diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index 80d39b9..575be3e 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -16,6 +16,6 @@ jobs:
     - name: Setup yt_experiments
       run: |
         python -m pip install --upgrade pip
-        python -m pip install -e .[test]
+        python -m pip install -e .[test,full]
     - name: Run tests
       run: pytest
diff --git a/examples/tiled_grids_intro.ipynb b/examples/tiled_grids_intro.ipynb
index 0d26e4d..7d6b8d6 100644
--- a/examples/tiled_grids_intro.ipynb
+++ b/examples/tiled_grids_intro.ipynb
@@ -8,7 +8,7 @@
    "outputs": [],
    "source": [
     "import yt\n",
-    "from yt_experiments.tiled_grid import YTTiledArbitraryGrid, YTPyramid, YTOctPyramid\n",
+    "from yt_experiments.tiled_grid import YTTiledArbitraryGrid\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import zarr\n",
@@ -59,17 +59,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "yt : [INFO     ] 2024-08-28 10:56:43,105 Sample dataset found in '/Users/chavlin/data/yt_data/DeeplyNestedZoom/DD0025/data0025'\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,237 Parameters: current_time              = 14.1336338797\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,237 Parameters: domain_dimensions         = [128 128 128]\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,238 Parameters: domain_left_edge          = [0. 0. 0.]\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,238 Parameters: domain_right_edge         = [1. 1. 1.]\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,238 Parameters: cosmological_simulation   = 1\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,238 Parameters: current_redshift          = 14.092558914923\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,238 Parameters: omega_lambda              = 0.6911\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,239 Parameters: omega_matter              = 0.3089\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,239 Parameters: omega_radiation           = 0.0\n",
-      "yt : [INFO     ] 2024-08-28 10:56:43,239 Parameters: hubble_constant           = 0.6774\n"
+      "yt : [INFO     ] 2024-10-03 14:47:05,892 Sample dataset found in '/Users/chavlin/data/yt_data/DeeplyNestedZoom/DD0025/data0025'\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,037 Parameters: current_time              = 14.1336338797\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,037 Parameters: domain_dimensions         = [128 128 128]\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,037 Parameters: domain_left_edge          = [0. 0. 0.]\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,038 Parameters: domain_right_edge         = [1. 1. 1.]\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,038 Parameters: cosmological_simulation   = 1\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,038 Parameters: current_redshift          = 14.092558914923\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,038 Parameters: omega_lambda              = 0.6911\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,039 Parameters: omega_matter              = 0.3089\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,039 Parameters: omega_radiation           = 0.0\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,039 Parameters: hubble_constant           = 0.6774\n"
      ]
     }
    ],
@@ -92,13 +92,41 @@
    "execution_count": 4,
    "id": "61d5da26-5095-4d0c-a657-66efd8f9d08a",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YTTiledArbitraryGrid with total shape of (400, 400, 400) divided into 64 grids: (4, 4, 4) grids in each dimension."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tag = YTTiledArbitraryGrid(\n",
+    "    ds.domain_left_edge,\n",
+    "    ds.domain_right_edge,\n",
+    "    (400, 400, 400),\n",
+    "    100,\n",
+    "    ds=ds,\n",
+    ")\n",
+    "tag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "610f692a-9f9f-4fa4-833f-9d66627ce720",
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Parsing Hierarchy : 100%|████████████████████████████████████████████████████████| 1825/1825 [00:00<00:00, 16480.51it/s]\n",
-      "yt : [INFO     ] 2024-08-28 10:56:46,212 Gathering a field list (this may take a moment.)\n"
+      "Parsing Hierarchy : 100%|████████████████████████████████████████████████████████| 1825/1825 [00:00<00:00, 20771.71it/s]\n",
+      "yt : [INFO     ] 2024-10-03 14:47:06,168 Gathering a field list (this may take a moment.)\n"
