diff --git a/docs/user_guide/00_liquid-handling/_liquid-handling.rst b/docs/user_guide/00_liquid-handling/_liquid-handling.rst
index 32ea5d7d564..3de4229f65b 100644
--- a/docs/user_guide/00_liquid-handling/_liquid-handling.rst
+++ b/docs/user_guide/00_liquid-handling/_liquid-handling.rst
@@ -24,3 +24,4 @@ Examples:
    moving-channels-around
    tutorial_tip_inventory_consolidation
    mixing
+   container_no_go_zones
diff --git a/docs/user_guide/00_liquid-handling/container_no_go_zones.ipynb b/docs/user_guide/00_liquid-handling/container_no_go_zones.ipynb
new file mode 100644
index 00000000000..5968a95c525
--- /dev/null
+++ b/docs/user_guide/00_liquid-handling/container_no_go_zones.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "h4mxa2wu6t9",
+   "metadata": {},
+   "source": [
+    "# Container No-Go Zones\n",
+    "\n",
+    "> **Mode:** Simulation (`STARChatterboxBackend`) - no hardware required.\n",
+    ">\n",
+    "> **Topic:** Defining obstructed regions and automatic channel avoidance using `Container` no-go zones.\n",
+    ">\n",
+    "> **Extra dependencies** (not included in pylabrobot):\n",
+    ">   - `matplotlib` - used for cross-section visualizations in this tutorial only\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## What are no-go zones?\n",
+    "\n",
+    "Some containers have internal obstructions - divider walls, support beams, baffles - where pipette tips physically cannot go. When multiple channels target the same container (e.g. aspirating from a trough with 4 channels), `LiquidHandler` needs to know where these obstructions are to position tips safely.\n",
+    "\n",
+    "A **no-go zone** is a cuboid region inside a container, defined as a `Tuple[Coordinate, Coordinate]` - the front-left-bottom and back-right-top corners, relative to the container's origin.\n",
+    "\n",
+    "## How it works\n",
+    "\n",
+    "When `LiquidHandler.aspirate` or `.dispense` targets a single container with multiple channels:\n",
+    "\n",
+    "1. The container's Y axis is split into **compartments** (free space between no-go zones)\n",
+    "2. Each compartment is shrunk by `edge_clearance` (default 2mm) from each boundary\n",
+    "3. Channels are distributed across compartments **center-out, then back-first**\n",
+    "4. Each channel group is centered within its compartment\n",
+    "\n",
+    "```{note}\n",
+    "Single-channel operations are unaffected - the tip goes to the container center as usual.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "v15b2poml7",
+   "metadata": {},
+   "outputs": [],
+   "source": "# Imports and visualization helper (collapse this cell in Jupyter via View → Collapse)\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nfrom matplotlib.lines import Line2D\n\nfrom pylabrobot.resources.container import Container\nfrom pylabrobot.resources.coordinate import Coordinate\nfrom pylabrobot.liquid_handling.channel_positioning import (\n    _get_compartments,\n    compute_channel_offsets,\n    MIN_SPACING_EDGE,\n)"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "uan4wnlungq",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MIN_CHANNEL_SPACING = 9.0  # mm, minimum center-to-center distance between adjacent channels\n",
+    "CHANNEL_DIAMETER = 9.0  # mm, minimum center-to-center spacing between adjacent channels\n",
+    "\n",
+    "def _draw_container_axis(ax, container, n_ch, spread=\"wide\", channel_spacings=None):\n",
+    "    \"\"\"Draw a single container cross-section with channels on the given axis.\"\"\"\n",
+    "    from pylabrobot.liquid_handling.errors import ChannelsDoNotFitError\n",
+    "\n",
+    "    size_y = container.get_absolute_size_y()\n",
+    "    size_x = container.get_absolute_size_x()\n",
+    "    center_y = size_y / 2\n",
+    "    compartments = _get_compartments(container)\n",
+    "    label_bbox = dict(facecolor=\"white\", alpha=0.5, edgecolor=\"none\", pad=0.3)\n",
+    "    dist_bbox = dict(facecolor=\"white\", alpha=0.8, edgecolor=\"none\", pad=1)\n",
+    "\n",
+    "    # Container outline\n",
+    "    ax.add_patch(plt.Rectangle((0, 0), size_x, size_y, fill=False, edgecolor=\"black\", linewidth=2))\n",
+    "\n",
+    "    # No-go zones (red)\n",
+    "    for flb, brt in container.no_go_zones:\n",
+    "        ax.add_patch(plt.Rectangle((0, flb.y), size_x, brt.y - flb.y,\n",
+    "                                    facecolor=\"red\", alpha=0.3, edgecolor=\"red\", linewidth=1))\n",
+    "\n",
+    "    # Free compartments (green)\n",
+    "    for comp_lo, comp_hi in compartments:\n",
+    "        ax.add_patch(plt.Rectangle((0, comp_lo), size_x, comp_hi - comp_lo,\n",
+    "                                    facecolor=\"green\", alpha=0.1, edgecolor=\"green\",\n",
+    "                                    linewidth=1, linestyle=\"--\"))\n",
+    "\n",
+    "    # Channel positions\n",
+    "    try:\n",
+    "        kwargs = dict(spread=spread)\n",
+    "        if channel_spacings is not None:\n",
+    "            kwargs[\"channel_spacings\"] = channel_spacings\n",
+    "        offsets = compute_channel_offsets(container, n_ch, **kwargs)\n",
+    "        tip_ys = []\n",
+    "        for i, o in enumerate(offsets):\n",
+    "            tip_y = center_y + o.y\n",
+    "            tip_ys.append(tip_y)\n",
+    "            if channel_spacings is not None:\n",
+    "                d = channel_spacings[i]\n",
+    "            else:\n",
+    "                d = CHANNEL_DIAMETER\n",
+    "            ax.add_patch(plt.Circle(\n",
+    "                (size_x / 2, tip_y), d / 2,\n",
+    "                facecolor=\"none\", edgecolor=\"navy\", linewidth=1, linestyle=\":\"))\n",
+    "            ax.plot(size_x / 2, tip_y, \"o\", color=\"navy\", markersize=4, zorder=5)\n",
+    "            ax.text(size_x * 0.78, tip_y, f\"ch{i}\", ha=\"left\", va=\"center\",\n",
+    "                    fontsize=6, color=\"navy\", bbox=label_bbox, zorder=6)\n",
+    "\n",
+    "        tip_ys_sorted = sorted(tip_ys)\n",
+    "\n",
+    "        # Channel-to-channel distances (right side)\n",
+    "        brace_x = size_x + 2\n",
+    "        for i in range(len(tip_ys_sorted) - 1):\n",
+    "            y_lo = tip_ys_sorted[i]\n",
+    "            y_hi = tip_ys_sorted[i + 1]\n",
+    "            gap = y_hi - y_lo\n",
+    "            mid = (y_lo + y_hi) / 2\n",
+    "            ax.annotate(\"\", xy=(brace_x, y_hi), xytext=(brace_x, y_lo),\n",
+    "                         arrowprops=dict(arrowstyle=\"<->\", color=\"#444\", lw=1))\n",
+    "            ax.text(brace_x + 1, mid, f\"{gap:.1f}\", ha=\"left\", va=\"center\",\n",
+    "                    fontsize=7, color=\"#444\", fontweight=\"bold\", bbox=dist_bbox)\n",
+    "\n",
+    "        # Edge distances (left side)\n",
+    "        edge_x = -2\n",
+    "        for comp_lo, comp_hi in compartments:\n",
+    "            group = [y for y in tip_ys_sorted if comp_lo - 0.1 <= y <= comp_hi + 0.1]\n",
+    "            if not group:\n",
+    "                continue\n",
+    "            edge_lo = group[0] - comp_lo\n",
+    "            if edge_lo > 0.5:\n",
+    "                mid = (comp_lo + group[0]) / 2\n",
+    "                ax.annotate(\"\", xy=(edge_x, group[0]), xytext=(edge_x, comp_lo),\n",
+    "                             arrowprops=dict(arrowstyle=\"<->\", color=\"#888\", lw=0.8))\n",
+    "                ax.text(edge_x - 0.5, mid, f\"{edge_lo:.1f}\", ha=\"right\", va=\"center\",\n",
+    "                        fontsize=6, color=\"#888\", bbox=dist_bbox)\n",
+    "            edge_hi = comp_hi - group[-1]\n",
+    "            if edge_hi > 0.5:\n",
+    "                mid = (group[-1] + comp_hi) / 2\n",
+    "                ax.annotate(\"\", xy=(edge_x, comp_hi), xytext=(edge_x, group[-1]),\n",
+    "                             arrowprops=dict(arrowstyle=\"<->\", color=\"#888\", lw=0.8))\n",
+    "                ax.text(edge_x - 0.5, mid, f\"{edge_hi:.1f}\", ha=\"right\", va=\"center\",\n",
+    "                        fontsize=6, color=\"#888\", bbox=dist_bbox)\n",
+    "\n",
+    "    except ChannelsDoNotFitError:\n",
+    "        ax.text(size_x / 2, size_y / 2, \"Cannot fit!\", ha=\"center\", va=\"center\",\n",
+    "                fontsize=12, color=\"red\", fontweight=\"bold\")\n",
+    "\n",
+    "    ax.set_xlim(-8, size_x + 14)\n",
+    "    ax.set_ylim(-2, size_y + 2)\n",
+    "    ax.set_xlabel(\"X (mm)\")\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "\n",
+    "\n",
+    "def plot_container_cross_section(container, num_channels_list, **kwargs):\n",
+    "    \"\"\"Plot container cross-sections for multiple channel counts.\"\"\"\n",
+    "    n_plots = len(num_channels_list)\n",
+    "    fig, axes = plt.subplots(1, n_plots, figsize=(2.2 * n_plots, 5), squeeze=False)\n",
+    "    axes = axes[0]\n",
+    "\n",
+    "    for ax, n_ch in zip(axes, num_channels_list):\n",
+    "        _draw_container_axis(ax, container, n_ch, **kwargs)\n",
+    "        ax.set_title(f\"{n_ch} channel{'s' if n_ch != 1 else ''}\")\n",
+    "        if ax != axes[0]:\n",
+    "            ax.set_yticklabels([])\n",
+    "        else:\n",
+    "            ax.set_ylabel(\"Y (mm)\")\n",
+    "\n",
+    "    legend_handles = [\n",
+    "        mpatches.Patch(facecolor=\"red\", alpha=0.3, edgecolor=\"red\", label=\"No-go zone\"),\n",
+    "        mpatches.Patch(facecolor=\"green\", alpha=0.1, edgecolor=\"green\", label=\"Free compartment\"),\n",
+    "        Line2D([0], [0], marker=\"o\", color=\"w\", markerfacecolor=\"none\", markeredgecolor=\"navy\",\n",
+    "               markersize=10, linestyle=\"None\", label=\"Tip diameter\"),\n",
+    "    ]\n",
+    "    fig.legend(handles=legend_handles, loc=\"lower center\", ncol=3, fontsize=8)\n",
+    "\n",
+    "    size_y = container.get_absolute_size_y()\n",
+    "    name = container.name\n",
+    "    model = container.model or \"\"\n",
+    "    fig.suptitle(f\"{name} ({model})\\nsize_y={size_y}mm, {len(container.no_go_zones)} no-go zone(s)\",\n",
+    "                 fontsize=11, fontweight=\"bold\")\n",
+    "    fig.tight_layout(rect=[0, 0.06, 1, 0.92])\n",
+    "    fig.subplots_adjust(wspace=-0.15)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def plot_spread_comparison(container, n_ch, title=None, channel_spacings=None):\n",
+    "    \"\"\"Plot wide vs tight side-by-side for a single channel count.\"\"\"\n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(8, 6), squeeze=False)\n",
+    "    axes = axes[0]\n",
+    "\n",
+    "    for ax, mode in zip(axes, [\"wide\", \"tight\"]):\n",
+    "        _draw_container_axis(ax, container, n_ch, spread=mode, channel_spacings=channel_spacings)\n",
+    "        ax.set_title(f'spread=\"{mode}\"')\n",
+    "        if ax != axes[0]:\n",
+    "            ax.set_yticklabels([])\n",
+    "        else:\n",
+    "            ax.set_ylabel(\"Y (mm)\")\n",
+    "\n",
+    "    if title is None:\n",
+    "        title = f\"{n_ch} channels on {container.name}: wide vs tight\"\n",
+    "    fig.suptitle(title, fontsize=11, fontweight=\"bold\")\n",
+    "    fig.tight_layout(rect=[0, 0, 1, 0.93])\n",
+    "    fig.subplots_adjust(wspace=-0.1)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nhdap5csut",
+   "metadata": {},
+   "source": [
+    "## Real-world examples: Hamilton troughs\n",
+    "\n",
+    "The Hamilton trough family has internal support structures that create no-go zones. These were measured from physical troughs using calipers (top of beams) and visual inspection through the transparent walls (base of tapered beams).\n",
+    "\n",
+    "### 60 mL trough - 1 center divider\n",
+    "\n",
+    "The `hamilton_1_trough_60mL_Vb` has a single center support wall (~1.2mm wide), creating 2 compartments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "72t56n0fkzm",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container: trough_60mL, size_y=90.0mm\n",
+      "No-go zones: [(Coordinate(x=0, y=44.4, z=5.0), Coordinate(x=19.0, y=45.6, z=60.25))]\n",
+      "Compartments: [(2.0, 42.4), (47.6, 88.0)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1980x500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_60mL_Vb\n",
+    "\n",
+    "trough_60 = hamilton_1_trough_60mL_Vb(name=\"trough_60mL\")\n",
+    "\n",
+    "print(f\"Container: {trough_60.name}, size_y={trough_60.get_absolute_size_y()}mm\")\n",
+    "print(f\"No-go zones: {trough_60.no_go_zones}\")\n",
+    "print(f\"Compartments: {_get_compartments(trough_60)}\")\n",
+    "\n",
+    "plot_container_cross_section(trough_60, [1, 2, 3, 4, 5, 6, 7, 8, 9])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shj0sw6dkcg",
+   "metadata": {},
+   "source": [
+    "### 120 mL trough - 3 tapered support beams\n",
+    "\n",
+    "The `hamilton_1_trough_120mL_Vb` has 3 internal support beams (~2.5mm wide at the base, ~0.8mm at the top), creating 4 compartments. The no-go zones use the base width since that is the worst case for pipette tip clearance.\n",
+    "\n",
+    "Dimensional breakdown (from outer y=0):\n",
+    "\n",
+    "| Element | Y range (mm) | Size (mm) | Modelled as |\n",
+    "|---|---|---|---|\n",
+    "| Back wall | 141.1 - 142.5 | 1.4 | Not modelled (included in `size_y`) |\n",
+    "| Compartment 3 | 109.8 - 141.1 | 31.3 | Free space |\n",
+    "| Beam 3 | 107.3 - 109.8 | 2.5 | `no_go_zones[2]` |\n",
+    "| Compartment 2 | 76.0 - 107.3 | 31.3 | Free space |\n",
+    "| Beam 2 | 73.5 - 76.0 | 2.5 | `no_go_zones[1]` |\n",
+    "| Compartment 1 | 42.2 - 73.5 | 31.3 | Free space |\n",
+    "| Beam 1 | 39.7 - 42.2 | 2.5 | `no_go_zones[0]` |\n",
+    "| Compartment 0 | 1.2 - 39.7 | 38.5 | Free space |\n",
+    "| Front wall | 0 - 1.2 | 1.2 | Not modelled (included in `size_y`) |\n",
+    "\n",
+    "```{note}\n",
+    "size_y=142.5` is the **outer** dimension of the trough.\n",
+    "The front and back walls are not modelled as no-go zones - they are accounted for by the `edge_clearance` parameter during channel positioning.\n",
+    "\n",
+    "The internal beams *are* modelled as no-go zones because they obstruct pipette access within the container's interior.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "20hbb649kmc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container: trough_120mL, size_y=142.5mm\n",
+      "No-go zones: [(Coordinate(x=0, y=39.7, z=12.0), Coordinate(x=19.0, y=42.2, z=70.0)), (Coordinate(x=0, y=73.5, z=12.0), Coordinate(x=19.0, y=76.0, z=70.0)), (Coordinate(x=0, y=107.3, z=12.0), Coordinate(x=19.0, y=109.8, z=70.0))]\n",
+      "Compartments: [(2.0, 37.7), (44.2, 71.5), (78.0, 105.3), (111.8, 140.5)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2420x500 with 11 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_120mL_Vb\n",
+    "\n",
+    "trough_120 = hamilton_1_trough_120mL_Vb(name=\"trough_120mL\")\n",
+    "\n",
+    "print(f\"Container: {trough_120.name}, size_y={trough_120.get_absolute_size_y()}mm\")\n",
+    "print(f\"No-go zones: {trough_120.no_go_zones}\")\n",
+    "print(f\"Compartments: {_get_compartments(trough_120)}\")\n",
+    "\n",
+    "plot_container_cross_section(trough_120, [1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2prx1963mj",
+   "metadata": {},
+   "source": [
+    "## Defining no-go zones on a new container\n",
+    "\n",
+    "Any `Container` (or subclass: `Trough`, `Well`, `Tube`, etc.) accepts a `no_go_zones` parameter. Each zone is a pair of `Coordinate`s - the front-left-bottom and back-right-top corners of the obstructed cuboid, relative to the container's outer origin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "zzzwi3r4vaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container: custom_trough, size_y=100.0mm\n",
+      "Compartments: [(2.0, 28.0), (34.0, 63.0), (69.0, 98.0)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylabrobot.resources.trough import Trough, TroughBottomType\n",
+    "\n",
+    "# Define a custom trough with two dividers\n",
+    "custom_trough = Trough(\n",
+    "    name=\"custom_trough\",\n",
+    "    size_x=19.0,\n",
+    "    size_y=100.0,\n",
+    "    size_z=50.0,\n",
+    "    max_volume=80_000,\n",
+    "    bottom_type=TroughBottomType.V,\n",
+    "    no_go_zones=[\n",
+    "        (Coordinate(0, 30.0, 0), Coordinate(19.0, 32.0, 50.0)),  # divider 1\n",
+    "        (Coordinate(0, 65.0, 0), Coordinate(19.0, 67.0, 50.0)),  # divider 2\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "print(f\"Container: {custom_trough.name}, size_y={custom_trough.get_absolute_size_y()}mm\")\n",
+    "print(f\"Compartments: {_get_compartments(custom_trough)}\")\n",
+    "\n",
+    "plot_container_cross_section(custom_trough, [1, 2, 3, 4, 6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ydm1lbbn8m",
+   "metadata": {},
+   "source": [
+    "## Edge clearance and tip size\n",
+    "\n",
+    "`edge_clearance` (default: `MIN_SPACING_EDGE = 2mm`) controls how close the automatic positioning places tip **centers** to compartment boundaries. It is a positioning safety margin, not a physical gate.\n",
+    "\n",
+    "It does **not** prevent a pipette from entering a container. Whether a tip physically fits is determined by tip diameter vs. container opening - e.g. a 1000 µL tip won't fit into a 1536-wellplate well. This is the **user's responsibility** to verify when choosing tips and containers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bee6v46lawm",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container size_y: 20.0mm\n",
+      "No-go zone: Y=8-12mm -> two 8mm raw compartments\n",
+      "After 2mm edge clearance: compartments = [(2.0, 6.0), (14.0, 18.0)]\n",
+      "1 channel: [Coordinate(x=0, y=6.0, z=0)]\n",
+      "2 channels (1 per compartment): [Coordinate(x=0, y=6.0, z=0), Coordinate(x=0, y=-6.0, z=0)]\n",
+      "3 channels (needs 2 in one 4mm compartment): ChannelsDoNotFitError - Cannot distribute channels across compartments while respecting spacing constraints.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A narrow container where compartments are smaller than the default 9mm channel spacing.\n",
+    "# 1 channel per compartment still works - the channel is simply centered.\n",
+    "# 2 channels in one compartment would fail (needs 9mm, only 4mm available).\n",
+    "from pylabrobot.liquid_handling.errors import ChannelsDoNotFitError\n",
+    "\n",
+    "tiny = Container(\n",
+    "    name=\"tiny_container\", size_x=10, size_y=20, size_z=10,\n",
+    "    no_go_zones=[(Coordinate(0, 8, 0), Coordinate(10, 12, 10))],\n",
+    ")\n",
+    "print(f\"Container size_y: {tiny.get_absolute_size_y()}mm\")\n",
+    "print(f\"No-go zone: Y={8}-{12}mm -> two 8mm raw compartments\")\n",
+    "print(f\"After 2mm edge clearance: compartments = {_get_compartments(tiny)}\")\n",
+    "print(f\"1 channel: {compute_channel_offsets(tiny, 1)}\")\n",
+    "print(f\"2 channels (1 per compartment): {compute_channel_offsets(tiny, 2)}\")\n",
+    "try:\n",
+    "    print(f\"3 channels: {compute_channel_offsets(tiny, 3)}\")\n",
+    "except ChannelsDoNotFitError as e:\n",
+    "    print(f\"3 channels (needs 2 in one 4mm compartment): ChannelsDoNotFitError - {e}\")\n",
+    "plot_container_cross_section(tiny, [1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "snfkeopew6",
+   "metadata": {},
+   "source": [
+    "## Spread modes with no-go zones\n",
+    "\n",
+    "The `spread` parameter on `aspirate` and `dispense` controls how channels are positioned within each compartment:\n",
