diff --git a/.gitignore b/.gitignore
index 445c130..e277d92 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,4 @@ examples/cthead1.zarr/
 examples/chelsea.zarr/
 .ipynb_checkpoints
 examples/sub-I46_ses-SPIM_sample-BrocaAreaS01_stain-GAD67_chunk-00_SPIM*
+examples/dask-worker-space/
diff --git a/examples/ConvertTiffFile.ipynb b/examples/ConvertTiffFile.ipynb
new file mode 100644
index 0000000..0186e51
--- /dev/null
+++ b/examples/ConvertTiffFile.ipynb
@@ -0,0 +1,1315 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c82b644e",
+   "metadata": {},
+   "source": [
+    "## Package install and setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cffe4de0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install --upgrade multiscale-spatial-image matplotlib tifffile zarr ome-types dask_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a3a9d3cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from multiscale_spatial_image import to_multiscale, Methods\n",
+    "from spatial_image import is_spatial_image, to_spatial_image\n",
+    "import tifffile\n",
+    "import os\n",
+    "from urllib.request import urlretrieve\n",
+    "import zarr\n",
+    "import ome_types\n",
+    "import dask.array as da\n",
+    "import datatree\n",
+    "from numcodecs import Blosc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa57d874",
+   "metadata": {},
+   "source": [
+    "## Download example image\n",
+    "\n",
+    "Lightsheet volume [from the human cortex](https://dandiarchive.org/dandiset/000026/draft/files?location=rawdata%2Fsub-I46%2Fses-SPIM%2F).\n",
+    "\n",
+    "Mazzamuto, Giacomo; Costantini, Irene; Gavryusev, Vladislav; Castelli, Filippo Maria; Pesce, Luca; Scardigli, Marina; Pavone, Francesco Saverio; Roffilli, Matteo; Silvestri, Ludovico; Brady, Niamh; Ramazzotti, Josephine; Hof, Patrick R.; Boas, David A.; Fischl, Bruce; Morgan, Leah; Yang, Jiarui; Chang, Shuaibin; Laffey, Jessie; Magnain, Caroline; Varadarajan, Divya; Wang, Hui; Frost, Robert; Kouwe, Andre van der; Player, Allison Stevens; Atzeni, Alessia; Gonzalez, Juan Eugenio Iglesias; Balbastre, Yael; Vera, Matthew; Cordero, Devani; Nestor, Kimberly; Ammon, William; Nolan, Jackson; Mora, Jocelyn; Pallares, Erendira Garcia; Augustinack, Jean; Diamond, Bram; Fogarty, Morgan; Boyd, Emma; Varghese, Merina; Dalca, Adrian V.; Edlow, Brian; Frosche, Matthew; Wicinski, Bridget; Chen, I-Chun Anderson (2022) Human brain cell census for BA 44/45 (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/000026/draft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "865974c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_name = 'sub-I46_ses-SPIM_sample-BrocaAreaS01_stain-GAD67_chunk-00_SPIM'\n",
+    "filename = f'{image_name}.ome.tif'\n",
+    "if not os.path.exists(filename):\n",
+    "    url = f'https://api.dandiarchive.org/api/assets/fcb6c425-ee14-4247-b2e4-d2550273f1f3/download'\n",
+    "    urlretrieve(url, filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d34a205",
+   "metadata": {},
+   "source": [
+    "## Inspect metadata, data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9d11d71c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tiff = tifffile.TiffFile(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18739faa",
+   "metadata": {},
+   "source": [
+    "[OME-TIFF](https://docs.openmicroscopy.org/ome-model/6.2.2/)'s contain useful, standardized metadata and have broad community adoption."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "432abb91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tiff.is_ome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eef1e9e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<ome:OME xmlns:ome=\"http://www.openmicroscopy.org/Schemas/OME/2016-06\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd\">\\n    <ome:Image ID=\"Image:0\" Name=\"sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM\">\\n        <ome:AcquisitionDate>2019-10-01T00:00:00</ome:AcquisitionDate>\\n        <ome:Pixels BigEndian=\"false\" DimensionOrder=\"XYZCT\" ID=\"Pixels:0\" Interleaved=\"false\" SizeC=\"1\" SizeT=\"1\" SizeX=\"3882\" SizeY=\"342\" SizeZ=\"242\" Type=\"uint16\" PhysicalSizeX=\"3.3\" PhysicalSizeXUnit=\"µm\" PhysicalSizeY=\"3.3\" PhysicalSizeYUnit=\"µm\" PhysicalSizeZ=\"3.3\" PhysicalSizeZUnit=\"µm\">\\n            <ome:Channel ID=\"Channel:00\" SamplesPerPixel=\"1\" Name=\"GAD67\" ExcitationWavelength=\"638\" EmissionWavelength=\"690\" ExcitationWavelengthUnit=\"nm\" EmissionWavelengthUnit=\"nm\" Fluor=\"Alexa Fluor 647\">\\n                <ome:LightPath />\\n            </ome:Channel>\\n        <ome:TiffData IFD=\"0\" PlaneCount=\"242\" /></ome:Pixels>\\n    </ome:Image>\\n</ome:OME>'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tiff.ome_metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "59062f84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "binary_only=None creator=None datasets=[] experimenter_groups=[] experimenters=[] experiments=[] folders=[] images=[Image(\n",
+      "   id='Image:0',\n",
+      "   name='sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM',\n",
+      "   pixels=Pixels(\n",
+      "      id='Pixels:0',\n",
+      "      dimension_order='XYZCT',\n",
+      "      size_c=1,\n",
+      "      size_t=1,\n",
+      "      size_x=3882,\n",
+      "      size_y=342,\n",
+      "      size_z=242,\n",
+      "      type='uint16',\n",
+      "      big_endian=False,\n",
+      "      channels=[<1 Channels>],\n",
+      "      interleaved=False,\n",
+      "      physical_size_x=3.3,\n",
+      "      physical_size_y=3.3,\n",
+      "      physical_size_z=3.3,\n",
+      "      tiff_data_blocks=[<1 Tiff_Data_Blocks>],\n",
+      "   ),\n",
+      "   acquisition_date=datetime.fromisoformat('2019-10-01T00:00:00'),\n",
+      ")] instruments=[] plates=[] projects=[] rights=None rois=[] screens=[] structured_annotations=[] uuid=None\n"
+     ]
+    }
+   ],
+   "source": [
+    "ome = ome_types.from_xml(tiff.ome_metadata)\n",
+    "print(ome)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "290db649",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['z', 'y', 'x']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dims = [d for d in tiff.series[0].axes.lower().replace('i', 'z').replace('s', 'c')]\n",
