in_source_id stringlengths 13 58 | issue stringlengths 3 241k | before_files listlengths 0 3 | after_files listlengths 0 3 | pr_diff stringlengths 109 107M ⌀ |
|---|---|---|---|---|
aimhubio__aim-2669 | `TensorboardFolderTracker` missing Image import
## 🐛 Bug
Currently the `TensorboardFolderTracker` will crash if there are any images that need to be parsed. This is because currently `aim.Image` is only imported during type checking, however `_process_tb_event` attempts to create an `Image` instance, without access
### To reproduce
- Create a tensorboard events file that contains an image
- Create a run using the `aim.ext.tensorboard_tracker.tracker.Run` runner.
- Observe the console
### Expected behavior
Images are converted and added to the RocksDB database.
### Environment
- Aim Version 3.17.3
- Python version 3.10.5
- pip version 22.0.4
- OS Ubuntu
- Any other relevant information
| [
{
"content": "from tensorboard.backend.event_processing.directory_watcher import DirectoryWatcher\nfrom tensorboard.backend.event_processing import event_file_loader\nimport tensorflow as tf\nfrom tensorboard.util import tensor_util\nimport time\nimport threading\nfrom pathlib import Path\nimport logging\nimpor... | [
{
"content": "from tensorboard.backend.event_processing.directory_watcher import DirectoryWatcher\nfrom tensorboard.backend.event_processing import event_file_loader\nimport tensorflow as tf\nfrom tensorboard.util import tensor_util\nimport time\nimport threading\nfrom pathlib import Path\nimport logging\nimpor... | diff --git a/CHANGELOG.md b/CHANGELOG.md
index 57c867207b..2017eb2edd 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,7 +13,8 @@
### Fixes
-- Convert NaNs and Infs in responses to strings (n-gao)
+- Convert NaNs and Infs in responses to strings (n-gao)
+- Import `Image` and `Audio` for `TensorboardFolderTracker` (alansaul)
## 3.17.4 May 4, 2023
diff --git a/aim/ext/tensorboard_tracker/tracker.py b/aim/ext/tensorboard_tracker/tracker.py
index ce00b5e1e0..2d86d3c92a 100644
--- a/aim/ext/tensorboard_tracker/tracker.py
+++ b/aim/ext/tensorboard_tracker/tracker.py
@@ -10,10 +10,8 @@
import weakref
import queue
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
- from aim import Audio, Image
+from typing import Any
+from aim import Audio, Image
class TensorboardTracker:
diff --git a/tests/README.md b/tests/README.md
index 3b7f693039..8dbe0c6f2e 100644
--- a/tests/README.md
+++ b/tests/README.md
@@ -5,6 +5,7 @@ Be able to test the correctness of the
- `aim engine`
- `aim sdk`
- `aim ql`
+ - `extensions`
### Folder Structure
@@ -16,6 +17,8 @@ tests
test_*.py
ql
test_*.py
+ ext
+ test_*.py
```
## Run
diff --git a/tests/ext/__init__.py b/tests/ext/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/ext/test_tensorboard_tracker.py b/tests/ext/test_tensorboard_tracker.py
new file mode 100644
index 0000000000..545af772e0
--- /dev/null
+++ b/tests/ext/test_tensorboard_tracker.py
@@ -0,0 +1,113 @@
+from queue import Queue
+import numpy as np
+from PIL import ImageChops as PILImageChops
+import tensorflow as tf
+from tensorboard.compat.proto.summary_pb2 import SummaryMetadata, Summary
+from tensorboard.compat.proto.tensor_pb2 import TensorProto
+from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
+from tensorboard.compat.proto.event_pb2 import Event
+from torch.utils.tensorboard.summary import image, scalar
+
+from aim import Image
+from aim.ext.tensorboard_tracker.tracker import TensorboardFolderTracker
+
+from tests.base import TestBase
+
+
+def images_same_data(image1: Image, image2: Image) -> bool:
+ """
+ Compare two Aim images to see if they contain the same values
+ """
+ image_diff = PILImageChops.difference(
+ image1.to_pil_image(), image2.to_pil_image()
+ )
+ return image_diff.getbbox() is None
+
+
+class TestTensorboardTracker(TestBase):
+
+ def test__process_tb_image_event(self):
+ # Given
+ queue = Queue()
+ tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue)
+ height, width, channels = 5, 4, 3
+ # Note channels is last
+ image_np = np.random.randint(0, 16, (height, width, channels)).astype(dtype=np.uint8)
+ # Create image summary in standard format
+ image_summary = image(tag='test_image', tensor=image_np, dataformats='HWC')
+ event = Event(summary=image_summary)
+
+ # When
+ tracker._process_tb_event(event)
+
+ # Then
+ tracked_image = queue.get().value
+ original_image = Image(image_np)
+ self.assertTrue(isinstance(tracked_image, Image))
+ self.assertTrue(tracked_image.size == original_image.size)
+ self.assertTrue(images_same_data(tracked_image, original_image))
+
+ def test__process_tb_image_plugin_event(self):
+ # Given
+ queue = Queue()
+ tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue)
+ height, width, channels = 5, 4, 3
+ # Note channels is last
+ image_np = np.random.randint(0, 16, (height, width, channels)).astype(dtype=np.uint8)
+ # Create image summary in format of plugin
+ plugin_data = SummaryMetadata.PluginData(plugin_name='images')
+ smd = SummaryMetadata(plugin_data=plugin_data, )
+ tensor = TensorProto(dtype='DT_STRING',
+ string_val=[
+ f"{height}".encode(encoding='utf_8'),
+ f"{width}".encode(encoding='utf_8'),
+ tf.image.encode_png(image_np).numpy(),
+ ],
+ tensor_shape=TensorShapeProto(dim=[TensorShapeProto.Dim(size=3)]))
+
+ image_summary = Summary(
+ value=[Summary.Value(tag='test_image', metadata=smd, tensor=tensor)]
+ )
+ event = Event(summary=image_summary)
+
+ # When
+ tracker._process_tb_event(event)
+
+ # Then
+ tracked_image = queue.get().value
+ original_image = Image(image_np)
+ self.assertTrue(isinstance(tracked_image, Image))
+ self.assertTrue(tracked_image.size == original_image.size)
+ self.assertTrue(images_same_data(tracked_image, original_image))
+
+ def test__process_tb_scalar_simple_value_event(self):
+ # Given
+ queue = Queue()
+ tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue)
+ scalar_np = np.array(0.32, dtype=np.float32)
+ scalar_summary = scalar('test_scalar', scalar_np, new_style=False)
+ event = Event(summary=scalar_summary)
+
+ # When
+ tracker._process_tb_event(event)
+
+ # Then
+ tracked_scalar = queue.get().value
+ self.assertTrue(isinstance(tracked_scalar, float))
+ self.assertTrue(np.allclose(tracked_scalar, scalar_np))
+
+ def test__process_tb_scalar_plugin_event(self):
+ # Given
+ queue = Queue()
+ tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue)
+ scalar_np = np.array(0.32, dtype=np.float32)
+ scalar_summary = scalar('test_scalar', scalar_np, new_style=True)
+ event = Event(summary=scalar_summary)
+
+ # When
+ tracker._process_tb_event(event)
+
+ # Then
+ tracked_scalar = queue.get().value
+ self.assertTrue(isinstance(tracked_scalar, np.ndarray))
+ self.assertTrue(np.allclose(tracked_scalar, scalar_np))
|
pex-tool__pex-2123 | Release 2.1.133
On the docket:
+ [x] python<=3.8 symlink with a suffix (eg 3.7m) can create a venv without a pythonX.Y symlink which breaks pex assumptions that pythonX.Y is always available #2119
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.132\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.133\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.rst b/CHANGES.rst
index dd0d64575..73c5d8e43 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,12 +1,21 @@
Release Notes
=============
+2.1.133
+-------
+
+This release fixes ``--venv`` mode PEX venv script shebangs for some
+scenarios using Python ``<=3.7`` interpreters.
+
+* Fix venv script shebangs. #2122
+ `PR #2122 <https://github.com/pantsbuild/pex/pull/2122>`_
+
2.1.132
-------
This release brings support for the latest Pip release with
-`--pip-version 23.1` or by using new support for pinning to the latest
-version of Pip supported by Pex with `--pip-version latest`.
+``--pip-version 23.1`` or by using new support for pinning to the latest
+version of Pip supported by Pex with ``--pip-version latest``.
* Add support for Pip 23.1 (#2114)
`PR #2114 <https://github.com/pantsbuild/pex/pull/2114>`_
diff --git a/pex/version.py b/pex/version.py
index ddfe692f1..600f0ec89 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.132"
+__version__ = "2.1.133"
|
pex-tool__pex-2104 | Release 2.1.130
On the docket:
+ [x] Pex fails to lock - missing artifact #2098
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.129\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.130\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.rst b/CHANGES.rst
index 46c800c11..7db19a69e 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,6 +1,15 @@
Release Notes
=============
+2.1.130
+-------
+
+This release fixes a regression locking certain complex cases of direct
+and transitive requirement interactions as exemplified in #2098.
+
+* Guard lock analysis against Pip-cached artifacts. (#2103)
+ `PR #2103 <https://github.com/pantsbuild/pex/pull/2103>`_
+
2.1.129
-------
diff --git a/pex/version.py b/pex/version.py
index 2553debad..2be2a4246 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.129"
+__version__ = "2.1.130"
|
pex-tool__pex-2081 | Release 2.1.126
On the docket:
+ [x] Resolve sdist builds can race and fail. #2078
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.125\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.126\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.rst b/CHANGES.rst
index ce3114e28..112d072a0 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,6 +1,16 @@
Release Notes
=============
+2.1.126
+-------
+
+This release fixes a long standing (> 4 years old!) concurrency bug
+when building the same sdist for the 1st time and racing another Pex
+process doing the same sdist build.
+
+* Guard against racing sdist builds. (#2080)
+ `PR #2080 <https://github.com/pantsbuild/pex/pull/2080>`_
+
2.1.125
-------
diff --git a/pex/version.py b/pex/version.py
index cafa484de..014827637 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.125"
+__version__ = "2.1.126"
|
pex-tool__pex-1987 | Release 2.1.114
On the docket:
+ [ ] Only insert "" to head of sys.path if a venv PEX runs in interpreter mode #1984
+ [x] venv_dir calculation doesn't correctly handle PEX_PYTHON_PATH with symlinks. #1885
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.113\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.114\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.rst b/CHANGES.rst
index c2f86b7b9..097046b8f 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,6 +1,17 @@
Release Notes
=============
+2.1.114
+-------
+
+This release brings two fixes for ``--venv`` mode PEXes.
+
+* Only insert "" to head of sys.path if a venv PEX runs in interpreter mode (#1984)
+ `PR #1984 <https://github.com/pantsbuild/pex/pull/1984>`_
+
+* Map pex python path interpreter to realpath when creating venv dir hash. (#1972)
+ `PR #1972 <https://github.com/pantsbuild/pex/pull/1972>`_
+
2.1.113
-------
diff --git a/pex/version.py b/pex/version.py
index 5a26142a6..1417a9b98 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.113"
+__version__ = "2.1.114"
|
pex-tool__pex-2258 | Release 2.1.148
On the docket:
+ [x] The Pex CLI should warn when it creates a PEX zip that requires zip64. #2247
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.147\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.148\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.md b/CHANGES.md
index 70201b9bb..84cd183cd 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -1,5 +1,16 @@
# Release Notes
+## 2.1.148
+
+Add support to the Pex for checking if built PEXes are valid Python
+zipapps. Currently, Python zipapps must reside in 32 bit zip files due
+to limitations of the stdlib `zipimport` module's `zipimporter`; so this
+check amounts to a check that the built PEX zip does not use ZIP64
+extensions. The check is controlled with a new
+`--check {none,warn,error}` option, defaulting to warn.
+
+* Add --check support for zipapps. (#2253)
+
## 2.1.147
Add support for `--use-pip-config` to allow the Pip Pex calls to read
diff --git a/pex/version.py b/pex/version.py
index 34e32d6eb..ae2ef1582 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.147"
+__version__ = "2.1.148"
|
pex-tool__pex-2214 | Release 2.1.142
On the docket:
+ [x] KeyError when locking awscli on Python 3.11 #2211
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.141\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.142\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.md b/CHANGES.md
index bb214e656..5bbc1c157 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -1,5 +1,12 @@
# Release Notes
+## 2.1.142
+
+This release fixes Pex to handle Pip backtracking due to sdist build
+errors when attempting to extract metadata.
+
+* Handle backtracking due to sdist build errors. (#2213)
+
## 2.1.141
This release fixes the Pex CLI to work when run from a read-only
diff --git a/pex/version.py b/pex/version.py
index eb2be6ed5..f385f96a2 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.141"
+__version__ = "2.1.142"
|
pex-tool__pex-2153 | Release 2.1.137
On the docket:
+ [x] A locked requirement with mixed artifact types fails to lock. #2150
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.136\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.137\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.rst b/CHANGES.rst
index e2b40876e..86b69c27c 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,6 +1,16 @@
Release Notes
=============
+2.1.137
+-------
+
+This release fixes a long standing bug in lock file creation for exotic
+locking scenarios pulling the same project from multiple artifact
+sources (any mix of URLs, VCS and local project directories).
+
+* Fix inter-artifact comparisons. (#2152)
+ `PR #2152 <https://github.com/pantsbuild/pex/pull/2152>`_
+
2.1.136
-------
diff --git a/pex/version.py b/pex/version.py
index 2f3f7bef4..5f72c8fd6 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.136"
+__version__ = "2.1.137"
|
pex-tool__pex-2055 | Release 2.1.122
On the docket:
+ [x] Support the latest Pip releases: 22.3.1 & 23.0 #2056
+ [x] Lock sdists with prepare-metadata-for-build-wheel. #2053
+ [x] Fix `execute_parallel` "leaking" a thread. #2052
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.121\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.122\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.rst b/CHANGES.rst
index 052808652..869877089 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,6 +1,24 @@
Release Notes
=============
+2.1.122
+-------
+
+This release fixes posix file locks used by Pex internally and enhances
+lock creation to support locking sdist-only C extension projects that
+do not build on the current platform. Pex is also updated to support
+`--pip-version 22.3.1` and `--pip-version 23.0`, bringing it up to date
+with the latest Pip's available.
+
+* Support the latest Pip releases: 22.3.1 & 23.0 (#2056)
+ `PR #2053 <https://github.com/pantsbuild/pex/pull/2056>`_
+
+* Lock sdists with ``prepare-metadata-for-build-wheel``. (#2053)
+ `PR #2053 <https://github.com/pantsbuild/pex/pull/2053>`_
+
+* Fix ``execute_parallel`` "leaking" a thread. (#2052)
+ `PR #2052 <https://github.com/pantsbuild/pex/pull/2052>`_
+
2.1.121
-------
diff --git a/pex/version.py b/pex/version.py
index 2513fd6e8..c30e2a6bb 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.121"
+__version__ = "2.1.122"
|
pex-tool__pex-1976 | Release 2.1.113
On the docket:
+ [x] Restore AtomicDirectory non-locked good behavior. #1974
| [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.112\"\n",
"path": "pex/version.py"
}
] | [
{
"content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.113\"\n",
"path": "pex/version.py"
}
] | diff --git a/CHANGES.rst b/CHANGES.rst
index 5765b0113..c2f86b7b9 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,6 +1,16 @@
Release Notes
=============
+2.1.113
+-------
+
+This is a hotfix release that fixes errors installing wheels when there
+is high parallelism in execution of Pex processes. These issues were a
+regression introduced by #1961 included in the 2.1.112 release.
+
+* Restore AtomicDirectory non-locked good behavior. (#1974)
+ `PR #1974 <https://github.com/pantsbuild/pex/pull/1974>`_
+
2.1.112
-------
diff --git a/pex/version.py b/pex/version.py
index ff657f9d8..5a26142a6 100644
--- a/pex/version.py
+++ b/pex/version.py
@@ -1,4 +1,4 @@
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
-__version__ = "2.1.112"
+__version__ = "2.1.113"
|
wemake-services__wemake-python-styleguide-1588 | Add "wrapper" to NESTED_FUNCTIONS_WHITELIST?
# Rule request
## Thesis and reasoning
Decorators are often created inside functions. These decorators are then supposed to be used on top of other functions, which means they must accept one, and also that they will often create and return a new function by wrapping the original one. It's a level-3 nesting of functions. Maybe WPS430 should be relaxed, or `NESTED_FUNCTIONS_WHITELIST` could include another name like `wrapper`?
Code example:
```python
from functools import wraps
def python(versions):
"""Run through multiple Python versions."""
def decorator(func):
@wraps(func)
def wrapper(context, *args, **kwargs):
for version in versions:
# do things with version and context
func(context, *args, **kwargs)
return wrapper
return decorator
@python(["3.6", "3.7", "3.8"])
def task(context):
...
```
What do you think 🙂 ?
| [
{
"content": "\"\"\"\nThis module contains list of white- and black-listed ``python`` members.\n\nWe add values here when we want to make them public.\nOr when a value is reused in several places.\nThen, we automatically have to add it here and document it.\n\nOther constants that are not used across modules\na... | [
{
"content": "\"\"\"\nThis module contains list of white- and black-listed ``python`` members.\n\nWe add values here when we want to make them public.\nOr when a value is reused in several places.\nThen, we automatically have to add it here and document it.\n\nOther constants that are not used across modules\na... | diff --git a/wemake_python_styleguide/constants.py b/wemake_python_styleguide/constants.py
index 25e04b158..c570a0f1f 100644
--- a/wemake_python_styleguide/constants.py
+++ b/wemake_python_styleguide/constants.py
@@ -278,6 +278,7 @@
NESTED_FUNCTIONS_WHITELIST: Final = frozenset((
'decorator',
'factory',
+ 'wrapper',
))
#: List of allowed ``__future__`` imports.
|
fedora-infra__bodhi-507 | setup.py test doesn't include extra_requires from fedmsg deps
```
======================================================================
ERROR: Failure: ImportError (No module named psutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/decause/.virtualenvs/bodhi-python2.7/lib/python2.7/site-packages/nose/loader.py", line 418, in loadTestsFromName
addr.filename, addr.module)
File "/home/decause/.virtualenvs/bodhi-python2.7/lib/python2.7/site-packages/nose/importer.py", line 47, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/home/decause/.virtualenvs/bodhi-python2.7/lib/python2.7/site-packages/nose/importer.py", line 94, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/home/decause/code/bodhi/bodhi/tests/test_masher.py", line 27, in <module>
from bodhi.consumers.masher import Masher, MasherThread
File "/home/decause/code/bodhi/bodhi/consumers/masher.py", line 30, in <module>
import fedmsg.consumers
File "/home/decause/code/bodhi/.eggs/fedmsg-0.16.0-py2.7.egg/fedmsg/consumers/__init__.py", line 25, in <module>
import psutil
ImportError: No module named psutil
----------------------------------------------------------------------
Ran 335 tests in 138.787s
FAILED (errors=1)
```
| [
{
"content": "import __main__\n__requires__ = __main__.__requires__ = 'WebOb>=1.4.1'\nimport pkg_resources\n\n# The following two imports are required to shut up an\n# atexit error when running tests with python 2.7\nimport logging\nimport multiprocessing\n\nimport os\nimport sys\n\nfrom setuptools import setup... | [
{
"content": "import __main__\n__requires__ = __main__.__requires__ = 'WebOb>=1.4.1'\nimport pkg_resources\n\n# The following two imports are required to shut up an\n# atexit error when running tests with python 2.7\nimport logging\nimport multiprocessing\n\nimport os\nimport sys\n\nfrom setuptools import setup... | diff --git a/setup.py b/setup.py
index 9f78b56417..46fa402cb1 100644
--- a/setup.py
+++ b/setup.py
@@ -57,7 +57,9 @@
# External resources
'python-bugzilla',
'simplemediawiki',
- 'fedmsg',
+
+ # "python setup.py test" needs one of fedmsg's setup.py extra_requires
+ 'fedmsg[consumers]',
'Sphinx',
|
scikit-image__scikit-image-1741 | peak_local_max Incorrect output type
This [function](http://scikit-image.org/docs/dev/api/skimage.feature.html#peak-local-max) is returning a `list` instead of an `ndarray` if no peaks are detected.
I traced the problem till this [line](https://github.com/scikit-image/scikit-image/blob/master/skimage/feature/peak.py#L122). However, I have to check if there is other case (beyond this line) that produces an incorrect output.
I will work on it this weekend and submit a pull-request or a code snippet here
| [
{
"content": "import numpy as np\nimport scipy.ndimage as ndi\nfrom ..filters import rank_order\n\n\ndef peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,\n exclude_border=True, indices=True, num_peaks=np.inf,\n footprint=None, labels=None):\n \"\"... | [
{
"content": "import numpy as np\nimport scipy.ndimage as ndi\nfrom ..filters import rank_order\n\n\ndef peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,\n exclude_border=True, indices=True, num_peaks=np.inf,\n footprint=None, labels=None):\n \"\"... | diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py
index 599a6e581cd..421abeec363 100644
--- a/skimage/feature/peak.py
+++ b/skimage/feature/peak.py
@@ -119,7 +119,7 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
if np.all(image == image.flat[0]):
if indices is True:
- return []
+ return np.empty((0, 2), np.int)
else:
return out
diff --git a/skimage/feature/tests/test_peak.py b/skimage/feature/tests/test_peak.py
index 90fe9b627cb..e62562083a6 100644
--- a/skimage/feature/tests/test_peak.py
+++ b/skimage/feature/tests/test_peak.py
@@ -11,6 +11,7 @@
def test_trivial_case():
trivial = np.zeros((25, 25))
peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True)
+ assert type(peak_indices) is np.ndarray
assert not peak_indices # inherent boolean-ness of empty list
peaks = peak.peak_local_max(trivial, min_distance=1, indices=False)
assert (peaks.astype(np.bool) == trivial).all()
@@ -89,7 +90,7 @@ def test_num_peaks3D():
image[5,5,::5] = np.arange(20)
peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
assert len(peaks_limited) == 2
-
+
def test_reorder_labels():
image = np.random.uniform(size=(40, 60))
|
digitalfabrik__integreat-cms-470 | Do not commit trivial changes to documentation
### Motivation
<!-- A clear and concise description of what the motivation for the new feature is, and what problem it is solving. -->
At the moment, our CircleCI jobs `build-documentation` and `deploy-documentation` automatically build our documentation and commit/push it to the `gh-pages` branch. Most of the times, this commit includes only trivial changes (see e.g. 6d6c89beda44ebd2448526a18636c464e1111355). This makes the "last changed" date very unreliable, because it is always the date of the last commit, not the date when the documentation actually changed the last time.
### Proposed Solution
<!-- A clear and concise description of the feature you would like to add, and how it solves the motivating problem. -->
Do not commit the changes to the documentation if only the "Last updated on ..." changes
### Alternatives
<!-- A clear and concise description of any alternative solutions or features you've considered, and why you're proposed solution is better. -->
Remove the "last changed" date
Do not commit trivial changes to documentation
### Motivation
<!-- A clear and concise description of what the motivation for the new feature is, and what problem it is solving. -->
At the moment, our CircleCI jobs `build-documentation` and `deploy-documentation` automatically build our documentation and commit/push it to the `gh-pages` branch. Most of the times, this commit includes only trivial changes (see e.g. 6d6c89beda44ebd2448526a18636c464e1111355). This makes the "last changed" date very unreliable, because it is always the date of the last commit, not the date when the documentation actually changed the last time.
### Proposed Solution
<!-- A clear and concise description of the feature you would like to add, and how it solves the motivating problem. -->
Do not commit the changes to the documentation if only the "Last updated on ..." changes
### Alternatives
<!-- A clear and concise description of any alternative solutions or features you've considered, and why you're proposed solution is better. -->
Remove the "last changed" date
| [
{
"content": "\"\"\"\nConfiguration file for the Sphinx documentation builder.\n\nThis file only contains a selection of the most common options. For a full\nlist see the documentation:\nhttps://www.sphinx-doc.org/en/master/usage/configuration.html\n\"\"\"\n\n# -- Path setup ------------------------------------... | [
{
"content": "\"\"\"\nConfiguration file for the Sphinx documentation builder.\n\nThis file only contains a selection of the most common options. For a full\nlist see the documentation:\nhttps://www.sphinx-doc.org/en/master/usage/configuration.html\n\"\"\"\n\n# -- Path setup ------------------------------------... | diff --git a/Pipfile b/Pipfile
index c8c609700d..a9bcd6b4c0 100644
--- a/Pipfile
+++ b/Pipfile
@@ -16,6 +16,7 @@ pylint-django = "*"
pylint-runner = "*"
sphinx = "*"
sphinxcontrib-django = "*"
+sphinx-last-updated-by-git = "*"
sphinx-rtd-theme = "*"
[packages]
diff --git a/Pipfile.lock b/Pipfile.lock
index 1ccafe893e..a30d979b26 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
- "sha256": "7f2b410d75f2b5983cc773585a3738d7a058acf8baf2417c7e5e76156c02d8e9"
+ "sha256": "f4294c69d8adfb109254979367353e64b3511216cf5bb13a51c7ad32143adebd"
},
"pipfile-spec": 6,
"requires": {
@@ -852,6 +852,14 @@
"index": "pypi",
"version": "==3.1.2"
},
+ "sphinx-last-updated-by-git": {
+ "hashes": [
+ "sha256:7017ba80de387cddbdf403201e950b8667e37c5773796874a7750098edd33e70",
+ "sha256:f103776539bb19fdf16714a653b6ccb5a4e31483fed9b18717e1797859e73cab"
+ ],
+ "index": "pypi",
+ "version": "==0.2.2"
+ },
"sphinx-rtd-theme": {
"hashes": [
"sha256:22c795ba2832a169ca301cd0a083f7a434e09c538c70beb42782c073651b707d",
diff --git a/sphinx/conf.py b/sphinx/conf.py
index 33ba9269e9..10428bf826 100644
--- a/sphinx/conf.py
+++ b/sphinx/conf.py
@@ -56,6 +56,7 @@ def setup(app):
"sphinx.ext.linkcode",
"sphinxcontrib_django",
"sphinx_rtd_theme",
+ "sphinx_last_updated_by_git",
]
# Enable cross-references to other documentations
|
meltano__meltano-6901 | ci: PyPi publish job fails in "Build distribution" step with error `module 'sqlalchemy' has no attribute 'orm'`
https://github.com/meltano/meltano/actions/runs/3267990463/jobs/5373871668
| [
{
"content": "\"\"\"add resource type to embed token\n\nRevision ID: 23ea52e6d784\nRevises: ceb00d7ff3bd\nCreate Date: 2020-02-12 09:29:31.592426\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\nfrom meltano.migrations.utils.dialect_typing import (\n get_dialect_name,\n max_string_length_for_... | [
{
"content": "\"\"\"add resource type to embed token\n\nRevision ID: 23ea52e6d784\nRevises: ceb00d7ff3bd\nCreate Date: 2020-02-12 09:29:31.592426\n\n\"\"\"\nimport sqlalchemy as sa\nimport sqlalchemy.orm\nfrom alembic import op\n\nfrom meltano.migrations.utils.dialect_typing import (\n get_dialect_name,\n ... | diff --git a/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py b/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py
index 70e6d097cf..fc4060a65e 100644
--- a/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py
+++ b/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py
@@ -6,6 +6,7 @@
"""
import sqlalchemy as sa
+import sqlalchemy.orm
from alembic import op
from meltano.migrations.utils.dialect_typing import (
|
liqd__a4-opin-388 | timeline wrong way?
the phases in the timeline seem to be sorted in the wrong direction:
| [
{
"content": "from django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\n\nfrom euth.modules import models as modules_models\n\nfrom . import content\nfrom .validators import validate_content\n\n\nclass... | [
{
"content": "from django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\n\nfrom euth.modules import models as modules_models\n\nfrom . import content\nfrom .validators import validate_content\n\n\nclass... | diff --git a/euth/phases/models.py b/euth/phases/models.py
index 0a2e799e7..bee6de8b5 100644
--- a/euth/phases/models.py
+++ b/euth/phases/models.py
@@ -26,6 +26,9 @@ class Phase(models.Model):
objects = PhasesQuerySet.as_manager()
+ class Meta:
+ ordering = ['type']
+
def __str__(self):
return '{} ({})'.format(self.name, self.type)
diff --git a/tests/projects/test_project_models.py b/tests/projects/test_project_models.py
index 1e2e46fa6..6d6538977 100644
--- a/tests/projects/test_project_models.py
+++ b/tests/projects/test_project_models.py
@@ -93,3 +93,12 @@ def test_image_deleted_after_update(project_factory, ImagePNG):
assert not os.path.isfile(image_path)
assert not os.path.isfile(thumbnail_path)
+
+
+@pytest.mark.django_db
+def test_phases_property(module, phase_factory):
+ project = module.project
+ phase1 = phase_factory(module=module, type='fake:30:type')
+ phase2 = phase_factory(module=module, type='fake:20:type')
+
+ assert list(project.phases) == [phase2, phase1]
|
vacanza__python-holidays-1699 | Update branch names
Rename:
- `master` -> `main`
- `beta` -> `dev`
| [
{
"content": "#!/usr/bin/env python3\n\n# python-holidays\n# ---------------\n# A fast, efficient Python library for generating country, province and state\n# specific sets of holidays on the fly. It aims to make determining whether a\n# specific date is a holiday as fast and flexible as possible.\n#\n# A... | [
{
"content": "#!/usr/bin/env python3\n\n# python-holidays\n# ---------------\n# A fast, efficient Python library for generating country, province and state\n# specific sets of holidays on the fly. It aims to make determining whether a\n# specific date is a holiday as fast and flexible as possible.\n#\n# A... | diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index d81d30823..159b16f62 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -41,5 +41,5 @@ Your PR description goes here.
Thanks again for your contribution!
-->
-[contributing-guidelines]: https://github.com/vacanza/python-holidays/blob/beta/CONTRIBUTING.rst
-[docs]: https://github.com/vacanza/python-holidays/tree/beta/docs/source
+[contributing-guidelines]: https://github.com/vacanza/python-holidays/blob/dev/CONTRIBUTING.rst
+[docs]: https://github.com/vacanza/python-holidays/tree/dev/docs/source
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index b26f34be8..204845091 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -5,11 +5,11 @@ updates:
schedule:
interval: weekly
day: wednesday
- target-branch: beta
+ target-branch: dev
- package-ecosystem: github-actions
directory: /
schedule:
interval: weekly
day: wednesday
- target-branch: beta
+ target-branch: dev
diff --git a/.github/workflows/pre-commit-autoupdate.yml b/.github/workflows/pre-commit-autoupdate.yml
index e347fcb4f..ab1cd089d 100644
--- a/.github/workflows/pre-commit-autoupdate.yml
+++ b/.github/workflows/pre-commit-autoupdate.yml
@@ -24,7 +24,7 @@ jobs:
- uses: peter-evans/create-pull-request@v6.0.0
with:
- base: beta
+ base: dev
body: Update pre-commit hooks to their latest versions.
branch: update-pre-commit-hooks
commit-message: 'chore: Update pre-commit hooks'
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index ec10a35d5..2935704e6 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -3,7 +3,7 @@ Contributing
============
.. _prs: https://github.com/vacanza/python-holidays/pulls
-.. _`beta branch`: https://github.com/vacanza/python-holidays/tree/beta
+.. _`dev branch`: https://github.com/vacanza/python-holidays/tree/dev
.. |contributors| image:: https://img.shields.io/github/contributors/vacanza/python-holidays
:target: https://github.com/vacanza/python-holidays/graphs/contributors
:alt: contributors
@@ -15,7 +15,7 @@ Basics
------
When contributing with fixes and new features, please start forking/branching
-from the `beta branch`_ to work on the latest code and reduce merging issues.
+from the `dev branch`_ to work on the latest code and reduce merging issues.
If you add/change holiday official dates or names you must include references to
all sources (government sites, archived web pages, wiki pages, etc) you've used
while working on this PR. Contributed PRs_ are required to include valid test
diff --git a/README.rst b/README.rst
index e87184a4b..28b5a7c09 100644
--- a/README.rst
+++ b/README.rst
@@ -42,7 +42,7 @@ flexible as possible.
:target: https://github.com/psf/black
:alt: Code style
- .. image:: https://img.shields.io/coverallsCoverage/github/vacanza/python-holidays?branch=master&color=%2341B5BE&style=flat
+ .. image:: https://img.shields.io/coverallsCoverage/github/vacanza/python-holidays?branch=main&color=%2341B5BE&style=flat
:target: https://coveralls.io/r/vacanza/python-holidays
:alt: Code coverage
@@ -60,8 +60,8 @@ flexible as possible.
:target: https://github.com/vacanza/python-holidays/graphs/contributors
:alt: GitHub contributors
- .. image:: https://img.shields.io/github/last-commit/vacanza/python-holidays/beta?color=%2341BE4A&style=flat
- :target: https://github.com/vacanza/python-holidays/commits/beta
+ .. image:: https://img.shields.io/github/last-commit/vacanza/python-holidays/dev?color=%2341BE4A&style=flat
+ :target: https://github.com/vacanza/python-holidays/commits/dev
:alt: GitHub last commit
@@ -74,14 +74,14 @@ The latest stable version can always be installed or updated via pip:
$ pip install --upgrade holidays
-The latest development (beta) version can be installed directly from GitHub:
+The latest development (dev) version can be installed directly from GitHub:
.. code-block:: shell
- $ pip install --upgrade https://github.com/vacanza/python-holidays/tarball/beta
+ $ pip install --upgrade https://github.com/vacanza/python-holidays/tarball/dev
-All new features are always first pushed to beta branch, then released on
-master branch upon official version upgrades.
+All new features are always first pushed to dev branch, then released on
+main branch upon official version upgrades.
Documentation
-------------
diff --git a/RELEASE.rst b/RELEASE.rst
index f327210d2..c710266eb 100644
--- a/RELEASE.rst
+++ b/RELEASE.rst
@@ -3,24 +3,24 @@ How to release a new version of Python Holidays
- Finalize the current development version
- - switch to ``beta`` branch and pull the most recent changes
- from https://github.com/vacanza/python-holidays remote ``beta`` branch.
+ - switch to ``dev`` branch and pull the most recent changes
+ from https://github.com/vacanza/python-holidays remote ``dev`` branch.
- generate release notes by running the following script
``scripts/generate_release_notes.py``
- insert the script's output into the top of ``CHANGES`` file
(see previous release notes for consistent formatting)
- - commit the updated ``CHANGES`` file to ``beta`` branch with the following
+ - commit the updated ``CHANGES`` file to ``dev`` branch with the following
commit message 'Finalize v<version>', e.g. 'Finalize v0.39'
- - push changes to https://github.com/vacanza/python-holidays ``beta`` branch
+ - push changes to https://github.com/vacanza/python-holidays ``dev`` branch
- make sure the push related CI/CD job(s) have been completed successfully
-- Merge the finalized changes into ``master`` branch:
+- Merge the finalized changes into ``main`` branch:
- - create a new PR for the recent changes from ``beta`` to ``master`` branch
+ - create a new PR for the recent changes from ``dev`` to ``main`` branch
using 'v<version>' as a PR title and the previously generated release notes
as a PR description
- get the PR reviewed by at least one of the code owners
- - merge the PR into ``master`` branch with 'Create a merge commit' action
+ - merge the PR into ``main`` branch with 'Create a merge commit' action
(**do not use 'Squash and merge'**)
- make sure the PR related CI/CD job(s) have been completed successfully
- make sure readthedocs.org documentation build jobs at
@@ -33,7 +33,7 @@ How to release a new version of Python Holidays
on the 'Draft a new release' button
- click on 'Choose a tag', enter 'v<version>' into the input field
(you should see something like 'Create a new tag: v0.39' on publish'
- - select **master** - instead of default ``beta`` in 'Target' dropdown
+ - select **main** - instead of default ``dev`` in 'Target' dropdown
- put 'v<version>' into 'Release title' field, e.g. 'v0.39'
- click on 'Generate release notes' button to collect new contributors and
full changelog link information (we normally keep it at the bottom with
@@ -57,12 +57,12 @@ How to release a new version of Python Holidays
- send "Python Holidays 'v<version>' has been released!" (or similar) message
to Vacanza Team Slack #release channel
- - pull the recent changes from ``master`` branch into ``beta``
+ - pull the recent changes from ``main`` branch into ``dev``
- bump the Python Holidays version at ``holidays/__init__.py`` file
- create a commit with 'Initialize v<version>' message, e.g.
- 'Initialize v0.40' and push it to ``beta`` branch (this may require
+ 'Initialize v0.40' and push it to ``dev`` branch (this may require
running ``make package`` to pass the tests locally)
- - make sure ``beta`` branch **is not behind** the master branch (there
+ - make sure ``dev`` branch **is not behind** the ``main`` branch (there
will be a message on top of the
- https://github.com/vacanza/python-holidays/tree/beta page in case it is)
+ https://github.com/vacanza/python-holidays/tree/dev page in case it is)
- make sure the push related CI/CD job(s) have been completed successfully
diff --git a/scripts/generate_release_notes.py b/scripts/generate_release_notes.py
index 8e21b6e1c..cfcf08139 100755
--- a/scripts/generate_release_notes.py
+++ b/scripts/generate_release_notes.py
@@ -25,7 +25,7 @@
sys.path.append(f"{Path.cwd()}")
import holidays # noqa: E402
-BRANCH_NAME = "beta"
+BRANCH_NAME = "dev"
HEADER_TEMPLATE = """
Version {version}
============
|
pytorch__examples-182 | Division error
Training a model for `fast-neural-style` raises a RuntimeError from variable division during input normalization.
- python2.7
- torch==0.1.12.post2
- torchvision==0.1.8
````
Traceback (most recent call last):
File "neural_style/neural_style.py", line 226, in <module>
main()
File "neural_style/neural_style.py", line 220, in main
train(args)
File "neural_style/neural_style.py", line 65, in train
style_v = utils.normalize_batch(style_v)
File "/home/paperspace/embro/neural_style/utils.py", line 42, in normalize_batch
batch /= Variable(std)
File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 793, in __idiv__
return self.div_(other)
File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 323, in div_
raise RuntimeError("div_ only supports scalar multiplication")
````
| [
{
"content": "import torch\nfrom PIL import Image\nfrom torch.autograd import Variable\n\n\ndef load_image(filename, size=None, scale=None):\n img = Image.open(filename)\n if size is not None:\n img = img.resize((size, size), Image.ANTIALIAS)\n elif scale is not None:\n img = img.resize((... | [
{
"content": "import torch\nfrom PIL import Image\nfrom torch.autograd import Variable\n\n\ndef load_image(filename, size=None, scale=None):\n img = Image.open(filename)\n if size is not None:\n img = img.resize((size, size), Image.ANTIALIAS)\n elif scale is not None:\n img = img.resize((... | diff --git a/fast_neural_style/neural_style/utils.py b/fast_neural_style/neural_style/utils.py
index d86b243440..525c25148c 100644
--- a/fast_neural_style/neural_style/utils.py
+++ b/fast_neural_style/neural_style/utils.py
@@ -39,5 +39,5 @@ def normalize_batch(batch):
std[:, 2, :, :] = 0.225
batch = torch.div(batch, 255.0)
batch -= Variable(mean)
- batch /= Variable(std)
+ batch = batch / Variable(std)
return batch
|
freqtrade__freqtrade-4302 | Invalid JSON returned by rest_client.py
## Describe your environment
* Operating system: Linux (Docker)
* Python Version: 3.8.6
* CCXT version: 1.40.99
* Freqtrade Version: 2021.1
## Describe the problem:
The JSON output from `rest_client.py` is not a valid JSON.
The reason of the problem is that the output uses single quotes instead of double quotes, and also some strings should be surrounded by quotes (At least `True, False and None`, and possibly some other strings).
### Steps to reproduce:
1. Start the bot
2. Make sure that there are some open trades
3. Run the `rest_client.py status` command to show the open trades, and pass the result to JQ to verify the validity of the JSON ouput
### Observed Results:
* What happened? : The JSON output is not a valid JSON
* What did you expect to happen? The JSON output to be a valid JSON.
### Relevant code exceptions or logs
Example of the error :
```
root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json status | jq .
parse error: Invalid numeric literal at line 1, column 13
```
To fix the JSON output, we need to :
- convert the single quotes `'` to double quotes `"`
- add double quotes around the words `True`, `False` and `None`, as JSON only allow numbers to not be quoted.
Example of the fix :
```
root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json status | tr "'" "\"" | sed -e "s|True|\"True\"|g" | sed -e "s|False|\"False\"|g" | sed -e "s|None|\"None\"|g" | jq -c .
[{"trade_id":2,"pair":"XRP/USDT","is_open":"True","exchange":"binance","amount":205.5,"amount_requested":205.5,"stake_amount":99,"strategy":"ichis","timeframe":60,"fee_open":0.001,"fee_open_cost":0.09897907500000001,"fee_open_currency":"USDT","fee_close":0.001,"fee_close_cost":"None","fee_close_currency":"None","open_date_hum":"7 minutes ago","open_date":"2021-02-01 01:44:14","open_timestamp":1612143854504,"open_rate":0.48165,"open_rate_requested":0.48165,"open_trade_value":99.07805408,"close_date_hum":"None","close_date":"None","close_timestamp":"None","close_rate":"None","close_rate_requested":"None","close_profit":"None","close_profit_pct":"None","close_profit_abs":"None","profit_ratio":-0.00935377,"profit_pct":-0.94,"profit_abs":-0.92675363,"sell_reason":"None","sell_order_status":"None","stop_loss_abs":0.385864,"stop_loss_ratio":-0.2,"stop_loss_pct":-20,"stoploss_order_id":"None","stoploss_last_update":"2021-02-01 01:44:51","stoploss_last_update_timestamp":1612143891511,"initial_stop_loss_abs":0.38532000000000005,"initial_stop_loss_ratio":-0.2,"initial_stop_loss_pct":-20,"min_rate":0.47578,"max_rate":0.48233,"open_order_id":"None","stoploss_current_dist":-0.09223600000000004,"stoploss_current_dist_pct":-19.29,"stoploss_current_dist_ratio":-0.19292198,"stoploss_entry_dist":-19.86229713,"stoploss_entry_dist_ratio":-0.20047121,"base_currency":"USDT","current_profit":-0.00935377,"current_profit_abs":-0.92675363,"current_profit_pct":-0.94,"current_rate":0.4781,"open_order":"None"}]
```
Another example with the command `stats` :
```
root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json stats | jq .
parse error: Invalid numeric literal at line 1, column 16
```
With the fix :
```
root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json stats | tr "'" "\"" | jq -c .
{"sell_reasons":{"trailing_stop_loss":{"wins":1,"losses":0,"draws":0}},"durations":{"wins":"1307.344079","draws":"N/A","losses":"N/A"}}
```
| [
{
"content": "#!/usr/bin/env python3\n\"\"\"\nSimple command line client into RPC commands\nCan be used as an alternate to Telegram\n\nShould not import anything from freqtrade,\nso it can be used as a standalone script.\n\"\"\"\n\nimport argparse\nimport inspect\nimport json\nimport logging\nimport re\nimport ... | [
{
"content": "#!/usr/bin/env python3\n\"\"\"\nSimple command line client into RPC commands\nCan be used as an alternate to Telegram\n\nShould not import anything from freqtrade,\nso it can be used as a standalone script.\n\"\"\"\n\nimport argparse\nimport inspect\nimport json\nimport logging\nimport re\nimport ... | diff --git a/scripts/rest_client.py b/scripts/rest_client.py
index 2232b842122..b6e66cfa4a2 100755
--- a/scripts/rest_client.py
+++ b/scripts/rest_client.py
@@ -379,7 +379,7 @@ def main(args):
print_commands()
return
- print(getattr(client, command)(*args["command_arguments"]))
+ print(json.dumps(getattr(client, command)(*args["command_arguments"])))
if __name__ == "__main__":
|
pydantic__pydantic-1204 | Mypy does not allow using the root validator with arguments other than pre or _func
# Bug
Output of `python -c "import pydantic.utils; print(pydantic.utils.version_info())"`:
```
pydantic version: 1.4
pydantic compiled: True
install path: /home/[username]/Desktop/[project]/env/lib/python3.6/site-packages/pydantic
python version: 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0]
platform: Linux-5.3.0-26-generic-x86_64-with-Ubuntu-18.04-bionic
optional deps. installed: ['typing-extensions']
```
Code:
```py
@root_validator(pre=False, skip_on_failure=True)
def test_validator(cls, values):
return values
```
Output:
```
test.py:5: error: No overload variant of "root_validator" matches argument types "bool", "bool"
test.py:5: note: Possible overload variants:
test.py:5: note: def root_validator(_func: Callable[..., Any]) -> classmethod
test.py:5: note: def root_validator(*, pre: bool = ...) -> Callable[[Callable[..., Any]], classmethod]
```
| [
{
"content": "import warnings\nfrom collections import ChainMap\nfrom functools import wraps\nfrom itertools import chain\nfrom types import FunctionType\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload\n\nfrom .errors import ConfigError\nfrom .... | [
{
"content": "import warnings\nfrom collections import ChainMap\nfrom functools import wraps\nfrom itertools import chain\nfrom types import FunctionType\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload\n\nfrom .errors import ConfigError\nfrom .... | diff --git a/changes/1192-samuelcolvin.md b/changes/1192-samuelcolvin.md
new file mode 100644
index 00000000000..da7a14ea329
--- /dev/null
+++ b/changes/1192-samuelcolvin.md
@@ -0,0 +1 @@
+fix mypy signature for `root_validator`
diff --git a/pydantic/class_validators.py b/pydantic/class_validators.py
index 19f2924ccfa..3d1032e789e 100644
--- a/pydantic/class_validators.py
+++ b/pydantic/class_validators.py
@@ -103,7 +103,9 @@ def root_validator(_func: AnyCallable) -> classmethod:
@overload
-def root_validator(*, pre: bool = False) -> Callable[[AnyCallable], classmethod]:
+def root_validator(
+ *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False
+) -> Callable[[AnyCallable], classmethod]:
...
diff --git a/tests/mypy/modules/success.py b/tests/mypy/modules/success.py
index f1aa5eab9fd..b80d77e4723 100644
--- a/tests/mypy/modules/success.py
+++ b/tests/mypy/modules/success.py
@@ -33,7 +33,7 @@ def check_age(cls, value: int) -> int:
def root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]:
return values
- @root_validator(pre=True)
+ @root_validator(pre=True, allow_reuse=False, skip_on_failure=False)
def pre_root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]:
return values
|
Pycord-Development__pycord-2345 | AttributeError: 'Interaction' object has no attribute 'entitlements'
### Summary
The newest verision (2.4.1.dev241+g9683629e) crashes when executing slash commands
### Reproduction Steps
Start the bot code below and run the `hello` command
### Minimal Reproducible Code
```python
import discord
bot = discord.Bot()
@bot.slash_command()
async def hello(ctx):
await ctx.respond("Hello!")
bot.run("")
```
### Expected Results
Bot responds with `Hello!`
### Actual Results
```
Traceback (most recent call last):
File "C:\Users\Timo\Desktop\pythonProject\main.py", line 11, in <module>
bot.run("...")
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 766, in run
return future.result()
^^^^^^^^^^^^^^^
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 745, in runner
await self.start(*args, **kwargs)
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 709, in start
await self.connect(reconnect=reconnect)
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 601, in connect
await self.ws.poll_event()
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\gateway.py", line 604, in poll_event
await self.received_message(msg.data)
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\gateway.py", line 554, in received_message
func(data)
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\state.py", line 816, in parse_interaction_create
interaction = Interaction(data=data, state=self)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\interactions.py", line 170, in __init__
self._from_data(data)
File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\interactions.py", line 187, in _from_data
self.entitlements: list[Entitlement] = [
^^^^^^^^^^^^^^^^^
AttributeError: 'Interaction' object has no attribute 'entitlements'
```
### Intents
-
### System Information
- Python v3.10.9-final
- py-cord v2.4.1-final
- aiohttp v3.9.1
- system info: Windows 10 10.0.22631
### Checklist
- [X] I have searched the open issues for duplicates.
- [X] I have shown the entire traceback, if possible.
- [X] I have removed my token from display, if visible.
### Additional Context
_No response_
| [
{
"content": "\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2015-2021 Rapptz\nCopyright (c) 2021-present Pycord Development\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the \"Software\"),\nto deal in the Software without r... | [
{
"content": "\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2015-2021 Rapptz\nCopyright (c) 2021-present Pycord Development\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the \"Software\"),\nto deal in the Software without r... | diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9210527012..ced04899ae 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -232,6 +232,9 @@ These changes are available on the `master` branch, but have not yet been releas
([#2337](https://github.com/Pycord-Development/pycord/pull/2337))
- Fixed `TypeError` due to `(Sync)WebhookMessage._thread_id` being set to `None`.
([#2343](https://github.com/Pycord-Development/pycord/pull/2343))
+- Fixed `AttributeError` due to `entitlements` not being included in
+ `Interaction.__slots__`.
+ ([#2345](https://github.com/Pycord-Development/pycord/pull/2345))
## [2.4.1] - 2023-03-20
diff --git a/discord/interactions.py b/discord/interactions.py
index 6443af1c04..0e254d514a 100644
--- a/discord/interactions.py
+++ b/discord/interactions.py
@@ -148,6 +148,7 @@ class Interaction:
"token",
"version",
"custom_id",
+ "entitlements",
"_channel_data",
"_message_data",
"_guild_data",
|
pyodide__pyodide-3562 | Error about `--user` and `--target` flag when installing xbuildenv
I sometimes get following error while installing xbuild environment:
```bash
$ pyodide build .
Downloading xbuild environment
Installing xbuild environment
stderr:
ERROR: Can not combine '--user' and '--target'
[notice] A new release of pip available: 22.3.1 -> 23.0
[notice] To update, run: /home/gitpod/.pyenv/versions/3.10.2/bin/python -m pip install --upgrade pip
```
It happens here, which installs host site packages:
https://github.com/pyodide/pyodide/blob/7cc1058358242a5a9012edbb8163d86a860a1a28/pyodide-build/pyodide_build/install_xbuildenv.py#L50-L57
I think we need to add `--no-user` flag explicitly to prevent this error.
| [
{
"content": "import argparse\nimport json\nimport shutil\nimport subprocess\nfrom pathlib import Path\nfrom urllib.request import urlopen, urlretrieve\n\nfrom .common import exit_with_stdio, get_make_flag, get_pyodide_root\nfrom .create_pypa_index import create_pypa_index\nfrom .logger import logger\n\n\ndef m... | [
{
"content": "import argparse\nimport json\nimport shutil\nimport subprocess\nfrom pathlib import Path\nfrom urllib.request import urlopen, urlretrieve\n\nfrom .common import exit_with_stdio, get_make_flag, get_pyodide_root\nfrom .create_pypa_index import create_pypa_index\nfrom .logger import logger\n\n\ndef m... | diff --git a/docs/project/changelog.md b/docs/project/changelog.md
index 7790fef37c2..5e447fee1ba 100644
--- a/docs/project/changelog.md
+++ b/docs/project/changelog.md
@@ -108,6 +108,9 @@ myst:
CPU cores in the system and uses them for parallel builds.
{pr}`3559`
+- {{ Fix }} Fixed pip install error when installing cross build environment.
+ {pr}`3562`
+
### Pyodide CLI
- Added `pyodide py-compile` CLI command that py compiles a wheel, converting
diff --git a/pyodide-build/pyodide_build/install_xbuildenv.py b/pyodide-build/pyodide_build/install_xbuildenv.py
index e5a40358312..23e6a68d91f 100644
--- a/pyodide-build/pyodide_build/install_xbuildenv.py
+++ b/pyodide-build/pyodide_build/install_xbuildenv.py
@@ -51,6 +51,7 @@ def install_xbuildenv(version: str, xbuildenv_path: Path) -> None:
[
"pip",
"install",
+ "--no-user",
"-t",
host_site_packages,
"-r",
|
joke2k__faker-1423 | Faker adds path objects to sys.path_importer_cache, breaking other packages
* Faker version: 6.6.3
* OS: Gentoo Linux
After importing `faker`, entries with `PosixPath` objects are added as keys to `sys.path_importer_cache`. However, the keys are supposed to be regular `str`s there, and the wrong type breaks software relying on `str` methods being available, e.g. astroid:
```
___________________________________________ ClassNodeTest.test_slots_added_dynamically_still_inferred ____________________________________________
self = <tests.unittest_scoped_nodes.ClassNodeTest testMethod=test_slots_added_dynamically_still_inferred>
def tearDown(self):
del sys.path[0]
datadir = find("")
for key in list(sys.path_importer_cache):
> if key.startswith(datadir):
E AttributeError: 'PosixPath' object has no attribute 'startswith'
tests/resources.py:41: AttributeError
```
Note that since Faker installs a pytest plugin, it is autoloaded by default in all programs' test suites.
### Steps to reproduce
```
import sys
import faker
print(sys.path_importer_cache)
```
### Expected behavior
The printed dict should only contain `str` keys.
### Actual behavior
```
[...] PosixPath('/usr/lib/python3.9/site-packages/faker/providers/address'): FileFinder(PosixPath('/usr/lib/python3.9/site-packages/faker/providers/address')), PosixPath('/usr/lib/python3.9/site-packages/faker/providers/automotive'): FileFinder(PosixPath('/usr/lib/python3.9/site-packages/faker/providers/automotive')), [...]
```
| [
{
"content": "import pkgutil\nimport sys\n\nfrom importlib import import_module\nfrom pathlib import Path\nfrom types import ModuleType\nfrom typing import List, Set\n\n\ndef get_path(module: ModuleType) -> str:\n if getattr(sys, 'frozen', False):\n # frozen\n\n if getattr(sys, '_MEIPASS', Fals... | [
{
"content": "import pkgutil\nimport sys\n\nfrom importlib import import_module\nfrom pathlib import Path\nfrom types import ModuleType\nfrom typing import List, Set\n\n\ndef get_path(module: ModuleType) -> str:\n if getattr(sys, 'frozen', False):\n # frozen\n\n if getattr(sys, '_MEIPASS', Fals... | diff --git a/faker/utils/loading.py b/faker/utils/loading.py
index 18ffb6482c..757a012565 100644
--- a/faker/utils/loading.py
+++ b/faker/utils/loading.py
@@ -22,7 +22,7 @@ def get_path(module: ModuleType) -> str:
else:
# unfrozen
path = Path(module.__file__).parent
- return path
+ return str(path)
def list_module(module: ModuleType) -> List[str]:
|
microsoft__DeepSpeed-2267 | [BUG] ImportError: cannot import name 'OrderedDict'
python3.6, master version
```
Traceback (most recent call last):
File "/opt/conda/bin/ds_report", line 3, in <module>
from deepspeed.env_report import cli_main
File "/opt/conda/lib/python3.6/site-packages/deepspeed/__init__.py", line 16, in <module>
from .runtime.engine import DeepSpeedEngine, DeepSpeedOptimizerCallable, DeepSpeedSchedulerCallable
File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/engine.py", line 30, in <module>
from deepspeed.runtime.bf16_optimizer import BF16_Optimizer
File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/bf16_optimizer.py", line 5, in <module>
from typing import OrderedDict
ImportError: cannot import name 'OrderedDict'
```
| [
{
"content": "\"\"\"\nCopyright 2022 The Microsoft DeepSpeed Team\n\"\"\"\n\nfrom typing import OrderedDict\nimport torch\nimport os\nfrom deepspeed import comm as dist\nfrom deepspeed.runtime.constants import PIPE_REPLICATED\nfrom deepspeed.ops.op_builder import UtilsBuilder\nfrom deepspeed.runtime import ZeRO... | [
{
"content": "\"\"\"\nCopyright 2022 The Microsoft DeepSpeed Team\n\"\"\"\n\nfrom collections import OrderedDict\nimport torch\nimport os\nfrom deepspeed import comm as dist\nfrom deepspeed.runtime.constants import PIPE_REPLICATED\nfrom deepspeed.ops.op_builder import UtilsBuilder\nfrom deepspeed.runtime import... | diff --git a/deepspeed/runtime/bf16_optimizer.py b/deepspeed/runtime/bf16_optimizer.py
index 303267f0494d..a9988e2c498f 100644
--- a/deepspeed/runtime/bf16_optimizer.py
+++ b/deepspeed/runtime/bf16_optimizer.py
@@ -2,7 +2,7 @@
Copyright 2022 The Microsoft DeepSpeed Team
"""
-from typing import OrderedDict
+from collections import OrderedDict
import torch
import os
from deepspeed import comm as dist
|
borgbackup__borg-540 | stats no longer shows day of week
borg 0.29.0
When using create --stats the output no longer shows the day of the week.
0.28.2 output:
Keeping archive: panda-121415_1835 Mon Dec 14 18:35:50 2015
Keeping archive: panda-121415_0925 Mon Dec 14 09:26:27 2015
Keeping archive: panda-121315_1835 Sun Dec 13 18:36:03 2015
....
0.29.0 output:
Keeping archive: panda-121515_1537 2015-12-15 15:37:50
Keeping archive: panda-121515_0654 2015-12-15 06:55:36
Keeping archive: panda-121415_1835 2015-12-14 18:35:50
Keeping archive: panda-121315_1835 2015-12-13 18:36:03
....
| [
{
"content": "from .support import argparse # see support/__init__.py docstring, DEPRECATED - remove after requiring py 3.4\n\nimport binascii\nfrom collections import namedtuple\nfrom functools import wraps\nimport grp\nimport os\nimport pwd\nimport re\ntry:\n from shutil import get_terminal_size\nexcept I... | [
{
"content": "from .support import argparse # see support/__init__.py docstring, DEPRECATED - remove after requiring py 3.4\n\nimport binascii\nfrom collections import namedtuple\nfrom functools import wraps\nimport grp\nimport os\nimport pwd\nimport re\ntry:\n from shutil import get_terminal_size\nexcept I... | diff --git a/borg/helpers.py b/borg/helpers.py
index 62b3278163..57f0a70226 100644
--- a/borg/helpers.py
+++ b/borg/helpers.py
@@ -462,7 +462,7 @@ def dir_is_tagged(path, exclude_caches, exclude_if_present):
def format_time(t):
"""use ISO-8601 date and time format
"""
- return t.strftime('%Y-%m-%d %H:%M:%S')
+ return t.strftime('%a, %Y-%m-%d %H:%M:%S')
def format_timedelta(td):
|
TheAlgorithms__Python-8746 | Revert "Create guess_the_number_search.py"
Reverts TheAlgorithms/Python#7937
@ChrisO345 the algorithm you merged failed tests, you shouldn't have merged it
> https://github.com/TheAlgorithms/Python/actions/runs/4997927546/jobs/8952811360
> https://results.pre-commit.ci/run/github/63476337/1684282951.oykZY7Z4R3qR94KO0YZS2Q
| [
{
"content": "\"\"\"\nguess the number using lower,higher and the value to find or guess\n\nsolution works by dividing lower and higher of number guessed\n\nsuppose lower is 0, higher is 1000 and the number to guess is 355\n\n>>> guess_the_number(10, 1000, 17)\nstarted...\nguess the number : 17\ndetails : [505,... | [
{
"content": "\"\"\"\nguess the number using lower,higher and the value to find or guess\n\nsolution works by dividing lower and higher of number guessed\n\nsuppose lower is 0, higher is 1000 and the number to guess is 355\n\n>>> guess_the_number(10, 1000, 17)\nstarted...\nguess the number : 17\ndetails : [505,... | diff --git a/DIRECTORY.md b/DIRECTORY.md
index 46bd51ce91ea..82791cde183d 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -605,6 +605,7 @@
* [Newton Raphson](maths/newton_raphson.py)
* [Number Of Digits](maths/number_of_digits.py)
* [Numerical Integration](maths/numerical_integration.py)
+ * [Odd Sieve](maths/odd_sieve.py)
* [Perfect Cube](maths/perfect_cube.py)
* [Perfect Number](maths/perfect_number.py)
* [Perfect Square](maths/perfect_square.py)
@@ -712,6 +713,7 @@
* [Gauss Easter](other/gauss_easter.py)
* [Graham Scan](other/graham_scan.py)
* [Greedy](other/greedy.py)
+ * [Guess The Number Search](other/guess_the_number_search.py)
* [H Index](other/h_index.py)
* [Least Recently Used](other/least_recently_used.py)
* [Lfu Cache](other/lfu_cache.py)
diff --git a/other/guess_the_number_search.py b/other/guess_the_number_search.py
index 0439223f2ec9..01e8898bbb8a 100644
--- a/other/guess_the_number_search.py
+++ b/other/guess_the_number_search.py
@@ -148,7 +148,7 @@ def answer(number: int) -> str:
break
print(f"guess the number : {last_numbers[-1]}")
- print(f"details : {str(last_numbers)}")
+ print(f"details : {last_numbers!s}")
def main() -> None:
|
encode__httpx-2125 | httpx cli --proxy and --proxies option problem
The `httpx --help` shows `--proxy` option, but actually it accepts `--proxies` option.
- `--proxy`
https://github.com/encode/httpx/blob/master/httpx/_main.py#L72
- `--proxies`
https://github.com/encode/httpx/blob/master/httpx/_main.py#L379
Which is correct? Is there an undocumented change from`--proxy` to `--proxies`?
| [
{
"content": "import functools\nimport json\nimport sys\nimport typing\n\nimport click\nimport httpcore\nimport pygments.lexers\nimport pygments.util\nimport rich.console\nimport rich.markup\nimport rich.progress\nimport rich.syntax\nimport rich.table\n\nfrom ._client import Client\nfrom ._exceptions import Req... | [
{
"content": "import functools\nimport json\nimport sys\nimport typing\n\nimport click\nimport httpcore\nimport pygments.lexers\nimport pygments.util\nimport rich.console\nimport rich.markup\nimport rich.progress\nimport rich.syntax\nimport rich.table\n\nfrom ._client import Client\nfrom ._exceptions import Req... | diff --git a/httpx/_main.py b/httpx/_main.py
index 7bd6b90846..ebcb65214f 100644
--- a/httpx/_main.py
+++ b/httpx/_main.py
@@ -69,7 +69,7 @@ def print_help() -> None:
)
table.add_row(
- "--proxy [cyan]URL",
+ "--proxies [cyan]URL",
"Send the request via a proxy. Should be the URL giving the proxy address.",
)
|
MongoEngine__mongoengine-2043 | Missuse of write_concern in Document.save
It is possible to define write_concern on the connection.
However, while calling save method on a document, the following code (line 229 in document.py) tells you that if it's not define on save call, it is erased, whatever is your settings on the connection:
```
if write_concern is None:
write_concern = {"w": 1}
```
The idea is to delete those two lines to fallback on connection settings.
| [
{
"content": "import re\nimport warnings\n\nfrom bson.dbref import DBRef\nimport pymongo\nfrom pymongo.read_preferences import ReadPreference\nimport six\nfrom six import iteritems\n\nfrom mongoengine import signals\nfrom mongoengine.base import (BaseDict, BaseDocument, BaseList,\n ... | [
{
"content": "import re\nimport warnings\n\nfrom bson.dbref import DBRef\nimport pymongo\nfrom pymongo.read_preferences import ReadPreference\nimport six\nfrom six import iteritems\n\nfrom mongoengine import signals\nfrom mongoengine.base import (BaseDict, BaseDocument, BaseList,\n ... | diff --git a/docs/changelog.rst b/docs/changelog.rst
index 356e2b65b..80b92b814 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -12,6 +12,7 @@ Development
- disconnect now clears `mongoengine.connection._connection_settings`
- disconnect now clears the cached attribute `Document._collection`
- POTENTIAL BREAKING CHANGE: Aggregate gives wrong results when used with a queryset having limit and skip #2029
+- Fix the default write concern of .save that was overwriting the connection write concern #568
- mongoengine now requires pymongo>=3.5 #2017
- Generate Unique Indices for SortedListField and EmbeddedDocumentListFields #2020
- connect() fails immediately when db name contains invalid characters #2031 #1718
diff --git a/mongoengine/document.py b/mongoengine/document.py
index 753520c7e..5ccedbfab 100644
--- a/mongoengine/document.py
+++ b/mongoengine/document.py
@@ -375,7 +375,7 @@ def save(self, force_insert=False, validate=True, clean=True,
self.validate(clean=clean)
if write_concern is None:
- write_concern = {'w': 1}
+ write_concern = {}
doc = self.to_mongo()
|
redis__redis-py-2316 | RediSearch: search command doesn't support asyncio Pipeline
The RediSearch search command returns an instance of the `Result` class except when the Redis client is a `Pipeline` because `Pipeline` returns itself instead of a result when you execute a command. There's code that checks for this in both the `SearchCommands` and `AsyncSearchCommands` classes:
https://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L414
https://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L883
However, this check doesn't work if the `Pipeline` is from the `redis.asyncio.client` module. The following modification should fix the issue:
```python
from redis.client import Pipeline
from redis.asyncio.client import Pipeline as AsyncPipeline
...
if isinstance(res, Pipeline) or isinstance(res, AsyncPipeline):
return res
...
```
I'm not sure if it makes sense to check for both `Pipeline` types or if the `SearchCommands` class should check for just `Pipeline` and the `AsyncSearchCommands` class should check for just `AsyncPipeline`. Let me know and I can make a PR. Or feel free to make the changes yourself if that's easier. Thanks!
| [
{
"content": "import redis\n\nfrom ...asyncio.client import Pipeline as AsyncioPipeline\nfrom .commands import AsyncSearchCommands, SearchCommands\n\n\nclass Search(SearchCommands):\n \"\"\"\n Create a client for talking to search.\n It abstracts the API of the module and lets you just use the engine.\... | [
{
"content": "import redis\n\nfrom ...asyncio.client import Pipeline as AsyncioPipeline\nfrom .commands import AsyncSearchCommands, SearchCommands\n\n\nclass Search(SearchCommands):\n \"\"\"\n Create a client for talking to search.\n It abstracts the API of the module and lets you just use the engine.\... | diff --git a/redis/commands/search/__init__.py b/redis/commands/search/__init__.py
index 923711b8c4..70e9c279e5 100644
--- a/redis/commands/search/__init__.py
+++ b/redis/commands/search/__init__.py
@@ -167,5 +167,5 @@ class Pipeline(SearchCommands, redis.client.Pipeline):
"""Pipeline for the module."""
-class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline):
+class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline, Pipeline):
"""AsyncPipeline for the module."""
diff --git a/tests/test_asyncio/test_search.py b/tests/test_asyncio/test_search.py
index aa52602588..ec83e565e5 100644
--- a/tests/test_asyncio/test_search.py
+++ b/tests/test_asyncio/test_search.py
@@ -16,7 +16,7 @@
from redis.commands.search.query import GeoFilter, NumericFilter, Query
from redis.commands.search.result import Result
from redis.commands.search.suggestion import Suggestion
-from tests.conftest import skip_ifmodversion_lt
+from tests.conftest import skip_if_redis_enterprise, skip_ifmodversion_lt
WILL_PLAY_TEXT = os.path.abspath(
os.path.join(os.path.dirname(__file__), "testdata", "will_play_text.csv.bz2")
@@ -1043,3 +1043,22 @@ async def test_aggregations_sort_by_and_limit(modclient: redis.Redis):
res = await modclient.ft().aggregate(req)
assert len(res.rows) == 1
assert res.rows[0] == ["t1", "b"]
+
+
+@pytest.mark.redismod
+@skip_if_redis_enterprise()
+async def test_search_commands_in_pipeline(modclient: redis.Redis):
+ p = await modclient.ft().pipeline()
+ p.create_index((TextField("txt"),))
+ p.add_document("doc1", payload="foo baz", txt="foo bar")
+ p.add_document("doc2", txt="foo bar")
+ q = Query("foo bar").with_payloads()
+ await p.search(q)
+ res = await p.execute()
+ assert res[:3] == ["OK", "OK", "OK"]
+ assert 2 == res[3][0]
+ assert "doc1" == res[3][1]
+ assert "doc2" == res[3][4]
+ assert "foo baz" == res[3][2]
+ assert res[3][5] is None
+ assert res[3][3] == res[3][6] == ["txt", "foo bar"]
|
mars-project__mars-1302 | [BUG] mt.linalg.norm failed and raises TypeError
<!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Describe the bug**
`mt.linalg.norm` failed and raises `TypeError: copy() got an unexpected keyword argument 'order'
`
**To Reproduce**
To help us reproducing this bug, please provide information below:
1. Your Python version
2. The version of Mars you use
3. Versions of crucial packages, such as numpy, scipy and protobuf
4. Full stack of the error.
5. Minimized code to reproduce the error.
```
In [2]: import mars.tensor as mt
In [3]: t = mt.random.rand(10, 10, chunk_size=5)
In [4]: mt.linalg.norm(t).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-900a2d2bec75> in <module>
----> 1 mt.linalg.norm(t).execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
559
560 def execute(self, session=None, **kw):
--> 561 self._data.execute(session, **kw)
562 return self
563
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
368
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
404 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
405 for t in tileables)
--> 406 result = self._sess.run(*tileables, **kw)
407
408 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
102 # set number of running cores
103 self.context.set_ncores(kw['n_parallel'])
--> 104 res = self._executor.execute_tileables(tileables, **kw)
105 return res
106
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
387 _kernel_mode.eager = False
388 _kernel_mode.eager_count = enter_eager_count + 1
--> 389 return func(*args, **kwargs)
390 finally:
391 _kernel_mode.eager_count -= 1
~/Workspace/mars/mars/utils.py in inner(*args, **kwargs)
481 def inner(*args, **kwargs):
482 with build_mode():
--> 483 return func(*args, **kwargs)
484 return inner
485
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose)
825 # build chunk graph, tile will be done during building
826 chunk_graph = chunk_graph_builder.build(
--> 827 tileables, tileable_graph=tileable_graph)
828 tileable_graph = chunk_graph_builder.prev_tileable_graph
829 temp_result_keys = set(result_keys)
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
387 _kernel_mode.eager = False
388 _kernel_mode.eager_count = enter_eager_count + 1
--> 389 return func(*args, **kwargs)
390 finally:
391 _kernel_mode.eager_count -= 1
~/Workspace/mars/mars/utils.py in inner(*args, **kwargs)
481 def inner(*args, **kwargs):
482 with build_mode():
--> 483 return func(*args, **kwargs)
484 return inner
485
~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph)
340
341 chunk_graph = super().build(
--> 342 tileables, tileable_graph=tileable_graph)
343 self._iterative_chunk_graphs.append(chunk_graph)
344 if len(self._interrupted_ops) == 0:
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
387 _kernel_mode.eager = False
388 _kernel_mode.eager_count = enter_eager_count + 1
--> 389 return func(*args, **kwargs)
390 finally:
391 _kernel_mode.eager_count -= 1
~/Workspace/mars/mars/utils.py in inner(*args, **kwargs)
481 def inner(*args, **kwargs):
482 with build_mode():
--> 483 return func(*args, **kwargs)
484 return inner
485
~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph)
253 # for further execution
254 partial_tiled_chunks = \
--> 255 self._on_tile_failure(tileable_data.op, exc_info)
256 if partial_tiled_chunks is not None and \
257 len(partial_tiled_chunks) > 0:
~/Workspace/mars/mars/tiles.py in inner(op, exc_info)
292 on_tile_failure(op, exc_info)
293 else:
--> 294 raise exc_info[1].with_traceback(exc_info[2]) from None
295 return inner
296
~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph)
233 continue
234 try:
--> 235 tiled = self._tile(tileable_data, tileable_graph)
236 tiled_op.add(tileable_data.op)
237 for t, td in zip(tileable_data.op.outputs, tiled):
~/Workspace/mars/mars/tiles.py in _tile(self, tileable_data, tileable_graph)
328 if any(inp.op in self._interrupted_ops for inp in tileable_data.inputs):
329 raise TilesError('Tile fail due to failure of inputs')
--> 330 return super()._tile(tileable_data, tileable_graph)
331
332 @kernel_mode
~/Workspace/mars/mars/tiles.py in _tile(self, tileable_data, tileable_graph)
191 t._nsplits = o.nsplits
192 elif on_tile is None:
--> 193 tds[0]._inplace_tile()
194 else:
195 tds = on_tile(tileable_data.op.outputs, tds)
~/Workspace/mars/mars/core.py in _inplace_tile(self)
160
161 def _inplace_tile(self):
--> 162 return handler.inplace_tile(self)
163
164 def __getattr__(self, attr):
~/Workspace/mars/mars/tiles.py in inplace_tile(self, to_tile)
126 if not to_tile.is_coarse():
127 return to_tile
--> 128 dispatched = self.dispatch(to_tile.op)
129 self._assign_to([d.data for d in dispatched], to_tile.op.outputs)
130 return to_tile
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
387 _kernel_mode.eager = False
388 _kernel_mode.eager_count = enter_eager_count + 1
--> 389 return func(*args, **kwargs)
390 finally:
391 _kernel_mode.eager_count -= 1
~/Workspace/mars/mars/tiles.py in dispatch(self, op)
113 return self._handlers[op_cls](op)
114 try:
--> 115 return op_cls.tile(op)
116 except NotImplementedError as ex:
117 cause = ex
~/Workspace/mars/mars/tensor/linalg/norm.py in tile(cls, op)
96 return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=out_chunks, nsplits=nsplits)
97
---> 98 r = cls._norm(x.astype(op.outputs[0].dtype), ord, axis, keepdims)
99 recursive_tile(r)
100 new_op = op.copy()
~/Workspace/mars/mars/tensor/base/astype.py in _astype(tensor, dtype, order, casting, copy)
154
155 if tensor.dtype == dtype and tensor.order == tensor_order:
--> 156 return tensor if not copy else tensor.copy(order=order)
157 elif not np.can_cast(tensor.dtype, dtype, casting=casting):
158 raise TypeError('Cannot cast array from {0!r} to {1!r} '
TypeError: copy() got an unexpected keyword argument 'order'
```
| [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2020 Alibaba Group Holding Ltd.\n#\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#\n# http://... | [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2020 Alibaba Group Holding Ltd.\n#\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#\n# http://... | diff --git a/mars/tensor/linalg/norm.py b/mars/tensor/linalg/norm.py
index c057c26728..29871d6599 100644
--- a/mars/tensor/linalg/norm.py
+++ b/mars/tensor/linalg/norm.py
@@ -64,7 +64,7 @@ def __call__(self, x):
@classmethod
def tile(cls, op):
- x = op.input
+ x = astensor(op.input)
axis = op.axis
ord = op.ord
keepdims = op.keepdims
diff --git a/mars/tensor/linalg/tests/test_linalg_execute.py b/mars/tensor/linalg/tests/test_linalg_execute.py
index 599d8e6a39..739ff6d394 100644
--- a/mars/tensor/linalg/tests/test_linalg_execute.py
+++ b/mars/tensor/linalg/tests/test_linalg_execute.py
@@ -811,6 +811,12 @@ def testNormExecution(self):
res = self.executor.execute_tensor(t)[0]
self.assertAlmostEqual(res, 3.14, delta=1)
+ raw = np.random.RandomState(0).rand(10, 10)
+ d = norm(tensor(raw, chunk_size=5))
+ expected = self.executor.execute_tensor(d, concat=True)[0]
+ result = np.linalg.norm(raw)
+ np.testing.assert_allclose(expected, result)
+
def testTensordotExecution(self):
size_executor = ExecutorForTest(sync_provider_type=ExecutorForTest.SyncProviderType.MOCK)
|
pallets__werkzeug-1726 | Pytest fails due to missing dependency
Reproduction:
Activate virtualenv and execute `pytest`
Expected result:
Tests are run
Actual result:
```(env) :~/git/werkzeug[master ?]🙂 pytest
========================= test session starts ==========================
platform darwin -- Python 3.6.8, pytest-5.3.2, py-1.8.0, pluggy-0.13.0
rootdir: /Users/latham/git/werkzeug, inifile: setup.cfg, testpaths: tests
plugins: mock-1.11.2, cov-2.8.1
collected 563 items / 1 error / 562 selected
================================ ERRORS ================================
_________________ ERROR collecting tests/test_debug.py _________________
tests/test_debug.py:372: in <module>
@pytest.mark.timeout(2)
../../Library/Python/3.6/lib/python/site-packages/_pytest/mark/structures.py:327: in __getattr__
PytestUnknownMarkWarning,
E pytest.PytestUnknownMarkWarning: Unknown pytest.mark.timeout - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/latest/mark.html
!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!