+    "\n",
+    "- `spread=\"wide\"` (default) - channels are spread as far apart as possible within each compartment\n",
+    "- `spread=\"tight\"` - channels are packed at minimum spacing (9mm), centered in each compartment\n",
+    "- `spread=\"custom\"` - no-go zones are ignored entirely; the user controls all positioning via offsets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bbjg3lf6qa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compartments: [(2.0, 58.0), (63.7, 116.0)]\n",
+      "spread='wide'   -> offsets (from center): ['+43.9', '+30.9', '+17.8', '-15.0', '-29.0', '-43.0']\n",
+      "spread='tight'  -> offsets (from center): ['+39.9', '+30.9', '+21.9', '-20.0', '-29.0', '-38.0']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare wide vs tight spread on the 200mL trough (2 large compartments, 6 channels)\n",
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_200mL_Vb\n",
+    "\n",
+    "trough_200 = hamilton_1_trough_200mL_Vb(name=\"trough_200mL\")\n",
+    "print(f\"Compartments: {_get_compartments(trough_200)}\")\n",
+    "\n",
+    "for mode in [\"wide\", \"tight\"]:\n",
+    "    offsets = compute_channel_offsets(trough_200, 6, spread=mode)\n",
+    "    positions = [f\"{o.y:+.1f}\" for o in offsets]\n",
+    "    print(f\"spread={mode!r:8s} -> offsets (from center): {positions}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "yayka43i62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_spread_comparison(trough_200, 6, title=\"6 channels on 200mL trough: wide vs tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98rmf1z9e0c",
+   "metadata": {},
+   "source": [
+    "### Container without no-go zones\n",
+    "\n",
+    "`compute_channel_offsets` also works on plain containers. Without no-go zones, it falls through to standard wide/tight spread across the full Y axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "rp2rrixry2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2-channel wide   -> ['+15.0', '-15.0']\n",
+      "2-channel tight  -> ['+4.5', '-4.5']\n",
+      "4-channel wide   -> ['+27.0', '+9.0', '-9.0', '-27.0']\n",
+      "4-channel tight  -> ['+13.5', '+4.5', '-4.5', '-13.5']\n",
+      "6-channel wide   -> ['+32.1', '+19.3', '+6.4', '-6.4', '-19.3', '-32.1']\n",
+      "6-channel tight  -> ['+22.5', '+13.5', '+4.5', '-4.5', '-13.5', '-22.5']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A plain trough without no-go zones\n",
+    "plain_trough = Trough(\n",
+    "    name=\"plain_trough\",\n",
+    "    size_x=19.0,\n",
+    "    size_y=90.0,\n",
+    "    size_z=65.0,\n",
+    "    max_volume=60_000,\n",
+    ")\n",
+    "\n",
+    "for n in [2, 4, 6]:\n",
+    "    for mode in [\"wide\", \"tight\"]:\n",
+    "        offsets = compute_channel_offsets(plain_trough, n, spread=mode)\n",
+    "        positions = [f\"{o.y:+.1f}\" for o in offsets]\n",
+    "        print(f\"{n}-channel {mode:6s} -> {positions}\")\n",
+    "\n",
+    "# Side-by-side: wide vs tight for 2, 4, 6 channels\n",
+    "channel_counts = [2, 4, 6]\n",
+    "n_pairs = len(channel_counts)\n",
+    "fig, axes = plt.subplots(1, n_pairs * 2, figsize=(3 * n_pairs * 2, 5), squeeze=False)\n",
+    "axes = axes[0]\n",
+    "\n",
+    "for pair_idx, n_ch in enumerate(channel_counts):\n",
+    "    for mode_idx, mode in enumerate([\"wide\", \"tight\"]):\n",
+    "        ax = axes[pair_idx * 2 + mode_idx]\n",
+    "        _draw_container_axis(ax, plain_trough, n_ch, spread=mode)\n",
+    "        ax.set_title(f'{n_ch}-channel \"{mode}\"')\n",
+    "        if pair_idx == 0 and mode_idx == 0:\n",
+    "            ax.set_ylabel(\"Y (mm)\")\n",
+    "        else:\n",
+    "            ax.set_yticklabels([])\n",
+    "\n",
+    "fig.suptitle(\"Plain trough (no no-go zones): wide vs tight\", fontsize=11, fontweight=\"bold\")\n",
+    "fig.tight_layout(rect=[0, 0, 1, 0.93])\n",
+    "fig.subplots_adjust(wspace=-0.05)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qpv4b5xxlcq",
+   "metadata": {},
+   "source": [
+    "### Per-channel occupancy diameters\n",
+    "\n",
+    "Each channel has an occupancy diameter - the physical space it takes up. On a Hamilton STAR, 1mL channels have a 9mm occupancy diameter, while 5mL channels have 18mm. The required gap between two adjacent channels is the sum of their radii: `spacing[i]/2 + spacing[j]/2`.\n",
+    "\n",
+    "`compute_channel_offsets` accepts `channel_spacings` - one occupancy diameter per channel - and computes all gaps accordingly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9wtrqbm2upq",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compartments: [(2.0, 93.0), (107.0, 198.0)]\n",
+      "spread='wide'    centers=['24.8', '47.5', '70.2', '129.8', '152.5', '175.2']  gaps=[22.8, 22.8, 59.5, 22.8, 22.8]\n",
+      "spread='tight'   centers=['34.0', '43.0', '56.5', '139.0', '152.5', '166.0']  gaps=[9.0, 13.5, 82.5, 13.5, 13.5]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 6 channels with mixed occupancy diameters on a wide demo trough\n",
+    "# 4 channels with 9mm occupancy (1mL), 2 with 18mm occupancy (5mL) - e.g. mixed Hamilton\n",
+    "mixed_spacings = [9.0, 18.0, 9.0, 18.0, 9.0, 9.0]  # 6 occupancy diameters for 6 channels\n",
+    "\n",
+    "# Create a wider trough to clearly show the difference between wide and tight\n",
+    "demo_trough = Trough(\n",
+    "    name=\"demo_trough\",\n",
+    "    size_x=37.0,\n",
+    "    size_y=200.0,\n",
+    "    size_z=95.0,\n",
+    "    max_volume=200_000,\n",
+    "    bottom_type=TroughBottomType.V,\n",
+    "    no_go_zones=[\n",
+    "        (Coordinate(0, 95, 0), Coordinate(37.0, 105, 95.0)),  # center divider\n",
+    "    ],\n",
+    ")\n",
+    "print(f\"Compartments: {_get_compartments(demo_trough)}\")\n",
+    "\n",
+    "for mode in [\"wide\", \"tight\"]:\n",
+    "    offsets = compute_channel_offsets(demo_trough, 6, spread=mode, channel_spacings=mixed_spacings)\n",
+    "    centers = sorted([demo_trough.get_size_y() / 2 + o.y for o in offsets])\n",
+    "    gaps = [round(centers[i + 1] - centers[i], 1) for i in range(len(centers) - 1)]\n",
+    "    print(f\"spread={mode!r:8s}  centers={[f'{c:.1f}' for c in centers]}  gaps={gaps}\")\n",
+    "\n",
+    "plot_spread_comparison(\n",
+    "    demo_trough, 6,\n",
+    "    title=\"6 channels, occupancy diameters [9, 18, 9, 18, 9, 9] mm\\n(demo trough, 200mm Y, center divider)\",\n",
+    "    channel_spacings=mixed_spacings,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "t7qstevvf8b",
+   "metadata": {},
+   "source": [
+    "## Try it yourself\n",
+    "\n",
+    "Edit the parameters below to experiment with any container geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cqrxs83dgvi",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compartments: [(2.0, 28.0), (34.0, 63.0), (69.0, 98.0)]\n",
+      "  1 ch: ['y=+33.5']\n",
+      "  2 ch: ['y=+33.5', 'y=-1.5']\n",
+      "  3 ch: ['y=+33.5', 'y=-1.5', 'y=-35.0']\n",
+      "  6 ch: ['y=+38.3', 'y=+28.7', 'y=+3.3', 'y=-6.3', 'y=-30.5', 'y=-39.5']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 880x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- Edit these ---\n",
+    "CONTAINER_SIZE_X = 19.0   # mm\n",
+    "CONTAINER_SIZE_Y = 100.0  # mm\n",
+    "CONTAINER_SIZE_Z = 50.0   # mm\n",
+    "\n",
+    "# List of (y_start, y_end) pairs for no-go zones\n",
+    "NO_GO_Y_RANGES = [\n",
+    "    (30, 32),   # divider 1\n",
+    "    (65, 67),   # divider 2\n",
+    "]\n",
+    "\n",
+    "NUM_CHANNELS = [1, 2, 3, 6]  # channel counts to plot\n",
+    "# ------------------\n",
+    "\n",
+    "no_go_zones = [\n",
+    "    (Coordinate(0, y_lo, 0), Coordinate(CONTAINER_SIZE_X, y_hi, CONTAINER_SIZE_Z))\n",
+    "    for y_lo, y_hi in NO_GO_Y_RANGES\n",
+    "]\n",
+    "\n",
+    "custom = Container(\n",
+    "    name=\"custom_container\",\n",
+    "    size_x=CONTAINER_SIZE_X,\n",
+    "    size_y=CONTAINER_SIZE_Y,\n",
+    "    size_z=CONTAINER_SIZE_Z,\n",
+    "    no_go_zones=no_go_zones,\n",
+    ")\n",
+    "\n",
+    "print(f\"Compartments: {_get_compartments(custom)}\")\n",
+    "for n in NUM_CHANNELS:\n",
+    "    try:\n",
+    "        result = compute_channel_offsets(custom, n)\n",
+    "        status = [f\"y={o.y:+.1f}\" for o in result]\n",
+    "    except ChannelsDoNotFitError:\n",
+    "        status = \"Cannot fit\"\n",
+    "    print(f\"  {n} ch: {status}\")\n",
+    "\n",
+    "plot_container_cross_section(custom, NUM_CHANNELS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gfshxjfrbkm",
+   "metadata": {},
+   "source": [
+    "## End-to-end simulation\n",
+    "\n",
+    "Below we set up a full `LiquidHandler` with `STARChatterboxBackend` (simulation - no hardware needed) to verify that `aspirate` correctly distributes channels around no-go zones.\n",
+    "\n",
+    "The Hamilton-specific trough definitions (`hamilton_1_trough_60mL_Vb`, `hamilton_1_trough_120mL_Vb`) already have `no_go_zones` pre-configured - you don't need to define them manually when using these resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "wnozu8h6wt8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trough: trough_60\n",
+      "Pre-configured no-go zones: [(Coordinate(x=0, y=44.4, z=5.0), Coordinate(x=19.0, y=45.6, z=60.25))]\n",
+      "Compartments: [(2.0, 42.4), (47.6, 88.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pylabrobot.liquid_handling import LiquidHandler\n",
+    "from pylabrobot.liquid_handling.backends.hamilton.STAR_chatterbox import STARChatterboxBackend\n",
+    "from pylabrobot.resources.hamilton import STARLetDeck\n",
+    "from pylabrobot.resources.hamilton.tip_racks import hamilton_96_tiprack_1000uL_filter\n",
+    "from pylabrobot.resources.hamilton.trough_carriers import Trough_CAR_5R60_A00\n",
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_60mL_Vb\n",
+    "from pylabrobot.resources import TIP_CAR_480_A00, set_tip_tracking, set_volume_tracking\n",
+    "\n",
+    "set_tip_tracking(True)\n",
+    "set_volume_tracking(True)\n",
+    "\n",
+    "backend = STARChatterboxBackend(num_channels=8)\n",
+    "deck = STARLetDeck()\n",
+    "lh = LiquidHandler(backend=backend, deck=deck)\n",
+    "await lh.setup()\n",
+    "\n",
+    "tip_car = TIP_CAR_480_A00(name=\"tip_carrier\")\n",
+    "tip_car[0] = hamilton_96_tiprack_1000uL_filter(name=\"tips_1000\")\n",
+    "deck.assign_child_resource(tip_car, rails=1)\n",
+    "\n",
+    "trough_car = Trough_CAR_5R60_A00(name=\"trough_carrier\")\n",
+    "trough_car[0] = hamilton_1_trough_60mL_Vb(name=\"trough_60\")\n",
+    "deck.assign_child_resource(trough_car, rails=10)\n",
+    "\n",
+    "trough = deck.get_resource(\"trough_60\")\n",
+    "print(f\"Trough: {trough.name}\")\n",
+    "print(f\"Pre-configured no-go zones: {trough.no_go_zones}\")\n",
+    "print(f\"Compartments: {_get_compartments(trough)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "w71fiy95wq",
+   "metadata": {},
+   "source": [
+    "### Aspirate with 2 and 4 channels\n",
+    "\n",
+    "Pick up tips, fill the trough, then aspirate. The chatterbox backend prints raw firmware commands - the `yp` values show absolute Y positions in 0.1mm units. Verify that channels land in separate compartments and avoid the divider at Y=44-46mm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bc6s5vhj5wh",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C0TTid0001tt01tf1tl0871tv10650tg3tu0\n",
+      "C0TPid0002xp01179 01179 01179 01179 00000&yp1458 1368 1278 1188 0000&tm1 1 1 1 0&tt01tp2266tz2166th2450td0\n",
+      "2 channels -> offsets: ['+22.8mm', '-22.8mm']\n",
+      "4 channels -> offsets: ['+29.5mm', '+16.1mm', '-16.1mm', '-29.5mm']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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M7jF0xDUCLwT2B0F/xDfwW+C2QWmdAwcOaJM3ALLfkOUF7wViK7g3IPNszZo1vN9qpZuBS8vcREVc7506dWJZxjFHEF5JSlBQrnHcV4oXL05ORePm3L9/X3PgwAHta8ztGDp0qFa7G05yS4so5VRgTehOwhIESzh48KCeJXD16lWtVYj5JLoThoXUA9e2Mj9v4cKFTv9+t1cmmFWKgw8TP2/evHoTwVq1amVR7Su1g9+guMow218QrAETayE7qH6AyYNwH+E1JselRg0owThw3+K8oJJFaty33F6Z3Lp1S9O+fXue0evl5cXxlapVq2omTZqkLV9iDbp+Y2MPexTAE5xLcjPTrfWVJ7cda+IzzgSj3Bo1avCMbcRXYI20a9dOs2vXrhRtTzceYUtsyZXlJC3ivFw4lYIMF/giBUEwDmZsK7O2BcEU6aBRTL4rCIIgCGl5BrwgCIKQdhBlYkMHQFcBxinSo5FmjdRfpB2inhNKuhiCCXETJ07kmflYV0nXRAE6S0EHPNRtQqopSpggLRWd8tIqmCiHFHOUxFG64ummrgrqBinTOGdK/xRLQYo0Pqebcu3WpHbQJq2QVrvfWQL6NhgLGGJS4tmzZ41m9hg+0A/mwYMHZr8Lk8iUbCDdB7Lo0DEvLWKsIyBKpwvqR+kvguSb27dvW528g8/h8/v27dO4O6JM3Jzjx49rb4CNGzfmHiZoYoVmUljWrFkz7br79+/Xrtu6dWvNw4cPNb///rt22eDBg5P9LvRTUfpIQPmg9eqhQ4e40RaWoa9LWmTdunWaL7/8kmcfY3a2KJO0A/ogKfKcElq0aMGf79atm8bdEWWi0fDNE3NKMJpEWQb8RUMgpYSFKcskuTRA3XIN6H6GmzJG+tg+Jnx99dVXSXpAm6JXr17acjBogGSsyxyaatk6qtadM4DUT6UkhWJx6E7m1B2JKRPY8PtQBsMUS5cu1X7+u+++S9JVT+nOaFhGAuVc0AETvx8KZ8OGDayYhgwZwooIad0otaM7ec7Wjo8pRSkRY6lloptOiq6bX3/9NTc8gzKHzBiWH7G0k6K5VF90ksTxQMovJiUaK20DcKwxyMBxVmQXzaYMG60ZklwDM93vSElnSJSFQQdSU11BMcdi+vTp3BkU88ewHs6/4US+p0+fasuwoLyRtfcEMHv2bK1l/Vzn2nRH3F6ZhIWFJem6pjtpMaXKRKlXhHklxtw6yvYtmVykTKzEAzWVDGuVIfdfd4ayNeCmnpwy0e2EiBpqyrJnz55p10XrYGV5ci1W0cZVWU+3ThRu5MpyXJyGNyPD86O0VzY8njNmzLBbx8fUUCaKhab7qFWrlnZdazopmgKK2VAeobgUS1T3Ro8bpynZhSwYtoy1VJngpmzt71GOKzqMGu6TYVdQ1NMz9d3jx4/XroftK8vR6dDaewKAG1h5b82aNRp3xu0D8Kg4q1S/RSc49C9AFVXU9jLVpEYn3qR9oFaVUnYbwXrUxkEFz+HDh/P7qAaKEtKoeooaYwB1uBCMNgdqYSnVf3///XftcqULIGovKQFfdORTgsDJPZTKorp9Y1B/C1WJ0Z0RHfYUlOOT0q52Cin9fIUKFfiz6CQIUOEW+4dKrfgdSu8OU13xHNXx0d7gd6EeFc6B0lAMtbaU5AZ7dFJEFWVlNsC8efO44ySanun2/wB4jQAz1kU/ENTDghx07tyZ39+zZw/XRjMFKlsr1wauKeX3oD/PRx99lOLfg9YL+A2muoKiu6PSlRJ1vZRKzugbpPx+RZ6VLqSoTabbf8WaewJqvynX/REndzZUHRo3ByN6pf95o0aNNN9//z2PMOAGsTQAj7iDMrJD1VaYzwAj3eSsFzxGjBhh0X7+9ttv2s+gEvCJEye0rxcvXmxxZVPlobhP4JaqWbNmsusq29et0qvrzurZs6d2eXKBSF13lq5rQrePt+L+0h3ZIhZhOAqsW7eu9vPK/mP/jFkmiM0osQ1lWe/evbXrKtVYdeNDqWGZwH+vMG3aNO1ypec5XFJKGZMXL14YPf4nT540+V1wAyoBY1R5UIDLSlmuWCZwbynb/OCDD7Tr4lgaq9SdHH369NF+Bu4nBWt+j+5xVWRPVx5QadlcZXHlsWrVKl4XMT5j58rSe4ICPk9E7HZ1Z9zeMsGIHmmxqLiKyqroD966dWte/uWXX5pVxrA2MPLFSA6NqGBpKP0ozHV+BBiFWgKqnyrbRS8JpToo0nh1R0tI09W1mEw9lBRnVGfFqA4NepDmi1Ehqgzr9pNQel+ktKudsfds7YpnTcfG1Or4aC3muhNa2kkRbVwNLVGM1vF5pe+Lbqc+HBfDRmf26GwKUN0alb7Bhx9+yC14bekMaa4rqD2uOWvvCanV2VBtuL0yAUOGDGEhRHtamP6tWrXiBlAQHMOGWrrghgj3FTrQoew1Gmyh9LexGyN6Vhu7qeu6rZIDFzz6U4O5c+fSokWLtGWrdTtLWuvmArhopk2bxu4fuOHg0lBuOngPbiZLutqh3Lnu7zcktbripXZXPUsx153Qmk6KxoA8Kt+h2zUR7jXDRmcp6VZpCJpZKc3r4B5Dcy1bf4+5Y6R7ze3bty/J9YYbP9o/6HYhhXIxLARi6T0B24N7LjU6G6oNt1cmDx48oJEjR7L/tFChQtSxY0fuhQAgYKY65MGPCj8sfL4YKUHgDPulYDtKz48JEyZwXxKMoBAXgDKoV68e3bhxw+KTBesBFxD2SbkZwxKxFew7elBDkaCTG/qAKPEDXFSKstLt6vjNN9/wxTZnzhzueqlYT8qoET5zQ8UFxauMgNE9D53pELdQetFgIqQlLY2dgbH9NwXOqdJNURmlKrKDh70sHks7KSJmZ3gThZzg3CCWB3CTRE06JQah26lSkV3FWkBsBOcJN00lTqG7P8ZQrF2A74R1Ynjzt6YzpKXodipEjAwTb3GtXr16laZMmaKN3egOXPDbFRm29p6AgZDSTruyszsbqg2Nm5NcpU+UpFcyVgxjJsjWsiQ1GPMwTGXE6MYuLAVpk8pn7TUvw1SHQMQl0OkxJZMWTXXXs3TSorHP654r3Qq7lnZntKbjozXdAc3FqZKrFG2qU6exroDWdFI0BbZlLBtK6UaqZFoBZMeZ+k2IdySXzYXtmPqscpyt+T3WdDlNLptLV0Z0U4N1z5Gl9wTdeJ+PpAZLzAQj5XfffZdHxIhJwIxG9znEDDZv3qz1pxtRwhYp6zfeeIM2bdrEGVfYPrYHfz3iLMieypUrl1XKH5aCgmKu2wpMeIwAMRKFHxrdGZFxhgw1Xb80gDsMVhb6buO3wK2AUiLIOlLcBskB6wUd8DDSg2sQ34kOefDzw1JTC4rbB+cnuTiQM7Gmk6IpYLWgVzriM7A4YU3jPCtWg27XxAEDBnDGITptIuMO34fPwYpA1pWpa8PS68Mev8cYcB2jBzy6cELG8MB+o3slLGIFXI/Kd+h2IbXmnqB8rkOHDnpZiW6J2aGMoCqUkRBGVHfv3k3t3XFZMKkPx9mSuRtprRsf5pooGVHI8EK2kjLydrfmacocrpSWU8EcL3x+//79GndHlEkaARc5ZgkrLgrU0xIcw82bN122RAbSXhW3DGbaY4a4okgwY9zczHZXpG3btvz7MRPfGpDWj89hgq+g0Ug/ExWA4GhyE8Cg9BEQRiYJXANIU5wxYwZX3RUEawgPD6f+/fuzewnZewjKo9oxgsxI3YVLSBBSgiiTNKJMBEEQ1IwoE0EQBMFm3H6eiSAIgmA7okwEQRAEmxFlIgiCINiMKBNBEATBZkSZCIIgCDYjykQQBEGwGVEmgiAIgs2IMhEEQRBsRpSJIAiCYDOiTARBEASbEWUiCIIg2IwoE0EQBMFmRJkIgiAINiPKRBAEQbAZUSaCIAiCzYgyEQRBEGxGlIkgCIJgM6JMBEEQBJsRZSIIgiDYjCgTQRAEwWZEmQiCIAg2I8pEEARBsBlRJoIgCILNiDIRBEEQbEaUiSAIgmAzokwEQRAEmxFlIgiCINiMKBNBEATBZkSZCIIgCDYjykQQBEGwGVEmgiAIgs2IMhEEQRBsRpSJIAiCYDOiTARBEASbEWUiCIIg2IwoE0EQBMFmRJkIgiAINiPKRBAEQbAZUSaCIAiCzYgyEQRBEGxGlIkgCIJgM6JMBEEQBJsRZSIIgiDYjCgTQRAEwWZEmQiCIAg2I8rEjfjiiy8oXbp09PjxY3Kl3yOkffr160cZMmQgV/o9BQoUIHdClIlKOHToEP3vf/+j0qVLU0BAAOXLl4+6dOlCFy9eTO1dEwQ9vvnmG1biZcqUkSMjaPF89VRITb7//nvas2cPde7cmcqVK0f379+nqVOnUqVKlWj//v1y4Qqq4Pbt2/Ttt9/ygEcQdBFlohJGjBhB8+fPJ29vb+2yrl27UtmyZWncuHH0999/p+r+CQL44IMPqEaNGhQfH+8y7lLBPoibSyXUqlVLT5GAokWLstvr3LlzFm3j/Pnz7BrLmjUr+fn5UfHixenTTz9Nsl5oaCj7dDNlykRBQUH0xhtvUEREhN46c+bMoUaNGlG2bNnIx8eHSpUqRdOnT0+yLfiFW7duTbt376Zq1aqRr68vFSpUiP766y+99f744w92jcD6guLEPmJ02759e3r06FGS7a5fv57q1q3L6wQGBlKrVq3ozJkzZo/Bpk2bqE6dOvzb4IPHMfjkk08sOn5C8uzcuZOWLFlCkyZNsvpQHThwgF577TXKnDkzn1NY3z///HOS9e7cuUPt2rXjcwcZgfKC4tLlxx9/5OslJCSE5bxy5cq8X4ZA3uA6XrFiBVv2kGNcTxs2bDAae7t8+bLZ6wJgYIfvxHcHBwdTt27d6NatW2aPwYIFC/hzkOeMGTPyQNHYMUiriDJRMRqNhh48eEBZsmQxu+7JkyepevXqtHXrVurfvz8LKS7K1atXJ1kXCufly5f03Xff8XPc6L/88ku9daA48ufPzzfin376ifLmzUtDhgyhX375Jcn2cBF26tSJmjZtyuvihoGL0tjNf+jQoXTixAkaM2YMDR48mPcPF7wuc+fOZeWBGwrcf5999hmdPXuWlcT169dNHgN8HxRbdHQ0jR07lvelTZs2rMAE28ANHefu7bff5pugNUDB16tXj8/hsGHD+Lw0bNiQ1qxZk+Q7mjdvzkoCCqN+/fq87syZM/XWg2xXrFiRzzFcbp6enuweXrt2bZLvxiAHcosb/vjx4ykqKoo6duxIT548SdF1gXhRnz59eKA3YcIEGj58OG3ZsoV/HwZpyR2D7t2787UBmYa3oUGDBq4lmxpBtcydO1eDU/Tbb7+ZXbdevXqawMBAzY0bN/SWJyQkaJ+PGTOGt/fmm2/qrdO+fXtNSEiI3rKIiIgk39G8eXNNoUKF9Jblz5+ft7lz507tsocPH2p8fHw077//vnbZnDlzeL0mTZro7dN7772n8fDw0ISGhvLrly9fajJlyqTp37+/3vfcv39fExQUpLdc+T0KEydO5NePHj1K9lgJ1jN16lQ+/ji3oH79+prSpUub/VxcXJymYMGCLCfPnj3Te09XDvr27cvnbuzYsXrrVKxYUVO5cuVkZTMmJkZTpkwZTaNGjfSWY3ve3t6ay5cva5edOHGCl0+ZMsXq6+L69essq998843eeqdOndJ4enrqLcfvwW9WGDZsmCZjxox8PFwVsUxUClxW77zzDtWsWZP69u2b7LpwE8EF8eabb3IWmC7GUmcHDRqk9xruJIzUXrx4oV0GE17h+fPn7B/HSPHq1av8Whe4wLANBbgn4F7CuoYMGDBAb5/wOYxIb9y4oR3BYYSHURy+U3l4eHiw5bVt2zaTxwHuCbBy5UpKSEgwuZ5gHZCNzz//nC1EnFtrOHbsGF27do1H8Mr5sVY2DeVIVzafPXvG8oj1jh49mmR7TZo0ocKFC2tfw70GF5Mx2TR3XSxbtozlClaLrmzmyJGDLRVzshkeHs7y7apIAF6FIJMLbh74beELxo00OZQLw9JUTUOFA9NbuTBxoQGY33BF7du3L4nfGBcv9s3U9pRtYnvWfDe4dOkS/0W8xhjK/hkDCQuzZ89mV8zIkSOpcePG1KFDB3bBpU8v46aUMnr0aI4NwM1lLVeuXLFYNhFvM1RWxuQI7rGvv/6ajh8/zi7N5JSTvWQTcgfZhMEDxWEMLy8vk78NrrZFixZRy5YtKXfu3NSsWTNWSi1atCBXQZSJysCNGgKH0fmuXbsoV65cdv8OU8op0TOQeAPAjbhEiRLsF0a8BMkB69ato4kTJyYZ9ZvbnjXrKttG3AQjPkPgHzcFRqyw0DBChP8cgdaFCxeyYvr333/NKmUhKbiBImaBoPvdu3e1yxF7iI2N5RgWbrRQNrZiyfnBNYE4GGIU06ZNo5w5c/JNHAkjyIa0dJsplU0oLCSHGFs3QzKTLpHIAuW3ceNG/jwe2GfEX/78809yBUSZqAhcoK+//jpPVNy8eTO7jywB2VPg9OnTdtkPBMUx4lu1apXeaC05M95eKC4JXHxwUVgLLBAoQjygCBGgRUYb9j0l23N3kF2Fm+i7777LD0MKFizIQXVTGV7K+YRs2uP4L126lC0Y3JSRnaWAG7OjwW+BYsFvLlasmNWf9/b25usbDxxTWCszZsxg92GRIkUorSO2v0pA3ABuGriVFi9ezLESS4FrACO133//nW7evGl2BGYOZdSl+1lYTM64YJHNg5EulABGvoYYSyNWePr0aZJlFSpU4L+67hDBcuCeWr58eZIHUmwx0MDzt956y+TnMekWN18oG8Nsp5TKJqwD3XRhWEdI/3U0cJni+5HhZbjvGo3GaIaYguF7GPQgfuNKsimWiUp4//332RLAqAU3RcNJir169Ur285MnT+bUWVy8CHLjAsZFBncPzGtrgD9XGUUNHDiQwsLCaNasWWwt3Lt3jxwJFAnSknv37s2/BSmdUJZQkvgttWvX5soAxkCqKNxciDchrfnhw4fsCsmTJw8fG8F6kJaOFHNDFEvE2HuGN02cT8gSFDvmbsA1hQQTpHLDwrAGnFtYnIg19OjRg88x0tUxskd6vKMtE8RqRo0axdcWfjvmjCDBYPny5XzdYV6MMRDHw3UNlyvkEQknU6ZM4WNSsmRJcgVEmagE5YYPF5OxuSHmlEn58uW57ApMZly8cJnhhoogn7UgEwuBfwRecXEgdoE5IbipI2PM0eAmgVgRcvF/+OEHHrkhaInsGtyMTAFfOi5yWGjIssGNEBloGEnqJgwIzgXWJtyMOA+YNwIXD27MmA9lLbgZ//bbbywbyBDDoAnzNnDeHa1MABI74OJC7FCZg4KYYrNmzVj+TIHrF7EnDG5goeGagicCEyZdJTkkHfKDU3snBEEQhLSNa6hEQRAEIVURZSIIgiDYjCgTQRAEwWZEmQiCIAg2I8pEEARBsBlJDTYD0hhRRgL55NJvXDAFkiJRvhwpzc5K9RTZFNQkl6JMzABFgjxyQbAENEnCpDRnILIpqEkuRZmYARaJcjKSq1gruDcoU45BhyIvzkBkU1CTXIoyMYPi2oIiEWUiWCovzkBkU1CTXEoAXhAEQbAZUSaCIAiCzYgyEQRBEGxGYiYqokqVKtyy191BRdXDhw+n9m4IOohsJiKyaRpRJioCigSd7QRBbYhsCuYQZaJCMLkIDYTcDTTeMuwvL6gLkU3BFKJMVAgUye3bt8ndwKQqsczUjcimYAoJwAuCIAg2I8pEEARBsBlRJoIgCILNSMxEhWjSaSg2PlavFIJn+sRTpbtcAe9hnfiEeErQ6Aew06dLTx7pPbh6aFxCXJLPenl48V+8h3V0wefweWwT23b0dvG7yXnVSIQUILIpmEKUidrwJooLjKNrz67pKYusAVn5+YOwB0kURoh/CHl7eNOL6BcUHhOu956/lz8F+QaxEnoc8VjvPSigHBly8PNH4Y+SKIXMfpnJ19OXwmLC6GX0S733fL18KbNvZlYGD8MfJvkZ2C62/yTiCcXEx+i9h/3BfkXERtDzqOfa5fjdFECkIX3lI6gEkU2RzWQQZaJC0sWnIx9Pn1ev06WjlzEv9Ub8ukTFRVF0fDQrGd3PAdyY8VlYB4bvAWW7nh6ebDHogm3GJsQa3W46SpfsdsNiw7SppD7p9N/HNvFZw+3id1NM4rYFdSKyKbJpClEmKoLdPN5kUmkktxxAGeCfMaCQkvus4kYzBlxS6T3SO3y76TRyoaoVkU2RTXNIAN7JREZG0t9//01xcUnjDMx/ysRtb1gZ/vsrOB0021q8eLHpFUQ2RTaTQZSJkzl48CBPzDt16pSzv1oQkmXHjh109epVt5wwK9iOKBMnEh0dTceOHePn+/fvp/h4/QwpQUgtnjx5wkrE29ub9uzZIydCsBpRJk7k6NGjlDlzZn7u6ekp1omgGvbu3UsFChQgf39/dneJdSJYiygTJ1olcHFVrVqVX9esWZP27duX1DoxEUoRBEdaJRcuXKBSpUpx9l3lypWNWycim0IyiDJxolUSHBxMefPm5dfFixdPYp1wNlOUG2c1Ie6OaTISf3e6VVKmTBkKCAjg1xjwGFonIpsim+YQZeIkcHHWq1ePU2n5wKdPT/Xr19erksuT9dK578Qonl/Ch8BNlWkq8ejRI7aUFfz8/Pj1rVu3tMtENkU2zSHzTJxEx44d+W9YWOJkPlCsWDF+aME9FINDN72Xckqwr6RfOps333yT/z579ky7rEaNGvoriWyKbJpBLBNBXcjwRlArIpvJIspEEARBsBlRJoIgCILNiDIRBEEQbEaUiYrg9MswN08NjpbUYDUisimyaQ5RJoJq4JTgWEkNthTMCzl06JB2foit5MyZ0y7bcUVENs0jykRtqbF+7psay3MZPNx3no2aEdkU2TSHJLupDePtSNwDePf83HeejS2lepYsWUKPHz+m3r17U9asWWn58uXc7gCtDho2bKitvKAwc+ZMypAhAz/HBMWCBQua/yKRTZHNZBBlIghpHJTl6dSpE23btk27rE2bNuTh4UHPnz+n9evXU7du3fQ+4+PjQz169EiFvRVcFVEmgpDGgdJA6XjDZSAmJoayZMmS5DNYPn/+fLZOmjZtyiVUBMEWRJkIgosyb948LpHSqlWrJO/16tWLFcjp06dp9+7drFAEwRZEmagJxJ2j3Dw1NiG1d8B16NmzJ7148YKWLl2aJCaiWCKoXn3ixAnzGxPZFNl05Wwu9AL57LPP+ELBxVG4cGH66quvSKN5dTfG888//5zTHrFOkyZN6NKlS6Ta9MM4902N5bkMEW48z8ZOQOaVPjleXl5JXGB4D4F5gMrASsO25BDZFNl0acvk+++/p+nTp9Off/5JpUuXpsOHD9Mbb7xBQUFB9O677/I648ePp8mTJ/M6UDpQPs2bN6ezZ8+Sr68vqQlOifWS1FjBehYvXkwPHz6kp0+fcm+Sc+fOJcqURsOtDwB656CnTqZMmXh9KBrEVl577TWRTcG9lQma+rRt21brE0bb0X/++Yc7GioX0qRJk2j06NG8Hvjrr78oe/bstGLFiiQZLqkOBuQ+7pt+yHMZAtx3no0tdO7cWe91xYoVk6xTtmxZ7fN+/fpZ9wUimyKbruzmqlWrFm3ZsoUuXrzIr+H7RTCxZcuW/PratWt0//59dm0pwGqpXr06t8w1lbMPP7PuQ3AibqpILcFQNpO0fBYci8im6yqTkSNHsnVRokQJNtkxGhs+fDgHHgEUCYAlogteK+8Z8t1337HCUR6Gk70EIbUwlM2TJ0/KyRBUQ5pWJosWLeL0R+TLo8c64iI//vgj/00po0aN4oleygOjQXvWP5J8fsFespkjRw7u1x4eHm6Xg3rv3j05OYJ7KpMPP/xQa53AH4xSEu+99x6P4AAuNvDgwQO9z+G18p4hmBmcMWNG7UOZ/OU0xHMhmMBQNvFwRdmMj0+gRw8j+Pn1qy/o9cYr6cTRR/x659bb9NXoA5SQIHE1tZGmA/ARERGUPr2+PsTNPyEhcbICsregNBBXqVChAi+Dr/nAgQM0ePBgq0dtKFeB7wsMDOSgP1KMYbXAxYaMGN2LOzQ0lNauXUvp0qXj91HeAjeD5OCU2Eg3To3F/QH3EJmMrTqcKZu9O26gkCx+NGV2Q8qew5+KFMtEfv6Jt6q7d8Lp5PFHlD594n6MG3uIGjfPR1Wr67uy7Y7Ipmsrk9dff52++eYbypcvH6cGHzt2jCZMmEBvvvkmv48bOWIoX3/9NRUtWlSbGpwrVy5q166dVd8FBQILCIphx44dWkWC+kZQNMgsa9GihXZ9pB137NiR/x4/fpwfCPwLZuYyJLjvPBt3BVbG3N/PUet2BVmJDHq3HGXKnDjwghKZOL2+dt1uvYvzA0SEx9L2zbepZKlgIgcrE5FNF1cmU6ZMYeUwZMgQzrGHkhg4cCBPUlT46KOP2Kc8YMAAthbq1KlDGzZssHqOCWoYKS4vWCfI5w8JCeFlefLk0SuyB3S3j/Wh2MzBKbEZ3Dc1ln+3j/v+fjXjSNkMfRZNP35zhHz9PKlrz2JUr1Eeiz7nH+BF63e0I2WO8ugP9lCe/IE0aGg5u++jyKaLKxNYC5hHgocpcBMfO3YsP+wBAp/Xr1+n+vXrs5tNQXfWvS5RUVFslRjOAxBM4CVHxl1APCRHLn8KDvGlnUc6U+Zg6ycR4/rGOA3XH6yYlGzDYkQ2XTcA72yQ2bVmzRqOj/j7+3PlVQVjlgfmAaxevZoaNWokWVyCakHfE2Oyqyvf9iYmJp56tFtH3489zK9tVQK4/j4dW50tG3DowAOTAzzBMYgysRAE9VetWkW1a9dm9xbqGT158oQvutu3b3NDIkPgTsMcGLjBBEGtoFGWYVrwxo0baf/+/Q77Tm9vD/ppWn0aMtz+LqnzZ59Su6araNum23bftmAaUSYWglpHd+/e5UA75rVg1n2VKlX4+a5du3g2PsAFiNgMCuhduHCB6yFhHdQNEwQ1Uq5cOdqzZ4/2NcrWo3adbvkVe3L65BO2GmrWyUlZs/nbffslSgXTwtWvUcOmMohzJmk6ZuJMkC2Gi86QkiVL6r2uUaMG/0UxvREjRlj3JbDKw4gohNwXeFbsf38RkqFatWo0Y8YMbZl6DJgg13rVhO0km7duvKTWDVfQNz/Vpp79SjjsvNSpn5v/PnkcyRlidkFkM1nEMlERSkqsu6bG8hyGGDeeZ5NKoLoDShGdOXOG3bawwhVL296ymSdfBlq8rjV16FqEHM32zbeoUrF5dOOa7fX1RDbNI8pERXD6oa/7psZyCf70UoI/tawTpNeHhYUltUrsKJsIlGOCoZ+f450i1WvlpO8m1qGs2Wy3TEQ2zSNuLjNgjgrqHyEl2B7lK4xlzsgZ+Y90/7m4xDBJFesEE3vPnz+fxCqx191izYqrtPDvizRnQTPy9HT8OBapwj366rvSAr0DU7StD4Z+QGERYTRr6iw77Z3rIZaJIAgMUtjRSMuSzospITDQm8ujOEORKNy/F05ff3aAHtx/NSfMkEFvb6CzZx4nWf75pzupeaOF1P+N9RQf757eAmsQZSIIgnYScM2aNR12NOo3zkM/Tk3s+mgN61Zdoya1llKhrL/zX7y2hlXLrtKdW8gesJxTJx/R3TthtHFrVypWPJhOHpcKrOYQN5cgCE4BmVyZg30oQ6B+T/rkgOLo32vzf7Pcic6fecqvZ/3dhF5rk5h9lhw5cgbQwTPd9ZYhLfn94VvpzKnH5OmZjvz8vWjm9ON07Wool2iZv6gNHdh/lxo1KcDrN2lWgLZ8fICK6iduCgaIZaImYElH//fXXXHn3+7istmg2mKOmVjDhHFHtYqEd0ODID7RxHFHU7wf69de5arDsDrW/tuFgoN9qVqNXLRyXSfy8fGgM6cfU+izKMqYMVHpZQzypogIEUxziDJREZx2GevmqcHhkhrsqrI5Z0Fzeq2teWtCl6uXnmsViQJeX7n03OJttGmykn6d8qor5YXzT6hO3VcTGqFYyldIrGCRO08gK5KgTD704kViOZkXz2PI31/S1s0hykRFcPqhp6TGCq4pm/Ua5qacuazrWFqoaBBbIrrgdeFiQRZvo0OXIlS4yKv1i5cIoT27b+uVwNetrQdlVb1GLtq+9Qa/3rLpOhUo5OQmeWkQUSZqAvKMenfuaZgkzmHwd995Nq4umzu33aG/55yz6jMjRlbSurZ4N/5zeWG5pfQbUJqatsyvfd2yVSGKi9NQs4YLqFWzRfT0aVSSz5Qrn42yZffnbK5zZ59Q2fIeXDVYZNM0EoAX1IUMb1yWvbvu0uH9D6jXG5ZHshFkR7AdMRK4tgoXDaIRoypRy9cLWlzm/tnTKKpQOavW+sDfiZMbG13/m3GvGnF9/d2r5zNmzkgsp2IAWoCjrlmHDh3I3RFlIgiCU/j4syoWNYkzplAsydwyxu8zztDm9Tdo78lu5AjQ6RXlZypWrKitbeauiDIRBMEpKIrk6ZMobojlDD77ujr1ffuVJfQy5mWKtvPj1B/pweMHlMs/l3bZnTt3uGIAmDt3rl6HV3dEnApqI8H5Xxkfn0CH9t/nSVogOjqewl46rjGS4L6yufDvC1Sj7ALOkHI0kGEvr/RUuGgmh2wfrSX8/Py4LfeuXbvo2jXrJlO6GqJM1JYaG+Gc1Nj9e+7Rr5NfpUv2aL+e1q28zs/37LhLJfL8qZ01DJ+zU7rW4StQukzi7y4rmw2a5KUvx9WgwIyO7YGLgRGU1r/rEjOy7C2bsEo2b95MlSpVYmWSI0cOtk7cGVEmbgQskLi4BK0y2brpFj/38EhP/+7uQF3+a3laulwITZxen3LlSUzjfKPbv/TxsN0O3z+ewxDvvvNs3AHU5urepwS7vCIj4hz2PehhMnBoOapeK4dDZBNWCXoWoc8RfkuPHj3c3joRZaIiOO0wwDHph1AiXVqvpWmTTvDrd96rQAtXvaZ9v2DhIJ7pq1zwnXsU0/q4kYbZ+T9Fc+d2mNYdZm/4d3tL+qU7yOaSfy5RrfIL6N7dcLI3kE/MZB/6fgWefGhv2Xzx4gVbJd26dSNPz8Swc9OmTdk6WbFiBbkrokzUhoMG5ajUinRKZaQGX7KlmTX1GuXhHhTg288PUve267UWjt2xvGyTkIZls1GzvGw55Mhp37aaKxZfpnqVF3MdMEfJpr+/P33wwQfUqlUr7Vuenp40ZswYViruimRzuTjLFl6m2LgE6tqzGL09pIzN20O7VbRCdWYZccH1QDbXoHcT22Bv2XiTHj2MpK69XlnDKXHhwl3bvFUBnpCYN3/K+pZYAhSHMaVRpIjju0eqGbkjOInDhw+zeazL06dP6fjx4w79XmRpHd5/327by5TZh7NjkPE1bOB2Onnskd22Lbgn+3bfs7qsvC6nTz6h+lUWc+8SNMTq1L2oXfdPsAyxTJzE/fv36dGjR1S3bl3tsu3bt1NQkOU1hlLCtxNqc+0he4Ntwjd99044lauYWCRPEFLC6K+qU0xMPFslO7fepknjj9Fv85tS5mDTc1EunHtKjx9FUe16uShX7gCqWDkbu26F1EOOvpNA06EzZ85w+1+lDAPy0qtXr/5qJdzzEY+0w73/2zEHafOGm3yBwvy3N+jhvWh1K2rROrHng91wXIKPYAt2lE1jeHsnFlL09fOkQkWC2AIGvTtuoN9/PU0Xzz+j7m3X0e2bibGQP2aepc8+3Msp63CZTZndkDO4HIrIZrKIZeIkQkJCqESJEnTkyBF+vW/fPqpQoQJlyJBBuw6nHaKonY2RTrigLl8MpfwFbe9ZnxxQVKHPoum36aep91sluTCeTdvDHIYoonSBkhqsNuwlm+aoVjMHPxRq1MlBWbMntvoN/C/bEHw4ugr5B3imOMZiLSKb5hFl4mTrZM6cOfz8+vXrNHDgwKTphz62p18iLfL3f5qRs/jrt3NcSK9x83w2bYfLm/M9S2Ytqg17yaa1IIVdYeZfTbTPnVWORUFk0zzi5nKydVK4cGF+Xr58eT2rRIuNE4PDw2I5oOmUGev/BeQPn+9hsyJhMMjEPEkxTNSJYyetqxuRTbOIZZIK1snly5epWrVqDtn+4QMPuDTKrqNd2PfsDBD4TJxdr2GrKNA7aVrmoLc30LvvVaFSpbPoLf/80510YN89ypc/I703+H2KjA6nWVNnOWW/BcFRjB07lo4ePUrh4eHUu3dv6tOnD509e5a+//57TsRBTa+qVavS8OHDydc3qZX1/vvv08mTr8odwUU+ZcoUVZ8wsUycDGbJYsJTxoyOiWfUrJuT1u9oRwUKWbd9pGY2qbWUCmX9nf9ak6oZFRVHJfP+xRPGrOHUyUecEYZe3MWKB9PJ4/FWfV4Q1IqXlxcPHHXx8PCg5s2b03vvvUdlypShLVu20Pr1601uI1++fPTJJ5/w44033iC1I5ZJKuDIoCGyYqxN1YXi6N9rs7aL3fkzT/k1mhJZ0kfC19eTi/dV/m+WPICb7f3hW+nMqcfk6ZmO/Py9aOb043Ttaij5B3jR/EVt6MD+u9SoSWI2WJNmBWjLxweoqOV9kwRBtYwaNYoOHTrEZVcUihcvzj1PwsLC6N69e7R3795kt4HaXzVq1GArJi0glonasLEy9y8Tj9PyRdZZCBPGHdUqEqC0SUV3O0tB8b4iOqW+16+9SunTp2OrY+2/XSg42Jeq1chFK9d1YlfYmdOPKfRZFGXMmJihg7pgERESeFc10pXAZjZv3kxdu3blCsOoONyyZUuT6546dYratGlDHTt2pI0bN5LaEWWiIjj9MMa2Mt9nTz2l2/+VjreUq5eeaxWJAl6jTaqlIOiP2fYKF84/oTp182hfQ7GUr5BoMeXOE8iKBEX4XrxIvEOhv4W/v+2/X1CvbKZl+HeH2f77q1WrRl9//TW7u44dO0a7dxuvxo3JzbBu8PDx8aFJkybRkydPSM2IMlERnH7oYVtq7C+/N+JqqdZQqGgQWyK64HXhYpYH8GdMOUkzp57Svi5eIoT27L6tN2Ne170HZVW9Ri7avjWx38SWTdepQKHEiWuCa8qmQJQlSxaeqNy/f392BaMKBoiPj6eYmBhKSEgsoNquXTtq2LAhNWrUiOrXr09xcXHsGlMzEjNRE7jXwj1q4+BPKXpnKSgxrxszUf5iuaVgXguaaCm0bFWINv17nZo1XMDZXoiZGFKufDae6Ni80ULKkzeQKlX3oKhoKUHvyrKZVuH5NX6Wy+b27du5Nzy4dOkSrVu3jm7dusVWRs6cOWn//v3aIDuYN28eu76+/fZbzvIaNmwYKxE03oJrDBlfyrpqRZRJKoDRB4TEESycd5G++nQ/nbzam11LloAgO4LtiJHAtVW4aBCNGFWJS9ZbCr5Lt5wFrJCJkxsbXfebcfW1z7/+7tXzGTNniF9eUC9WGM6zZ8/mkkkAigOPQYMG0ZIlSyg0NJRr8qGEPdKGTVkwCxYsoIiICMqbNy+9/fbbDssAtReiTJwM2n3+/fffPPsd2Rr2pkKlLPT+qMoUG5vAgW5LgUKxJHPLGAf33acpPx6nn2c2cMjMZPiWv/zyS5o6dSrlyfMqDiMIauXvv/82uhzBdGNgHgoeCp999hmlNUSZOBnU5AIYqbRo0cLu2y9eMpgfzgS+X19/T21xvpcxKWtM9OPUH+nB4weUyz+X3vK//vqLJ3/BFfDxxx/bZZ8FQbAvEoB3Ipj5ippcABWEDfubMHaIbz64H0Hfjz1EkZHOKXNavVZOmjW3icVuNWtAtYDTp09r/dCPHz+2+3cIFiKxdyEZRJk4kT179lCpUqW0XdkUK0WB0w7DbU8/RH2uv2afoxvXjCgrO7No3kVtWXC73Kyi9G9acBegphlA8HLhwoX2+S7BKuwlm5Zaui//Sxm/ef0F9em8gc6ceqJ1qU6dcNwhPXqslU1BH1EmTrRKrly5whOVAGa2YsRt1DqxEdTkOnCmG5Uo5Vh3Fyyfid8fpeWLr9hle1zePO5VmXNYJVDATZokVotFbv7atWvFOnFx3u65iUZ/mDg7HEkduhbv2dNPaeXSK9plv04+qVU0jsRQNoWkiDJxEiidUK5cOW2lYGRrFC1aVJsiCDjtMMA+qbEZAr35Zo/GQuhi5wjQIOvf3R1o8LDEXt62wnMYvF7NZYBVkitXLq0CRt9tpEiKdeJ87CmbSbat0dCqZVd44iro2K0odelZjJ8HZPCiPxY2p9JlE63Tfv1LscyBiPBYmvvbOW7b62gMZVNIiigTJ4GaPLVq1dJb1qBBg6TZSXYc+Jw7/ZS+/+ownThm3zhDZEQcfTX6AF/8gRm9uXGRXcBv93l1DG7evElvvvkmF8gDqFGEVMqrV6/a5/sE68+PA3jyOIo+HLqLFQpAViHa8Zrcjf8mv6LG286jnalj1yL8+oevD9M/f513zE4ayKaQFMnmchKwSgCKvCkgb1yJoTiCSlWz0f5T3biXNnzMYS9juQaWrdy6+ZJWLLlCrdoW5O9wFMjVx3ycEydOaJe1b9+e2rZt67DvFJwHEkVCsvhSlqx+tHV/J8qd10h/HzMok3Nh3Tx6GKnNKBScj1gmLg4UCfhj5hlqVGOJNrBpLbhYN6y5zi6zYiUy057jXRyqSICpiZ2OmvApOA/IUedWa2j8V4f5dUoUiaG1Mn5yXer/Tll+jZ7xjgJlT5YvX05RUa8qPigZmuhh4q7IVekmtHi9AJdHgVsK5Va2b77Ff61xRQzovZnWr76uLTsvCLa0Shj9VXXq/ab9ew5cOPeUGtdYyjLuCKKjo2nmzJm0cuVKPQXz9ddfmyzc6A6IMlETiO1FOib9MFfuDNSjbwl+fmDvferZYQOdPpEYuDx2+CFnxEC54G/os2heDh92p1Zr2CqBK2Ld9nbUtmNi22GHIf2xXF42b91ITCVv9lp+yps/aVdOW4Hl/OufjalOg9wOkc3AwEDOLFy8eDErEbBhwwZ6+vQpu2HdFVEmKoLTDuMdn35Ys05O2ri7A5WrmNhCd9L4YzTuy0McWG9Wexnt2JJY7RcKBFk0EeGJkx/LlNdvuWtveA5DpPuWOXcH2bxzO4waVl9CC/++QI4CLi/E85AYApl2hGx2796dqzIgvR+DrX/++Yer/KKOlrsivgoVwWmX3o6vmouLrUy5xFRLMHNuE3ryOJKzY9Zua0f5CyaOFmvVzcUPQbCXbObI6U/T5jSiWnVyOvyg7tp+h97qsYm2H+rElrk9yZ49O1snaL2Lwq1Pnz6lnj17kjsjlonasD3ZympQEBIXGyaCVaicVRu0dzZ8o8ogJehdWTaRfdWsZX6eB+VoKlTKSsM+rED+Rtof2EM2YZ0oPUgaurlVAsQyEQTBKWxaf4NWL79KE6bVt9/cpGRAssk77+k3igv0TlmM5oP/fUBhEWE0a+osPeukdOnS3F63p5tbJUAsE0EQnALmOiH+5gxFovD4UST9MvE4u3FNMejtDXT2jP7E3ufPo6lBnXmUM2RKkvd0+fzzz2nkyJFub5UAUSaCIDiF5q0K0Ox5Ta3+3LpV16hJraVUKOvv/BevLSU6Kp5+/uE4Xb383Krv9Pf3pCXL21Pb9kWTXQ89iRo3Nt4Ezt0QN5faiE3tHRAEx8gmJsz6+XtaZZlAcei2lD5/5im/RmdQS5q55coTQOdv99UrFonsq/eHb6Uzpx6Tp2c6bik9c/pxunY1lJNQ5i9qQ15eHpQlq3+Kf6s7IpaJiuC0w2g3To1FbBPVZqSWnkvKZsVi8+jPWWet+syEcUe1igTgL16jxbSlmYuGfXbWr73KyzZu7Upr/+1CwcG+VK1GLlq5rhMno5w5bcKthSklIpuuq0zQBrdXr17c8wKFAMuWLUuHDyeWaFBGIfBr5syZk99HOfNLly6RGuGKpLhw3FRilTkMUubbNWVzwrR61Lh5Pqs+c/XSc60i0e6LhujKJcvdVr07bqA/Z79SYhfOP6E6dV8VWIViKV8hKz/PnSeQQp/pl0nRRWTTRZXJs2fPqHbt2uTl5UXr16+ns2fP0k8//USZM2fWrjN+/HiaPHky/frrr3TgwAEKCAjg/HDDujqqAPfSAPetTMppl76SGqxK7CCbbToUpgKFMlr1mUJFg9gS0duVdESFiwVZvI3K1bNRcMirdPfiJUJoz+7EiblKYoBSiRgYKi8tniKbLhsz+f777zmLYs6cOXql3nWtkkmTJtHo0aO1lWbRTxwpfStWrKBu3bqlyn4LriqRQnIcOfiAbt8Ks6okD+rJ6cZMlL9YbinDP9Jft2WrQrTp3+vUrOEC8vJKzzETY3Rsu4xOnXxEly49o4JFYqlkYg1JwRUtk1WrVlGVKlWoc+fOlC1bNqpYsSLNmvUqD/zatWt0//59bac+EBQURNWrV0/SMle3iBu6H+o+BEENOFo2Q0NDaevWreQo1q26Tr9MfNVOwBIQZEewvWTpYI5n4O/seU2o5evmg+/g3t1wun71BQ8sFWCFTJzcmP7d1o1jJktWtKdSpRNLBX0zrj7VrZ84+XDpyg508dpA2rKjO1WtblzhPH/+nHbs2GHVb3JV0rQyQZOk6dOnc8fCjRs30uDBg+ndd9+lP//8k9+HIgGwRHTBa+U9Q7777jtWOMrD3We1CurB0bIJRXLo0CGHtUX+cHRl2rjL+kKIUCib9nakq4/e5L+WKhIwfdIJLlbqKMaOHcvVgm/ffuU2c1fStDJBGQO0dP3222/ZKhkwYAD179+f4yMpZdSoUTzaUB4YDeICQ6zFHiAJIFncM/YupEA2b92yX4n1ly9f8uDM09OTW0w7QjbRtgBWAdpJO4uRY6rS7/ObamMiL2Nepujx49Qf6cvxX+pt++HDh1zoEcyfP5/cnTStTJChZdipsGTJktzuFeTIkYP/PnjwQG8dvFbeM8THx4c7ICoPpWWsM+C0y3A3Tw1G9XtRqBbJJh72AskpuJ4waEK2o6F1Yi/ZXLbwMlUv/Q+Fh8U6pQEX5o2Uq5iYqWVv2VywYAF5e3tzs7YtW7a4vXWSppUJMrkuXNAvZX3x4kXKnz+/NhgPpYETrQA/My6cmjVrWvVd9+7do7lz59K8efM4VhMfH0/nz5/nZRAqQ/81/M9YFyMW9D2AhSMkD6ddxkr6pbOBVYLWyBiYYfBUpkwZ09aJjVSvlYMGDytHPr4eDi+jUrfSItq26ZZDZBNWCTJIK1SowMokc+bMbm+dpGll8t5779H+/fvZzXX58mU+meiA9s477/D7MG2HDx/OPk0oABRk69OnD+XKlYvatWtn1XehIQ6yv1DQDf5qjN7g/urRowfVqVMnycXn6+tLHTt25PcR0zl+/LhlqbH+7pt+yHMYkH4ppolTweCqUKFC2pR6DLQMrRN7ySba8w4eVp5nwcfFWd7p01r8/Dy5p0nZClkcIpsYQPr7+/O8NtxnunXr5vbWSZpWJlWrVuVezGhMg9HUV199xanAuhU8P/roIxo6dCjHU7B+WFgYd0XDzd4aMmTIwPNZAEYi6F+AiZIYyeXJk4cePXqktz62r3wH1tfNY3fdM2IjOEQ4ZG7q5Ustzp07x1a+Atxn5cqV43lbjpJNVA9uUHUxPbgfQfYGnUIDMnjR59/U4AZv9pZNWHKwSpBFCjcXeO2111gZL1u2jNyVNH/rat26NVscmISIiwIBeF1wE0fGBbK3sM7mzZupWLFiKf4+BD6vX7/OCkQRJKCbeqgLvhNWCUYwgqBGBg0axKn1uqB4oa6CsTdVa2RnyyEki31756xZcZVqV1hIt28mtgZ2VOzqrbfe0vNueHt783w2a93nroRMEbMCxD3WrFnDoxAoD6X/MzBmeSCusnr1amrUqJH5LC5BSCUUi1sXWNOOJEfOABr1RTV+vn/PPXoeGs1VhVMKrkdcg/Ua5qHhH1Vkd5qjgOLo1KlTkuVlypQhdybNWybOTENG3AWjNbi3YNI+efKEFQZyzLNmTZoxAndaiRIl2IoRBME4K5dcoVnTEnupp4QL555S64Yr6emTKMoY5E3930mMYwjORSwTC4EL7e7duxxoxwPzWjD7HkF/5Oa3atWK10NCABQI/KrININbDG44uNawfrLgWkLJMPeMvyfiuJisYAsOlM1vJ9TmVGEogAN779GMKae4G2OmzD4mP3PnVhg9ehjJbaZRdwuP6Oh4cigim8kiysRC0J4TQUlDMK9Flxo1amib5owYMYKsgdMO49w3NZbnMEQQpQtwz9+vZhwpm1AiSk94NLNCFd+gTImv33lzK9VpkIuq1cxBP317lEZ/XY1y5c5AP/9wjA7uu0/bDnairNn8ae7SFnbfL719FNk0iygTFcFph16SGiu4r2zWa5SHHwqIfaT3SM+VfR8/jqTYmETzYPjHFTn9V9xZ6kGUiZrAoA+WvZsOzHkOQ4D7zrNRNakkm598mRikB4tWJ7qSAawTZyKyaR4JwJshPDyc56fgrz2IjIy0y3ZcFjdVpCkBpU/sWTcO5VSEZBDZTBaxTATBBVLWlyxZwjPWe/fuzZmFmMyLgUtcXBw1bNgwSYVhVIrARFyAuRG6fYAEISWIMhGENA6yCTHvYdu2bdplbdq04eoMyCbEbG3DRnCYeIdSP4JgL0SZqA3nVecWXAQoDd1qDMoygIm1WbIkrU+F5Uhrh3XStGlTyybVimwKySDKREVw+mGUm5egR2hKigXYBVStfvbsmXYOlC69evViBYJ+HLt372aFkhwimyKb5hBloiKUtEt3rZrLcxjQ51sinXYBBU/RGmHp0qVJYiKKJVK8eHEuP28OkU2RTXNINpeagEGCmKi7GiZICfaR1GCbj6NGw2V+lLpbhi4wvIfAPEC3RqX0fLKIbIpsmkEsE0FdJK05KFgAGrChYRNaI6DgIMr/KIqlXr16/BxlfYKDg7k6A9aHokFsBYVLBZFNWxFlIgguAHpr6ILacYbotkHo16+fU/ZLcB/EzSUIgiDYjCgTQRAEwWZEmagITr8Mc+PUYPCq35jg5FI/9+7dM/meyKbIpjlEmQiqgW9YMW6uTAVVIrJpHlEmakuN9XPf1Fiey+DhvvNs1IzIpsimKrO5UJjuwIEDdOPGDYqIiODCdMg+kWJzqINB7gsMEsylE8NEnYhsimyqRZns2bOHfv75Z1q9ejXFxsZSUFAQz8RFbjwUTKFChWjAgAE0aNAgCgwMdOauCYIgCGnBzYUqpl27dqUCBQrQv//+yz3Snzx5Qrdv32br5NKlSzR69GjasmUL90vftGmTs3ZNEARBSCuWCYrNoUYQZt0aA1YJHn379qWzZ88mm1mSFsFMZGMtRk0tFwRnYUwGsQyIbAqqs0wGDhxoUpEYUqpUKWrcuDG5EgsXLqQrV67oLYPSXLly5asFuH6j//vrrrjzb08lZsyYwa5mXXbt2sVuaS0imyKbas/mCgsL48qmug9XpHDhwnyBKiM+FNvD6yJFimjX4Wq5se5bNZfTL8MlNdjZ5MuXj/bu3at9jXkrhw8fFtkU2VS/Mrl27Rq7vdC7GkF4VC3FAwXoLKpgmgapUKECK87r169rrRK4EGCFKXBKrKekxgrOpVatWnT+/HmOYwJkWubPn59y5MghsimoOzUYjXkwQv/9998pe/bsbuGXhYuvevXqdPDgQX69f/9+qlOnDqVPr6PPcRh83Tf9kOcyBLjvPJvUAoM4DGrOnDlDCQkJdPz48aQtfUU2RTbVqEzQjOfIkSPcmMedgHWyb98+fg4lomuVCP/hpopUDdbJrFmzuCS9oVUi/IfIpvrcXKgnhKY87gask0qVKvHzGjVq6FslgpDK1gmUCOZ/1a5dW86FkDYsk9mzZ/PExDt37nAjH8Msr3LlypGrAkWKtOfSpUun9q4Igh4NGjQgX19fsUqEtKNMHj16xGmyb7zxhnYZ4iZKvrvSctQV8fHxSdLISA/X/emCysmWLRtPLjaJyKagNmXy5ptvci2uf/75x20C8Banxka6cWos4u6R/9XnElSFyKbIpiqVCQo8rlq1Si+PXfiPdInZTLHxsdpDkj5devJI78GWW1xCXJJD5eWR6CbEe8o8FgV8Dp9P0CRQfIL+0BJK3DN9ogjofp8C3sM6jtouPofPv/pg4ujXXefZqB6RTZFNtSmTRo0acUaXKBN9OCXWl0jjoaHoOEyFT8TXy5cCvQP5pv4o/FGS4xnsF8x/n0Q8oZh4/e5SmXwzkZ+XH4XHhFNEXITee96e3rxdKIr70feTbDdThkysMJ5GPtXbHxDoE0gZvDNQZGwkhcaEJlFumX0T5wvdD7ufRBFlDMjICiU0KlRvu/jdUoJenYhsimyqUpm8/vrr9N5779GpU6eobNmySQLwyfptXd2V8ILIM9CTCmYuaNQywQ3clGUCpWHKggjwDqAQ/xCTFgQ+a8qCMLfdzH6ZTW7X19PX5Hb9vfz1LBPPl55EUTL6UyMimyKbqlQmyOQCY8eOTfKeqwfgzaJJvHAVBWF4bIwtV1Bu4MbAjT+9h+lU5NTYLhQS/undsGS+onoR2RTUpkwwy1YQBEFwHVJFmQiCvbh79y7179+fYmJi6NtvvyVvb2/64IMP9NbBcszvMTao+fPPP2n9+vVc3BAFDydOnMhzLQQhpURFRdGIESN4Yja8CejPNGzYMH49d+5c7uGEKQL16tWjd955h6sOGIJMVzQRxDQKzLv76aefVH9CUk2ZHDp0iLZt20YPHz5MYqlMmDAhtXZLSGNMmTLFaCWBnj178oxupWKzMZYvX07z58+nunXrsrK5cOGCWM2CzWg0Gp760LZtW1YgaD/x22+/ccIRmgMiZox7H5QFBjDt2rVLso24uDhq2LAhLVq0KM2ckVRRJhgpoqsianMZzjNx9zknGBMHQrk+f07uBn63NTbBjh07ePJry5YtWTHogsoK5cuXT7aHDpq1oQbVqFGj+DW2I5hGZNMy/Pz86K233uJ2GsHBwaxMADrNKvKIAc7//vc/niZhjN69e7O1LcrEDOgDj4rB/fr1k2tX92LVaAh5bAUjIojmz3e7Y9M2IoKuEdERg8wxY8AtNX36dE7mgNvAkE8++YQHJiiuiedodaBLZGQkuxBCQkKoU6dOFB0dzSnrcJFJzbSkiGxaLpsAJZOU+xsqC8AVqzuwQb8YZdDjKqSKZYKLVYrJJcVLoyEk2UbBOnPRvi7Jgd+d+b/jYA6M2FCcENbtuXPneNmTJ0+oRIkSNGDAAMqbNy/35VizZg37qTEK1AWjPvDs2TNOU0fZ9U2bNlGVKlVYqQj6iGxaLpsgS5Ys9N1337HljIEzrBPEURSLGjJZv359l5K1VFEmuHh/+eUXmjRpUmp8verhaXwBAeRu6E+LtKy+m651iyDlV199pa19BjcXlMnNmzf5NVLO8fD09GRLBYF2f39/atGiBVsoW7Zs4RGlYOYciWyaxcfHhwcmeGzevJljJFAm+Dtu3DiqVq0ajRw5UuvW15XNtGoZp4oygSsBnRbhN0RPD0O/9rJly8id8cF/4eHkjr87ysJ1EdxEGX9lpLdz505u6IQunuiVA9nCX6D0zZk3bx6PCJXsrsaNG9O6det41Hj69GmXczs4ApFN8+zcuZOOHTvGcofK6IiLFC1alC1lKJKMGTNyJhfadiNmV7JkySSyefLkSW085enTpyynyOrKkycPqZVUUSbvvvsua2hkK2BE6O5Bd4XYdOnoGWImMKWf4Zn7+eXv/XcczIELVVESSitkKAJkBm7fvp3TfREIxaAFmV3GQJA0NDSU/vrrL7ZUBg8ezNaMkBSRTctlMygoiMtFbdy4ka1fKAfI1tatW1k+IXPjx4/ndZs2bcrKxJANGzaw2xUgJoiUdQzC1axM0mkM62Q4gcDAQFqwYAFf6GoHGRkQjufPn/OIwpFAUJ7cuUMFcubUxgHcCVxU1+/do5DcufWC6rgwcSH98ccflDt3bnJ3OUmN7xTZNC6bK1asoJkzZ7LloEacKSOpYpkgXc5U7r+7AzfPS/hMDbKP3AH8bkvdXILzEdkUkiNVIj1ffPEFjRkzhiKQAisIgiCkeVLFMpk8eTJn4mDCImaEGgbgjx49mhq7JQiCIKQlZWKsfIDwCg3pN8eyqeGUA5tr2Xu73DNDcjFUjcimoCplAheXYAIPogSfBLr2DPNt9ZVF1oCs/PxB2IMkCgO9Srw9vOlF9AtuhKUL+oYE+QaxEnoc8VjvPSigHBly8HM03jJUCuhVgp4kYTFh9DL6pd57aNqFJlhQBg/DHyb5Kdgutm+saRf2B/sVERtBz6MSS8fEBcYRZfxPqQjqQ2RTZFMNygQjU0kBNnOMcBP1I9J4asjHkzP6teDYvYx5abJHSFRcFEXHR7OSMfwsRpP4LM6B4XtA2a6nhydbDLpgm7EJsUa3i/a6yW03LDaM/2ISlk86/fexTXxWd7vpEsQsUSsimyKbqlEmpUuXps8//5w6dOjAZcJNcenSJa4ajIqvmCHqdnibbyplTcMpZzTXstd2WZlEEqXLIBeuKhHZFNlUgzJBqfCPP/6YhgwZwhN1UGYgV65cPKkH9ZHOnj1Lu3fvpjNnznAdJUzycVXEShPUisimoPrUYJSuQKXMVatWcRVNlA+A0sDsZKQKwyLp06cPTwj6/vvvk1R5tQSUKsAoefjw4XqNatCABjPtM2TIQB07dqQHDx5QajZz+uGHH3gWrGDElZJB/NKpxcGDB9krgF4agj4imyoMwNepU4cfjmi2NWPGDK5fY1hUcu3atbR48WJWUFBgcLXt2bOHUoN9+/bx6A91epo3b54q+yAIhkAmUcsMiuTUqVPc3EkQrCFtlqc0ICwsjC2cWbNmUWad0u0oIYAOZxhtodRz5cqVac6cObR3717av3+/0/cTlW5RiBCgsCBKHSRBBoVCKoAuk7GxsRQQEMADHqPWicim4OrKBG4s1Plq0qSJ3nKMtHCB6C5Hvwu0ysQF42ygxJSibignY6jQ0mnScc0K/isITrRKYKlDNpEcg/LpsE5ENgW3UiYoGIkZ82hEY8j9+/f54kATJV0w8x7vGQMd92Ax6D7sZZUgLlSpUiV+jfLpuGB1t48UXkza47+C4CTZhFWC2GKhQoU45ojGdYbWicimoCplguCzPbl16xYNGzaMg/nICrMHUEqIrSgPdOyzB7g4Ec9BxWSQNWtWKlKkCMdOtMAgQU8sdzVMoEMx31J0qVNlE1ZJzZo1ycMjMaUcpf1hnSg9XhiRTZFNNSkTzDWZb8fe5nBjPXz4kEf76FCGBxolofYXnsMCQXtWw8wpZHOhKY0xRo0axbEW5QGFZQ/gWqtVq5besgYNGnB6tPBqEmSiceau2jR5HCWb6AOjm7gC6wTdJ1OSUemqiGyqLJvrm2++oYEDB9Ly5cs58wql6G1NNzb07b7xxhscF8GcFozcUEQS7ViREqyY9GjjipGYMTAiw8PeVKhQQZssoKCMMAWd9EtfSQ02haNks3r16kmWqbVvTGohsqkyZYIJiy1btuQOd2jXi+yr119/PcXbg8vIsM0qslEwp0RZju9C72UoLjSHGTp0KCsSpeWroDJSpVqcIFiAyKa6Dk/BggW5feXUqVN5vgcySOCSclQJerS7RG0oWCYIYGJux7Rp0+y2fUEQBCGVdO2NGzdo2bJlPCekbdu2SZSJLaD/ty4IzP/yyy/8UDucEhxGlC5EYgaCuhDZFFSnTODaev/993nuB+pwIatJEARBSNs4VZkgQwT1f+DiQh0uwUSZb3ft54GfHU18DAR1IbIpsqkqZRIfH08nT56kPHnyOPNr0xbGq8e7T/plrKQGWwqSTVCTDn/tQc6cOZNfQWRT0tbVokw2bdrkzK8T0hg8y9pDKgAI6kNk0zyS7Caoh3T/ubgk/8AqkKW4ZMkSevz4MfXu3ZvjkJjLFRkZySVRGjZsmGS2/MyZM7klA0CqPLIsBZFNWxBlIghpHGRDdurUibZt26Zd1qZNGy6Pgpny69evp27duul9BpMfe/TokQp7K7gqokzUFuSLktpUgnVAaRi2wlbqbKGcUJYsWZJ8BstR2gjWCTqf+vmZyXoQ2RTMIMpEbQHoOAnyCfYBBVDREhvtGQzp1asXKxAUc0S7bCgUkU3BFkSZqC3I5+XmAeiE1N4B1wEN41CmfunSpUliIoolggrBJ06cMLstkU2RTZfvZ+JSIPDs474BaJ5lHSHNwezR7App+ACFTg1dYHhP6VWCysO63UlNnxyRTZHN5BHLRBBcgMWLF3M7hqdPn3KR03PnzmkVS7169fg5Kmyj4CmaxWF9KBrEVl577bVU3nvBFRBlIqhrlnWAG1cAsIHOnTvrva5YsWKSdcqWLat93q9fP6fsl6sgsmkecXMJ6sJNXXxCGkBkM1lEmTi5ZIXZFMxEV7cgqA+RTSEZRJmoLQAdKQFowTLCw8OpatWq/Nce3Lt3T3WyGRMTT5cvhVJEeCy/jotL4DiQoD4kZmLFhYYZxmi0hQ6PyN2/dOkSWy0IZCKIiU6OCug7v3btWu6njfcxI9kRLVcFwdXYs/Muhb2MoeatCtCtGy+pfuXFtGDVa1S3QW6aM+MMzfzlFB043Z3Sp09H0dHx5OPjxhUoVYRYJhYCBYKSFMjdR992RZGgJEWdOnVo7969SZpyobsj3i9atCgdP37csiBfBjcOQONnR0gFADXiaNnUtTZmTztNG9bc4OcFCwfRknWtqVyFxFn8NWrnpGEfVmRFAiulQdXFNPf3xMw1hyKyaRZRJhaCshOwMPigpU/PKZjoNY/USpTUf/ToURJlgoeyPiwUwYIKAAlSAcDdePI4kl5vtJItEvDzjAY0YVpiOjOURs06OSkoU6JVX7ZCFur1Rkl+Hh+vof5DylDVGtn59e2bL9kt5ghENs0jysRKUDjv+vXrrEB0J4OZ8uNGRUWxVaKblikYh0e9Pm5smbkpmYN9qUTpYAoISBysZQzytmjwBffWm4PKUIlSwXz9vdVzE73z1qtil/ZEZNM8EjOxstT3mjVrOD4C4UWxPAVjwo+ZxqtXr6ZGjRqZz+ISEkm8nwhuwJ+zz1KV6tmpdNkQ+nFqoiWSUnD9/fRLfcqY0YECpLNpXP+G17zmvwGlu3ohxDKxkISEBFq1ahXVrl2b3VsoQfHkyRNWGLdv3zbay37Dhg1UokQJ6SwpqJqjR49qy68oPHjwgG7evOmw74Q7asHcC/TvusTYiD0oUy6E8hXISOFhsfTRsF30PBQ9oB3jnUDs9MqVK3rLp06dStOmTSN3RZSJhaA8xd27dznQjtLdFy9epCpVqvDzXbt2Ua1atXi9/fv3cyYXah5duHCBS1hgncOHD5v/EgxswiQALTiXI0eOcPVgrRhqNLRu3Tr9OKCdZdPb24OWrmtNwz9KOlPfHjGYXdvu0MULoeTI+Olff/2lXXbz5k32WuTLl4/cFXFzWUjp0qWpXLlySZaXLJkYDFSoUaMG/0X9oxEjRlgf5NP5KwjOANb2zp07tWXoL1++zHNXdOXdXrIJRfXtmIPUvU8JKlQkiBwBrJNdR7uQp6djxspIukEJ//Hjx2u9DvPnz2ePRfPmzcldEctERXCQz9fNA9CvwlCCk4ArFjdIJJbgZo/+JhgUKdmL9pTNO7fCaP3q63TtynNyJFAkjx9F0qxfTlFCgsbusok4aK5cuXh6AI7Ztm3bqHv37kkqNLsTokzUhhvbijy7OkYqADgbpK7DOoErNzY2lq2S8uXLO0Q28+QLZKuhcXPHu4OgsMaNPUSXL4baXTYV6+TatWscbwpxc6sEiDJxcsmKyMhIu2zHFeEGTOndvDlYKlonUCphYWFJrBJ7AUvh/r1wp2U7IVPs2MVeVKyEBf1aUiCbsE4wgRmWSXc3t0qAKBNBPeAe4y/VWVMDKBKUrceI26hVYgdWLb1CdSst4pnrzgBKC3NWcLNX0nYDvQNT9Phg6Ac05osxepWDcaxQ5QIKpbmbWyVAlIkgCEzNmjVp+PDhDrFKQOv2hei3+c0cFhg3xsXzz6h84b/pzKmnJtcZ9PYGOnvmsd6yw4fuUeP6/1CLxgvpjd5reba9MWCRLFmyxO2tEiDKRE1AXpEaL14eIZXw9PR0mGxmy+5P9Rrmtvpz61Zdoya1llKhrL/zX7y2lDx5M1Cft0pRpkzWuaDy5AmkNRs60YYtXSl//ox05qTU3zeHG4d71QenXcZKarDgmrL58w/HuPJvparZLP4MFEf/XpsJYRZ4qs6fecqvZ/3dhF5rU9Ds5/0DvOiDTyvrLYPL6/3hW+nMqcfk6ZmO/Py9aOb043TtaiivP39RG8qRM4N2fS9vD0onw26zyCFSERzc83TzALQb/3RXl81//jpvdWbVhHFHtYqE90ODWAjRxHFHLd7GoQMPuJS9wvq1V7mA5MatXWntv10oONiXqtXIRSvXdeJ6X2dOv3J53bzxgrZuuUGlykiZe3OIMlETGPSh0LCbzlnktMtwSQ12Vdncf6o7delZzKrPXL30XKtIFPD6yiXL56kM7reFS7coXDj/hOrUTZxsCKBYyldILIeUO08ghT6L4ucvXkTTgDfX06+zmpOHh6Stm0OUiSAIqqVQ0SC2RHTB68LFLJ89v3FXe+rRr4T2dfESIbRn923ta0xq1E1XhrJCxhkC7yM/rUlFiwXb+jPcAlEmgmrg2dX+bl4BwIWZNP4o9e60warPjBhZSevaAorLC8stJSSLH+XO8yoG0rJVIYqL01CzhguoVbNF9PRpoiWiy+KF5+nwofs0/rv99FrTRXT8aBxXDRbZNI0E4AV1IcMbl6VUmRDy97cu7RhBdgTbESOBa6tw0SAaMaoStXzdfPBdCeDv332Pvvy+ptb6wN+JkxsbXf+bcfW1z7v3LKV9PmPmDApDF1AD1q9fT4sXL6ZZs2bxvBN3RpSJ2nDOfC5BcLpsNnstf4o+B4ViSeaWMZ49jaZHDyO1iuRlzKtAvDX8OPVHevD4AeXyz6WXFYbKwY8fP6bt27dT48bGFZS7IONAtQWgIyQALbiubN65HUZ/z3FCz/b/6NmvBE3/wzE3+X379rEiAX///XeSnjDuhigTNyY+PnGoGRubQB+9u4t2bk0MSt69E0Z/zDxDL55LCV/Bvhzad5/GfXnIKbK1cskVioqKc8i2Faskd+7c7N66ffs2WyfujCgTFcHBvQDnBPmmTjhOXVqv5edeXunp7u0wev7fBX7m5BP68pP92tLdG9dep9Mn9MtNOAR8HepgSvzdZWUTJVW2HezENbMcyY1rL2j4oO20af1Nh8gmrBJ0WkQRWNQ1q1GjhttbJ6JM1IYD55icO/OU3QygbPks1KrtKz/038ta0uvtC/Hzpi3z04W7/ShTZh9+PeXH4zT/z1d5+o6CZ1fHSwUAV5ZN1OXKms2fIsJjacOa6+Qo8hfMSDuPdKHW7VIWa0lONhWrpEKFCmyZgF69erm9dSLKxE1A3vxb3f+lqT8d59f1G+ehNweVSbatqsKKTW1o5Jiq/Bw3gDkzzmirsNoTHvV6S/qlO7B88RUa2n8bu1TtCfq+oyEW5DNv/kC7lbvXlU205YZbq2/fvtr3ixcvTnXr1qVjx46RuyLZXC5OdHQ8u6v8/Dzpz8XNuaVpSkaTilvi5LHHdOnCM+r9Vkmua2R33LslhNvQo29xLvqYK3cGiomJZxnDTHRb2b3jLk0af4xavl6AG3E5QjYzZ85MS5cuJR8fH25xrPDpp59SQoL7pmOKZeIkTI3kHTHC12X4wO00bGBiYLBo8cxce8gWPvqsCs2c24Qv/gf3Ixy6/7t27eJmTbo8efKEDh486LDvFJwDLAZYDgAB+T6dNqRYlvC5A3vv8XO4bncf62J/RWIAFIkhHh4eDivfnxYQZeIkFi1aRFevXtVbdvbsWVq1atWrBbiW0NDRjvdnlJHoamU9JEtuBMjGaVF3Gf056yw5imnTptFvv/2mt2zSpEk0d+5ch32nYAIHyKZCo2Z5qVW7gixXL1/E0PWrL6z6/P4996lDizV09vQTfp05GEXEBGcjysRJFCxYkEfayugL5jBeFyqUGPTWBvlQOsIOkc5nT6P4u1Dy2xH9tuH2GvlFNc7OcRRt27alDRs20LNnz/g1+m3v37+f2rdv77DvFIxjT9k0pE793NS9T2LtrKULLlHDaotZfsGp44/pyeNITl+HJYy/4M/ZZ+mr0Qf4ebWa2emfla/xDHsh9RBl4iSQ+fHixQu6ceOG1ioBpUuX1g/y+dgegEawvUOL1fT9V4fJkcDiyZLVj2MydnN3xekrE39/f9q6dSu/XrFiBeXNm5fq139V8kJwDvaSTXN07V2cMwsV62Jgn800bdJJOn70EVUqNo+uXk4sYa9J0FBsTDzLnYdH+hQ13bIax0xZcRkkAO8kvL29qXr16lp/P0bYtWrV4hx1PezgckUCy4ejq1ChIpZXVk0pt2++pM6t19LkWQ2pavXsNm2LZ1dHEaULTBz9+vn5UefOnemPP/7g1ydOnKBRo0a5fQ2kVMMJ4QAkitSu96pkyaI1rbgoI9LU/1zUnAP2oN+AV4MwZ2Aom0JSxDJxsnWiG1DWtUrsCUZqqGVUopTjS2fnypOBWrQuQJn/m5NiC9x4ib0pGj3rRAl25siRQ6wSNwOB9AKFMrIyadIiHwVmTJ10P2OyKegjysTJ1knFihX5OWbMJrFK7AA6yo0YsoP9zM4A6Zxjvq1BRYpl0i4L9A5M0eODoR/QmC/H6E2Og3XSqFEjfo5YibtXZhVSCchkgPs2rrMEcXM5mWrVqtHdu3epTBnTEwZt4eGDCDpx9JHdJmtZAuYJrF91ncpWyGLStTbo7Q307ntVqFTpLNplz59HU9tWS+jCuae0ZWd3k9sfMGAABQQEULt27Ryy/4Jgb8aOHUtHjx6l8PBw6t27N/Xp04fjpN9//z09evSIB0koxTJ8+HDy9U2affb+++/TyZMnta9LlChBU6ZMUfWJEmXiZOCy6dq1q+kVbKx/V7ladtqyv5PVn0PfB/TbRptUdLdD8yFLy36jpekHQ3fSp2OrWxWn8ff3pCXL29PoUTuTXQ8X3ttvv23xdgUHIXU/LcbLy4tq1qxJmzdv1i6DVd28eXPKmjUr7d69m7Zs2cIz501lJ+bLl4/LtICgIMfHP21FlImK4CBfjPNL0EOR9O+1WdvF7vyZp/waTYksUSiI0Ry90FPPn40sm/eHb6Uzpx7zTHk/fy+aOf04XbsaSv4BXjR/URvy8vKgLFn9HfzrhLQsm2mVUaNG0aFDh/SUCRQHpgggbnrv3j3au3dvstvIlCkTu8MxmEoLSMxERXBwz8O2IF/75qto9Ad7rPoMLBJFkfB+/NcmFd3tLMUwMLp+7VWOp2zc2pXW/tuFgoN9qVqNXLRyXSeehX/mtBOqEAuqkk2BWLnAM4GJt5UqVaKWLVuaPCynTp2iNm3aUMeOHWnjxo2qP3yiTNQEBn0YhNgw+OvzVilq1c66iYRwbRlOE8FrtEm1lPFfHaaJ379SPhfOP6E6dfNoX0OxlK+QlZ/nzhNIoc+S9t1mZPTrsrKZlmGLLMx2y6xatWr09ddfs7sLRSHh7jIGikbCusEDrnFUfkApITUjysTFaN+lCNWsk9OqzyBGYhivx+vCxSz30yJ1MyH+lUYqXiKE9uxObLYFMLFRNynAwSXJBEGVZMmSheeb9e/fn13BSkMtdGmMiYnRFopEsknDhg05kxGTdOPi4tg1pmYkZuJioKT38SOPrOqZjWC7bsxE+YvlljLgf2X1XrdsVYg2/XudmjVcwM23EDMxRse2y+jUyUd06dIzKlgklkqWkRL0gvrgmf9+lsvm9u3b6dy5xPbEly5donXr1tGtW7fYysiZMydPWlaC7GDevHns+vr22285y2vYsGGsRDB9AK4xZHwp66qVNK1MvvvuO1q2bBmdP3+eg1SYUY7UOwS6FKKiojjNbsGCBRQdHc3mJQoIZs9u22ztlIIRCLq01a5d2yHpuzu23KaPh+2mC3f6kZ+/ZacXigfBdsRI4NoqjGyuUZWo5euWKSQ0OoqMjKOQLK8ChfhtEycb7739zbhX5VCWruygfT5j5gwKi4i26DsFwelYMcVp9uzZ9ODBA34OxYHHoEGDaMmSJdwPBdlZrVq14rRhUxYM7lkRERFcQgjZjBkzWt8+wpmkaWWyY8cOeuedd1iTwwz85JNPqFmzZpzPjXkJ4L333qO1a9fS4sWL+QT+73//ow4dOtCePdYFqe3F8ePH+bsxm7tIkSJJV7DR/dOmQ2Fq3a6QxYpEV6FYY83osmrZVfrgfzvpxJVeegpFcDHENWkxf//9t9HlCKYbA/NQ8FD47LPPKK2RppUJKsrqghpO2bJloyNHjlC9evXo+fPnXMJ8/vz52lnUc+bMoZIlS/JIAWl3zgT+0MOHE4svIi2wcOHCetYJB/fCidJlSbnFEpDBSxujsEezIUtAa9TgEF+tInkZ8zJF2/lx6o/04PEDyuX/qjaToA7sIZuCa+NSAXgoDxAcnFiTCkolNjaWmjRpojeTFL5HuJqMAVcYqvvqPuwFfKhwcwGYuiip7ghQtrt6mX+4tIqjQRAxQ6A3NXstv8O/y91xpGyqFcjX0ydR3DFUeS2oE5dRJhj1ozQBYhFKqZL79+9zPSxM/tEF8RK8ZyoOA3eY8oC/0l77B2ukSpUq/Bp/4e7SvTg4uBdgewAas9AbNM6jtVIcRXx8AvVot54Wzbtonw3iZyNjWO4XTpVNi06NnWTTEs6cekI7t93h55cuhFLZgnPp0P7E6xXN2JrUXMqWN1D+OhyRTfdRJoidnD59moNWtoC8blg4ygMZGPa0SpTkABR8RNOnJNaJHbwIUCI/TKnHridHjuRQGrxoicyUv6B9WqRy46U4xzRgcgUcJZsW46TT8s1nB+ifP8/zcySDIDmkZOlEb0OpsiHUvkthduFCkTSrs8x+g5lkENl08ZiJAoLqa9asoZ07d1KePK8myiHIjdxtuJR0rRNkWeA9YyB1z1h/Z3sE3lGrR6l6C4sJE5iwXLfbor1HeAiM/zyjARUrkdmu24bbATPZx35f027b5NnVXjLL2hSOks3UBl0V3+2/nTMIK1bJRhN/bcADId12CgrVaubghyKDLVsXoOIlE2UblbLRVMsRsUKRTRe3TDDqhiJZvnw5d+ND3RtdKleuzAXXUFBN4cKFC3Tz5k2+sTsTTEIqV66c3jJMXmrRooXDvjN3ngxcGj5zsH1vQOfPPqVa5RfSkYOJqY92A/cA7KoYJm4FSvF4+6SnqKjEuEj2HP48N8mSRlrvf1KZylfKyveCvp030v/e2uaYnRTZdG3LBK4tZGqtXLmSAgMDtXEQ+JMx7wR/33rrLRoxYgQH5ZGnPXToUFYkzs7kUlKVdUEmF9rSOgrMSp8yq6G2NP3Bffc5bdhW0KyoXafCdrd2BPdi5ZIr2rYFv81vZtO2cC19+lV1Cg5xPcstrZCmLZPp06ez77hBgwY8q1R5LFy4ULvOxIkTqXXr1pzfjXRhuLcw0VGVILyBnlYOCHMsW3iZPn1/L718kbI64s9Do+nN7v/StSvPydfXkz77unqqdb0T0r5sogfOpPFHadF8+8U7UEaoeMlgdn999+UhnkzrCJBFN3jw4CQxqz/++IOnHrgraVqZwLQ19ujXr592HZQh+OWXX+jp06fcqAaKxFS8JLXhIF+8YwLQA4eWpY2727MCgI96yBtb6eb15FNLo6LiuHEVwCTI+3fD6fFjEwUaBZfG3rLp7e1Byza8Th9+Wpnsze2bL2n+H+fpxDHHVKaG1wODWJQ/Ubh//z4n/2TIkNij3h1J08rE1eC0S2/HpF/CDZAjZ6Kr7c6tMLp08Rn3FQG/Tj7JD5REGTNyH50+kXgR/jn7LLVutJLCXsbwxb9mWzuqWt3BZWgS3eaCi8omBnvTfz5B9++Fc7AcAXZ7U7hoJjp4prvVBU8tlU3EYbt37871tzBIBf/88w+72uEFcVdEmagNJ3iOypTPQpv2dKQsWRNnrMMt8OB+BD/fufW29jlKs6z4t412voqjZ9TzLOtIacDkyrJ5+2YYzZhyig4fsHPyhgGwpDEIWvLPJbukxxvKJmr8oX4WKlpg+xs3bqQuXbqkmUZWjiBNB+AF+zDsw4ra59sOdtY+z5krgB+CYC/y5g+kg2e7W5StZSvHjjyi9wbvoDLlQ6hEqcR5KvYCqf2wTiZPnsxWf1BQkFtbJUAsE0E1sAslg5Sgd1VCn0XTi+eJLlNHVMw2pE79XKy47KFIjMkmrBPESGCZdHFzqwSIMhEEwSmsXHqFapZdQHFxiQ2gHA0UlqFlHegdmKLHB//7gMZ8NCaJdYIy8kjyae3mVgkQZaI2HJPNKAipLpsNm+Sh7yfXIU9P5912Ll8KpfpVFvNEW1MMensDnT2jn/n18EE4NWnwD7VsspBaN19ML54bV4DoM7J69Wq3t0qAKBMVwcG9aAlAC64pm/kKZEzRpNl1q65Rk1pLqVDW3/kvXltKjhz+7O5C6R9rQDuFf7d2o/Wbu1L3nqXo4P44q/fb3ZAAvIrg+j9omStlcwUXlE2kmletkZ1KlQmx+DNQHLotpc+fecqvUfzRkmZuaI/wzU+19ZYhxvH+8K105tRj8vRMxy2lZ04/TteuhnK6/PxFbfRSll+GxVD2HDLuNoccITWBQV+AG9emwn0qTErQu6psjvvyEB05+NCqz0wYd1SrSAD+4jVaTFvKpQvP6PEjTN9PZP3aq5zmvnFrV1r7bxcKDvalajVy0cp1ndiCOXM60eV18sRDalh3PiuaPHnTE6F4hLRHMIkoE0E1KLOrpQS9a3LuVl/q/WZJqz5z9dJzrSJRwOsrlxIb4VlCj/brac6MM9rXF84/oTp1X1UXh2IpXyErP8+dJ5BCnyVWeShXPhtt29WDRo+pRVs3JQaMRDZNI8pEUA2cdukrqcHCKwoVDWJLRBe8LlwsyOLDtGDla9ShaxHt6+IlQmjP7tva1+iLopuqDGWF2mEKGYN8yAvzdj1FNpNDYiaCuhCJdFlmTD1Jp449pqm/NbL4MyNGVtKLmSh/sdya8iq6tGxViDb9e52aNVzAkycRMzEELq7RI3eSh0c68vH1pAaNvYjSS6plcsilqzbEJyukEmfOnOF6U0OGDDE+qdBG2cyRI4BeFrLuhowgO4LtiJHAtYXOi2ii1fJ188F3sG3TLTp66CF/RvlN+DtxcmOj638zrr72+YYtXbXPZ8ycQWGJVYYEE4gyURGcdhlOlC6Lu0bghdQCGU4HDhygsLAwOn/+PJUsWdLustm2U+EUfQ4KxZLMLWNcvhjKTdycMePe3RFlYkFTq0OHDhltbpUS3L3kgqBO0H0U7a0hn3v27KESJUo45AaM9gd7dt61S5M2S+j/Tll6e0gZ7euXMS9TtJ0fp/5IDx4/oFz+uey4d66FBODVFoD2d+MgH352tLj6nH7YNRravXs3FS9enEuDxMTEsHXiCNn8d90NGjFkJ9focjQ7ttzm0i12UYoim2YRy8RC7t27R9u2baP06dNz3wLU5Ll06RJbLehv8Nprr3FbYAWM8tauXcuCjPfbtGlDPj4WtBR1Y/XOaZexkn6ZGlbJo0ePqGrVqnT37l1ua23UOrGDbLbrXIRq1M5JGYMc22vhxrUX1LvTBm5bnVL3mi4im+Zx41uXdUCBdOvWjXr27MnlphVF0qNHD6pTpw7t3btXb32M8NAqGO8XLVqUjh8/nmr7nlbg2dVIv5QsBKcCqwSKBIULQdmyZY1aJ/YAkwLzF8xIsbEJtG/3PXIU+I71O9vT6x3s404T2TSPKBMLQalpWBh80NKn5w5rISEh5OHhQXny5OGRnaEywUNZXwKAFoBBMA6ZxEqdCuS4SpUq2teenp7UtGlTuzSVMsW8P85Tz/br6e4dlDywH5ERcdqGWKXLhtivoZvIpllEmVgJej9fv36dFYgykgOmLryoqCi2SjDaEwQ10qJFiyQuWFjTpUqVcth39n27JK3Z1pZy5c5A8fEJdlNcG9dep1EjdnNHR8G5iDKxgujoaFqzZg3HR/z9/dkVoGDM8oiPj+fy1I0aNbIsiwvXEyo5uGn8XVAxdpZNXC9KwcdJ3x+jwW9stUmhKCXmEZPZcbgzd3QUnIsoEwtJSEigVatWUe3atdktkDlzZnry5AkrjNu3b1PWrIm1fXTZsGEDBzFhxVgc5IuTALSgPhwpm6XKBlPFKllZwcBNpVuU0RL277lHTWoupbOnn/BrWDuC85FsLgs5d+4cZ7og0I5HxYoV2c88f/589jEjuwvs37+fFcjLly/pwoUL7BY7deoUFStWTM8vbTLI5+XmAWjnNOETrMSRsqk7m33Zosv0+cd76fC5HpQ52JeuXXlO2XP4k5+/J8XEJHD5E8RBEBe5czuMhn1YkSpXy06//N6ISpa2b5/3JIhsJosoEwspXbo0lStXLslyw5nCNWrU4L+ZMmWiESNGkFVg0OfjvgFonmUdQZQuwE0PgJpxkmy+1qYAZQ72YUUC+nTeSM1ey08tXi9A7Zquoq0HOlLxksH08EEEXb2MisIaVjBtO9qe/pscIpvmEWUiCIJqgBLRLZ0yfU4j8vBMT9my+9GkX+tT9hyJlSiGDC+finspGEOUiaAaeHZ1gBtXAEjlUj85c+YktVGmfBbt8849iqXafohsmkcC8GYIDw/nCV34aw8iI60LLrod4uES1IrIZrKIZaI24lJ7B4S0mLK+ZMkSevz4MfXu3ZszC5cvX84Dl7i4OGrYsCHlzZtX7zMzZ87kibgA5VMKFrSgKq/IppAMokxUBAf5ov77KwgWgmzCTp06ce04BdSCQ3UGZBOuX7+eSwHpgkmKKPUjsinYC1EmKkJJu3Tr1GDBaqA0dKsxKMsAJtZmyfIq7qCA5Uhrh3WC0inmJtWKbArmEGWiJmCQwPPgroYJdChCU9LyxS7MmzePnj17pp0DpUuvXr1YgZw+fZoLPUKhJIvIpsimGUSZCKqBZ1ejz7fbalP7ggrXL168oKVLlyaJiSiWCHqYnDhxIpX2MO0gsmkeyeYSVAOnX/pIarDNx1Gj4TI/AJWuDV1geA+BeXDr1i0uDSSIbNqKWCaCukis8i9YyeLFi+nhw4fcGqFMmTJc/kdRLPXq1ePnKOsTHBzM1RmwPhQNYisoXCqIbNqKKBNBcAE6d+6s9xq14wzRbYPQr18/p+yX4D6Im0tFcEpwmKQGC+pDZFMwhygTQRAEwWZEmagtAO3n5gHoV/3GBCeX+rl3z3RPdpFNkU1ziDJRG4lzzdzXlRIjbj7VIrIp1SmSQZSJoBp4lrWHVAAQ1IfIpnlEmQjqAXMVMZdO5iwKakNk0yyiTARBEASbEWWiJhB3j/7vryCoCZFNwQyiTJwEylbExsYm6UNx584d/fo/sVKbSnAu165d45nyuqCmF/qjiGwKliLKxEns27ePDh48qLds165ddOzYMf0gn6ebB6Dd+KenFuh3cvHiRb1l69ato8uXL2tfi2yKbJpDlImTqF69OvfrhjUCwsLCuFprtWrV9IN8vu4bgObU4HBJv0wN2dyzZ4/WOoEVjTkn5cuX1zk5Ipsim8kjysRJ5MuXj7Jly0YnT57k17BSChUqxMsEITWB0kCLX8XlCsVSpUoVsw2zBEEXUSZOIl26dFS7dm1t7wgoFbwWDGZZB7h5BYBUavtbo0YNbpSFuB6sEigT4RUim+YRZeJk6yQkJISfo1mRWCVGcFMXnxqsE7hg4X4Vq8QEIpvJIsrEydYJ/NOgVq1axldK7GkkCE63TkqXLs3PTVolIptCMkg/EydTrFgx6tOnD+XIkcN4ADpSAtBC6tCoUSPueWIsViKyKZhDLJNUIGfOnKnxtYKQLOnTp6fs2bPLURJShCgTtQX5MrhxABo/O1Ly+dWIyKbIpjnEzaVG0hPFxr+aLZ8+XXrySO/B8wDiEuKSrO7lkdg4He8ZzmTG5/D5BE0CxSfEJ4nheKZPFAHd71PAe1jHUdvF5/D5Vx9M9MtzJQBBnYhspvYZUC2iTNRGfOIoMDoucXIj8PXypUDvQL6pPwp/lOQjwX7B/PdJxBOKidfvLpXJNxP5eflReEw4RcRF6L3n7enN24WiuB99P8l2M2XIxArjaeRTvf0BgT6BlME7A0XGRlJoTGgS5ZbZNzM/vx92P4kiyhiQkRVKaFSo3nY1HlKCXtWIbLp3dQoziDJRERzkfEHkGehJBTMXNGqZ4AZuyjKB0jBlQQR4B1CIf4hJCwKfNWVBmNtuZr/MJrfr6+lrcrv+Xv56lonnS0+iKLFM1IjIpsimOdxGmfzyyy/0ww8/0P379zmnfsqUKfqlTNSCJvHCVRSELrgBG1uuoNzAjYEbf3oP0yGy1NguFBL+6d2wZOCnXkQ2BXcPwC9cuJBGjBhBY8aMoaNHj7Iyad68OT18+DC1d00QBMElcAtlMmHCBOrfvz+98cYbVKpUKfr111/J39+ffv/999TeNUEQBJfA5d1cMTExdOTIERo1apRePn2TJk24LLwhKCmhVPZV+jo4G9RGypMnD7kb+N2CaUQ2Uw+RTfO4vDJBg5/4+Pgkk7Hw+vz580nW/+677+jLL7+k1CQhIUGvaZYgAJFNQc24vDKxFlgwiK/oWiZ58+Z1yncbK7HijshxMI7IZuojsunGyiRLlizk4eFBDx480FuO18YEw8fHhx+pweHDh1Ple4W0gcimoGZcPgDv7e1NlStXpi1btui5kfC6Zs2aqbpvgiAIroLLWyYAbqu+fftyaW3MLZk0aRKFh4dzdpcgCIJgO26hTLp27UqPHj2izz//nCctVqhQgTZs2CAVUgVBEOyEWygT8L///Y8f1qKUEUmNFGEh7aDIh2HZGUcisimoSS7dRpmklJcvX/JfZ2V0CWkbyEtQUJDTvguIbApqkMt0GmcOpdIgCNbfvXuXAgMDuTaWI1HSkG/dukUZM2YkdyatHQtcRrhgc+XKxZNinYHIZuqQlmRT40S5FMvEDDgBzp6NDgFVu5A6i7R0LJxlkSiIbKYuaUU2g5wkly6fGiwIgiA4HlEmgiAIgs2IMlHZDGeUyU+tGfhqQo6FupDzIcfCHBKAFwRBEGxGLBNBEATBZkSZCIIgCDYjykQQBEGwGVEmgiAIgs2IMlERv/zyCxUoUIB8fX2pevXqdPDgQXL1zoFVq1bl6gLZsmWjdu3a0YULF/TWiYqKonfeeYdCQkIoQ4YM1LFjxyS9aQTH4m5yCUQ2rUeUiUpYuHAhl8pHavDRo0epfPny1Lx5c3r48CG5Kjt27GBFsX//ftq0aRPFxsZSs2bNuD2AwnvvvUerV6+mxYsX8/oobdOhQ4dU3W93wh3lEohspgDU5hJSn2rVqmneeecd7ev4+HhNrly5NN99953GXXj48CHqxGl27NjBr0NDQzVeXl6axYsXa9c5d+4cr7Nv375U3FP3QeQyEZFN84hlogJiYmLoyJEj1KRJE726S3i9b98+cheeP3/Of4ODg/kvjgmsFd3jUqJECcqXL59bHZfUQuTyFSKb5hFlogIeP35M8fHxSZp14TWaebkDqIA7fPhwql27NpUpU4aX4bej7XKmTJnc9rikJiKXiYhsWoZUDRZUAWInp0+fpt27d6f2rgiCHiKbliGWiQrIkiULeXh4JMlSwuscOXKQq4MOmGvWrKFt27bplfvHb4erJTQ01C2PS2rj7nIJRDYtR5SJCoArp3LlyrRlyxY90xqva9asSa4KGvfgYl2+fDlt3bqVChYsqPc+jomXl5fecUHq8M2bN136uKgFd5VLILKZAiwI0gtOYMGCBRofHx/NH3/8oTl79qxmwIABmkyZMmnu37/vssd/8ODBmqCgIM327ds19+7d0z4iIiK06wwaNEiTL18+zdatWzWHDx/W1KxZkx+Cc3BHuQQim9YjykRFTJkyhW+c3t7enJK5f/9+jSuDsYyxx5w5c7TrREZGaoYMGaLJnDmzxt/fX9O+fXtWOILzcDe5BCKb1iMl6AVBEASbkZiJIAiCYDOiTARBEASbEWUiCIIg2IwoE0EQBMFmRJkIgiAINiPKRBAEQbAZUSaCIAiCzYgyEQRBEGxGlImgB2pfoYjfy5cvnX5kNmzYQBUqVOD6T4JgiMimuhFl4mKgL0qtWrWStLZFc5+8efPSp59+muznR40aRUOHDuW+7M6mRYsWXNhx3rx5Tv9uwfGIbLo4KSjBIqicCxcuaPz8/DR///23dlnv3r015cqV00RHR5v83I0bN7hN7u3btzWpxdSpUzVVqlRJte8XHIvIpusiysRF+fnnn7k44t27dzUrVqxgJXH8+PFkP/PDDz8kuZGj6CIq+65evVpTrFgxVlIdO3bUhIeHcyXZ/PnzcxXZoUOHauLi4rSfw/KvvvqKlVhAQAAXCly5ciX30m7Tpg0vK1u2rObQoUNJFBrGOJcvX7bzERHUgsimayLKxEVJSEjQNGjQQNO4cWNNtmzZ+MZuDtzkUfLdUJlAETVt2lRz9OhRzY4dOzQhISGaZs2aabp06aI5c+YMKxpUlEW5cl1lEhwcrPn11181Fy9e5JLeGTNm1LRo0UKzaNEiHqG2a9dOU7JkSd5XXbJnz65XOVhwLUQ2XRNRJi7MuXPneJQPCyA2Ntbs+uXLl9eMHTtWbxlu6oaWwsCBA7kc/MuXL7XLmjdvzst1lUmvXr20r1E2Htv57LPPtMv27dvHywxLylesWFHzxRdfpOAXC2kFkU3XQwLwLszvv/9O/v7+dO3aNbp9+7bZ9SMjI8nX1zfJcmyjcOHC2tfZs2enAgUKUIYMGfSWPXz4UO9z5cqV03sflC1bNskyw8/5+flRRESEhb9SSIuIbLoeokxclL1799LEiRO5t3q1atXorbfe4lak5np+P3v2LMlyZFjpki5dOqPLDFN6ddfB+6aWGX7u6dOnlDVrVgt+pZAWEdl0TUSZuCAY1ffr148GDx5MDRs2pN9++40OHjxIv/76a7Kfq1ixIp09e5ZSk6ioKLpy5Qrvi+B6iGy6LqJMXBDMFYEVMm7cOH4Nl9SPP/5IH330EV2/ft3k55o3b0779u3j+QCpxf79+8nHx4dq1qyZavsgOA6RTddFlImLsWPHDvrll19ozpw5HOtQGDhwIE9mTM7d1bJlS/L09KTNmzdTavHPP/9Qz5499fZdcA1ENl0b6QEv6AFFtGrVKtq4caPTj8zjx4+pePHidPjwYSpYsKCcGUFkMw3hmdo7IKgLWDChoaFcm8vZJVXggps2bZooEkFkMw0ilokgCIJgMxIzEQRBEGxGlIkgCIJgM6JMBEEQBJsRZSIIgiDYjCgTQRAEwWZEmQiCIAg2I8pEEARBsBlRJoIgCILNiDIRBEEQnFtOBdVkY2Njbf9WQRAEIU2AHkQeHh72UyZhYWHcrc9cgyVBEAR7gntOaGgMRUTEkb+/J2XK5K1trCY4HhzrPHny6HVWNboeevdaYpFcunSJy4KjA56cSEEQHE1oaBT9/fdpmjXrGF28+FS7vFixYOrfvyL16lWGMmVK2mZasB9QD48ePeKmZkWLFk3WQrFImaD7HfqIo8kS+nMLgiA4ko0bL1OXLksoIiKWOnYsyY/Mmf3o2bNIWrr0HD/8/b1o0aJO1Lx5ETkZDiQyMpIreqMthK+vr31iJnoWSWQkUUwMOQRvbyJRWk4nPiGeEjT6/djtQfp06ckjvXmfqyAoiqR163+oefPCNHt2G8qRQ9+90rlzabp/P4zefnsVr7dmTXdRKA7EUk+UVZaJVjNBkaxcSfTsGTmEzJmJ2rY1q1BgKWF/Tp8+zR0CQZUqVbhFbYMGDRyzby6sSG49v0Ux8fYfIHh7eFPeoLxmFQrOJ1r2KtYvzuXs2bPJndm+fTtffy1atHDad2IUumHDBho0aBClhmsrf/5JVLduPlqxoht5eppOOI2LS6B27RbQrl036caN4Va7vCpUqMB/Y2Ji6MKFC1S2bFl+jQZtygNdP20hS5Ys3OwNsv3aa6/RxIkTebuOBj2Jfv31Vxo5cqTN20py/7drcyxYJFAkuOiT2XiKiIpK3Da+wwLrJDo6mn777Tdu6iSkHFgkUCS44Xumt1/PtLiEON4utu9B5q2ThQsXai9yU/E7SzJLXIG4uDhWJrgxOFuZ4EaUGsrkzz+Ps2sLFklyigTg/VmzXqd8+SbRX3+doHffrW7Vdx0/flz7eyFzymtHsW7dOnIWkJlx48alSJlA7pTBufPmmUCRBATY92Glcvriiy/oq6++4gCRIQ8fPqQOHTrwiKNMmTI0Y8YMk9u5d+8eNWvWjEqVKsV/u3XrxttWMtnefPNN3gYeX375pdFtjB07loUSD6wH8/DGjRt8A/zwww+1nx86dCiPhkC/fv1YETZu3JiKFSvG+6u8hzRsCEO1atV4m126dKFnjrIG/wOKxMvDy24PWxXTH3/8QQ0bNqSOHTvyeTx48CAdOnSIGjVqxJZLxYoVafHixdr10W64Tp06VLlyZT5u27ZtM7rdO3fuUKdOnXib5cqVo88++8yszGB0OXr0aKpVqxblzZuXb7hz5syhmjVr8nsLFizQrotzj3Wxfziv8+bN076H0S72Hd/bqlUrun//vvamlilTJvr444+pUqVKNHXqVP4OfBbnH/KlrIP9xToIiu7Zs4fee+89rdzBUleYO3cuVa9endetV68enThxQntcmzRpQt27d+ffiv25evUqvwclgpE6ttemTRtyFnCSTJ9+mOMjhq4tU+TMGUgdOpSkadMO2TXTFNflpEmT+DnuA5A/yFyJEiXo9ddfpydPnhj93KpVq6hkyZJ8bj/66CO99yAjisKaMGECVa1alY8x/u7bty9FcmbqWsA5RLdUbB/vAcgZ7iG4LnDO8R263wm5w3t9+/ZN2UHTWEBkZKTm7Nmz/JcJDdVopk3TaP75R6NZtcq+D2wT28Z3mCF//vyaY8eOaXr16qX5+uuveVnlypU127Zt4+ddunTRjBw5kp8/ePBAkydPHs2+ffuMbqtTp06azz//nJ/fu3dPkz17ds2YMWP49UcffaTp0aOHJj4+XhMWFqapUKGCZsGCBcnuW//+/TVvvvkmP582bZqmfv36mqioKE1sbKymZcuWmnHjxvF7ffv21VSrVk0THh6uiYuL09SqVUszf/58fu+bb77RjB07VrtNPB8yZIjGEcTExWguPLqguf7suubOizt2e2B72C62bw6cz2LFimnKly/Pj2XLlmnmzJmj8fPz05w/f57XefbsGR//u3fv8utHjx5p8ubNq7l9+7bmypUrmho1amieP3/O7126dEmTI0cOPu6GNGjQQPPtt99qXz98+NCszGD/hg8frt22r6+v5quvvuLXBw8e1GTJkkW7PVxao0eP5ufYr8yZM2uuXbum913gu+++0wwcOJCf43187s8//9S+DxkcNmyY9rWyzvLly/n17NmzNQEBAZqtW7fy6/Hjx7Msg927d7OsKb9/586dmlKlSvFzHNeMGTNqrl69yq8//vhjzYABA/g5rh8cf2fz6FG4hugLzaJFp6363MKFp/lzjx+Hp+h7cUyDgoL0luG6nDhxovYcZM2ale8LYPDgwXx9G/LgwQNNcHCw5syZM/x6xowZfK6U867crwxlAPJVvHhx7WtL5Sy5a8HYb2rWrJlm+/bt/Bz3oebNm2sWLVqk/c633npLk5CQYP7+bwKX6AEPywQa1dAs37x5Mx05coSfZ8uWjUecWFajRo0k29iyZQvHWkCOHDmodevWetv56aefKH369BQQEEB9+vShTZs2UdeuXY3uz9dff003b96kNWvWaD+PkQ7iAaB///70yy+/8EgAtG/fntOuAX7HlStX+PmKFSvo+fPntHTpUn4NiwUjCFfG0M2FETRGaIqfee/evTyCbtmypd7nMJI+f/48Xb58mUfgCjhnOBcYwSvA0ty9ezdbMQpIebdEZpRzXqRIEfYfw7oBGP09ffqU3QuwHMDbb7/NfwsVKsT7tHPnTj5/8+fPZ4sBvmg84FfXnSDWq1evZI8Rvrddu3ba70X+P6w3RX4UK2jlypVsicAyUcA+IjsHYKQLP7jyfMqUKZSahIUlWuTI2rKGzJkTvRkvX8ZQSEjidWRvYEHivgAGDBjAcmHI/v372SKBdwO89dZb7IUwxrFjx+ibb75hCwcuJcgvzosSL7REzpK7FiBzuoSHh/M97sGDB3rXAdZVwD3KlmkfLqFMcIH26NGDb+LJoRwonAglQI+Lafny5SbXTW47xvjrr79o2bJlfOMw5Xc0/LxuUAvxAPgsAQa4uMDhdnNndCdL4ZiULl2aLyRDzp07R02bNuWbtb0wd66U11gPD+XcmdoWlNjkyZPZrQFlBbfI559/rl0HgwoowORQBiWG+2BMfuCy+Pbbb41ux9TnUosMGbz5L9J/reHZsyj+GxiY+HlnYMlNN52JdTAohDKCCxYurhcvXlBQUBDHfxVlYomcJXctwB2qi+IChMIzFUQ3NynRbWpzwf/3999/0927d7XL4BOeNWsWP8fEG9zkcbPByBG+SzwURQK/I0bBANpbsSqU7SDIjxMCDY9RpbEbPEawsJLWrl2rd2LweSgZCBGEABlKligIjD6R/aHEg/D3zJkz5M7ASkFmCY61As4jjm3z5s15+cmTJ7XvIcZiCM4NLAVYmwqQj+RkJiXAz61c2Lt27aK6detyzCswMJBCQkJ4n5OL44GMGTOydZoSEO/ANQHLDCQkJHBmkTls+U5bCAnxo+LFQ3gOiTVgfXwuONjPocFzZVSP6xdyYkjNmjVZ9mAhg99//10b/9QF1iiW58uXj1+n1CJM7lrAOYSlo3y/Yr0iKK+AeyWqmtiL9DZnXoWH2/eBbaYAuAreffddDqQrYASI0SqCTTiQn376qZ7Jr8vPP//MFzxMVARIsZ7irkCwE+4HbAfLcZEikGUIzFbc8GF2KoF4nDCYxQiA4oFlsKSGDx9u9jfBDYaRC74T5jNcLY7OOEH2VWx8rN0e2J49yZw5MytrjLbLly/P5wtJCrhRwiUAqwQJDXgPgVAliGoIBgS4sWJkh3OCYLe1MmMOJF4gKIqBA7aL846sLCXtFMolucw1xQWKc64E4K0B2x8/fjxvA8cDv1U3eGsKyBrWRTDfmQF4jLgHD67CygHzSCzh3r2XtGzZORoypKpDK3PgWML7gQA8kmqMWXtZs2ZlBaIcb1QNwaDBENzo4UWBSxKJIt6YV2fnayE4OJjd8TiXSgAe7k+4gXFeId+wjkwlEqSEND3PxJ5Ai0NhwDWFA4wbN0Z1Kb2RpDXUMM/ElcCNDVaIMiAR1DfPxFKQzQXXuKmBiasT5dB5JrjJ42bvQjPgMYqAJoduhWk4ZMgQt1EkADd63PBlBryQmkAhoEQKZrZDUWAeCdJ/jVkk/fuvpo0br9DatT2kRpcKSJllIgiC4KTaXJhHkliby5eD7XCDwbWF2lyLF3emZs0Ky7lIs5aJIAiCA0HxRriuMLMdExIXLXqVeIJg+08/NaO+fctTUJAMbtWCVA0WBEHVwHny9GkkzyNB+i+ytqQNRhqtGozANE4eUiWln4kgCM4mIAAThhNvZJiPITi3nwnu/9ADNlsmQDotCoIguB/p7NlpUUF6wAuCILgXXhb2gLdKmQiCIAiCS5dTEQRBEFIPUSaCIAiCzYgyEQRBEMhW/g+rNtnKrI69nQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 440x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tip_rack = deck.get_resource(\"tips_1000\")\n",
+    "await lh.pick_up_tips(tip_rack[\"A1:D1\"])\n",
+    "trough.tracker.set_volume(50_000)\n",
+    "\n",
+    "# Show expected offsets before aspirating\n",
+    "for n in [2, 4]:\n",
+    "    offsets = compute_channel_offsets(trough, n)\n",
+    "    print(f\"{n} channels -> offsets: {[f'{o.y:+.1f}mm' for o in offsets]}\")\n",
+    "\n",
+    "plot_container_cross_section(trough, [2, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "mvpra250mkk",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation stopped.\n"
+     ]
+    }
+   ],
+   "source": [
+    "await lh.stop()\n",
+    "print(\"Simulation stopped.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/pylabrobot/liquid_handling/backends/backend.py b/pylabrobot/liquid_handling/backends/backend.py
index dc3253986e3..a16034577ea 100644
--- a/pylabrobot/liquid_handling/backends/backend.py
+++ b/pylabrobot/liquid_handling/backends/backend.py
@@ -3,6 +3,7 @@
 from abc import ABCMeta, abstractmethod
 from typing import Dict, List, Optional, Union
 