+    "dims"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5679e320",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'z': 3.3, 'y': 3.3, 'x': 3.3}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scale = { d:getattr(ome.images[0].pixels, f\"physical_size_{d}\") for d in dims}\n",
+    "scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "59e371b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'z': 'micrometer', 'y': 'micrometer', 'x': 'micrometer'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "axis_units = { d:str(getattr(ome.images[0].pixels, f\"physical_size_{d}_quantity\").units) for d in dims}\n",
+    "axis_units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "33d1afd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "            <table>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 612.81 MiB </td>\n",
+       "                        <td> 126.61 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (242, 342, 3882) </td>\n",
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+       "                    <td> uint16 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "dask.array<array, shape=(242, 342, 3882), dtype=uint16, chunksize=(50, 342, 3882), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_store = tiff.series[0].aszarr()\n",
+    "zarr_data = zarr.open(data_store, 'r')\n",
+    "data = da.array(zarr_data)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4650f030",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "name = ome.images[0].name\n",
+    "name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "18ed5e3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
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+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
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+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.SpatialImage &#x27;sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM&#x27; (z: 242,\n",
+       "                                                                     y: 342,\n",
+       "                                                                     x: 3882)&gt;\n",
+       "dask.array&lt;array, shape=(242, 342, 3882), dtype=uint16, chunksize=(50, 342, 3882), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * z        (z) float64 0.0 3.3 6.6 9.9 13.2 ... 782.1 785.4 788.7 792.0 795.3\n",
+       "  * y        (y) float64 0.0 3.3 6.6 9.9 ... 1.119e+03 1.122e+03 1.125e+03\n",
+       "  * x        (x) float64 0.0 3.3 6.6 9.9 ... 1.28e+04 1.28e+04 1.281e+04</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.SpatialImage</div><div class='xr-array-name'>'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 242</li><li><span class='xr-has-index'>y</span>: 342</li><li><span class='xr-has-index'>x</span>: 3882</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e8b423c7-771f-4b73-874c-c526e9075b7a' class='xr-array-in' type='checkbox' checked><label for='section-e8b423c7-771f-4b73-874c-c526e9075b7a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(50, 342, 3882), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 612.81 MiB </td>\n",
+       "                        <td> 126.61 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (242, 342, 3882) </td>\n",
+       "                        <td> (50, 342, 3882) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 6 Tasks </td>\n",
+       "                        <td> 5 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint16 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"201\" height=\"108\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"37\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"42\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"46\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"50\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"55\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 31.08469041486957,21.08469041486957 31.08469041486957,58.96802878764755 10.0,37.88333837277798\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 151.08469041486956,21.08469041486957 31.08469041486957,21.08469041486957\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"58\" x2=\"151\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"151\" y1=\"21\" x2=\"151\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"31.08469041486957,21.08469041486957 151.08469041486956,21.08469041486957 151.08469041486956,58.96802878764755 31.08469041486957,58.96802878764755\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"91.084690\" y=\"78.968029\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3882</text>\n",
+       "  <text x=\"171.084690\" y=\"40.026360\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,171.084690,40.026360)\">342</text>\n",
+       "  <text x=\"10.542345\" y=\"68.425684\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.542345,68.425684)\">242</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-5f448cbf-6bf5-4fcc-a8ba-61e6abc42aab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f448cbf-6bf5-4fcc-a8ba-61e6abc42aab' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.3 6.6 ... 788.7 792.0 795.3</div><input id='attrs-d1e7b367-49c6-4c52-8fbb-2ae53388fbfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1e7b367-49c6-4c52-8fbb-2ae53388fbfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d67f089b-695e-48aa-8cdf-c2be1f22905f' class='xr-var-data-in' type='checkbox'><label for='data-d67f089b-695e-48aa-8cdf-c2be1f22905f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>z</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   3.3,   6.6, ..., 788.7, 792. , 795.3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.3 6.6 ... 1.122e+03 1.125e+03</div><input id='attrs-510b79bf-fa24-463c-ae37-6dd716fe7d8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-510b79bf-fa24-463c-ae37-6dd716fe7d8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e0796a-c43c-4a35-aec2-528f022c000f' class='xr-var-data-in' type='checkbox'><label for='data-50e0796a-c43c-4a35-aec2-528f022c000f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>y</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([   0. ,    3.3,    6.6, ..., 1118.7, 1122. , 1125.3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.3 6.6 ... 1.28e+04 1.281e+04</div><input id='attrs-c7acd22a-5b4f-49df-825e-16b9a0e6d958' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7acd22a-5b4f-49df-825e-16b9a0e6d958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc6cf74b-0020-462c-bdcb-ef7d17df2e07' class='xr-var-data-in' type='checkbox'><label for='data-bc6cf74b-0020-462c-bdcb-ef7d17df2e07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>x</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([0.00000e+00, 3.30000e+00, 6.60000e+00, ..., 1.28007e+04, 1.28040e+04,\n",