=========================== 1 error in 1.60s ===========================```
| [
{
"content": "import io\nimport re\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nwith io.open(\"README.rst\", \"rt\", encoding=\"utf8\") as f:\n readme = f.read()\n\nwith io.open(\"src/werkzeug/__init__.py\", \"rt\", encoding=\"utf8\") as f:\n version = re.search(r'__version__ =... | [
{
"content": "import io\nimport re\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nwith io.open(\"README.rst\", \"rt\", encoding=\"utf8\") as f:\n readme = f.read()\n\nwith io.open(\"src/werkzeug/__init__.py\", \"rt\", encoding=\"utf8\") as f:\n version = re.search(r'__version__ =... | diff --git a/setup.py b/setup.py
index 157d884ab..de09ac3c7 100644
--- a/setup.py
+++ b/setup.py
@@ -57,6 +57,7 @@
"watchdog": ["watchdog"],
"dev": [
"pytest",
+ "pytest-timeout",
"coverage",
"tox",
"sphinx",
|
cisagov__manage.get.gov-1181 | Update "User management" title to read "Domain managers"
### Issue description and context
During user testing, we consistently saw that participants didn't know where to add another domain manager. The section title "User management" didn't resonate with anyone we talked to.
Therefore, we need to update that title to read: "Domain managers."
### Acceptance criteria
- Navigation title is changed from "User management" to "Domain managers"
- Page title is changed from "User management" to "Domain managers"
### Links to other issues
_No response_
| [
{
"content": "\"\"\"Views for a single Domain.\n\nAuthorization is handled by the `DomainPermissionView`. To ensure that only\nauthorized users can see information on a domain, every view here should\ninherit from `DomainPermissionView` (or DomainInvitationPermissionDeleteView).\n\"\"\"\n\nimport logging\n\nfro... | [
{
"content": "\"\"\"Views for a single Domain.\n\nAuthorization is handled by the `DomainPermissionView`. To ensure that only\nauthorized users can see information on a domain, every view here should\ninherit from `DomainPermissionView` (or DomainInvitationPermissionDeleteView).\n\"\"\"\n\nimport logging\n\nfro... | diff --git a/src/registrar/templates/domain_detail.html b/src/registrar/templates/domain_detail.html
index e0d672093..4ddbd673a 100644
--- a/src/registrar/templates/domain_detail.html
+++ b/src/registrar/templates/domain_detail.html
@@ -52,7 +52,7 @@ <h2 class="margin-top-neg-1"> DNS name servers </h2>
{% include "includes/summary_item.html" with title='Security email' value='None provided' edit_link=url %}
{% endif %}
{% url 'domain-users' pk=domain.id as url %}
- {% include "includes/summary_item.html" with title='User management' users='true' list=True value=domain.permissions.all edit_link=url %}
+ {% include "includes/summary_item.html" with title='Domain managers' users='true' list=True value=domain.permissions.all edit_link=url %}
</div>
{% endblock %} {# domain_content #}
diff --git a/src/registrar/templates/domain_sidebar.html b/src/registrar/templates/domain_sidebar.html
index 1acd87eeb..ac45ad04c 100644
--- a/src/registrar/templates/domain_sidebar.html
+++ b/src/registrar/templates/domain_sidebar.html
@@ -100,7 +100,7 @@
<a href="{{ url }}"
{% if request.path|startswith:url %}class="usa-current"{% endif %}
>
- User management
+ Domain managers
</a>
</li>
</ul>
diff --git a/src/registrar/templates/domain_users.html b/src/registrar/templates/domain_users.html
index 22b9d18d1..f66eef5a6 100644
--- a/src/registrar/templates/domain_users.html
+++ b/src/registrar/templates/domain_users.html
@@ -1,10 +1,23 @@
{% extends "domain_base.html" %}
-{% load static %}
+{% load static url_helpers %}
-{% block title %}User management | {{ domain.name }} | {% endblock %}
+{% block title %}Domain managers | {{ domain.name }} | {% endblock %}
{% block domain_content %}
- <h1>User management</h1>
+ <h1>Domain managers</h1>
+
+ <p>
+ Domain managers can update all information related to a domain within the
+ .gov registrar, including contact details, authorizing official, security
+ email, and DNS name servers.
+ </p>
+
+ <ul>
+ <li>There is no limit to the number of domain managers you can add.</li>
+ <li>After adding a domain manager, an email invitation will be sent to that user with
+ instructions on how to set up an account.</li>
+ <li>To remove a domain manager, <a href="{% public_site_url 'contact/' %}" class="usa-link">contact us</a> for assistance.
+ </ul>
{% if domain.permissions %}
<section class="section--outlined">
diff --git a/src/registrar/tests/test_views.py b/src/registrar/tests/test_views.py
index 0e8f895af..1262347a1 100644
--- a/src/registrar/tests/test_views.py
+++ b/src/registrar/tests/test_views.py
@@ -1199,14 +1199,14 @@ def test_domain_overview_blocked_for_ineligible_user(self):
self.assertEqual(response.status_code, 403)
-class TestDomainUserManagement(TestDomainOverview):
- def test_domain_user_management(self):
+class TestDomainManagers(TestDomainOverview):
+ def test_domain_managers(self):
response = self.client.get(
reverse("domain-users", kwargs={"pk": self.domain.id})
)
- self.assertContains(response, "User management")
+ self.assertContains(response, "Domain managers")
- def test_domain_user_management_add_link(self):
+ def test_domain_managers_add_link(self):
"""Button to get to user add page works."""
management_page = self.app.get(
reverse("domain-users", kwargs={"pk": self.domain.id})
diff --git a/src/registrar/views/domain.py b/src/registrar/views/domain.py
index aa71a7551..d9b671a65 100644
--- a/src/registrar/views/domain.py
+++ b/src/registrar/views/domain.py
@@ -656,7 +656,7 @@ def form_valid(self, form):
class DomainUsersView(DomainBaseView):
- """User management page in the domain details."""
+ """Domain managers page in the domain details."""
template_name = "domain_users.html"
|
pytorch__TensorRT-1849 | Add Test Suite for `torch.compile` backend Partitioning/Lowering Phases
- Add robust test suite for `torch.compile` backend, ensuring each phase functions correctly
- Add general-purpose utilities for test expansion as the backend evolves
| [
{
"content": "import torch\n\nfrom typing import Any, Union, Sequence, Dict\nfrom torch_tensorrt import _Input, Device\n\n\ndef prepare_inputs(\n inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],\n device: torch.device = torch.device(\"cuda\"),\n) -> Any:\n if isinstance(inputs, _Input.Input):... | [
{
"content": "import torch\n\nfrom typing import Any, Union, Sequence, Dict\nfrom torch_tensorrt import _Input, Device\n\n\ndef prepare_inputs(\n inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],\n device: torch.device = torch.device(\"cuda\"),\n) -> Any:\n if isinstance(inputs, _Input.Input):... | diff --git a/.circleci/config.yml b/.circleci/config.yml
index 88b547729e..1604bea3df 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -727,6 +727,22 @@ commands:
- store_artifacts:
path: /tmp/testlogs
+ test-dynamo-torch_compile-core:
+ description: "Test the Dynamo torch_compile path"
+ steps:
+ - run:
+ name: Run Dynamo torch_compile core tests
+ command: |
+ cd py/torch_tensorrt/dynamo/torch_compile
+ pushd test/
+ pytest --junitxml=/tmp/artifacts/test_results/dynamo/torch_compile/test_results.xml
+ popd
+
+ - store_test_results:
+ path: /tmp/artifacts
+ - store_artifacts:
+ path: /tmp/testlogs
+
test-dynamo-torch_compile:
description: "Test the Dynamo torch_compile path"
steps:
@@ -953,6 +969,7 @@ jobs:
# We install torch after torch-trt because pip automatically enforces the version constraint otherwise
- dump-test-env
- test-dynamo-torch_compile
+ - test-dynamo-torch_compile-core
- test-dynamo-fx_ts
package-x86_64-linux:
diff --git a/py/torch_tensorrt/dynamo/test/utils.py b/py/torch_tensorrt/dynamo/test/utils.py
index ff6bc39158..b1e6632ec3 100644
--- a/py/torch_tensorrt/dynamo/test/utils.py
+++ b/py/torch_tensorrt/dynamo/test/utils.py
@@ -13,42 +13,3 @@ def cosine_similarity(gt_tensor, pred_tensor):
res = res.cpu().detach().item()
return res
-
-
-def same_output_format(trt_output, torch_output):
- # For each encountered collection type, ensure the torch and trt outputs agree
- # on type and size, checking recursively through all member elements.
- if isinstance(trt_output, tuple):
- return (
- isinstance(torch_output, tuple)
- and (len(trt_output) == len(torch_output))
- and all(
- same_output_format(trt_entry, torch_entry)
- for trt_entry, torch_entry in zip(trt_output, torch_output)
- )
- )
- elif isinstance(trt_output, list):
- return (
- isinstance(torch_output, list)
- and (len(trt_output) == len(torch_output))
- and all(
- same_output_format(trt_entry, torch_entry)
- for trt_entry, torch_entry in zip(trt_output, torch_output)
- )
- )
- elif isinstance(trt_output, dict):
- return (
- isinstance(torch_output, dict)
- and (len(trt_output) == len(torch_output))
- and (trt_output.keys() == torch_output.keys())
- and all(
- same_output_format(trt_output[key], torch_output[key])
- for key in trt_output.keys()
- )
- )
- elif isinstance(trt_output, set) or isinstance(trt_output, frozenset):
- raise AssertionError(
- "Unsupported output type 'set' encountered in output format check."
- )
- else:
- return type(trt_output) is type(torch_output)
diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/test_compiler_utils.py b/py/torch_tensorrt/dynamo/torch_compile/test/test_compiler_utils.py
new file mode 100644
index 0000000000..da7157c3e5
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/torch_compile/test/test_compiler_utils.py
@@ -0,0 +1,57 @@
+from torch_tensorrt.dynamo.torch_compile.utils import prepare_device, prepare_inputs
+from utils import same_output_format
+import torch_tensorrt
+import unittest
+import torch
+
+
+class TestPrepareDevice(unittest.TestCase):
+ def test_prepare_cuda_device(self):
+ gpu_id = 0
+ device = torch.device(f"cuda:{gpu_id}")
+ prepared_device = prepare_device(device)
+ self.assertTrue(isinstance(prepared_device, torch.device))
+ self.assertTrue(prepared_device.index == gpu_id)
+
+ def test_prepare_trt_device(self):
+ gpu_id = 4
+ device = torch_tensorrt.Device(gpu_id=gpu_id)
+ prepared_device = prepare_device(device)
+ self.assertTrue(isinstance(prepared_device, torch.device))
+ self.assertTrue(prepared_device.index == gpu_id)
+
+
+class TestPrepareInputs(unittest.TestCase):
+ def test_prepare_single_tensor_input(self):
+ inputs = [torch.ones((4, 4))]
+ prepared_inputs = prepare_inputs(inputs)
+ self.assertTrue(
+ same_output_format(inputs, prepared_inputs, enforce_tensor_type=False)
+ )
+
+ def test_prepare_trt_input(self):
+ inputs = [torch_tensorrt.Input(shape=(4, 3), dtype=torch.float)]
+ prepared_inputs = prepare_inputs(inputs)
+ self.assertTrue(
+ same_output_format(inputs, prepared_inputs, enforce_tensor_type=False)
+ )
+
+ def test_prepare_mixed_type_compound_tensor_input(self):
+ inputs = {
+ "first": [
+ torch.ones((4, 4)),
+ torch_tensorrt.Input(shape=(4, 3), dtype=torch.float),
+ ],
+ "second": (
+ torch.rand((5, 1)),
+ (torch.rand((5, 1)), torch_tensorrt.Input(shape=(2, 3))),
+ ),
+ }
+ prepared_inputs = prepare_inputs(inputs)
+ self.assertTrue(
+ same_output_format(inputs, prepared_inputs, enforce_tensor_type=False)
+ )
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/test_lowering.py b/py/torch_tensorrt/dynamo/torch_compile/test/test_lowering.py
new file mode 100644
index 0000000000..f8f181827f
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/torch_compile/test/test_lowering.py
@@ -0,0 +1,54 @@
+from functools import partial
+from utils import fx_dynamo_testing_backend
+from torch.testing._internal.common_utils import run_tests, TestCase
+import torch
+
+
+class TestTRTModule(TestCase):
+ def test_lowering_inplace_op(self):
+ class FullySupported(torch.nn.Module):
+ def __init__(self, *args, **kwargs) -> None:
+ super().__init__(*args, **kwargs)
+
+ def forward(self, x, y):
+ x = torch.ops.aten.add_.Tensor(x, y)
+ x = torch.ops.aten.relu_.default(x)
+ return x
+
+ # Operations expected to be included in the traced graph after decompositions
+ expected_ops = {torch.ops.aten.add.Tensor, torch.ops.aten.relu.default}
+
+ # Trace module and set up custom backend to track intermediate graphs
+ fx_graph = torch.fx.symbolic_trace(FullySupported())
+ partitioned_graphs = []
+ custom_backend = partial(
+ fx_dynamo_testing_backend,
+ store_intermediate_graphs=partitioned_graphs,
+ )
+
+ # Invoke compilation
+ compiled_graph = torch.compile(fx_graph, backend=custom_backend)
+ compiled_graph(
+ torch.rand(
+ 5,
+ ).cuda(),
+ torch.rand(
+ 5,
+ ).cuda(),
+ )
+
+ # Iterate over intermediate graphs, attempt to match nodes
+ for fx_module in partitioned_graphs:
+ for _, submodule in fx_module.named_children():
+ for node in submodule.graph.nodes:
+
+ if node.op == "call_function" and node.target in expected_ops:
+ expected_ops.remove(node.target)
+
+ self.assertEqual(
+ len(expected_ops), 0, "All operators should have been decomposed"
+ )
+
+
+if __name__ == "__main__":
+ run_tests()
diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/test_partitioning.py b/py/torch_tensorrt/dynamo/torch_compile/test/test_partitioning.py
new file mode 100644
index 0000000000..b068f9c413
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/torch_compile/test/test_partitioning.py
@@ -0,0 +1,68 @@
+from torch_tensorrt.dynamo.torch_compile.lowering import partition
+from torch.testing._internal.common_utils import run_tests, TestCase
+import torch
+from copy import deepcopy
+import numpy as np
+
+
+class TestPartitioning(TestCase):
+ def test_partition_fully_supported_one_op(self):
+ class FullySupportedOneOp(torch.nn.Module):
+ def __init__(self, *args, **kwargs) -> None:
+ super().__init__(*args, **kwargs)
+
+ def forward(self, x, y):
+ return torch.ops.aten.add.Tensor(x, y)
+
+ fx_graph = torch.fx.symbolic_trace(FullySupportedOneOp())
+ partitioned_graph = partition(deepcopy(fx_graph))
+ self.assertEqual(
+ len(list(partitioned_graph.named_children())),
+ 0,
+ "Single operators should not be segmented",
+ )
+
+ def test_partition_fully_supported_multi_op(self):
+ class FullySupportedMultiOp(torch.nn.Module):
+ def __init__(self, *args, **kwargs) -> None:
+ super().__init__(*args, **kwargs)
+
+ def forward(self, x, y):
+ sum_ = torch.ops.aten.sub.Tensor(x, y)
+ concat_ = torch.ops.aten.cat.default(x, sum_)
+ relu_ = torch.ops.aten.relu.default(concat_)
+ pow_ = torch.ops.aten.pow.Tensor_Scalar(relu_, 2)
+ return pow_
+
+ fx_graph = torch.fx.symbolic_trace(FullySupportedMultiOp())
+ partitioned_graph = partition(deepcopy(fx_graph))
+ self.assertEqual(
+ len(list(partitioned_graph.named_children())),
+ 1,
+ "All operators are supported, there should be one segment",
+ )
+
+ def test_partition_partially_supported_multi_op(self):
+ class PartiallySupportedMultiOp(torch.nn.Module):
+ def __init__(self, *args, **kwargs) -> None:
+ super().__init__(*args, **kwargs)
+
+ def forward(self, x, y):
+ sum_1 = torch.ops.aten.add.Tensor(x, y)
+ sum_2 = torch.ops.aten.add.Tensor(x, sum_1)
+ sum_ = np.sum(sum_1) + np.sum(sum_2)
+ relu_ = torch.ops.aten.relu.default(sum_)
+ pow_ = torch.ops.aten.pow.Tensor_Scalar(relu_, 2)
+ return pow_
+
+ fx_graph = torch.fx.symbolic_trace(PartiallySupportedMultiOp())
+ partitioned_graph = partition(deepcopy(fx_graph))
+ self.assertEqual(
+ len(list(partitioned_graph.named_children())),
+ 2,
+ "Unsupported operators interleave supported ones, expected 2 segments",
+ )
+
+
+if __name__ == "__main__":
+ run_tests()
diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/utils.py b/py/torch_tensorrt/dynamo/torch_compile/test/utils.py
new file mode 100644
index 0000000000..bdcbbfcc4a
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/torch_compile/test/utils.py
@@ -0,0 +1,94 @@
+from copy import deepcopy
+from functools import partial
+from typing import List, Sequence
+import torch
+from torch_tensorrt.dynamo.torch_compile.lowering._decompositions import (
+ get_decompositions,
+)
+from torch_tensorrt.dynamo.torch_compile.lowering._partition import (
+ partition,
+)
+
+from torch._dynamo.backends.common import fake_tensor_unsupported
+
+from torch._functorch.aot_autograd import aot_module_simplified, make_boxed_compiler
+
+
+@fake_tensor_unsupported
+def fx_dynamo_testing_backend(
+ gm: torch.fx.GraphModule,
+ sample_inputs: Sequence[torch.Tensor],
+ *,
+ store_intermediate_graphs: List,
+):
+ """Helper Dynamo backend exclusively for testing"""
+ custom_backend = partial(
+ compile_module_testing,
+ store_intermediate_graphs=store_intermediate_graphs,
+ )
+
+ # Invoke AOTAutograd to translate operators to aten
+ return aot_module_simplified(
+ gm,
+ sample_inputs,
+ fw_compiler=make_boxed_compiler(custom_backend),
+ decompositions=get_decompositions(),
+ )
+
+
+def compile_module_testing(
+ gm: torch.fx.GraphModule,
+ example_inputs: Sequence[torch.Tensor],
+ *,
+ store_intermediate_graphs: List,
+) -> torch.fx.GraphModule:
+ """Helper compiler exclusively for testing"""
+ partitioned_module = partition(gm)
+
+ # Store intermediate graph from partitioned module
+ store_intermediate_graphs.append(deepcopy(partitioned_module))
+
+ return partitioned_module
+
+
+def same_output_format(trt_output, torch_output, enforce_tensor_type=True):
+ # For each encountered collection type, ensure the torch and trt outputs agree
+ # on type and size, checking recursively through all member elements.
+ if isinstance(trt_output, tuple):
+ return (
+ isinstance(torch_output, tuple)
+ and (len(trt_output) == len(torch_output))
+ and all(
+ same_output_format(trt_entry, torch_entry, enforce_tensor_type)
+ for trt_entry, torch_entry in zip(trt_output, torch_output)
+ )
+ )
+ elif isinstance(trt_output, list):
+ return (
+ isinstance(torch_output, list)
+ and (len(trt_output) == len(torch_output))
+ and all(
+ same_output_format(trt_entry, torch_entry, enforce_tensor_type)
+ for trt_entry, torch_entry in zip(trt_output, torch_output)
+ )
+ )
+ elif isinstance(trt_output, dict):
+ return (
+ isinstance(torch_output, dict)
+ and (len(trt_output) == len(torch_output))
+ and (trt_output.keys() == torch_output.keys())
+ and all(
+ same_output_format(
+ trt_output[key], torch_output[key], enforce_tensor_type
+ )
+ for key in trt_output.keys()
+ )
+ )
+ elif isinstance(trt_output, set) or isinstance(trt_output, frozenset):
+ raise AssertionError(
+ "Unsupported output type 'set' encountered in output format check."
+ )
+ elif enforce_tensor_type:
+ return type(trt_output) is type(torch_output)
+ else:
+ return True
diff --git a/py/torch_tensorrt/dynamo/torch_compile/utils.py b/py/torch_tensorrt/dynamo/torch_compile/utils.py
index c096eb9397..ba76536338 100644
--- a/py/torch_tensorrt/dynamo/torch_compile/utils.py
+++ b/py/torch_tensorrt/dynamo/torch_compile/utils.py
@@ -64,3 +64,5 @@ def prepare_device(device: Union[Device, torch.device]) -> torch.device:
raise ValueError(
"Invalid device provided. Supported options: torch.device | torch_tensorrt.Device"
)
+
+ return device
|
projectmesa__mesa-398 | error launching Flocker
I've Anaconda with python 3.6 & Mesa 0.8.1
I launch Flocker's run.py and I get this error:
```
Flockers e$ python run.py
Traceback (most recent call last):
File "run.py", line 1, in <module>
from flockers.server import server
File "/Users/e/Dropbox/devlib/notebooks/mesa-master/examples/Flockers/flockers/server.py", line 20, in <module>
server = ModularServer(BoidModel, [boid_canvas], "Boids", model_params)
File "/Users/e/anaconda3/lib/python3.6/site-packages/mesa/visualization/ModularVisualization.py", line 287, in __init__
self.reset_model()
File "/Users/e/anaconda3/lib/python3.6/site-packages/mesa/visualization/ModularVisualization.py", line 313, in reset_model
self.model = self.model_cls(**model_params)
TypeError: __init__() got an unexpected keyword argument 'N'
```
| [
{
"content": "from mesa.visualization.ModularVisualization import ModularServer\n\nfrom .model import BoidModel\nfrom .SimpleContinuousModule import SimpleCanvas\n\n\ndef boid_draw(agent):\n return {\"Shape\": \"circle\", \"r\": 2, \"Filled\": \"true\", \"Color\": \"Red\"}\n\nboid_canvas = SimpleCanvas(boid_... | [
{
"content": "from mesa.visualization.ModularVisualization import ModularServer\n\nfrom .model import BoidModel\nfrom .SimpleContinuousModule import SimpleCanvas\n\n\ndef boid_draw(agent):\n return {\"Shape\": \"circle\", \"r\": 2, \"Filled\": \"true\", \"Color\": \"Red\"}\n\nboid_canvas = SimpleCanvas(boid_... | diff --git a/.travis.yml b/.travis.yml
index 22c39694fff..c7a0e5fd874 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -16,5 +16,6 @@ script:
# * E123 - indentation on data structures
- flake8 . --ignore=F403,E501,E123,E128 --exclude=docs,build
- nosetests --with-coverage --cover-package=mesa
+ - ./tests/test_end_to_end_viz.sh
after_success:
- coveralls
diff --git a/examples/Flockers/flockers/server.py b/examples/Flockers/flockers/server.py
index 73d665cb214..72cc2455a19 100644
--- a/examples/Flockers/flockers/server.py
+++ b/examples/Flockers/flockers/server.py
@@ -9,7 +9,7 @@ def boid_draw(agent):
boid_canvas = SimpleCanvas(boid_draw, 500, 500)
model_params = {
- "N": 100,
+ "population": 100,
"width": 100,
"height": 100,
"speed": 5,
diff --git a/tests/test_end_to_end_viz.sh b/tests/test_end_to_end_viz.sh
new file mode 100755
index 00000000000..04405b8aaba
--- /dev/null
+++ b/tests/test_end_to_end_viz.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+cd examples/Flockers
+python run.py &
+PID=$!
+sleep 3
+curl localhost:8521 | grep Boids
+kill $PID
|
hylang__hy-2220 | Add header notice to "stable" line documentation to point users to the alpha cycle documentation
I was reading documentation and noticed that hy.contrib.walk is mentioned there:
https://docs.hylang.org/en/stable/contrib/walk.html
however it appears that hy.contrib.walk file is no longer on the master branch.
https://github.com/hylang/hy/blob/6ba90fd3f853b2ddc391aa3358f9386c41d831c4/hy/contrib/walk.hy
is it a bug in documentation or I'm missing something?
| [
{
"content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport re, os, sys, time, html\n\nsys.path.insert(0, os.path.abspath('..'))\n\nextensions = [\n 'sphinx.ext.napoleon',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.autodoc',\n 'sphinx.ext.viewcode',\n '... | [
{
"content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport re, os, sys, time, html\n\nsys.path.insert(0, os.path.abspath('..'))\n\nextensions = [\n 'sphinx.ext.napoleon',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.autodoc',\n 'sphinx.ext.viewcode',\n '... | diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
new file mode 100644
index 000000000..5a39ae454
--- /dev/null
+++ b/docs/_templates/layout.html
@@ -0,0 +1,15 @@
+{% extends "!layout.html" %}
+
+{% block extrabody %}
+{% if has_active_alpha %}
+<div class="wy-nav-content-wrap">
+ <div id="dev-warning" class="wy-nav-content" style="overflow-wrap: breakword; padding: 1em 1em 0.1em 1em; background: #ffe761;">
+ <p>
+ Hy is currently in an active alpha cycle. Make sure you're looking
+ at the correct version of the documentation for your install by
+ using the version selector in the bottom-left corner of this page.
+ </p>
+ </div>
+</div>
+{% endif %}
+{% endblock %}
diff --git a/docs/conf.py b/docs/conf.py
index 9f184c6e1..a384609de 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -56,7 +56,9 @@
html_show_sphinx = False
html_context = dict(
- hy_descriptive_version = hy_descriptive_version)
+ hy_descriptive_version = hy_descriptive_version,
+ has_active_alpha = True,
+)
highlight_language = 'clojure'
|
pallets__werkzeug-2001 | Update docs: werkzeug escape utility also translates single quotes
This is a bit nitpicky. The escape utility now uses python's built-in html library for escaping. This will also escape single quotes (') in addition to double quotes ("). It would be helpful if someone could update the docs as escaping single quotes can have implications for XSS vulnerabilities in html.
Environment:
- Python version: >=3.5
- Werkzeug version: latest
| [
{
"content": "import codecs\nimport io\nimport mimetypes\nimport os\nimport pathlib\nimport pkgutil\nimport re\nimport sys\nimport typing as t\nimport unicodedata\nimport warnings\nfrom datetime import datetime\nfrom html.entities import name2codepoint\nfrom time import struct_time\nfrom time import time\nfrom ... | [
{
"content": "import codecs\nimport io\nimport mimetypes\nimport os\nimport pathlib\nimport pkgutil\nimport re\nimport sys\nimport typing as t\nimport unicodedata\nimport warnings\nfrom datetime import datetime\nfrom html.entities import name2codepoint\nfrom time import struct_time\nfrom time import time\nfrom ... | diff --git a/src/werkzeug/utils.py b/src/werkzeug/utils.py
index f5dbfad1f..2713413ba 100644
--- a/src/werkzeug/utils.py
+++ b/src/werkzeug/utils.py
@@ -446,7 +446,8 @@ def secure_filename(filename: str) -> str:
def escape(s):
- """Replace ``&``, ``<``, ``>``, and ``"`` with HTML-safe sequences.
+ """Replace ``&``, ``<``, ``>``, ``"``, and ``'`` with HTML-safe
+ sequences.
``None`` is escaped to an empty string.
|
great-expectations__great_expectations-1500 | Use cleaner solution for non-truncating division in python 2
Prefer `from __future__ import division` to `1.*x/y`
| [
{
"content": "# -*- coding: utf-8 -*-\nfrom great_expectations import DataContext\n\nGREETING = \"\"\"<cyan>\\\n ___ _ ___ _ _ _\n / __|_ _ ___ __ _| |_ | __|_ ___ __ ___ __| |_ __ _| |_(_)___ _ _ ___\n| (_ | '_/ -_) _` | _| | _|\\ \\ / '_ \\/ -_) _| _/ _` | _| ... | [
{
"content": "# -*- coding: utf-8 -*-\nfrom great_expectations import DataContext\n\nGREETING = \"\"\"<cyan>\\\n ___ _ ___ _ _ _\n / __|_ _ ___ __ _| |_ | __|_ ___ __ ___ __| |_ __ _| |_(_)___ _ _ ___\n| (_ | '_/ -_) _` | _| | _|\\ \\ / '_ \\/ -_) _| _/ _` | _| ... | diff --git a/great_expectations/cli/cli_messages.py b/great_expectations/cli/cli_messages.py
index fbc9de052a84..57f0a8a585d5 100644
--- a/great_expectations/cli/cli_messages.py
+++ b/great_expectations/cli/cli_messages.py
@@ -17,6 +17,7 @@
great_expectations
|-- great_expectations.yml
|-- expectations
+ |-- checkpoints
|-- notebooks
|-- plugins
|-- .gitignore
|
zulip__zulip-28952 | Add instructions to download .zuliprc file
https://zulip.com/api/configuring-python-bindings describes .zuliprc files, but does not give instructions for where download them. We should fix this.
- [ ] Add instructions for downloading a bot's .zuliprc file and your .zuliprc file to https://zulip.com/api/configuring-python-bindings. We'll might want to add some section headings to this page as part of this change. The instructions should have tabs for downloading the file for a bot vs. for yourself.
- [ ] Your own .zuliprc file is downloaded via the "Show/change your API key" on SETTINGS / ACCOUNT & PRIVACY. While we're here, let's rename that button to "Manage your API key".
| [
{
"content": "import re\nfrom typing import Any, Dict, List, Mapping, Optional\n\nimport markdown\nfrom markdown.extensions import Extension\nfrom markdown.preprocessors import Preprocessor\nfrom typing_extensions import override\n\nfrom zerver.lib.markdown.priorities import PREPROCESSOR_PRIORITES\n\nSTART_TABB... | [
{
"content": "import re\nfrom typing import Any, Dict, List, Mapping, Optional\n\nimport markdown\nfrom markdown.extensions import Extension\nfrom markdown.preprocessors import Preprocessor\nfrom typing_extensions import override\n\nfrom zerver.lib.markdown.priorities import PREPROCESSOR_PRIORITES\n\nSTART_TABB... | diff --git a/api_docs/api-keys.md b/api_docs/api-keys.md
index 2c6dc2b1de332..1f902fa9e88d4 100644
--- a/api_docs/api-keys.md
+++ b/api_docs/api-keys.md
@@ -17,7 +17,7 @@ Anyone with your API key can impersonate you, so be doubly careful with it.
{settings_tab|account-and-privacy}
-1. Under **API key**, click **Show/change your API key**.
+1. Under **API key**, click **Manage your API key**.
1. Enter your password, and click **Get API key**. If you don't know your
password, click **reset it** and follow the
diff --git a/api_docs/configuring-python-bindings.md b/api_docs/configuring-python-bindings.md
index d5b51b43c145b..25720f98f4019 100644
--- a/api_docs/configuring-python-bindings.md
+++ b/api_docs/configuring-python-bindings.md
@@ -5,15 +5,65 @@ easily, called the [Python bindings](https://pypi.python.org/pypi/zulip/).
One of the most notable use cases for these bindings are bots developed
using Zulip's [bot framework](/api/writing-bots).
-In order to use them, you need to configure them with your API key and other
-settings. There are two ways to achieve that:
+In order to use them, you need to configure them with your identity
+(account, API key, and Zulip server URL). There are a few ways to
+achieve that:
- - With a file called `.zuliprc`, located in your home directory.
- - With
- [environment variables](https://en.wikipedia.org/wiki/Environment_variable)
- set up in your host machine.
+- Using a `zuliprc` file, referenced via the `--config-file` option or
+ the `config_file` option to the `zulip.Client` constructor
+ (recommended for bots).
+- Using a `zuliprc` file in your home directory at `~/.zuliprc`
+ (recommended for your own API key).
+- Using the [environment
+ variables](https://en.wikipedia.org/wiki/Environment_variable)
+ documented below.
+- Using the `--api-key`, `--email`, and `--site` variables as command
+ line parameters.
+- Using the `api_key`, `email`, and `site` parameters to the
+ `zulip.Client` constructor.
-A `.zuliprc` file is a plain text document that looks like this:
+## Download a `zuliprc` file
+
+{start_tabs}
+
+{tab|for-a-bot}
+
+{settings_tab|your-bots}
+
+1. Click the **download** (<i class="fa fa-download"></i>) icon on the profile
+ card of the desired bot to download the bot's `zuliprc` file.
+
+!!! warn ""
+
+ Anyone with a bot's API key can impersonate the bot, so be careful with it!
+
+{tab|for-yourself}
+
+{settings_tab|account-and-privacy}
+
+1. Under **API key**, click **Manage your API key**.
+
+1. Enter your password, and click **Get API key**. If you don't know your
+ password, click **reset it** and follow the
+ instructions from there.
+
+1. Click **Download zuliprc** to download your `zuliprc` file.
+
+1. (optional) If you'd like your credentials to be used by default
+ when using the Zulip API on your computer, move the `zuliprc` file
+ to `~/.zuliprc` in your home directory.
+
+!!! warn ""
+
+ Anyone with your API key can impersonate you, so be doubly careful with it.
+
+{end_tabs}
+
+## Configuration keys and environment variables
+
+`zuliprc` is a configuration file written in the
+[INI file format](https://en.wikipedia.org/wiki/INI_file),
+which contains key-value pairs as shown in the following example:
```
[api]
@@ -29,7 +79,7 @@ can be found in the following table:
<table class="table">
<thead>
<tr>
- <th><code>.zuliprc</code> key</th>
+ <th><code>zuliprc</code> key</th>
<th>Environment variable</th>
<th>Required</th>
<th>Description</th>
@@ -102,3 +152,10 @@ can be found in the following table:
</td>
</tr>
</table>
+
+## Related articles
+
+* [Installation instructions](/api/installation-instructions)
+* [API keys](/api/api-keys)
+* [Running bots](/api/running-bots)
+* [Deploying bots](/api/deploying-bots)
diff --git a/help/add-a-bot-or-integration.md b/help/add-a-bot-or-integration.md
index f1ac70b91539c..f65ac9427ee88 100644
--- a/help/add-a-bot-or-integration.md
+++ b/help/add-a-bot-or-integration.md
@@ -38,7 +38,7 @@ is visible and available for anyone to use.
as the **bot type**.
Depending on the type of bot you're creating, you may need to download its
-`.zuliprc` configuration file. For that, click the **download**
+`zuliprc` configuration file. For that, click the **download**
(<i class="fa fa-download"></i>) icon under the bot's name.
## Related articles
diff --git a/templates/zerver/integrations/google-calendar.md b/templates/zerver/integrations/google-calendar.md
index b700ba54ba5c6..e9a21b3afbb35 100644
--- a/templates/zerver/integrations/google-calendar.md
+++ b/templates/zerver/integrations/google-calendar.md
@@ -20,7 +20,7 @@ your reminders directly in your Zulip feed.
then clicking on **Personal settings**.
1. Click on the tab that’s labeled **Account & privacy** and click on
- **Show/change your API key**. Enter your password if prompted, and
+ **Manage your API key**. Enter your password if prompted, and
download the `zuliprc` file. Save this file as `.zuliprc` to your `~/`
directory.
diff --git a/web/templates/settings/account_settings.hbs b/web/templates/settings/account_settings.hbs
index 057a1b4738c40..5bb89c528768f 100644
--- a/web/templates/settings/account_settings.hbs
+++ b/web/templates/settings/account_settings.hbs
@@ -127,7 +127,7 @@
</p>
<div id="api_key_button_container" class="inline-block {{#unless user_has_email_set}}disabled_setting_tooltip{{/unless}}">
<button class="button rounded" id="api_key_button" {{#unless user_has_email_set}}disabled="disabled"{{/unless}}>
- {{t "Show/change your API key" }}
+ {{t "Manage your API key" }}
</button>
</div>
</div>
diff --git a/web/templates/settings/api_key_modal.hbs b/web/templates/settings/api_key_modal.hbs
index c4c94d75e638e..90821f9d7558e 100644
--- a/web/templates/settings/api_key_modal.hbs
+++ b/web/templates/settings/api_key_modal.hbs
@@ -36,7 +36,7 @@
</button>
<div id="api_key_buttons">
<button class="modal__btn dialog_submit_button" id="regenerate_api_key" aria-label="{{t 'Generate new API key' }}">{{t "Generate new API key" }}</button>
- <a class="modal__btn dialog_submit_button" id="download_zuliprc" download="zuliprc" tabindex="0">{{t "Download .zuliprc" }}</a>
+ <a class="modal__btn dialog_submit_button" id="download_zuliprc" download="zuliprc" tabindex="0">{{t "Download zuliprc" }}</a>
</div>
</footer>
</div>
diff --git a/zerver/lib/markdown/tabbed_sections.py b/zerver/lib/markdown/tabbed_sections.py
index 52d0945fd0152..c707eeb2be698 100644
--- a/zerver/lib/markdown/tabbed_sections.py
+++ b/zerver/lib/markdown/tabbed_sections.py
@@ -112,6 +112,8 @@
"v6": "Zulip Server 6.0+",
"v4": "Zulip Server 4.0+",
"all-versions": "All versions",
+ "for-a-bot": "For a bot",
+ "for-yourself": "For yourself",
}
|
ivy-llc__ivy-15263 | eigh
| [
{
"content": "# local\nimport ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n to_ivy_arrays_and_back,\n from_zero_dim_arrays_to_scalar,\n)\n\n\n@to_ivy_arrays_and_back\n@from_zero_dim_arrays_to_scalar\ndef eigvalsh(a, /, UPLO=\"L\"):\n return ivy.eigvalsh(a, UPLO=UPLO)\n\n\n@to_ivy_arr... | [
{
"content": "# local\nimport ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n to_ivy_arrays_and_back,\n from_zero_dim_arrays_to_scalar,\n)\n\n\n@to_ivy_arrays_and_back\n@from_zero_dim_arrays_to_scalar\ndef eigvalsh(a, /, UPLO=\"L\"):\n return ivy.eigvalsh(a, UPLO=UPLO)\n\n\n@to_ivy_arr... | diff --git a/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py b/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py
index d974f7e7ff21f..33412f60265d2 100644
--- a/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py
+++ b/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py
@@ -17,6 +17,7 @@ def eig(a):
return ivy.eig(a)
+@to_ivy_arrays_and_back
@from_zero_dim_arrays_to_scalar
def eigh(a, /, UPLO="L"):
return ivy.eigh(a, UPLO=UPLO)
|
hylang__hy-2312 | New release
It's time for a new release soon. Here are the things I'd like to get done, or at least try to get done, first. If you think you'll make a PR soon that you'd also like to get in for this release, mention that, too. Volunteers to take these tasks on are also welcome.
- ~#2291~; ~#2292~ - These are more difficult than I thought. I don't think I'm going to make the release wait for them.
- Install bytecode (for Hy and for Hyrule): hylang/hyrule#42; at least partly addresses #1747
| [
{
"content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport html\nimport os\nimport re\nimport sys\nimport time\n\nsys.path.insert(0, os.path.abspath(\"..\"))\n\nextensions = [\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.autodoc\"... | [
{
"content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport html\nimport os\nimport re\nimport sys\nimport time\n\nsys.path.insert(0, os.path.abspath(\"..\"))\n\nextensions = [\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.autodoc\"... | diff --git a/NEWS.rst b/NEWS.rst
index 9d548edaf..02fd8bba7 100644
--- a/NEWS.rst
+++ b/NEWS.rst
@@ -1,24 +1,40 @@
.. default-role:: code
-Unreleased
+0.24.0 (released 2022-06-23)
==============================
+This release is a direct successor to 1.0a4. We've returned to 0.*
+version numbers to work around the inflexibility of PyPI and pip
+regarding the default version to install. (We skipped some version
+numbers because this release is several major releases since 0.20.0.)
+Sorry for the mess.
+
Removals
------------------------------
* Tag macros have been removed. Use reader macros instead, rewriting
`(defmacro "#foo" [arg] …)` as
`(defreader foo (setv arg (.parse-one-form &reader)) …)`.
-* `hy.read-str` has been removed. Use `hy.read`, which now accepts
- strings, instead.
* `with-decorator` and `#@` have been removed in favor of decorator
lists (see below).
-* `hy.cmdline.run_repl` has been replaced with `hy.cmdline.HyREPL.run`.
+* Fraction literals have been removed. Use `fractions.Fraction`
+ instead.
+* Unrecognized backslash escapes in string and byte literals are
+ no longer allowed. (They've been `deprecated in Python since 3.6
+ <https://docs.python.org/3.6/reference/lexical_analysis.html#index-23>`_.)
+* A bare `#` is no longer a legal symbol.
+* `u` is no longer allowed as a string prefix. (It had no effect,
+ anyway.)
+* `hy.read-str` has been removed. Use `hy.read`, which now accepts
+ strings, instead.
-Breaking Changes
+Other Breaking Changes
------------------------------
-* `if` now requires all three arguments. For cases with less than
- three arguments (i.e., with no else-clause), `when` is a drop-in
- replacement.
+* Tuples are now indicated with `#( … )`, as in `#(1 2 3)`, instead of
+ `(, … )`, as in `(, 1 2 3)`.
+* Tuples have their own model type, `hy.models.Tuple`, instead of
+ being represented as `Expression`\s.
+* `if` now requires all three arguments. For the two-argument case
+ (i.e., with no else-clause), `when` is a drop-in replacement.
* `cond` has a new unbracketed syntax::
(cond [a b] [x y z]) ; Old
@@ -26,68 +42,61 @@ Breaking Changes
* `defmacro` once again requires the macro name as a symbol, not
a string literal.
-* The parser has been completely rewritten. It is mostly
- backwards-compatible, with a few exceptions:
-
- - Unescaped double quotes are now allowed inside replacement
- fields of f-strings.
- - Unrecognized backslash escapes in string and byte literals are
- now syntax errors. (They've been `deprecated in Python since 3.6
- <https://docs.python.org/3.6/reference/lexical_analysis.html#index-23>`_.)
- - ``u`` is no longer allowed as a string prefix. (It had no effect,
- anyway.)
- - A bare `#` is no longer a legal symbol.
-
+* Annotations are now indicated by `#^` instead of `^`.
+* `annotate` (but not `#^`) now takes the target first and the type
+ second, as in `(annotate x int)`.
+* The way f-strings are parsed has changed, such that unescaped double
+ quotes are now allowed inside replacement fields.
+* Non-ASCII whitespace is no longer ignored during tokenization like
+ ASCII whitespace.
* The mangling rules have been refined to account for Python's
treatment of distinct names as referring to the same variable if
they're NFKC-equivalent. Very little real code should be affected.
-* Non-ASCII whitespace is no longer ignored by the lexer like ASCII
- whitespace.
-* Tuples are now defined by the form `#(1 2 3)` and compile to the model
- `hy.models.Tuple`
-* Tuples now wrap to `hy.models.Tuple` during model promotion
-* Fraction literals have been removed
-* Annotations now use `#^` instead of `^`
-* ``annotate`` takes the target first and the type second. i.e. ``(annotate x int)``
+* `hy.cmdline.run_repl` has been replaced with
+ `hy.cmdline.HyREPL.run`.
Bug Fixes
------------------------------
-* Readline is now imported only when necessary to avoid triggering a
- CPython bug regarding the standard module `curses` (`bpo-2675`_).
-* Elements of `builtins` such as `help` are no longer overridden until
- the Hy REPL actually starts.
-* Crash when using Keywords as `match` value.
+* Fixed a crash when using keyword objects in `match`.
* Fixed a scoping bug in comprehensions in `let` bodies.
-* Tab completion in the Hy REPL now properly unmangles symbol names.
-* `!=` with model objects is now consistent with `=`.
-* Module names supplied to `hy -m` are now mangled.
* Literal newlines (of all three styles) are now recognized properly
in string and bytes literals.
-* ``defmacro`` no longer allows arguments after ``#* args``
-* Tracebacks from code parsed with `hy.read` now show source positions.
-* Hy now pre-compiles .hy files during setup/installation.
+* `defmacro` no longer allows further arguments after `#* args`.
+* `!=` with model objects is now consistent with `=`.
+* Tracebacks from code parsed with `hy.read` now show source
+ positions.
+* Elements of `builtins` such as `help` are no longer overridden until
+ the REPL actually starts.
+* Readline is now imported only when necessary, to avoid triggering a
+ CPython bug regarding the standard module `curses`
+ (`cpython#46927`_).
+* Module names supplied to `hy -m` are now mangled.
+* Hy now precompiles its own Hy code during installation.
New Features
------------------------------
* Added user-defined reader macros, defined with `defreader`.
-* Python reserved words are allowed once more as parameter names and
- keyword arguments. Hy includes a workaround for a CPython bug that
- prevents the generation of legal Python code for these cases
- (`bpo-46520`_).
* `defn` and `defclass` now allow a decorator list as their first
argument.
-* You can now set the variable `_hy_export_macros` to control what macros are
- collected by `(require module *)`
-* New macro `export`
* `...` is now understood to refer to `Ellipsis`, as in Python.
+* Python reserved words are allowed once more as parameter names and
+ keyword arguments. Hy includes a workaround for a CPython bug that
+ prevents the generation of legal Python code for these cases
+ (`cpython#90678`_).
+* New macro `export`.
+
+ - Or you can set the variable `_hy_export_macros` to control what
+ macros are collected by `(require module *)`.
+
+* New macro `delmacro`.
* New function `hy.read_many`.
-* new function `hy.model_patterns.parse_if`
+* New function `hy.model_patterns.parse_if`.
+* New function `hy.model_patterns.in_tuple`.
* Added a command-line option `-u` (or `--unbuffered`) per CPython.
-* new function `hy.model_patterns.in_tuple`
-* new core macro `delmacro` to delete user defined or require'd macros.
+* Tab-completion in the REPL now attempts to unmangle names.
-.. _bpo-2675: https://bugs.python.org/issue2675#msg265564
-.. _bpo-46520: https://bugs.python.org/issue46520
+.. _cpython#46927: https://github.com/python/cpython/issues/46927#issuecomment-1093418916
+.. _cpython#90678: https://github.com/python/cpython/issues/90678
1.0a4 (released 2022-01-09)
==============================
diff --git a/README.md b/README.md
index 62b85ccbe..8cc4d9270 100644
--- a/README.md
+++ b/README.md
@@ -11,13 +11,13 @@ Hy is a Lisp dialect that's embedded in Python. Since Hy transforms its Lisp
code into Python abstract syntax tree (AST) objects, you have the whole
beautiful world of Python at your fingertips, in Lisp form.
-To install the latest alpha of Hy, just use the command `pip3 install --pre
+To install the latest release of Hy, just use the command `pip3 install
--user hy`. Then you can start an interactive read-eval-print loop (REPL) with
the command `hy`, or run a Hy program with `hy myprogram.hy`.
* [Try Hy with a web console](https://hylang.github.io/hy-interpreter)
-* [Why Hy?](http://docs.hylang.org/en/alpha/whyhy.html)
-* [Tutorial](http://docs.hylang.org/en/alpha/tutorial.html)
+* [Why Hy?](http://docs.hylang.org/en/stable/whyhy.html)
+* [Tutorial](http://docs.hylang.org/en/stable/tutorial.html)
Project
-------
@@ -25,8 +25,7 @@ Project
* Code: https://github.com/hylang/hy
* Documentation:
* master, for use with the latest revision on GitHub: http://docs.hylang.org/en/master
- * alpha, for use with the latest alpha release: http://hylang.org/en/alpha
- * stable, for use with version 0.20.0: http://hylang.org/en/stable
+ * stable, for use with the latest release on PyPI: http://hylang.org/en/stable
* Bug reports: We have no bugs! Your bugs are your own! (https://github.com/hylang/hy/issues)
* License: MIT (Expat)
* [Hacking on Hy](http://docs.hylang.org/en/master/hacking.html)
diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
deleted file mode 100644
index 5a39ae454..000000000
--- a/docs/_templates/layout.html
+++ /dev/null
@@ -1,15 +0,0 @@
-{% extends "!layout.html" %}
-
-{% block extrabody %}
-{% if has_active_alpha %}
-<div class="wy-nav-content-wrap">
- <div id="dev-warning" class="wy-nav-content" style="overflow-wrap: breakword; padding: 1em 1em 0.1em 1em; background: #ffe761;">
- <p>
- Hy is currently in an active alpha cycle. Make sure you're looking
- at the correct version of the documentation for your install by
- using the version selector in the bottom-left corner of this page.
- </p>
- </div>
-</div>
-{% endif %}
-{% endblock %}
diff --git a/docs/conf.py b/docs/conf.py
index 986817839..8c00214b3 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -61,9 +61,7 @@
html_show_sphinx = False
html_context = dict(
- hy_descriptive_version=hy_descriptive_version,
- has_active_alpha=True,
-)
+ hy_descriptive_version=hy_descriptive_version)
highlight_language = "clojure"
diff --git a/docs/index.rst b/docs/index.rst
index 7d7cc8de4..2b77e6199 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -14,7 +14,7 @@ Hy is a Lisp dialect that's embedded in Python. Since Hy transforms its Lisp
code into Python abstract syntax tree (AST) objects, you have the whole
beautiful world of Python at your fingertips, in Lisp form.
-To install the latest alpha of Hy, just use the command ``pip3 install --pre
+To install the latest release of Hy, just use the command ``pip3 install
--user hy``. Then you can start an interactive read-eval-print loop (REPL) with
the command ``hy``, or run a Hy program with ``hy myprogram.hy``.
|
Azure__azure-cli-extensions-4911 | `az webpubsub client start` errors with `TypeError: As of 3.10, the *loop* parameter was removed from Lock() since it is no longer necessary`
- If the issue is to do with Azure CLI 2.0 in-particular, create an issue here at [Azure/azure-cli](https://github.com/Azure/azure-cli/issues)
### Related command
```console
$ az webpubsub client start --name twitch-pubsub --resource-group twitchRG --user user1 --hub-name hub1
The command failed with an unexpected error. Here is the traceback:
As of 3.10, the *loop* parameter was removed from Lock() since it is no longer necessary
Traceback (most recent call last):
File "/opt/az/lib/python3.10/site-packages/knack/cli.py", line 231, in invoke
cmd_result = self.invocation.execute(args)
File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 663, in execute
raise ex
File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 726, in _run_jobs_serially
results.append(self._run_job(expanded_arg, cmd_copy))
File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 697, in _run_job
result = cmd_copy(params)
File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 333, in __call__
return self.handler(*args, **kwargs)
File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/command_operation.py", line 121, in handler
return op(**command_args)
File "/home/anthony/.azure/cliextensions/webpubsub/azext_webpubsub/client.py", line 58, in start_client
asyncio.get_event_loop().run_until_complete(connect(token['url']))
File "/opt/az/lib/python3.10/asyncio/base_events.py", line 646, in run_until_complete
return future.result()
File "/home/anthony/.azure/cliextensions/webpubsub/azext_webpubsub/client.py", line 43, in connect
async with websockets.connect(url, subprotocols=['json.webpubsub.azure.v1']) as ws:
File "/home/anthony/.azure/cliextensions/webpubsub/websockets/client.py", line 517, in __aenter__
return await self
File "/home/anthony/.azure/cliextensions/webpubsub/websockets/client.py", line 535, in __await_impl__
transport, protocol = await self._create_connection()
File "/opt/az/lib/python3.10/asyncio/base_events.py", line 1089, in create_connection
transport, protocol = await self._create_connection_transport(
File "/opt/az/lib/python3.10/asyncio/base_events.py", line 1107, in _create_connection_transport
protocol = protocol_factory()
File "/home/anthony/.azure/cliextensions/webpubsub/websockets/client.py", line 69, in __init__
super().__init__(**kwargs)
File "/home/anthony/.azure/cliextensions/webpubsub/websockets/protocol.py", line 235, in __init__
self._drain_lock = asyncio.Lock(
File "/opt/az/lib/python3.10/asyncio/locks.py", line 78, in __init__
super().__init__(loop=loop)
File "/opt/az/lib/python3.10/asyncio/mixins.py", line 17, in __init__
raise TypeError(
TypeError: As of 3.10, the *loop* parameter was removed from Lock() since it is no longer necessary
```
### Extension name (the extension in question)
webpubsub
### Description of issue (in as much detail as possible)
appears this just needs an upgrade
I was able to work around by running (I'm in azure cloud shell):
```bash
/opt/az/bin/python3.10 -m pip install websockets --upgrade --target ~/.azure/cliextensions/webpubsub/
```
| [
{
"content": "#!/usr/bin/env python\n\n# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# ------------------... | [
{
"content": "#!/usr/bin/env python\n\n# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# ------------------... | diff --git a/src/webpubsub/setup.py b/src/webpubsub/setup.py
index 7d0b6786e17..0429389e714 100644
--- a/src/webpubsub/setup.py
+++ b/src/webpubsub/setup.py
@@ -34,7 +34,7 @@
# TODO: Add any additional SDK dependencies here
DEPENDENCIES = [
- 'websockets~=8.1'
+ 'websockets>=8.1'
]
with open('README.rst', 'r', encoding='utf-8') as f:
|
benoitc__gunicorn-1699 | "Connection refused" when using keep-alive with gthread
Gunicorn version: 19.7.1 (also tried master branch)
This bug could be reproduced with attached files:
- test_http_gunicorn_raw_keep-alive-requests.py (test runner)
- myapp.py (gunicorn app that should be in the same directory)
Test case starts command:
```
gunicorn --worker-class gthread --workers 1 --threads 20 --keep-alive 9999 \
--log-level DEBUG --bind localhost:7777 myapp:app
```
with logs redirected to `/tmp/_test_gunicorn.out`
and then create separate threads to open http session and send 1000 requests within it.
In my case gunicorn server received request `/request/1096` and then reset connection (see `tcp.stream eq 10` from attached `gunicorn_reset_keep_alived_connection.pcapng`
NOTE: this is race condition so it may happen that all requests finish with success (see myapp.py -> without `time.sleep` it almost never fails) but with that `time.sleep` it usually fails and then script should output line similar to:
`[1] Failed request id=1096 with ('Connection aborted.', error(104, 'Connection reset by peer'))`
it mean that thread sent request `GET /request/1096` and received `Connection reset by peer` (so the last one request from that session that succeed is `GET /request/1095`).
| [
{
"content": "# -*- coding: utf-8 -\n#\n# This file is part of gunicorn released under the MIT license.\n# See the NOTICE for more information.\n\n# design:\n# a threaded worker accepts connections in the main loop, accepted\n# connections are are added to the thread pool as a connection job. On\n# keepalive co... | [
{
"content": "# -*- coding: utf-8 -\n#\n# This file is part of gunicorn released under the MIT license.\n# See the NOTICE for more information.\n\n# design:\n# a threaded worker accepts connections in the main loop, accepted\n# connections are are added to the thread pool as a connection job. On\n# keepalive co... | diff --git a/gunicorn/workers/gthread.py b/gunicorn/workers/gthread.py
index 5f918e2e7..862f873d8 100644
--- a/gunicorn/workers/gthread.py
+++ b/gunicorn/workers/gthread.py
@@ -74,11 +74,6 @@ def set_timeout(self):
def close(self):
util.close(self.sock)
- def __lt__(self, other):
- return self.timeout < other.timeout
-
- __cmp__ = __lt__
-
class ThreadWorker(base.Worker):
|
ManimCommunity__manim-2740 | Documentation Bug: Cylinder.get_direction()
The documentation `get_direction` method for `Cylinder` mentions a function called `shoelace_direction` which returns a string, either "CW" or "CCW". However, the implementation of `get_direction` returns a 3d vector. This is the correct behavior in this context, but the documentation is incorrect.
| [
{
"content": "\"\"\"Three-dimensional mobjects.\"\"\"\n\nfrom __future__ import annotations\n\n__all__ = [\n \"ThreeDVMobject\",\n \"Surface\",\n \"Sphere\",\n \"Dot3D\",\n \"Cube\",\n \"Prism\",\n \"Cone\",\n \"Arrow3D\",\n \"Cylinder\",\n \"Line3D\",\n \"Torus\",\n]\n\n\nfrom ... | [
{
"content": "\"\"\"Three-dimensional mobjects.\"\"\"\n\nfrom __future__ import annotations\n\n__all__ = [\n \"ThreeDVMobject\",\n \"Surface\",\n \"Sphere\",\n \"Dot3D\",\n \"Cube\",\n \"Prism\",\n \"Cone\",\n \"Arrow3D\",\n \"Cylinder\",\n \"Line3D\",\n \"Torus\",\n]\n\n\nfrom ... | diff --git a/manim/mobject/three_d/three_dimensions.py b/manim/mobject/three_d/three_dimensions.py
index 80aa100788..6f04b7ccf6 100644
--- a/manim/mobject/three_d/three_dimensions.py
+++ b/manim/mobject/three_d/three_dimensions.py
@@ -687,6 +687,7 @@ def set_direction(self, direction):
self._rotate_to_direction()
def get_direction(self):
+ """Returns the direction of the central axis of the cylinder."""
return self.direction
|
boto__botocore-658 | Pin jmespatch dependency version
Can this library pin its jmespath dependency to a specific version? Currently, it depends on the development branch of the jmespath GitHub repo - which is not stable nor deterministic.
Currently, this project's setup.py requires version 0.7.1 but the upstream GitHub repo/branch does not deliver that version - so this project's dependency graph is disconnected.
This can result in runtime errors for downstream consumers - like my organization did today.
| [
{
"content": "#!/usr/bin/env python\nimport botocore\nimport sys\n\nfrom setuptools import setup, find_packages\n\n\nrequires = ['jmespath==0.7.1',\n 'python-dateutil>=2.1,<3.0.0',\n 'docutils>=0.10']\n\n\nif sys.version_info[:2] == (2, 6):\n # For python2.6 we have a few other dependen... | [
{
"content": "#!/usr/bin/env python\nimport botocore\nimport sys\n\nfrom setuptools import setup, find_packages\n\n\nrequires = ['jmespath>=0.7.1,<1.0.0',\n 'python-dateutil>=2.1,<3.0.0',\n 'docutils>=0.10']\n\n\nif sys.version_info[:2] == (2, 6):\n # For python2.6 we have a few other d... | diff --git a/setup.py b/setup.py
index ea3260e993..b1ed6c109d 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
from setuptools import setup, find_packages
-requires = ['jmespath==0.7.1',
+requires = ['jmespath>=0.7.1,<1.0.0',
'python-dateutil>=2.1,<3.0.0',
'docutils>=0.10']
|
nltk__nltk-2595 | Move from nose to pytest or nose2
https://nose.readthedocs.io/en/latest/ -- nose is on life support. I personally prefer pytest, but nose2 may also be considered. Has this been discussed very much yet?
| [
{
"content": "# Natural Language Toolkit: Corpus Readers\n#\n# Copyright (C) 2001-2020 NLTK Project\n# Author: Edward Loper <edloper@gmail.com>\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\n# TODO this docstring isn't up-to-date!\n\"\"\"\nNLTK corpus readers. The modules in this pac... | [
{
"content": "# Natural Language Toolkit: Corpus Readers\n#\n# Copyright (C) 2001-2020 NLTK Project\n# Author: Edward Loper <edloper@gmail.com>\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\n# TODO this docstring isn't up-to-date!\n\"\"\"\nNLTK corpus readers. The modules in this pac... | diff --git a/.gitignore b/.gitignore
index 0a9e9afadc..c9f8051b17 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,7 +14,7 @@ web/_build
*.tox
*.errs
.noseids
-.coverage
+.coverage*
nltk/test/*.html
nltk/test/tweets*
model.crf.tagger
diff --git a/AUTHORS.md b/AUTHORS.md
index 1bb005ec94..46d1e22d41 100644
--- a/AUTHORS.md
+++ b/AUTHORS.md
@@ -270,6 +270,7 @@
- Jacob Moorman <https://github.com/jdmoorman>
- Cory Nezin <https://github.com/corynezin>
- Matt Chaput
+- Danny Sepler <https://github.com/dannysepler>
- Akshita Bhagia <https://github.com/AkshitaB>
## Others whose work we've taken and included in NLTK, but who didn't directly contribute it:
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index d48909b3e8..92ba3f4e32 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -136,6 +136,15 @@ For a better design of your code, we recommend using a technique called
where you write your tests **before** writing the actual code that implements
the desired feature.
+You can use `pytest` to run your tests, no matter which type of test it is:
+
+```
+cd nltk/test
+pytest util.doctest # doctest
+pytest unit/translate/test_nist.py # unittest
+pytest # all tests
+```
+
## Continuous Integration
@@ -162,7 +171,7 @@ The [`.travis.yml`](https://github.com/nltk/nltk/blob/travis/.travis.yml) file c
- `py-travis` tox test environment generally
- the `extras = all` dependencies in needed to emulate `pip install nltk[all]`, see https://tox.readthedocs.io/en/latest/config.html#confval-extras=MULTI-LINE-LIST
- for the `py-travis-third-party` build, it will run `tools/travis/third-party.sh` to install third-party tools (Stanford NLP tools and CoreNLP and SENNA)
- - calls `tools/travis/coverage-pylint.sh` shell script that calls the `nltk/nltk/test/runtests.py` with [`coverage`](https://pypi.org/project/coverage/) and
+ - calls `tools/travis/coverage-pylint.sh` shell script that calls `pytest` with [`pytest-cov`](https://pytest-cov.readthedocs.io/) and
- calls `pylint` # Currently, disabled because there's lots to clean...
- before returning a `true` to state that the build is successful
diff --git a/Makefile b/Makefile
index 7c8bf8cabc..537be973f4 100644
--- a/Makefile
+++ b/Makefile
@@ -17,16 +17,14 @@ all: dist
########################################################################
# TESTING
########################################################################
-
-DOCTEST_DRIVER = nltk/test/runtests.py
DOCTEST_FILES = nltk/test/*.doctest
DOCTEST_CODE_FILES = nltk/*.py nltk/*/*.py
doctest:
- $(PYTHON) $(DOCTEST_DRIVER) $(DOCTEST_FILES)
+ pytest $(DOCTEST_FILES)
doctest_code:
- $(PYTHON) $(DOCTEST_DRIVER) $(DOCTEST_CODE_FILES)
+ pytest $(DOCTEST_CODE_FILES)
demotest:
find nltk -name "*.py"\
diff --git a/jenkins-job-config.xml b/jenkins-job-config.xml
index d9d411917d..957a90d427 100644
--- a/jenkins-job-config.xml
+++ b/jenkins-job-config.xml
@@ -133,7 +133,7 @@
<sourceEncoding>ASCII</sourceEncoding>
</hudson.plugins.cobertura.CoberturaPublisher>
<hudson.tasks.junit.JUnitResultArchiver>
- <testResults>**/nosetests_scrubbed.xml</testResults>
+ <testResults>**/coverage_scrubbed.xml</testResults>
<keepLongStdio>false</keepLongStdio>
<testDataPublishers/>
</hudson.tasks.junit.JUnitResultArchiver>
diff --git a/jenkins.sh b/jenkins.sh
index 58becb130c..5be27c07db 100755
--- a/jenkins.sh
+++ b/jenkins.sh
@@ -2,15 +2,15 @@
cd `dirname $0`
-#download nltk python dependencies
+# Download nltk python dependencies
pip install --upgrade -r pip-req.txt
pip install --upgrade matplotlib
pip install --upgrade https://github.com/PyCQA/pylint/archive/master.zip
-#download nltk data packages
+# Download nltk data packages
python -c "import nltk; nltk.download('all')" || echo "NLTK data download failed: $?"
-#download external dependencies
+# Download external dependencies
pushd ${HOME}
[[ ! -d 'third' ]] && mkdir 'third'
pushd 'third'
@@ -76,17 +76,16 @@ echo "---- CLASSPATH: ----"
echo $CLASSPATH
echo "---- MODELS: ----"
echo $STANFORD_MODELS
-echo "---- NLTK runtests.py ----"
+echo "---- Running tests ----"
-#coverage
-coverage erase
-coverage run --source=nltk nltk/test/runtests.py -v --with-xunit
-coverage xml --omit=nltk/test/*
-iconv -c -f utf-8 -t utf-8 nosetests.xml > nosetests_scrubbed.xml
+# Coverage
+rm -f coverage_scrubbed.xml
+pytest --cov=nltk --cov-report xml nltk/test/
+iconv -c -f utf-8 -t utf-8 coverage.xml > coverage_scrubbed.xml
# Create a default pylint configuration file.
touch ~/.pylintrc
pylint -f parseable nltk > pylintoutput
-#script always succeeds
+# Script always succeeds
true
diff --git a/nltk/corpus/__init__.py b/nltk/corpus/__init__.py
index 3821907dad..3d7ba1e77a 100644
--- a/nltk/corpus/__init__.py
+++ b/nltk/corpus/__init__.py
@@ -482,7 +482,7 @@ def demo():
# demo()
pass
-# ** this is for nose **
+# ** this is for unit testing **
# unload all corpus after tests
def teardown_module(module=None):
import nltk.corpus
diff --git a/nltk/test/Makefile b/nltk/test/Makefile
index f2e4f7a25e..984182ed44 100644
--- a/nltk/test/Makefile
+++ b/nltk/test/Makefile
@@ -9,7 +9,7 @@ HTML = $(TESTS:.doctest=.html) # $(IPYNB:.ipynb=.html)
IPYNB = $(wildcard *.ipynb)
.doctest.errs:
- python ./runtests.py $< > $@
+ pytest $< > $@
.doctest.html:
rst2html.py $< > $@
diff --git a/nltk/test/childes.doctest b/nltk/test/childes.doctest
index 7900c541b6..6c7455f497 100644
--- a/nltk/test/childes.doctest
+++ b/nltk/test/childes.doctest
@@ -4,6 +4,12 @@
Read the XML version of the CHILDES corpus.
+Setup
+=====
+
+ >>> from nltk.test.childes_fixt import setup_module
+ >>> setup_module()
+
How to use CHILDESCorpusReader
==============================
diff --git a/nltk/test/childes_fixt.py b/nltk/test/childes_fixt.py
index daac182ba7..ccda7c2930 100644
--- a/nltk/test/childes_fixt.py
+++ b/nltk/test/childes_fixt.py
@@ -1,16 +1,14 @@
# -*- coding: utf-8 -*-
-
-def setup_module(module):
- from nose import SkipTest
+def setup_module():
+ import pytest
import nltk.data
try:
nltk.data.find("corpora/childes/data-xml/Eng-USA-MOR/")
except LookupError as e:
- print(e)
- raise SkipTest(
+ pytest.skip(
"The CHILDES corpus is not found. "
"It should be manually downloaded and saved/unpacked "
"to [NLTK_Data_Dir]/corpora/childes/"
- ) from e
+ )
diff --git a/nltk/test/classify.doctest b/nltk/test/classify.doctest
index 26d14e6c69..d3ab385b9d 100644
--- a/nltk/test/classify.doctest
+++ b/nltk/test/classify.doctest
@@ -5,6 +5,9 @@
Classifiers
=============
+ >>> from nltk.test.classify_fixt import setup_module
+ >>> setup_module()
+
Classifiers label tokens with category labels (or *class labels*).
Typically, labels are represented with strings (such as ``"health"``
or ``"sports"``. In NLTK, classifiers are defined using classes that
diff --git a/nltk/test/classify_fixt.py b/nltk/test/classify_fixt.py
index cf1f0b66b6..0d0d0c7ac5 100644
--- a/nltk/test/classify_fixt.py
+++ b/nltk/test/classify_fixt.py
@@ -2,10 +2,10 @@
# most of classify.doctest requires numpy
-def setup_module(module):
- from nose import SkipTest
+def setup_module():
+ import pytest
try:
import numpy
- except ImportError as e:
- raise SkipTest("classify.doctest requires numpy") from e
+ except ImportError:
+ pytest.skip("classify.doctest requires numpy")
diff --git a/nltk/test/corpus.doctest b/nltk/test/corpus.doctest
index 73b8fd792f..42671b509d 100644
--- a/nltk/test/corpus.doctest
+++ b/nltk/test/corpus.doctest
@@ -2196,4 +2196,8 @@ access to its tuples() method
[('NUM:dist', 'How far is it from Denver to Aspen ?'), ('LOC:city', 'What county is Modesto , California in ?'), ...]
+Teardown
+========
+ >>> from nltk.corpus import teardown_module
+ >>> teardown_module()
diff --git a/nltk/test/corpus_fixt.py b/nltk/test/corpus_fixt.py
deleted file mode 100644
index 17b011bd6f..0000000000
--- a/nltk/test/corpus_fixt.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from nltk.corpus import teardown_module
diff --git a/nltk/test/discourse.doctest b/nltk/test/discourse.doctest
index a5dabe8ae1..3ea674ef23 100644
--- a/nltk/test/discourse.doctest
+++ b/nltk/test/discourse.doctest
@@ -9,6 +9,12 @@ Discourse Checking
>>> from nltk.sem import logic
>>> logic._counter._value = 0
+Setup
+=====
+
+ >>> from nltk.test.childes_fixt import setup_module
+ >>> setup_module()
+
Introduction
============
diff --git a/nltk/test/discourse_fixt.py b/nltk/test/discourse_fixt.py
index c213ad90ea..6f97c85cf2 100644
--- a/nltk/test/discourse_fixt.py
+++ b/nltk/test/discourse_fixt.py
@@ -3,14 +3,14 @@
# FIXME: the entire discourse.doctest is skipped if Prover9/Mace4 is
# not installed, but there are pure-python parts that don't need Prover9.
-def setup_module(module):
- from nose import SkipTest
+def setup_module():
+ import pytest
from nltk.inference.mace import Mace
try:
m = Mace()
m._find_binary("mace4")
except LookupError as e:
- raise SkipTest(
+ pytest.skip(
"Mace4/Prover9 is not available so discourse.doctest is skipped"
- ) from e
+ )
diff --git a/nltk/test/gensim.doctest b/nltk/test/gensim.doctest
index 386e3e0dad..c2ea04733e 100644
--- a/nltk/test/gensim.doctest
+++ b/nltk/test/gensim.doctest
@@ -4,6 +4,9 @@
=======================================
Demonstrate word embedding using Gensim
=======================================
+
+ >>> from nltk.test.gensim_fixt import setup_module
+ >>> setup_module()
We demonstrate three functions:
- Train the word embeddings using brown corpus;
diff --git a/nltk/test/gensim_fixt.py b/nltk/test/gensim_fixt.py
index 0b11d13988..517385b3f4 100644
--- a/nltk/test/gensim_fixt.py
+++ b/nltk/test/gensim_fixt.py
@@ -1,10 +1,10 @@
# -*- coding: utf-8 -*-
-def setup_module(module):
- from nose import SkipTest
+def setup_module():
+ import pytest
try:
import gensim
- except ImportError as e:
- raise SkipTest("Gensim doctest requires gensim") from e
+ except ImportError:
+ pytest.skip("Gensim doctest requires gensim")
diff --git a/nltk/test/gluesemantics_malt.doctest b/nltk/test/gluesemantics_malt.doctest
index a76e96f355..20e0fabb0b 100644
--- a/nltk/test/gluesemantics_malt.doctest
+++ b/nltk/test/gluesemantics_malt.doctest
@@ -7,6 +7,9 @@
Glue Semantics
==============================================================================
+ >>> from nltk.test.gluesemantics_malt_fixt import setup_module
+ >>> setup_module()
+
>>> from nltk.sem.glue import *
>>> nltk.sem.logic._counter._value = 0
diff --git a/nltk/test/gluesemantics_malt_fixt.py b/nltk/test/gluesemantics_malt_fixt.py
index a566c259cf..4583a633cf 100644
--- a/nltk/test/gluesemantics_malt_fixt.py
+++ b/nltk/test/gluesemantics_malt_fixt.py
@@ -1,11 +1,11 @@
# -*- coding: utf-8 -*-
-def setup_module(module):
- from nose import SkipTest
+def setup_module():
+ import pytest
from nltk.parse.malt import MaltParser
try:
depparser = MaltParser("maltparser-1.7.2")
except LookupError as e:
- raise SkipTest("MaltParser is not available") from e
+ pytest.skip("MaltParser is not available")
diff --git a/nltk/test/inference.doctest b/nltk/test/inference.doctest
index 5bf15015a0..006a3206e2 100644
--- a/nltk/test/inference.doctest
+++ b/nltk/test/inference.doctest
@@ -5,6 +5,9 @@
Logical Inference and Model Building
====================================
+ >>> from nltk.test.inference_fixt import setup_module
+ >>> setup_module()
+
>>> from nltk import *
>>> from nltk.sem.drt import DrtParser
>>> from nltk.sem import logic
diff --git a/nltk/test/inference_fixt.py b/nltk/test/inference_fixt.py
index 1c2df992be..754aa5dd4e 100644
--- a/nltk/test/inference_fixt.py
+++ b/nltk/test/inference_fixt.py
@@ -1,14 +1,14 @@
# -*- coding: utf-8 -*-
-def setup_module(module):
- from nose import SkipTest
+def setup_module():
+ import pytest
from nltk.inference.mace import Mace
try:
m = Mace()
m._find_binary("mace4")
- except LookupError as e:
- raise SkipTest(
+ except LookupError:
+ pytest.skip(
"Mace4/Prover9 is not available so inference.doctest was skipped"
- ) from e
+ )
diff --git a/nltk/test/nonmonotonic.doctest b/nltk/test/nonmonotonic.doctest
index ea17c60cde..45b5fd1095 100644
--- a/nltk/test/nonmonotonic.doctest
+++ b/nltk/test/nonmonotonic.doctest
@@ -5,6 +5,9 @@
Nonmonotonic Reasoning
======================
+ >>> from nltk.test.nonmonotonic_fixt import setup_module
+ >>> setup_module()
+
>>> from nltk import *
>>> from nltk.inference.nonmonotonic import *
>>> from nltk.sem import logic
diff --git a/nltk/test/nonmonotonic_fixt.py b/nltk/test/nonmonotonic_fixt.py
index eff6ae9f37..f03be1aff3 100644
--- a/nltk/test/nonmonotonic_fixt.py
+++ b/nltk/test/nonmonotonic_fixt.py
@@ -1,14 +1,14 @@
# -*- coding: utf-8 -*-
-def setup_module(module):
- from nose import SkipTest
+def setup_module():
+ import pytest
from nltk.inference.mace import Mace
try:
m = Mace()
m._find_binary("mace4")
except LookupError as e:
- raise SkipTest(
+ pytest.skip(
"Mace4/Prover9 is not available so nonmonotonic.doctest was skipped"
- ) from e
+ )
diff --git a/nltk/test/portuguese_en.doctest b/nltk/test/portuguese_en.doctest
index 84cee4a60f..63dc00294d 100644
--- a/nltk/test/portuguese_en.doctest
+++ b/nltk/test/portuguese_en.doctest
@@ -18,6 +18,9 @@ Chapter 1 of the NLTK book contains many elementary programming examples, all
with English texts. In this section, we'll see some corresponding examples
using Portuguese. Please refer to the chapter for full discussion. *Vamos!*
+ >>> from nltk.test.portuguese_en_fixt import setup_module
+ >>> setup_module()
+
>>> from nltk.examples.pt import *
*** Introductory Examples for the NLTK Book ***
Loading ptext1, ... and psent1, ...