+from pylabrobot.liquid_handling.channel_positioning import GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS
 from pylabrobot.liquid_handling.standard import (
   Drop,
   DropTipRack,
@@ -158,6 +159,27 @@ async def request_tip_presence(self) -> List[Optional[bool]]:
 
     raise NotImplementedError()
 
+  def get_channel_spacings(self, use_channels: List[int]) -> List[float]:
+    """Get the per-channel occupancy diameter for each channel being used.
+
+    Each value is the channel's occupancy diameter - the physical space it takes up.
+    The required center-to-center distance between two adjacent channels is the sum of
+    their radii: ``spacing[i]/2 + spacing[j]/2``.
+
+    Args:
+      use_channels: The channels being used, in order.
+
+    Returns:
+      List of per-channel occupancy diameters (mm), length = ``len(use_channels)``.
+      Defaults to ``GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS`` (9mm) for all channels.
+      Backends with variable channel spacing should override this.
+
+    Note: This assumes channels spread along Y. Backends where channels are fixed in Y
+    (e.g. OT2 with 2 channels at fixed X spacing) should either override this or
+    signal that Y-spread is not supported via a ``supports_y_spread`` property.
+    """
+    return [GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS] * len(use_channels)
+
   @abstractmethod
   def can_pick_up_tip(self, channel_idx: int, tip: Tip) -> bool:
     """Check if the tip can be picked up by the specified channel. Does not consider
diff --git a/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py b/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py
index eae7f0a50b1..93181c0cf69 100644
--- a/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py
+++ b/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py
@@ -3,7 +3,6 @@
 import enum
 import functools
 import logging
-import math
 import re
 import sys
 import warnings
@@ -40,6 +39,11 @@
 )
 from pylabrobot.liquid_handling.backends.hamilton.common import fill_in_defaults
 from pylabrobot.liquid_handling.backends.hamilton.planning import group_by_x_batch_by_xy
+from pylabrobot.liquid_handling.channel_positioning import (
+  MIN_SPACING_EDGE,
+  get_tight_single_resource_liquid_op_offsets,
+  get_wide_single_resource_liquid_op_offsets,
+)
 from pylabrobot.liquid_handling.errors import ChannelizedError
 from pylabrobot.liquid_handling.liquid_classes.hamilton import (
   HamiltonLiquidClass,
@@ -62,11 +66,6 @@
   SingleChannelAspiration,
   SingleChannelDispense,
 )
-from pylabrobot.liquid_handling.utils import (
-  MIN_SPACING_EDGE,
-  get_tight_single_resource_liquid_op_offsets,
-  get_wide_single_resource_liquid_op_offsets,
-)
 from pylabrobot.resources import (
   Carrier,
   Container,
@@ -1358,15 +1357,16 @@ def __init__(
     self._setup_done = False
 