+       "       1.28073e+04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3eea9676-0401-4075-bd22-a18ccd2c1896' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3eea9676-0401-4075-bd22-a18ccd2c1896' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.SpatialImage 'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM' (z: 242,\n",
+       "                                                                     y: 342,\n",
+       "                                                                     x: 3882)>\n",
+       "dask.array<array, shape=(242, 342, 3882), dtype=uint16, chunksize=(50, 342, 3882), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * z        (z) float64 0.0 3.3 6.6 9.9 13.2 ... 782.1 785.4 788.7 792.0 795.3\n",
+       "  * y        (y) float64 0.0 3.3 6.6 9.9 ... 1.119e+03 1.122e+03 1.125e+03\n",
+       "  * x        (x) float64 0.0 3.3 6.6 9.9 ... 1.28e+04 1.28e+04 1.281e+04"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image = to_spatial_image(data, dims=dims, name=name, scale=scale, axis_units=axis_units)\n",
+    "image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd03e2b4",
+   "metadata": {},
+   "source": [
+    "## Generate isotropically chunked, multiscale spatial image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "dff01f94-1ad6-4d3d-92fe-2cb358444508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function to_multiscale in module multiscale_spatial_image:\n",
+      "\n",
+      "to_multiscale(image: spatial_image.SpatialImage, scale_factors: Sequence[Union[Dict[str, int], int]], method: Optional[multiscale_spatial_image.Methods] = None, chunks: Union[int, Tuple[int, ...], Tuple[Tuple[int, ...], ...], Mapping[Any, Union[NoneType, int, Tuple[int, ...]]], NoneType] = None) -> multiscale_spatial_image.MultiscaleSpatialImage\n",
+      "    Generate a multiscale representation of a spatial image.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    \n",
+      "    image : SpatialImage\n",
+      "        The spatial image from which we generate a multi-scale representation.\n",
+      "    \n",
+      "    scale_factors : int per scale or dict of spatial dimension int's per scale\n",
+      "        Integer scale factors to apply uniformly across all spatial dimension or\n",
+      "        along individual spatial dimensions.\n",
+      "        Examples: [2, 2] or [{'x': 2, 'y': 4 }, {'x': 5, 'y': 10}]\n",
+      "    \n",
+      "    method : multiscale_spatial_image.Methods, optional\n",
+      "        Method to reduce the input image.\n",
+      "    \n",
+      "    chunks : xarray Dask array chunking specification, optional\n",
+      "        Specify the chunking used in each output scale.\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    \n",
+      "    result : MultiscaleSpatialImage\n",
+      "        Multiscale representation. An xarray DataTree where each node is a SpatialImage Dataset\n",
+      "        named by the integer scale.  Increasing scales are downscaled versions of the input image.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(to_multiscale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a0eba925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataTree('None', parent=None)\n",
+      "├── DataTree('scale0')\n",
+      "│       Dimensions:                                       (z: 242, y: 342, x: 3882)\n",
+      "│       Coordinates:\n",
+      "│         * z                                             (z) float64 0.0 3.3 ... 795.3\n",
+      "│         * y                                             (y) float64 0.0 ... 1.125e+03\n",
+      "│         * x                                             (x) float64 0.0 ... 1.281e+04\n",
+      "│       Data variables:\n",
+      "│           sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(128, 128, 128), meta=np.ndarray>\n",
+      "├── DataTree('scale1')\n",
+      "│       Dimensions:                                       (z: 242, y: 342, x: 1941)\n",
+      "│       Coordinates:\n",
+      "│         * z                                             (z) float64 0.0 3.3 ... 795.3\n",
+      "│         * y                                             (y) float64 0.0 ... 1.125e+03\n",
+      "│         * x                                             (x) float64 1.65 ... 1.281e+04\n",
+      "│       Data variables:\n",
+      "│           sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(128, 128, 128), meta=np.ndarray>\n",
+      "├── DataTree('scale2')\n",
+      "│       Dimensions:                                       (z: 121, y: 171, x: 485)\n",
+      "│       Coordinates:\n",
+      "│         * z                                             (z) float64 1.65 ... 793.6\n",
+      "│         * y                                             (y) float64 1.65 ... 1.124e+03\n",
+      "│         * x                                             (x) float64 11.55 ... 1.279...\n",
+      "│       Data variables:\n",
+      "│           sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(121, 128, 128), meta=np.ndarray>\n",
+      "└── DataTree('scale3')\n",
+      "        Dimensions:                                       (z: 30, y: 42, x: 60)\n",
+      "        Coordinates:\n",
+      "          * z                                             (z) float64 11.55 ... 777.1\n",
+      "          * y                                             (y) float64 11.55 ... 1.094...\n",
+      "          * x                                             (x) float64 103.9 ... 1.256...\n",