      ]
     },
     {
@@ -110,35 +138,33 @@
     }
    ],
    "source": [
-    "tag = YTTiledArbitraryGrid(\n",
-    "    ds.domain_left_edge,\n",
-    "    ds.domain_right_edge,\n",
-    "    (400, 400, 400),\n",
-    "    100,\n",
-    "    ds=ds,\n",
-    ")\n",
     "vals = tag.to_array(\n",
     "    (\"gas\", \"density\"),\n",
-    "    ops=[\n",
-    "        np.log10,\n",
-    "    ],\n",
     ")\n",
     "print(vals.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "id": "1456d42a-29e8-45e4-9ba6-86582546c033",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "id": "e3fb96a7-7619-403c-8d2f-dc9a91fd1ea8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x33668e0e0>"
+       "<matplotlib.image.AxesImage at 0x320945150>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -154,16 +180,74 @@
     }
    ],
    "source": [
-    "plt.imshow(vals[:, :, 200])"
+    "plt.imshow(np.log10(vals[:, :, 200]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c18e530-d63e-47e1-95d3-57102e664b88",
+   "metadata": {},
+   "source": [
+    "applying operations by-chunk during grid generation (beware units!!)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "1d308443-2fc1-4af1-8428-730e3cd39dba",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_array([-26.92235472, -26.92235472], 'g/cm**3')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vals = tag.to_array(\n",
+    "    (\"gas\", \"density\"),\n",
+    "    ops=[\n",
+    "        np.log10,\n",
+    "    ],\n",
+    ")\n",
+    "vals[0:10, 0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8f58397b-ddcd-4a7e-ab8b-c57d9ba3f9f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x320bc95d0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(vals[:, :, 200])"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -175,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "7e537448-7764-4db8-9bee-60c22acf107b",
    "metadata": {},
    "outputs": [],
@@ -195,24 +279,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "396121e2-96bf-4fa7-8081-b4fe2a9d7c3a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zarr.hierarchy.Group '/'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zarr_store = initialize_store(\"tiled_grid_full.zarr\")\n",
+    "zarr_store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d13ecb23-ecbc-48a8-b4a6-c993b1950005",
+   "metadata": {},
    "source": [
-    "zarr_store = initialize_store(\"tiled_grid_full.zarr\")"
+    "create an empty array to fill "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1353735f-7021-4f86-b8d0-267573893765",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((2000, 2000, 2000), (100, 100, 100))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tag.dims, tag.chunks"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "883012fe-9c66-40c8-a8c4-64e6c1758466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zarr_field = zarr_store.empty(\"gas_density\", shape=tag.dims, chunks=tag.chunks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "52b4d798-09cc-455f-b877-6d2eb0942ecb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zarr.core.Array '/gas_density' (2000, 2000, 2000) float64>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zarr_field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "id": "0f563615-94b4-4829-b339-70e3143ca331",
    "metadata": {},
    "outputs": [],
    "source": [
-    "dens = tag.to_zarr(\n",
+    "_ = tag.to_array(\n",
     "    (\"gas\", \"density\"),\n",
-    "    zarr_store,\n",
+    "    full_domain=zarr_field,\n",
     "    ops=[\n",
     "        np.log10,\n",
     "    ],\n",
@@ -221,14 +377,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "4e0311cf-441b-4569-b5ac-7c1f62adbe8e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table class=\"zarr-info\"><tbody><tr><th style=\"text-align: left\">Name</th><td style=\"text-align: left\">/gas_density</td></tr><tr><th style=\"text-align: left\">Type</th><td style=\"text-align: left\">zarr.core.Array</td></tr><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(2000, 2000, 2000)</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(100, 100, 100)</td></tr><tr><th style=\"text-align: left\">Order</th><td style=\"text-align: left\">C</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">False</td></tr><tr><th style=\"text-align: left\">Compressor</th><td style=\"text-align: left\">Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)</td></tr><tr><th style=\"text-align: left\">Store type</th><td style=\"text-align: left\">zarr.storage.DirectoryStore</td></tr><tr><th style=\"text-align: left\">No. bytes</th><td style=\"text-align: left\">64000000000 (59.6G)</td></tr><tr><th style=\"text-align: left\">No. bytes stored</th><td style=\"text-align: left\">1169123996 (1.1G)</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">54.7</td></tr><tr><th style=\"text-align: left\">Chunks initialized</th><td style=\"text-align: left\">8000/8000</td></tr></tbody></table>"