@@ -563,3 +566,9 @@ and print them in descending order of frequency:
ter 249
dois 231
+
+Teardown
+---------
+
+ >>> from nltk.corpus import teardown_module
+ >>> teardown_module()
diff --git a/nltk/test/portuguese_en_fixt.py b/nltk/test/portuguese_en_fixt.py
index f417bc6a48..548d745ffb 100644
--- a/nltk/test/portuguese_en_fixt.py
+++ b/nltk/test/portuguese_en_fixt.py
@@ -1,10 +1,8 @@
# -*- coding: utf-8 -*-
-from nltk.corpus import teardown_module
+def setup_module():
+ import pytest
-def setup_module(module):
- from nose import SkipTest
-
- raise SkipTest(
+ pytest.skip(
"portuguese_en.doctest imports nltk.examples.pt which doesn't exist!"
)
diff --git a/nltk/test/probability_fixt.py b/nltk/test/probability_fixt.py
index a3d092906d..4388f430b4 100644
--- a/nltk/test/probability_fixt.py
+++ b/nltk/test/probability_fixt.py
@@ -6,9 +6,9 @@
def setup_module(module):
- from nose import SkipTest
+ import pytest
try:
import numpy
- except ImportError as e:
- raise SkipTest("probability.doctest requires numpy") from e
+ except ImportError:
+ pytest.skip("probability.doctest requires numpy")
diff --git a/nltk/test/pytest.ini b/nltk/test/pytest.ini
new file mode 100644
index 0000000000..f9973618ca
--- /dev/null
+++ b/nltk/test/pytest.ini
@@ -0,0 +1,13 @@
+[pytest]
+doctest_optionflags=ELLIPSIS NORMALIZE_WHITESPACE IGNORE_EXCEPTION_DETAIL
+
+# --doctest-continue-on-failure allows the test to always
+# get to the teardown, if it exists
+addopts = --doctest-glob *.doctest --doctest-continue-on-failure
+
+# Other options for creating valid teardowns for doctests:
+# 1. Turn doctests into unittest tests
+# - https://docs.python.org/3/library/doctest.html#unittest-api
+# 2. Use sphinx doctests
+# - https://www.sphinx-doc.org/en/master/usage/extensions/doctest.html
+# 3. Convert the tests that require setup/teardown into pytest tests with fixtures
diff --git a/nltk/test/runtests.py b/nltk/test/runtests.py
deleted file mode 100755
index 9dc06ec9be..0000000000
--- a/nltk/test/runtests.py
+++ /dev/null
@@ -1,75 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-import sys
-import os
-import nose
-from nose.plugins.manager import PluginManager
-from nose.plugins.doctests import Doctest
-from nose.plugins import builtin
-
-NLTK_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
-sys.path.insert(0, NLTK_ROOT)
-
-NLTK_TEST_DIR = os.path.join(NLTK_ROOT, "nltk")
-
-if __name__ == "__main__":
- # there shouldn't be import from NLTK for coverage to work properly
- try:
- # Import RedNose plugin for colored test output
- from rednose import RedNose
-
- rednose_available = True
- except ImportError:
- rednose_available = False
-
- class NltkPluginManager(PluginManager):
- """
- Nose plugin manager that replaces standard doctest plugin
- with a patched version and adds RedNose plugin for colored test output.
- """
-
- def loadPlugins(self):
- for plug in builtin.plugins:
- self.addPlugin(plug())
- if rednose_available:
- self.addPlugin(RedNose())
-
- super(NltkPluginManager, self).loadPlugins()
-
- manager = NltkPluginManager()
- manager.loadPlugins()
-
- # allow passing extra options and running individual tests
- # Examples:
- #
- # python runtests.py semantics.doctest
- # python runtests.py --with-id -v
- # python runtests.py --with-id -v nltk.featstruct
-
- args = sys.argv[1:]
- if not args:
- args = [NLTK_TEST_DIR]
-
- if all(arg.startswith("-") for arg in args):
- # only extra options were passed
- args += [NLTK_TEST_DIR]
-
- # Activate RedNose and hide skipped test messages from output
- if rednose_available:
- args += ["--rednose", "--hide-skips"]
-
- arguments = [
- "--exclude=", # why is this needed?
- # '--with-xunit',
- # '--xunit-file=$WORKSPACE/nosetests.xml',
- # '--nocapture',
- "--with-doctest",
- # '--doctest-tests',
- # '--debug=nose,nose.importer,nose.inspector,nose.plugins,nose.result,nose.selector',
- "--doctest-extension=.doctest",
- "--doctest-fixtures=_fixt",
- "--doctest-options=+ELLIPSIS,+NORMALIZE_WHITESPACE,+IGNORE_EXCEPTION_DETAIL",
- # '--verbosity=3',
- ] + args
-
- nose.main(argv=arguments, plugins=manager.plugins)
diff --git a/nltk/test/segmentation_fixt.py b/nltk/test/segmentation_fixt.py
deleted file mode 100644
index 09ddc68fdc..0000000000
--- a/nltk/test/segmentation_fixt.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# -*- coding: utf-8 -*-
-
-
-# skip segmentation.doctest if numpy is not available
-def setup_module(module):
- from nose import SkipTest
-
- try:
- import numpy
- except ImportError as e:
- raise SkipTest("segmentation.doctest requires numpy") from e
diff --git a/nltk/test/semantics.doctest b/nltk/test/semantics.doctest
index 32c0f84025..5565ff6221 100644
--- a/nltk/test/semantics.doctest
+++ b/nltk/test/semantics.doctest
@@ -5,6 +5,10 @@
Semantics
=========
+ >>> # Setup tests by setting the counter to 0
+ >>> from nltk.sem import logic
+ >>> logic._counter._value = 0
+
>>> import nltk
>>> from nltk.sem import Valuation, Model
>>> v = [('adam', 'b1'), ('betty', 'g1'), ('fido', 'd1'),
diff --git a/nltk/test/semantics_fixt.py b/nltk/test/semantics_fixt.py
deleted file mode 100644
index 8d67144d6a..0000000000
--- a/nltk/test/semantics_fixt.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# reset the variables counter before running tests
-def setup_module(module):
- from nltk.sem import logic
-
- logic._counter._value = 0
diff --git a/nltk/test/translate.doctest b/nltk/test/translate.doctest
index 87966fb4c0..e93f9eec27 100644
--- a/nltk/test/translate.doctest
+++ b/nltk/test/translate.doctest
@@ -240,3 +240,8 @@ Here are some examples:
*Statistical Machine Translation*, Cambridge University Press
+Teardown
+========
+
+ >>> from nltk.corpus import teardown_module
+ >>> teardown_module()
diff --git a/nltk/test/translate_fixt.py b/nltk/test/translate_fixt.py
deleted file mode 100644
index 17b011bd6f..0000000000
--- a/nltk/test/translate_fixt.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from nltk.corpus import teardown_module
diff --git a/nltk/test/unit/conftest.py b/nltk/test/unit/conftest.py
new file mode 100644
index 0000000000..5ff8204bd4
--- /dev/null
+++ b/nltk/test/unit/conftest.py
@@ -0,0 +1,15 @@
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def mock_plot(mocker):
+ """ Disable matplotlib plotting in test code """
+
+ try:
+ import matplotlib.pyplot as plt
+
+ mocker.patch.object(plt, 'show')
+ except ImportError:
+ pytest.skip(
+ "mock_plot imports matplotlib which doesn't exist!"
+ )
diff --git a/nltk/test/unit/test_seekable_unicode_stream_reader.py b/nltk/test/unit/test_seekable_unicode_stream_reader.py
index c5d15831ba..d59d84becc 100644
--- a/nltk/test/unit/test_seekable_unicode_stream_reader.py
+++ b/nltk/test/unit/test_seekable_unicode_stream_reader.py
@@ -91,7 +91,7 @@ def test_reader():
try:
# skip strings that can't be encoded with the current encoding
string.encode(encoding)
- yield check_reader, string, encoding
+ check_reader(string, encoding)
except UnicodeEncodeError:
pass
@@ -120,7 +120,7 @@ def test_reader_on_large_string():
def _check(encoding, n=1000):
check_reader(LARGE_STRING, encoding, n)
- yield _check, encoding
+ _check(encoding)
except UnicodeEncodeError:
pass
diff --git a/nltk/test/wordnet.doctest b/nltk/test/wordnet.doctest
index 54c597511c..b5bbbb7303 100644
--- a/nltk/test/wordnet.doctest
+++ b/nltk/test/wordnet.doctest
@@ -602,3 +602,11 @@ Patch-1 https://github.com/nltk/nltk/pull/2065 Adding 3 functions (relations) t
Synset('french.n.01')
>>> wn.synsets("slang")[1].in_usage_domains()[18]
Synset('can-do.s.01')
+
+
+-------------
+Teardown test
+-------------
+
+ >>> from nltk.corpus import wordnet
+ >>> wordnet._unload()
diff --git a/nltk/test/wordnet_fixt.py b/nltk/test/wordnet_fixt.py
deleted file mode 100644
index 09ba27cc71..0000000000
--- a/nltk/test/wordnet_fixt.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-
-
-def teardown_module(module=None):
- from nltk.corpus import wordnet
-
- wordnet._unload()
diff --git a/pip-req.txt b/pip-req.txt
index 85a1ecf581..2f7d5ee4e6 100644
--- a/pip-req.txt
+++ b/pip-req.txt
@@ -1,6 +1,5 @@
nose>=1.3.0
tox>=1.6.1
-coverage>=3.7.1
pylint>=1.1.0
numpy>=1.8.0
scipy>=0.13.2
@@ -11,4 +10,3 @@ gensim>=0.11.1
pyparsing>=2.0.3
twython>=3.2.0
regex>=2019.08.19
-
diff --git a/requirements-test.txt b/requirements-test.txt
index ffb8d8782f..290c3ec8d0 100644
--- a/requirements-test.txt
+++ b/requirements-test.txt
@@ -1,4 +1,6 @@
tox
nose
-coverage
pylint
+pytest>=6.0.1
+pytest-mock
+pytest-cov>=2.10.1
diff --git a/tools/travis/coverage-pylint.sh b/tools/travis/coverage-pylint.sh
index 7383a94e49..ad70587ba5 100644
--- a/tools/travis/coverage-pylint.sh
+++ b/tools/travis/coverage-pylint.sh
@@ -16,10 +16,9 @@ pip -V
echo "$(pwd)" # Know which directory tox is running this shell from.
#coverage
-coverage erase
-coverage run --source=nltk $(pwd)/runtests.py -v --with-xunit
-coverage xml --omit=$(pwd)/*
-iconv -c -f utf-8 -t utf-8 nosetests.xml > nosetests_scrubbed.xml
+rm -f coverage_scrubbed.xml
+pytest --cov=nltk --cov-report xml
+iconv -c -f utf-8 -t utf-8 coverage.xml > coverage_scrubbed.xml
# Create a default pylint configuration file.
##touch $HOME/.pylintrc
diff --git a/tox.ini b/tox.ini
index 488a929d31..f803c80c88 100644
--- a/tox.ini
+++ b/tox.ini
@@ -19,12 +19,13 @@ passenv = *
deps =
numpy
nose >= 1.2.1
- coverage
text-unidecode
twython
pyparsing
+ pytest
+ pytest-cov
+ pytest-mock
python-crfsuite
- rednose
regex
changedir = nltk/test
@@ -33,39 +34,44 @@ commands =
; they can't be installed in one command
pip install scipy scikit-learn
- ; python runtests.py --with-coverage --cover-inclusive --cover-package=nltk --cover-html --cover-html-dir={envdir}/docs []
- python runtests.py []
+ ; pytest --cov=nltk --cov-report html:{envdir}/docs nltk/test/
+ pytest
[testenv:pypy]
; numpy is bundled with pypy; coverage is extra slow and
; the coverage results are not that different from CPython.
deps =
nose >= 1.2.1
+ pytest
+ pytest-mock
twython
commands =
- python runtests.py []
+ pytest
[testenv:py35-nodeps]
basepython = python3.5
deps =
nose >= 1.2.1
- rednose
-commands = python runtests.py []
+ pytest
+ pytest-mock
+commands = pytest
[testenv:py36-nodeps]
basepython = python3.6
deps =
nose >= 1.2.1
- rednose
-commands = python runtests.py []
+ pytest
+ pytest-mock
+commands = pytest
[testenv:py37-nodeps]
basepython = python3.7
deps =
nose >= 1.2.1
- rednose
-commands = python runtests.py []
+ pytest
+ pytest-mock
+commands = pytest
# Use minor version agnostic basepython, but specify testenv
# control Python2/3 versions using jenkins' user-defined matrix instead.
diff --git a/web/dev/jenkins.rst b/web/dev/jenkins.rst
index e5b3025b82..0a210a046f 100644
--- a/web/dev/jenkins.rst
+++ b/web/dev/jenkins.rst
@@ -68,25 +68,21 @@ installed beforehand, and to make them run a series of extra environment
variables are initialized. These dependencies will not be detailed until the
last section.
-The test suite itself consists of doctests. These are found in each module as
-docstrings, and in all the .doctest files under the test folder in the nltk
-repo. We run these tests using nose_, find code coverage using `coverage.py`_
-and check for `PEP-8`_ etc. standard violations using `pylint`_.
+The test suite itself consists of doctests and unittests. Doctests are found in
+each module as docstrings, and in all the .doctest files under the test folder in
+the nltk repo. We run these tests using pytest_, find code coverage using
+`pytest-cov`_ and check for `PEP-8`_ etc. standard violations using `pylint`_.
All these tools are easily installable through pip your favourite OS' software
-packaging system. For testing, only nose_ is really needed. This is also the
-only software that does not work properly out of the box. To use the options
-+ELLIPSIS and +NORMALIZE_WHITESPACE in our doctests, we have installed nose
-from source with `a patch that allows this`_ applied.
+packaging system. For testing, you can install the requirements with ``pip install -r requirements-test.txt``
The results of these programs are parsed and published by the jenkins instance,
giving us pretty graphs :)
-.. _nose: http://readthedocs.org/docs/nose/
-.. _`coverage.py`: http://nedbatchelder.com/code/coverage/
+.. _pytest: https://docs.pytest.org/
+.. _`pytest-cov`: http://pytest-cov.readthedocs.io/
.. _`PEP-8`: http://www.python.org/dev/peps/pep-0008/
.. _`pylint`: http://www.logilab.org/project/pylint
-.. _`a patch that allows this`: https://github.com/nose-devs/nose/issues/7
The builds
diff --git a/web/dev/local_testing.rst b/web/dev/local_testing.rst
index 110987855d..d72d67ed32 100644
--- a/web/dev/local_testing.rst
+++ b/web/dev/local_testing.rst
@@ -14,8 +14,8 @@ NLTK testing
4. Make sure all NLTK data is downloaded (see ``nltk.download()``);
5. run 'tox' command from the root nltk folder. It will install dependencies
- and run ``nltk/test/runtests.py`` script for all available interpreters.
- You may pass any options to runtests.py script separating them by '--'.
+ and run ``pytest`` for all available interpreters.
+ You may also pass any pytest options here (for example, `-v` for verbose).
It may take a long time at first run, but the subsequent runs will
be much faster.
@@ -30,7 +30,6 @@ that failed in the last test run::
tox -e py36 -- -v --failed
-
Run tree doctests for all available interpreters::
tox -- tree.doctest
@@ -48,9 +47,9 @@ In order to skip numpy & friends, use ``..-nodeps`` environments::
It is also possible to run tests without tox. This way NLTK would be tested
only under single interpreter, but it may be easier to have numpy and other
libraries installed this way. In order to run tests without tox, make sure
-``nose >= 1.2.1`` is installed and execute runtests.py script::
+to ``pip install -r test-requirements.txt`` and run ``pytest``::
- nltk/test/runtests.py
+ pytest nltk/test/
Writing tests
@@ -71,7 +70,7 @@ unittests. Test should be written as unittest if some of the following apply:
* test deals with non-ascii unicode and Python 2.x support is required;
* test is a regression test that is not necessary for documentational purposes.
-Unittests currently reside in ``nltk/test/unit/test_*.py`` files; nose
+Unittests currently reside in ``nltk/test/unit/test_*.py`` files; pytest
is used for test running.
If a test should be written as unittest but also has a documentational value
@@ -85,13 +84,13 @@ There are some gotchas with NLTK doctests (and with doctests in general):
* Don't write ``+ELLIPSIS``, ``+NORMALIZE_WHITESPACE``,
``+IGNORE_EXCEPTION_DETAIL`` flags (they are already ON by default in NLTK).
-* Do not write doctests that has non-ascii output (they are not supported in
+* Do not write doctests that have non-ascii output (they are not supported in
Python 2.x). Incorrect::
>>> greeting
u'Привет'
- The proper way is to rewrite such doctest as unittest.
+ The proper way is to rewrite such a doctest as a unittest.
* In order to conditionally skip a doctest in a separate
``nltk/test/foo.doctest`` file, create ``nltk.test/foo_fixt.py``
@@ -100,10 +99,10 @@ There are some gotchas with NLTK doctests (and with doctests in general):
# <a comment describing why should the test be skipped>
def setup_module(module):
- from nose import SkipTest
+ import pytest
if some_condition:
- raise SkipTest("foo.doctest is skipped because <...>")
+ pytest.skip("foo.doctest is skipped because <...>")
* In order to conditionally skip all doctests from the module/class/function
docstrings, put the following function in a top-level module namespace::
@@ -111,10 +110,10 @@ There are some gotchas with NLTK doctests (and with doctests in general):
# <a comment describing why should the tests from this module be skipped>
def setup_module(module):
- from nose import SkipTest
+ import pytest
if some_condition:
- raise SkipTest("doctests from nltk.<foo>.<bar> are skipped because <...>")
+ pytest.skip("doctests from nltk.<foo>.<bar> are skipped because <...>")
A good idea is to define ``__all__`` in such module and omit
``setup_module`` from ``__all__``.
@@ -150,7 +149,7 @@ If the code requires some external dependencies, then
* tests for this code should be skipped if the dependencies are not available:
use ``setup_module`` for doctests (as described above) and
- ``nltk.test.unit.utils.skip / skipIf`` decorators or ``nose.SkipTest``
+ ``@pytest.mark.skipif / @pytest.mark.skip`` decorators or ``pytest.skip``
exception for unittests;
* if the dependency is a Python package, it should be added to tox.ini
(but not to ..-nodeps environments).
|
open-mmlab__mmdetection-6034 | Missing '**kwargs' parameters passing to imshow_bboxes() in show_result() of rpn.py
https://github.com/open-mmlab/mmdetection/blob/bde7b4b7eea9dd6ee91a486c6996b2d68662366d/mmdet/models/detectors/rpn.py#L155
'**kwargs' parameters haven't passed to mmcv.imshow_bboxes() in show_result() of mmdetection/mmdet/models/detectors/rpn.py
| [
{
"content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\n\nimport mmcv\nimport torch\nfrom mmcv.image import tensor2imgs\n\nfrom mmdet.core import bbox_mapping\nfrom ..builder import DETECTORS, build_backbone, build_head, build_neck\nfrom .base import BaseDetector\n\n\n@DETECTORS.register... | [
{
"content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\n\nimport mmcv\nimport torch\nfrom mmcv.image import tensor2imgs\n\nfrom mmdet.core import bbox_mapping\nfrom ..builder import DETECTORS, build_backbone, build_head, build_neck\nfrom .base import BaseDetector\n\n\n@DETECTORS.register... | diff --git a/mmdet/models/detectors/rpn.py b/mmdet/models/detectors/rpn.py
index c829c26233a..c70ede2ba37 100644
--- a/mmdet/models/detectors/rpn.py
+++ b/mmdet/models/detectors/rpn.py
@@ -152,4 +152,4 @@ def show_result(self, data, result, top_k=20, **kwargs):
Returns:
np.ndarray: The image with bboxes drawn on it.
"""
- mmcv.imshow_bboxes(data, result, top_k=top_k)
+ mmcv.imshow_bboxes(data, result, top_k=top_k, **kwargs)
|
gratipay__gratipay.com-1953 | CRLFInjection reports in Sentry
We keep getting CRLFInjection exception reports in sentry that @whit537 keeps marking 'ok' :smile:
If they are ok, we should be catching them.
One of the later ones is a GET for
```
/Allan
ns:netsparker056650=vuln/
```
| [
{
"content": "\"\"\"Wireup\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport os\nimport sys\n\nimport aspen\nimport balanced\nimport gittip\nimport raven\nimport psycopg2\nimport stripe\nfrom gittip.models.community import Community\nfrom gittip.models.participa... | [
{
"content": "\"\"\"Wireup\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport os\nimport sys\n\nimport aspen\nimport balanced\nimport gittip\nimport raven\nimport psycopg2\nimport stripe\nfrom gittip.models.community import Community\nfrom gittip.models.participa... | diff --git a/gittip/wireup.py b/gittip/wireup.py
index 3c3fbbdd28..5cd99c4ce7 100644
--- a/gittip/wireup.py
+++ b/gittip/wireup.py
@@ -60,7 +60,7 @@ def tell_sentry(exception, request=None):
# Decide if we care.
# ==================
- if exception.__class__ is aspen.Response:
+ if isinstance(exception, aspen.Response):
if exception.code < 500:
|
imAsparky__django-cookiecutter-14 | [FEAT]: Add Tox, Pytest and test config
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
| [
{
"content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration... | [
{
"content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration... | diff --git a/.github/workflows/test_contribution.yaml b/.github/workflows/test_contribution.yaml
new file mode 100644
index 00000000..34c9e868
--- /dev/null
+++ b/.github/workflows/test_contribution.yaml
@@ -0,0 +1,36 @@
+name: Test Contributions
+
+on:
+ pull_request:
+ branches: [main]
+
+ # push:
+ # branches: [main]
+
+ workflow_dispatch:
+
+jobs:
+ test_contribs:
+ strategy:
+ matrix:
+ python-version: ["3.6", "3.7", "3.8", "3.9"]
+ os: [macos-latest, ubuntu-latest, windows-latest]
+
+ runs-on: ${{ matrix.os }}
+ steps:
+ - uses: actions/checkout@v2
+ with:
+ fetch-depth: 0
+
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install tox tox-gh-actions
+
+ - name: Test with tox
+ run: tox
diff --git a/docs/source/requirements.txt b/docs/requirements.txt
similarity index 100%
rename from docs/source/requirements.txt
rename to docs/requirements.txt
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 7ad9709a..5a30952f 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -16,7 +16,7 @@
import sys
sys.path.insert(0, os.path.abspath('.'))
-
+__version__ = "0.4.0"
# -- Project information -----------------------------------------------------
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 00000000..39baf331
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,2 @@
+[pytest]
+testpaths = tests/
diff --git a/requirements_dev.txt b/requirements_dev.txt
index 2ecd9682..92218998 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -1,2 +1,5 @@
Django==3.2.7
pre-commit==2.15.0
+pytest==6.2.5
+pytest-cookies==0.6.1
+tox==3.24.4
diff --git a/tests/test_fake.py b/tests/test_fake.py
new file mode 100644
index 00000000..6078684d
--- /dev/null
+++ b/tests/test_fake.py
@@ -0,0 +1,4 @@
+def test_fake_test():
+ """A Fake test"""
+
+ assert 3 == 3
diff --git a/tox.ini b/tox.ini
new file mode 100644
index 00000000..fcdaabe9
--- /dev/null
+++ b/tox.ini
@@ -0,0 +1,39 @@
+[tox]
+skipsdist = true
+skip_missing_interpreters = true
+envlist =
+ py36
+ py37
+ py38
+ py39
+ py38 pypy
+ py39-docs
+
+[testenv:docs]
+basepython=python
+changedir=docs/source
+deps= -r{toxinidir}/docs/requirements.txt
+commands=
+ sphinx-build -b html -d {envtmpdir}/doctrees . {envtmpdir}/html
+
+[gh-actions]
+python =
+ pypy-3.8: pypy3
+ 3.9: py39
+ 3.8: py38
+ 3.7: py37
+ 3.6: py36
+
+[gh-actions:env]
+PLATFORM =
+ ubuntu-latest: linux
+ macos-latest: macos
+ windows-latest: windows
+
+[testenv]
+setenv =
+ PYTHONPATH = {toxinidir}
+deps =
+ -r{toxinidir}/requirements_dev.txt
+commands =
+ pytest -v {posargs:tests}
|
saulpw__visidata-1304 | [undo develop] undoing a reload blanks the entire sheet
Since v2.5 undo for reload has been removed, and replaced with quitguard+confirm! However, in that case an undo should not be set.
Current behaviour is that it blanks the sheet.
| [
{
"content": "import itertools\nfrom copy import copy\n\nfrom visidata import vd, options, VisiData, BaseSheet, UNLOADED\n\nBaseSheet.init('undone', list) # list of CommandLogRow for redo after undo\n\nvd.option('undo', True, 'enable undo/redo')\n\nnonUndo = '''commit open-file'''.split()\n\ndef isUndoableComm... | [
{
"content": "import itertools\nfrom copy import copy\n\nfrom visidata import vd, options, VisiData, BaseSheet, UNLOADED\n\nBaseSheet.init('undone', list) # list of CommandLogRow for redo after undo\n\nvd.option('undo', True, 'enable undo/redo')\n\nnonUndo = '''commit open-file reload-sheet'''.split()\n\ndef i... | diff --git a/visidata/undo.py b/visidata/undo.py
index 175512acf..8a4b20dd5 100644
--- a/visidata/undo.py
+++ b/visidata/undo.py
@@ -7,7 +7,7 @@
vd.option('undo', True, 'enable undo/redo')
-nonUndo = '''commit open-file'''.split()
+nonUndo = '''commit open-file reload-sheet'''.split()
def isUndoableCommand(longname):
for n in nonUndo:
|
scipy__scipy-12486 | scipy.stats.poisson docs for rate = 0
I noticed that the docs for [scipy.stats.poisson](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.poisson.html) are not clear about the intended behaviour when `\lambda = 0 `.
Strictly speaking, the pmf for the Poisson is ill-defined at `\lambda = 0`; however, the `\lambda \to ` limit of a Poisson seems to me well-defined and intuitive,
P(n | \lambda = 0) = { 1 if n = 0
{ 0 otherwise
The mean and variance are `$E(n) = Var(n) = \lambda = 0$` as expected. The Poisson is implemented this way (requring only non-negative rate paramater) in [R](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/Poisson.html), which should give some confidence that it isn't an obviously silly thing to do.
Indeed, the current behaviour appears to be just this,
```
>>> from scipy.stats import poisson
>>> poisson.pmf(1, 0.)
0.0
>>> poisson.pmf(0, 0.)
1.0
```
so, can we add a note to the docs that this is the intended treatment?
See https://github.com/scikit-hep/pyhf/issues/293#issuecomment-627207254 where this issue arose indirectly.
| [
{
"content": "#\n# Author: Travis Oliphant 2002-2011 with contributions from\n# SciPy Developers 2004-2011\n#\nfrom functools import partial\nfrom scipy import special\nfrom scipy.special import entr, logsumexp, betaln, gammaln as gamln\nfrom scipy._lib._util import _lazywhere, rng_integers\n\nfrom n... | [
{
"content": "#\n# Author: Travis Oliphant 2002-2011 with contributions from\n# SciPy Developers 2004-2011\n#\nfrom functools import partial\nfrom scipy import special\nfrom scipy.special import entr, logsumexp, betaln, gammaln as gamln\nfrom scipy._lib._util import _lazywhere, rng_integers\n\nfrom n... | diff --git a/scipy/stats/_discrete_distns.py b/scipy/stats/_discrete_distns.py
index 88b06a6ebc6d..1db68980ce56 100644
--- a/scipy/stats/_discrete_distns.py
+++ b/scipy/stats/_discrete_distns.py
@@ -584,6 +584,8 @@ class poisson_gen(rv_discrete):
for :math:`k \ge 0`.
`poisson` takes :math:`\mu` as shape parameter.
+ When mu = 0 then at quantile k = 0, ``pmf`` method
+ returns `1.0`.
%(after_notes)s
|
StackStorm__st2-4064 | Packs are not listed in WEBUI for users with observer role
Hi Team,
In stackstorm WebUI , page **does not render** on clicking packs tab by logged in user having **observer role**.
It highlighted the below error when checked in debug logs of browser
`{
**"faultstring": "User \"arul\" doesn't have required permission \"pack_search\""**
}`
How can the permission `pack_search` be appended to observer role?
Is it possible to provide `pack_search` permission for all the packs as
`---
name: "pack_search_role"
description: "Role which grants pack_search permission to all packs"
permission_grants:
-
resource_uid: "packs"
permission_types:
- "pack_search"
`
Is this a know issue?
**Please look into this and advice. Also find screenshot for the same**.
Thanks,
Suraj S
| [
{
"content": "# Licensed to the StackStorm, Inc ('StackStorm') under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"Li... | [
{
"content": "# Licensed to the StackStorm, Inc ('StackStorm') under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"Li... | diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 5f83d9ee74..6e351a4f36 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -103,6 +103,9 @@ Fixed
expressions and default values. (bug fix) #4050 #4050
Reported by @rakeshrm.
+* Make sure ``observer`` system role also grants ``pack_search`` permission. (bug fix) #4063 #4064
+
+ Reported by @SURAJTHEGREAT.
2.6.0 - January 19, 2018
------------------------
diff --git a/st2api/tests/unit/controllers/v1/test_packs_rbac.py b/st2api/tests/unit/controllers/v1/test_packs_rbac.py
index 08805fa125..3227337266 100644
--- a/st2api/tests/unit/controllers/v1/test_packs_rbac.py
+++ b/st2api/tests/unit/controllers/v1/test_packs_rbac.py
@@ -17,8 +17,10 @@
import six
from st2common.router import Response
+from st2common.services import packs as pack_service
from st2api.controllers.v1.actionexecutions import ActionExecutionsControllerMixin
from tests.base import APIControllerWithRBACTestCase
+from tests.unit.controllers.v1.test_packs import PACK_INDEX
http_client = six.moves.http_client
@@ -90,3 +92,24 @@ def test_get_all_limit_minus_one(self):
resp = self.app.get('/v1/packs?limit=-1')
self.assertEqual(resp.status_code, http_client.OK)
+
+ @mock.patch.object(pack_service, 'fetch_pack_index',
+ mock.MagicMock(return_value=(PACK_INDEX, {})))
+ def test_pack_search(self):
+ user_db = self.users['no_permissions']
+ self.use_user(user_db)
+
+ data = {'query': 'test'}
+ resp = self.app.post_json('/v1/packs/index/search', data, expect_errors=True)
+
+ expected_msg = 'User \"no_permissions\" doesn\'t have required permission \"pack_search\"'
+ self.assertEqual(resp.status_code, http_client.FORBIDDEN)
+ self.assertEqual(resp.json['faultstring'], expected_msg)
+
+ # Observer role also grants pack_search permission
+ user_db = self.users['observer']
+ self.use_user(user_db)
+
+ data = {'query': 'test'}
+ resp = self.app.post_json('/v1/packs/index/search', data)
+ self.assertEqual(resp.status_code, http_client.OK)
diff --git a/st2common/st2common/rbac/resolvers.py b/st2common/st2common/rbac/resolvers.py
index 6e76935791..f5fafd8da1 100644
--- a/st2common/st2common/rbac/resolvers.py
+++ b/st2common/st2common/rbac/resolvers.py
@@ -60,7 +60,8 @@
# "Read" permission names which are granted to observer role by default
READ_PERMISSION_NAMES = [
'view',
- 'list'
+ 'list',
+ 'search'
]
|
imAsparky__django-cookiecutter-16 | [FEAT]: Add Tox, Pytest and test config
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
| [
{
"content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration... | [
{
"content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration... | diff --git a/.github/workflows/test_contribution.yaml b/.github/workflows/test_contribution.yaml
new file mode 100644
index 00000000..34c9e868
--- /dev/null
+++ b/.github/workflows/test_contribution.yaml
@@ -0,0 +1,36 @@
+name: Test Contributions
+
+on:
+ pull_request:
+ branches: [main]
+
+ # push:
+ # branches: [main]
+
+ workflow_dispatch:
+
+jobs:
+ test_contribs:
+ strategy:
+ matrix:
+ python-version: ["3.6", "3.7", "3.8", "3.9"]
+ os: [macos-latest, ubuntu-latest, windows-latest]
+
+ runs-on: ${{ matrix.os }}
+ steps:
+ - uses: actions/checkout@v2
+ with:
+ fetch-depth: 0
+
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install tox tox-gh-actions
+
+ - name: Test with tox
+ run: tox
diff --git a/docs/source/requirements.txt b/docs/requirements.txt
similarity index 100%
rename from docs/source/requirements.txt
rename to docs/requirements.txt
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 7ad9709a..5a30952f 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -16,7 +16,7 @@
import sys
sys.path.insert(0, os.path.abspath('.'))
-
+__version__ = "0.4.0"
# -- Project information -----------------------------------------------------
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 00000000..39baf331
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,2 @@
+[pytest]
+testpaths = tests/
diff --git a/requirements_dev.txt b/requirements_dev.txt
index 2ecd9682..92218998 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -1,2 +1,5 @@
Django==3.2.7
pre-commit==2.15.0
+pytest==6.2.5
+pytest-cookies==0.6.1
+tox==3.24.4
diff --git a/tests/test_fake.py b/tests/test_fake.py
new file mode 100644
index 00000000..6078684d
--- /dev/null
+++ b/tests/test_fake.py
@@ -0,0 +1,4 @@
+def test_fake_test():
+ """A Fake test"""
+
+ assert 3 == 3
diff --git a/tox.ini b/tox.ini
new file mode 100644
index 00000000..fcdaabe9
--- /dev/null
+++ b/tox.ini
@@ -0,0 +1,39 @@
+[tox]
+skipsdist = true
+skip_missing_interpreters = true
+envlist =
+ py36
+ py37
+ py38
+ py39
+ py38 pypy
+ py39-docs
+
+[testenv:docs]
+basepython=python
+changedir=docs/source
+deps= -r{toxinidir}/docs/requirements.txt
+commands=
+ sphinx-build -b html -d {envtmpdir}/doctrees . {envtmpdir}/html
+
+[gh-actions]
+python =
+ pypy-3.8: pypy3
+ 3.9: py39
+ 3.8: py38
+ 3.7: py37
+ 3.6: py36
+
+[gh-actions:env]
+PLATFORM =
+ ubuntu-latest: linux
+ macos-latest: macos
+ windows-latest: windows
+
+[testenv]
+setenv =
+ PYTHONPATH = {toxinidir}
+deps =
+ -r{toxinidir}/requirements_dev.txt
+commands =
+ pytest -v {posargs:tests}
|
pyca__cryptography-1244 | RSAPublicNumbers should have a nicer repr
Instead of:
```
<cryptography.hazmat.primitives.asymmetric.rsa.RSAPublicNumbers object at 0x106547290>
```
Something like:
```
<RSAPublicNumbers(e=65537, n=<some big product of primes>)>
```
would be great
| [
{
"content": "# 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#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, so... | [
{
"content": "# 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#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, so... | diff --git a/cryptography/hazmat/primitives/asymmetric/rsa.py b/cryptography/hazmat/primitives/asymmetric/rsa.py
index 15ec52ac4d02..398b3763b1a6 100644
--- a/cryptography/hazmat/primitives/asymmetric/rsa.py
+++ b/cryptography/hazmat/primitives/asymmetric/rsa.py
@@ -402,3 +402,6 @@ def n(self):
def public_key(self, backend):
return backend.load_rsa_public_numbers(self)
+
+ def __repr__(self):
+ return "<RSAPublicNumbers(e={0}, n={1})>".format(self._e, self._n)
diff --git a/tests/hazmat/primitives/test_rsa.py b/tests/hazmat/primitives/test_rsa.py
index 8e850737b5db..e53ff06bac64 100644
--- a/tests/hazmat/primitives/test_rsa.py
+++ b/tests/hazmat/primitives/test_rsa.py
@@ -27,6 +27,7 @@
)
from cryptography.hazmat.primitives import hashes, interfaces
from cryptography.hazmat.primitives.asymmetric import padding, rsa
+from cryptography.hazmat.primitives.asymmetric.rsa import RSAPublicNumbers
from .fixtures_rsa import (
RSA_KEY_1024, RSA_KEY_1025, RSA_KEY_1026, RSA_KEY_1027, RSA_KEY_1028,
@@ -1973,3 +1974,7 @@ def test_invalid_private_numbers_argument_values(self, backend):
n=33
)
).private_key(backend)
+
+ def test_public_number_repr(self):
+ num = RSAPublicNumbers(1, 1)
+ assert repr(num) == "<RSAPublicNumbers(e=1, n=1)>"
|
openstates__openstates-scrapers-2707 | MO failing since at least 2018-12-09
MO has been failing since 2018-12-09
Based on automated runs it appears that MO has not run successfully in 2 days (2018-12-09).
```
06:11:33 INFO pupa: SB158
06:11:33 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=20875
06:11:34 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=20875
06:11:35 INFO pupa: save bill SB 158 in 2019 as bill_716103c4-fc42-11e8-b9f8-02f1fb7ee550.json
06:11:35 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=39332
06:11:36 INFO pupa: SB159
06:11:36 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=39332
06:11:37 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=39332
06:11:38 INFO pupa: save bill SB 159 in 2019 as bill_732da4be-fc42-11e8-b9f8-02f1fb7ee550.json
06:11:38 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=39145
06:11:39 INFO pupa: SB160
06:11:39 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=39145
06:11:40 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=39145
06:11:41 INFO pupa: save bill SB 160 in 2019 as bill_74f5473e-fc42-11e8-b9f8-02f1fb7ee550.json
06:11:41 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=51423
06:11:42 INFO pupa: SB161
06:11:42 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=51423
06:11:43 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=51423
06:11:44 INFO pupa: save bill SB 161 in 2019 as bill_76bf4ed4-fc42-11e8-b9f8-02f1fb7ee550.json
06:11:44 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=53657
06:11:45 INFO pupa: SB162
06:11:45 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=53657
06:11:46 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=53657
06:11:47 INFO pupa: save bill SB 162 in 2019 as bill_788977b2-fc42-11e8-b9f8-02f1fb7ee550.json
06:11:47 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=53656
06:11:48 INFO pupa: XXXXXX
loaded Open States pupa settings...
mo (scrape, import)
bills: {}
Traceback (most recent call last):
File "/opt/openstates/venv-pupa//bin/pupa", line 11, in <module>
load_entry_point('pupa', 'console_scripts', 'pupa')()
File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/__main__.py", line 68, in main
subcommands[args.subcommand].handle(args, other)
File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/commands/update.py", line 274, in handle
return self.do_handle(args, other, juris)
File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/commands/update.py", line 320, in do_handle
report['scrape'] = self.do_scrape(juris, args, scrapers)
File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/commands/update.py", line 175, in do_scrape
report[scraper_name] = scraper.do_scrape(**scrape_args)
File "/opt/openstates/venv-pupa/src/pupa/pupa/scrape/base.py", line 112, in do_scrape
for obj in self.scrape(**kwargs) or []:
File "/opt/openstates/openstates/openstates/mo/bills.py", line 627, in scrape
yield from self._scrape_upper_chamber(session)
File "/opt/openstates/openstates/openstates/mo/bills.py", line 600, in _scrape_upper_chamber
session,
File "/opt/openstates/openstates/openstates/mo/bills.py", line 193, in _parse_senate_billpage
action_url = action_url[0].attrib['href']
File "src/lxml/etree.pyx", line 2457, in lxml.etree._Attrib.__getitem__
KeyError: 'href'
```
Visit http://bobsled.openstates.org for more info.
| [
{
"content": "import re\nimport pytz\nimport datetime as dt\nfrom collections import defaultdict\n\nimport lxml.html\nfrom pupa.scrape import Scraper, Bill, VoteEvent\n\nfrom openstates.utils import LXMLMixin\n\nfrom .utils import (clean_text, house_get_actor_from_action,\n senate_get_actor_f... | [
{
"content": "import re\nimport pytz\nimport datetime as dt\nfrom collections import defaultdict\n\nimport lxml.html\nfrom pupa.scrape import Scraper, Bill, VoteEvent\n\nfrom openstates.utils import LXMLMixin\n\nfrom .utils import (clean_text, house_get_actor_from_action,\n senate_get_actor_f... | diff --git a/openstates/mo/bills.py b/openstates/mo/bills.py
index ce8fcea385..ca00cec489 100644
--- a/openstates/mo/bills.py
+++ b/openstates/mo/bills.py
@@ -157,6 +157,10 @@ def _parse_senate_billpage(self, bill_url, year):
self.info(bid)
+ if bid == 'XXXXXX':
+ self.info("Skipping Junk Bill")
+ return
+
bill = Bill(
bill_id,
title=bill_desc,
|
hylang__hy-1898 | `lfor` can't see an imported name in `hy -c`
This one is deeply bizarre.
```
$ CODE='(import math) (print (lfor j [1] (math.sqrt 5)))'
$ hy -c "$CODE"
Traceback (most recent call last):
File "/usr/local/bin/hy", line 8, in <module>
sys.exit(hy_main())
File "<string>", line 1, in <module>
File "<string>", line 1, in <listcomp>
NameError: name 'math' is not defined
$ echo "$CODE" | hy
hy 0.18.0 using CPython(default) 3.7.5 on Linux
=> [2.23606797749979]
=>
now exiting HyREPL...
$ echo "$CODE" > /tmp/foo.hy && hy /tmp/foo.hy
[2.23606797749979]
```
| [
{
"content": "# Copyright 2020 the authors.\n# This file is part of Hy, which is free software licensed under the Expat\n# license. See the LICENSE.\n\nfrom __future__ import print_function\n\nimport colorama\ncolorama.init()\n\nimport argparse\nimport code\nimport ast\nimport sys\nimport os\nimport io\nimport ... | [
{
"content": "# Copyright 2020 the authors.\n# This file is part of Hy, which is free software licensed under the Expat\n# license. See the LICENSE.\n\nfrom __future__ import print_function\n\nimport colorama\ncolorama.init()\n\nimport argparse\nimport code\nimport ast\nimport sys\nimport os\nimport io\nimport ... | diff --git a/NEWS.rst b/NEWS.rst
index 220b9f764..8c97e0f16 100644
--- a/NEWS.rst
+++ b/NEWS.rst
@@ -19,6 +19,7 @@ Bug Fixes
* Quoted f-strings are no longer evaluated prematurely.
* Fixed a regression in the production of error messages for empty
expressions.
+* Fixed a scoping bug for code executed with `hy -c`.
0.18.0
==============================
diff --git a/hy/cmdline.py b/hy/cmdline.py
index b9ffcdb55..6277d3d7f 100644
--- a/hy/cmdline.py
+++ b/hy/cmdline.py
@@ -429,7 +429,7 @@ def run_command(source, filename=None):
return 1
with filtered_hy_exceptions():
- hy_eval(tree, None, __main__, filename=filename, source=source)
+ hy_eval(tree, __main__.__dict__, __main__, filename=filename, source=source)
return 0
diff --git a/tests/test_bin.py b/tests/test_bin.py
index 6c8e51c75..9bb63c8f4 100644
--- a/tests/test_bin.py
+++ b/tests/test_bin.py
@@ -266,6 +266,10 @@ def test_bin_hy_cmd():
_, err = run_cmd("hy -c \"(koan\"", expect=1)
assert "Premature end of input" in err
+ # https://github.com/hylang/hy/issues/1879
+ output, _ = run_cmd(
+ """hy -c '(setv x "bing") (defn f [] (+ "fiz" x)) (print (f))'""")
+ assert 'fizbing' in output
def test_bin_hy_icmd():
output, _ = run_cmd("hy -i \"(koan)\"", "(ideas)")
|
deepset-ai__haystack-3705 | Bad Semaphore initialization in RequestLimiter
**Describe the bug**
RequestLimiter takes a number as parameter and use it to set up a Semaphore. The issue is that the environment variable indicates the concurrent allowed requests per worker. When the semaphore is created (https://github.com/deepset-ai/haystack/blob/6790eaf7d8be05c5674d97a75cc5783e00a66875/rest_api/rest_api/controller/utils.py#L13), this value is set down by 1. This is clearly not what the project tried to achieve (at least per naming).
**Error message**
REST API will always return it's busy, error 503 when CONCURRENT_REQUEST_PER_WORKER is equal to CONCURRENT_REQUEST_PER_WORKER -1. When user set the concurrency to 1, it will never be able to call the API, since the Semaphore declaration will be Semaphore(0)
**Expected behavior**
Being able to set the request limits using the env variable CONCURRENT_REQUEST_PER_WORKER
**Additional context**
**To Reproduce**
**FAQ Check**
- [x] Have you had a look at [our new FAQ page](https://haystack.deepset.ai/overview/faq)?
**System:**
- OS: Ubuntu
- GPU/CPU: i7/ Nvidia
- Haystack version (commit or version number): 1.9
- DocumentStore:
- Reader:
- Retriever:
| [
{
"content": "from typing import Type, NewType\n\nimport inspect\nfrom contextlib import contextmanager\nfrom threading import Semaphore\n\nfrom fastapi import Form, HTTPException\nfrom pydantic import BaseModel\n\n\nclass RequestLimiter:\n def __init__(self, limit):\n self.semaphore = Semaphore(limit... | [
{
"content": "from typing import Type, NewType\n\nimport inspect\nfrom contextlib import contextmanager\nfrom threading import Semaphore\n\nfrom fastapi import Form, HTTPException\nfrom pydantic import BaseModel\n\n\nclass RequestLimiter:\n def __init__(self, limit):\n self.semaphore = Semaphore(limit... | diff --git a/rest_api/rest_api/controller/utils.py b/rest_api/rest_api/controller/utils.py
index 5579d3f0b5..49587968ed 100644
--- a/rest_api/rest_api/controller/utils.py
+++ b/rest_api/rest_api/controller/utils.py
@@ -10,7 +10,7 @@
class RequestLimiter:
def __init__(self, limit):
- self.semaphore = Semaphore(limit - 1)
+ self.semaphore = Semaphore(limit)
@contextmanager
def run(self):
|
saulpw__visidata-2269 | save as csv actually saves tsv when file existing file extension is CSV, i.e. uppercase - PR available #2269
**Small description**
Slightly hperbolic ;-) Corruption of file format, opening a csv but saving as csv results in TSV data in a file named csv
**Expected result**
CSV, not TSV
**Actual result with screenshot**
If you get an unexpected error, please include the full stack trace that you get with `Ctrl-E`.
No error, contents:
header1 header2
1 one
2 two
**Steps to reproduce with sample data and a .vd**
Datafile, called bug.CSV
header1,header2
1,one
2,two
1. open data file, MUST have uppercase CSV on end (works fine for lower). E.g., `visidata bug.CSV`
2. save (ctrl-s)
3. hit enter to accept current filename
4. hit `y` to overwrite
5. Display will say saving TSV
6. sanity check file contents
First try reproducing without any user configuration by using the flag `-N`.
e.g. `echo "abc" | vd -f txt -N`
Please attach the commandlog (saved with `Ctrl-D`) to show the steps that led to the issue.
See [here](http://visidata.org/docs/save-restore/) for more details.
**Additional context**
Please include the version of VisiData and Python.
Windows:
(py311csv) C:\code\py>python
Python 3.11.3 (tags/v3.11.3:f3909b8, Apr 4 2023, 23:49:59) [MSC v.1934 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> ^Z
(py311csv) C:\code\py>visidata --version
saul.pw/VisiData v3.0.2
| [
{
"content": "import collections\nimport os\nfrom copy import copy\n\nfrom visidata import vd\nfrom visidata import Sheet, BaseSheet, VisiData, IndexSheet, Path, Progress, TypedExceptionWrapper\n\nvd.option('safe_error', '#ERR', 'error string to use while saving', replay=True)\nvd.option('save_encoding', 'utf-8... | [
{
"content": "import collections\nimport os\nfrom copy import copy\n\nfrom visidata import vd\nfrom visidata import Sheet, BaseSheet, VisiData, IndexSheet, Path, Progress, TypedExceptionWrapper\n\nvd.option('safe_error', '#ERR', 'error string to use while saving', replay=True)\nvd.option('save_encoding', 'utf-8... | diff --git a/visidata/save.py b/visidata/save.py
index e2e9e0320..a4574209e 100644
--- a/visidata/save.py
+++ b/visidata/save.py
@@ -110,7 +110,7 @@ def saveSheets(vd, givenpath, *vsheets, confirm_overwrite=True):
vd.warning('no sheets to save')
return
- filetypes = [givenpath.ext, vd.options.save_filetype]
+ filetypes = [givenpath.ext.lower(), vd.options.save_filetype.lower()]
vd.clearCaches()
|
RedHatInsights__insights-core-2879 | 'TypeError' object has no attribute 'tb_frame'
While fetching object details from insight inspect, getting kicked out from the ipython console with the following error.
'TypeError' object has no attribute 'tb_frame'
(gss-rules) ⌊gss-rules⌋»$ insights inspect insights.parsers.installed_rpms.InstalledRpms ~/scripts/rhel7_sosreport/
IPython Console Usage Info:
Enter 'InstalledRpms.' and tab to get a list of properties
Example:
In [1]: InstalledRpms.<property_name>
Out[1]: <property value>
To exit ipython enter 'exit' and hit enter or use 'CTL D'
Starting IPython Interpreter Now
In [1]: InstalledRpms
'TypeError' object has no attribute 'tb_frame'
| [
{
"content": "import os\nimport sys\nfrom setuptools import setup, find_packages\n\n__here__ = os.path.dirname(os.path.abspath(__file__))\n\npackage_info = dict.fromkeys([\"RELEASE\", \"COMMIT\", \"VERSION\", \"NAME\"])\n\nfor name in package_info:\n with open(os.path.join(__here__, \"insights\", name)) as f... | [
{
"content": "import os\nimport sys\nfrom setuptools import setup, find_packages\n\n__here__ = os.path.dirname(os.path.abspath(__file__))\n\npackage_info = dict.fromkeys([\"RELEASE\", \"COMMIT\", \"VERSION\", \"NAME\"])\n\nfor name in package_info:\n with open(os.path.join(__here__, \"insights\", name)) as f... | diff --git a/setup.py b/setup.py
index 43283af7b2..d3aadc939e 100644
--- a/setup.py
+++ b/setup.py
@@ -70,7 +70,9 @@ def maybe_require(pkg):
'ipython',
'colorama',
'jinja2',
- 'Pygments'
+ 'Pygments',
+ 'jedi<0.18.0' # Open issue with jedi 0.18.0 and iPython <= 7.19
+ # https://github.com/davidhalter/jedi/issues/1714
])
testing = set([
|
GeotrekCE__Geotrek-admin-4021 | Problème de thumbnail avec les SVG
Bug détecté à partir de la version 2.101.4 de Geotrek Admin.
Celui est déclenché par l'ajout d'un SVG comme pictogramme sur un lieu de renseignement.
Explication : la dernière version de easy_thumbnail n'accepte pas de faire le thumbnail d'un SVG. -> l'api V2 plante
| [
{
"content": "#!/usr/bin/python3\nimport os\nimport distutils.command.build\nfrom pathlib import Path\nfrom setuptools import setup, find_packages\nfrom shutil import copy\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n\nclass BuildCommand(distutils.command.build.build):\n def run(self):\n di... | [
{
"content": "#!/usr/bin/python3\nimport os\nimport distutils.command.build\nfrom pathlib import Path\nfrom setuptools import setup, find_packages\nfrom shutil import copy\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n\nclass BuildCommand(distutils.command.build.build):\n def run(self):\n di... | diff --git a/debian/rules b/debian/rules
index 4f314f4e5b..6394839dd1 100755
--- a/debian/rules
+++ b/debian/rules
@@ -10,7 +10,7 @@ override_dh_virtualenv:
--python /usr/bin/python3.8 \
--upgrade-pip \
--preinstall wheel \
- --preinstall setuptools \
+ --preinstall setuptools==69.2.0 \
--builtin-venv \
--extra-pip-arg --no-cache-dir \
--extra-pip-arg --quiet
diff --git a/docs/changelog.rst b/docs/changelog.rst
index 936c514cd5..2e74944dbb 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -5,6 +5,10 @@ CHANGELOG
2.103.2+dev (XXXX-XX-XX)
------------------------
+**Bug fixes**
+
+- Fix api crash when using an svg file for information desks (fixes #3860)
+
**Breaking changes**
- Geotrek-rando v2 support is deprecated, `sync_rando` command and Sync rando menu view are removed.
diff --git a/geotrek/api/tests/test_v2.py b/geotrek/api/tests/test_v2.py
index 2802e0003e..9db3d252e0 100644
--- a/geotrek/api/tests/test_v2.py
+++ b/geotrek/api/tests/test_v2.py
@@ -2156,12 +2156,32 @@ def test_informationdesk_list(self):
tourism_models.InformationDesk
)
+ def test_informationdesk_list_with_svg(self):
+ info_desk = tourism_factory.InformationDeskFactory(
+ photo=get_dummy_uploaded_image_svg(), name='test!')
+ info_desk.save()
+ self.treks[0].information_desks.add(info_desk)
+ self.check_number_elems_response(
+ self.get_informationdesk_list(),
+ tourism_models.InformationDesk
+ )
+
def test_informationdesk_detail(self):
self.check_structure_response(
self.get_informationdesk_detail(self.info_desk.pk),
INFORMATION_DESK_PROPERTIES_JSON_STRUCTURE
)
+ def test_informationdesk_detail_with_svg(self):
+ info_desk = tourism_factory.InformationDeskFactory(
+ photo=get_dummy_uploaded_image_svg())
+ info_desk.save()
+ self.treks[0].information_desks.add(info_desk)
+ self.check_structure_response(
+ self.get_informationdesk_detail(info_desk.pk),
+ INFORMATION_DESK_PROPERTIES_JSON_STRUCTURE
+ )
+
def test_informationdesk_filter_trek(self):
response = self.get_informationdesk_list({'trek': self.treks[0].pk})
self.assertEqual(response.json()["count"], 1)
diff --git a/requirements.txt b/requirements.txt
index 03d999bcc8..3ec14c5ac3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -52,7 +52,9 @@ cffi==1.16.0
# pyvips
# weasyprint
chardet==5.2.0
- # via geotrek (setup.py)
+ # via
+ # geotrek (setup.py)
+ # reportlab
charset-normalizer==3.2.0
# via requests
click==8.1.3
@@ -85,6 +87,7 @@ coreschema==0.0.4
cssselect2==0.7.0
# via
# cairosvg
+ # svglib
# weasyprint
datetime==5.2
# via appy
@@ -172,8 +175,9 @@ drf-yasg==1.21.5
# via
# django-large-image
# geotrek (setup.py)
-easy-thumbnails==2.8.5
+easy-thumbnails[svg]==2.8.5
# via
+ # geotrek (setup.py)
# mapentity
# paperclip
env-file==2020.12.3
@@ -217,7 +221,9 @@ large-image==1.20.3
large-image-source-vips==1.17.2
# via geotrek (setup.py)
lxml==4.9.3
- # via mapentity
+ # via
+ # mapentity
+ # svglib
mapentity==8.7.2
# via geotrek (setup.py)
markdown==3.4.4
@@ -258,6 +264,7 @@ pillow==10.2.0
# geotrek (setup.py)
# large-image
# paperclip
+ # reportlab
# weasyprint
prompt-toolkit==3.0.39
# via click-repl
@@ -297,6 +304,10 @@ rcssmin==1.1.1
# via django-compressor
redis==4.5.4
# via geotrek (setup.py)
+reportlab==4.1.0
+ # via
+ # easy-thumbnails
+ # svglib
requests==2.31.0
# via
# coreapi
@@ -326,12 +337,15 @@ soupsieve==2.5
# via beautifulsoup4
sqlparse==0.4.4
# via django
+svglib==1.5.1
+ # via easy-thumbnails
tif2geojson==0.1.3
# via geotrek (setup.py)
tinycss2==1.2.1
# via
# cairosvg
# cssselect2
+ # svglib
# weasyprint
transaction==3.1.0
# via zodb
diff --git a/setup.py b/setup.py
index 7be60e8fc2..558ba1f1a0 100644
--- a/setup.py
+++ b/setup.py
@@ -72,6 +72,7 @@ def run(self):
# prod,
'gunicorn',
'sentry-sdk',
+ 'easy-thumbnails[svg]',
],
cmdclass={"build": BuildCommand},
include_package_data=True,
|
PaddlePaddle__models-3377 | 如果eos发生变化无法做infer的操作
[attention_model.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/ocr_recognition/attention_model.py)这个文件中如果eos发生变化无法做infer的操作（比如因为要识别有数字所以eos改成了其他值）
attention_infer的方法中
```xml
selected_ids, selected_scores = fluid.layers.beam_search(
pre_ids,
pre_score,
topk_indices,
accu_scores,
beam_size,
eos, # 这里应该从1改成eos，否则eos如果发生变化会导致无法做infer操作
#level=0
)
```
| [
{
"content": "# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.\n#\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#\n# http://www.apache.org/licenses/LICENSE-... | [
{
"content": "# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.\n#\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#\n# http://www.apache.org/licenses/LICENSE-... | diff --git a/PaddleCV/ocr_recognition/attention_model.py b/PaddleCV/ocr_recognition/attention_model.py
index 4d3b4da50a..963d2168fd 100755
--- a/PaddleCV/ocr_recognition/attention_model.py
+++ b/PaddleCV/ocr_recognition/attention_model.py
@@ -325,7 +325,7 @@ def attention_infer(images, num_classes, use_cudnn=True):
topk_indices,
accu_scores,
beam_size,
- 1, # end_id
+ eos, # end_id
#level=0
)
|
dmlc__gluon-nlp-184 | API doc examples are currently not easy to copy/paste
users may want to use a snippet from example directly, so making the notebooks copy-friendly is important
currently the code blocks have python shell prefix ">>>" in them. see http://gluon-nlp.mxnet.io/api/notes/data_api.html
| [
{
"content": "# -*- coding: utf-8 -*-\n#\n# documentation build configuration file, created by\n# sphinx-quickstart on Thu Jul 23 19:40:08 2015.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# au... | [
{
"content": "# -*- coding: utf-8 -*-\n#\n# documentation build configuration file, created by\n# sphinx-quickstart on Thu Jul 23 19:40:08 2015.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# au... | diff --git a/docs/_static/copybutton.js b/docs/_static/copybutton.js
new file mode 100644
index 0000000000..a8e45151ef
--- /dev/null
+++ b/docs/_static/copybutton.js
@@ -0,0 +1,65 @@
+// Copyright 2014 PSF. Licensed under the PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+// File originates from the cpython source found in Doc/tools/sphinxext/static/copybutton.js
+
+$(document).ready(function() {
+ /* Add a [>>>] button on the top-right corner of code samples to hide
+ * the >>> and ... prompts and the output and thus make the code
+ * copyable. */
+ var div = $('.highlight-python .highlight,' +
+ '.highlight-default .highlight,' +
+ '.highlight-python3 .highlight')
+ var pre = div.find('pre');
+
+ // get the styles from the current theme
+ pre.parent().parent().css('position', 'relative');
+ var hide_text = 'Hide the prompts and output';
+ var show_text = 'Show the prompts and output';
+ var border_width = pre.css('border-top-width');
+ var border_style = pre.css('border-top-style');
+ var border_color = pre.css('border-top-color');
+ var button_styles = {
+ 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0',
+ 'border-color': border_color, 'border-style': border_style,
+ 'border-width': border_width, 'color': border_color, 'text-size': '75%',
+ 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em',
+ 'border-radius': '0 3px 0 0'
+ }
+
+ // create and add the button to all the code blocks that contain >>>
+ div.each(function(index) {
+ var jthis = $(this);
+ if (jthis.find('.gp').length > 0) {
+ var button = $('<span class="copybutton">>>></span>');
+ button.css(button_styles)
+ button.attr('title', hide_text);
+ button.data('hidden', 'false');
+ jthis.prepend(button);
+ }
+ // tracebacks (.gt) contain bare text elements that need to be
+ // wrapped in a span to work with .nextUntil() (see later)
+ jthis.find('pre:has(.gt)').contents().filter(function() {
+ return ((this.nodeType == 3) && (this.data.trim().length > 0));
+ }).wrap('<span>');
+ });
+
+ // define the behavior of the button when it's clicked
+ $('.copybutton').click(function(e){
+ e.preventDefault();
+ var button = $(this);
+ if (button.data('hidden') === 'false') {
+ // hide the code output
+ button.parent().find('.go, .gp, .gt').hide();
+ button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
+ button.css('text-decoration', 'line-through');
+ button.attr('title', show_text);
+ button.data('hidden', 'true');
+ } else {
+ // show the code output
+ button.parent().find('.go, .gp, .gt').show();
+ button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
+ button.css('text-decoration', 'none');
+ button.attr('title', hide_text);
+ button.data('hidden', 'false');
+ }
+ });
+});
diff --git a/docs/conf.py b/docs/conf.py
index dc8c203b58..93de5392d4 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -216,6 +216,7 @@ def setup(app):
}, True)
app.add_transform(AutoStructify)
app.add_javascript('google_analytics.js')
+ app.add_javascript('copybutton.js')
sphinx_gallery_conf = {
|
scrapy__scrapy-6347 | Set METAREFRESH_IGNORE_TAGS to ["noscript"] by default
I was wrong in https://github.com/scrapy/scrapy/issues/3844. The default value should be `["noscript"]`, to deal with [antibot behaviors](https://github.com/scrapy/scrapy/commit/ec1ef0235f9deee0c263c9b31652d3e74a754acc).
Found by @mukthy.
Set METAREFRESH_IGNORE_TAGS to ["noscript"] by default
I was wrong in https://github.com/scrapy/scrapy/issues/3844. The default value should be `["noscript"]`, to deal with [antibot behaviors](https://github.com/scrapy/scrapy/commit/ec1ef0235f9deee0c263c9b31652d3e74a754acc).
Found by @mukthy.
| [
{
"content": "\"\"\"\nThis module contains the default values for all settings used by Scrapy.\n\nFor more information about these settings you can read the settings\ndocumentation in docs/topics/settings.rst\n\nScrapy developers, if you add a setting here remember to:\n\n* add it in alphabetical order\n* group... | [
{
"content": "\"\"\"\nThis module contains the default values for all settings used by Scrapy.\n\nFor more information about these settings you can read the settings\ndocumentation in docs/topics/settings.rst\n\nScrapy developers, if you add a setting here remember to:\n\n* add it in alphabetical order\n* group... | diff --git a/docs/topics/downloader-middleware.rst b/docs/topics/downloader-middleware.rst
index 1abbc49684f..d4cd062fe38 100644
--- a/docs/topics/downloader-middleware.rst
+++ b/docs/topics/downloader-middleware.rst
@@ -884,6 +884,10 @@ Meta tags within these tags are ignored.
The default value of :setting:`METAREFRESH_IGNORE_TAGS` changed from
``['script', 'noscript']`` to ``[]``.
+.. versionchanged:: VERSION
+ The default value of :setting:`METAREFRESH_IGNORE_TAGS` changed from
+ ``[]`` to ``['noscript']``.
+
.. setting:: METAREFRESH_MAXDELAY
METAREFRESH_MAXDELAY
diff --git a/scrapy/settings/default_settings.py b/scrapy/settings/default_settings.py
index 2b3d95a0e14..d7ac7ec350f 100644
--- a/scrapy/settings/default_settings.py
+++ b/scrapy/settings/default_settings.py
@@ -239,7 +239,7 @@
MEMUSAGE_WARNING_MB = 0
METAREFRESH_ENABLED = True
-METAREFRESH_IGNORE_TAGS = []
+METAREFRESH_IGNORE_TAGS = ["noscript"]
METAREFRESH_MAXDELAY = 100
NEWSPIDER_MODULE = ""
diff --git a/tests/test_downloadermiddleware_redirect.py b/tests/test_downloadermiddleware_redirect.py
index 10b8ca9afb9..83ff259823a 100644
--- a/tests/test_downloadermiddleware_redirect.py
+++ b/tests/test_downloadermiddleware_redirect.py
@@ -395,9 +395,8 @@ def test_ignore_tags_default(self):
"""content="0;URL='http://example.org/newpage'"></noscript>"""
)
rsp = HtmlResponse(req.url, body=body.encode())
- req2 = self.mw.process_response(req, rsp, self.spider)
- assert isinstance(req2, Request)
- self.assertEqual(req2.url, "http://example.org/newpage")
+ response = self.mw.process_response(req, rsp, self.spider)
+ assert isinstance(response, Response)
def test_ignore_tags_1_x_list(self):
"""Test that Scrapy 1.x behavior remains possible"""
|
dbt-labs__dbt-core-1826 | Agate type inference is too clever
### Describe the bug
We’re trying to set a value from a {% call statement %} and within the call, one line is SELECT 0 AS my_value...and it then treats it as a boolean (false) in the returned values.
The same happens if we try SELECT 1 AS my_value, but as soon as we do SELECT 2 AS my_value it treats it like a number (as it should).
### Steps To Reproduce
Create a call statement that selects 0, or 1. false, and true respectively will be returned.
### Expected behavior
0, or 1 to be returned, as integers.
### Screenshots and log output
### System information
**Which database are you using dbt with?**
- [ ] postgres
- [ ] redshift
- [x] bigquery
- [ ] snowflake
- [ ] other (specify: ____________)
**The output of `dbt --version`:**
```
installed version: 0.15.0-a1
latest version: 0.14.2
Your version of dbt is ahead of the latest release!
```
FYI, we run a fork, but that shouldn't have affected anything here.
**The operating system you're using:**
Mojave
**The output of `python --version`:**
Python 3.7.1
### Additional context
We'd love a quick fix for this, even if it's ugly!
| [
{
"content": "from codecs import BOM_UTF8\n\nimport agate\nimport json\n\n\nBOM = BOM_UTF8.decode('utf-8') # '\\ufeff'\n\nDEFAULT_TYPE_TESTER = agate.TypeTester(types=[\n agate.data_types.Number(null_values=('null', '')),\n agate.data_types.TimeDelta(null_values=('null', '')),\n agate.data_types.Date(... | [
{
"content": "from codecs import BOM_UTF8\n\nimport agate\nimport json\n\n\nBOM = BOM_UTF8.decode('utf-8') # '\\ufeff'\n\nDEFAULT_TYPE_TESTER = agate.TypeTester(types=[\n agate.data_types.Number(null_values=('null', '')),\n agate.data_types.TimeDelta(null_values=('null', '')),\n agate.data_types.Date(... | diff --git a/core/dbt/clients/agate_helper.py b/core/dbt/clients/agate_helper.py
index 8139a554853..215ceed7aaf 100644
--- a/core/dbt/clients/agate_helper.py
+++ b/core/dbt/clients/agate_helper.py
@@ -46,7 +46,7 @@ def table_from_data_flat(data, column_names):
row.append(value)
rows.append(row)
- return agate.Table(rows, column_names)
+ return agate.Table(rows, column_names, column_types=DEFAULT_TYPE_TESTER)
def empty_table():
diff --git a/test/integration/022_bigquery_test/macros/test_int_inference.sql b/test/integration/022_bigquery_test/macros/test_int_inference.sql
new file mode 100644
index 00000000000..852accbb0e7
--- /dev/null
+++ b/test/integration/022_bigquery_test/macros/test_int_inference.sql
@@ -0,0 +1,13 @@
+
+{% macro test_int_inference() %}
+
+ {% set sql %}
+ select
+ 0 as int_0,
+ 1 as int_1,
+ 2 as int_2
+ {% endset %}
+
+ {% do return(run_query(sql)) %}
+
+{% endmacro %}
diff --git a/test/integration/022_bigquery_test/test_bigquery_query_results.py b/test/integration/022_bigquery_test/test_bigquery_query_results.py
new file mode 100644
index 00000000000..b7d6109f91e
--- /dev/null
+++ b/test/integration/022_bigquery_test/test_bigquery_query_results.py
@@ -0,0 +1,39 @@
+from test.integration.base import DBTIntegrationTest, use_profile
+import json
+
+class TestBaseBigQueryResults(DBTIntegrationTest):
+
+ @property
+ def schema(self):
+ return "bigquery_test_022"
+
+ @property
+ def models(self):
+ return "models"
+
+ @property
+ def project_config(self):
+ return {
+ 'macro-paths': ['macros'],
+ }
+
+ @use_profile('bigquery')
+ def test__bigquery_type_inference(self):
+ _, test_results = self.run_dbt(['run-operation', 'test_int_inference'])
+ self.assertEqual(len(test_results), 1)
+
+ actual_0 = test_results.rows[0]['int_0']
+ actual_1 = test_results.rows[0]['int_1']
+ actual_2 = test_results.rows[0]['int_2']
+
+ self.assertEqual(actual_0, 0)
+ self.assertEqual(str(actual_0), '0')
+ self.assertEqual(actual_0 * 2, 0) # not 00
+
+ self.assertEqual(actual_1, 1)
+ self.assertEqual(str(actual_1), '1')
+ self.assertEqual(actual_1 * 2, 2) # not 11
+
+ self.assertEqual(actual_2, 2)
+ self.assertEqual(str(actual_2), '2')
+ self.assertEqual(actual_2 * 2, 4) # not 22
|
keras-team__keras-16145 | EfficientNetV2 does not match google implementation
Hi,
I may be wrong, but checking the efficientnetv2 implementation I think there is a difference with the [google one](https://github.com/google/automl/blob/387d5ddb92bb8fbbec4b012e5636a81ea65fffda/efficientnetv2/effnetv2_model.py)
The ["survival_probability"](https://github.com/keras-team/keras/blob/d8fcb9d4d4dad45080ecfdd575483653028f8eda/keras/applications/efficientnet_v2.py#L990) is defined as `survival_probability=drop_connect_rate * b / blocks` but b is set to zero, while according to the [google implementation](https://github.com/google/automl/blob/387d5ddb92bb8fbbec4b012e5636a81ea65fffda/efficientnetv2/effnetv2_model.py#L619) it should increase with the "number" of the block.
I think that line should be replaced with:
`survival_probability=drop_connect_rate * i / blocks`
where i is the counter of the for loop
| [
{
"content": "# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\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#\n# http://www.apache.org/licenses/LICENSE-2.0\... | [
{
"content": "# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\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#\n# http://www.apache.org/licenses/LICENSE-2.0\... | diff --git a/keras/applications/efficientnet_v2.py b/keras/applications/efficientnet_v2.py
index a329ed23e101..6a1b5be8d0fb 100644
--- a/keras/applications/efficientnet_v2.py
+++ b/keras/applications/efficientnet_v2.py
@@ -991,6 +991,7 @@ def EfficientNetV2(
name="block{}{}_".format(i + 1, chr(j + 97)),
**args,
)(x)
+ b += 1
# Build top
top_filters = round_filters(
|
xorbitsai__inference-1092 | DOC: Add doc about installing from source code
Note that the issue tracker is NOT the place for general support.
| [
{
"content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup -------------------------------------------... | [
{
"content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup -------------------------------------------... | diff --git a/doc/source/conf.py b/doc/source/conf.py
index b22c286a07..0c5441f239 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -75,7 +75,7 @@
html_theme_options = {
"show_toc_level": 2,
- "header_links_before_dropdown": 6,
+ "header_links_before_dropdown": 7,
"icon_links": [
{
"name": "GitHub",
diff --git a/doc/source/development/contributing_codebase.rst b/doc/source/development/contributing_codebase.rst
new file mode 100644
index 0000000000..ace1729d21
--- /dev/null
+++ b/doc/source/development/contributing_codebase.rst
@@ -0,0 +1,100 @@
+=============================
+Contributing to the code base
+=============================
+
+.. contents:: Table of contents:
+ :local:
+
+Code standards
+--------------
+
+Writing good code is not just about what you write. It is also about *how* you write it.
+During Continuous Integration testing, several tools will be run to check your code for stylistic errors.
+Good style is a requirement for submitting code to Xinference.
+
+In addition, it is important that we do not make sudden changes to the code that
+could have the potential to break a lot of user code as a result. Therefore
+we need it to be as backwards compatible as possible to avoid mass breakages.
+
+Autofixing formatting errors
+----------------------------
+
+Moreover, Continuous Integration will run code formatting checks
+like ``black``, ``flake8``, ``isort``, and others using `pre-commit hooks <https://pre-commit.com/>`_
+Any warnings generated by these checks will cause the Continuous Integration to fail. Therefore,
+it is advisable to run the check yourself before submitting code. This
+can be done by installing ``pre-commit``::
+
+ pip install pre-commit
+
+and then running::
+
+ pre-commit install
+
+from the root of the Xinference repository. This setup ensures that all styling checks are
+automatically executed each time you commit changes without your needing to run each one manually.
+In addition, using ``pre-commit`` will also allow you to more easily
+remain up-to-date with our code checks as they change.
+
+Note that if needed, you can skip these checks with ``git commit --no-verify``.
+
+If you don't want to use ``pre-commit`` as part of your workflow, you can still use it
+to run its checks with::
+
+ pre-commit run --files <files you have modified>
+
+without needing to have done ``pre-commit install`` beforehand.
+
+If you want to run checks on all recently committed files on upstream/main you can use::
+
+ pre-commit run --from-ref=upstream/main --to-ref=HEAD --all-files
+
+without needing to have done ``pre-commit install`` beforehand.
+
+.. note::
+
+ You may consider periodically running ``pre-commit gc`` to clean up repos
+ which are no longer used.
+
+.. note::
+
+ If you have conflicting installations of ``virtualenv``, if could lead to
+ errors - refer to `here <https://github.com/pypa/virtualenv/issues/1875>`_.
+
+ Also, due to a `bug in virtualenv <https://github.com/pypa/virtualenv/issues/1986>`_,
+ you may run into issues if you're using conda. To solve this, you can downgrade
+ ``virtualenv`` to version ``20.0.33``.
+
+Backwards compatibility
+-----------------------
+
+Please try to maintain backward compatibility. If you think breakage is necessary,
+clearly state why as part of the pull request. Also, be careful when changing method
+signatures and add deprecation warnings where needed. Also, add the deprecated sphinx
+directive to the deprecated functions or methods.
+
+You'll also need to
+
+1. Write a new test that asserts a warning is issued when calling with the deprecated argument
+2. Update all of Xinference existing tests and code to use the new argument
+
+Type hints
+----------
+
+Xinference strongly encourages the use of :pep:`484` style type hints. New development should
+contain type hints and pull requests to annotate existing code are accepted as well!