+      "        Data variables:\n",
+      "            sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(30, 42, 60), meta=np.ndarray>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Isotropic 128x128x128 chunks\n",
+    "chunks = 128\n",
+    "\n",
+    "multiscale = to_multiscale(image,\n",
+    "                           [{'x':2,'y':1,'z':1},{'x':4,'y':2,'z':2},{'x':8,'y':4,'z':4}],\n",
+    "                           method=Methods.DASK_IMAGE_GAUSSIAN,\n",
+    "                           chunks=chunks)\n",
+    "print(multiscale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f7108355",
+   "metadata": {},
+   "outputs": [
+    {
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+       "                    </tr>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 612.81 MiB </td>\n",
+       "                        <td> 4.00 MiB </td>\n",
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+       "dask.array<rechunk-merge, shape=(242, 342, 3882), dtype=uint16, chunksize=(128, 128, 128), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now the data has isotropic chunking\n",
+    "multiscale['scale0'].ds[name].data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2342795c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f48ea350ac0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multiscale['scale2'].ds[name].isel(x=250).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "998fe844",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f489e93c430>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Ltjx3WOZ2qbRoApKSq3YN8iFhtQTnfEbqGJQ5TzgtGIt7bv1ZLpN0MPBi4APAW+Uz/D4X+J3Q5WzgPcAngBPCPsD5wMdD/xOAc82sDtwp6Xbg6cB/92WSfSBqMpFIJFKRwiZTZavAPwFvo5NEYC9gi5kVboz3AgeF/YOAewDC+a2hf7u9xzULgiWlyZhZTPc/HYV244IGI9fzeakoeZt7u4RXeiwoPV4zUBb64e0ZRS2XPAO1hGu2Yx3bXlt5AlYzzBmWGmqp7WFG6iaVUZaBawF1QR7mLbCasNRoDNXYETST+sQQedNhTd8vcyk4m+wMJvMeZSbIBS3vBafca18YHY82Fe7M6tTPCd5vFmvLLHM6rvIV2FvStaXjM8zsDABJLwEeMrPrJB3b3zkuLJaUkIlEIpFB4itjVhYyG83sqCnOPQt4qaQXASPAGuCjwDpJadBWDgbuC/3vAw4B7pWUAmvxDgBFe0H5mgXB0hAyMd3/7NAUfyRmPjamHBMDbQ2mMGIUMTHKg/0k95qKhZjOvKhQiYJXWbBz1Iy85Que5bVOrEyeirzmUPDgKqpfupaPr5GpbT/JMx+82ZpImQBvYxlPIRNqBNWlsBMVtp/i65UKnikTagZNrVUYfKxTWbNUG82cprZxReYUOc1run8z0bA9/60xs3cA7wAImsxfmNnvSvoK8HK8h9nJwNfDJReG4/8O5y83M5N0IfBFSR8GDgSOAH6wxxPsI4tfyEQBM3ume2a5BcHhje8AhCUzlwVBE5wAlPvIfpl1lsYEzqksk9pGdLWEWsHtOe+UZPZZnZ1fMjPvKg2gFjiEtTo/+oVjQavmaOVBuEwkuFZHWJjDB4EWTg2FDCkO/esoruGX9fzSXxAmKgWeBqFjRdBqzI83Lyy0TMz5YONk3g6cK+lvgB8BZ4b2M4HPB8P+I3iPMszsJknnATcDLeANZguh6k6HgQkZSb8EfLnU9Bjgr/G+3l8GDgXuAl5pZpuDp8RHgRcBY8BrzeyHg5pfJBKJzBZv+O+vVmtmVwJXhv2f4b3DuvtMAK+Y4voP4D3UFiQDEzJmditwJLQDju4DvgacBlxmZqdLOi0cvx14IV7VOwJ4Bt5t7xmDmt+SZybjdG6g3AcplpplhYbil8EKzcW1jBxwRcriSbdQl9F88luna0GeiTyDLO/kB/M3s6DpOKyWQJ5jKW2NglaImSzuZX6Jzk04LBNqiWTCa0iupU5WaKe272Svd2DhNSW15B0MDN/fec2G0nJZJ+VOXDKbV5wWQFaZWRn+I8ydC/PzgDvM7G68X/fZof1s4LfC/gnAOea5Cm8AO2CO5heJRCIzUhj+q2wRz1zZZF4FfCns72dm94f9B4D9wv5U/t73l9qQdCpwKsAIKzonQk2Z6MY8DXnu06Pk5k0yvda5zYoas752S0i3YkGb6WQqDloNNtn9l45tox1AmYdx8467c7v2DPjqlDWRpw6ZyBO1jf/KCvtIeY4iScGCjScdD5pMFpwMiutdZw5tSgMp81pM2yYTtKzCpZlgp/GuzK6j0UTml3nOxJz1KRhzuTBwISNpCHgpwZOiTPCOmJVFL/iZnwGwRhsWjjUwEokseQzRtMXvLzWXzMXTeiHwQzN7MBw/KOkAM7s/LIc9FNp32987pvufBWZe28syIJmcqiPYPZTl/m0xM2+jaBkO71Ujs7Z9xIKbssw6GoOEqaTxiFK5AB+8aQm4oIkUHmkWAkSNEACpjgajVtAuinsYWF1YYrimSCYI3mXgEu/JVta4Ji+hB8+3wlU5o22TkTqaTFt7EliqTt0dCH7aMSBzXpjndP+DMPwvZIKL9JSY2UtnGmMuhMxJdJbKoOPvfTq7+oG/UdK5eIP/1tKyWiQSicw7hpbbctkxeDPGl4CrYfb+2wMVMpJWAs8H/rDUfDpwnqRTgLuBV4b2i/Huy7fjXZh/f5BzW9bkuU9IaXkwRFiXncbbTpQbhk+RT8t7iZhTJ95E5tPHlLSMQjPxSS8L24wFDcF7hlka7DApnXCdsg0nOJ4VGoZrlrzLclD4V2uJt6kk9aDFtCyUHijZZEoaVYEVGkth88k645dtQRQFPp1Cypvl8wa74JnHdP/LzKi/P/43/CR84s5vAF8ys5uqDjBQIWNmO/FJ3Mptm/DeZt19DXjDIOcTKZHnU/6hygjLaqUfd/ChXs4gaUuZ4OpcXNgRMirKGZeWwyzzffNM5Am44bBCVxjay3PIOwLANc1ngsYfW8tH6lvihUtSt0mGf2UWnAs6GQfKvwuFkEH+mrKQKQJQXVZaFkx8zjKXxEzMyx0zlpULcwjs/BbwrVDT5iTgSknvNbOPVxkjWrAikUikIt7wv7wMwEG4vBgvYA4FPoaPeazE4hcyRb6pSDVKtWYspI/pueyQm88Mo5BexYQSkeNKhn7fh0lLaLSN9m3De1LSHkwo9wGYWVNBmyjq14SqnLmhzHDIp39phpo2Bkp8sKcy2hqTaxQaifmlsraLtJ+nCq0G2tpWsZzWWS6zjnOBdYz/hGdAEt2XI55lZvg/B3gS3pzxXjO7cbZjLH4hE4lEInOEob4VLVsk/B6wE1/B802lGEThrRxrZhpg6QiZmChz9vRyww3/iHxwoiEMS5w/zgxH7itgAlb8g8s7tVtM5jPl5l1ajYINxYIy1ezUnGm7C5eSYBbBkC4zXDDq+zl4rWSSJtPsVPFEFvrQDuq0DG9LouyYENLO5GHskuZSnkORONOcgyTxyRqjRjOvzHcm5uWkyZjtuQFq6QiZSCQSGTAG5MvI8C9pw3TnzeyRmcZYGkImajGzo2SX8c8uUP7bMaOdOjPLMVx4gwy2CwcquYQVb/eSH1IE24mTr0ZZpKjBp+93LW8/UXAT9pqFvFZU0koK+4hrFVqFH7cwAhWaTtvLrW1zkfcyc2VX6s55pdapW9MsaVPtL1TYovw1bZvMgkjSuPxYOOn+K5dWXipcN805w2fXn5alIWQikUhkDjBYVt5lZnbYno6xtIRMSJIZqUCRLLOb8ttisa+QOsbw2o15laRs/1SRlwVQSKhZJJ2kpNE4QsmAzHuZmRPZsMhGHFk9Ial7NaHQSpQFbSVoUIVm0vZEy6zjeWaF3cVrTkXfcumBIjgzt04STq8lWdt21PYws/aXmxyQGWNl5o951iTNtNyWy/YF/go4HLgeON3Mts1mjCX5tBaGWr0AqFIyWK6zDNRNeI6+kmRwMS5cnq3jdtzestx/tgy1cp/zLMu9gMiK64OhPQvLZym0VkBjlaO5OiEf8nMugjHbwZJtF2V/vWsZrmkkTf/Zc2tNda4YwwuYYslt0v1KAaLmhKVBuBTPKZZjnl/mMftCZq7SNh2SRiT9QNL/SLpJ0ntD++ck3Snpx2E7MrRL0sck3S7peklPLY11sqTbwnZyn7/uOXjvsv8HrMbHyMyKpaXJRCKRyADx9WT6slpSB55rZjsk1YDvS/pmOPeXZnZ+V/+eRR2DYf7dwFFhetdJutDMNvdjksABZvbOsH+JpFlXK14yQiZmYp6CKhmDpckbdJbKCq3FOf+MXTsE01/apTVOCsoMCXND3KZfmsIvObisE7CZjRr19cJcgmtZ2zVZeUlraRVOANZ2e+7Um7HJ7sfYLq7T7fkVy2fqLKPt4jhQDi5tOxuEmjKSN0JX/h8Q6Tvzmom5P5UxQxqtHeGwFrbp/lm1izoCV0kqijoeC1xaeHlJuhQ4nslJifcISevphLwn5eMq3mVR549EIpGK+JVjVdqAvSVdW9pOLY8lKZH0Y3y5k0vN7Opw6gNhSewjIaULTF3Ucar2frEW72FWbGuAH4b9a6sMsGQ0mUgXZZtBL22mOB/quOyixZS7e/UDoXaAZjcdI7m168IoDxpD1gnWLF5AC/sKgtYo1Nd5baE2nnTO0bGPFPacdvLNwskg79h3lJVdqn1iy8Klut2eBM2mMOYXcwlaEuyaUBPwLsyuMPxH55L5YBc35nnIxDzL3GUbzeyoKcfyySePlLQO+JqkJ+GLOz4ADOGLM74deN8eTXoPMLND93SMqMlEIpHILMhxlbaqmNkW4ArgeDO73zx14LPA00O3qYo67naxxypIerSktaXj50j6qKQ/C1WPZ2TpCZk8rpZXxhWeZS5Uo5wiCWROxzYTXHsnbWWPs8ITrQhkzK1dOkCFR1rQVAotxVIjW2G0VkA2FJShlgXX5Y4W07a9tPdD4GbJe63Y9x5j3sutsOm0zzVtkq2n7f1WVNcM7tLF92trPWmCYl2ZZY1P9a9K23RI2idoMEgaxdds+UmwsyCfJOy3gCIh5YXAa4KX2dF0ijpeAhwnaX2wlRwX2vrFecDKMKcjga8APweOBP6lygBxuSwSiURmQZ8SZB4AnC2pWJw9z8wuknS5pH3whvUfA38U+vcs6mhmj0h6P3BN6Pe+Ksb4WTBqZr8I+78HnGVmH5LkwvxmZGkImZjuf2am8jIr4mSkjl6rEI1IKcAxD21FYOM06+Ft240LthMLgZqTUukXcSzCNdXxqwm2laTZCbpse5BlFtLZMMn7q7DZqGxLKipclm0ype/nsHZgprIQjJkV46rjHSf/Hbx3mfcsa9uwIssOn4W5L95l1wNP