From d002139f608c6e2e3ba9e7433e33419e1df50a8e Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 12 Aug 2020 13:39:26 +0800
Subject: [PATCH 01/37] [DLMED] Add LoadImage transform

---
 examples/notebooks/IO_factory_test.ipynb | 173 +++++++++++++++++++++++
 monai/data/__init__.py                   |   1 +
 monai/data/image_reader.py               |  49 +++++++
 monai/transforms/io/array.py             |  73 ++++++++++
 4 files changed, 296 insertions(+)
 create mode 100644 examples/notebooks/IO_factory_test.ipynb
 create mode 100644 monai/data/image_reader.py

diff --git a/examples/notebooks/IO_factory_test.ipynb b/examples/notebooks/IO_factory_test.ipynb
new file mode 100644
index 0000000000..cbb1582c08
--- /dev/null
+++ b/examples/notebooks/IO_factory_test.ipynb
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multi GPU Test\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/multi_gpu_test.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU \"monai[itk]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU itk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MONAI version: 0.1.0+317.gd00d0f5.dirty\n",
+      "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
+      "Numpy version: 1.18.1\n",
+      "Pytorch version: 1.6.0\n",
+      "\n",
+      "Optional dependencies:\n",
+      "Pytorch Ignite version: 0.3.0\n",
+      "Nibabel version: 3.1.1\n",
+      "scikit-image version: 0.15.0\n",
+      "Pillow version: 7.0.0\n",
+      "Tensorboard version: 2.1.0\n",
+      "\n",
+      "For details about installing the optional dependencies, please visit:\n",
+      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Copyright 2020 MONAI Consortium\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "from monai.config import print_config\n",
+    "from monai.transforms import LoadImage\n",
+    "\n",
+    "print_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test loading Nifti files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Can't downcast to a specialization of MetaDataObject",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-ca80851c4218>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoadImage\u001b[0m\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;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/workspace/data/medical/MONAI/monai/transforms/io/array.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     88\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcompatible_meta\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mmeta_key\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\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;32m---> 90\u001b[0;31m                     \u001b[0mmeta_datum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmeta_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m                     if (\n\u001b[1;32m     92\u001b[0m                         \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta_datum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/itk/ITKCommonBasePython.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdown_cast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetMetaDataObjectValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\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;32m/opt/conda/lib/python3.6/site-packages/itkExtras.py\u001b[0m in \u001b[0;36mdown_cast\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m   1198\u001b[0m         raise RuntimeError(\n\u001b[1;32m   1199\u001b[0m             \u001b[0;34m\"Can't downcast to a specialization of %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1200\u001b[0;31m             className)\n\u001b[0m\u001b[1;32m   1201\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Can't downcast to a specialization of MetaDataObject"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
+    "loader = LoadImage()\n",
+    "data, meta = loader(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/monai/data/__init__.py b/monai/data/__init__.py
index 42c76c7cb4..6106ca11ff 100644
--- a/monai/data/__init__.py
+++ b/monai/data/__init__.py
@@ -14,6 +14,7 @@
 from .dataset import ArrayDataset, CacheDataset, Dataset, PersistentDataset, ZipDataset
 from .decathalon_datalist import load_decathalon_datalist
 from .grid_dataset import GridPatchDataset
+from .image_reader import *
 from .nifti_reader import NiftiDataset
 from .nifti_saver import NiftiSaver
 from .nifti_writer import write_nifti
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
new file mode 100644
index 0000000000..0b9746bd07
--- /dev/null
+++ b/monai/data/image_reader.py
@@ -0,0 +1,49 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict
+from abc import ABC, abstractmethod
+import numpy as np
+import itk
+
+
+class ImageReader(ABC):
+    """Abstract class to define interface APIs to load image files.
+
+    """
+    def __init__(self, img: Any = None):
+        self._img = None
+
+    @abstractmethod
+    def read_image(self, filename: str):
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
+    @abstractmethod
+    def get_meta_data(self) -> Dict:
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
+    @abstractmethod
+    def get_array_data(self) -> np.ndarray:
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
+    def uncache(self):
+        self._img = None
+
+
+class ITKReader(ImageReader):
+    def read_image(self, filename: str):
+        self._img = itk.imread(filename)
+
+    def get_meta_data(self) -> Dict:
+        return self._img.GetMetaDataDictionary()
+
+    def get_array_data(self) -> np.ndarray:
+        return itk.array_from_image(self._img)
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 132ccd5df8..637b0219eb 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -21,6 +21,7 @@
 
 from monai.config import KeysCollection
 from monai.data.utils import correct_nifti_header_if_necessary
+from monai.data.image_reader import ImageReader, ITKReader
 from monai.transforms.compose import Transform
 from monai.utils import ensure_tuple, optional_import
 
@@ -28,6 +29,78 @@
 Image, _ = optional_import("PIL.Image")
 
 
+class LoadImage(Transform):
+    """
+    Load image file or files from provided path based on reader, default reader is ITK.
+    All the supported image formats of ITK:
+    https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO
+    If loading a list of files, stack them together and add a new dimension as first dimension,
+    and use the meta data of the first image to represent the stacked result.
+
+    """
+
+    def __init__(
+        self,
+        reader: ImageReader = None,
+        suffixes: Optional[Union[str, Sequence[str]]] = None,
+        image_only: bool = False,
+        dtype: Optional[np.dtype] = np.float32,
+    ) -> None:
+        """
+        Args:
+            reader: use reader to load image file and meta data, default is ITK.
+            suffixes: file suffixes that supported by the reader, if None, support all kinds of files.
+            image_only: if True return only the image volume, otherwise return image data array and header dict.
+            dtype: if not None convert the loaded image to this data type.
+
+        Note:
+            The transform returns image data array if `image_only` is True,
+            or a tuple of two elements containing the data array, and the meta data in a dict format otherwise.
+
+        """
+        if reader is None:
+            reader = ITKReader()
+        self.reader = reader
+        self.suffixes = suffixes
+        self.image_only = image_only
+        self.dtype = dtype
+
+    def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
+        """
+        Args:
+            filename: path file or file-like object or a list of files.
+        """
+        filename = ensure_tuple(filename)
+        img_array = list()
+        compatible_meta: Dict = dict()
+        for name in filename:
+            if self.suffixes is not None and name.split(".")[-1] not in self.suffixes:
+                raise RuntimeError("unsupported file format.")
+            img = self.reader.read_image(name)
+            header = self.reader.get_meta_data()
+            header["filename_or_obj"] = name
+
+            img_array.append(self.reader.get_array_data().astype(dtype=self.dtype))
+
+            if self.image_only:
+                continue
+
+            if not compatible_meta:
+                for meta_key in header.GetKeys():
+                    meta_datum = header[meta_key]
+                    if (
+                        isinstance(meta_datum, np.ndarray)
+                        and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None
+                    ):
+                        continue
+                    compatible_meta[meta_key] = meta_datum
+
+        img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
+        if self.image_only:
+            return img_array
+        return img_array, compatible_meta
+
+
 class LoadNifti(Transform):
     """
     Load Nifti format file or files from provided path. If loading a list of

From 496c3905a46f85198a9256c3faa6b24e4dfc62ae Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Wed, 12 Aug 2020 05:46:43 +0000
Subject: [PATCH 02/37] [MONAI] python code formatting

---
 monai/data/image_reader.py   | 6 ++++--
 monai/transforms/io/array.py | 2 +-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 0b9746bd07..8b6236fec7 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -9,16 +9,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict
 from abc import ABC, abstractmethod
-import numpy as np
+from typing import Any, Dict
+
 import itk
+import numpy as np
 
 
 class ImageReader(ABC):
     """Abstract class to define interface APIs to load image files.
 
     """
+
     def __init__(self, img: Any = None):
         self._img = None
 
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 637b0219eb..842e26e4cd 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -20,8 +20,8 @@
 from torch.utils.data._utils.collate import np_str_obj_array_pattern
 
 from monai.config import KeysCollection
-from monai.data.utils import correct_nifti_header_if_necessary
 from monai.data.image_reader import ImageReader, ITKReader
+from monai.data.utils import correct_nifti_header_if_necessary
 from monai.transforms.compose import Transform
 from monai.utils import ensure_tuple, optional_import
 

From 46ee38ce8c2f6a5ebd907e46e393112f9b14fe99 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Fri, 14 Aug 2020 15:54:06 +0800
Subject: [PATCH 03/37] [DLMED] add logic to load affine

---
 examples/notebooks/IO_factory_test.ipynb | 20 ++++----
 monai/data/image_reader.py               | 63 +++++++++++++++++++-----
 monai/transforms/io/array.py             | 32 ++++++------
 3 files changed, 79 insertions(+), 36 deletions(-)

diff --git a/examples/notebooks/IO_factory_test.ipynb b/examples/notebooks/IO_factory_test.ipynb
index cbb1582c08..6401d78768 100644
--- a/examples/notebooks/IO_factory_test.ipynb
+++ b/examples/notebooks/IO_factory_test.ipynb
@@ -70,7 +70,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.1.0+317.gd00d0f5.dirty\n",
+      "MONAI version: 0.1.0+319.g496c390.dirty\n",
       "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
       "Numpy version: 1.18.1\n",
       "Pytorch version: 1.6.0\n",
@@ -120,6 +120,13 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "!!!!!!!!!!!!!data shape: (55, 512, 512)\n"
+     ]
+    },
     {
      "ename": "RuntimeError",
      "evalue": "Can't downcast to a specialization of MetaDataObject",
@@ -128,7 +135,9 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-2-ca80851c4218>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoadImage\u001b[0m\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;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/workspace/data/medical/MONAI/monai/transforms/io/array.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     88\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcompatible_meta\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mmeta_key\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\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;32m---> 90\u001b[0;31m                     \u001b[0mmeta_datum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmeta_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m                     if (\n\u001b[1;32m     92\u001b[0m                         \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta_datum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/workspace/data/medical/MONAI/monai/transforms/io/array.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     82\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m             \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m             \u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"filename_or_obj\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"affine\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_affine\u001b[0m\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;32m/workspace/data/medical/MONAI/monai/data/image_reader.py\u001b[0m in \u001b[0;36mget_meta_dict\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mmeta_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetMetaDataDictionary\u001b[0m\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;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/workspace/data/medical/MONAI/monai/data/image_reader.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mmeta_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetMetaDataDictionary\u001b[0m\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;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/itk/ITKCommonBasePython.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdown_cast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetMetaDataObjectValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\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;32m/opt/conda/lib/python3.6/site-packages/itkExtras.py\u001b[0m in \u001b[0;36mdown_cast\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m   1198\u001b[0m         raise RuntimeError(\n\u001b[1;32m   1199\u001b[0m             \u001b[0;34m\"Can't downcast to a specialization of %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1200\u001b[0;31m             className)\n\u001b[0m\u001b[1;32m   1201\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mRuntimeError\u001b[0m: Can't downcast to a specialization of MetaDataObject"
@@ -140,13 +149,6 @@
     "loader = LoadImage()\n",
     "data, meta = loader(filename)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 8b6236fec7..f936dbd187 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict
+from typing import Any, List, Optional, Union, Sequence
 
 import itk
 import numpy as np
@@ -20,32 +20,73 @@ class ImageReader(ABC):
     """Abstract class to define interface APIs to load image files.
 
     """
+    def __init__(
+        self,
+        suffixes: Optional[Union[str, Sequence[str]]] = None,
+        img: Any = None
+    ):
+        self.suffixes = suffixes
+        self.img = img
 
-    def __init__(self, img: Any = None):
-        self._img = None
+    def verify_suffix(self, suffix: str):
+        return False if self.suffixes is not None and suffix not in self.suffixes else True
+
+    def uncache(self):
+        self.img = None
 
     @abstractmethod
     def read_image(self, filename: str):
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
-    def get_meta_data(self) -> Dict:
+    def get_meta_dict(self) -> List:
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
-    def get_array_data(self) -> np.ndarray:
+    def get_affine(self) -> List:
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
-    def uncache(self):
-        self._img = None
+    @abstractmethod
+    def get_spatial_shape(self) -> List:
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
+    @abstractmethod
+    def get_array_data(self) -> np.ndarray:
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
 
 class ITKReader(ImageReader):
     def read_image(self, filename: str):
-        self._img = itk.imread(filename)
+        self.img = itk.imread(filename)
+
+    def get_meta_dict(self) -> List:
+        meta_dict = self.img.GetMetaDataDictionary()
+        return {key: meta_dict[key] for key in meta_dict.GetKeys()}
+
+    def get_affine(self) -> List:
+        """
+        Construct Affine matrix based on direction, spacing, origin information.
+        Refer to: https://github.com/RSIP-Vision/medio
+
+        """
+        direction = itk.array_from_vnl_matrix(self.img.GetDirection().GetVnlMatrix().as_matrix())
+        spacing = itk.array_from_vnl_vector(self.img.GetSpacing().GetVnlVector())
+        origin = itk.array_from_vnl_vector(self.img.GetOrigin().GetVnlVector())
+
+        direction = np.asarray(direction)
+        affine = np.eye(direction.shape[0] + 1)
+        affine[(slice(-1), slice(-1))] = direction @ np.diag(spacing)
+        affine[(slice(-1), -1)] = origin
+        return affine
 
-    def get_meta_data(self) -> Dict:
-        return self._img.GetMetaDataDictionary()
+    def get_spatial_shape(self) -> List:
+        # don't support spatial dims greater than 3
+        spatial_rank = min(self.img.GetImageDimension(), 3)
+        meta_dict = self.img.GetMetaDataDictionary()
+        spatial_shape = list()
+        for i in range(1, spatial_rank + 1):
+            spatial_shape.append(int(meta_dict[f"dim[{i}]"]))
+        return spatial_shape
 
     def get_array_data(self) -> np.ndarray:
-        return itk.array_from_image(self._img)
+        return itk.array_from_image(self.img)
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 842e26e4cd..4075b1d8e9 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -72,30 +72,30 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
         """
         filename = ensure_tuple(filename)
         img_array = list()
-        compatible_meta: Dict = dict()
+        compatible_meta: Dict = None
         for name in filename:
-            if self.suffixes is not None and name.split(".")[-1] not in self.suffixes:
+            if not self.reader.verify_suffix(name.split(".")[-1]):
                 raise RuntimeError("unsupported file format.")
-            img = self.reader.read_image(name)
-            header = self.reader.get_meta_data()
-            header["filename_or_obj"] = name
-
+            self.reader.read_image(name)
             img_array.append(self.reader.get_array_data().astype(dtype=self.dtype))
-
             if self.image_only:
                 continue
 
-            if not compatible_meta:
-                for meta_key in header.GetKeys():
-                    meta_datum = header[meta_key]
-                    if (
-                        isinstance(meta_datum, np.ndarray)
-                        and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None
-                    ):
-                        continue
-                    compatible_meta[meta_key] = meta_datum
+            header = self.reader.get_meta_dict()
+            header["filename_or_obj"] = name
+            header["affine"] = self.reader.get_affine()
+            header["original_affine"] = header["affine"].copy()
+            header["spatial_shape"] = self.reader.get_spatial_shape()
+
+            if compatible_meta is None:
+                compatible_meta = header
+            else:
+                assert np.allclose(
+                    header["affine"], compatible_meta["affine"]
+                ), "affine data of all images should be same."
 
         img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
+        self.reader.uncache()
         if self.image_only:
             return img_array
         return img_array, compatible_meta

From 7de36759f96c2db5425a7291dc05cf747a41cd38 Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Fri, 14 Aug 2020 08:00:47 +0000
Subject: [PATCH 04/37] [MONAI] python code formatting

---
 monai/data/image_reader.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index f936dbd187..03b10a9f3b 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional, Union, Sequence
+from typing import Any, List, Optional, Sequence, Union
 
 import itk
 import numpy as np
@@ -20,11 +20,8 @@ class ImageReader(ABC):
     """Abstract class to define interface APIs to load image files.
 
     """
-    def __init__(
-        self,
-        suffixes: Optional[Union[str, Sequence[str]]] = None,
-        img: Any = None
-    ):
+
+    def __init__(self, suffixes: Optional[Union[str, Sequence[str]]] = None, img: Any = None):
         self.suffixes = suffixes
         self.img = img
 

From f39ba1f730d305f1785b8c525f152f51ee64639d Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Sat, 15 Aug 2020 00:34:06 +0800
Subject: [PATCH 05/37] [DLMED] update according to comments

---
 examples/notebooks/IO_factory_test.ipynb | 56 ++++++++++++++++--------
 monai/data/image_reader.py               | 29 +++++++-----
 2 files changed, 54 insertions(+), 31 deletions(-)

diff --git a/examples/notebooks/IO_factory_test.ipynb b/examples/notebooks/IO_factory_test.ipynb
index 6401d78768..fec0aebd46 100644
--- a/examples/notebooks/IO_factory_test.ipynb
+++ b/examples/notebooks/IO_factory_test.ipynb
@@ -70,7 +70,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.1.0+319.g496c390.dirty\n",
+      "MONAI version: 0.1.0+325.g7de3675.dirty\n",
       "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
       "Numpy version: 1.18.1\n",
       "Pytorch version: 1.6.0\n",
@@ -119,35 +119,53 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
+    "loader = LoadImage()\n",
+    "data, meta = loader(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "!!!!!!!!!!!!!data shape: (55, 512, 512)\n"
+      "{'ITK_FileNotes': '5.0.10', 'aux_file': '', 'bitpix': '32', 'cal_max': '0', 'cal_min': '0', 'datatype': '16', 'descrip': '5.0.10', 'dim[0]': '3', 'dim[1]': '512', 'dim[2]': '512', 'dim[3]': '55', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '0.976562', 'pixdim[2]': '0.976562', 'pixdim[3]': '5', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-499.023', 'qoffset_y': '-499.023', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '0', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '1', 'sform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0.976562 0 0 -499.023', 'srow_y': '0 0.976562 0 -499.023', 'srow_z': '0 0 5 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '10', 'origin': array([499.02319336, 499.02319336,   0.        ]), 'spacing': array([0.97656202, 0.97656202, 5.        ]), 'direction': array([[-1.,  0.,  0.],\n",
+      "       [ 0., -1.,  0.],\n",
+      "       [ 0.,  0.,  1.]]), 'filename_or_obj': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
+      "       [  0.        ,  -0.97656202,   0.        , 499.02319336],\n",
+      "       [  0.        ,   0.        ,   5.        ,   0.        ],\n",
+      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'original_affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
+      "       [  0.        ,  -0.97656202,   0.        , 499.02319336],\n",
+      "       [  0.        ,   0.        ,   5.        ,   0.        ],\n",
+      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'spatial_shape': [55, 512, 512]}\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-     "ename": "RuntimeError",
-     "evalue": "Can't downcast to a specialization of MetaDataObject",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-ca80851c4218>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoadImage\u001b[0m\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;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/workspace/data/medical/MONAI/monai/transforms/io/array.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     82\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m             \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m             \u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"filename_or_obj\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"affine\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_affine\u001b[0m\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;32m/workspace/data/medical/MONAI/monai/data/image_reader.py\u001b[0m in \u001b[0;36mget_meta_dict\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mmeta_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetMetaDataDictionary\u001b[0m\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;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/workspace/data/medical/MONAI/monai/data/image_reader.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mmeta_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetMetaDataDictionary\u001b[0m\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;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmeta_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/itk/ITKCommonBasePython.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdown_cast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetMetaDataObjectValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\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;32m/opt/conda/lib/python3.6/site-packages/itkExtras.py\u001b[0m in \u001b[0;36mdown_cast\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m   1198\u001b[0m         raise RuntimeError(\n\u001b[1;32m   1199\u001b[0m             \u001b[0;34m\"Can't downcast to a specialization of %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1200\u001b[0;31m             className)\n\u001b[0m\u001b[1;32m   1201\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: Can't downcast to a specialization of MetaDataObject"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(55, 512, 512)\n"
      ]
     }
    ],
    "source": [
-    "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
-    "loader = LoadImage()\n",
-    "data, meta = loader(filename)"
+    "print(data.shape)"
    ]
   }
  ],
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 03b10a9f3b..05dbce01db 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -57,8 +57,17 @@ def read_image(self, filename: str):
         self.img = itk.imread(filename)
 
     def get_meta_dict(self) -> List:
-        meta_dict = self.img.GetMetaDataDictionary()
-        return {key: meta_dict[key] for key in meta_dict.GetKeys()}
+        img_meta_dict = self.img.GetMetaDataDictionary()
+        meta_dict = dict()
+        for key in img_meta_dict.GetKeys():
+            # ignore deprecated, legacy members that cause issues
+            if key.startswith('ITK_original_'):
+                continue
+            meta_dict[key] = img_meta_dict[key]
+        meta_dict['origin'] = np.asarray(self.img.GetOrigin())
+        meta_dict['spacing'] = np.asarray(self.img.GetSpacing())
+        meta_dict['direction'] = itk.array_from_matrix(self.img.GetDirection())
+        return meta_dict
 
     def get_affine(self) -> List:
         """
@@ -66,9 +75,9 @@ def get_affine(self) -> List:
         Refer to: https://github.com/RSIP-Vision/medio
 
         """
-        direction = itk.array_from_vnl_matrix(self.img.GetDirection().GetVnlMatrix().as_matrix())
-        spacing = itk.array_from_vnl_vector(self.img.GetSpacing().GetVnlVector())
-        origin = itk.array_from_vnl_vector(self.img.GetOrigin().GetVnlVector())
+        direction = itk.array_from_matrix(self.img.GetDirection())
+        spacing = np.asarray(self.img.GetSpacing())
+        origin = np.asarray(self.img.GetOrigin())
 
         direction = np.asarray(direction)
         affine = np.eye(direction.shape[0] + 1)
@@ -77,13 +86,9 @@ def get_affine(self) -> List:
         return affine
 
     def get_spatial_shape(self) -> List:
-        # don't support spatial dims greater than 3
-        spatial_rank = min(self.img.GetImageDimension(), 3)
-        meta_dict = self.img.GetMetaDataDictionary()
-        spatial_shape = list()
-        for i in range(1, spatial_rank + 1):
-            spatial_shape.append(int(meta_dict[f"dim[{i}]"]))
+        spatial_shape = list(itk.size(self.img))
+        spatial_shape.reverse()
         return spatial_shape
 
     def get_array_data(self) -> np.ndarray:
-        return itk.array_from_image(self.img)
+        return itk.array_view_from_image(self.img)

From a9acc9fe874ee54d677cb23c69c4d8d9e6105fba Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Fri, 14 Aug 2020 16:38:38 +0000
Subject: [PATCH 06/37] [MONAI] python code formatting

---
 monai/data/image_reader.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 05dbce01db..f4ea928819 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -61,12 +61,12 @@ def get_meta_dict(self) -> List:
         meta_dict = dict()
         for key in img_meta_dict.GetKeys():
             # ignore deprecated, legacy members that cause issues
-            if key.startswith('ITK_original_'):
+            if key.startswith("ITK_original_"):
                 continue
             meta_dict[key] = img_meta_dict[key]
-        meta_dict['origin'] = np.asarray(self.img.GetOrigin())
-        meta_dict['spacing'] = np.asarray(self.img.GetSpacing())
-        meta_dict['direction'] = itk.array_from_matrix(self.img.GetDirection())
+        meta_dict["origin"] = np.asarray(self.img.GetOrigin())
+        meta_dict["spacing"] = np.asarray(self.img.GetSpacing())
+        meta_dict["direction"] = itk.array_from_matrix(self.img.GetDirection())
         return meta_dict
 
     def get_affine(self) -> List:

From 46867245ac0c95fe3a25cf5edf6b5528dc149150 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Sun, 16 Aug 2020 08:21:18 +0800
Subject: [PATCH 07/37] [DLMED] update axis

---
 .../{IO_factory_test.ipynb => io_factory_test.ipynb}  | 11 ++++++-----
 monai/data/image_reader.py                            |  6 ++----
 2 files changed, 8 insertions(+), 9 deletions(-)
 rename examples/notebooks/{IO_factory_test.ipynb => io_factory_test.ipynb} (94%)

diff --git a/examples/notebooks/IO_factory_test.ipynb b/examples/notebooks/io_factory_test.ipynb
similarity index 94%
rename from examples/notebooks/IO_factory_test.ipynb
rename to examples/notebooks/io_factory_test.ipynb
index fec0aebd46..920e027b4c 100644
--- a/examples/notebooks/IO_factory_test.ipynb
+++ b/examples/notebooks/io_factory_test.ipynb
@@ -4,9 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multi GPU Test\n",
+    "# IO factory test notebook\n",
+    "This notebook shows the basic usage of `ImageLoad`, `ITKReader` and `NibabelReader` for Nifti and PNG data.\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/multi_gpu_test.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/io_factory_test.ipynb)"
    ]
   },
   {
@@ -70,7 +71,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.1.0+325.g7de3675.dirty\n",
+      "MONAI version: 0.1.0+327.ga9acc9f.dirty\n",
       "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
       "Numpy version: 1.18.1\n",
       "Pytorch version: 1.6.0\n",
@@ -143,7 +144,7 @@
       "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'original_affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
       "       [  0.        ,  -0.97656202,   0.        , 499.02319336],\n",
       "       [  0.        ,   0.        ,   5.        ,   0.        ],\n",
-      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'spatial_shape': [55, 512, 512]}\n"
+      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'spatial_shape': [512, 512, 55]}\n"
      ]
     }
    ],
@@ -160,7 +161,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(55, 512, 512)\n"
+      "(512, 512, 55)\n"
      ]
     }
    ],
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index f4ea928819..259800c237 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -86,9 +86,7 @@ def get_affine(self) -> List:
         return affine
 
     def get_spatial_shape(self) -> List:
-        spatial_shape = list(itk.size(self.img))
-        spatial_shape.reverse()
-        return spatial_shape
+        return list(itk.size(self.img))
 
     def get_array_data(self) -> np.ndarray:
-        return itk.array_view_from_image(self.img)
+        return itk.array_view_from_image(self.img, keep_axes=True)

From 162c2e807ce10c903de8c0183bc9dfdbe6f549cf Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Sun, 16 Aug 2020 09:52:52 +0800
Subject: [PATCH 08/37] [DLMED] itk to optional import

---
 .github/workflows/pythonapp.yml               |    2 +-
 docs/requirements.txt                         |    1 +
 docs/source/installation.md                   |    4 +-
 examples/notebooks/io_factory_test.ipynb      |   41 +-
 examples/notebooks/mednist_GAN_workflow.ipynb | 4519 ++++++++++++++++-
 monai/data/image_reader.py                    |    4 +-
 requirements-dev.txt                          |    1 +
 setup.cfg                                     |    3 +
 8 files changed, 4526 insertions(+), 49 deletions(-)

diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml
index 728c84d1ef..6dfe983694 100644
--- a/.github/workflows/pythonapp.yml
+++ b/.github/workflows/pythonapp.yml
@@ -76,7 +76,7 @@ jobs:
         python -m pip install torch==1.4 -f https://download.pytorch.org/whl/cpu/torch_stable.html
         python -m pip install torchvision==0.5.0
         # min. requirements for windows instances
-        python -c "f=open('requirements-dev.txt', 'r'); txt=f.readlines(); f.close(); print(txt); f=open('requirements-dev.txt', 'w'); f.writelines(txt[1:11]); f.close()"
+        python -c "f=open('requirements-dev.txt', 'r'); txt=f.readlines(); f.close(); print(txt); f=open('requirements-dev.txt', 'w'); f.writelines(txt[1:12]); f.close()"
     - name: Install the dependencies
       run: |
         python -m pip install torch==1.4
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 93cc2b5845..3969823f8c 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -2,6 +2,7 @@
 torch>=1.4.0
 pytorch-ignite==0.3.0
 numpy>=1.17
+itk
 nibabel
 parameterized
 scikit-image>=0.14.2
diff --git a/docs/source/installation.md b/docs/source/installation.md
index 1148d7a7c6..1cc4ff511c 100644
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -129,9 +129,9 @@ Since MONAI v0.2.0, the extras syntax such as `pip install 'monai[nibabel]'` is
 
 - The options are
 ```
-[nibabel, skimage, pillow, tensorboard, gdown, ignite]
+[nibabel, skimage, pillow, tensorboard, gdown, ignite, itk]
 ```
 which correspond to `nibabel`, `scikit-image`, `pillow`, `tensorboard`,
-`gdown`, and `pytorch-ignite` respectively.
+`gdown`, `pytorch-ignite`, and `itk` respectively.
 