+
+Test-driven development
+-----------------------
+
+Xinference is serious about testing and strongly encourages contributors to embrace
+`test-driven development (TDD) <https://en.wikipedia.org/wiki/Test-driven_development>`_.
+This development process "relies on the repetition of a very short development cycle:
+first the developer writes an (initially failing) automated test case that defines a desired
+improvement or new function, then produces the minimum amount of code to pass that test."
+So, before actually writing any code, you should write your tests. Often the test can be
+taken from the original GitHub issue. However, it is always worth considering additional
+use cases and writing corresponding tests.
+
+Adding tests is frequently requested after code is pushed to Xinference. Thus,
+it is worth getting in the habit of writing tests ahead of time so this is never an issue.
\ No newline at end of file
diff --git a/doc/source/development/contributing_environment.rst b/doc/source/development/contributing_environment.rst
new file mode 100644
index 0000000000..80aa266dfb
--- /dev/null
+++ b/doc/source/development/contributing_environment.rst
@@ -0,0 +1,114 @@
+==================================
+Creating a development environment
+==================================
+
+.. contents:: Table of contents:
+ :local:
+
+Before proceeding with any code modifications, it's essential to set up the necessary environment for Xinference development,
+which includes familiarizing yourself with Git usage, establishing an isolated environment, installing Xinference, and compiling the frontend.
+
+Getting startted with Git
+-------------------------
+
+Now that you have identified an issue you wish to resolve, an enhancement to incorporate, or documentation to enhance,
+it's crucial to acquaint yourself with GitHub and the Xinference codebase.
+
+To the new user, working with Git is one of the more intimidating aspects of contributing to Xinference.
+It can very quickly become overwhelming, but sticking to the guidelines below will help simplify the process
+and minimize potential issues. As always, if you are having difficulties please
+feel free to ask for help.
+
+The code is hosted on `GitHub <https://github.com/xorbitsai/inference>`_. To
+contribute you will need to sign up for a `free GitHub account
+<https://github.com/signup/free>`_. We use `Git <https://git-scm.com/>`_ for
+version control to allow many people to work together on the project.
+
+`GitHub has instructions <https://help.github.com/set-up-git-redirect>`__ for installing git,
+setting up your SSH key, and configuring git. All these steps need to be completed before
+you can work seamlessly between your local repository and GitHub.
+
+Some great resources for learning Git:
+
+* `Official Git Documentation <https://git-scm.com/doc>`_
+* `Pro Git Book <https://git-scm.com/book/en/v2>`_
+* `Git Tutorial by Atlassian <https://www.atlassian.com/git/tutorials>`_
+* `Git - Concise Guide <http://rogerdudler.github.io/git-guide/index.zh.html>`_
+
+.. note::
+ If the speed of ``git clone`` is slow, you can use the following command
+ to add a proxy:
+
+ ::
+
+ export https_proxy=YourProxyAddress
+
+Creating an isolated environment
+--------------------------------
+
+Before formally installing Xinference, it's recommended to create an isolated
+environment, using Conda recommended, for ease of subsequent operations.
+
+::
+
+ conda create --name xinf
+ conda activate xinf
+
+``xinf`` can be replaced with a custom Conda environment name.
+
+Afterward, you'll need to install Python and Node.js (npm) in the newly created
+Conda environment. Here are the commands:
+
+::
+
+ conda install python=3.10
+ conda install nodejs
+
+Install from source code
+------------------------
+
+Before we begin, please make sure that you have cloned the repository.
+Suppose you clone the repository as ``inference`` directory, ``cd`` to this directory
+where the ``setup.cfg`` and ``setup.py`` files are located, and run the following command:
+
+::
+
+ pip install -e .
+ xinference-local
+
+If the commands run successfully, you can use Xinference normally. For
+detailed usage instructions, refer to
+`using_xinference <https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html>`__.
+
+If errors occur or the process freezes during execution, the next step
+is to compile the frontend.
+
+Frontend Compilation
+--------------------
+
+Navigate to the ``inference/xinference/web/ui`` directory. Then, execute the following command
+to clear the cache:
+
+::
+
+ npm cache clean
+
+If the command fails to execute, you can try adding the ``--force`` option.
+
+.. note::
+ If the ``node_modules`` folder already exists in this directory,
+ it's recommended to manually delete it before cleaning the cache.
+
+Next, execute the following command in this directory to compile the
+frontend:
+
+::
+
+ npm install
+ npm run build
+
+Still, if the first command fails to execute, you can try adding the ``--force`` option.
+
+After compiling the frontend, you can ``cd`` back to the directory
+where the ``setup.cfg`` and ``setup.py`` files are located,
+and install Xinference via ``pip install -e .``.
diff --git a/doc/source/development/index.rst b/doc/source/development/index.rst
new file mode 100644
index 0000000000..9b423ae03a
--- /dev/null
+++ b/doc/source/development/index.rst
@@ -0,0 +1,11 @@
+.. _development_index:
+
+===========
+Development
+===========
+
+.. toctree::
+ :maxdepth: 2
+
+ contributing_environment
+ contributing_codebase
\ No newline at end of file
diff --git a/doc/source/index.rst b/doc/source/index.rst
index e5ed21d85c..d5f3bce3e9 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -13,6 +13,7 @@ Welcome to Xinference!
user_guide/index
examples/index
reference/index
+ development/index
Xorbits Inference (Xinference) is an open-source platform to streamline the operation and integration
diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_codebase.po b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_codebase.po
new file mode 100644
index 0000000000..e2138c7e45
--- /dev/null
+++ b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_codebase.po
@@ -0,0 +1,202 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2023, Xorbits Inc.
+# This file is distributed under the same license as the Xinference package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2024.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: Xinference \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2024-03-07 15:03+0800\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_CN\n"
+"Language-Team: zh_CN <LL@li.org>\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.14.0\n"
+
+#: ../../source/development/contributing_codebase.rst:3
+msgid "Contributing to the code base"
+msgstr "代码库开发指南"
+
+#: ../../source/development/contributing_codebase.rst:6
+msgid "Table of contents:"
+msgstr "目录"
+
+#: ../../source/development/contributing_codebase.rst:9
+msgid "Code standards"
+msgstr "代码规范"
+
+#: ../../source/development/contributing_codebase.rst:11
+msgid ""
+"Writing good code is not just about what you write. It is also about "
+"*how* you write it. During Continuous Integration testing, several tools "
+"will be run to check your code for stylistic errors. Good style is a "
+"requirement for submitting code to Xinference."
+msgstr ""
+"写出好的代码不仅在于你写了什么，更在于你是如何写的。在持续集成测试期间，会有多个工具来检查您的代码是否存在风格错误。良好的编程风格是提交代码到 "
+"Xinference 的要求之一。"
+
+#: ../../source/development/contributing_codebase.rst:15
+msgid ""
+"In addition, it is important that we do not make sudden changes to the "
+"code that could have the potential to break a lot of user code as a "
+"result. Therefore we need it to be as backwards compatible as possible to"
+" avoid mass breakages."
+msgstr "此外，不要对代码进行突然的更改，这可能会导致大量用户代码出现问题。所以，我们需要尽可能地保持向后兼容，以避免大规模的故障。"
+
+#: ../../source/development/contributing_codebase.rst:20
+msgid "Autofixing formatting errors"
+msgstr "自动修复格式错误"
+
+#: ../../source/development/contributing_codebase.rst:22
+msgid ""
+"Moreover, Continuous Integration will run code formatting checks like "
+"``black``, ``flake8``, ``isort``, and others using `pre-commit hooks "
+"<https://pre-commit.com/>`_ Any warnings generated by these checks will "
+"cause the Continuous Integration to fail. Therefore, it is advisable to "
+"run the check yourself before submitting code. This can be done by "
+"installing ``pre-commit``::"
+msgstr ""
+"此外，持续集成将使用 `pre-commit hooks <https://pre-commit.com/>`_ 运行诸如 ``black``、``flake8``、``isort`` "
+"等代码格式检查工具。任何由这些检查生成的警告都将导致持续集成失败。因此，建议在提交代码之前自行运行这些检查。"
+"可以通过在 Xinference 仓库的根目录下安装 ``pre-commit`` 来完成这一操作："
+
+#: ../../source/development/contributing_codebase.rst:30
+msgid "and then running::"
+msgstr "然后执行命令："
+
+#: ../../source/development/contributing_codebase.rst:34
+msgid ""
+"from the root of the Xinference repository. This setup ensures that all "
+"styling checks are automatically executed each time you commit changes "
+"without your needing to run each one manually. In addition, using ``pre-"
+"commit`` will also allow you to more easily remain up-to-date with our "
+"code checks as they change."
+msgstr ""
+"安装好了以后就能确保每次提交更改时都会自动执行所有样式检查，无需手动逐个运行。"
+"此外，使用 ``pre-commit`` 也能让您更轻松地在我们的代码检查发生更改的时候保持同步。"
+
+#: ../../source/development/contributing_codebase.rst:39
+msgid ""
+"Note that if needed, you can skip these checks with ``git commit --no-"
+"verify``."
+msgstr "请注意，如果需要，您可以通过使用 ``git commit --no-verify`` 命令来跳过这些检查。"
+
+#: ../../source/development/contributing_codebase.rst:41
+msgid ""
+"If you don't want to use ``pre-commit`` as part of your workflow, you can"
+" still use it to run its checks with::"
+msgstr "如果您不想将 ``pre-commit`` 作为工作流程的一部分，仍然可以运行如下命令来使用它进行检查："
+
+#: ../../source/development/contributing_codebase.rst:46
+#: ../../source/development/contributing_codebase.rst:52
+msgid "without needing to have done ``pre-commit install`` beforehand."
+msgstr "而不需要事先执行 ``pre-commit install``。"
+
+#: ../../source/development/contributing_codebase.rst:48
+msgid ""
+"If you want to run checks on all recently committed files on "
+"upstream/main you can use::"
+msgstr "如果您想在所有最近提交的文件上运行检查，您可以使用以下命令："
+
+#: ../../source/development/contributing_codebase.rst:56
+msgid ""
+"You may consider periodically running ``pre-commit gc`` to clean up repos"
+" which are no longer used."
+msgstr "您可以考虑定期运行 ``pre-commit gc`` 命令来清理不再使用的存储库。"
+
+#: ../../source/development/contributing_codebase.rst:61
+msgid ""
+"If you have conflicting installations of ``virtualenv``, if could lead to"
+" errors - refer to `here "
+"<https://github.com/pypa/virtualenv/issues/1875>`_."
+msgstr "如果您安装了冲突的 ``virtualenv`` 版本，可能会导致错误 - 可以参考"
+" `这里 <https://github.com/pypa/virtualenv/issues/1875>`_ 。"
+
+#: ../../source/development/contributing_codebase.rst:64
+msgid ""
+"Also, due to a `bug in virtualenv "
+"<https://github.com/pypa/virtualenv/issues/1986>`_, you may run into "
+"issues if you're using conda. To solve this, you can downgrade "
+"``virtualenv`` to version ``20.0.33``."
+msgstr ""
+"此外，由于 ``virtualenv`` 中的一个 `错误 <https://github.com/pypa/virtualenv/issues/1986>`_ ，如果您使用 conda，可能会遇到问题。"
+"要解决这个问题，您可以将 ``virtualenv`` 降级到版本 ``20.0.33``。"
+
+#: ../../source/development/contributing_codebase.rst:69
+msgid "Backwards compatibility"
+msgstr "向后兼容"
+
+#: ../../source/development/contributing_codebase.rst:71
+msgid ""
+"Please try to maintain backward compatibility. If you think breakage is "
+"necessary, clearly state why as part of the pull request. Also, be "
+"careful when changing method signatures and add deprecation warnings "
+"where needed. Also, add the deprecated sphinx directive to the deprecated"
+" functions or methods."
+msgstr ""
+"请尽量保持向后兼容性。如果您认为必须进行更改，请在拉取请求中说明清楚原因。同时，在更改方法签名时要小心，并在需要时添加弃用警告。此外，为弃用的函数或方法添加弃用的"
+" sphinx 指令。"
+
+#: ../../source/development/contributing_codebase.rst:76
+msgid "You'll also need to"
+msgstr "同时你还需要"
+
+#: ../../source/development/contributing_codebase.rst:78
+msgid ""
+"Write a new test that asserts a warning is issued when calling with the "
+"deprecated argument"
+msgstr "编写一个新的测试样例，在调用带有弃用参数时会发出警告。"
+
+#: ../../source/development/contributing_codebase.rst:79
+msgid "Update all of Xinference existing tests and code to use the new argument"
+msgstr "更新所有 Xinference 现有的测试样例和代码，以使用新的参数。"
+
+#: ../../source/development/contributing_codebase.rst:82
+msgid "Type hints"
+msgstr "类型提示"
+
+#: ../../source/development/contributing_codebase.rst:84
+msgid ""
+"Xinference strongly encourages the use of :pep:`484` style type hints. "
+"New development should contain type hints and pull requests to annotate "
+"existing code are accepted as well!"
+msgstr "Xinference 强烈鼓励使用 :pep:`484` 风格的类型提示。新的开发应包含类型提示，并且对现有代码进行注释的拉取请求也是可以接受的！"
+
+#: ../../source/development/contributing_codebase.rst:88
+msgid "Test-driven development"
+msgstr "测试驱动开发"
+
+#: ../../source/development/contributing_codebase.rst:90
+msgid ""
+"Xinference is serious about testing and strongly encourages contributors "
+"to embrace `test-driven development (TDD) <https://en.wikipedia.org/wiki"
+"/Test-driven_development>`_. This development process \"relies on the "
+"repetition of a very short development cycle: first the developer writes "
+"an (initially failing) automated test case that defines a desired "
+"improvement or new function, then produces the minimum amount of code to "
+"pass that test.\" So, before actually writing any code, you should write "
+"your tests. Often the test can be taken from the original GitHub issue. "
+"However, it is always worth considering additional use cases and writing "
+"corresponding tests."
+msgstr ""
+"Xinference 非常重视测试，并强烈鼓励贡献者采用 `测试驱动开发(TDD) <https://en.wikipedia.org/wiki"
+"/Test-driven_development>`_ 。这种开发过程 "
+"\"依赖于非常短的开发周期的重复：首先，开发者编写一个（初始为失败的）自动化测试样例来定义所需的改进或新功能，然后用最少的代码来通过该测试。\"因此，在实际编写任何代码之前，您应该编写您的测试样例。通常，测试样例可以从原始的"
+" GitHub issue 中获取。然而，值得考虑额外的情况并编写相应的测试样例。"
+
+#: ../../source/development/contributing_codebase.rst:99
+msgid ""
+"Adding tests is frequently requested after code is pushed to Xinference. "
+"Thus, it is worth getting in the habit of writing tests ahead of time so "
+"this is never an issue."
+msgstr "在将代码推送到 Xinference 之后，经常会要求添加测试样例。因此，养成提前编写测试样例的习惯非常重要，这样就不会出现问题。"
+
+#~ msgid "Pre-commit"
+#~ msgstr "Pre-commit"
+
diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_environment.po b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_environment.po
new file mode 100644
index 0000000000..8ced444523
--- /dev/null
+++ b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_environment.po
@@ -0,0 +1,202 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2023, Xorbits Inc.
+# This file is distributed under the same license as the Xinference package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2024.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: Xinference \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2024-03-06 16:29+0800\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_CN\n"
+"Language-Team: zh_CN <LL@li.org>\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.14.0\n"
+
+#: ../../source/development/contributing_environment.rst:3
+msgid "Creating a development environment"
+msgstr "创建开发环境"
+
+#: ../../source/development/contributing_environment.rst:6
+msgid "Table of contents:"
+msgstr "目录"
+
+#: ../../source/development/contributing_environment.rst:8
+msgid ""
+"Before proceeding with any code modifications, it's essential to set up "
+"the necessary environment for Xinference development, which includes "
+"familiarizing yourself with Git usage, establishing an isolated "
+"environment, installing Xinference, and compiling the frontend."
+msgstr ""
+"在进行任何代码修改之前，建立起适用于 Xinference 开发的必要环境至关重要。包括熟悉 Git 的使用、建立一个独立的环境、安装 "
+"Xinference 以及前端部分的编译。"
+
+#: ../../source/development/contributing_environment.rst:12
+msgid "Getting startted with Git"
+msgstr "Git 的使用"
+
+#: ../../source/development/contributing_environment.rst:14
+msgid ""
+"Now that you have identified an issue you wish to resolve, an enhancement"
+" to incorporate, or documentation to enhance, it's crucial to acquaint "
+"yourself with GitHub and the Xinference codebase."
+msgstr "当你有一个需要修复的问题、需要添加的增强功能或需要改进的文档时，熟悉 GitHub 和 Xinference 代码库很重要。"
+
+#: ../../source/development/contributing_environment.rst:17
+msgid ""
+"To the new user, working with Git is one of the more intimidating aspects"
+" of contributing to Xinference. It can very quickly become overwhelming, "
+"but sticking to the guidelines below will help simplify the process and "
+"minimize potential issues. As always, if you are having difficulties "
+"please feel free to ask for help."
+msgstr ""
+"对新用户来说，使用 Git 是参与 Xinference "
+"开发最令人畏惧的方面之一。很快就会感到压力山大，但以下指南将有助于简化流程并减少潜在问题。如果您遇到难以解决的问题，欢迎在社区寻求帮助。"
+
+#: ../../source/development/contributing_environment.rst:22
+msgid ""
+"The code is hosted on `GitHub <https://github.com/xorbitsai/inference>`_."
+" To contribute you will need to sign up for a `free GitHub account "
+"<https://github.com/signup/free>`_. We use `Git <https://git-scm.com/>`_ "
+"for version control to allow many people to work together on the project."
+msgstr ""
+"Xinference 的代码托管在 `GitHub <https://github.com/xorbitsai/inference>`_ 。要参与"
+" Xinference 代码贡献，你需要注册一个 `免费的 GitHub 账户 "
+"<https://github.com/signup/free>`_ 。我们使用 `Git <https://git-scm.com/>`_ "
+"进行版本控制，以便大家共同参与项目的开发。"
+
+#: ../../source/development/contributing_environment.rst:27
+msgid ""
+"`GitHub has instructions <https://help.github.com/set-up-git-redirect>`__"
+" for installing git, setting up your SSH key, and configuring git. All "
+"these steps need to be completed before you can work seamlessly between "
+"your local repository and GitHub."
+msgstr ""
+"你可以参考 `GitHub 指南 <https://help.github.com/set-up-git-redirect>`_ 来安装 "
+"git，设置 SSH 密钥以及配置 git。你需要完成这些步骤以确保你的本地仓库和 GitHub 可以正常工作，后续的工作才可以顺利进行。"
+
+#: ../../source/development/contributing_environment.rst:31
+msgid "Some great resources for learning Git:"
+msgstr "以下是一些很好的学习 Git 的资源："
+
+#: ../../source/development/contributing_environment.rst:33
+msgid "`Official Git Documentation <https://git-scm.com/doc>`_"
+msgstr "`Git 官方文档 <https://git-scm.com/doc>`_"
+
+#: ../../source/development/contributing_environment.rst:34
+msgid "`Pro Git Book <https://git-scm.com/book/en/v2>`_"
+msgstr "`Pro Git 书籍 <https://git-scm.com/book/en/v2>`_"
+
+#: ../../source/development/contributing_environment.rst:35
+msgid "`Git Tutorial by Atlassian <https://www.atlassian.com/git/tutorials>`_"
+msgstr "`Atlassian 提供的 Git 教程 <https://www.atlassian.com/git/tutorials>`_"
+
+#: ../../source/development/contributing_environment.rst:36
+msgid ""
+"`Git - Concise Guide <http://rogerdudler.github.io/git-"
+"guide/index.zh.html>`_"
+msgstr "`Git-简明指南 <http://rogerdudler.github.io/git-guide/index.zh.html>`_"
+
+#: ../../source/development/contributing_environment.rst:39
+msgid ""
+"If the speed of ``git clone`` is slow, you can use the following command "
+"to add a proxy:"
+msgstr "如果在 ``git clone`` 代码的时候速度较慢，可以通过如下命令添加代理"
+
+#: ../../source/development/contributing_environment.rst:47
+msgid "Creating an isolated environment"
+msgstr "创建一个隔离环境"
+
+#: ../../source/development/contributing_environment.rst:49
+msgid ""
+"Before formally installing Xinference, it's recommended to create an "
+"isolated environment, using Conda recommended, for ease of subsequent "
+"operations."
+msgstr "在正式安装Xinference之前，建议使用 Conda 创建一个隔离环境方便后续操作。"
+
+#: ../../source/development/contributing_environment.rst:57
+msgid "``xinf`` can be replaced with a custom Conda environment name."
+msgstr "``xinf`` 可替换为自定义的 Conda 环境名。"
+
+#: ../../source/development/contributing_environment.rst:59
+msgid ""
+"Afterward, you'll need to install Python and Node.js (npm) in the newly "
+"created Conda environment. Here are the commands:"
+msgstr "随后需要在新建的 Conda 环境中安装 Python 以及 Node.js (npm)。命令如下："
+
+#: ../../source/development/contributing_environment.rst:68
+msgid "Install from source code"
+msgstr "从源码安装"
+
+#: ../../source/development/contributing_environment.rst:70
+msgid ""
+"Before we begin, please make sure that you have cloned the repository. "
+"Suppose you clone the repository as ``inference`` directory, ``cd`` to "
+"this directory where the ``setup.cfg`` and ``setup.py`` files are "
+"located, and run the following command:"
+msgstr ""
+"在开始之前，请确保您已经克隆了存储库。假设您将存储库克隆到名为 ``inference`` 的目录中，请进入该目录，其中包含 "
+"``setup.cfg`` 和 ``setup.py`` 文件，并执行以下命令："
+
+#: ../../source/development/contributing_environment.rst:79
+msgid ""
+"If the commands run successfully, you can use Xinference normally. For "
+"detailed usage instructions, refer to `using_xinference "
+"<https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html>`__."
+msgstr ""
+"如果命令能够成功运行，接下来就能正常使用 Xinference 了，使用教程详情见 `使用 "
+"<https://inference.readthedocs.io/zh-cn/latest/getting_started/using_xinference.html>`__。"
+
+#: ../../source/development/contributing_environment.rst:83
+msgid ""
+"If errors occur or the process freezes during execution, the next step is"
+" to compile the frontend."
+msgstr "如果出现报错或者在运行过程中卡死，那就需要进行下一步前端编译。"
+
+#: ../../source/development/contributing_environment.rst:87
+msgid "Frontend Compilation"
+msgstr "前端编译"
+
+#: ../../source/development/contributing_environment.rst:89
+msgid ""
+"Navigate to the ``inference/xinference/web/ui`` directory. Then, execute "
+"the following command to clear the cache:"
+msgstr "首先需要进入 ``inference/xinference/web/ui`` 目录下，随后执行如下命令清除缓存："
+
+#: ../../source/development/contributing_environment.rst:96
+msgid ""
+"If the command fails to execute, you can try adding the ``--force`` "
+"option."
+msgstr "如果命令执行失败，您可以尝试添加 ``--force`` 选项"
+
+#: ../../source/development/contributing_environment.rst:99
+msgid ""
+"If the ``node_modules`` folder already exists in this directory, it's "
+"recommended to manually delete it before cleaning the cache."
+msgstr "如果该目录下已经存在 ``node_modules`` 文件夹的话建议先手动删除该文件夹"
+
+#: ../../source/development/contributing_environment.rst:102
+msgid ""
+"Next, execute the following command in this directory to compile the "
+"frontend:"
+msgstr "接着在该目录下执行以下命令进行前端编译："
+
+#: ../../source/development/contributing_environment.rst:110
+msgid ""
+"Still, if the first command fails to execute, you can try adding the "
+"``--force`` option."
+msgstr "如果第一个命令执行失败，您仍然可以尝试通过添加 ``--force`` 选项解决"
+
+#: ../../source/development/contributing_environment.rst:112
+msgid ""
+"After compiling the frontend, you can ``cd`` back to the directory where "
+"the ``setup.cfg`` and ``setup.py`` files are located, and install "
+"Xinference via ``pip install -e .``."
+msgstr "编译完前端后，您可以返回到包含 ``setup.cfg`` 和 ``setup.py`` 文件的目录，然后通过 ``pip install -e .`` 安装 Xinference。"
+
diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/development/index.po b/doc/source/locale/zh_CN/LC_MESSAGES/development/index.po
new file mode 100644
index 0000000000..54e58a5b8e
--- /dev/null
+++ b/doc/source/locale/zh_CN/LC_MESSAGES/development/index.po
@@ -0,0 +1,25 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2023, Xorbits Inc.
+# This file is distributed under the same license as the Xinference package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2024.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: Xinference \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2024-03-06 12:05+0800\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_CN\n"
+"Language-Team: zh_CN <LL@li.org>\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.14.0\n"
+
+#: ../../source/development/index.rst:5
+msgid "Development"
+msgstr "开发指南"
+
|
open-telemetry__opentelemetry-python-contrib-1541 | Add readthedocs documentation tortoiseorm instrumentation
Part of [1491](https://github.com/open-telemetry/opentelemetry-python-contrib/issues/1491)
| [
{
"content": "# Copyright The OpenTelemetry Authors\n#\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#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by... | [
{
"content": "# Copyright The OpenTelemetry Authors\n#\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#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by... | diff --git a/docs-requirements.txt b/docs-requirements.txt
index a1b55877a1..ed47dddac0 100644
--- a/docs-requirements.txt
+++ b/docs-requirements.txt
@@ -37,6 +37,7 @@ redis>=2.6
remoulade>=0.50
sqlalchemy>=1.0
tornado>=5.1.1
+tortoise-orm>=0.17.0
ddtrace>=0.34.0
httpx>=0.18.0
diff --git a/docs/instrumentation/tortoiseorm/tortoiseorm.rst b/docs/instrumentation/tortoiseorm/tortoiseorm.rst
new file mode 100644
index 0000000000..af84ebf65a
--- /dev/null
+++ b/docs/instrumentation/tortoiseorm/tortoiseorm.rst
@@ -0,0 +1,6 @@
+.. include:: ../../../instrumentation/opentelemetry-instrumentation-tortoiseorm/README.rst
+
+.. automodule:: opentelemetry.instrumentation.tortoiseorm
+ :members:
+ :undoc-members:
+ :show-inheritance:
diff --git a/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py b/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py
index a8061a99cc..3b0e58c928 100644
--- a/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py
+++ b/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py
@@ -13,7 +13,7 @@
# limitations under the License.
"""
-Instrument `tortoise-orm`_ to report SQL queries.
+Instrument tortoise-orm to report SQL queries.
Usage
-----
|
pypa__setuptools-3715 | [FR] The way to not overwrite but inherit DEFAULT_EXCLUDE when define find package exclude
### What's the problem this feature will solve?
Setuptools users become able to omit redundant definition when they want to add exclude directory or file in `flat-layout`.
And they also become able to manage many packages because they can reduce toils of maintain `pyproject.toml` in each project.
<details>
<summary>pyproject.toml when exclude as same as DEFAULT_EXCLUDE by tool.setuptools.packages.find.exclude</summary>
```toml
[tool.setuptools.packages.find]
where = ["."]
exclude = [
# Additional ecludsion from Setuptools default.
"htmlcov",
# Setuptools default.
# see:
# - setuptools/discovery.py at 92ebeed732b08ac29576634ad4814b9efd07bb37 · pypa/setuptools
# https://github.com/pypa/setuptools/blob/92ebeed732b08ac29576634ad4814b9efd07bb37/setuptools/discovery.py
# FlatLayoutPackageFinder
"ci",
"ci.*",
"bin",
"bin.*",
"doc",
"doc.*",
"docs",
"docs.*",
"documentation",
"documentation.*",
"manpages",
"manpages.*",
"news",
"news.*",
"changelog",
"changelog.*",
"test",
"test.*",
"tests",
"tests.*",
"unit_test",
"unit_test.*",
"unit_tests",
"unit_tests.*",
"example",
"example.*",
"examples",
"examples.*",
"scripts",
"scripts.*",
"tools",
"tools.*",
"util",
"util.*",
"utils",
"utils.*",
"python",
"python.*",
"build",
"build.*",
"dist",
"dist.*",
"venv",
"venv.*",
"env",
"env.*",
"requirements",
"requirements.*",
# ---- Task runners / Build tools ----
"tasks", # invoke
"tasks.*", # invoke
"fabfile", # fabric
"fabfile.*", # fabric
"site_scons", # SCons
"site_scons.*", # SCons
# ---- Other tools ----
"benchmark",
"benchmark.*",
"benchmarks",
"benchmarks.*",
"exercise",
"exercise.*",
"exercises",
"exercises.*",
# ---- Hidden directories/Private packages ----
"[._]*",
# FlatLayoutModuleFinder
"setup",
"setup.*",
"conftest",
"conftest.*",
"test",
"test.*",
"tests",
"tests.*",
"example",
"example.*",
"examples",
"examples.*",
"build",
"build.*",
# ---- Task runners ----
"toxfile",
"toxfile.*",
"noxfile",
"noxfile.*",
"pavement",
"pavement.*",
"dodo",
"dodo.*",
"tasks",
"tasks.*",
"fabfile",
"fabfile.*",
# ---- Other tools ----
"[Ss][Cc]onstruct", # SCons
"[Ss][Cc]onstruct.*", # SCons
"conanfile", # Connan: C/C++ build tool
"conanfile.*", # Connan: C/C++ build tool
"manage", # Django
"manage.*", # Django
"benchmark",
"benchmark.*",
"benchmarks",
"benchmarks.*",
"exercise",
"exercise.*",
"exercises",
"exercises.*",
# ---- Hidden files/Private modules ----
"[._]*",
]
```
</details>
cf. [Package Discovery and Namespace Packages - setuptools 65.3.0.post20220826 documentation](https://setuptools.pypa.io/en/latest/userguide/package_discovery.html)
### Describe the solution you'd like
For example, adds parameter `additional_exclude` which inherits `DEFAULT_EXCLUDE` .
### Alternative Solutions
#### Alternative Solution A
(If it can) User rename target directory with starting `.` .
Because Setuptools ignore by `DEFAULT_EXCLUDE` .
However, directory starting with `.` behaves as hidden by system.
It's intension isn't always same as excluding when build.
#### Alternative Solution B
Setuptools adds directory to exclude when user report in GitHub Issues.
However, Setuptools shouldn't cover every tools because there are infinite minor tools.
### Additional context
In my case, I would to exclude `htmlcov` directory additionally which is dump of coverage.
see: [Command line usage — Coverage.py 6.3.2 documentation](https://coverage.readthedocs.io/en/6.3.2/cmd.html#html-annotation-coverage-html)
### Code of Conduct
- [X] I agree to follow the PSF Code of Conduct
| [
{
"content": "\"\"\"Automatic discovery of Python modules and packages (for inclusion in the\ndistribution) and other config values.\n\nFor the purposes of this module, the following nomenclature is used:\n\n- \"src-layout\": a directory representing a Python project that contains a \"src\"\n folder. Everythin... | [
{
"content": "\"\"\"Automatic discovery of Python modules and packages (for inclusion in the\ndistribution) and other config values.\n\nFor the purposes of this module, the following nomenclature is used:\n\n- \"src-layout\": a directory representing a Python project that contains a \"src\"\n folder. Everythin... | diff --git a/changelog.d/3594.change.rst b/changelog.d/3594.change.rst
new file mode 100644
index 0000000000..c0642d783a
--- /dev/null
+++ b/changelog.d/3594.change.rst
@@ -0,0 +1 @@
+Added ``htmlcov`` to FlatLayoutModuleFinder.DEFAULT_EXCLUDE -- by :user:`demianbrecht`
diff --git a/setuptools/discovery.py b/setuptools/discovery.py
index 98fc2a7f48..6244a18558 100644
--- a/setuptools/discovery.py
+++ b/setuptools/discovery.py
@@ -273,6 +273,7 @@ class FlatLayoutModuleFinder(ModuleFinder):
"benchmarks",
"exercise",
"exercises",
+ "htmlcov",
# ---- Hidden files/Private modules ----
"[._]*",
)
|
certbot__certbot-3432 | Nginx plugin selection
We want to slowly roll out the Nginx plugin to make sure it's working for people. To do this we should:
- Mark the nginx plugin as hidden (we did this for the manual plugin)
- Disable automatic selection if it's the only available configurator (if not already the case, we should disable automatic selection of hidden plugins).
- Make sure the description of the Nginx plugin is sane (it used to say it was broken).
| [
{
"content": "\"\"\"Nginx Configuration\"\"\"\nimport logging\nimport os\nimport re\nimport shutil\nimport socket\nimport subprocess\nimport time\n\nimport OpenSSL\nimport zope.interface\n\nfrom acme import challenges\nfrom acme import crypto_util as acme_crypto_util\n\nfrom certbot import constants as core_con... | [
{
"content": "\"\"\"Nginx Configuration\"\"\"\nimport logging\nimport os\nimport re\nimport shutil\nimport socket\nimport subprocess\nimport time\n\nimport OpenSSL\nimport zope.interface\n\nfrom acme import challenges\nfrom acme import crypto_util as acme_crypto_util\n\nfrom certbot import constants as core_con... | diff --git a/certbot-nginx/certbot_nginx/configurator.py b/certbot-nginx/certbot_nginx/configurator.py
index a1c24b5c8df..049ba9a2013 100644
--- a/certbot-nginx/certbot_nginx/configurator.py
+++ b/certbot-nginx/certbot_nginx/configurator.py
@@ -55,7 +55,9 @@ class NginxConfigurator(common.Plugin):
"""
- description = "Nginx Web Server - currently doesn't work"
+ description = "Nginx Web Server plugin - Alpha"
+
+ hidden = True
@classmethod
def add_parser_arguments(cls, add):
|
mne-tools__mne-bids-pipeline-680 | Doc deployment step failing
The latest CI run failed to execute documentation deployment:
https://app.circleci.com/pipelines/github/mne-tools/mne-bids-pipeline/3557/workflows/3458e5cc-c471-4664-8d0a-b0cc4961f9eb/jobs/41986/parallel-runs/0/steps/0-107
```shell
#!/bin/bash -eo pipefail
./.circleci/setup_bash.sh
CIRCLE_JOB=deploy_docs
COMMIT_MESSAGE=68c63d6878992fb7c298f24420f1d349c6811079 MAINT: Use mike for doc deployment (#676)
COMMIT_MESSAGE_ESCAPED=68c63d6878992fb7c298f24420f1d349c6811079 MAINT: Use mike for doc deployment (#676)
CIRCLE_REQUESTED_JOB=
Running job deploy_docs for main branch
./.circleci/setup_bash.sh: line 35: sudo: command not found
Exited with code exit status 127
CircleCI received exit code 127
```
| [
{
"content": "#!/bin/env python\n\"\"\"Generate steps.md.\"\"\"\n\nimport importlib\nfrom pathlib import Path\nfrom mne_bids_pipeline._config_utils import _get_step_modules\n\npre = \"\"\"\\\n# Processing steps\n\nThe following table provides a concise summary of each step in the Study\nTemplate. All steps exis... | [
{
"content": "#!/bin/env python\n\"\"\"Generate steps.md.\"\"\"\n\nimport importlib\nfrom pathlib import Path\nfrom mne_bids_pipeline._config_utils import _get_step_modules\n\npre = \"\"\"\\\n# Processing steps\n\nThe following table provides a concise summary of each step in the Study\nTemplate. All steps exis... | diff --git a/.circleci/config.yml b/.circleci/config.yml
index 63076d580..bb419bcfe 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -1132,13 +1132,9 @@ jobs:
- store_artifacts:
path: docs/site
destination: site
- - persist_to_workspace: # For documentation deployment to gh-pages
- root: ~/
- paths: project/docs/site
deploy_docs:
- docker:
- - image: node:10
+ <<: *imageconfig
steps:
- add_ssh_keys:
fingerprints:
@@ -1157,15 +1153,23 @@ jobs:
<<: *git
- run:
<<: *bashenv
+ - run:
+ # This is a bit computationally inefficient, but it should be much
+ # faster to "cp" directly on the machine rather than persist
+ # 1GB doc build to workspace then go retrieve it
+ name: Build documentation again
+ command: |
+ make doc
- run:
name: Deploy docs to gh-pages branch
# https://github.com/jimporter/mike
command: |
- git config --global user.email "circle@mne.com"
- git config --global user.name "Circle CI"
# Arguments used in all mike commands
ARGS="--config-file docs/mkdocs.yml"
- echo "Deploying dev"
+ # First we need to actually check out our current version of
+ # gh-pages so we don't remove it!
+ git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*"
+ git fetch origin
# If it's tagged as v*, deploy as "v*" and "stable" as well
if git describe --tags --exact-match $(git rev-parse HEAD); then
VERSION="$(git describe --tags --exact-match $(git rev-parse HEAD))"
@@ -1184,7 +1188,7 @@ jobs:
fi
git checkout gh-pages
git reset $(git commit-tree HEAD^{tree} -m "Deploy and squash docs [ci skip]")
- git log -n1
+ git log -n3 # should just be one, but let's be sure
git push origin --force gh-pages
diff --git a/README.md b/README.md
index 94ffe6ef0..662062cdd 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,27 @@
-[](https://circleci.com/gh/mne-tools/mne-bids-pipeline)
+# <img src="https://raw.github.com/mne-tools/mne-bids-pipeline/main/docs/source/assets/mne.svg" alt="MNE Logo" height="20"> The MNE-BIDS-Pipeline
-# MNE-BIDS-Pipeline
+**The MNE-BIDS-Pipeline is a full-flegded processing pipeline for your MEG and
+EEG data.** It operates on data stored according to the [Brain Imaging Data
+Structure (BIDS)](https://bids.neuroimaging.io/). Under the hood, it uses [MNE-Python](https://mne.tools).
-The MNE-BIDS-Pipeline is a full-flegded processing pipeline for your MEG and
-EEG data. It operates on data stored according to the Brain Imaging Data
-Structure (BIDS).
+## 💡 Basic concepts and features
-# Documentation
+* 🐾 Data processing as a sequence of processing steps.
+* ⏏ Your data can be "ejected" from the pipeline at **any** stage. No lock-in!
+* 🧾 Extensive processing and analysis summary reports.
+* 🎬 Process just a single participant, or as many as several hundreds of participants – in parallel.
+* 🛠 Configuration via a simple text file.
+* 💻 Execution via an easy-to-use command-line utility.
+* 🆘 Helpful error messages in case something goes wrong.
-Please find the installation and usage instructions at
-[mne.tools/mne-bids-pipeline](https://mne.tools/mne-bids-pipeline).
+## 📘 Installation and usage instructions
-# Acknowledgments
+Please find the documentation at
+[**mne.tools/mne-bids-pipeline**](https://mne.tools/mne-bids-pipeline).
-The original pipeline for MEG/EEG data processing with MNE python was built
+## ❤ Acknowledgments
+
+The original pipeline for MEG/EEG data processing with MNE-Python was built
jointly by the [Cognition and Brain Dynamics Team](https://brainthemind.com/)
and the [MNE Python Team](https://mne.tools), based on scripts originally
developed for this publication:
diff --git a/docs/source/features/gen_steps.py b/docs/source/features/gen_steps.py
index 463d32bf4..e7c82ca7a 100755
--- a/docs/source/features/gen_steps.py
+++ b/docs/source/features/gen_steps.py
@@ -12,6 +12,7 @@
Template. All steps exist in the `steps`/ directory.
"""
+print('Generating steps …')
step_modules = _get_step_modules()
# Construct the lines of steps.md
|
liqd__a4-opin-766 | cannot delete user in django admin if user has not uploaded avatar
| [
{
"content": "from django.db.models import signals\nfrom django.dispatch import receiver\n\nfrom adhocracy4.images import services\n\nfrom . import models\n\n\n@receiver(signals.post_init, sender=models.User)\ndef backup_image_path(sender, instance, **kwargs):\n instance._current_image_file = instance.avatar... | [
{
"content": "from django.db.models import signals\nfrom django.dispatch import receiver\n\nfrom adhocracy4.images import services\n\nfrom . import models\n\n\n@receiver(signals.post_init, sender=models.User)\ndef backup_image_path(sender, instance, **kwargs):\n instance._current_image_file = instance.avatar... | diff --git a/euth/users/signals.py b/euth/users/signals.py
index e29186dff..d74a64923 100644
--- a/euth/users/signals.py
+++ b/euth/users/signals.py
@@ -20,4 +20,4 @@ def delete_old_image(sender, instance, **kwargs):
@receiver(signals.post_delete, sender=models.User)
def delete_images_for_User(sender, instance, **kwargs):
- services.delete_images([instance.avatar])
+ services.delete_images([instance._avatar])
|
networkx__networkx-3958 | Misleading description in the doc
In this page
https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.structuralholes.effective_size.html
The description of *Return* is "Dictionary with nodes as keys and the constraint on the node as values."
But this is effective size. I think it should be "Dictionary with nodes as keys and the **effective size of** the node as values."
| [
{
"content": "\"\"\"Functions for computing measures of structural holes.\"\"\"\n\nimport networkx as nx\n\n__all__ = ['constraint', 'local_constraint', 'effective_size']\n\n\ndef mutual_weight(G, u, v, weight=None):\n \"\"\"Returns the sum of the weights of the edge from `u` to `v` and\n the edge from `v... | [
{
"content": "\"\"\"Functions for computing measures of structural holes.\"\"\"\n\nimport networkx as nx\n\n__all__ = ['constraint', 'local_constraint', 'effective_size']\n\n\ndef mutual_weight(G, u, v, weight=None):\n \"\"\"Returns the sum of the weights of the edge from `u` to `v` and\n the edge from `v... | diff --git a/networkx/algorithms/structuralholes.py b/networkx/algorithms/structuralholes.py
index cdd799703fe..3938faa258e 100644
--- a/networkx/algorithms/structuralholes.py
+++ b/networkx/algorithms/structuralholes.py
@@ -98,7 +98,7 @@ def effective_size(G, nodes=None, weight=None):
Returns
-------
dict
- Dictionary with nodes as keys and the constraint on the node as values.
+ Dictionary with nodes as keys and the effective size of the node as values.
Notes
-----
|
liberapay__liberapay.com-2234 | Enabling or disabling a specific visibility level as a creator
This issue is for the upcoming feature mentioned in <https://medium.com/liberapay-blog/lifting-the-veil-of-anonymity-479dadd369be>.
Patrons page doesn't mention the lack of support for secret donations through PayPal
I just clicked the option to explictly not show who my patrons are in the settings. On the settings page it shows "You've chosen not to see who your patrons are." However on the donation page it shows "This donation won't be secret, you will appear in bjorn3's private list of patrons." Which of those two statements is true?
| [
{
"content": "from base64 import b64decode, b64encode\nfrom binascii import hexlify, unhexlify\nfrom datetime import date, datetime, timedelta\nimport errno\nimport fnmatch\nfrom hashlib import sha256\nimport hmac\nfrom operator import getitem\nimport os\nimport re\nimport socket\n\nfrom pando import Response, ... | [
{
"content": "from base64 import b64decode, b64encode\nfrom binascii import hexlify, unhexlify\nfrom datetime import date, datetime, timedelta\nimport errno\nimport fnmatch\nfrom hashlib import sha256\nimport hmac\nfrom operator import getitem\nimport os\nimport re\nimport socket\n\nfrom pando import Response, ... | diff --git a/liberapay/utils/__init__.py b/liberapay/utils/__init__.py
index 9112e9e826..1a678ca68a 100644
--- a/liberapay/utils/__init__.py
+++ b/liberapay/utils/__init__.py
@@ -538,8 +538,8 @@ def word(mapping, k, pattern=r'^\w+$', unicode=False):
return r
-FALSEISH = {'0', 'f', 'false', 'n', 'no'}
-TRUEISH = {'1', 't', 'true', 'y', 'yes'}
+FALSEISH = {'0', 'f', 'false', 'n', 'no', 'off'}
+TRUEISH = {'1', 't', 'true', 'y', 'yes', 'on'}
NULLISH = {'', 'null', 'none'}
diff --git a/style/base/base.scss b/style/base/base.scss
index c5b7b43e8c..6326b2dcac 100644
--- a/style/base/base.scss
+++ b/style/base/base.scss
@@ -929,3 +929,8 @@ abbr[title] {
/* Bootstrap 3.3.6 adds a bottom border but doesn't disable text-decoration */
text-decoration: none;
}
+
+.preview {
+ border-left: 2px solid $gray-lighter;
+ padding: 11px 0 1px 2ex;
+}
diff --git a/style/base/utils.scss b/style/base/utils.scss
index da69bf9c58..5ef82676ee 100644
--- a/style/base/utils.scss
+++ b/style/base/utils.scss
@@ -85,6 +85,10 @@
max-width: 500px;
}
+.max-width-750 {
+ max-width: 750px;
+}
+
@for $i from 0 through 100 {
.width-#{$i} {
width: $i * 1%;
diff --git a/templates/macros/your-tip.html b/templates/macros/your-tip.html
index 7799db4355..97f123c52b 100644
--- a/templates/macros/your-tip.html
+++ b/templates/macros/your-tip.html
@@ -252,8 +252,37 @@ <h5 class="list-group-item-heading">{{ _("Manual renewal") }}</h5>
</label>
</li>
</ul>
- % set patron_visibilities = tippee_p.recipient_settings.patron_visibilities
- % set paypal_only = tippee_p.payment_providers == 2
+ {{ tip_visibility_choice(
+ tippee_name,
+ tippee_p.recipient_settings.patron_visibilities,
+ tippee_p.payment_providers,
+ tip
+ ) }}
+ <br>
+ <button class="btn btn-primary btn-lg btn-block" {{ disabled }}>{{
+ (_("Modify your pledge") if pledging else _("Modify your donation"))
+ if tip.renewal_mode > 0 else
+ (_("Pledge") if pledging else _("Donate"))
+ }}</button>
+ % if tip.renewal_mode > 0
+ <br>
+ <button class="btn btn-danger btn-lg btn-block" name="selected_amount" value="0">{{
+ _("Cancel the pledge") if pledging else _("Discontinue the donation")
+ }}</button>
+ % elif tippee_p.payment_providers
+ <p class="text-center">{{ payment_methods_icons(tippee_p, new_currency) }}</p>
+ % elif not pledging
+ <p class="text-muted">{{ glyphicon('info-sign') }} {{ _(
+ "{username} hasn't configured any payment method yet, so your donation "
+ "cannot actually be processed right now. We will notify you when payment "
+ "becomes possible.",
+ username=tippee_name
+ ) }}</p>
+ % endif
+% endmacro
+
+% macro tip_visibility_choice(tippee_name, patron_visibilities, payment_providers, tip)
+ % set paypal_only = payment_providers == 2
% if paypal_only
% if patron_visibilities == 0
% set patron_visibilities = 2
@@ -332,25 +361,4 @@ <h5 class="list-group-item-heading">{{ _("Public donation") }}</h5>
username=tippee_name
) }}</p>
% endif
- <br>
- <button class="btn btn-primary btn-lg btn-block" {{ disabled }}>{{
- (_("Modify your pledge") if pledging else _("Modify your donation"))
- if tip.renewal_mode > 0 else
- (_("Pledge") if pledging else _("Donate"))
- }}</button>
- % if tip.renewal_mode > 0
- <br>
- <button class="btn btn-danger btn-lg btn-block" name="selected_amount" value="0">{{
- _("Cancel the pledge") if pledging else _("Discontinue the donation")
- }}</button>
- % elif tippee_p.payment_providers
- <p class="text-center">{{ payment_methods_icons(tippee_p, new_currency) }}</p>
- % elif not pledging
- <p class="text-muted">{{ glyphicon('info-sign') }} {{ _(
- "{username} hasn't configured any payment method yet, so your donation "
- "cannot actually be processed right now. We will notify you when payment "
- "becomes possible.",
- username=tippee_name
- ) }}</p>
- % endif
% endmacro
diff --git a/www/%username/patrons/index.spt b/www/%username/patrons/index.spt
index b0963f04bd..dc997c5fbf 100644
--- a/www/%username/patrons/index.spt
+++ b/www/%username/patrons/index.spt
@@ -7,10 +7,27 @@ if user != participant and user.recipient_settings.patron_visibilities < 2:
if not user.is_acting_as('admin'):
response.redirect(user.path('patrons/'))
+patron_visibilities = participant.recipient_settings.patron_visibilities
if request.method == 'POST':
- see_patrons = request.body.parse_boolean('see_patrons')
- participant.update_recipient_settings(patron_visibilities=(7 if see_patrons else 1))
- form_post_success(state)
+ if 'see_patrons' in request.body:
+ # Temporary support for legacy form
+ see_patrons = request.body.parse_boolean('see_patrons')
+ participant.update_recipient_settings(patron_visibilities=(7 if see_patrons else 1))
+ form_post_success(state)
+ new_patron_visibilities=(
+ (1 if request.body.get('allow_secret_donations') == 'on' else 0) |
+ (2 if request.body.get('allow_private_donations') == 'on' else 0) |
+ (4 if request.body.get('allow_public_donations') == 'on' else 0)
+ )
+ if new_patron_visibilities == 0:
+ raise response.error(400, _("You have to check at least one box."))
+ elif new_patron_visibilities == patron_visibilities:
+ raise response.redirect(participant.path('patrons/'))
+ if request.body.parse_boolean('confirmed', default=False):
+ participant.update_recipient_settings(patron_visibilities=new_patron_visibilities)
+ form_post_success(state)
+else:
+ new_patron_visibilities = patron_visibilities or 1
title = participant.username
subhead = _("Patrons")
@@ -42,37 +59,57 @@ subhead = _("Patrons")
) }}</p>
% endif
-% set patron_visibilities = participant.recipient_settings.patron_visibilities
-<form action="" method="POST">
+<h3>{{ _("Visibility levels") }}</h3>
+<form action="" method="POST" id="patron_visibilities">
<input type="hidden" name="csrf_token" value="{{ csrf_token }}" />
- % if patron_visibilities == 0
- <p class="text-info">{{ glyphicon('info-sign') }} {{ _(
- "Liberapay now supports non-anonymous donations, do you want to know "
- "who your patrons are?"
- ) }}</p>
- <p class="buttons">
- <button class="btn btn-primary" name="see_patrons" value="yes">{{ _(
- "Enable non-anonymous donations"
- ) }}</button>
-
- <button class="btn btn-default" name="see_patrons" value="no">{{ _("No") }}</button>
- </p>
- % elif patron_visibilities > 1
- <p class="text-muted">{{ _(
- "You have opted-in to see who your patrons are. If you change your mind, "
- "then {link_start}click here to disable non-anonymous donations{link_end}.",
- link_start='<button class="link" name="see_patrons" value="no">'|safe,
- link_end='</button>'|safe
- ) }}</p>
+ <p>{{ _(
+ "Liberapay supports three visibility levels for donations. Each level can "
+ "be turned on or off, but at least one of them must be enabled."
+ ) }}</p>
+ % if participant.payment_providers == 2 and new_patron_visibilities._and__(1)
+ <p class="text-danger">{{ glyphicon('exclamation-sign') }} {{ _(
+ "Secret donations aren't possible with PayPal. You should either disable "
+ "secret donations or {link_start}add a Stripe account{link_end}.",
+ link_start=('<a href="%s">'|safe) % participant.path('payment/'),
+ link_end='</a>'|safe,
+ ) }}</p>
% else
- <p class="text-muted">{{ _(
- "You've chosen not to see who your patrons are. If you change your mind, "
- "then {link_start}click here to enable non-anonymous donations{link_end}.",
- link_start='<button class="link" name="see_patrons" value="yes">'|safe,
- link_end='</button>'|safe
- ) }}</p>
+ <p class="text-warning">{{ glyphicon('info-sign') }} {{ _(
+ "Secret donations aren't possible when the payer uses PayPal."
+ ) }}</p>
% endif
+ <div class="checkbox">
+ <label><input type="checkbox" name="allow_secret_donations"{% if new_patron_visibilities.__and__(1) %} checked{% endif %} /> {{ _("Allow secret donations") }}</label><br>
+ <label><input type="checkbox" name="allow_private_donations"{% if new_patron_visibilities.__and__(2) %} checked{% endif %} /> {{ _("Allow private donations") }}</label><br>
+ <label><input type="checkbox" name="allow_public_donations"{% if new_patron_visibilities.__and__(4) %} checked{% endif %} /> {{ _("Allow public donations") }}</label>
+ </div>
+ <button class="btn btn-primary">{{ _("Preview") }}</button>
</form>
+<br>
+% from "templates/macros/your-tip.html" import tip_visibility_choice with context
+<p>{{ _("This is what your prospective donors currently see:") }}</p>
+<div class="preview max-width-750">{{
+ tip_visibility_choice(
+ participant.username,
+ patron_visibilities,
+ participant.payment_providers,
+ participant.get_tip_to(participant)
+ )
+}}</div>
+% if request.method == 'POST'
+ <br>
+ <p>{{ _("This is what your prospective donors will see with the new settings:") }}</p>
+ <div class="preview max-width-750">{{
+ tip_visibility_choice(
+ participant.username,
+ new_patron_visibilities,
+ participant.payment_providers,
+ participant.get_tip_to(participant)
+ )
+ }}</div>
+ <br>
+ <button class="btn btn-success btn-lg" form="patron_visibilities" name="confirmed" value="true">{{ _("Save") }}</button>
+% endif
% if patron_visibilities > 1
<h3>{{ _("Data export") }}</h3>
|
pypi__warehouse-1177 | Permanent URL (Heroku "No such app" error)
I noticed that https://warehouse.python.org/ produces a `Heroku | No such app` error at the moment. Is this intentional? Are we permanently at https://pypi.io/ now?
If so, we should probably update the URL in a few places: https://github.com/pypa/warehouse/search?utf8=%E2%9C%93&q=%22warehouse.python.org%22
| [
{
"content": "# 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#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, softw... | [
{
"content": "# 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#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, softw... | diff --git a/docs/index.rst b/docs/index.rst
index 369425f7bc30..2d108d74a33f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -15,7 +15,7 @@ Warehouse is a new code base that implements a Python package repository.
It is being actively developed and
the plan is that it will eventually power PyPI_ and
replace an older code base that is currently powering PyPI.
-You can see Warehouse in production at https://warehouse.python.org
+You can see Warehouse in production at https://pypi.io
The goal is to improve PyPI by making it:
diff --git a/warehouse/__about__.py b/warehouse/__about__.py
index 5bc3396e5461..1c67a3bb22c8 100644
--- a/warehouse/__about__.py
+++ b/warehouse/__about__.py
@@ -26,7 +26,7 @@
__title__ = "warehouse"
__summary__ = "Next Generation Python Package Repository"
-__uri__ = "https://warehouse.python.org/"
+__uri__ = "https://pypi.io/"
__version__ = "15.0.dev0"
|
dask__distributed-529 | consider truncating huge strings in logging error when deserialization fails
I was getting enormous errors due to this line, which made it difficult to debug. May want to truncate the string logged here if it is huge:
https://github.com/dask/distributed/blob/master/distributed/core.py#L76
| [
{
"content": "from __future__ import print_function, division, absolute_import\n\nfrom collections import defaultdict\nfrom datetime import timedelta\nimport logging\nimport six\nimport socket\nimport struct\nfrom time import time\nimport traceback\nimport uuid\n\nfrom toolz import assoc, first\n\ntry:\n imp... | [
{
"content": "from __future__ import print_function, division, absolute_import\n\nfrom collections import defaultdict\nfrom datetime import timedelta\nimport logging\nimport six\nimport socket\nimport struct\nfrom time import time\nimport traceback\nimport uuid\n\nfrom toolz import assoc, first\n\ntry:\n imp... | diff --git a/distributed/core.py b/distributed/core.py
index d1907462085..4a08ee63300 100644
--- a/distributed/core.py
+++ b/distributed/core.py
@@ -73,7 +73,7 @@ def loads(x):
try:
return pickle.loads(x)
except Exception:
- logger.info("Failed to deserialize %s", x, exc_info=True)
+ logger.info("Failed to deserialize %s", x[:10000], exc_info=True)
raise
|
rasterio__rasterio-1477 | Python crashes while building overviews
After performing the below code Python crashes:
```python
import rasterio
from rasterio.enums import Resampling
factors = [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
dst = rasterio.open('rasterio/tests/data/RGB.byte.tif', 'r+')
dst.build_overviews(factors, Resampling.average)
```
```
*** Error in `python': malloc(): memory corruption: 0x0000000002e0f9c0 ***
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fbe1c3fd7e5]
/lib/x86_64-linux-gnu/libc.so.6(+0x8213e)[0x7fbe1c40813e]
/lib/x86_64-linux-gnu/libc.so.6(__libc_malloc+0x54)[0x7fbe1c40a184]
/home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(CPLMalloc+0x20)[0x7fbe19ab2700]
/home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(CPLCalloc+0x1c)[0x7fbe19ab27ac]
/home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(_ZN12GTiffDataset15IBuildOverviewsEPKciPiiS2_PFidS1_PvES3_+0x10f0)[0x7fbe19554bd0]
/home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(_ZN11GDALDataset14BuildOverviewsEPKciPiiS2_PFidS1_PvES3_+0x38)[0x7fbe198059f8]
/home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/_io.cpython-35m-x86_64-linux-gnu.so(+0x3613a)[0x7fbe0595713a]
python(PyCFunction_Call+0x77)[0x4e9ba7]
python(PyEval_EvalFrameEx+0x614)[0x5372f4]
python[0x540199]
python(PyEval_EvalCode+0x1f)[0x540e4f]
python[0x60c272]
python(PyRun_InteractiveOneObject+0x2b1)[0x46b89f]
python(PyRun_InteractiveLoopFlags+0xe8)[0x46ba48]
python[0x46cfa0]
python[0x4cf2bd]
python(main+0xe1)[0x4cfeb1]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf0)[0x7fbe1c3a6830]
python(_start+0x29)[0x5d6049]
```
| [
{
"content": "\"\"\"Errors and Warnings.\"\"\"\n\nfrom click import FileError\n\n\nclass RasterioError(Exception):\n \"\"\"Root exception class\"\"\"\n\n\nclass WindowError(RasterioError):\n \"\"\"Raised when errors occur during window operations\"\"\"\n\n\nclass CRSError(ValueError):\n \"\"\"Raised wh... | [
{
"content": "\"\"\"Errors and Warnings.\"\"\"\n\nfrom click import FileError\n\n\nclass RasterioError(Exception):\n \"\"\"Root exception class\"\"\"\n\n\nclass WindowError(RasterioError):\n \"\"\"Raised when errors occur during window operations\"\"\"\n\n\nclass CRSError(ValueError):\n \"\"\"Raised wh... | diff --git a/CHANGES.txt b/CHANGES.txt
index 51549d733..61843b88d 100644
--- a/CHANGES.txt
+++ b/CHANGES.txt
@@ -6,6 +6,9 @@ Changes
Bug fixes:
+- If the build_overviews method of a dataset is passed a list of factors that
+ specify more than one 1x1 pixel overview (#1333), Rasterio raises an
+ exception.
- Calling calculate_default_transform for large extents should no longer result
in the out of memory error reported in #1131. The rio-warp command should
also now run more quickly and with a smaller memory footprint.
diff --git a/rasterio/_io.pyx b/rasterio/_io.pyx
index 79f744b4c..062e1f4a2 100644
--- a/rasterio/_io.pyx
+++ b/rasterio/_io.pyx
@@ -6,6 +6,7 @@ from __future__ import absolute_import
include "directives.pxi"
include "gdal.pxi"
+from collections import Counter
import logging
import sys
import uuid
@@ -24,7 +25,7 @@ from rasterio.enums import ColorInterp, MaskFlags, Resampling
from rasterio.errors import (
CRSError, DriverRegistrationError, RasterioIOError,
NotGeoreferencedWarning, NodataShadowWarning, WindowError,
- UnsupportedOperation
+ UnsupportedOperation, OverviewCreationError
)
from rasterio.sample import sample_gen
from rasterio.transform import Affine
@@ -1549,6 +1550,11 @@ cdef class DatasetWriterBase(DatasetReaderBase):
['Resampling.{0}'.format(Resampling(k).name) for k in
resampling_map.keys()])))
+ # Check factors
+ ovr_shapes = Counter([(int((self.height + f - 1) / f), int((self.width + f - 1) / f)) for f in factors])
+ if ovr_shapes[(1, 1)] > 1:
+ raise OverviewCreationError("Too many overviews levels of 1x1 dimension were requested")
+
# Allocate arrays.
if factors:
factors_c = <int *>CPLMalloc(len(factors)*sizeof(int))
diff --git a/rasterio/errors.py b/rasterio/errors.py
index 7e80f8817..97f63303f 100644
--- a/rasterio/errors.py
+++ b/rasterio/errors.py
@@ -98,3 +98,7 @@ class WarpOptionsError(RasterioError):
class UnsupportedOperation(RasterioError):
"""Raised when reading from a file opened in 'w' mode"""
+
+
+class OverviewCreationError(RasterioError):
+ """Raised when creation of an overview fails"""
diff --git a/tests/test_overviews.py b/tests/test_overviews.py
index d0098605d..4b26d9e33 100644
--- a/tests/test_overviews.py
+++ b/tests/test_overviews.py
@@ -9,6 +9,8 @@
import rasterio
from rasterio.enums import Resampling
from rasterio.env import GDALVersion
+from rasterio.errors import OverviewCreationError
+
gdal_version = GDALVersion()
@@ -79,3 +81,12 @@ def test_test_unsupported_algo(data):
with rasterio.open(inputfile, 'r+') as src:
overview_factors = [2, 4]
src.build_overviews(overview_factors, resampling=Resampling.q1)
+
+
+def test_issue1333(data):
+ """Fail if asked to create more than one 1x1 overview"""
+ inputfile = str(data.join('RGB.byte.tif'))
+ with pytest.raises(OverviewCreationError):
+ with rasterio.open(inputfile, 'r+') as src:
+ overview_factors = [1024, 2048]
+ src.build_overviews(overview_factors, resampling=Resampling.average)
|
dotkom__onlineweb4-425 | "Startet studie" in Profile -> Medlemskap requires defined format without specifying it
"Started studie" is a datefield. The problem is that most browsers (like FF, Chrome) don't render these fields with any additional tools which makes filling them out a pain in the ass (Safari@iOS has that fancy datepicker-shit).
The field requires the format 'yyyy-mm-dd', but does not specify this anywhere. This should be fixed somehow.
| [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom django import forms\nfrom django.utils.translation import ugettext as _\n\nfrom apps.profiles.models import Privacy\nfrom apps.authentication.models import OnlineUser, FIELD_OF_STUDY_CHOICES\n\nclass ProfileForm(forms.ModelForm):\n\n class Meta:\n model = ... | [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom django import forms\nfrom django.utils.translation import ugettext as _\n\nfrom apps.profiles.models import Privacy\nfrom apps.authentication.models import OnlineUser, FIELD_OF_STUDY_CHOICES\n\nclass ProfileForm(forms.ModelForm):\n\n class Meta:\n model = ... | diff --git a/apps/profiles/forms.py b/apps/profiles/forms.py
index 6e2b39a1f..ed1080b7d 100644
--- a/apps/profiles/forms.py
+++ b/apps/profiles/forms.py
@@ -61,3 +61,7 @@ def __init__(self, *args, **kwargs):
class Meta:
model = OnlineUser
fields = ['field_of_study', 'started_date', ]
+
+ widgets = {
+ 'started_date' : forms.TextInput(attrs={'placeholder' : 'YYYY-MM-DD'}),
+ }
diff --git a/files/static/js/profiles.js b/files/static/js/profiles.js
index b78c63825..12e372443 100755
--- a/files/static/js/profiles.js
+++ b/files/static/js/profiles.js
@@ -316,7 +316,11 @@ $(document).ready(function() {
JS for membership
*/
- $(".hasDatePcker").datepicker({ dateFormat: "yy-mm-dd" });
+ $(".hasDatePicker").datepicker({
+ yearRange: "2004:" + new Date().getFullYear(),
+ changeYear: true,
+ dateFormat: "yy-mm-dd"
+ });
$("#membership-details").submit(function(event) {
event.preventDefault();
diff --git a/templates/profiles/membership.html b/templates/profiles/membership.html
index 497d0c476..9a40b4746 100644
--- a/templates/profiles/membership.html
+++ b/templates/profiles/membership.html
@@ -15,7 +15,7 @@ <h3>Medlemskap</h3>
</form>
{% else %}
{% if user.ntnu_username %}
- Du er ikke registrert som medlem av Linjeforeningen Online.
+ <p>Du er ikke registrert som medlem av Linjeforeningen Online.</p>
{% else %}
<p>For å aktivere ditt medlemskap kreves det at du har knyttet din studentkonto til din profil.</p>
<p>Gå til Epost-innstillinger og registrer din @stud.ntnu.no epost.</p>
|
comic__grand-challenge.org-2027 | Integrate Forums into challenges
Navigating to the forum of a challenge currently takes the participant outside of the challenge environment. Navigating back to the challenge is not possible through the breadcrumbs on the forum page and instead requires going via the Challenge tab and searching for the respective Challenge again. It would be nicer if the forums were visually integrated into the challenge page layout and if the breadcrumbs reflected their nesting in the challenge rather than their nesting under all forum on GC.
See here: https://github.com/DIAGNijmegen/rse-roadmap/issues/83#issuecomment-919250835
| [
{
"content": "from actstream.models import Follow\r\nfrom django import template\r\nfrom django.contrib.contenttypes.models import ContentType\r\n\r\nfrom grandchallenge.notifications.forms import FollowForm\r\n\r\nregister = template.Library()\r\n\r\n\r\n@register.simple_tag\r\ndef get_follow_object_pk(user, f... | [
{
"content": "from actstream.models import Follow\r\nfrom django import template\r\nfrom django.contrib.contenttypes.models import ContentType\r\n\r\nfrom grandchallenge.notifications.forms import FollowForm\r\n\r\nregister = template.Library()\r\n\r\n\r\n@register.simple_tag\r\ndef get_follow_object_pk(user, f... | diff --git a/app/grandchallenge/forum_conversation/templatetags/forum_extras.py b/app/grandchallenge/forum_conversation/templatetags/forum_extras.py
index deb1d274f8..18b12b1d6b 100644
--- a/app/grandchallenge/forum_conversation/templatetags/forum_extras.py
+++ b/app/grandchallenge/forum_conversation/templatetags/forum_extras.py
@@ -51,3 +51,9 @@ def get_content_type(follow_object):
except AttributeError:
ct = None
return ct
+
+
+@register.simple_tag()
+def is_participant(user, challenge):
+ if challenge.is_participant(user):
+ return True
diff --git a/app/grandchallenge/forums/templates/board_base.html b/app/grandchallenge/forums/templates/board_base.html
index ecf95b9ac3..56268e2f07 100644
--- a/app/grandchallenge/forums/templates/board_base.html
+++ b/app/grandchallenge/forums/templates/board_base.html
@@ -2,6 +2,8 @@
{% load static %}
{% load i18n %}
{% load forum_permission_tags %}
+{% load forum_extras %}
+{% load guardian_tags %}
{% block title %}{% block sub_title %}{% endblock sub_title %} - {{ request.site.name }} Forums{% endblock title %}
@@ -12,31 +14,63 @@
{% block body %}
{% include "grandchallenge/partials/navbar.html" with hide_userlinks=False %}
- {% include "partials/breadcrumb.html" %}
-<div class="my-5 container" id="main_container">
- <div class="row">
- <div class="col-12">
- <div class="float-right controls-link-wrapper">
- {% if not request.user.is_anonymous %}
- <a href="{% url 'notifications:follow-list' %}" class="d-inline-block ml-3"><i class="fas fa-bookmark"> </i>{% trans "Subscriptions" %}</a>
- <a href="{% url 'forum_member:user_posts' request.user.id %}" class="d-inline-block ml-3"><i class="fas fa-comments"> </i>{% trans "View my posts" %}</a>
- {% endif %}
- {% get_permission 'can_access_moderation_queue' request.user as can_access_moderation_queue %}
- {% if can_access_moderation_queue %}
- <a href="{% url 'forum_moderation:queue' %}" class="d-inline-block ml-3"><i class="fas fa-gavel"> </i>{% trans "Moderation queue" %}</a>
- {% endif %}
+ {% if forum.challenge %}
+ <div class="container-fluid bg-primary">
+ <div class="container">
+ <nav aria-label="breadcrumb">
+ <ol class="breadcrumb">
+ <li class="breadcrumb-item"><a href="{% url 'challenges:list' %}">Challenges</a></li>
+ <li class="breadcrumb-item"><a href="{{ forum.challenge.get_absolute_url }}">
+ {% firstof forum.challenge.title forum.challenge.short_name %}</a></li>
+ <li class="breadcrumb-item"><a
+ href="{% url 'forum:forum' forum.slug forum.id %}">Forum</a></li>
+ {% if topic %}
+ <li class="breadcrumb-item"><a
+ href="{% url 'forum_conversation:topic' forum.slug forum.id topic.slug topic.id %}">{{ topic.subject }}</a>
+ </li>
+ {% endif %}
+ </ol>
+ </nav>
</div>
</div>
- </div>
- <div class="row">
- <div class="col-12">
- <br />
- {% block messages %}{% include "partials/messages.html" %}{% endblock messages %}
+ {% else %}
+ {% include "partials/breadcrumb.html" %}
+ {% endif %}
+ <div class="container pb-3 mt-3">
+ {% block outer_content %}
+ {% block messages %}
+ {% if forum.challenge %}
+ {% include 'challenges/challenge_banner.html' with challenge=forum.challenge %}
+ {% endif %}
+ {% include "grandchallenge/partials/messages.html" %}
+ {% endblock %}
+ {% if forum.challenge %}
+ {% block topbar %}
+ {% get_obj_perms request.user for forum.challenge as "user_perms" %}
+ {% is_participant request.user forum.challenge as is_challenge_participant %}
+ {% include 'challenges/challenge_topbar.html' with challenge=forum.challenge challenge_perms=user_perms user_is_participant=is_challenge_participant %}
+ {% endblock %}
+ {% endif %}
+ {% endblock %}
+
+ <div class="row">
+ <div class="col-12">
+ <div class="float-right controls-link-wrapper">
+ {% if not request.user.is_anonymous %}
+ <a href="{% url 'notifications:follow-list' %}" class="d-inline-block ml-3"><i class="fas fa-bookmark"> </i>{% trans "Subscriptions" %}</a>
+ <a href="{% url 'forum_member:user_posts' request.user.id %}" class="d-inline-block ml-3"><i class="fas fa-comments"> </i>{% trans "View my posts" %}</a>
+ {% endif %}
+ {% get_permission 'can_access_moderation_queue' request.user as can_access_moderation_queue %}
+ {% if can_access_moderation_queue %}
+ <a href="{% url 'forum_moderation:queue' %}" class="d-inline-block ml-3"><i class="fas fa-gavel"> </i>{% trans "Moderation queue" %}</a>
+ {% endif %}
+ </div>
+ </div>
</div>
+
+ {% block content %}
+ {% endblock content %}
</div>
- {% block content %}
- {% endblock content %}
-</div>
{% endblock %}
{% block js %}
|
speechbrain__speechbrain-1504 | Torch 1.12 not compatible?
working to install speechbrain 0.5.12, and getting the error that "speechbrain 0.5.12 requires torch<=1.11,>=1.7, but you have torch 1.12.0 which is incompatible." read elsewhere that it should work with >=1.7.
| [
{
"content": "#!/usr/bin/env python3\nimport os\nimport sys\nimport site\nimport setuptools\nfrom distutils.core import setup\n\n\n# Editable install in user site directory can be allowed with this hack:\n# https://github.com/pypa/pip/issues/7953.\nsite.ENABLE_USER_SITE = \"--user\" in sys.argv[1:]\n\nwith open... | [
{
"content": "#!/usr/bin/env python3\nimport os\nimport sys\nimport site\nimport setuptools\nfrom distutils.core import setup\n\n\n# Editable install in user site directory can be allowed with this hack:\n# https://github.com/pypa/pip/issues/7953.\nsite.ENABLE_USER_SITE = \"--user\" in sys.argv[1:]\n\nwith open... | diff --git a/requirements.txt b/requirements.txt
index 6f78e41680..7a9360a981 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,6 +8,6 @@ pre-commit>=2.3.0
scipy>=1.4.1
sentencepiece>=0.1.91
SoundFile; sys_platform == 'win32'
-torch>=1.9.0,<=1.11.1
-torchaudio>=0.9.0,<=0.11.1
+torch>=1.9.0
+torchaudio>=0.9.0
tqdm>=4.42.0
diff --git a/setup.py b/setup.py
index 49aab61b1b..92d2cd2b40 100644
--- a/setup.py
+++ b/setup.py
@@ -37,7 +37,7 @@
"packaging",
"scipy",
"sentencepiece",
- "torch>=1.7,<=1.11",
+ "torch>=1.9",
"torchaudio",
"tqdm",
"huggingface_hub",
|
mlcommons__GaNDLF-453 | Pickle5 may cause setup errors on Python 3.8 (future-proofing)
**Describe the bug**
When installing GaNDLF on Python 3.8, an error occurs when installing the dependency "pickle5".
Note that pickle5 is redundant in 3.8 -- the backported functionality is the default/standard [[ref](https://github.com/pitrou/pickle5-backport/issues/12)].
You can solve this by adding this annotation in setup.py so that pickle5 is only installed on Python versions 3.7 or lower (example of this syntax: https://stackoverflow.com/a/32643122).
If pickle5 is imported directly in your code, you may also need to do a version check at import time, something like this:
``` python
# Both these should come standard if you have setuptools anyway
import platform
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.8.0"):
import pickle5 as pickle
else:
import pickle
```
**To Reproduce**
Steps to reproduce the behavior:
1. Create a Python 3.8 environment using your mechanism of choice.
2. Install GaNDLF per instructions.
3. Receive error message while installing pickle5.
**GaNDLF Version**
Latest master (0.0.14.dev0 I think)
**Desktop (please complete the following information):**
Occurs in any system with Python 3.8 or greater. At least for me on Ubuntu-based machines.
**Additional context**
This issue is just a heads up for supporting 3.8 and greater. Hope this helps.
| [
{
"content": "#!/usr/bin/env python\n\n\"\"\"The setup script.\"\"\"\n\n\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.install import install\nfrom setuptools.command.develop import develop\nfrom setuptools.command.egg_info import egg_info\n\nwith open(\"README.md\") as readme... | [
{
"content": "#!/usr/bin/env python\n\n\"\"\"The setup script.\"\"\"\n\n\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.install import install\nfrom setuptools.command.develop import develop\nfrom setuptools.command.egg_info import egg_info\n\nwith open(\"README.md\") as readme... | diff --git a/Dockerfile-CPU b/Dockerfile-CPU
index 5a5c6a80f..411dc67b5 100644
--- a/Dockerfile-CPU
+++ b/Dockerfile-CPU
@@ -2,19 +2,20 @@ FROM ubuntu:18.04
LABEL github="https://github.com/CBICA/GaNDLF"
LABEL docs="https://cbica.github.io/GaNDLF/"
LABEL version=1.0
+
# Install fresh Python and dependencies for build-from-source
-RUN apt-get update && apt-get install -y python3.7 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.7-dev libffi-dev
-RUN python3.7 -m pip install --upgrade pip
+RUN apt-get update && apt-get install -y python3.8 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.8-dev libffi-dev
+RUN python3.8 -m pip install --upgrade pip
# EXPLICITLY install cpu versions of torch/torchvision (not all versions have +cpu modes on PyPI...)
-RUN python3.7 -m pip install torch==1.10.0+cpu torchvision==0.11.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
+RUN python3.8 -m pip install torch==1.8.2+cpu torchvision==0.9.2+cpu torchaudio===0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu
COPY . /GaNDLF
WORKDIR /GaNDLF
-RUN python3.7 -m pip install --upgrade pip && python3.7 -m pip install openvino-dev==2022.1.0 && python3.7 -m pip install torch==1.8.2+cpu torchvision==0.9.2+cpu torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
-RUN python3.7 -m pip install -e .