6dH+3Cn6G1MUdTSzs4Cz9nhSvSn/Q38u3maEmeWq+DewNIRMgeVxKWO2OHWyDDvXLn0Mk92TfQ0ZlRwCrPdiaxHA2SVoihxlxVhFPrKkDskEKJPfbxhJPQi2hElLYH6pLIxf/Ps2cFneXtIr5l7INaCdgaAt4ZyfYdttuVgaa+XIibx4BqZO1uZUWJL4LAlRwMw785WJ2aeVWVa/MZdLOg+4H1gPXA4QlvUaVQYY6NMKvtznS/qJpFskHSNpg6RLQ3TqpWEdcdqI1kgkElkYeE2myrZEeAvwVeAu4Nlm1gzt+wPvnOKaSQxak/ko8C0ze3nwRFiBz4NzmZmdLuk04DS8m17PiNbKd4qZmGeNisBC58AFTcOFpStnWK5dtJmCSVrNTH9PFv4TDOmFe7Nrea0lGfe1ZdKdRjpuJPUMU4KltJfWJi2JdZN1nAs6c6Wt6Je1Gq+WhGW/IrOylTQl83nWiuBRCMGYiSB1Pn9ZksT8ZfPJPOcv61PE/6IgLNOd26P9R+VjSf9tZsf0GmNg4ja4vf06cGaYVCO46p0AnB26nY33oIBSRKuZXQUUEa2RSCSyIOiXd9kSZGSqE4PUZA4DHgY+K+nJ+AjRNwP7Bdc78EFH+4X9qSJX7y+1EaJmTwUYYcXAJr8kqZJiZjpKWZk7Y87Qr5SKpqiuKXnjvtdkoLYTXMOo7YR0PMM1chKndgXCsnty2e7SfT/lXZpWcAiRTzTTDvAMk6Ntn8lLwZgiaDYhk3N4azUn8pojSRKfYmc3H2Fk8bOElsL6yZR/EoN8WinwVOATZvYUfCbP0ybNyqtis/p7NbMzzOwoMzuqxvDMF0QikUif8N5lldPKRBisJnMvcG8pH8/5eCHzoKQDzOz+sBz2UDjf18hVM6Oqi92ypjsx5mzo9YqSd9rbnl6FNpOH25Q1mYaR7oR0wqiNGcl4C9fIOkkqnUJgpJWCPnt8DbNJgbgd92kIelRHm3FhnkVtHAvjm9dclJmvnGn+em+TAUscliYoSTq2rD3RDCN7hhxz7WJmQCtqMr2Y8gdkYE/LzB4A7pH0S6HpecDN+MjVk0PbycDXw/5UEa2RSCSyYFhm3mVVefVUJwbtXfanwBeCZ9nP8FGqDjhP0inA3cArQ9+eEa2zwb8xRy+zGQtq9TpvwR2r0Bamolvj6S4N0D1mu0JmuUyAtYMfXdN8nvOdOenODDfeQlmGS32qm+COBplN0jp2oZS+BnUHg0JZmyEXcjYpONSP4WNqZN5zThkoCY5mIRjTe5fFJJnzznyl+19mS2GSHsG7MH8JuNymqAhpZjf2aocBCxkz+zG+mE43z+vRd8qI1siAcJpU7XGSQb3XKlAQKDbd0lq3/Cr7D5d/+MM51/IG93Q890b/etMb3usZLnGdTMnBetcWMOVS22UBE8ZVyBXdDiKdStCUv7OF9knjFYZ/78ZswfAfg37nh/nOxGwsLxdmvPPWj/GZoM+RdD7wpeABXIn4lxKJRCKzYJkZ/nea2cfN7FnAMXg7+b9I+pmkv60yQBQyy42qtelDt2m1liqoh5ZUnArZln0qmQzXyKDZgqZfMlOW+y0vXJeDcT8vaTN5l0tz+a22SEHT1nDouEAXec5KWyejNB0HA/PfwRzkNQepN/7H/GXLE2PZCZn2FzGzn5vZ35vZU/GmjXqVAZZW7rJIJBIZIIZo5cvq3fyKXo1m9hPgvVUGWDpCppyJubxevxzJ8+oaS8mFuZzZGGi7+u6izUzK1ty1PzmHyy7Hk8YKNWHIQc0cNb32AvhUMVmR2LK323J7riVbzKRPqf1voZ1NunA8KGwzPcZR0HasbQPySUTzVORDCUk9AbfMnUuWMcvJJmNmb93TMZaVSI5EIpE9wpbXcpmkl0naEPb3kXSOpBskfVnSwVXGWHJCxmJlzGpITKolU9RJcepqY9etuL78Cb2PYXrvn9xXonTNHFq518JsBjdq6FStLPpnu/Zva2SFHadcsbOryuekIM9QUqDwaAOCTUZYLcFqoUJmVW0xMhA0D6sVy9Am84FSEbSPAz/CJzP+Jr489IzEv5JIJBKZBf0QMpJGJP1A0v9IuknSe0P7YZKuDiVPvhxiDJE0HI5vD+cPLY31jtB+q6QX9PnrlteFDzezj5jZvWb2OWCfKgMsLSFjMcXHrHHylR+LCpDyQYdWaDSuS9vp3mBq7WUGjaStSRSJL3toMerhLTbpHnlIoFmuilk6X8S8dDSfydpMZ9t1buV5mIM8hXzIYTVfvCymLZpHylrMHP5/MESWu0rbDNSB55rZk4EjgeNDppMPAh8xs8OBzcApof8pwObQ/pHQD0lPAF4FPBE4Hu9e3E+D4ZWS3idpNOy/LNz3OcDWKgMsLSETmR3FH2r5X0EhbBIXgg+LypmTt12Ybf6znM4PfXmMgmkEVHsZrHBrtrKLc9f1ZpOW4MqCpuOuXBJmYYmw7QRRjCcfjJnXgpAp3JjjktmyI0eVtukIJU12hMNa2Axf4vj80N5dCqUokXI+8Dz5t5wTgHPNrG5md+Izpjy9T18V4I34v6pbgVcAF0jaDryeaVLJlFk63mWRSCQyYCwY/iuyt6RrS8dnmNkZxUHQOK4DDgf+GbgD2GJmrdClKHcCpVIoZtaStBXYK7SXo+/L1+wxoRLme4D3hBphqZltms0YS1rIxEzMFSiej5N/W3fqZE+2Tp/uZatef2cKgYu7uDFP6mOdWi8BC/c1yb//dbsi09E0egV17kKvVVPXmVORS63s2tw9l14u8OYgT0SeBqN/TC0z/xTpvecQqy5kNppZr7RaYRzLgCMlrQO+Bjx+z2fXfySleGP/48PxzcAlJWE4LfGvJBKJRCrT/3oyoWLwFfi0LevCjzpMLnfSLoUSzq8FNrGHJVIkvWSG8wcBNwF/DhyI15LeBtwk6cAq91iaQiY6AExPD7djU7C1JMLSYHspElSW7THBXtN2dy5t3nmAng4Cxd/cLoGdZddpV5pbOXVML8oaSAWXZ28D6upXcm1u30fq1LLpyjBtDv9saqXUMpH5Y540STNV2qYjxJysC/ujwPOBW/DC5uWh28lMLoVycth/OZ2MyBcCrwreZ4cBRwA/mMXXed8M5z+ALzx5rJn9mZm9xcz+N3557++q3GBJL5dFIpFIPzGDLO/Ly8UBwNnBLuOA88zsorAUda6kv8HHpJwZ+p8JfF7S7cAjeI8yzOwmSefha3W1gDeEZbiqzPRljjaz13Y3mtnHJN1a5QbTCpki0nMG8qDuLQws1pMBZk4t0+2aHFyW27YR1zbIeBNL4Wa8q7kkNACJQnXJ2dGxg7hpU7d3uxW3tZP2QCFpzCzscO3vU1ziOtpYd5odcyKveQ8zaqlPLRNtfnPOLun+55h+pJUxs+uBp/Ro/xk9vMPMbALv3dVrrA/gNY7d4Q9nOD8+zbmxKjeYSZP5Rdime6oJ8KgqN4tEIpHFjH/PWdwvFpJW4G0sjzKz10s6AvglM7uoR/e1kn671zDAmir3m0nI3GJmu0jbrgn/qMqNIguUUvClpSJPvFaTl1P054ac2in3vfdWV0xL6GulVCxVadt/Uoclzo/cjuGZ4g96pjfZQonLu44D03qptW0y3fPEJ8kM6WXmI61JZL5ZEiljPot3nT4mHN8HfAXoJWS+C/zmFON8r8rNZhIyx8xwvmqfuaGrCuOyN8zOtGQm147st8QLmLzmwpJR59kVpZKVW1g264rExwsgCwGWu/yA9wq4bFfZDEGOQw7lKW6ohjVbnaWzmejKBO1LNpfaktL9K4xXZD+gLGTK+cvSkMMsdTjnMOcgm4cywJEOcjArM8SeMY8rdf3isWZ2oqSTAMxsTFPEepjZ7081iKT9qtxsWvcMM5uQlEj6yXR9ppnEXSFj54+LoCRJGyRdKum28Lk+tEvSx0IOnuslPbXKF4hEIpG5pB/eZfNMI3i0GYCkx1KxAJmkdZJOkXQZ3jFhRmb0LjOzLCRee5SZ/bzKoF08x8w2lo5PAy4zs9MlnRaO344P9jkibM8APhE+Z43lRl+z9yxlVKqVUnPkQ37fwuuHrxYplBmuhTfu5wZ0gjbJfdCrnKG8hwd595tfe4kOcJAPiRYJJnATNRxMSmdTGOFFl0aUhBfYdkAp4NykQNFJWtWkuja9+7RryfR4VKYiGFOT8pct/hfbxYuc5jTzuvcuW/SRH+8GvgUcIukLwLOA107VOQikE4DfwTsrrManu+nLclnBenzwzQ+AnUWjmb204vVlTgCODftnA1fihcwJwDnB9/uqIDEPMLP7d+MekUgkMhAW+3KZmV0q6YfA0fhF4Td3KQJtJH0R+DXg28D/Ay4HbjezK6ver6qQeVfVAbsw4NuSDPhUyNuzX0lwPAAU63rt3DyBIgfPJCEj6VTgVIARVuzmtJYR09ll2vaYYHRPRTbsyGpekwC8jSU3XEu+7kvLUKb2q75PUKmQCTl85pNVgW4bjcmnsCFoUFlN5AlYkpDUh7yx3uGDPttaDOGzY8uRMdlbvdCQ2k4L5rVaemswPdtCLZneeXPAEq/NWOKgHZTq/HOOzC1OUJhipnF97zcLfClsSnqYIIrf1keFlaof9rjsCfhs0LfgHcGy8HtemUpCxsy+K+nRwBFm9p3gAldlQerZZnafpH2BS7ttO2Zms51wEFRnAKzRhkX+ThGJRBYTxoK3t0zHh6Y5V2SAntxodqSkxwMnAd+RtBFYLWk/M3uwyk0rCRlJr8drDxuAx+I1jE8Cz5vuOjO7L3w+JOlr+CCjB4tlMEkHAA+F7nuUgycyDT20GZW9vAqNYki0hkU27L2ooGSTaeG1mZbhMnzgYw4yr9W4Zkifr9BWqixpvcKsiqSYTuQpmHNYYiQrEtRKUZZP9hQL7yKdoMnCFqK2J2FbQwoY8o5HsIu3oXUn/WyfMFxmWGK7ljRQSC2T+PQyJNHwtxxZrG+2Zvac3bzuJ3g7zrslPQ1vm7lG0r1m9syZrq9qwXoD3ji0Ldz0NmDf6S6QtFLS6mIfOA64kck5eLpz87wmeJkdDWyN9phIJLKgMLBclbaFinxVzrdK+qqkCyS9RdJIlWvN7Doz+3Pg0XinrRmpapOpm1mjePsNWUBnEuj7AV8L16TAF83sW5KuAc6TdApwN/DK0P9i4EX4ojtjwJT+2dNi1hUnGNP9A1PbZgptoua3bBhaoyKvdbooB2VeW3FN4bKg3eTWPmfOtzmFapT5rqk/ygujFrSCQqPJa/4zHXGkY97WYakDeZsQGW1NpFtjKWxLxbidexVlBYCkdE1Yv2+fa0+wmFzXM1IpfiYJAZkhcDSm+19+LOLlsoJzgO14Qz54zeTz9EhbI+mJ+LiaC8PxR/AZoAE+XuVmVYXMdyX9FTAq6fnAnwD/Pt0FIQfPk3u0b6LHMlvwKntDxfnMTMxhtitdgqZwv/UVMP0SWTYUlsuGaLsxQxAymXANcM0iMFOhHZKG74MIgZtMMp7LbJdsAH65rLMEhdHOPGCEwFDANXMvSDrp1DpLWeUXiFKUv4lOYGjhw6By/6LRdnVzVuez/Ay8sIE8wc8tTbzxPzJ/yCGXz2Us5qL3LgOeZGZPKB1fERJz9uJ0JmdbfgHeEWwF8Nd0KndOSdW/kNOAh4Eb8AnVLjazd1a8NhKJRJYERe6yRR6M+cNgkgBA0jOAa6foe4CZ/VfpeJuZXWBmnwf2rnKzqprMn5rZR4FPlyb25tC28IhaTHXK6V1c0GiGoDVqWIp/ew9dXQauoaDJFM4AXrPJE79vzn+qpLX4fbXT0/gbBgN69xJVCHgkLOEBkIukpcmvRFIn/U3337OjrcWUXzrbNXOKIaxH8Kgra1il2jLFGEHraucvU0h/E92Y55R5y8Qc/i0vcp4G/JekIrj+UcCtkm7ALyr9Sqnv6vKFZnZ06XBau3xBVSFzMtAtUF7boy0SiUSWNEtguez4WfT9haRnmNnV5cagCf2iygAz1ZM5CW8UOkzShaVTq/GFcxY2MUnm7Aj2i0KbyYcNc4bVgjOFgZrCNYRaClqMt9OkNf/pNZmOUwAEx4Gg2SgpaTmFbUVdcwhaTDbkgvbjsFZI8VxoXona11oI7GwT3KcnLVmEfu0gU4LtqMguHe5tIUloYadq22eK6UmYM6/JJAkucYvWpTWyOyxsz7EqmNndIWfkIZRkwBTBmG8Hvizpc0Bx/ml4xePEKvebSZP5L3xU