 - `pip install 'monai[all]'` installs all the optional dependencies.
diff --git a/examples/notebooks/io_factory_test.ipynb b/examples/notebooks/io_factory_test.ipynb
index 920e027b4c..a81ee1a558 100644
--- a/examples/notebooks/io_factory_test.ipynb
+++ b/examples/notebooks/io_factory_test.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
@@ -28,29 +28,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[33m  WARNING: monai 0.2.0 does not provide the extra 'itk'\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
    "source": [
-    "%pip install -qU \"monai[itk]\""
+    "%pip install -qU \"monai[itk, nibabel]\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%pip install -qU itk"
+    "# temporarily need this, FIXME remove when d93c0a6 released\n",
+    "%pip install -qU git+https://github.com/Project-MONAI/MONAI#egg=MONAI\n",
+    "%pip install itk"
    ]
   },
   {
@@ -71,7 +66,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.1.0+327.ga9acc9f.dirty\n",
+      "MONAI version: 0.1.0+328.g4686724.dirty\n",
       "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
       "Numpy version: 1.18.1\n",
       "Pytorch version: 1.6.0\n",
@@ -120,7 +115,23 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "Optional import: import itk.\n\nFor details about installing the optional dependencies, please visit:\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-ca80851c4218>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoadImage\u001b[0m\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;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/workspace/data/medical/MONAI/monai/transforms/io/array.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverify_suffix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"unsupported file format.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m             \u001b[0mimg_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_array_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_only\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/workspace/data/medical/MONAI/monai/data/image_reader.py\u001b[0m in \u001b[0;36mread_image\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mITKReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImageReader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\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;32m---> 59\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/workspace/data/medical/MONAI/monai/utils/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    184\u001b[0m                 \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mWhen\u001b[0m \u001b[0myou\u001b[0m \u001b[0mcall\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m             \"\"\"\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0m_kwargs\u001b[0m\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;32m/workspace/data/medical/MONAI/monai/utils/module.py\u001b[0m in \u001b[0;36moptional_import\u001b[0;34m(module, version, version_checker, name, descriptor, version_args, allow_namespace_pkg)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_namespace_pkg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0mis_namespace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__file__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__path__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_namespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# user specified to load class/function/... from the module\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mthe_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: Optional import: import itk.\n\nFor details about installing the optional dependencies, please visit:\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies"
+     ]
+    }
+   ],
    "source": [
     "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
     "loader = LoadImage()\n",
diff --git a/examples/notebooks/mednist_GAN_workflow.ipynb b/examples/notebooks/mednist_GAN_workflow.ipynb
index e77a90c738..6065de5e2c 100644
--- a/examples/notebooks/mednist_GAN_workflow.ipynb
+++ b/examples/notebooks/mednist_GAN_workflow.ipynb
@@ -43,9 +43,11 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "Note: you may need to restart the kernel to use updated packages.\n"
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
     }
    ],
    "source": [
@@ -60,9 +62,11 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "Note: you may need to restart the kernel to use updated packages.\n"
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
     }
    ],
    "source": [
@@ -78,9 +82,11 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "Note: you may need to restart the kernel to use updated packages.\n"
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
     }
    ],
    "source": [
@@ -103,9 +109,25 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "MONAI version: 0.2.0+74.g8e5a53e\nPython version: 3.7.5 (default, Nov  7 2019, 10:50:52)  [GCC 8.3.0]\nNumpy version: 1.19.1\nPytorch version: 1.6.0\n\nOptional dependencies:\nPytorch Ignite version: 0.3.0\nNibabel version: NOT INSTALLED or UNKNOWN VERSION.\nscikit-image version: NOT INSTALLED or UNKNOWN VERSION.\nPillow version: 7.2.0\nTensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n\nFor details about installing the optional dependencies, please visit:\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n\n"
+     "output_type": "stream",
+     "text": [
+      "MONAI version: 0.2.0+74.g8e5a53e\n",
+      "Python version: 3.7.5 (default, Nov  7 2019, 10:50:52)  [GCC 8.3.0]\n",
+      "Numpy version: 1.19.1\n",
+      "Pytorch version: 1.6.0\n",
+      "\n",
+      "Optional dependencies:\n",
+      "Pytorch Ignite version: 0.3.0\n",
+      "Nibabel version: NOT INSTALLED or UNKNOWN VERSION.\n",
+      "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n",
+      "Pillow version: 7.2.0\n",
+      "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n",
+      "\n",
+      "For details about installing the optional dependencies, please visit:\n",
+      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -160,9 +182,11 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "/home/bengorman/notebooks/\n"
+     "output_type": "stream",
+     "text": [
+      "/home/bengorman/notebooks/\n"
+     ]
     }
    ],
    "source": [
@@ -199,9 +223,12 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "file /home/bengorman/notebooks/MedNIST.tar.gz exists, skip downloading.\nextracted file /home/bengorman/notebooks/MedNIST exists, skip extracting.\n"
+     "output_type": "stream",
+     "text": [
+      "file /home/bengorman/notebooks/MedNIST.tar.gz exists, skip downloading.\n",
+      "extracted file /home/bengorman/notebooks/MedNIST exists, skip extracting.\n"
+     ]
     }
    ],
    "source": [
@@ -235,9 +262,11 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "[{'hand': '/home/bengorman/notebooks/MedNIST/Hand/003676.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/006548.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/002169.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004081.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004815.jpeg'}]\n"
+     "output_type": "stream",
+     "text": [
+      "[{'hand': '/home/bengorman/notebooks/MedNIST/Hand/003676.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/006548.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/002169.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004081.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004815.jpeg'}]\n"
+     ]
     }
    ],
    "source": [
@@ -307,9 +336,11 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "10000/10000 Load and cache transformed data:  [==============================]\n"
+     "output_type": "stream",
+     "text": [
+      "10000/10000 Load and cache transformed data:  [==============================]\n"
+     ]
     }
    ],
    "source": [
@@ -494,10 +525,10 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "scrolled": true,
     "pycharm": {
      "is_executing": true
     },
+    "scrolled": true,
     "tags": [
      "outputPrepend"
     ]
@@ -535,22 +566,4322 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "scrolled": true,
     "pycharm": {
      "is_executing": true
-    }
+    },
+    "scrolled": true
    },
    "outputs": [
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125.103778 53.547086 \nL 125.462063 48.159696 \nL 125.820349 47.408544 \nL 126.53692 52.437825 \nL 126.895206 50.207096 \nL 127.253491 54.934416 \nL 127.611777 51.972845 \nL 127.970063 50.988521 \nL 128.328348 64.02667 \nL 129.044919 48.328976 \nL 129.403205 51.068935 \nL 129.761491 51.542737 \nL 130.119776 58.951391 \nL 130.478062 57.179748 \nL 130.836348 51.241913 \nL 131.552919 55.820235 \nL 131.911204 55.320583 \nL 132.26949 56.292857 \nL 132.627776 53.097337 \nL 132.986061 56.57601 \nL 133.344347 49.501398 \nL 133.702633 62.087225 \nL 134.060918 53.900585 \nL 134.419204 50.039012 \nL 134.777489 49.562905 \nL 135.135775 49.9709 \nL 135.494061 51.06364 \nL 135.852346 54.464921 \nL 136.210632 53.356966 \nL 136.927203 42.709299 \nL 137.285489 50.537958 \nL 137.643774 54.94199 \nL 138.00206 48.894123 \nL 138.360346 50.922625 \nL 139.076917 52.457867 \nL 139.435203 50.790174 \nL 139.793488 50.744126 \nL 140.151774 51.485626 \nL 140.510059 46.941937 \nL 140.868345 51.430914 \nL 141.226631 49.714338 \nL 141.584916 53.154453 \nL 141.943202 46.075785 \nL 142.301488 51.906544 \nL 142.659773 54.40507 \nL 143.018059 45.527631 \nL 143.376344 49.673649 \nL 143.73463 48.732778 \nL 144.092916 52.276161 \nL 144.451201 49.415247 \nL 144.809487 50.297865 \nL 145.167772 48.058008 \nL 145.526058 49.715925 \nL 145.884344 55.51364 \nL 146.242629 50.83412 \nL 146.600915 51.700326 \nL 146.959201 49.934261 \nL 147.317486 51.130485 \nL 147.675772 50.634105 \nL 148.392343 53.768799 \nL 148.750629 51.403774 \nL 149.108914 51.284448 \nL 149.825486 57.617373 \nL 150.183771 59.183075 \nL 150.900342 48.847482 \nL 151.258628 50.763807 \nL 151.616914 49.428481 \nL 151.975199 55.627614 \nL 152.333485 47.241195 \nL 152.691771 52.446909 \nL 153.050056 59.903159 \nL 153.408342 61.293234 \nL 153.766627 51.440163 \nL 154.124913 53.267364 \nL 154.483199 50.347265 \nL 155.19977 54.008366 \nL 155.558056 49.353003 \nL 156.274627 54.300516 \nL 156.632912 52.245376 \nL 156.991198 60.449013 \nL 157.349484 19.801227 \nL 157.707769 55.843998 \nL 158.066055 57.829921 \nL 158.424341 58.44884 \nL 158.782626 55.34588 \nL 159.140912 54.540163 \nL 159.857483 56.32981 \nL 160.215769 56.049444 \nL 160.574054 56.719469 \nL 161.290626 53.929673 \nL 161.648911 57.250576 \nL 162.007197 57.085501 \nL 162.365482 53.034951 \nL 162.723768 60.775667 \nL 163.082054 54.42521 \nL 163.440339 52.919202 \nL 163.798625 59.272804 \nL 164.15691 56.566247 \nL 164.515196 61.797026 \nL 164.873482 54.481665 \nL 165.231767 55.94343 \nL 165.590053 60.646177 \nL 165.948339 59.929665 \nL 166.306624 55.288256 \nL 166.66491 56.880335 \nL 167.023195 57.739502 \nL 167.381481 54.620124 \nL 167.739767 52.926934 \nL 168.098052 57.919458 \nL 168.456338 57.415878 \nL 168.814624 59.116611 \nL 169.172909 62.083109 \nL 169.531195 40.724382 \nL 169.88948 52.484682 \nL 170.247766 54.603564 \nL 170.606052 62.978619 \nL 170.964337 52.597936 \nL 171.322623 58.396486 \nL 171.680909 57.803685 \nL 172.039194 61.172009 \nL 172.39748 44.942836 \nL 172.755765 55.96752 \nL 173.114051 52.631384 \nL 173.472337 55.009657 \nL 173.830622 61.513622 \nL 174.547194 55.440152 \nL 174.905479 56.272902 \nL 175.263765 54.027305 \nL 175.62205 59.771002 \nL 175.980336 58.640851 \nL 176.338622 55.511328 \nL 176.696907 54.509708 \nL 177.055193 63.839485 \nL 177.413479 58.721622 \nL 177.771764 57.996336 \nL 178.13005 57.584688 \nL 178.488335 58.632697 \nL 178.846621 52.657301 \nL 179.204907 56.125903 \nL 179.563192 49.337372 \nL 179.921478 57.552531 \nL 180.279763 48.963931 \nL 180.638049 62.535452 \nL 180.996335 66.865309 \nL 181.35462 61.903749 \nL 181.712906 67.290076 \nL 182.787763 58.868143 \nL 183.146048 58.579242 \nL 183.504334 56.429359 \nL 183.86262 52.120759 \nL 184.220905 58.554197 \nL 184.579191 68.58393 \nL 184.937477 56.513857 \nL 185.295762 58.882418 \nL 185.654048 55.106273 \nL 186.012333 62.139891 \nL 186.370619 47.841893 \nL 186.728905 63.166544 \nL 187.445476 56.141245 \nL 187.803762 56.775304 \nL 188.162047 53.853014 \nL 188.520333 49.543128 \nL 188.878618 76.59738 \nL 189.236904 63.389565 \nL 189.59519 65.456766 \nL 189.953475 62.70256 \nL 190.311761 62.213225 \nL 190.670047 68.204702 \nL 191.028332 66.892335 \nL 191.386618 62.516218 \nL 191.744903 54.68745 \nL 192.103189 64.199567 \nL 192.461475 55.617422 \nL 193.178046 76.563324 \nL 193.536332 62.14946 \nL 193.894617 63.246125 \nL 194.252903 55.549649 \nL 194.611188 83.231291 \nL 194.969474 52.37308 \nL 195.32776 85.459208 \nL 195.686045 76.783507 \nL 196.402617 69.157219 \nL 196.760902 68.313176 \nL 197.119188 61.968578 \nL 197.477473 66.341721 \nL 197.835759 64.61548 \nL 198.194045 64.888479 \nL 198.55233 65.588713 \nL 198.910616 67.662015 \nL 199.627187 62.396779 \nL 199.985473 68.558032 \nL 200.343758 59.847814 \nL 201.06033 66.842075 \nL 201.418615 74.275035 \nL 201.776901 61.954823 \nL 202.135186 69.635435 \nL 202.851758 60.287042 \nL 203.210043 62.279463 \nL 203.568329 60.161473 \nL 203.926615 65.269442 \nL 204.2849 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236.888892 67.883632 \nL 237.247177 65.90278 \nL 237.605463 71.411855 \nL 237.963749 66.47925 \nL 238.322034 64.850566 \nL 238.68032 61.272833 \nL 239.038606 65.250271 \nL 239.396891 65.845397 \nL 239.755177 65.167204 \nL 240.113462 69.57456 \nL 240.471748 65.927315 \nL 240.830034 68.93273 \nL 241.188319 63.208921 \nL 241.546605 65.027433 \nL 241.904891 72.81423 \nL 242.263176 64.2145 \nL 242.621462 60.922109 \nL 242.979747 61.753388 \nL 243.338033 69.48614 \nL 243.696319 63.367515 \nL 244.054604 66.204437 \nL 244.41289 66.240082 \nL 244.771176 71.377841 \nL 245.129461 64.366204 \nL 245.487747 71.489497 \nL 245.846032 59.68066 \nL 246.204318 56.479149 \nL 246.562604 60.844882 \nL 246.920889 68.543885 \nL 247.279175 55.956063 \nL 247.995746 69.515479 \nL 248.354032 68.162277 \nL 248.712317 68.037532 \nL 249.070603 68.50288 \nL 249.428889 64.581582 \nL 249.787174 70.107004 \nL 250.503746 67.03812 \nL 250.862031 60.52811 \nL 251.220317 65.758542 \nL 251.578602 63.797214 \nL 251.936888 66.634917 \nL 252.295174 65.881995 \nL 252.653459 67.990159 \nL 253.011745 72.635368 \nL 253.37003 62.772727 \nL 253.728316 64.239334 \nL 254.086602 60.995762 \nL 254.803173 58.26632 \nL 255.161459 56.359891 \nL 255.519744 61.504571 \nL 255.87803 48.501233 \nL 256.236315 60.607442 \nL 256.594601 36.864935 \nL 256.952887 71.343604 \nL 257.311172 66.434552 \nL 257.669458 64.219559 \nL 258.027744 66.171172 \nL 258.386029 63.90427 \nL 258.744315 67.979884 \nL 259.1026 69.783586 \nL 259.460886 63.541856 \nL 259.819172 64.071702 \nL 260.177457 66.072615 \nL 260.535743 62.131407 \nL 260.894029 68.324545 \nL 261.252314 63.73029 \nL 261.6106 64.360657 \nL 261.968885 70.040822 \nL 262.327171 65.416418 \nL 262.685457 72.52005 \nL 263.043742 61.801781 \nL 263.402028 62.384179 \nL 263.760314 68.106777 \nL 264.118599 66.279504 \nL 264.476885 62.744547 \nL 264.83517 64.638128 \nL 265.193456 58.61498 \nL 265.551742 67.404153 \nL 265.910027 65.042645 \nL 266.268313 69.781277 \nL 266.626599 67.688415 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282.391166 64.525968 \nL 282.749452 60.011311 \nL 283.107737 63.696278 \nL 283.466023 70.566681 \nL 283.824308 62.554065 \nL 284.54088 73.695084 \nL 285.257451 62.951202 \nL 285.615737 63.665021 \nL 285.974022 67.002067 \nL 286.332308 66.600706 \nL 286.690593 55.90626 \nL 287.048879 65.254833 \nL 287.407165 60.893228 \nL 287.76545 62.277201 \nL 288.123736 70.889541 \nL 288.482022 66.969039 \nL 288.840307 78.580103 \nL 289.198593 64.635178 \nL 289.556878 63.668373 \nL 289.915164 73.416248 \nL 290.27345 64.929698 \nL 290.631735 71.086747 \nL 291.348306 62.596055 \nL 291.706592 63.823241 \nL 292.064878 56.79411 \nL 292.781449 69.067972 \nL 293.139735 63.071053 \nL 293.49802 62.730962 \nL 293.856306 61.650926 \nL 294.214591 69.677733 \nL 294.572877 63.047191 \nL 294.931163 60.751031 \nL 295.289448 57.654714 \nL 295.647734 61.194694 \nL 296.00602 66.110034 \nL 296.364305 62.948411 \nL 296.722591 69.079416 \nL 297.080876 64.100752 \nL 297.439162 64.39111 \nL 297.797448 66.763292 \nL 298.155733 62.581506 \nL 298.514019 66.074325 \nL 298.872305 66.887608 \nL 299.23059 62.480378 \nL 299.588876 66.073279 \nL 299.947161 57.887173 \nL 300.305447 68.841498 \nL 300.663733 70.175435 \nL 301.022018 57.358456 \nL 301.380304 69.088544 \nL 301.73859 64.712202 \nL 302.096875 62.741099 \nL 302.455161 58.879792 \nL 302.813446 73.50311 \nL 303.171732 72.964673 \nL 303.530018 65.630113 \nL 303.888303 68.695503 \nL 304.246589 66.782094 \nL 304.604875 66.585542 \nL 304.96316 60.907622 \nL 305.321446 57.654669 \nL 305.679731 67.993845 \nL 306.038017 61.688822 \nL 306.396303 69.6595 \nL 306.754588 63.330009 \nL 307.112874 73.617375 \nL 307.829445 59.620921 \nL 308.187731 63.831405 \nL 308.546016 63.694168 \nL 308.904302 67.362405 \nL 309.262588 64.634927 \nL 309.620873 64.341638 \nL 309.979159 70.759957 \nL 310.337444 71.403612 \nL 310.69573 62.448351 \nL 311.054016 61.880091 \nL 311.412301 56.713021 \nL 311.770587 57.184845 \nL 312.128873 59.437846 \nL 312.487158 66.279609 \nL 312.845444 64.971262 \nL 313.203729 59.320952 \nL 313.562015 71.660911 \nL 313.920301 65.372486 \nL 314.278586 67.943103 \nL 314.636872 67.187816 \nL 314.995158 65.422101 \nL 315.353443 64.684924 \nL 315.711729 64.827986 \nL 316.070014 66.264428 \nL 316.4283 69.373845 \nL 316.786586 71.484336 \nL 317.144871 62.380502 \nL 317.503157 63.379337 \nL 317.861443 74.077001 \nL 318.578014 59.241723 \nL 318.936299 70.937944 \nL 319.294585 69.063883 \nL 319.652871 70.488996 \nL 320.011156 65.358603 \nL 320.369442 70.312697 \nL 320.727728 63.998936 \nL 321.086013 70.591286 \nL 321.802584 66.353653 \nL 322.16087 68.988926 \nL 322.519156 64.78313 \nL 322.877441 67.581418 \nL 323.235727 63.931203 \nL 323.594013 65.555266 \nL 323.952298 60.434196 \nL 324.310584 58.62053 \nL 324.668869 68.810742 \nL 325.385441 67.054835 \nL 325.743726 60.700827 \nL 326.102012 74.887118 \nL 326.460298 68.312457 \nL 326.818583 67.961926 \nL 327.176869 64.926698 \nL 327.535154 67.157488 \nL 327.89344 60.349654 \nL 328.251726 59.45626 \nL 328.610011 55.931744 \nL 328.968297 59.094622 \nL 329.326582 69.738586 \nL 329.684868 66.520391 \nL 330.043154 59.504319 \nL 330.401439 56.91315 \nL 330.759725 60.210435 \nL 331.118011 67.149884 \nL 331.476296 68.241171 \nL 331.834582 74.621351 \nL 332.192867 63.629032 \nL 332.551153 62.628925 \nL 332.909439 67.082527 \nL 333.267724 65.871025 \nL 333.62601 67.186715 \nL 333.984296 69.573993 \nL 334.342581 66.266903 \nL 334.700867 65.916534 \nL 335.059152 64.944943 \nL 335.417438 68.910311 \nL 335.775724 49.542431 \nL 336.134009 64.846491 \nL 336.850581 73.122086 \nL 337.208866 66.897865 \nL 337.567152 64.903741 \nL 337.925437 69.898264 \nL 338.283723 69.038893 \nL 338.642009 70.937528 \nL 339.000294 64.883601 \nL 339.35858 66.592366 \nL 339.716866 66.668782 \nL 340.075151 65.382634 \nL 340.433437 69.577519 \nL 340.791722 68.821617 \nL 341.150008 61.534207 \nL 341.508294 70.822832 \nL 341.866579 66.144306 \nL 342.224865 63.278872 \nL 342.583151 65.587582 \nL 342.941436 67.121492 \nL 343.299722 55.640135 \nL 343.658007 66.184069 \nL 344.016293 69.169446 \nL 344.374579 67.693591 \nL 344.732864 64.229865 \nL 345.09115 61.863494 \nL 345.449435 51.221182 \nL 345.807721 65.66331 \nL 346.166007 67.516427 \nL 346.524292 66.285399 \nL 346.882578 64.45204 \nL 347.240864 73.351172 \nL 347.599149 63.18252 \nL 347.957435 71.784596 \nL 348.31572 71.032038 \nL 348.674006 71.310356 \nL 349.032292 64.648347 \nL 349.390577 68.285305 \nL 349.748863 63.79556 \nL 350.465434 71.740257 \nL 350.82372 55.274526 \nL 351.182005 64.140935 \nL 351.540291 70.093632 \nL 351.898577 68.952492 \nL 352.256862 75.246659 \nL 352.615148 66.4161 \nL 352.973434 66.692128 \nL 353.331719 66.217336 \nL 353.690005 67.98381 \nL 354.04829 62.052248 \nL 354.406576 59.197497 \nL 354.764862 70.755736 \nL 355.123147 67.841923 \nL 355.481433 70.020926 \nL 355.839719 63.108393 \nL 356.198004 68.156784 \nL 356.55629 63.096202 \nL 356.914575 62.019659 \nL 357.272861 85.926931 \nL 357.631147 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114.03443 \nL 149.108914 116.715851 \nL 149.4672 116.227563 \nL 149.825486 103.111025 \nL 150.183771 107.984985 \nL 151.258628 127.004799 \nL 151.616914 125.221273 \nL 151.975199 114.684442 \nL 152.333485 111.031499 \nL 152.691771 115.394723 \nL 153.050056 126.240348 \nL 153.408342 86.606436 \nL 153.766627 101.410047 \nL 154.124913 120.865939 \nL 154.483199 117.483193 \nL 154.841484 111.752507 \nL 155.19977 115.963561 \nL 155.558056 103.616345 \nL 155.916341 120.764393 \nL 156.274627 111.249248 \nL 156.991198 114.12376 \nL 157.349484 76.954141 \nL 157.707769 62.324575 \nL 158.066055 90.939846 \nL 158.424341 109.761016 \nL 158.782626 104.489233 \nL 159.140912 112.462189 \nL 159.499197 109.966112 \nL 159.857483 108.797971 \nL 160.215769 109.562266 \nL 160.574054 112.343218 \nL 160.93234 110.911948 \nL 161.290626 115.0725 \nL 161.648911 107.159221 \nL 162.007197 110.095065 \nL 162.365482 111.052245 \nL 162.723768 111.610611 \nL 163.082054 109.817324 \nL 163.440339 111.642121 \nL 164.15691 106.204102 \nL 164.515196 106.200963 \nL 164.873482 107.56087 \nL 165.231767 110.302483 \nL 165.590053 102.675179 \nL 165.948339 101.102678 \nL 166.66491 112.270452 \nL 167.023195 109.406834 \nL 167.381481 111.436906 \nL 167.739767 103.354585 \nL 168.098052 102.350287 \nL 168.456338 108.452036 \nL 168.814624 106.648204 \nL 169.172909 106.939284 \nL 169.88948 47.444558 \nL 170.247766 99.889743 \nL 170.606052 95.826274 \nL 170.964337 102.271939 \nL 171.322623 101.3593 \nL 171.680909 103.13196 \nL 172.039194 103.178898 \nL 172.39748 98.210774 \nL 172.755765 101.604056 \nL 173.114051 117.596943 \nL 173.472337 116.547127 \nL 174.188908 102.969514 \nL 174.547194 109.658767 \nL 174.905479 107.960177 \nL 175.263765 98.896266 \nL 175.62205 102.546268 \nL 175.980336 102.035554 \nL 176.338622 106.80735 \nL 177.055193 103.436428 \nL 177.413479 103.084147 \nL 177.771764 104.965196 \nL 178.13005 100.040881 \nL 178.488335 105.526367 \nL 178.846621 106.882647 \nL 179.204907 106.600585 \nL 179.563192 99.142562 \nL 179.921478 107.079887 \nL 180.279763 95.017363 \nL 180.638049 95.124283 \nL 180.996335 89.082426 \nL 181.35462 93.491228 \nL 181.712906 96.439814 \nL 182.071192 89.397442 \nL 182.429477 99.295474 \nL 182.787763 95.686899 \nL 183.146048 104.999614 \nL 183.504334 101.377262 \nL 183.86262 101.464131 \nL 184.220905 102.238517 \nL 184.579191 97.663697 \nL 184.937477 94.779344 \nL 185.295762 95.907865 \nL 185.654048 96.660062 \nL 186.012333 99.259487 \nL 186.370619 87.019538 \nL 186.728905 92.899782 \nL 187.445476 100.798337 \nL 187.803762 94.050697 \nL 188.162047 95.69125 \nL 188.520333 103.910714 \nL 188.878618 82.982623 \nL 189.236904 83.841286 \nL 189.59519 95.596037 \nL 189.953475 92.852468 \nL 190.311761 96.135364 \nL 190.670047 94.417484 \nL 191.028332 86.805352 \nL 191.386618 96.535902 \nL 191.744903 87.926495 \nL 192.103189 94.690397 \nL 192.461475 92.860756 \nL 192.81976 95.507258 \nL 193.178046 83.113606 \nL 193.894617 94.567006 \nL 194.252903 96.548283 \nL 194.611188 88.278096 \nL 194.969474 44.646933 \nL 195.686045 73.244848 \nL 196.044331 80.515912 \nL 196.402617 81.817527 \nL 196.760902 82.321486 \nL 197.119188 87.117272 \nL 197.477473 86.095148 \nL 197.835759 86.814797 \nL 198.194045 90.405095 \nL 198.55233 89.82306 \nL 198.910616 88.541509 \nL 199.268901 90.688832 \nL 199.627187 97.282204 \nL 199.985473 94.694593 \nL 200.343758 87.762812 \nL 200.702044 91.977492 \nL 201.06033 85.958472 \nL 201.418615 89.049363 \nL 201.776901 90.305221 \nL 202.135186 88.564442 \nL 202.493472 89.038043 \nL 203.210043 92.159404 \nL 203.568329 92.324808 \nL 203.926615 91.925626 \nL 204.2849 77.084323 \nL 204.643186 91.084291 \nL 205.001471 91.1943 \nL 205.359757 93.406768 \nL 205.718043 91.508914 \nL 206.076328 88.510164 \nL 206.434614 78.996585 \nL 206.7929 88.897751 \nL 207.151185 93.234106 \nL 207.509471 87.802559 \nL 207.867756 93.830709 \nL 208.226042 85.773489 \nL 208.942613 91.597877 \nL 209.300899 90.479602 \nL 209.659185 91.409075 \nL 210.01747 94.777684 \nL 210.375756 87.189784 \nL 210.734041 92.252408 \nL 211.092327 92.610182 \nL 211.450613 86.970121 \nL 211.808898 90.847136 \nL 212.167184 87.385094 \nL 212.52547 85.003826 \nL 212.883755 66.485219 \nL 213.242041 83.226582 \nL 213.600326 88.582832 \nL 213.958612 90.534756 \nL 214.316898 87.056612 \nL 215.033469 89.611818 \nL 215.391755 86.800675 \nL 215.75004 91.412379 \nL 216.108326 86.69665 \nL 216.466611 92.69856 \nL 216.824897 86.888194 \nL 217.183183 84.151747 \nL 217.541468 91.59053 \nL 217.899754 87.445735 \nL 218.258039 88.035182 \nL 218.616325 88.201052 \nL 218.974611 89.06085 \nL 219.332896 94.651277 \nL 219.691182 83.228395 \nL 220.049468 88.767888 \nL 220.766039 86.576244 \nL 221.124324 88.547478 \nL 221.48261 89.716598 \nL 221.840896 93.138442 \nL 222.557467 81.442926 \nL 222.915753 90.199087 \nL 223.274038 84.387787 \nL 223.632324 88.983723 \nL 223.990609 90.107429 \nL 224.348895 86.890659 \nL 224.707181 89.18771 \nL 225.065466 88.2719 \nL 225.423752 91.373502 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240.471748 87.354717 \nL 240.830034 88.377067 \nL 241.188319 91.168659 \nL 241.546605 87.940302 \nL 241.904891 91.659998 \nL 242.263176 86.387851 \nL 242.621462 91.429356 \nL 242.979747 90.423056 \nL 243.338033 86.474955 \nL 244.054604 90.929616 \nL 244.771176 88.559545 \nL 245.129461 85.653833 \nL 245.487747 89.116375 \nL 245.846032 85.458374 \nL 246.204318 85.794463 \nL 246.562604 88.264355 \nL 246.920889 89.824381 \nL 247.279175 73.887171 \nL 247.637461 90.668776 \nL 247.995746 85.980423 \nL 248.354032 86.807862 \nL 248.712317 88.363899 \nL 249.070603 81.921581 \nL 249.428889 88.311484 \nL 249.787174 81.351537 \nL 250.14546 87.463229 \nL 250.503746 90.844169 \nL 250.862031 82.562508 \nL 251.220317 94.522998 \nL 251.578602 88.763467 \nL 252.295174 84.309434 \nL 252.653459 85.124653 \nL 253.011745 86.403006 \nL 253.37003 91.813617 \nL 253.728316 92.154081 \nL 254.086602 90.788462 \nL 254.444887 90.25801 \nL 254.803173 90.144779 \nL 255.161459 86.167871 \nL 255.519744 88.937967 \nL 255.87803 83.697933 \nL 256.236315 85.685441 \nL 256.594601 33.385336 \nL 257.311172 83.364122 \nL 257.669458 85.947092 \nL 258.027744 89.859423 \nL 258.386029 83.91171 \nL 258.744315 89.564405 \nL 259.1026 88.180227 \nL 259.460886 91.043273 \nL 259.819172 90.248445 \nL 260.177457 85.690706 \nL 260.894029 87.971781 \nL 261.252314 86.989142 \nL 261.6106 92.810081 \nL 261.968885 91.323414 \nL 262.327171 87.886387 \nL 262.685457 89.837074 \nL 263.043742 85.802718 \nL 263.402028 87.697753 \nL 264.118599 86.174501 \nL 264.476885 88.061774 \nL 264.83517 88.38446 \nL 265.193456 83.072411 \nL 265.551742 86.284657 \nL 265.910027 82.507639 \nL 266.268313 83.76394 \nL 266.626599 87.921947 \nL 266.984884 84.105642 \nL 267.34317 91.310094 \nL 267.701455 82.727417 \nL 268.418027 92.11063 \nL 268.776312 88.54706 \nL 269.134598 89.028231 \nL 269.492884 84.90822 \nL 269.851169 87.515257 \nL 270.209455 82.064464 \nL 270.56774 87.107179 \nL 271.284312 84.726419 \nL 271.642597 88.029159 \nL 272.000883 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85.85813 \nL 287.407165 88.121673 \nL 287.76545 84.479698 \nL 288.123736 82.695744 \nL 288.482022 89.882471 \nL 288.840307 84.219577 \nL 289.198593 87.931856 \nL 289.556878 83.110916 \nL 289.915164 82.065727 \nL 290.27345 88.211281 \nL 290.631735 88.500441 \nL 290.990021 86.066815 \nL 291.348306 89.570771 \nL 291.706592 87.45367 \nL 292.064878 83.578472 \nL 292.423163 88.587913 \nL 292.781449 80.392332 \nL 293.139735 86.638741 \nL 293.49802 90.80007 \nL 293.856306 84.199194 \nL 294.214591 83.896369 \nL 294.572877 80.755619 \nL 294.931163 91.189425 \nL 295.289448 87.846574 \nL 295.647734 86.072258 \nL 296.00602 85.774046 \nL 296.722591 92.964448 \nL 297.080876 81.226014 \nL 297.439162 85.230508 \nL 297.797448 83.671721 \nL 298.155733 87.505887 \nL 298.514019 90.031445 \nL 298.872305 87.246897 \nL 299.23059 89.983469 \nL 299.588876 81.210118 \nL 299.947161 83.161073 \nL 300.305447 85.738933 \nL 300.663733 85.457117 \nL 301.022018 71.13359 \nL 301.380304 87.414473 \nL 301.73859 89.608077 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331.118011 79.460773 \nL 331.476296 87.95914 \nL 331.834582 75.227318 \nL 332.192867 84.219106 \nL 332.551153 86.218213 \nL 332.909439 86.51035 \nL 333.267724 82.565325 \nL 333.62601 84.959329 \nL 333.984296 84.766046 \nL 334.342581 78.920613 \nL 334.700867 83.148742 \nL 335.059152 82.224892 \nL 335.417438 84.348835 \nL 335.775724 61.254432 \nL 336.134009 75.375781 \nL 336.492295 82.805448 \nL 336.850581 87.102015 \nL 337.208866 78.918866 \nL 337.567152 87.941025 \nL 338.283723 80.764453 \nL 338.642009 82.269577 \nL 339.000294 88.813281 \nL 339.35858 88.776422 \nL 339.716866 90.211516 \nL 340.075151 83.867183 \nL 340.433437 80.131132 \nL 340.791722 82.04123 \nL 341.150008 81.162538 \nL 341.508294 86.838389 \nL 341.866579 83.973694 \nL 342.224865 86.567117 \nL 342.583151 86.083738 \nL 342.941436 85.404455 \nL 343.299722 68.988933 \nL 343.658007 81.120893 \nL 344.016293 86.233475 \nL 344.374579 77.810932 \nL 344.732864 88.839772 \nL 345.09115 81.893297 \nL 345.449435 85.522677 \nL 345.807721 62.021157 \nL 346.166007 80.681326 \nL 346.524292 85.102639 \nL 346.882578 85.075714 \nL 347.240864 87.811884 \nL 347.599149 89.544835 \nL 347.957435 86.910358 \nL 348.31572 82.030101 \nL 348.674006 84.912506 \nL 349.032292 83.394316 \nL 349.390577 81.044532 \nL 349.748863 82.73549 \nL 350.107149 86.926679 \nL 350.465434 88.285421 \nL 350.82372 88.35407 \nL 351.182005 84.355934 \nL 351.540291 85.655573 \nL 351.898577 83.941245 \nL 352.256862 73.544698 \nL 352.615148 85.190495 \nL 352.973434 82.412459 \nL 353.690005 88.219844 \nL 354.04829 85.703115 \nL 354.406576 85.632811 \nL 354.764862 87.991622 \nL 355.123147 84.665656 \nL 355.481433 79.445948 \nL 355.839719 84.802704 \nL 356.198004 83.709357 \nL 356.55629 83.873408 \nL 356.914575 84.231037 \nL 357.272861 79.496676 \nL 357.631147 86.552251 \nL 357.989432 86.230604 \nL 358.706004 83.15618 \nL 359.064289 85.469171 \nL 359.422575 83.634555 \nL 359.78086 85.816549 \nL 360.139146 85.77982 \nL 360.497432 79.408462 \nL 360.855717 93.792677 \nL 361.214003 86.538288 \nL 361.572289 86.396333 \nL 361.930574 83.731481 \nL 362.28886 85.09918 \nL 362.647145 83.312069 \nL 363.005431 67.550611 \nL 363.363717 87.630784 \nL 363.722002 80.141358 \nL 364.080288 84.947161 \nL 364.438573 87.508016 \nL 364.796859 88.331129 \nL 365.155145 83.26697 \nL 365.51343 84.752999 \nL 365.871716 83.191405 \nL 366.230002 87.577349 \nL 366.588287 78.266654 \nL 367.304858 90.276632 \nL 367.663144 86.112053 \nL 368.02143 84.900491 \nL 368.379715 87.904966 \nL 368.738001 85.542713 \nL 369.096287 86.646316 \nL 369.454572 83.390076 \nL 369.812858 85.45346 \nL 370.171143 80.069937 \nL 370.529429 90.150395 \nL 370.887715 88.859982 \nL 371.246 83.297529 \nL 371.604286 87.347567 \nL 371.962572 76.096914 \nL 372.320857 86.056713 \nL 372.679143 87.566489 \nL 373.395714 82.642867 \nL 373.754 84.014313 \nL 374.112285 82.035499 \nL 374.470571 88.111176 \nL 374.828857 81.726442 \nL 375.187142 79.982779 \nL 375.545428 84.512058 \nL 375.903713 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83.601236 \nL 407.432848 89.220891 \nL 407.791134 88.910745 \nL 408.149419 87.739639 \nL 408.507705 85.794545 \nL 408.865991 81.572827 \nL 409.224276 82.456694 \nL 409.582562 86.810071 \nL 409.940848 88.603994 \nL 410.299133 85.601373 \nL 410.657419 89.223211 \nL 411.015704 83.979119 \nL 411.37399 82.139775 \nL 411.732276 81.417683 \nL 412.090561 87.378786 \nL 412.448847 84.427151 \nL 412.807133 84.53758 \nL 413.165418 88.224207 \nL 413.523704 71.706299 \nL 413.881989 90.697325 \nL 414.240275 93.451714 \nL 414.598561 83.652708 \nL 414.956846 86.661176 \nL 415.315132 84.19251 \nL 415.673418 87.794943 \nL 416.031703 89.809982 \nL 416.389989 78.812418 \nL 416.748274 84.32287 \nL 417.10656 85.53961 \nL 417.464846 89.478089 \nL 417.823131 88.378099 \nL 418.181417 89.821207 \nL 418.539702 80.975579 \nL 418.897988 87.116355 \nL 419.256274 87.209153 \nL 419.614559 86.694584 \nL 419.972845 88.692245 \nL 420.331131 87.233282 \nL 420.689416 83.984966 \nL 421.047702 88.497832 \nL 421.405987 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\n"