   def _min_spacing_between(self, i: int, j: int) -> float:
-    """Return the conservative minimum Y spacing required between channels *i* and *j*.
-
-    For adjacent channels, the constraint is the larger of the two channels' individual minimum
-    spacings, ceiling'd to 1 decimal place for safe movement.
+    """Return the firmware-safe minimum Y spacing between channels *i* and *j*.
 
-    For non-adjacent channels, the spacing is the sum of all intermediate adjacent-pair spacings.
+    Uses max() of both channels' spacings for firmware safety (conservative).
+    For adjacent channels, ceiling-rounded to 0.1mm.
+    For non-adjacent channels, the sum of all intermediate adjacent-pair spacings.
     """
     lo, hi = min(i, j), max(i, j)
     if hi - lo == 1:
+      import math
+
       spacing = max(self._channels_minimum_y_spacing[lo], self._channels_minimum_y_spacing[hi])
       return math.ceil(spacing * 10) / 10
     return sum(self._min_spacing_between(k, k + 1) for k in range(lo, hi))
@@ -2214,10 +2214,6 @@ def _compute_channels_in_resource_locations(
           )
           min_ch = min(use_channels)
           offsets = [all_offsets[ch - min_ch] for ch in use_channels]
-
-          if num_channels_in_span % 2 != 0:
-            y_offset = 5.5
-            offsets = [offset + Coordinate(0, y_offset, 0) for offset in offsets]
         # else: container too small to fit all channels — fall back to center offsets.
         # Y sub-batching will serialize channels that can't coexist.
 
@@ -2328,8 +2324,8 @@ async def probe_liquid_heights(
     Notes:
       - All specified channels must have tips attached
       - Containers at different X positions are probed in sequential groups (single X carriage)
-      - For single containers with odd channel counts, Y-offsets are applied to avoid
-        center dividers (Hamilton 1000 uL spacing: 9mm, offset: 5.5mm)
+      - For single containers with no-go zones, Y-offsets are computed to avoid
+        obstructed regions (e.g. center dividers in troughs)
     """
 
     if move_to_z_safety_after is not None:
@@ -4548,9 +4544,9 @@ async def move_channel_z(self, channel: int, z: float):
     stop disk Z position used with :meth:`move_channel_stop_disk_z` for the same
     physical height above the deck.
     """
-    # TODO: remove after 2026-09
+    # TODO: remove in v1
     warnings.warn(
-      "move_channel_z is deprecated and will be removed after 2026-09 in legacy PLR. "
+      "move_channel_z is deprecated and will be removed in v1. "
       "Use move_channel_stop_disk_z() for moves without a tip attached "
       "or move_channel_tool_z() when a tip/tool is attached.",
       DeprecationWarning,
@@ -4677,6 +4673,9 @@ async def move_channel_z_relative(self, channel: int, distance: float):
     current_z = await self.request_z_pos_channel_n(channel)
     await self.move_channel_z(channel, current_z + distance)
 
+  def get_channel_spacings(self, use_channels: List[int]) -> List[float]:
+    return [self._channels_minimum_y_spacing[ch] for ch in sorted(use_channels)]
+
   def can_pick_up_tip(self, channel_idx: int, tip: Tip) -> bool:
     if not isinstance(tip, HamiltonTip):
       return False
diff --git a/pylabrobot/liquid_handling/backends/hamilton/planning.py b/pylabrobot/liquid_handling/backends/hamilton/planning.py
index c050114bfc6..dfba2fa0a5f 100644
--- a/pylabrobot/liquid_handling/backends/hamilton/planning.py
+++ b/pylabrobot/liquid_handling/backends/hamilton/planning.py
@@ -1,6 +1,6 @@
 from typing import Callable, Dict, List
 
-from pylabrobot.liquid_handling.utils import MIN_SPACING_BETWEEN_CHANNELS
+from pylabrobot.liquid_handling.channel_positioning import MIN_SPACING_BETWEEN_CHANNELS
 from pylabrobot.resources import Coordinate
 