+       "<table class=\"zarr-info\"><tbody><tr><th style=\"text-align: left\">Name</th><td style=\"text-align: left\">/gas_density</td></tr><tr><th style=\"text-align: left\">Type</th><td style=\"text-align: left\">zarr.core.Array</td></tr><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(2000, 2000, 2000)</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(100, 100, 100)</td></tr><tr><th style=\"text-align: left\">Order</th><td style=\"text-align: left\">C</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">False</td></tr><tr><th style=\"text-align: left\">Compressor</th><td style=\"text-align: left\">Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)</td></tr><tr><th style=\"text-align: left\">Store type</th><td style=\"text-align: left\">zarr.storage.DirectoryStore</td></tr><tr><th style=\"text-align: left\">No. bytes</th><td style=\"text-align: left\">64000000000 (59.6G)</td></tr><tr><th style=\"text-align: left\">No. bytes stored</th><td style=\"text-align: left\">1169123997 (1.1G)</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">54.7</td></tr><tr><th style=\"text-align: left\">Chunks initialized</th><td style=\"text-align: left\">8000/8000</td></tr></tbody></table>"
       ],
       "text/plain": [
        "Name               : /gas_density\n",
@@ -241,37 +397,26 @@
        "Compressor         : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n",
        "Store type         : zarr.storage.DirectoryStore\n",
        "No. bytes          : 64000000000 (59.6G)\n",
-       "No. bytes stored   : 1169123996 (1.1G)\n",
+       "No. bytes stored   : 1169123997 (1.1G)\n",
        "Storage ratio      : 54.7\n",
        "Chunks initialized : 8000/8000"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dens.info"
+    "zarr_field.info"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "8de51d61-c92e-4b67-adfa-52860d22ecfb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/Users/chavlin/data/yt_data/tiled_grid_full.zarr'"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
@@ -284,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "id": "6b68d880-46a5-4768-b06d-eb162ce8b10f",
    "metadata": {},
    "outputs": [
@@ -303,7 +448,7 @@
        " '15.16.12']"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,17 +459,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "id": "3593b84e-4ca0-4495-8f33-7b9d71f43ff6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x35833aa10>"
+       "<matplotlib.image.AxesImage at 0x3480bdc30>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -340,7 +485,7 @@
     }
    ],
    "source": [
-    "plt.imshow(dens[800:1200, 800:1200, 1000])"
+    "plt.imshow(zarr_field[800:1200, 800:1200, 1000])"
    ]
   },
   {
@@ -348,42 +493,87 @@
    "id": "63030b8d-6bd9-4b60-aaac-293d568b7b36",
    "metadata": {},
    "source": [
-    "## a pryamidal on-disk zarr"
+    "## a yt image pryamid"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 4,
+   "id": "99ca8d0c-4452-46b5-b927-075d9fa22a08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from yt_experiments.tiled_grid import YTArbitraryGridPyramid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "89f37edd-31b6-40f1-9ef0-98c802a59b75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mInit signature:\u001b[0m\n",
+       "\u001b[0mYTArbitraryGridPyramid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mleft_edge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mright_edge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlevel_dims\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlevel_chunks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mds\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0myt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_objects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatic_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mfield_parameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mdata_source\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m      <no docstring>\n",