+RUN python3.8 -m pip install openvino-dev==2022.1.0
+RUN python3.8 -m pip install -e .
# Entrypoint forces all commands given via "docker run" to go through python, CMD forces the default entrypoint script argument to be gandlf_run
# If a user calls "docker run gandlf:[tag] gandlf_anonymize", it will resolve to running "python gandlf_anonymize" instead.
# CMD is inherently overridden by args to "docker run", entrypoint is constant.
-ENTRYPOINT python3.7
+ENTRYPOINT python3.8
CMD gandlf_run
# The below force the container commands to run as a nonroot user with UID > 10000.
diff --git a/Dockerfile-CUDA10.2 b/Dockerfile-CUDA10.2
index 011fcca7f..79649a93d 100644
--- a/Dockerfile-CUDA10.2
+++ b/Dockerfile-CUDA10.2
@@ -6,11 +6,15 @@ LABEL version=1.0
# Install instructions for NVIDIA Container Toolkit allowing you to use the host's GPU: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
# Note that to do this on a Windows host you need experimental feature "CUDA on WSL" -- not yet stable.
-RUN python3 -m pip install --upgrade pip
+# Explicitly install python3.8
+RUN apt-get update && apt-get install -y python3.8 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.8-dev libffi-dev
+RUN python3.8 -m pip install --upgrade pip
+RUN python3.8 -m pip install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
COPY . /GaNDLF
WORKDIR /GaNDLF
-RUN python3 -m pip install --upgrade pip && python3 -m pip install openvino-dev==2022.1.0 && python3 -m pip install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
-RUN python3 -m pip install -e .
+RUN python3.8 -m pip install openvino-dev==2022.1.0
+RUN python3.8 -m pip install -e .
+
# Entrypoint forces all commands given via "docker run" to go through python, CMD forces the default entrypoint script argument to be gandlf_run
# If a user calls "docker run gandlf:[tag] gandlf_anonymize", it will resolve to running "python gandlf_anonymize" instead.
# CMD is inherently overridden by args to "docker run", entrypoint is constant.
diff --git a/Dockerfile-CUDA11.3 b/Dockerfile-CUDA11.3
index f773f1999..66be6da58 100644
--- a/Dockerfile-CUDA11.3
+++ b/Dockerfile-CUDA11.3
@@ -6,18 +6,19 @@ LABEL version=1.0
# Install instructions for NVIDIA Container Toolkit allowing you to use the host's GPU: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
# Note that to do this on a Windows host you need experimental feature "CUDA on WSL" -- not yet stable.
-# Install Python 3.7 -- base image has 3.8, but pickle5 is redundant in 3.8 (and will cause setup errors). This could be resolved at setup time but this simplifies the build for now.
-RUN apt-get update && apt-get install -y python3.7 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.7-dev libffi-dev
-RUN python3.7 -m pip install --upgrade pip
-RUN python3.7 -m pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+# Explicitly install python3.8 (this uses 11.1 for now, as PyTorch LTS 1.8.2 is built against it)
+RUN apt-get update && apt-get install -y python3.8 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.8-dev libffi-dev
+RUN python3.8 -m pip install --upgrade pip
+RUN python3.8 -m pip install torch==1.8.2 torchvision==0.9.2 torchaudio===0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu111
COPY . /GaNDLF
WORKDIR /GaNDLF
-RUN python3.7 -m pip install --upgrade pip && python3.7 -m pip install openvino-dev==2022.1.0 && python3.7 -m pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
-RUN python3.7 -m pip install -e .
+RUN python3.8 -m pip install openvino-dev==2022.1.0
+RUN python3.8 -m pip install -e .
+
# Entrypoint forces all commands given via "docker run" to go through python, CMD forces the default entrypoint script argument to be gandlf_run
# If a user calls "docker run gandlf:[tag] gandlf_anonymize", it will resolve to running "python gandlf_anonymize" instead.
# CMD is inherently overridden by args to "docker run", entrypoint is constant.
-ENTRYPOINT python3.7
+ENTRYPOINT python3.8
CMD gandlf_run
# The below force the container commands to run as a nonroot user with UID > 10000.
diff --git a/Dockerfile-ROCm b/Dockerfile-ROCm
index 51a406674..9cf61ee63 100644
--- a/Dockerfile-ROCm
+++ b/Dockerfile-ROCm
@@ -1,11 +1,12 @@
-FROM rocm/pytorch:rocm5.0.1_ubuntu18.04_py3.7_pytorch_1.9.0
+# NOTE: ROCm is NOT supported by PyTorch LTS (1.8.2)
+FROM rocm/pytorch:rocm4.5.2_ubuntu18.04_py3.8_pytorch_1.10.0
LABEL github="https://github.com/CBICA/GaNDLF"
LABEL docs="https://cbica.github.io/GaNDLF/"
LABEL version=1.0
# Quick start instructions on using Docker with ROCm: https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/quick-start.md
-# The base image contains ROCm, python 3.7 and pytorch already, no need to install those
+# The base image contains ROCm, python 3.8 and pytorch already, no need to install those
RUN python3 -m pip install --upgrade pip
COPY . /GaNDLF
WORKDIR /GaNDLF
diff --git a/docs/setup.md b/docs/setup.md
index 037d3f145..38c5171ff 100644
--- a/docs/setup.md
+++ b/docs/setup.md
@@ -18,12 +18,12 @@
## Installation
-The instructions assume a system using NVIDA GPUs with [CUDA 10.2](https://developer.nvidia.com/cuda-toolkit-archive) (for AMD, please make the appropriate change during PyTorch installation from [their installation page](https://pytorch.org/get-started/locally)).
+The instructions assume a system using NVIDIA GPUs with [CUDA 10.2](https://developer.nvidia.com/cuda-toolkit-archive) (for AMD, please make the appropriate change during PyTorch installation from [their installation page](https://pytorch.org/get-started/locally)).
```bash
git clone https://github.com/CBICA/GaNDLF.git
cd GaNDLF
-conda create -n venv_gandlf python=3.7 -y
+conda create -n venv_gandlf python=3.8 -y
conda activate venv_gandlf
### PyTorch LTS installation - https://pytorch.org/get-started/locally
## CUDA 10.2
diff --git a/setup.py b/setup.py
index a85d0a251..0b153016c 100644
--- a/setup.py
+++ b/setup.py
@@ -60,7 +60,7 @@ def run(self):
"pylint",
"scikit-learn>=0.23.2",
"scikit-image>=0.19.1",
- "pickle5>=0.0.11",
+ 'pickle5>=0.0.11; python_version < "3.8.0"',
"setuptools",
"seaborn",
"pyyaml",
|
pytorch__pytorch-116517 | Missing packaging dependency in torch 2.1.x
### 🐛 Describe the bug
Hi,
[torch.utils.tensorboard requires "packaging"](https://github.com/pytorch/pytorch/blob/fa1ccc34c4f65756bc50c3e3ab135c88b175b18c/torch/utils/tensorboard/__init__.py#L2C1-L3C1) to be installed but that dependency is [missing on torch 2.1.x](https://github.com/pytorch/pytorch/blob/v2.1.2-rc1/requirements.txt).
Here's some example code:
```python
from torch.utils.tensorboard import SummaryWriter
```
The links above point to a RC version of 2.1.2 but this is also the case for 2.1.1. Would it be possible to make a patch release to add the dependency?
### Versions
Python version: 3.9.16 (main, Dec 7 2022, 10:16:11) [Clang 14.0.0 (clang-140[0.0.29.202](http://0.0.29.202/)
)] (64-bit runtime)
Python platform: macOS-13.6.1-x86_64-i386-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Intel(R) Core(TM) i5-7287U CPU @ 3.30GHz
Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.1.1
[pip3] torchvision==0.16.1
[conda] Could not collect
| [
{
"content": "import tensorboard\nfrom packaging.version import Version\n\nif not hasattr(tensorboard, \"__version__\") or Version(\n tensorboard.__version__\n) < Version(\"1.15\"):\n raise ImportError(\"TensorBoard logging requires TensorBoard version 1.15 or above\")\n\ndel Version\ndel tensorboard\n\nf... | [
{
"content": "import tensorboard\nfrom torch._vendor.packaging.version import Version\n\nif not hasattr(tensorboard, \"__version__\") or Version(\n tensorboard.__version__\n) < Version(\"1.15\"):\n raise ImportError(\"TensorBoard logging requires TensorBoard version 1.15 or above\")\n\ndel Version\ndel te... | diff --git a/torch/utils/tensorboard/__init__.py b/torch/utils/tensorboard/__init__.py
index 39ac891165696f..cca0fb95146039 100644
--- a/torch/utils/tensorboard/__init__.py
+++ b/torch/utils/tensorboard/__init__.py
@@ -1,5 +1,5 @@
import tensorboard
-from packaging.version import Version
+from torch._vendor.packaging.version import Version
if not hasattr(tensorboard, "__version__") or Version(
tensorboard.__version__
|
bookwyrm-social__bookwyrm-514 | shelve actions are always posted and always public
| [
{
"content": "''' views for actions you can take in the application '''\nfrom io import BytesIO, TextIOWrapper\nfrom uuid import uuid4\nfrom PIL import Image\n\nimport dateutil.parser\nfrom dateutil.parser import ParserError\n\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.aut... | [
{
"content": "''' views for actions you can take in the application '''\nfrom io import BytesIO, TextIOWrapper\nfrom uuid import uuid4\nfrom PIL import Image\n\nimport dateutil.parser\nfrom dateutil.parser import ParserError\n\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.aut... | diff --git a/bookwyrm/view_actions.py b/bookwyrm/view_actions.py
index 3de1dd9d74..b909a8f72c 100644
--- a/bookwyrm/view_actions.py
+++ b/bookwyrm/view_actions.py
@@ -402,7 +402,7 @@ def shelve(request):
request.user,
desired_shelf,
book,
- privacy='public'
+ privacy=desired_shelf.privacy
)
return redirect('/')
|
ansible__molecule-3313 | Question: accessing values of variables as they are being used for provisioning an instance inside Testinfra tests
I want to use Testinfra tests to test my role.
Inside an Testinfra test I would like to access the values of variables as they are being used for provisioning the machine when `playbook.yml` is converged for some instance.
I need this since the instance's state, which I want to check, depends on the chosen values of the variables defined in role's `default/main.yml` or `vars/main.yml` file.
I tried using the following ['trick' suggested by Testinfra maintainer](https://github.com/philpep/testinfra/issues/61#issuecomment-178503626), but it only works for Ansible facts and `group_vars`/`host_vars`, not for variables defined within a role, either in `default/main.yml` or `vars/main.yml`.
Here is an example test for PostgreSQL service:
``` python
def test_postgresql_running_and_enabled(Ansible, Service):
postgresql_unit_name = Ansible("debug", "msg={{ postgresql_unit_name }}")["msg"]
postgresql = Service(postgresql_unit_name)
assert postgresql.is_running
assert postgresql.is_enabled
```
Variable `postgresql_unit_name` is defined in role's `vars/main.yml`, but apparently the invocation of Ansible through Testinfra is unable to find it. Here is the error:
``` python
def test_postgresql_running_and_enabled(Ansible, Service):
postgresql_unit_name = Ansible("debug", "msg={{ postgresql_unit_name }}")["msg"]
postgresql = Service(postgresql_unit_name)
> assert postgresql.is_running
E assert <service 'postgresql_unit_name' is undefined>.is_running
```
Any ideas how I could achieve this?
| [
{
"content": "# Copyright (c) 2015-2018 Cisco Systems, Inc.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to\n# deal in the Software without restriction, including without limitation the\n# right... | [
{
"content": "# Copyright (c) 2015-2018 Cisco Systems, Inc.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to\n# deal in the Software without restriction, including without limitation the\n# right... | diff --git a/src/molecule/util.py b/src/molecule/util.py
index 3dc1c528f1..68df87e257 100644
--- a/src/molecule/util.py
+++ b/src/molecule/util.py
@@ -143,6 +143,7 @@ def run_command(
else:
env = cmd.env or env
args = cmd.cmd
+ cwd = cmd.cwd
stdout = cmd.stdout
stderr = cmd.stderr
else:
|
python-trio__trio-2334 | Embedded use vs. signal handler
If you install your own signal handler from C and then run an embedded Python interpreter, you can't use Trio:
```
Traceback (most recent call last):
File "/etc/kamailio/main.py", line 65, in background
trio.run(bg_main)
File "/usr/lib/python3/dist-packages/trio/_core/_run.py", line 1929, in run
runner = setup_runner(
File "/usr/lib/python3/dist-packages/trio/_core/_run.py", line 1846, in setup_runner
ki_manager.install(runner.deliver_ki, restrict_keyboard_interrupt_to_checkpoints)
File "/usr/lib/python3/dist-packages/trio/_core/_ki.py", line 180, in install
not is_main_thread()
File "/usr/lib/python3/dist-packages/trio/_util.py", line 79, in is_main_thread
signal.signal(signal.SIGINT, signal.getsignal(signal.SIGINT))
File "/usr/lib/python3.9/signal.py", line 56, in signal
handler = _signal.signal(_enum_to_int(signalnum), _enum_to_int(handler))
TypeError: signal handler must be signal.SIG_IGN, signal.SIG_DFL, or a callable object
```
| [
{
"content": "# coding: utf-8\n\n# Little utilities we use internally\n\nfrom abc import ABCMeta\nimport os\nimport signal\nimport sys\nimport pathlib\nfrom functools import wraps, update_wrapper\nimport typing as t\nimport threading\nimport collections\n\nfrom async_generator import isasyncgen\n\nimport trio\n... | [
{
"content": "# coding: utf-8\n\n# Little utilities we use internally\n\nfrom abc import ABCMeta\nimport os\nimport signal\nimport sys\nimport pathlib\nfrom functools import wraps, update_wrapper\nimport typing as t\nimport threading\nimport collections\n\nfrom async_generator import isasyncgen\n\nimport trio\n... | diff --git a/newsfragments/2333.bugfix.rst b/newsfragments/2333.bugfix.rst
new file mode 100644
index 000000000..efab3a638
--- /dev/null
+++ b/newsfragments/2333.bugfix.rst
@@ -0,0 +1 @@
+Python raises a `TypeError` if you try to (re-)install a C signal handler.
diff --git a/trio/_util.py b/trio/_util.py
index b5a2ceabd..539fc540f 100644
--- a/trio/_util.py
+++ b/trio/_util.py
@@ -78,7 +78,7 @@ def is_main_thread():
try:
signal.signal(signal.SIGINT, signal.getsignal(signal.SIGINT))
return True
- except ValueError:
+ except (TypeError, ValueError):
return False
|
docker__docker-py-806 | Auth fails with long passwords
See https://github.com/docker/docker/issues/16840
docker-py is encoding `X-Registry-Auth` with regular base64 and not the url safe version of base64 that jwt tokens use.
| [
{
"content": "# Copyright 2013 dotCloud inc.\n\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\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless requir... | [
{
"content": "# Copyright 2013 dotCloud inc.\n\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\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless requir... | diff --git a/docker/auth/auth.py b/docker/auth/auth.py
index 366bc67e9..1ee9f8125 100644
--- a/docker/auth/auth.py
+++ b/docker/auth/auth.py
@@ -102,7 +102,7 @@ def decode_auth(auth):
def encode_header(auth):
auth_json = json.dumps(auth).encode('ascii')
- return base64.b64encode(auth_json)
+ return base64.urlsafe_b64encode(auth_json)
def parse_auth(entries):
diff --git a/tests/utils_test.py b/tests/utils_test.py
index b1adde267..04183f9f8 100644
--- a/tests/utils_test.py
+++ b/tests/utils_test.py
@@ -19,7 +19,9 @@
exclude_paths, convert_volume_binds, decode_json_header
)
from docker.utils.ports import build_port_bindings, split_port
-from docker.auth import resolve_repository_name, resolve_authconfig
+from docker.auth import (
+ resolve_repository_name, resolve_authconfig, encode_header
+)
from . import base
from .helpers import make_tree
@@ -376,12 +378,21 @@ def test_decode_json_header(self):
obj = {'a': 'b', 'c': 1}
data = None
if six.PY3:
- data = base64.b64encode(bytes(json.dumps(obj), 'utf-8'))
+ data = base64.urlsafe_b64encode(bytes(json.dumps(obj), 'utf-8'))
else:
- data = base64.b64encode(json.dumps(obj))
+ data = base64.urlsafe_b64encode(json.dumps(obj))
decoded_data = decode_json_header(data)
self.assertEqual(obj, decoded_data)
+ def test_803_urlsafe_encode(self):
+ auth_data = {
+ 'username': 'root',
+ 'password': 'GR?XGR?XGR?XGR?X'
+ }
+ encoded = encode_header(auth_data)
+ assert b'/' not in encoded
+ assert b'_' in encoded
+
def test_resolve_repository_name(self):
# docker hub library image
self.assertEqual(
|
tornadoweb__tornado-2649 | LogFormatter DEFAULT_COLORS needs a logging.CRITICAL entry
https://github.com/tornadoweb/tornado/blob/c447875a1058f4768c2995ddd9ebb0eaddd3f32e/tornado/log.py#L108-L113
Currently `ERROR` will log red, but `CRITICAL` will log in white. Curses `COLOR_MAGENTA` (5) is probably the best option.
| [
{
"content": "#\n# Copyright 2012 Facebook\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicab... | [
{
"content": "#\n# Copyright 2012 Facebook\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicab... | diff --git a/tornado/log.py b/tornado/log.py
index 435cd71858..e1f7c6a677 100644
--- a/tornado/log.py
+++ b/tornado/log.py
@@ -110,6 +110,7 @@ class LogFormatter(logging.Formatter):
logging.INFO: 2, # Green
logging.WARNING: 3, # Yellow
logging.ERROR: 1, # Red
+ logging.CRITICAL: 5, # Magenta
}
def __init__(
|
ansible-collections__community.aws-990 | Invalid import path for BotoCoreError in redshift_info module
### Summary
In case of any AWS related error (like missing permissions) the module will throw a gigantic python stack trace with error summary as:
```
line 304, in find_clusters
NameError: name 'BotoCoreError' is not defined
```
This is due to an invalid import path that is present in the module https://github.com/ansible-collections/community.aws/blob/main/plugins/modules/redshift_info.py#L280
Instead of `from botocore.exception` it should be `from botocore.exceptions`. Once that is done, ansible no longer hides the real error with the stack trace.
### Issue Type
Bug Report
### Component Name
redshift_info
### Ansible Version
```console (paste below)
$ ansible --version
ansible 2.10.8
config file = None
configured module search path = ['/home/wojtek/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules']
ansible python module location = /usr/local/lib/python3.6/dist-packages/ansible
executable location = /usr/local/bin/ansible
python version = 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0]
```
### Collection Versions
Non-relevant
### AWS SDK versions
```console (paste below)
$ pip show boto boto3 botocore
Name: boto
Version: 2.49.0
Summary: Amazon Web Services Library
Home-page: https://github.com/boto/boto/
Author: Mitch Garnaat
Author-email: mitch@garnaat.com
License: MIT
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires:
---
Name: boto3
Version: 1.20.54
Summary: The AWS SDK for Python
Home-page: https://github.com/boto/boto3
Author: Amazon Web Services
Author-email: None
License: Apache License 2.0
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires: jmespath, s3transfer, botocore
---
Name: botocore
Version: 1.23.54
Summary: Low-level, data-driven core of boto 3.
Home-page: https://github.com/boto/botocore
Author: Amazon Web Services
Author-email: None
License: Apache License 2.0
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires: jmespath, urllib3, python-dateutil
```
### Configuration
```console (paste below)
$ ansible-config dump --only-changed
```
### OS / Environment
Ubuntu 20.04
### Steps to Reproduce
Run the module without DescribeClusters permission.
### Expected Results
AWS API error on missing permissions is shown.
### Actual Results
Python stack trace ending with
```
line 304, in find_clusters
NameError: name 'BotoCoreError' is not defined
```
### Code of Conduct
- [X] I agree to follow the Ansible Code of Conduct
| [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: re... | [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: re... | diff --git a/changelogs/fragments/970-redshift_info-boto-import.yml b/changelogs/fragments/970-redshift_info-boto-import.yml
new file mode 100644
index 00000000000..568c6cdf605
--- /dev/null
+++ b/changelogs/fragments/970-redshift_info-boto-import.yml
@@ -0,0 +1,2 @@
+bugfixes:
+ - redshift_info - fix invalid import path for botocore exceptions (https://github.com/ansible-collections/community.aws/issues/968).
diff --git a/plugins/modules/redshift_info.py b/plugins/modules/redshift_info.py
index b79b28b3074..a6a8a578a37 100644
--- a/plugins/modules/redshift_info.py
+++ b/plugins/modules/redshift_info.py
@@ -277,7 +277,7 @@
import re
try:
- from botocore.exception import BotoCoreError, ClientError
+ from botocore.exceptions import BotoCoreError, ClientError
except ImportError:
pass # caught by AnsibleAWSModule
|
django-json-api__django-rest-framework-json-api-817 | Tag new version to release in pip
Hi @sliverc, great work on DJA. May I know whether we can have a new release? I'm keen to use #781.
Thanks 😄
| [
{
"content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.1.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n",
"path": "rest_framework_json_api/__init__.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.2.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n",
"path": "rest_framework_json_api/__init__.py"
}
] | diff --git a/CHANGELOG.md b/CHANGELOG.md
index 97546f10..51c13dc1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,17 +8,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
Note that in line with [Django REST Framework policy](http://www.django-rest-framework.org/topics/release-notes/),
any parts of the framework not mentioned in the documentation should generally be considered private API, and may be subject to change.
-## [Unreleased]
+## [3.2.0] - 2020-08-26
### Added
* Added support for serializing nested serializers as attribute json value introducing setting `JSON_API_SERIALIZE_NESTED_SERIALIZERS_AS_ATTRIBUTE`
+ * Note: As keys of nested serializers are not json api spec field names they are not inflected by format field names option.
### Fixed
* Avoid `AttributeError` for PUT and PATCH methods when using `APIView`
* Clear many-to-many relationships instead of deleting related objects during PATCH on `RelationshipView`
-* Allow POST, PATCH, DELETE for actions in `ReadOnlyModelViewSet`. It was problematic since 2.8.0.
+* Allow POST, PATCH, DELETE for actions in `ReadOnlyModelViewSet`. Regression since version `2.8.0`.
* Properly format nested errors
### Changed
diff --git a/rest_framework_json_api/__init__.py b/rest_framework_json_api/__init__.py
index a15ece29..28a440ce 100644
--- a/rest_framework_json_api/__init__.py
+++ b/rest_framework_json_api/__init__.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
__title__ = 'djangorestframework-jsonapi'
-__version__ = '3.1.0'
+__version__ = '3.2.0'
__author__ = ''
__license__ = 'BSD'
__copyright__ = ''
|
graspologic-org__graspologic-176 | change semipar and nonpar names?
What do people think? @jovo brought up that the current names are uninformative. I agree, but don't really have a strong opinion on it
| [
{
"content": "from .semipar import SemiparametricTest\nfrom .nonpar import NonparametricTest\n\n__all__ = [\"SemiparametricTest\", \"NonparametricTest\"]\n",
"path": "graspy/inference/__init__.py"
}
] | [
{
"content": "from .latent_position_test import LatentPositionTest\nfrom .latent_distribution_test import LatentDistributionTest\n\n__all__ = [\"LatentPositionTest\", \"LatentDistributionTest\"]\n",
"path": "graspy/inference/__init__.py"
}
] | diff --git a/docs/reference/inference.rst b/docs/reference/inference.rst
index b388033c4..025b4e8fe 100644
--- a/docs/reference/inference.rst
+++ b/docs/reference/inference.rst
@@ -6,6 +6,6 @@ Inference
Two-graph hypothesis testing
----------------------------
-.. autoclass:: SemiparametricTest
+.. autoclass:: LatentPositionTest
-.. autoclass:: NonparametricTest
\ No newline at end of file
+.. autoclass:: LatentDistributionTest
\ No newline at end of file
diff --git a/docs/tutorial.rst b/docs/tutorial.rst
index 8fd7311bc..774ac66d4 100644
--- a/docs/tutorial.rst
+++ b/docs/tutorial.rst
@@ -33,8 +33,8 @@ are tutorials for robust statistical hypothesis testing on multiple graphs.
.. toctree::
:maxdepth: 1
- tutorials/inference/semipar
- tutorials/inference/nonpar
+ tutorials/inference/latent_position_test
+ tutorials/inference/latent_distribution_test
Plotting
========
diff --git a/docs/tutorials/inference/latent_distribution_test.ipynb b/docs/tutorials/inference/latent_distribution_test.ipynb
new file mode 100644
index 000000000..1cf86e3a7
--- /dev/null
+++ b/docs/tutorials/inference/latent_distribution_test.ipynb
@@ -0,0 +1,349 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Latent Distribution Two-Graph Testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "np.random.seed(8888)\n",
+ "\n",
+ "from graspy.inference import LatentDistributionTest\n",
+ "from graspy.embed import AdjacencySpectralEmbed\n",
+ "from graspy.simulations import sbm, rdpg\n",
+ "from graspy.utils import symmetrize\n",
+ "from graspy.plot import heatmap, pairplot\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate a stochastic block model graph\n",
+ "\n",
+ "We generate a stochastic block model graph (SBM), which is shown below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x128644518>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x12881a048>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n_components = 4 # the number of embedding dimensions for ASE\n",
+ "P = np.array([[0.9, 0.11, 0.13, 0.2],\n",
+ " [0, 0.7, 0.1, 0.1],\n",
+ " [0, 0, 0.8, 0.1],\n",
+ " [0, 0, 0, 0.85]])\n",
+ "\n",
+ "P = symmetrize(P)\n",
+ "csize = [50] * 4\n",
+ "A = sbm(csize, P)\n",
+ "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n",
+ "heatmap(A, title='4-block SBM adjacency matrix')\n",
+ "pairplot(X, title='4-block adjacency spectral embedding')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Latent distribution test where null is true\n",
+ "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between SBM 1 and 2 (up to a rotation) are no greater than those differences observed by chance.\n",
+ "\n",
+ "In other words, we are testing\n",
+ "\n",
+ "\\begin{align*}\n",
+ "H_0:&X_1 = X_2\\\\\n",
+ "H_\\alpha:& X_1 \\neq X_2\n",
+ "\\end{align*}\n",
+ "\n",
+ "and want to see that the p-value for the unmatched test is high (fail to reject the null)\n",
+ "\n",
+ "We generate a second SBM in the same way, and run an unmatched test on it, generating a distance between the two graphs as well as a null distribution of distances between permutations of the graph. We can see this below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x12d1420f0>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x12d52da90>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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d/TI06t45aGIwbtRptw+a7JnZvbChvY5P77lXX9dj53XtazuE3jdzbR/cjnEu5lKQPd6DRbaH5rsenc8EmsDw/22v1SVo4q3+aEHbW6BxqVyEZqHye/sYTmu+zvvajnMBmnoqN+60vXP7fnyt+37NtblXO95HR67RM9t2B3c+r93P0OzP9e4l/uEfC39mdRQIIQYQkb3QuKn2cZ3HCSwrbbzKqWh+UEcDqQkhqwdjVgixxayK7y+rzqIsL0XjvvOuT0IIWS0Ys0KIAdqA1iehqS1zGZrHFSwtIrIlmmJ+90ITr3Kkc66b9UYIIQC4WCHECvdA8xyfbwN4vHPOt87GVLk5mpTdy9EEEB803JwQssowZoUQQgghpklWVjZt3OgA4BVb3OM6xw+/8g8V4buvlWR+HoDfXIbmPnttaByfNj50x4kdd1G/7rWZx+f9XDS/IebH9Wm/qH+IrRg7qfNJPU9rlLiWpYg5l9j3c8iWpWvq833m09/nOyXmszLUpm/uqd/7i5gf5wi3IbhqM4mHAbaEEEIIMU2yG+hFsoMD9NQEH0JsxcwrpypU8jqNobWjK7lDHFKZFu2mfJQya9RUhbqvh7axcI1TlYIQG7mVh9T5hFyL2oqgJbVpnr55bbb55lRWCkJlhRBCCCGmUVNWZlhSDvqwEo9ilZQdTeiuzMIuSmsO2mqC9rUZUpk0YprG2ofOM3Q8n7mHqBsxcxhC+/qH2PQZU0vVGbLjE3uX67tAK3aIMSv1oLJCCCGEENNwsUIIIYQQ0yS7gSylLofaDEkNzh3U29d/RkzanVaqXgi1g/MWkSPtdKzNUOCpVpBlagpud34h+AQv+4yvndob4m6ZiqsydB417zMt15O2ezZHYDMDbMtCZYUQQgghplEPsO1DIyi1pFITEgSX037J84yhZHGtXJRIKc1NyV1oyripY9d4H2KUgtQ2IfMaUiX6GLOZU4VMaTtPzvtzfvyxtHwG2JaFygohhBBCTFNEWZmx7Gm/PvikKY75/lOvn09BtRis7aCtKyIhlCi+tuzXy9J4tYgpBJiaclzqvgpRtjRS26mslIXKCiGEEEJMU1RZmaGVLbOsCk1KZk/INcnpXy61i8ppZ5kUHgtz8EFLQVpFZaVGxlBqIcFFfYbUHCvfLcwGKguVFUIIIYSYhosVQgghhJimihtohlY6sm+qWS1i0wuXMXV5qH8u6TclldMiKUXOyPUZus98isxp2dd6P2OCSGNtasw99HNeqnAi3UC2oLJCCCGEENNUVVbmsaSELAOpZdB9mPKOPmZHWKI8uPa4PgGQKTZid8UxfaZ0n9VM180VGF6jaKC2suUzHpUVm1BZIYQQQohpzCgrM0IKEaWMnzrmMihBNcqW17ZpYbcY0mas31jf3Ne9RHn21HFyxzh0+/b111LltGJCtK+bj02fcbVTqrVi3frGobJSFiorhBBCCDGNOWVlRmqRMwuKR6z6olHgLaSQHNFjivEV1rH2WIFSSk0JtK+tj60SsVEac6CyYgsqK4QQQggxjTllZcinWkMtyR2bUjP2xepuzwetLIGaZfutlnC3Oq8+QmKQSsbvdNFSg2LGy1nHxKdvjUy5FPWL2UA2mYyyYsGtQ8gqYX2hQvTh9yyxymQWK4QQQghZTYq4gTRcHT5SX45dQcrY1tObre2cSwY+LhMhj27wkedrulJWmdTHTVh6b0q4En3u0+4c+j4rsfOjG6gsVFYIIYQQMoqIbC8i/ygiXxSRy0TEicieAf3vISKfEZHLReQiEfl3EdnOq6+WshKiImik5mrY8pnHojZaqknqOBrKD8lX1nu+v6Wd7zylCuWlUnNeJQq+pfTR7O87fk4bIfOIue4+vwcMsL0+7cLkiwC+D+CXAP4EwEOdc+s9+t4WwOkALgHwDgBbAXglgA0AdnfOXT3U/wYJ8yaEEELI6vB1ANs5534tIk8E8LGAvgcD2BzAfZxzPwMAETkNwPEA9gHw3qHO5lKXQ2Cas854qZTc8Vrd9adiRTWoNQdtShYR037vYsfLNVcrj8rQHCdU+ex7bRWVlXnmFiu+ysovAHzeOfeszvHvAvihc+5RQ/2prBBCCCErhIhcMtbGObeNor3bALgFgK/1vHwagEeMjlFTWQlVQlLiR6xk5oydQ99rGuP3vTbFndKicYfG9tlFWfHD+7Do/rCgyvjOI+T6L6uaNiNE2bKkwM2TUpQvx3tf4vvmCLehqLKi6cX4N/zo0rE2Y4uVEGVFRHYF8FUAz3LOfbDz2qEADgRwA+fcNYvGmIyysqxfVMQWvM/qwutPSH40VRNPNm//vqrntU1zbS5fNABTlwkhhBCSk43t3+t6Xtus06aXbG6gVLdGjGundoBtKbRdRkPjWNnpagXKpdjuo0bBqxrj5MJ6ITQtN0RKYGuM/RzjhIwXYrN2AbmxPov6lQ6w1XQDabiwAt1AtwHwUwB/5Zx7W+e19wN4hHPulkNjTMYNRAghhKwqayece+Sc+5mI/ArArj0v7wbgjLExJhVgGzN27iJ1IeP3tc8V+BtSgnr+NespjbmpoaKkYv19sF4gLzRIOGTuFgJifYJdUwvb5QpYj7nWfeS4/qWVlZes0VNW/uXavMqKiNwZAJxz580dexeaeip3n6uzsheAEwD8uXPuPUP2qKwQQgghxAsReU37z9nqcB8ReRCAS5xz72yPfb79e4e5rm8G8FQAXxSRWQXbAwGcCeCYUbupysqmjRuvM0CJWJFSqklov6lhYVdbGys7fCvzGGMq8/RBu0y+T/9UxaFmKX4r7712/FpsUcTSysrL1t5RTVn552t+GD13EVk0jx8553Zo22wAgNn/5/reC8DbADwIwG8BfBLAXzrnfjVml8oKIYQQQrxwzo0udLqLlLnjZwN4ZIxdtcVKSQUiJO5jyJfqOz6J3xFa9dnPsJJtUypLw6e/tWKBueY1FLOV6/3UjvsIbVOKkEJvPm3n8SkcF3LP1FaKiB9UVgghKw03J2QKTDkbSAMWhSOEEEKIadRTl1NTcXM/w6d28GzK+cU+AynkeUQaRZ5qo11QbYb189ZG67lLQ/20C/dpB8vmdI3lmldICm/NtGQfLBdHLB1ge8AN9AJsD/tdfIBtLegGIoQQQoyzVia3vlDFbLn9IbTUl9wqjnWs7FaspERqEFvEymIZ9ClRqux+n41ctnO8V6U+a6GF9sbGCQ2eDUnCiL0WpZWVV97wTmrKyluv/sHkVj6MWSGEEEKIaaqW289J7sJx2nPQ7u+jbNXYrVmJcVh2csfbWItLyUUJmyGl70uMk4JW7FDK4z/6+i1DzMqrbqSnrBzyWyorhBBCCCGqZMsGmmcqMSE+fs0QpWZGaPaORluffrE7HAuxKlNSWmoqR9YymrTuwVA7of1jiN3Z5yrNH0JIdtEyKGXzTCVmZdWVFWYDEUIIIcZhNlCBBxnWzLqJsV27FkspYncU1lWNZVATfMbLrcL02QhR2mJ3sRZiL1aFVMWni3adG2001arSysrB6+6spqy8+arzJrfyYcwKIaSX0IBpDZZ5c0DsYGHhRMKgG4gQQggxzqo/G8h86nJI4Z8S9mvu/LTdaUwV/gM5AzJLFSzTLkQXmmaucZ6xMr21NN1SNrVdbX2synfADF9XZ2k30N9upucGesMmuoEIIYQQQlQpUm5/hnV/tM+DunIVaItFq3BcDWoEUjJ4M42S12uZUma1yVXOfoqPY9AqrxDap7Sy8rrN76KmrLxu4/eprBBCCCGEaKKWuhziA50yWudn4TpZ2zlZ3/HWUH6sUHNeueNkQubQZyu16J+WglSzuFyNmJeY6671KAOAykppmA1ECCGEGGfV3SDmsoFCotO1ytjHYEEZScVKcbKSOyQLlCjqZvURBjHZLLkeV1C7nH3NLLGYz5O2wqWlxsTOPfU7pbSy8npFZeXvJqisrPpijRBiiBqLzykueEkaU95krip0AxFCCCHG4bOBjLmBpkbOFO0QV1OMa6xk+qmVZ4ZMzX1kZb4p88gZnBozvykGSocE4YZcL5/xYtAu7pfTZRf7XpV2A71pi7uq/db+zZXnTm7lQzcQIYQQQkwzaWXFQpBrqLKiMefQInOLbE6p3L61+cSwDOcwZawqebGPS4gZVyNQPVTlsHDdU+dgIXX5H7fUU1b++goqK4QQQgghqqgXhZuRs8R8Lpap4Fsfy5D2O4SFAmElyRUPNKX7w1JKe+37YkrvGxCfuhyT/h4zr7ExqKyUhdlAhBBCiHGYDWQ0ZmXowVq5HgY4o2Y2T6h9bTUoF1PMwCg9fg4sZGNp73xrkzuTJmQOubKN+tAuBqddTDJEqfHJUBvqD5RXVt6y1d3UfmsPvPx7k1v5TEZZmfKXGyGEEJLC2sktL3RhgC0hhBBCTFPVDZQzCFfbZZQyntVg42VQqywWi8pBarpojWfThJBzLhbPE1g8Hx/X3dgYMfMZs1niGWAx480o7WIu7QZ624313EB/eRndQIQQQghRhgG2SspKarl3jSJpOQNZteZRoyiczzja5NpFxQTD1U4pXUSNwNPUa1GiiJh1LKXIl7i2MYXo+tqHlNLPfZ9pXLfSysrbb3J3NWXl5b/57uRWPoxZIYQQQohpzKYuWyVXqnDseFqxNGPU3h2XUmpqj2cpviKVmDgZ7QfgxVIiNThF2Roao+S1WPS9E6K+lHzsh1bZfQA4wm0oqk68c2s9ZeWll1JZIYQQQghRxbyy4vMQrqF+NQqshdiaETNPzf7LRGzcVMz4OR+UlqNPzJz6bCyTAuRDjvdcg9h5aSuB3TmE9NVQObrjlHhvSsesrLqyMplsIEspv4QQQkhJmA1UUFkpUS7f0qImdF4llZ2uzWUg504thVw73ZCYkJKxIDUfrUA1qExNHY3xQt+rmipOH6WVlXdts6OasvLiS86Z3MpnqWJWLC1U5ol97kZulv1LOwSr904I2veZ1Wti9fNkFe33Medms5RNMj0m4wYihBBCVpVVfzZQNjdQaErdIolvFVfUpc5dKxUxhJJFrGqkEfvc41Z2/SmF9vraprhdarqOco5XI9i1RtB3rvs+Zg7z7XPeV6XdQEduq+cGeuHFdAMRQgghhKiSrKxs2rjRAX5pZN3XrKgmWoXVcvlyQ1K2U23NyLUjqV1crstUdvTWrltJll2hWTSulQJ5WmgHsNdOvy6trBx1Uz1lZf+LqKwQQgghhKhivigcKZPyrYH27r+2mlAyZsXSbtjSXPpYtvRkH+U09/n4FFIM6W/1Hk/9fM5Tutz+qisrzAYihBBCjMOicJliVvrQVghSSur39RvyFdeIr/GJWdGKt9Emd5GzGsRks1khNqtiRkw2kPa8tNBSLrTe85pZPCUpmaVXQq0qHbNy9M3uoaas7Pvr70xu5cOYFWIWKwHYZLmx9qNO8sP3fHrQDUQIIYQYh0XhlijAtvYzgnxcMhrpyFrnWTIV1NqzSUKk96nI8loBybGSeYo7w6rrwgcr7kvtAnQp7pYpvY9TSV1+/3Z6bqB9LqQbiBBCCCFElUkqKzWCXlMUEJ+damyAstUAWwtoKSIztEum107N7qJVlCyXzdj3c1WK8llSNaZwvbqEqqullZUP3Pyear+1z/rVt6msEEIIIYRoUuRBhjHl9q2V5F9E7TiZ7jxiY1+65Nplh4wTanPRudfe2ZUqz2593FCbudLBLSkQoeRKQU9Fu4BiTP+c3y19/UoXhVt1ZYXZQIQQQohxmA1UQVnxIUQpiGkbOy/rxF6LMXx2IkOxNNqF1KxmKdVkCnECY9kjNeJkSvcPtdNnSzvzJydj8wj93ggdX5OaysqHbqGnrDz9l9NTVhizQgghhBDT0A1ECCGEGIfPBjKSulzzuUFTCeYNwcfdVUJitRKsOVWsnkusey/lfEKKr4W4FlLdEKlz79rWtJGD2sUHU9B0S5dOXT72lvdScwM95RdnT27lQzcQIYQQQkYRkXUicoiInC8iG0XkFBHZy7Pvn4rIehH5tYhcLCIni8jTvG2XfOpySRapJakp1TE2a+NzLRZRI3itZGG1nGX2c49nHStl6EthLVA0pBhlrvT3obFTlZXcRRvHKK2sfOxWesrKk34ep6yIyAcB7A3gcABC1c/zAAAgAElEQVTfB7AvgF0B7OGcO3mg3+MAfBzASQD+qz38DAAPBPAC59xRY7YZs0IIIYSQQURkNzQLjAOcc4e3x44BcBaAQwA8ZKD7SwBcAGAv59xVbd93A/gBgOcCyL9YsaAi9CkHi+Y1NN/UkvVDaZg1SZlX7G6lVInz1J1Syk6ur78VNUEjHiK2KN9Q25BiXSG2Q+aQa5zUxwAMqTHan2Gf+J2h/iE2F9lKVVC1lJQppYFX5ikArgbwntkB59wmETkKwJtEZHvn3AUL+t4EwMWzhUrb9yoRuRjARh/jVFYIIYQQ42hmA4nIJWNtnHPbdA7tAuAc59zlneOnARAA90GjnvTxJQCvFpE3ADi6PbYvgLsBOMBrziWzgawpDhaocU1WfHcQjRXVRAsLhbd8isLlLrvfN6aFLDYrKpOPjRmpCt4iSsbx9LXta1M6ZuXj2/+RWszKn/387EvH2nQXKyJyFoCfOece2Tl+TwBnYyD2RES2BPBeAE9Fs7ABgMsBPNU59xmfOVNZIYSsNFNcaBKSQo9q4sPmAK7qOb5p7vVFXAXgewA+AuBjANYCeCGAD4vIXs65r44Z52KFEEIIMc6a+kXhNgJY13N8s7nXF/EOALsBuL9z7loAEJEPo1FkDkeTFTSIWuryjJKpoNaZUkG1lIA9n4JXPuNpu8Jyjzs0dmpwZEiwZSylgqCnCL/Hyrn+QvrkmE8spd1An7z1vdXcQI87/1vBcxeR4wHc0jm3U+f4XgBOAPAY59yne/rdCMAVAN7snHtt57W3A/i/ADZ3zv1uyD6LwhFCCCFkjDMA7CgiW3WO797+feaCfjdD48VZ2/PaDdvXRhdPRZWV3ISklva9tmr4BDNqERNAOTSXkPF8+/u2yZlS2h03tN+iefWNq3H/1w5StfB9M3WFwBfrBSK1bPnaLq2s/M/tdlJTVh77k2/GKCu7AzgF162zsg5NnZVfOOce1B67PYAtnHPntP9fC+BCNJlCOzvnrm6PbwXgOwAucc7de8w+Y1YIIYQQMohz7lQR+QiAQ0VkewDnAXgegDugSUOecQyAPdCqJc65a0TkrQDeCOBkEfkPNCrL/gBuC+CVPvbVlZWaTHnXMk+NB5LN0FJYfHYpIapEqbiWoXmGzCs0fmfs/LRiVqx8RlYl7qMksQqBRSykjo+xasoKAIjIZgDeAOA5ALYF8E0ABzvnTphrsx5N+X3p9H0WgJejqa2yru17iHPuYz62qawQQgghxlmztno2EJxzmwAc2P5Z1GbPBcc/AOADsbbVisJZWL3n2DXWKJwVU2gp1X/eJbX0t0ZMSGixqFzj9LUZazvUPyZ7KlZZmcIOddXh9bJP33tUWln59A47qykrj95wZv2VTyDMBiJkDlZXJoQQe1RRVqzuJKzMS+PBWqGZUYsIURNC56uVBTT2Wmz9l77+3TFirn9qHI+Fuhch41iJkwnByndBDCXr8Ghfp1yPVoiZg7VsoM/ceRc1ZeVR551OZYXkh7t/QgghqwQXK4QQQggxzVKlLg9RQk4PwYJ8qlV4bKh/SEG1oePWS/HHpIumBiaHjBdCiAuxRiB7SRtTdgdNkSk9SqK0G+izd7uv2m/tI7/3DbqBCCGEEEI0WarU5VSsqS8lCUmdXdQnVgnRLpOfs//8GEOE3ktjQcu1700LgY/a5FRNahR21E5lp6o0DJWVsrAoHCGEEGIcC0XhalIkZsUnpXFGSR+4RtnyKe06QkrKa5XAD7n+qdc0NV4kBW11qORnhYwzpGBM8bsghimeZ845l1ZWjr/H/dSUlYd/5+uTW/kwZoUQQgghplGLWZkxxbLeU9wxaJArE6bvtT6bPhlDff1SiFFdQlSO2AyfRaSqV9rU/KxM8XM6xTmHMJXzCynE2D2+qF9pZeWEe91fTVn507O/SmWFkCnDgnuEEGIPLlYIIYQQYhr1ANvYZ7aQxZRINwxJnY15Bk+Ma0d7vDEbsWPMj5PrmUWxT12eGqHPXbJ0DVLTiC0UxstxbbWL8eW+TlafDfSFnXZTcwM97Jun0Q1ECCGEEKLJypTbt0LNdGmtJ/qGpOfGjOOj5gy18Zmn1hOQfdAu7d8dVwufa5uyS07pF9qXDBMbVFoay0kUVFbKwqJwhBBCiHGEReFWQ1nJ5ee0uhOZJ+TBd4v6xqYlh9gaG39svJgU45TYglBir2X3desxKzVKulu4Jta+C2JIPQct9TdlDrE2Q22VVla+uMvuar+1Dz391MmtfBizQgghhBDTmFNWtCPHa2JdzUlVQrRVCa3y/SHjhcxD6xy6bWPm2GdrVYrDhTIWGxXSZ6xfzFxyfYaH2ud+ZEBtlalE1lNpZWX9rg9Q+63d82snU1khZMpM4ceXEEJWDS5WCCGEEGIatWcD+UiqVnet1uYXE2SWmlLatRWT0utTqG0oULRE4beYYNkYd1KqiyfURgo+987U3LM+Ls6SQb19bcc+R7VdKTO0SyYsC6XdQF/e/U/U3EAPOfUkuoEIIYQQQjTJVm4/tCiYhVV3TInnKZ3Dorbz7UspDyn9UsYLOU/ta6B1vqXKjKcGgy7rjjoXJQvu5RpPW5FN/S4OsRkKlZWysCgcIYQQYhxZM7n1hSrmUpdLklKAyOquMXZeMfEj3b4+pPrsQ9qEEBIfE7JrjJ2XdQWvj2X4TGh8J4TaKlk8byqUPN+pFIX7ygMeqPZb++CTT5zcyocxK4QQQggxTVVlJWQnUntnWaMM9KI5xKomM2ILly0az4eYgm+xcSSpO9WYuB0fUhSW0AJ+ue/PKcWmWVIlfNTCoX41CgHWyJ7Sth1iw/fzVFpZOfFBD1ZTVh74v1+hskLIlMmVZkwIISSeosqKpR0OSc8GConGHyIkHiamtktOtBSjkPEWjdvHFHfgNW1qKxgWas1o2/ZRynJmFVmBykpZmA1EzEKVgxBCGmTt5NYXqtANRAghhBDTTCbAdpUJKUMfE6Tnw1DKcYitkDL0Q21jS5KnBrf6zstnDiHnVzKQNbZYl5aN0kz5eyg0YLdUGrcWWqUYclDaDXTSHg9RcwP9yZe+PDmZhm4gQgghxDir7gZaqqJwsTskrZS6EMUhZp7a5Cwxvyio1KcoXKitELTShxe9pl0ULjXAeSrUDGQtETBqhZop0NrUTvkurayc/LA91H5rH/CFL01u5cOYFUIIIYSYJllZeZHscJ0Bppy2uGrEqicxqbhabUPGG7IRc+4hilkJVvWzFlsEz1q8TUq5/RJK1KI2NVSmGqnQYzZLKyunPvyhasrK7sd/kcoKIYQQQogmVWJWrJbqTtnplMRH5Yi9tilxFbGl62NiQvrOYVFf3/ZD/ULHCGXs+ltQNMbQiBsJjQFbJnzinlLGTR1naOxS380lbPlCZaUsk8kGsnKDknLUeM95nxFCLLLq2UB0AxFCCCHENGpuICtukRmWdsi155fiQglpE1rELcV2t61vew1i0699xum+FuIemVJRuEXjWXf7+rb3sbFqLhRLZRv6CH0/SruBTnvUw9TcQLt95guTk2km4wYihBBCVpU1aya3vlDFfFG42qpEDD7l2bV3xVqpeSmKSF+/PltjbVIDY7WLr/m01VJWYoKWp0iuc7AaqGthDvNYUDmsXZMZvt9DR7gNRVcPX3vMXmq/tbt+6vOTW/kwZoUQQgghpjGvrISgVTa7tt88RSUJ8f3nLF0fU7wtJAYmdpxF44WqX762Fx2LGadvrsuGtfiKXMpPjrFjsKSwxNqvpdCUjln5+uMfrvZbe79PHE9lhZApU6MSLSGEkGGSA2xDVuYpJZ59yDVuaWLjTkLH0Mre8ZlX6nhdhsYLydDpXqfDr/zO79vHxp/EtA2Zs6Uy6LEsOoeSMTpDqlWqrVznpxWb1iVXdpcPoba7NrSys0LakPIwG4iQOaisEEIssoZF4QghhBBC7JItwLZkIOuUZLuQFNVScxmiRLruovFi3VRagbpDc++SWmBPax4axN6TU/ocWsb6dVy1oO9FlA6wPf2Jj1ALsN3luM9NTqahG4gQQggxzqo/G6hquf2h9E5rK/VlCGrsErvTD0mFjlFAUlOhY9SgmOJwQ+19Cr4N2a8ZbOnbvztGqc9I6HgapQAW2Q8dN9TWKsDUZT/O2PuRasrKfT762cmtfBizQgghhBDTmHEDpayOS6ystVP+UnZnOdScMWXBJ37EZ7yhcXzSdX0ISfv1OZeQ1OechfbG2oamqy9iaLyh/iHXNAZtJTCWmmm+2mjPoUTqsnb/GRbejyFk7WprC6t99oQQQggxj3o2UGr5eO1sGa3Vco1Vt5avPaR8fGqGzyKbqTErIfOIKQoXey187lONLCCruz1trGaalIhNs7qzD