 
diff --git a/pylabrobot/liquid_handling/channel_positioning.py b/pylabrobot/liquid_handling/channel_positioning.py
new file mode 100644
index 00000000000..535dc8a8681
--- /dev/null
+++ b/pylabrobot/liquid_handling/channel_positioning.py
@@ -0,0 +1,489 @@
+import logging
+import math
+import warnings
+from typing import List, Optional, Tuple
+
+from pylabrobot.liquid_handling.errors import ChannelsDoNotFitError
+from pylabrobot.resources.container import Container
+from pylabrobot.resources.coordinate import Coordinate
+from pylabrobot.resources.resource import Resource
+
+logger = logging.getLogger(__name__)
+
+GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS = 9
+MIN_SPACING_BETWEEN_CHANNELS = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS
+# minimum spacing between the edge of the container and the border of a pipette
+MIN_SPACING_EDGE = 2.0
+
+
+def _get_compartments(
+  container: Container,
+  edge_clearance: float = MIN_SPACING_EDGE,
+) -> List[Tuple[float, float]]:
+  """Compute the usable Y compartments within a container created by no-go zones.
+
+  Each compartment is the free-space region between no-go zones and/or container walls,
+  shrunk by ``edge_clearance`` on each side.
+
+  Args:
+    container: The container whose no-go zones define the compartments.
+    edge_clearance: Minimum clearance between the pipette border and a compartment
+      boundary (container wall or no-go zone) in mm.
+
+  Returns:
+    List of (y_min, y_max) tuples representing usable Y ranges for channel centers.
+  """
+  container_y = container.get_size_y()
+  zones = sorted(container.no_go_zones, key=lambda z: z[0].y)
+
+  raw_compartments = []
+  prev_end = 0.0
+  for flb, brt in zones:
+    if flb.y > prev_end:
+      raw_compartments.append((prev_end, flb.y))
+    prev_end = max(prev_end, brt.y)
+  if prev_end < container_y:
+    raw_compartments.append((prev_end, container_y))
+
+  usable = []
+  for lo, hi in raw_compartments:
+    raw_width = hi - lo
+    usable_lo = lo + edge_clearance
+    usable_hi = hi - edge_clearance
+    if usable_hi > usable_lo:
+      usable.append((usable_lo, usable_hi))
+    elif raw_width > 0:
+      logger.info(
+        "Compartment Y=[%.1f, %.1f] (width=%.1fmm) is smaller than "
+        "2 * edge_clearance (%.1fmm). Automatic channel positioning will "
+        "skip this compartment. Ensure the attached tip physically fits in the container.",
+        lo,
+        hi,
+        raw_width,
+        2 * edge_clearance,
+      )
+  return usable
+
+
+def _resolve_channel_spacings(
+  num_channels: int,
+  channel_spacings: Optional[List[float]] = None,
+) -> List[float]:
+  """Resolve channel_spacings to a validated per-channel list.
+
+  Args:
+    num_channels: Number of channels.
+    channel_spacings: Per-channel occupancy diameters (length = num_channels).
+      Each value is the physical space the channel occupies.
+      If None, defaults to GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS for all.
+
+  Returns:
+    List of per-channel spacings, length = num_channels.
+  """
+  if channel_spacings is None:
+    return [GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS] * num_channels
+  if num_channels <= 1:
+    return channel_spacings[:num_channels]
+  if len(channel_spacings) != num_channels:
+    raise ValueError(
+      f"channel_spacings has {len(channel_spacings)} entries, "
+      f"expected {num_channels} (one per channel)."
+    )
+  return channel_spacings
+
+
+def required_spacing_between(channel_spacings: List[float], i: int, j: int) -> float:
+  """Compute the required center-to-center distance between channels i and j.
+
+  Each channel's spacing value is its diameter - the minimum distance its center must maintain
+  from any neighbor. For adjacent channels, the required distance is the sum of both channels'
+  radii (half-spacings), ceiling-rounded to 0.1mm for safety. For non-adjacent channels, the
+  distance is the sum of all intermediate adjacent-pair required spacings.
+
+  Args:
+    channel_spacings: Per-channel spacing values (one per channel). Each value is the channel's
+      spacing diameter.
+    i: Index of the first channel.
+    j: Index of the second channel.
+
+  Returns:
+    Required center-to-center distance in mm, ceiling-rounded to 0.1mm.
+  """
+  lo, hi = min(i, j), max(i, j)
+  if hi - lo == 1:
+    return math.ceil((channel_spacings[lo] / 2 + channel_spacings[hi] / 2) * 10) / 10
+  return sum(required_spacing_between(channel_spacings, k, k + 1) for k in range(lo, hi))
+
+
+def _position_channels_wide(
+  resource_size: float,
+  channel_spacings: List[float],
+) -> List[float]:
+  """Compute channel Y centers spread wide across a single region.
+
+  Distributes channels as far apart as possible while respecting per-channel spacing constraints.
+  Edge clearance = each edge channel's radius (half its occupancy diameter).
+  Returns centers in front-to-back order (ascending Y).
+  """
+  num_channels = len(channel_spacings)
+  if num_channels == 1:
+    return [resource_size / 2]
+
+  gaps = [
+    required_spacing_between(channel_spacings, i, i + 1) for i in range(len(channel_spacings) - 1)
+  ]
+  needed = sum(gaps)
+  first_radius = channel_spacings[0] / 2
+  last_radius = channel_spacings[-1] / 2
+  usable = resource_size - first_radius - last_radius
+
+  if usable < needed:
+    raise ValueError("Resource is too small to space channels.")
+
+  max_gap = max(gaps)
+  classic_gap = resource_size / (num_channels + 1)
+  if classic_gap >= max_gap:
+    return [(i + 1) * resource_size / (num_channels + 1) for i in range(num_channels)]
+
+  # Can't achieve equal spacing; center block like tight
+  surplus = usable - needed
+  start = first_radius + surplus / 2
+  centers = [start]
+  for g in gaps:
+    centers.append(centers[-1] + g)
+  return centers
+
+
+def _position_channels_tight(
+  resource_size: float,
+  channel_spacings: List[float],
+) -> List[float]:
+  """Compute channel Y centers packed tight in the center of a single region.
+
+  Channels are placed at minimum gap distances, centered in the region.
+  Edge clearance = each edge channel's radius (half its occupancy diameter).
+  Returns centers in front-to-back order (ascending Y).
+  """
+  num_channels = len(channel_spacings)
+  if num_channels == 1:
+    return [resource_size / 2]
+
+  gaps = [
+    required_spacing_between(channel_spacings, i, i + 1) for i in range(len(channel_spacings) - 1)
+  ]
+  needed = sum(gaps)
+  first_radius = channel_spacings[0] / 2
+  last_radius = channel_spacings[-1] / 2
+  usable = resource_size - first_radius - last_radius
+
+  if usable < needed:
+    raise ValueError("Resource is too small to space channels.")
+
+  surplus = usable - needed
+  start = first_radius + surplus / 2
+  centers = [start]
+  for g in gaps:
+    centers.append(centers[-1] + g)
+  return centers
+
+
+def _centers_to_offsets(centers: List[float], resource: Resource) -> List[Coordinate]:
+  """Convert absolute Y centers to offsets relative to the resource center, sorted back-to-front."""
+  center_y = resource.center().rotated(resource.get_absolute_rotation()).y
+  offsets = [Coordinate(x=0, y=c - center_y, z=0) for c in centers]
+  offsets.sort(key=lambda o: o.y, reverse=True)
+  return offsets
+
+
+def _space_needed(spacings: List[float]) -> float:
+  """Compute the minimum space needed for a contiguous group of channels."""
+  if len(spacings) <= 1:
+    return 0.0
+  return sum(required_spacing_between(spacings, i, i + 1) for i in range(len(spacings) - 1))
+
+
+def _distribute_channels(
+  compartments: List[Tuple[float, float]],
+  num_channels: int,
+  spacings: List[float],
+) -> List[int]:
+  """Distribute channels across compartments proportionally to compartment width.
+
+  Uses largest-remainder rounding to convert fractional per-compartment ideal_channel_counts into integers,
+  then iteratively shifts channels from overloaded compartments to neighbors until all groups
+  fit. Detects cycles to avoid infinite loops.
+
+  Args:
+    compartments: List of (y_min, y_max) usable compartment ranges.
+    num_channels: Total number of channels to distribute.
+    spacings: Per-channel spacing values (length = num_channels).
+
+  Returns:
+    List of channel counts per compartment.
+
+  Raises:
+    ChannelsDoNotFitError: If no valid distribution exists.
+  """
+  n_comp = len(compartments)
+  widths = [hi - lo for lo, hi in compartments]
+  total_width = sum(widths)
+
+  # Proportional ideal_channel_counts (largest-remainder rounding)
+  ideal_channel_counts = [w / total_width * num_channels for w in widths]
+  channel_distribution = [int(t) for t in ideal_channel_counts]
+  remainders = [t - int(t) for t in ideal_channel_counts]
+  deficit = num_channels - sum(channel_distribution)
+  # Give extra channels to compartments with largest remainders.
+  # Break ties by preferring back compartments (higher index = further back).
+  for _ in range(deficit):
+    idx = max(range(n_comp), key=lambda i: (remainders[i], i))
+    channel_distribution[idx] += 1
+    remainders[idx] = -1  # don't pick again
+
+  # Validate: check each group fits
+  def _fits(dist: List[int]) -> bool:
+    idx = 0
+    for comp_idx, n_ch in enumerate(dist):
+      if n_ch == 0:
+        idx += 0
+        continue
+      group = spacings[idx : idx + n_ch]
+      needed = _space_needed(group)
+      if needed > widths[comp_idx]:
+        return False
+      idx += n_ch
+    return True
+
+  # Shift channels from overloaded compartments to neighbors until everything fits.
+  seen = set()
+  while not _fits(channel_distribution):
+    key = tuple(channel_distribution)
+    if key in seen:
+      raise ChannelsDoNotFitError(
+        "Cannot distribute channels across compartments while respecting spacing constraints."
+      )
+    seen.add(key)
+
+    improved = False
+    idx = 0
+    for comp_idx in range(n_comp):
+      n_ch = channel_distribution[comp_idx]
+      if n_ch == 0:
+        continue
+      group = spacings[idx : idx + n_ch]
+      needed = _space_needed(group)
+      if needed > widths[comp_idx] and n_ch > 1:
+        if comp_idx + 1 < n_comp:
+          channel_distribution[comp_idx] -= 1
+          channel_distribution[comp_idx + 1] += 1
+          improved = True
+          break
+        if comp_idx - 1 >= 0:
+          channel_distribution[comp_idx] -= 1
+          channel_distribution[comp_idx - 1] += 1
+          improved = True
+          break
+      idx += n_ch
+    if not improved:
+      raise ChannelsDoNotFitError(
+        "Cannot distribute channels across compartments while respecting spacing constraints."
+      )
+
+  return channel_distribution
+
+
+def compute_channel_offsets(
+  resource: Resource,
+  num_channels: int,
+  spread: str = "wide",
+  channel_spacings: Optional[List[float]] = None,
+) -> List[Coordinate]:
+  """Compute Y offsets for positioning pipette channels in a resource.
+
+  Single entry point for all channel positioning logic. Handles containers with no-go zones
+  (distributing channels across compartments) and plain resources (wide/tight spread).
+
+  Args:
+    resource: The target resource (Container, Trough, Well, etc.).
+    num_channels: Number of channels to position.
+    spread: Positioning strategy:
+      - "wide": spread channels as far apart as possible (respects no-go zones if present)
+      - "tight": pack channels at minimum spacing (respects no-go zones if present)
+      - "custom": return zero offsets (caller controls positioning)
+    channel_spacings: Per-channel occupancy diameters in mm (length = num_channels).
+      Each value is the physical space the channel occupies. The required gap between
+      channels i and i+1 = spacing[i]/2 + spacing[i+1]/2 (sum of radii).
+      If None, defaults to 9mm for all channels.
+
+  Returns:
+    List of Y offsets relative to the resource center, sorted back-to-front (descending Y).
+
+  Raises:
+    ChannelsDoNotFitError: If channels cannot fit within the resource's compartments.
+    ValueError: If spread is not "wide", "tight", or "custom".
+  """
+  if spread == "custom":
+    return [Coordinate.zero()] * num_channels
+  if spread not in ("wide", "tight"):
+    raise ValueError(
+      f"Invalid value for 'spread': {spread!r}. Must be 'wide', 'tight', or 'custom'."
+    )
+
+  spacings = _resolve_channel_spacings(num_channels, channel_spacings)
+
+  # channel_spacings are back-to-front (ch0=backmost first).
+  # Compartments are processed front-to-back (ascending Y).
+  # Reverse for compartment assignment so spacings[0] = frontmost channel.
+  spacings_front_to_back = list(reversed(spacings))
+
+  # Container with no-go zones: distribute across compartments
+  if isinstance(resource, Container) and len(resource.no_go_zones) > 0:
+    compartments = _get_compartments(resource)
+    if not compartments:
+      raise ChannelsDoNotFitError(
+        f"Cannot fit {num_channels} channels into the compartments of "
+        f"'{resource.name}' while respecting its no-go zones. "
+        f"Use fewer channels or spread='custom' with manual offsets."
+      )
+
+    distribution = _distribute_channels(compartments, num_channels, spacings_front_to_back)
+
+    container_center_y = resource.get_size_y() / 2
+    offsets = []
+    spacing_idx = 0
+
+    for (comp_lo, comp_hi), n_ch in zip(compartments, distribution):
+      if n_ch == 0:
+        continue
+      comp_width = comp_hi - comp_lo
+
+      # Slice per-channel spacings for this group (front-to-back order)
+      group_spacings = spacings_front_to_back[spacing_idx : spacing_idx + n_ch]
+      spacing_idx += n_ch
+
+      if n_ch == 1:
+        if comp_width <= 0:
+          raise ChannelsDoNotFitError(
+            f"Cannot fit {num_channels} channels into the compartments of "
+            f"'{resource.name}' while respecting its no-go zones. "
+            f"Use fewer channels or spread='custom' with manual offsets."
+          )
+        centers = [(comp_lo + comp_hi) / 2]
+      else:
+        group_gaps = [
+          required_spacing_between(group_spacings, i, i + 1) for i in range(len(group_spacings) - 1)
+        ]
+        needed = sum(group_gaps)
+        # Edge clearance: first and last channel's radius must not extend past compartment
+        first_radius = group_spacings[0] / 2
+        last_radius = group_spacings[-1] / 2
+        usable = comp_width - first_radius - last_radius
+
+        if usable < needed:
+          raise ChannelsDoNotFitError(
+            f"Cannot fit {num_channels} channels into the compartments of "
+            f"'{resource.name}' while respecting its no-go zones. "
+            f"Use fewer channels or spread='custom' with manual offsets."
+          )
+
+        if spread == "wide":
+          max_gap = max(group_gaps)
+          classic_gap = comp_width / (n_ch + 1)
+          if classic_gap >= max_gap:
+            centers = [comp_lo + (i + 1) * comp_width / (n_ch + 1) for i in range(n_ch)]
+          else:
+            # Can't achieve equal spacing; center block like tight
+            surplus = usable - needed
+            start = comp_lo + first_radius + surplus / 2
+            centers = [start]
+            for g in group_gaps:
+              centers.append(centers[-1] + g)
+        else:
+          # Tight: minimum gaps, centered within usable space
+          surplus = usable - needed
+          start = comp_lo + first_radius + surplus / 2
+          centers = [start]
+          for g in group_gaps:
+            centers.append(centers[-1] + g)
+
+      for c in centers:
+        offsets.append(Coordinate(0, c - container_center_y, 0))
+
+    # Validate cross-compartment gaps: channels at adjacent compartment boundaries
+    # must respect their required spacing across the no-go zone.
+    all_centers = sorted([container_center_y + o.y for o in offsets])
+    ch_idx = 0
+    for comp_i in range(len(distribution) - 1):
+      n_a = distribution[comp_i]
+      n_b = distribution[comp_i + 1]
+      if n_a == 0 or n_b == 0:
+        ch_idx += n_a
+        continue
+      last_in_a = all_centers[ch_idx + n_a - 1]
+      first_in_b = all_centers[ch_idx + n_a]
+      # Spacing indices: last channel of group A and first channel of group B
+      spacing_idx_a = sum(distribution[: comp_i + 1]) - 1
+      spacing_idx_b = spacing_idx_a + 1
+      required = required_spacing_between(spacings_front_to_back, spacing_idx_a, spacing_idx_b)
+      actual = first_in_b - last_in_a
+      if actual < required - 0.05:
+        raise ChannelsDoNotFitError(
+          f"Cannot fit {num_channels} channels into the compartments of "
+          f"'{resource.name}' while respecting spacing constraints across no-go zones "
+          f"(gap {actual:.1f}mm < required {required:.1f}mm between compartments "
+          f"{comp_i} and {comp_i + 1}). "
+          f"Use fewer channels or spread='custom' with manual offsets."
+        )
+      ch_idx += n_a
+
+    offsets.sort(key=lambda o: o.y, reverse=True)
+    return offsets
+
+  # Plain resource (no no-go zones): wide or tight across full Y
+  resource_size = resource.get_absolute_size_y()
+  if spread == "wide":
+    centers = _position_channels_wide(resource_size, spacings_front_to_back)
+  else:
+    centers = _position_channels_tight(resource_size, spacings_front_to_back)
+  return _centers_to_offsets(centers, resource)
+
+
+# ---------------------------------------------------------------------------
+# Deprecated wrappers (remove in v1)
+# ---------------------------------------------------------------------------
+
+
+def get_wide_single_resource_liquid_op_offsets(
+  resource: Resource,
+  num_channels: int,
+  min_spacing: float = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS,
+) -> List[Coordinate]:
+  """Deprecated. Use ``compute_channel_offsets(resource, num_channels, spread='wide')`` instead."""
+  warnings.warn(
+    "get_wide_single_resource_liquid_op_offsets() is deprecated and will be removed in v1. "
+    "Use compute_channel_offsets(resource, num_channels, spread='wide') instead.",
+    DeprecationWarning,
+    stacklevel=2,
+  )
+  per_channel = [min_spacing] * num_channels
+  return compute_channel_offsets(
+    resource, num_channels, spread="wide", channel_spacings=per_channel
+  )
+
+
+def get_tight_single_resource_liquid_op_offsets(
+  resource: Resource,
+  num_channels: int,
+  min_spacing: float = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS,
+) -> List[Coordinate]:
+  """Deprecated. Use ``compute_channel_offsets(resource, num_channels, spread='tight')`` instead."""
+  warnings.warn(
+    "get_tight_single_resource_liquid_op_offsets() is deprecated and will be removed in v1. "
+    "Use compute_channel_offsets(resource, num_channels, spread='tight') instead.",
+    DeprecationWarning,
+    stacklevel=2,
+  )
+  per_channel = [min_spacing] * num_channels
+  return compute_channel_offsets(
+    resource, num_channels, spread="tight", channel_spacings=per_channel
+  )
diff --git a/pylabrobot/liquid_handling/channel_positioning_tests.py b/pylabrobot/liquid_handling/channel_positioning_tests.py
new file mode 100644
index 00000000000..78f9fa6612e
--- /dev/null
+++ b/pylabrobot/liquid_handling/channel_positioning_tests.py
@@ -0,0 +1,406 @@
+import unittest
+
+from pylabrobot.liquid_handling.channel_positioning import (
+  _centers_to_offsets,
+  _distribute_channels,
+  _get_compartments,
+  _position_channels_tight,
+  _position_channels_wide,
+  _resolve_channel_spacings,
+  _space_needed,
+  compute_channel_offsets,
+  required_spacing_between,
+)
+from pylabrobot.liquid_handling.errors import ChannelsDoNotFitError
+from pylabrobot.resources.container import Container
+from pylabrobot.resources.coordinate import Coordinate
+from pylabrobot.resources.resource import Resource
+
+
+def _make_container(size_y, no_go_zones=None, name="test"):
+  return Container(
+    name=name,
+    size_x=19.0,
+    size_y=size_y,
+    size_z=50.0,
+    no_go_zones=no_go_zones or [],
+  )
+
+
+class TestGetCompartments(unittest.TestCase):
+  def test_no_zones(self):
+    c = _make_container(100)
+    result = _get_compartments(c)
+    self.assertEqual(result, [(2.0, 98.0)])
+
+  def test_single_center_zone(self):
+    c = _make_container(
+      100,
+      [
+        (Coordinate(0, 48, 0), Coordinate(19, 52, 50)),
+      ],
+    )
+    result = _get_compartments(c)
+    self.assertEqual(len(result), 2)
+    self.assertAlmostEqual(result[0][0], 2.0)
+    self.assertAlmostEqual(result[0][1], 46.0)
+    self.assertAlmostEqual(result[1][0], 54.0)
+    self.assertAlmostEqual(result[1][1], 98.0)
+
+  def test_zone_at_front(self):
+    c = _make_container(
+      50,
+      [
+        (Coordinate(0, 0, 0), Coordinate(19, 10, 50)),
+      ],
+    )
+    result = _get_compartments(c)
+    self.assertEqual(len(result), 1)
+    self.assertAlmostEqual(result[0][0], 12.0)
+    self.assertAlmostEqual(result[0][1], 48.0)
+
+  def test_zone_at_back(self):
+    c = _make_container(
+      50,
+      [
+        (Coordinate(0, 40, 0), Coordinate(19, 50, 50)),
+      ],
+    )
+    result = _get_compartments(c)
+    self.assertEqual(len(result), 1)
+    self.assertAlmostEqual(result[0][0], 2.0)
+    self.assertAlmostEqual(result[0][1], 38.0)
+
+  def test_compartment_too_narrow_is_skipped(self):
+    # 3mm raw compartment < 2*2mm edge clearance -> skipped
+    c = _make_container(
+      20,
+      [
+        (Coordinate(0, 3, 0), Coordinate(19, 17, 50)),
+      ],
+    )
+    result = _get_compartments(c)
+    # front compartment [0, 3] -> too narrow (3mm < 4mm)
+    # back compartment [17, 20] -> too narrow (3mm < 4mm)
+    self.assertEqual(result, [])
+
+  def test_custom_edge_clearance(self):
+    c = _make_container(10)
+    result = _get_compartments(c, edge_clearance=1.0)
+    self.assertEqual(result, [(1.0, 9.0)])
+
+  def test_multiple_zones(self):
+    c = _make_container(
+      100,
+      [
+        (Coordinate(0, 30, 0), Coordinate(19, 35, 50)),
+        (Coordinate(0, 65, 0), Coordinate(19, 70, 50)),
+      ],
+    )
+    result = _get_compartments(c)
+    self.assertEqual(len(result), 3)
+
+  def test_overlapping_zones(self):
+    c = _make_container(
+      100,
+      [
+        (Coordinate(0, 30, 0), Coordinate(19, 50, 50)),
+        (Coordinate(0, 40, 0), Coordinate(19, 60, 50)),
+      ],
+    )
+    result = _get_compartments(c)
+    # Should merge: gap is [0,30] and [60,100]
+    self.assertEqual(len(result), 2)
+    self.assertAlmostEqual(result[0][0], 2.0)
+    self.assertAlmostEqual(result[0][1], 28.0)
+    self.assertAlmostEqual(result[1][0], 62.0)
+    self.assertAlmostEqual(result[1][1], 98.0)
+
+
+class TestResolveChannelSpacings(unittest.TestCase):
+  def test_none_returns_defaults(self):
+    result = _resolve_channel_spacings(4)
+    self.assertEqual(result, [9.0, 9.0, 9.0, 9.0])
+
+  def test_explicit_spacings(self):
+    result = _resolve_channel_spacings(3, [9.0, 18.0, 9.0])
+    self.assertEqual(result, [9.0, 18.0, 9.0])
+
+  def test_wrong_length_raises(self):
+    with self.assertRaises(ValueError):
+      _resolve_channel_spacings(3, [9.0, 9.0])
+
+  def test_single_channel(self):
+    result = _resolve_channel_spacings(1, [18.0, 9.0])
+    self.assertEqual(result, [18.0])
+
+  def test_zero_channels(self):
+    result = _resolve_channel_spacings(0)
+    self.assertEqual(result, [])
+
+
+class TestRequiredSpacingBetween(unittest.TestCase):
+  def test_equal_spacings(self):
+    spacings = [9.0, 9.0, 9.0]
+    result = required_spacing_between(spacings, 0, 1)
+    self.assertAlmostEqual(result, 9.0)
+
+  def test_mixed_spacings(self):
+    spacings = [9.0, 18.0]
+    result = required_spacing_between(spacings, 0, 1)
+    # (9/2 + 18/2) = 13.5, ceil to 0.1 = 13.5
+    self.assertAlmostEqual(result, 13.5)
+
+  def test_non_adjacent(self):
+    spacings = [9.0, 9.0, 9.0]
+    result = required_spacing_between(spacings, 0, 2)
+    self.assertAlmostEqual(result, 18.0)
+
+  def test_ceiling_rounding(self):
+    # 7.0/2 + 8.0/2 = 7.5 -> ceil(75)/10 = 7.5
+    spacings = [7.0, 8.0]
+    result = required_spacing_between(spacings, 0, 1)
+    self.assertAlmostEqual(result, 7.5)
+
+    # 7.0/2 + 7.1/2 = 7.05 -> ceil(70.5)/10 = 7.1
+    spacings = [7.0, 7.1]
+    result = required_spacing_between(spacings, 0, 1)
+    self.assertAlmostEqual(result, 7.1)
+
+
+class TestPositionChannelsWide(unittest.TestCase):
+  def test_single_channel_centered(self):
+    result = _position_channels_wide(100.0, [9.0])
+    self.assertAlmostEqual(result[0], 50.0)
+
+  def test_two_channels_equal_spacing(self):
+    result = _position_channels_wide(90.0, [9.0, 9.0])
+    self.assertAlmostEqual(result[0], 30.0)
+    self.assertAlmostEqual(result[1], 60.0)
+
+  def test_too_small_raises(self):
+    with self.assertRaises(ValueError):
+      _position_channels_wide(5.0, [9.0, 9.0])
+
+  def test_mixed_spacings(self):
+    # With mixed spacings, classic gap may not work; should still produce valid result
+    result = _position_channels_wide(100.0, [9.0, 18.0, 9.0])
+    self.assertEqual(len(result), 3)
+    # Channels should be sorted ascending
+    self.assertLess(result[0], result[1])
+    self.assertLess(result[1], result[2])
+    # Gaps should respect minimum required spacings
+    gap_01 = result[1] - result[0]
+    gap_12 = result[2] - result[1]
+    self.assertGreaterEqual(gap_01, required_spacing_between([9.0, 18.0, 9.0], 0, 1) - 0.01)
+    self.assertGreaterEqual(gap_12, required_spacing_between([9.0, 18.0, 9.0], 1, 2) - 0.01)
+
+
+class TestPositionChannelsTight(unittest.TestCase):
+  def test_single_channel_centered(self):
+    result = _position_channels_tight(100.0, [9.0])
+    self.assertAlmostEqual(result[0], 50.0)
+
+  def test_two_channels_centered(self):
+    result = _position_channels_tight(100.0, [9.0, 9.0])
+    gap = result[1] - result[0]
+    self.assertAlmostEqual(gap, 9.0)
+    center = (result[0] + result[1]) / 2
+    self.assertAlmostEqual(center, 50.0)
+
+  def test_too_small_raises(self):
+    with self.assertRaises(ValueError):
+      _position_channels_tight(5.0, [9.0, 9.0])
+
+  def test_three_channels(self):
+    result = _position_channels_tight(100.0, [9.0, 9.0, 9.0])
+    self.assertEqual(len(result), 3)
+    self.assertAlmostEqual(result[1] - result[0], 9.0)
+    self.assertAlmostEqual(result[2] - result[1], 9.0)
+
+
+class TestCentersToOffsets(unittest.TestCase):
+  def test_sorted_back_to_front(self):
+    resource = Resource(name="r", size_x=10, size_y=100, size_z=10)
+    centers = [20.0, 50.0, 80.0]
+    offsets = _centers_to_offsets(centers, resource)
+    # Should be sorted descending by y
+    self.assertGreater(offsets[0].y, offsets[1].y)
+    self.assertGreater(offsets[1].y, offsets[2].y)
+
+  def test_offset_relative_to_center(self):
+    resource = Resource(name="r", size_x=10, size_y=100, size_z=10)
+    offsets = _centers_to_offsets([50.0], resource)
+    self.assertAlmostEqual(offsets[0].y, 0.0)
+
+  def test_x_and_z_are_zero(self):
+    resource = Resource(name="r", size_x=10, size_y=100, size_z=10)
+    offsets = _centers_to_offsets([30.0, 70.0], resource)
+    for o in offsets:
+      self.assertAlmostEqual(o.x, 0.0)
+      self.assertAlmostEqual(o.z, 0.0)
+
+
+class TestSpaceNeeded(unittest.TestCase):
+  def test_single_channel(self):
+    self.assertAlmostEqual(_space_needed([9.0]), 0.0)
+
+  def test_zero_channels(self):
+    self.assertAlmostEqual(_space_needed([]), 0.0)
+
+  def test_two_equal(self):
+    self.assertAlmostEqual(_space_needed([9.0, 9.0]), 9.0)
+
+  def test_three_equal(self):
+    self.assertAlmostEqual(_space_needed([9.0, 9.0, 9.0]), 18.0)
+
+  def test_mixed(self):
+    result = _space_needed([9.0, 18.0])
+    expected = required_spacing_between([9.0, 18.0], 0, 1)
+    self.assertAlmostEqual(result, expected)
+
+
+class TestDistributeChannels(unittest.TestCase):
+  def test_equal_compartments(self):
+    compartments = [(0.0, 40.0), (50.0, 90.0)]
+    result = _distribute_channels(compartments, 4, [9.0] * 4)
+    self.assertEqual(sum(result), 4)
+    self.assertEqual(result, [2, 2])
+
+  def test_unequal_compartments(self):
+    compartments = [(0.0, 60.0), (70.0, 90.0)]
+    result = _distribute_channels(compartments, 3, [9.0] * 3)
+    self.assertEqual(sum(result), 3)
+    # Wider compartment should get more
+    self.assertGreaterEqual(result[0], result[1])
+
+  def test_single_compartment(self):
+    compartments = [(0.0, 100.0)]
+    result = _distribute_channels(compartments, 3, [9.0] * 3)
+    self.assertEqual(result, [3])
+
+  def test_channels_dont_fit_raises(self):
+    # Tiny compartments can't hold many channels
+    compartments = [(0.0, 5.0), (10.0, 15.0)]
+    with self.assertRaises(ChannelsDoNotFitError):
+      _distribute_channels(compartments, 10, [9.0] * 10)
+
+  def test_shifting_needed(self):
+    # One narrow + one wide compartment, proportional would overload the narrow one
+    compartments = [(0.0, 10.0), (15.0, 100.0)]
+    result = _distribute_channels(compartments, 8, [9.0] * 8)
+    self.assertEqual(sum(result), 8)
+    # Narrow compartment can fit at most 1 channel (10mm width, 9mm spacing)
+    self.assertLessEqual(result[0], 1)
+
+
+class TestComputeChannelOffsets(unittest.TestCase):
+  def test_custom_returns_zeros(self):
+    resource = Resource(name="r", size_x=10, size_y=100, size_z=10)
+    result = compute_channel_offsets(resource, 3, spread="custom")
+    for o in result:
+      self.assertAlmostEqual(o.x, 0.0)
+      self.assertAlmostEqual(o.y, 0.0)
+      self.assertAlmostEqual(o.z, 0.0)
+
+  def test_invalid_spread_raises(self):
+    resource = Resource(name="r", size_x=10, size_y=100, size_z=10)
+    with self.assertRaises(ValueError):
+      compute_channel_offsets(resource, 2, spread="invalid")
+
+  def test_wide_plain_resource(self):
+    resource = Resource(name="r", size_x=10, size_y=90, size_z=10)
+    result = compute_channel_offsets(resource, 2, spread="wide")
+    self.assertEqual(len(result), 2)
+    # Should be sorted back-to-front (descending y)
+    self.assertGreater(result[0].y, result[1].y)
+
+  def test_tight_plain_resource(self):
+    resource = Resource(name="r", size_x=10, size_y=90, size_z=10)
+    result = compute_channel_offsets(resource, 2, spread="tight")
+    self.assertEqual(len(result), 2)
+    gap = abs(result[0].y - result[1].y)
+    self.assertAlmostEqual(gap, 9.0)
+
+  def test_single_channel(self):
+    resource = Resource(name="r", size_x=10, size_y=90, size_z=10)
+    result = compute_channel_offsets(resource, 1)
+    self.assertEqual(len(result), 1)
+    self.assertAlmostEqual(result[0].y, 0.0)
+
+  def test_with_no_go_zones(self):
+    c = _make_container(
+      100,
+      [
+        (Coordinate(0, 48, 0), Coordinate(19, 52, 50)),
+      ],
+    )
+    result = compute_channel_offsets(c, 2)
+    self.assertEqual(len(result), 2)
+    # Channels should be in different compartments (one positive, one negative offset)
+    center_y = 50.0
+    positions = sorted([center_y + o.y for o in result])
+    # First should be in [2, 46], second in [54, 98]
+    self.assertLess(positions[0], 48.0)
+    self.assertGreater(positions[1], 52.0)
+
+  def test_no_go_zones_too_restrictive_raises(self):
+    # Almost entire container is a no-go zone
+    c = _make_container(
+      20,
+      [
+        (Coordinate(0, 2, 0), Coordinate(19, 18, 50)),
+      ],
+    )
+    with self.assertRaises(ChannelsDoNotFitError):
+      compute_channel_offsets(c, 2)
+
+  def test_channel_spacings(self):
+    resource = Resource(name="r", size_x=10, size_y=100, size_z=10)
+    result = compute_channel_offsets(resource, 2, channel_spacings=[9.0, 18.0])
+    self.assertEqual(len(result), 2)
+    gap = abs(result[0].y - result[1].y)
+    self.assertGreaterEqual(gap, 13.5 - 0.01)
+
+  def test_many_channels_no_go_zones(self):
+    # 3 no-go zones creating 4 compartments
+    c = _make_container(
+      142.5,
+      [
+        (Coordinate(0, 39.7, 0), Coordinate(19, 42.2, 50)),
+        (Coordinate(0, 73.5, 0), Coordinate(19, 76.0, 50)),
+        (Coordinate(0, 107.3, 0), Coordinate(19, 109.8, 50)),
+      ],
+    )
+    for n in [1, 2, 3, 4, 5, 6, 7, 8, 9]:
+      result = compute_channel_offsets(c, n)
+      self.assertEqual(len(result), n)
+
+  def test_offsets_respect_no_go_zones(self):
+    c = _make_container(
+      90,
+      [
+        (Coordinate(0, 43, 0), Coordinate(19, 47, 50)),
+      ],
+    )
+    center_y = 45.0
+    for n in range(1, 9):
+      result = compute_channel_offsets(c, n)
+      positions = [center_y + o.y for o in result]
+      for p in positions:
+        # No channel center should be inside the no-go zone
+        self.assertFalse(
+          43.0 <= p <= 47.0, f"Channel at y={p} is inside no-go zone [43, 47] with {n} channels"
+        )
+
+  def test_wide_vs_tight_gap_difference(self):
+    resource = Resource(name="r", size_x=10, size_y=100, size_z=10)
+    wide = compute_channel_offsets(resource, 3, spread="wide")
+    tight = compute_channel_offsets(resource, 3, spread="tight")
+    wide_gap = abs(wide[0].y - wide[-1].y)
+    tight_gap = abs(tight[0].y - tight[-1].y)
+    self.assertGreaterEqual(wide_gap, tight_gap - 0.01)
+
+
+if __name__ == "__main__":
+  unittest.main()
diff --git a/pylabrobot/liquid_handling/errors.py b/pylabrobot/liquid_handling/errors.py
index 55eb6daf600..5ec290b1ab7 100644
--- a/pylabrobot/liquid_handling/errors.py
+++ b/pylabrobot/liquid_handling/errors.py
@@ -10,6 +10,11 @@ class NoChannelError(Exception):
   """
 
 
+class ChannelsDoNotFitError(Exception):
+  """Raised when channels cannot be positioned within a resource's compartments while respecting
+  no-go zones and spacing constraints."""
+
+
 class ChannelizedError(Exception):
   """Raised by operations that work on multiple channels: pick_up_tips, drop_tips, aspirate, and
   dispense. Contains a key for each channel that had an error, and the error that occurred."""
diff --git a/pylabrobot/liquid_handling/liquid_handler.py b/pylabrobot/liquid_handling/liquid_handler.py
index 7654d4af444..ef194c5a3a8 100644
--- a/pylabrobot/liquid_handling/liquid_handler.py
+++ b/pylabrobot/liquid_handling/liquid_handler.py
@@ -24,15 +24,14 @@
   cast,
 )
 
+from pylabrobot.liquid_handling.channel_positioning import (
+  compute_channel_offsets,
+)
 from pylabrobot.liquid_handling.errors import ChannelizedError
 from pylabrobot.liquid_handling.strictness import (
   Strictness,
   get_strictness,
 )
-from pylabrobot.liquid_handling.utils import (
-  get_tight_single_resource_liquid_op_offsets,
-  get_wide_single_resource_liquid_op_offsets,
-)
 from pylabrobot.machines.machine import Machine, need_setup_finished
 from pylabrobot.plate_reading import PlateReader
 from pylabrobot.resources import (
@@ -348,6 +347,20 @@ def _check_args(
 
     return extra
 
+  def _compute_spread_offsets(
+    self,
+    resource: Resource,
+    use_channels: List[int],
+    spread: str,
+  ) -> List[Coordinate]:
+    """Compute channel spread offsets for a single-resource multi-channel operation."""
+    return compute_channel_offsets(
+      resource=resource,
+      num_channels=len(use_channels),
+      spread=spread,
+      channel_spacings=self.backend.get_channel_spacings(use_channels),
+    )
+
   def _make_sure_channels_exist(self, channels: List[int]):
     """Checks that the channels exist."""
     invalid_channels = [c for c in channels if c not in self.head]
@@ -768,10 +781,7 @@ async def discard_tips(
       raise RuntimeError("No tips have been picked up and no channels were specified.")
 
     trash = self.deck.get_trash_area()
-    trash_offsets = get_tight_single_resource_liquid_op_offsets(
-      trash,
-      num_channels=n,
-    )
+    trash_offsets = compute_channel_offsets(trash, num_channels=n, spread="tight")
     # add trash_offsets to offsets if defined, otherwise use trash_offsets
     # too advanced for mypy
     offsets = [
@@ -947,18 +957,8 @@ async def aspirate(
     if len(set(resources)) == 1:
       resource = resources[0]
       resources = [resource] * len(use_channels)
-      if spread == "tight":
-        center_offsets = get_tight_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "wide":
-        center_offsets = get_wide_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "custom":
-        center_offsets = [Coordinate.zero()] * len(use_channels)
-      else:
-        raise ValueError("Invalid value for 'spread'. Must be 'tight', 'wide', or 'custom'.")
+
+      center_offsets = self._compute_spread_offsets(resource, use_channels, spread)
 
       # add user defined offsets to the computed centers
       offsets = [c + o for c, o in zip(center_offsets, offsets)]
@@ -1130,18 +1130,8 @@ async def dispense(
     if len(set(resources)) == 1:
       resource = resources[0]
       resources = [resource] * len(use_channels)
-      if spread == "tight":
-        center_offsets = get_tight_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "wide":
-        center_offsets = get_wide_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "custom":
-        center_offsets = [Coordinate.zero()] * len(use_channels)
-      else:
-        raise ValueError("Invalid value for 'spread'. Must be 'tight', 'wide', or 'custom'.")
+
+      center_offsets = self._compute_spread_offsets(resource, use_channels, spread)
 
       # add user defined offsets to the computed centers
       offsets = [c + o for c, o in zip(center_offsets, offsets)]
diff --git a/pylabrobot/liquid_handling/liquid_handler_tests.py b/pylabrobot/liquid_handling/liquid_handler_tests.py
index d31cfed45d0..d27eba719e2 100644
--- a/pylabrobot/liquid_handling/liquid_handler_tests.py
+++ b/pylabrobot/liquid_handling/liquid_handler_tests.py
@@ -9,12 +9,14 @@
 
 from pylabrobot.liquid_handling.backends.backend import LiquidHandlerBackend
 from pylabrobot.liquid_handling.backends.chatterbox import LiquidHandlerChatterboxBackend
+from pylabrobot.liquid_handling.channel_positioning import (
+  get_tight_single_resource_liquid_op_offsets,
+)
 from pylabrobot.liquid_handling.errors import ChannelizedError
 from pylabrobot.liquid_handling.strictness import (
   Strictness,
   set_strictness,
 )
-from pylabrobot.liquid_handling.utils import get_tight_single_resource_liquid_op_offsets
 from pylabrobot.resources import (
   PLT_CAR_L5AC_A00,
   TIP_CAR_480_A00,
@@ -70,6 +72,7 @@ def _create_mock_backend(num_channels: int = 8):
   type(mock).num_arms = PropertyMock(return_value=1)
   type(mock).head96_installed = PropertyMock(return_value=True)
   mock.can_pick_up_tip.return_value = True
+  mock.get_channel_spacings.side_effect = lambda channels: [9.0] * len(channels)
   return mock
 