+       "\u001b[0;31mInit docstring:\u001b[0m\n",
+       "Parameters\n",
+       "----------\n",
+       "left_edge\n",
+       "right_edge\n",
+       "level_dims\n",
+       "level_chunks\n",
+       "ds\n",
+       "field_parameters\n",
+       "data_source\n",
+       "\u001b[0;31mFile:\u001b[0m           ~/src/yt_/yt_dev/yt_experiments/yt_experiments/tiled_grid/tiled_grid.py\n",
+       "\u001b[0;31mType:\u001b[0m           type\n",
+       "\u001b[0;31mSubclasses:\u001b[0m     YTArbitraryGridOctPyramid"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "?YTArbitraryGridPyramid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "463c8b75-da09-4e8b-baa7-b953eeb23d7e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(2000, 2000, 2000),\n",
-       " (1800, 1800, 1800),\n",
-       " (1600, 1600, 1600),\n",
-       " (1400, 1400, 1400),\n",
-       " (1200, 1200, 1200),\n",
-       " (1000, 1000, 1000),\n",
-       " (800, 800, 800),\n",
-       " (600, 600, 600),\n",
-       " (400, 400, 400)]"
+       "[(1000, 1000, 1000), (800, 800, 800), (600, 600, 600), (400, 400, 400)]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "level_dims = [(res,) * 3 for res in range(2000, 200, -200)]\n",
+    "level_dims = [(res,) * 3 for res in range(1000, 200, -200)]\n",
     "level_dims"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 7,
    "id": "825cfeb4-bf26-4e0a-85d1-4fa01bce73fa",
    "metadata": {},
    "outputs": [],
@@ -393,82 +583,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 8,
    "id": "35e07369-ab57-4eae-95dd-3f23e64caeca",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Decomposing [2000 2000 2000] into 1000 chunks for level 0\n",
-      "Decomposing [1800 1800 1800] into 729 chunks for level 1\n",
-      "Decomposing [1600 1600 1600] into 512 chunks for level 2\n",
-      "Decomposing [1400 1400 1400] into 343 chunks for level 3\n",
-      "Decomposing [1200 1200 1200] into 216 chunks for level 4\n",
-      "Decomposing [1000 1000 1000] into 125 chunks for level 5\n",
-      "Decomposing [800 800 800] into 64 chunks for level 6\n",
-      "Decomposing [600 600 600] into 27 chunks for level 7\n",
-      "Decomposing [400 400 400] into 8 chunks for level 8\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pyr = YTPyramid(\n",
+    "pyr = YTArbitraryGridPyramid(\n",
     "    ds.domain_left_edge, ds.domain_right_edge, level_dims, level_chunks, ds=ds\n",
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a4468ee-0a0c-4b00-bec6-377864c3ea79",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "8c412bea-b2e2-446d-a69e-dc1d75c71767",
    "metadata": {},
    "source": [
-    "individual levels are comprised of `YTTiledArbitraryGrid` objects, accessible at:"
+    "individual levels are comprised of `YTTiledArbitraryGrid` objects, accessible by indexing the object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "2000f66c-bbbc-426b-b825-6e6624c8dde7",
+   "execution_count": 9,
+   "id": "4624c137-e6fc-444d-8849-4d1016d72d4f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<yt_experiments.tiled_grid.tiled_grid.YTTiledArbitraryGrid at 0x358558490>"
+       "YTTiledArbitraryGrid with total shape of (1000, 1000, 1000) divided into 125 grids: (5, 5, 5) grids in each dimension."
       ]
      },
-     "execution_count": 27,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pyr.levels[0]"
+    "pyr[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b337d331-6a92-4e27-85ca-d7949a4e6948",
+   "metadata": {},
+   "source": [
+    "or indexing the `levels` attribute:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "c77799fb-c510-4d7c-90a8-5b3da93c8dff",
+   "execution_count": 11,
+   "id": "2000f66c-bbbc-426b-b825-6e6624c8dde7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2000, 2000, 2000])"
+       "<yt_experiments.tiled_grid.tiled_grid.YTArbitraryGridPyramid at 0x1475215a0>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pyr.levels[0].dims"
+    "pyr"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c77799fb-c510-4d7c-90a8-5b3da93c8dff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "09582a88-0f3e-4caf-b646-949cecccb685",
diff --git a/pyproject.toml b/pyproject.toml
index 268ba0c..7aa47fe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,8 +36,6 @@ test = [
 ]
 full = [
     "xarray",
-    "zarr",
-    "dask[array,distributed]"
 ]
 