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McWbtoTVyGLZKumnnoEyUscb6UcJuu9bzHXNsInwee36ziFRisJLtotKHKn3LxvElZH0i1mZ8P5ZdpZIZsDJ39Ga3Yd0sGxqfi7FjcmCWOXcxVizrB2PLLF2WBWN8Lct+SIsi5+9Lly61ss0vFZt3xfpSsQeSXnm90WvLsvsuwZZo86a9Q7MNmu2CsD1M8m/ZGy2rGu13tOWynrymZWWyPliZf21esrGC7UBb8DBsnR9pRaTNuLJ2qWTumdLPt+M4WYJtcLupWPZsLDcqY7P1L4tZw8b43nFxyvhaWXNVshsNw9b2PXDgQCtzPuZ7evfdd1uZY5BZQ4l9b7LenPtZ18p3a2VM6LVTVWy/1r69drv5z6whhBBCCCGEEEIInxDyh5oQQgghhBBCCCGEmbBWR17J/tCbBYhUpIL2W7M+UQZF+VVvFipKW1mmDerixYutTGnY/v37W9lk5MzcQtZJsfjMPI+76rPMTBas01tvvbWyfnPYJX6KhWrKvZaWvaJSR5M6E5MiEpNNVqSCZlUx+aFZG4hZqMwaSXm1ZcowCxWvSak5ZZ7DsDW+TH5pMc970EZCWwjrR8sG60RZOeOaknJev2IT7bVAkooNqmLRmZI5bqcxaxLfLeG74rzD8/lOaNNhf2YcmUWiMv/Srrdnz56V9Tl37lwrv/POOyvvZWMtY5Mxy2dnm7BcsRON/812oYSbz8by7t27W/ny5cutbJYVs7tU1ka9WUAq9NqpNnX9uVKZy8yuW5HwWxuz3/Ic9iOzYNDu9Pbbb7fy+++/38q04j744IMr69CbWYVYPS2uqxZVy5Jl9GaA6rX+bSpGtjvDaeX8pcUm68u1Etc7vVY+UrEo9m6RQSrrYYvHiv2lYqvlnGPrZLM3jq/PuZlzJbMJcwxiu9y6dWvl8Ur7sk4cR/juOZ6ynrRMkopd09ailSx9lbX0lEzYUdSEEEIIIYQQQgghzIT8oSaEEEIIIYQQQghhJpSzPlUytvRSyT5TsVpQJkcZFLOjmBypkmWDdglKq65fv97KtBPduHGjlSnhNkkq5V1sZ/52XNdKBilCqTrtUcx2QTtHxRKzqawxletXjts5n9SsT73y7HU7wH8E+2pvVriKTJqSRsad7SJvcnHGx6FDh1qZcUq5OK/PZ+dxnv/hhx8OhL8x2TfHKco4iVkq+FtK4U32yux0ZqfiM/C+JimuWGH5Dqyv8N1U5O42Dy0Ba0uTDFvftiwzPG5tbLJcYhlb7rnnnlZmn+Jx2jFos7JMTBYrZu2141UJus33tHXZGMRzeNzWFqR37tvUfNRrp9pum8acYJ+07E69mQcrWP+vZGakpZHzFzONsj+yzDitWA0q9pDxuvQj2J5m1RzPb5U1bSXOrR0t9nvXkKRileq1Odj5U7K/Vcb9OcG44HNYVkyzC/U+a2879WbY67W9VixOvXWr9ItxPXk/zsGWVY4WNY5B/K6sZJwyy7zZ4Tg+mvXJ/o5gsWa2JvvuqmQTNKtbxSYaRU0IIYQQQgghhBDCTMgfakIIIYQQQgghhBBmQm1r9jX0ymB75V52DmWStBQx0xHl5ZREmQTJJISUQVF6+sADD7Qy5d/c8dp2qqaMy2S0Y5moZY3hM1DqyuvSpsU2YoYLZrGinKySjcW4k7LqKedXZIVzZcpz98ajSQhNVlyx0bBfM3sauXnzZitXbDrMhvPUU0+18v3339/KlI5bvzOL0lj+TamnxbzJHSmb5HUpB63shH/q1KlW5s78V65caWU+M9+fWWjM8tq7Q37lWTaZTWSpVGTSlQxa9q7Y9jzOMmXFDz30UCvv3bu3lRmzNpfZs3BuqVhCzJL8ceYKszWZLZFzPMcOyy5pY6jVu1d232uz2o45ejuyR+00ZjuqyOQNO7/3PbMO7Gu0gTDbGuc+ZmthH7dxnVg82vxrfYHxNLY3mUVzO9gO+88Um0qvrak3e87SYtOeg/2tsvarXJNsx/i3qb7Wm3nZ5hxi88/YVm6WbPt+ZtZRbhPANVtlOxJbf3MuNpsVrde9aymz1ffGXe+7t/dElvt1GkIIIYQQQgghhHCXkT/UhBBCCCGEEEIIIcyEjVqfjGp2ho8w2bP9lhLmJ554opXPnDnTysx8QqmR7WZNuZbtcs/sTrRE0WbE3e9ZZn2YqWpdtiW2C+tnu+3T7mX1oEXi8uXLrcyMMJZxZDvY1A7pvdkulrArPqlkQyMmpTaLn1mZbAd0O7+SCYFl2ivY79iXmfWIdWC/3r9/fys/8sgjrcwY57hBqSbHB1ocaJsY2xL5G8q5TfZtUnLLlGN2oePHj7cybSpHjhxZ+Qznz59fWTfel+fzXfK+ZrOxrFVmkzPrC+m1DSyBKbJZs6zx/Zhkmm1MqTJjhxa6xx57rJWZLZAWOusLpDImsD9adqbezDBj2EZ8Zs7BLNNGwjGFMcKsFmaDqlCxLG0q42Hlt3bcxvolxKaNN9vxHJa5jH24kkGJcB7gGpLrNZZ5jmUp6c1CZfW0TFW85theYJllKvTWz8bEKdl67F6bitMpY8gS4pGwvmaRMbvupu5bOW7n9Gbbq5xTyaJp1zQrj8X4+HzOZWZRNFu1jVNcu/Peti0ILU60HlsWOvue6W0vG1usTStZ3uzvDlxvGFHUhBBCCCGEEEIIIcyE/KEmhBBCCCGEEEIIYSastT717jjem2GgsmM65UKURx09erSVH3300Vam9YmSIpNY8vo837IxUNJFGRetGbRE7d69u5Up3apIFMdyLauTyTsp7TZZKSVtrB/PXydd7WFTsswpmaQ2dc2dpjdLVa+80/paJcOP2VzM1keZNM+nfYcwmxvvxb68b9++VqaUspLphZJPy9Yy7i9mwWJmDo5f9luTX/Ic1pVZ25j9js/JcY32Fdq9Ku+PbcFn5Pszeb3Znax/WB2WxpRMMZXjJgu3bFpsV/Z/9iP2EZZ5vr0Tmx9Yf/YX1pMZahg3lD+zDh9nzK5YMTmO8PnZRrR+ce6vZMfozd7Rm3WCVCypRm+GqSVg76Ri57K2t99y3D18+HArs8+fPXu2lW1+5DUZF5T8M+so+ymzKLI+Ns+YdYAxa/YT/pbPSIvlOHOpZT/cFL0Z0KZcv3K8cp0pmcYq/XKumIWF6xrOEcadzO41pU/dyXfSm2FsGLaOF4xhftOyr9qWBJbV1Po5xwjel2t69m2uD5i5uLLmJFNizaisXSuZTJe7Ag4hhBBCCCGEEEK4y8gfakIIIYQQQgghhBBmwsfK+lTZ9dyoSBEpPaa08vTp0638q7/6q6385JNPtrLJpnhN2gJoI6B0izYFs1pwN+ubN2+2MjNAUSbGMuXSlPOtk2hxJ3SzUZiE0s63DBeUk9EKUskwNCVjReX83uuYxK6SoWSuVLIc9ErXK5l5eD5ljIxTa2+z+DAWbt261coHDx5sZWY3unLlSivTgsBY5jjAPs5zKDmsZF6qxNP4NyZVp5TcJN+2uz6fjRZQy5JlfYL3tfpw3GQ7cnxgO3KMMstkxU5mFrsp1sudoCIzrsynjMdKtsRKRhTLksbMa3znnDc5V9o7rFhIzIplVDJbVcdvswbYccZCb3YJxlSvrWm7582KtZUs2YpYGVd6297Ot37BdWPlmvztO++808rvvfdeK3Me4PUtY4nV2bK7WCZD6ws8n3PCOCsg51GOR7b+qNCbfWdTGaAqNsNNZZiy629q7b0T2NqEmL3Ozpny3NuR9am3v9jauzKfsg0r9Rn3HbbvPffc08q27mdsV9aWVg+OA9yOw7by4PjC+rCelex69n1iY2jFrmjfDBwf+VzGcmfcEEIIIYQQQgghhLuM/KEmhBBCCCGEEEIIYSZ8LOuTMSUDlP2Wcut/82/+TSv/yq/8Siu/+eabrfzhhx+2MrMvmQyfZbMzWEYYSqUoSaWFiDLU69evrzzHJKa0TQyDS+Io9+JxXotQ5k4JOy1YtGlRusb27ZUI9+5C37ujekXaWsk0VrF37TSWsYL07lRfkd6bJND6prUrz6d08dKlS61M28UjjzzSyg899FArM6ZMOsvrs48zOwZ3i7csVOvk2Lw3f2/nWMyy3pSeMpaZDYsZEdimtD4xZs1yxnHNxmKOj2xH2j6tr5jE1GLTbD+VHfJ3Gqu7vf/KO6ctiHMK72XZWCoZ3DjPUorL+/L9MxsS61kZg/m8lFTzmpZtzOa0j2N34nPa81g7ckyxbGVm8TM2ZU+YYn/ttbVvymZwp6jMlb3PxPdsMT5lfWFZn7jmpD2f/XFsNfqIyjqxUmezcnAc41qSdq1h2Dp/MR7NwtDLptZAvdfZVGaZCpW6LcGuaH3Jvo8qzGF8mmJrq9hSGWss27rS7suYG4atW4cQvgP7lub8aJme7JqWKZlrBVKxL9latBIX22FL7P52vu0ZIYQQQgghhBBCCOGOkD/UhBBCCCGEEEIIIcyEjWZ96pUKmgSJViNahP7Fv/gXrUyZJOWg586da2VKqCgNNakYJeV2fdsVnzYCZqLhfSklo13r0KFDK+vDzCrD4JYB2xWf5/NalLnTBkVZKq0mfM7Lly+3Mi0ellGA9GaMmpL1qSItuxuzyVTO75Xhm92JksaKRNPeP+XZ7F+MO9aZffPAgQOtTIsPLRtm/aGssrLrPhk/i9kfCK1chllZOJadOHGilWnf+uCDD1qZz8w4ZYzzfJOGsszxi+1rUlhrBx43q0hVnjtHrC+x7pbpj/YiZl/i+cwqSImxtaVl0LB50OxlJrfm3MK+wH5n4wnLlDazzpyL2T5cJ6ybN/hvW1uwb/MeHOPY1hybOH5V4qjXRtFrKa/Ma5UsFXeL3YlYZkOjt+0tS97DDz/cyq+//vrKOlSuz7ig5ZQWdq45Od6zPmZ5Z9+fYhPjdTg+8F7DsDXmT5482cq0dVWyjm4qQyiZYlfcjmxuVrfetd1cYXvwW4zjrvWxSgbX7R63KpalTV2Tz8Ixh/NyJYsRGc/7lkXVsilxrOH7M+uT2ZDN+sT68Ppcx9o6fFPxUulPdk3L7mSWLhJFTQghhBBCCCGEEMJMyB9qQgghhBBCCCGEEGbCWuvTFOnrlN/y+LFjx1qZO8RT1vTMM8+08nPPPdfKlCebNK6SicYkZLwmrQaUnvJZKB/jvSgRX5fhgv+2djT5u+2qTVkpM1/QXmHPT/sWpWhT5J13UkrdK2ueE5WdyysZlyo2tYqVyaSVFQkopZHsa+xflA1yHDh9+vTK37I/WpYcWgBZpizcMkmN25zWCZbZLpRu2nUtmxttMIxTwmejjPXWrVutTNuMWcJ4X16Hxy0LkfWDimXJzl9y1ifCun/5y19u5aNHj7YyY4E2AcqKae/lcfbhd999t5XNpsPjZqtl21Nqzr7D2DQrB98t68z3bFkXaZ2gfaNiFRqfx3vwWmw7w2xQNt5ZO/ZaITYl37d72XWszlMyJO00lhmtd05k27B/MhZYps3uypUrK69jGeKsTMk/Y4rzF/spx2lilkY+L+cuG4PNjnHw4MFWHtswee+33367lfft29fK3ErAshNWuJPrvSnxUlmr9WbVXQLsG4xNxhfXHb0207mNT1MsrfaMbCt+V3LusjYZr9HMEsl50zJ+cm1h6357xxwraX1ifTiWVTKc2tqY2PdSZVy2bzDeyzLwjbM7r6zbbc8IIYQQQgghhBBCCHeE/KEmhBBCCCGEEEIIYSas1ZH3yn965Vv2W8qFKGWiRIi2iN///d9v5fPnz6+8L+tPiZ3JtSjLsl2reR3WkxJOylDNusRzeM3xDvkm2zXZOutNaRklWJSx8X48n9eknPeFF15oZcvWY9mgpshQNyV5nbMs8naYnNDke6RXcmvvkP3I7muZfCr1ZD+iZefGjRut/NRTT628ptkPGRN8Lsam7XDPcWmcwYlxYbvwm53M5O+sB2OT59gO/JS8m0S+IiM3OTvfDSW2lV33zQ7H629HFo+dhm1AO9rnP//5VqYFh5J/ns+5j++BEmDrI2xvvn/2F7Mj8f2wbNYP1oexxj7Cc/jsvCZjiBkSaAFkvI/tFfw9ZdW0V4xtxh/Btuid13ptTZVMTBVLqmV9smtafYwlZF4zerPxVNqJ/Y0xxeufPXu2lbk+tKxclTma47pZnNhnLXsp4butrP+tztbXGH/DsNWiyW0Orl692sq0V7DtLJtdJaZ6t2zonY967YqVuJuSLW4J8ybHafY3y1Rp77aSSc2+ASt9p5I1aEpm5N7r2BhCu6JlaLWsnsOwda7lOsBsR70WRZvXzT7K61gmU1vrVGyuveVeGyPptfNHURNCCCGEEEIIIYQwE/KHmhBCCCGEEEIIIYSZsFZzM0XG2yv9olSKsrRXX321lf/oj/6olb/5zW+2Mi04/C3lWmZZMqm+ZWIxuTHPpyyLv2WZcifKVmmhoiVqGLZma7JMG7bbNq/F8yljo53jgQceaGVaMJhxh+3Lc2g/sww6JgmrsKkd0klF2jsnTJY8BbPgUCZtNjuzAvTKje1ZKGmkFJrSywMHDqy8F2OFFhLaK2iDYExwXKKUdFxPthHPY1zYLvwWC4wvWp94fZO9cky5du1aK1uGJmLvg89o1pop8lFi2ab47HOlkmHg5MmTK48zy4H1Ecr/LWtfJSNBxTpgx3lfez92X7MYmxWRliiTRa+Tu/MenNcYXzaHssy5jLFs8ct6mAV0iiVqU/NphV5LwFyxfmL90OwDPJ9js9lsaQtg3zEq8ybryXg06y77L5+Llgdb93I+YTxatkCbN8f99PDhw61MSycz4dH2zPme5U19n0xZW9p1ejM0bcqytIR4JFZf9ltaXPmtZFYmi/E5tE1vP7LjjCmuN8yCb+s+jhvDsPV70MYsW0PYd7JlkeQYxPuO6/QRtMDZmtYyh1ayO1Uy5hI7zrHPtlNhnzaiqAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMKFufKudsKusT5UuUt/3O7/xOK1+5cmXl+ZR4UVplNiuTqlYyJBiWKYJlPpdJosYyfz4Py5bZgdelLJ7WEcrHWSfuzk+pK2VmlJHTOvL1r3+9lc+dO7fyt5TSTZF6TtkJv5LJbMn0yvcs4xD7EeOL/chsGpUsJRUovaZEmtZISiYtDvhcjAlaTg4dOtTK169fb+XxrvjEsklZhhTLmsOxac+ePa28f//+lfXmM1MKTjklZfcViTDhcY45lQwfZpdg2WwG/K1JeJcG+wLl/I888kgrMwaPHDnSyq+//vrKcziOMoMK28lky2x7kw/zt4wdvivaYZm9gRYtsxVfuHChlRnj7NeMO16fcxTjlHEwrjfnLJYrlg/OjxW7E5ky9m0qq5Qd780aY+VNWXC3k8rcRPh8Nj5xfVSxmFdsc4ZZZDg2mx2B9WF8VWxAlgXU1lCMOdaH681h2BprtINy7PjlX/7llfemxZ6xyTmUbMoeZedXrnMn15ZLXsfyHTJeaHnhOTa+boftrBIvleyyvbY5y9TG2Cdcb1hGTcbvOCMbY57XsiyiXHPaN6mNKdzWg3M8z+f1Kxa4ShZFsqltMex9c0y09jHmP7OGEEIIIYQQQgghfELIH2pCCCGEEEIIIYQQZsJaHfl274xdkZxRIvTKK6+0su1yT4kXy73WDFLJrmC2EdahYlHicWYTGF+LUlrCe1OSazIw2rEs8xahpH7v3r2tTLkaJcLMzkUb1DvvvLPyvrYj+RQqMkQ7/26kN6OE2RDYp2g3YJ+y91mREJpdgpmbaAmhJchikLFGu9NTTz3Vyt/4xjda2STfY0xWbvJcHrfMSrQ+sa6Ma9aJEmHanShPtbg2OTvbi2XGu41fFRuUtanZ7dbZz+aCZQ9g2eLIxir2Z8uIZVmZDN6X8wntGxUbH/smrQ2cB2xM4HHOCZz7KIumXYmWK8YE5/ph2BrDbHdbK1BWzXaxZ9iUjcjOL0mjO21HvXOczRlLsDuRSoZBO9/GNvYvjruMQcZsJYtXpQ5mReW9bCxnnPL8iq2J2PhgtqzxdRjnnNePHTvWyox/zq0854033mhlrjMZs+NxYVWdKraITWXr6aV3nKn0rTnBOlpfZZ+0Mdiuad9fNg5MydxFKtc3qzfLnLtsPcDMp7QD27YebGeOY+Pz+HuuITneWXyZfYvfj8ePH29lrnv5bGwjjhWW6djWrnZNG0Mt021lHrcMrbwmrVvGsmbZEEIIIYQQQgghhLuY/KEmhBBCCCGEEEIIYSZsNOvTlOMmU6JUyiRIhPIrWgEoOzLJXO8u0YTyNkrIKJ2u2B14X8q5h2GrjIp1ovzMZGO8Fo/zmpVn5vPwfF6H2UpMRs+2oDWjYkXrxeSgvTK2OVGR61asfCYNNZke+xH7OSXMzNjArCnsO9bGJp/ks/CalEAy6w0zN1nmGlonGDes2x//8R+3smVdGwa3EHKHfD5DJTYfeuihVmZWNZ5j97p69WorV9q9InmvSElJr92D74aYbWCuWGzy+Pe///1W5tjJ2GF7MLsZY8QsvfYObcwzqxxlubQgWCYlSpgpvTZJMvvsjRs3WpljC98/78X6MN7HUnP+xuyRFevmpqT2dv52Z3fqpdcavIR506w6trbk+ewvnFPYbxlHXNexf1X6kcG+zTmI9hAbfxhHfBZ7n4wDPguPc+wyqxfbajxXcC63LDNHjx5tZT7/iRMnWvn06dOt/L3vfa+VOW4yW6Rl3qpYn8zKUsmGNiVLbi9LW9Na3DGmKmuZCr1W7Mr5vVYpYr9lfHGNzTUjLVEcB3gOY5ZjHeNxnJGN8PfMWMk1N88h9m18+PDhVuY3I5+H16cl2b7zLX4rdiejYvu1LR6s3YkdJ1HUhBBCCCGEEEIIIcyE/KEmhBBCCCGEEEIIYSastT6RXlluZWd7Sod4vu16TfmSyeEppa5Id3vtIYbVrZJlwiRU47rxPMpMTdJJuwSlrpSM0yLB5+R1KDOj9JD14278lNBxF3LWme+A8lTLmmAWAtK70/6Ss1eQTWWpYntQjsf3QJk3M/8w8wstRdeuXWtl2pR4zUpGNhsrWGfKDNl/+SyUWlsGGfZBWjloAxn3QT4Dz6P1z2yNtsM845fjCO/F57HnN1mmybnNlsU6UIZrWRmIjaeU9vId8BnXjYlzxOKRz8E+8t3vfreV2QYcpxl3vZlZbP61+jD7EvsRyyZn5njP+KL1w2L28uXLrUyLEq/JPkjpNC1jYwm2ZYfiHMz5i32M8WvZCafYjjZlT9iOOcCuP0XiPydszCO0F+3fv7+VOe5+7nOfa+U//MM/bGWbv3rbz96J1Zl9mXBuMStiJeMb15K2pmMM8tk5hox/w/odPHhw5TMw5lk/jhEcj5hZ5plnnmllrpl7bWm2btxULEzJ1tRrrZoTtsZjn9wOC9emvicMi/eKJc7GKK4N2Wdp4bdsQmZR5Fps/G9+V9u3q9nheQ7j8YknnmhlrnXsO5fPY1mfKu1YyeRnGS4r8W5zOscZPlelTy/36zSEEEIIIYQQQgjhLiN/qAkhhBBCCCGEEEKYCWutT2YxMHtCRR5bkSYRyrooh6e0yjLU2G7KJm/c1A78lHyzzpRkUj5KmRVlXOMMONYWlKrzHN6P9gSTsfJ5KHszS4xJRvlb3pdyVr4/PsuFCxdamdlHTHa+KZa2Qz7jztqjksmiIsPmdWwHfr6fXbt2tTItMiaZ5nvmNQnfCaWXfBb2R8okaZdgHeydM35Zpm1iLBPl/RhHlIxyjKBElTBemMmGsWwZaizDFrF3zDGB0nk+P+vMd0CbisU1YfvweWktoDSd2DXnRMUOzP7C90lJL8t8tyyTypxLTFZ86dKlVqZUmXHELA1mq6UdwayIfJ+MFdaNscbrs49QCj5+Xo5HtGgypjiOmO3XrCPWvpUMMr1Uflt59xVreuX6S7NXVLLxmLz92LFjrUzLOOcvk+Gz7/S2MTGrwjhD6Kp7WX+0rCn8LecEs0Tx2W29MbZr8X60UvNalqHKtj+gXZm/Zfm5555r5TfeeKOVmVmm1wZVyfo0JTOf3ddYwjqW2Ps0G7fF6XZkyqqsn4ltZ9H7bi0eOZ5wjU0413GssPgdX4drPJ7HtQLXtLwHxwuuG774xS+28qOPPtrKXGdyPOWagLFp1qpKmVQy5rLMdmebcJ1gFlCbGyrfs1HUhBBCCCGEEEIIIcyE/KEmhBBCCCGEEEIIYSZMzvpk5/TS+9uK/aoiP6tkfTIofaJsn3JxyjkpBzMJHGXt47qZJM7sUhV5NtvFMjrx+jyfu7FTEkZJOSVqlLdRhkrpP9/ZuXPnWvndd99d+SzbIe9cspybVPq29RGLEcsGxb5DK55lCqKNhu/cbDQW77QHmfyb9TQJJPsjn/Hhhx9uZT7Xun5nsmfLcGQWRT6P2TsZO7Slme3Nnp9jmWVt41jGuvFZ+Izj7Dur7sXrU6rLeOc4w7rNFWtjk0/b+ZZ1oWJdtBg3OT/nHfYpypCZWeno0aOtTDsS60kLws2bN1den33k+vXrrcx5jBJsSq3ZF8wWPf63ZV5kDFqmSdIrW++1LFUyVlgdzFJhEutKxkMb0xjLS8vIZjYaPivnBY5DfFZaBadkcarA9uZ4zOPsv5V1smVgtOxRlXWlZZYZn894JpxfrO/ZdVlv2u15L7YR3/FLL73UyhUb1JS14qa+qWyttrRMpuzDZlG1rRrIpmxQpHKditWVZT4vj7Ofsi+bFYm/5bcn51bLREzG8c61OGOBscn53mzyn/3sZ1v5ySefbGVaSe1bomLzr3z/2xxayVJK7Hwbi+xvEGZjM5YVySGEEEIIIYQQQgh3MflDTQghhBBCCCGEEMJMWGt9quxoXtkluyLJppSLMijDdlKvyLx7d3C3+1JCduLEiVamDYrybErZTTJsFodxnUx+zN+zHe23ltWCO61Tikf5Gc/hTuC0uPC3bC/K7JiVg1I3yuoodavYuyqZxowl7JbP92ySS8uQ05uxxOSaPIf3Ypnvzd4nZa7sO7TUmD3OrDmWfYj9zixd7NeUm1LyOZaJmt2AMk6OBZSV8pn5DCZtJ7w+y7bDfKX/VzLC8HmtjcxKaRJewrGL78YyHs2JyvximVYsjirZW3ol3zYOsG6MOxtPKHlmv3vsscda+a233lpZtvGBNivCGGJ8EMrjh2Hr/MIy5ybaBtkulmVnp+aR3mya9ls7buNY5TpLwKxg1n6MBfbPGzdutDL7vMnYK3OuwXfCvs05yKwQ7LOWAYnPSPhbxoqtz3mcdbM15jB4pijem/MI68p2ZJzy2cw+zXWD2fkvXrzYyhwfSCWbV+UbaTuOL8GKSOwdWrZb0ts2m6Kyfq5sKWB2W7MfMm645uL17buK8ybPt3uN78GxgPYl1unTn/70yvLp06dbmZY2rgN4HY65Zm82W1MlM25lSxCbMyp/g7B47LUlRlETQgghhBBCCCGEMBPyh5oQQgghhBBCCCGEmfCxsj5VzjFZ0JQMA5Xds/lbswTZTswVKFGjtIwZLsziwPua3JTSS5aHwXfr5v0sYwftJRWZO+Vnlh2DclDuCs4yn9+krTyHGUQsm5XZrzYlbVyytJtUZJkVeWwlpsyKZbYOvkPa5nh99gXaK9h39u3bt7KehO/TslYx1tinGFt8xrFMlHU1CbhlpbJ6s70oBaZMlGXet5IZyMZW1pn3Zf1ZZhtxjOJ7Zbvzt7y+7fzPvrKETBZmQ+Bxs65S0luR1RsVC4vZ11gHvgdmZbp8+XIrM8MD+w4l1nzPlrmL57A+hH2HsmvOxZz3xnWizZD14P16ZdK91gZSsSNVzmEfMnuqWUXYPjaf2niyNCrvhG3GMYn9n+3EOLW4610/V94548XWVpZdxNZ9ZtW1+jATolmP2R95r2Fwqz77JOOZljPbVsAyHvJeHC94PrPS8BnOnj3bypxzp8T+prI+bcpeMSdse4be77U7yZR3aHZbxoFlIGR/ZKxYX7AMU+Prc83NcYFrPMYgz+e2BcxEZVthcFywbS4qttXebVkqGRUr20BYf7Xrk0qcLjeSQwghhBBCCCGEEO4y8oeaEEIIIYQQQgghhJmw1vpkWZkqO4vb8YqtidjxisyssvtyRcbLZ6f0+vDhw61MSZfZvlgHk/NRYja2PpkViJJRSt94P1qfLOsKJaCUtFLexjrx+teuXWtls47wOKWnfH5aq2hrYTveunWrld99992V9e+VwJElZH0ilWftPb9SZj+nbNIsUfwt+yzfId8J+86hQ4dauSJzNsmz9Xd7FvY7Ss1poRiGrf3WZNuV3fwtM5zZFSkTZXtVMvARe36zh/AcPjvlr9yxn++b53NMY1zTVmmZHpaAWSHsndj7Mdkzr2l9h+/Nxkiew+uwzHMYs+yPlpWGUmjWgXYJXt+sJZRLs004n4yz0jDDi2Wv4D1s3WNZdipWqd7sI/Zbk1vb+yOV5+J75Vqksn5awrxpmUBsvWrWnEpGxcqattJmZlUwbI7mWMt5ieOuxQGP06bAcdoyKrLdmPFtGLa2r4077Ie01fO42SZtnW1rYz4n68Dn5DOwDpW1SOV7Y1MZ5ZYQj4R9hv2T79wy2c6NitXVtghgv+N8xy0izGLMeZNzrsH7cl02DFvXuIwvs7HT4sQ6ccyyMcXsTvatyraozMWk8m6sXLEW2lzC81m2+XrLb297RgghhBBCCCGEEEK4I+QPNSGEEEIIIYQQQggzYa31qddSQUzeWZEXVbKUWCaaivXJpEx2jkmsaX2iTMwyelDiZPelrHQs46KVgLIxSgP5G8oHaWWiRI2WkldffXXlvWhr4vXffvvtVr569WorU0rLtqOsmm1HixOlbpTPsb34Dig9Zbl3V/ClUZHdWT8n22GDsvpYe7Mvs49cunSpldkXKPs0+T8lmSZnpq2H16R9h23IGBo/CyWqjHnGkWUDMnsJ68TjNvZVsviY3YvyVI4PzOjDe/F5eQ7HnJs3b7YypeOU57LMtqJMn89bkf7vNGz7XnuvZUupzKcVybRJj63vMJsZ44Uyaf6W53BOZFZEWpEuXrzYyrTVmuXEoHR6bEvk7y0jEvsVxw6LTYupKTaoyrrBrGWWTcMyirGezM5WsQP12irnhMWjZQLdlH2e9GaHsT5iWZ/M9sjn4hqN8xrXYuxTrAPP55rLbFZcJ/K+w7C1T3Ie4f0s+xTjjtmgWG+zPdtWBZz72b6nT58eVkEbFNfule8Kw9ZSUzLvLg2+H45/7C9Leb6K3dzi2jJqco4yGw3PsfGB8chYHteD629+b1pdzW7NcYH14BjBNWFvlq9K/6+smVlnwvrYmrzyXi17n7GMnh5CCCGEEEIIIYTwCSB/qAkhhBBCCCGEEEKYCWutT6SSyaj3eEXiV5EN9u7kX9n1mXWj5Gz//v2tPJZYr6oDr8PdrCnho+SbdaBMbBi2Pg9/QykXZWO8H6/Fe1MWT0uRZWChRI02h+vXr2u9P4LtbtL2o0ePtjJlfJR5s90pw6X83e5ru3YvzQbVa2Wyc2zn+U2db9J4qxv7LPsU5YFmtaBlh33cdp1n3LBM+TPjbJ3thv9nGZEYF2wLnsPrsB6WXYJwnOI12UaMEbM+MdYYgza2mESWv+X5LHNsMZk+6ZXC7gRmYemVvdt8ZHHH89mWfD+sD/u22WhojWV2FPZxxjL7F8d1Wl1Zf86nX//611uZ8mobs82iNLZKcb5gu7C9WG+zjvA6jLXKmsOwvmI2M1pT+D74zngdtgXLfPdmS+Rz9Y7jc6WyLq1Yn7bDQl25DuPO+nLFEkKLscWy9SP2F85FlqGF8x7tsMOwNaY4BnHM4nzB82mNZvzSyse24DMwvngvyzzFcZDX57OZpbe331TWqBXL4dJi02yatubkOZXsXjtF5T2zzuzLXPdZhkReh8c5R7GPWMa68brS1sqcdzg3mZ2HcW3vjM/M9mLmKtaBv61k0rKsmbbeqoyntiYz+zrHHLZ1rE8hhBBCCCGEEEIICyJ/qAkhhBBCCCGEEEKYCWXrk0n2TOpbOW477dvO7iYjr8jCjUoWG0rOjh07tvK42Ywoa+Izcod4PiMtR5SxDYPv3G2yOcpMaWuifJpyLNu1n/YiXpPHTWLNetIqZe1FCfeRI0da2bI+sQ6Uy5s9hMxBFvlxqUjRK1lmjIpEvDd7RS+UD1++fLmVKaWkNJKyaB5nHDHueH3LrMJrsv+ue0bGEevKdqR8mrFgdg7er7IrPi0lrA8zKzGOaDmkzJvnWCYaYuM168/xgW1tz26ZO+aKZRUwCa3NZZVMNCzbmEr4HiiN5lzG+YEZmmiTtYx8Zq/hPHPw4MGVdaPVkfMMY5bzg80VzFI4xmxNzG7FGGH801pJSTqlzhwjKplfLMsh78X3wXfG2OS7f/PNN1v5ypUrrWw2YbNVViziS8j0RCpSd1LJnNhL7/xo/cWsu8TWhixzPuH6lr9lv2B8MQYZ75x/+VvGx/g3jGGzQfG49XnWyTIx8dk473CM43HGJudWjmt8r7SWsX2tDpVvqt5tI5aSFWkV7Oecy+ZscerF3rlZkyyjIteSlbUHrUv83mSsDMPWTGqcd1hXxgjHFLNVM5Y5bzJG+L5Zb853nKPNutmbXdHsUfZ3h4o9iuMJ1xgcB9m2xnIjOYQQQgghhBBCCOEuI3+oCSGEEEIIIYQQQpgJa61PJs3qzbxhEm5imZLMEmXY7v12jsmXKNEyqSNl3pRiWUYUStQoB6Nlh5IxytKGYesO2yZxs6wevJZZoijvpAzMZF3sE2bLMssDn5nvibI8yyBj1hTKX3mvyq77S5NOVmR3fA+V+K1kd+ptsyntyt+yL1POv2fPnla2rEeUVFucmv3OrCLjDGO8ltkrKpYl3pu/5f04dvActgWtE+wrr7/+eiufOHGilSnL5DMz1nicdgmOLcwKx/HOxgeT+dr4ZvLUudKbkaMSdzxOWwHficUp288yazDbmGUhM+sE5dMcj2mt47ulNJhjPOdZs2BYZquxhLsiXa7IpxlfLHPseOutt1qZ8mbLzEhbE5+BWbIsKw3fGed0ZsO5ePFiK7N/TBnTp1hqd5re5zOrlNkuptiBK+tSWg3M2mBrYPZTjs2Ma8vKwjGeMWhZlQivOV7T8t6WKYm2Z8a2ZSBl/WjfsGxVrB/HHcYg453zL9eojK+rV6+2Mtud9eeapmKPMu7GjGzsJ+yHFWvm0p57HXz/bAf7DiWWhcmypo7XtGxr/h/XBGbJZqwxxvkM9p54X8vORns+1/pmdeS9bPyy7yiLQbObst05PvBbm3XjWt2IoiaEEEIIIYQQQghhJuQPNSGEEEIIIYQQQggzYa2OvDebDM8ZZyy63fkm17Qd0ClT4r1MJl2Rp1qmJ9oCKJOkzMpkzrTyEJOOMzMD6zA+j//H5zFpLDNq8Hlo52Db8Tkpk7XMASZPJCbj43FaMyixo0Scz0hZHZ+FctOKXJIsQcJt0mjb3byyGzrZDrvYpmxQjDtKZBmD7ON8/+xTtM3xOOPM7GOUYQ7DVksCY4d9lbYryrYrNjbbtZ9lnsN4ob2Edidm5uBvmbGCUM7KZzl//nwrMyMIz7GYsoxUbGuzuMyVXmtIJR7NamGyXMsqyHuxz1I+zOPsXzzO+YdxxNixMmOCcffwww+38pkzZ1qZY7nZbWlPpg1oGNwux77H56dEmf2N8wvjiFms2O6c48zqa5Zem09pr2CsvfDCC638xhtvrDx/Stai3rXgXOnNwlaxCW8Ks7AwRhiPtOCYvY/9y+wkloXPspFyHjh+/PjKe9kakGPFMGyNO8YpbUGMHa4PaS+iPYr1Y73NXkTYJ3h9xjvHqSeeeKKV+Q4ee+yxVua8RmskbVm8F+ffSrY/6zdLyJBoWHavStaspWDrPj4744M2IMsmals+sO8zBi1D7zBsbV/rhzzOe1gmZltPc43HZ+PYx/nXskHxW7WS+dLmMsuoZ1uxWKzZuorPxfWQEUVNCCGEEEIIIYQQwkzIH2pCCCGEEEIIIYQQZkL+UBNCCCGEEEIIIYQwE9buUWPest59MCr+NmKpuumBo6fN0nDR22heavpi6Y07duxYK3O/B3pv6Z2lN858xIR1HqdFW3XOMGz1uNF/Zyl0uY8APb+21wQ9kNzXgj47889OSVdpbcrUoqyD+RZZZ9vvYopPfwmY77WS5rH3ve2UL5jjAFMAHzhwoJUtPaD5uenbpV+c4wyfl+kBh8HTCHJc4LUsTTKvw37OuONxxgXjndfhcRs3LD0560+//8svv9zK3DeAe2xxfwzbD4KwPktOOVqJNRuHbA6tpOq2a9KHzmuyP9q+RLwX5zXOrZauk32QMUsslSXjkfHBfsrrW38fBt8Hg1g8cu7nvbknDud4lhkvPJ/72FgaesL3x/f0/e9/v5W5pw/n+u2OlyXEI5my35rFoF3fjlf29WG/YJ+y/Z1sbmE88pqMXztObO9Errn4jOynnEO4hh3/3vZX5PqY+9DZXjS8N+egyn5DfDeMO45fbHfu0cN03pW9Inn9s2fPtvI3v/nNlc9i+48YS9tLylLD8/3bt+ESnm+MzeO2pyb7oLUJYXtW1gzj9QP7uY2VrIftBWn7tDB+OQYxdjg+cBy0dYPtuWr7HNnfMlgfawfbS4hjMZ/X9u0dj4mriKImhBBCCCGEEEIIYSbkDzUhhBBCCCGEEEIIM6GcntuOm9WCsiuTqPXaZSp1M+yaloqU6fgop6KsmHJQSsD27Nmz8reUU1HSaBaqsVyc0mvKw0wSyfNZpvySsjG2qUnBKym2LYVZJfUsn5lyVlpcKDHl+6O8nJJXpmvl9U32tgQZpdXR0t9VLIebSsO93fBZGI8mi2U/ZV+mJYr9aO/evSvvxbim1WIYtlo1+A5o52F/tnSEZv9h/fg8/K1JLikxpXSc9kDKVk1Sfu7cuVZ+5ZVXWpnjoKVErKTnZp17rQVzwmTGlRTHlePE2tVi3MYEzkEcazkf0Yo6TrN7u3uZjYv1ZP9l37RzWObYz1S9w7B1jOB1zUpssmfaHSl1Zpp7zjVcT5htm/UxuT/n6+985zutTLsT7VSbihHrf3x/S5s3yaZsSptqb7Yl+x3td5x3zOrIvsy+zz5idkJL2815hnHDOZH9mv2d8cT18PgZLDYZa4TPxmeo2EIq75514zj42muvtTLXz7ReM4W3fRfxub74xS+2Mt8B7Y0cBzbVd+eE9X+u5dknK1bEOWN15jPa+Yxl9heuH22daNtIjGOF/YdzmVl4uLbkfGx2TcY4780YYVuYVZ/PQ6sU4frG2rey1QvrzPocOXJkZf05PpIrV660sq2ZSRQ1IYQQQgghhBBCCDMhf6gJIYQQQgghhBBCmAlrrU9T5Oe2gzLlVDzea52xTFJ2jknsaX/gDu6UdlPGee3atVam1ItQ+kSpF+VqtBfQBsV7UW45DFslW5QDsh0po2K78Lr8LZ+H8jZaHuydVTK5mPzd3istGGyvt99+u5UpnzWJHWXqlADynVk/WxomP7cYtPPvpMx7CqwD5c98tzyHUk3amojJMCkZtYxnY8wqyL5NuabJSvnOCOXWfE7GhVm/CGOT4wZlom+88cbKMu0xJt20fmbxbtmJ7Py5Yn2jYmXqtf32Zl20tjSLE+cHns93zhjZvXt3K7N/cSzne+aYbfJsszeaxXCckY19lb9hPRh3vAdjk23HcYS/5fm0YHAdwHjkXMznpM3h2WefXVlmm+5UNsM5W2RXUbEibuqZeu0pjGWuPymrZz/nnGXZUbmOY1/jOZyvaBukjYBz3zi+PoJxw/XturnSrNqWPc6sB5X1TcUWZO+e9eE648UXX2xly2zHMY5jAsc7zum09jMDJd9lxR6zZBsU5yPWnW1p59v2BnOm8q4ss5tdh+ew3Rg3jC2OD8OwtU9yPmZ83bhxo5VtPGKZ6wazsZnFiddhvWk95hrF+grXt5yLWWY7cmzhO9i1a1crM0s0z2eZ/ZJtNd5GYRVR1IQQQgghhBBCCCHMhPyhJoQQQgghhBBCCGEmlLM+mf2l9xxiMlSrQ+W4QRkYJZ2nT59uZdpoaCOg/JDSUO5Sb9Iyk0/yt5Q0MmsEd5Efhq3SNMrSeJxtSmmZydwpYzUpODH5Xe/7qEhPzbLE9rKMCGxryogtI0LFljBXzIJWsThZNpalSNr5ns1ewXhkn6U8m9Y6xhZhe45lopUxkbJn9lXaIohJMS07gslN2S4sW6anF154oZXPnj3byhyb+CzEJOiV7DD2vDavzJXerE+VtrE+xf7ce1+zQfHdsl9w7KTFifOmZSdjfL311lutzLmY4zTnPo79jDvWme3Aug3D1hgxaTjfAW1NlHZzXOD5HF84pxiW3enSpUutTIsTM79w7t6O7E5TzpnzPLGKijVpyjzYm7WN/ZT2F8rqKZ+3DJyMF5sHOI5aTNGaw3pybmXZ7La85tiix+dhvc3iZVapO2n54TNzXLN1P78NGL9sL66rmUWR3xtmYd7UN9JOY99KNlea5Xpp49AwuI3Xsu/yHM4h/K5kmbHMeGT8jbOrcY3KOZVzItcHdi2OL7YVgNmd2Ict4x23LFmXxeoj2D9YfzvHxlmew/tyDcDYtz5tNjYSRU0IIYQQQgghhBDCTMgfakIIIYQQQgghhBBmwu01NyuoSMtMIlSRK/bKTe2alJNRinXq1KlWpmyKEiRKGl966aVWZkYMyrJMrkbpE2WMlEeZZWNsr6A8knI3y4LD821Xbd6D51MSRokay2a5qchQTc5ofYXPRSsapfMm7yOWCWkpVp+PMFlrxQZllrWlSWWHYWsfodTT+jv7C2XIZj9i/FJ6yewu4/uxrSl35BjEtqbNkljmHl7H7su4pmXp3Llzrcz24th04cKFlb+t7IpfydZk2T1sbFlavzTbkc1Tho2LPG7ZfnrbjG3PDC9XrlxpZWZX4BhC+yxtCpxbOZ9y7mI/5fhNuwdh/6UcmzExjmX+27K7cYxgW9CCwkxMtEGZrZrvgHM8pd20gdHi9N3vfreVGac7NTdV5vElsKk1Z++9DLalZSNlvFumFGYyYd802b7ZRrjuO3jwYCtzbch451jBvs9Y4W/HmZFYV86DPI/PxnpXtlfYDixbIrPLcb1qtg6+D74njokcKzkuWbYwsrTY5DPZWsPWPsT69pypZGzk+MBYMeut2eIZN4y/8XYXzGrEudks7Vwrs35m1eb74295Pu9r1i/2CY6bjB2u12ktJIwvi02Ov/x+4LthmTZqZtBct15ZRRQ1IYQQQgghhBBCCDMhf6gJIYQQQgghhBBCmAlrrU8mcbJzCOU/Jmk3ualJ2nrlt5RHUa5JyTRlR5QyUaZEu5PJnSqyKdbTMrFQ5knJ8/jflV21WQ/K2ng+Jaa0Odh7NbmpnV/Zkb4i62f7so1og6K8jfJHltk+LC/NAmR9vmKFMFtJJQvG3LBxw7JjsC+YVY6ST7aPZUgbhq2yTJMoc7zguMO6mkycMk5ek/diXNOy8txzz7Xy888/38qUuVcy6xCOAxUrXWWstwxGS6bXamHz45TMHmbxNFsW3/+ZM2damZmYaAOk3Jr3ohWC0mbaJfhbjt+0PjEmOEdZFkTWbRi29ivOoWYNZmxSwn3kyJFWNiupZaVhXbme+LM/+7NW/s53vrOynjam30nulswyZFNZnKYcZ3/Zs2dPKzMDGtd6XPvYOs7WiZwH2ae4/rR13OOPP97KjF/GB9fGfEZm6RyvablG5/hilli2I3/LmL2TfZL15BzNdQbryXWDrUXtPfVa6WzcnyusO213HM/ZPzl3jNdjS4PPxZjls1smU/YR/pbff7Y25pp0PM9wXLC1Ge3Q/D3HBdaJccGY5bNxLmdd2T94nP2c1+d4xNhkPc2Gb2tUnsNx8Pz5863MsZv2b66BOHZbNjcSRU0IIYQQQgghhBDCTMgfakIIIYQQQgghhBBmwlrrU0UmbRJuyhUpF7LrV+5bOYcyKMqyuPsyZWOU/9NGc/bs2ZXn8FlsJ22TvJo8lbYDSrcocx1fi1Iu1oO/N4kXJd8mY2VdaRHhb61smQnsWXrtUbw+LWpsL7O78LmWnMmikunM+ifljhV5pN13DlTshMzGQPkhJYpsE/YXSk85boztFZZ9ie3IHek5PlJWWhlfLMvbjRs3Wpk2ih/84AetTEsU62ZZFmzH/krGjd6MfZWsFjaXzJWKpbJiG+3Nnmfvx+w+LLMvcG7ivWhr2rdvXytzruQ5zJ7EujEbFOXu7Gucu21esnYYBn8HlB8ztgll27wuZdu8t8WyZXd69tlnW5nz19zGWbJkG9Sm5vze31ofZL+j5ZZrV7PXcCxnTDEuaCey9R3HXcsayvvSAszrsO9zLmKcMaPguN779+9feV1aGGy9Ypn2prwnUvmt2YRtrWb1t3tVnsuss0uDdefa7OTJk61MG+zdZH3iGtXW8+xrjFP7TrdvMv52vLZi3LJOjHOOWYxNns/7cd1sGYc51rBOXNPzOfls7De8Ps+pZAS2NqpkJaZ1i2Mx+7FZW40oakIIIYQQQgghhBBmQv5QE0IIIYQQQgghhDATPlbWp4r01WRElUwgJgUnJuszeRHLlGhZNgZKvk2GbBIwyzJBSRelWJahiOVh2CoBpY2Cz2AWIT4zj7O9KN8ym4NleCEm0azYACrnU7rGNjL5Y8WWtjTrk7WNZSMhPM72q2Qum3M7Wb+wcYZScFoZbEd9xso6ewXjiG1H2Seva1nJbJd7ZtFgRqcXX3yxlWmvoL1xPKasupeN++w3NkZXJOgVi4T1rcrcsNNUrExT2qZiJ61Yg82mw9+aFffy5cutTGsS7Qtm6zDrB+9LuTHr/NBDD608vq5fWFYIk4lXskFV1jGWFeLb3/52K1fWGdvBFJtS71ptTvTatrbDzmVWRM5HbGPaCd94441Wpu2bfZxrQ5Y5/1BuT0sB11C0lnB84JrW1glcq77zzjsrn2UYtm43QHsk24gWSo4jvBZj075VNvUuK5n5KvYlvrMKZnGfMpfMFfuO43uubK8xZ9iP+Iy0ErPvV7KBmYWX7cnzac1hzI7vwXmTY4fZmjiOmH2Y9k5aJflbjh28fsWSz75iVNZqFu+sJ22oPJ/f5nwHfBaO+1rP254RQgghhBBCCCGEEO4I+UNNCCGEEEIIIYQQwkwoZ30ilR3KbXf23l3VK+dT+kR5kWVyoQWJ0kvKryx7EmVNvD4lZ3x2SrdoWaBsk9JTsyiN783fUO7F39hu4LZjNnerNouIycAqmcCIWTxMGmr9gNdh2xGTxpl0fgkyyortyGR9lN2xT95///2tTMme7TY/NxjX169fb2XGmmVbYptY9hz2cbOVjf/P7By8FuvNdme9aZ145plnWplZY2hHoTy9kiHMpJ6VbH9TsFizcWYJsdlrceq1O1Wk95VMI2ZhqdigOK9xDj137lwr08pgGdYs8xhjyMqWjWHcnpZBhBZF3tts271Zspg58qtf/WorUya9U/25knWxcn7vdXaa3myTdrxiPyS2bmSmM9p6aBdgxjDakWhFZFyYdZfrO/ZZi0f+lv2UdWNGRbYP5x9aKpjlbVxXjimsK8cdW9OaDaY3m5JRsTXZd84UO/CUTIFLw7JZckxlf1t6pieuv/iNxbUorUk8n7Fv9mGWGe+WXXNsFbJvK7MlW2ZS3s+yJfIZ+F5ZB8v4aBmzOG5UbFA2v7P+LHOM47PwXlzb2/uuWCCjqAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMWGt9Mkx6XTnH5H5TMh7wOpRx8pq0EVBKx8wMJvus3ItSLJOlWVYdk1nRBjEMW2V/vB+vRRmVydX4/HweysyYvYPtYvK2XltE7zmVDB8Vub9RsVnNicrz8bhZeHj85MmTrUxLlEklrT47hckSzV5BCTflioxrkzyPLWBmP+TvLdscxybK3Gl3+tM//dNW/t73vtfKFbtmb3+eYoWYYvUxWbvZXeZKJevIlMxXdk5vFsVKZkazEVCazv5LawOP09bBuYsxyHtVZNfGeDysWBY5XlCWzHvTwsEY51jJrDx/+Id/2MovvfRSK1vmtTvJpsaEJcyVpNfmUpnvKtdkrLF/McPLnj17WpmSeWZ34nqVfdDWkGZdNLsE5yXOm1zTWqYyns86cBwYZ31inNO2cOrUqVbmeMEy25HjCOdis2rbO55i8eu1I02xQVWyrc05Q+cqWEfLkEt7nPXzOWNbO3DbAc5RBw4caGXOM4RzMePD5jq2J88Zz5uc+xjbnIMtoxN/a7Yr1oNluw7HCvtOtgyqxLY2IPxtxYbPOnB85Dl8N1y3M/uVEUVNCCGEEEIIIYQQwkzIH2pCCCGEEEIIIYQQZkI561PF2mKyoIoMv1dmWJEBUs5MySjLzJ5UkUlSKmXWCcpKKcWiRI0yarNmjKVulFTx2Sibo6TKdqum3I33owSLz/DAAw+0srV1RYJvVHbRr1jRTMpvTLEl7DQWX/ae7TizsfzWb/1WK7OvXbp0qZWZdYJYxq07SSXDGLNJsL9Y3zEr4RjLOsHjlPOyHRmzr776aiszu9O3v/3tVqb8l3Wq2FAr42YlfnvHZbMvsa1pOTNJakXyvdNMsYJNsawZvXO3jbs25rD/cm5lFrJDhw61stlYzVrIMu9rWZ/Gz2g2Y87NlC6b7YrPxvh9+eWXW/nP//zPW5kWRcsQcSf5pNqdtoOKzcWyCtLOcOTIkVam/J+Z1JgljH3W+hHHUa5R77333pW/5drV5jg+L9cMFls2B9JCNQxb1982HzMzlmVnM9ss6Y273vNt7K7YqSpMyRK1BNge7Cccm2kfWYrdybB1GS19jM3jx4+vPN/WqxxzCMcHwm++cf0Yd1zHMs55XftWtbmV38Z892aH43XGdsqP4LhjWz+Y3bryfcX24bhsVnBmwaxkoSLzX/WGEEIIIYQQQgghfELIH2pCCCGEEEIIIYQQZsLHyvpk8naTfW6HLcLkSLQUUVbJ8ymhsp3DTUJosm2T89PWRLk05XwmO6cUdhi2ZoR57LHHVj4Pr8v7sa5sI9qaKD87duxYK1PaXsn4NYVeC9WULDNLl07eDttJnVJEyhWZgeLf//t/38qvvPJKKzP70IULF1p5DtYnPiNlnJSaUw7KcywzDPsOY3xd1ieWeS1KIq9fv97Kr732Wis/99xzrXzmzJlWpu3C5OZT5NC9lpteS6Ndk+/A2p3taTv5z4mKbWtT0vjeDCQV61OlbpbBkP2U/Z2ZazjnUAJMS4W9cytX283qSjgPcm6lHeXcuXOt/PWvf72Vz54928p8/ilZLaewHXanpdmEyZS629rPsqtwrmFGo6NHj7Yy7UiUyXNurfQjs04wpmgbYewwXrhepZyfdWOZvzUbLtch4/Gbz8a1K9clbEdal2nV53NyTJmSYW1TY3Hv+tP6qM0fRu9WADsNn4nvmfFlsbYUbD3J7St4nP3avr34PccYN0sUsaxKw7A1bhmbLNu6kXOl3Y/n0+7E52E/YCzzOMcjjn18Zh7neMJvIXuWXsuhtTvfDc/ncSOKmhBCCCGEEEIIIYSZkD/UhBBCCCGEEEIIIcyEj6Ujs12TiUkfK7K+3jqYDNvkfib7JFZPSu8osbRrUtrJMiWmJuEety0lcZSBcWd/2p2487a9A8pHaRHhs1G2a+3SKw3ttU5U3pndqzfT2BIsUbZ7OmPBns+kpMwmxEwTn//851v5j/7oj1bed6ewMWTXrl2tzP67d+/eVrbxgW1o9iiTko5hnLJ933zzzVam9emFF15oZcpHacHo7as2vliWO6OSnc36nI3vbB+ObxzTlibhJhXpuo1zm8qQaNhve7OXsG+yzPGB8w9lzrR+MKY4/1j2OusX437KNuW8S4sT5dzk4sWLrfz666+38rPPPtvKzNRm2aPmTK9lY8nWp954sWelXJ0yfPb53bt3t7JlG2P/Yj+iNZZ91urGeGHdGDtml+C6lDHB/mtxbVsH2Pp8bEXiv3ke575HH320ldnWHC9olWK5Mnf0Wk83NW6SKXG0tBg0+Bxcd7FfsH8uYZ0+hvFIOw77tWXZ5XFuR0ErD9vQrIjcEsQyIw/D1jmb61Wu06wezBTL4xzLOE6xTpallfXhcWZH5pYdlUxwFVu1jSG8PuE75ljE971nz57b1o1EURNCCCGEEEIIIYQwE/KHmhBCCCGEEEIIIYSZsNb61CvbtixIttMzJUK2o3VFrshzKtL4XluM7fBP6SWf13bON8koZVbr7BX8Pa9LWZrJ2swSQ3gOpa68Jp+zYlebYpUivdmg7LdLtjsZJtMj7Le0Apn1gFknKKuem7TfdtGnrPLQoUMrz6EskX2fv7XxbTzOmKWI7UXppmXEoT2K40ilr06xJvVi9anEl8nFOb7x2fmelmB9srnSypuSzFesxJW+Y3Mxxwebf5mBge+Q51BizXMOHz7cyrS+8beVrIPjccn6GOORcxwtw88///zKMi0rlgVjzmwqu9PSbBeVzDkWd5w7aEM4fvx4K3Ou5HVoBWCZcwXHfq6/bJ619TZtv6wPz2eZMcgyY8Us/FxLsJ42Zo+fxdbBHCMYaxwXLEsQz2EsT7H4bYfd3sZoO96byXRpsWmZy9i3l7hON8sk45TjCeG63bayMKuj9X2ec/78+VZmXA/DVvshr8XvSsuqxlimjY1rCMYs+zzHDpuvad3kWMlnoOXKxiaWzXZeyWzNPmrbo3Cs4zd7pU9HURNCCCGEEEIIIYQwE/KHmhBCCCGEEEIIIYSZsG1ZnywrjUm+KYmqyOcrVoBe243Jv83iREmXSUkpGeOO5ZRBmfR9DKVclHi99dZbrcxsPZSo8dlYb8rG+AyUnvL92U7XU6wZvefY+b1Sz6VJQ4llwbK+RImiZWxh3/na177WypQes69tyvrUm32DULp43333tfJDDz3UyrajfsU6YRLusTzZdoxnzHMsoEWEknfb/X5Tkt/tjkE7bmMxx9ZKeQ52u9sxJbOMHa9Yk6Zk1TPLTuWaZv35/ve/38rM3GLZxpgNipYKy4plkupxe5rlkPfjccrBX3zxxZXHGadzsztNsXj0Xn9pc2hvvDDWKGPnPMJsmVw3MS44b1qWtIqF3yT5tPvQUmE2AtaNfZ/1IZZtrWK75jnj2Ld1I+OLcyWfjfM916W0zdCGYFkqp4yhletMyS7WS8V2O1fYfvyGYp+pZtucE2x7zmvM/GMZgYjNcZwHuc63rIaMd2YZpR1nfB6va1t12FYm/G6tZGcjNg4yljlmcS1NSxjbziygPIf35XXYL/nObF1i390cr/mejChqQgghhBBCCCGEEGZC/lATQgghhBBCCCGEMBPKWZ9MOkQsIxCla72ZLCpZnywbhcnn7ZpmbeBvKX+ldIuyL8pfaXeg/MzqaVaOYdgqiaOdg5Js2p0oCbPsBXxm20mcdTW52qYyKE2RaG5KqrqEneUr7cR3y2diP+T7fP3111uZsr59+/a1MrNE0TrQa0nZlDyf/fTUqVOtTGn63r17W9msTIwbnkNM8j2un1mWKOe+detWK1NiaraQKWzKykR6s05YhhJKQEllvpkrvZlzKhmxescnG5srY3alzmb1ozSYfZxzEeORVgb2C3texhAlwxyvKC8fhq2SY7NCMIsEM94xIxufoTemtmN+2ZStrrfcW4c5YbL3in2Y/YjrQEL7AMucK832blTannVj/7dshOzLlsmR7cP6M1Z6txcYP4udx7ry3hwjLMsjn5/xvh021N6se5VsMr0xa+P40uZNwviidebBBx9s5Zs3b7bynNfsfA9cP5vdic/Ifs04rfQFriu5vmU2J85146xP/B6sWEPtG5MxyLJR2VrF4toyrvJ7me+Az0jblNlKx9/kH2EZS3l9jrm0c7JuxnIjOYQQQgghhBBCCOEuI3+oCSGEEEIIIYQQQpgJa3VIZv8hlPzQbrB79+5WpqSKUmKzZphVqkJFWliRBPJ5bbdmyru4qz8lr9evX29lSrX57LwOZX6UvQ3DMNx///2tTAk425dyL8quCGVjPMcsWJZdY12mjVXcyaxPJgHtlerOFZMEmnXGZM987meffbaVb9y40cqHDx9uZetTU6jIhC1+Dx482MpHjx5t5RMnTrQy+y9jjbHC9uEO9/ztuj7O37ONGKeU6lLObZk/trtPTrn+lKxCtmM/3yvHH8v6M1cqlqLeLE4VG1TlfML7VuzAdk3LAsEx5PLly63MtYHJiu39E0qMOW+OsfGfcE5kn+Q9erOw3Um70HZYnHrLS5tDezOHEvZzWt25vuUYz35UGR8Ma3vafdhPORexzuzjrCfP5/VtDU8Yg5xDLYbG97CxhlYrtjXncta7YrWo2JemrEUr73U71sNLjkfWne/N1mZLgd9JnONoeWH80ppj227QykQYp7b2vHTp0srzx21r/ce+ARmPti2IWUYr4wDP4RrStnWw7Vc4TnF9b5ao/fv3r/wt3+XZs2dX3pfvgNe0rJZGFDUhhBBCCCGEEEIIMyF/qAkhhBBCCCGEEEKYCWutT5Q1mYSQEieW/92/+3etTJkSsyB9/etfb2VKuSiZplSSck1KmSw7VSWLhEm4TYZnkiXanWiDsh31iWVBYXaMYdj6bLRRUb7GtrbnoRSVUlLuem1WNKu3yct7JeKV324qW1Ov3HlOVOwMFVsE3y1teuwjlPWxP1cyVlidp9jRGI+MkUceeaSVKVfkLvq2M71JvqvSdMYmr2U2H7Yvy71tuhTs3VN62pvhb670ZvDozeJUuU6lDkbFomVzgsmWWaYc2KyFlEhTks1zaNngmDC2DFvMcy7nusQse1OYg93J+pAdt1ir2PbmSm9WK0rdmaGs0j97Mzr1Wp9sfc4y1+ScZxi/ZiMgjFmeb/2L9103ZlfWeFxzsK05z/Le1hZm9ZyS6exOWg7t+mRTc8BOYOP0umybc8VsiY8++mgr21YhjB3acfitxn5NWxPnMX5TM8sQr7Mu9q3/8N6Vb2y+M447lTVNBZuPbJsG1ofvhnVjRi7OAdxqgeMpbWzMdsn2IbbliBFFTQghhBBCCCGEEMJMyB9qQgghhBBCCCGEEGbCWuuTyego26Gc/+rVq63853/+563867/+66388MMPtzLlQm+88UYrP/PMM61MOdKVK1dW1s3kcJRBmUS3Ir+y31IOSruT7aJPKImiVJuWDUpth2GrTNzkoBX7ANuLdeXz0C5CKR4lvyxTDleR9VfsLpvawb43C8sSMOmuvf+KZNQk/5RKmuVhChXbHOOF2c+efvrpVmbWpz179rSySQsr9o2qDNOyalF+ascr0vOlU+mvJn3uzdi309h4sx2ZRnqZIre3rEc25rC/0xrM+eTkyZOtTJm3ZT+zMYHz7HisswyOnHdZ5jlm+Ta2O9NT5Tq9kvLefjDF5rzTWB3NjnnkyJFWfuihh1aeT8u42XF610QVy63Z+Cw2WTeuHxmntk4wqz3l/4xrzmkfJ4srz+O1aPPgGtXen80jdq8p9GZ6qsTyprKCLS02K3PQnJ+P8wa/6Y4dO9bKnO8Yv9z+wmxfjGXGI8cibmVw8eLFVqY9at16vrfvVWzPliHSysSsizbG2X0tyzLtZzyfawO2Hd8TM9Px3XPtwXtxLuHayJj/qjeEEEIIIYQQQgjhE0L+UBNCCCGEEEIIIYQwE8rWJ8qOaD2gJJISxW9961utzB2ULUsU5UWUNPKaZikya0NvlgNimXEoM+NxSp/MymASfj4724rXGYatsk/u4l2xTpgMl9JVPhttV7SomfR8SjaWXhvUlGxDZG5yyR4qfd6y5Vh70MrGvl3J/tFLb2YDjhUnTpxoZcpKOS5RZsj6mxyS7cZ+bc847oOW3YkSc5ODLyWTwaawDALWX/k+lpAV607aXCq/rWRWmXJNuw77NW21fLeMa8vQwvPvu+++Vubag+WxnNusT5xrK1bqXqZk/+q1UFXuZeP4lIwzS8v6ZHVk23Ae2bdvXysfPny4lZmZlOM6x3uurcgUe5ytG1k2ux7ryXjkWtrah+fwubjuZSzbt8O6tbfd2zJUsR60jjDGaU9gu0zJ+kSm2Am3I+vNkuFzsz8zOxLfLWPQtpi4k3AMOXDgQCt//vOfb+XHHnts5W9p1WeftW8sxhrnPn4jX7t2rZUt09M6erO0Vq5TiYXKeGHZnSzLHduR3zm0ovHvDmY/43Fud8I+yra2b3uz+RtR1IQQQgghhBBCCCHMhPyhJoQQQgghhBBCCGEmrLU+mQSY0inKCSk1ojXnd37nd1r5v//3/97KlB9S/mPSLF7fJPCWncrkoCZ55nUoLTMrg+1mbXUjZksZtwOlbMwaY7vqVyw/bFNKtiiNpdyL1/w4u/n3sCn5/hyyrWwak+6apYbHKeXjcfYpswhth92pIoekNPQzn/lMKx86dKiV2U8phTb4XDyf8smPI+1nPHO84Dhi8Xu3wrbj83Kc4RhN6xqPL7mtemXCvWMbqWT/s75t5YrtzOZTxgFjlrZaSoN5L8YT+wKlyrdu3Vp5zjC4fJp1tWw9m8oytqmMS71ZmSrHN3WdJcyhZndlG3AuoNWOayXL2mcZOCuxU1k3ss4cI1lnyw5DSy7LFeu8ZQrlcc5v9uzr4qmyXuW9+T7MKtub9aliiTJ6Lfa93wlT5oYlWIYJ624xtSmL8RTYv2h/+cIXvtDKp0+fbmXGLG0xXHPyGTlv8vuM22AwaxDLtIZVvp3XYX3JYtvG1soWDLaG4G/N6sbnNJskYfvSBsUtFTgHcNzkWtQyI3N9y7GLax1mkjKiqAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMWGt9IpQjcedtls1qQemaSfZ6sx+YhLKSfaiSscKyDJklxH5rx012brtZD4PbKNjWlZ20TdLJa1KORUtJRRa/KXvMlHPs/N4d+OcK+3klq1HlWTeVHYZsKnPNvffe28qUkjJDBzOVcVyqSCYtVioWs2HwDDeMWcpVKYPcbgvhnKE8lW3NsYiy1SXEaSVbwk5RGbOnZCOx8ZX3YnZB2pTYFxizXD/wOOPGMiqsg3LlSma73ufvzV5h15mSialyL2NTWWnmhPV/y9xkax/K281qU7HDE8tKxPvyHM5xnHMYL4TnmITfYPvQ9kXLIZ+dfceyuVUxKwifhxZK2kuYtev69eut3JuZtde2bWyH/ZXcLesHsyKaJfZOwv7MNecTTzzRyl/+8pdb+eTJk618/PjxVmaccn3L2LRvT55DSxT7uF2nyqbW7pU1UO86yezwthWLnc8xi+PaQw891Mr8xmB8cfzhWM/7Xr16deV1aEurjInzX/WGEEIIIYQQQgghfELIH2pCCCGEEEIIIYQQZsJa6xPlUrQM0BZDaeE777zTypQC8fwpWSTseK8Fya5TuZdJUitySLOrUJZl5wzDVrsE30fFvkUs2wUl6czaRckW5Xpmv9oOeiWmld/eLRmgKOGnpNeeqdLnSW97TJE38hzKEpn1ae/eva3MnfZpf+iVWLMNiT37+PomS2XMWla1pWVkmIqNpxU76N3YVr3ZQnrnrN4sIpsaK8zuxFizeYwyZLMD0/phWe2GwS0o/M2HH37YyozNTdkSK+/AjldsTbbOYJ3teCXuyMfJhDd32K9oUT1z5kwrc345d+7cyuuwv0yxZlhsmsWW8z6fhbE2pV8zbngdy2RosTnu7+xLZtVm/RinXKNyHWAWaJZpobLvB2OKRbyy5pxi7+0dZ+YE3wO/M/g++Ry0lWxq/W7vh2vRw4cPt/LnPve5VqbF6dSpU63MLIe0cXF8oMWSML7Y3/mtzXagpaa3X6/rI7Z2N3uvnW/rCbMvcdwxm3TvdQjXJXxPrD+zc3FusAzQhNZQvj9mrOSYZkRRE0IIIYQQQgghhDAT8oeaEEIIIYQQQgghhJlQzvpEaQ93vCfHjh1rZcquKPHidShvs4xRJsfiNXvlw3ZO5XglI0zFLmF1Y9vSNjEMWy0V3Fm6YgcwiSnbmpJW2jRYtsxTvTI7oyLR7JVeV7JvTLFW7QTs/+wzJh+lDPBOWr4qkkmrD7M3UFb68MMPt7JlbrH3zP5uskpeh+25TpLM31BO+dZbb7UyZZMV6+LdSiW+2HeXZrWwuak325o9d2W835Q91Ogdd03mTVttJaMHf2s25HHd+DwcLyhnZzxaJjKjYlPqHQdJb/YZ6zdmfapYMCp9dMm2RNadFp7nn3++ldkGZjHmfDHFGm8ZBtneXANyzqGsnv2a60meX8nYyXM4NnPNSGsY16psz/E6sdIPeW8+M+0ftD6xzBhn7FvfnpKxr7KWupPrzKXZ+VlHrv1+8zd/s5UPHjzYyq+//norsx9OsaDZPMVv2yeffLKVn3766VamRYYxyGexbGi25QW3DaHF6cKFC63MTE/8Jpu6ZUGvralCxUptdnhi2R/tfGtrHiccNzjmmNXTMu/ye5ltxd8y45cRRU0IIYQQQgghhBDCTMgfakIIIYQQQgghhBBmwlrrk2UHovSREkeeT5khz6nIREklI0avXHc7JIe9dhI+O8uUp453g6YMrmJ9qrQdf8syd6t+9dVXW5mSX8q3NpUdo/ed9UpSl2BrqlDZPZ3PWpETTsEkw2ZHorSQv6V8lLuwf+lLX2rl++67r5VN2t9rRWRGCJN2cgwcY2MZd3qnbHLJNoGpVMYfwvdhu+vf7fRmd6pkfbLrmBXC3k9Fwsy+b+MALZyWPcfKjL9xH7GsUZQl05bIec2yS/SyKev1lPnL3mtlzWS2tIptZk7YPMjj6/rSR/TGXSUeK1mPWE/Lpsp1I+0bnIv4297sq4ybK1eurLwv16dvvvlmK/NbYHzdyhjHcYHPQBvM+B4fYZkWK32id51pbCobX+9vl4CN57Q70V7327/92638n//zf25l9nP2Q75bzjX8VqUV98iRI618+vTpVmamY9aHGZ3MOsN1Js+xfsdtJzhH8fiUdeW6b6OK3WmKVdBi0J6BNiXLhGdbrth6gmMC3439nYKWUY6DtqUC24S/pT3TtpIhUdSEEEIIIYQQQgghzIT8oSaEEEIIIYQQQghhJpSzPpnsziwvVq5kapiSrakiv5qSEcPuZeebZNhkZZRZUYI9DFstZ2z3CpV3Y/JWuxdl5JuS3E3J4FU53ySGU3Yy3wlYR8qzTQZIKpLeXuuE2Z1st3WTfXIHdNqdmPWJ9ijKnClhJRZTJjmsyIcZi8OwVd5KiSrjiHVdmmXgTmDzxDrL2RypZPghlfHY5pTeOlRsgFY30jvecxyoZOHj+WYrNKuIZYwan8exgPHMcu9z2jhotsxK/5iSJarXMmf37c1oswSqWcNuR6WNe69TuSbnfbNCcE7kHMVyb904HjPzEqX9XDOus+lPaSOrB+OX64zKmtbYlF28km2qEne94/USYrNitWF/Zjaof/7P/3kr/97v/V4r/9Ef/VErs22YxYn2pf3797fyPffc08q0O9FO+NBDD7Uy5yxe07L62vMyXmgtZHxxa4reDKLVb69K1qfKNgd23LKwWXZCti/HEcY449q+B3ic75jWNWbqoiWKbc1x1uxt/FY5cOBAK9NyxXsZUdSEEEIIIYQQQgghzIT8oSaEEEIIIYQQQghhJpStT7YLMpmSLWJTdiSTCU/ZMb13936TXptckXIqSt1ooRifV5E49rap7a5vEvOKfaMis+uVf/ce75WOLyEjT0WKXtlJ3a5px60tKUtk2aSelASy/IUvfKGVf+M3fqOV9+zZ08qML8oGK/HFfs379rbnWDpNG4W1i7VFbxwt3SrVmx1jyc9bGfN6rUyGSeN7rYsVa2RvVgdaM1555ZVW/uIXv9jKtD1arNDuwRik/HldXU26zPpRlmxrnSl2XRtD7R1v6l1uKqNi7/XnxBwy50yxp1SyiNJia1lZp4yvfOe0GZkVft29prwD3oPjAi0inH8tM8uUdf+U75beZ59iz19abPL90J5imXNojd+1a1crf+Yzn2nl8+fPtzKtT7yOZQTi8aNHj7Yys0TZus/sO/Z9ajZcK1foXcOP/202JcveyuP8rV3Tvh/GdVp1fTtOCxK/EzhW0tLG87n+4NjC75CLFy+2Mq1u7CsnTpxo5QcffLCV9+7d28qlOf22Z4QQQgghhBBCCCGEO0L+UBNCCCGEEEIIIYQwE9ZanyqWlErmnEoWgikWGTtOCRLlTiZLs/PtuEmWKrI3XpOSzPfee6+VKb1bd+/KLtwVy1JFxtybNaS3br1ZmSpluxfb3eT1c6Uig50ib+7d8Z2SQ7Y3JYSsA8+nDJBS1UceeaSVTQ5pcW3vnzv2c5f3SpYcSjXHmYjuu+++Vuaz7d69e+XvWafttg/MQfbca6+oZPeZK5blwPptJYPHpiT2lYwivXOFjdmE1kBany5cuNDKzKBhEmaz5NrYMgxbY5XZGa5fv97KzKLB7DC96xWb403Cbccr8529M/utvUvLsmHH70Z6s1pNscZX6jBlvOzNHlWp56bsquuuU81Gc7t7rLMof0RlTWM2lV56s7NV+pPFZiUL71yxOeXw4cOtbFm/uLaiDeXJJ59sZVqouOY0my1tVpyDuNbjNXvXovYOWX/arF5++eWV9SSVvmbfYeN5k89g63s7btmdeI7ZqdjWXK+zzN/yfJaZwYvnWz/je6Utmm3Kb0PLKkV4fdqvuMao2NiiqAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMKFufTIpesc707rBudSC2GzqpyBUrdpyKjM2k15Y1ghJFWpwogxrLNqfI2ir1tndmcvZeGbaVe99lpd/YdSoWmrGtZY6YdcZ2UjdJrGHyeZP7UZbI+phtgce5k/qhQ4dWntMr47U+y0xPtmO9sa4d2H94D7M/mJ2ycu9qRo1VzEECbX2L75vwPdG6sgQqGfMq9L7zKVYIG4/NrmXPaJksaH1iJo4DBw7ctg6MIcqQq5lYeL/vfve7rfzaa6+1MjPlVOY+k3xvKnuQSbXtfdg8bjYQe0abM2zusevPlU2Nnb2/7bUR3UlrVeV8G8cqfX/cVhULx6bmrMoWCVMyhFbapWLXqqyNLfYr48Bc4fhhmXNsvOEagVYm2llohaG9iNCeUrHXmB3exshK3HGOO3jwYCvzWUhlixKzK/F8ttv4N7Y2s6yrvJY9s2WGMosan58ZIu098buCx+098ZqWKZJ96MaNGyvrwLUOMz1Zv3zhhReG2xFFTQghhBBCCCGEEMJMyB9qQgghhBBCCCGEEGbCWuuTWQMqUj5KikivPHBTO6ZXbEBmdyK9dqKKHJQS7nXPXpHA9lrF7Dkprey1PlUyVpBKX6k8r/UJy8JFKRrfgbXJnDCLE6WIlA1yF3OWabszK4HtVG/SRTvOLEuU/jHbC+0PfBbbmb8ivSZ2HVKxA45/y/8b757/EbbLfWWctZ3nzZJKKvabXqn5piysfHbLyPX++++38hJik89E2ewUO0Mls5KdX7Fs2LxWsdxV3jl/ywxQzz77bCszu4dJ0Gl9Y7/g9SlhHoZhuHTpUit//etfb+XnnnuulTkmVqTqZp0wG0IlrisSdru+nWNjQsXKxOtYhkQer2TB2GkqcWFMGSN7j/dmgJpiy7JrViw4U7IzrbuH1aly3d4tAozKnF6xI5HKdwLpzVBrdnezrswJju22FuA74TrIti7gmEQrjK2naFtnm5nt3bIY8V1VLPy2XQDXw48//ngr/8mf/MnKOhDWmete9pF162G2hWVxIpUMWHzH9vy2HmZdrcw6V/7WwHtZRknW+cMPP1x5Pp/d1gO8zjvvvNPKN2/eHG5HFDUhhBBCCCGEEEIIMyF/qAkhhBBCCCGEEEKYCWXrE+VhFaZIQyvn9O6cb8dZtl3HTTZVsW6ZHI7XNyvDujavvA+ra8XOMcX+QCrHN2VvMxuIZbAye89YOj9HmB2JNoFTp0618smTJ1v52LFjrXzr1q1WPnfuXCt/61vfauV33323lWmPMtscrUyUbnJ3/QceeGBlPZ9++ulWPnLkSCtT0lixB5GK7aA3K9y6TGjc6d1sabZrvcW/xY7VySxOlr3Fzu/NQlUpsx3YPmyT48ePrzx+5cqVVmZfnCt8PrY935VJ4HuZYseoWKjsfZrc2vqvZVE4c+ZMK3Mc4xjFsYLyYY5RvCavMwzD8Pzzz7fyD37wg1Zmdicb1yrZDCu2MTIlW48dr2QF6+0rleuYdXGuTMmEaFTatbdfbEc/6rUZfRwr0+2uuV30fg9MycJFKjawKZm6rA6co1k2mzrtQ3OFYzjXjWZ35jqTbcDjHBc5L1S+Ayp2tEqMVKxJhGMq68zsV0ePHm1lZh+iNYfPaNluK89SZYrN256ZZbPD8RnM+sRvCdbTbOqcG2hNokWamJ2M74DvifZvvjMjipoQQgghhBBCCCGEmZA/1IQQQgghhBBCCCHMhB+bIvkMIYQQQgghhBBCCJsjipoQQgghhBBCCCGEmZA/1IQQQgghhBBCCCHMhPyhJoQQQgghhBBCCGEm5A81IYQQQgghhBBCCDMhf6gJIYQQQgghhBBCmAn5Q00IIYQQQgghhBDCTMgfakIIIYQQQgghhBBmQv5QE0IIIYQQQgghhDAT8oeaEEIIIYQQQgghhJmQP9SEEEIIIYQQQgghzIT8oSaEEEIIIYQQQghhJuQPNSGEEEIIIYQQQggzIX+oCSGEEEIIIYQQQpgJ+UNNCCGEEEIIIYQQwkzIH2pCCCGEEEIIIYQQZkL+UBNCCCGEEEIIIYQwE/KHmhBCCCGEEEIIIYSZkD/UhBBCCCGEEEIIIcyE/KEmhBBCCCGEEEIIYSbkDzUhhBBCCCGEEEIIMyF/qAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMyB9qQgghhBBCCCGEEGZC/lATQgghhBBCCCGEMBPyh5oQQgghhBBCCCGEmZA/1IQQQgghhBBCCCHMhPyhJoQQQgghhBBCCGEm5A81IYQQQgghhBBCCDMhf6gJIYQQQgghhBBCmAn5Q00IIYQQQgghhBDCTMgfakIIIYQQQgghhBBmQv5QE0IIIYQQQgghhDAT8oeaEEIIIYQQQgghhJmQP9SEEEIIIYQQQgghzIT8oSaEEEIIIYQQQghhJuQPNSGEEEIIIYQQQggzIX+oCSGEEEIIIYQQQpgJ+UNNCCGEEEIIIYQQwkzIH2pCCCGEEEIIIYQQZkL+UBNCCCGEEEIIIYQwE/KHmhBCCCGEEEIIIYSZkD/UhBBCCCGEEEIIIcyE/KEmhBBCCCGEEEIIYSb85Lr/fOCBB/5p1fF//Md/bOV/+qd/um2Z2PEf//Hb/83IfmvnsMw6s0x4nPX5sR/7sVb+6Z/+6VbevXt3K//bf/tvW/nhhx9u5aeeeqqVjxw50soPPPDAbe81FT7PX/zFX7Tyu+++28ovvPBCK/+v//W/WvlrX/taK9+6dauV/+7v/m7lvazePF55NnvHlevwtzzH+tZP/MRP3PZeN27c2NwL2SBf+MIX2sP+1V/91cpzePwnf/L/hbq1B8/5+7//+5XnsI15/j/8wz+sPE4YO+ybPJ/v5Od//udb+VOf+lQr79+/v5Utvu67775W3rVrVyv/wi/8Qiv/7M/+7Mr6sI9/8MEHrfz++++38tmzZwfyve99r5WfeeaZVr5582YrMwb5DmycImxf/pbns9/yuPVz6wc/9VM/tfKavA5/W+lbVgfe62//9m9XXoe/ZVs9//zzs4zNe++9t1WScWTvtjLm2fHKbyvz5qbqY8crcxzvy/MZ+4xrxjJ/+84772y5LuOWfczuvam2s+tYnFbmLMPmRyvb+NP7Wx7nOPDWW2/NMjaPHz++8oXamMqxpzIG8xyW7ZzKb4mtYyv91N7bz/zMz7Tyvn37Wvlf/st/2crHjx9v5d/4jd9oZcYj59O/+Zu/aWWudTmnj+tR+Wbg+Zyn2RaM/zfffLOV//f//t+t/Nxzz7XymTNnWplztN3X6kYsvuycyvrWjlt/tbn74sWLs4zNT33qUx973tzkd9Mq7PqVsZBtb2soHuc62c6pXNOw9ZrNjcOw9X3Yu7F5jUz51uVvOT5WvvOn/G3Cxih7Rpsr2e58x1wDnz17dmWjRFETQgghhBBCCCGEMBPyh5oQQgghhBBCCCGEmbDW+vTXf/3XrUypDqU9JtHslf2apKgiy61ImUyKaFKxinSRkuz33ntvZf0vXrzYyqdOnVp5TkWGPBW+J9o5fvjDH7byjRs3Wpn2KMpBrd4muaxIBnvfH7F+YDI5+61ZX+YKY5Ple++9t5X53iivM5sSJY08zuvzONup9zp8PzzOfsTj999/fyt/+OGHrfzggw+uPId2J0qyKb1mf+H4Rng+Y2XcZ1lXnme2C5NEmrWsV9pO2KZmObPj9i55nPflcbY7j1Nqz+OMR5Pasp3nSqWOvfai3uvYOb3zZuV4ZW6tyITNBsJ+R0sFrce0eY5tm4y7ytg+xXZkUvDKGohU1gG9VuJey3ClvOR5k2M+x6S//Mu/bGWzXVpf5Tm27qhsF9D7/s3+UlljMz5+9KMftTJtQ0ePHm1lrisOHz7cymxbPjvnw7H1idjz2PjCNQ3vfc8997QyrcdcH/A6HC84jlRsKhXrR+W4jXd2vPfbycaZObGp8aPXrjrlmpW51d4h+69dk32T/Y5tZf2rspXBOouZWQutTsTWrpU5yyymvK+1kbV7ZesTUvnbQWUNZ7+192FEURNCCCGEEEIIIYQwE/KHmhBCCCGEEEIIIYSZsNb6ZBIek3SSXvn0pnZoJr07dVdkWZbRhm1y5cqVVqZklBJTk72ZxHId1l6Url29erWVX3rppVY+d+5cK9NSUnnHm6JX5rupLCZ8xt5+sNPQVsL3bDJLywDQa1UglXaqWAvtmnZ9ywa1Z8+eVuYzMjsMrRM8xyw7lMFTHj/uU/w/1slkqZWMHZXze6XOlfHUpN0mk63IaiuWALMQmB1srlTmr8pvezMhVsbI3n5B7Pq9v52S6YVZZg4cONDKnE8Zf8MwDC+//HIr0/a7qSxcm7Ir98qqp7yzyvm9mWh6LWM7AfsJx3myqWw8U/pCrzW8105n4zEtRG+//XYrX7t2rZW5juX8SMsR15LrrKt8B71jllkaadu3+ZFZrM6fP9/KZvPvfce9a1rrc5V1UuW+S4jNKd96U+bcTVG5r9l6CPuyWX/MJm5rWouDdfZ6s3eSTY1BlcxK9gxmcepdf26K3q0/KrE5/+gNIYQQQgghhBBC+ISQP9SEEEIIIYQQQgghzIS1OvIpUvopVhW7fkVaZXWoYDvNE0pnmWGH9fzFX/zFledUsql8HKx9bdduSlq5yz3lqr2ZCXozFvRK4Oy3U+T4ZAnSUGKZASo7plfklNaWFneV7ASUZVqfZyYOlpmxgZmeWKblgXFqGcmY4YKybUKbFTM4McaHYRh+7ud+bmU9LLNDZRf6SvaO3hisxJRJRnutW6QiASV8drYbLWZLYIpFqCK935QNdFOZoSrZTgzrU+wLtD4x9mlvHGdwe+utt1rZrA0mjZ5ia+k9x2Kt9zq91oleu13lOnPF7OQVuXpv1h1SGaft/EqGqd44td9y7KdNmDYjZnGi9YkxyPlwXT15XbNkV7LXcC7n3M/MVbYe5nhRsU5U+hDptUb2vjM7v2KfmxNTrE87Re9WHqQydlYyAtt2GTaW025ome+GYRhu3brVyrQsclywe9j6vtfOXbFrVspT3kdvVuaKdda+SYxlfZ2GEEIIIYQQQggh3MXkDzUhhBBCCCGEEEIIM2Gt56Z3x3GjV0pdkZVWpMG99e+VOFHeefDgwVb+/Oc/38rMTFGxMqyThlXsKL1tTbsTJa2UiW4qq4XVYYrUs1eCT0ziPmep5UdYX+2V1Jk1h5g9im1s8mTKkNmnbNd6xhSfxTI98be8PiWdbCuTbbLM3zImKK8ey5/N+mX0Wi2mWFxs93+T6dsYZLJzs9v1yoKtTe7kjv2bYIolYTvqMMWm05uFr3d+6G0HjgMcW2hFHPdfzs3vvPNOK5u1weq3qSySrDfHoykWQmPKc9lxs4gvIVsiMfuAlW3snJJJzerTy6bsMjzOeZBzK+1OBu0VnA/XZTKlDYo2DLYLY4ftTusyM7vRjmXWA2LWiUqGpop9tNde3ruNgLGENe0ngcp3W2UdbuNSJasU44xxM86Cx38zpsxSZHWyZ+vN9ETYFpUtBaZY9e1Zeq2OFTurEUVNCCGEEEIIIYQQwkzIH2pCCCGEEEIIIYQQZsJajf4USWdFTlvJBNEr1zTpvcmy7Lhd3+T/lDCbrJLnMIPMvn37Vt5rbKEwiRd35KZclRku3nvvvVa+ceNGK5u1oyIL347MXr12iSk2vMr1lwD7BfuYvUPL+mTPbX3epHyWsYdU5I20OVDCzMwvJqs064xZw2i54nHG0zqJIu1YlJb2ZmsivZm3KljssEyZu/WVSnYBG2etDibbrVjD5oS1a+98t91sKsNe73UqY7wdZ2xRmr3OPkx5N+OU82OvxcDeMeds2jdo2eDxmzdvtnJvRp8Km7JKVSTfS6BirzRZusWyWUvtuK3jSEXOvyn7O39r6wdaC7netMyJlsFp/Lz2DpiNlHO/rY8Zy5yz2RaMfc73lknQ5vveDGukd+6z3y5tjfpJY4rdza5jW2dYH6ysNxhDXG8Pw9ZvQxvXLBYqc2XF7lSxPk2xoW7Ht2TFGmnv1YiiJoQQQgghhBBCCGEm5A81IYQQQgghhBBCCDNhrfWpNxtALxVZUK/EryK3J70ybMvucurUqVZ+8MEHW5lyS9qPbt261cq0clDmOa4D60p7AuXgFUkrj1My2mt32lSmkLmxBGl3JWOFSSWNilXQ+pdZVRgjlZiixJoZ0x566KFWPnbsWCszXtjHaYswa5g9C+vM31JSPZYrmiS7N4MIMVl4L1OsqqwzxxxaOXh9O27yTrNWWeasuyXrk52/3WPPpuS9lfOt75utw9qK/c4yX4xl2+Tee+9tZY4LFYsBsbagdYLj0Z49e1qZ6wCuD775zW+2MsemXqZYk6bYo3qtHzsN+56NSZUsc722hYoFtvLbyjw+xWJplnrGINuE1if+lv19nRXa/s+yO1kfY9y9++67rczYZ50eeOCBlXWoZDGrjHF2vtFrtzR2KsvgJljK90GFTX07W5uYtdCwMYfzJte3wzAM999/fyu//fbbrcysb73bkVTqZzYrUrEJ9/ahTfW57bAozn9mDSGEEEIIIYQQQviEkD/UhBBCCCGEEEIIIcyEtdan7cjgUMmCUZHx9kpG7Ryrj13fZJ+WQYLSLZ5P69Ojjz562/uO/49yMu6Qz2dgZine74MPPmjlDz/8sJVpoaow5d1sh8ysVxq6BIuTQWsI+xjLlgWpYomq2J2sn5tdphKbtCYcPXp0ZfnQoUMrr8l7sV/zXibbpP3QMnTwuXid8f0oGSe9/fNOypgr47iNlWYPW2cV+wj244rcfyzPXSq9cuUKlawLvef32uZYZiyTSmY6wv7Fedbk3+PnNVvilPHfnpN2J45TzDLDMi0bXB9ULH7W7hU70hSb0nZc805hc2JvVsnKdWxeNmuVZQGakpGyd5zhfblO5NqQ60qewzmRdkD+dtyvOZ7TmlQZC4jZZlk/jjtcKzAeKxlbKtmgbK1emWenbNnQe07YHJV5tvLNZOugynxnYz/Ppx2QmdrG8zXj2azolWxzlT5v42kla+xO9fPeNfOU79/5z6whhBBCCCGEEEIInxDyh5oQQgghhBBCCCGEmbDW+lTZxXxKhh+7jkmHjIqMqGKDsrLVgXJLyjspIaO0k3LQvXv3tjJ3uGd5DKVvlG5SvkZ5J+9NCRmtGZSxWhvZzva9u3lXjpPe3fKnZK9YGuwnJnes9HnLutMr1zU7FY/b9a1MWwAzpbC/0/bHvs/d7HmOZTkz21/FVjb+P0qpORaY5aPCHPqqyb/NpsRzOPZV7AS9svM5UZG4TskEUrnvpjL/9NaT4xKtDGY5YhxUsp/xfM6B6/qXxeMUqw7Htd27d7cyM9Xt27dvZV0tS+Om4PNXsq31ruFsfF+C9Wm7LYeVjECVtqxYArcbxrJlJGNc83yz5jMOxr+xccTa1+xOltWGsc8yxweumS1TKpmS+bRynEzJIDiH9cPt2JQVdw7YOGDjca9tyqxI9luez3nZLP/DsNUiZTZ2e088x74NKnMQ2Y6tM7abyt8XKs8y/5k1hBBCCCGEEEII4RNC/lATQgghhBBCCCGEMBO2LetT73WmSJ96r99rx6FEi7Kx+++//7b3ouyLcsuKjG1d9grLEEBpKSWnzPrE3bxZrsg7K7viW50rxytMsQ1Udl23dpgTlAZXrAHW3pVsFBXriWVHos3OfstnsQw/fBbGlGWzosXJMrX94i/+4srfWpaod955p5Vv3ry55RlM2s5nq8hBlyLvtHpOkez3ZuCbKyZXnpKxZVM2qCnZoMxWyb7MuYyyal6TsTzOnrbqvpWsbbQTjW2MNqZsKvMRr0m7Jo/z+d96661W3lSGuF56s3X22lnnSm9GFZPqE8v0ZDZQu/6mxr8p2cx4X86h7Kc2Jlgd1tnlbY1SWTeavcrGGs73XA8fPHiwlWmD4lhjayzWp2IztPOnjNfG3TKHzs0G1WtTqtj8K9mNevuLzcuMg3VbR/TGeYVKG/VmE54bvX83qXxvzn9mDSGEEEIIIYQQQviEkD/UhBBCCCGEEEIIIcyEtdan3ow6vZKwigyq11pVydzUa8fhb/fs2dPKR48ebeVHHnmklbnTvNk3Pvjgg5XHTT45xmS1lt2JMlazPpnUjbI5k6j1ZtUyKv2DVOTWFSld7w7kO03lmaZkxKq0t9mdLCMQsyhY9gbaFihn/uEPf9h1HdbhzTffXPkslZ3zP/zww1ambWr8W9o8WH7vvfda2aSoJotfIjYOWFYtwvapZK5ZGr2y7U3F7JRzbFw06y6tP8yGxHmQccqYsj7CPsVzeB3OueO2NQvSFIsaLY3MSEfrBNuCVknOuRzjzKqwKVt47/qscq8lzJVkyjs324KNTxWbC8u7du1q5evXr7cy+3xv9qEp8L5cS5qdl23CNS2Pj7Oamq2a51l/s8yJzGRqdmPGLOfoQ4cOtTLnfrO0VWxsFSoxVfk+qVj15soUi679druz6pGKPYh9kJkK7XvOrm/3snazrE2swzieuFUH53jLsNZr9+K9uaZn2dbGS18n99Z/WZEcQgghhBBCCCGEcBeTP9SEEEIIIYQQQgghzITba////2yHbNasJ1OyTU3JBkV4nDJMyrlpcTAJqGVEuXHjRiufOnWqlU2iNr6WyVIpq2Z2CUqvKTenzGxTkuaKDLtX+r8du+LfSbnkprHMRxXZZK982uTGZkkg7F/2W0osWX/aCExuzP7OelK2TNsU24Hxa5JhWiosg9swbI1bZoazLDMmnV9CxrEqJgVnPzBLwKayDOwEFUn2prIl9NKbTcWsT3yHJu3ev39/K1PmzIwrlFFXrCg2PnCOHvcdXov16IX1Y+YMWlb27t172+twPJoi7Z6y3iKMU8syUrF1LGHssmetrD9tzLa52NrJ1opPPvlkKx85cqSVn3/++VamrWeKVa6CWYssU1klg9M4NtlenCtt3qzY/DkWWDYo1unAgQOtfOzYsVamZZrXsQxQvZlrercOqPTRJWfMqdh5Kr/djkyzFSqWKM53nDeIfc8xviw2zW7J9SrXvbatxfgetk1AZX5hPe69995WZtZkwm9Vy2J8N1EZr6OoCSGEEEIIIYQQQpgJ+UNNCCGEEEIIIYQQwkxYa32qSPmmyP16M9SYvM12Xrc6VI4TyqWZyYHSLUq6KBOzTC+W3YbXH2PPz2emrYm72V+9erWVTdLZu4M3qdiITKpr1+ktk95sYUvL+mR9wWTClSw6lDpWpMsm2+d1rMzzKW+k7JP2pX379rWyZX7hcdoiaL/icWZC4315DtuKx8dWL7YLbREcO8ziVdnxf+nYM/LdmxWhkuljTkwZnzaVXbEX9nPaDth/zXZgcy5tg1auyN1tbGEdrP7jf1vGmgo8n+MRsz9yHcC5mHYnjjV3MuNb73qrsmYy6+JcmWI5tOxFXNcxXji2WdYUXv8//sf/2MqMkVdffbWV/8N/+A+t/O6777Zyb6avSl9gPc2uSmxeZvmee+7Z8hs+A8cR2oftfmxfex/cqsAyrPG9njx5spVfeeWVVuZa2izcZg+0Od3Ww73fNsTWakuwJdp7rlhiK2N575YM2zGesW/SgkQbFNelb7/9diszVmzdZHXmvRhbNl8Nw9b16pTMzZx/uTamNdrOp036brI+9VrZo6gJIYQQQgghhBBCmAn5Q00IIYQQQgghhBDCTChnfTJ6Mw/Y8YqcqpKZgudMkRVTonbw4MFWfvDBB1vZbErWJrROUCJr2RXWZX1i/fgb2jx4DqVylqHH7AabylximLyzIqsjn6SsTyZ35HGziZiVxLIZ2Dl2vtmaKllHbHd6SjQtI8TY8rCqnpa5hXJQnsM6UF49lnCz3izzPEpaCZ+f7XU3YeOyxSzPWZeZYO5sSn7eO7b12lYYO+zztGCY1YJ9llkqOM/QJmyxUpED27jHOoxjiH2J97NMFsSyP1rGCs7rlK1/8MEHrUwb1JQ+McViTCz7TGWetaw3c6XSZjaH8v0TtgffP/sX12V2PvsmLTvM9MQ5i33KxtfeDI+Ev+X4ULHn2nzK48Ow9ZkrdlDWie+DdkKOWbRL0Gpx8eLFVmZb8z098cQTK69/7dq1VuacXtl2oWJFm5IJsbcfzInK92BlDVmZRzaVMc/Or9yX8ywzj5k1llsBsF9zvrPvJNu+Y52l0dZgpLJFAr97LWsy70ULlq3p7yZifQohhBBCCCGEEEJYEPlDTQghhBBCCCGEEMJMWKv9rci3zHa0qd31K9Iq2/GbMsZeOTClXnv37m1lStR4DiWphHWj7PPQoUO3rcNYrmgSUMrDKIOjnIzyWUrSKzaEivSQbMo6ZH1iirRxStaxOUHJrbW3ZUXplX+bhNbsTmY5rGT1oZzZMjpR8s3noryTv71x40YrU/Zpz8g6UGLK42P7IC1OJg1nXSvZbnrjbonYO+jNMDQnNmUVrYxhU8Zs9kH23+PHj7cyrb4vvvhiKzMGGWucc2n9MasvZdGMr8p7Zh3WybTNcsx509qRdaVU+4EHHmhljilmfeF4beMm2ZTVza5ZscZNyZS5NKZk3WH/+vSnP93KXO9dvny5ldnvOI+wT1lmIcaXrYF5zpR3aOsBs9SzzuxrtDeNsTV6ZS1itg1e0+Z7ruOZ0Yn2KK77H3744ZV1YDZVWlYq3xtTMtfavGK/XcK8Sexbb0rGOVtzWl+r1I1U5mLLQEi7HscNxhczINEGZf3C1rSMD24pMN4qge1r34mVeZP34HqC4x2/TyuZZZdOZSsPEkVNCCGEEEIIIYQQwkzIH2pCCCGEEEIIIYQQZsJa61Ov1GiKlK+yy7fJCU0CaTK5yq71PN9kcryOWVFYpqSNclDK4Uxiuq5+JnW9efPmynvwtyybJLtiXyEVWVelf5DKfY270UJiO8NPkdVbPzc7nVlSKjFrclNajSgLtz5r9i7KmRmbvCb7kWW5YpxSProuIxul8JS0UobN91fJBHE30Wu9Y/uYzXWubIeFpff8Xlk4+ynlyezLlbHC7FGWgYLHbY62sd+y6ozrN8X6xExPe/bsWflb1puWS87FrCuZYpmryNF77T2VzFtLsAkTG2PMMsCy9T3a3o8cOdLKhw8fXnn+448/vvK+nF94nHOWZVMiU2zc9oxci3KOZl+2dmP9x2s9y3LI6zJOeW+zSPA4r2+xT0sjxyluT8BMNGxTZrmj1dOeecp80Ds39G73MCcsqy/LjBebLypjXu9ve6lkYiJcP3L+pYXo+vXrrVyxJZnd2NbPw7A1FnozJHKcYqYnljlu2pYKlW/1pWD9oNK2UdSEEEIIIYQQQgghzIT8oSaEEEIIIYQQQghhJpSzPlVkub2ZeUjFpkGJUG/mpl47lVmr+IyUTFrGCstowx3l7fxxu9n/sX62MzjLlffam/2h0icMO8dsDlOyUG2HXHgnMPk8qdjL2MaWccmsRlOkodZHKFVmmXJNypbN9sXf0gbF82nNYJnXJ7Q+UPI8DFtlrJSossx3wF3u70ZrXpWKBcNsaXNlU3bP7cDam7HPPkvpNWXLFy9ebGXGmtkJeX1mmOI8yBg0aTqtUpzfLXvUMGydp2lHojzbbBS8Fp//4MGDrUwbFOdfPs8Pf/jDldc3eucjO78309OULGJLGLtsjVrBbD68zqVLl1rZ3j9jhxaZP/7jP27lK1eutPKzzz7bypx3Kmtmy5hD7P2b1dfswFYHjg/juZXPzzKtRpxbK3ZowuNme+b1OcczQw0tbXwGWhr5jvnMUzIxVd5Z7zueK71jjNmgOEdwDrLMe8S+xSrfOobV2eYvswBzXratOWze5FzH+7KtaCscn1f5vmOZY4TN2Xxn/K1ZK5fQh9dh/dUscFt+uy01CiGEEEIIIYQQQgjd5A81IYQQQgghhBBCCDPh9tsN34ZKRiAer2Rfsh3c7ThlRMxMYXYGyirNykGpGOWQPJ8yLkrpTLZvcksepzzTZKXje9NGYVmjTpw40crMOENpmVlHrI0qtqZeyWDFHtArWa6cv7Qd8vneLJNFJSsT5b2UKLJPvfXWW63MflHJFjIlK42NJ4xNyo15Pvs444h1s4wzlsGJsnOOCcOw1V5Fu4hlr7h27dqwiiX0ve3Cnr03y9vS6LWZbiqTlGVbY5lzAvsv51nGBa/JuYxwzGHcMYY4zjCWGfv87bqsgCYrZ9myRfIcy7jDGOeag5k5+AzbYXvrHWcrmTh7swbulJ2vh95MT2aRYP/k+6dN6emnn25lZgTiuuzdd99t5VdffbWVX3vttVa2+DJ7Pqn0i0qmGFoZeJyxVrFvjPsOY6qSIZJtzTHInsesUoTjGs/n8/CdMVsNrZs2JpJKBqhe+9KmMv/tNJV5iliWoePHj7cy+96FCxdameN0b2ahKd8fFgu2RiWMKcu6SGx8YL8m43awzLIWp7ZG51qZMWjfLeuyxC2NSqanWJ9CCCGEEEIIIYQQFkT+UBNCCCGEEEIIIYQwEz6W9clk2LYTtUl+KlYFk1bxfF6T2Y1YH0q/rA68/q5du1aWmUGCEnE+L49TDmm735vsayxFo+SS/8esE2fOnGnlw4cPt/I777yz8jjbkXJNtlevfHZKBqjejAWV6xi9Nq45YdY0sz6ZNY+yu6eeeqqVz507t/Kcq1evtjJtR7Y7e+Ud2jtn/WnFMgkzr8/2oezcMtRwTLBMEZSMUrI+DFvjnOMXf8P2mpIx65OA2Uf5/uZKb5Ye0ms36cUsBZyzmLlo3759rcy5j5mbGAu8Pi3GnFtoDWTcMZMS5yVaong+zzGbxjBsjUHaHS0TBuGcy7YwGxQl9Rw7NjV/bcc8ayxtTqxgNh97VrMtEI5P7OfMCMT4oq2J2Z04tvE67NtcN9oakvWZklHQ5ke2A2OQa2/GDeOA5w/D1ixsXJdyzcHfmA2Scy7bjusStgXHCMusw3UDn4fX5NqbawCOidZvtnveX5ptpDK2EfbJQ4cOtfJnPvOZVjZbyeXLl1uZa0vL1lWpW2VOt+vYVh72TW3HzU7EazJurF+P72Hfg1Yn+2bmuoFwjLBtHZZIJYveui1OPuLum4lDCCGEEEIIIYQQFkr+UBNCCCGEEEIIIYQwE9Zan0xGZRKvSkYnSg4pY6QkmfIolimPojXDJFqsg9klKJ+jBIlyLdvNnbIxtgnldrbrvNWN54/luGw71ptSNmYaYLaeL3/5yyuvw7LJdk32NsW+UdnBvlf21msPWLK026xM1mZmj2IfZp8/efLkyt+avcgyvFTeoVkwTM5MeTLl38Ssjqwz49r6gkm+OV4Nw1apM/+Pv+GzcSd8ZofZlMVlKVRkxHwHS2ufKdaDitx6U5Yai3HOv4yX3bt3tzJl5HYd3pfX4fm8po0tFdvjOJY5P/J5OI5Y9hlmEOGYyHmT1izGMq2O1i6kYney4xUJfiVTiDEl69ic6F2/VDKq8Bzaf7iG4nH2f84JlSyKFguVelbgWMt5jPMVz7HsRqwnn51Wp2Go9VtaKK2ujHleh+MI1zpmobXMr7a+ZxsdO3aslfnubW1k9Gb+u1ti07ahsPbgmGoWN64PmaGLfYprRcsIPOVbpNJP2Uc4L/Ebmf23kiXK7FHss+uyJbJNLQOUZUikvfnAgQOtzLjj2Mf1M63XvRm55gz7AfulZeEiy/1SDSGEEEIIIYQQQrjLyB9qQgghhBBCCCGEEGZC2fpkEiTKqyh9orTHMjqZ7NNsBJSo8V6WsYF1qFhF+Cy0gfD6lM8dOXJk5XGTorEOFbvWWPLH3/D/KJ2iJJs70rNNKefm+ZYdxzJZVOSAJvWbskP6dmDZhuYK34+VzUbH/nbhwoVWZlYTxintAjxOaT8l0BVZscGxghJIs2hRnmzyTEqvKxnWLPsGGT+jZdqwMYVtapmhPglYjJsNYAlZAGycs/Hb4sKkyxX7Uq8VwiyNnEM5D3Ju4XHGAaXTLJstkfYKxqxlaKHl6C/+4i9a2TJLDMNW2wnjjr/hPZiJyrJOUi5P61Mle0WvZakic+c7q1h9WDZLNuu/ZKtFr12MbVDJOMe+zfnLshhZG/M4+13vGEIq4wxjjZYFWhmsnpaJ8uLFiyuPD8PW+XX//v2tzLbjdwJ58MEHV16X8zrfh2WTNWst52uOd5YJjN8AXK9w/bypdexOrZO3k8q3gmUy5RhMex3He84vp0+fXnkO18Psz71tbNl+eC/GDo/bXGRzl7WVWZgJ23xsfarYjG3soA2Q3/B8T4xrxsg4M9zSsGyC7AeVTE8kipoQQgghhBBCCCGEmZA/1IQQQgghhBBCCCHMhI+V9cmsIZT5UCr4yiuvtDLlhJQGUx71/vvvtzJ3T6e8mfeiRI31pMyKUiPW33ZzJyYpt928KQ1jnXk+5ZmUsbFNaOsYhq0yUdaVv+G1+PyU/b3zzjut/PDDD7cy5aas69mzZ1ee05sNyjLr9GbB6D2n8tspGax2ApOiVySx/C1jjXH6xBNPtDLljbt27WrlN998s5UZp2ajsHdltgAe530pw+ZxxhTl/zyHkmSOOZZ5jXFAqeZYFsrfUALK45Ud/E3+u4Q+ORWTjPJdLsH6RCrZOazM52Z/4xjP/sV5wOT/Vh/OcZxn+Fuz2BJeh/2dcnRmT2Ic8BkZ4yyzbuwjtPaOZd5mF+F5tFGwj9HOwPtxbmXWK9qHWdfe8dqoWNd6+xnpzSZjltq5Ymtaez8VG7TNd+yT1vaWEWhTmX8q742xfOjQoVZ+6KGHWplrUZa5rmS2GsswNt6mwOKOcFwzK6bNm3x/PN/ai+/DMsFxzOKagN88tHEx++p2zF93iw2q8q1gWxRwHfvaa6+1MvsLx3ubv2i5tXnQ6lzBtrawbTEsTtkHeQ77F5+dfZnf4LTaM87G/7Z3Y1ZBbqPAMYL34/qAbW33nRuVWOO7ZDvYezWiqAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMWGt9ojyHskHKqyj/+dKXvtTKTz31VCv/7u/+bitT/kMpGq9p0qrdu3e3MqVSrBulx/xtRcZFOxFlXNxtm+cz64RlvzLrBGXklMtSojbe7Z73oKWKxymtY/vyfGZ94j0++9nPtrJJ61566aVWXieb+4iKfHpT9qXe45a9YAmZLMzKZLvimzzS5IeUTT722GOtTIsT3z8lo5R88/q98mzKkHlN9lk+L5+R/Z3nMO5Mtm/yZFqlxv2d45rZGnmc57PM9uU9lpCJbBjcAkrsuM03ZAnWp4rdpNdSwX5kmYhoaaUUnPFiNmHWh7Ye1pkWwkceeaSVv/71r7cy453X533Zr/meOebweWl94vXN6jWGbcS5nM/DNuU9WCfej2Ml27cyjkzJ7mRya85fXEPQVsk5nWsXe/dcP7BNrK2XMG8a1vY2b9oa1ca/7Zbw99rEGXeML9qdaIvns9v8y77D/sI5jXPd+Dxah4jZh4mtgRmzrLdZa3gdxs7hw4dbmese2hZ4nBlhaSm37QI2xZIzspnlx9bsLHM8vnr1aitzDPv0pz/dyhzv+a1n9+I1ubbs3bbBMrhxbck5h9+SnBMtK7FZq3hNy0g1zkRkNkP+nv3frE9mDeV4we8HWxvdSVufjemV9Zxtp8Jxlt8z69YurQ6VSocQQgghhBBCCCGE7Sd/qAkhhBBCCCGEEEKYCWutT5QXUU5LaQ+lZb/8y7/cyr/2a7+28nzKKSmJpPTr4sWLrUyJLqXdlKIx+4zt2E+ZmWXQ4E7tlCyNM7x8BKWUlGSarJJQPkfJ2brMCfyNSbNYV8syY1Jw213fJL8vvvhiK/NdUt5m72NTGZ02JYeb8+7it8PaoCLZY3+jXYJ9jfJGykfZv2g55FhBObBZAUwiTjkr72V2KrMdMK5t53/2TcowGR8mux0Gt/6xrS0eOfZRGm474c/B/mOZwPju2YfYD2wc4PtgmzAzCOeAuVKxPNgcYX3M7LHsR5yzOKcw6wjrQ8mtZdB4/fXXW5nvgdeh5JlzIutTsY2wzAwyvK+tPVifsT2CfZX1MCk5YZ9kDPIelDFbppDtiFmzsrBP0IJBWwvHOHLt2rVWZv15nP3DMunMFVsTTsmiWMmu2MumMnTZcY4hHL85V1bWgGZrYN8xm/743pWsT/w972frXt6b493Y5rHqXox9ywDF63MMYTvS0sW6sf7bwdJsUDZ+2FYbZt3lGMxvw9OnT7cy3y3XXOwXXPudOXOmlZlViu/T1jVmn7RvI9aB/Y7xyDrzHF6T84BhWbTGz2BzDevKWLYtCdjnuSakDapiGbb+3Jstz2xKbDu2tW2jwL9HcJxhm/BvH1xXVWzwUdSEEEIIIYQQQgghzIT8oSaEEEIIIYQQQghhJpSzPpnckRLaP/iDP2hlypJfffXVVqacjDIoSmuvX7/eyh988EErW6YFk58R2wGbEmvuVM0dmk0WzXuxDnxGSkBZf0rG2La8zlgmSskZ5VV8HrYp24jns8xrssz3d+LEiZXPY3YUSqNtF3LDLAEm552SWaEiF54rm8pqxTL7xZUrV1r55MmTrcx+wZ3zn3zyyVbmO7906VIrs/9bPDKz2zhDxEdQ6sl7mWSSMkazN1LybNYwMo5NSmkpiWRM8fnZjrw3n5/1u3z5citTJrrdfdVsorS2MivY448/3spf+cpXWvlb3/pWK9MqUrkvsWxQc6KS8aFitbCx9tatW61MaS3nHc5ZvO+NGzdamfHO+1LOb/My5fwHDhxoZcuARGhfY2zavMlnsYxMfEbG2TBs7Uv8ja0D+AwWv5xnK5JsUhm7K/OgZe+gVYxrGstcY5lFCK3NZrVeAr0WkEoGt4rVnVRk+JVzpmR6orX00KFDrcx1L/uFZXMz6yXnZfY1xtkwbP1+2Lt3bytzjGM88t4cIxiPLDNmK98GvJdlUrPsrZY57oknnmhlzn3c1mG7WfKa1uZNsy6yzO9H2qD4TcO1G+c1zkfWtzk/WjYoXp9l24KC8xKf0a5j2ZNtXGfdzOo1DFv7PO9nNlvGNtuI78Di17K5TRlbDbOA0ibMzMjsK4xfe0azBnNM45p/3XYnHxFFTQghhBBCCCGEEMJMyB9qQgghhBBCCCGEEGZCOesTJVKWpYjnUwZFCRbPYTaKCxcutPLLL7+88vqU/FMSZVYbk+hSKnb06NFWpmSYElD+ltekfInSMtuBnjJMy6Jl2RiGYaskjthO2jxOqatJrdh2rB+l55RScxdrk6qabJVUJL9mvavIfytyOLMizBWT8Vo8VjJQ8LeU8nFHc8YLpZLMKEIpNfsjZaI8Tnkgs0rx+ux3jDXLzkYZsmVjMBsN44bSTp5PKegYxhrrx+dhv+U9KJM2uSmtpLSv9GYZsR3v2V7Hjh1rZcpBn3766VbmuGRZefiMlomDMc6+aOPvXLEsFb02TbPWMsMa25jSXc5flHOzXTnX2HjCOOX8xWdk7PNd0S5TsSmxv1t8WNYeu/6qf6+6n2WD4705LnDNYZlcpswplWxhFr9m72J9uIYzKyKtbjavVKync8IsEjZXVjIV9lKxeFSsT5Vrmn2HNjiWab21+/IcznWMG44bjK2xVY5zis0jHKc497MPmw2GmI2XMct1D+cptl3FRsIx2r4x2EZLWHNuN5Xxo7Ltgb1P2sfZ79iHCddBXNOyX5g93bKAErMBWV/gOayzWZ8Yp4wt9lOO3+PvQsaz2aq5LufamvMm3wEZW5RX0Wv1tHGT62e+M66ZuKal3YnPwjZl5ia+D64N2A9o+eZ3cbI+hRBCCCGEEEIIISyI/KEmhBBCCCGEEEIIYSastT7Zjv6URFFmRukT7RKUBlMeRtk7z6e0jNfn+ZRpmXSIUlLKnWjLoiyccnGew3uZpJMyLkojKSumJIptRVklJVrcpXz8PJRg8T2xfmYx4P3sHMrj2C58TmYD4rNxN3te0zJA9cp5ezM3GL0Wqjlhcn4er2Q5IOznlOm99NJLK69P+Sj7CGWSn/nMZ1r5V37lV1qZ/YW2OUopbZd7lin15H3NJsn44vNathqTNrNfD8PWuDCZJeWRrDelpIxNG4MYs5RY8zix5+d9P/e5z7Uy7U7/7J/9s5XXZ0auN954Y+W9zp8/38omBTZbE9vaLB5zxexOUzIYMJZpBaCdkPMO5fZ8V5yLOeeaDYTzL+OU8yb7tWVoIrwmYVzb++ez8zo8f2wRZkwRy7xYsVLzHpRGm32jMpf1ZhiybHnMwsU2tXbgWM/+xLWLjY8Vm9CS2VQs99q7p2AZFTkOMFscY5ljiMUvx2CWOT+wT3GcoYVkGLb2K853lkGH8W+ZLC1rHS1eFgtcN7CfWwZKy2LLZ2Fbs3zu3LmV19wOlrC+nZIxr2KD4vzFfkRrjq1j2RfYh22c5tjJcZHrL7M6cq3E+GAsc1xntjRek32Q36q2hhpn/OO/OUbwOTnX0NbHeZNliy/GMsuVbzTLBMa2tuzOp0+fbmVuO8BnYTvQDsx3YxZxfidwXOod66OoCSGEEEIIIYQQQpgJ+UNNCCGEEEIIIYQQwkxYa32iZIkSMitT4khZE6VclCZRHma7rZv1qZJtgFIxSrcogeRxSt14fUrmKFmi9Il1o2WJbULZF6WUfEZKvSgfHdePcmtKwm7cuNHKJl3lPVgnvidekzI7yuYopzt16lQrM5uXvVeTTFcyIlQwe4/Jl5cgDSWWucva0qwNZkejfY39k32KNgfKL3k+JYSWOY51o8yQdaOMkcfNrse6mS3CrGGURTPmLLPKuH58BsYOZbUV6xPvzbGS9lGzgHKcpS2C7+mpp55q5c9//vMr60PJKGWfHONYH+6Ez7GFz8u2o0yU8cg+ZHLWuVIZe0jFIsP2Y9twjjDJN9ubNiWLQR63sYJSX/ZT9hfGptmU7D2b9YnzHvsOzx9nZLMsF5aFzTLlMNY491mWN17HxuveTGC8po0tHHMZy4TPy/fEcebKlSutbBnCzNIzVypZY6ac32t3szVIr8XDrs8+zj5imW4qGcxYZ47xXDNcunTptucPw1abLS2HjG3ONTzfLCVclxNe06wZPIfPz7i2bLIWm2xrjl8Vm/onicr40RuzbFf2z7Nnz7ayZS6ztR/XUJaZkedzrmSfZSyw7/Pb0NarvD7jmvMVj9v307o5ivfmvM71La18rBPnFMaU2WZZb5uvK+OprXX4fcosldy+gZY2zu/8hmV9OFfaWMnjZluuWIajqAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMWKsjNyk6LTKU7TA7DOWKjz/+eCtTlsaMJZSBmR2DZbNyUAZFORUtTpRrUWZFKEWjdI1SsUcffbSVKfWiBI42ILM7VbJfjWG9eT+2BSVnttu2ZRNh+1IOyOtTGkg5INuF75hyWJN79UqNjSkZo5Yg4abUl8/E4+xXlewV1t7sRxcvXmzlM2fOrLwv44tjhWVaYB1YZ/YR9lnGTkUuTpkh68O4piyW/Z3WAZPRjq9rGdYop2R7caxhTPF87khPC5JZR3gdyj4PHjzYyg8//HArM0sQf8vn4pj+3HPPtfJrr73WyjYuUfJt2Xb4niivXWdrmSMV+6ZZFK1scx/fD22vHO8pgWZ/Zh80+y3vZRJmSp75ntl/bQ5lHfhueZz9wuY6nrMuMxj/z8ZQs44wrnk/SqNPnDjRypVMSYbZY1h/WhopTec7oPyb12S/oYSbfYjrAbNuLc0yTHptRHbcrlNpmynWp8oa2NZoXIvavMz5juMx+w7Ltp5nfJgVbxi2jgtcN3MdzPuZxcXi2tY0Zv3jOsa+fxgXnKf4LBwHOc9yvGN9NrX+XFps9s6bxNa0toakZZhbW9A2amsWvk/2TY7HZhXkvfjOWeYcx7jj2M+2Yn2I2bhsThtnfeLvOV6wHfnMPN+sxGYr5jzFudXmTesrfE+MX1omue5lBiiuk9ifuD6ntf/ChQutbN/Otv2ErbeMKGpCCCGEEEIIIYQQZkL+UBNCCCGEEEIIIYQwE8pZnyhZMvklZV2Ub1G+RAsSs4Xw/Ir9wXaDpsyMMktKnCwjDK9vGZ0oH6U0jnKya9euDatg3SizMovKGNbVMlRZtgxKzymVo4yT16Fcle/GMvfweSgz4315Hcpk+fy9VjeTDlekzNanl2B9Mim6ZQ+o2J3YNmY/5Ls6f/58KzOTGvsF447vnxJTyj7NusV+av3RssZQYmm731sGHJMujiW4rDfvx3bkuMN68N58Tkpd2b6/9mu/1sqUiVpWPFqfeF/ucs+xjHCM/r//9/+2Mm2ulP9aZjo+I+9LO6S9J5aXlvWpMiZV7BKWIcRik+MD38/NmzdbmfJbYlmf2PaMQcukxPdJWfTYNrjqmryOZc3gM7KvjfsI44iWCtaPbcc1CuvN+nF9wLjmbymv53xq85rJ+vnuWWfKzjmeUsLOdmQd2A/OnTvXypbtj1RsP3PF2ruy1ujN5kZ6bSiVMcHmdPZ/rvW4RjMbLvu4vWfOs+xHjC1aiHjf8bPQ8sBrmW2QfdvWMTzO8zlGWDYdnm82Dfs2sEy0LLMtaM3glgef1AxQFkcWj6QSX+xH/Fbl+GdZN2kt5fxoWXBZH65vzcbLOY7nsw5mp7O5iHXm+bY+HbetZfvlvENLFM+3PszrmM2f1+HaxcZowvZlW1hWRN6X7ct1OLd7oM2f37C9c2WlT5MoakIIIYQQQgghhBBmQv5QE0IIIYQQQgghhDAT1urITfZvu62zbDJh2iVMPm2ywYr9xTK2sEx5FGWftgs324G/pWyT0idKOCntp+TKbFzcYXqc9amS0YfyLZNjcYdqns9nZpuyrjyfUlKWKf+2LBgvvPBCK1NubRnFtkN2bH1oCVh9zSJhsWzyYb5nk+/xXXE3dMob2RcOHDiw8jrj3eZX1cHeM+WTlk3ILFGUPTKeGDeWcYWy0mFw2SfHCP6eklHGNc9nXXk+3wfHNWZs4b0Y74QSWD7z2bNnW/krX/lKKz/zzDOtzFjmu+FYYVY3/pbvj+1g/dssf3OiEpsm3a1kfiEV+wbfrUmSbby37ISV2GQfNPm3ZbXgNa0OZlEc2/jMDkx7Bp+H9WOMmD2SMbt///5WZj/nvTjHcwwllsGN4+mRI0damXJ0Pj/fJefZV155pZU5btj8a/bypWWWmWJl6s3otN3YO2FMcV6yzGjsa+yz7Au0DlhWFvYvxgrH9XFsco1rWRVZJ9oNbCyzDDqWbY7X4TNzfWB9gufzvrb24jxOCzBtkrzOpphDf70dFUu+UcmWyOPsF8yiya0tOKaalZ7rQMs8Rosb5y/LdsqY4Nxi6zXei7Fi37lmbR6vw9kPLZMgn41jDa1crCvvbd+MnI/YFnw2wrjm+2CGU7MJW8YsxiNtibTJsQ8Rs+2TXpvwsr5OQwghhBBCCCGEEO5i8oeaEEIIIYQQQgghhJlQzvpkcjKTwVLixOtYZoopEiFKvCh9ogzZLCEmrWI9CeValhHDpGsmd6dVyjLdDMNWKRqlobaDPY8z4wPrYb9lPVi2ncopgaNEj9Kyhx56qJUp56Y9jM9IuTiPs33NCmGybZMLLyFjhVGRhvZmd7LYZLuaXYjvzaT0tls+78vr075jlkn2O/YR6+8/+tGPWpnxZO3A42NborUFf0PZs1mEzLbAsYljCscdtinLfGbWm++VMfjf/tt/a+XvfOc7rcwYtHgx25hl+mH/YL+kDJfvaQkZMSrWwgqVDBeV61eyJRg2PrAvsO/bfEJ5svVNy17I+DD7r40n43uYxJztwntTzs05y8ZTSqz5PMwywuw4tHLY+EgJ98mTJ1uZMn0+M/sEr/+DH/yglWlvZHzZvGn0nr/TbCrrU2+GyYo1o0LFZsi+wDnH6sb4YkzwmoQxyLne1vBch4/rz/mCc7w9p2Wo4vm0FrJOvBd/y9hkzLI+nHN5TY6DlUw0HFsee+yxVmZ2RbMVV+i1zi6BUlYc6XvsL3w/9p7ffPPNVqYdh23Jbx3OCTY/0GrDeZDjLq/Pfmq2Ic5Ldo6db2ur8VYZfAb2ST4/4XhhWU0ZR1zf8vmZKZYWJMsOzHbnWMNMqXyX/E4gHMteffXVVv7e977XyvxWr1iDK1TOj6ImhBBCCCGEEEIIYSbkDzUhhBBCCCGEEEIIM2Gt9cl2pTYsU4zZAirSIctEY7vcU/pESZRJrCmBpAyf9aRszDJiUBrGZ6G0jHW2Xd4pv6Jsb3xvSvQo5WKZ9eZz8h4mMbdnYBtRJstrUm7LOtCWRpkcd79nG/EZ2V7MMGT2qErGKNulfQlYjFR2zjeJrsm8zQJpdgZmImJ92Hf4W/YR1s3sSOx3NiawPhwHiGWWsXGDfWQsKeezsa9ybLId+W1nfz6n7fLP5+R7sqxXllnjq1/9aiszu1NFjl7JqlbJ2EfMeleZh3YaswNXMrJV2pJU5s2K1YLH2cbss2Z1tXGAfbmScYVzEeOGFoFr1661MuOM9Rzb4/hsjDVey94N68FnNosfZe4c1/jMtCNdvHixlRnvHINOnz7dyrRBWcYK1u3FF19sZdqdaA/pzUA2t+xHH5deu9OmskFVrtlrrTLrk2XS41qJ6zuzI5hl2NbnjLN11kvbeoBrUcuQyHNYtrU768F5jVZEjnFsLz6zWa54jtkr2C7M2nb06NFW5rp3CVkON8UUq2AlE52N8eyTHJtpRzPrEK3qPMfuyzhlX+BajLHJmDArYiWuK1mCx9e3fs5tNBibltGJ9+a8xnpw3uR92f+ZccmyanF7DVqfeA7HMrbXlStXWvnP/uzPWplZn6xNeufBXotiFDUhhBBCCCGEEEIIMyF/qAkhhBBCCCGEEEKYCflDTQghhBBCCCGEEMJMWLsxB/1htn8LoWeLXi5Lq2WeekuhaF5q2+eAdaYXjf5E89vanjY8hx5Gev3o4aMnj3Uwbxzbavy8lsKNe4Kw3R944IGVx3ld1tX8wjxu6dLoq2WKUqZaMz8jn8vSn/M69DPS4//GG2+0Mveusfdn+7AsAfOSV551SqzxXvTVmkecMcjzWU/GCJ/LUs7a9dl3bK8mPhf7OM/ndTgOrIPX5f3oeefzWEpjXoftwv7MeOd1eJzXpKeabffd7363lb/2ta+1Mv37lTHXyrYvkvl8bb8DvuMlxKml5+5NjV3ZE8OOV/YUsbZnH+HYz70cOJZznrH90lhmO5innp5y+tE5D7z88ssr68n+Owxb49HSl3Is4PPbXlqMNT4b37f56Hkdm+9szxGOCawbY4rz4HPPPdfKTC06Ze+LJe9LY/siTtmjhlT22ZiSntuuwz7FvsNz2McJ+5HtM8G+bPvFsQ6MCUvnOww+l3PO4t6GTElv78/mb0tpbHvecTzifVk3rmlsrW97o3AMsT00bD3Xy9Ji1uZNi19i35g2/3INxffJbxq+H47H7DtMAc13aHsk2v6FrI/te8R+yrHc9n9jmb+19OLDsDVe+J1o846NEbwu247j1PXr11uZawvuOcs2ZRux3bkvDeOL12Q/YPtyPcz9cPiObS21qbIRRU0IIYQQQgghhBDCTMgfakIIIYQQQgghhBBmwlrrU29KRksVWjleqYOdT+kXJVGWgpJSJkqfmC6NEtATJ06srAPvy7RulJmZpI0SO0rgLJX5+Lr8P5ZZb5Nts96UQ/MZ2Hasqz0PZWmUyFOGThmbWUVMqsh3Qxki5XO8l6UVrqQJ7rUo7ARm2zI7IanIvO1ehFLlW7dutTL7He/Fvsn3Q/kk+xHPp2SS/d3sMmYDYrwzlilVZjxWZfD8N9uF/bMSj+znfK+0l9j5lJ6arYM2y9/7vd9bebw3XW9Fgmzpnw2ewz69hHSlFVtYxVJmcUq247jZoDhXUKrMWGO/rqQrNRsUrX7Hjh1rZcYmLbCUZrNu4/sxLszOTbuiWRgIZdWcKwnrzTgllLZzzOJxezecu3/wgx+0Mq3KjJ2KhNuelyzNUlGhMuZXxrzeVN2kMtZyPrF7sY/bcc4hLPP67Gucrzm38Jq0I9iadBi2xhfX5YxnptxlH2adzArOOvH6tn7m2pUWKq6HOS8zvsy6xTUA53G2HVN18x1Msf0uLTbZN8zaY9i3qtmQbSxk37bvCc6DXJeald5seXxGziGV9Nk859ChQ63M/stn5JxmabtZt2HY2l6cj+w5LVU9MZs044jPfPz48ZVlrg8Ya/w25L34LKz/tWvXWplWats6ozJXVrYC6P07SBQ1IYQQQgghhBBCCDMhf6gJIYQQQgghhBBCmAlrrU9TdhmvXKeSgaciPaVcjZJJSi7tHEq0KGOjrJrSNcrPbty40cqUU1EmxzqY3Yl14w73Fy5cGAglYZRiUrLGuto9TPZJSR+laGwjs51QysV7mcXDZHWUw/EZKWPjDvyU2NKiRukan4v1N4napvr9dmJyuUrmpil2J4tTywxGWwRjh5JeywrHGLRd69k3K8/OmGW/sIxv9ryM6/EzsA+zz1v2KUqdLUMNpau8PvsB6802orzzO9/5TiufP3++lS2up8j3TYJsMnXCtuZvl5D1qYKNN5sqV+zJvdmgaFO4dOlSK1uGNct2wX5ttiGOJ7T1fPrTn25lji2cK/jbcb0ZIyyzT5pVg2MW24XPzzHO5Nz8rVk0LbsJn41zHO1OXDdYXJPe7ESbymC0E1SyH24q1uwd9lrKzLpnNnzakAl/y9jkOpH91OwCXN/yOC19/C3jg7E4/j3ncs79NlZy/q5Y2jlGWKZMth1tJGw7jnG0WZpVjFlQ+Z5s3OSYaJarXpZgg2Ibsy+xbTj+2Thk34+2hrR1I78T2Rf43syCY5nUbJ3JPsK1JJ+LccdvIN6LNmHa2W1+tGyn43+zn/Mb1dZybBfLbGgWaL57lvl9blnu2C62jQDfJbM78dvA7E5mN2U9ba6fYjGOoiaEEEIIIYQQQghhJuQPNSGEEEIIIYQQQggzYa31yawhpCKnteOV31akQ4RSLFpwLFuCXZ9SLEqezRJEWRqlWLQsUM5JKZrZrNbtkM96U4pGCR2fkxJTSt/YRrw+z7f2MnsJ24vXpOSMUjFKBtnPKEW7fPlyK9MGxV2++T5MJtqbyWyumNSzV4pesUFVMEmjSQUpB2X92RcIr0PJM3e8t+xRHBMYpyZbZX0s+9DY+sT+bLYdk5XzOOvNOGLbWbYL3ovP+e1vf7uVv/GNb7SyjY/GlAwoNm5U5MtsqyVYn6zP23GT7W/3mFQZK+w4x1pKrDn/0NbDODp8+HArM444b9KCwDGebcjYZyyPsyWalYnSaLYFbSFm5+A6gOdbdkmW+ZxWB86bHAcox6fd6YUXXlh5jsUaqVifKr9dGtth7Z9iFSW8DvuLZVLjPGAZV8yGzH5ttgP2Qa5pbZxmzLHvj9eM7KtmWWFb8N7WFjYX87esH8uWBZVrUa4/Gb+sM+tgWwHwvrSH8D2NvwHuZtgeXAdZ27CNef7Vq1dbmX2vsqUGz2EcXbx4ceW99u3b18pcc3FOsOyljBf+lsdpMeZ3Huv58MMPtzLnWfY7xi/nSmYbY9sOw9bvUj4zLconT55cWW+ebxZNztM839qIbWpbJLDMNf2bb77Zyq+//norP/fcc63Md1BZo1qWskp2z97vtChqQgghhBBCCCGEEGZC/lATQgghhBBCCCGEMBM2mvWp1+40JWMFMZuSSY/NykM5me1yT9mjSRptZ3Je8+DBgyvrT9kXrz8MnnWCcjeTSlbsCXx+StQqdie7DmVylABWJNmUf/McylApf2Qd2FaVjADsE/aMc8Ji0+Sd9tsp1ie2E/sgy3yHzGZgVkqTGVqWJMpiGYNmkbF3y7rRasFd8Rm/4z7Lfkj5JWOYsck2Mvk05aAmSef5tGMwo9PXvva1VrZMV71MGcdNLk9s5/wlZJbppTeLTq9NbVNUxnhKjCkLNzkwY4XX4Tm85uOPP97KnKMYE4yn8b05XtCmxfinxJoWEd6Pv2WMW4Ybk3Dzvmaz5HqCEvznn3++lTeVHabC3WJ36s36ZEyxG5sdkv3IbBR855X1DvsybRSWLdBsCpZpkfMmf8tr0iY5vhbLBw4caGVaNRgvtCbR5sA4sq0ALAMlx6CzZ8+2Mp+T74NZdmgD4bvhusTmdMLxy7J83Y3zoK3N+M1Fqw7bm/3t93//91uZWybwnVRin/2ItjlekzZe9guuFTnnsJ+yL/CdWxZUrt14HWZkOnXqVCu/+uqrwyps6wu28zBsjWfbGoCxQ2xrA/ZhPqdZnxg7rAOz/bLtWGden+/+zJkzrcx3aVsBmN2pkq2JTInfKGpCCCGEEEIIIYQQZkL+UBNCCCGEEEIIIYQwEz5W1qcpWZl67RiUpVkdKLOi/MzkkJTJmSyRMihKL00WTXkXy/ytZTp66KGHWtl2KR8GlzezTpSSWxvxOSkHrbw/k7BbnzBbGo+bNM7ks5bZi1T6Jct8riXIStl+lfdAtiPbhclWTfJPTErNWDBLI/uO2ddMrkh5KiXPlNdaHLAO4/qZbL0iSWdsWhYJwud/+eWXW/kP/uAPWvncuXMr67kd1qdN9T+zRy3BlmjvymwONv5NyZxIerPS9L5Ds0WwzDhgfFBuzTLji+ez/1LyTCn0+BmZzYLxdfz48VamfJxWZ5Ob2/jCZ2a7cP7lmoPncAxiHRjXX/3qV1fWmXNihd5MT3dj1idjU5mberE+QrsP+4VldSGWOZBjqmUiYp9iFjaWaeuwrKSWTWUYtq6PLasa78F4ZF0vXLjQyrROMDZ5HbOIs70Ym1x7M075XcF2fOSRR1Ze07JBcSyz7FwVeuN3TticyD5Dq9Gv//qvtzLnCI7xf/iHf9jK3//+91uZ743YuoZ9gTYo2u/sHfL9Wx9krHGuZL+jXZF9kNkP+Vtmg7KtP8h4TOO4Qysi7UK2zQFjk++Gz8x4MXuRWZ94Du/FMYTxxYzDLLPOlQxNRuVvIrbtRrI+hRBCCCGEEEIIISyI/KEmhBBCCCGEEEIIYSbckaxPlPlUMhKYRJNlszvR+sNzKJmkXMssS5SiUbrF3/L6LFMa98EHH7QyZVwms6K8a7yrNOtHOSmlqJSSUgJISTapSLAs+05FEkbZn1kY7JqUt7FstqneDA3Wt5Zgr7AdxzeV0alCRRpqWYwYF7QgVd6n2RX5bk0KTqkqY8KyKlk7UzI5vp9ZHDmmsI/xefhbk6haVoAf/OAHrcyMFWxfsik59KbsrMRicwm2RLPo9sassR3ZoHqlu4Qxy7mI0mNKuCtZmMziwPpw3qcsfFxP9jezQfFatF3w3iZhp6yc51esX4xljomvvPJKKzNrG7Pm2PhgbMrKtAQbhbGpWOs9h7D9ON7bmpbXN7sT5xZ7P7wX7UGMA8KYZTxy7cl70brI3zJWaNkYhq3Pw3mKGeOY9Y31vnr1aiszjjgG0YJkduNr1661Mp/NbIy8DtcTtCjy+szEwxhnu5uFqjezzJLhOM3+TIvniy++2Mqf+9znWnn//v2tzHfOLHkcU3n9im3UYpB147jO+YixTHsU18l857yXZR3l9xDjgxZg9h3amPiNSMvgOGNwZZzn8/PZ+DyEdeJ4xPfHtTWf3yyB9p3IWGYWVFqfGHeVb0Ozr1e2epliRfzkjAIhhBBCCCGEEEIIMyd/qAkhhBBCCCGEEEKYCR8r6xPlPGZlqmTXqciFbKdkygZtt21KxShvs6wxrA+vbztmU7plO5ZTJsfrUN7F83nOWD5GmRbPo8yUcq9du3a1Mi0YfDbLUFKRyBOzt5nlwfoE5b9mIeHz2vVNcmbSNevrc2VT2XWmWElMWsksZrQzsD9TAk0Jt71/SiB5HUogLX7NBkWLA6/zcdqzkkXC+hX7JH/LMYvXoSSb0l7K0yn/trjYbqZkW1ty9gr2YWKZ60hvhqZeejNGVTBZOOcosyaxb5oViTYI1p82Z16H0u5h8PiiJYPvw2KQ1+FzmiWZczzHMp5P2fq3v/3tVv7Wt77Vyq+//norW7YSo2Jx6o01W5MtgV77V8WuOAVen2sc9hFb31UyLXIs4hxNmxLjgOtbxi/nE9aH87LZsiwT3DBsjVW2+40bN1Y+A/s/n59zH8cF2kv4bcDY5LhjNrBKNiDWgRYn+0ayjKtsU1uvbvc8sRPYtwj74ZkzZ1r5P/2n/9TKX/jCF1qZfY/tx+9E9lXbhsHg+zSbLDMKsq/xWbgGti0vePzEiROtTLse164cHxjv7FOs8zoLGOOFliqbH9m+9j3MtuCawDIo27uxb0mOTbRGPvvss63MODVLI+n9m0UvyfoUQgghhBBCCCGEsCDyh5oQQgghhBBCCCGEmVDO+tSbTcakSWY1qtTB5IGUblKmRNkjy7REVTKQ2K7SZoOivNMy2vBZbMf3sWWD/7bd4+1+ZiPib01aZlJ1UrGLVGwOhG1tu3NXbBQV+bJl5JkrfIdT7Ay9Uj62JfvqY4891sqHDx9uZfY7yh4p+2SZbc8YP3ToUCuzz5rNhP2F8cjjvM54x/uPsD4yzrhi9jpKOi2mzL7FtjbJ7BtvvNHKly5damW2dSU2t0M+bXNApb+yDZeW9anXblI5p7f9yHa3Ga/PfscsGzyHdj3GNeXotC9wHCBmmxpL6Bn/HLM4hjKbjFkOLSskY5NZYHicz8b6/Mmf/EkrP/PMM63MzBSVrCS9VDJ7VayI251Z8E7Rm7XDfluxjLPM+Yv9mes79iOz/tg1aS+wMsdX9k3a8mgpYEzYmpx14/m0XQzD1rnMrMs8h5ZIWhjsfox3ro35zIxNjgnE3rHZOth2n/3sZ1fei9e0rQwss9uUOWauVL6P2H7f+c53WvnVV19tZVpwWGZccM1l46vFF98/+5RlK6JtyDJb0bJkGQIPHDiw8jpc97E+nEP5vLw+23bdvMm44HUJ12y0VvLZOM/yGbhFAu2Qtv0By7w+x4qXXnqplZmdin2osjauZDruXdv1zpVR1IQQQgghhBBCCCHMhPyhJoQQQgghhBBCCGEmrLU+9VKxR1UkQmY9oQyKdifK28zyYHInqw+ll5RZUTJKqZdJLymHo0TLdptetws1n4FlXpfSUmZ9otyN7WWZqyryLWL2oko2JT4n61ORh/VmbrA6VDI9zAnLTmBMsRmavJk72z/11FOtfPDgwVamBJh1phSR8cLsEvv3729lWheZBcYyJ1BKTIk045ESY8pKWR/KU9fFB+PfMldRlmqZCXg+Y5zPzx3/KfVkXS3TRK/daco5FneVLICV43PFsgyx7jZ3EGuDXhtKrxy+d6wgfBbG8pUrV1qZGV14nGOLzVc2R1vmmmHY2o6MQWa14Dmsh40FlGfzeVjmvMx1yfPPP9/KP/jBD1q5kilmu5nSV5ZgtaiMJZVYq8jhK/ZrHmcfYb+zNaGNr+y/ZlmwLCu03DE2Gcscuzif2lzMeYznD8PWeY315nmc73mc9+A8WLFdsK5sF65dOO5Y5kjCPmHZ3+zbwKxYlk3SWFo8EpvLLAMvx3J+63ENxXfOPs++xjY2qyvhu6pkImK8MB4Z7xz7ObfY+WZlsr7DOvO3tGWNLWA2NvE3XEPzmfk+LNsl60H4jW3rataHdmOWuU5mv6lsbdG71UslI9sUm/D8V70hhBBCCCGEEEIInxDyh5oQQgghhBBCCCGEmVDO+lSRC1lWEzuHmKyUUEJFSRslUZSNUU5W2S3fds6nRJoSa8rkWDaLg2V+oESNu9SPZaK8Lq9FTGZKGSelcrwfJYa9kq1Klq/KddheJjGs3HenMt3cKSjXtXaqWFIq74Tns/9zp3br25QAs3+xzN+y/pbtotJ3KIWmHZDHLdMTMUvjWBbLNqK8k+MIf0/JKOtBSS5l4RcuXGhlWrl4TbZpRdY/5bidU7EcVixObJMlx+mmqNipiI27vRLdXhsUy4xZzsuUJ9MqRNuBjVG0LHDuYsytkxVzHjSsfTlOcW3BOOVz0t7J7Gyvv/56K3N83I6sSb1S7Yp9bsmZnqZYArvl6jL+WX9m2fqz1ZN2D/ZxxhTXsTyH9mRanBiblcxIXFdahjSuYYfBMzLSvjT+zar68R6cH2mj4HqCz885nusb1oExXrEVsx0Z+2Z55bxv65LKGL1k6xPfP+nNbGiWIs4XjBeznle+IdgXLPuf2Y7YB/lbjgPs48ymapndCJ+La0Ou280mNgxb18psO96b2xPwe5NjBNeojCm2ndmjeC+zMPMd0655+fLlVra+RaZYyo2KVbXSv6OoCSGEEEIIIYQQQpgJ+UNNCCGEEEIIIYQQwkxYa32akinGzqlg17RduykfpuSQ8qiK5cruxTIlWiyb1JMyNspHTQZFmRhlX8Pgklnem7LPkydPrnwGStQofTOZWcVSZPWsHK9kVqhch/Rmh1maTJRSRGIWCcusZbvWWxub5PDixYsrr1/Z/Z3nUGLM+vC+lHHympQ3mmWBliPey+w44wwyHzFuH7NWst68N20R/C3lsC+99FIrf//73195fRsv+A4qtpCK7LM3u5PJi81iavVZGjb2WGya3JrjulkazVpH+FurA9mUnYXXYR+s2Bgpw2Y70F5BeTWfa9wOvJbZfnkPiyObyxnjHFM4Jp47d66VOd7tlHWoEte9MbiEmN2ODHKV57Z7VTK+Ve7LPstsn8yYtHfv3pX3tYyCnN/ZXxh3Fis2vo1tQxwX7LqMX8bd/fff38rMVmUWY9o0WA+zMll2VMvCxTLrQDvGo48+uvJeZlOxulXsw0uIR2JbbVTsfpV1rM079n1n/ZZls2vbFhRHjx5tZWYapeWO8wbrzHmGFiJug0ALlW1lwfbhfDjOwsT/sy0DeF3GC8cO1oNxzfvZWMN3wH7ONrVMT9zyoJJFcafmwVifQgghhBBCCCGEEBZE/lATQgghhBBCCCGEMBPKWZ96ZdKVbDwVKT2lUpR4EVqfmHWhIrFmHSjRooyLsjTbwZ5yZsoe2Q5mLWJ5neyR/zaZOyVqbAvem7Ixy2QzJROEYfIz2wG79/pGRQ76Sc1ewf5pmYtMPmp9h1Jly25EGbZZ8SiNZPYKSjJ5Pq9D6SnLrD93y6fsmvfl9dm2Y+sZz2MM8360ZTLTBssca77xjW+08quvvtrKjGU+G2WvbOvKDvO9fcjGL7OGmly2kvXJ+uhcqWTtsLJJ7O25GQu8DuPX2rs3e88UmzOfhXMlJc/2jIwnto/Z+2iJGIatsWrZXmxMZEyxzGfgPHv+/PlWZqY2Pmclg+Z2Y+++khFzU/PyTrMpy7VZfqx/2XUqNheLZc5f7P9cM/O37Mu05tA6z/Utn5HxxC0IeA7X5LzOOJMp50fbGoDWLI4FtDwwHjnWcI63zFU8zjZi23FNsy7746rjbFNCixrPMftrhaXZnYhZynrjsbJtg93L7FTEshWxH7Fvct1Hix6vY/Yr21KD5zMm7BuZz8g1Ju/FtcQwbI1VZoxj+/I425pjEMcaxhHfAedljhf2/hiDXD8zwxrbvRJHU+ay3u04ujMIdtcohBBCCCGEEEIIIWwL+UNNCCGEEEIIIYQQwkyYnPWp97eV45QIUY5FmRUtFZQNUhI1JbPBup3qP4LSOLNd8LeUdFHGxvNZ/7G9ib9hmfWg3ItyUO4SbtI9s3lsaif5Tf22YqWrZHfgb5dgqSDsb2adsJ3wTYJnGXvY12zndUKpMvsj60DpMXdnZ7xToslnpNyaMWKZWDhW8L6Up7IdeB3ajFh/tskwuEyU96Ys85lnnmllk91fvny5lSmZ5bshfAbW2zIW8Hyz6/TGl1lTKudX7ruEOK1YuypxZ3YJvkNaCG0etGyGlB6b1bFXdl6x1Ni4wXikLYBQOk3bBOH4MAxbxxEb+9j/TYZu2dxu3LjRyrSRMPbnZneaYl+yvrsE20VlXVOxRNm6yTJ48jqM3145vI0t7LO0P3A+5fkcKzj/WqZUyzBFGxT7O+dQs2GO4bXYppYBjnHN+dHWw/wt7RhsO9aBY+uePXta2Z7Txj62KbcmIFyXcN1eaTubE5dmUey1fPXa/yu2xCkZMhkX/C2tsVwb873x+4x2PfZTy/RrcyLHKMuuuXv37pX1GQbPJsUxxSxVrB+tWbYtiGVUtOPMjMV1Mo9XYrN3qwijt//1zpXzX/WGEEIIIYQQQgghfELIH2pCCCGEEEIIIYQQZkI561NFRjcliwShZItSLkqMKZOmtNCk/RXJvGUmMWkc70t5G6XdrA+fhRI4SrQo2+b1x79nPXgPyjIpCaM8znYV3yl6s0tUbGwVKdqmLF07AaWMluGHmNTTbBHWv3gv24WeckVKJlkHy9jA3eJtd3rLPMXMKrymxS/tFWYrHMfgR4ztFXxm1s8smrRIVCyRbEezpZmNyI5XMrxUMiuY/cHOsXJlvlmC9cmojE+W8bDSNpTtMx75nu04Ybz3ysgr/YixQ1k045F9n2OC2Ul4Pq2Hw+BZFc3SSSqSbM79tDbwnLlRWZ/Ze52SvWKnmTKesT04prI/V9bAU7KwcUygBeHkyZOtfOrUqVampYJrzqtXr7Yys6Zw7WoZS1lmO3CONisxy8Pg7c65n3CM43zKLFOMQdaPx20c4Pm0I+3fv7+Vr1271spmRyEcs15++eVWfvTRR1v5yJEjK69jY3Rl/ljamtYs2qR324PKGr9i7+39/rU+xUyAhw8fbmWuGdn3uY5lH+f1+VuORdY3bf3ImBiGre0yzgj1EYwRwnVGJUuWWb7t+5/tyLU05+Lx9gQfMWVbj8q7791mpcJyV70hhBBCCCGEEEIIdxn5Q00IIYQQQgghhBDCTPhYWZ+m2FMquy+blI82AsoJeyVOVh9C6ZbJM812QbkWZV8mnbYsGGMqklxei1I5Hh9L3D6CslLKWy27hjEls8IUm9IUSdvS5Nzsn3yfZpdgXzWJMi0DvOb169dbmVJH9mdKqXlN9kHbOd6kp5RxUhr5C7/wC63M2GdMVSTAlELz2Xk+49p2ux/fj7auc+fOtTLlmsy0wXa0evOd2fuzzF58l5bFxuLFrKQVi5OdYxZTY2nZKywLTGWu5BjM98N3SMk/s5HQwsDjtNYxkwmlxIxNvvNKNqjKWMtxiWMX487GActeZzaocYYL/tusZbwuxx2Od2ZdZtmyTs6NKVk5l5aFjdg4xHdl8WtZ7BizNs/a2Nxrg+I1aZFghhbGFK0T7Ke0xXNtaPdirHAsskw9ZsceZxfkdWnTolWfMcjxyGwUfAe0QrBsVmqzfJvVjeuDyjcG11K0dDLTDzNYmQ218g3Gdp9D1rnbYfZ8sh1j6pTvXOvb7NeMQZ7DuDt48GArW9bgStY5q79td8F6jtvBbFQ8buMg44jxy2fg+MXvBMssy3Z8/fXXW5lWRLNuGnfSHpisTyGEEEIIIYQQQgh3AflDTQghhBBCCCGEEMJMKGd96t3teMruyDzHpEyUYlV2Rjd4DiWdlHdRNkb51a1bt1aWKaU0iTjlbfaMlHCO62EydN6bEkrKSnfv3r3yHmahoRStkiVqU3YnUrHJVejNYrIETJbM57Dd3RlH7F/sF7w+j7Ovced13ov2IkLZI/uXWZkMyrlZH8otKSumLYmZHBgTJm1m3x/bB2nnoMSaNpXKDv5mWerNiGTyfbNm9FohejM6TclGMUUyutPYWGW2CD4rpfFjO89HHDt2rJU/+9nPtvJ3v/vdVqbF6ejRo61MK7FlTpySwaAiHee9aGm07DY2F1kmqXVUrHyEccp3ZlL1OVufjEqdtyOrxZ2i8p7NGsAxlfHCa1ofqWSWsXraGtXihf2Ra0DL9GSx3wufhdfkHEqL5RiOiWaX4LNxbWGZpbguf+ONN1bei2NHxdZEi4dlq7H1JOd91of1vHTp0srf9m45UbESzQmrb2Xt05tRkfRmvLTsTuzbXAPa1hSMWcYdbX8sc31becaxzfAj2Mcts9swbF0rmiXbMsWafYnxYusMjh2MccYIrZtsa34z9K4bp8xfle9Hex8VoqgJIYQQQgghhBBCmAn5Q00IIYQQQgghhBDCTChnfarI7XslapXrmJSpIlu2+vfuBk0pFqGVgXJLk/DxWXg+7RjcvZ9ys/G/K+1F28X58+dbmRI6StcoN6Ws1qSxFdluxfJQkRtOkdpXrjPFTrUTUEZHawDfp+0Mzz5G2aBlTuE1eQ77GvsUMxrZrvWW0Yn9hbJP1pm2KcaOZWZgO/A6rA+tgayDWZ/27ds3ED4/JbAsm92J9bDMIhVLEccHPjOPsw58f9bnrZ4VW5M9C49XMv0sIR5JpT1MJsy4O3HiRCvzHV68eLGV//W//tcrf3vq1KlWfu6551r5i1/8YivTRvG9732vlTk3sb8QywZl87JlHuOcw/ZhjPM4y5zfOF+NYdxaVh6TJduzmdViinVku6nMfVMsjUvA5kTrt7TEcj6ipYjXsTWh0ZvRju3NPm+WAq7dWDezwJp1guMP28Gyo1bl/xxH+Ay2fQDnY7Mb2PYBtHtx3Dl9+vTKuvLdcD1h1icbEwn7BC2pXMfwnfVuFWFz9BKsT2RK1qdNrRes/ew4257rPvZlxg77pmVuYozbPGZrV/Yji007fxjcbm1ZnzjH85l5nPfgMzMueA5tUzzO9rW/EZA7aZ/f1HYcZFmzbAghhBBCCCGEEMJdTP5QE0IIIYQQQgghhDATPlbWp0q5ch1iWZwoJzSmSPxMmk4oM6P8itJrHjfZJ5+F9hDKaynJHGOZmIjtMM4d/9nWlNZR0klJK49T0tYr897Urtp2TZP0kYo0dAky0YqEm+/HLDI8h/3FpNGEvzXJt0m7+R5oR7IMVoT9l/c9ePBgK7N9GF/8re2oz98yxllnWjOGwTNNUKrNczgW9MrfzX5o446Na6xDxfphlgf7bW82At6L5/B57VnmhD2rZfSy52P/tIxRZ8+ebeUnnniilS2bGy0b7PPMjGaWjSmZa/jsnFsq79bGN44V62xMfE72ec61ltmOz8w53mzIS6TXDmz9ewnzJvsb+4JlG7x27Vor0ybMPkUrAPuOZTXp3QqA59DmzKxwPE67Ii0CnJcso6LZnzkWcc3IcYxtyFix+gyDZyy1rKicj3kttrVZRLjmZtuxfhybWB+znNm4QWxNZv2j1/Y75btrTmxHHXszZRmVb0ybK9i/eM6hQ4damfHIufjAgQOtzDjg/MMyr2PfzmZdGs+/vJZ9l9qcbRlk7R3Y8/B8jr+2XUIvvVa63uzDlayfJcvrbc8IIYQQQgghhBBCCHeE/KEmhBBCCCGEEEIIYSaUsz7Z8V7ZXeW3FUtNRZZr9algO1JTxvb++++3ssnMeF+ThlLStu7ZTd5tbc37USZ6/fr1VqakjXJQQjloJWOLMUXGOSWjk12f9Wd7LiHLjGXoomyQthtadWiR4Dk//OEPW9kyibFtKhnArF15PqWYlH3yHMtYYRkYeA7j67777ltZH9aT8c77UubNaw6Dy+grWc8qMlyzG5h1xDLo8Pp834bJv61uJt8nlfHaMmGZbWpO2HuwOYXtYZZTjs3nzp1rZUrvX3/99ZX34vm0SPD98/xdu3a1sll6rV+YtZTj1d69e1fei+ewHWi7sCxyfK5xtgq2tdm9LBMVxyazHlt2s51iipWpYtuz473rgZ2G4zT7hdkHGAucCzjmmc2ud+w0+N5oGea6lHMfMwtxDWhxanHA8YfxaFlcLAvT+Bmtz3B+5RrFLMqVeZZjB9vrvffea2WOuZbJle++8r1R+eaxdqhYd+y+S8v6NGXsrGT7rcRg5bitgS3zGvuXZWoz+5JlVTKrpo1jZjdeZ51nfFWs+r0ZnTmfMtY4vnD+5XhUifftwPpWb+bp7mx//VUNIYQQQgghhBBCCNtB/lATQgghhBBCCCGEMBPKWZ/suEn2KsftOr0yQKtbBbsOJZ2Uk1FKSvmkycns+pR6sUzZ6lh6R6kcZWMVGRWla5bdh1I5Ss5M5m471ZMpFrjK8V65XcVqsQQJN98VZZN79uxZeZz95cSJE638ve99b+X1LU4rskmTg7IOrBuzMtEKQWuh2Q8tAxT7OOvA89kXKvJRMrYoMl4oGbXsO9a+lonJ+iefweyXdrySza03O0IvFWno0rI+EXu3Zhnhc9Oa8/DDD688n1khmEWC75Ntxn564cKFVmY2qErWJ9bfspfwHMY+60PLUmVO6LXvVK9r5zN+uSaglJxy9iny5kp9KvFYsU/a8YrdaW5Wrx5snjJL3Trbzqrjdp3erDGW0Yx2J8YyYSxzDqWFiJhFkRaE/fv3tzJjlnFgc6utdYdh6zObBYnjpllGK98D/C1tn2+++WYrs335Li3D1BQbVGWdWRkHjSWsY8mUsWSK9cTOr1ifrMw+y8xxfCe0AO/bt6+VGe8PPPBAK3NMGNt7V2E2K44bZrMaBrcpERvXKlskWHvZeMrnYRv1fgtvit6+ZeVYn0IIIYQQQgghhBAWRP5QE0IIIYQQQgghhDAT1urIKzsr99pfTEo6xbI0JZuQ1Y3SSFoqKpmeemWJZuVYJ/PvleURe2baoOyadl+7fu97miJT7812YedUnnGnoZzy3XffbWW2Gd8nbREmKzaLmJ1TkuzhOpQuHjlypJW/+MUvtjKl17QvUbZMOWQlswaf17LDUMJNaSjl35Rb8prD4Lvz834V64FlCTL7D5+/kpHNsnqYTJ/H2UZm0bJ6WhYnqz+Psz0rkt+dxjJTmZXt4MGDrXzq1KlW/s3f/M1W5ru6detWK3O+ePnll1deh1m/GHe0TXGssHfLtq9Y+nicMcUy+5SVCePMrJTjMZ7XoiXM5hqzcnH8Ynvx+puS79vxylxm44xZlni8YoPqzZIyJ+yd95bZzysZPyt2fnsPjH3CcZGZPC2TI/ss68b+S+v08ePHV9bNxns+I+drjhVjyzDjkZYts4HxmVkny4jD52S9eV9bP/E6HHNt64Bem1zl+CeJih2Y9NqXes+pjHO2biL8fqS9j+V33nmnlWl5Zmxa3ycWp7bu5TVp7xvf26yvxLYS4P3Yz3kdm5dZb2ae41y8qXlnUxmdKvXptSXOf2YNIYQQQgghhBBC+ISQP9SEEEIIIYQQQgghzIRy1qcpUr5KpidjSqanXksUJY2UYlHeWMmOUqmD7WTP64+hRYq/r0iwKtJKytVMwj6Wrq6iIi+u0GuV6j1nCVJtg3JK9hlKdCmZvnnzZitfvny5lSmT5nue8g5NPmq7tlN+yeszywpjhM/OOGCcXr16tZXZDnwuWj/YDqwPjzMjBOs2DFvj4u233155Hutt8dVrG6zYHNhGtsu/2ZRMOm70xlSl/qzP2HI2RypjsGVXoTWJGdB4TdqaaBdgf2asPf30061M6y6zTlB6TbsEZcuWScrGBHtGHmeZcUd7FGPL7DjrpPJ8BtrArO+ZnYOwHzIzh9lKzU5FKjFeyWA4xeJk7Vvp00vIAGXrl14pus2JZg/l3MHjtCSzDrQ/8L0xZm2tSEuFWYZZT8bgZz/72Va2+ZHXoTWB62eeT0vFeN40WxTblG3B+DW7MduFbc0y68G1AsdBricqa3TrW9tha6p8byzNTlUZe4xei9OULD08v9ceyn7EDMIcH8xuX8kCatmWPk6WMPu9tdeUTLHjceEjOI5YRuR1tudVVCxOm8reWOlblblnuV+qIYQQQgghhBBCCHcZ+UNNCCGEEEIIIYQQwkxYa32qyH92KstBr3y4YpuqWJy2w65lO9xTJjcMW6WllIxWZJC92ZpYp4qks1c21vuejClSN2KywrlCqa/JL2lzoMyS8l5mPKAEumJzqUj1K9kraMuyneAZC5RCcyd4Xp9yZsYKpeP79+9vZduNfvfu3a1Mi8P42Tl2mPXAmJK9wCyQFQmvxaPZpgw7f1M2KLPJzZV12fo+wuxotMfRgmR9mzHyzDPPtPJv/dZvrTyf8wttU+wLPMeykFX6Hc+nPJkyb1oZeF8bN3ol8eueoZKtyjK9sd05Rtx3332tzDHLrDKVOdqskdbPzErK4ybfN/trbxaWuVLJUtprmbcMUHw/tDhxzvrc5z7XyowLxr7Z5Lkus3GDlgLWmXF38uTJVv7Sl7608vrnzp1rZc6/jFnel/VkedzmlqGJa13aqywzKanYMXh9wu0F7L1WtnvYKdvR0qyIxLJcVtYRFZsS6f2uNGwctetwruf8yzGB60z2d/7WLLY2n9gYz3YeZ9Sk/bjXWkbs+4T3Y4zTlmnfKr22rinfifbs9r57+1BpO4muK4YQQgghhBBCCCGEbSN/qAkhhBBCCCGEEEKYCeWsT5uyg9xJOV5lt2aTRlbkSJVzeiVRrA93yx+GrVYWSuIq9TDpsv22kt2JbEpKSHplnFNkn7aL+lzhbuiWBcmsQyYhZJ+y3eMrfc1sHZR3WmYGZq0yWbXt8s5MN7Q1Ua5I2TYtFSappl2A/WJsO+D/sa5vvvlmKzN+rY9ZP6zIsC1mTT5b6fN2L7a72aN6xyVrX74/ynGXgLUN5b3sF+z/Fy5caGVmYOG8wNinnP+1115rZcYa++bzzz+/8rc2t5jM2+ZNxinLfHZKnmnTsDGH9+V11tmTezMh2pjId8n2YpYOjnEco2mpsPualNoybJnFcixhv931TbI+xXo5V3qzPlXGMOt7bJsrV660Mscwxu/hw4dbmXZg2umuXbvWyuxT58+fb2XL+sR+wXfFuZL9mvWnTYMxy7WEZZbkOWM7P9uOZbMgcRyxrDEWs5V3XMl2WTk/9MO1Vu+4UrE7Wca8ipWtYoshtnUA+ybjlFsTHD9+vJXZr2mJ4thv430ly1U1q6fZCStbRtjfEbgW5ZrGrsMxgedbJtNKuTdDGOm9jr2nWJ9CCCGEEEIIIYQQFkT+UBNCCCGEEEIIIYQwEz5W1ic7x45XMo1MqUPv+b0SRZPMTdkh3CRzlH9S9jYMW2VzlYwFvVlXKnaMyjUrMjs7n0y5V+WcXqnbnDB7ETFJtsl4e2XhxOSNJpNkhgtaP2jZYP+nJPvTn/70ymvyvTGrhY0/bDfL2kR5KstjCw7tJdypnmXKNa1OJjGt2BMMk3/32v2mjHeVGDfp7dJik8/BPmZZLWjhoQT60qVLrcy+Q3sU5cAmJeb57Gu0CprlweKXmPyZz0XZNq0cvf3OLLyW6WiM2etMAs570OLEsYnWDrapZasxWT/byyTTZn2y7E42tpCKfN/mht41xhKYYmGx+fTtt99uZYsRzlk2PrBPvfHGG63MccNsQ2YReOWVV1Y+C+cuWiY5D7Ivc7y6fv16K1umqmHwtYhlwuM4VZnLbN1j9FqZ5mx3WloGqEp2ykoGPHtum8sq16/MfbZOsd+yPowRxi/jhefwXlx72r0q3whj+7+tV2xbBNbJxhrLXPzcc8+18sMPP9zKtHRyvKMFdPydvOq+dtwsxhXbb8VyVrHVVbj7ZtkQQgghhBBCCCGEhZI/1IQQQgghhBBCCCHMhHLWJ2OKJWpTWZMqsvpKFqPKzu52/d4drykZpUydu+VT8jkMWyVevdYns1FYdo1KlqwptjQ73muV2o4d4ZeQ9cl2STcpH/ubvX+TOpKKlJi/pbzx6tWrrUyLwMWLF1uZkmnLWrVr165WPnjwYCtTirhnz55WpiycliVm4ti7d28rM87YJpR2M3vUMGx9NkrGeW/LAESrgmHSU2Ky+ykZ73rtEla3iq2J96XEn9jxOVEZk2hn4HNzzGefomSYfZiZVtgnKRmmpNmsA2Y7MEm5Wdb47IxTltnfLdMc28cyjFlmsHFfs/GR2HGOC3x+Wrlo3XzsscdameMRrS8sEz4zGWeY+4je2OyNcZOCk0rbzok7aQexNQWzgTErIG2/u3fvbmXG+Llz51qZMU67RMXqyj774osvtvKrr7668lnM4mBzvWUXrGKWLbIpW8Gmfjs3lhCPhNY/+9ax75jKWFixPvWOqRwjK7YsYmsAzhuWXZDrf2ZC49xasQOzj/A643qzbOsGjju2puU75rh27NixVuZzfuELX1h5L46h3/zmN1eew7YjZkurWJ96syuazdWy3RlR1IQQQgghhBBCCCHMhPyhJoQQQgghhBBCCGEmrLU+VTJyVGRpFYlir4WFmF2mkm3KMjBYFgXLhEB5Zq/E2Ha4p7xtGFxmbNJzkwaaJcbkWNYulWxKvXan3gxhlfta+5jkrCJF22loGWB9TXbH47ZTfa981Kw8hOdT8v/WW2+1Mi1Olh2F5/yP//E/WpkZZE6ePNnKtCC89NJLK+tGC8mv/uqvtjItJPwtZeqUXg7DVmsWn9Mk4JRNmsSUbMpaaJkMWM+KXNgsc6QyLlWslHxPS8syY9kPOLZzfmFGFfZ5sw6ZVcoyQFHebNlUKhanSuxT6sv+zuvwGStZjyrZh8Z9pGJRJjY+0o7CeD98+HAr04LyS7/0S63MzFBnzpxZeb6NFRZHtt6yOOU7s2tanFasAkuwjfSOo1OeydYgfG+0yfIc9i9aBF5++eVWZp+qWF3NimUWAfvtptpnzBysOtv1bOH23H///a1MizMzjtl3SSWDX8X6VFl/sQ6cTytbTXAtw9/Sqs9xmudzruRxyxDHcsXCPLbycJ1R+UaztRmvy3P4vtlebAs+G8/nXMwsUf/lv/yXVuY2Irb1A5/R6mnzoPUV+762ebn0t4/bnhFCCCGEEEIIIYQQ7gj5Q00IIYQQQgghhBDCTFhrfSImraxkgek9bvJDk1lVJP9m/ahI8i0DhcnnrH2snpS0UeY6lpf3ylvtt8Qk7JUMUL0ZmipS0sp9TdrYm8nCpPxLyPrE+trO66SS4cfOqVgLK7vxs59XMjmYzJ/2KMYLYUYnysj5zikfPXr0aCvTukQ7CTNu0LIwDFufhxkLeG+TnzIDFo9z3GHb8TqWocfksCbFpD3Grm/2lUqsGfztz/3cz7Uys+qYFHau8J3zvdk7vHXr1spzmDHM3g/L/C3tDHyHlJSznrRBWSxXxhBSmYtMnm0ZGCyGqjaFynxkcz/vzT5p7ch7PfjggyvPoTzbJP6W3crqaWO6jYOW0ac3w2PFDrfT3Ek7i7UT25vzSMWWfe3atVbm+6zM1xWJfeX8ylqvynZskWB12o4sUXOjNyPqnOAYaWMh50H7jjMqW2dU2q8yHhusMzMg8dl5nHM3rVK2Drc5tJIBedw+le9w0rvtCMvcbsC+1TnHHTlyZOX1n3zyyVbm2Mp+w7qxTc0aRyrWJ16fda5Yko0oakIIIYQQQgghhBBmQv5QE0IIIYQQQgghhDAT1mrTKXU3Oa3J7c22QCq7cFdk9RXJkmHn876Un9ku4r2yOsueU2m38Xl2vCLtrtTP7tWbocneccWK1nt9O87n4o7fvD4tGHPFbFuVd1WRG/e+c/utZVgzyahleOG7suvQBkWLh9l3aFF6/vnnVz4LM2ussyUSWrMqmdp4LRvXKNE0KtJbk8+yXcwGWBnvLMYrcnxad3j8/fffX1nPuWJ922w7fCdsex6nzYHjEy1OZnPhcbNi2fxOGI88x+LLrDb33HNPK/MZ+Vy0JVq7TcWe2eyBrAfHF2bkYjY4s6bwt7RQse0s4waP8/0RHrf5zt69zRM2RllGk7li4+t22J17bSicNyjbt3pWsqza2qoSR73WmY+Tka+y3q1YKirWjMo77rWH7RSVNfCSbVCcC/hMHDstY62tzSqWH1v708JCqzptSjbuclznXLF3795WpvWeNiDOlZYplMdtjUnMVjxeW1n/6d3+oJLNk+sA+5sC253txTmUz8zjZ8+ebWXOyyxznWlrncrfIyzrE5+RfaXyvRlFTQghhBBCCCGEEMJMyB9qQgghhBBCCCGEEGbCWusTpbKUZVZkdBXZXa8cj5KrSgYku9emzrcdqSuZdHqlqmMonbIMQCYr/TjZMlZRsTNMefdWT7tvRSJs8lde87777rtt3XYa9rfeWNjUO68ct7r1ynJpr+A1zV7AzCqWoYhljm+V3dmrY5dZ+XptBZRNmiXVJL+UVnJMN2sZ284sNDZ+2fs2OSufyyyKPGcJ9gqzsBCzoZh1iOdY3+b7Z7yY9cksbmZhtv5vcmZKuDmmUsLMccz6qcXQx5HzVzJ88N58H5aBg7/lMzBzCX/LZ2Z2M3tms36xf9ACV7Fz99obzWbFc/gsc4Xv06yWm6IyV9p7Zl+w92Y2B8av2T0qNiM7vslsUFPW4r3rmE294znYiHot7kuYNzlfWCY69n+OebSxW//nGMz243hMaxXHcs5Zhw4damW2q63LOPfx/N27d7cyLVS8l40PhPe1eZxtWP3GrKzXezNmVc63dSOfje+Jz8asT2zrp59+upWZvZVZF1lmfzK7OGE92Z/Y52x9U9nWIIqaEEIIIYQQQgghhJmQP9SEEEIIIYQQQgghzIQfm8MO5iGEEEIIIYQQQgghipoQQgghhBBCCCGE2ZA/1IQQQgghhBBCCCHMhPyhJoQQQgghhBBCCGEm5A81IYQQQgghhBBCCDMhf6gJIYQQQgghhBBCmAn5Q00IIYQQQgghhBDCTPj/AAC5zj7HjzajAAAAAElFTkSuQmCC\n"
+      "image/png": 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+       "   <g clip-path=\"url(#p87c80878c8)\">\n",
+       "    <image height=\"193\" id=\"imagea26df1f2c8\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"238.096552\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-6.613793\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_3\">\n",
+       "   <g clip-path=\"url(#p2a39d3380b)\">\n",
+       "    <image height=\"193\" id=\"imagebe40b8aa70\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"468.993103\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-6.613793\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_4\">\n",
+       "   <g clip-path=\"url(#p056e7b2292)\">\n",
+       "    <image height=\"193\" id=\"image5fbbcd8e86\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"699.889655\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-6.613793\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_5\">\n",
+       "   <g clip-path=\"url(#p36d6b3e236)\">\n",
+       "    <image height=\"193\" id=\"image6a278c2b2e\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"930.786207\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-6.613793\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_6\">\n",
+       "   <g clip-path=\"url(#p9e49c69866)\">\n",
+       "    <image height=\"193\" id=\"image5f66dae4a0\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"7.2\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-243.821066\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_7\">\n",
+       "   <g clip-path=\"url(#pb62119b4d5)\">\n",
+       "    <image height=\"193\" id=\"image546263cae6\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"238.096552\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-243.821066\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_8\">\n",
+       "   <g clip-path=\"url(#p912fd50499)\">\n",
+       "    <image height=\"193\" id=\"image474004b5b9\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"468.993103\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-243.821066\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_9\">\n",
+       "   <g clip-path=\"url(#p4a5f2d5c77)\">\n",
+       "    <image height=\"193\" id=\"image5fc508d902\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"699.889655\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-243.821066\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       "  <g id=\"axes_10\">\n",
+       "   <g clip-path=\"url(#pa069cefbe4)\">\n",
+       "    <image height=\"193\" id=\"image504e749f9d\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"930.786207\" xlink:href=\"data:image/png;base64,\n",
+       "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\" y=\"-243.821066\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p8dd11fa1e8\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"7.2\" y=\"7.2\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p87c80878c8\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"238.096552\" y=\"7.2\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p2a39d3380b\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"468.993103\" y=\"7.2\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p056e7b2292\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"699.889655\" y=\"7.2\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p36d6b3e236\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"930.786207\" y=\"7.2\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p9e49c69866\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"7.2\" y=\"244.407273\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"pb62119b4d5\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"238.096552\" y=\"244.407273\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p912fd50499\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"468.993103\" y=\"244.407273\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p4a5f2d5c77\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"699.889655\" y=\"244.407273\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"pa069cefbe4\">\n",
+       "   <rect height=\"192.413793\" width=\"192.413793\" x=\"930.786207\" y=\"244.407273\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1440x576 with 10 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -643,7 +5102,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 259800c237..2ccd18e01f 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -11,8 +11,10 @@
 