 
@@ -1207,3 +1210,121 @@ async def test_load_state_backward_compatible(self):
     # Old state format without head96_state or arm_state
     old_state = {"head_state": {c: self.lh.head[c].serialize() for c in self.lh.head}}
     self.lh.load_state(old_state)  # should not raise
+
+
+class TestNoGoZoneIntegration(unittest.IsolatedAsyncioTestCase):
+  """Integration tests for no-go zone channel distribution through LiquidHandler."""
+
+  async def asyncSetUp(self):
+    self.backend = _create_mock_backend(num_channels=8)
+    self.backend.get_channel_spacings.side_effect = lambda channels: [9.0] * len(channels)
+    self.deck = STARLetDeck()
+    self.lh = LiquidHandler(backend=self.backend, deck=self.deck)
+
+    # A trough-like container with a center divider
+    from pylabrobot.resources.trough import Trough
+
+    self.trough = Trough(
+      name="trough",
+      size_x=19.0,
+      size_y=90.0,
+      size_z=65.0,
+      max_volume=60_000,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(19, 46, 65))],
+    )
+    self.deck.assign_child_resource(self.trough, location=Coordinate(100, 100, 0))
+
+    self.tip_rack = hamilton_96_tiprack_300uL_filter(name="tip_rack")
+    self.deck.assign_child_resource(self.tip_rack, location=Coordinate(0, 0, 0))
+    await self.lh.setup()
+
+  async def test_aspirate_2_channels_avoids_no_go_zone(self):
+    """2 channels on a trough with a center divider should be placed in separate compartments."""
+    t0 = self.tip_rack.get_item("A1").get_tip()
+    t1 = self.tip_rack.get_item("B1").get_tip()
+    self.lh.update_head_state({0: t0, 1: t1})
+    self.trough.tracker.set_volume(50_000)
+
+    await self.lh.aspirate([self.trough] * 2, vols=[100, 100], use_channels=[0, 1])
+
+    ops = self.backend.aspirate.call_args.kwargs["ops"]
+    y_offsets = [op.offset.y for op in ops]
+    # Both offsets should be non-zero (not centered) and in opposite compartments
+    self.assertEqual(len(y_offsets), 2)
+    # One positive (back compartment), one negative (front compartment)
+    self.assertTrue(y_offsets[0] > 0 and y_offsets[1] < 0, f"offsets: {y_offsets}")
+    # Neither should be near the divider (Y=44-46, container center=45, so offset ~0 is bad)
+    for y in y_offsets:
+      self.assertGreater(abs(y), 5, f"offset {y} too close to divider")
+
+  async def test_single_channel_respects_no_go_zone(self):
+    """Single channel on a container with no-go zones should be placed in a safe compartment."""
+    t = self.tip_rack.get_item("A1").get_tip()
+    self.lh.update_head_state({0: t})
+    self.trough.tracker.set_volume(50_000)
+
+    await self.lh.aspirate([self.trough], vols=[100], use_channels=[0])
+
+    ops = self.backend.aspirate.call_args.kwargs["ops"]
+    # Single channel should be placed in a compartment, not at container center
+    # (container center Y=45 is inside the no-go zone at Y=44-46)
+    self.assertNotAlmostEqual(ops[0].offset.y, 0.0)
+    # Should be in the back compartment center: (48+88)/2 = 68, offset = 68-45 = 23
+    self.assertAlmostEqual(ops[0].offset.y, 23.0)
+
+  async def test_dispense_uses_no_go_zones(self):
+    """Dispense should also use no-go zone logic."""
+    t0 = self.tip_rack.get_item("A1").get_tip()
+    t1 = self.tip_rack.get_item("B1").get_tip()
+    self.lh.update_head_state({0: t0, 1: t1})
+    t0.tracker.add_liquid(volume=100)
+    t1.tracker.add_liquid(volume=100)
+
+    await self.lh.dispense([self.trough] * 2, vols=[100, 100], use_channels=[0, 1])
+
+    ops = self.backend.dispense.call_args.kwargs["ops"]
+    y_offsets = [op.offset.y for op in ops]
+    self.assertTrue(y_offsets[0] > 0 and y_offsets[1] < 0, f"offsets: {y_offsets}")
+
+  async def test_no_go_zones_skipped_for_custom_spread(self):
+    """spread='custom' should bypass no-go zone logic."""
+    t0 = self.tip_rack.get_item("A1").get_tip()
+    t1 = self.tip_rack.get_item("B1").get_tip()
+    self.lh.update_head_state({0: t0, 1: t1})
+    self.trough.tracker.set_volume(50_000)
+
+    await self.lh.aspirate([self.trough] * 2, vols=[100, 100], use_channels=[0, 1], spread="custom")
+
+    ops = self.backend.aspirate.call_args.kwargs["ops"]
+    # Custom spread: offsets should be zero (user controls positioning)
+    for op in ops:
+      self.assertAlmostEqual(op.offset.y, 0.0)
+
+  async def test_no_go_zones_tight_vs_wide(self):
+    """spread='tight' should pack channels closer than spread='wide' within compartments."""
+    tips = [self.tip_rack.get_item(f"{chr(65 + i)}1").get_tip() for i in range(4)]
+    self.lh.update_head_state({i: t for i, t in enumerate(tips)})
+    self.trough.tracker.set_volume(50_000)
+    self.backend.get_channel_spacings.side_effect = lambda channels: [9.0] * len(channels)
+
+    # wide (default): channels spread far apart within each compartment
+    await self.lh.aspirate(
+      [self.trough] * 4, vols=[100] * 4, use_channels=[0, 1, 2, 3], spread="wide"
+    )
+    wide_ops = self.backend.aspirate.call_args.kwargs["ops"]
+    wide_offsets = sorted([op.offset.y for op in wide_ops])
+
+    self.lh.update_head_state({i: t for i, t in enumerate(tips)})
+    self.trough.tracker.set_volume(50_000)
+
+    # tight: channels packed at minimum spacing within each compartment
+    await self.lh.aspirate(
+      [self.trough] * 4, vols=[100] * 4, use_channels=[0, 1, 2, 3], spread="tight"
+    )
+    tight_ops = self.backend.aspirate.call_args.kwargs["ops"]
+    tight_offsets = sorted([op.offset.y for op in tight_ops])
+
+    # within each compartment, wide channels should be further apart than tight
+    wide_gap_lower = abs(wide_offsets[1] - wide_offsets[0])
+    tight_gap_lower = abs(tight_offsets[1] - tight_offsets[0])
+    self.assertGreater(wide_gap_lower, tight_gap_lower)
diff --git a/pylabrobot/liquid_handling/utils.py b/pylabrobot/liquid_handling/utils.py
deleted file mode 100644
index a59ed6e4e2d..00000000000
--- a/pylabrobot/liquid_handling/utils.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from typing import List
-
-from pylabrobot.resources.coordinate import Coordinate
-from pylabrobot.resources.resource import Resource
-
-GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS = 9
-MIN_SPACING_BETWEEN_CHANNELS = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS
-# minimum spacing between the edge of the container and the center of channel
-MIN_SPACING_EDGE = 1.0
-
-
-def _get_centers_with_margin(dim_size: float, n: int, margin: float, min_spacing: float):
-  """Get the centers of the channels with a minimum margin on the edges."""
-  if dim_size < margin * 2 + (n - 1) * min_spacing:
-    raise ValueError("Resource is too small to space channels.")
-  if dim_size - (n - 1) * min_spacing <= min_spacing * 2:
-    remaining_space = dim_size - (n - 1) * min_spacing - margin * 2
-    return [margin + remaining_space / 2 + i * min_spacing for i in range(n)]
-  return [(i + 1) * dim_size / (n + 1) for i in range(n)]
-
-
-def get_wide_single_resource_liquid_op_offsets(
-  resource: Resource,
-  num_channels: int,
-  min_spacing: float = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS,
-) -> List[Coordinate]:
-  resource_size = resource.get_absolute_size_y()
-  centers = list(
-    reversed(
-      _get_centers_with_margin(
-        dim_size=resource_size,
-        n=num_channels,
-        margin=MIN_SPACING_EDGE,
-        min_spacing=min_spacing,
-      )
-    )
-  )  # reverse because channels are from back to front
-
-  # offsets are relative to the center of the resource, but above we computed them wrt lfb
-  # so we need to subtract the center of the resource
-  # also, offsets are in absolute space, so we need to rotate the center
-  return [
-    Coordinate(
-      x=0,
-      y=c - resource.center().rotated(resource.get_absolute_rotation()).y,
-      z=0,
-    )
-    for c in centers
-  ]
-
-
-def get_tight_single_resource_liquid_op_offsets(
-  resource: Resource,
-  num_channels: int,
-  min_spacing: float = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS,
-) -> List[Coordinate]:
-  channel_space = (num_channels - 1) * min_spacing
-
-  min_y = (resource.get_absolute_size_y() - channel_space) / 2
-  if min_y < MIN_SPACING_EDGE:
-    raise ValueError("Resource is too small to space channels.")
-
-  centers = [min_y + i * min_spacing for i in range(num_channels)][::-1]
-
-  # offsets are relative to the center of the resource, but above we computed them wrt lfb
-  # so we need to subtract the center of the resource
-  # also, offsets are in absolute space, so we need to rotate the center
-  return [
-    Coordinate(
-      x=0,
-      y=c - resource.center().rotated(resource.get_absolute_rotation()).y,
-      z=0,
-    )
-    for c in centers
-  ]
diff --git a/pylabrobot/resources/container.py b/pylabrobot/resources/container.py
index 633cc6c9653..26106be6045 100644
--- a/pylabrobot/resources/container.py
+++ b/pylabrobot/resources/container.py
@@ -1,4 +1,4 @@
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 from pylabrobot.serializer import serialize
 from pylabrobot.utils.interpolation import interpolate_1d
@@ -24,6 +24,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones: Optional[List[Tuple[Coordinate, Coordinate]]] = None,
   ):
     """Create a new container.
 
@@ -35,6 +36,9 @@ def __init__(
         ``compute_volume_from_height`` and ``compute_height_from_volume`` are auto-generated
         via piecewise-linear interpolation if not explicitly passed. The data is also available
         for direct use (e.g. building firmware segments from calibration knots).
+      no_go_zones: List of cuboid regions within the container where tips must not be positioned.
+        Each zone is a tuple of two Coordinates: (front_left_bottom, back_right_top), relative to
+        the container's front-left-bottom origin.
     """
 
     super().__init__(
@@ -77,6 +81,46 @@ def compute_height_from_volume(v: float) -> float:
     self.tracker = VolumeTracker(thing=f"{self.name}_volume_tracker", max_volume=self.max_volume)
     self._compute_volume_from_height = compute_volume_from_height
     self._compute_height_from_volume = compute_height_from_volume
+    self.no_go_zones: List[Tuple[Coordinate, Coordinate]] = self._validate_no_go_zones(
+      no_go_zones or []
+    )
+
+  def _validate_no_go_zones(
+    self, zones: List[Tuple[Coordinate, Coordinate]]
+  ) -> List[Tuple[Coordinate, Coordinate]]:
+    """Validate no-go zones to ensure they are inside the container and well-formed.
+
+    Each zone is defined as (front_left_bottom, back_right_top).
+    """
+    validated: List[Tuple[Coordinate, Coordinate]] = []
+    for idx, (flb, brt) in enumerate(zones):
+      if not isinstance(flb, Coordinate) or not isinstance(brt, Coordinate):
+        raise TypeError(
+          f"no_go_zones[{idx}] must be a tuple of Coordinate instances, got {type(flb)!r}, {type(brt)!r}."
+        )
+
+      # Ensure front-left-bottom is not beyond back-right-top on any axis.
+      if flb.x > brt.x or flb.y > brt.y or flb.z > brt.z:
+        raise ValueError(
+          f"no_go_zones[{idx}] has invalid ordering: front_left_bottom must not exceed "
+          f"back_right_top on any axis (flb={flb}, brt={brt})."
+        )
+
+      # Ensure all coordinates lie within the container bounds.
+      for coord_label, coord in (("flb", flb), ("brt", brt)):
+        if coord.x < 0 or coord.y < 0 or coord.z < 0:
+          raise ValueError(f"no_go_zones[{idx}].{coord_label} has negative coordinates: {coord}.")
+        if (
+          coord.x > self.get_size_x() or coord.y > self.get_size_y() or coord.z > self.get_size_z()
+        ):
+          raise ValueError(
+            f"no_go_zones[{idx}].{coord_label}={coord} is outside the container bounds "
+            f"(size_x={self.get_size_x()}, size_y={self.get_size_y()}, size_z={self.get_size_z()})."
+          )
+
+      validated.append((flb, brt))
+
+    return validated
 
   @property
   def material_z_thickness(self) -> float:
@@ -99,6 +143,7 @@ def serialize(self) -> dict:
       if self.height_volume_data is not None
       else serialize(self._compute_height_from_volume),
       "height_volume_data": self.height_volume_data,
+      "no_go_zones": [(flb.serialize(), brt.serialize()) for flb, brt in self.no_go_zones],
     }
 
   def serialize_state(self) -> Dict[str, Any]:
diff --git a/pylabrobot/resources/container_tests.py b/pylabrobot/resources/container_tests.py
index 7d737092c2c..d301f54f40c 100644
--- a/pylabrobot/resources/container_tests.py
+++ b/pylabrobot/resources/container_tests.py
@@ -1,9 +1,11 @@
 import json
 import unittest
 
+from pylabrobot.liquid_handling.errors import ChannelsDoNotFitError
 from pylabrobot.serializer import serialize
 
 from .container import Container
+from .coordinate import Coordinate
 
 
 class TestContainer(unittest.TestCase):
@@ -41,6 +43,7 @@ def compute_height_from_volume(volume):
         "compute_volume_from_height": serialize(compute_volume_from_height),
         "compute_height_from_volume": serialize(compute_height_from_volume),
         "height_volume_data": None,
+        "no_go_zones": [],
         "parent_name": None,
         "rotation": {"type": "Rotation", "x": 0, "y": 0, "z": 0},
         "type": "Container",
@@ -165,3 +168,115 @@ def test_height_volume_data_validates_minimum_points(self):
       Container(
         name="c", size_x=10, size_y=10, size_z=10, height_volume_data={0: 0}
       )  # only 1 point
+
+  def test_no_go_zones_default_empty(self):
+    c = Container(name="c", size_x=10, size_y=10, size_z=10)
+    self.assertEqual(c.no_go_zones, [])
+
+  def test_no_go_zones_stored(self):
+    zones = [(Coordinate(0, 44, 0), Coordinate(10, 46, 10))]
+    c = Container(name="c", size_x=10, size_y=90, size_z=10, no_go_zones=zones)
+    self.assertEqual(len(c.no_go_zones), 1)
+    self.assertEqual(c.no_go_zones[0][0], Coordinate(0, 44, 0))
+    self.assertEqual(c.no_go_zones[0][1], Coordinate(10, 46, 10))
+
+  def test_no_go_zones_serialize(self):
+    zones = [(Coordinate(0, 44, 0), Coordinate(10, 46, 10))]
+    c = Container(name="c", size_x=10, size_y=90, size_z=10, no_go_zones=zones)
+    serialized = c.serialize()
+    self.assertEqual(
+      serialized["no_go_zones"],
+      [
+        (
+          {"type": "Coordinate", "x": 0, "y": 44, "z": 0},
+          {"type": "Coordinate", "x": 10, "y": 46, "z": 10},
+        )
+      ],
+    )
+
+  def test_no_go_zones_multiple(self):
+    zones = [
+      (Coordinate(0, 34.6, 0), Coordinate(10, 36.6, 10)),
+      (Coordinate(0, 70.2, 0), Coordinate(10, 72.2, 10)),
+      (Coordinate(0, 105.9, 0), Coordinate(10, 107.9, 10)),
+    ]
+    c = Container(name="c", size_x=10, size_y=142, size_z=10, no_go_zones=zones)
+    self.assertEqual(len(c.no_go_zones), 3)
+
+
+class TestNoGoZoneCollision(unittest.TestCase):
+  def _make_container(self, size_y, no_go_zones=None):
+    return Container(name="c", size_x=10, size_y=size_y, size_z=10, no_go_zones=no_go_zones)
+
+  def test_no_zones_uses_standard_spread(self):
+    from pylabrobot.liquid_handling.channel_positioning import compute_channel_offsets
+
+    c = self._make_container(90)
+    result = compute_channel_offsets(c, num_channels=1)
+    self.assertEqual(len(result), 1)
+    # No no-go zones: single channel goes to center (offset 0)
+    self.assertAlmostEqual(result[0].y, 0.0)
+
+  def test_1_channel_in_2_compartments(self):
+    from pylabrobot.liquid_handling.channel_positioning import compute_channel_offsets
+
+    # 90mm container, divider at Y=44-46 -> 2 compartments [0,44] and [46,90]
+    # edge_clearance = 2.0
+    # Usable: [2.0, 42.0] and [48.0, 88.0]
+    # 1 channel -> center-out back-first -> goes to back compartment (index 1)
+    # Center of back usable = (48.0 + 88.0) / 2 = 68.0
+    # Container center = 45.0, offset = 68.0 - 45.0 = 23.0
+    c = self._make_container(
+      90,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(10, 46, 10))],
+    )
+    result = compute_channel_offsets(c, num_channels=1)
+    self.assertEqual(len(result), 1)
+    self.assertAlmostEqual(result[0].y, 23.0)
+
+  def test_2_channels_across_2_compartments(self):
+    from pylabrobot.liquid_handling.channel_positioning import compute_channel_offsets
+
+    c = self._make_container(
+      90,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(10, 46, 10))],
+    )
+    result = compute_channel_offsets(c, num_channels=2)
+    self.assertEqual(len(result), 2)
+    # Sorted descending by Y (back-to-front)
+    self.assertGreater(result[0].y, result[1].y)
+
+  def test_4_channels_across_2_compartments(self):
+    from pylabrobot.liquid_handling.channel_positioning import compute_channel_offsets
+
+    c = self._make_container(
+      90,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(10, 46, 10))],
+    )
+    result = compute_channel_offsets(c, num_channels=4)
+    self.assertEqual(len(result), 4)
+
+  def test_raises_when_impossible(self):
+    from pylabrobot.liquid_handling.channel_positioning import compute_channel_offsets
+
+    # Entire container is no-go
+    c = self._make_container(
+      12,
+      no_go_zones=[(Coordinate(0, 0, 0), Coordinate(10, 12, 10))],
+    )
+    with self.assertRaises(ChannelsDoNotFitError):
+      compute_channel_offsets(c, num_channels=1)
+
+  def test_3_compartments_6_channels(self):
+    from pylabrobot.liquid_handling.channel_positioning import compute_channel_offsets
+
+    # 150mm container, 2 dividers -> 3 compartments, 6 channels -> 2 per compartment
+    c = self._make_container(
+      150,
+      no_go_zones=[
+        (Coordinate(0, 49, 0), Coordinate(10, 51, 10)),
+        (Coordinate(0, 99, 0), Coordinate(10, 101, 10)),
+      ],
+    )
+    result = compute_channel_offsets(c, num_channels=6)
+    self.assertEqual(len(result), 6)
diff --git a/pylabrobot/resources/hamilton/troughs.py b/pylabrobot/resources/hamilton/troughs.py
index 71c3b80b369..49026ba0bcd 100644
--- a/pylabrobot/resources/hamilton/troughs.py
+++ b/pylabrobot/resources/hamilton/troughs.py
@@ -2,6 +2,7 @@
 
 import warnings
 
+from pylabrobot.resources.coordinate import Coordinate
 from pylabrobot.resources.trough import Trough, TroughBottomType
 
 # --------------------------------------------------------------------------- #
@@ -43,11 +44,9 @@ def hamilton_1_trough_60mL_Vb(name: str) -> Trough:
   Trough 60 mL, w lid, self standing (V-bottom).
   True maximal volume capacity ~80 mL.
   Compatible with Trough_CAR_?? (194057 <- not yet integrated into PLR!).
+  Has a center support wall (~1.2mm wide at Y=44-46mm) but is still open
+  at the bottom.
   """
-  warnings.warn(
-    "hamilton_1_trough_60mL_Vb has a center support that can interfere with pipetting.\
-     If using an odd number of channels, use spread='custom' and define offsets for each channel to avoid collision."
-  )
 
   return Trough(
     name=name,
@@ -60,6 +59,9 @@ def hamilton_1_trough_60mL_Vb(name: str) -> Trough:
     model=hamilton_1_trough_60mL_Vb.__name__,
     bottom_type=TroughBottomType.V,
     height_volume_data=_hamilton_1_trough_60mL_Vb_height_volume_data,
+    no_go_zones=[
+      (Coordinate(0, 44.4, 5.0), Coordinate(19.0, 45.6, 60.25)),  # center divider
+    ],
   )
 
 
@@ -92,12 +94,9 @@ def hamilton_1_trough_120mL_Vb(name: str) -> Trough:
   Trough 120 mL, without lid, self standing (V-bottom).
   True maximal volume capacity ~120 mL.
   Compatible with Trough_CAR_?? (194058 <- not yet integrated into PLR!).
+  Has 3 in-container support beams (~2.5mm wide at base, ~0.8mm at top, tapered)
+  but is still open at the bottom.
   """
-  warnings.warn(
-    "hamilton_1_trough_120mL_Vb has 3 (!) in-container support beams that can interfere with "
-    "pipetting. If using an odd number of channels, use spread='custom' and define offsets "
-    "for each channel to avoid collision."
-  )
 
   return Trough(
     name=name,
@@ -110,6 +109,11 @@ def hamilton_1_trough_120mL_Vb(name: str) -> Trough:
     model=hamilton_1_trough_120mL_Vb.__name__,
     bottom_type=TroughBottomType.V,
     height_volume_data=_hamilton_1_trough_120mL_Vb_height_volume_data,
+    no_go_zones=[
+      (Coordinate(0, 39.7, 12.0), Coordinate(19.0, 42.2, 70.0)),  # beam 1
+      (Coordinate(0, 73.5, 12.0), Coordinate(19.0, 76.0, 70.0)),  # beam 2
+      (Coordinate(0, 107.3, 12.0), Coordinate(19.0, 109.8, 70.0)),  # beam 3
+    ],
   )
 
 
@@ -136,6 +140,7 @@ def hamilton_1_trough_200mL_Vb(name: str) -> Trough:
   Trough 200 mL, w lid, self standing (V-bottom).
   True maximal volume capacity ~300 mL.
   Compatible with Trough_CAR_4R200_A00 (185436).
+  Has a center support wall (~1.2mm wide at Y=59-61mm) which is open at the bottom.
   """
   return Trough(
     name=name,
@@ -148,6 +153,9 @@ def hamilton_1_trough_200mL_Vb(name: str) -> Trough:
     model=hamilton_1_trough_200mL_Vb.__name__,
     bottom_type=TroughBottomType.V,
     height_volume_data=_hamilton_1_trough_200mL_Vb_height_volume_data,
+    no_go_zones=[
+      (Coordinate(0, 60, 8.0), Coordinate(37.0, 61.7, 60.0))  # center divider
+    ],
   )
 
 
diff --git a/pylabrobot/resources/petri_dish.py b/pylabrobot/resources/petri_dish.py
index 410c105d1ee..2d9cbfe5dbf 100644
--- a/pylabrobot/resources/petri_dish.py
+++ b/pylabrobot/resources/petri_dish.py
@@ -20,6 +20,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     super().__init__(
       name=name,
@@ -33,6 +34,7 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.diameter = diameter
     self.height = height
diff --git a/pylabrobot/resources/petri_dish_tests.py b/pylabrobot/resources/petri_dish_tests.py
index 956f191a964..a9e916c4b3e 100644
--- a/pylabrobot/resources/petri_dish_tests.py
+++ b/pylabrobot/resources/petri_dish_tests.py
@@ -25,6 +25,7 @@ def test_petri_dish_serialization(self):
         "compute_volume_from_height": None,
         "compute_height_from_volume": None,
         "height_volume_data": None,
+        "no_go_zones": [],
         "parent_name": None,
         "type": "PetriDish",
         "children": [],
diff --git a/pylabrobot/resources/trash.py b/pylabrobot/resources/trash.py
index 98ecd3e7f52..40c633d5072 100644
--- a/pylabrobot/resources/trash.py
+++ b/pylabrobot/resources/trash.py
@@ -17,6 +17,7 @@ def __init__(
     compute_volume_from_height=None,
     compute_height_from_volume=None,
     height_volume_data=None,
+    no_go_zones=None,
   ):
     super().__init__(
       name=name,
@@ -30,4 +31,5 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
diff --git a/pylabrobot/resources/trough.py b/pylabrobot/resources/trough.py
index 1bdab42aec8..55aca488c44 100644
--- a/pylabrobot/resources/trough.py
+++ b/pylabrobot/resources/trough.py
@@ -31,6 +31,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     if isinstance(bottom_type, str):
       bottom_type = TroughBottomType(bottom_type)
@@ -47,6 +48,7 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.through_base_to_container_base = through_base_to_container_base
     self.bottom_type = bottom_type
diff --git a/pylabrobot/resources/tube.py b/pylabrobot/resources/tube.py
index b684f5bb854..85e48d740a7 100644
--- a/pylabrobot/resources/tube.py
+++ b/pylabrobot/resources/tube.py
@@ -38,6 +38,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     """Create a new tube.
 
@@ -67,6 +68,7 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.tracker.register_callback(self._state_updated)
 
diff --git a/pylabrobot/resources/well.py b/pylabrobot/resources/well.py
index 3cb6f5a4003..47983ca31c9 100644
--- a/pylabrobot/resources/well.py
+++ b/pylabrobot/resources/well.py
@@ -50,6 +50,7 @@ def __init__(
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     cross_section_type: Union[CrossSectionType, str] = CrossSectionType.CIRCLE,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     """Create a new well.
 
@@ -99,6 +100,7 @@ def __init__(
       compute_height_from_volume=compute_height_from_volume,
       material_z_thickness=material_z_thickness,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.bottom_type = bottom_type
     self.cross_section_type = cross_section_type
diff --git a/pylabrobot/resources/well_tests.py b/pylabrobot/resources/well_tests.py
index f322d88521b..284c7cca82a 100644
--- a/pylabrobot/resources/well_tests.py
+++ b/pylabrobot/resources/well_tests.py
@@ -39,6 +39,7 @@ def test_serialize(self):
         "compute_volume_from_height": None,
         "compute_height_from_volume": None,
         "height_volume_data": None,
+        "no_go_zones": [],
       },
     )
 
diff --git a/pylabrobot/scales/scale.py b/pylabrobot/scales/scale.py
index 5fbbc1e4a37..977c28d1ecf 100644
--- a/pylabrobot/scales/scale.py
+++ b/pylabrobot/scales/scale.py
@@ -65,12 +65,12 @@ async def read_weight(self, **backend_kwargs) -> float:
     return await self.backend.read_weight(**backend_kwargs)
 
   # # # Deprecated # # #
-  # TODO: remove after 2026-06
+  # TODO: remove in v1
 
   async def get_weight(self, **backend_kwargs) -> float:
     """Deprecated: use :meth:`read_weight` instead."""
     warnings.warn(
-      "scale.get_weight() is deprecated and will be removed in 2026-06. "
+      "scale.get_weight() is deprecated and will be removed in v1. "
       "Use scale.read_weight() instead.",
       DeprecationWarning,
       stacklevel=2,