 [tool.pytest.ini_options]
diff --git a/yt_experiments/tiled_grid/__init__.py b/yt_experiments/tiled_grid/__init__.py
index 44a995d..2be86b4 100644
--- a/yt_experiments/tiled_grid/__init__.py
+++ b/yt_experiments/tiled_grid/__init__.py
@@ -1 +1,5 @@
-from .tiled_grid import YTOctPyramid, YTPyramid, YTTiledArbitraryGrid  # noqa: F401
+from .tiled_grid import (
+    YTArbitraryGridOctPyramid as YTArbitraryGridOctPyramid,
+    YTArbitraryGridPyramid as YTArbitraryGridPyramid,
+    YTTiledArbitraryGrid as YTTiledArbitraryGrid,
+)
diff --git a/yt_experiments/tiled_grid/tests/test_tiled_grid.py b/yt_experiments/tiled_grid/tests/test_tiled_grid.py
new file mode 100644
index 0000000..08895a1
--- /dev/null
+++ b/yt_experiments/tiled_grid/tests/test_tiled_grid.py
@@ -0,0 +1,96 @@
+import numpy as np
+import unyt
+from numpy.testing import assert_equal
+from yt.testing import fake_amr_ds, requires_module
+
+from yt_experiments.tiled_grid import (
+    YTArbitraryGridOctPyramid,
+    YTArbitraryGridPyramid,
+    YTTiledArbitraryGrid,
+)
+
+
+def test_arbitrary_grid():
+    ds = fake_amr_ds()
+    tag = YTTiledArbitraryGrid(
+        ds.domain_left_edge,
+        ds.domain_right_edge,
+        (20, 20, 20),
+        5,
+        ds=ds,
+    )
+    assert tag._ngrids == (20 // 5) ** 3
+    _ = tag.__repr__()
+
+    fld = ("stream", "Density")
+    den = tag.to_array(fld)
+    assert isinstance(den, unyt.unyt_array)
+    den2 = np.empty(tag.dims)
+    _ = tag.to_array(fld, output_array=den2)
+    assert not isinstance(den2, unyt.unyt_array)
+    assert_equal(den, den2)
+
+    den3 = tag[fld]
+    assert_equal(den3, den)
+
+    assert np.min(den) > 0.0
+    assert np.all(np.isfinite(den))
+
+    den = tag.to_array(fld, dtype=np.float32)
+    assert den.dtype == np.float32
+
+
+def test_arbitray_grid_pyramid():
+    ds = fake_amr_ds()
+    levels = [(16, 16, 16), (10, 10, 10)]
+    pyr = YTArbitraryGridPyramid(
+        ds.domain_left_edge, ds.domain_right_edge, levels, 2, ds=ds
+    )
+
+    assert pyr.n_levels == 2
+    fld = ("stream", "Density")
+    for ilev in range(pyr.n_levels):
+        vals = pyr[ilev][fld]
+        assert vals.shape == levels[ilev]
+        assert np.all(np.isfinite(vals))
+
+    level_arrays = pyr.to_arrays(fld)
+    for ilev in range(pyr.n_levels):
+        assert level_arrays[ilev].shape == levels[ilev]
+
+
+def test_arbitrary_grid_oct():
+    ds = fake_amr_ds()
+    expected_levels = [(16, 16, 16), (8, 8, 8)]
+    oct = YTArbitraryGridOctPyramid(
+        ds.domain_left_edge, ds.domain_right_edge, (16, 16, 16), 2, 2, ds=ds
+    )
+
+    assert oct.n_levels == 2
+    fld = ("stream", "Density")
+    for ilev in range(oct.n_levels):
+        vals = oct[ilev][fld]
+        assert vals.shape == expected_levels[ilev]
+        assert np.all(np.isfinite(vals))
+
+    level_arrays = oct.to_arrays(fld)
+    for ilev in range(oct.n_levels):
+        assert level_arrays[ilev].shape == expected_levels[ilev]
+
+
+@requires_module("xarray")
+def test_arbitrary_grid_to_xarray():
+    import xarray as xr
+
+    ds = fake_amr_ds()
+    tag = YTTiledArbitraryGrid(
+        ds.domain_left_edge,
+        ds.domain_right_edge,
+        (20, 20, 20),
+        5,
+        ds=ds,
+    )
+
+    vals = tag.to_xarray(("stream", "Density"))
+    assert isinstance(vals, xr.DataArray)
+    assert hasattr(vals, "coords")
diff --git a/yt_experiments/tiled_grid/tiled_grid.py b/yt_experiments/tiled_grid/tiled_grid.py
index dae9bbf..05a4d6d 100644
--- a/yt_experiments/tiled_grid/tiled_grid.py
+++ b/yt_experiments/tiled_grid/tiled_grid.py
@@ -1,7 +1,7 @@
-from typing import Any, Optional
+from typing import Any, Callable, Optional
 