 from abc import ABC, abstractmethod
 from typing import Any, List, Optional, Sequence, Union
+from monai.utils import optional_import
 
-import itk
+itk, _ = optional_import("itk")
+nib, _ = optional_import("nibabel")
 import numpy as np
 
 
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 8346888221..ee1e3dc30e 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -5,6 +5,7 @@ parameterized
 pytorch-ignite==0.3.0
 gdown>=3.6.4
 scipy
+itk
 nibabel
 pillow
 tensorboard
diff --git a/setup.cfg b/setup.cfg
index 6e843b4dfd..9a32814ff6 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -26,6 +26,7 @@ all =
     tensorboard
     pytorch-ignite==0.3.0
     gdown>=3.6.4
+    itk
 nibabel =
     nibabel
 skimage =
@@ -38,6 +39,8 @@ gdown =
     gdown>=3.6.4
 ignite =
     pytorch-ignite==0.3.0
+itk =
+    itk
 
 [flake8]
 select = B,C,E,F,N,P,T4,W,B9

From ac2874e00c4d4a526772ef2b40608415261631da Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 06:52:35 +0800
Subject: [PATCH 09/37] [DLMED] fix optional import issue

---
 examples/notebooks/io_factory_test.ipynb | 35 +++++++-----------------
 monai/config/deviceconfig.py             | 10 +++++++
 monai/data/image_reader.py               |  2 +-
 3 files changed, 21 insertions(+), 26 deletions(-)

diff --git a/examples/notebooks/io_factory_test.ipynb b/examples/notebooks/io_factory_test.ipynb
index a81ee1a558..f517598ad3 100644
--- a/examples/notebooks/io_factory_test.ipynb
+++ b/examples/notebooks/io_factory_test.ipynb
@@ -43,7 +43,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# temporarily need this, FIXME remove when d93c0a6 released\n",
+    "# temporarily need this, FIXME remove when MONAI v0.3 released\n",
     "%pip install -qU git+https://github.com/Project-MONAI/MONAI#egg=MONAI\n",
     "%pip install itk"
    ]
@@ -66,17 +66,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.1.0+328.g4686724.dirty\n",
+      "MONAI version: 0.1.0+329.g162c2e8.dirty\n",
       "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
       "Numpy version: 1.18.1\n",
-      "Pytorch version: 1.6.0\n",
+      "Pytorch version: 1.5.0a0+8f84ded\n",
       "\n",
       "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.3.0\n",
+      "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
       "Nibabel version: 3.1.1\n",
       "scikit-image version: 0.15.0\n",
-      "Pillow version: 7.0.0\n",
+      "Pillow version: 5.3.0.post1\n",
       "Tensorboard version: 2.1.0\n",
+      "ITK version: 5.1.1\n",
       "\n",
       "For details about installing the optional dependencies, please visit:\n",
       "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
@@ -113,25 +114,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "Optional import: import itk.\n\nFor details about installing the optional dependencies, please visit:\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-ca80851c4218>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoadImage\u001b[0m\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;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/workspace/data/medical/MONAI/monai/transforms/io/array.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverify_suffix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"unsupported file format.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m             \u001b[0mimg_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_array_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_only\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/workspace/data/medical/MONAI/monai/data/image_reader.py\u001b[0m in \u001b[0;36mread_image\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mITKReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImageReader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\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;32m---> 59\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_meta_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/workspace/data/medical/MONAI/monai/utils/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    184\u001b[0m                 \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mWhen\u001b[0m \u001b[0myou\u001b[0m \u001b[0mcall\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m             \"\"\"\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0m_kwargs\u001b[0m\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;32m/workspace/data/medical/MONAI/monai/utils/module.py\u001b[0m in \u001b[0;36moptional_import\u001b[0;34m(module, version, version_checker, name, descriptor, version_args, allow_namespace_pkg)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_namespace_pkg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0mis_namespace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__file__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__path__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_namespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# user specified to load class/function/... from the module\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mthe_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: Optional import: import itk.\n\nFor details about installing the optional dependencies, please visit:\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
     "loader = LoadImage()\n",
@@ -140,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -165,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index 9d57366c99..7bdb6196f5 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -58,6 +58,14 @@
 except (ImportError, AttributeError):
     tensorboard_version = "NOT INSTALLED or UNKNOWN VERSION."
 
+try:
+    import itk
+
+    itk_version = itk.Version.GetITKVersion()
+    del itk
+except (ImportError, AttributeError):
+    itk_version = "NOT INSTALLED or UNKNOWN VERSION."
+
 
 def get_config_values():
     """
@@ -84,6 +92,8 @@ def get_optional_config_values():
     output["scikit-image"] = skimage_version
     output["Pillow"] = PIL_version
     output["Tensorboard"] = tensorboard_version
+    output["ITK"] = itk_version
+
 
     return output
 
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 2ccd18e01f..56b232b8d1 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -13,7 +13,7 @@
 from typing import Any, List, Optional, Sequence, Union
 from monai.utils import optional_import
 
-itk, _ = optional_import("itk")
+itk, _ = optional_import("itk", allow_namespace_pkg=True)
 nib, _ = optional_import("nibabel")
 import numpy as np
 

From 2c1c244410983d3801a924ecf907e515d60ec8b2 Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Sun, 16 Aug 2020 22:57:00 +0000
Subject: [PATCH 10/37] [MONAI] python code formatting

---
 monai/config/deviceconfig.py | 1 -
 monai/data/image_reader.py   | 4 +++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index 7bdb6196f5..b19e996de4 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -94,7 +94,6 @@ def get_optional_config_values():
     output["Tensorboard"] = tensorboard_version
     output["ITK"] = itk_version
 
-
     return output
 
 
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 56b232b8d1..50b469505e 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -11,11 +11,13 @@
 
 from abc import ABC, abstractmethod
 from typing import Any, List, Optional, Sequence, Union
+
+import numpy as np
+
 from monai.utils import optional_import
 
 itk, _ = optional_import("itk", allow_namespace_pkg=True)
 nib, _ = optional_import("nibabel")
-import numpy as np
 
 
 class ImageReader(ABC):

From adceec83b33912eb3a1cb19d6a480cfa71e4a153 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 07:24:45 +0800
Subject: [PATCH 11/37] [DLMED] fix flake8 issue

---
 monai/data/image_reader.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 50b469505e..12717417a1 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional, Sequence, Union
+from typing import Any, List, Dict, Optional, Sequence, Union
 
 import numpy as np
 
@@ -40,7 +40,7 @@ def read_image(self, filename: str):
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
-    def get_meta_dict(self) -> List:
+    def get_meta_dict(self) -> Dict:
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
@@ -60,7 +60,7 @@ class ITKReader(ImageReader):
     def read_image(self, filename: str):
         self.img = itk.imread(filename)
 
-    def get_meta_dict(self) -> List:
+    def get_meta_dict(self) -> Dict:
         img_meta_dict = self.img.GetMetaDataDictionary()
         meta_dict = dict()
         for key in img_meta_dict.GetKeys():

From 774ed7745589342bcb813116a32bb20ade1f6abe Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 07:37:50 +0800
Subject: [PATCH 12/37] [DLMED] format code

---
 monai/data/image_reader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 12717417a1..5b74d7226c 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, List, Dict, Optional, Sequence, Union
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 import numpy as np
 

From 2fcd74b07e1329db90b777ecf02967b7e9a3a8ab Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 08:01:17 +0800
Subject: [PATCH 13/37] [DLMED] add for test

---
 .github/workflows/pythonapp.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml
index 6dfe983694..fddd821231 100644
--- a/.github/workflows/pythonapp.yml
+++ b/.github/workflows/pythonapp.yml
@@ -34,6 +34,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip wheel
         python -m pip install -r requirements-dev.txt
+        python -m pip install itk
     - name: Lint and type check
       run: |
         # clean up temporary files

From 0b3eac784823f1ab5f4f20c98e3b960e8a3e09aa Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 09:55:15 +0800
Subject: [PATCH 14/37] [DLMED] test flake8

---
 .github/workflows/pythonapp.yml | 1 +
 monai/config/deviceconfig.py    | 9 +++++++++
 2 files changed, 10 insertions(+)

diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml
index fddd821231..bbb342f719 100644
--- a/.github/workflows/pythonapp.yml
+++ b/.github/workflows/pythonapp.yml
@@ -34,6 +34,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip wheel
         python -m pip install -r requirements-dev.txt
+        python -m pip uninstall -y itk
         python -m pip install itk
     - name: Lint and type check
       run: |
diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index b19e996de4..7cd0db15ee 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -58,6 +58,14 @@
 except (ImportError, AttributeError):
     tensorboard_version = "NOT INSTALLED or UNKNOWN VERSION."
 
+try:
+    import gdown
+
+    gdown_version = gdown.__version__
+    del gdown
+except (ImportError, AttributeError):
+    gdown_version = "NOT INSTALLED or UNKNOWN VERSION."
+
 try:
     import itk
 
@@ -92,6 +100,7 @@ def get_optional_config_values():
     output["scikit-image"] = skimage_version
     output["Pillow"] = PIL_version
     output["Tensorboard"] = tensorboard_version
+    output["gdown"] = gdown_version
     output["ITK"] = itk_version
 
     return output

From 60cf0f2f7bd9057fe4db7a2a05052a959281094a Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 16:14:54 +0800
Subject: [PATCH 15/37] [DLMED] add unit tests

---
 .github/workflows/pythonapp.yml          |   2 -
 docs/source/data.rst                     |  14 ++
 docs/source/transforms.rst               |  12 ++
 examples/notebooks/io_factory_test.ipynb | 137 +++++++++++++++--
 monai/data/image_reader.py               | 182 ++++++++++++++++++++++-
 monai/transforms/io/array.py             |  17 ++-
 monai/transforms/io/dictionary.py        |  37 ++++-
 tests/test_load_image.py                 | 115 ++++++++++++++
 tests/test_load_imaged.py                |  43 ++++++
 9 files changed, 533 insertions(+), 26 deletions(-)
 create mode 100644 tests/test_load_image.py
 create mode 100644 tests/test_load_imaged.py

diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml
index bbb342f719..6dfe983694 100644
--- a/.github/workflows/pythonapp.yml
+++ b/.github/workflows/pythonapp.yml
@@ -34,8 +34,6 @@ jobs:
       run: |
         python -m pip install --upgrade pip wheel
         python -m pip install -r requirements-dev.txt
-        python -m pip uninstall -y itk
-        python -m pip install itk
     - name: Lint and type check
       run: |
         # clean up temporary files
diff --git a/docs/source/data.rst b/docs/source/data.rst
index fd9118fba8..214edcaae2 100644
--- a/docs/source/data.rst
+++ b/docs/source/data.rst
@@ -49,6 +49,20 @@ Patch-based dataset
   :members:
 
 
+Image reader
+------------
+
+ITKReader
+~~~~~~~~~
+.. autoclass:: ITKReader
+  :members:
+
+NibabelReader
+~~~~~~~~~~~~~
+.. autoclass:: NibabelReader
+  :members:
+
+
 Nifti format handling
 ---------------------
 
diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index 58878a775e..2521fc823f 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -195,6 +195,12 @@ Intensity
 IO
 ^^
 
+`LoadImage`
+"""""""""""
+.. autoclass:: LoadImage
+    :members:
+    :special-members: __call__
+
 `LoadNifti`
 """""""""""
 .. autoclass:: LoadNifti
@@ -630,6 +636,12 @@ IO (Dict)
     :members:
     :special-members: __call__
 
+`LoadImaged`
+""""""""""""
+.. autoclass:: LoadImaged
+    :members:
+    :special-members: __call__
+
 `LoadNiftid`
 """"""""""""
 .. autoclass:: LoadNiftid
diff --git a/examples/notebooks/io_factory_test.ipynb b/examples/notebooks/io_factory_test.ipynb
index f517598ad3..cf7d7a61dc 100644
--- a/examples/notebooks/io_factory_test.ipynb
+++ b/examples/notebooks/io_factory_test.ipynb
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
     "tags": []
    },
@@ -66,17 +66,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.1.0+329.g162c2e8.dirty\n",
+      "MONAI version: 0.1.0+335.g0b3eac7.dirty\n",
       "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
       "Numpy version: 1.18.1\n",
       "Pytorch version: 1.5.0a0+8f84ded\n",
       "\n",
       "Optional dependencies:\n",
-      "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
+      "Pytorch Ignite version: 0.3.0\n",
       "Nibabel version: 3.1.1\n",
       "scikit-image version: 0.15.0\n",
-      "Pillow version: 5.3.0.post1\n",
+      "Pillow version: 7.2.0\n",
       "Tensorboard version: 2.1.0\n",
+      "gdown version: 3.12.0\n",
       "ITK version: 5.1.1\n",
       "\n",
       "For details about installing the optional dependencies, please visit:\n",
@@ -101,7 +102,7 @@
     "\n",
     "from monai.config import print_config\n",
     "from monai.transforms import LoadImage\n",
-    "\n",
+    "from monai.data import NibabelReader\n",
     "print_config()"
    ]
   },
@@ -109,12 +110,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Test loading Nifti files"
+    "## Test loading Nifti files with ITK"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -134,10 +135,10 @@
      "text": [
       "{'ITK_FileNotes': '5.0.10', 'aux_file': '', 'bitpix': '32', 'cal_max': '0', 'cal_min': '0', 'datatype': '16', 'descrip': '5.0.10', 'dim[0]': '3', 'dim[1]': '512', 'dim[2]': '512', 'dim[3]': '55', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '0.976562', 'pixdim[2]': '0.976562', 'pixdim[3]': '5', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-499.023', 'qoffset_y': '-499.023', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '0', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '1', 'sform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0.976562 0 0 -499.023', 'srow_y': '0 0.976562 0 -499.023', 'srow_z': '0 0 5 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '10', 'origin': array([499.02319336, 499.02319336,   0.        ]), 'spacing': array([0.97656202, 0.97656202, 5.        ]), 'direction': array([[-1.,  0.,  0.],\n",
       "       [ 0., -1.,  0.],\n",
-      "       [ 0.,  0.,  1.]]), 'filename_or_obj': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
+      "       [ 0.,  0.,  1.]]), 'filename_or_obj': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'original_affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
       "       [  0.        ,  -0.97656202,   0.        , 499.02319336],\n",
       "       [  0.        ,   0.        ,   5.        ,   0.        ],\n",
-      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'original_affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
+      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
       "       [  0.        ,  -0.97656202,   0.        , 499.02319336],\n",
       "       [  0.        ,   0.        ,   5.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'spatial_shape': [512, 512, 55]}\n"
@@ -148,10 +149,126 @@
     "print(meta)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(512, 512, 55)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test loading JPEG files with ITK"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"/workspace/data/medical/MedNIST/Hand/008334.jpeg\"\n",
+    "loader = LoadImage()\n",
+    "data, meta = loader(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n",
+      "       [0., 1.]]), 'filename_or_obj': '/workspace/data/medical/MedNIST/Hand/008334.jpeg', 'original_affine': array([[1., 0., 0.],\n",
+      "       [0., 1., 0.],\n",
+      "       [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
+      "       [0., 1., 0.],\n",
+      "       [0., 0., 1.]]), 'spatial_shape': [64, 64]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(64, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test loading Nifti files with Nibabel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
+    "loader = LoadImage(NibabelReader())\n",
+    "data, meta = loader(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sizeof_hdr': array(348, dtype=int32), 'data_type': array(b'', dtype='|S10'), 'db_name': array(b'', dtype='|S18'), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'regular': array(b'r', dtype='|S1'), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  55,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.976562, 0.976562, 5.      , 0.      , 0.      ,\n",
+      "       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'descrip': array(b'5.0.10', dtype='|S80'), 'aux_file': array(b'', dtype='|S24'), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-499.0232, dtype=float32), 'qoffset_y': array(-499.0232, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.976562,    0.      ,    0.      , -499.0232  ], dtype=float32), 'srow_y': array([   0.      ,    0.976562,    0.      , -499.0232  ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'intent_name': array(b'', dtype='|S16'), 'magic': array(b'n+1', dtype='|S4'), 'as_closest_canonical': False, 'filename_or_obj': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'original_affine': array([[   0.97656202,    0.        ,    0.        , -499.02319336],\n",
+      "       [   0.        ,    0.97656202,    0.        , -499.02319336],\n",
+      "       [   0.        ,    0.        ,    5.        ,    0.        ],\n",
+      "       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.97656202,    0.        ,    0.        , -499.02319336],\n",
+      "       [   0.        ,    0.97656202,    0.        , -499.02319336],\n",
+      "       [   0.        ,    0.        ,    5.        ,    0.        ],\n",
+      "       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'spatial_shape': array([512, 512,  55], dtype=int16)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 5b74d7226c..a2e62411a3 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,11 +10,12 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional, Sequence, Union
+from typing import Any, Dict, List, Optional, Sequence
 
 import numpy as np
 
 from monai.utils import optional_import
+from monai.data.utils import correct_nifti_header_if_necessary
 
 itk, _ = optional_import("itk", allow_namespace_pkg=True)
 nib, _ = optional_import("nibabel")
@@ -22,45 +23,123 @@
 
 class ImageReader(ABC):
     """Abstract class to define interface APIs to load image files.
+    users need to call `read_image` to load image and then use other APIs
+    to get image data or properties from meta data.
 
-    """
+    Args:
+        img: image to initialize the reader, this is for the usage that the image data
+            is already in memory and no need to read from file again, default is None.
+        as_closest_canonical: if True, load the image as closest to canonical axis format.
 
-    def __init__(self, suffixes: Optional[Union[str, Sequence[str]]] = None, img: Any = None):
-        self.suffixes = suffixes
+    """
+    def __init__(self, img: Optional[Any] = None, as_closest_canonical: bool = False):
         self.img = img
+        self.as_closest_canonical = as_closest_canonical
+        self._suffixes: Optional[Sequence] = None
+
+    def get_suffixes(self):
+        """
+        Get the supported image file suffixes of current reader.
+        Default is None, support all kinds of image format.
+
+        """
+        return self._suffixes
 
     def verify_suffix(self, suffix: str):
-        return False if self.suffixes is not None and suffix not in self.suffixes else True
+        """
+        Verify whether the specified file matches supported suffixes.
+        If supported suffixes is None, skip the verification.
+
+        """
+        return False if self._suffixes is not None and suffix not in self._suffixes else True
 
     def uncache(self):
+        """
+        Release image object and other cache data.
+
+        """
         self.img = None
 
+    @abstractmethod
+    def convert(self):
+        """
+        Convert the image if necessary.
+
+        """
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
     @abstractmethod
     def read_image(self, filename: str):
+        """
+        Read image data from specified file.
+        Note that different readers return different image data type.
+
+        """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
     def get_meta_dict(self) -> Dict:
+        """
+        Get the all the meta data of the image and convert to dict type.
+
+        """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
-    def get_affine(self) -> List:
+    def get_affine(self) -> np.ndarray:
+        """
+        Get or construct the affine matrix of the image, it can be used to correct
+        spacing, orientation or execute spatial transforms.
+
+        """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
     def get_spatial_shape(self) -> List:
+        """
+        Get the spatial shape of image data, it doesn't contain the channel dim.
+
+        """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
     def get_array_data(self) -> np.ndarray:
+        """
+        Get the raw array data of the image, converted to Numpy array.
+
+        """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
 
 class ITKReader(ImageReader):
+    """
+    Load medical images based on ITK library.
+    All the supported image formats can be found:
+    https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO
+
+    """
+
+    def convert(self, as_closest_canonical: Optional[bool] = None):
+        """
+        Convert the image as closest to canonical axis format.
+
+        """
+        # FIXME: need to add support later
+        pass
+
     def read_image(self, filename: str):
+        """
+        Read image data from specified file.
+        Note that the returned object is ITK image object.
+
+        """
         self.img = itk.imread(filename)
 
     def get_meta_dict(self) -> Dict:
+        """
+        Get the all the meta data of the image and convert to dict type.
+
+        """
         img_meta_dict = self.img.GetMetaDataDictionary()
         meta_dict = dict()
         for key in img_meta_dict.GetKeys():
@@ -73,8 +152,10 @@ def get_meta_dict(self) -> Dict:
         meta_dict["direction"] = itk.array_from_matrix(self.img.GetDirection())
         return meta_dict
 
-    def get_affine(self) -> List:
+    def get_affine(self) -> np.ndarray:
         """
+        Get or construct the affine matrix of the image, it can be used to correct
+        spacing, orientation or execute spatial transforms.
         Construct Affine matrix based on direction, spacing, origin information.
         Refer to: https://github.com/RSIP-Vision/medio
 
@@ -90,7 +171,94 @@ def get_affine(self) -> List:
         return affine
 
     def get_spatial_shape(self) -> List:
+        """
+        Get the spatial shape of image data, it doesn't contain the channel dim.
+
+        """
         return list(itk.size(self.img))
 
     def get_array_data(self) -> np.ndarray:
+        """
+        Get the raw array data of the image, converted to Numpy array.
+
+        """
         return itk.array_view_from_image(self.img, keep_axes=True)
+
+
+class NibabelReader(ImageReader):
+    """
+    Load NIfTI format images based on Nibabel library.
+
+    Args:
+        img: image to initialize the reader, this is for the usage that the image data
+            is already in memory and no need to read from file again, default is None.
+        as_closest_canonical: if True, load the image as closest to canonical axis format.
+
+    """
+    def __init__(self, img: Optional[Any] = None, as_closest_canonical: bool = False):
+        super().__init__(img, as_closest_canonical)
+        self._suffixes: [Sequence] = ["nii", "nii.gz"]
+
+    def convert(self, as_closest_canonical: Optional[bool] = None):
+        """
+        Convert the image as closest to canonical axis format.
+
+        """
+        if as_closest_canonical is None:
+            as_closest_canonical = self.as_closest_canonical
+        if as_closest_canonical:
+            self.img = nib.as_closest_canonical(self.img)
+
+    def read_image(self, filename: str):
+        """
+        Read image data from specified file.
+        Note that the returned object is Nibabel image object.
+
+        """
+        img = nib.load(filename)
+        img = correct_nifti_header_if_necessary(img)
+        if self.as_closest_canonical:
+            img = nib.as_closest_canonical(img)
+        self.img = img
+
+    def get_meta_dict(self) -> Dict:
+        """
+        Get the all the meta data of the image and convert to dict type.
+
+        """
+        meta_data = dict(self.img.header)
+        meta_data["as_closest_canonical"] = self.as_closest_canonical
+        return meta_data
+
+    def get_affine(self) -> np.ndarray:
+        """
+        Get the affine matrix of the image, it can be used to correct
+        spacing, orientation or execute spatial transforms.
+
+        """
+        return self.img.affine
+
+    def get_spatial_shape(self) -> List:
+        """
+        Get the spatial shape of image data, it doesn't contain the channel dim.
+
+        """
+        ndim = self.img.header["dim"][0]
+        spatial_rank = min(ndim, 3)
+        return self.img.header["dim"][1 : spatial_rank + 1]
+
+    def get_array_data(self) -> np.ndarray:
+        """
+        Get the raw array data of the image, converted to Numpy array.
+
+        """
+        return np.array(self.img.get_fdata())
+
+    def uncache(self):
+        """
+        Release image object and other cache data.
+
+        """
+        if self.img is not None:
+            self.img.uncache()
+            super().uncache()
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 4075b1d8e9..5dce96afa9 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -41,15 +41,13 @@ class LoadImage(Transform):
 
     def __init__(
         self,
-        reader: ImageReader = None,
-        suffixes: Optional[Union[str, Sequence[str]]] = None,
+        reader: Optional[ImageReader] = None,
         image_only: bool = False,
         dtype: Optional[np.dtype] = np.float32,
     ) -> None:
         """
         Args:
             reader: use reader to load image file and meta data, default is ITK.
-            suffixes: file suffixes that supported by the reader, if None, support all kinds of files.
             image_only: if True return only the image volume, otherwise return image data array and header dict.
             dtype: if not None convert the loaded image to this data type.
 