1EWQ8bLWeSvRGxU6ZiVM5/2aLWYlZ0//OnJxayYcQMRQgghpJ9VLwpn5kGGGuqLb/9c89Kw42tLY15atUFC5uvTXyvuo2+8Ra/F1nQJaTM0z5g6MtYJKW0+j8975Gvbt19InI31ku0WVODY7+Tc11bzt6K0svKtZz5GTVm59wc/NbmVz1LFrEzlSzwVreJi5Prw2tal5o87WR1W5bdimViqxQohhBCyjMgaUfsTPQeRdSJyiIicLyIbReQUEdkroP+zROQ0EblCRC4SkS+JyG5efa24gbSZWmBtjeJyWgG2fWgVjhsbXysYt3t8/rVY90OobZ8+fbZK3C+5gyJD3Ac+br15anyecrtyQ9yXMeP29fcJIq/tmlk0dg63Umk30FnPfqzab+0f/ef/RM1dRD4IYG8AhwP4PoB9AewKYA/n3Mkjfd8I4FUA3g/gJABbAtgZwHHOuY+P2WaALSGEEGKcNZXrrLQKyDMAHOCcO7w9dgyAswAcAuAhA33/BMDBAPZ2zn0syn6NBxmWSEcuVfrbh9QdV+qOxid4UzvIdcjWoteGzlc7/Vp7HB+FIETFsRokqb1TLRE8m2Ir1WZJct8zVspI5MZ3nqWVlW8/9/Fqyso9j/lE8NxF5FAArwBwU+fc5XPHXw3gTQBu45y7YEHfDwHYwTm3u4isAbDF/Bg+MGaFEEIIIWPsAuCcnkXGaQAEwH0G+u4F4Ksi8mYAlwK4TEQ2iMizfY0XiVmpEfcxw4Kyoo2WX14rviJVTfAhpOBbyZiQXAXtfNqGzDOE2PGmsnMOoaSaoBFzYplSyk+sjdD5lVZWvrPfE/SUlaM/celYG+fcNvP/F5GzAPzMOffIzvF7AjgbwAucc0d1xxGRbQFcBODXAK4B8Pft/18C4EEAnuzjGmLMCiGEEELG2BzAVT3HN8293sdW7d83A/DHzrlTAUBEPoYmSPfvANhYrJQqcuZjs2R2kJUdb7e/D7muU6zy4HPuKWpOyK4s9BxSVBef90F7pxo7XondrCYhcXWxMXhacWcxbX3IneWVOnau8+0bc9mUrCG6qoknGwGs6zm+2dzri/oBwA9nC5V2DleJyLEAXi4iW43FsFBZIWSOVfrCIg18z1ePKb7nBp66fAGA7XuOz46dv6DfRWgUmV/0vPYLNPEuWwMYXKxUP3tCCCGEmOcMADuKyFad47u3f5/Z18k5d23b9zY9L98WTRzLRWPGl6oonJXg2ZqybqqtXAGsQ2il9i6aT0xad6pNn3mEBoQvatPXNlcQbs4iW6QeU3rPQspS5HQjHeE2FA2wPecFT1T7rd3xPcfFpC7vDuAUXLfOyjo0dVZ+4Zx7UHvs9mhSk8+Z6/tXAN4K4BHOuePbYzdBE7NyjnNuYY2WGXQDEUIIIcaRNXUdIc65U0XkIwAOFZHtAZwH4HkA7oCmku2MYwDsgca9M+NdAF4A4KMichiAiwHsD2AbAK/2sZ+srLxIdnCA/RThKe0ccpOryFksITZnxCoaY7uyoQBIn7kP2dQiZu65iqdZ+5wPYeE7wNr1slB8cKqUTl3+7gufrKas3P3I/44tt78ZgDcAeA6AbQF8E8DBzrkT5tqsR1N+Xzp9bwXgLQAeiyZz6Ott3y/72KayQgghhJBRnHObABzY/lnUZs8Fx38OYJ9Y2+oxKywo5UdMAaiQMvShc/BRCGLHXoSGQuOjJvjE3YSoEqFxPCkF33xsDrXPlZKqbbvE579k3I72+DXfT2s2QmJWfPrGzq+0snLui5+ipqzc9V3HFp27BswGIoQQQohpzCgrXaa44tdiTBnQKv0dq37EKCIxykNo/MiifjExJ2Ov+dr27R+iqIz1mTKlS6ZPhSlnYVmal2ZhzdLZQKuurDBmhRBCCDGOgaJwVVntsyeEEEKIecylLk9Z7pwyIa4nrYDYsXFypP/GuFt8xgsJ1I0pLqf1eciZOqsd+LhM1Himz7JfUy1i3eylA2y//9KnqbmB7vLOD9MNRAghhBBdVt0NVKXcvvVy9H3kth9SKjq2rHRMgG3XTl+/kHL2Q2OHnIPPeLH9x+YVm7rcnUOschQynrVUV41U+1UuixBSOJHo0XcPlVZWznv5M9SUlTu//b8mp6ys9lKNEEIIIebJpqzU8I1rjx9qI1fadondbEqRuiFSC7OF2PIhJK3ZZ4xF/WOvn0YBOV+bPgrNotempFZYUHJDi/ot6m+h0J41NSfl2oT2m6e0svKDA56lpqzc6bAPUFkhhBBCCNGkSszKMjClneUiUoudxYxTkthy/mP9tGNNYubQN96Uqfl5sqYUaDPl87OS6dYHlZWyMBuIkDmm9mVOCFkNZO3a2lOoirnFSslo95TdXI1siJzjpdQLGRpn7HjovIbadOcZY2e+/yLbvuOPzavkwii2LkcpxaPmItH3vZ/qQjbHvEvFDYaMP9X3h/gxmZgV3oh1yRXwmTpeiUDIGJbhfrXq3qvBMryfWvC+IDUwp6wQQggh5LqwKNyEA2y1S06XkLxDXCm55xWSxqpVjl6LVLfSjBjFKDUwdtG4oTa0WIZg8S4lSvxP8bqlpNqHjG91vBCb8/TZLx1gu+FVz1P7rd3hkH+fXIDtai/VCCGEEGKebG6gEoFp2jENJdSXmKJaNXcVqaXhY/BRJ0KKzM23jbnuQ4GxGuPVJqaAnU8Adg1CAsS1gtJDqB2wqxGM3td/6JqGBJaHjFcy6LvvOh3hNmS132XNmtXWFlb77AkhhBBiHrWYlZppmTNq71qmzJgvO7U0/BA+CsaieYakHPvaGjsfH2UlNOZk7Nxzlmv3OQcLn6fYHfVYv9TztKAoWZrHImrGn+SwWTpm5cev3k8tZuX2//C+ycWsMBuIkDmsuWgIIQRgNpCZbCDru4JFWNt9zgjJhggl93g5M49isnVSFJtYYpSVVPWlBMu2uw613fe5nIqyFYvFAnIalFZWfvKa/dWUldu98ajJKSurvVQjhBBCiHnoBiKEEEKMQzdQQTfQVF09qcTKuSWvV0ygaJfQYNeY8fpe6xLiBvJJJY85F+1U79qfmWX87IYUlZxHq2jjMl5TLabwjKbSbqCfvvbP1dxAt/37d9MNRAghhBCiSdUA25BgM59xtEs8a45pkaHr3z2+qF8KIWXQF80rpDjcmI0UUq/bmKIVUkjLl1UJdtWwmfM7wZLCUlsFjgk2rhVYXlpZ+dnf/x81ZeU2r/03KiuEEEIIIZpkU1ZiVRMLykoqIfPR2hVYSs+0TK57sCa5ivKljpM7nbvPVvd4jnksmldoyrzF+8va/IZU1tTHHcyILRZIZaUszAYiZA4WhSOEWITZQAViVnziDUJUCJ9o/Nw+VBKGNUUjRv2K3dnPsBIf0+1Xsujd2DUIta1VUHAR2oUG+/rlYirfXdbUHF9KKyvnv/HFasrKrV/zrskpK5NZqnHHSwghhKwmdAMRQgghxqEbyMizgcbI8WRUjUCtGunSPml8udIrc4zdtREr+y/Ttejaik2/tqBIxhYLDHGtDbnGYgobxowXWlCw5veOlhs0xpZV91TovEq7gS74h5eo/dZu/+p/oRuIEEIIIUSTlVFWfMbWVkmspSum7NKtnINVchWg8xl3RokCftbSf8fa+rbXwEe11Q681vpcxqp0Fr4LSqpE85RWVn5x6F+o/dbe8qB3UFkhhBBCCNHEjLISUl7cZ4yYonIptofGtbD7AOrtQEKxdt2GsHCdhiiRjuw7h3lbKWnTqfEoXWJTjkNUr5D5xDKlz80yQGWlLMwGIoQQQowja1bbEaKurMQW/7JQNKxm9khIG+6c6qIdJ9A3jnamj7bCkkuVKRFrYkFtmrflg5b6a/07xPr85imtrPzyrS9XU1Zu8cq3T05ZmcxSzUIKJiGEEELKkzVmxfoquYaaY/Wa5IrbsUbNkv7aGVWxC/hctURSbKcSEhcTaj8mxis09qYWObL8xjKhcp53ye+x0srKrw47QE1ZufkBh1FZmUElhKwyIcXOShLyw+uDtc95LvdZqs2S7qUYlmEzZvXaEh0m4wYihBBCyGqi5gbKvZOcYlGy2CJKpQKJ55nKNSWLpe7QgNGU9OE+fAqghYyn0aevfxefz4OWzdSgah+sFqXMRa3EiNJuoAv/+a/U3EDbveyf6AYihBBCCNHEXFG4GJ/6Mu4WrJArwFhzzGUg5WFvuYrDpQakphZNC7GlPW6I7VgFydL9bynoV4MS50NlpSwsCkcIIYQYh0XhChSFs1DOPnanar1Am5V5dLFQ5C+V3GnrqSqTT6xJlyGVwyfVVyvLJoQamTQh56lVbj9XzMqyk+t3YIzSysqv33mgmrJys5e+ZXLKymov1QghhBBinirl9kOoUa9Ce8eb2j/EJ16i4FLJHduYzdhHGWjtxpaxrkSI+hI7tvWaGCH3SUjRuZzKls+8LGE9fs1aNtDF7/prNWVl2xf/I5UVspzU+HGx/oNGCEnH4kKF2IOLFUIIIYSYRr0onPVVcqj0WKNA24yYIls+tlJcW5q2rN8zJd/7mGDLXOm/Q6TajAkGzm0z1X0TMs7YmIuw/llZRUq7gS458mA1N9A2L3wz3UCEEEIIIZokKysvkh0csDor/mUoZa0dABzTdx5LO8yc72eK2uQTJDzUpo8QxaFvHmOklu8fG9e3f8hjBUJspo5jHYsB9SXnMI+FANtVV1ZYFI4QQggxjqxdbUeImXL7Y8TueGMK0uVMl7awc0glV6rlIjtD4+SMQUpRObRtDrUNIaRYY6giUrLMfsi8co+nXVJ/Gb4jpozv9S+trFx61GvUfmu33v+Nk1NWVnupRgghhBDzZCsKN4/1HYKFwl654iO0xi5RKG9ZC9vlsp0aK7FIWUnNZEmJk4nFR+VLGS8HpTIMa3/mLNrWoLSy8pv3/Z2asnKT/V5PZYUQQgghRBMuVgghhBAyioisE5FDROR8EdkoIqeIyF4R43xKRJyIHO7dR7so3IxQaS+lf2xQolbQpjZjgZ053QhTlWRrYUFG1yLVDZRSdM2HEkG0MWnOsYHEY/eMtQKWucjxfVviWpR2A112zOvU3EA3fu7rouYuIh8EsDeAwwF8H8C+AHYFsIdz7mTPMR4L4EMAtgTwdufcK3z6UVkhhBBCyCAishuAZwA4yDl3kHPuSAAPA/BjAId4jnEjAIcBODTYfq7U5ZLqRM5VtMXdSmoat6UibH02tINxaxSiq/EehaJR1Ey7EF1OrNlOecSClaKIFgrHaRW5nOFbZPEIt2GllBURORTAKwDc1Dl3+dzxVwN4E4DbOOcuGBnjQAAvA3B3AFcgQFlhUThCCCHEOAaKwu0C4Jz5hUrLaQAEwH0ALFysiMitAPwtgJc4564UCVsvZXuQYe24j0WsyrxKpBqXHEeb3KXvS5CqEPgUgUsZt+R4MaX9Q9SOnFj7bIyh9V1l9bthiJrKyuX/+Xo9ZeU5r710rI1zbpv5/4vIWQB+5px7ZOf4PQGcDeAFzrmjFo0nIu8D8EcAdnPOORFxoLJCCCGEEEU2B3BVz/FNc6/30sa7PBdNIG7UomvllJU+asw9pTx7aEZTl9SS8ClYyUCa8v3apcbuP7UMfYjNkMycbt+Y9vPEnmfI9Sr5mZvavV2S0O/A0tlAV3zwjWrKypbPfE1MzEqUsiKNv+ckAD9xzj1t7jiVFUJi4Zf56sH3nBAvLgCwfc/x2bHzF/R7EoDdABwsIjt0XrtJe+wXzrmNQ8arR+wQQgghxDxnANhRRLbqHN+9/fvMBf1uj2at8QUAP5z7AwD7tf/eY8x4kacuW0hvi+nr0z80FXFqQWWx851KmnRuOzltDRVSjClqFuPy8HG3xBZNC6HUM4eG2syT+tymGKb23TJFagbYXvmhf1BzA23x9FfHuIF2B3AKgAOcc4e3x9YBOAuNMvKg9tjtAWzhnDun/f+dAdy7Z8iPAfgkgKMAnOSc++WQfbqBCCGEEDKIc+5UEfkIgENFZHsA5wF4HoA7oKlkO+MYNEqJtP3Oa9tehzZ1+Tzn3HE+9osoKz5Y2BXEFBqLLeCU63xrBvKRfjRUviEFIzXw1Cf9OrZw1qLxFo3rO/eU/tqKVMxc+mxYCDTXajslYj8Hq6asAICIbAbgDQCeA2BbAN8EcLBz7oS5NuvRZP0M2mCALSGEELJsrFlbewZwzm0CcGD7Z1GbPT3HClowmVNWZqTGOoQU9Brrs0pYugappb8tnUsOQpS7kGJpfWjFhMSk9NYojx8yh1jFZxnvy6l85jRiykqnLl/5kUP1lJWnHlR07howG4gQQgghplmKBxn62NRe8dcobuajIKXG0miT25Z26e8+rCo1McXSrJFSbG6+fY0y+X3zGbNvLSMyRp1LzQzUiuXLHW8z9h1fXFn56Fv1lJW9X0llhRBCCCFEEzMxKylYr+OSGnvh02aZapT42M6plGlk7ZRQ+WJsWkOrBot2TE3qtY3JjFp2StZeymWrajbQiisrzAaaIDV+eKz+2Gmzyj8mFliV+4yQUGRt/WygmtANRAghhBDTqLuBagZ45gy2zDXXXK6AkMJeWlgJqk4JvNN2B+VEoyCaNXzK98eOWSPoNn+BaIwAABvWSURBVFeRRq1yDaE2lpHY8ywdYLvxuMPU3ECbP/EAuoEIIYQQooyBonA1WarU5ZxY32WsasqytjI1JXzKxqeQOl5qQTqfYNcaxKiY3b5j/ReNV/PzZO2zUvs7oLiy8vF/1lNWnvCyySkrjFkhhBBCiGmqpC5rFTcbGi+lzTwxJcm1sFQgKRQLu7Cc6eUWSC233yVX+m8qVhSVRYR+p4T0TyH1M6hdLsDCd4ImxZWVT/6LnrLyuJdQWSFkyizLFykhhCwTRZQV7ZLwuQqCTfGHKiYDgNfPfvxOrCo0IyYmJIahuJahDKsYpSf1+6JvzovaxMw3tN+UPi+Anc+5lfgaKitlYTYQIWSlsepeImQeWbPajpDVPntCCCGEmGcpng20DJR02+SiRJCqVpDfjJDnvFglt6sn5zipAewaLiyf9zyH+jL2OZ9i0LcP1l2wvpR2A2361LvUfms3e8yL6QYihBBCiDIsCmdLWdFIYfZtH4OV4K4xQq+FhTnPKBlcPWViipL1YT01eEZIwG5sQbuShehSUoKnqABaQPP7o7iy8pkj9ZSVR72QygohhBBClKGyUv5BhjUJ3ZH4pAb7jOM77rJRuyT2KhEa96ERN+KTNu2T3jw0ng+5C9FpKTV9r3WJLfa3jJ8Ny5/74srK547SU1Yesf/klBVmAxFCCCHENNmUlXlSVsVT3Dmk+qB9/PCl5jUlLKg4NUqSW481GUL7cQCpCkupYnrz4+SiZFyLxmdmvr+FwpVjvz2llZWrTnifmrKy7k/3o7KSi2X9gSW2mPIPPyGELCuTWawQQgghZDVRcwOFpPTOsObWWCY3iVX32RTmNaPk/DTcP6HPxclVUC2kTarNlNTlEinbMQG2JahRmG2Gpc+9L33Xq7gb6AvH6LmBHvZcuoEIIYQQQjTJpqzE7qBzr/hrlLXPqSZMRQ1KfSpuqo2QNtrEnJ+PUpBbGYgtNpcyLy3lR2s+qTZnlFBWYgpoWvushJD7+3psXCorZWFROEIIIcQ6LAqnq6zMWMU4Eq30vRmWUr5Tx7OQVpzDVs04rBiVI7U0f4it2Da50J5fauG4mp+jXONNxbYGxZWV9f+pp6zs+ezJKSuMWSGEEEKIaZKVlRfJDg4Y9tFa2EGk4lN2v1SRp5xxH7mwlg2xDKSWx9cuyJYb7WJsMWXz+4hVaiwWaAsZfx6tOBktxmxpvB+llZXffuW/1JSVGz34GVRWCCGEEEI0US+3b40pZvjktlmjJHwuSmZ31c7qiskC8tnZa9RbiZ1frK2uTZ95+Yw31me+X2q2Uy4sfT5Dya0QaypcVFbKwmygRGp8IUzxS2iZqBmMSAhZUdastiNktc+eEEIIIeap6gYqKatPWRqNwWpZ+xBSJVvtIllaaBXkyq22pBZoCymPX4Ocadwx9nOXf+gbe9W+F1OZv5ZHuA1l3UAnfljPDfTAp9ENRAghhBBlWBROV1mxErzJHcN0sKCClbCp9UiK7nh9Yyx6LWfQq096/9g4ofNbdM+EqAk+ys+QChNSSqBEsUbrhd60fyNqnW/xANuTP6qnrDxg78kpK4xZIYQQQohpzCkr2libz4yShZtCyowvGien0lWyhHhKXIC1e0m7OJqPIpLaZirp0jEp31oxPiUp+bkcm0OfrRqKpy+llZWrTz1OTVm54e5PpLJCCCGEEKJJtmyg2B1XCDExANZ8vFOh5OMTrGUyWSgs6HPfxpTfjx2nb7yQAm1Dnz3tIm4p5MwYsnBvD7Eq34tUVqYBs4GIWZb9S5JcHwspzYSYhEXhCCGEEELsku2py/PEyLpD1ExvLhkMWmM+uWznOs+c7ikrKZLztoeIdcn0vZYyjxrEuMJSi6WlBvdOObh7hvZnpETqsxbF3UBf/bieG+j+T6AbiBBCCCG6CIvC2Sy3P2XFYWgOudWIKQey+pBboZnS/RajIsQGsuZWVEIKtIWOs6jNjNTvoVg1Z8yWT5JCKMtacDEHY9+PpZWV3339f9SUlRvc77GTU1YYs0IIIYQQ06gpK1NdPY+Rcl6x/tdcu/8hLBSAKoHV+zRFRQshJI1Yu5jb0DjaRd2GbM+IteVTFsGnb6mYuVX8zGnYNKesnP4ZPWVll0dRWSGEEEII0aRqzEosKSXT+8aZSlR+Dh92ClZVilyUPN/UgofdtqlZMovapGYXLerb179EJlJIKf3U+VlUOXKqMNYyKVPHpbJSFmYDEUIIIdZhUThCCCGEELsUcQOlpOvWlN5rj6NFzeDZKRV5AuoHI4YQ4nZJddvEFJkbauPjQtF2A6WmNfuO79vPwv0V4+4qMR/t4o85KO4GOvNzem6gnR9BNxAhhBBCdJG1LAqXNIBWgK1Gum7szj6FGiv+2rsMS0rIELkL8NXGZ1dck5jg3nm0A1lLXpMYFackU7zfx4j9XoxVf0srK9ec9Xk1ZWXtH+01OWWFMSuEEEIIMU2RBxnGkOpLteRvrTVOd7wZy5SSWEJlspCuHloCP1d8hnYqtBY+SqpPOfvc8+p7jSymxHWL/XwXV1a+vV5PWbnnnlRWCCGEEEI0US+3P6NkobZu39T+IVkRNc4zZPycNmKYUtGplPFLxk+FqC+hCk0XH6XAQrxMLDGF8WLHtabUWbIZopCl2pxMzMqKKyvMBiKEEEKss4bZQEkDaNdZ0cZC1HuNLKVQLKgSVmN+tGzFXIOhnXguBSNHaXkfW4tsljzPmP6xtVhSsPb90cVHQaqtMoVgQlk55yt6ysqOD56cssKYFUImQM2FPm2SnFhfqJA/ICLrROQQETlfRDaKyCkispdHvyeLyIdE5IcicqWInCMibxGRrX1t0w1ECCGEGEdsPBvoaAB7AzgcwPcB7Avg0yKyh3Pu5IF+RwI4H8D7AfwYwL0BvAzAo0VkV+fcpjHD6qnLyyBPhowzpfOsGdTrE5CZ8jgG3/6Lxumbp6X3NnReFlyvfcS8RzF9U6lRHsF60bpVU6DG3KGl3UDXfu9ENTfQmrs9MHjuIrIbgFMBHOCcO7w9thmAswCc75x7yEDfPZ1z6zvHngvg3wHs55w7enTOoRMmhBBCyMrxFABXA3jP7ECriBwF4EEisv2ijt2FSsvH2r+9VsFVy+1r7f5TdzghO7jUlLqQ+Sza/ZQoljaVFMfQ8TRS42tfEwvB0FbGrpEOngutQntD36VjNkKDjqeWrh6bmt537Ai3oayy8v1T9AJs7/qAS8faOOe2mf+/iBwP4JbOuZ06x/cCcAKAxzjnPu07BxG5K4DvATjIOfeWsfZUVgghhBAyxvYALug5Pjt268DxXgXgGgD/7dPYXFG4RfjEQ6xyWmzIOLl3o0Nja5XPLvl+Wo9PikX7vih1nbTjimrFA419Z6aqFamp1YvmV0JFyVWcT5MpKytr7vLHMTEr5wE42zn3hM7xOwE4D8BfOOfe6TnWswD8J4B/cM4d7NOH2UCEEEKIdaS6I2QjgHU9xzebe30UEXkwmjiX/wHwt77G1WNWUkuS+6Ct4lgvfa91vsuqEIxRIn5nma5tibgsbaaiCvXZnJGqrPjEuMWoLCkF81L7U1n5A9eed5qesnLn3WKUleSYFRHZGcB6NLEqD3POXeE959AJE0LIMjGV4FBCKnMGgB1FZKvO8d3bv88c6iwidwbwGQC/BPDYkIUKwMUKIYQQYh9Zo/cnjmMB3BDAC34/JZF1APYDcKJz7vz22O1FZMfrTF3kVgA+B+BaAI90zl0YfPo1U5dDSH3WhE8KaAkpONZNFtrHqstiVYryaWH9PGsU0atduC9XgG0Xn+8qLULGTXXxlCRnYbvibqAffE3PDXSnXaPmLiIfBvBEAIehCap9HoD7A3ioc+7Ets16AHs452Su3xkAdgZwKIBvdYY9b6T6LQAG2BJCCCHEj+cCeEP797YAvokmVuXEkX47t38f1PPavwMYXaxkU1ZWZQdde5cXQs3y5UNoFeWric85WFC0YhQ9337aWHqvc1yTlGBXn8JlsWpJyPw0lB4tpaa04lNaWbnmh9/QKwp3x/vyqcuEEEIIIZqYi1mJiSPxSbWcEbobGrPvs+PSeqxAyjxXESvX2EKxNGvxWDVsW7kfFvX3STnOGYOxyOaUSVGbxqCyUhbGrBBCCCHWqV8UripFlBVLu/7UQnQhO7ch1SV3yXtrMRM+WMlk0rAxpVgmS9QqfV+K2B19iLLrM85UVBPtLCXN8YorKxvO0FNWdrjP5JSV1V6qEdJhKl/ihBCyStANRAghhFhHJieGqGImwLbU83ligyNnTOUJyD62S47jU5RvUZ9QW9ao+dyZKV83H6b2nLA+WynEphxrE+NeCU1OsEZxN9CPztRzA91h58mtfOgGIoQQQohp1JSVmKJTsU9a1lYlQoJSSwSB1kyXXpUd+SJSi3/VVK9yjjM2/pCNEIUsh5pm9Z4OCQL1SW8OsRkTaOvTZ2ju3TmEEBI8G2ujO87YGMWVlZ98S09Zud29qawQQgghhGhSpNz+DJ8Vdu50Re2dW2oqdB9jhYxyKTZafbQoGbNipcR8SVLuL6tqhQ8xxddyzkMrnXhK8R7z1Ci3r6HUUFkpC7OBCCGEEOM4FoXTVVZisj5y4FOeOkWxyHmeqTE9i+bVxWrhOAtzSKXEIxa6tkrGeJS815dV8Vn0mfUpJtmHdmn5lP451JIuqZlHY7asxaz87qdnqykrN7jtvSanrKz2Uo0QQggh5lHPBppHyxeusbPSiknIuXPTiMWxqpZoE5sRZQEr87KQKTTUr2RMWo14Lp+YIa1snZpl9n1+B3IoH2NjpF6L4srKz76jp6zc5h5UVmbUSDucSkBZH1Oeu3UsLVTmsTovbbQCz63b1KbGtahBzEKlhE1iC7qBCCGEEGKaokXhhgK/crlkSqYplnK31HYjhAQvj/XxsTOP9V1iLKVcMst6/VII/TzFpHynuni6xLp1QorLWVcfYovUaVHcDXT+d/XcQLe+O91AhBBCCCGaZFNWYnfZNdN/Q8aZQsDuMtpa1kBijbnXVtwsUVuVqxFrF5Omq91WixDVZMZQuYCQcfvG73s/N9t8cyorBWFROEIIIcQ6LAqXp9x+Krl35kNjhxT0mkIK7ZQVhxkx8UkztFQYn7iD1DTW1HFC7GioOKFjxMR7aBESa5UaYzWV7MXcaoxP+yEFo0ZBO595ARWUlQvO1VNWtr/r5JSV1V6qEUIIIcQ8ycrKi2QHB6QVgOtrr11wKbbP2G4sdKdvPa4lFxZ20Nrj57SRikYhxdpYndcitJWVHBk6IdlAWjbH5pLTRgihcTKllZWrf36emrJyw1vdmcoKIYQQ4ouFhQqxDxcrhBBCCDGNmhtoho90WfL5G6lYmLu1ccbG72MqqcZTcvHMsDJPi+9nCbSCjbvH59F4Ls78OFpF4UqlNfu4jLRcWb7nVLoo3NW/+KGeG+iWd6QbiBBCCCFEE/UAWx9ypoBqUDswdiy9M3b3HzNOSNCxNloppSSdUkX+crznqWnIvn1TP5fdvrmCaX3baqk5FsiRCk1lpSwsCkcIIYRYRya3vlAlW1G4ErvimNLrOdOlF81hiFVTCEKuf4n4kWVSaqYQb0OuTw3FIrWcvabtUIVdq9Db2HjWisJd/csNesrKLXaY3MqHMSuEEEIIMY2ZcvspO1wfhWWGT8R4zkJaVnfyWkX0pob1+KlYrL43IeX2Y7I+poR2rEpM/1zl9rVt12Dsniwes/KrH+spKze/PZUVQqaM1S9OQghZZbhYIYQQQohpirqBchU3C0k1DknXHWrjM68QtIMjpxxsGRM47TNeX7+azyzK+R7VdANZdUHNsDK/kGcDWVL8ahRo6+tf2y1V2g302wt/quYGutF2t6UbiBBCCCFEkyrKSh8pu5ycKce55qVRxj+0n8+8YtrW2KnGnINv+5pY2fXHMJW5a6tzWvPw+X60UNa+r31N5cdHYc9xbamslIVF4QghhBDrrFltR4h6uX2f8uwldmC54ke00lqHxom5XiXSrpcZrUcslIDv32KmdG001AgrcS01i8zVoriyctH5esrKTW89OWVltZdqhBBCCDGPesxKjcyckg8607I1I8bmssRg5N4F17QdSqn5WLl3rF3/GTGP6Ui1pYX1+JES/bvjzNDKIKpZbv+3F/9cT1nZ9lZUVnJh7UuNEEIIIWWYzGKFEEIIIatJ0acu+5Aiw/qkro09STN0fqHnmSvN2lpqccocrKZCW7iOZNqUTAfOlbYbWhTOUiBt7LXt+8wXdwNd8ks9N9A2t6AbiBBCCCFEE/Pl9nPuZrWD6XI9TkCrb6mA1iEbVgI8tdFOaY9J85/itc11T5Z4lEHI+EPKrhaLVANtm1MsHBfD2HkWT11ecWWFReEIIYQQ68hqO0KKFIXrYq3IWUnFYUboTjm07xTQKBZYcgedy86QzSkpDiloK4I+hR1Lxo0N9dOiZkl+rfgYCyqM7xyKKyuXXqinrGy93eSUldVeqhFCCCHEPEViVkJ20Ll92FYyTCxkn1jdZYeQeg7WVbkQpvh+WlXTSly/UvEjqTEr2opILvUk5Fpo2C6trFz1m4vUlJV1N7kplRVCCCGEEE2qlNu3tBudRytOJkQ16UNDIYiNkymJldgBcl1SH3Fh9X2wMK8asRha6ou2IlKjFsuQzdDzK66sXHaJnrJy422orBBClgNriw2SnxqLqVWxSdLgYoUQQgghpqmSujxDy1VRw/UUK4PXSJOekZIiPCVSrv+qBKlq265lv4uFwoyhNnzIFcBqIZ041h2Ucp4a7qDibqDLL9VzA221Nd1AhBBCCCGaqAXYTmlHo8GQmmP9HEqqX9avxQwr88yV0m41RduCKqT1KINQlalG6fvc+AT4hyQX5HpMwZDqOtR+vg2VlbKw3D4hhBBiHZbbz1MUrkS5a2vjpNjS8v3H7szH/Laxu0+f+JEUm1Mm5D23FhtiFa3yA9qEfOYWvd5HarxHTN+cWJ1XH8WVlSsu01NWtrzx5JSV1V6qEUIIIcQ8Vcrtx7YJ6etTJG2GNX95qYyhEpkOqXEVllSWnEpejTgUS1iLqSmh2qaoBiWzeEIyabTJmYm06LysFoXbdOUVasrKZltsSWWFEC0sS8CEkOmyDAv8GojIOhE5RETOF5GNInKKiOzl2fc2IvJhEblERH4jIseJyB19bXOxQgghhBAfjgZwAID/APByANcC+LSIPGCok4hsBeCLAB4M4E0AXgvgvgDWi8i2PobVi8L1sUhmy+UWKjFed9zUsUsEUA7JnVPZaYwFJS4rU3jW06qTGmCeioZNH1d6TjfQIldMqk2flGqf8efbbrb55mXdQB4hF77EzF1EdgNwKoADnHOHt8c2A3AWgPOdcw8Z6HsQgH8EcD/n3OntsR3bvm92zv3dmH0qK4QQQggZ4ykArgbwntkB59wmAEcBeJCIbD/S95TZQqXtew6AzwN4mo/xIgG2iygR9FojkDUlWHKon/WAytiiWBYCiXOmaIdgNfXWB0vvp7aNVBWyZnBq3zymTK6gW/MBtorKyuZbbHHpWBvn3Dbz/xeR4wHc0jm3U+f4XgBOAPAY59ynu+OIyBoAVwI40jn3ss5rbwDwNwC2cs5dOTQfFoUjhBBCjKPsdrokos/2AH7Wc/yC9u9bL+h3UwDr5tp1+0o79nlDxqsqKz5MKeV4Udt5Qgrkae2yre3AY1iGcyDXp8R9a1Xx0SgKF4qGjZyl72MK22mdg3VlpTYich6As51zT+gcvxOahcZfOOfe2dPvdgB+DOCvnHNv67z2fDRupHs7584ass+YFUIIIYSMsRGNQtJls7nXF/VDZN/fUyQbyALaRadCdoQlHj0wNN6qqRJWVaaShfJi2lgrzFaDGiX4a5CrYFwNYgp/hs63z8YKKitVY1aorBBCCCFkjDMA7NjWTJln9/bvM/s6OeeuBfAtALv2vLw7gHPHFioAFyuEEEIIGedYADcE8ILZARFZB2A/ACc6585vj92+raHS7fvHIrLLXN+7A3gYgI/4GC/qBqopsdYoQNdnO1dBvClK8BbmHPJ+9BHjQqnpbpliIcApknpfdfF5jo3F4NkhG7lSj2PH9ikct8puIAAQkQ8DeCKAw9AE1T4PwP0BPNQ5d2LbZj2APZxzMtfvxgBOB7AlgH8C8DsAf4kmE+g+zrlfj9lm6jIhhBBCfHgugDe0f28L4JtoYlVOHOrknLtMRPZEs8j5WzRenS8CeIXPQgUwmLockkbmUwY6pE3IvELmrqWiTHlHnqtQHglDu6Cd9uMmSiigVgr4WQhKnaE1PwsBt7HzYOqybRizQgghhBDTZFNWQsrHD7UZauuTojo0fu7YkNRiUdbVhJJKT6lron3fps7D+j1Qg2VR3MZ28KmFy4biWhb1D1VWfGJpahKj0HePL+pPZaUsVFYIIYQQYhr1bCALRbeG7Gv57LWLr4XMXbusdyolC9FRcVguShRFtEqMslKSUiqJz3kOtUktCherDlFZKQuVFUIIIYSYxkydFe3sHR98VtK5yuPHxGsMzWuoT65zSCV3zErsNY5RtnKrV7HzYa2e8b6x/VNJiUPpjuHbRyv2xXd+ff182oQQo6yk2gSorJRmMsqKlYAtC0zpx2Rq8NquHnzP61Lju52/J9NjMosVQgghhKwmk3nqcqjEFzJmrpLpVgNPa8j9U3QxaLPKwaRdrATi15pD3zy00A6MzR1oGxIWoDUvjeBluoHKQmWFEEIIIabJlrrcR0wp7VR1IvcOLmeQ3tg1CbWpHZCpNa6VnS6gd21rB2/WIFZt7Ht9nhoqZOo9qVV0zTqlVJzY6+YTkBwbQLzZ5ptTWSkIlRVCCCGEmKbIgwxz7Zw1dnKxtkr43EOKH5VQXyxS4jxT0oBD+6VQW83JHQO2bJQqtpYzPblGynGXkJICPvMZsjEPY1bKQmWFEEIIIaYpoqx00S6LnJPUXXVoXy1Cd9khMUNaNn3nENtfe7ycWLhnhtCa3zJmosWOH1OgbYhF932suqCt/GjHo2jYXjSPLn3vMWNWyjIZZcXalzchhBBCyjCZxQohhBBCVpOiAbYxz8GxHtQ4hNWgQe2A0VxBzLFttG2WoMazfLTLBdQ4h9zjDdmYoXXfh5BaLM0npTem7VD/GqnasYHDi5gfhwG2ZaGyQgghhBDTqCkryxg412cjpgBazsDTXOmhVhSHZYDXfTG5zre2yhoS5FpTeRgiJWA3tYhbasqxxrUcG5fKSlmorBBCCCHENNnK7Q+trGuUz7aG9m7beuprCUrdBznjd6Z4L8/QKGI4hTT/nPZLEfO4g9i4D22VI3dKte99y9TlslBZIYQQQohpuFghhBBCiGmS3UCEEEIIITmhskIIIYQQ03CxQgghhBDTcLFCCCGEENNwsUIIIYQQ03CxQgghhBDTcLFCCCGEENNwsUIIIYQQ03CxQgghhBDTcLFCCCGEENNwsUIIIYQQ03CxQgghhBDTcLFCCCGEENNwsUIIIYQQ03CxQgghhBDTcLFCCCGEENP8f3UVQdQf3LjPAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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yAnx/xv3z6+PdQGu9DyfmoBf4jlKqpuheC4vu8S2tdbro0i+437cppd5cel/lBPseVSPoGvGfjzMb/4pS6rrx8lpEXov1kWLhwhWQywo2rgPTAzia3W8qpYJF163GiXM4EfJ52FhSZguAH1BGuHC1X19y//wfpdTpxefdAMlvK0l/k5vn+5VSZ5beUykVVkpdMcqLZqJMqk611o/jeLv+FY5X8rNa62dK042FUmqjUmqEUK6UasaJuQnlt1ddCvybKrJBUUqdjxMmyeaI8IvW+lkcj+I1OOU/osyUE4j/IyWHJ1oueY1YOSGpUibcxifDFMqkmiicPloI8O62g5vdPwvj2hT6yDdx2seH3faST+vB6aOjafDyz/6MKtpMQSkVwDGZqdQ5bdJorXfjmBoFga+7z87noxXHJrpi3N+80f3zR2Ol5Yhz05UTeYZwDHM0XdblU9mH6Q203o4TDzMfNHxYeJai6x6jTAgTnPAaL7vnunA8KO/jSFDuRykfaP1GjoTD2ILjvfgbJhZovZUjAYM/X+Hvb8UJXJ8P7/FT97encOLn5UMElf7O5qK85YMT/xon3Md9TCDQOs4SVId7/DBHAmEP4AgHv3DPXVlyL8WREDuWm9e7cEwGRgu0/nWOhG/ZghP77m6cEE9p99zqovTnu8ceG6X8rqRMCJaJ1mnJdR8ruu7Dk2jT+Ril7TiBtX+E45Gbco/fXZI+X4bfwWnzr7h5fZQjAao/V+Y59RzRZieKyv9eHCHZBg5Nsa2fwJHA4H/EmXB8n+FhcnS5up6GNv5YuePjlP2Ey4RxQjExRmiv0donwwOt78EJ4H4PjtPXkHvuIcoHWp9QH3GvuZ4j/fBR9zfvdNN/h9H7SD7EUcatk5/gjAVTCbQ+WjneQflxZBmOA5V2v+/G2Ugh5dZbvl0urqD+L3LT9lEmVFKZdpn/7cVhqz7nlvVTOGZQGie01VNFn7+b6Lggn9n/EQ3msctu4FycznshzsD9CnAVzg45FaGd8Bqvwwng3IXjZHERzmB7NfCXerj2Mn/d9TjOGL/Asft8D04A4/3Ap3EEpvGe3YYTSmYvcL1S6uaxryhccw7OS8QHvA3H9unLwMU4A1u56/a7v/P7OM4BG3AGzC/i2BCO++iie/XhhOK5HWdQvxRnCfe/cMKh9I+SB60dW7F34QiVJ+OU26k4O4+M2BpOa/1xnADIP8OZDLwNZymuFufl9i6OhH6ZElOo09+73/1MLpzP/wVuwxHWz8Wpj1NwhJ33U2ZJ0uUpYD3O778Ep36fBd6vtf5CaWLt2IW+GWeP5c045X85jkNRGies2LvKXFdxuWgndMxlOMLe6ThOex9201fEZNv4ZJhsmVSRGE4f+iWOXeBbcVY6PoezEcGIyBOT6SNa6xtx2tazOO3mEpzdk96I065G4+9w+ulOnDH3QpxyOxdnTK462tlu+Fyc8cfLkbHsJpzfdAKOsB2r4HZXut/36PKhkoqf24ZjluBneJ9ciVOGr8MJHYWbr9cVfYojOgjHCPlo/IIgTAKl1GGcl1aT1rp7pvMzG1HOfuM34Tg9/fNReN4dOILb32on+oIgCIBSaj2OneZLWuvTxksvCFNBNJiCMElcm7dGoE+Ey/K4jmb/jLPUOK7dryAIU0Mp5R3FZvlk4HvunxWHCROEySJhigRhgiilzsYJcn0+jt3knTOaoVmI6/2/Fmd5tRH4th59NxVBEKaPILBFKbUXx+Z7ACfk0jk47/w/Al+bsdwJxw0iYArCxFmGExalE8c+8LMzm51ZyaU4MV4P42guPz2z2RGE44Y0jrf7hTixlOtxNjd4Fsfm9Fv66MVsFY5jxAZTEARBEARBmFbEBlMQBEEQBEGYVkTAFARBEARBEKYVETAFQRAEQRCEaWXWC5hKqX1KqX3jpxSE6iJtUZgtSFsUZgvSFoXRmAte5HV1dXV1FO2UIhwTqPGTzDqkLR67zLX2KG3x2EXaojBbmFJbnPUaTEEQBEEQBGFuIQKmIAiCIAiCMK2IgCkIgiAIgiBMKyJgCoIgCIIgCNOKCJiCIAiCIAjCtDIXvMiPKi3XPFhx2r1furSKORGEkaRMi/19SZrnhwn5jZnOjiAcl0g/PDaQeqwuMyZgKqVOA87WWt85U3kQhLlEyrS48YGX6BrI0DQvwPWXrSHkN2SQFISjSGk//NTFq+lNZKT/zTFiQybX3beNpGmxqD5YGE+F6WMmNZgbgBsBETAFoQL29yXpGsgA0DWQKQiV5YROQRCqQ3E/7Iynue7+bWSytvS/OUTKtLju/m08vSeG3+dYCu7vS7JqYe0M5+zYQmwwBWGO0Dw/TNO8AABN8wI0zw+XFToFQagexf0w7DdIZixA+t9cYn9fkmTGwu/1YGZtwn6D5vnhmc7WMce0ajCVUtdPIPl50/lsQTjWCfkNrr9szbDl8PzLLq/BlEFSEKpLcT9siAS45aEd0v/mGM3zwyyqDwIQDhjctGGtaJ6rgNJ6+nZ3UkrZONtFVbq9kNZaj1mrSql4XV1dXTwen3L+KkGcfI4ac207tKPeFitFbDCnhTnVHmdrWzweqUL/k7Z4FJBxsyKm1Ban2wazH3gGuKaCtB8GPjrNzxeE446Q3xDbIUGYIaT/zU2k3qrPdAuYW4BlWutnx0uolHrrND9bEARBEARBmAVMt5PPc8BKpdS8CtIq5thSgCAIgiAIgjA+0y1gfg9n6buS+94GrJjm5wuCIAiCIAgzzLQukWutdwI7K0w7AAxM5/MFQRAEQRCEmUe2ipyjVOrtLp7ugiAIgiAcbSTQuiAIgiAIgjCtiIApCIIgCIIgTCsiYArCLCBlWuw6PEjKtCZ1XhCE2UFpX5W+O/uQ8fboIDaYgjDDpEyLGx94qbDd3KcuXk1vIlPYYaL4fDTiZ+O65bQ21cruE4Iwy0iZFtff/yIdsSRLo2GuveQUbnloB53xNGG/wU3vWEu0xj/T2TyuKa2jGzecBlDY1QcYNh5ff9kaGWsniQiYgjDD7O9L0jWQAaAznua6+7eRydoFYXNLe4zOeBqATa/20B5LcmJjZIQgKgjCzNLWNcQTbT2kTIu9vQlOPqGW/bEUr/YMYWZtrrt/G7defqb016NM8baQbV1DbHq1BzNr096X5Nl9fdz19D6SGYtF9UGuOHd5YTzuGsiwvy8pO/5MEhEwBWGGaZ4fpmlegK6BDGG/QTJjYXiUI2zet42kadHRlyQa9oOGkM8onMvkbJllC8IsIZ216E9lMXMWgxnFg1s76RnKkDEtAj6nb4vAcnQpXSF611lLQDvntIZvPdrGzsOD+L15i0FdGI+b5gUKWk1h4lRdwFRKrQJWAQ2U2blHa31ntfMgCLOZkN/g+svWsL8vSUMkwC0P7XCEzYDzQgKIRvy8/9xlbH61h1giS8DnKQiiMssWhNlB0OehNmAwqCFn23gNRcuCCACGUiyqD4rAcpQpXiHqGsgQ9HlZ39pAeyxFbcBLLGni9SrMnE04YNDaVFsYj2V1aGpUTcBUSi0Efgi8JX+oTDINiIBZRSqNlwkSM3MmCfmNgoBYLGze/JvtbHq1BzRsfrWHay85ld5EZpggKrNsQZgdLKkPEw54SWdtDI+HoM+geX5IzFlmkOIVoqZ5AVqbarhxw1rauga5fdNeXj40gEJxzvJ6btqwtlA/MmGfOtXUYH4DR7j8NvAI0FvFZwnCMUOxsLlx3XLaY0n8hoeOWIoD8RRrm+sAZJYtCLOMA/EUC2oCLKgJEPQafHDdcta3NhLyG+LcM0MUrxAVO04eHkjTlzA5qamWdNbiqgtWSR1NM9UUMN8CfEdrfVUVnyEIxzStTbUsi4YLWsw7N+/hRneWXSyIFhuxi7ApCEeflGlx5+Y9HOhLgYL1KxcUhEvpnzNL6Vh5/f3b2NuTpCeRYXk0worGCK1NNTOcy2OPagqYHuCFKt5fEI55Qn5jmBazPZairWuQtc31hTQSxkgQZp79fUliiSwnLawllbXYuG45AP+7p5fv/2kPmZzNovqgOOTNMG1dg2xq68XM2fi8HjacubggXMpEYHqppoD5J+CMKt5fEI4LHC1miE1tvaDgzs37uHHDEQGyrWuIPd0JfF7PsDBG8iIThKNHsa3fsmiITNbmX37xAk/s6mUgnSMacZZfxSFvplHDPEJ+++IhcrZ260cTS2TLxiMWJk41BcxPAI8qpR7RWv+8is8RhGOe809eyN7eJEGvQXssWdBi5pfl2mNJcrbG8ChCPkM8ywXhKJO39cs7j9zwq5c4EE+BbQOOdizsN8Qhb4Zpbaph/coFdMSS1IV8JE2LrGUzkM7iUYqagJfOeJpr791KPJVlWTRUMEsSJkY1BcxvA0PAT5VSB4HdQOm+S1prfWEV8yAIc5LYkMnm3T3UB33c98IBugdNeoYypLM2ytViXntJmC3tMQ4NZFhSH8LSmsbaADlLi2e5IFSZcsupzrdi8+4e+oZMsrbG51GE/Yql0TCfvPhkEVRmmJDf4NpLTmHz7l5CXsUXfr2d/oSJ12twTks9ZlYT8HnYur+fbM6mo2RCL0volVNNAfNEnDBE7e7fy6r4rHGZSLgeQZhJYkMm7/7OJg7G04CmLuhleUMN88N+zJxNyG9waMDZ8WcwleO5jj4Mj4doxMfX3382STMnA6AgTAOjCRR5u+fyW0BqcpYma2vQYGsYTNtsPzTAh374NL/4hzeKt/IMkjItbv7Ndv7U1o2ZtfGgGTItdMZiW8cA//6+M8jkNJ/46fPYWpOzNZmsPSJgu5ggjU/VBEytdUu17i0IxzKbd/fQPWhi5mw00J3Iks4NUhf2YlmQiVuEA15aGiIkzByZnE3Qq0hnbWKJzAgHIJlxC6VIuxifcgIFODaU6axNZzzNzq5BzKzNtfdu5aoLWmltqqW1qZYzl9bz+CtdaIUjaAJYmgN9aR7f2cU7z26ewV82d6hGO23rGmJ75wB9iawjQFoaDXgUJMwcL3TEealzAIVmKJOjJuDlf57ey4fWr5QtJCeIbBUpCLOIlGnx8PYucpad380MtDP4pTI2WVvjNxTRsB+/18P2Q0ksW2NatptYDbuXzLiFUqRdVEbpDjBtXYPc9XS7G63BR8Drwcw6nsgvHIhz04PbC851n3/7abzvu5vpS5pYplXoy7bWPLD1IG89bZGU+ThUo53mbdYPDaTJWjaGUng9oJRCazBzNr9+8RAH+1PkbE3W0gxmcmx+NcYV5y6XLSQnyNHYKnIecBHOkjk4tpi/11oPVvvZgjDX2N+XZCCV5Zzl89l2oJ+cZaFR5GzweiDk89CXzKJJEfIbNNeH8Hoc7eXpS+qGxXIrfUHKjFsAaReVUroDDKhCucUSWT523okYhqJ7IMOBeGqYcx1AczTEQDqLz1BordEoGiJ+LFs8ySuhGu20EEqqqZZn2/vwGgo0nHLCPGw0QZ+BoRRm1sa0NEoBGiytCfq8srnFBKmqgKmU+jvgVqCGI6oVDQwppT6htf6vaj5fEOYaxS+1C09ZSDxhsu1AHK014YBBTcBH1rJBa3Z2DRLxe1nVVEttyDtsm7PSe8mMW8gj7aIySneAAYaV22lL6rn18jNp6xrizs17CuFtGiIBnmuP8XxH3NkyUsGKxggLIgGCPkP2I6+QarTT/D13dyeYH/aRszWWbaPRfGHDadz2h128sD+OAmoCHiKBAFrD+tYGWptqhgVsF8ZHaa3HTzWZGyv1duA+HI3lbcBL7qk1wMdxNJrv0Fr/apz7xOvq6uri8fiU8lMNJ5+Z3Lv7GPg95famn9VMV1scj7zdUUMkwKd+9gJP740R9hmAJloTYF9PwvFONTycubSeD61vKewYMtq9ZMY9LnOqPU61LUq7mBxjOf3k++zNv3mZP2zvoi+ZBRyNyvywj3OWzedj561kzZK68cr8uGqLY1GNdpoyLdq6BvnGI208s6+PVNYiYCgCPoOFNQFe6Roi7DcI+Aw+/Zcn09pUczxvXDGltuiZrlyU4dPAduBMrfVtWus/uJ/bgLOBHcBnqvh8QZiVpEyLXYcHSZmlUbsc8rPkA/EUL3cOYOZsx5ZLQ03AS9DroSbgJeT1UB/ysb61EaDsPfP3Ok4HR2EUyrX7kZHxAAAgAElEQVSL8dqlMHp/CrnxLbe0x9jb4yyRKwUeD/gMhd9QPN8R5/Yn98xEtmc1Y7W7aoxfIb/B2uZ6bn7X6Zy8sBafR5HMWvQMZWiPp9A4nv9KOZtcrG2ul/FzklRzifwM4Eat9VDpCa31oFLqh8DnpvqQYy380LH2e4ThTMxw3VldCPoMvB7F2sV1DJk55oX9RMN+GmsD3PSOtQDitCFMCXH8mRzDNZfb2duboGsoQ8Bn0OT10LIggt/jYeuBfkJ+g2TGEvvLImaq3aVMiwPxJAtqA3g8CtsGr8eDAhprAiyqD9LSEGFJfYhdhwdF0z9Jqilgjqdarc7avCDMYvKG65at2dOdoK1riLXNdWXTLqkPE/R5SJkWkYCBxsayNBpnx555IS8hv1HYKjIoO/gI4zDakqM4/kycYuHI61FsPdCPmbPxKPjkxSezZnEdS+pDfOHBF/EZClvbBLweGiKBmc76rGGi7W46lsxTpsX1929jx6EhugfT+L0eUBDwejh7WT2f37CWpJmjIRLglod2yKRrClRzifwF4EqlVKT0hFKqBrjSTSMIxw3N88NEI352dg3SHkty5+Y9w5aGipeLehMZlkcjrGyMMJjO8VzHAB3xFEOpHIZHEUtkaesaLGwVubNrkGjELw4EQlnyAtGXfrODGx94aVi7yzs/AOL4UyHFwlEsYZKzbIYyOQZSWX75/EGW1IfoTWQYSFmsaqrFzGl6hjLc8tAOMUNwmUi7G6v9ToS2rkE2tfVysC9JXzJLIpPD61FYtgYU0YifVQtr6U1kRgi/wsSopgbzFuBeYItS6jbgZfd43smnFXhXFZ8vCEeNSmfWIb/BxnXLaY8lCfkMYolsYdZeulz0qYtX01gb4OXOAZKmhUKDNqgJegn6jELolFgiy0kLa0llLTauWy6zbKEs42mLrjh3GaAK3rLC2OSFo854mmjET03Qy5Z9fZiWzSuHB7nuvm3ODj8RH4/tj5M0LQ72p6kN+kRD7FLqqV/OcSp/fPq07MpZX1XKGVNxwrz5vR5e6hygrWuQ1qZa0lmbaMRXiA4gk66JU82dfO5TSl0FfBn4OkeWxBWQAK7SWt9frecLwtFiInZE+Vn3smiYWMIcNnCVDqC9iQwb1y1nb0+C9liCeCqHYSjOWR7lI29aQWuTM7jmQ3mc2BgpHBOEUkYL++IsGb5IRyzJ0miYGzecNsM5nRuE/Aafung11923jaRpMT/iZ83iOl45PEjA5yHprkJsXLeCvT1J9sdTZHM2YdchSHAoDf2T9/K+c/O+whh5/WVrpi1sUWtTDetXLuCVw4N0D3pYWBvg5c5Bgj4DpSCdtQvjeTTi5+qLVh3PXuRToqpxMLXW31JK3QW8BVjhHs4HWu+v5rMF4WhR6cy6WBCNRnxcfdFJBW1RyrRGnTG3LAiTMi1snWb1onkkMjmCPqMw4EnwX6ESymmLUqbFIzu6eKKth2zOpr0vOaZdsDCc3kSGTM7G8CgGUlk+8ZaTuH3THpKmRWOtn3TWZkl9iNaFNUQCXsIBY0S8WuEI+TFyd3eCjr4kKxtrhtmqT8dYF/Ib3LjhtEL80u5BkyXzgyyoCdDSECnYsgN0D2Y4PJCWifskqfpOPlrrOHBPtZ8jCDNFpTPrYgef9lgK0IWX/GgzZkfjqQgFDGqyPgylRjxDgv8KpYxmslHcVvLtru3wEP3JrJNOg/hfVk5p31+zpI5b33tmQQP3tYd3FsxdehMZmQSOQ36MDPkMtIYdnQMA3L7pVb74zjOmbaxzQhXVceOGtYUoAPn6AQqmDx19SX70VDt/2NElTj6TQPYiF4QpMpYdUTENkQBeQ/Fy5wBKwZ2b93HjhtoRzgLF2klnazOTiN8LNXD5a5ZyweqmEc+QwNlCnkpNNvLtLugzCAc8LKwNcuriWtHWTIDR+n7QZxBLmIVoEQfiqYJWWPpqeUpXcdYsnscLHXFsDX/e3Udb1yBrm+un9ZnFAmu0xl9Ynn/3Wc0cHkxzzzP7MTxKIitMkmkTMJVSj+BMff9Sa51z/x4PrbW+cLryIAgzxXgz65RpcfNvXuZAzLHDWt4QoXswU3jRFGtBGiKBQuy1vNf5pld7QMNjrxzmgtVNI+4tMQyFPMUTls54mk1t3Zy9LDpCg+a0LR+b2nrxGQbN80Nce8mphfMiCFVGub5f2m/v3LyHGzdIzNrRKLeKk8naXPXjLaRNC5+hKI18ON3tMx++aFNbLyh43YoGGmv94uQzBaZTg3kiYHOkFZyIrLUIAnAkNEY6a9GfypI0rYIwWawFyQdsLna4KOd13jw/POxbYhgKeRoiAQJeD4OZHAfjKX745D5u/s0Ols4Ps6g+OGy5duO6FeztdcKvZHI2vYlMQZMjgtDEKBV4yvVbQPpqGfb3JemMp0lnLSxbE/QZRCMBEhmLVNZCa03XYJpt+3VBwz5d7TNfb+msTXsshZmzAeiMp/jspacS9HlkkjVJpk3A1Fq3jPW3IBzPZLI2OVuTszSWhnTW4lB/isd3dnHeSU2FF35b1xCbXu3BzB5xuGhtqmVZNFwQOhsigRHhjKbDu1KY+6RMi1se2kHSjVawuC5E1rLpT2aJhnO8cmiQj/94Cz7DQ2NtgA1nLKZnKMNgKkdd2FcIAl46aWnrcrxs5UVbnnICeWtTLSc2Rkb0y2jEX+jL0lcdGiIBOvqS9CezhXa4pT2G31Ao5SGRsbj67ueoCfh4Y2sDG9etKNiz7+5OTGr5vNRbPRrxsbg+SEcsCQqWRsMSsmuKiA2mIFSZlGlx9zPtGB5VUOlrIJ3TfOvRNv7v73fSsiBC8/wQ7zprCcMSFf2h3e8D8dSIcEbiSS7AEcHQ8CiwIRwwSJoWtSEfHX1J4qksWsP8sI9XDiteOeQE9V+xIEIk4C1oMPNa0Lw3dGnIGGljwxltFaGc135xXxYcehMZls4Ps6DGIuQz6E1kOHtZlJqgj/ZYEg0Mpi3MnHb3etcF8w6K7NkrbZel3uonNdUSS2S5+qJVfGj9CiQe7PRwVAVMpZQX2ABEgV9prQ8dzecLwkzgOOpkOXlhLYf6U7zaM4RlO7YkXUMZkqZFwswBEPR5Wd/aQHssxbJoiNam2sL1NQEvsUQW0CM0luJJLsBwr+bi5fB40uTzv3yZVCZHKmeTMJ0XeV3IR08ig8ejWFQfpHl+eJgWNBww2HBmM994ZJdsRToGo0WSKO2XpX1ZytKh2TXfKC6/lGlRG/AOE8NtrYnW+GltqmXjuhV0xFIFh6qJlGWxtzruitIKN46wCJXTR9UETKXUV4A3a61f6/6tgIeBN+G8W7+olHq91vrVauVBEGYDxTt+ZCxNNBLAzNkEvIpM1sbWMJjOsbcnwZL6UCF0Rl5wLH15tTaN1IwIApT3ao7W+IkNmSSyFmnL2cf+nGV1zI8EGEjlWL9yARvXLS+8XHcdHixoQZMZi/9+ai/t7rLh+pULZFm3DJVGkpiuYOHHGqXlB3DtvVvZ1T1USOMzFOtXLuDmd55OyG/Q2lTDijImCJVQXA/r3SV30VhOP9XUYL4VR6DM8zbgL4CvAM/j7O5zDfCRKuZBEGYF7zprCS/s78d6pRsU9CVM3veaJfz2pS62dw5geBQtDRF6ExlW1dQOm4mP9vISzYdQjnI7o2xpj9E8P8SCGj8AV735JAI+D6BYUh/iQDxV2CKv+OUb9jtL7LIV6fhUsopQqSB6PJKfTDsONxaxpAmAz6MIehUfedNKPrCupeCEtr8vWdDQN0QCEypTqYejQzUFzKXArqK/3wbs0VpfA6CUWgP8TRWfLwgzTmzI5Np7t7L1gLNxVcDrIZ21GEjn+OZju3ntivm8tiVKJmcXlijLIUvgwmTI25p1xtMcjKdYOj9MY22Au59pp3vQJOD1EPAZPLMvBhrWtzZw44a1w6Ia3PLQDtmKdBqRvlye0lBFJ9QF2duTwNZQEzTY0hGnc3AHn7p4daFN5p0ci/+u1EZY6qH6VFPA9AO5or/fzHCN5m5gURWfLwgzSmzI5J/vfo6tHXHSOZuI36AmYOBVXsycTTprcyie4fq3nepeoca8nyBMlGKnn8V1IS5Y3cgpi+q47Q+72H6on7Rp4zMUHo8HgB2HhgoeufmXr2h6hKNBsaPUoYE0tq1ZGg0T9BmO0xqwuzvB5t09wxyqHt/ZxYsHBqgP+cRGeJbhqeK9O4B1UNBWngg8XnS+CRgqc50gzHlSpsV1923j5YMDTlw1rUmaFn2JLBnLJpV14rv1JDJEIwHuerqdrz28kxsfeMn1NBWEqZNf7rZszcH+FI/s6Oaup/fyavcQvYksyaxFOmejcNrnof4Ut2/aO6wN5jU9IlwK1aS4re7tSbC9c4COvhRoCPsMdh4epKMvycPbu4hGfADMCxn8xx92sfPwIM/u62NeyBC71llENQXMnwAfVEo9ADwADAC/Ljp/FiAOPsIxyf6+JEnTIuDzEA54WX1CLTUBA1trhjI5ljeEOX1JHcujEbZ39o8IcSII00HIb/Cpi1dzwepGFteFMDyKg/EM9WE/Po8Hw6OoCXjZuK7FbZ/w5z29tHXJ3F84uuTtIj/w+mUsjYZJ52ziSZPdPUO897VLWRYNc1JTLQOpLBvXreCaS1Zz0SmLGErnqAl48Xs9XHTKIpkIzSKqKWDeDNyBo8XUwEatdRxAKVUHvB34QxWfLwgzRj7sxklNtbxm+XyuOHc5hkcxlMkxlMkRT2YBaKz1c/ayKE3znADX4lkqTCf5kEOP7OjmYH8Ky9Ysi4Y4+YQa5od9hP0G565oYM3iWnK2Rtt2SfxVQTh6hPwG61sbCfsMHBc0SGRy/Ocfd7OoPojhUW4kjRpWLaxl3YkN1IV9KKChxs+6Extm+BcIxVTNBlNrnQE+7H5KGcSxvxRVjXBMkp+N53eKeODFTidQsOW8uONJk9qgD3dXMq44dznprEXQV805n3C8UWzXFg37ufw1zVyweiGxhMl7dm/CsjTP74+xdX+coUwOj4L1Jy0Y15lH9ikXqkXKtGjrHiSdc3Y/My2LLe19vCnQyNUXrSIaCbCprZuzl0WJ1vi556NvYEt7rPC3MHuYkZ18tNY20D8TzxaEo0XIbxSCAGdzNl5DgaO4JJ3THB5IE0+afPpnL5CzNR19ycJ+0bJbytiIgFMZzfPDw3Y8eeyVbi5YvZDnO/qIJbLYWnMonsGjFCiwNbxxZeOYZTrd+5RLXR4fVFrPW9pjDKadHY/y5GxNZzxFfyrH1Xc/X9hS8p6PvoFojZ+LTj3hKPwCYaJUVcB0g6tfBKwCGhjpJqu11l+oZh5mAy3XPDjTWRBmiCNB1jW1AS9JM4e2QSscRx8zx7PtfbQ0hOkdMomG/eIJOQ7TLeAcy4T8xrAdT7oHM2xq66Y+6MPwKGxLY3hAKUXWctTpv3mpk7efuaSwtWGpUDDatoiTQery+KDSek6ZFnUhPyG/QX9q+LmuoQzferTNMS/Smt4hk827e7j09MVH6VcIE6WaO/msAu4DVjN6/BUNHPMCpnD8kl8qf+lAP994tI1MzgIUXkORyuTI5GySmRzbOwdRCvb2JnjjKtktZSwmIuCIdgyW1IeIRvwMpnMc7E/xo6faaawNcOEpTezrTbJkfoiugTQvdPRjKNjROVgIul5OKJjO3WgqrUupx7lNcT13xtNsautmfetwTXmxEHrWsnoOxYNsO9BPzrYxlOKkploMjyLo89AzZOJR8IstB1h34gJZGp+lVNPg6+vASuAzwGuAFWU+J1bx+YIwa7h90x5ePNCPz2uwYkGEr15+Jqc11xHwe6kNeh1vc79B1tIkM7lh16ZMi12HByV8kUtewIGxnaLyL6wv/WbHcRv+qXhfcRQsrnc8yWMJk42vb+Gjf3EiQa8HrcFrKMJ+x5M8k7XLCn9wZNJ0zSWrp6xxrKQupR7nPsUhiDr6kvzoqfYRdVnc3pIZiytet4zaoJewzwDl7BfeHA3x5XefwcrGGiJ+L891xLnu/m3SJmYp1VwifxPwNa31V6v4DEGY9eRDFvl9HsysTeO8AKcumkfTvCCBrgQeBUGfh5SZI+w3yOR0QZMjS4gjKXagGis4/XQu5c5VigOtY+O2L5toxMdPn+mgI5akPZZkZWMN9SE/Zs7C8Cjufqaday85dVRN5XTtglLJln3j1aNoN2c/+XBZD2w9wO9f7sLwqBF1OVIz7kyGMpYGVOEeAIPpLP3pLD7Dw2Aqd1z27blANQXMDLCnivcXhDlBPmQROC/4mzaspTeRYSCV49RF80hlLT7+5lbu+t92khlr2JaRIiSNzl1Pt48peE/nUu5cpbgMFtUHC3s3p7M2X3t4J0FXO2RaNmc019GbMKkJeIklsvQmMkdlF5/xhNWx6lEmYHODvCa9M57mYH+q4MxYOmkpbm8AZzTX8ec9fYT8Bj7DQ2/CGQtbGiKksxa2rakNeo/Lvj0XqKaA+TtgPfDdKj5DEGY95bQ0Ib9ReGme2BjhnJYo57RER7zMRUgqTyWCdyXasWOdcmUQrfGTMq1Cu1q/cgEb1y1nSX142J7O+fQzPaEZqx5lAjY3KNakL50f5gOvXzbCBhNGTjZuftcZXHffNpLm8Il3c9TRboYDzoT9eOzbc4FqCpifAP6olPok8HWttVnFZwnCnGK0l6YISZVRLHhHI37SWYuUaY37wjoeKVcGo7WrSkwPZhMyAZsblGrSyzn4lBvjojV+bn3vmcPOpUyLK85dDmham2QL09lMNQXMTUAE+ArwJaXUQaDUEldrrVdWMQ+CMOOMtoxXqfAjQtJIjthhDnHn5j187eFdNM0LcNWbV7G9s1+CLlfAaO1qPNODyTBRO8ni9MCoy+AyAZv95Osyb55RXE8p0yr04VgiW7bNFbfTcmOpMHuppoDZjuw3JggjlvHaugYJ+gx5IU4RJ5C9h1jCiV6/P5bir7+32YmlVxSEWRidUsGvGkvOE7WTLE1/xbnLxsyTTMBmL2PVff7cnu4E7bEkJy2sHbfN7e9L0hlPk8paWHEtJhGznGpuFXl+te4tCHOJ4cu5Pu7cvI9YwhSnhGmgIRIg4POQzDhheJKmhQL6k1m2tMdkh48xKPfyr8aS80SF1nx6y9bs7k6QztqyDD5HKVf3zfPD7O9Lks5adA1kCo5mqazFiY2RMeu3IRKgoy9Z2MmnIRI4Wj9FmAQzslWkIBxPFC/j5b13QZwSpkohxmPGIuw3+OTFJ/P3//1M4eVz9rLoTGdxVjOa4DfdS84TFVpLt7f86TMdXHvJKSOWV4XZT2ndN0QChUlNNOInGvERS2QLjmbj2VT2JjIsnR+mscYi6DPoTWRklWIWU3UBUyn1F8DFwELgVq31DqVUDXA2sFVrHa92HgRhpskv4xV774o2ZmoUe6Zmcja21tzz0TewpT3G2cuihPwGuw4PilAyCqMJftO95DwRO8n8kv37XrOssL1lLGHSm8jIRGwOUlr3xZOaWMLk6otOIujzjLDLHK2t5EO+yfg5N6jmVpEGcBfwHhyXRA38GNgB5HC2kfwq8MVq5UEQZhvilDB9lBOQQn6Di049QeIjVsDRbIuVCK3FdRaN+FgaDRdMSUSQmLsU131pn21tqhl1u8jRHH5k/Jw7VFOD+Rng3Tjhin4LbM+f0FqnlVK/AP4KETCF4wxxSpgeJD7i1JlNbXG4divL1RetEme4Y4zxBMRK49vOljYrjE019yLfCNyptf4PoKfM+e04e5ULgiBMiPz+7ODEDh1tFx8Ye79yYfZQWmdL6qXOjkXyAmK5SUNpG2iIBNh1eFD2Gp+jVFOD2QLcOsb5ODC/is+vOi3XPDjTWRCE445Klr+LNSUNkYAsqc1SSu3tiuuseFchMXGY+1QSC1XawLFFNQXMQWAsN85WoLuKzxcE4RgkHwsvYeYYSGdp6xpibXPdsDT5l5m8pGYv+YlCZzxd2PIvWuNn1cJatu3vZ093gqDPcGPHDo1wBhHmBrEhk827e3h4excDqfLB1PMUC6Fi5jL3qaaA+QTwAaXUV0pPKKXmAx/Csc0UBEGomIZIgH2xBAfjaQyP4vZNu/ncpWsKYWzgyM4v+RiZhkfJS2qWkZ8o7Dw8iJmzue6+bdz63jMBuHPzHtpjSVDwuhUNhZ1eohEfG9etGOEcIsxOYkMml3/3SXqHTEzL5qyl9ezpTow6KSxemfjUxasl4sYcp5oC5r/hCJmPAHe4x85QSq0CrsHZRvJLVXy+IAjHIL2JDA2RAH2JLIaC/X0prr13K/2pLEujYTauW14I1N01kKEh4idna3lJzTKa54cJBwzMnI3P66F76IimMpbIctLCWhKZHGsW1fJEWy8Am9p66YilWNEYEW30HGBLe4z+pNNPLVvzcucAPsPD7Zt288V3nj4sNNGmtm463Ulj10CG3kRGPMbnOFVz8tFaP4PjRb4a+IF7+KvAt4EQ8E6t9cvVer4gCMceKdMinbVYVB/EZyg8HkWN38vz+/vYcWiQP7V1k8naRCM+dh4e5GB/inDA4OqLThKBZJYR8jvL4ucsd0zxD/SluH3TbuJJk2jECZ7dkzD59YuH2NOboHswg43GZ3jY3Z2grWtwJrMvVMDZy6LUhX0opWiI+PEAmazFU7tjtHUNAU6fvv7+bXz38d3s6U0wkM4SjfjFdvoYoKqB1rXWDyqlWoC3AKfgxMPcBfxOa52s5rMFQTi2KLbZ29M7hK01SkHO1gykcuRsjZnzoNFsXLeiEKh7IJUj6PPIS2oWEq3xc9UFrdz04Hb8hoc/74lxMJ5mWTTEhjMX8+Xf7WCgxySZtYks8BL0GuzqGsTjUdy5eR83bhh75xdhZonW+AubHyjg4z95nqxlk7U16azjGd7WNcimtl4yOZukmaPG7yVn29z8m5eJJca22RRmN1XfyUdrnQEecD+CIAiTIm/0n85a9CdzABgKOvtTRPxecrYm5DcI+ry0NtWwojEi9ltzgNamWk5sjLCnOwEa/IaH9liKN6wEDwpLg601kYBBJGC4Gmo/sYQpNrVzgGiNn4tOPYFt++PMC3pJZ22CPg9BX34BVYG7hG5r58j+WMpZnQh4xXZ6DiN7kQuCMCfIx8jrjGvmR3ykTIuBdA5fJkck4KU26GXVwlqW1IfY35fkUxevlv2r5wD50DRtXUPcvulV/ry7DxT8cVcP57TUs7srScLMEvF7aawNANp1+PGTzlqkTEvqd5bjxLFUrFsZ5WA8w7JoiNYmR2Bsbaph/coF7O5OsPPwAJ39KcJ+L2ctryOZsWWCOIepqoCplLoC+D/AKqChTBKttRYhVxCEcQn5DT518Wq2tMc4ZVEdT+/p5b+e2MO8kI/dPUPMUz5ytsW1925lMJ3D8Ci+9O7TRfiYIwR9Hq44t4V9vSnMnM3BvhReQxHweVhUV8vHzlvJmiWO53Fb1xB3bt7D1x7eJUuos5y8jWV7LMXi+iDXXXoKS+rDrg2mprWplqsvOokfP70Pj4J9sSSprIWZ1Vx90Spam8QMYq5Szb3IrwM+DxwGngT6qvUsQRCqSyVBko9GHvIxLaORQ+RsiCVNugYzKDQ5y+bJthhZyyZhWniAv/7eZu7/P28kWuOfkTwfS1SrDRSHp4kEvOw8PEg6a6G1IujzYGtN2Gdw+5N7uPXyM10zCMfTHCRG4mwnb2Np5mw6YknefdYSvvFIG1sP9KOAc1rqefHAAAOpHOmsRcDrIegzyORsgj5jxF7lMz0OzTVmssyqqT38R+Ax4K1a62wVnyMIQhUpF59uJpaeiwMvt8dSKOCkplr6U1leOTRAb8JEAyGfF9vWGIYiaVpsaY9x0aknHLV8HotUsntSafpKX2rF9bq3N4GhFH7DQzJrkzQtNBDxGyQzVkGQzJtLiI3tXMCxsQTQwLcf382LBwdImRY1AYNXOofoS2TxehQBr4eWBRHmBX0sqg8Oq9eJtsHjgfH62UyXWTUFzHnAT0W4FIS5TbEA0BlPc91928jk7KM2YBXvypMXKpZFQ4AiljCpDXoJeD0oBcmsjW3bGB5FJOClPuzj7GVjbSgmVMJEdlWZ6EutIRIg4PWQNC1aGiLs7hoklbUB8Ciwgcba4DCBo3hLQdFmzW6W1IdYu7iOWNIkGvYzmM5h5mxytk3C1KyZV4dpWQymLSIBL7e85wxsrUfUq+zsM5xK+tlMl1k1BczngKVVvL8gCEeBYm1ROGBUfWec4lk5MKr2FOClA/1sP9jPs/tiJE3b1WAarF1Sy6Wnn8Blpy+R5fFpYCIaw4kKo7c8tIPBdA4UbDjjBJ7e04smR8K00BpCfg+XnHYC7z5n6bAXaMhvHNcCxlwgX7+ZnE1jbYDP/tWp/NuDLxP2eYj4PWggYeZYNj9CPJ2lJuDl+0/sHiYslZtgita6sn4205r+agqY1wE/V0r9XGv9XBWfIwhCFSnWFpXu7V1uwJqKzU/prPyKc5cPG0R7E5nCIBobMvnMvVvpGcxgWjYhr4ec1lgaGmr9XP6aZaLZmibG0xgW1/lEhdHOeJpXu4cwcza3b9qHUgqf4SHg1SyuC3IwnuI//7SHXzx/gJ9/bL1MGOYQeSHI8CgyWSfO5U3vWMt1929zhKJ4koPxFL0JE4WiocaP4VFsautmfWsjMPoE83jv25X2syvOXU7emeqYscHUWj+ulPow8JRS6ilgL2CNTKY/XK08CBOj5ZoHK0q390uXVjknwmwjry1KmRZXnLsMUGX3g56qzU/pcnx7LFGIeVg6iG5pj9GXMElmLSwblEczL+jlzKXzuWnD2uP+BTTdjKYxjA2ZXHffNpKms8PS9ZetqXj5unl+uLA8HvIbeJTi9CXOcmrPkEnI6yFra6ysxf6+FI/v7OKdZzdX82cK00g5ISi/g9PjO7v4yf92sKNzAAWOiYtpsbcnwQ+f3Me9Ww7wt+tbRp1gHu8UR9U4e1m0ovUh3wEAACAASURBVLH4aFNNL/LXAT8EfMCb3E8pGhABUxDmAOUGrFJt5VRtfo7EukzT0Zfknmf201jr5x/OW0nAN3xn21MW1ZHO2eQsjccDNUEvJ8wLMT/iF+HyKJEyLa67fxtP74nhd+snX+eV1HvKtLBsjc9wvEBOqAty7SWnFPab//1Lh/iX+7eRcyNw//y5/bz1tEVSv3OEcprv2JDJtfe+wAv7+wGIBLxOfSpYuaAGDbzaM4SZtbFsTTTiK+zoc7wui5dbFSqOqvGHHV0jJvMzbX8J1V0i/w/ABDYAf9Jax6v4LEEQqkR+cIsnTXZ3Jwj5DLoGMrR1DXLn5n10xJIsjYa5ccNpU7b5yb+QNrV186On2jE8ikP9GW57ZBcepQoaMoDtnf2sXVLHrsOD5GyN1+NhQW2A7sFMYYlNBJHppdyEIpmx8Hs9mFmbgNdDOmsTGzLHXcpMmRbX/mIrz+zrI+B19qp+72uWEq3xF5bBT10yj/qQo8EG2H5wgEd2HOaC1QulbucIxZrvlGlx3X3b+POePlJZi4jPw+L5Yf6f17dQEzRYfcI8Pv+rl8i4bSmTs9m4bgVBn+e4XRYv3iI34PXwkTedyJoldWUFyOb54UmZqlSLagqYpwM3aK1/VcVnCIJQRYoHt32xJOmshQLWtzaQydpserUHM2uzL5YsvPin6t0b8husb23kDzu66Iyn2dubIGlaBQ1mW9cQdz29j854mp6hDGsW1xH2ewgHvPQlsnT0JfnRU+1lZ/XC5CmnwW6eH2ZRfRCAgM9DwGtw4wMv0TOUYXk0UpgQlKuDtq5Btu7vJ5W1GEjb2Bp++kw7py2pK6RvbarlrKX1PL6rG4WzhHrHpr080dYjdTsH2d+XLJhDpLIWyZxNz5DJd/7YRnN9mIP9KRbOCxL2G7Q0OO2nnCnO8UTeTnnHoQFiCZMt7X2cf3IT115yyjABsiESGNE/ZzrSQjUFzC4cDaYgCHOU/Cw5lbUYTGVZ0RjBUIqN61YAOv+P/lSWHxS9+Ke6FFOsyfzhk/scJ5CsTdhvALrgOLC4LsRbTm3istOXEPIbwzSfEspkehltyS3/EutPZvmnu58jmbFIZS0WRALj1IFCKQj6DLTWLI2GiSWyw9KH/AZfec8ZXHvvVg64ziBBn8Ge7gRtXUOsba47iiUgTJXiCUnWskllLRSKV7uHiPhM+pNZGmsCrGys4QOvXyarEByxU+5NmFi2ZiCdY29vgt5EZpgAOVr/nMnxzzN+kklzO/ABpZRsBSkIc5T8MkvIZ1AX9hH0GkRr/Cypd/YSXt/awJL6EPOCXmoC3sLANl3Uh/yEAwYrG2s498QoN71jLa1NtTTNC2DZmoP9KR7Z0c0tD+0AYH1rY+EFdjzbbFWDfFuA4WWbXwLVaLI5G48Cw6PQjF0H+T2oT1pYw5L5YeYFfYX0sSGTh18+RGzIJFrj52t/fRY3v+t0Xn9ilFd7hmiPJblz8x53j2thrpCfOH720lP4+vvPZmVjDQf7na1BDw8m8XhUwRRGhEuHkN/g7960grqgF5/HsVWOhv0FreSqhY53eL5/WrYm4PPQEAnMcM6rq8F8ArgMx4v8W8AeRnqRo7X+YxXzIAjCFCj2VFyxoIZbH3qFZMYxLr/+sjXcuGEtbV1D3L5pN/v7UkTD/mkZ2Jz9i19k06s9aA1rFs3jb9+wgpDfKGunWU6jdrzabFWL0Rw28nvD3/V0B+AIlxee0sjfv2nluKFRNq5rATRL6sMFm82UaXH5d5+kP5mlLuzjno++gWiNn7XNdXxo/QoOxtOEfMYIbacwNyi2ydy4bjntsSRWnWbrgThBH3T0Jbnl8jOk7xZx2pJ63ry6iVe7Ek4/fNtI85CUaXFuS5QHt3YOG6NnshyrKWA+XPT/7+OspBWj3GPSigRhllLsqRjwOeFkDI+iM57mkR1dLIuGiEYCDKSy7On5/9m79/i4zvrA/5/nnJkzN11HF8e2rNiWHOzYTkK4JiZlgeA2JF1z+UEpbQNL6dLdH/sq261psqTprhualMvvB9tsWyilJb/utiUlTdqkoRCSsME40GASO74klm+SbFm3mZHmfm7P74/RTCRZsmV7Rtfv+/VyQKPROUc65zzzPc/zfb5Phj7D4P4nD7PnCssE9Sdz9CVy2E6pePpLZ1J88buvsLEtVmk0y3ma05PYpQB37Uz+2yYydiUQDAUNXF9jexrLVHz0rRvobq+fNdCfqbTRprrSdvf2DDOWKy0AN5Zzpiz12d1ez8a2mBTbXia62+vpjEd46sgQrq8puqXlQY8MjLG2ObLQh7doRCyTT9/6Ou565ABoePCZY5XJjv3JHFErwJ3f+DGjGRvb83nD1c0MpAoLPtmxlgHmv6vhtoUQ82ByXk+u6BG1THK2x8nRLL//jwcxlSJgGIwXXGzXoy4UoDeRv+KepY7mKOviUXqTuVIZIkVl9np529N71ACODaal57IGZiqTsr83UQkE03mHoleaqOMYBgXHrUwOi1om9713e2Vm+IVKG0Gp/FRk4jqbvtSnLBG5fJSvqV96YyfHh7IcOZfG9Ut51rK861R52+NzTxzmyNlxrEDpnnn66CDPvjJEIutQcDxG0kVMBZ6vSWZt8o634JMda1lo/Zu12rYQYn5MLnWxuinM7p2b2XdihHsfO8RY3kUp0FpjKIXra5SCznjkinuWIpbJnl3b6BnKUHA8vvVC74y18CYXgL+SAu9iduV0hcnlqCITQUBjNMhYzqE+EiTilXqgwpbBWMFjIFXg1aE0tuNzz2MH+dIHb5ixtFF0In+svK8HnznG+pYYKHjg/dedt3KP9FAvfZPv13gsSFd7HbFQYNZzvtKVZ99bQYOi7XFqNMtf/egUZ5J5utrqOD6cpeCWllZd1Rji19+2gcdePLvgkx1lAo4QYlbTl4kczRYJB02yRRdPa/DBCihioQCmUvzX92ypWiHsiGVWZglvm6j7Nluv1WIoKrxc9QxlKuWoepO5yuzteJ3Fw5+8uZKD+eWnXuXUSJaGSIDGcKBUx9DxMQ1FOu9WzsnkmcTRkDll1aXyeSz30uRsd8F+b1E7k+/XRNbh07duIhw0pVd6wvQRg8n3jOdrUGAqBapUwQM0dVYA29N0tdazrjlKW7214AXqqxZgKqV+Dl6btFP++mJkko8Qi1u5gbv3sYOcGsnRn8rha42iNKGjOWqxrjnK+tZozVZZuViv1WIoKrx86dcy6HXlPwDE66xKfuTdt23h7kcOcODMGJ/59gG2dTRiGuC6HmfH8pXJXxca5pbzuDJMP88LsU72YjXbaMzkB/1yXvyOrlbee/0aPvPIAUaLLgGlODIwxp7HD9PVHuPTt25a0L9tNXswnwW0UiqitbbLX1/g/TLJR4glomcozd6eUXK2R952sUyF72uaokE2rarnozddzY7uNuC1PEhg3nLlJDevdsrlqHoTeTrjpfJU0+Vtj/29CRI5G8f10VrzrycT2J6PZRisqg8zmi1Whj5ne2CQ87gyyHme3eTe