 import numpy as np
-import xarray as xr
+from yt._typing import FieldKey
 from yt.data_objects.construction_data_containers import YTArbitraryGrid
 from yt.data_objects.static_output import Dataset
 
@@ -19,7 +19,6 @@ def __init__(
         *,
         ds: Dataset = None,
         field_parameters=None,
-        parallel_method: Optional[str] = None,
         data_source: Optional[Any] = None,
     ):
         """
@@ -33,7 +32,7 @@ def __init__(
             chunk size (or sizes in each dimension), not number of chunks
         ds
         field_parameters
-        parallel_method
+
         data_source
 
         Notes
@@ -50,7 +49,7 @@ def __init__(
         self.ds = ds
         self.data_source = data_source
         self.field_parameters = field_parameters
-        self.parallel_method = parallel_method
+
         self.dims = dims
         if isinstance(chunks, int):
             chunks = (chunks,) * self._ndim
@@ -73,6 +72,17 @@ def __init__(
         self._left_cell_center = self.left_edge + self.dds / 2.0
         self._right_cell_center = self.right_edge - self.dds / 2.0
 
+    def __repr__(self):
+        nm = self.__class__.__name__
+        shape = tuple(self.dims)
+        n_chunks = tuple(self.nchunks)
+        n_tot = self._ngrids
+        msg = (
+            f"{nm} with total shape of {shape} divided into {n_tot} grids: "
+            f"{n_chunks} grids in each dimension."
+        )
+        return msg
+
     @property
     def _chunks(self):
         return np.array(self.chunks, dtype=int)
@@ -117,37 +127,19 @@ def _get_grid(self, igrid: int):
         ijk_grid = np.unravel_index(igrid, self.nchunks)
         return self._get_grid_by_ijk(ijk_grid)
 
-    def to_dask(self, field, chunks=None):
-        from dask import array as da, delayed
-
-        if chunks is None:
-            chunks = self.chunks
-
-        full_domain = da.empty(self.dims, chunks=chunks, dtype="float64")
-        for igrid in range(self._ngrids):
-            _, _, le, re, slc, shp = self._get_grid(igrid)
-            vals = delayed(_get_filled_grid)(
-                le, re, shp, field, self.ds, self.field_parameters
-            )
-            vals = da.from_delayed(vals, shp, dtype="float64")
-            slc = self._grid_slc[igrid]
-            full_domain[slc] = vals
-        return full_domain
-
     def _coord_array(self, idim):
         LE = self._left_cell_center[idim]
         RE = self._right_cell_center[idim]
         N = self.dims[idim]
         return np.mgrid[LE : RE : N * 1j]
 
-    def to_xarray(self, field, chunks=None, backend: str = "dask") -> xr.DataArray:
+    def to_xarray(self, field, *, output_array=None):
+
+        import xarray as xr
 