@@ -61,7 +59,6 @@ def __init__(
         if reader is None:
             reader = ITKReader()
         self.reader = reader
-        self.suffixes = suffixes
         self.image_only = image_only
         self.dtype = dtype
 
@@ -74,7 +71,14 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
         img_array = list()
         compatible_meta: Dict = None
         for name in filename:
-            if not self.reader.verify_suffix(name.split(".")[-1]):
+            supported_format = False
+            suffixes = name.split(".")
+            for i in range(len(suffixes) - 1):
+                if self.reader.verify_suffix(".".join(suffixes[-(i + 2) : -1])):
+                    supported_format = True
+                    break
+
+            if not supported_format:
                 raise RuntimeError("unsupported file format.")
             self.reader.read_image(name)
             img_array.append(self.reader.get_array_data().astype(dtype=self.dtype))
@@ -83,8 +87,9 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
 
             header = self.reader.get_meta_dict()
             header["filename_or_obj"] = name
+            header["original_affine"] = self.reader.get_affine()
+            self.reader.convert()
             header["affine"] = self.reader.get_affine()
-            header["original_affine"] = header["affine"].copy()
             header["spatial_shape"] = self.reader.get_spatial_shape()
 
             if compatible_meta is None:
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index b53caf5fcf..f3bc5f3c02 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -21,7 +21,8 @@
 
 from monai.config import KeysCollection
 from monai.transforms.compose import MapTransform
-from monai.transforms.io.array import LoadNifti, LoadNumpy, LoadPNG
+from monai.data.image_reader import ImageReader
+from monai.transforms.io.array import LoadImage, LoadNifti, LoadNumpy, LoadPNG
 
 
 class LoadDatad(MapTransform):
@@ -83,6 +84,39 @@ def __call__(self, data):
         return d
 
 
+class LoadImaged(LoadDatad):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.LoadImage`,
+    must load image and metadata together. If loading a list of files in one key,
+    stack them together and add a new dimension as the first dimension, and use the
+    meta data of the first image to represent the stacked result. Note that the affine
+    transform of all the stacked images should be same. The output metadata field will
+    be created as ``key_{meta_key_postfix}``.
+    """
+
+    def __init__(
+        self,
+        keys: KeysCollection,
+        reader: Optional[ImageReader] = None,
+        dtype: Optional[np.dtype] = np.float32,
+        meta_key_postfix: str = "meta_dict",
+        overwriting: bool = False,
+    ) -> None:
+        """
+        Args:
+            keys: keys of the corresponding items to be transformed.
+                See also: :py:class:`monai.transforms.compose.MapTransform`
+            dtype: if not None convert the loaded image data to this data type.
+            meta_key_postfix: use `key_{postfix}` to store the metadata of the nifti image,
+                default is `meta_dict`. The meta data is a dictionary object.
+                For example, load nifti file for `image`, store the metadata into `image_meta_dict`.
+            overwriting: whether allow to overwrite existing meta data of same key.
+                default is False, which will raise exception if encountering existing key.
+        """
+        loader = LoadImage(reader, False, dtype)
+        super().__init__(keys, loader, meta_key_postfix, overwriting)
+
+
 class LoadNiftid(LoadDatad):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.LoadNifti`,
@@ -174,6 +208,7 @@ def __init__(
         super().__init__(keys, loader, meta_key_postfix, overwriting)
 
 
+LoadImageD = LoadImageDict = LoadImaged
 LoadNiftiD = LoadNiftiDict = LoadNiftid
 LoadPNGD = LoadPNGDict = LoadPNGd
 LoadNumpyD = LoadNumpyDict = LoadNumpyd
diff --git a/tests/test_load_image.py b/tests/test_load_image.py
new file mode 100644
index 0000000000..0d9e2c7ad8
--- /dev/null
+++ b/tests/test_load_image.py
@@ -0,0 +1,115 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import tempfile
+import unittest
+
+import itk
+import nibabel as nib
+from PIL import Image
+import numpy as np
+from parameterized import parameterized
+from monai.data import NibabelReader
+from monai.transforms import LoadImage
+
+TEST_CASE_1 = [
+    {"reader": NibabelReader(), "image_only": True},
+    ["test_image.nii.gz"],
+    (128, 128, 128),
+]
+
+TEST_CASE_2 = [
+    {"reader": NibabelReader(), "image_only": False},
+    ["test_image.nii.gz"],
+    (128, 128, 128),
+]
+
+TEST_CASE_3 = [
+    {"reader": NibabelReader(), "image_only": True},
+    ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
+    (3, 128, 128, 128),
+]
+
+TEST_CASE_4 = [
+    {"reader": NibabelReader(), "image_only": False},
+    ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
+    (3, 128, 128, 128),
+]
+
+TEST_CASE_5 = [{"image_only": True}, ["test_image.nii.gz"], (128, 128, 128)]
+
+TEST_CASE_6 = [{"image_only": False}, ["test_image.nii.gz"], (128, 128, 128)]
+
+TEST_CASE_7 = [
+    {"image_only": True},
+    ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
+    (3, 128, 128, 128),
+]
+
+TEST_CASE_8 = [
+    {"image_only": False},
+    ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
+    (3, 128, 128, 128),
+]
+
+
+class TestLoadImage(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
+    def test_nibabel_reader(self, input_param, filenames, expected_shape):
+        test_image = np.random.rand(128, 128, 128)
+        with tempfile.TemporaryDirectory() as tempdir:
+            for i, name in enumerate(filenames):
+                filenames[i] = os.path.join(tempdir, name)
+                nib.save(nib.Nifti1Image(test_image, np.eye(4)), filenames[i])
+            result = LoadImage(**input_param)(filenames)
+
+            if isinstance(result, tuple):
+                result, header = result
+                self.assertTrue("affine" in header)
+                self.assertEqual(header["filename_or_obj"], os.path.join(tempdir, "test_image.nii.gz"))
+                np.testing.assert_allclose(header["affine"], np.eye(4))
+                np.testing.assert_allclose(header["original_affine"], np.eye(4))
+            self.assertTupleEqual(result.shape, expected_shape)
+
+    @parameterized.expand([TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8])
+    def test_itk_reader(self, input_param, filenames, expected_shape):
+        test_image = np.random.rand(128, 128, 128)
+        with tempfile.TemporaryDirectory() as tempdir:
+            for i, name in enumerate(filenames):
+                filenames[i] = os.path.join(tempdir, name)
+                itk_np_view = itk.image_view_from_array(test_image)
+                itk.imwrite(itk_np_view, filenames[i])
+            result = LoadImage(**input_param)(filenames)
+
+            if isinstance(result, tuple):
+                result, header = result
+                self.assertTrue("affine" in header)
+                self.assertEqual(header["filename_or_obj"], os.path.join(tempdir, "test_image.nii.gz"))
+                np.testing.assert_allclose(header["affine"], np.eye(4))
+                np.testing.assert_allclose(header["original_affine"], np.eye(4))
+            self.assertTupleEqual(result.shape, expected_shape)
+
+    def test_load_png(self):
+        spatial_size = (256, 256)
+        test_image = np.random.randint(0, 256, size=spatial_size)
+        with tempfile.TemporaryDirectory() as tempdir:
+            filename = os.path.join(tempdir, "test_image.png")
+            Image.fromarray(test_image.astype("uint8")).save(filename)
+            result, header = LoadImage(image_only=False)(filename)
+            self.assertTupleEqual(tuple(header["spatial_shape"]), spatial_size)
+            self.assertTupleEqual(result.shape, spatial_size)
+            np.testing.assert_allclose(header["affine"], np.eye(3))
+            np.testing.assert_allclose(header["original_affine"], np.eye(3))
+            np.testing.assert_allclose(result, test_image)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_load_imaged.py b/tests/test_load_imaged.py
new file mode 100644
index 0000000000..736409e269
--- /dev/null
+++ b/tests/test_load_imaged.py
@@ -0,0 +1,43 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import tempfile
+import unittest
+
+import nibabel as nib
+import numpy as np
+from parameterized import parameterized
+
+from monai.transforms import LoadImaged
+
+KEYS = ["image", "label", "extra"]
+
+TEST_CASE_1 = [{"keys": KEYS}, (128, 128, 128)]
+
+
+class TestLoadImaged(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1])
+    def test_shape(self, input_param, expected_shape):
+        test_image = nib.Nifti1Image(np.random.rand(128, 128, 128), np.eye(4))
+        test_data = dict()
+        with tempfile.TemporaryDirectory() as tempdir:
+            for key in KEYS:
+                nib.save(test_image, os.path.join(tempdir, key + ".nii.gz"))
+                test_data.update({key: os.path.join(tempdir, key + ".nii.gz")})
+            result = LoadImaged(**input_param)(test_data)
+
+        for key in KEYS:
+            self.assertTupleEqual(result[key].shape, expected_shape)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 1b7fb4c3b614aa25b9ffb3ab54056eea3326a313 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 16:24:45 +0800
Subject: [PATCH 16/37] [DLMED] remove test notebook

---
 examples/notebooks/io_factory_test.ipynb | 307 -----------------------
 1 file changed, 307 deletions(-)
 delete mode 100644 examples/notebooks/io_factory_test.ipynb

diff --git a/examples/notebooks/io_factory_test.ipynb b/examples/notebooks/io_factory_test.ipynb
deleted file mode 100644
index cf7d7a61dc..0000000000
--- a/examples/notebooks/io_factory_test.ipynb
+++ /dev/null
@@ -1,307 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# IO factory test notebook\n",
-    "This notebook shows the basic usage of `ImageLoad`, `ITKReader` and `NibabelReader` for Nifti and PNG data.\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/io_factory_test.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33m  WARNING: monai 0.2.0 does not provide the extra 'itk'\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU \"monai[itk, nibabel]\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temporarily need this, FIXME remove when MONAI v0.3 released\n",
-    "%pip install -qU git+https://github.com/Project-MONAI/MONAI#egg=MONAI\n",
-    "%pip install itk"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 0.1.0+335.g0b3eac7.dirty\n",
-      "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
-      "Numpy version: 1.18.1\n",
-      "Pytorch version: 1.5.0a0+8f84ded\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.3.0\n",
-      "Nibabel version: 3.1.1\n",
-      "scikit-image version: 0.15.0\n",
-      "Pillow version: 7.2.0\n",
-      "Tensorboard version: 2.1.0\n",
-      "gdown version: 3.12.0\n",
-      "ITK version: 5.1.1\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Copyright 2020 MONAI Consortium\n",
-    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License.\n",
-    "\n",
-    "import torch\n",
-    "\n",
-    "from monai.config import print_config\n",
-    "from monai.transforms import LoadImage\n",
-    "from monai.data import NibabelReader\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test loading Nifti files with ITK"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
-    "loader = LoadImage()\n",
-    "data, meta = loader(filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'ITK_FileNotes': '5.0.10', 'aux_file': '', 'bitpix': '32', 'cal_max': '0', 'cal_min': '0', 'datatype': '16', 'descrip': '5.0.10', 'dim[0]': '3', 'dim[1]': '512', 'dim[2]': '512', 'dim[3]': '55', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '0.976562', 'pixdim[2]': '0.976562', 'pixdim[3]': '5', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-499.023', 'qoffset_y': '-499.023', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '0', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '1', 'sform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0.976562 0 0 -499.023', 'srow_y': '0 0.976562 0 -499.023', 'srow_z': '0 0 5 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '10', 'origin': array([499.02319336, 499.02319336,   0.        ]), 'spacing': array([0.97656202, 0.97656202, 5.        ]), 'direction': array([[-1.,  0.,  0.],\n",
-      "       [ 0., -1.,  0.],\n",
-      "       [ 0.,  0.,  1.]]), 'filename_or_obj': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'original_affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
-      "       [  0.        ,  -0.97656202,   0.        , 499.02319336],\n",
-      "       [  0.        ,   0.        ,   5.        ,   0.        ],\n",
-      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'affine': array([[ -0.97656202,   0.        ,   0.        , 499.02319336],\n",
-      "       [  0.        ,  -0.97656202,   0.        , 499.02319336],\n",
-      "       [  0.        ,   0.        ,   5.        ,   0.        ],\n",
-      "       [  0.        ,   0.        ,   0.        ,   1.        ]]), 'spatial_shape': [512, 512, 55]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(meta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(512, 512, 55)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test loading JPEG files with ITK"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = \"/workspace/data/medical/MedNIST/Hand/008334.jpeg\"\n",
-    "loader = LoadImage()\n",
-    "data, meta = loader(filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n",
-      "       [0., 1.]]), 'filename_or_obj': '/workspace/data/medical/MedNIST/Hand/008334.jpeg', 'original_affine': array([[1., 0., 0.],\n",
-      "       [0., 1., 0.],\n",
-      "       [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
-      "       [0., 1., 0.],\n",
-      "       [0., 0., 1.]]), 'spatial_shape': [64, 64]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(meta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(64, 64)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test loading Nifti files with Nibabel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = \"/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\"\n",
-    "loader = LoadImage(NibabelReader())\n",
-    "data, meta = loader(filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'sizeof_hdr': array(348, dtype=int32), 'data_type': array(b'', dtype='|S10'), 'db_name': array(b'', dtype='|S18'), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'regular': array(b'r', dtype='|S1'), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  55,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.976562, 0.976562, 5.      , 0.      , 0.      ,\n",
-      "       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'descrip': array(b'5.0.10', dtype='|S80'), 'aux_file': array(b'', dtype='|S24'), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-499.0232, dtype=float32), 'qoffset_y': array(-499.0232, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.976562,    0.      ,    0.      , -499.0232  ], dtype=float32), 'srow_y': array([   0.      ,    0.976562,    0.      , -499.0232  ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'intent_name': array(b'', dtype='|S16'), 'magic': array(b'n+1', dtype='|S4'), 'as_closest_canonical': False, 'filename_or_obj': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'original_affine': array([[   0.97656202,    0.        ,    0.        , -499.02319336],\n",
-      "       [   0.        ,    0.97656202,    0.        , -499.02319336],\n",
-      "       [   0.        ,    0.        ,    5.        ,    0.        ],\n",
-      "       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.97656202,    0.        ,    0.        , -499.02319336],\n",
-      "       [   0.        ,    0.97656202,    0.        , -499.02319336],\n",
-      "       [   0.        ,    0.        ,    5.        ,    0.        ],\n",
-      "       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'spatial_shape': array([512, 512,  55], dtype=int16)}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(meta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(512, 512, 55)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data.shape)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From b74197b7f16334c488b56a510e6cd3f6c8e674c9 Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Mon, 17 Aug 2020 08:27:50 +0000
Subject: [PATCH 17/37] [MONAI] python code formatting

---
 monai/data/image_reader.py        | 4 +++-
 monai/transforms/io/array.py      | 5 +----
 monai/transforms/io/dictionary.py | 2 +-
 tests/test_load_image.py          | 4 +++-
 4 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index a2e62411a3..39542b2c21 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -14,8 +14,8 @@
 
 import numpy as np
 
-from monai.utils import optional_import
 from monai.data.utils import correct_nifti_header_if_necessary
+from monai.utils import optional_import
 
 itk, _ = optional_import("itk", allow_namespace_pkg=True)
 nib, _ = optional_import("nibabel")
@@ -32,6 +32,7 @@ class ImageReader(ABC):
         as_closest_canonical: if True, load the image as closest to canonical axis format.
 
     """
+
     def __init__(self, img: Optional[Any] = None, as_closest_canonical: bool = False):
         self.img = img
         self.as_closest_canonical = as_closest_canonical
@@ -195,6 +196,7 @@ class NibabelReader(ImageReader):
         as_closest_canonical: if True, load the image as closest to canonical axis format.
 
     """
+
     def __init__(self, img: Optional[Any] = None, as_closest_canonical: bool = False):
         super().__init__(img, as_closest_canonical)
         self._suffixes: [Sequence] = ["nii", "nii.gz"]
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 5dce96afa9..ebe191c934 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -40,10 +40,7 @@ class LoadImage(Transform):
     """
 
     def __init__(
-        self,
-        reader: Optional[ImageReader] = None,
-        image_only: bool = False,
-        dtype: Optional[np.dtype] = np.float32,
+        self, reader: Optional[ImageReader] = None, image_only: bool = False, dtype: Optional[np.dtype] = np.float32,
     ) -> None:
         """
         Args:
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index f3bc5f3c02..7d2d8e40d1 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -20,8 +20,8 @@
 import numpy as np
 
 from monai.config import KeysCollection
-from monai.transforms.compose import MapTransform
 from monai.data.image_reader import ImageReader
+from monai.transforms.compose import MapTransform
 from monai.transforms.io.array import LoadImage, LoadNifti, LoadNumpy, LoadPNG
 
 
diff --git a/tests/test_load_image.py b/tests/test_load_image.py
index 0d9e2c7ad8..1faa2a35cf 100644
--- a/tests/test_load_image.py
+++ b/tests/test_load_image.py
@@ -15,9 +15,10 @@
 
 import itk
 import nibabel as nib
-from PIL import Image
 import numpy as np
 from parameterized import parameterized
+from PIL import Image
+
 from monai.data import NibabelReader
 from monai.transforms import LoadImage
 
@@ -111,5 +112,6 @@ def test_load_png(self):
             np.testing.assert_allclose(header["original_affine"], np.eye(3))
             np.testing.assert_allclose(result, test_image)
 
+
 if __name__ == "__main__":
     unittest.main()

From be5b69c2f14f60e6b4d9b174604d3aef693d6930 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 17 Aug 2020 16:41:11 +0800
Subject: [PATCH 18/37] [DLMED] restore notebooks

---
 examples/notebooks/mednist_GAN_workflow.ipynb | 4519 +----------------
 1 file changed, 30 insertions(+), 4489 deletions(-)

diff --git a/examples/notebooks/mednist_GAN_workflow.ipynb b/examples/notebooks/mednist_GAN_workflow.ipynb
index 6065de5e2c..e77a90c738 100644
--- a/examples/notebooks/mednist_GAN_workflow.ipynb
+++ b/examples/notebooks/mednist_GAN_workflow.ipynb
@@ -43,11 +43,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
+     "name": "stdout",
+     "text": "Note: you may need to restart the kernel to use updated packages.\n"
     }
    ],
    "source": [
@@ -62,11 +60,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
+     "name": "stdout",
+     "text": "Note: you may need to restart the kernel to use updated packages.\n"
     }
    ],
    "source": [
@@ -82,11 +78,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
+     "name": "stdout",
+     "text": "Note: you may need to restart the kernel to use updated packages.\n"
     }
    ],
    "source": [
@@ -109,25 +103,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "MONAI version: 0.2.0+74.g8e5a53e\n",
-      "Python version: 3.7.5 (default, Nov  7 2019, 10:50:52)  [GCC 8.3.0]\n",
-      "Numpy version: 1.19.1\n",
-      "Pytorch version: 1.6.0\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.3.0\n",
-      "Nibabel version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "Pillow version: 7.2.0\n",
-      "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
+     "name": "stdout",
+     "text": "MONAI version: 0.2.0+74.g8e5a53e\nPython version: 3.7.5 (default, Nov  7 2019, 10:50:52)  [GCC 8.3.0]\nNumpy version: 1.19.1\nPytorch version: 1.6.0\n\nOptional dependencies:\nPytorch Ignite version: 0.3.0\nNibabel version: NOT INSTALLED or UNKNOWN VERSION.\nscikit-image version: NOT INSTALLED or UNKNOWN VERSION.\nPillow version: 7.2.0\nTensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n\nFor details about installing the optional dependencies, please visit:\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n\n"
     }
    ],
    "source": [
@@ -182,11 +160,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "/home/bengorman/notebooks/\n"
-     ]
+     "name": "stdout",
+     "text": "/home/bengorman/notebooks/\n"
     }
    ],
    "source": [
@@ -223,12 +199,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "file /home/bengorman/notebooks/MedNIST.tar.gz exists, skip downloading.\n",
-      "extracted file /home/bengorman/notebooks/MedNIST exists, skip extracting.\n"
-     ]
+     "name": "stdout",
+     "text": "file /home/bengorman/notebooks/MedNIST.tar.gz exists, skip downloading.\nextracted file /home/bengorman/notebooks/MedNIST exists, skip extracting.\n"
     }
    ],
    "source": [
@@ -262,11 +235,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "[{'hand': '/home/bengorman/notebooks/MedNIST/Hand/003676.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/006548.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/002169.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004081.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004815.jpeg'}]\n"
-     ]
+     "name": "stdout",
+     "text": "[{'hand': '/home/bengorman/notebooks/MedNIST/Hand/003676.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/006548.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/002169.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004081.jpeg'}, {'hand': '/home/bengorman/notebooks/MedNIST/Hand/004815.jpeg'}]\n"
     }
    ],
    "source": [
@@ -336,11 +307,9 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "10000/10000 Load and cache transformed data:  [==============================]\n"
-     ]
+     "name": "stdout",
+     "text": "10000/10000 Load and cache transformed data:  [==============================]\n"
     }
    ],
    "source": [
@@ -525,10 +494,10 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
+    "scrolled": true,
     "pycharm": {
      "is_executing": true
     },
-    "scrolled": true,
     "tags": [
      "outputPrepend"
     ]
@@ -566,4322 +535,22 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
+    "scrolled": true,
     "pycharm": {
      "is_executing": true
-    },
-    "scrolled": true
+    }
    },
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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172.39748 44.942836 \nL 172.755765 55.96752 \nL 173.114051 52.631384 \nL 173.472337 55.009657 \nL 173.830622 61.513622 \nL 174.547194 55.440152 \nL 174.905479 56.272902 \nL 175.263765 54.027305 \nL 175.62205 59.771002 \nL 175.980336 58.640851 \nL 176.338622 55.511328 \nL 176.696907 54.509708 \nL 177.055193 63.839485 \nL 177.413479 58.721622 \nL 177.771764 57.996336 \nL 178.13005 57.584688 \nL 178.488335 58.632697 \nL 178.846621 52.657301 \nL 179.204907 56.125903 \nL 179.563192 49.337372 \nL 179.921478 57.552531 \nL 180.279763 48.963931 \nL 180.638049 62.535452 \nL 180.996335 66.865309 \nL 181.35462 61.903749 \nL 181.712906 67.290076 \nL 182.787763 58.868143 \nL 183.146048 58.579242 \nL 183.504334 56.429359 \nL 183.86262 52.120759 \nL 184.220905 58.554197 \nL 184.579191 68.58393 \nL 184.937477 56.513857 \nL 185.295762 58.882418 \nL 185.654048 55.106273 \nL 186.012333 62.139891 \nL 186.370619 47.841893 \nL 186.728905 63.166544 \nL 187.445476 56.141245 \nL 187.803762 56.775304 \nL 188.162047 53.853014 \nL 188.520333 49.543128 \nL 188.878618 76.59738 \nL 189.236904 63.389565 \nL 189.59519 65.456766 \nL 189.953475 62.70256 \nL 190.311761 62.213225 \nL 190.670047 68.204702 \nL 191.028332 66.892335 \nL 191.386618 62.516218 \nL 191.744903 54.68745 \nL 192.103189 64.199567 \nL 192.461475 55.617422 \nL 193.178046 76.563324 \nL 193.536332 62.14946 \nL 193.894617 63.246125 \nL 194.252903 55.549649 \nL 194.611188 83.231291 \nL 194.969474 52.37308 \nL 195.32776 85.459208 \nL 195.686045 76.783507 \nL 196.402617 69.157219 \nL 196.760902 68.313176 \nL 197.119188 61.968578 \nL 197.477473 66.341721 \nL 197.835759 64.61548 \nL 198.194045 64.888479 \nL 198.55233 65.588713 \nL 198.910616 67.662015 \nL 199.627187 62.396779 \nL 199.985473 68.558032 \nL 200.343758 59.847814 \nL 201.06033 66.842075 \nL 201.418615 74.275035 \nL 201.776901 61.954823 \nL 202.135186 69.635435 \nL 202.851758 60.287042 \nL 203.210043 62.279463 \nL 203.568329 60.161473 \nL 203.926615 65.269442 \nL 204.2849 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236.888892 67.883632 \nL 237.247177 65.90278 \nL 237.605463 71.411855 \nL 237.963749 66.47925 \nL 238.322034 64.850566 \nL 238.68032 61.272833 \nL 239.038606 65.250271 \nL 239.396891 65.845397 \nL 239.755177 65.167204 \nL 240.113462 69.57456 \nL 240.471748 65.927315 \nL 240.830034 68.93273 \nL 241.188319 63.208921 \nL 241.546605 65.027433 \nL 241.904891 72.81423 \nL 242.263176 64.2145 \nL 242.621462 60.922109 \nL 242.979747 61.753388 \nL 243.338033 69.48614 \nL 243.696319 63.367515 \nL 244.054604 66.204437 \nL 244.41289 66.240082 \nL 244.771176 71.377841 \nL 245.129461 64.366204 \nL 245.487747 71.489497 \nL 245.846032 59.68066 \nL 246.204318 56.479149 \nL 246.562604 60.844882 \nL 246.920889 68.543885 \nL 247.279175 55.956063 \nL 247.995746 69.515479 \nL 248.354032 68.162277 \nL 248.712317 68.037532 \nL 249.070603 68.50288 \nL 249.428889 64.581582 \nL 249.787174 70.107004 \nL 250.503746 67.03812 \nL 250.862031 60.52811 \nL 251.220317 65.758542 \nL 251.578602 63.797214 \nL 251.936888 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282.391166 64.525968 \nL 282.749452 60.011311 \nL 283.107737 63.696278 \nL 283.466023 70.566681 \nL 283.824308 62.554065 \nL 284.54088 73.695084 \nL 285.257451 62.951202 \nL 285.615737 63.665021 \nL 285.974022 67.002067 \nL 286.332308 66.600706 \nL 286.690593 55.90626 \nL 287.048879 65.254833 \nL 287.407165 60.893228 \nL 287.76545 62.277201 \nL 288.123736 70.889541 \nL 288.482022 66.969039 \nL 288.840307 78.580103 \nL 289.198593 64.635178 \nL 289.556878 63.668373 \nL 289.915164 73.416248 \nL 290.27345 64.929698 \nL 290.631735 71.086747 \nL 291.348306 62.596055 \nL 291.706592 63.823241 \nL 292.064878 56.79411 \nL 292.781449 69.067972 \nL 293.139735 63.071053 \nL 293.49802 62.730962 \nL 293.856306 61.650926 \nL 294.214591 69.677733 \nL 294.572877 63.047191 \nL 294.931163 60.751031 \nL 295.289448 57.654714 \nL 295.647734 61.194694 \nL 296.00602 66.110034 \nL 296.364305 62.948411 \nL 296.722591 69.079416 \nL 297.080876 64.100752 \nL 297.439162 64.39111 \nL 297.797448 66.763292 \nL 298.155733 62.581506 \nL 298.514019 66.074325 \nL 298.872305 66.887608 \nL 299.23059 62.480378 \nL 299.588876 66.073279 \nL 299.947161 57.887173 \nL 300.305447 68.841498 \nL 300.663733 70.175435 \nL 301.022018 57.358456 \nL 301.380304 69.088544 \nL 301.73859 64.712202 \nL 302.096875 62.741099 \nL 302.455161 58.879792 \nL 302.813446 73.50311 \nL 303.171732 72.964673 \nL 303.530018 65.630113 \nL 303.888303 68.695503 \nL 304.246589 66.782094 \nL 304.604875 66.585542 \nL 304.96316 60.907622 \nL 305.321446 57.654669 \nL 305.679731 67.993845 \nL 306.038017 61.688822 \nL 306.396303 69.6595 \nL 306.754588 63.330009 \nL 307.112874 73.617375 \nL 307.829445 59.620921 \nL 308.187731 63.831405 \nL 308.546016 63.694168 \nL 308.904302 67.362405 \nL 309.262588 64.634927 \nL 309.620873 64.341638 \nL 309.979159 70.759957 \nL 310.337444 71.403612 \nL 310.69573 62.448351 \nL 311.054016 61.880091 \nL 311.412301 56.713021 \nL 311.770587 57.184845 \nL 312.128873 59.437846 \nL 312.487158 66.279609 \nL 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240.471748 87.354717 \nL 240.830034 88.377067 \nL 241.188319 91.168659 \nL 241.546605 87.940302 \nL 241.904891 91.659998 \nL 242.263176 86.387851 \nL 242.621462 91.429356 \nL 242.979747 90.423056 \nL 243.338033 86.474955 \nL 244.054604 90.929616 \nL 244.771176 88.559545 \nL 245.129461 85.653833 \nL 245.487747 89.116375 \nL 245.846032 85.458374 \nL 246.204318 85.794463 \nL 246.562604 88.264355 \nL 246.920889 89.824381 \nL 247.279175 73.887171 \nL 247.637461 90.668776 \nL 247.995746 85.980423 \nL 248.354032 86.807862 \nL 248.712317 88.363899 \nL 249.070603 81.921581 \nL 249.428889 88.311484 \nL 249.787174 81.351537 \nL 250.14546 87.463229 \nL 250.503746 90.844169 \nL 250.862031 82.562508 \nL 251.220317 94.522998 \nL 251.578602 88.763467 \nL 252.295174 84.309434 \nL 252.653459 85.124653 \nL 253.011745 86.403006 \nL 253.37003 91.813617 \nL 253.728316 92.154081 \nL 254.086602 90.788462 \nL 254.444887 90.25801 \nL 254.803173 90.144779 \nL 255.161459 86.167871 \nL 255.519744 88.937967 \nL 255.87803 83.697933 \nL 256.236315 85.685441 \nL 256.594601 33.385336 \nL 257.311172 83.364122 \nL 257.669458 85.947092 \nL 258.027744 89.859423 \nL 258.386029 83.91171 \nL 258.744315 89.564405 \nL 259.1026 88.180227 \nL 259.460886 91.043273 \nL 259.819172 90.248445 \nL 260.177457 85.690706 \nL 260.894029 87.971781 \nL 261.252314 86.989142 \nL 261.6106 92.810081 \nL 261.968885 91.323414 \nL 262.327171 87.886387 \nL 262.685457 89.837074 \nL 263.043742 85.802718 \nL 263.402028 87.697753 \nL 264.118599 86.174501 \nL 264.476885 88.061774 \nL 264.83517 88.38446 \nL 265.193456 83.072411 \nL 265.551742 86.284657 \nL 265.910027 82.507639 \nL 266.268313 83.76394 \nL 266.626599 87.921947 \nL 266.984884 84.105642 \nL 267.34317 91.310094 \nL 267.701455 82.727417 \nL 268.418027 92.11063 \nL 268.776312 88.54706 \nL 269.134598 89.028231 \nL 269.492884 84.90822 \nL 269.851169 87.515257 \nL 270.209455 82.064464 \nL 270.56774 87.107179 \nL 271.284312 84.726419 \nL 271.642597 88.029159 \nL 272.000883 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71.100373 \nL 421.764273 82.064857 \nL 422.122559 87.970455 \nL 422.480844 89.666875 \nL 423.197416 85.415465 \nL 423.555701 86.428512 \nL 423.913987 86.073875 \nL 424.272272 89.18967 \nL 424.630558 91.286328 \nL 424.988844 86.197985 \nL 425.347129 84.86785 \nL 425.705415 80.93294 \nL 426.063701 91.891948 \nL 426.421986 84.777565 \nL 426.780272 86.660854 \nL 427.138557 84.564786 \nL 427.496843 84.795551 \nL 427.855129 88.382661 \nL 428.213414 89.096903 \nL 428.5717 88.600787 \nL 428.929986 76.757027 \nL 429.288271 86.028723 \nL 429.646557 84.866231 \nL 430.004842 88.122519 \nL 430.363128 87.33214 \nL 430.721414 78.452096 \nL 431.079699 88.997026 \nL 431.437985 88.702314 \nL 431.796271 85.242128 \nL 432.154556 85.05794 \nL 432.512842 85.070695 \nL 432.871127 86.951882 \nL 433.229413 76.223283 \nL 433.945984 87.359973 \nL 434.30427 86.658333 \nL 434.662556 89.947153 \nL 435.020841 87.336122 \nL 435.379127 87.342408 \nL 435.737412 86.395569 \nL 436.095698 87.010906 \nL 436.812269 91.253363 \nL 437.52884 87.520171 \nL 438.245412 78.819342 \nL 438.603697 80.335226 \nL 438.961983 68.144756 \nL 439.320269 86.867068 \nL 439.678554 86.464428 \nL 440.03684 85.02474 \nL 440.395125 85.344938 \nL 440.753411 83.998899 \nL 441.111697 90.265454 \nL 441.469982 90.314661 \nL 441.828268 92.172779 \nL 442.186554 91.085998 \nL 442.544839 83.008339 \nL 443.26141 87.290631 \nL 443.619696 81.233702 \nL 443.977982 84.444903 \nL 444.336267 94.97923 \nL 444.694553 86.502667 \nL 445.052839 82.359071 \nL 445.411124 86.659556 \nL 445.76941 86.840458 \nL 446.127695 85.809083 \nL 446.485981 86.871206 \nL 446.844267 85.207578 \nL 447.560838 91.437276 \nL 447.919124 81.107131 \nL 448.277409 86.381214 \nL 448.635695 82.537855 \nL 448.99398 87.315103 \nL 449.352266 86.175763 \nL 449.710552 82.774003 \nL 450.068837 86.110956 \nL 450.427123 86.925601 \nL 450.785409 83.599765 \nL 451.143694 91.856031 \nL 451.50198 88.715419 \nL 451.860265 90.283537 \nL 452.218551 80.54498 \nL 452.576837 82.451122 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467.983118 90.553753 \nL 468.341404 91.319855 \nL 468.69969 81.34739 \nL 469.057975 85.288607 \nL 469.416261 91.636314 \nL 469.774547 81.829218 \nL 470.132832 85.40079 \nL 470.491118 90.359609 \nL 470.849403 89.232867 \nL 471.207689 81.68561 \nL 471.565975 88.523052 \nL 471.92426 78.129435 \nL 472.282546 83.905353 \nL 472.640832 80.432085 \nL 472.999117 87.427323 \nL 473.715688 83.797939 \nL 474.073974 83.17282 \nL 474.43226 88.576442 \nL 474.790545 89.739975 \nL 475.148831 81.578254 \nL 475.507116 90.644799 \nL 475.865402 77.600554 \nL 476.223688 83.635595 \nL 476.581973 85.228056 \nL 476.940259 75.324026 \nL 477.298545 86.119563 \nL 477.65683 92.264307 \nL 478.015116 85.396678 \nL 478.373401 87.089143 \nL 478.731687 84.748353 \nL 479.089973 87.309618 \nL 479.448258 86.84092 \nL 479.806544 85.948314 \nL 480.16483 88.538356 \nL 480.523115 89.964021 \nL 480.881401 83.109226 \nL 481.239686 84.006037 \nL 481.597972 87.31923 \nL 481.956258 85.162334 \nL 482.314543 85.390466 \nL 482.672829 86.100218 \nL 483.031115 86.052309 \nL 483.3894 80.389602 \nL 483.747686 84.515313 \nL 484.105971 86.318689 \nL 484.464257 89.217728 \nL 484.822543 83.916197 \nL 485.180828 87.290273 \nL 485.539114 88.867307 \nL 485.8974 87.398903 \nL 486.255685 77.629322 \nL 486.613971 83.159255 \nL 486.972256 80.179309 \nL 487.330542 83.708594 \nL 487.688828 82.070474 \nL 488.047113 83.99139 \nL 488.405399 86.885695 \nL 488.763685 83.325094 \nL 489.12197 88.825954 \nL 489.838541 85.118152 \nL 490.196827 86.417459 \nL 490.555113 84.474243 \nL 490.913398 85.44315 \nL 491.271684 84.121259 \nL 491.62997 87.126771 \nL 491.988255 88.173035 \nL 492.704826 75.384303 \nL 493.063112 90.594394 \nL 493.421398 80.899658 \nL 493.779683 87.393506 \nL 494.137969 90.694764 \nL 494.496254 82.662564 \nL 494.85454 84.655584 \nL 495.212826 85.974967 \nL 495.571111 88.24211 \nL 495.929397 83.235057 \nL 496.287683 82.183376 \nL 496.645968 85.478655 \nL 497.004254 91.043512 \nL 497.362539 75.562871 \nL 497.720825 82.520085 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -4911,143 +580,15 @@
    },
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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"iVBORw0KGgoAAAANSUhEUgAAAMEAAADBCAYAAAB2QtScAAAYoklEQVR4nO2dWa8VVbeGl1r2vSiIoAK6kZioqJELL0z8rf4FL42JMcQYotEEDSBNYEsjnQ327bk5zvOsSj2bUd+G72z2fN+rQVGralatNTPe/Y7utrfeeuufxf/ivvvu+9dc7N27t9krKyvN3rVrV7OvXLnS7BMnTjT7o48+avbVq1eb/eOPPzb79ttvb/YwDM1+5JFHmn3PPfc0+8EHH2z2Y489NrnO1157rdkvvvhisx966KFm33nnnc2+7bbbJtdD8Pjff/89edw++88//0zaf/75Z7N///33pc/wnX788cfNfu+995p99OjRZp88ebLZf/zxx3XvTfB8Pttff/3VbL4jrpvXtPN53MDzKzZ/K3fddVez+bvZvXv35PHV1dVmL/0Wr7vKINjkyCYIusfw9ddf/98/4GouXLjQ7A8++KDZP/3006RN90I3S1d2xx13TN6LFOXatWuTx+n6ePz48ePN/vbbb5v9wAMPNJt0jjCqw7VxzXwuHud6jBrx+qQS4/NJAUmVSEW+++67Zn///ffNNlrCe/C40SejMXacz8brGw0j7HweJ/gefvvtt0n7119/bTZ/B3xXfPZ4gqB7ZBME3WOgy6VL+eGHH5pN92sKgYGuuKIikIqYWyMN42epUJ07d67ZO3bsmFwb7zVX+eG7IjUy6mH0hG75ev/3L/idcd12PsF1GEWz73XuORWs5zpG7Yyyk1LzdxxPEHSPbIKgewxGRX755Zdm03XQhVZgNIMuncfpvkgzSI3oKnn84sWLzabq9dJLLzX7/vvvb7YpEITRIb4fqkNcs12f6x8Hy0izeA9+T/w+eNzojeFGUZf/L9hvi78J+z6WKOzNWmAQ3CrIJgi6x1BxoaZyEPZZu765MrsmaYIpKN98802zP/vss2bv2bOn2QcOHLjuOkk3SHVIPUjbuB4qEKbEWBBssVhW4vhsly5dmvz8XDo0N5i10WABtcrzkgIR8QRB98gmCLrHUMnXMLpC0C3PDarQjVtKL226NdIJUpSDBw82e8uWLc1mKvi2bduaTSWGKhlBqmOBtrXygqaOj+/FtG+jaKSGN1sR2sio/HYrVD6eIOge2QRB91iiQxb0oW2qTiWnqOKKK8E4O4fujmmzH374YbPffPPNZpN6WACLwTW+B352HPCauqa5YqZOLxbLitDjjz/ebFb93Xvvvc22dPDNRHvmwvK0SGeTSh0EQDZB0D2GSkGzKR6V3Ju5MNXIziEsOMU8IlKj5557rtl0lbQrag/vxbyVu+++u9mWozUOllmuy9atW5tNilZR7jYrjPZUvqel82/C2oLglkI2QdA9llKprVjZeubQdd9sNcKKuc3FcT0///xzs7/66qtms0ifQTRWsfH61ijAVDI7Zy0aye+DPXOefPLJZlM1OnPmTLOtcH6zovKMVN+ofi79bm7ssoLg1kM2QdA9lhJXrIDbgj5zU6znpvHaZ40aEVaAzuozpl4z+MVgFFs+VsB3SFds72qsgJE2cR0PP/zw5JpI3Zj/NLcC8FZEpbdShTrHEwTdI5sg6B6DVSZZD5tKMKvSWm89Rd5zg3T8LHOKjhw50mwqLgxyMd2aao+lW1taOJUJrmd8HSpQpDrMHeJaSZPYhtJS2zcr+H75ThlYTN+hIBBkEwTdY7A8C+vMXPkrnK6b1V50WVQy1kONKjZpCdUhFuMzWMaBIOy2TXpC8P2wfaUFaqp0jpSG75QunqoWvzMGPjcTLL+N34112Lb+WfEEQffIJgi6x2Cdok35sUENVgi+9Fe4UK8bpRpVlCgGszjv6/z5881mng7zjqzY3yrvbFjJWrPSSJsYLCMdMiXE+upsVlhOm3U8T7AsCATZBEH3UHXI0pLpco0ambtnzsvly5ebbb2G5qIyO4t0iMGlTz/9dHKdHPCxc+fOZltaNWHvcC11iNciHbLcpsp42s0ULLMKMms4sFRQL99HPEHQPbIJgu4xmKusFLlb4OKpp55qNtOV9+3b12zSDxtGYah0t547DpTr5MR4TkfntHkqSHS5VqT/nwTIbGwt6RDznGgbPbjVYd89KyErXcKTOxQEQDZB0D0G63Nj1MIm0dPtvPHGG80+dOhQs5n2y/NJh9aDijpkAcGzZ882+4knnmg2A2r79+9vtk235/uxyrK1wM/zHTE3xlKprT3jzVaK5g5usc9W+knxuYwiEvxuUmgfBIJsgqB7zB7SwfOZz0I3vrKy0uz333+/2RcuXGg2K7zWEyC7UZVrpGTHjh1r9uuvv95sKkikTPZODGupNRaY5LORGnEACfsUnTt3rtmVSfcVVAZh3CjYd2mBMBv9S0qamWVBIMgmCLrHYCnBlYozVmBRpXj66aeb/fbbbzebwTK6a9KkG0WN5p7DnCKOUT1x4kSzWTV29erVZpOG2MjQyijbxWL5vfPzrCZ79NFHm81u1aRopEw28nU9SlGl51SFJlUUocpn7bls7t2S4nTdOwXBJkc2QdA9lnKATY2wABOL0Jm7QbfD3BtOlqeKshEqouhCqTpQNeKwDw74IN0gDeF7YKBmLUpigUm+LwbRSMWY9s1u1VROLDA5VyVcqzrueqjkq5k6VEn957u2fKrQoSAAsgmC7jFYboUNmLA0VdpUKejimEdEN2X9iP6bMMWGFXCkfwycUa0hbOo9MVbDjBoy6MP3S8Vq7969zSYFYtowKR0pbKXTt63NnvNGBdH4m7PGApUeT1YVGU8QdI9sgqB7TFeIj2DVUizyJlVg8Oudd95p9unTp5tNmrHRYBVLDPCxHxHPsXlndMtr5fJYcIeUhoEzVvFZsJP347ovXbo0eY6pN+tRkOYGxUx9MvpuPZ4sYLlE9ydXEAQdIZsg6B6DuVDrpWPT2Jlv8+677zabReukTONJ7hsJFXWEqowN5rB3SDe+1j0YhOO1LABk6h6rrnj9Tz75pNlUu0w1qnT9njtHjKjMw7OgZmVgjCqek58Mgo6QTRB0j6GSA6Lt63CcSganxtO1mjvdaKDbpAJmAT4GppheTspHSsJnp/I2Bv+Pnb55b97P8o6oWNFmu0kGMpl3ZLRvPSN4iUqRfmWEsKXgV3pRxRME3SObIOgeQyW/w/7yZuCF12G3Z1NONnLXZFIXBqOYtmwqGd8Jg1p012vlFFmrQAu22Tu1GWdUh0iH2Fby8OHDzWa7TFbTkeYSlRaZlQrGitpjQTHLcbLz4wmC7pFNEHSPoRIAoVu2gRdUL2hXultvNFRaKdK+du1as416kJ7wvY3pAP/Ne1v+j+UUGQ2lwrV9+/Zmv/rqq80mBeQ1OfJ2dXW12fy+jarZ78CqGQn7jZryQ9g104YxCIBsgqB7DJbfYa6M7pc2K8LoHm2E5kaDtTmkTfpABYnPSKpD9cXSs3n+YrHccZprIo1hdZgN6WBAjd+T9d4h7aFSdODAgclrMpDHvDEqg0bbbLI8aWgld4io5L1FHQoCQTZB0D00WGYzr0xBmpsnstHUIcuhIsWwwm6qQ3TFPG50Zlykb/lGRqGoxrAAn6AyxWvys1w3g4LWTIHPz/wi5h2dOnWq2awk5H0t56dCaSpDZYzuE/EEQffIJgi6R2mEa0UpulXSpA10m0yf5nFSEg4ZIaiMkQKxqs5Uk8Visdi2bVuz+a5JxZi6TVjQzlQRqjp8NlIdUikqV2xDyWdgR3KmeR85cqTZpEaVtpCVvkM2S89o+lJ13uQKgqAjZBME3WPpT3BLfa0UW5v7ulVyh+haGSCjIkLKQApAmsChGebq+U7GM84s5ZrviwrPlStXmk0qRjpkbQm5Dn7Wqt0siGg04+WXX5685vHjx5vNHCSrQjQ6bmnY1shAA22TR4OgI2QTBN1D6ZC5TcsFmqsObYTAmeXOMIBlFV1sNUk6xEAW3TsVIVM4xrDeTPw8lSLm7VCZsXvYDDZrK1kpZqeyxDl2zz//fLOteo5tISu9j6ynkDUoINKGMQiAbIKge2iwzLojV1KjKy33NoI6RDduFWF8RlNZSCUY7GJrQ6pAFy9ebDbVlMViOYBl+UlWyUU1inSC9yBds+EoPM7zt2zZMnmcwUXel3SIaeVWrffll182m9SIVLIS3DVKat9fPEHQPbIJgu6hQzrWmrp+PVSqgtYzz+pGUSkrOmcqMW0qLqQnXA/pDN2vKW9nz55dWhPVGConpD20rWKLFIL0w5QTG/PKKjM+D5+f1yTFtAAcadUrr7wy+VxUb6jE8T1aarcFGdOGMQgE2QRB98gmCLrHUOH+lcjw3Okjc2H3mvv3gXWVIE9lxNimv5Dv8u8AXscmytAeJ9lZYy7WI/AzvMfc75IRZtrWV5bH2X5/z549zWaJJ9fDv60oR1NepdTM98C/b7gegs9lAyX5NxOl03iCoHtkEwTdQ+kQjzNKPDc5znK+Kwl3RoHWI9nSFbMUcMeOHc1mpJM23bINzKNUZ2WB1oVifC1GrikfWgcMwqLKRsV4DukdJVxGwJl8x/nUzz77bLN37tzZbL4XrpmyM6kk6zIokZIy8XdpNQSW7cDz4wmC7pFNEHQPbc0+1zZUqFGlBHM9fUxJH5555plJm0oRz6eKQMpg16eqQTWCEVnrBzoGn5nUhfcgtaCL51p5vilLRqvOnz/fbNISXoclnqRP+/fvbzYVG1Ij0lOC9NHeFyPGXD+p4LjX69Q14wmC7pFNEHSPoUJRKklw1vOx0pHCyvasrLNCw0gNOHllZWWl2QzsUKWwBDVSI2vNTptKBt+JdYUY/5/RKbp+vgueU6FM1u+T6hWvaU3DLGnOKBwVN66H75fqk9VA0GYwjmuwd5XW7EEAZBME3WOoKDNzc4QqsJwioxYVCkT3znI+BnCoUlgZJd2v5c7ws6QJpACkQza7d/zeSEW4Jl7LWrbz3lROxgG5qXtb2axRQwuskoowuMaSUn43fF6eQ1WK+T82hJA2v2OWaZq6F08QdI9sgqB7LMkDRlHmpknb+Rb0sHbvlW4WphDs2rWr2SwRJEh7rCSPIKWxnqH2LFwnFRfm6SwWPu+YNMO6RBiFtfdOCkGKRXpXUfoq3yWvaRSL74X0z9KhGXSjTbrF3wQVJ77neIKge2QTBN1Du00QleF+do4F3SpNvHh9G5rHgBcrwhj8MpXFUp2pLiz1rJTzqcQw+EW6QTpAtz9+dqoZvJbRHioqdP2kNzbTmOfQtpycCt3i+dYMjPTGVDaqRrwO3xevw+tz/UYv+bzxBEH3yCYIusdgQRyiUvlVcZV0xZYvRNdNOkF6Q7fPtugcCMdZwda8yQI+dJu8F9OtK4PluH66d1M7FotlKkb6SLfOz3OAoKkxfAbm7RC8Pm3rG1oJXtpsYVOTqBqt9Y6mzrffE6kRv5vQoSAAsgmC7qHBMsJUoKXqHEmNrtAGuiwGOkh1CCoBVEeY50M6QHA9ViBPV0kqwXXacDuujc/IYXWkW6xuG8NoAz/Pqi7apDQseOf75fpI9fjMFkQ0RY/fPb+Dc+fONZsNDqxCjc9YGR/A6/MZKwMJ4wmC7pFNEHSPwQJSldRoUwsqA+forqmIcNIL18YCbnObVAtMBbKCcptjTFWKx+1d2QQb2lSKxnlNdj/SBktRtjnLpFzs7WN5OKSD4znL/6JCUSytnGtmgNOUroqyROrFNGxTkNJ3KAiAbIKgeyypQzdqkoylOpMOkfbQjVPhoduka60McjPQhZrqRWrAgBfXz3NIybhmKhanTp2avOb4PfN9kRrZM1vgjO+UwUJ2kLZ5yvb8RiVNNSKl4TpJB/n8fMa5QyFNMSNIL5e6hE+eHQQdIZsg6B6D9Q4y21KjCetzYzSAqcWWHmsBOFuDVXtxbVRBqJow2GKF3VyDBawOHjzYbNI80hwGcxYLH7JHBY3fh1EOVlGdOHGi2TZQg++R92Kg7fDhw83md2N5Y5U0esstI+ZWMFrA0nolxRME3SObIOgepcoyQ0WZsUojBjEYSLHghqV5V1Qsy1kiBWJ7xt27d09en5VepBukOp9//nmzrZqMKsUY1ona3gUpk6UlW34V6aB15WYeEQNb1ibR6JBRIK6nQnmNvleCd6RDVKjiCYLukU0QdI8SHaq0arTz+Ze6DbCo5IlYYMuUKwMpwPbt25v9wgsvNJtun26TrpXPxSAV3Tur2/jsvOYYNrPMKqdIe0i5LN2ceTWkd1ZhyEo0UiPmcll7Q8JaNVINs7R7C3Ba3hvPJ/21fkfxBEH3yCYIuocO6VgPrICd7sgCLObWTAmwijbrhUO6wco1qiMWdCLtMRrCPB1rPUhqNKZwdh7pDdWbpZRg+S5JP9j20QZ5EKSPHHPLXChexxQeU66obvFZKgGySuG/Bci45niCoHtkEwTdY7C/tolK7pBRFKM6PG5t/4wOVYq8LTWalVxM567072HOj6Uh09Vbfg2vz4DY+H7WPrGS12UKHekQz7FZYKRDzCmigsY1816E0eJK2ro9e0UlNJq39NnJM4KgI2QTBN1jqPSSmRssI8x1G5WyAvaKimVp21u3bm02g2I8/8iRI81mMItF6qRPpDFUk2gTfBZL8x7fm89s41ntuhZoY5E+KSBh1I25VqSVVNBIdbg2a8FJ+sSifqPF62n/aFVv8QRB98gmCLrHUKnSIizIZbCxp0ZvzI1XOhyT3rDV3759+5q9d+/eZh86dKjZLIrn9UmlqA5VxrzyOjY9fvwebCgIMVaUpmCdwTkE5OTJk81mIMzSx0kxqQ5RNSLFst8KKR+vTyplv4NKJSRRmZ8XTxB0j2yCoHvoRPvK6E6jJZWqoEpQzNZjKhMDO5xiz8J50h4WoDOIZEExXp/nM1V5KSdFnmWtYI6NWLVzLEfK6Bcpx5kzZ5rNBgFUgawlpbWtZODTKs4sl8lojwW8TA2zVo1Rh4JAkE0QdI9S7lCF6hBGV+iyzMVVKooIGzRBl86AjFEAG4LBz1KVsSo5GwRhyti475B9hjk2FhSsKFAWqDp9+nSzGQizOW0WpON6LM+M75Gf5buoqIpG/0y1tM/GEwTdI5sg6B5DhXIQRoEq1KhSSF1RgWzembliFryzIsoK/KmgsFLMprJbM4FKF+ex6+azWYqyUShTS6zvknXTJk2kIkQaY6nnlb5ANteMqAQaK6qRtXYk4gmC7pFNEHSPkjpEVNKbK/O8zEVXcoTMVfI4U4aZL2OVT6ZSsE/P0aNHm02aRGpE5caq5+z4YrFMFazo3nJsrGjdqreo/JACMYjI74OUie+Ia7Pnr7TUtCBiZa6eVS0S+lucPDsIOkI2QdA9ShPtDZUeMJXi/cr1zd1ZLxnmCNHVk0qYm6Xrvnz5crPZcdr6C5EmmHphtGWxWM5P4vPzupYDQ0pn7RxNOaF97NixyfvyOakOserNvpv1tNQ0dYjH7beY3KEgKCCbIOgeg7lQwlrrEZVgWaVfzhoLbbYFZ8y9E6QiVpVGCkD6xKAbVSZeh9fne7P5YwxGjT9jnagtOEfwfixg53tnVZcNy1hdXW02lSVSI3t++24McxsuGF2295bcoSAQZBME3WOoKEKmzNhf8JV8JHNrc2F5OOYSiUruCa9JCmCUzPKgeE0WqbPqbbGodZmuBPxIgazbM6/DcyxPiUFBy5GqjPW1c4wWz1UY7bj9VuIJgu6RTRB0j8HSki3IQFRyPSoVYZYjVBnSUTnfcpMqhd2V61ueDs/nQBBOiadSNF4r+/NY20cL/nEdtEljeJz34vNYFVjld1N510Sl+3SlyrESrCXiCYLukU0QdI+hQmOMGlXOr6gClUCbqS52/twWkeZ+6cZNATNqR7pBRYgpzOMApY16Za7S1atXm00KUaEi/D4sBZrX5HFen89GVAa0zP2eKhV69vuw73LpXV13BUGwyZFNEHSPwQrVK7A03spI1koAjpirVtHmc80NzFnwytZg9I9rYJ4OO0OPP8MeSayOo2LDd8d7GI2ztTIoxutU8rT4XixHqDLoZW5rzspMO3sPS1R+8upB0BGyCYLuMVRyeCrt/ewvcjvfXJzl/FRGwRLq+irBE3kuU0rMFfN8Phfp0BdffKHrYEUc6YqlcVcoEGF01mhrRSmrtNG0NVcGcFRUIHsuo2rxBEH3yCYIuseSzzT3XcnVsb/szfWZu6vQIVsDUaFAFddNe25uFcEcH2vhuFgsPz/dd6WL91xU8m1sDRYstCEl9puYG9SsHK9gaZ3/8VWCYJMgmyDoHoO5L7p+S9edO+neclgIc5trzfmaWtvcqqOKmjRX+bAqLlKMMR2yIJRVh9m9KwpPJdXZpsnbdUyBqfwmKvlnc9PoDek7FARANkHQPQamxNr4TVNFKu3xKm7Kev7Ycea28Ljd16hdJX2Y7to6Ltt1jIawCN76Bq31DBYMsm7VlWfm+ZW8IAtCVb57m/FmgUbCApa2fsuN4+8+niDoHtkEQff4H0iZTMc6G7CCAAAAAElFTkSuQmCC\" y=\"-6.613793\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_3\">\n",
-       "   <g clip-path=\"url(#p2a39d3380b)\">\n",
-       "    <image height=\"193\" id=\"imagebe40b8aa70\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"468.993103\" xlink:href=\"data:image/png;base64,\n",
-       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y=\"-6.613793\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_4\">\n",
-       "   <g clip-path=\"url(#p056e7b2292)\">\n",
-       "    <image height=\"193\" id=\"image5fbbcd8e86\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"699.889655\" xlink:href=\"data:image/png;base64,\n",
-       "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\" y=\"-6.613793\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_5\">\n",
-       "   <g clip-path=\"url(#p36d6b3e236)\">\n",
-       "    <image height=\"193\" id=\"image6a278c2b2e\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"930.786207\" xlink:href=\"data:image/png;base64,\n",
-       "iVBORw0KGgoAAAANSUhEUgAAAMEAAADBCAYAAAB2QtScAAAYsElEQVR4nO2dSY8ftdeFCyjmMdCBDARQABEJxJflA7DgK7BDbEGCFaAIoQgyEkICYZ6n/+bFPK63nuSWOo268Tkrp+JyuVw/654+d/BtV69e/Wv6Pzz22GN/N6fbb799WsNff7Xu0x9//LHa/vPPP296/bbbbmvtO+6446bPned59V6bG8ckeC/n9ttvv7X2t99+29qnT59u7VdeeaW133nnndb+7rvvWpvveNddd63O/+67716dz/IergWvE/fcc09r8/3vvPPO1ua7sf+9997b2seOHWtt/g6eeOKJ1n7kkUdam+/DcTjP+++/v7V//fXX1Tlwbr///ntrf/HFF6195cqV1r506VJrP/74463N783n8jq/6+XLl1t7/RcXBAMhmyAYHjPNF005zZRdp/klJeCYv/zyS2vT9HOchx56aHVM9jdqQUpjdIBto1ucz48//tjan3zySWufP3++ta9fv746B87ZnlWhf1VwXTgPPoM0g2tk70x688MPP7S2UTqCa81vb78hzp9z3tnZaW3+tgi+108//dTaDz74YGuTzvG9OGYsQTA8sgmC4THTrNFUfv/9961NU0YTRLNDs0Yl4OrVq6v9CZoymmWqCDT199133+qzaK5//vnn1bkZXWF/KgdUhzg3U70IowNcwyWt4Lh8f74n35+KB8fiOIS9v9FcvgPb7EPwG3N8vjOfxXcx2sox7RuzzbXmmjz11FOt/fXXX//zrNU3CYKBkE0QDI+Z5oUmiKoITR/N8osvvtjaNJXffPNNa3/22WetbWaKY9JMkQ7QgWPqEE0922a6CZrrTz/9dHU+BMfke/G69aEKspwb382cf3adlIBrZ2qUKWtcC74/aSXHZ5v0zJRHzoEOL6qE/H78DdGJxt8N8fDDD7c2nYBfffVVaz/wwAP/zGd1lCAYCNkEwfCYaZposi5evNjaVIpOnjzZ2jRHdD68//77rU1a9cwzz7T2o48+2to013wWr7NtCofF0VCBMLrC+dMpRjpgKlCFAlmfG41lcU4Vh6KpZuxvSgvpEKkt1RuC1y9cuNDaXHfO/8iRI6tzPnr06E3H53txzvx9cN2p6PF9OxVr9alBMBCyCYLhMVtIM//KJ3WhWfvoo49a+/XXX2/tN954o7VpukmHaI5oBqkQ0GzSofbss8/+8wIwcezP96J5J30yKlVpV2C0xxxnN4LRVqOJ9jyCzzb1ietbGcdCvo2qPfnkk61NhZHqDR2udLSRwnIOpOD87ZJq87mxBMHwyCYIhsdsWWDmIHvzzTdb+7XXXmttmiyaNZrxjz/+uLVpHg8dOtTajO84depUaxs1otlkRhGdcRbDY/1PnDjR2symosPOQpiNAlWvc36mLln4tX1L9jfVyGiZzYH9LUOP17m+DGkmRTElx7LV+DvgOLzOODBSIz4rliAYHtkEwfCYzZQzfub48eOt/eqrr7Y2TY0pBxyfJpTmiH/lf/nll63NWI+XX365tRkbYiG6VJkIy5gjZaDZZIYTr9MsG90y3MhxZnSI86s45AymjlXa9iyqUuxDSkOQwtLhZTFRpKR0wFncG+fAe/ks0rNYgmB4ZBMEw6NzljGpmioQ+9CJVjG/hNEAtjn+hx9+2Nr8658Kx/PPP9/apFJWC8dUDbapVtGpQtWBShfp0Nb1qdIhu8ccZISFmBNGJyz0mrA5mxPNlChSVdIVUk/SXMa3cZ4WK8Z7+Z1iCYLhkU0QDI+Zf4UzNoYZPGbittIhg1Ej0oxz5861Nmkb6//QyWXxL1QIeJ1Uh6G7zHxiWDVpEpUuzq0SF7RcQ1Na7DpRUW8slLqiDnFd+G68bnWKCCo2ptbxe1ChY5aZhXbTqcnvx98QnxtLEAyPbIJgeHT2yuoOWZzMvwmrWPzee++1Np1ZdLSRAlE14rvQtFqcDu/ls5jMTXNNalBxoi2fvRVbnWhGtwxG7yxUndetEjUVG9JK+350tJHKU00iVSVFJsViBlwsQTA8sgmC4dHRIZpEy6i6VYrQbkBzSmr09ttvtzapy3PPPdfaNLmMQaI5ZZu0h/SG2XCcD6sgM/6qUsJwmlzJMcWGMApE2meKTaWatmWHVSpUsw+dWXbACWklnWVWd8iy0viNLRMtliAYHtkEwfCYaTb51zOdUDRl1cTwfws09Qztfvfdd1ubtZJoHrtSfHI+GMen+aWJ5pgMO+d6VsoHTtP2ukWV89sqdZEqIdymcPF97BAQgk6ra9eutTbX2koyct35GzVHGymQzS2WIBge2QTB8Jgr1ZtvVEV5P4FUjeoCzS/VHlIXi3nhu5MmWfwLyxZa2DZDvpf0suK0IioxP1aVuqI42bMIO0zEzpnjOKRDnCeVJX4nqkCsXcU15VpTDWT4e7cm//+VgmAsZBMEw2O2v5it1N9+hsU+MUPthRdeaG2aYobfmupA6kI6xOtUNRjGy1gYZjUtY7FMyTFVjtTCSmqSihjVM6pjdNlUJlIgc5DZGXV2Lhudjlx3riNhqpSeXbfaOwgGQjZBMDxmM7k0m1urMe8HkGYwI4wmlJSJqg7jjqw0IOkDTTQdcKQJDAfmfJZnopnjyWKBaPqNDlFRISWwkHGjv3x/qmlcL95LasjfFteC6845UN3j+lIFIrjW/AZ8X35vzi2WIBge2QTB8Ojiamk27fT2/ewsI2geaXIZS8JK2nSiWWgwqRRNNGkIr5uziOPQ7E9TT49IG/gNLKaI1MhOhzdU6BBhhQxIjdiH34CqnJ1Ez/dlfzrI2IfxW1x3zodqXeconIJgcGQTBMOjix0iDTCnykGBOc5IgYwaMTnbqKDVL2KoL1UKJuNTbVuqQ5cuXWptc7Dx2QTnaifa87qFj1vFbf4++KzDhw+3Np1clgFYORiGDjX24TqQ8nFN+JvmdSpaPPQlliAYHtkEwfDonGWkB1Yh+KDATCvNMmOEnn766damY4eqER1ZFlNE9cIcjqQenMM09cfcWsnBzz//vLVJP86fP796nZTGzgur1GDiu3EtqIjxXv6e2N/W0c5Zq9Q74nvxffkNWByhU4pWRw+CgZBNEAyPeaviUclE22+g2aSJptJAWsLaQbzONeE4XB/SAaoyVIqsrtE09VSBihLHtfKJVJYIiy8yJyLjavib4HVSI3NgVTIVLXnf6iBZEQB+Y66hKYBUtGIJguGRTRAMj44OkR7QvOy3WkNbYfE1dEbxOh1YpC5moi0jyugAqc3yoAk7FMPUJbZJV9jfQq/pPCI1MiWHz7J4JK4pqUglFs3Wy+osWSg4x6cyaIphLEEwPLIJguHRxQ4xHJVm3ZK5D4o6RFgYMk03nS0s7chkfJpcOmpIH6gI8TpVpmVJRtIVjss21SijT1bGkM8jHaJjju/M53J8S3jnu5nzqxKLVkn8tzpQFjLN79o966azCYL/OLIJguHR0SE7BZ6OBTpkaLr3MzWyJHUqGVTASAfMdJOSWNivJXyTkixr5NB8U5my41OtdKEdkMH+VmeK91qWFvvzfRiPRFiMEN+lcsiI1TI6dOhQa/MdOTc6QZNoHwRANkEwPGY7AIHmhSGop0+fbm0rd7efQdNqzhyL/6F6ZjFXXE+OQ9WEKsVSsSCF4v0c15xNVm7RnE2cN2mPHY1qpRdJgag4mRpDmApkmY2kMcwOY9vCxfm9O+XqprMMgv84sgmC4dHVHeJf0oyfsUwdmqmDEl9EM853vHLlymr/LuQWptvokDnISEOoJi0P5eAzGP9DU8577HR4+2Z2QIipgabS2AEkFQpkSpGFSXMOnCcLIlD5oWrEUHhSXq5nLEEwPLIJguGhJ9rTlJ85c2b1+kGHKRxmokkraFotpNdCj81JNU29KefzSD8Y9sz7qegxY41zJTgPUlurU0T6aAdecP5WhpKolJS0auAWQ0W6aRSfKlYsQTA8sgmC4dFllvFsKJpQmjhLKqeJPiigKaZ55LvTpFuVaN67s7PT2hYHRBO9zCyjusR1J+WgwkPqQsXDakVRvSHF4jsQHMfKMJIy8fdEp1XleFnea45G0ryTJ0+uzpnfjzSX69lV7V4dJQgGQjZBMDxmM0E0vzSVNOUVx8h+hmXJWSgu6YaFT3NNzCnETKylOkRzb7WG2OY8OD/LgiMVIVWw6s2kENaHDikLVbffijlfSb1ISa0UJOdDZakS1xRLEAyPbIJgeMx27Cev24nlNCkHMQGfppum3moumTpEE221f2j2aepZJXuaemWDZ3XxfiaVE6QubFsFbXtnfj/Oh+2zZ8+2Nqtkk+pRbeR1rp395ixjjn3sABG+r43P940lCIZHNkEwPLqADpp1O/PLwmkPCgUiKhSOSgPfl9dJe9iH68asNIvlmaaeNhgl4FikTKYgcX5d+UEoRaRGpkpx3nTYsTABnaZW2tFqJVlGH0H6eO7cudYmXWTdIYJz4LvEEgTDI5sgGB4zzR3/2qa6QNNEM7LMijoIsDlT9bL+jOshHbLzvqwC9I0S7UnL6IQipWH8Fr8TqRSpmIXIW2agHcrCOVhFa4YrW8i4xRERNmfStgsXLrQ2153rwCw5Pcp2dQZBMBCyCYLhMdMM0qQwDLZSKu+gwA7aoLrAPnTCWBlCU8xofo0CLOkA6RFNtpXL5LNJVUlX7B14L+kT34HOP45JymFH/1rItB0yYutlRQ2s7hXf0fp0atVqjyAYCNkEwfDIJgiGR+cxpjexSz8TbkdOeVBg9TT57pZqyT7kxOTr9veE1f0kn54mr/pg34DPIK+3g+z4tw/5shUE4/zorWWba2TBlrZG/B5W0MvqrbL/1vyXbq2mIBgc2QTB8OjyCZiSZ9n+5nE9iPkEBgtco9k3KdPkPFKDG3ndudYmUxtNYp6BBalZESxe57xJJ3hKESkHqYvRHisbXzmBp0KTSMNIVUkX6dnuCqNNQTA4sgmC4dHZRouRtzjvgx5AZ5SBIHVhcJgddGce9atXr7Y215lBedPUlxhnIBtTGC04jv35/Sy9klSB/Tm+lTY3ZYawFF3zsFtkgqlb1scKwXH8lGYPAiCbIBges5kmC/Iyk0XsB6XITLQ5najEWHl0Cw7jdTtRxtIFl2qNOblYyIqOLasq0cXLSzlzvrPVOiUFJDWycvSVmqMEx7FTcax4mDnaLCXUDi2MJQiGRzZBMDw6W0wHEJ0kFQq032Bn91pFBppHO9PY4uztxBsDKRDpzHIepCIsZEVFiWqSOaRIjXgvqzIwVdGoi6FCf+30n8ohfpV7LW/A6FlKswcBkE0QDI+ZJoIHv7HAkzln9hsqczPzaCXYaWa5JjTRx44da22rucnCZnRSLU+IIQUiRbHQZastaifmWGySVckwJ1Tl/GHCYooIU4oqTjdTkMx5mfTKIACyCYLh0alDFg+zVS34N2Fm1toVJyCdURZ6TCpBGknqyDZNcXdKyoI+kPaQ3pCikXKxHudLL73U2gyL5/w4jjkLrRS6xY1V2oQpjBYmbdSrUtyrQqtiCYLhkU0QDI/Z1AXLriLMMbLX8UIVqlM5AcVOLiEdoqpj8Th8X9IZKhC2JstapDy4j3SK/VgK3QoEmLPz1KlTq334bnQEUq2qFGGrfAPLCGN/Xq/EgVUor32DWIJgeGQTBMNjtnBdmlbSJPvLfi8oUCVjqRLGW3HOkG7QLJvjiPcy68vqgTIMmeMfP368mxP/z04L4vfguHRmXblyZfVe0h7GHTGGifFF7E8FyWiGxfaYA6sSR1RJwK+cSW31i2IJguGRTRAMj9myyRjfYn+13yoKVHGwWExKRQUypcEcKUalaEJJPUgTSCnp1KISw3GY6TVNPf2w2KOLFy+ujkuaxPm99dZbrX306NHWtlKbVIcYL2XqUKVtVLXi5DLnHa+zP2sKcT3tlJ5YgmB4ZBMEw2M2c2SHt90qVCgQYdSl0t/UC+tvdXqouJCesKYQKRAdZxyHdGipDtFkW3hzRUUx9eqDDz5obVIjOulIJ/iejEdiSUZzChp13ppcb3TWqqLbYYukizm4LwiAbIJgeMzmWGCFYzpSdkONTIExlami2JhqVIGZZZp3xs5YEjwzy+iYYlyPOXzoXFuCJvvIkSOtfe3atdamQ47PI4VgHypLdg4w1S6qQ+xD9bBSX2jr78Z+E5VCD1y3ShXuWIJgeGQTBMNjtr+27Rwqi92oOJ4Ic1ptdcBVzGYlLJfqi1VKJuwADTtnt0vsxposE+3NSUTzTapKxcrG4TyoFJ09e7a1qQIdPny4te0QFwsrtwM7bB1NiTMFjLDfnDnFuIZ831iCYHhkEwTDo/vTmU4S/vXPWBL2oZk107Q17mg3CdwVKmXzrChFNPvLjLC/QdNtdIigQ22aenpEykGFzupDsZq0fRuOT5WJTj7WLOL4bJNOcByjQ5VaQESlT6VOkaUBdNlwq6MHwUDIJgiGx2ymhrFDZgZJCSqxPZX4kb0u87ibOCVLKKeqYSqTZaXRXE9T7/AiJWU/cwCZQ9Ey6FiDiM/ls0jDSIt5nZTXaJ9REVPxiMr3IDgHO++Mv+lYgmB4ZBMEw2M2BwUVCCoKlQRrouII202MUCUZfzdlAreewUVTbOHPbJOSTFOv0pC6WCI8HWdU7khp7GwvPovOUbZJi0nP+PugU89Us1ulBlYUQPutmGMuliAYHtkEwfCY+Rcz/5K2ukNWx6VCeyomca+x9VkVpYtrZU6qylle09SHaJNa0JTzm3F+diq9ZWxx3nTaMcvMSnCSklExtHBuK+iwVTWyytIWp0SKSCSzLAiAbIJgeHSh1FQg7LRzM9FbyzPeKgq0tU5RJROt4pzZ6hSzcZYhxlZHiN/GzpljH6vJQ1AFYvwPQ6mpPpGG0XHWZWlJWDWxNVy+ogAa+I6k8lTGYgmC4ZFNEAyP2f7aplmz08INe31IRwUVE2p0pVIQoHKmVsVcLynD0nn2N/gNaNYr545ZBiDHJB1iKUlmk1FpsQrPFjpPVNZlKwWqhMXrN77pbILgP45sgmB4dHTIQnRvpGas9dkNKrE9u+mzNY6oYt4rDkRT0pbrZrE9dGDxflIUfj/G+VgME9+ZKgrpEK9TrTJFzJxrFQq0VVmqxJlZuD/fK5YgGB7ZBMHwmM2pwqRtxrMYtoa7VrAXR8TuxTx386zldcbeUCmiekP6RXNPOsR25TALgs9lXSPSDPaxcHyichhHBRVaVSnVmFDqIACyCYLh0alDFi9kobi7wVaVpnLvbsavOMgqjjbLODPn2rK/1XWyORkFskridq+FhlMpYpaZOeBMKbN6RJW6VBX1zcDnMi6LtDOWIBge2QTB8JiNHtAss21HphJ7fcbZbvpUsDX+hzCqY06eJR0y56VVaaZKQ3Nvh4LYPDg+x7l+/XprU4li26gOUanftPWoXRu/As45liAYHtkEwfCYLROKichs7yY8lrB6PrupL2TXt6pPRCVjrqIIVcz+8v9IS0hJeVo9lRzLojIVyJLTOQerX1RRCe1bVpS1rQ61rWsdZ1kQANkEwfDoqlLT9NHM2hlQW51Qe3F9a0J95cT1SgEB62MxMpXT3aepd+IwZuvMmTOtTSpCZ5aFNxtdIyxZ3ipumzpUqUpt2Yy7yfQjKmU9iViCYHhkEwTDQ0+0t+TxStJ9yQSJIrSV9tyqc80qGWGEXTe1rVqGkf0uX77c2qRGpoQYLGPQvrdlElbqKxGVMp2VpHj7fVQcavY77mjr6uyDYCBkEwTDo7OTFQcQs5Qqjo69CHUmKqaS2BoXVAmlrig/1XO3bFyqRlbnx75ZFycjipXBQp3NWWbKklG4rQqPxUHZvfy92rePJQiGRzZBMDxm++uZdWtYmbhybpdRpt1gKx2qHGdqB1+Yw4t96CyyejmVmkVL5YZrx7YlxVvdIaO29j52iAbbnI8dKVup1m3rUknGNypIVGh95xxc7REEAyGbIBges53+zUTtEydOtDZjWHhvpTxeNX5m7fpWOmQUoGJyrdSkncHFZ1XuNQVlOZZlmfF+ywDkt6ncW6kdZFTHDm5hf3NgVeis0SHOgffyufbuPGctliAYHtkEwfCYacpoRowa0TRRLaiYoK1VmnndnCT217+ZWVOBKkneFi9TmX/17DarI0TqwnfrjiItxBFR9WPbqK1RzEoFc6JyiIbRnorjrOIcJToFbLVHEAyEbIJgeMw8CII1bOwsLJ5kzoRvUz8qSc9bw62NDm11flViWCrmtGLSOU9SG67tNPXra44tzolUlWqJOc5IYTknUiOjwhZ6TSpFWszsRIOpUpwnn2U0zL49S0dyTF6PJQiGRzZBMDzmixcvtn9QKaLqQOzs7LQ2zZ0dPbo1lqaSeG3HkNo4piiYAkGKYmqNObzsWTwBnmvLuKxp6k0215TryPpCHIvfz9Q0UjFSINIDvj/78KR7xg5xTP4mGP5tlIkZcxzT6i9ZnJLRUNI5Osg62jkFweDIJgiGx/8A/fLfyQbMcRsAAAAASUVORK5CYII=\" y=\"-6.613793\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_6\">\n",
-       "   <g clip-path=\"url(#p9e49c69866)\">\n",
-       "    <image height=\"193\" id=\"image5f66dae4a0\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"7.2\" xlink:href=\"data:image/png;base64,\n",
-       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\" y=\"-243.821066\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_7\">\n",
-       "   <g clip-path=\"url(#pb62119b4d5)\">\n",
-       "    <image height=\"193\" id=\"image546263cae6\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"238.096552\" xlink:href=\"data:image/png;base64,\n",
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\" y=\"-243.821066\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_8\">\n",
-       "   <g clip-path=\"url(#p912fd50499)\">\n",
-       "    <image height=\"193\" id=\"image474004b5b9\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"468.993103\" xlink:href=\"data:image/png;base64,\n",
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y=\"-243.821066\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_9\">\n",
-       "   <g clip-path=\"url(#p4a5f2d5c77)\">\n",
-       "    <image height=\"193\" id=\"image5fc508d902\" transform=\"scale(1 -1)translate(0 -193)\" width=\"193\" x=\"699.889655\" xlink:href=\"data:image/png;base64,\n",
-       "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\" y=\"-243.821066\"/>\n",
-       "   </g>\n",
-       "  </g>\n",
-       "  <g id=\"axes_10\">\n",
-       "   <g clip-path=\"url(#pa069cefbe4)\">\n",
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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -5102,7 +643,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,