6N5MDuTZDlxf5QaRSCSypOjDW4WkQ/BeXvuFEc8ws49Keg/werwNHOCvzOzicM07gFPwvpZvMrNLQvvx+FWlBPiMmZ0+w73fj1+JuqP0baYKxvxBsNm8kU5+s5vwFTP3PBjTzO7Guxkf0xXxPwqM4oVNZBFiRb37LEet3GslWSlljMBqhqU5GslIajlyOXnuyJsOazloOtQQyYQjr4mkATjhGsHzLC+COfEBm1lnX7mFNDDentNxL6adsNMSr404+cBIFSlj2vYjte0nk79ckVamq7lwmS5c8ZPggp1bW+vNE/kkobWOLWZSbZngcZYnQOp8QGZM9z//zFW6/24NefdpAX9uZj8M8YTXSbo0nPuImf1jubOkJ+BLLj8ROBAfff+4cPqfgefjU3FdI+lCM5vKWwx82MhjzaxRZaJm9hDek2y3qORdFiL+zwc+FZoOBv5td28aiUQiixaruE03hNn9xfKUmW3H5wY7aJpLTgDONbO6md2Jjyd8ethuN7OfBaFxbug7HTcC62boMyWSehU3m5Kqhv834L/M1eAj/kM+sgVLTPc/BXneSYeSG2QZrpGRTPislOmI0VohWi3aGk1SyxldUWfFUJPE5TSzhFbmmGjUaDUTmhMp+VCNZMJrGkld7ViZQlNQBsrU9jwrvMwKjcUnzPRtbU83F5IehliaXK5tf/EeaOH6Kexu3WljvD2FtlZSlDBQ5j3fTEVAqk/Y6fuUBygGDrab1JE45z2dYsDv/CFHJ1PmnNywase9izpagTNC7sXJo0mH4lPoX43PrPJGSa/BuxX/uZltxgugq0qXFQmEYdfEwjOVSPk74EeSbqRUR2YWWfWnE4a7MMiI/8hCxcwLmzxDzRZuosnQtoSskWAu9VmZQ6bhLId8VCQyVg41WDs0TupyWrljIqtRz1K214d5ZHglzbGUPKmRjoNaKi2Nebfn9pZZ+7yFCHqAJLMgRGgvo8n80pkyFwJHJy+n5clkwdH+iqLdWCybFcKrQGbtfGxFqucif5s5f51rhkDSshtzMP5bcLWOzCNO/mUJ2pkcBk51T/WNZnbUdB0krQIuAN5iZtskfQJ4P/739f14W/gf7P5ke3I28EF83OPu+N3/aDadBxbxH4lEIkuOPsbJSKrhBcwXzOyrAGVjuqRPA8XS1HQJhGebWHjMzD62u/M2s1kJvapC5jS8V0M74h/4zOymFllQmEGzhU3Ucc6hzEhqCa4xjGvVUCtBmWitSKgnQ4wNZaweqTOUZKytjZPIyMxrODtaQ/y8toHNY6NsT1dQH0vaDgTtoM2mX0Zzde/inNRBLXzwZeqdAggGdpPaFSiVW/gMS2apN/hnNR8Q6TM4F9eVvl6XxlLo3WWXaZ+PDZJmJ+I0r0E2RNtl2+TT6RTpb9rjC58PzbnJdU0iS55+KEvyy0Jn4mu0fLjUXi7U+DK8/QR8AuEvSvow3vB/BPAD/L/UIyQdhhcur8KHnUzHf0j6uzBmebmslwtzMa9nAe/BJ8YMYdqYmT1mpu9atZ5Mjo/2//RMfSORSGRJ058VuWcBrwZukPTj0PZXwEmSjgx3uQv/Uo+Z3STpPOBmvGfaG8y8O52kNwKX4F2YzzKzm2a491PCZzl6v6cLc4kzgT8DrmOWr1RV68m8BL8+2C3F1szmZnNGzMQ8PSFRpmUZqte9k0SjiZyjNjZMMj5KunOYdKJGc6VQnjCuETYmOcNpi1Q5w67FaNJkZVpnXTpGTTmbR0b5xdBato8Pk4c66GbQannngNZ4QrLD4VqQjImk6adjAkKCTQvBoEXCTuVq17YxhzfKp6I1EvaHQu2boMm07S8lzcNX7Oy0FyjzGlVSp13N0xv0w/kcvO91GC/Yf7xWJfKklIk5ujEvH/qwXGZm36e3B8HF01zzAeADPdovnu66Hv2fU7Vvia1m9s3duK7yctk/Ab8N3GDzkjAoEolEFgazq+W7MJH0YnzMTbuOjJm9b5pLrpD0D8BXqbjEVlBVyNwD3LjoBExMlDk1hTbTbEGWQ7Ph38gn6iTjdUZ2jpKOr6S5KvU2mixlvLWSuxopW9aMMpy2WDcyzj4jO9hQ28mGoZ2srk2wdmiCna0h8hBEmZuYyGpsmRhl885RxraNQD0hGXG4enBpDmk61CpSxVinAqWjXWsmGxKtUciHRHMlZKMWyhL4/rjSP08HlnSO1Spl+hSQ+2SfSV0k4/Lu1WVFpAgilbC8o2EV10+qjpk4lCm6W84jcpqbdP8mWORpZSR9Ep+q/zl42/rL8fad6SjcosvecjMtsQHVhczbgIslfZfJUuzDU18SiUQiS5DF/zbxTDP7FUnXm9l7JX0ImHYpbHdLN0N1IfMBYAdetRra3ZvNKVGLmZkiNb0ZlmU+zqDV8lujQa3eJB0dJmmsJKkPkY4lTGwfZdPaYWwo54GVLe5bNc5Ba7dy+OqHWZuOc9DwZmrKGFGTmjIyHPW8xoPNNdw1thd3rtzA9olhdm4foTmWoobDNXyuFhe8zQoK+0k25FO8ZKOisRqyEWiuyclXZrjRFkktI0mMJMmRDMmoJRlDaYaTkeWORishy127HkiWORr1Go2dKRpPQgxPKGMQirF1Kn5O1mSUQ2sEWqMOG05REuocxHT/c0o73f9ce/ctfiEzHj7HJB0IbAIO6NVR0u+Z2b9Kemuv81UUjapC5kAze1LFvpHFRvmH0QzLcy9osgyNjTPUbJGMr2R42zDjm1Lq6xKy4YTWyhrb1g2zc/9hVqQNaisyHjfyAIfUNrHOTTCijASjYY6t+TB3jO7Lj0cexcbGKu5auYFHdq5gbGyY1s5aO7uzhVowvkpmEXgJ+TA01kB9Q46NZoysn2Cv1TtZNzLOqlqdVDmjSZNh12LYNRlNmqxwDRLlTOQ1dmTDjGdDNM2Rm2N7a5hNEyvZuGMlO3aOkLUc2ViKm3C4iVATJxPWDPNpB4j60tJINLY7RlcMkaYp1moBzXn53xeZYxa/kLlI0jrgH/CZlY2pQ1JWhs/VU5yfkapC5mJJx5nZt2d7A0kJPj3CfWb2kuDPfS6wF94d7tUhm8AwPivp0/CS9UQzu2u294tEIpGBsQSKlpnZ+8PuBSEP2YiZbZ2i76fC53t3935VhcwfA38hqUHnda2qC/Ob8cnfir4fxGcZPTcYoE4BPhE+N5vZ4ZJeFfpVqlcwLSG7bnRjrkh5Ca3egEYTspy03iDZNsrQI6O0VtfIhhyN1Y6J9Y4djRXctmIfchNPXHEfK9Vgg2ux2iUkiJoSmtZkn+RuVrtxHm6t4aCRfbhr5V7ct3MtD9TWUB9fQT6RkCdq5wvztV18bZvGaqOxT8bKfXeybsU4v7z+QQ5f8RB7p9tZ6erUlLHS1Vnh6qxUgxFljCgjQ4zlKZvyFWzPR5nIa0xYjYdbq7lneAM/Tzfw8PBKxupD7EhHaKkGJNDw6UqscJ9OIBvq1NbJRhzpmKO1okZaS6GRTKo7E5kH5igT82L3LpP0CuBbITHnXwJPlfR+M5syXYykEfxvdLdH2ozR/5WyMJvZajNzZjYS9ldXETCSDgZeTFDFQpTrc/EZncHn0PmtsH9COCacf56iVIhEIguNPmRhnmfeZWbbJT0b+A18oOUnZ7jm88D+wAuA7+LT11Qq9VJVk0HSS4FfD4dXmlmVdM//hPdMK9bz9gK2mFkrHJcziR5EyCZqZi1JW0P/jV3zOBU4FWCEFdPePGZi3gOCRmMQUtA0YKJObfsY6egwpAmjK4cZ2WsEqLFlzRpubqbsP7IdR8722iOscHVGlLFadVY6MSx4VLqZvZId7JXuYL/aNm6t7U+WO+7ZPEI25CbVbcmHIBuG5mqjsVfGhoO2cNR+9/DokUd4xsrbOTTdygpBIuGAESUMq0ZN3jcls5wWGVvzBsPZdh5Rk02sghySrl+BwmGguL9PGwNZ4l2hbciwkQw3knn/iFpKc3ONbMR1UstE5o85zMS82DUZOg/qxfjM0N+Q9DczXHO4mb1C0glmdrakLwL/UeVmVevJnI5f9ro5bG8OuW+mu+YlwENmdl2Ve1TFzM4ws6PM7Kgaw/0cOhKJRGbGVG1buNwn6VN4c8TFwR4+kywozCRbJD0JWAtUKvdSVZN5EXBkyGGGpLPx6Z7fMc01zwJeKulF+DW8NfgSoeskpUGbKWcMLbKM3htKCazFOwBE5ouS15nluQ/arNdhfBzkSLYNMTq2CkvW0FpZY2Lbai7nCO7aewP7jOxgyLUYdi32qu3kgKEt7JtuY4XqDCljQ7KD2nCLDPFQfRX3Dq/HksIA4pNkZkPQGoXG+ow1+2/nmP3v5rh1N/CodDOH14xVbhVNy6hbkwwjwxizBvU8Z0sOD2ej7MxX8Ei2L5uyVWxsrubBxhq2NUd4cHw1G3esZOfYMM2JFDKhiYR0pyMJkWDeDoOvEFrLccMZaS3zBUWtFtycQ4XRfPG/3i4JBp3uf+EvhVXhlcDxwD+a2RZJB+BtMwBIWh9q2JQ5Q9J64P/iE2uuAt5V5WaVNJnAutL+2pk6m9k7zOxgMzsUnxn0cjP7XeAKfIQpwMnA18P+heGYcP7yRZdhIBKJLH0WuU3GzMbM7Ktmdls4vr/Lc/iyHpddZmabzex7ZvYYM9sXqORtXFWT+Vt8JbUr8KvWv45P/787vB04N6wB/ghvdCJ8fl7S7cAjeMEUWQh0B202Q+Bhs4GyjNHUsW5oDRObHDvG1nDzhlXYcB6qfoEbbbFmtQ/a/KXVD7K+NsbBQ4XNpsnKtIESbw9Rbj5ZZbDJtFbm1DZM8L/2vZ9nr/kpR9Q2stp5TeXu1jib8lEeaG1gIq+xNVvJ1myUra1R7htfxy92rmWsWWOsPkSjkdCqp9hYipoi3elIx8RwHUYyfBqZDJKGodyXEciGoLkGWubIDPK8RkM11BIjGxNWPGAMba7DRN3HFcV3omWBln68ba+1vguAp3a1nY8POZmWGYWMJIevnnY08Kuh+e1m9sBM1xaY2ZXAlWH/Z/hSzt19JoBXVB0zEolE5oWl/y7R/oaSHo93W14r6bdLfdZQcmWejhmFjJnlkt5mZufhl7QWB13p/iN9oDszQEht7zYlrJIYfWiIkc3DNFY78tS79fl0MDXqa0e5ZZ91/GTf/Vi5os6j12/m4BVbGHItNtdXkDcS0lCeuShQ1ho1slU5B67fzuNXPcC6ZIy7W+vZkq3glokDuW3Hvtw/toZHdq6g0Uypj9WwiQTVHelOR22bSBpQm4DhluEakNYN14J0PMM1clzTQnlltQuVIWitSGiNOibWieYqkdf890ka4Oowuiln1b0TpA9vwybqPuI/ppRZ8siWhHfZbPgl4CV4c8lvltq3A6+vMkDV5bLvSPoL4MvAzqLRzB6peP38EXOYDYbCxbnZgh07cFmGG6qRbh4lH675qpFmIJEPpzTW1BjfO2V83xU0Vq7gxn3WcPNe+7NyRZ16IyV5JKW2XaT1LFTD9FmW3aom+67w7vhX73wsN2w7kPt2rOWB+9ZT21ijtk2kYzDSgNVjRlI3koaRjreo7WyhZo5rZJAZyjLUzCDLUSvz9V8Kg73zqWKQsDRhaMUwNpQyvHaI1srEL+UVwqmekW6bwG3ege3YiTUacalsnmjnL2OOMzEvbdpf0My+Dnxd0jFm9t+7M1hVw/+JwBuA7+FTwVyHTxUTiUQiy4s+GP4lHSLpCkk3S7pJ0ptD+wZJl0q6LXyuD+2S9DFJt0u6XtJTS2OdHPrfJunkqe7ZYw6HS/o/kp7Qdep5Pbq/TNIaSTVJl0l6WNLvVblP1fLLh1Xpt+CIWszgyTLMDMtylDjYOYZLJj/zJE1JR4cZeWAFqzaM0BpNGNsnYWLvFUysHQVg5SYxtMVId/hXUZ8UM2d4qEUrT/jhlkO4bdM+jN25huHNjn3uMUY3ZtR2tkjGW6iVo4kWaobEnq3goJBlWJb7fwslV+NiqW9SuqEioDJJcNtrkKaMbBmGWvgzaWWo0YRWhtUbWD0uky0Y5jATc5+Wy1rAn5vZDyWtBq6TdCnwWrwn1+mSTsM7WL0deCFwRNiegU/F9QxJG4B34+u8WBjnwh4uyATHrVeY2UZJr8a7IH8PeI+kM8zs/8GUK1THmdnbJL0MXxb6t8O1/zrTF60ajPmGkLWzOF4v6U+qXBuJRCJLhlDMrso27TDebfiHYX87Pr/jQUxOr9Wddusc81yFjzc