-        if backend == "dask":
-            da = self.to_dask(field, chunks=chunks)
-        elif backend == "numpy":
-            da = self.to_array(field)
-        else:
-            raise NotImplementedError()
+        # ToDo: import from on_demand_imports
+
+        vals = self.to_array(field, output_array=output_array)
 
         dims = self.ds.coordinates.axis_order
         dim_list = list(dims)
@@ -156,7 +148,7 @@ def to_xarray(self, field, chunks=None, backend: str = "dask") -> xr.DataArray:
         xrname = field[0] + "_" + field[1]
 
         xr_ds = xr.DataArray(
-            data=da,
+            data=vals,
             dims=dim_list,
             coords=coords,
             attrs={"ngrids": self._ngrids, "fieldname": field},
@@ -193,10 +185,10 @@ def single_grid_values(self, igrid, field, *, ops=None):
 
     def to_array(
         self,
-        field,
+        field: FieldKey,
         *,
-        full_domain=None,
-        ops=None,
+        output_array=None,
+        ops: list[Callable] = None,
         dtype=None,
     ):
         """
@@ -206,9 +198,10 @@ def to_array(
         ----------
         field
             the field to sample
-        full_domain
+        output_array
             the array to fill. if not provided, defaults to an empty
-            np array. Can provide a numpy or zarr array.
+            np array. Can provide any array type (np, zarr) that supports
+            np-like indexing.
         ops
             an optional list of callback functions to apply to the
             sampled field. Must accept a single parameter, the values
@@ -226,72 +219,24 @@ def my_func(values):
             a filled array
 
         """
-        if full_domain is None:
-            full_domain = np.empty(self.dims, dtype="float64")
 
         if dtype is None:
             dtype = np.float64
 
+        if output_array is None:
+            output_units = self.ds._get_field_info(field).units
+            output_array = self.ds.arr(np.empty(self.dims, dtype=dtype), output_units)
+
         for igrid in range(self._ngrids):
             vals, slc = self.single_grid_values(igrid, field, ops=ops)
-            full_domain[slc] = vals.astype(dtype)
-        return full_domain
-
-    def to_zarr(
-        self,
-        field,
-        zarr_store,
-        *,
-        zarr_name: str | None = None,
-        ops=None,
-        dtype=None,
-        **kwargs,
-    ):
-        """
-        write to a zarr Store or Group
+            output_array[slc] = vals.astype(dtype)
+        return output_array
 
-        Parameters
-        ----------
-        field
-        zarr_store
-        zarr_name
-        ops
-        kwargs
-            passed to the empty zarr array creation
-
-        Returns
-        -------
+    def __getitem__(self, item: FieldKey):
+        return self.to_array(item)
 
-        """
 
-        import zarr
-
-        _allowed_types = (zarr.storage.Store, zarr.hierarchy.Group)
-        if not isinstance(zarr_store, _allowed_types):
-            raise TypeError(
-                "zarr_store must be a zarr `Store` or `Group` but has "
-                f"type of {type(zarr_store)}."
-            )
-
-        if dtype is None:
-            dtype = np.float64
-
-        if ops is None:
-            ops = []
-
-        if zarr_name is None:
-            zarr_name = "_".join(field)
-
-        full_domain = zarr_store.create(
-            zarr_name, shape=self.dims, chunks=self.chunks, dtype=dtype, **kwargs
-        )
-        full_domain = self.to_array(
-            field, full_domain=full_domain, ops=ops, dtype=dtype
-        )
-        return full_domain
-
-
-class YTPyramid:
+class YTArbitraryGridPyramid:
     _ndim = 3
 
     def __init__(
@@ -320,6 +265,7 @@ def __init__(
         levels = []
 
         n_levels = len(level_dims)
+        self.n_levels = n_levels
 
         if isinstance(level_chunks, int):
             level_chunks = (level_chunks,) * self._ndim
@@ -339,15 +285,11 @@ def __init__(
                 level_chunks[ilev] = (level_chunks[ilev],) * self._ndim
 