From c9ba20a7fd3cb8204ce74bc06e05b5419e14c6ab Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 19 Aug 2020 11:24:38 +0800
Subject: [PATCH 19/37] [DLMED] refactor image readers and load image transform

---
 monai/data/image_reader.py   | 226 +++++++++++++++++++----------------
 monai/transforms/io/array.py |  45 ++-----
 2 files changed, 136 insertions(+), 135 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 39542b2c21..1f44149b85 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,12 +10,12 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional, Sequence
+from typing import Any, Dict, Tuple, Optional, Sequence, Union
 
 import numpy as np
 
 from monai.data.utils import correct_nifti_header_if_necessary
-from monai.utils import optional_import
+from monai.utils import optional_import, ensure_tuple
 
 itk, _ = optional_import("itk", allow_namespace_pkg=True)
 nib, _ = optional_import("nibabel")
@@ -23,22 +23,20 @@
 
 class ImageReader(ABC):
     """Abstract class to define interface APIs to load image files.
-    users need to call `read_image` to load image and then use other APIs
-    to get image data or properties from meta data.
+    users need to call `read` to load image and then use `get_data`
+    to get the image data and properties from meta data.
 
     Args:
         img: image to initialize the reader, this is for the usage that the image data
             is already in memory and no need to read from file again, default is None.
-        as_closest_canonical: if True, load the image as closest to canonical axis format.
 
     """
 
-    def __init__(self, img: Optional[Any] = None, as_closest_canonical: bool = False):
+    def __init__(self, img: Optional[Any] = None) -> None:
         self.img = img
-        self.as_closest_canonical = as_closest_canonical
-        self._suffixes: Optional[Sequence] = None
+        self._suffixes: Optional[Sequence[str]] = None
 
-    def get_suffixes(self):
+    def get_suffixes(self) -> Sequence[str]:
         """
         Get the supported image file suffixes of current reader.
         Default is None, support all kinds of image format.
@@ -46,7 +44,7 @@ def get_suffixes(self):
         """
         return self._suffixes
 
-    def verify_suffix(self, suffix: str):
+    def verify_suffix(self, suffix: str) -> bool:
         """
         Verify whether the specified file matches supported suffixes.
         If supported suffixes is None, skip the verification.
@@ -54,7 +52,7 @@ def verify_suffix(self, suffix: str):
         """
         return False if self._suffixes is not None and suffix not in self._suffixes else True
 
-    def uncache(self):
+    def uncache(self) -> None:
         """
         Release image object and other cache data.
 
@@ -62,51 +60,19 @@ def uncache(self):
         self.img = None
 
     @abstractmethod
-    def convert(self):
-        """
-        Convert the image if necessary.
-
-        """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
-
-    @abstractmethod
-    def read_image(self, filename: str):
-        """
-        Read image data from specified file.
-        Note that different readers return different image data type.
-
-        """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
-
-    @abstractmethod
-    def get_meta_dict(self) -> Dict:
-        """
-        Get the all the meta data of the image and convert to dict type.
-
-        """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
-
-    @abstractmethod
-    def get_affine(self) -> np.ndarray:
+    def read(self, filename: str) -> Union[Sequence[Any], Any]:
         """
-        Get or construct the affine matrix of the image, it can be used to correct
-        spacing, orientation or execute spatial transforms.
+        Read image data from specified file or files and save to `self.img`.
+        Note that it returns the raw data, so different readers return different image data type.
 
         """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
     @abstractmethod
-    def get_spatial_shape(self) -> List:
-        """
-        Get the spatial shape of image data, it doesn't contain the channel dim.
-
+    def get_data(self) -> Tuple[np.ndarray, Dict]:
         """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
-
-    @abstractmethod
-    def get_array_data(self) -> np.ndarray:
-        """
-        Get the raw array data of the image, converted to Numpy array.
+        Extract data array and meta data from loaded image and return them.
+        This function must return 2 objects, first is numpy array of image data, second is dict of meta data.
 
         """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
@@ -118,42 +84,77 @@ class ITKReader(ImageReader):
     All the supported image formats can be found:
     https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO
 
+    Args:
+        img: image to initialize the reader, this is for the usage that the image data
+            is already in memory and no need to read from file again, default is None.
+        keep_axes: default to `True`. if `False`, the numpy array will have C-order indexing.
+            this is the reverse of how indices are specified in ITK, i.e. k,j,i versus i,j,k.
+            however C-order indexing is expected by most algorithms in numpy / scipy.
+
     """
+    def __init__(self, img: Optional[itk.Image] = None, keep_axes: bool = True):
+        super().__init__(img=img)
+        self.keep_axes = keep_axes
 
-    def convert(self, as_closest_canonical: Optional[bool] = None):
+    def read(self, filename: str):
         """
-        Convert the image as closest to canonical axis format.
+        Read image data from specified file or files.
+        Note that the returned object is ITK image object or list of ITK image objects.
+        `self.img` is always a list, even only has 1 image.
 
         """
-        # FIXME: need to add support later
-        pass
+        filenames: Sequence[str] = ensure_tuple(filename)
+        self.img: Sequence[itk.Image] = list()
+        for name in filenames:
+            self.img.append(itk.imread(name))
+        return self.img if len(filenames) > 1 else self.img[0]
 
-    def read_image(self, filename: str):
+    def get_data(self):
         """
-        Read image data from specified file.
-        Note that the returned object is ITK image object.
+        Extract data array and meta data from loaded image and return them.
+        This function returns 2 objects, first is numpy array of image data, second is dict of meta data.
+        It constructs `affine`, `original_affine`, and `spatial_shape` and stores in meta dict.
+        If the loaded data is a list of images, stack them at first dim and use the meta data of first image.
 
         """
-        self.img = itk.imread(filename)
+        img_array: Sequence[np.ndarray] = list()
+        compatible_meta: Dict = None
+        for img in self.img:
+            header = self._get_meta_dict(img)
+            header["original_affine"] = self._get_affine(img)
+            header["affine"] = header["original_affine"].copy()
+            header["spatial_shape"] = self._get_spatial_shape(img)
+            img_array.append(self._get_array_data(img))
+
+            if compatible_meta is None:
+                compatible_meta = header
+            else:
+                if not np.allclose(header["affine"], compatible_meta["affine"]):
+                    raise RuntimeError("affine matrix of all images should be same.")
+                if not np.allclose(header["spatial_shape"], compatible_meta["spatial_shape"]):
+                    raise RuntimeError("spatial_shape of all images should be same.")
+
+        img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
+        return img_array, compatible_meta
 
-    def get_meta_dict(self) -> Dict:
+    def _get_meta_dict(self, img: itk.Image) -> Dict:
         """
-        Get the all the meta data of the image and convert to dict type.
+        Get all the meta data of the image and convert to dict type.
 
         """
-        img_meta_dict = self.img.GetMetaDataDictionary()
+        img_meta_dict = img.GetMetaDataDictionary()
         meta_dict = dict()
         for key in img_meta_dict.GetKeys():
             # ignore deprecated, legacy members that cause issues
             if key.startswith("ITK_original_"):
                 continue
             meta_dict[key] = img_meta_dict[key]
-        meta_dict["origin"] = np.asarray(self.img.GetOrigin())
-        meta_dict["spacing"] = np.asarray(self.img.GetSpacing())
-        meta_dict["direction"] = itk.array_from_matrix(self.img.GetDirection())
+        meta_dict["origin"] = np.asarray(img.GetOrigin())
+        meta_dict["spacing"] = np.asarray(img.GetSpacing())
+        meta_dict["direction"] = itk.array_from_matrix(img.GetDirection())
         return meta_dict
 
-    def get_affine(self) -> np.ndarray:
+    def _get_affine(self, img: itk.Image) -> np.ndarray:
         """
         Get or construct the affine matrix of the image, it can be used to correct
         spacing, orientation or execute spatial transforms.
@@ -161,9 +162,9 @@ def get_affine(self) -> np.ndarray:
         Refer to: https://github.com/RSIP-Vision/medio
 
         """
-        direction = itk.array_from_matrix(self.img.GetDirection())
-        spacing = np.asarray(self.img.GetSpacing())
-        origin = np.asarray(self.img.GetOrigin())
+        direction = itk.array_from_matrix(img.GetDirection())
+        spacing = np.asarray(img.GetSpacing())
+        origin = np.asarray(img.GetOrigin())
 
         direction = np.asarray(direction)
         affine = np.eye(direction.shape[0] + 1)
@@ -171,19 +172,19 @@ def get_affine(self) -> np.ndarray:
         affine[(slice(-1), -1)] = origin
         return affine
 
-    def get_spatial_shape(self) -> List:
+    def _get_spatial_shape(self, img: itk.Image) -> Sequence:
         """
         Get the spatial shape of image data, it doesn't contain the channel dim.
 
         """
-        return list(itk.size(self.img))
+        return list(itk.size(img))
 
-    def get_array_data(self) -> np.ndarray:
+    def _get_array_data(self, img: itk.Image) -> np.ndarray:
         """
         Get the raw array data of the image, converted to Numpy array.
 
         """
-        return itk.array_view_from_image(self.img, keep_axes=True)
+        return itk.array_view_from_image(img, keep_axes=self.keep_axes)
 
 
 class NibabelReader(ImageReader):
@@ -198,63 +199,87 @@ class NibabelReader(ImageReader):
     """
 
     def __init__(self, img: Optional[Any] = None, as_closest_canonical: bool = False):
-        super().__init__(img, as_closest_canonical)
-        self._suffixes: [Sequence] = ["nii", "nii.gz"]
+        super().__init__(img)
+        self._suffixes: [Sequence[str]] = ["nii", "nii.gz"]
+        self.as_closest_canonical = as_closest_canonical
 
-    def convert(self, as_closest_canonical: Optional[bool] = None):
+    def read(self, filename: str):
         """
-        Convert the image as closest to canonical axis format.
+        Read image data from specified file or files.
+        Note that the returned object is Nibabel image object or list of Nibabel image objects.
+        `self.img` is always a list, even only has 1 image.
 
         """
-        if as_closest_canonical is None:
-            as_closest_canonical = self.as_closest_canonical
-        if as_closest_canonical:
-            self.img = nib.as_closest_canonical(self.img)
+        filenames: Sequence[str] = ensure_tuple(filename)
+        self.img: Sequence[nib.nifti1.Nifti1Image] = list()
+        for name in filenames:
+            img = nib.load(name)
+            img = correct_nifti_header_if_necessary(img)
+            self.img.append(img)
+        return self.img if len(filenames) > 1 else self.img[0]
 
-    def read_image(self, filename: str):
+    def get_data(self):
         """
-        Read image data from specified file.
-        Note that the returned object is Nibabel image object.
+        Extract data array and meta data from loaded image and return them.
+        This function returns 2 objects, first is numpy array of image data, second is dict of meta data.
+        It constructs `affine`, `original_affine`, and `spatial_shape` and stores in meta dict.
+        If the loaded data is a list of images, stack them at first dim and use the meta data of first image.
 
         """
-        img = nib.load(filename)
-        img = correct_nifti_header_if_necessary(img)
-        if self.as_closest_canonical:
-            img = nib.as_closest_canonical(img)
-        self.img = img
+        img_array: Sequence[np.ndarray] = list()
+        compatible_meta: Dict = None
+        for img in self.img:
+            header = self._get_meta_dict(img)
+            header["original_affine"] = self._get_affine(img)
+            header["affine"] = header["original_affine"].copy()
+            if self.as_closest_canonical:
+                img = nib.as_closest_canonical(img)
+                header["affine"] = self._get_affine(img)
+            header["as_closest_canonical"] = self.as_closest_canonical
+            header["spatial_shape"] = self._get_spatial_shape(img)
+            img_array.append(self._get_array_data(img))
+
+            if compatible_meta is None:
+                compatible_meta = header
+            else:
+                if not np.allclose(header["affine"], compatible_meta["affine"]):
+                    raise RuntimeError("affine matrix of all images should be same.")
+                if not np.allclose(header["spatial_shape"], compatible_meta["spatial_shape"]):
+                    raise RuntimeError("spatial_shape of all images should be same.")
+
+        img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
+        return img_array, compatible_meta
 
-    def get_meta_dict(self) -> Dict:
+    def _get_meta_dict(self, img: nib.nifti1.Nifti1Image) -> Dict:
         """
         Get the all the meta data of the image and convert to dict type.
 
         """
-        meta_data = dict(self.img.header)
-        meta_data["as_closest_canonical"] = self.as_closest_canonical
-        return meta_data
+        return dict(img.header)
 
-    def get_affine(self) -> np.ndarray:
+    def _get_affine(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
         """
         Get the affine matrix of the image, it can be used to correct
         spacing, orientation or execute spatial transforms.
 
         """
-        return self.img.affine
+        return img.affine
 
-    def get_spatial_shape(self) -> List:
+    def _get_spatial_shape(self, img: nib.nifti1.Nifti1Image) -> Sequence:
         """
         Get the spatial shape of image data, it doesn't contain the channel dim.
 
         """
-        ndim = self.img.header["dim"][0]
+        ndim = img.header["dim"][0]
         spatial_rank = min(ndim, 3)
-        return self.img.header["dim"][1 : spatial_rank + 1]
+        return img.header["dim"][1 : spatial_rank + 1]
 
-    def get_array_data(self) -> np.ndarray:
+    def _get_array_data(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
         """
         Get the raw array data of the image, converted to Numpy array.
 
         """
-        return np.array(self.img.get_fdata())
+        return np.array(img.get_fdata())
 
     def uncache(self):
         """
@@ -262,5 +287,6 @@ def uncache(self):
 
         """
         if self.img is not None:
-            self.img.uncache()
-            super().uncache()
+            for img in self.img:
+                img.uncache()
+        super().uncache()
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index ebe191c934..483904ba58 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -40,7 +40,7 @@ class LoadImage(Transform):
     """
 
     def __init__(
-        self, reader: Optional[ImageReader] = None, image_only: bool = False, dtype: Optional[np.dtype] = np.float32,
+        self, reader: Optional[ImageReader] = None, image_only: bool = False, dtype: np.dtype = np.float32,
     ) -> None:
         """
         Args:
@@ -63,44 +63,19 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
         """
         Args:
             filename: path file or file-like object or a list of files.
-        """
-        filename = ensure_tuple(filename)
-        img_array = list()
-        compatible_meta: Dict = None
-        for name in filename:
-            supported_format = False
-            suffixes = name.split(".")
-            for i in range(len(suffixes) - 1):
-                if self.reader.verify_suffix(".".join(suffixes[-(i + 2) : -1])):
-                    supported_format = True
-                    break
-
-            if not supported_format:
-                raise RuntimeError("unsupported file format.")
-            self.reader.read_image(name)
-            img_array.append(self.reader.get_array_data().astype(dtype=self.dtype))
-            if self.image_only:
-                continue
-
-            header = self.reader.get_meta_dict()
-            header["filename_or_obj"] = name
-            header["original_affine"] = self.reader.get_affine()
-            self.reader.convert()
-            header["affine"] = self.reader.get_affine()
-            header["spatial_shape"] = self.reader.get_spatial_shape()
+                will save the filename to meta_data with key `filename_or_obj`.
+                if provided a list of files, use the filename of first file.
 
-            if compatible_meta is None:
-                compatible_meta = header
-            else:
-                assert np.allclose(
-                    header["affine"], compatible_meta["affine"]
-                ), "affine data of all images should be same."
-
-        img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
+        """
+        self.reader.read(filename)
+        img_array, meta_data = self.reader.get_data()
         self.reader.uncache()
+        img_array = img_array.astype(self.dtype)
+
         if self.image_only:
             return img_array
-        return img_array, compatible_meta
+        meta_data["filename_or_obj"] = ensure_tuple(filename)[0]
+        return img_array, meta_data
 
 
 class LoadNifti(Transform):

From 1270df13a28a9f97b5f32b236318ddb43580d5c3 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 19 Aug 2020 16:29:35 +0800
Subject: [PATCH 20/37] [DLMED] update LoadImage transform

---
 monai/data/image_reader.py        | 67 +++++++++++++++++++++--
 monai/transforms/io/array.py      | 50 +++++++++++++----
 monai/transforms/io/dictionary.py | 91 ++++++++++++++++++++-----------
 tests/test_load_image.py          | 17 +++++-
 tests/test_load_imaged.py         | 19 ++++++-
 5 files changed, 192 insertions(+), 52 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 1f44149b85..b2d01000bb 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -44,13 +44,33 @@ def get_suffixes(self) -> Sequence[str]:
         """
         return self._suffixes
 
-    def verify_suffix(self, suffix: str) -> bool:
+    def verify_suffix(self, filename: str) -> bool:
         """
-        Verify whether the specified file matches supported suffixes.
-        If supported suffixes is None, skip the verification.
+        Verify whether the specified file or files match supported suffixes.
+        If supported suffixes is None, skip the verification and return True.
 
+        Args:
+            filename: file name or a list of file names to read.
+                if a list of files, verify all the subffixes.
         """
-        return False if self._suffixes is not None and suffix not in self._suffixes else True
+
+        if self._suffixes is None:
+            return True
+
+        supported_format: bool = True
+        filenames: Sequence[str] = ensure_tuple(filename)
+        for name in filenames:
+            suffixes: Sequence[str] = name.split(".")
+            passed: bool = False
+            for i in range(len(suffixes) - 1):
+                if ".".join(suffixes[-(i + 2) : -1]) in self._suffixes:
+                    passed = True
+                    break
+            if not passed:
+                supported_format = False
+                break
+
+        return supported_format
 
     def uncache(self) -> None:
         """
@@ -65,6 +85,9 @@ def read(self, filename: str) -> Union[Sequence[Any], Any]:
         Read image data from specified file or files and save to `self.img`.
         Note that it returns the raw data, so different readers return different image data type.
 
+        Args:
+            filename: file name or a list of file names to read.
+
         """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
@@ -102,6 +125,9 @@ def read(self, filename: str):
         Note that the returned object is ITK image object or list of ITK image objects.
         `self.img` is always a list, even only has 1 image.
 
+        Args:
+            filename: file name or a list of file names to read.
+
         """
         filenames: Sequence[str] = ensure_tuple(filename)
         self.img: Sequence[itk.Image] = list()
@@ -114,7 +140,8 @@ def get_data(self):
         Extract data array and meta data from loaded image and return them.
         This function returns 2 objects, first is numpy array of image data, second is dict of meta data.
         It constructs `affine`, `original_affine`, and `spatial_shape` and stores in meta dict.
-        If the loaded data is a list of images, stack them at first dim and use the meta data of first image.
+        If loading a list of files, stack them together and add a new dimension as first dimension,
+        and use the meta data of the first image to represent the stacked result.
 
         """
         img_array: Sequence[np.ndarray] = list()
@@ -141,6 +168,9 @@ def _get_meta_dict(self, img: itk.Image) -> Dict:
         """
         Get all the meta data of the image and convert to dict type.
 
+        Args:
+            img: a ITK image object loaded from a image file.
+
         """
         img_meta_dict = img.GetMetaDataDictionary()
         meta_dict = dict()
@@ -161,6 +191,9 @@ def _get_affine(self, img: itk.Image) -> np.ndarray:
         Construct Affine matrix based on direction, spacing, origin information.
         Refer to: https://github.com/RSIP-Vision/medio
 
+        Args:
+            img: a ITK image object loaded from a image file.
+
         """
         direction = itk.array_from_matrix(img.GetDirection())
         spacing = np.asarray(img.GetSpacing())
@@ -176,6 +209,9 @@ def _get_spatial_shape(self, img: itk.Image) -> Sequence:
         """
         Get the spatial shape of image data, it doesn't contain the channel dim.
 
+        Args:
+            img: a ITK image object loaded from a image file.
+
         """
         return list(itk.size(img))
 
@@ -183,6 +219,9 @@ def _get_array_data(self, img: itk.Image) -> np.ndarray:
         """
         Get the raw array data of the image, converted to Numpy array.
 
+        Args:
+            img: a ITK image object loaded from a image file.
+
         """
         return itk.array_view_from_image(img, keep_axes=self.keep_axes)
 
@@ -209,6 +248,9 @@ def read(self, filename: str):
         Note that the returned object is Nibabel image object or list of Nibabel image objects.
         `self.img` is always a list, even only has 1 image.
 
+        Args:
+            filename: file name or a list of file names to read.
+
         """
         filenames: Sequence[str] = ensure_tuple(filename)
         self.img: Sequence[nib.nifti1.Nifti1Image] = list()
@@ -223,7 +265,8 @@ def get_data(self):
         Extract data array and meta data from loaded image and return them.
         This function returns 2 objects, first is numpy array of image data, second is dict of meta data.
         It constructs `affine`, `original_affine`, and `spatial_shape` and stores in meta dict.
-        If the loaded data is a list of images, stack them at first dim and use the meta data of first image.
+        If loading a list of files, stack them together and add a new dimension as first dimension,
+        and use the meta data of the first image to represent the stacked result.
 
         """
         img_array: Sequence[np.ndarray] = list()
@@ -254,6 +297,9 @@ def _get_meta_dict(self, img: nib.nifti1.Nifti1Image) -> Dict:
         """
         Get the all the meta data of the image and convert to dict type.
 
+        Args:
+            img: a Nibabel image object loaded from a image file.
+
         """
         return dict(img.header)
 
@@ -262,6 +308,9 @@ def _get_affine(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
         Get the affine matrix of the image, it can be used to correct
         spacing, orientation or execute spatial transforms.
 
+        Args:
+            img: a Nibabel image object loaded from a image file.
+
         """
         return img.affine
 
@@ -269,6 +318,9 @@ def _get_spatial_shape(self, img: nib.nifti1.Nifti1Image) -> Sequence:
         """
         Get the spatial shape of image data, it doesn't contain the channel dim.
 
+        Args:
+            img: a Nibabel image object loaded from a image file.
+
         """
         ndim = img.header["dim"][0]
         spatial_rank = min(ndim, 3)
@@ -278,6 +330,9 @@ def _get_array_data(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
         """
         Get the raw array data of the image, converted to Numpy array.
 
+        Args:
+            img: a Nibabel image object loaded from a image file.
+
         """
         return np.array(img.get_fdata())
 
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 483904ba58..9bfaa647cb 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -34,8 +34,10 @@ class LoadImage(Transform):
     Load image file or files from provided path based on reader, default reader is ITK.
     All the supported image formats of ITK:
     https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO
-    If loading a list of files, stack them together and add a new dimension as first dimension,
-    and use the meta data of the first image to represent the stacked result.
+    Automatically choose readers based on the supported subffixes and in below order:
+    - User specified reader at runtime when call this loader.
+    - Registered readers from the first to the last in list.
+    - Default ITK reader.
 
     """
 
@@ -44,7 +46,8 @@ def __init__(
     ) -> None:
         """
         Args:
-            reader: use reader to load image file and meta data, default is ITK.
+            reader: register reader to load image file and meta data, if None, still can register readers
+                at runtime or use the default ITK reader.
             image_only: if True return only the image volume, otherwise return image data array and header dict.
             dtype: if not None convert the loaded image to this data type.
 
@@ -53,23 +56,50 @@ def __init__(
             or a tuple of two elements containing the data array, and the meta data in a dict format otherwise.
 
         """
-        if reader is None:
-            reader = ITKReader()
-        self.reader = reader
+        self.defaut_reader: ITKReader = ITKReader()
+        self.readers: Sequence[ImageReader] = list()
+        if reader is not None:
+            self.readers.append(reader)
         self.image_only = image_only
         self.dtype = dtype
 
-    def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
+    def register(self, reader: Optional[ImageReader]) -> List[ImageReader]:
+        """
+        Register image reader to load image file and meta data.
+        Return all the registered image readers.
+
+        Args:
+            reader: registered reader to load image file and meta data based on subfix,
+                if all registered readers can't match subfix at runtime, use the default ITK reader.
+
+        """
+        self.readers.append(reader)
+        return self.readers
+
+    def __call__(
+        self,
+        filename: Union[Sequence[Union[Path, str]], Path, str],
+        reader: Optional[ImageReader] = None,
+    ):
         """
         Args:
             filename: path file or file-like object or a list of files.
                 will save the filename to meta_data with key `filename_or_obj`.
                 if provided a list of files, use the filename of first file.
+            reader: runtime reader to load image file and meta data.
 