8AJ/m5VIzeyQIlkvxMTC92MfMigwqbwKOMbPX4YXWTPaVWvh8MfCVqRJq9qKqTeb1ZvbPxYGZbZb0euBfqt5oXglJMiMDorDPFO7NXZhzaGwct30nI5tHsKEaow+uoL5hmObqhDwV6XhGujNjaGuD1kr/71kmWq2EOzdvYPuWFQz/bJh9b88Z3txk9P6duG1jPoFnq+W1lTzYWCzHcvNr9V3OCr3YpVUtrNH0Gs74eCfxZZZjQXMjLzSjqMUsO6prMntLKpsVzjCzM7o7SToUeApwNbCfmd0fTj0A7Bf222m3AkVKrqnae9GUdJCZ3YevD1bY1+vMvOTz75J+gq9F88eS9gEmZrgGqC5kEkkqgiND+v7FUbwsEolE+kl1IbPRzI6aroOkVfgYlLeY2bZyTmAzM6mvvmx/Bnxb0gXATcDlki4Bng18droLzew0SX8PbDWzTNIYXrsqvsfzzezSXtdWFTLfAr4c8t0A/GFoW1TEdP8DZLo3+sLdudXyaWmShGT7TlZsGsFGh7FaAnmOWj51jRtKcC1Idjgyhtm+cZgV9yWsvy1j1c+2o50TsG2Ht4lk+aRg0cpzmgkz/1vSanU0mfL4UYNZmMxBuv9+/exLquEFzBfM7Kuh+UFJB5jZ/WE57KHQXqTdKihSct0HHNvVfmWv+5nZlZKeCfwOPmnxdXht5E/N7CczzbdsqzGznZQ8jfGlWfZIyLwdL1j+OBxfytSV1CKRSCQyDaGMyZnALV0ljIv0Wqeza9qtN0o6F29D2RoE0SXA3xZeaMBxTJNTMthSPlGax75m9tBU/WfzlaY6UdW7LA8T+8RMfRcaMd3/AqCcaDPLOjaPiTpscyhJOhpmrYabGGZoxwgjDzvyLQlD22H1PS1W3rkN9/AWrNHAJuodzWIQWkXUVBYfc5Xuvz+azLOAVwM3SPpxaPsrvHA5T9IpwN34ZJYAF+MTFd8OjAG/D167kPR+4JrQ731TxS8GT7RufiDpKYD2MO5xyqcyrZCRdJ6ZvVLSDb0GMbNf2YNJRZYbvTIGSD4PmhTcUHPcWIORjU2UeweA4S0thn+xA7dxMzY27q+LRvdIoByQGRoGFxxr/cldZmbfZ+q3/13iVII9/A1TjHUWcFaF227EC64yBwE/xP++P6bCGLNmJk3mzeHzJYO4eSQSiSw6Fm9yh78Eng/8pZndACDpzj7FQd411YlphUzhSmdmd4cJrZnpmkikEt1aDYCEAG0fY/iBhKGtNTDDbRtH23Z4LabV2vX6SGSOEIs3d5mZfUjSl4GPSLoHH8S529+m7FFmZr89Vb9KAkPSHwLvxXsiFJMamHoViUQiC5ZFKmQAzOxe4BWSXop34Jq+hv30nAk8aqZOVbWSvwCeVIoWXRyUMzHHgMyFS6GVOOcTbu4cw2UZJInXcup18nojJLSMGkxkHlkiWZjN7MKQxuax0/WTNFXmfQF7VblXVSFzB96jIRKJRJY3i/Q9R9Jbp2g/DqDLlbrg14Dfw2cImHQZPeqC9aKqkHkH8F+SrsanICBM6k0Vr59fLA/ujZEFTZ57baYRtJZA9CaLVGUu0v0vYk1m9W5ccxUwZmbf7T4h6dYqA1QVMp8CLgduYNHK8UgkEukDi1TImNl7d+OaF05z7terjFFVyNTMrKeqteCJ6f4XF0WyzZjCJTJbyun+BxUrU6FWzGJC0g/N7KkV+u1HJ/HmfWb2YNV7VBUy35R0KvDvTF4umzJCVNIIvt7AcLjP+Wb2bkmHAefijUbXAa82s4akYeAc4GnAJuBEM7ur6heJLDGiYIksUBbxclkvpvWGCtkAPgGsxedJAzhY0hbgT4pyBdNRVcicFD7LOXFmcmGuA881sx0hEdz3JX0TeCvwETM7V9IngVPClzgF2Gxmh0t6FT7h2okV5xeJRCJzw9ISMt+Y4fxngT80s6vLjZKODueePNMNKlnDzeywHtu0MTKhuE7hkVALmwHPBc4P7d1FeYpiPecDz9MAUiZbrMUeiSxtBuzk04+iZfONpP1DrMzVkvafpuvKbgEDEAqnraxyr2n/b5TrSO9OH0lJSP72ED7w5w5gi5kVla3KBXbaxXfC+a308MOWdKqkayVd2+ys3EUikcjgsVlsCxRJrwN+gC+h/HLgKkl/MEX3b0r6hqQTJT0zbCdK+gYVy73MtFz2WUnHMv263Zn4qm67YGYZcGQo3fw14PFVJjUdobLcGQBrtKHS/0rLDbnoABCJLHkGnIlZzGDEWBz8JfAUM9sEIGkv4L/okWTTzN4k6YX4laa24R/4ZzO7uMrNZhIya/HG+eme68Mz3cTMtki6AjgGX5s6DdpKUXgHOkV57pWUhntvmmnsSCQSmVMWsJZSkU3A9tLxdqb5rTWzbwLfLI5D7ZotVW82U4LMQ6sO1E2oAd0MAmYUn/3zg8AVeBXtXHYtynMy8N/h/OUWDSiRSKQCu6T7H+S9FumvUini/3a8LebreJF5AnD9FNf8NXCemf0keAB/EzgSaEn6HTP7zkz3HaSF7ADgCknX4wvqXGpmF+GrbL5V0u14m8uZof+ZwF6h/a3AaQOcWyQSiewefbLJSDpL0kOSbiy1vUfSfZJ+HLYXlc69Q9Ltkm6V9IJS+/Gh7XZJ0/1urg7bHcC/lWb5deDOKa45ESgi+0/Gy4x9gP8N/O3M33KAafvN7Hp62GrM7Gf0yHljZhPAKwY1n0gkEtlj+lS0LPA54OP4+MAyHzGzfyw3SHoC8CrgicCBwHckPS6c/mf8StG9wDWSLjSzm3eZ+m5E/AON0orSC4AvBVv7LcGsMSNLvzZMzMQciSw/5BhYErM+LZeZ2fckHVqx+wnAuWZWB+4MKz7Fy/rt4eUdSeeGvrsImYJgH+9V6fi5PbrXJT0JeBB4Dj4jf0GlMgFV68lcBnyo7E0g6QwzO7XK9ZFIJLJUmIVNZm9J15aOzwjesTPxRkmvAa4F/tzMNuM9u64q9SmHf9zT1f6MGcYvC4oR4P8ArSn6vhkft7gPXsO6EyAs4/1o5q9SXZM5DHi7pF8tqVxHVbx2QWC5oejBHIkseeSE5QO0zlcfeqOZzfZ38hPA+8Nd3g98CJgqhmW3MLPrupr+U9IPpuh7NT1CT4LC0RcX5oItwPOAj0n6d3x9gUgkEll2DNK7rJx4UtKngYvCYRHiUVAO/5iqvSeSNpQOHV5hWDvT3CS9GG8TGinN930zXVdVyCjEtfyJpNcC3wfWV7x2/on1ZCKR5cGgMzEbAy12IukAM7s/HL4MKDzPLgS+KOnDeMP/EfiofQFHhMTD9+GdA35nhttch/8mAprAXfjckdPN65N4G8xzgM/gw0x6aj/dVBUynyx2zOxzkm4A3lDx2kgkElkSiP5pMpK+BByLt93cC7wbOFbSkXghcBfwhwBmdpOk8/AG/RbwhuDlhaQ3ApfgU5qcZWY3zXDrtwPfMrNtkt4FPJWZKx8/08x+RdL1ZvZeSR+iFKA5HZWEjJl9quv4Ovq8ThiJRCKLgv55l53Uo/nMHm1F/w8AH+jRXtk+Evi/ZnaepGfjExb/I94WNJ3DwHj4HJN0ID5DwAFVbhbXkCKRSGQWyKzStoApFhRfDHzazL4BDM1wzUUhB+U/AD/Ea1lfqnKzpR8n0wMzYwBVBCKRyFJngWdYrsh9kj5FSPUV0sVMq3CY2fvD7gWSLgJGzGxrlZstL03GFniRh0gksufIoQEGXcuqbQuYV+JtOC8wsy3ABnxm5imRtELSuyR9OgSE7ivpJVVutiw1mUgkEtldFnpBspkwszHgq6Xj+4H7p74C8FUwr8Nn0gfvyfYVOi7WU7J8NJmoxUQiS5o5WwJf5EXLdpPHmtnf412eC0FV6YFHTSYSiUSqsvCXwgZFI5RsMQBJj4VqpYmjkIlEIpHZsDyFzLvx5ZYPkfQF4FnAa6tcGIVMJBKJVKSfwZiLBUkOn+Hlt4Gj8Y/hzWa2scr1y0PImPkUExDT/Uciy4UBpfvXIJNvLkDMLJf0NjM7D/jGbK9fPob/SCQS2VOqGv2Xnhz6jqS/kHSIpA3FVuXC5aHJBGK6/0hkmSBHJ7C9z0MvT0fVE8NnOWelAY+Z6cKBaTJB4l0h6WZJN0l6c2jfIOlSSbeFz/WhXZI+FupUXy/pqYOaWyQSWSYMwq15GWoyZnZY90aPOjO9GORyWQtf1e0JeGPRG0Kd6tOAy8zsCOCycAzwQnz66iOAU/EJ2yKRSGRBsQQi/neboAw8T9KZTK7IOSUDEzJmdr+Z/TDsbwduwZcLPQE4O3Q7G/itsH8CcI55rgLWSaqU5TMSiUTaDNKxx/CORFW2JYSkoyV9DLgb+DrwPRaAJtNG0qHAU4Crgf1KRXkeAPYL+wexa63qg+hC0qmSrpV0bbNaLFAkEon0DeXVtqWApL+VdBu+xMD1+N/xh83sbDPbXGWMgRv+Ja0CLgDeEorktM+ZmUmzUyzN7AzgDIA12lD9WrNJSRBiJuZIJDJblmGczOuAn+LNF/9uZvXZ/mYPVJORVMMLmC+YWZGQ7cFiGSx8PhTap6thHYlEIvNP1aWyCstlks6S9JCkG0tts3aMknRy6H+bpJP7/I0PAP4G+E3gDkmfB0YlVVZQBuldJnyVt1vM7MOlUxcCxYM4Gb++V7S/JjzMo4GtpWW1/hETZUYiy4JBpfvvo+H/c8DxXW2zcowKsSrvxle1fDrw7kIw9QMzy8zsW2Z2MvBY4N+A/8TXpPlilTEGqck8C3g18FxJPw7bi4DTgeeHdb7fCMfgy4f+DLgd+DTwJwOcWyQSiewefXJhNrPvAY90Nc/WMeoFwKVm9kiwkVzKroKrL5hZ3cwuMLOXA4fjc5nNyMBsMmb2faZOBf28Hv2NyYE+A5hUDsRozEhkqSIJs5A6ajCxmLOxyewt6drS8RnBpjwds3WMquQw1Q8kvRh4IjAym+uWVcR/m5i/LBKJ7A4GZJWlzEYzO2q3b7UbjlGDQtIngRXAc4DPAC8HflDl2pi7LBKJRGbBgIMxZ+sYNVcOU880s9cAm83svfgKmY+rcuGyFjK2xAKmIpFIFxrAT9xggzFn6xh1CXCcpPXB4H9caOs34+FzTNKB+AqZlYLll+dyWSQSiewm/VrAkvQl4Fi87eZevJfY6cB5kk7BR9e/MnS/GHgR3jFqDPh9ADN7RNL7gWtCv/eZWbczQT+4SNI64B+AH+IXDj9T5cJlJ2RiJuZIZJkwiEzMfUx+aWYnTXFqVo5RZnYWcFZ/ZtUbM3t/2L1A0kXAiJltrXLtsl4ui0QikdkgQJlV2pYSkt4QNBnMrA44SZXCTKKQiUQiS5s+p4+SWaVtifF6M9tSHISYnNdXuXDZLZdFIpHIbrMEa8VUJJGksGyHpAQYqnJhFDKRSCRSmaWXxr8i3wK+LOlT4fgPme+I/wVJVybmSCQSmS0LIzxyznk7XrD8cTi+lOhdFolEIgNgGWoyZpaHapjfxy8Y3mpmlVz3lqeQiTnMIpFlgZyo9lNYEWPJeY5VQdKx+ISdd+HXgw6RdHJI8jkty1PIRCKRyO6y/GQMwIeA48zsVgBJjwO+BDxtpguXn5CJWkwksqRpZ2Ie1PjLcLkMqBUCBsDMfhqKUs7I8hMykUgksicsTyFznaTPAP8ajn8XuHaa/m2Wr5CJ6f4jkaXNIGrKGLA8i+v+ET6tzZvC8X8A/1LlwuUrZCKRSGSWiCUZzT8tIfDyf8zs8cCHZ3v9sk8rs2TT/btl/782EvH0O91/nlfblgjBVflWSY/anesH9ksk6SxJD0m6sdS2QdKlkm4Ln+tDuyR9TNLtkq6X9NRBzQt8JuYlTRQwkYin3wKmWC6rsi0t1gM3SbpM0oXFVuXCQS6XfQ74OHBOqe004DIzO13SaeH47cALgSPC9gzgE+EzEolEFhTLbbks8K7dvXBgQsbMvifp0K7mE/BFesAH9lyJFzInAOeE5GtXSVon6YBQ+S0SiURmxS5uzFL/4luWoZAxs+8W+5L2BjZZRVvDXK+r7FcSHA8A+4X9g4B7Sv3uDW27IOlUSddKurZJfXAzjUQikV2oWHq5wu+vpLsk3SDpx5KuDW0LwqRQmuPRkq6U9FVJTwnmjxuBByUdX2WMeVu8D1Jw1q8EZnaGmR1lZkfVGB7AzBY50R4TiQwOAzKrtlXjOWZ2pJkdFY4Lk8IRwGXhGCabFE7FmxTmgo8Df4uP7r8ceJ2Z7Q