         # should be ready by this point
-        self._validate_levels(levels)
+        self._validate_levels(level_dims)
 
         for ilev in range(n_levels):
             chunksizes = np.array(level_chunks[ilev], dtype=int)
             current_dims = np.asarray(level_dims[ilev], dtype=int)
-            n_chunks_lev = int(np.prod(current_dims / chunksizes))
-            print(
-                f"Decomposing {current_dims} into {n_chunks_lev} chunks for level {ilev}"
-            )
             tag = YTTiledArbitraryGrid(
                 left_edge,
                 right_edge,
@@ -362,7 +304,8 @@ def __init__(
         self.levels: [YTTiledArbitraryGrid] = levels
 
     def _validate_levels(self, levels):
-        for ilev in range(2, len(levels)):
+
+        for ilev in range(1, self.n_levels):
             res = np.prod(levels[ilev])
             res_higher = np.prod(levels[ilev - 1])
             if res > res_higher:
@@ -373,45 +316,31 @@ def _validate_levels(self, levels):
                 )
                 raise ValueError(msg)
 
-    def to_zarr(
-        self,
-        field,
-        zarr_store,
-        zarr_name: str | None = None,
-        ops=None,
-        dtype=None,
-        **kwargs,
-    ):
-        import zarr
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__} with {self.n_levels} levels and base resolution "
+            f"{self.base_resolution}"
+        )
 
-        _allowed_types = (zarr.storage.Store, zarr.hierarchy.Group)
-        if not isinstance(zarr_store, _allowed_types):
-            raise TypeError(
-                "zarr_store must be a zarr `Store` or `Group` but has "
-                f"type of {type(zarr_store)}."
-            )
+    def base_resolution(self) -> tuple[int, int, int]:
+        return tuple(self[0].dims)
 
-        if zarr_name is None:
-            zarr_name = "_".join(field)
+    def to_arrays(self, field, output_arrays=None):
+        if output_arrays is None:
+            output_arrays = [None for _ in range(len(self.levels))]
 
-        if zarr_name not in zarr_store:
-            zarr_store.create_group(zarr_name)
+        for ilev, yttag in enumerate(self.levels):
+            output_arrays[ilev] = yttag.to_array(
+                field, output_array=output_arrays[ilev]
+            )
 
-        field_store = zarr_store[zarr_name]
+        return output_arrays
 
-        for lev, tag in enumerate(self.levels):
-            print(f"writing level {lev}")
-            tag.to_zarr(
-                field,
-                field_store,
-                zarr_name=str(lev),
-                ops=ops,
-                dtype=dtype,
-                **kwargs,
-            )
+    def __getitem__(self, item: int) -> YTTiledArbitraryGrid:
+        return self.levels[item]
 
 
-class YTOctPyramid(YTPyramid):
+class YTArbitraryGridOctPyramid(YTArbitraryGridPyramid):
     def __init__(
         self,
         left_edge,
@@ -419,7 +348,7 @@ def __init__(
         dims: tuple[int, int, int],
         chunks: int | tuple[int, int, int],
         n_levels: int,
-        factor: int = 2,
+        factor: int | tuple[int, int, int] = 2,
         ds: Dataset = None,
         field_parameters=None,
         data_source: Optional[Any] = None,
@@ -429,6 +358,11 @@ def __init__(
         if isinstance(chunks, int):
             chunks = (chunks,) * self._ndim
 
+        if isinstance(factor, int):
+            factor = (factor,) * self._ndim
+
+        factor = np.asarray(factor, dtype=int)
+
         level_dims = []
         for lev in range(n_levels):
             current_dims = dims_ / factor**lev
@@ -437,7 +371,7 @@ def __init__(
         super().__init__(
             left_edge,
             right_edge,
-            dims,
+            level_dims,
             chunks,
             ds=ds,
             field_parameters=field_parameters,