         """
-        self.reader.read(filename)
-        img_array, meta_data = self.reader.get_data()
-        self.reader.uncache()
+        if reader is None or not reader.verify_suffix(filename):
+            reader = self.defaut_reader
+            if len(self.readers) > 0:
+                for r in self.readers:
+                    if r.verify_suffix(filename):
+                        reader = r
+                        break
+
+        reader.read(filename)
+        img_array, meta_data = reader.get_data()
+        reader.uncache()
         img_array = img_array.astype(self.dtype)
 
         if self.image_only:
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index 7d2d8e40d1..b987744702 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -25,10 +25,10 @@
 from monai.transforms.io.array import LoadImage, LoadNifti, LoadNumpy, LoadPNG
 
 
-class LoadDatad(MapTransform):
+class LoadImaged(MapTransform):
     """
-    Base class for dictionary-based wrapper of IO loader transforms.
-    It must load image and metadata together. If loading a list of files in one key,
+    Dictionary-based wrapper of :py:class:`monai.transforms.LoadImage`,
+    must load image and metadata together. If loading a list of files in one key,
     stack them together and add a new dimension as the first dimension, and use the
     meta data of the first image to represent the stacked result. Note that the affine
     transform of all the stacked images should be same. The output metadata field will
@@ -36,36 +36,35 @@ class LoadDatad(MapTransform):
     """
 
     def __init__(
-        self, keys: KeysCollection, loader: Callable, meta_key_postfix: str = "meta_dict", overwriting: bool = False,
+        self,
+        keys: KeysCollection,
+        reader: Optional[ImageReader] = None,
+        dtype: Optional[np.dtype] = np.float32,
+        meta_key_postfix: str = "meta_dict",
+        overwriting: bool = False,
     ) -> None:
         """
         Args:
             keys: keys of the corresponding items to be transformed.
                 See also: :py:class:`monai.transforms.compose.MapTransform`
-            loader: callable function to load data from expected source.
-                typically, it's array level transform, for example: `LoadNifti`,
-                `LoadPNG` and `LoadNumpy`, etc.
-            meta_key_postfix: use `key_{postfix}` to store the metadata of the loaded data,
+            dtype: if not None convert the loaded image data to this data type.
+            meta_key_postfix: use `key_{postfix}` to store the metadata of the nifti image,
                 default is `meta_dict`. The meta data is a dictionary object.
-                For example, load Nifti file for `image`, store the metadata into `image_meta_dict`.
+                For example, load nifti file for `image`, store the metadata into `image_meta_dict`.
             overwriting: whether allow to overwrite existing meta data of same key.
                 default is False, which will raise exception if encountering existing key.
-
-        Raises:
-            TypeError: When ``loader`` is not ``callable``.
-            TypeError: When ``meta_key_postfix`` is not a ``str``.
-
         """
         super().__init__(keys)
-        if not callable(loader):
-            raise TypeError(f"loader must be callable but is {type(loader).__name__}.")
-        self.loader = loader
+        self.loader = LoadImage(reader, False, dtype)
         if not isinstance(meta_key_postfix, str):
             raise TypeError(f"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.")
         self.meta_key_postfix = meta_key_postfix
         self.overwriting = overwriting
 
-    def __call__(self, data):
+    def register(self, reader: Optional[ImageReader] = None):
+        self.loader.register(reader)
+
+    def __call__(self, data, reader: Optional[ImageReader] = None):
         """
         Raises:
             KeyError: When not ``self.overwriting`` and key already exists in ``data``.
@@ -73,7 +72,7 @@ def __call__(self, data):
         """
         d = dict(data)
         for key in self.keys:
-            data = self.loader(d[key])
+            data = self.loader(d[key], reader)
             assert isinstance(data, (tuple, list)), "loader must return a tuple or list."
             d[key] = data[0]
             assert isinstance(data[1], dict), "metadata must be a dict."
@@ -84,10 +83,10 @@ def __call__(self, data):
         return d
 
 
-class LoadImaged(LoadDatad):
+class LoadDatad(MapTransform):
     """
-    Dictionary-based wrapper of :py:class:`monai.transforms.LoadImage`,
-    must load image and metadata together. If loading a list of files in one key,
+    Base class for dictionary-based wrapper of IO loader transforms.
+    It must load image and metadata together. If loading a list of files in one key,
     stack them together and add a new dimension as the first dimension, and use the
     meta data of the first image to represent the stacked result. Note that the affine
     transform of all the stacked images should be same. The output metadata field will
@@ -95,26 +94,52 @@ class LoadImaged(LoadDatad):
     """
 
     def __init__(
-        self,
-        keys: KeysCollection,
-        reader: Optional[ImageReader] = None,
-        dtype: Optional[np.dtype] = np.float32,
-        meta_key_postfix: str = "meta_dict",
-        overwriting: bool = False,
+        self, keys: KeysCollection, loader: Callable, meta_key_postfix: str = "meta_dict", overwriting: bool = False,
     ) -> None:
         """
         Args:
             keys: keys of the corresponding items to be transformed.
                 See also: :py:class:`monai.transforms.compose.MapTransform`
-            dtype: if not None convert the loaded image data to this data type.
-            meta_key_postfix: use `key_{postfix}` to store the metadata of the nifti image,
+            loader: callable function to load data from expected source.
+                typically, it's array level transform, for example: `LoadNifti`,
+                `LoadPNG` and `LoadNumpy`, etc.
+            meta_key_postfix: use `key_{postfix}` to store the metadata of the loaded data,
                 default is `meta_dict`. The meta data is a dictionary object.
-                For example, load nifti file for `image`, store the metadata into `image_meta_dict`.
+                For example, load Nifti file for `image`, store the metadata into `image_meta_dict`.
             overwriting: whether allow to overwrite existing meta data of same key.
                 default is False, which will raise exception if encountering existing key.
+
+        Raises:
+            TypeError: When ``loader`` is not ``callable``.
+            TypeError: When ``meta_key_postfix`` is not a ``str``.
+
         """
-        loader = LoadImage(reader, False, dtype)
-        super().__init__(keys, loader, meta_key_postfix, overwriting)
+        super().__init__(keys)
+        if not callable(loader):
+            raise TypeError(f"loader must be callable but is {type(loader).__name__}.")
+        self.loader = loader
+        if not isinstance(meta_key_postfix, str):
+            raise TypeError(f"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.")
+        self.meta_key_postfix = meta_key_postfix
+        self.overwriting = overwriting
+
+    def __call__(self, data):
+        """
+        Raises:
+            KeyError: When not ``self.overwriting`` and key already exists in ``data``.
+
+        """
+        d = dict(data)
+        for key in self.keys:
+            data = self.loader(d[key])
+            assert isinstance(data, (tuple, list)), "loader must return a tuple or list."
+            d[key] = data[0]
+            assert isinstance(data[1], dict), "metadata must be a dict."
+            key_to_add = f"{key}_{self.meta_key_postfix}"
+            if key_to_add in d and not self.overwriting:
+                raise KeyError(f"Meta data with key {key_to_add} already exists and overwriting=False.")
+            d[key_to_add] = data[1]
+        return d
 
 
 class LoadNiftid(LoadDatad):
diff --git a/tests/test_load_image.py b/tests/test_load_image.py
index 1faa2a35cf..e2d84d78cf 100644
--- a/tests/test_load_image.py
+++ b/tests/test_load_image.py
@@ -19,7 +19,7 @@
 from parameterized import parameterized
 from PIL import Image
 
-from monai.data import NibabelReader
+from monai.data import NibabelReader, ITKReader
 from monai.transforms import LoadImage
 
 TEST_CASE_1 = [
@@ -112,6 +112,21 @@ def test_load_png(self):
             np.testing.assert_allclose(header["original_affine"], np.eye(3))
             np.testing.assert_allclose(result, test_image)
 
+    def test_register(self):
+        spatial_size = (32, 64, 128)
+        expected_shape = (128, 64, 32)
+        test_image = np.random.rand(*spatial_size)
+        with tempfile.TemporaryDirectory() as tempdir:
+            filename = os.path.join(tempdir, "test_image.nii.gz")
+            itk_np_view = itk.image_view_from_array(test_image)
+            itk.imwrite(itk_np_view, filename)
+
+            loader = LoadImage(image_only=False)
+            loader.register(ITKReader(keep_axes=False))
+            result, header = loader(filename)
+            self.assertTupleEqual(tuple(header["spatial_shape"]), expected_shape)
+            self.assertTupleEqual(result.shape, spatial_size)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_load_imaged.py b/tests/test_load_imaged.py
index 736409e269..46cfd7a774 100644
--- a/tests/test_load_imaged.py
+++ b/tests/test_load_imaged.py
@@ -12,11 +12,11 @@
 import os
 import tempfile
 import unittest
-
+import itk
 import nibabel as nib
 import numpy as np
 from parameterized import parameterized
-
+from monai.data import ITKReader
 from monai.transforms import LoadImaged
 
 KEYS = ["image", "label", "extra"]
@@ -38,6 +38,21 @@ def test_shape(self, input_param, expected_shape):
         for key in KEYS:
             self.assertTupleEqual(result[key].shape, expected_shape)
 
+    def test_register(self):
+        spatial_size = (32, 64, 128)
+        expected_shape = (128, 64, 32)
+        test_image = np.random.rand(*spatial_size)
+        with tempfile.TemporaryDirectory() as tempdir:
+            filename = os.path.join(tempdir, "test_image.nii.gz")
+            itk_np_view = itk.image_view_from_array(test_image)
+            itk.imwrite(itk_np_view, filename)
+
+            loader = LoadImaged(keys="img")
+            loader.register(ITKReader(keep_axes=False))
+            result = loader({"img": filename})
+            self.assertTupleEqual(tuple(result["img_meta_dict"]["spatial_shape"]), expected_shape)
+            self.assertTupleEqual(result["img"].shape, spatial_size)
+
 
 if __name__ == "__main__":
     unittest.main()

From 21ba37d0eab59526d927d4f1ee2425b9d7c82777 Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Wed, 19 Aug 2020 08:33:21 +0000
Subject: [PATCH 21/37] [MONAI] python code formatting

---
 monai/data/image_reader.py   | 5 +++--
 monai/transforms/io/array.py | 4 +---
 tests/test_load_image.py     | 2 +-
 tests/test_load_imaged.py    | 2 ++
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index b2d01000bb..0846c487fa 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,12 +10,12 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Tuple, Optional, Sequence, Union
+from typing import Any, Dict, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
 from monai.data.utils import correct_nifti_header_if_necessary
-from monai.utils import optional_import, ensure_tuple
+from monai.utils import ensure_tuple, optional_import
 
 itk, _ = optional_import("itk", allow_namespace_pkg=True)
 nib, _ = optional_import("nibabel")
@@ -115,6 +115,7 @@ class ITKReader(ImageReader):
             however C-order indexing is expected by most algorithms in numpy / scipy.
 
     """
+
     def __init__(self, img: Optional[itk.Image] = None, keep_axes: bool = True):
         super().__init__(img=img)
         self.keep_axes = keep_axes
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 9bfaa647cb..2fe6353224 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -77,9 +77,7 @@ def register(self, reader: Optional[ImageReader]) -> List[ImageReader]:
         return self.readers
 
     def __call__(
-        self,
-        filename: Union[Sequence[Union[Path, str]], Path, str],
-        reader: Optional[ImageReader] = None,
+        self, filename: Union[Sequence[Union[Path, str]], Path, str], reader: Optional[ImageReader] = None,
     ):
         """
         Args:
diff --git a/tests/test_load_image.py b/tests/test_load_image.py
index e2d84d78cf..17ec674911 100644
--- a/tests/test_load_image.py
+++ b/tests/test_load_image.py
@@ -19,7 +19,7 @@
 from parameterized import parameterized
 from PIL import Image
 
-from monai.data import NibabelReader, ITKReader
+from monai.data import ITKReader, NibabelReader
 from monai.transforms import LoadImage
 
 TEST_CASE_1 = [
diff --git a/tests/test_load_imaged.py b/tests/test_load_imaged.py
index 46cfd7a774..0d553d58e6 100644
--- a/tests/test_load_imaged.py
+++ b/tests/test_load_imaged.py
@@ -12,10 +12,12 @@
 import os
 import tempfile
 import unittest
+
 import itk
 import nibabel as nib
 import numpy as np
 from parameterized import parameterized
+
 from monai.data import ITKReader
 from monai.transforms import LoadImaged
 

From d7f3c18e9151ca637411388742b7ad5ed7c33230 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 19 Aug 2020 16:43:23 +0800
Subject: [PATCH 22/37] [DLMED] fix packaging error

---
 .github/workflows/pythonapp.yml   | 1 +
 monai/transforms/io/dictionary.py | 6 +++---
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml
index 6dfe983694..7d601ea750 100644
--- a/.github/workflows/pythonapp.yml
+++ b/.github/workflows/pythonapp.yml
@@ -158,6 +158,7 @@ jobs:
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
         python -m pip install torch>=1.4 torchvision
+        python -m pip install -r requirements-dev.txt
     - name: Test source archive and wheel file
       run: |
         git fetch --depth=1 origin +refs/tags/*:refs/tags/*
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index b987744702..e9c8b6647e 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -55,14 +55,14 @@ def __init__(
                 default is False, which will raise exception if encountering existing key.
         """
         super().__init__(keys)
-        self.loader = LoadImage(reader, False, dtype)
+        self._loader = LoadImage(reader, False, dtype)
         if not isinstance(meta_key_postfix, str):
             raise TypeError(f"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.")
         self.meta_key_postfix = meta_key_postfix
         self.overwriting = overwriting
 
     def register(self, reader: Optional[ImageReader] = None):
-        self.loader.register(reader)
+        self._loader.register(reader)
 
     def __call__(self, data, reader: Optional[ImageReader] = None):
         """
@@ -72,7 +72,7 @@ def __call__(self, data, reader: Optional[ImageReader] = None):
         """
         d = dict(data)
         for key in self.keys:
-            data = self.loader(d[key], reader)
+            data = self._loader(d[key], reader)
             assert isinstance(data, (tuple, list)), "loader must return a tuple or list."
             d[key] = data[0]
             assert isinstance(data[1], dict), "metadata must be a dict."

From 1f5eb08eee474434b041ee036000cb4ea3f04e25 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Thu, 20 Aug 2020 08:01:28 +0800
Subject: [PATCH 23/37] [DLMED] update according to comments

---
 monai/data/image_reader.py   | 46 ++++++++++--------------------------
 monai/transforms/io/array.py |  1 -
 tests/test_load_image.py     |  2 +-
 tests/test_load_imaged.py    |  2 +-
 4 files changed, 14 insertions(+), 37 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 0846c487fa..b1523c9d74 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -29,21 +29,14 @@ class ImageReader(ABC):
     Args:
         img: image to initialize the reader, this is for the usage that the image data
             is already in memory and no need to read from file again, default is None.
+        other_args: other particular arg for every sub-class reader.
 
     """
 
-    def __init__(self, img: Optional[Any] = None) -> None:
+    def __init__(self, img: Optional[Any] = None, **other_args) -> None:
         self.img = img
         self._suffixes: Optional[Sequence[str]] = None
 
-    def get_suffixes(self) -> Sequence[str]:
-        """
-        Get the supported image file suffixes of current reader.
-        Default is None, support all kinds of image format.
-
-        """
-        return self._suffixes
-
     def verify_suffix(self, filename: str) -> bool:
         """
         Verify whether the specified file or files match supported suffixes.
@@ -72,13 +65,6 @@ def verify_suffix(self, filename: str) -> bool:
 
         return supported_format
 
-    def uncache(self) -> None:
-        """
-        Release image object and other cache data.
-
-        """
-        self.img = None
-
     @abstractmethod
     def read(self, filename: str) -> Union[Sequence[Any], Any]:
         """
@@ -110,15 +96,16 @@ class ITKReader(ImageReader):
     Args:
         img: image to initialize the reader, this is for the usage that the image data
             is already in memory and no need to read from file again, default is None.
-        keep_axes: default to `True`. if `False`, the numpy array will have C-order indexing.
+        other_args: acceptable args: `c_order_axis_indexing`.
+            if `c_order_axis_indexing=True`, the numpy array will have C-order indexing.
             this is the reverse of how indices are specified in ITK, i.e. k,j,i versus i,j,k.
             however C-order indexing is expected by most algorithms in numpy / scipy.
 
     """
 
-    def __init__(self, img: Optional[itk.Image] = None, keep_axes: bool = True):
+    def __init__(self, img: Optional[itk.Image] = None, **other_args):
         super().__init__(img=img)
-        self.keep_axes = keep_axes
+        self.c_order_axis_indexing = other_args.get("c_order_axis_indexing", False)
 
     def read(self, filename: str):
         """
@@ -224,7 +211,7 @@ def _get_array_data(self, img: itk.Image) -> np.ndarray:
             img: a ITK image object loaded from a image file.
 
         """
-        return itk.array_view_from_image(img, keep_axes=self.keep_axes)
+        return itk.array_view_from_image(img, keep_axes=not self.c_order_axis_indexing)
 
 
 class NibabelReader(ImageReader):
@@ -234,14 +221,15 @@ class NibabelReader(ImageReader):
     Args:
         img: image to initialize the reader, this is for the usage that the image data
             is already in memory and no need to read from file again, default is None.
-        as_closest_canonical: if True, load the image as closest to canonical axis format.
+        other_args: acceptable args: `as_closest_canonical`.
+            if `as_closest_canonical=True`, load the image as closest to canonical axis format.
 
     """
 
-    def __init__(self, img: Optional[Any] = None, as_closest_canonical: bool = False):
+    def __init__(self, img: Optional[Any] = None, **other_args):
         super().__init__(img)
         self._suffixes: [Sequence[str]] = ["nii", "nii.gz"]
-        self.as_closest_canonical = as_closest_canonical
+        self.as_closest_canonical = other_args.get("as_closest_canonical", False)
 
     def read(self, filename: str):
         """
@@ -335,14 +323,4 @@ def _get_array_data(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
             img: a Nibabel image object loaded from a image file.
 
         """
-        return np.array(img.get_fdata())
-
-    def uncache(self):
-        """
-        Release image object and other cache data.
-
-        """
-        if self.img is not None:
-            for img in self.img:
-                img.uncache()
-        super().uncache()
+        return np.asarray(img.dataobj)
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 2fe6353224..925139c706 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -97,7 +97,6 @@ def __call__(
 
         reader.read(filename)
         img_array, meta_data = reader.get_data()
-        reader.uncache()
         img_array = img_array.astype(self.dtype)
 
         if self.image_only:
diff --git a/tests/test_load_image.py b/tests/test_load_image.py
index 17ec674911..948242d42d 100644
--- a/tests/test_load_image.py
+++ b/tests/test_load_image.py
@@ -122,7 +122,7 @@ def test_register(self):
             itk.imwrite(itk_np_view, filename)
 
             loader = LoadImage(image_only=False)
-            loader.register(ITKReader(keep_axes=False))
+            loader.register(ITKReader(c_order_axis_indexing=True))
             result, header = loader(filename)
             self.assertTupleEqual(tuple(header["spatial_shape"]), expected_shape)
             self.assertTupleEqual(result.shape, spatial_size)
diff --git a/tests/test_load_imaged.py b/tests/test_load_imaged.py
index 0d553d58e6..c44f176213 100644
--- a/tests/test_load_imaged.py
+++ b/tests/test_load_imaged.py
@@ -50,7 +50,7 @@ def test_register(self):
             itk.imwrite(itk_np_view, filename)
 
             loader = LoadImaged(keys="img")
-            loader.register(ITKReader(keep_axes=False))
+            loader.register(ITKReader(c_order_axis_indexing=True))
             result = loader({"img": filename})
             self.assertTupleEqual(tuple(result["img_meta_dict"]["spatial_shape"]), expected_shape)
             self.assertTupleEqual(result["img"].shape, spatial_size)

From c1a2145820005a255da05f7143c0eda6fdc0a32e Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Thu, 20 Aug 2020 09:48:45 +0800
Subject: [PATCH 24/37] [DLMED] remove constructor of ImageReader

---
 monai/data/image_reader.py | 71 +++++++++++++++++++-------------------
 monai/data/utils.py        | 29 +++++++++++++++-
 2 files changed, 63 insertions(+), 37 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index b1523c9d74..8f1bbf4dd3 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -16,6 +16,7 @@
 
 from monai.data.utils import correct_nifti_header_if_necessary
 from monai.utils import ensure_tuple, optional_import
+from .utils import is_supported_format
 
 itk, _ = optional_import("itk", allow_namespace_pkg=True)
 nib, _ = optional_import("nibabel")
@@ -26,44 +27,19 @@ class ImageReader(ABC):
     users need to call `read` to load image and then use `get_data`
     to get the image data and properties from meta data.
 
-    Args:
-        img: image to initialize the reader, this is for the usage that the image data
-            is already in memory and no need to read from file again, default is None.
-        other_args: other particular arg for every sub-class reader.
-
     """
-
-    def __init__(self, img: Optional[Any] = None, **other_args) -> None:
-        self.img = img
-        self._suffixes: Optional[Sequence[str]] = None
-
+    @abstractmethod
     def verify_suffix(self, filename: str) -> bool:
         """
-        Verify whether the specified file or files match supported suffixes.
-        If supported suffixes is None, skip the verification and return True.
+        Verify whether the specified file or files format is supported by current reader.
 
         Args:
             filename: file name or a list of file names to read.
                 if a list of files, verify all the subffixes.
-        """
 
-        if self._suffixes is None:
-            return True
+        """
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
-        supported_format: bool = True
-        filenames: Sequence[str] = ensure_tuple(filename)
-        for name in filenames:
-            suffixes: Sequence[str] = name.split(".")
-            passed: bool = False
-            for i in range(len(suffixes) - 1):
-                if ".".join(suffixes[-(i + 2) : -1]) in self._suffixes:
-                    passed = True
-                    break
-            if not passed:
-                supported_format = False
-                break
-
-        return supported_format
 
     @abstractmethod
     def read(self, filename: str) -> Union[Sequence[Any], Any]:
@@ -96,16 +72,27 @@ class ITKReader(ImageReader):
     Args:
         img: image to initialize the reader, this is for the usage that the image data
             is already in memory and no need to read from file again, default is None.
-        other_args: acceptable args: `c_order_axis_indexing`.
-            if `c_order_axis_indexing=True`, the numpy array will have C-order indexing.
+        c_order_axis_indexing: if `True`, the numpy array will have C-order indexing.
             this is the reverse of how indices are specified in ITK, i.e. k,j,i versus i,j,k.
             however C-order indexing is expected by most algorithms in numpy / scipy.
 
     """
 
-    def __init__(self, img: Optional[itk.Image] = None, **other_args):
-        super().__init__(img=img)
-        self.c_order_axis_indexing = other_args.get("c_order_axis_indexing", False)
+    def __init__(self, img: Optional[itk.Image] = None, c_order_axis_indexing: bool = False):
+        super().__init__()
+        self.img = img
+        self.c_order_axis_indexing = c_order_axis_indexing
+
+    def verify_suffix(self, filename: str) -> bool:
+        """
+        Verify whether the specified file or files format is supported by ITK reader.
+
+        Args:
+            filename: file name or a list of file names to read.
+                if a list of files, verify all the subffixes.
+
+        """
+        return True
 
     def read(self, filename: str):
         """
@@ -227,10 +214,22 @@ class NibabelReader(ImageReader):
     """
 
     def __init__(self, img: Optional[Any] = None, **other_args):
-        super().__init__(img)
-        self._suffixes: [Sequence[str]] = ["nii", "nii.gz"]
+        super().__init__()
+        self.img = img
         self.as_closest_canonical = other_args.get("as_closest_canonical", False)
 
+    def verify_suffix(self, filename: str) -> bool:
+        """
+        Verify whether the specified file or files format is supported by Nibabel reader.
+
+        Args:
+            filename: file name or a list of file names to read.
+                if a list of files, verify all the subffixes.
+
+        """
+        suffixes: [Sequence[str]] = ["nii", "nii.gz"]
+        return is_supported_format(filename, suffixes)
+
     def read(self, filename: str):
         """
         Read image data from specified file or files.
diff --git a/monai/data/utils.py b/monai/data/utils.py
index 1d95d496e5..e19f631112 100644
--- a/monai/data/utils.py
+++ b/monai/data/utils.py
@@ -20,7 +20,7 @@
 from torch.utils.data._utils.collate import default_collate
 
 from monai.networks.layers.simplelayers import GaussianFilter
-from monai.utils import BlendMode, NumpyPadMode, ensure_tuple_size, first, optional_import
+from monai.utils import BlendMode, NumpyPadMode, ensure_tuple, ensure_tuple_size, first, optional_import
 
 nib, _ = optional_import("nibabel")
 
@@ -507,3 +507,30 @@ def compute_importance_map(
         raise ValueError(f'Unsupported mode: {mode}, available options are ["constant", "gaussian"].')
 
     return importance_map
+
+
+def is_supported_format(filename: str, suffixes: Sequence[str]) -> bool:
+    """
+    Verify whether the specified file or files format match supported suffixes.
+    If supported suffixes is None, skip the verification and return True.
+
+    Args:
+        filename: file name or a list of file names to read.
+            if a list of files, verify all the subffixes.
+        suffixes: all the supported image subffixes of current reader.
+
+    """
+    supported_format: bool = True
+    filenames: Sequence[str] = ensure_tuple(filename)
+    for name in filenames:
+        tokens: Sequence[str] = name.split(".")
+        passed: bool = False
+        for i in range(len(tokens) - 1):
+            if ".".join(tokens[-(i + 2) : -1]) in suffixes:
+                passed = True
+                break
+        if not passed:
+            supported_format = False
+            break
+
+    return supported_format

From 6da75e4ceb262f5940e035751e1062880e6612ca Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Thu, 20 Aug 2020 20:47:08 +0800
Subject: [PATCH 25/37] [DLMED] update read API

---
 monai/data/image_reader.py | 84 +++++++++++++++++++++++---------------
 1 file changed, 50 insertions(+), 34 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 8f1bbf4dd3..3b6292e38c 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -42,13 +42,15 @@ def verify_suffix(self, filename: str) -> bool:
 
 
     @abstractmethod
-    def read(self, filename: str) -> Union[Sequence[Any], Any]:
+    def read(self, data: Union[Sequence[str], str, Any], **kwargs) -> Union[Sequence[Any], Any]:
         """
-        Read image data from specified file or files and save to `self.img`.
+        Read image data from specified file or files, or set a loaded image.
         Note that it returns the raw data, so different readers return different image data type.
 
         Args:
-            filename: file name or a list of file names to read.
+            data: file name or a list of file names to read, or a loaded image object.
+            kwargs: additional args for 3rd party reader API.
+
 
         """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
@@ -70,17 +72,15 @@ class ITKReader(ImageReader):
     https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO
 
     Args:
-        img: image to initialize the reader, this is for the usage that the image data
-            is already in memory and no need to read from file again, default is None.
         c_order_axis_indexing: if `True`, the numpy array will have C-order indexing.
             this is the reverse of how indices are specified in ITK, i.e. k,j,i versus i,j,k.
             however C-order indexing is expected by most algorithms in numpy / scipy.
 
     """
 
-    def __init__(self, img: Optional[itk.Image] = None, c_order_axis_indexing: bool = False):
+    def __init__(self, c_order_axis_indexing: bool = False):
         super().__init__()
-        self.img = img
+        self._img = None
         self.c_order_axis_indexing = c_order_axis_indexing
 
     def verify_suffix(self, filename: str) -> bool:
@@ -94,21 +94,28 @@ def verify_suffix(self, filename: str) -> bool:
         """
         return True
 
-    def read(self, filename: str):
+    def read(self, data: Union[Sequence[str], str, itk.Image], **kwargs):
         """
-        Read image data from specified file or files.
+        Read image data from specified file or files, or set a `itk.Image` object.
         Note that the returned object is ITK image object or list of ITK image objects.
-        `self.img` is always a list, even only has 1 image.
+        `self._img` is always a list, even only has 1 image.
 
         Args:
-            filename: file name or a list of file names to read.
+            data: file name or a list of file names to read, or a `itk.Image` object for the usage that
+                the image data is already in memory and no need to read from file again.
+            kwargs: additional args for `itk.imread` API. more details about available args:
+                https://github.com/InsightSoftwareConsortium/ITK/blob/master/Wrapping/Generators/Python/itkExtras.py
 
         """
-        filenames: Sequence[str] = ensure_tuple(filename)
-        self.img: Sequence[itk.Image] = list()
+        self._img: Sequence[itk.Image] = list()
+        if isinstance(data, itk.Image):
+            self._img.append(data)
+            return data
+
+        filenames: Sequence[str] = ensure_tuple(data)
         for name in filenames:
-            self.img.append(itk.imread(name))
-        return self.img if len(filenames) > 1 else self.img[0]
+            self._img.append(itk.imread(name, **kwargs))
+        return self._img if len(filenames) > 1 else self._img[0]
 
     def get_data(self):
         """
@@ -121,7 +128,10 @@ def get_data(self):
         """
         img_array: Sequence[np.ndarray] = list()
         compatible_meta: Dict = None
-        for img in self.img:
+        if self._img is None:
+            raise RuntimeError("please call read() first then use get_data().")
+
+        for img in self._img:
             header = self._get_meta_dict(img)
             header["original_affine"] = self._get_affine(img)
             header["affine"] = header["original_affine"].copy()
@@ -206,17 +216,14 @@ class NibabelReader(ImageReader):
     Load NIfTI format images based on Nibabel library.
 
     Args:
-        img: image to initialize the reader, this is for the usage that the image data
-            is already in memory and no need to read from file again, default is None.
-        other_args: acceptable args: `as_closest_canonical`.
-            if `as_closest_canonical=True`, load the image as closest to canonical axis format.
+        as_closest_canonical: if True, load the image as closest to canonical axis format.
 
     """
 
-    def __init__(self, img: Optional[Any] = None, **other_args):
+    def __init__(self, as_closest_canonical: bool = False):
         super().__init__()
-        self.img = img
-        self.as_closest_canonical = other_args.get("as_closest_canonical", False)
+        self.img = None
+        self.as_closest_canonical = as_closest_canonical
 
     def verify_suffix(self, filename: str) -> bool:
         """
@@ -230,23 +237,29 @@ def verify_suffix(self, filename: str) -> bool:
         suffixes: [Sequence[str]] = ["nii", "nii.gz"]
         return is_supported_format(filename, suffixes)
 
-    def read(self, filename: str):
+    def read(self, data: Union[Sequence[str], str, nib.nifti1.Nifti1Image], **kwargs):
         """
-        Read image data from specified file or files.
+        Read image data from specified file or files, or set a Nibabel Image object.
         Note that the returned object is Nibabel image object or list of Nibabel image objects.
-        `self.img` is always a list, even only has 1 image.
+        `self._img` is always a list, even only has 1 image.
 
         Args:
-            filename: file name or a list of file names to read.
+            data: file name or a list of file names to read.
+            kwargs: additional args for `nibabel.load` API. more details about available args:
+                https://github.com/nipy/nibabel/blob/master/nibabel/loadsave.py
 
         """
-        filenames: Sequence[str] = ensure_tuple(filename)
-        self.img: Sequence[nib.nifti1.Nifti1Image] = list()
+        self._img: Sequence[nib.nifti1.Nifti1Image] = list()
+        if isinstance(data, nib.nifti1.Nifti1Image):
+            self._img.append(data)
+            return data
+
+        filenames: Sequence[str] = ensure_tuple(data)
         for name in filenames:
-            img = nib.load(name)
+            img = nib.load(name, **kwargs)
             img = correct_nifti_header_if_necessary(img)
-            self.img.append(img)
-        return self.img if len(filenames) > 1 else self.img[0]
+            self._img.append(img)
+        return self._img if len(filenames) > 1 else self._img[0]
 
     def get_data(self):
         """
@@ -259,7 +272,10 @@ def get_data(self):
         """
         img_array: Sequence[np.ndarray] = list()
         compatible_meta: Dict = None
-        for img in self.img:
+        if self._img is None:
+            raise RuntimeError("please call read() first then use get_data().")
+
+        for img in self._img:
             header = self._get_meta_dict(img)
             header["original_affine"] = self._get_affine(img)
             header["affine"] = header["original_affine"].copy()

From ec64108954ef5b7aff594bb60251cd32f8a0e806 Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Thu, 20 Aug 2020 12:49:25 +0000
Subject: [PATCH 26/37] [MONAI] python code formatting

---
 monai/data/image_reader.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 3b6292e38c..56d13c88a1 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -16,6 +16,7 @@
 
 from monai.data.utils import correct_nifti_header_if_necessary
 from monai.utils import ensure_tuple, optional_import
+
 from .utils import is_supported_format
 
 itk, _ = optional_import("itk", allow_namespace_pkg=True)
@@ -28,6 +29,7 @@ class ImageReader(ABC):
     to get the image data and properties from meta data.
 
     """
+
     @abstractmethod
     def verify_suffix(self, filename: str) -> bool:
         """
@@ -40,7 +42,6 @@ def verify_suffix(self, filename: str) -> bool:
         """
         raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
 
-
     @abstractmethod
     def read(self, data: Union[Sequence[str], str, Any], **kwargs) -> Union[Sequence[Any], Any]:
         """

From 8b939d2c63275da5b5c4f4b01f8140a640022c99 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Thu, 20 Aug 2020 23:51:27 +0800
Subject: [PATCH 27/37] [DLMED] ignore pytype issue

---
 monai/config/deviceconfig.py | 2 +-
 monai/data/image_reader.py   | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index 7cd0db15ee..2b65a62029 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -67,7 +67,7 @@
     gdown_version = "NOT INSTALLED or UNKNOWN VERSION."
 
 try:
-    import itk
+    import itk  # type: ignore
 
     itk_version = itk.Version.GetITKVersion()
     del itk
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 56d13c88a1..ad1487ccd0 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -81,7 +81,7 @@ class ITKReader(ImageReader):
 
     def __init__(self, c_order_axis_indexing: bool = False):
         super().__init__()
-        self._img = None
+        self._img: Sequence[itk.Image] = None
         self.c_order_axis_indexing = c_order_axis_indexing
 
     def verify_suffix(self, filename: str) -> bool:
@@ -108,7 +108,7 @@ def read(self, data: Union[Sequence[str], str, itk.Image], **kwargs):
                 https://github.com/InsightSoftwareConsortium/ITK/blob/master/Wrapping/Generators/Python/itkExtras.py
 
         """
-        self._img: Sequence[itk.Image] = list()
+        self._img = list()
         if isinstance(data, itk.Image):
             self._img.append(data)
             return data
@@ -223,7 +223,7 @@ class NibabelReader(ImageReader):
 
     def __init__(self, as_closest_canonical: bool = False):
         super().__init__()
-        self.img = None
+        self._img: Sequence[nib.nifti1.Nifti1Image] = None
         self.as_closest_canonical = as_closest_canonical
 
     def verify_suffix(self, filename: str) -> bool:
@@ -250,7 +250,7 @@ def read(self, data: Union[Sequence[str], str, nib.nifti1.Nifti1Image], **kwargs
                 https://github.com/nipy/nibabel/blob/master/nibabel/loadsave.py
 
         """
-        self._img: Sequence[nib.nifti1.Nifti1Image] = list()
+        self._img = list()
         if isinstance(data, nib.nifti1.Nifti1Image):
             self._img.append(data)
             return data

From b7e08d62fa2ede1850ffed90a1e34a9f34f7c9ed Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Fri, 21 Aug 2020 00:06:37 +0800
Subject: [PATCH 28/37] [DLMED] fix typo

---
 monai/config/deviceconfig.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index d1a5db3838..0bcecb1813 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -66,7 +66,7 @@
 except (ImportError, AttributeError):
     gdown_version = "NOT INSTALLED or UNKNOWN VERSION."
 
-try
+try:
     import torchvision
 
     torchvision_version = torchvision.__version__

From 2d906f8df25289761f7e0dc077a9ee417f03c72c Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Fri, 21 Aug 2020 00:25:22 +0800
Subject: [PATCH 29/37] [DLMED] fix typehints

---
 monai/data/image_reader.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index ad1487ccd0..6d4c96c090 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Sequence, Tuple, Union
+from typing import Any, Dict, Sequence, Tuple, Union, List
 
 import numpy as np
 
@@ -127,7 +127,7 @@ def get_data(self):
         and use the meta data of the first image to represent the stacked result.
 
         """
-        img_array: Sequence[np.ndarray] = list()
+        img_array: List[np.ndarray] = list()
         compatible_meta: Dict = None
         if self._img is None:
             raise RuntimeError("please call read() first then use get_data().")
@@ -235,7 +235,7 @@ def verify_suffix(self, filename: str) -> bool:
                 if a list of files, verify all the subffixes.
 
         """
-        suffixes: [Sequence[str]] = ["nii", "nii.gz"]
+        suffixes: Sequence[str] = ["nii", "nii.gz"]
         return is_supported_format(filename, suffixes)
 
     def read(self, data: Union[Sequence[str], str, nib.nifti1.Nifti1Image], **kwargs):
@@ -271,7 +271,7 @@ def get_data(self):
         and use the meta data of the first image to represent the stacked result.
 
         """
-        img_array: Sequence[np.ndarray] = list()
+        img_array: List[np.ndarray] = list()
         compatible_meta: Dict = None
         if self._img is None:
             raise RuntimeError("please call read() first then use get_data().")

From 4e372fb617e0a3e4e691b4ea61ef1beef51b294a Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Thu, 20 Aug 2020 16:31:34 +0000
Subject: [PATCH 30/37] [MONAI] python code formatting

---
 monai/data/image_reader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 6d4c96c090..8ddc65c576 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Sequence, Tuple, Union, List
+from typing import Any, Dict, List, Sequence, Tuple, Union
 
 import numpy as np
 

From 3f27f459659d377ab5375eef2e7f7d0fb045c2b7 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Fri, 21 Aug 2020 00:43:26 +0800
Subject: [PATCH 31/37] [DLMED] fix typehints

---
 monai/data/image_reader.py        | 8 ++++----
 monai/transforms/io/array.py      | 2 +-
 monai/transforms/io/dictionary.py | 2 +-
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 8ddc65c576..5587a08330 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -147,8 +147,8 @@ def get_data(self):
                 if not np.allclose(header["spatial_shape"], compatible_meta["spatial_shape"]):
                     raise RuntimeError("spatial_shape of all images should be same.")
 
-        img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
-        return img_array, compatible_meta
+        img_array_ = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
+        return img_array_, compatible_meta
 
     def _get_meta_dict(self, img: itk.Image) -> Dict:
         """
@@ -295,8 +295,8 @@ def get_data(self):
                 if not np.allclose(header["spatial_shape"], compatible_meta["spatial_shape"]):
                     raise RuntimeError("spatial_shape of all images should be same.")
 
-        img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
-        return img_array, compatible_meta
+        img_array_ = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
+        return img_array_, compatible_meta
 
     def _get_meta_dict(self, img: nib.nifti1.Nifti1Image) -> Dict:
         """
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 925139c706..7ef32f92c5 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -63,7 +63,7 @@ def __init__(
         self.image_only = image_only
         self.dtype = dtype
 
-    def register(self, reader: Optional[ImageReader]) -> List[ImageReader]:
+    def register(self, reader: ImageReader) -> List[ImageReader]:
         """
         Register image reader to load image file and meta data.
         Return all the registered image readers.
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index e9c8b6647e..145c54458f 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -61,7 +61,7 @@ def __init__(
         self.meta_key_postfix = meta_key_postfix
         self.overwriting = overwriting
 
-    def register(self, reader: Optional[ImageReader] = None):
+    def register(self, reader: ImageReader):
         self._loader.register(reader)
 
     def __call__(self, data, reader: Optional[ImageReader] = None):

From 51af48dbdfe8f1a0b5f9632a01f5939baf5e00ae Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Fri, 21 Aug 2020 01:12:42 +0800
Subject: [PATCH 32/37] [DLMED] fix pytype

---
 monai/config/deviceconfig.py |  2 +-
 monai/data/image_reader.py   | 14 +++++++-------
 monai/transforms/io/array.py |  4 ++--
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index 0bcecb1813..bba5f48ea7 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -75,7 +75,7 @@
     torchvision_version = "NOT INSTALLED or UNKNOWN VERSION."
 
 try:
-    import itk  # type: ignore
+    import itk
 
     itk_version = itk.Version.GetITKVersion()
     del itk
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 5587a08330..4e00dc5e50 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Sequence, Tuple, Union
+from typing import Any, Dict, List, Sequence, Tuple, Union, Optional
 
 import numpy as np
 
@@ -31,7 +31,7 @@ class ImageReader(ABC):
     """
 
     @abstractmethod
-    def verify_suffix(self, filename: str) -> bool:
+    def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
         """
         Verify whether the specified file or files format is supported by current reader.
 
@@ -81,10 +81,10 @@ class ITKReader(ImageReader):
 
     def __init__(self, c_order_axis_indexing: bool = False):
         super().__init__()
-        self._img: Sequence[itk.Image] = None
+        self._img: Optional[Sequence[itk.Image]] = None
         self.c_order_axis_indexing = c_order_axis_indexing
 
-    def verify_suffix(self, filename: str) -> bool:
+    def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
         """
         Verify whether the specified file or files format is supported by ITK reader.
 
@@ -223,10 +223,10 @@ class NibabelReader(ImageReader):
 
     def __init__(self, as_closest_canonical: bool = False):
         super().__init__()
-        self._img: Sequence[nib.nifti1.Nifti1Image] = None
+        self._img: Optional[Sequence[nib.nifti1.Nifti1Image]] = None
         self.as_closest_canonical = as_closest_canonical
 
-    def verify_suffix(self, filename: str) -> bool:
+    def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
         """
         Verify whether the specified file or files format is supported by Nibabel reader.
 
@@ -329,7 +329,7 @@ def _get_spatial_shape(self, img: nib.nifti1.Nifti1Image) -> Sequence:
         """
         ndim = img.header["dim"][0]
         spatial_rank = min(ndim, 3)
-        return img.header["dim"][1 : spatial_rank + 1]
+        return list(img.header["dim"][1 : spatial_rank + 1])
 
     def _get_array_data(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
         """
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 7ef32f92c5..d0b4e74eb1 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -57,7 +57,7 @@ def __init__(
 
         """
         self.defaut_reader: ITKReader = ITKReader()
-        self.readers: Sequence[ImageReader] = list()
+        self.readers: List[ImageReader] = list()
         if reader is not None:
             self.readers.append(reader)
         self.image_only = image_only
@@ -77,7 +77,7 @@ def register(self, reader: ImageReader) -> List[ImageReader]:
         return self.readers
 
     def __call__(
-        self, filename: Union[Sequence[Union[Path, str]], Path, str], reader: Optional[ImageReader] = None,
+        self, filename: Union[Sequence[str], str], reader: Optional[ImageReader] = None,
     ):
         """
         Args:

From 6ced0f0d194b003b08f78c4f32cc999d75c114cb Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Thu, 20 Aug 2020 17:20:12 +0000
Subject: [PATCH 33/37] [MONAI] python code formatting

---
 monai/data/image_reader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 4e00dc5e50..c63b4e142d 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Sequence, Tuple, Union, Optional
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 

From b9ff481da4b5f505652a85acb443e9365b6f0a3f Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Fri, 21 Aug 2020 01:29:19 +0800
Subject: [PATCH 34/37] [DLMED] ignore itk error

---
 monai/config/deviceconfig.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index bba5f48ea7..0bcecb1813 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -75,7 +75,7 @@
     torchvision_version = "NOT INSTALLED or UNKNOWN VERSION."
 
 try:
-    import itk
+    import itk  # type: ignore
 
     itk_version = itk.Version.GetITKVersion()
     del itk

From 114e28b3e042ddb50217b8c3acf63283c9cecf19 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Fri, 21 Aug 2020 01:39:08 +0800
Subject: [PATCH 35/37] [DLMED] fix mytype issue

---
 monai/data/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/data/utils.py b/monai/data/utils.py
index e19f631112..aae1836567 100644
--- a/monai/data/utils.py
+++ b/monai/data/utils.py
@@ -509,7 +509,7 @@ def compute_importance_map(
     return importance_map
 
 
-def is_supported_format(filename: str, suffixes: Sequence[str]) -> bool:
+def is_supported_format(filename: Union[Sequence[str], str], suffixes: Sequence[str]) -> bool:
     """
     Verify whether the specified file or files format match supported suffixes.
     If supported suffixes is None, skip the verification and return True.

From 18758e660d27c5aed625cc8459a00b7558648f34 Mon Sep 17 00:00:00 2001
From: Wenqi Li <wenqil@nvidia.com>
Date: Thu, 20 Aug 2020 20:12:35 +0100
Subject: [PATCH 36/37] fixes mypy errors

---
 monai/data/image_reader.py | 10 +++++++---
 setup.cfg                  |  4 ++--
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index c63b4e142d..857f4c98a1 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -19,8 +19,12 @@
 
 from .utils import is_supported_format
 
-itk, _ = optional_import("itk", allow_namespace_pkg=True)
-nib, _ = optional_import("nibabel")
+if TYPE_CHECKING:
+    import itk  # type: ignore
+    import nibabel as nib
+else:
+    itk, _ = optional_import("itk", allow_namespace_pkg=True)
+    nib, _ = optional_import("nibabel")
 
 
 class ImageReader(ABC):
diff --git a/setup.cfg b/setup.cfg
index 51d13683b2..2c161f2a18 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -81,8 +81,8 @@ ignore_missing_imports = True
 no_implicit_optional = True
 # Warns about casting an expression to its inferred type.
 warn_redundant_casts = True
-# Warns about unneeded # type: ignore comments.
-warn_unused_ignores = True
+# No error on unneeded # type: ignore comments.
+warn_unused_ignores = False
 # Shows a warning when returning a value with type Any from a function declared with a non-Any return type.
 warn_return_any = True
 # Prohibit equality checks, identity checks, and container checks between non-overlapping types.

From 76988b9c86873335eab19d47c44f2f01400a018a Mon Sep 17 00:00:00 2001
From: Wenqi Li <wenqil@nvidia.com>
Date: Thu, 20 Aug 2020 20:29:47 +0100
Subject: [PATCH 37/37] revert packaging pipeline

---
 .github/workflows/pythonapp.yml |  3 +--
 monai/data/image_reader.py      | 32 ++++++++++++++++++--------------
 2 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml
index 648f1ea714..ea43b66fe1 100644
--- a/.github/workflows/pythonapp.yml
+++ b/.github/workflows/pythonapp.yml
@@ -156,7 +156,7 @@ jobs:
         ln -s /usr/bin/python$PYVER /usr/bin/python`echo $PYVER | cut -c1-1` &&
         curl -O https://bootstrap.pypa.io/get-pip.py && \
         python get-pip.py && \
-        rm get-pip.py ; fi 
+        rm get-pip.py ; fi
     - name: Install dependencies
       run: |
         which python
@@ -205,7 +205,6 @@ jobs:
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
         python -m pip install torch>=1.4 torchvision
-        python -m pip install -r requirements-dev.txt
     - name: Test source archive and wheel file
       run: |
         git fetch --depth=1 origin +refs/tags/*:refs/tags/*
diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py
index 857f4c98a1..d2bdede4bd 100644
--- a/monai/data/image_reader.py
+++ b/monai/data/image_reader.py
@@ -22,9 +22,13 @@
 if TYPE_CHECKING:
     import itk  # type: ignore
     import nibabel as nib
+    from itk import Image  # type: ignore
+    from nibabel.nifti1 import Nifti1Image
 else:
     itk, _ = optional_import("itk", allow_namespace_pkg=True)
+    Image, _ = optional_import("itk", allow_namespace_pkg=True, name="Image")
     nib, _ = optional_import("nibabel")
+    Nifti1Image, _ = optional_import("nibabel.nifti1", name="Nifti1Image")
 
 
 class ImageReader(ABC):
@@ -85,7 +89,7 @@ class ITKReader(ImageReader):
 
     def __init__(self, c_order_axis_indexing: bool = False):
         super().__init__()
-        self._img: Optional[Sequence[itk.Image]] = None
+        self._img: Optional[Sequence[Image]] = None
         self.c_order_axis_indexing = c_order_axis_indexing
 
     def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
@@ -99,7 +103,7 @@ def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
         """
         return True
 
-    def read(self, data: Union[Sequence[str], str, itk.Image], **kwargs):
+    def read(self, data: Union[Sequence[str], str, Image], **kwargs):
         """
         Read image data from specified file or files, or set a `itk.Image` object.
         Note that the returned object is ITK image object or list of ITK image objects.
@@ -113,7 +117,7 @@ def read(self, data: Union[Sequence[str], str, itk.Image], **kwargs):
 
         """
         self._img = list()
-        if isinstance(data, itk.Image):
+        if isinstance(data, Image):
             self._img.append(data)
             return data
 
@@ -154,7 +158,7 @@ def get_data(self):
         img_array_ = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
         return img_array_, compatible_meta
 
-    def _get_meta_dict(self, img: itk.Image) -> Dict:
+    def _get_meta_dict(self, img: Image) -> Dict:
         """
         Get all the meta data of the image and convert to dict type.
 
@@ -174,7 +178,7 @@ def _get_meta_dict(self, img: itk.Image) -> Dict:
         meta_dict["direction"] = itk.array_from_matrix(img.GetDirection())
         return meta_dict
 
-    def _get_affine(self, img: itk.Image) -> np.ndarray:
+    def _get_affine(self, img: Image) -> np.ndarray:
         """
         Get or construct the affine matrix of the image, it can be used to correct
         spacing, orientation or execute spatial transforms.
@@ -195,7 +199,7 @@ def _get_affine(self, img: itk.Image) -> np.ndarray:
         affine[(slice(-1), -1)] = origin
         return affine
 
-    def _get_spatial_shape(self, img: itk.Image) -> Sequence:
+    def _get_spatial_shape(self, img: Image) -> Sequence:
         """
         Get the spatial shape of image data, it doesn't contain the channel dim.
 
@@ -205,7 +209,7 @@ def _get_spatial_shape(self, img: itk.Image) -> Sequence:
         """
         return list(itk.size(img))
 
-    def _get_array_data(self, img: itk.Image) -> np.ndarray:
+    def _get_array_data(self, img: Image) -> np.ndarray:
         """
         Get the raw array data of the image, converted to Numpy array.
 
@@ -227,7 +231,7 @@ class NibabelReader(ImageReader):
 
     def __init__(self, as_closest_canonical: bool = False):
         super().__init__()
-        self._img: Optional[Sequence[nib.nifti1.Nifti1Image]] = None
+        self._img: Optional[Sequence[Nifti1Image]] = None
         self.as_closest_canonical = as_closest_canonical
 
     def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
@@ -242,7 +246,7 @@ def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
         suffixes: Sequence[str] = ["nii", "nii.gz"]
         return is_supported_format(filename, suffixes)
 
-    def read(self, data: Union[Sequence[str], str, nib.nifti1.Nifti1Image], **kwargs):
+    def read(self, data: Union[Sequence[str], str, Nifti1Image], **kwargs):
         """
         Read image data from specified file or files, or set a Nibabel Image object.
         Note that the returned object is Nibabel image object or list of Nibabel image objects.
@@ -255,7 +259,7 @@ def read(self, data: Union[Sequence[str], str, nib.nifti1.Nifti1Image], **kwargs
 
         """
         self._img = list()
-        if isinstance(data, nib.nifti1.Nifti1Image):
+        if isinstance(data, Nifti1Image):
             self._img.append(data)
             return data
 
@@ -302,7 +306,7 @@ def get_data(self):
         img_array_ = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
         return img_array_, compatible_meta
 
-    def _get_meta_dict(self, img: nib.nifti1.Nifti1Image) -> Dict:
+    def _get_meta_dict(self, img: Nifti1Image) -> Dict:
         """
         Get the all the meta data of the image and convert to dict type.
 
@@ -312,7 +316,7 @@ def _get_meta_dict(self, img: nib.nifti1.Nifti1Image) -> Dict:
         """
         return dict(img.header)
 
-    def _get_affine(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
+    def _get_affine(self, img: Nifti1Image) -> np.ndarray:
         """
         Get the affine matrix of the image, it can be used to correct
         spacing, orientation or execute spatial transforms.
@@ -323,7 +327,7 @@ def _get_affine(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
         """
         return img.affine
 
-    def _get_spatial_shape(self, img: nib.nifti1.Nifti1Image) -> Sequence:
+    def _get_spatial_shape(self, img: Nifti1Image) -> Sequence:
         """
         Get the spatial shape of image data, it doesn't contain the channel dim.
 
@@ -335,7 +339,7 @@ def _get_spatial_shape(self, img: nib.nifti1.Nifti1Image) -> Sequence:
         spatial_rank = min(ndim, 3)
         return list(img.header["dim"][1 : spatial_rank + 1])
 
-    def _get_array_data(self, img: nib.nifti1.Nifti1Image) -> np.ndarray:
+    def _get_array_data(self, img: Nifti1Image) -> np.ndarray:
         """
         Get the raw array data of the image, converted to Numpy array.