/8OvB3VQaY61+kByUdABA+Hwrt9wGHlPodHNoikUhkQTHgomUn4E0JhM/fKrWfY56rgHXFb+mASc3s22b2FeCBcG/M7CdVB5hrIXMhcHLYPxn4eqn9NUElPBrYOjB7TPl//lLzMitrMUvIhTISWVBUXy7bu1jaD9up3SMB35Z0XencHpsU+kz5h2S861ylH9CBGf4lfQlv5N9b0r3Au4HTgfMknQLcDbwydL8YeBFwOzAG/P6g5hWJRCK7jTGbl9ONpWWwXjzbzO6TtC9wqaRJ2oGZmTTv1WueLGkbPvPyaNgnHI9UGWCQ3mUnTXHqeT36Gj5lwdxhef996OeTaIuJRHrS33T//auMaWb3hc+HJH0NeDrBpGBm9y8Ek4KZ7XE24eX5y2RLbCmpW8DEpbJIZHD0wbtM0kpJq4t94Di819b8mxT6TMxdFolEIlUxIOvLS9x+wNckgf8d/qKZfUvSNSwxk0IUMpFIZOnS90zM1peVEDP7GfDkHu2bWCgmhT4RhUwkEonMhmUY8b8nRCGDz8Qc1NbFR7THRCIz0y8nn9l5l0WIQiYSiURmR9RkZsWyFTKWG3I5sMceevNHdFuORGZGjr4aZqKQmRXLVshEIpHIrDGDrN81nZc2UcgsVnppMdEeE4m02SXdf7+ImsysiEImN+/muJiIAiYSmT+ikJkVUchEIpFIZSx6l82S5SlkzHx6t8VI1GIikVmjfgVlGthSS0s1YJankIlEIpHdpT9pZZYNy1rIWG5oEXswRyKR6ZGE9dOD2SyuHMySZS1kIpFIZNZEw/+sWL5CZjHWk4n2mEhk3rH4Nzcrlq+QiUQikVnTv6Jly4UoZBYLUYuJRHafmCBz3lhQ60WSjpd0q6TbJZ02l/ceSGRwv+iVaTkKmMhSY5C5+PoUcG2AZVmlLeJZMEJGUgL8M/BC4AnASZKeML+zikQikRIWipZV2WZgPl+q55IFI2SApwO3m9nPzKwBnAucMPC7LvTAqlgvJrKnONd7m8959Do3IPpdK8pyq7TNMKdl81K9kITMQcA9peN7Q9skJJ0q6VpJ1zapz9nkIpFIBOiXJjM/L9XzwKIz/JvZGcAZAJK2f8fOv3X3BgqfGdDsz9ymYW9g48DvsufEefaPxTBHWF7zfPSeTmI7my/5jp2/d8XuI5KuLR2fEX6/oPdL9TP2dH4LkYUkZO4DDikdHxzapuNWMztqcFPqD5KujfPsH4thnothjhDnOVvM7Pj5nsNiYyEtl10DHCHpMElDwKuAC+d5TpFIJDIIduelelGyYISMmbWANwKXALcA55nZTfM7q0gkEhkIy+aleiEtl2FmFwMXz+KSM2busiCI8+wvi2Gei2GOEOc5L5hZS1LxUp0AZy3Vl2ot6CDESCQSiSxqFsxyWSQSiUSWHlHIRCKRSGRgLFohM58pGSQdIukKSTdLuknSm0P7BkmXSrotfK4P7ZL0sTDX6yU9tTTWyaH/bZJOHtB8E0k/knRROD5M0tVhPl8OhkckDYfj28P5Q0tjvCO03yrpBQOY4zpJ50v6iaRbJB2zEJ+npD8L/89vlPQlSSML4XlKOkvSQ5JuLLX17flJepqkG8I1H5N2L4x+inn+Q/j/fr2kr0laVzrX8zlN9fc/1f+LyDxiZotuwxvK7gAeAwwB/wM8YQ7vfwDw1LC/GvgpPjXE3wOnhfbTgA+G/RcB3wQEHA1cHdo3AD8Ln+vD/voBzPetwBeBi8LxecCrwv4ngT8O+38CfDLsvwr4cth/QnjGw8Bh4dknfZ7j2cDrwv4QsG6hPU98AN2dwGjpOb52ITxP4NeBpwI3ltr69vyAH4S+Cte+sI/zPA5Iw/4HS/Ps+ZyY5u9/qv8XcZu/bd4nsFuThmOAS0rH7wDeMY/z+TrwfOBW4IDQdgA+WBTgU8BJpf63hvMnAZ8qtU/q16e5HQxcBjwXuCj8SGws/VG3nyXe0+WYsJ+Gfup+vuV+fZrjWvyPt7raF9TzpBOlvSE8n4uAFyyU5wkc2vXj3ZfnF879pNQ+qd+ezrPr3MuAL4T9ns+JKf7+p/u3Hbf52xbrclmlPGdzQVgCeQpwNbCfmd0fTj0A7Bf2p5rvXHyPfwLeBhTJlPYCtpiPS+q+Z3s+4fzW0H/Q8zwMeBj4bFjW+4yklSyw52lm9wH/CPwcuB//fK5j4T3Pgn49v4PC/qDnC/AHeE1pd+Y53b/tyDyxWIXMgkDSKuAC4C1mtq18zvyr1Lz6h0t6CfCQmV03n/OoQIpfQvmEmT0F2Ilf3mmzQJ7nenwSw8OAA4GVwKJIM7IQnt9MSHon0AK+MN9zifSPxSpk5j0lg6QaXsB8wcy+GpoflHRAOH8A8FBon2q+g/4ezwJeKukufJbX5wIfBdZJKgJxy/dszyecXwtsmoN53gvca2ZXh+Pz8UJnoT3P3wDuNLOHzawJfBX/jBfa8yzo1/O7L+wPbL6SXgu8BPjdIBB3Z56bmPr/RWSeWKxCZl5TMgTPmjOBW8zsw6VTFwKFR87JeFtN0f6a4NVzNLA1LGNcAhwnaX14Sz4utPUFM3uHmR1sZofin9HlZva7wBXAy6eYZzH/l4f+FtpfFbylDgOOwBuC+zXPB4B7JP1SaHoecDML7Hnil8mOlrQi/Bso5rmgnmeJvjy/cG6bpKPD935Naaw9RtLx+CXdl5rZWNf8ez2nnn//4dlO9f8iMl/Mt1Fodze8h8xP8V4m75zjez8bv/RwPfDjsL0IvyZ8GXAb8B1gQ+gvfIGiO4AbgKNKY/0BcHvYfn+Acz6WjnfZY/B/rLcDXwGGQ/tIOL49nH9M6fp3hvnfym56Fs0wvyOBa8Mz/Te8d9OCe57Ae4GfADcCn8d7Ps378wS+hLcTNfGa4Sn9fH7AUeE73wF8nC4njT2c5+14G0vxt/TJmZ4TU/z9T/X/Im7zt8W0MpFIJBIZGIt1uSwSiUQii4AoZCKRSCQyMKKQiUQikcjAiEImEolEIgMjCplIJBKJDIwoZCKRSCQyMKKQiQwcSYdKGpf041le91LNcRmHKeZxrKRn7sZ1vyZfDuLGmXtHIkuTKGQic8UdZnbkbC4wswvN7PQqfUP0+qD+PR8LzErISErN7D/wQYORyLIlCpnIHiHpV0OxqRFJK+ULej1phmsODUWqPifpp5K+IOk3JP1nKJb19NDvtZI+Hvb3CwWt/idszwzj3CrpHHw0+iHyBbBulC+wdWK49lhJ35X0dUk/k3S6pN+V9IPQ77Gh3z6SLpB0TdieJZ9l+4+AP5P046Cd7NIvXP8eSZ+X9J/4bACRyLInnblLJDI1ZnaNpAuBvwFGgX81syrLQ4cDr8CnMbkG+B18up6XAn8F/FZX/48B3zWzl0lKgFX41DNHACeb2VWS/g8+Pc2Tgb2BayR9L1z/ZOCXgUfwxbg+Y2ZPl69q+qfAW/DJQz9iZt+X9Ch83q5flvRJYIeZ/SOApC929wtjgy+09WwzG6/wDCKRJU8UMpF+8D68oJgA3lTxmjvN7AYASTcBl5mZSboBX9Sqm+fiEzNiZhmwNSRxvNvMrgp9ng18KZx/UNJ3gV8FtgHXWKitIukO4NvhmhuA54T93wCeoE5l4TXy5Ry6ma7fhVHARCIdopCJ9IO98JpFDZ8UcmeFa+ql/bx0nDO7f5dV7lX1fg442swmyhdq13L20/WrOp9IZFkQbTKRfvAp4F34YlMfHNA9LgP+GEBSImltjz7/AZwYzu+Dryc/mxT638YvnRHuc2TY3Q6srtAvEol0EYVMZI+Q9BqgaWZfBE4HflXScwdwqzcDzwnLadfhbR/dfA1fKuB/gMuBt5mvVVOVNwFHBUeGm/EGf4B/B15WGP6n6ReJRLqIqf4jAyd4aF1kZtN6nS1FlvN3j0QgajKRuSED1s42GHOxE7Sefwc2zvdcIpH5ImoykUgkEhkYUZOJRCKRyMCIQiYSiUQiAyMKmUgkEokMjChkIpFIJDIw/j/3Xm+xgM6xeAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multiscale['scale2'].ds[name].isel(y=100).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c0382d35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f489e87bbb0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multiscale['scale2'].ds[name].isel(z=100).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd7eec14",
+   "metadata": {},
+   "source": [
+    "## Serialize as OME-NGFF Zarr \n",
+    "\n",
+    "Generate an Open Microscopy Environment-Next Generation File Format [OME-NGFF](https://ngff.openmicroscopy.org/latest/) dataset.\n",
+    "\n",
+    "Here we also uses the [*bioformats2raw* transitional layout](https://github.com/ome/ngff/pull/112) to preserve the OME-XML metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "554a5c53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "store = zarr.storage.DirectoryStore(f'{image_name}.zarr', dimension_separator='/')\n",
+    "group = zarr.group(store, overwrite=True)\n",
+    "group.attrs['bioformats2raw.layout'] = 3\n",
+    "store['OME/METADATA.ome.xml'] = tiff.ome_metadata.encode('utf-8')\n",
+    "\n",
+    "# Compression options\n",
+    "compressor = Blosc(cname='zstd', clevel=5, shuffle=Blosc.SHUFFLE)\n",
+    "\n",
+    "# For OME-NGFF, large datasets, use dimension_separator='/'\n",
+    "store = zarr.storage.DirectoryStore(f'{image_name}.zarr/0', dimension_separator='/')\n",
+    "multiscale.to_zarr(store, encoding={ name: { 'compressor': compressor }})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2dc8ff6",
+   "metadata": {},
+   "source": [
+    "### Deserialize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1ac7998c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f48884e1430>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "store = zarr.storage.DirectoryStore(f'{image_name}.zarr/0', dimension_separator='/')\n",
+    "\n",
+    "new_multiscale = datatree.open_datatree(store, engine='zarr')\n",
+    "new_multiscale['scale2'].ds[name].isel(x=250).plot.imshow()"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "5108159e926a957efbf78924adfcf51570e7c16c3091fc603e070659d85c3c31"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}