3ekTdcr3zPT6lxtb67A9n6Ltkco5OL5mOFPk1i2rZrxf50s1A8yPUwoYnYmvZZKPEMtE0fErOZauhpBShIIm16yqp6M5wo7uttLSf4+8xGjWYV08TMAwSWTtecuJlNy82ijlw26fMRjI2x49Qxke2neS4bTNSKZIMGCUimgXXIquT155nE5k51y+Ss7jyiDneWbl3t2BVOG8VcngtYf2cnDZEgvREY+AglfOjVP0NNmiR972+YvnTvLEwQHu27V9QfJaqxZgaq3/atrXMslHiGUgb3v83QulepOuD60xi854lHDQ5KM3X10JLv/T3+znhdNJAE6NZNjQVkdDOCg5kcvATMFAeSjv5HCW04kca5sidDRF+fCb15HI2Pz+Px1CA1pD60T+rkzeEOLCZqvzW14itzwxClTlAf5T79jE94+cw/M0J0ezZIouIVMxMF6gN5HjnkcP8qUP3TDvPcUyyUcIcUH9yRzDaZu1TRF8rWmrC+H6mvaGUCW4/K2//RkvnxnH8fxK8nk8alXeJ7l0y095KC8YMEjlHDIFl5Y6i/b6MLbrYyqYmI9AXTgg14AQl2BVQ5i2+lAliCwvkQvQm8ijgFgowECqwOeeOEzO9hhMF+huqyNqlerSvtQ3hjVRv7hnKEM4aMxrSkLNAkyl1M3A7cA1QAMwDrwCPKG13ler/QohqqslFuJ0Iksy69AcC/LHv3wjOdutBAz3PHaQw2fHsD2foGkQs0xu2dTG792+tbI6i+RYLT/lobyeoQwASsHgeIHPf+coHfEoVzWFSWYc6iMBPv9/ycosQszF9BJOn771Grrb6wAqE6M64xFAMZwu4mlNuuhimQbrmqOVNdzztldZzKCtPsRD+05WZpXPVxm3qgeYSqkG4G+AX6D08Drd3UqpJ4Bf0Vqnq71/IUR1nUnlKDg+AAXHJ5Etsr2jCShN6MkVPULBUmO1eXUDn751E9vWNhGxTBkSXcbKQ3lPHx3kDx4/TMEpFVoHSGZtXtfewFC0SFdbHfGYXAdCzMX0Ek7hoFEJBidP7ikHkOmCy9mxPOsmShmVJwRFLJMvfegG+pM5Co7Hl586BsxvGbda9GD+PXArpbXI/wI4QKn3sgG4DvgEpTXK/w54Tw32L4SoKoVSEDBKz4u9iVxl7eiWWGhKTcPPvudaqV24gkQsk3duXsWzrwxxfDjL4HiBcNAkFDRIZGziUYtk1l7wJeuEWCouVKprci50fzJH0fWxAqWeyw++sYPO+NQ0lPL7ExmbgKlIZGzWt0bnLV2lqgGmUurnKQWXX9Ja757hLT8DvqmU+iLwn5VS79Zaf6+axyCEqK7u9jp2dLVyajTLSKbI3/6kjy9+9xVa60Ksb4lx921bKsFmuT6brKazckQsk7tvu5Z7Hj1IJGjia03AMDgzlkdrCAeNBV+yToilYq4lnCYHom31Fs++MkwiaxOPWdx509WV+pd52+P+Jw9zoH8Mz9d0xOcvF7raPZi/DJwGPnOR930G+ADwEUACTCEWsfKyjU8fHeSv9p4iW3Q5myqQzDqcSeU5k8qzvaORY4NpBlIF8o6Hl9Iyc3wFGc0WK2vUZ/IOylBsuaqBnOMRMo0FX7JOiKXkQiWcSqXB0oBi987NjGaLFByfLz/1Kp6v2Xt8hN5Ejo1tsUqg2pvI47ilNKeBVH7JDpG/AXhUa32hAutorX2l1KOUejuFEEvAs68Mc2YsT9HxAY2hSiVoehNZutvraImF6EvmGMs5NEaDc657KJa+juYoUcskX3TJ2KU1kV/sS/HurasIB4wFX7JOiKUub3u8fGaMr/7gOIcGxlEKdnS1smfXNqA0AejkcBY0RIJm5YGuozlKZzxCXyIHCtbFl+gQObCW0kzxuXgF+FiV9y+EqIFSqaIiqxsinB7NUh8KgFIETMVf/PAkj+zv5/br1rCmKUJbXYhw0JS6h8tUeZ3k8tr05WG8/7Lzdbz/T0YqE308rXnH69q5bdtqWbFFiCuQtz3ufexlnnlliLGcg2Eo6kIB+hK5Svmh3Ts3c2I4w58/d4Ki61ce6MoLJZR7Pbvb6yr34fQ1z6ut2gFmAzDXmeFpoK7K+xdCVFG5AYpaAfqSOUYzNrbnc8O6JlJZmzNjeQZTeVwNL/WlqI8EuToeY3VTWHqrlqFyCZXyKiPlmau7d27mS997Bd/3K+/1PE1jWEotC3ExFwv0+pM5To1mGc+XloE0dGkEqb0hzIPPHKPo+LTVl2plFl2faMhk987NlW1FLLNS+WPyPmda87yaqn33G1x4/fGZ3i+EWITKT819iRyNkSBrGiPEoxZ9E2UvRrM22aLLRGoPjl+qmVmuwya9VctPuYRKwfEYyzm01nkMpAo8fuAM6byLFTDA9lFAwFT875/08Z1D586rv1frnhMhloq5BHodzVHiUQulFEFD0xAO8Dvvfh1PvzLEC6cSGIZiXXOUYMCgLhSg6PiVEaTZ7rXJ5ZBqlR9di8fL9yilrprD+95Qg30LIaqkZyjD3uMj2I5PMGBw3dpGrIDBTfEWhsYL2G4p105RKrIdCRpsaI1KcLmMvbZOsqYxGsQyDfqSOb53eIj+VI6AaWKZHhqIxyzGCw6ZoktdKDAlJ6zWPSdCLBVzCfQilsm9v7iVl7/6IzIFl4aoRVd7HY8fHCDv+jieTyhQ5E0bmhjPe5Xh8QsFrxcqh1QttQgwPzLxby4upbdTLBLr73piTu879cDtNT4SUVu6cocq4BO3bKApalFwPL703VcJmAaGoWgIBVjfEuMTt2zgnZtXSbCwjE0uodISC7G/N1FZL7k1FqIY9ulqr6NnKMO6eJT1LTFAT5nkMx89J0IsFXMN9HK2y6b2evKOR2Si1mx9JEAkaFIXCrChLcbHd3RNWQ7y2GB61nttruWQrkS1A8x3VHl7QogF0t1ez47uFnoTeTrjkcrqPHnbY3VTGM/XnBrNsr41RkdzRILLFWJyCZUd3W18/+gQQ+NF1rdGAUUia/P2a1q586YNlSXuJn+IzUfPiRBLxaXUvVzdFK7cN93t9dy3a3tlOcjVTeEpE3jKP3Ohe+1C5ZCqQV2kotCCU0qlGhsbG1Op1Izfn2tvmph/F+nBnGkZ0UXtYtficjRb/s5sM4mXsCV1PS6ma3HyNQLMqUdEcjAvSK5FMaOZ7puL3UtXeK9d0bUoU/yEELOa7Ql38utSimhlm36NzKVHpNY9J0IsRzPdNxe7lxbyXpNZ3EIIIYQQoqokwBRCCCGEEFW1FHIwfUA1NjbO+P2m3/xf83tAYs5Sf/Yrs35vbGysV2t99TwezhW72LUolq6ldj3Ktbh8ybUoFosrvRaXQoDpUuppHV/oYxFVNbaUGlGQa3GZW1LXo1yLy5pci2KxuKJrcdEHmEIIIYQQYmmRHEwhhBBCCFFVEmAKIYQQQoiqkgBTCCGEEEJUlQSYQgghhBCiqiTAFEIIIYQQVSUBphBCCCGEqCoJMIUQQgghRFVJgCmEEEIIIapKAkwhhBBCCFFVEmAKIYQQQoiqkgBTCCGEEEJUlQSYQgghhBCiqiTAFEIIIYQQVSUBphBCCCGEqCoJMIUQQgghRFVJgCmEEEIIIapKAkwhhBBCCFFVEmAKIYQQQoiqkgBTCCGEEEJUlQSYQgghhBCiqiTAFEIIIYQQVSUBphBCCCGEqCoJMIUQQgghRFVJgCmEEEIIIapKAkwhhBBCCFFVEmAKIYQQQoiqkgBTCCGEEEJUlQSYQgghhBCiqiTAFEIIIYQQVSUBphBCCCGEqCoJMIUQQgghRFVJgCmEEEIIIapKAkwhhBBCCFFVEmAKIYQQQoiqkgBTCCGEEEJUlQSYQgghhBCiqhZ9gKmUOq2UOr3QxyGEXItisZBrUSwWci2K2QQW+gDmoLGxsbER0At9IKKq1EIfwGWQa3H5WmrXo1yLy5dci2KxuKJrcdH3YAohhBBCiKVFAkwhhBBCCFFVEmAKIYQQQoiqkgBTCCGEEEJUlQSYVZS3PY4Npsnb3kIfihBCLHrSZq4ccq5XnqUwi3xJyNseex4/xNB4kfaGEPfesZWIZc7p5/qTOTqao3N6v1hZ1t/1xJzed+qB22t8JEJU16W0mdJOLm2Tz3U8ZnHnTVfT3V4v53KZq0mAqZRqBF4HDGqtZ6yPpZTaANyitX6oFscw3/qTOYbGiwAMjRfpT+bYtKr+gj9zuUGpEEIsdXNtM6WdXPrK59rzNXuPj9CbyLGxLSbncpmr+hC5UupuYBDYB5xQSj2jlOqa4a03A39Z7f0vhLztUXA84jELgPaGEB3N0YsOCczUwAohxErQ0RylvSEEzN5m5m2PvT3DDKQKgLSTS1X5XBccDzREguaUczn5vMtQ+vJR1R5MpdTPA58DXgUeB9YB7wN+qpTapbX+QTX3N98SGZv9vQlu7IwTrysFk+Wn64FUgVDA4D+8vYutaxsBLvrUXb7pyu/paI7O++8khBALIWKZ3HvHVvqTOVpiIXqGMjy07yTDaZuoZfLZ26/lwWeOMZAqcDqRozVmsb41Ju3kElNOb9i9czNnUnke2neSRNYhHrMoOB6JjM39Tx6mN5FnTVOYgKFIZB3prV4Gqj1E/jvAEeANWusCgFLqeuAR4J8ngsynqrzPqpot1yeRsfngV3/EWM6hMRrk4U/eTLzOoj+ZYyBV4NWhNLbjY5on+dIHb5jT8M/kBlZyi4QQK1HB8bj/ySP0JXKcTuTQaFxXc9cjBzCVqryn4PrIYjFLy0zpDXt2ba88THzpu69iez4nhjO4nubkSJbOeJTGSHDOqWZi8ar2EPm1wF+Wg0sArfVLwFuAY8A/KqV2VnmfVVO+GR548ih7Hj80pYt+34lRRjI2tuczkrHZd2IEKPVCRi0T2/GxAga54msB6uThn5ZYiIP9YxzsT03ZbsQy2bRKkp2FEMvXTMOe5fb2vieOsPf4CMGAgedr8raPUjCWsck7HiOZ0oN6S8wikXVkiHwJKXfAJLM2B/rGePlMqvJZd2okx9Fz4xzoS5HKOWjANBQtsSCAjOotA9XuwWwEEtNf1FqPKKXeAXwfeFQp9f4q7/eSzdRTObnXcSBVYG/PMDd2xjmTyvP4wTNkCg6eBlMpvnNogHduXkXEMrnvvdu557GD5Ioeq5vClW1OHv65/8lSI4qGHd0t7Nm1XYJKIcSyN9sknXJ7GwmaoMFxfd66sYWfnBxlJF0kkbUJmAYxyyQULAWf5fZVLA0tsRAnRzP0JfIA7P77A/yvT7yVh/ad5MRwhkTOwVQQDBisbgyzZXUDd9+2hdFsUUb1loFqB5h9lGaPn0drnVRKvYtSkPkPwLervO85m63BK/c6DqQK9CVzfPNHp7n/yaPEoxanRrNEgiZZ2yMUVJxLFSvB4/7eBJ99z7XkbHfKTVHunTw2mKYvkcN2fAB6E3np+hdCrAizpQtNzkF/y8Y4t25ZRVM4yI+OD6MBT4Pn+tiuT33YJGu7fOodmyrtq5QuWvxGs0XqQ0EMVUApSOYcHvrRSY4NZRgvlHotXQ2m7/Oe7Vfx4TddXTqX2Zm3J+d8aal2gLkP2AX87kzfnBRkPgV8hAVKqJmtwSv3Ou7tGeavn++l4HilnMtwEM/3yTkeGsjZPufSeQyl+OBXf0Qq5xC1TP72398040Xf0RxlXTxKbzKH1tAUCdISC83zby2EEPNvtsmM5fb20Jkx/vyHJ3j4hX5CQYOAMTVzSwNFx+f4cJbff+xlfuvWTaxtivKF7x6V0kWLXEdzlA1tMXqGM2gNru/z8E/7yRRdXP+192kU/3zwHMeGsgQMZpzkI+Wqlp5qB5iPArcppf6N1vrZmd4wLch8fZX3PyczNXiTn4x2dLfxL4cG6RnMAJrToxl8XUpYNVSpwbMMk2deGSSVc8gUXcYLDnd9+wBfu/ON5130Ectkz65tlYa06Ph84btH5QYRQix7F5vM+OfPneD5E6MEAwbXtNezbU0jRwbGOTNWwFSlnkzH12jbZe/xERI5m7pQAK3BChgyGWSRCwcMrr2qAdfX9CeypIsuCgiaBnqij0kBvYkc58YLtNWFiFgmY3mHnqE03e319CdzFBz/kmtNi4VV1QBTa/2PwD/O4X0p4I3V3PelmN7gwdSSQrt3bqbguhw+N4bjalClBPOAaeBpjfYha7u81D9GxDIZLzgEzdJT90wXfTl4DQUNXE9jGkpuECHEilFOF5quZyjDS/0p0kUXinBqNMMb1sdZ1RgmlbfRGvKOP9H7VQpG+hI5fA1Ry6SrrU7yMhex/mSORNahOWYxXnAwTIWi1Elz04Y477luNd/6115e7B/DKbqoIoznbVwfQgGDrz13gnDAmChrFCQes0hkbZkAtESs2KUiJzd4xwbTU56M9vcmOD2Sx/MnxvA1FByfHd0tXN/RzJOHBmiNhUjlHD74hrXsO55Aa0CBoRTHBtOVJ/XpS2TFY8FK939LLFR5LyC5JUKIFUbjeD6K0uhQU7SUA4+GUMDEUArHcwCN1lAfMvH80kP6uuYov/rWTm7sjEvbuUh1NEeJx4L0JvK01ofwPM1xL0PAMLACBl1tMUzDIGAoPF+DBkMZGEpjBUxODmVRhqpUEPj0rZsIB00510vEig0wJ5s8uScaMtmyupGu9hjHhtL4WhMKGLxlQ5z/vmsbX37qVcbyDsmsTabocvjMGNGwieeVarW990/2csO6JjqaI5Ve0nLwmsjafPrWawgHDVpioUoOUWkFIC3FZVeIua4vLsRyF4+FKLo+E52TrG2KcGxonDOpAo6nMRT4GoIGXNUY4SsfvoFP/92L5GyPwXSBLasbJRdz0VO4nua5V4cpOh7eRGrDz/pSfP25k3TEw/QlLfK2R6boUpxIzgyZioxd+no0U2RHd4usX77ESIBJqTdz987N3PNoqdTQg88c4/du38pH3nw1fcks65pjbFvbSM9Qhr5Ejq62OgbHC+RtD6UUqawLqlS+qOB4jOUdLNOYUg+z3AB2t9cRscwpvaZ9iRwaqAsFZOhcCLFiHBkYwwoY2BNB5kimQEsszEjGxvU8tIagqVjVEOaB922jIRKkq62OguMRDpocGRiTvLxFrDREbuP4PoWJVAetwZlIFSu6Pr9xy0bSeY9Xz6XJOx6xoIkC3vf6tbwymAFKnTd33rRBgsslpuprkS9Vo9kiRdev5EeOZou8aUOc99+4jjdtiAPw0L6T9CZyHB/OsPmqBppiQVxf0xQL0BKzMBWEgyaNkWAlR6Sc73nXbZunPF1PLsS+Lh6lMx4BpLisEGLluLEzTswKlCZOmgaWaRIOGtSFAoQCBkETTFWaffzYSwO0xEKsbgoTCwVY3RTmxs74eeuZi8Wj/DnXGAkSCZqYhiJkKlbVW7xuVT2rm8JoNIcHxskUHTwflFK01of40Js6iU58Xm5o3FXRgAAAIABJREFUi9HdXrfAv424VNKDOeFi64KXk5WvWVVP3vH4tbdejfkTOD6Upas9xu/s3MyRgTG2rG6ctR7mZDNNNJI8IiHEShKvs/jbf38Tdz1yAM/TDKYLrGmMcH1HI7/21vUMZYr8zY97iYUCJLI2o9nieTPSZbndxWvy+YlaAV7sS9Feb9HVVl8ppt4zlCnNXzAMWusNfuOWLm7btpoHnzlGzvaIhkojjHJul54VH2BOLk907x1b6RlKU3B8eoYyleHsvO1RcPzKBJ2NbTHCQYPxvEtbfYjxvEvOdrn12qsuad/TA08Z2hFCrBST296v/dobK/WHAVJ5l8ZokDesj/OvpxIMpAqEggZRK3BeMDnbDHWxOEw+P2ubI+cVS+9ur+MtG+IcO5ehtcHi+o4mEtkiQ+PF0jC64zOaLRKvsxb4NxGXakUHmNMLt+7euZmH9p1ib88oKNjR1crdt22ZMhnn07duoru9dLNcqMdTCCHEzGYqml2qP3yu0v4+tO80e3Ztq+THpwsud37jx6xrjrK6KSwTepagmc572WC6wPGRLK+c+xlv3RifUnFFPl+XppoHmEqpTcAmoIVSPdUptNYP1foYZlOe4e35mhPDWfadGKE3kceemMXWl8ixvzcxZRZ4OGhWGjUZmhFCiEs322pqd960gb5EnqBp0JvI0TOUJhw0Kbo+juszlnNorfNkQs8SNdN5BzibKmC7Po7nkSsqzqYK3HP7tYSDhny+LmE1CzCVUquAbwLvLr80w9s0sGABZrlG13PHRvC05juHzrGmKVSZ1d0YCbJldSPtDUMz9lTK0IwQQly62XLeu9vrWBePVNrkb+w9ye/dvpX4RKHu+onJItKrtTRNKQlombTESqv2rGkK8VKfj+dD0fVZ0/RaxRWxdNWyB/NBSsHlnwJPA6M13NdliVgmH3pjJz94dQTfh5+eSvKVD7+ej7x5PV9/7jhF1+fBZ46xe+fmSkKyXPBCCHFlZpucM71N/vGJBMeHS0v2Gkpxw7oGPr6jS4KPJapSEvCxUknA8pLJH3nzen54bJSQq7ECio+8eb2c32WglgHmu4E/01p/qob7uGLhoEnAVPh+aUnIcLBUJsP1wTQUA6kC+3sT7OhukwterChzLQh/6oHba3wkYjmabQRoepvcl8zSm8gTCZqM5z3CQUPa4iVsNFuk6LxWErBnKM1QukDANNDaJxgwCAfl/C4HtQwwDeClGm6/Krrb69jR1UpfIse6eLRSa6vcjd+XzPHXz/fy/aNDklQuhBA1NrlNXt1UGi7vS+ZAw47uFhkaX+Imp0fEY0Ee2nea4XRxIt8ywvoWqXm5XNQywHwOuL6G26+KiGWyZ9e284Zq7r1ja6VsRvlJS5LKhRCitia3yQXH48tPHeOa9npZzWWZmJweUXB8vvzUq5iG4up4jF99a6eMFi4jtQwwfxt4Rin1tNb62zXczxWbrRD6ju42vn905gk+QoiFJ8P4y1O5Tc7bXqW3S1ZzWT5mOr+rm8ISXC4ztQww/xTIAN9SSp0FTgDetPdorfW7angMV0RWiRBCiIUjbfDyJud3eatlgLmRUhmi3omvO2u4r5qRUkRCCLFwpA1e3uT8Ll81CzC11utrtW0hhBBCCLF4GQt9AEIIIYQQYnmZj6UiG4BbKQ2ZQykX83ta63St9y2EEEIIIeZfTQNMpdQngC8Bdby2VKQGMkqp39Za/0Ut9y+EEEIIIeZfLdci/7fA1yj1WP4ecGjiW1uB/wR8TSk1pLX+p1odgxBCCCGEmH+17MH8DHAEeIvWOjPp9e8rpf4SeB74XUACTCGEEEKIZaSWk3yuB/5qWnAJwET+5TdZAiv9CCGEEEKIS1PLAFNd5Pu6hvsWQgghhBALpJYB5kvAx5RSsenfUErVAR+beM+8ydsexwbT5O3pCwoJIYSoNmlzhbh8S/3+qWUO5heAR4D9Sqn/ARyeeL08yacbeH8N9z9F3vbY8/ihyrri996xVZalEkKIGpE2V4jLtxzun5r1YGqtHwU+BawB/hh4auLf/5h47VNa68dqtf/p+pM5hsaLAAyNF+lP5uZr10IIseJImyvE5VsO909NV/LRWv8JsA74JeDuiX8fAjq01n9ay31P1xILEQoYeL6mvSFER3P0oj+z1LunhRBiPiQyNk8dPkciY1de62iO0t4QAphzmyuEKLmc+2d6zLLQMUzNV/LRWqeAh2u9Hyj9MXuGMoCmu72+0p18JpnnrkcO4HmapliQ3Ts3X7SreTl0TwshRC3lbY9DZ8bY/e2XSOddGqNBHvr4W0hkbQqOx/tfv5ZwMEB3e520n0Jcgohlcu8dW+lP5uhojk65f2aKdRIZm3seO0iu6LG6KczunZu5/8nD9CbydMYj7Nm1fd7vwZoHmPOh9MdO8429p/jxyVHQsKO7hT27tpO3PT78tX2cGy8QNAzecHUzo9ki8Tqr8rMzncCZuqc3rapfkN9PCCEWm0TG5u5HDnBiJMPZVIE6yySVtfnM37/EiZEsY3mHhnCAWza1smfX9oU+XCGWnIhlTok7Zop13rKxmY+8eT1ff+4EPz2dxAqUBqb3nRhhb88otuvTl8jRM5Rme0fTvB5/1QJMpdTTlEoP/bzW2p34+mK01vpdV7Lfck/jieEsJ0eyeL5GAb2JPP3JHK8OphkvuBhK4fg+KCpdzeWfHUgViIZMPvuea8nZLh3N0Ur3dLkHU4Z3hBCiJG973P3IAZ59dRgA1/MpuoqwZZDI2uRsD8fzKTh+pS2WB3QhLl+5h3J4vMjpRA7PL1V6/OGxUY6dy3JuvABoiq5PNGSyqj78WrFIVfnPvKpmD+ZGwOe132Ij81DrsmcozYnhLJZpYBqlXQcMRWc8QkssxHcOHaPolvIPVtWHeeD91xGxTPK2x9NHB3nlXJqzqTyO6/Phr+2jq62O1U1h7r1j66zd00IIsVLlbY+9PcMMp0sjPL6vCRgKH02m4JItZoFSO2waUB8O0BILLeQhC7GklR/ofnwyQSigMJQCQ6EBQ2kGM0USORvTULTXh/jse64lHrPY0dVKXyLHuniUtU0Rjg2mp8Qzs43gVkvVAkyt9foLfV0LiYzNg0/3cHq01HO5bU0jv37LRhojAbrb6+kZyvDT00miwdIf7o/ev521zRHytse9jx3khz2jJHM22tfURwLkbI/xvMNo1ublMynetKFFnrqFEGJCImNzz6MHSRddknmHpmiAoqvJF11sV+P6mkhAsS4eoTFqcWoky6Gz49z/5OEZc8Bq/QEnxHLw8pkUL5xOkrVdsja8rauV//sd3Wg0//OZHvb2jOJrMIGGcJBE1mZtc4Q9u7bRn8zREgvxhe8enTKnBKj5PJOaziKvpbztcc+jB/nXUwmSOZtk1ubg2RSPvXhm0gQfDRoMpQgHTRqiwcrT96mRHLbrEzJNoqEAnfEYdWGTI+fSvDqY5jPfPjhlRqQQQqxkedvjnscO8pOTCY4PZ+hojnDvHdv4/TuupSlqVYbs8m5pWNx2fLQG2/E4ei5Dz1D6vO3tefwQDzx5lD2PH5JqHULMIG97/NkPTpDM2TiexvM1Wmm2rm2kq62egltK/TMUuJ5maDzPg0+/yplkvvLwNpotnjenZD7KIM1rgKmUCiilPqCU+g2l1FVXsq3+ZI6xvEPW9rBdjafB9zTHhzM8/EIviYxNd3s9O7pb6GqvY0d3C2uboux5/BB//XwvA+N50gWHvOMSCZr89q2b+PS7riEUMKgLBUjnHfb3Jqr1qwshxJKVyNg8/EIvqayDFTCwHZ/6UIB3bm7n7de0s741NiXDy/F9zo0XCJiKvOMzkiny0L7TU8qn7O0ZZiBVAJZunT8haq0/mWM872AaCgWYhmIs5/L00UF2//2LHBlIEzAUEcskZhkYhsnzJ5P80lf38bknjnDvYy+Tyjk0REwyRZd4LDhlngnUroxYzWaRK6U+D7xDa/2mia8VpULrt1DK0/xDpdRbtdbHL2f7LbEQvYkcvq8xJqJ3D3h1MM1Xvt/DN/ed5uFP3syeXdsrXcT7exP0J/Nkiy7Dabv0JADkbZf//ZM+Pnv7tbTUWYzlHBqjQW7sjFfpryGEEEtTImPzwa/+iFTOwfZ8tq9ppCkW5L6JmeFf+O5Rio43JeFea1jbHOGGdU08fyJBS51FImtXelTKkyv7kjnWNUdZ3RSWiZRCzKCjOcr61hi9iRzJvE1TOEAqb/ONH57i8MAYvq/RShExDdCa0WwR01AUHI/6SICjg+OcHMkymrVprbMoT5O5UBmkaqllmaJfoBRQlv0i8HPA54EXKa3ucxfwG5ez8dFskfUtMQqOh+v5WAGTWMjkTKqAZWpGMzb7Toxy+3WrKw1afyLPi/0ptAbH8zENhetpUIrhdJFEtsjDn7yZfSdGaa+35i0RVgghFqv9vQnGcg4KsEyDX9i2ig++sZOIZXJsMM1AqoDjaUwDXH/ihzQcGRinP5ElXfQZThd426Y2OpqjlaE501Csa47yq2/tZEd3m7StQswgYpncfdsWxvM2w2mbiGViTASQ/kQKoKv1RBxkYKjSfZq3PfoSOXwf3Hqf5ESAWX7Q27Sq/rwySNVWywBzHXBs0te/CJzUWt8FoJTaCvzK5W68ozlKRzyCaSgczyNb9EDB2VSBTNElYBg8dWSAd25urzRojucTVNAUsxgY9wmaBq6psUzFmbE8D+07zadvvYYnDp6dUqx0enKsNIRCiJXixs44jdEgYzmHpmiQO65bW2kDW2Ih+pI5kpkioAgYGtcvlQ8pOj6ur0FrCo4iZ7sAU0rArW4KS3ApxDTTO7VGs0VcH2KhAKOZIg3RIKah8HyNM5H7HImaOJ7GCpgYShEKGmxqq2dgvJSLaXs+/ck8b+uOzdtoQS0DTAtwJ339Dqb2aJ4AVl/uxsvduz1Dab723HF+1juMpzXxmEVTJEhz1OJsqsjLZ1KlCT6RIKOZIgVXk8o7tDeE+a13dbOqLswfP9NDOGgynC5y17cPcGRgvFKsdH9vQgquCyFWrHidxcOfvJn9vQlu7IxXFqmA0kjSuuYoMStAbyJH1naBUuqRBvBKKUyxkEnR8Svtp5SAE2JmpSo3L1fKC+3ZtY2O5igNkQBPHjyH7flYpuKaqxqoswxcrbBdj7b6MM1RC8f3yRRcRjJFrKDB9R1NJLI24aCJ7fncedPV83bP1XKSTx9wE1R6KzcCP5j0/XYgcyU7iFgm4aDJ0Hip2zgSNFnTGKEzHuHUaJbeRI7PfPsgn//OK+zvTXB6NIenNaah6GyOsn1tE1vWNBKPlRrMUMAga7sEAwb2RLHSGzvjtDeE8HxNKGBIPTchxIoTr7PY0d3GaLY4ZbZ3SyxE1DJpjAQJBYxSytEkPqVA8+p4bEqeZXloToJLIabqGcqw9/jIlP+NWCa3bGrD8ycqM3iaouNh+1B0PUIBg//87mv42M2d9CfyDIwViFgBfmfnNdz7i1tpawhhGoqNbTG62+evg6yWAebfAh9VSj0OPA6MA/886fuvBy5rgk9Z3vZI5WzqQiZBUxENmWxsi/GJW7rojEfpaI6QzjuM5R3Gci7l9CDHK9W9LNeGytkeoYDCCirOjZVmNb7h6mbu27WdeJ3F7p2biYZMcrbHF757VMppCCFWlJlKCuUn2sOcXZpM8N9+8VqswPmrhRhK8aYNzVPSi/K2x7HBtLSlQjD9ftCvLVGjK/9hXXME03gtZGuOWdzQ0cSWqxrY3tHEt17o465HXmYoXUAp6GiKEA4GePCZY+SKHlHLZPfOzfP6UFfLIfL7KeVhvhcYA+7UWqcAlFKNwL8F/t/L3Xi5WPrenlE0sHVNI7/59i62rW0EYENbjIFUgcZokMZIkOZYkILjE7VMrlvbyH27tldqQ5mGIpV3UXm4ZlU9ecfjU+/srgwFjWaLFJ3SpCAZJhdCrDSz1cwrt59Fx6cjHuU926/ixd4U/ck8zsQTfdBU/PKbr54SXNa6wLMQS8X0+2H3zs3s6G6hN5GnMx6p9Dh2tdWzqjHEaNqmPhLgc+/dztd/eIKh8SKhgMFwuojva5QqTV6uDwcA/do96vqMZotTUlxqrWYBpta6CPz6xL/p0pTyLy+78Fl/Mlcq5jsxbTFbdGmKBisNVTnHpyUWYjRbpCUW4kwqByi62+tKQ+qWWUk274xHAEUia5/XjSzrkgshVrLZ2sDJr3W31/OH77uep48Oct8TR0qzXH3N//NLN7C2OVLZ1kzBqjywi5Vq+v0wmi1WyitOzlEezRbZ0FLH6gaPcNDE13pKnHP/k4c5k8oTDBpcv7aJ+967fUqMsxCxSy17MGeltfYp9Wpeto7mKJ3xCH2JHChYF49O+eNNnn5fjtinR+7T60ABMyaez0e9KCGEWKxmawNneu2dm1fx7CvDlUkKN3e1TtmWPLAL8ZqZ7oeZygd1TNSLne19e3Ztn1gt67VONJj5Hp0vNQ0wJ4qr3wpsAlqA6Qk6Wmv9B5ez7YhlzvoHvdTtTD6Rsz1J17pelBBCLGYztYGzvVZeA3mmDzV5YBfiNXO9Hy72vohlsr2jacafW6jYpZYr+WwCHgU2c35gWaaBywowYfY/qBBCiIVzsQ81eWAX4jVzvR+W2n1Tyx7MPwa6gN8FngZGa7gvIYQQQgixSNQywLwF+LLW+os13IcQQgghhFhkalkHswicrOH2hRBCCCHEIlTLHsx/AXYAX63hPoRYNNbf9cRCH8KyIX9LIYRY2mrZg/nbwE1Kqf+ilJq/yp5CCCGEEGJB1bIHcy8QAz4PPKCUOgtMXxdMa627angMQgghhBBintUywOzltRU1hRDL1FyHs089cHuNj0QIIcRiUculIv9NrbYthBBCCCEWr1rmYAohhBBCiBWo5gGmUurnlFL3KaX+XCm1eeK1uonXZRkeIYQQQohlpmYBplLKVEr9HfAM8F+BjwNrJr7tUlpG8j/Wav9CCCGEEGJh1LIH83eBD1AqV7SFSeuRa60LwD8A76nh/sUM8rbHscE0eXv6hH4hhBAXIu3n0ifncP7Uchb5ncBDWuuvKKVaZvj+ESTAnFd522PP44cYGi/S3hDi3ju2ErHMhT4sIYRY9KT9XPrkHM6vWvZgrgf2XeD7KaC5hvsX0/QncwyNFwEYGi/Sn8wt8BEJIcTSIO3n0ifncH7VMsBMA/ELfL8bGK7h/sU0Hc1R2htCALQ3hOhoji7wEQkhxNIg7efSJ+dwftVyiPyHwK8qpT4//RtKqWZKk36+U8P9i2kilsm9d2ylP5mjozkqQwNCCDFH0n4ufXIO51ctezA/B2wCngbumHjteqXUJ4H9lJaRfKCG+xcziFgmm1bVT7mxJOlZCCEubqb280KkbV18LnQO5XxVVy1X8nlBKfUB4OvAX068/EVKs8mHgPdprQ/Xav9ibiTpWQghqk/a1qVFzlf11XKIHK31E0qp9cC7ea1U0THgX7TWkl17hfK2N2tXf9726BlKA4ru9rrzeizLP1dOevZ8zYnhLD1DabZ3XFn9+wsdlxBCLCfl9q4lFuJMKg9outvrp0wo6U/kefiFXu64bi0Ry5T2cYGVPh8zFByXcDDA2qYI+3sTDKQKAPQMZnj66CDv3LxKztEVqGmACaC1LgKPT/wTVXKhp6287XHvYwfZ2zMKCnZ0tbJn1zYilkkiY3PPowdJF13qQwH+y87XETAVhwfGUQoe2neaPbvmPgR0KcclhBDLSbm9G0gVOJ3IUnB8FPCWjc184MZOAqYinXc5eHaMQwNjfO3/nOT1VzeRK3rSPi6Q0ufjy/ywZ4SxnENDJEDECtDRFKE3mSVT8MjZLn/w+GH+8cWz3P/+64jXWZWflYeDuZO1yJeoC5Vb6E/m6E3ksV0f2/HpS+ToT+bI2x73PHaQH59M8NPTSZ4/McrH/vInDKdLPZhdbXUksvYVlW6QMhBCiJWi3N7lHY9k1iFvexRdn+eOjfLb33qRA/1j5B2XgKHIFj3OjRfYe2wEz9fSPi6Q/mSOvkTp89DxfbJFj2TWpuB4ZIseBcfD15DMOfz4ZIJ7HjtI3vYqDxMPPHmUPY8fkjzNOahpgKmU+ohSaq9Sakgp5c3wz63l/pezC5Vb6GiO0hmPYAUMrKDBuniUjuYoPUNphsaLGAoc18f1Ndmii6kUAUPhuH5lW5eb7CxlIIQQK0W5vYsETZpjQSKWiaHA830czydve2gfLNPA8zVBU2EFDAqOJ+3jAulojrIuXuqBDBgGllma+JMtupgG1IcDaA1aa8JBg1yx1GspnSeXrmZD5Eqpe4D/DgwCPwKStdrXSlPupt+9czOj2eJ53fURy2TPru2VHMy1TRF6htJ8Y+8pzo7lMRQETYWpwPE1YctkR3cLd960ge72OoDLHuaWMhBCiJVicnsXtQL8+OQo/7C/j8Nn06QKDgHDIFmwuaGziZ+cTGCZipu64nx8R9d5ufFifpQ+H7dx6MwY//OZHl7qTzGSLlB0PBoiQdY1R7ECoDBwfc3qpnDlQaC9IVT5XJSHg4urZQ7mfwSeBX5Ba+3UcD8rylxzHCOWSXd7PT1Dae5/8gi9iRynE1nWNEbIOx4tdSFioQDhgMlHb7qaHd1tle0cG0xXJv6cHM7SM5Rhe0fjnI+xXAZCCCGWs8kTfO5/8jBHzqY5PpIhaBoYSrGhJUrAMBhJ25WH94/v6Lqk9lRUX8QyCQUNBtMFErlSeJLMOayLR/nozaXPQ+C8jhLpPLk0tQwwG4BvSXBZXTN1088UzJUD0RPDWfqSOdbHY4znXHLFDK7nUx8OkMjZXL+2iS2rG6fcNB3NUeIxi73HR0DDQ/tOsmfX9ku6oSQZWgixXJVnIT+07ySJrEPAUBw4M0ZuIgczaBoYaM6OFQiZipzjEwoYNMcs1jZFFvrwV7y87fHQvtOcSeYrr2ngbCrPjZ3xyoTY06NZWmKhymeYdJ5cmloGmD8D1tVw+ytSOfjrS+QquZVl08sPDaQK+L7G9zUjmSL1YRPb0+QdjeuD0pqRjM2d3/gx65qjrG4KV3pE77zpanoTOSJBk0TWmTWQnYnMJBdCLFfl9u3kcJbeRI6utjoGszau5xNQEAqYrGkM05vIk7M9MlrjawgYijWNEc6kcjOmNon50zOU4fhwBk/rKa9HQiZnUjnyjseHv7aPnO3RFA3y8CdvrswkF3NXywDzHuDbSqlva61/VsP9rEAaPfG/k4dovvDdowyNF4nHLN57/RpOJ7KM5xxsz2d1Q4Ss7eH4pRmNtusTChoYBozlHFrrPAZSBfb2DLOju43u9no2tsUYSBUIBQ1aYqE5H91ce1mFEGKpKbdv4aCJBo6eGydgGsRCAeIxi8ZwkI1tMQZ/dgbH9/H90s+NFVyKjss39p5kPO/Kw/cCydsef/zMMV7qTTF9Cmu24PKVp46RLrqcGy9gKAXA/t4Et1571fwf7BJXy5V8fqCU+nXgeaXU88ApOO98aq31r9fqGJaCSx1K7k/mSGQd6kIBhtOlmpZF1ydgKhIZm2DA4AevDnNkYJyC43NVY4Rz4wUCAcWq+hBWMMqpkSye7xOxTHxfUx8JYpkGfckcf/18L/9y6Bx33rSBT71jE5974jC5oscXvnt0zo1heWalJEMLsXhJGsvlmdy+Xbe2kUTWJhYK4OnSaNGRc+O81J+k4Ph4/tQessGMTX+qwDWr6uXhe46qfZ3+9FSCpw4N4k96LRxQRIImQdPgX08nUfgEDIU7MfN/y2rJmb0ctZxF/hbgm0AQuGXi33QaWLEB5uUMJU9u3KIhk1yxFLMfHhjH9XzSRRff1xilBy98NKGAQe9oFl/DtjUNrG4MUXR8RrNFbE9zw7oGbt1yFQ+/0A/A3p5R+hJ5GiPBSuN5KY2hzCQXYnGTNJa5mSm4mT5z/HNPHCZddFGUesCSOYei68+4vXTeIRQ0yTseG9ti8vB9EdW6Tiefx6OD4+hp33d9TcBQFF2fouPiaai3DAzDJGIZ/NF3jvJHH7hO7pFLVMsh8q8ANrALeE5rnarhvpakyxlKnty4lYfFTwxnUQo641F6hrMEjVKdy3DQZGSiiHrWdolZAQ4PpEkXHGxPo7VmfUsd43mPzniM1U1hTgxnQUHQNDhwZgxv4sbb0d1ySY2hJEMLsXhJGsvFXSi4KU+G3PP4IdIFl1OjWdY1RxnOFvH8mYNLQ0Gm6HJjZzO/desmutsvf8W0laIa1+n08/iJt23ky997lYxdOk8K8HzI2V4p7UGDoRSuVqSzDqNZh95EgQ+9cR1v29Ra7V9xWatlofXrgC9qrf9JgsuZXW5R8nLwFq+zuPeOrdxz+xZ2dLVSHw7SWmexZXUj165t5KrGMFqDp0Fr8DU4XqnrX2mNp+GVc+PEY0G62+umbMv2fJSC111VT2c8yp03bZDGUIhlQhZEuLiLFdbuGcpwcjhLwS2t8uJpzcbWOq5d00hAvfY+BZiUPmzrQwE++faNbO9okvZ0DqpxnU4/j77W/N0nb6a7PYplKibSLAlbBmuao8TCAepCpb63ck+nrzVHB8au+PdZaWrZgzlEqQdTzOJyh5InT+wZzRbpbq9nz65tU14r1WU7wsB4gYCvaY5GaKsP0VYf4icnRhmfGFrPuz7vvX5tZd/bO5rYs6tUP/OhfacZThepl9IaQiwZMw3rzvTaR97cCSgp+D2LC+WSl8rcnKQ3kUMD9ZEAkaDJ6qYw/+1tW/nsPxzk1aE0rldKUUoXHFwfwpZJV5v0FF+K8nW6tiky5Rqea27m9PPYEgtx9yMHGEk7NEeDNEUskjkbT0Om6PC27jaGxgu01IV49pVBihOjgbdtXzN/v/QyUcsA8xvAryqlHtRay5KQs7jUoeRyd/9AqkBfMjelvFB5OxHLpD+Z4+7btnAmlQc0a5uinEnleGjfadrqw4zmHMyJJ7dUYWqp0oiYtMgqAAAgAElEQVRlsr2jibtvi3LPYwcveZKPEGJhzDSsC1NX5tq9c3Ol4sTk94ipLtQBUJ5sec2qevKOx+/sfB1N0WCl4Lrra25c18THdmzk/9t3ihdOJzENxcbWOkazRSl5MweTr+V4zAI0iawz6zU822fT9PPYM5ThwJkx8o5H3oHu9npa6kJkii6NkSC/ccsGwsFSCkQia/P9I+d415arWNssnSyXqpYB5g+BOyjNIv8T4CTnzyJHa/1/angMS8LkJzE4f/WAycrd/XnHYyzn0FbnTclNuVDe0Gi2SCJr01ofIjyaJWAYtNZb3LRx5ryS0WyRouNjGkrytIRYAmYa1i04PieHs4SDJkPjRfb3JqqS17YSJvHN1gEwuVdsY1uMbWsbiVgmB/tT7O0ZxXZ9rIBBYyTA/e+/rvKgPnnZQXFh5VrOBcdjvOBgKEVdKMBAqsDjB84wkCrM+bNp6nnUKKAuFMAwFB/fcTV/+OQrjOUcsrbL2qZo5QFgrRXhzps31P6XXaZqGWA+Nen/fx3Om7ilJl5bvq3THEzukQwFFNFQ4II10soNm5fSNEaDhIPmlOGbCyVFT24Uf2Hbam7d0s5NG1tnfZouFXUP0pvI0xmPSMMoxCI303Dg/U8epjeRAwVv2RCnMWIRj1kksvZl5bXJDPQL9W6q0ifbxP8FRcQy+Q9v7wa0TOy5BC2xEKcTWZJZh6ZokNd3NpLMuvQlc3zv8BBnx/KVEbxLuYa72+vZ0d1S+Vxrb4iyrjlKW11pks9otlgZBVzuD1C1VssA89/VcNvLRvkp7dXBNNmii2kotq5pZGi8SM9QutJVX8456RlK84HXdxAKGqxtilZWhIDSGuItsdCseUOXl/OpJtpLdZH3CSEW2vR7fPJQbqbokit6/NkPjhOPBfnNt3ehgJ6h9JS2ZHq7UG53yvmaMgO9ZKbeze72OnZ0tVZWWlvbFJkxZUFc3JlUjmzBoeB65IrwkTevZyxv89fP92IainXNUT74xg7a68P0DGXmnEscsUz27No+ZdRwdVN4ykPZSn+AqpZaFlr/Zq22vZx0NEcJBQ2yRRfb8/FdODqY5m3dcR7ad7rSy7B752buf/IwP+wZxfM1O7pb+cP3bWdT3fnD4rt3bp71w+JScj5LH06lOpiJrL1iP0iEWEom3+PTezRzRQ/TUAynbb7+3AkOnh3D9zURy+TqiVJl5SCoXOdxzz+9zEv9YxiGYkdXK3fftkUWUphFKXjZNiXAl2D88gyliwxnHDRQdHzGCw47utv4/tGhyojfdw4N8NNTKVCwo6uVPbu2zTnInHwepj+UyTmrjlr2YIqLKPcMhAImpqHwXWiOBlkXj3LrltU8sr9U+LycN3VqpBTw+Vqzt2eEnqEM2zsaz7shRrPFqtwQsiKPEEvbTHVzy4s0DKeL2I6P62sKjs+q+tJysU8fHeLZVwYZTtscH84wXnApOB51lklforSOtiykMLsLBfjShs7dq+fSlbw6PfH1u7asYvfOzdz9yAHOpPKcGy+gdWl8rS+Ru+xgUM5ZbVQtwFRK/Ry8Nmmn/PXFrNRJPuVexxPDWfqSOa5ZVc+rE0NV3e113LSxhWdfGaIvkWN1U4TGSJDGSBAoFYHVWlNwSnOmZsq7OjaYvuLGX1bkEWLpK3945m2P27et5ujgOO943f/P3r3Hx1Wdh97/rb3nPrqOLNlG8t0m5mJDnAsxJE2bEpIAPTQhSU9JSluSnOT0Tduc9kBDQzk9lIS8EN43aTht2qS0cBLSXAttCCc0ISXBNaEEAgZsbNmyJdmydRmN5rJnZt/W+WM045Es2bI9I42s5/v58MGjGc/e1t577Wc/a61nLedLT+7ncCpPwNdEgiZF12NwPM/fPXWAoxMFutuiWLZHyFQUHDBMxapErNIWSFbn1KQNPTN526M9HsJUpfrNSpUm5SSzNjsPjPHCYIq841N0PJojAYIBo3Juni05ZrVTywzmvwFaKRXVWtvl1yf5/JKe5FPOOkYnVw44MJojZBo0R03euLaDvOMBGs/X/GJgnNFMkVAALlub4JWjaQwFX3pyH7CJi7tbZ8xSnKq7fC7kRrI4rP3ko3P63MHPXlPnPRGNpNxLUnR8Hni6jx/tHsH1fP5+x0H+9rdezy+/povWSICv/cchnto7imV7OJ6PYSg00BYLcl5blFjQ5MNvWV+ZLS3mTtrQk5tekSBve9z+yC4Ojlp0toRIWy6O6/OFH+3lKzv6aIsEGckUMZQiYCr++1Wb2dAZJxys3boxcsxqo5YB5k2UAsZyUUWZ5FNl+kVUnXXc2t1KMmdjKMXT+5M83z9BSyTA2o44pqFIWw57ihl8z2d1R4yVzRGOpPP8ZN8YLw6mefOmDj7wxrWEgwaHU/lKd/lQqsBtD++i6PoyWFmIJaQUWGa5f8d+fnZgHMfzydsu1uTyeMfSRT70wH+wqasZx/N5ZShNtuihgWSuyFvP7+IPr9x00sk/Qpyt6bUub9y+hrTl8OTeUWzXJ1d00b7GBVJ5l4mCSypYxNMAmpZIkPWdMb7z/KBMymlANQswtdb/MO21TPKZNFtZj+qs412PvcIPdw+XJvroUuJ3LFekPRYiFg6QK7oUXJ+DYxZ5x6PglG4UE3mHn+wdY0dvkoChuGx9e6UESSxsVgb1y2BlIZaGcnvTN5Jj/2gO1/XIFF3cqiWyDQUFx+OlIxNkiy6Od7yzKRYO8LG3bmBLTxuAFAUXdVPuyfN8zY79oxwczXFkIk/KsrG9qR2gnq8JmgrL8TEUGIZiS3crkWBAJuU0qHquRS4mVU/CGUoV2NE7Qt72pqwp/v7Xr2Z5SwQFOJ4mnXc4nMyxfyTL53/jUi46r5Vo0CRoGgQNA3Oy3JrraVy/1JVuuz5HUkVu3L6GT75rM3det4WVbRFA1hsWYqkotzeRoImpIOd4leDSVBAwFGs6YhQcj5TlTAkugdKwHSCZtdl3LEPePmF9DCFqotyTV3A8tIZs0SWTdypJlmqRoMGW7hZaI0GawgHaokE+9taNdLdFCQcNPF/Lfa7B1G0WuVLqcuAa4HygBUgDrwKPaq131mu7jah8EZWXd/zq0/38aM9wJZOZzJZKhoxmClS39ZarsVybe37wKu++9DzCAYO84/Hi4ATo0ngEQ4Hn+0SDAQylWJ2ITinmK4OVhVhaqoffXNLTxtGJAoPjOcbzLqZhsKI1RFM4QNHVJwySN4GU5XDX93czUXCmLEUr7YeotXJP3suHJ/jjb/+C/SNZXG/mqRsGmmXNEcZyNilLEwubFFyfux57BavoEQuZ3HzVZjlPG0jNA0ylVAvwdeCdzFyd+1al1KPAB7TWmVpvvxGVL6IdvSOVIrHlVH5Pe4zbHt7Fzw+NV8otmApcDf5kl8DzAyl2H82QiAf5g7edj6EUQxN5Do5ZxMMBAgo+/Jb1XLYuccJKETJYWYilZabSRO3xEAEDrt56Hl3NEX7/68/NPANTga9LvSEzLUUrRK1FQyatsSAdsXBl+UelIIDGqkqeFz147tA4q9pjLGuKcDiV5+4f7GE0U+T85c0UXV/WeW8w9chgfhu4ktJa5H8HvEgpe9kCbAU+TGmN8m8AV9dh+w0pGjIrRWKr62sNjlulUiBBg1zRIxwwCAdNQgHFytYo/WM50gUXgHReEQooehJRAI5ligQMRVssyHte2yMXlhACmPpgOb0XY9dgipA5dXRUuaRHOGDQHg/THg+Rd70TlqIVoh562mN0NodQqhRctsWC9LRFefFwCtcrnZvlc7SzJUzKclAKWqNBRrNF8o7H+s64nKcNpqYBplLqHZSCy3u11jfP8JHngQeUUp8D/ptS6u1a63+t5T40spnqa/VMdkEBleByOF1gVSLGdZes5DPf30PRzeH6mljIZPv6Zbxt8/LKKhu7hybYtjohwaUQYkbTezE2djVz6ao2nj4whtY+Ra+UtQwYiq09bXz63VvwtaYjHpYZ5GJeREMmd73nEm797oskLZtELETR9XnDmg52D6UxJrOal/aUSvIlc8XKSndXbFjGjdvXyDrvDajWGczfBA4Bt5zic7cA1wM3AEsmwIQTG/vpQSdQNbN8N6mCQ2s0yIbOJu5+7yWVQLL8Hd3t0fn/RwghFq3SzXwrtz2yC6vo0R4P8Ka1y/jX3UdxffjKUwcqYy7lwVXMl0RTiM//59eeUM/5ygu7uO7SHv73zoMUXZ/7fryP26+9aMqSnBJYNqZaB5ivAx7WeoYpYFW01r5S6mFK2c4lb3rQuWl5M/uOZUjmbM7vaqbgeHzyXRdIMCmEqIlEU4h733fplPWXH999DNOQUi9i4cw2tGNw3ML19Qkl9+QcbWy1LlPUTWmm+Fy8CvTUePvnjPJMUNNQrOuMs7GraaF3SQhxDinfzKsXfgApaSYag5yfi1+tM5gtwFxnhmcAiZpmIeuhCiHmi7Q3opHJ+bk41TqDaXDy9cfrvf1zSvUT3FzkbU8KIwshTtv0pWyFWGjT72enez8UC68eZYquVkqtmMPnXleHbS9Zsy1HKYQQJyNth2g0ck6eG+oRYN4w+d9cnE62U5xE9XKUMkhfCDFX0naIRiPn5Lmh1gHmr9T4+8QcVS8PJ4OghRBzJW2HaDRyTp4bahpgaq2frOX3ibmTQdBCiDMhbYdoNHJOnhvq0UUuFoisOy6EOBPSdohGI+fk4iezuIUQQgghRE1JgCmEEEIIIWpqMXSRt0xMTNDW1rbQ+yFqaGJi4pDWes1C78dpOum52Paxr83z7py+07mOav3vaeRtL8LzUdrFc5Sci6JRnO25qE6xbPiCU0q5lDKt6YXeF1FTE4usEZVz8dy2qM5HORfPaXIuikZxVudiwweYQgghhBBicZExmEIIIYQQoqYkwBRCCCGEEDUlAaYQQgghhKgpCTCFEEIIIURNSYAphBBCCCFqSgJMIYQQQghRUxJgCiGEEEKImpIAUwghhBBC1JQEmEIIIYQQoqYkwBRCCCGEEDUlAaYQQgghhKgpCTCFEEIIIURNSYAphBBCCCFqSgJMIYQQQghRUxJgCiGEEEKImpIAUwghhBBC1JQEmEIIIYQQoqYkwBRCCCGEEDUlAaYQQgghhKgpCTCFEEIIIURNSYAphBBCCCFqSgJMIYQQQghRUxJgCiGEEEKImpIAUwghhBBC1JQEmEIIIYQQoqYkwBRCCCGEEDUlAaYQQgghhKgpCTCFEEIIIURNSYAphBBCCCFqSgJMIYQQQghRUxJgCiGEEEKImpIAUwghhBBC1JQEmEIIIYQQoqYkwBRCCCGEEDUlAaYQQgghhKgpCTCFEEIIIURNNXyAqZQ6pJQ6tND7IYSci6JRyLkoGoWci2I2gYXegTlobW1tbQX0Qu+IqCm10DtwBuRcPHcttvNRzsVzl5yLolGc1bnY8BlMIYQQQgixuEiAKYQQQgghakoCTCGEEEIIUVMSYAohhBBCiJqSAHMRytse+45lSGZt9h3LkLe9hd4lUWflYz7bsT7V+0IIIY6brc2UtrR2FsMsclElb3vc8b2XGUoVGBi3WNUeY2VbhNuvvYhoyFzo3RN1UD7mw+kiXS3hE471qd4XQtTf2k8+OufPHvzsNXXcE3Eqs7WZ0pbWVl0ymEqpLqXUJqWUqvrZOqXU/1RKfVEpdUP1e2LuBscthtNF8o7HhOVQcDyG00UGx63KZ072BCZPZ4vP4LjFUKpAtugylCqccKx39I4wlCoAnHAuCCGEmKp3OMOBkRyer6e0mb3DWfpm+Lk4MzXNYCqlDOBvgN+lVD/pVaXU1UAC+AkQm/zo7wG/rZS6Wmstkc5p6GmP0dUSxktpWmNBIkGTrpYwPe2lX20ya3Pbw7uwbI+VbRFuvmozY7li5f1y9tM04Nqt5/HW87tINIXI2x6D4xY97bHKE9tMPxPzo/y774iHOTZRoHc4Q97xaYkGSVk2edsjb3vc9sguUjmHQ8kcy1sibOiMV461EEKIqfK2x/07+jgwmsVUiktWtZHOO9z16CscTFr0Jy00sLW7lY54eKF3d1GrdRf5bwEfAr4P9FMKNL8EBIG/BB4AosAfAL8NfGTyfTFH0ZDJ7ddeVAk+ysFjOb1/2yO7eKYvSSho4Hg+f/iPzwPQHA1w0+XrGEoV2D2UZiRb5Mm9o3S3R3jow9u578f7pnQLANJVsEDK3TSDyTwHRrPkiy4Z26MpZDKes7nrsT1s6IyTzrs8ezCJZXv4WpO3PdZ0xBd694UQomH1Dmd4+kCSouOTdzz6RrK89292oifLxF/a08rhVJ5kzuaex/fIve8s1DrA/CjwuNb6WgCl1CvAF4BvaK3/tOpzNymlLgZuQALM0xYNmWxa3lz5cznLODhuYRU9QgGDouOzfySL7XgUPE00aOJ5GtOAZM7G1+BrzZFUge88O8DLR9K0RoNTugWG00U8X3NgJEfvcIYtPW0L+c8+p1Vni8td4q8eyzCWLeJPNnzjeReAV46kATANhWEqHN/HUAZKKQbHLXb0jnDFxk5pFIUQYpqi45d6gRwfgN7h3JQliF4ZmqA1FgZFZUhS+X4rTk+tA8yNwF9Uvf4/lLrKZxr9/DDwxzXe/pIyfUDyzVdtZmVbBADP13i+Zt9wBsd2aAoHyBRcNnbFCAcNnOLkyASt+dozB0nlPUxD8Y6LVlS6WFuiJk/tG8M0FQ/uPMQd1zVL0FIHMx3HWNjE80sPBN60QSQF16dgu6xsi7Gpq5mDozls1ydgGoxmi3z16X5+tGdYnryFEIKpD/DhoEEkYFJwfDTgT/us48F4zsZxXSLBAEbVdBEZNnZ6ah1gNgPZqtfjk/8fmuGzRyc/L85QecIPlJ60nutPVsZcdsTD3PP4HkxDcXAsR2dTmOf6k/zHwTF8XYr69eR/yZxLU8hAK4MrL+iqdLdbRR/H0wRMg5FMUZ7k6qT6OA6ni4zlitx53RZu/e6LPDeQYjxn4/l6ylN270iOsZzNxd2tXLK6lf7RPM2RAEHTwDRUJRMtx0sIsZTN9AD/po0JfrDrWKVNbQ4qir7G80EpcH1NtuCRLfrc8OWn+e7vXUEiHpJhY6ep1rPIR4GuqtcO8HMgPcNnlwOpGm9/SalM+PE1A+MWX326n3se31MZm3nzVZu5/dcu5EsffB0HRnMUXc1krwDhgEHAAM8Hx9dkHZ/2eJDt65eRtz2e2DPMaLZIJFgKNsMBQyaP1En5OAIk4iFSlsPhlMVvbV+L72vcacGlopShLrg+h5IWz/aNcyxd4FDSIhYuNXjVE7+EEGKpmukB/kOXr2ddZ4xosBQCZRxNUzjAsqZgZUiSq0uB5ki2yC3ffoHe4cyU75EZ5qdW6wzmy8Al5Rda6zTwhlk+ezFwoMbbX1LKE3529I7w1af7MQ3FUKrAbY/swip6xEImn7rmQu74lxfJFd1KkGIaivZ4iNFMAaUgaChWd8T481+7mMMpi/t3HGTngVEmLBeNJhowCQWlqlS9lI9j73CW+3cc4A+/8TxouHBlM7Y3tQMnGjSwXR9fQ6bgElAKy/EIBUyCwEfevJ7WWFC6cIQQguMP8OXMY0c8zF8+sZexjE3RPd6+Ji2XzSuasexS316meHxsUqmsn5ryPfIAf2q1DjAfArad6kNKqQTwbuC+Gm9/yYmGTK7Y2MmP9gwznC4SC5lk8i77R7MUbI+PPPgfxEIBggEDXJ+WaIC733sJm1e08D/++SV+MZAiYCq2dLfxyC8G6U/m6Rst1QELmqo0Q9k0+PnBlEz0qaNoyCQSNBhKFbAn08ypvMv5XU38vP94oj9kwvplLeQdl9GsTU97jL3DWTzfx1ABVrZF6W6PLtQ/QwghGkp15ZWe9hgvHZ7gR7tHKDpeJVtZNjSRpzUaIO/4xIGAoYgEDTZ0NbGxq2nK98gD/KnVNMDUWj8IPDiHj04AKwHJMZ+B6QONp5cuuu3hXRRsj4mCS6aQJRQw2NLdSsg0+Oz1W0nEQwyOW9x9/SUcTlmUR2R+/of7iAZNTKOUrTSUgeMZBEyj9BEki1lPPe0xViVi9I9boGHdshgfe+tGPvq/n2X/SI6gUTomY7kitufj+RqlSpOxfB8KjsenH32Fe99/qTR+QggxqbryynCmgDc9spwUCZh89j1bKbgeF6xs5Ugqz3CmwPb1yyptqoxrn7sFWSpysrj6xEJse7GbbSmraMislLj51DUX8vtff45dgxMYhiIcMLhmywre9/rVwMz1LfO2V0n/v3ljB7/x+tVoNA89M8BQKs+qRIyNXU1z3kd5yjt90ZDJHdddTO9wFtB0t8W45/E9dDVHODJRIGwqwsHScT6UzBEOGMRCAS5c2crz/SmiIRNr8ncvjaAQQpxo+/plnNcWYSRjY7ulGsKuDwEDtnS38rq1CaC0qs83n+0nmXN4qndUJvWcAVmLfJGZPmC5HExMDzzvee8l/M7fP4Nle7TFgly7tZtoyGTfsRMHKm9a3nxCN0L5Qrq4u+20gkVZy/XsREMmW3paASrHKhoyubSnjaLnEQmY9CctfE8Tmcw2m4YqDYEAOptlbJAQQswm0RTiqx96Ezd/+wVeHUoznncIm4rmSICPvnU9UErC9I3k6E9anL+8WapynCEJMBeZ6QOWy8HE9MDT15p//C/b+dHuo/zqBStINIUqfz8RD9I3atERD05ZCqu6G+FkPzuZ2QJgcXrytkfB8WmJmhxJFWmLBgi5pSCyqzlCJKEImiaxyazlRStbyDseN25fIwG9EEKcRDJXWkTkNStaePVomvZ4iC09LZWEynC6VEFFA2M5m80rmuTB/QxIgLnIzJZpnGmm3D2P72E4XWT30cyUTKLra/qTFodTirse280d111cs6BktgBYzF05CzyUKpUeWtYUIhYOEAsrftY3BhouW5/gpivWVbrRh9NF1nfG2dglwbwQQswmb3s8uPMQA+MWWkNTNEB7PETAmHovHUoViARNIgEDmX9wZiTAXIRmyzRWB56zZRIHxy2OpAr4vsb3NQNJq6ZZxuMldzLIRXlmyscu73hk8g7Lm8Ok8y7v2dbNQNJCAeM5h0jQJNEUkpmNQggxR4PjFsmczfldzYxmi6AhHgqQzNmVe+H08n/V74m5q3WhdbGAyoFnecJPuXh3dSaxpz3G6kSUUMAgFDRYlYjVJcv40DP9fP6He7njey9P1hATc1U+dtGgSWssSCRo0tUS5tJV7YxbNn2jOQYmKwbA1OMuhBBidtX3xpztMWbZ7D2WIREPVu6F5fJ/5aWXpTfuzEgG8xw1W1c6wI3b1/L+168mEjTZ2NVU88BExmGenellp8ZyxUpWelV7jM4mj0jQZCxXrIytnY3M6BdCiKlueONq+pN5vvXsAFAq8Xbj9nVT2siT3UPF3NQ9wFRKbQI2AR3M0Gc6WTtT1MH0rvT5muEt4zDPXvWxq56gtbItMuffq8zoF0KI46rbxEQ8SGdzmGTOZl1nfMYyfKc7yVVMVbcAUym1HHgAeHv5RzN8TDO3wuyiBuYrsyhPfvVxur9XySSLRrf2k4/O6XMHP3tNnfdELAXVbWIy5/CJKzcRmawtLPep2qtnBvM+SsHlXwNPAGN13JaYg/nMLMqTX32czu9VMslCCHHc9DZxY5eMXa+negaYbwe+pLX+eB23IU7D8RnepZViRGM72/GTkkkWQojjTrdNlDHsZ6eeAaYBvFDH7xdn6KFnDsm4vAZXq/GTkkkWQojj5tomyhj2s1fPMkU/BS6p4/eLMzDTuDzReOQ4CSHEwpE2+OzVM8D8I+DdSqnr67gNcZpmq48pGoscJyGEWDjSBp+9enaR/zWQBb6plDoCHACmV9zWWutfreM+iGlkXN7iIMdJCCEWjrTBZ6+eAeZ6SjNJ+idfr67jtsRpkHF5i4McJyGEWDjSBp+dugWYWuu19fpuIYQQQgjRuGQtciGEEEIIUVPzsVRkC3AlpS5zKI3F/Fetdabe2xZCCCGEEPOvrgGmUurDwL1AE8eXitRAVin1R1rrv6vn9oUQQgghxPyr51rk/wn4W0oZyz8DXp586yLg94G/VUoNa63/pV77IIQQQggh5l89M5i3ALuBy7TW2aqf/0gp9ffA08CfABJgCiGEEEKcQ+o5yecS4B+mBZcATI6/fABZ6UcIIYQQ4pxTzwBTneJ9XcdtCyGEEEKIBVLPAPMF4HeUUvHpbyilmoDfmfyMEEIIIYQ4h9RzDOY9wHeB55RSfwm8Mvnz8iSfjcB76rh9IYQQQgixAOq5ks/DSqmPA/8v8EWOd4krIAd8XGv9SL22L4QQQgghFkZd62Bqrf9KKfUQ8HZg3eSPy4XWJ+q5bSGEEEIIsTDqvpKP1joFfKve2xFCCCGEEI1B1iIXQgghhBA1VbMMplLqCUrjLN+htXYnX5+K1lr/aq32QQghhBBCLLxadpGvB3yO179cj9S6FEKIc8LaTz46p88d/Ow1dd4TIcRiULMAU2u99mSvhRBCCCHE0iBjMIUQQgghRE3Na4CplAoopa5XSn1EKbViPrfdiPK2x75jGfK2t9C7IsSiI9ePEIuHXK9LT93KFCml7gZ+RWv9hsnXCvgh8BZK4zQ/o5R6k9Z6f732oZHlbY87vvcyw+kiXS1hbr/2IqIhc6F3S4hFQa4fIRYPuV6XpnrWwXwnpYCy7NeAXwLuBn5BaXWfTwIfqeM+1Fze9hgct+hpj53VBTI4bjGcLgIwnC4yOG6xaXlzrXZTiHPa9OundzhLJGic9XV5rpjrhByQSTmi/k7nflere6xYePUMMFcB+6pe/xrQp7X+JIBS6iLgA3Xcfs3V8imspz1GV0u48l097bEa760Q567q6ycRD/Hgzj6SOUeyI0I0oLne7yTTeW6pZ4AZAtyq17/C1IzmAWBlHbdfcydmTTJEguYZPWlFQya3X3uRPKkJcQaiIZObr9rMc/1JWqNBvvTkAcBiIkYAACAASURBVEB6A4RoRHO93/UOZzgwkiMaNOVaPgfUM8AcALYDX57MVq4Hbq96vwvI1nH7p2UuafmpWZMgD+48RDJnk4iHuHH7GjZ2NZ9WoBgNmXLxCHEG8rbHPY/vYShVIBwwiIUNhtM2qxNR6Q04TafTnb4Q3yfODdFQKRkz/T5bvvd2xMM8uPMQA+MWaLhiY4dcy4tcPQPMfwT+TCnVBVwEpIHvV73/WqAhJvhUp+UT8SA3bl/Hxq6mE4LF6qewguPz+R/uxfM1O/aP0p+0WN8Zl5S+EDU208Pf4LjFUKrA3uEMRccnEjQ4ry3K8XUehBCNZKbub6Dys3DQIJN36W6NAnDj9nVyL13k6hlg3kVpHOavAxPAjVrrFIBSqhX4T8D/X8ftz1m569vzNTt6xxhI5lk3S7BYzjrmbY+uljC9x7I4ro+pFH0jOXqHs2zpaZ11W9NvljKgWYjZzTYmq6c9RixkYjs+hoJM3iEfCzGSKVaup3JWZCxXlOtLiAVSvscVHL9ynz0wkqsMMRtOF7Fdn8OpPOm8g+36tMaCdLdFF3rXxVmqW4CptS4CH5r8b7oMpfGXVr22fzrKXd8HRnKgIGgavHo0wz/sOMAb1nZwUXfrjMHgzVdt5tbvvsjAuMUvBlO0RAI8uLOPO67bMuPNbPrN8uarNnPP43tkQLMQM8jbHjt6RxhKFQA4MJLjpcMTtMWC9LTHuPPXt3Drd1/guUNJcrbH3mMZUpaDoRR3fO9lhlIFBsYtVrXHWNkWketLiHk2vXewJRpg5/4xXF9z7+N7+cib1xELG/z7/jFczydgKl6zvJmgaXA4ZZFoCi30P0GchXpmMGeltfYpZTUbQrnru3c4w/07+ti5f4zhTJFfDKQImgaXr+/gzndv4b4f75sSDI7liri+prstSm44y3ltUZI5Z9aBydMnCT3Xn5RSRUJMk7c9eoezPLizj5GMzaFkjoLjA3DLd15gTSJeCRhvfNNanu5LopTC9TXpgsOf//PLmIYiW3QZy9okYiG5voRYANX3vGTO4dqtK3lq3yiZgstP9o3w0pEUaxJxHK9UfL3oag4lLaIhkwd3HuKO65or3yO9EItPXQPMyeLqVwKbgA5OHCCltdZ/Uc99mKtoyGRLTxs3XbGel4+kGc0UcTUUXZ+neke55dsvoDVM5B3Gcza9wxm622IEDMXhVAHP1xxJ5dnQGT9hYHL1IObqUg3bVif40Z5hKVUkxKRyxqNvJEd/0uL85c0si4cpej7hgEHfSI684zGUKvDEnmN869kBrKKL65cal6CpyBQcwkGTwXEL2/MZTOV587KYXF9CzLNy7+BgMg8KwkED1/dxPR+twXE1vcMZ3NLzI4aC5S0RVrRESOZseoezPPTMoSm9fjLkZfGo50o+m4CHgc3MPvJeAw0RYJZt7GriovNaOTSWw3V16YcK0pZN72gpk2Io+Osn9xMPmYzlbDzts6W7lbztcd0l3SeMsSx318VCJp+65kIs2628L6WKhDiunPGIBE1QkHc81i6LAYqRTJHWWJCQadA3luVzP3iVoXQepQwUPorSWMzdxQxNYRNDKV67qg3P1zJhQIgFEA2ZfPCyNdx4/zN4vub5Q0lcX+PrUjAZMMDTBkHDw9fQFg2wJhHD9TVdLWEKjlspWzSUKnDbw7sour4MKVsk6pnB/CKwAfgT4AlgrI7bOm3V4ynztsdz/Um2rU6QaArxmXdv4fptPfx///oqLx0u9eQfzZS6w6EUFe89mqGjKUxTOFCa4DOawzDgz/75ZdYkjo/5qp7tajs+n/7+K9z7vksrF4aUKhLiuOpSYJetS/CWTctY1R5nQ2cTY7kiHfEwT+4d5k//6SUcz8P1IR4yShlMDbYPyvfJAs2RAJ6vWdcZZ2NX00L/04RYcpJZmw898B8kczZKgQGgFKahiIcDrGyNMpItUHRNYkHFxuUt3PLOzfha0xEPc9djuytli7b0tGLZHqahZMjLIlHPAPMtwOe11p+r4zbOSPXA45aoyS8G0mTyDq2xIN/66OUkmkK8edMyWqNB7viXl/F8TdZ2SOUdoBRgtkSCdLWEGE7bXHReM+mCBxr6RnMUHK9yAVTPdg0FDKyiJxeGELOYOh76IJ97fC9aw9buVu56z1aik70GvtZoDUpBUySA62uKk/1sGvC1ZsOyJv77Va+pTNITQsyv5/qT2K6PUqA1GKWOCTRgFV0OjGZpiQbZtCzO0WyRPUMZfvv+Z/jGR7dzOJVnIGmxobMJx/X5yJvX853nB2VI2SJSzwCzCPTV8fvPWPXA495hi/GcTcBQTFgOz/UnufLCFUCpu3ztshhP9Y6RsmxcT6MpZUoGUnnGLJuupjAt0RCJuFHpwosETbpawnTEwwyOW3zqmgv59PdfwSp6rGyLyIUhxElEQyaRoMlQKk/R8ckWXX7Wl+TW775ISzTA0YkiQUPha4X2Na7r0xYNkMw5OJO9DMbkoJz7/72PO2ep6iCEqK9tqxO0x0szwUMBg/91wzbue6KXp/aP4vgax9e4vk1Xc4SJvIPj+WSKDv/1qz9nzbIo/UkLFFyxYRkXdbdyUXerDClbROoZYP4AuAL4mzpu44xUd8Nt7IqRLbqVDOa21YnK56Ihk994/WpeGJwgV3SxHR+f0s2r4Hg4rs+KlgjpvMsnrtxEJGhW6u51xMNTShDded0WGZwsxBz1tMdYlYhxYDQHQCRokMzZpPIOTeEA569oJpmzmcg7eBpWtUY5ry3K/uEshqnA1+wbztA7DLc9vIt733+pXHdCzLNEU4hvffTyKUPQ3vv6Hp49NI7rlVaSjgZNrr5oBbuPTjD5fMj+4QxD6QKblzdjez43bl9TuX6l92/xqGeA+UfAT5RSfwx8UWtt13Fbp2X65JryGMwLVrYylisSDZmVCTrfeLafdN6h6HigyrOVFLGgQSwcIGQahIMG3W2xSs2uRFOIfccyU0oQjeWKJ70wpOC6EMdFQyZ3XHcxLx+e4MtPHaDo+HQ2hwFNMufQ3R4hYBgcSxfwNSQtm49csY4n941i2R4vDKawHZegaZApuDIsRYgFEg2ZrOmIA7BrcILWSICOphC2V+o6v2xDBw892w9aodCYQHM0iFIK2/NZ3xlnY5dcu4tRPQPMHUAcuBv4rFLqCOBN+4zWWm+o4z7MqnpyTTRkcsXGzuOzvcMmn7r6QnYPTXA0XSwVXw+YRA3FumVxDKW46c1ruXRVO59+tNT1fddjr0xZYrI6S3qq8SKzrVYixFIWDZm8fl1iSrcYwEuHU9zynV2MZQq4vs/qRIwjqQL3/nAf7fEgH3vLBvJO6YHN19AcDciwFCEWQHUVlUNJi7zjYqB43do2/uBXN9HVFGbX4QmefHUEgIChWL8sRkdzhOZwgA+/ZR0Xd7fJ/XCRqmeA2U9pLO+iUJ7t/eqxDJbt8Rt/s5N1y+IcHMvhuJqmsIlSinDAYF1nnLdtXs7guFWZWLCjd4yDYxadTWHu/PUtJJpCcy5BNL0Au2RbxGIxH5n36ZUWJvIOacum6Po4HhwcLQWS0aBJ2nL45xePcCxdIGAabO1u5VNXXyi9A0IsgPK9reB4JLNFfA0hUzGcttnU1cRDz/Tz6lAG19MYpZEtRIImA0mLtR1xvvv8YS7ublvof8aCW6w9nPVcKvKX6/XdtVRdBD0cKI3z0lpTcDxWtkZYO5naNw1FZ3NoxizlgZEcGhgcz9M3kuO2R3ZVShHNJVA8nWynEI1iPjPv1Q3sttUJYuEAEwUXQ0EsHMB2S3X0miMBDENx/vJm8o7Hh9+y7oQVuBZTAy3EYna80LqPr8H2fDxfcV5bGFCTlVyCdDQFMA0T3/cJBU3ydgHb8yXhwuLu4VyQpSJr5Wyj+rztcfsjLzGQtFiViPFbb1rNcwMpPM+vLE3Xk4jOunpAdUmV+57o5eeHxs+oFJEUXBeNarZrrHqd8HrXpZupgf3H/7KdW779AvtHshhK8brXLOOdF63k0lXtlYByfWecSDAgvQNCLJDyvW1H7wjsLE2OBbjpig1s7GqiqyXMUKpAPByiPRZk3HKIBE1aY0Gik9VYlnrCpbqHcyhVYEfvCFds7KzME2nkuKHuAaZS6peAq4DlwL1a6z1KqSZgG/Ci1jp1Jt9bi6i+dzjLjv2j2I5P/7jFb7x+Fb98/jL6k3nOawtXLoJoyKxM4JmuvMTkXe/Zym2P7DrjUkRScF00mtmusepxVQPjFqvaY3UtvzXbEJKv/PYb6B3OApqNXc2V67/6YQ2Q3gEhFlB5jkP1ssjl+2o5+Pzq0/2YhqIlEuSDb1rNttUJqboyqZwFLre3X326nx/tGebmqzZPqVTTiJnNei4VaQIPAe/leG3VrwN7AJfSMpKfAz5zJt9fm3GL+vgoUV1aJ/WO67ac0RNBoinEve+7tKGfJoQ4HbNdY+Wfm4ZiVXuMD75pdeWJuh5mG0JSerhrPeHz0x/WpHdAiIU1Wy/d9OBzZVuk0pbMltRZamYKxIfTRZ7rTzZ870w9M5h/AlxPqVzR/wF2l9/QWheUUv8EXM0ZBpi1GLe4sauZKzZ20J/MszoRrWRBzvQgSRZSnEtmu8aqf159Q6iXsx1CItelEAtvtutQhoid2kxZ4G2rE1NeN2LvTD0DzBuBB7XWX1BKdczw/m5KAeYZqcVJWaq1d2YZyzPR6OMlhKh2sqzDfN8Q5hokyjUmxOJRfb3KQ+DJzdTuNnpgXs8Acy1w70neTwHtZ7OB8kzu6b/g07nJzFd2YzHPBBNL1/Tro/ramunaW0hyjQmxeMj1evrONF5ZqAfvegaYGSBxkvc3AiNns4GZTlBgTiftfP/CpdalWOyqr7dEPAgokjmbRDzEjdvXTJlosxDkGhNi8ZDr9exMbY9nb4MXMpCvZ4D5FPBBpdTd099QSrUDN1Eam3nGZjpBy3+u/llPe4ze4SwTVpGRrE1nU4hHXhhiJFMkFja587otdR9QLLUuxWKWtz2e2HOM3mNZIkGTPUezRIIG8VCAp3pH2XM0w+YVTdz6rgsrsz+BeX2Ik2tMiMVDrtdTq06EAVOqZvQOZ+kbyREMGDzVO8qrRzO8ZkUzt77rgikz8BcykK9ngPlpSkHmE8A/TP7sEqXUJuCTlJaR/OzZbGC2E7Q8pT8WNomFAtz+yEs8ufcYo1kHgKBp0BYNYBgGjutz28O7uPf9lwL1uyEuhvESQswkmbW55du/4PmBCSzbBSAWMmiOhPDjMGE52I7HaLbIrf/0Iq6np2Q45+upWa4xIRYPuV5PbnqPkevDz/rGQMNl69sBxaGkRcHxKDg+tuNxLJPnSCpP0DTobA5z4/Y1dLctXCBfz5V8nlVKXQ98Bfj7yR9/jlLJomHg3VrrV85mG9GQyc1XbWbngTFaIya9w1m626Jc/9oevvzTA1hFj08/+gpHJwokcw7+ZEki1/OxHB9TaWJhE8v26B3O8tAzh2ZMI9eqO11ms4rFJpm1+f2vP8fP+sbwfDAUKAVF18AsOrxtcyeHUxaup/F8zUimSNA0mMg7BAxFPByY16dmucaEWDzkei2ZKcaozjz2J/PkHY+i4+P6mhcH0rRGg/i+T9Hx8H2NrxXjOZdd9gThgIFpKPqTFus747MuFlNvdS20rrV+VCm1Fng7cAGl4HIf8AOttXW235+3Pe56bDdP9Y6SshxiQUU4GGBFa4SjEwXOX96MZXtEQyaq6u+VlpQzuGBFG56vWdkWATRDqQLZoks679A7nGFLT5sMRBZLVt72uO3hXbx0JI3nl0rGehrQkCu65G3F84cmiIYCJGJBWiJBekczZPIerbEA21a3k8670v0lztraTz46p88d/Ow1dd4TIWprtrkkBccnEQ+SzDmsTkRJF1z2DKVxPE3BdpgoBJmwHAzDAMArLT5Iruhh2R4BU7HJNBhOFxnLFRckkK/7Sj5a6yLwvcn/aqp3OMurxzLkii4FxyPvgMIlU3SJBQ3yjsfqRIzrLjmPSNCkfzzHwVGLaMBAa4OPvXU9bbEQHfEw+0cy9I3lODpRWvru/h0H+cy7t8hAZLFkDY5bWLZHLGSSKzq4/vH3NOBrTdHziAQNlFIcSxcYzTiEAga2q7nhjWtoiwWl+0sIIWYxPcboHc7y4M6DDCQtVrZF+cSVm0jEw7z3SzuwvVI3rOND3vExlCIWNNBKsbw5zHCmSKbgEAsFiAYNbM+nJxGlpz22IDPJF+1a5Hnb48GdfYxkihRdv3pBHrIFF88z+Ohb1vEvu47wxR/30tUS4j2X9vDAzkO4no9SoFAUHJ+/ePQleoctsoXSOqgBQzGUylcG1CbiocpYMsnEiKWiZ3IJSE9rCrbHeN6Z8r4GXhnKEDAUvtZEAwZF18c0wPUUCuRhTAghTmL6XJKC401ZwvqmK9axe2gCy/Yqf8fXEAmYBEImjqcJmAYD43lMVcpk+r4mHDR492u72dDZRN72FmRZyboGmEqpG4D/B9gEzFRsXWutz2gfBsctkjmHjZ1N7HImcCaDTA2YhiISMDgwmuPpA+NYtseLgz6DyQLRkMGyplJE/41n+zk4avHykTSRyYxnUzhAwDSIhwPcv+MA6bxLIh7kE1eeX1k/VYiloHqJsvufOshz/UmKbulRTld9zp0c3Jx3StdgwfHxfM3XnjnERd2tcs0IIcQspk926h3OTFnCGjTbVidoCgdIF1y0hrCpWNsRoy0WYmiiwHC6gOP6WJNtcc72cNM+9z7+KsGAwZbzWrEcD8f1GUrpeeuJreda5LcB/xM4Bvw7MF7L7+9pj5GIh3j1WIZIwCDcHMKyPRxfEwuatMdDrOtsYiLvYLs+vtYoBWsScT74ptUsb4nw+R/uA8DTGl9DWyzEJ962iSdePcZYzmH30TTndzWTzDlEgobcKMWSU71EmWW77D6aIRwwyBZLDV11oKlUaRJQJGgSNhVDqYIMKRFCiFOonuw02xLW3/zo5dz87RcoOB5rOqJ84I1r+dozB3nxcJFM0UVVTTTRgOuD7Wl87TOQtEgXHAqOT2ssSEc8PC//rnpmMH8P+DfgnVpr5xSfPUOasGkQDhp0xMN0Noe55R2b6RvNsm11gsMpi9ZokFzRpeh6oKmsnQylckZ5x6MtGuS8tigbu5q4sLuFx3cfoykcAA0Fx2NdZ1y6xsWSdsMbV9M7nOPuH+zBdn3CAYOAUhRcD19DwADTMAgFTMIBhWkarErE5LoRQojTMNsS1t3tUe77zW08159k2+oEY7ki6bzHps4mdh9Ns7I1woGRLL6vMYxSmSLb9ZgouAxnNKahWLcsTjwcYCxXrHvtb6hvgNkCfLNewWW5izweDmCnNK6vaYmUAsUNXU1A6UC9aX07T/UmiYVMOpvD3HzV5soB+/ivbOI//+1OHLdUw+++G7aRiIcq4yGu2NjBjdvXSde4WLKm12J70/oE+4dzDGcKbOpqJme7BE2DvpEMYzkH19fEQ2Hufs9Wtq1NyHUjhBCnaabyTdXjKH+0Z5ibr9pMIh5kx1CacKBUGu6SVe0EDMV//eWNrFsW55Zvv8BLR9IETAMUGIZiZVtk3h786xlgPg+sqteXlwfGHhjJoRQkYqWJONVdctGQyU1XbGAoVSQSNHF9PSVy3z00Qd72MAyDvO2xe2iCKy9csaDFXxdqzVCx8Brx2FfPcEzmHD5x5SZA8eDOPpI5h55EM9e/toc/fXgX43kXTWn8T971GubfIIQ4dzRiOzkfps82H8sVuXH7OgaSeTyt6RvJAaUhSstbwliTD//xsInt+LxuTTsff9vGeV3St54B5m3Ad5RS39FaP1/rLy8PjH3pcIqv/LSPouvPOMt7Y1cT6zrjM1ax37Y6QWusVEuqNRZk2+pE5bsXYtyY1Nxcuhr12E+f4VhunKq7cPK2x+r2GANJC19De/z4tSSEELXSqO3kfJht5cJ1nXGGUgVaY0GiQXPKe6Ua3xALmdz56ycuiV3vYL2eK/k8qZT6EPC0Uupp4CDgnfgx/aGz2c53nz9M0fWJTa7qM/2XdLLlqBJNIb710csrYxoSTaEFfTqSmptLV6Me+9mun/JDWDJrc9vDuyh6PldsWMbVW1fwy+cvn5fxPUKIpaVR28n5csMb11Bei7zcFpfb5454+ITVek7WGzsfwXo9Z5FfBjwABIG3TP43nQbOOMAsn2ymoSi6/qwDV2cbz1D+xV954YrKz2b7hc9H4DnbE4o49zXysY+GTHraY1PO//zk8qr3/XgfPz84TihocH5XM1u62yS4FELURSO3k/U0PTa5+arNU9rjcnwzve2dqe0um49gvZ5d5F8AbOA64Kda61StN3CmJ1ve9rj9kZcYSFqsSsS447qLiYbMWX/h85WWP1m2tXrfl+L4k3NZ+Zgu1HqxpzJT43bXY7t59ViG4XSBgKmwnVIvwlJp8IUQ82/6PRJg37FMw7WZtVYdmwylCqVeo8lhgeV4ZKbY4GSxy3wE6/UMMLcCf661/pd6bWAuAdlMeoezUyrl9w5n2dLTOusvfD7T8icb/1nujrRsj5VtkSU1/uRcNdvDTiOZfv7vPDDKjv2jFB2fvOOxeUUzK1oj3HndlobbdyHE4jVT0FS+Ry6l8ZjVsUksbGIVPUxDVeKRnvbYjL+Lk8UuZxo/nY56BpjDlDKYdXVmE3L0CZXyy9810y+8EdLyedvjtkd28UxfklCwtLj9Uht/ci6a7WGnkUw//7uaI6BBAa3RIB968zretnn5Odu4CyHm36kCyKU0HrM6NumIh6cs+1juAp/pd3Gq2KXeE5rrGWDeD3xQKXWf1tqt43ZO20yV8stm+oXXO9KfS7f34LiFVfQIBYxKd2RHPLwkugfOFTMd54LjlpY5rSyLo0/6HQthpm6p6utHgkshRK0NjlsMpQrkHQ9v2vKGeduj4Pgk4kGSOWdJjMesjk2mxyOzBZLzkaU8mXoGmE8B11KaRf5XQB8nziJHa/2TOu7DjGarlH+qv1OPSH+uaf6e9tjxkgNhk09dfeGCLF4vzsxMxxngm88OEjANXKW5bH37lIedRjL9/D/d60cIIU5HRzzMwLhVKSNYXt5w6uIPIT5x5aZ5re3YCKa3xycLJBeq7CLUN8D8YdWfv8KJqRk1+bMFOSsW8pdeba5p/vIJ1DucARTJnL1kugfOBTMdZ4CRTJGe9igAN12xYdE0ko1y/Qghzk1juSLntUWJhwO0RoOVKjFTF3+wiQTNRdNu1lMjtsn1DDB/t47fvWhVd5NCaa3zRLy0CtFc0vwPPdNfWbbvdP6eWFgd8TDhgFGZoNXTHiOZs9k/ksWyPdpiQbrbogu9m0II0RA64mGOpPJMWA65olvJYHbEwwQMRdKyWdsRl3tfA6tnofUH6vXdi9X01D5okjmHRDzI716xjom8Td6efYm9mZbtiwTNk3ZTSlmjhVdeQzZTcEHBh9+8nt7hLF/44V7SeYdgwOC81uisdVyFEOJcNT3pUv7zWK7IqvYYy5o8QqZRWRDlrsd288LhFJ6n6WmPLPDei5OpZwZTTFMdIA4kLTTQFA5wdKLIrd99kbzt0RwNcM/1l7C+s4nDKQtQdLeVgo+OeHjGZftms5TKODSicsOZsmx6j2UZGLfI2x6/ff8zdDaH2T2UxteagusTCxnyJC6EWFJmS7p0tYT5+K9sIhY2sV2fg2M57n/qIA+Yh8gVXRxX4/mawWRBhoc1sJoFmEqpX4Ljk3bKr09lISb5LJTqmV6rEjHKF5Pna9IFF0P7DCZtbvnOC3haU3R8lFKEAoqupghrl8W49V0XVoLN8njMjV1NAFNen6oGlqit6ZnicsM5lCpwKGkxYRVJWi4KsGyPlGVTdH1MA9oiAd5x8cqF/icIIcS8qr5HHRjJMVGwaQ4HGMsW+aNvPk++4HF4Io+nNb8YSBEJGkSCJpbt4vmaIxN5YiHJkzWqWh6ZfwO0UiqqtbbLr0/y+QWd5DPfZlqtBUpB4Zd/2oc9UCq7oIGB8TxoXblwxrIeE5bL4VSewymLjV3N3P7ILnb0joGCy9Z14PoeTx8YJ2Ao3rxxGXdcd3FD1O9cCmbKFJcbzrzjMWHZZIulAgoacHyNYx8vqBAOmPzT84f54SvHuPPXt0g3uRBiSSjfowbH87x6dALL8fE1GAq0phIlBE2FrzXxiMny5jCRgMFEwaXg+Hz60Ve49/2XSu9cA6plgHkTk/fPydcyyWfSqbqqj6ULvGZFM3uPZXG80gVmGoqgqUApbM/HNFTpYkMxOG7Rn8xjuz5QWirrcCpPtuhiKMXBsVwlW7mQNbCWipkyxeWG00tpQgEDvzDz340GA3S1RNg/ksV2fG57ZBf3vm9qYynjaIVYOGs/+ehC78I5q1wd5VvP9vPiQApDaXxdyktVlwX2NbTHQmzqbObIRJ6JvINllybIWpPto/TONZ6aBZha63+Y9lom+Uw62RrnD+48xMC4he9rOuIhjkzk8X0IBwxuu+ZC+pMWP+tLMpF32LS8mY1dTeQnZx0HAwZKwYqWCCPZAoatAEjEQlMKrVZfeBKs1N5MmeLjZaWyDCSz/I9/foVxy8H1jyf1FRAPmyTiIfqTFoahyORdeoczlclbgIyjFUKcs6Ihk2u3dvP3/36QQqqAr3UplzKp/OcVLRHedfEK/urJ/ShAKTivNUp7PMjeYxk64mHp/WkwMnhhHpxsjfNkzub8rmYKjsflGzr44hP7QIHt+HzmsT1M5B1cz6ctFmDT8qbKjGSr6LF+WZzfe+sGLjivlbse283B0RyJeIi73rN1xiBEJv3Ux8mK3D64s4+f7htlwrKZTDhXGsyWaJBNXc387uVrufWfdmEVXQZTFvfv6COdd+lqCXPDG9fIOFohxDkt0RTiOx+7gp0HRsnmHb7+zAAHxzKkCqVhY66v2X00zRee2EvAMDCUoi0a5H2v7+ZvfnKAn+4dpTUWAVZg2gAAIABJREFU5FsfvVyCzAZStwBTKXU5cA1wPtACpIFXgUe11jvrtd1GNJc1ztd1xtm2up1gwMTxfJQqTQbxfI2vwXZhKFXguf4kQ6lCqUvV9fnaM/3c+/5LueO6i6eUephpCUmZ9FM/MxW5LQ9lKDg+nmbKU3nAgKABy5qDtMZCbOhsouB4eFpzJFWgKRyYPFZaxtEKIc55iaYQ12w9j7zt8WTvKPtHc4Bfed/zIW05bF7RQjhosnZZjOZIiEy+tBL1hOXwXH+SKy9csUD/AjFdzQNMpVQL8HXgnUy9p5bdqpR6FPiA1jpT6+03qlOtcd4RD3M4ledXL+jk0Gie7kSE3UcyDE0U0BpiYYOVbRHaoqWns0zRJR4KTBl/Uu52ny1LKZN+5ldPe4zViSiHxnIUHAPH0xgKUNAUMnE8GE7bJOIhOpvDDCRLDwgBg0qpjo1dMo5WCLF0REMmd163hT/+5i/49wOjuJ6uPKB7KExT0dkc5tZ3XQhAayxYWU5y2+rEwu68mKIeGcxvA1dSWov874AXKWUvW4CtwIcprVH+DeDqOmx/USkvVF8uaRMOGPzZtRdwcXcbyZzNYy8Nsa4jSks0zDefHeALT+zj+f5xio6H6/m0x0NTAsWTZSkXeuH7paa85n3vcIaC45PJ2/yvf+vlxcE0qXypZNErRya443svEwuZaEqZzXIpqupjJJlmIcRSEw4Y2G6p4oZpKNrjIVqjIYquz1iuyKblzXzro5dXirBL93hjqWmAqZR6B6Xg8l6t9c0zfOR54AGl1OeA/6aUervW+l9ruQ+L0eC4xVCqwN7hDLbjY5qKO6/bwn0/3sdwusj+kTDXv7aHgaRFtuhSdP1KCaOLVk4NPE6VpWzE9UrPZdGQyZaeNgB2DU4wMF5Ao9GUJkjmXZ+RTJGQadAUDpDMORxO5YkEjQXdbyGEWAh52+PW777Ajv2jOJ6u1DIMBww2dTVhGoquljAd8XBlKNgVGzsZHLeIhmRd8kZS6wzmbwKHgFtO8blbgOuBG4AlH2B2xMP4k4XVwwEDq+ix88AYfSM5IkGToVSBL//0AP1JC19DJGBgKnA0PLmvNFal3BUuWcpGVpod6VdN9okGDDqbw7REgoxkioQDBvfvOFCZ5CMTsYQQS8nguMVYzpk6k9yAzpYwd7/3Eiy7tC75PY/vmVwBKAgokjlb2swGU+s0yeuAh7XWJyuwjtbaBx4GXl/j7S865VnhWkMsZLKhs4nO5jA/3D1Ef9Ji73CGcMCg6Pqcv7yZNR0xvvSB1/GJt5/Plu5WbNdnKFVaLqusnKWUi6yxdLfFCJiKcknTlmiQi7vbuOvdW7n1XRcQC5uM5Wx+1pfE83VliIMQQiwVPe0x1i2L0RYLAqXsZXs0wFdufAPd7VE2LW9mLFesDAXrT+YZSJbaSWkzG0utA8xuSjPF5+JVoKfG2190ymMmQwGD/8vevcfHcdaH/v88M3tfXVeWHMeyfFOci50LJhCMoRQaUiApgXCgPSkNFPr6ld8pPYeW0oYmpNRNmtDAr9ByWqCHFHwolEBDXAjQNEkTEsdJmhoSO/FNvkmyZa2s1a72PrMzz++P2d2sZNmS413r9n2/XnrZ0o5mRjvPPPud5/J91nY28aE3ruSWTSsZzzusW9rMivYIv/Pm1SxrC2EaijWdUTauinHDFcsZHi9wMO6tcd0RDc72nyKmMZotsjIWxWca+AxF2G9wz/uuINYUYDRbpGi7NAV9oKFgOzIRSwix6FTGrn/4javwGV7r5XjRZe+J8eo2laFgAD2xcHnpZaTOnGPq3UXeAsx0ZngaaKrz8eed2jGTy9pCbO7tBKj+bE1nlA3L29iwvG1Ct/fgWI4V7RE6mxz8psHO/gSbezul1XIO626P0Bb1E/EbaKVYs6SJnFWqvla55pt7O7hl0+rqmvJCCLGYhAMmr1vVgc80sB0XQ8HS5tCE12uHggEyLGwOqneAaXDm9cen2n5RO92Yyal+Vpmck7ccCrZDZ3OQkXSRgbEc33qmn0f3xmX8yRwWDpjc9q7L+I2v7SBnOQynC9WW58kpq0azxVk+WyGEmD1rOptY2hJiNGvREQ2wrC08Ib/z5AmrMnl17mlEmqJ3KaVmkun0tQ049rx0uhyZU90wtXkuY1E/7796Bd97fgDTUJI8fR7IWaVqUvWQ32Q0W6ym1qhNWSWrLQkhFrPRbJFVHVEuaAnh9xnc9eOXKdqu1IvzSCMCzJvLXzNxNq2dgol5LhNZm55YmGVtIUmePk90t0fOeL1ktSUhhJhYV1ayq0hDyvxS7wDzrXXen5hkcp5LWellfpkujZSstiSEEKcOG6qkJZJ6cf6oa4CptX6invsTpzpdgCJPc/PHmZLdSx5TIYTw1NaVUi/OP43oIhcNJqvxLGxyfYUQYiKpF+cfCTCFEEIsOqtufWi2T0GIBW3RpwkSQgghhBD1NR9aMFtSqRRtbW2zfR6ijlKp1FGt9crZPo+zJGVxgZqH5fGMZbHtY/90nk9nbjibe3M236MznedCK4ti/jrXsqimWTZ81imlSngtrePTbSvmldQ8q0SlLC5s86o8Sllc0KQsirninMrinA8whRBCCCHE/CJjMIUQQgghRF1JgCmEEEIIIepKAkwhhBBCCFFXEmAKIYQQQoi6kgBTCCGEEELUlQSYQgghhBCiriTAFEIIIYQQdSUBphBCCCGEqCsJMIUQQgghRF1JgCmEEEIIIepKAkwhhBBCCFFXEmAKIYQQQoi6kgBTCCGEEELUlQSYQgghhBCiriTAFEIIIYQQdSUBphBCCCGEqCsJMIUQQgghRF1JgCmEEEIIIepKAkwhhBBCCFFXEmAKIYQQQoi6kgBTCCGEEELUlQSYQgghhBCiriTAFEIIIYQQdSUBphBCCCGEqCsJMIUQQgghRF1JgCmEEEIIIepKAkwhhBBCCFFXEmAKIYQQQoi6kgBTCCGEEELUlQSYQgghhBCiriTAFEIIIYQQdSUBphBCCCGEqCsJMIUQQgghRF1JgCmEEEIIIepKAkwhhBBCCFFXEmAKIYQQQoi6mvMBplLqqFLq6GyfhxBSFsVcIWVRzBVSFsXp+Gb7BGagtbW1tRXQs30ioq7UbJ/AqyBlceGab+VRyuLCJWVRzBXnVBbnfAumEEIIIYSYXyTAFEIIIYQQdSUBphBCCCGEqCsJMIUQQgghRF3N+wAzbzkcGE6Tt5zZPhUhFiW5BxcXud5CiJk477PIlVIxYFxrXTrXfeUthy0/eon4eJGuliB33LCecMCsw1kKIWZC7sHFRa63aKRVtz40o+2O3HN9g89E1MN5bcFUSq0FRoB312N/g2M54uNFAOLjRQbHcvXYrRBihuQeXFzkegshZqquLZhKqY3TbLICL6/Smsq2Wuudr/Z43e0RulqC1afp7vbIq92VEOJVkHtwcZHrLYSYqXp3kT/P9MlWNfC5mu9fdf9KOGByxw3rGRzL0d0eka4aIc4zuQcXF7neQoiZasQYzAzwtfK/k3UAHwe+B7xcj4OFAyYXLW2ufp+3nAmV3+TvhRDnrva+EovL5DoXvPLQF08Dit6uJqlrhRB1DzCvB/4O+HXgf2mtf1D7YnkM5seB72qtH6jzsasD0IeSBSIBk9uuv4wv/8cBGZAuRJ1UAomtO46SyFrEogFAk8jaco8tUnnL4Y5tu9jeNwoKNq9dwpYbN1TLgTzkC7E41TXA1Fr/RCm1HrgLuF8p9VPg97TW/fU8TsXkimtwLMdQssD+eBrLdrn1X17ENBSmoaoD0ic/eb/aYwmxmHiBZYatOw7Tn8jTn8jR3R5mvGBjKEVT0HfO95iY205XB/bF0+w9kaFYclHAQCJXLQcy61yIxavuXeRa6xzwB0qpfwL+AXhZKbUF+EI9jzNVxdXdHiESMLFsl4DPmyAfCZoUbfdVDUivVKgd0SD3PrxXKkmxKFXutcMjWfoTOVZ1REnlbfKWQ3s0wFUrWhjPO9V7TB7GFp7TBYp5y2HrjqPE03myxRJtET8rYpFqXTvVrHN5ABFicWhYHkyt9fNKqauBPwL+DPggXpA53SSgGTldxXXney7n9m27yBUdlrWF+NR1l3AsmaNou/TF0/R2Nc+o66a2Qg36DHKWU5eWUCHmm8q9FvKboGA8bxPxG3S3R/CZBje/fhVtET8d0WC1lVO6zBeW09W3ffE0R05mUSgCPoNlrSE+ce26avBZsF1iUX+1PMiYXSEWj4YmWtdaO8DnlFLfB74C/CN1CjBPly4j1hTgC++/asIEhK07jrL94EnQsLm3gy03Xg5wxq6b2go1Zznn1BIqxHxWe69dszrGeL7EyWyR/cMZWiI+7n9+gE+/81LufXhvtZVz3dJmeRhbQKaqbyutl0cTOVJ5G0NBfyLPXT9+mTtvvLza6xOLBvjEtRdNeLgXQix852UlH631QeDtSqn3Aj3AL851n2dKl1E7y/HAcJqBRA7LdtHA3hMZ+uJpQn6zGkAOJQts7xthc29ndT+1FWqlJXQ0W5RuP7HoVO61vniavniWbz97lAvbwhTsLCvaIiSyFjv7ExNaOfO2w5rOqDyMLRBT1bcHhtMkshYXX9DMS8dTOI7GUJDOl9jZn2AoWSBvOziuJuQ3pd4UYpE5r0tFTp5Vfq4qgWRlbdzacT+VSrC7PcKKWMR7ys7ZjKQLfPmxPu74tfV0tQQZShYYGMvxrWf6eXRvvNqSOVWFGmsK1PP0hZhXtu44wlN9o6TyNs1BH+GAge26+AzFpcta6WqJEx8vsnntEm7ZtFJarBaYyemJah/C39TbyfNHEmSKJQ6NZLAcl6OJLOl8idby8AkhxOJy3tcir7fasZKxqB9QJLLWhG7vLTdu4Ke7h/iHnx0ikbP5r6Nj1W6cHYdG+cbTh4FTWzKnyvcmxGKTyFj86MVjHBzJYpdcQn4Dy3ExHNh7Is2JVIEvPrKPT7/zMo4l84CW4HIRCAdMPnXdJezsT6CU4um+k+Rth0yxxJ8+sAufYbCyI0JzyM9otigP6EIsMvM+wKwdK9mfyOO6GsNQOEldbX3si2f42YE4I1mL8bxNS8hHMmez49Aoj+wZYnAsT8lxiQZ9p7RkCrGYJTIW7//q0ySzFkXHpSnow3HBdjVFx8VxNAXb5WA8ywM/H+SFwTHi4xY9sTBbbrxc7qEFYqoJkXnL4d6H9zKULLBveJxkzqKkvbWA0/kShqE4OprjtSvbpQVTiEVoXgeYlVmKLWGT48kiXS0Bdh9Lk87btEb8RAK+CelV1nREeWEwSbHksmswyf/OWiRyFlp7H5JQAiSdhhAVO/sTpHI2SimCpsGH3riSZw8neObgKJbzyny9l4dS7BtOU7AdWsN+BhI5+uJpLu9um8WzF/VwuhRFffE0h8qt2omMRaU4aMAFTAXpok1/IsfdP3lZHjiEWGTmbYBZu2rP0USOJdEAlq3pbg9jNwfx+wwe3XOCoWShOvEgZzu0Rf20RwIcjGc4lipQsEv4DYNoyMRQBgXbYbVMThACgI09MVojfpI5m0jQxxUXtvHEvpNEAj5M28FnGtiOS6GkAQcAy9GEAuC1ZYn5bqoURd3tEbbuOMrAWA6r5KKUwlQaFwj6DBxXE/QZZC2H48k8qbw94YFDcqUKsfDN2wCzUullrRJjWYsl0QDFkktz0EdOOQyM5fj3l+McT+VZ0R5h89olfODqbu5/fpAjJ7P4TANTgaEM/D4DQxm8YU07H9m8luVtYVnPXAi8tF9bP3INt/7LiwB8/+eD+E0vcHBdF9vVBH2v3BOGgp72MOuXt9Lb1TRbpy3qaKoURYNjORJZi3VdzYwXbHyG4kA8Q87yWrBdDdmi1yOUs0r4fUa5l+j0LaJCiIVl3gaY3e0RYtEALw+NYzkuA2M5Nq2N8ZuvX8VwusD3nh/ENBQr2iO8/+oV9MTC9HY1s2F5G33xNF978hD7htKMF2wuWtqMXXL5yOa19HY1Taj8PnXdJbKKj1jUclaJQLlVasehUQq2i+O4oLw2SttxCfsNQj6DjatibFoT450bLpT7ZIGoncyzsSdWzc5RycKRyttc2BrmtT3taKBge8uKGmh8CrRSKOD+5wfYsLxVVvcRYpFoeICplLoIuAjoYIo+M6311lez33DA5JZNK+lP5AiYXtd2rujy908cJBYN0NkcIJG16WwO8vi+4Qkri/R2NRPymbRG/NiuxlSK7s4ovV1Np1R+lfx+le+lMhSLTSWYODySxXE0SmuvM9wF0weXLGvmQ5tW09kU4DP/upsX+pN857kBvve7b5SZwwtAZTJPfLw4YQLkHTesZ3vfCN96ph/TUGjgY29Zw9882oflOORKGu2C36dYt7SZRNaq9gRNtUiGEGJhaViAqZRaCnwTeHvlR1NspoFXFWAC9HY1s6YzSny8SMBncCyZpz0aIJG1+MS16wj5vcDzi48cAF4JEAESWYuWkJ9owMcH39BTTU00ufLb2BPj0b1xqQzFolUJJnYfS/GVJw7wwkAKv6kwUIQDJs1BP29Z18nO/gTpvNctmsrZ7OxPcO1lF8zy2YtzdboWx3DAZHNv54T6UaHIWSVAYQDRiJ9QufW7Un+eaZEMIcTC0cgWzC/jBZd/DzwGjDbiIDe/vod4usjtD+4ikbUxDcWvrr+A3q6m6vjJSldOJGDSEQ0SDpjVILKzOcjSltCkfa6kNpefVIZisaqMP44EfHzliYMMpSwuW9ZaHlPn0J/wHtjufXgvv/OmNYQDJjnLoS3iZ2NPbJbPXtTDdC2ON7++B1AsbwvzZz/cxUvHx3G0xmcqVrVHWN0V5SOb11TrZCHE4tDIAPPtwFe01h9vxM5rZ5HvPTHOaMYCNCGfj2sv7apWZJXxQ7dv20Wu6HX13HHDeu64YT0vHUvxD08d4gsP72dZW4iPv/UitvzwJRI5i1UdUbbcuKG6D+kWF4tN7T22b3ic4XIrVtBn8Ge/tp7mkI9/fm6AvO2w5/g4n3lwNyvaI5iG4p73XSHd4wtE7UN2RzRYfdgGJoxXv+k1y9nRN0qh5E3mQWvSxRLew3rThPyZMslHiIWvkQGmAbzQqJ1Xum3ytkPRdnC1xtVQcjVXrWifsO1otkjRdjENNSHNxn3bD/NfR8aqExj++Psv8PzRMbTW5Tx+GS7vbm3UnyDEnFa5xwq2QzpfQpfzHBZLLt/acZSLLmjiSCLDiVQRx9X4DUV7NMAlF7SUu0nFQlEZPlQbGN78+p4JXecDY/lyQOmxXRjJFHn20NiEFEW1Xe6TV08TQiwcRgP3/SRwZaN2Xum2CftNmkJ+WsI+2iN+rlzRdsqHW2VbYEKajZzlEPAbWCVvRmy2UMJ2XGxHk8zbFGz5kBSLV+W+CflNOpoDBH0GPgMCpkF7NMBAIk+m4OCWA08XKNgukaApY5UXoMljMUFNqFdXtEdoDvqr2ytAqXKqgZoh+JVy5biagbEc33qmny0/eom85Zy/P0YI0XCNbMH8Q+A/lFKPaa3/pd47r+22iQR83PXQy+Qsh2VtoVM+3Crb9sXTVCq67vYIy9q8sZeRgMlt11/Glh/uxnfCmw3ZFvYT8s/bLE5CnLPJXaMHR9IMjOV58sAI4/kSkYDB4JiBz1DYjqYl5Ofqle3cWV6xRfLHLiyTx2L2djVNGJ8O8OZ1S3jywEnQmkjQR2dTkCVNQZa3hav7mWoGumToEGLhUVrr6bd6NTtW6jGgG1gLHAcOUVnq4xVaa/0r0+wn2dra2ppMJs94vOk+zKYa9wNM+J1ExuLTD7w4YQymfDA2zLxb5mWmZXGhq9xrHdEgd/9kD0dOZmkJ+/nYW9awYXlbNbicZ+Ps5lV5nK2yOJN6ti+eATSxaJC7fvwyuaL34D+5DMzDMnK+LNqyuOrWh2a03ZF7rj/nY4kZOaey2MgmujV4aYj6y9/3NPBYp52IU6kQC7ZLfNwbK3ZoJFsdE1T7O7GmAF/8jddIq4sQk3iBg9cD0NvVVL1vtty4Ycr7RZJpL0zTTXjMWw7D43k29sSmHPte+7uSoUOIha1hAabWelWj9j1TtU/Isag3TvPZQwlQsHXHUbbc2HxKpSYzxoWYKG853LFtF9v7RkHB5rVLqq37p7tfJJn24pPIWLz/q0+Tytm0Rvxs/cg105YBqW+FWLgW9CDD2laURNbmpo3LGUoWCPnN6qoSUrkJcWaDYzn6E3lvMhwwkMhNe+9I69Tis7M/QSpnA16i/T1DKSkDQixi52OpyBbgWrwuc/DGYv671jrd6GNPbkXZtGYJT/WdlFYVIc5Cd3uEnliYgUQOFKyIRWZ070jr1OKysSdGa8RfbcGsrFsuZUCIxamhAaZS6neALwBNvDJYVAMZpdQfaq2/fq7HONOg86laUeSJWoiZq9xfn37nZRxL5qiMwZR7R0wWawrwvd99Izv7E1y6rJXRbLE6jEIIsfg0ci3ydwNfw2ux/AzwUvml9cDvA19TSsW11j98tceYySzEyU/Q8kQtxMzILF9xtmJNATb3dkq5ERPMdHa4WFgamWj9j4E9wFVa67/RWj9a/vobYCOwF/iTcznAVDNVhRD1IfeXeDWk3AghoLEB5pXAN7TWmckvlMdffpNzXOlnqhV6hBD1IfeXeDWk3AghoLFjMKdL0HnOGd5lTKUQjSP3l3g1pNwIIaCxLZgvAB9WSkUnv6CUagI+XN7mnFTGVEolJkT9yf0lXg0pN0KIRrZg3gs8AOxUSv0N8HL555VJPr3ATQ08vhBCCCGEmAWNXMnnQaXUx4HPAX/LK13iCsgCH9dab2vU8YUQQgghxOxoaB5MrfXfKaW+DbwdWF3+cSXReqqRxxZCCCGEELOj4Sv5aK2TwPcafRwhhBBCCDE3NHKSjxBCCCGEWITq1oKplHoMb5zlr2qtS+Xvp6O11r9Sr3MQQgghhBCzr55d5GsAl1fyX66hDrkuhRBCCCHE/FK3AFNrvepM3wshhBBCiMVBxmAKIYQQQoi6avgs8lpKKR9wIxADfqi1PnE+jy+EEEIIMR+tuvWhGW975J7rG3gmM9OwFkyl1F8ppf6z5nsFPALcD3wV2KWUWtuo4wshhBBCiNnRyC7ydwBP1nz/a8Av4S0heXP5Z7c28PhCCCGEEGIWNLKLfAVwoOb7XwMOa61vBVBKrQd+s4HHF0IIIYQQs6CRLZgBoFTz/VvxusgrDgHLGnh8IYQQQggxCxoZYA4Am6DaWrkGeKLm9S4g08DjCyGEEEKIWdDILvJ/Bj6jlOoC1gPjwI9rXn8NcLCBxxdCCCGEELOgkS2YdwPfwGvF1MAtWuskgFKqFXg38GgDjy+EEEIIIWZBw1owtdZF4KPlr8nSeOMvc406vjizvOUwOJajuz1COGDO9umIcyDXUoi5R+5Lsdid10TrFVprF0jNxrGFV/Ft+dFLxMeLdLUEueOG9VIBzlNyLYWYe+S+nDtmmpx8LiQmX2gaGmCWk6tfC1wEdABq0iZaa/0XjTwHcarBsRzx8SIA8fEig2M5LlraPMtnJV4NuZZCzD1yXwrRwABTKXUR8CBwCacGlhUakADzPOtuj9DVEqw+XXe3R2b7lMSrJNdSiLlH7kshGtuC+bfAWuBPgMeA0QYeS5yFvOXwpt4lLG0OsX55q3TdzFF5y6EvngYUvV1NU16ncMDkjhvWy1gvIRpgpuMoJ28n96UQjQ0w3wx8UWv9+QYeQ5RNVRFO9bNExuL9X32aVM6mOezj3vddKUHmHJS3HO7YtosnD5yk5Lpc2d3Gvf/tKsIB85RrGg6Y0v0mRJ3NZByl9xCYYeuOwySy9oTt8pbD0dEsHdGg1K9iUWpkgFkEDjdw/6JsqooQmLJy3HHoJKMZCwPN8WSBz/7wJXpiEe58z+XEmgKz/JeIisGxHIdP5khkLWxX8+SBUT55/y8I+AyKJZdlbaEJH2TSUiJEfU03jrJS7x4eydKfyLFuaXN1u0jAxwe++jSZokMs6udfPrZZ6lex6DQyD+a/AZsbuH9RVqkIHVdzeCRLXzwzZeWYtxwe2ROnWHJIFx0UmpMZi+cOJ7h92y7ylnPKvvOWw4Hh9JSvifqZ/D53t0foiPrRVAYwa34+kOTpQ6PsG04zlCxUr+kd23Zx+4O7ueM011AIcfYq4yiBKcdRVupYv2lguy6ZYomuliAd0SCf+v4LHE8VSBdsjo/l+e7zR9k1mJL7UywqjWzB/EPgZ0qpTwJ/q7W2GnisRanSctURDRKLBth+8CRo2LrjMJ9+52UTBplHAj6+93w/w+MFWsMBssUSYb+B7bgEfQa5onPaJ3RJtdFYp3uf777pSj55/y/4+UASrTWFkoPWkChZBH0G3e0R+uJptveNUrQdDo1k2H0syetWd8z2nyTEvDfdOMru9gixqJ/tfaP4DIOOaIBPXXcJo9kiuWIJNLiA7Wq+/uRh/u8z/Wxeu4RPv/NSRrNF6XEQC14jA8ztQBT4K+AepdRxYPLjm9Zar23gOSxYlaBkKFkgEjC5+fU99CdyhP0miazNaLZYrRwjAR+33PcsyZxNwXYwDUVTyMfqjiimqUBDJGDSEQ1OOIak2jg/Tvc+hwMmbdEAflNhORrtaiJBHz7T4HfevJpwwKRou9iOS6bcMvLVJw4R8pv0djXLh5cQ5+hM45vDAZNbNq1mIJEn5DcpuZrRbJGOaJBk3kYpQHs9ECVXo22XPUPjfPqBFym5Wh7axYLXyC7yfuBl4GfAU8Ah4Oikr/4GHn9BGxzLMZQssD+e5rnDCf7vM0foiUUwDVXtzqlUjr8YGGM0Y6G1xnZcvHpPs7w9zD03XUFz2EfOcrj34b0TunCm6yIS9XG699m7xnlcDQXbxdUaV2vesCbGhuVt5C2H7z7fjwa0hraIn5eGxrnzoT1s+dFL0h0nRIP1djWxujM6od4dzRbpbA7SFPTabxwN44USWavEifECLx4iVSxgAAAgAElEQVRL4bi6+jApxELVyKUif7lR+xZeUBIJmFi2W574obll00pCfq8lsi+eATSxaJAf7DxGseTiuC6mYbD+wlYKJYeLlzZxPJmjaLuYhpqylfLm1/dwpjQ54tzVdsV1RIPVfwu2Q1dLiP3xNGiNaXijMUfTFnnL4VgyR38iz9olTRyIp72Wk5xF2G9Ki7MQ58HkbnSAZM6iNeSv3q8ArgbHceluCxNPFynYDqs7o/LQLha0WVkqUpybytjL266/jLt+/DK5osOythC9XV4wcce23Ww/eBLX1WitsUou0YDBkqYw3e0RcrbDvhNpDp/MEg34uHxFC4mMTU8sXK3wEhmL27ftqu67MjNdNEY4YNLdHqkOexgYy3Fha5jBZI7lbRGOj+VwNBRsh13HkvzW13fQHglyMJ4hazm0hEyWtQa5+IIo43lHWpyFOA9qMzgkshZ//P0XOBDPYJUcVnZE8akciZyNo8F24ReDKX51/VI+/taL5KFdLHgNDzCVUr8EXAcsBb6gtd6rlGoCNgIvaq2TjT6HhWTyhJA7b7x8woDxXYMp9g2nKdgO6XwJ19W4gM9UBP0m//2aHv72sQNYJZe87ZAplLCOuKzsiFCZr5zIWPyvf/45Lx9PEfR7FeDZtoZJ6pyzVxmLWbAdUjmbaMDHeL7EeN4iXx5rabtQKMFLQxkgg98AlGJZaxOZosPH3tJLW8Qv77sQDTQ5/2VL2Mczh0aJp4rViQapwRQblreQsUrkbV393VWxCJd3t87OiQtxHjVyqUgT+Dbw36A87A++A+wFSnjLSH4e+MtGncNCNHlCyGi2WA388pbD1h2HGUkXyRZLOFpX3/mo36DkuHzup3tJZosoBa4LPgNKJQdTKRJZi754mr9//CAvD42TL7kARILmWbWGJTIWtz+4i5zlTMjXKM6sMhZzKKlpjfhpCvowFYxkSoB3A01WcsFvao4mcoT8Jvc/P8CWGzfI+y1Eg0zOf7m2s4ldx1KczFgTZrFq4MCJcUz1Sle5At591fLzfcpCzIpGTvL5E+B9eOmKLqVmPXKtdQH4AfCuBh5/QfJSY3hphmLRwITAb3AsRyJr09vZxPK2MM0hL0gJ+gwuaA2TLjqMpAuMZm1cV6OAlqBJazSA3zTwGYq+eIZ0sUTQbxD2e5OE3n7p0hlPGMlbDrdv28VzhxPsj7+Sr1FMrzKe65PXXcyfvvNilILWkJcLsxJcTr5hTQUtIT+O603gOpGS91uIRqrmv/R5ad72nEiRzFq4WmOqids6QLb0yqPhRV1RcpJbWCwSjewivwXYqrX+klJqqsR8e5AA81XS5YBjYptWbV42V+vqpJ4LWoN8ePNqvvToAXKWgyo/UfsMcFF85l2X8t3nB3nxWIoXj6UI+Q3WdjYRMA36RjLc9dAevvwffXzvd9847WoUg2M5ckWHgM/Asl0igbNr/RReHtPH959kPG/TFDBQypslDl5ePZ+hMBQEfQa9XU0ETJNdx1LkbZsj5aXpKmSoghD1k7ccCrZDS9jPs4dHyz9VrFvazMGRDDmrRKbooAFDgd/wgtCKI6M5/uj7v2B1R5P07swxq259aEbbHbnn+gafycLRyBbMVcCOM7yeBNobePwFqdJK2RT0kcjaE1qrKnnZemIRVsQiBE2DnliEVR1NrL+whTes6SBgKkwDXK0xlCLgN8iXNKm8jV1ysUsuS6JBPrRpJTdtXE624HXPpnI2O/sT055fd3uEZW0h1i1t5vVrYtz5nsulAj0Lg2PezHDX9SLKkusNb6gVMBUblrfy5os6+fLNr6WjOUA4YNIeCbBqSZTRrDeEotKVd89P9kraIiHOUeV++uIjB8hZJZa3hrnkghYCPgNHa67qaWNpcwhDeQ/v0YBJdyxCbaOm32eQypXI246kKRILXiNbMNNA7Ayv9wIjDTz+glQZp1eZ5DO5dbCSl20oWaAtGqAjGqjOMP/I5lUcT+YxlWJ/PM0FLSEuXdbCpjUdPL4vTv9YDjSsWhJhc28necuhNeInlbNpjfjZ2HOmy+mZbvULcWbd7RF6YmEGEjkCvgBXLG/lQ5tWcst9z1EqD6l907pOfr9mFuqdN14+YcxrbR5NSZQvRH3U3k9F26WzOUix5LJ57RJu2bSS5W0R/uKh3RxPFXC0ZklziL//zddy549eYudACqvk0Bz0EQ6YhP2mZHoQC14jA8yngA8qpf5q8gtKqXbgI8BPG3j8BWm6AG5yTsXaGea9Xc2s6YwSHy/y1os7uWXT6mqQsuXGDdXcmbWrwHzupisYThfYtGbJtN3jtecggcyr412Ly+mLpwHF8rYwo9ki2z7+Jv71hWNcsyrGpt7OCdc91hTgCx+46pQyMd3DiBBi5mrvp2VtoeqykLX33F++90pufn2SeNriqhVt5KwSX/jAazhWzjcc9Bssb4vIUpFiUWhkgHkXXpD5GPCN8s+uVEpdBNyKt4zkPQ08/oI1XQBX+3ptUHim4DQcMCekzpicDultlyxtwF8ipuJdi7azWgt+qjIhrclC1M9U99Pkh+5wwOR1qzumvXdn+rAuxHzWsDGYWuvn8WaRXwL8Y/nHnwf+HggD79Vav9yo44upVQKR6YKNqbpXxflVj2sw0+sthJie1J9CzFxDE61rrR9SSq0C3s4rqYoOAP+mtZY7bg6T7tXZJ9dAiPlJ7l0hzsNKPlrrIvCj8peYJ6R7dfbJNRBifpJ7VwhZi1ycgUzWmX1yDYSYn+TeFYtdI/NgopS6WSm1XSkVV0o5U3yVGnl8IYQQQghx/jVyLfLbgT8HhoGngbFGHUsIIYQQQswdjewi/x/A48A7tNZ2A48jhBBCCCHmkEZ2kbcA90tw+erlLYcDw2nyljPh/2J+qlzDRMaSaynOm6nqDqlPhBCN1sgWzJ8DKxq4/wUtbzncsW0X/Yk8F7aF8BmKRNaeNuG2mJsqiZeHkgUGxnKsKK/ZXrmWecuRGaei7qZK+A1Uy2Ik6C01Kom/hRD11sgWzNuBjymlXtPAYyxYffE02/tGORjPsL1vlMMnvbShQ8kC2/tGpOVhnqkkXi7YDqmcTd52qgmYExmLT97/C+56aA9bfvSSXFtRN1Ml/B4cyzGULLB/OM1zhxLc/uCuacuctHgKIc5Ww1owtdZPKKU+CjyjlHoGOAJMrp201vqjjTqHua7SahUJ+NgzlGJjT6ymJUF5aekB01B0RP0US5qBsRzfeqafR/fGp1wLV8xNHdEgQb+BVTJojfgJ+01i0QDDqTyf/deX2HsiTdDvPe/1xTOE/IZcV3HOKgm/h5IFIgGTjmiQcMAkEjSxSi4Bv0G6WGJ73wibezsBJrSk5y2HvniGrTsOSw+KEOKsNHIW+TXANwE/8Oby12QaWJQBZqXrajCR5xeDSQKmQVvEz/d+943EmgL0djWxee0SBhI5lrWFufn1K4inLb73/ACmoRhKFrh92y6KtiuV/hyXtxzufXgvuaJDc8jH1vddw1Ayz1eeOMgf3P8CqbyNaXjBZdBnyIe5qJtwwORT113C7dt2kSt65fCOG9Zz542Xc/uDu0gXSxxP5vnm00f53vODRIIm4/kSXS1BPnXdJdz78F4Oj2TpT+RYt7S52goq+R2FENNpZBf5lwALuBGIaa2NKb4WzSfn5AkeffE08fEiybxNwXZwXE0qZ/PE/jiPvHzCC0Bv3MBt11+Gz4CvPHGIx/cN09kcBCASMMkVvQZhWet2bqt0SRZsh5zlMJTM89eP7OeZQ6OkCjZKKQKm4pILmnnHhgsYSVuAXFdRH6PZIkXbxTRUtUzFmgLc+Z7Lecu6JbQE/fSNZHj2cIKn+k5ilVwOjWTZcegk8fEiIb8JCvK2U132ULrMhRDTaeQknyuAz2qtf9jAY5xXr3YixlQTPDqbg8SifqySS8hvYhqKppCPLz66n2zBobXcmhnyGySy3kT8RNbmE9deRMjvdXXd+/DeKde6lQkjc0tHNMjAWI5UzqY57OPL/9HH80fHKJZcFNAR9XPVijaawybbfnF8wiQgWcN4ZqTMn17tutixqJ+C7ZLIWNz9k5d58sBJknkbraE94kdrzb7hND5T8cieYWLRAImsxea1S7hl00p6u7yWy8kThxrxnss1FWJ+a2SAGcdrwVwQppqNOdNKb/IEjyVNDomsxSeuXUfIb1THYKYLJe56aA8AqZzNjkMn6YlFiUX91S7TSgU/OJabcgzmuZynqK/KB2TBdljRHmFJk9dSHU8XsR0vuPSZ8KnrLuay5S188ZEDmIbiwtYwb7ukkxuuWC7XbgakzE80OTCrrIvdF0+zdcdRvvjIfoI+g5F0kZKjifi99+rC1jDhgEHedmkJ+RnPl6oPtLV1zIHh9CkTh+rdZS7XVIj5r5EB5n3AB5VSX9Zaz/slIaeajTnTSvWVgfa6OsHDCxabqpVmLBpg97EUzWEf6XyJ5rCPR/bEGc/btIT93LRxOZvWLAGopi/qiYXZcuPlEyreczlPUT+1H5AtYT8+U+EWNStiIdJ5h4MjGVytaQ35WNvVRG9Xc3UyxvFUnsf2jrDnRFo+WGdAyvwrTheYhQMmIb9JIus986eLJUxDoRQEfSavWxUjVywxlrMZy9m0hPzVB9rJ5a+2RXRy70m9yDUVYv5rZID5FHAD3izyvwMOc+oscrTWP2vgOdTNuVSqlRaEwbEcHdHgGVsdNyxv4cruNi5d2sJ9Tx/BcTXPHh7leDLPU30nuek1y3nywEkyxRIH42l2X53kdas76nKeon4qH5COq9lx8CSFkovfUHTHIvzB29cynC5yLJFFKcU/PXeUv3zvFdxxw3q2943wrWf6J4yXkw/WM5My/4ozBWYd0SA+QxEfLzCcLuK4LgCXLWvhPVcu409+sAvX0QT8Ju+/egVvu6Rryoeb2vqsUd3Xck2FmP8aGWA+UvP//4M3Y7yWKv9szjbPTO5qOpdKNRwwqxV9bVLjvOXw2N44+4bSuK7mhYEch0dyvLQ0TSzqZyCRBw1hv8lQssDP9o8wPF5EA5miw5YfvcwXf/01uFrP6DxlXNP5UfmA7BvOkMzbOI7GUPDz/gQf/eZJxvM2edulNWTy5P4Rvvazg1zd005rOEB71MfxZJGeWLg6oUKu2dQq742k7PJMSEsUNIkEfOwaTFGwS3z7uQF+PpBgLFdCuxoUBE2D0UyRrz15mGTORgExn0FXc5C+eBpQLG8Ln/Le1tZnjTD5oVzKvxDzTyMDzN9u4L4b7nRdTbWV6lQf/GcTDHir9ezmyQMjxMtBowbGCyVG0gU+/MY1vGPDMu7/zwFytkN/Ist/HRmdEKm/dHycd3/5Ka5a0UZr2M+d7/FW5Ziq8pdxTedP5QPyp7uHuOuhPYwXbIqO5shofsJ2qYKDxuFLjxxAKW/CTyToL2cLUNUUR3LNPJX7qyMa5FgyLymdJqmmJXpwF+lCiQ9+/VnyVomC7eC6LjnLpVSpQDSUXJc9QylawgEMpXC15qKlTXz7uSM8e2gMDYT8Bitj0QkrTzVKbf0JULBd7v7Jy3KNhZiHGplo/ZuN2vf50BdPc2gkS9hvTtlVeaYl2GYaDAyO5RhI5CjYLoaiWvHbjmYkbfF3jx/A0XD5ha2YpiJTcCiUJjYEmwZYJZc9J8ZRKP7Xd3/Ol379NVMu/SbjmhrndA8WTx88id9nEPSb2E7plGZ8X/m6uwDayxRQdOCClhCJrMXO/oRcs7LJ2Rhi0QCDY3mWt4VxXL2o35tao9kixZKLXXJJZC1yRRvbPf32RQeSeS9ThQLskmYwUcAquZRcTd5y6Gp2Glb+ah8aKg9TsagfUAwkcpKDU4h5qpEtmPNW3nLYuuMoA2M50LC5t6P6RF07M3jyB3/l/7U/O1Nl2N0eYUUswtFEjrxdwnU0rqbaklkouZQczf54mtVLohgKDEMRNBRrlkRJ5m2KVomio3FdKJRKvHQsxe0P7uILH7hqVgbnL0anaxkeHMuRyNpcvLSZ4XFvaT6nJsI0FbRH/aTy3jUEUAqaQz5C5YlgG3tiPLo3LteMVx6Q8uVsDG3hAKm8Td5yaI/6Sea8/y/2Fq7aSYXNQR/Zol0dj6QAQ0FT0EeqUDP3Umsc7SX6N5SitcnPsWQeP14LZmViYr3LX+29E/QZ5CwH01D0J/IomJCDc01nVNKxCQBW3frQbJ/CnDfT9+jIPdc37BzqFmAqpX4JXpm0U/l+OnNtkk/ectjeN8JIusjaJU2k8jYfuLqnumxapQUl6DNoCZuM573kw16XXa6aN67yswPD6dNWgOGAyZYbN9AXz5DKFfnSo338YsDrlnI1uK7GMBRoTdhv8oa1HZxIFYhFAtx90xUA7OxPsKw1zEe/+Z9kijYFpUgXS1MGt+djcP5idLqW4dqA/pJlTZgKdg+lq78X9puYpslt16/jx7uGKNgOS1tC3PFr68lZpbqM/V0Ialu4ulqCOOVsDOC9hx1RP4msxed+upeLljZVA/zFGoDUlplIwMefbdvN80cSjBdL+E2DlpCP//m2i/jrf9/HeKGEq6Hklh9qbZdIwOTu917BsWSO043BrJfKIgR528EqGUSCJomMRWvET3PIz3je5prVHVx7aVc1i8aB4fQpeYCl61yIuaeeLZiPA1opFdZaW5Xvz7D9nJvkUxtAHk1kKdguSsH9zw+wYXlrtTLcP5zGKrlcuaKNmzYu56oV7RO6dj5x7TqWt4VnVAGGAyaXd7eStxxuvsbiyGgWV4PPVFzYGiLoMzmWzGMYipDP5I4bLqvmwuyLp1naEvbGTXU145SD0uag77QtDY0enL8Yna5leHL+QdM08BmA66VTMA0DpeA1Pe28/+oVpw2GFvM1m9w6XJnMEwn4yuMMLRI5y8spOpajKeirvo+LebxxbZn525s3svtYkq8+cYhU3mbVkgjXX3EhLx5L0hfPcuhkhvF8CQWYhuLtl3YRawpMGGYz1ZCbepi8CMGly1o4mshhJhVvWNPO//uWtXz3+X4e2HmMx/eNAN7wIVdrtIaAz5CucyHmqHoGmB/BCxjt8vfzZpJPIuONdWsN+4mPFzENxZJokKLjEosEGEkX2d43wqXLWnG0pmg7BPwmLw+Nk9lR4qFdQ+SKXtdOImsT8huMZosz7i6vDWybQn6WNAVY1RHl0++8lJ39iWramkTW8rqM8HJhbu8bBQXXrO7ggtYgpqGIBE3unJQbUzTWmVoZK/kHT4wX2D+cqQ6BCPoUAZ/imtUd1Xyo8gE5kZdhYZh9Q2kMQ+G4mtFskYuWNrNrMMVLx8dxy+NJTFN5rW9BLyn4dGOoF5NwwOR1qzvYsLxtQhndcuPl/GT3cf70gV3VlgDTgGeOJHj3a7rPSx0ymi2yoj1CLFIibzv0n8zhOC4Fy+XFgXF+6aJcdSWzgUQOx9UcS+Upllta13Y2yYpXQsxRdQswtdbfmPT9vJjkc2wsz298bQdZq0TIZ3Lx0mY0sGpJBFCcSBU4Mprlvu1HGEoVaAv7CAd8dLUEOZEqEDANTqQKhP0mYEyo7KZq1Zqq267SxWoaipWxCB98Qw+bezsJB0w293aeMgZvcCxHfyKPVfJG7g8l89x2/WWE/Mai6w6cK84UIHa3R1B4M3ZNQ+G6mjVLmmgO+/nI5lVyvabwSoaFk4xkipgKOpuDGEpxYDjNeM5Ca40qbx/yGVx8QTO3vesyALbuOEp/IkfJ1bypNyYBCKeW0XDAJOgz0ShM5Y3BXN0RZTw/9RCbestbDgXbGz+798Q4xZJLJOgjU/QmumWsLP/fv++nt7OJZM4i4DdpCvqq66qvmFRXCiHmlkU9ySdvOdz6wIucGC/guF7ja84ucc2qDj79Tu+D6ve/s5PxQon9J9KkiyXGst6qGL/1hpX87ECcZw6NkcrbtIR8XLmijU9dd0m1spvcqpXIWNy+bRe5ojMh5UdtF+uyttCECnOq1rHu9gg9sTADiRwoWBGLTFgVaPLfuBjHoc0l4YDJ3Tdd4T3IFEtYjksoYNITC1eHO4hXVMZBHxnNUrAdysOQyRRLfPgfn2NVR5TBZA6FximPHcxaDkdGs3z+4X1ce2kXJ8YLANgll1zxDFOoF7lNa5ZwYVuIRMai5HoBXizqb8hkntp6qHbog6NdkjkbpzxjfUlziOHxIkrB8HiBVM4iYzn4DMXSlhABn8KyXYbHC2zsiUm9Jua9hTppqWEBplLqjcD1wDqgBRgH9gEPaa13NOq4M1X5EHMcjWkoSo5GKVCuZihVqA5wPziSIW+VyGmNYRjVrqTe8vJ+B0d2eek8HE0yZzOaLVbHK9W2GOQth9sf3MVzhxMEfAZAtZVguokcU7U8bLnx8moi5DMFl4t5HNpcsrw9zLbfexM7Dp3k314aJj5egGr7m6ioHS5yMlMk5DcplBxcVxMwDXLFEqm8TTpfoi0cYDRXwtRe9oVMocRje4Y5OJxmOF2sdpkXS+6i7iI/k1hTgH/52OaGlsup6qHala4Gk3kc7SV+11rT2RTkZKboDX/AW1DCxUvflszZ9MQiNIV8hP3mhPpWCDG31D3AVEq1AN8B3sHUNdWnlVIPAb+ptU5P8XrD1X6IDacLXNndxuGTGbTrkrU1I5ki920/whtWd5DK27jaC0KbAoqS9tJ2LG/zAsGLlzZzMlMETXXllakMjuXIWQ4Bv4FVM1as4mzH4HmTg9rOuI3kvZxbYk0B1i1t5oGdx4gGfSSyllyTSSYOF4ny/qu7aQv52frsUXJFh+OpPK1hP1mrRFdziOF0gWLJBVeTtx20hl3HxzEVGKYiHDDobF7c6Z2m0+hyOVU91N0eIRYNsP3gyfLDg8JnmrSGTU5mi+WZn5pIwCSZf2WF4eaQj3UXRKvZO+S6CjF3NaIF8/vAtXhrkX8deBGv9bIFuAL4Hbw1yr8LvKsBx59W7YfYinZvHM/Gnhg7Dp3kG08fIeQ3eebQKLsGk2gN0YCPQsnBcsBnaFrDfo4lc1ze3VZNMwSa3q7m07YkFmyXzuYA0EwkcH4m4kjey7lHrsmZ1b4/nc1BemIRerua2bgqVk1VNJotVtOCxdNF9p9IczJb4Jvbj1LJ7OhoaA6YdDaHuGXTSmm5n0Yjy+VU+w4HTG7ZtJL+RI6w38RxNW+/rIuAz+SObbtxXG9MaCzgw1d08JteLs5/uOVq1nQ2ybAfIeaBugaYSqlfxQsuv6C1/tQUm/wc+KZS6vPAHyil3q61/vd6nsNMnG7M49suWcpTfSfZdyLNWM6iYHlpZDqbg6QLNqm8TcbS5E+kuW/7Ef7yvV6Q2NvVVE20Pllt91AsGuCT162bEIg2coyk5FCce+SanNkrqZ0ybN1xmC8+cqCanqjy+kVNzeXFEI6wvc9bOlWXE4VX+AyF3zTp7fIm60kC9jNrZLk83b57u5pZ0xmt1sPvv7qH/zqSwNFUr2Wl5TNoQtHRfP7f9vKH111y2mFBQoi5o94tmP8dOAr88TTb/THwPuBm4LwHmKer8Crr+P7+d3aiNeRsl/aIn997ay8/3T3EM4fHcLVDJGAylMzTF89QsEv8nycPUyy5U67VW9s9VEkzVBtcNnqMpKS/mXvkmpyZl9rJqKanGUoWuP3BXRRLbvU+6Ytn2HciQ7G8nGHOKmEa4LjQHvHzmp5WfvuNa9j2wnG++Mh+GYM8A40sl1Ptu7Ye7ogGGRzLEfKbdDZ5KzQVSt5kuKLtMlZwMZTLzw6cJJXfzcXLmuV6CjHH1TvAfC3woNb6TAnW0Vq7SqkH8Vo7Z8XpKtPRbBG/aRCLBshbDld2t/KODct4y7ouPv3Ai7x4LIVSsKwtzH3bD/LUgVHGCyVi0QCOq9neNzJhFviZup5qc/UNJQun/K4Qi1XtfRMJmtU8s/HxIn3xDPdtP8ixZI5s0SESNHFdL2VRwGfwJ++4lHdfdWF5qU4Lx9UcGsnSF09PO25ZnH+pnM3nH95HsTyMaNPaJRwdzbJ/OI3taIzySH5DKVytsUruhDHlkilDiLmp3gHmcryZ4jOxD/hwnY9/zrrbIyxrCwF4YyXf43WDV1LNPL4/jgK628N8/uH9uOWpjrliiSOjWb71TD+P7o1Xn65P11pau965Lk8cmvy7QixWk1u3alfFKtgldhxMkC6UcLXmwtYmfAosx0tg7/d5D2/L2yLEov7qggRbdxxly41Tj5MW518l1+kT+0dI5W3aI36ghU9et46Q36R/NMPdP95HyGdwcDSLz/Cyb7RHA9WHdcmUIcTcVe8AswWY6czwNNBU5+Ofs6kCwrzl0Bf3xl0+e3gUNFyzJsaFbUEGEjn8foMVbWGylouj9Skztiu5K2v3WWldWdfVTCJnETSNaguNzCwWYmIvQ+092RfPUHJdXK0xlCLo85ZbPZGyGC/Y3P3jvZiG4sruNn5r0yoGEnlCflNm7c8xg2M5BhI5So6L1l4OzEjQpLermUTW4h+eOsKxVB5XQ2ezn998/WquW78UV+tqPXpgOC2ZMoSYo+odYBqcef3xqbafcybnr9zyo5c4NJLl8MksTrkr7niywO3XX8ZHNmtSOZvbtu0ur/qj+NX1F0zoCp/qKbu2C/Dipc2AJpG1ZWaxEFOovSeXt4VpCvoZz3vd5he2h/AZiuGUxUi6iKM1jqt55tBodSGCRNaSe2uO8XqLwuVhR4qmkI/b3nUZecvhN762g6FUAVdrmoI+Crbm8f1x+seyE1opJSuDEHNXI9IUvUspdcEMtnttA45dd5VJOmG/iVkeDOQzVHkVFm8m4yMvnyBbKNEUMHE0XHtp12kn+tQ+Zde2ylS2k3FEQpzZaLbIqo4onc1BAN6x/gIe2HmMUMCsPt1qwG8qirbLLW9dSchvyr01x4QDJh/ZvIrBhJeBIxr0kbNK7BlKkSuWvGVVHe+B3meoKdeVl6wMQsxdjQgwby5/zcTZtHbOiton5Df1dvDrV/cQ9BsTUiGpgCoAAApGSURBVA1t7InRGvGTytl0RP1sWrPktPuofcqePNFIunaEmF5lnHTlftq0ZglP9Z3EcTUXtoVoC/uJp4usWdLEsrbQafPTitnX29VM79KmCXVjRzRIWzQAWIQCQba8+zJ++tKJ0/bwSFYGIeYmNc2E77PbmVJvOdvf0Vo/Mc0+k62tra3JZPLVn9g5msksxUTGYmd/go09sSmXLpOZjqeYd+skzoWyKDxTrW09ORH7aLZ4NvfbvCqPC6ksTlU3Tq5PF1n9ueDK4kJaa/vIPdfXfZ+z+f5M8/ecU1msawvmdMHifDWTJ+RYU4BrLzv9yAB5yhaifibfT7XfVx7wZI3q+WGqunFyfSr1pxDzz5ycZCOEEEIIIeYvCTCFEEIIIURdNWKSjxBCCCHEgnM24yUbMV5zPpkPAWZLKpWirU2WeFtIUqnUUa31ytk+j7MkZXGBmoflUcriArUQy2Lbx/7pPJ7O3DHT+3M2358zneO5lsW6ziJvBKVUCa8rf3y2z0XUVWqeVaJSFhe2eVUepSwuaFIWxVxxTmVxzgeYQgghhBBifpFJPkIIIYQQoq4kwBRCCCGEEHW1aAJMpdSHlVJaKfXLs30u9aaUOqKUeny2z0PMjJRFMVdIWRRziZTHhWXeBZhKqV8uF8DKl6OUGlNK7VZKfVMp9Q6l1LxaamshUEpdrJT6vFLqMaVUsnxtPjvb59VIUhbnJqXUu5VS/6iU2quUyiqljiulHlFKvWO2z61RpCzOTUqpDyml/k0pNaiUKiilRpRSO8qB1IJd81LK4/yglHpnzTW6uu77n2+TfMpPNv8BfAf4Md5amc3AxcB7gB7gEeD9Wutkze+ZgB+wtNbueT7thlJKBQGttbZm8Rw+DNwHHAT6gbcBf661/uxsnVOjSVk81RwpiyfwZrRuA/YBMeC3gUuA27XWd83WuTWKlMVTzZGy+CVgKfACEAeagOuBtwP3aa0/Olvn1khSHk81F8pjLaVUFHgJ6MArl6/TWj9f14NorefVF/DLgAb+aIrXTOAL5dd/Mtvnupi+8D7E28r/v7p8DT472+fV4L9ZyuIc/ALeNsXPInjBpgW0z/Y5NuBvlrI4j76AhwAXuGC2z6VBf5+Uxzn+Bfw1MFhzLa6u9zHmXRf5mWitHa31J4GngHcopd5UeW2qsR01P/sVpdQdSqmjSqm8UupZpdQbytu8RSn1VLmrbUgp9Zmpjq2Uulop9QOl1EmlVFEptU8pdZtSyjdpu8fLYzEuVEp9p9xtkCt3o6ybtG1IKfXZ8r5y5a7nXUqpeydtN+XYDqXUe5RS28vnnin//8YptjtSPq9LlFIPKaXSSqmUUur7SqkLZvjeJ3TNk+hiJ2XxlHM6n2XxsSl+lgN+hNc6cvFM9rNQSFk85ZzOW1k8g6N4rXqt57ifeUfK4ynndN7Lo/K6w38f+ASQPpvfPRsLKsCs8fXyvzNdp+kevGb7LwF/DqwBHlZKvQd4AHgS+CNgL7BFKfXB2l9WSl0PbAfW4T0N/E9gB7AFr4tgsijwM8AB/hT4Mt4T3zY1cVzO/wb+DHgG+APgNuBRvO7nM1JK/Q/gB3gti1uAvyj//0Gl1P8zxa8sBx7H697+FPBt4CZg63TH+v/bO7dQqcoojv8WiSR5OUeUQgTTkkjDwOotzMgKChKLgiLELIhCoR68PAQFPdiDRAhdKLBCKbBC025eCFO6KNHdB81OPYhWlBdKCyJXD+ub424zM2f2cbvnzJz/D4Zv9jrfZc3sP/us+fb6vi2aIi0OHS1OTuUvZ9lPpyIttkmLZjbOzCaY2XQzWwIsBg4AB4v002VIj23QYwqmXwK2ufubrbYbFO2eph3EtO5cGky9Z+rMTnXeytgWJdvcOrYvgJEZ+23J/g+ZaWNgJHAE+DRjOx/4mRDiiJwfj9YZc2eyLc/VXZbsN2dsR4H3WvhOfgJ2Zo57gT+Ji9fYjH0skSP5B+l2dqa9A3fl+n022S8reI6G/S1yaXFoaDHT/sr0He5qt26kxeGnReDz1MaJW+PbgGnt1o30OPz0CKwATgFT0/ET6BZ5IWqPrBrbYv3n/f+Jt7tTucczSa+pzl5geqbujUQS98tAT/qVOsHMJhDJzQA35cY7DazJ2Wq39bJ9nwBmmtkVLX6OrE8XAGvcvf/xXen9GiKhd16uzWF339CCT6IY0mKbtWhmE4kZjr+AB4q27yKkxfZp8eE0/kJgA5Gq0VugfTciPVasRzO7hJhtfdLdfyzob2FGDFylI6kJttVno/ZlD9z9mMUOCvVOwDFi1VWNy1O5tkn/F+aOD7v73znb76nM9v0IsA741sz6iFV5W4At3nyF3dRU7qvzt5ptWs7el6/YwCdRDGkxaIsWzWw8sB2YBNzq7geKtO8ypMWgci26+97M4TozWwXsMrNZ7v5Dq/10GdJjUKUeXyC+r9Ut1D1rujXAnJXK/S3W/7egPUttL69lwFcN6hwu0G//3mDu/raZXQzcAlxH/Jq5H9htZvO83O0OWvJJFEZaLE4pWkzB5Q5ie6L5XmfxzzBDWizOubouvgqsJG7/1l2QMgyQHoszaD2a2YLk22Jgip3ZhnR8Kieb2XGgb4DAuGW6NcCs7S32bgVjfZ/Kk+6+o+zO3f0osB5Yb6GIp4DlwHzgjQbNar9yZhLJxllm5OqIc4u0GFSqxUxwOQNY4O5byx6jA5EWg6FwXRyVyvFNa3U30mNQlR6npLLRLO7GVE4EfitjwK7KwTSz88xsNXAtkXT7cQXDbiU20F2Z/qnlfRplZmOKdpo+S0/W5pGR+2U6bHZh2g6cBJZmx07vlxKJxduL+iRaR1rsp3Itmllv6nMmcIe7v19m/52GtNhPpVo0sxFm1ui25dJUflbWeJ2C9NhP1dfGd4A767xqAfCKdNxqysKAdPIM5uzMNgTZJwRMIVbo3VOFE+5+0swWApuA/Wa2llgV1kPcmrsdWECsSivCGOCImW0mxPorkbPxEJFfsqWJT8fNbDmxumyPmb2S/rQIuBR40N1PFPSnKWY2jjMXzUmpnGNmj6X3m939mzLHHEJIi419qlyLxEV5NrH1SG9+uxLgE3fv1hl8abGxT1VrcTRwyMw2At8R22NdRJyPq4lZq9dKHG8oIj029qlSPbr7Qepsi5VZnPShl/wkn04OMO9Or9NEpH8I+Ah43d0/qNIRd99qZtcQOTX3ElPMx4itBp4GBhNYnQKeAW4g8iZGE1svbAZWuXs+XyTv03NmdoTIOXk8mb8mbhduGoQ/A9FL7OGV5fr0gjg/3RpgSovNfapai1elsnZe8txH96aISIvNfapSi6eI4GEOsUK5h9h6Zh+wBHjR3VvJH+xkpMfmPlV9bayUjnsWuRBCCCGEGNp0VQ6mEEIIIYRoPwowhRBCCCFEqSjAFEIIIYQQpaIAUwghhBBClIoCTCGEEEIIUSoKMIUQQgghRKkowBRCCCGEEKWiAFMIIYQQQpSKAkwhhBBCCFEqCjCFEEIIIUSp/Ae5L/NvzX6LNgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "A1 = sbm(csize, P)\n",
+ "heatmap(A1, title='4-block SBM adjacency matrix A1')\n",
+ "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n",
+ "pairplot(X1, title='4-block adjacency spectral embedding A1')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot of Null Distribution\n",
+ "\n",
+ "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0.94. Thus, we cannot reject the null hypothesis that the two graphs come from the same generating distributions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ldt = LatentDistributionTest()\n",
+ "p = ldt.fit(A, A1)\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(10, 6))\n",
+ "ax.hist(ldt.null_distribution_, 50)\n",
+ "ax.axvline(ldt.sample_T_statistic_, color='r')\n",
+ "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n",
+ "plt.show();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Latent distribution test where null is false\n",
+ "\n",
+ "We generate a seconds SBM with different block probabilities, and run a latent distribution test comaring the previous graph with the new one."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x1286af128>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x12e1eaf98>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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df2z8/X1DWCmlDJXUi2kOM53JfSVtvBLKrpNZ4CIhxAU229+HLtydtNhRV/qOmO3i2vwdQo+1+f787UZfeqDIea1ASXF8p4mpXXyzEGKZzf7/U8E1rzP+/mCKcesYNjExDfvNR9DDl/0EuKaEsUexAKiVTkMx8wSAfxGW4MNCD/D9SePrl6a6gJTyNHrMQRdwlxCi3nKtZZZrfEVKGbec+lnj778IId6Sf12hB/suqBE0jPgvQ5+Nf0EIsWuqslowtVgftQoXhoBsK9gYDkwPoWt2/1UIUWc5byN6nMNyMMuwM6/OlgJfx0a4MLRfnze+/rcQ4tXW/UaA5HflHb/HKPODQoiL8q8phPALIa4tMNCUS0XPVEr5C3Rv13egeyX/Wkr5bP5xxRBC7BRCTBLKhRDt6IMW2KdXXQl8TlhsUIQQl6GHScowIfwipfw1umZlE3r9T6ozoQfi/2je5nLrxdSI2QlJpVJ2G6+EadRJNRHo72g2wLvRDm4zvmb7tWm8I/+K3j4+bLQX81gH+jtaSINn3vuTwpJMQQjhRTeZKdU5rWKklCfQTY3qgC8Z9zbL0YluE10yxm/eaXy9t9ixTDg3XWc534fet16CvhL3+wUclhQLkWq5p6tP5R9mNtB6N3o8TDNoeE54Fst5+7AJYYIeXuOwsa8f3YPyASaCcj+OfaD13UyEw3gO3XvxEcoLtN7JRMDgvy/x93eiB643w3t8x/jtMfT4eWaIoPzf2W4pmxmc+Efo4T4eoIxA6+hLUD3G9j4mAmGPogsHPzD2XZd3LcFEiB3NKOt96EtLhQKtf4mJ8C3Poce++zZ6iKe4sW+j5fjLjG37CtTfddiEYCn3mead92eW8z5cQZs2Y5R2owfWvhfdIzdmbP923vFmHd6F3uZfMsr6OBMBqj9jc58mJrTZEUv9348uJGeAV6bZ1s9jIjD4L9EnHF8jN0yOtHvWM9DG99ltn6Luy64TpgjFRJHQXoXaJ7mB1k+iB3D/LrrT17ix71HsA62X9Y4Y59zCxHv4uPGbXzaOv4vC74gZ4ihhPJNvofcF0wm0Xqge78G+H+lAd6CSxt9voydSiBnPzWyX55fw/K8wjh3GJlSSTbs0f3vQ2Pb/M/FufJfc5ALWzxvL7RfUp/Y/c14A9bF5KDMjYN5qvPDfMTrjOPAC8JdYAh9bzttHgcEH3TnjVvQBJWZ06M+ix6ybFGfQct5lRof2CvrSYB/6AHUjuXEzC3ak6HZLJ439t5VYB51GZ3bWKO9R4x7eKX7neeiZOF5hIgPMLejhNMxnsrrAs1plc63/NPbHjd/wT+gD9j3YDAyWc38ffXAaNOrtLPAz4C8LHP8W4zmfQe/cQ+iZSu4xruXOeyYVCZjlPNO8c9YZ1wwD/gra9JvQA1z/r3G/BPqg/Ri605Az7/hs/aLbMf8IfYCMoi+bfrDIvVzGeY8Z9Z8yfuuz6FEB3jDdejGe1+NGfWTIa/tMIWBW2sYLbS+h/suqE6orYN6D4YGMHuMygf6e7qKIAEQZ74jlnA8a7SVqtJ8fGe0pWxabcxzofexB9Pd+wLhvZ6F6KXe7XTu32Xe+UUdmX/YSumOY1/j9Wn67LHCPe417TMr+VuD4p43jb8gr41Qf275Qfeb3x4zGr1AoKkAI0Yeu5W2TUg5MdfxiROj5xvegOz39zSzc7x70aAt/LPXoCwqFAhBCbEe30zwkpdw81fEKxXRQNpgKRYUYNm+twLASLu0xHM3+Bl1jMqXdr0KhmB5CCFcBm+VXAV81vpYcJkyhqBQVpkihKBMhxFb0INeXodtN7p3TAtUghvf/FvSlyVbg32ThbCoKhWLmqAOeE0KcQjedGEUPufQ69DH/l8Cdc1Y6xaJBCZgKRfl0oIdF6UU3Yv/03BanJnkneozXPnTN5SfmtjgKxaIhju7tfjm6zWgTuh3pr9Gdjr4iZy9mq2IRo2wwFQqFQqFQKBQzirLBVCgUCoVCoVDMKErAVCgUCoVCoVDMKErAVCgUCoVCoVDMKDUvYAohTgshTk99pEJRXVRbVNQKqi0qagXVFhWFmA9e5I2NjY2N6NH+FQsHMfUhNYdqiwuX+dYeVVtcuKi2qKgVptUWa16DqVAoFAqFQqGYXygBU6FQKBQKhUIxoygBU6FQKBQKhUIxoygBU6FQKBQKhUIxoygBU6FQKBQKhUIxo8wHL/JFxeqbHp7R6536/Dtn9HqK6RFLapwZjtLe7Mfncc51cRSKaaHas0JRHovpnZkzAVMIsRnYKqXcO1dlUChmk1hSY/dDh+gfTdC2xMstV29a8B2MYuGi2rNCUR6L7Z2ZyyXyHcDX5/D+CsWscmY4Sv9oAi0jOTkQoat/fK6LpFBUzJnhKL3hOJFEmt5wnDPD0bkukkJRdWJJjWN9Y8SSWtnnmmMAQP9oYsG/M8oGU6GYJdqb/QQDHl7uH6M7FGXvgZMVdVIKRS3QEvDSMxylq3+cnuEoLQHvXBdJoagqpgby848cZfdDh8ruv9ub/bQt0d+TtiVe2pv91ShmzTCjS+RCiFvKOPzNM3lvhaLW8Xmc7Ny2iu5QFJ/bSSiS4sxwlPXLGua6aApF2QxFEqxs9rO0XsPndjIUSRCs98x1sRSKqmGngSyn//Z5nNxy9SZlg1kht6Kniyo1vZBKLaVYVHS2NbC2NUD/aIJgwEM8pRFLagu+o1EsPNqb/SxvqsvakxXTxiwmxwbFwsXUQBZr81O1dZ/HuWiUCjMtYI4AzwI3lXDsh4E/neH7KxQ1jTmD7eofZ++Bk9z52LFFYeytWHiUqo1ZbI4NioXLVG1etfVcZlrAfA7okFL+eqoDhRBvn+F7KxTzAr3DkXSHYvjczoqWWhSKWsCqjSmkuZnusqJCUUsU00BOp60vRC3/TAuYvwHeLIRYIqUcneJYQelL6QrFgiGW1Nh74DQ9w1GQsL2zZcEbeysWNsU0N6UsKyoUC4FK2/pC1XzOtID5VeAgpXmn/wvwXzN8f4Wi5jkzHCUUSbKhrYF4SmPntjULojNRLF6KaW4Wm2ODYvFSaVtfqFr+GQ1TJKV8WUr5DSlluIRjR6WUp2fy/gpFLWPGT2sJeAkG3MRSGiuDPjrb6ue6aArFtKgk/Mp04gkqFLNJOW3VXEIvZyJVjfBFtfB+qVSRCkUVsNrTAFmnnlAkRTDgJp0x7UOUlYiitinFNqyY5sZu+Q9g90OH6A3H8Xuc7HnPFhXiSFGTxJIatzx4kO5QjI6gj907tuDzOGfUZnKmtfy1suSuBEyFYoaxvtzBgBsQ9ISidIeibFjWQHcohgACXhehSHLBLIcoFh7lDFSFnB8KZS/pDcd5uX+MZCrDrgcPcscHLlLL54qao6t/jP1dQyTTGXpCUbr6x+hsa5hxAW4mwxfVypK7yuSjUMww1pSQR18Z59RghDq3EwTEUhodQR8rg7pmUzk9KGqZYqntSl2Cs1v+a2/24/c4SaYyeFwOogltwafNU9QG5S8di4mFJqH/U+spH2slY5DSYCoUM4yeEtLN/q4hJOB1OZDAJWuCXL99DZ1t+kxSOT0oap1CXrH5ms0br9zIUCRh254LLf/tec8Wdj14kGhCY3lTnZpoKapOJUvHK5p8bDm/kVAkyeqlgazNfC1HRqgVxzolYCoUM4yeEnINPaEYbpeDrv5xkukMLoeDzrYJ42+1LK6odQoNVFYNTm84zq4HD5JIZQoO2nbLf8F6D3d84KI5HwQVi4dyl45jSY3bHz1KIp2htcHLzVddkG2ntSDAFaMWMgapJXKFYgbIX3ZZ0eTLpoIUQDDgydpb1oJ3n0JRKnZesbqW3kMkkcZrLHFrGcmJgQhd/WPTuraJek8WL/nPfqbaQrlLx6ZA6nQIEukMQ5FEdp9VgFPt1B6lwVQopkm+l+HNV13I7Y8eJZrUCPrdtDf7GI2laVvipSXgrQnvPoVi+kgk4Pc68HudPH0iBAL2HjjN7h3lhWnJJzSeZNcDB4km9eVz9Z4sHuzML25/9GhJfWYpecDL0TxOFTi9Vry1a5WqC5hCiPXAeqAFm5gsUsq91S6DQlFN8r0MD5wYzM560xm4fvsa6txO2pv9nBmO0huOE09p9Ial8iBXzEv0ZAEp6r0uRmMa7926gt5wHLfTQbfhabulvamia8eSGrsePMgzJ0N43I7s/dR7sjjIX8Z+rjtU0rJ2KcJeuaGFphJIa8Vbu1apmoAphFgGfAN4m7nJ5jAJKAFTMc/J9TJc1lBHMODOajRXNPmzSyt+j4vjA+NEkxpNfjctAe/cFVuhqJD2Zj9LfE66+qN0tvnZtnYp+17qZ3/XEAi4e/9Jrt++ls62+rI1OmeGo0QTGh6Xg2Qqg9/jrDknCkX1yNcabu0I8rOj/VM61BQT9mJJLScWcama0JaAt6Dzml1ZVTvNpZoazC+jC5f/BvwcGKrivWqa1Tc9PNdFUFSRzrZ6tq9bSk8oysqgn7Wt9aQzklhSI57WuO2RI4QiSYIBD8ORJKOxFG6n4PxGH0ORhAowrZh3xJIaz/eMMhxJMp5IA3DN6zt46ZVx6r0unj4RojccZ2XQz85tq3Kc26aivdnP8qY6APxeJ3uMwNaKxYGd1rCUZe2pIh6cHIhkYxGXogntDcfpGY6y0miPVoHUqgmdqmwzGZB9vlFNAfNtwF1SyhuqeA+FYs7xeZzs3rE524l09Y/x9IkQyXSG3pEYq5cGWFLn5tRghFNDEeLpDPG0PniqGa9iPvJcd4ixWAqXQzAWS3HgxCD7XhpgMJKgdzSO0wFul4P9xwfpDkVZ2xoo2T6tVkKsKOaOfA/oUjyip4p4YI1FvLY1MKUmNJ7SGImmWFqv5Qikdkvxhcq22G00qylgOoDfVvH6CkXNkNsBTiyZO52CoN9DOiMJBjycCceo97pwOgQffePaRdXZKBYOWzuCNPrdjERTNPrdLGuoIxRJsqGtgUgyTdDvYSSWAgk+t7Ns+7RaCLGimH/YtRurZnP7uqVTatTN43vDkka/G5/bmaMRLcfucrHbaFZTwPwV8JoqXl+hqEnyl8xvvuoChiIJWgJebnvkCD2hKMubfHjdDmJJTQmZinlHsN7Dd//0DTzXHWJrRxCfx5kdxDvb6rnxyo2cDcdybN6KaesX8zKiorqUqxG3Hm9ng1mO3aUZzsscCxbbipWQUlbnwkK8Cngc+Csp5fencZ1wY2NjYzgcnrnCzTJzaYN56vPvnLN7T4Gd01dNU05bLDRgWo3NXxnRZ7a3vnsTGSnV4Dq3zKv2WCv9orWdx5JaVuAM1nuybR1kUY3RYl9GtEG1xRpHb9tjxFMZ6txOVjT5bJ2B8kPY7Z5/9sTTaovV1GD+GzAOfEcIcQ44AeRHIpVSysurWAaFYk4otMTn8zipczt4ZSTBs6eHSWkZ3v3lJ3jNyiaafG72vGeLcvqZBkoTNntYBcNgwANIQpEUPzvaPyl24Y1Xbpz0XMxnFU9lcrIC7e8aYHtnq3p+ihmllL6h1GO6+se4e/9Jnj4RQgJ1bgergoFJzkDWcF6hSEotkc8ga9HDEHUb3zuqeC+FYl4QGk/yct84iZRGIq0hJUSTGkd6R3EKwa4HD3LHBy5atIPrdAREpQmbWaZ6Flb7sp5QFAnUe12TYhfapZI0Y11GExqtDR6CAQ8DYwl6hqPc+1Q3Pzvazy1Xb8reR00YFNPBrm8AcpbBWwLeKQO6m9c5MRDhxOA4mQxoRsSQtgZtkp2lvkQ+EbJusS2RV03AlFKurta1FYr5yNnhGB/86gGiSQ23U+B2CjIZyEiJlpH4vE6iCW3RzXJNig0CpWgd4iltURvUzyTFhHWzvv0eF163niZyZdCPqcHMj13o9+jt2ukQ9I8m6Oof49/2HdcDqbscQAMfv3ID3aEY9zx5EsA4bpz7njmtJgyKaZPvbNPVP8Z9z3TTG45zOhRlab2HoN9DIp3B6RAFNenmdXxuJ04hEA7wuBzUuR2TnIEmEMY6s/7vYlplUakiFYpZIJbUuOn+F3hlNI5DCAJeJ69d2Uw0qXFeUx2ReBqHECxvqlt0s1yTQoNAKRoFc5k2GHCX5FSiKE4h79f8GIHnN/lo8Lq4+aoLAHJsMK2OElbNEAiiSQ2P2wik7nUHxYVHAAAgAElEQVSyosnP3gOnODscAwHb1y0FpJowKGaEfMcc0Cc7kUSac+EYw5EkdR4Hr1nRRCKdKahJbwl4s9e5dP1Srnl9R1EbTH2JPEnA6yIUSZbUpy0kZiNV5BLgCvQlc9BtMX8qpRyr9r0Virkif5Z6ZjiKpkmcQqBlJAGPizuuuYhQJMneAyezA+2NV25c0B1OMQoNAmAvYMSSGvu7BugNx3E6BKFIko9dsYE6t2NRaAeqSSFPWVPwjCTSDI0nafS5SaYzHB8Y5/7fnKF/NJEdlE075FhS49qLOwBBZ1s9wEQgdY8eSH0okiAUSbFhWQOxlJYNJaOypCimQ6GA6ABtS7yMxlI4hcDpEDgQfOTSNYzEUtz7VLdF4647ZZrL3DdfdWF2Sd0qVNrZzpfbpy00qipgCiE+AtwB1DPhjSSBcSHE30op/7Oa91co5gK75cWWgJezIzEcAnxeJzdf9SqCAQ/RZJpQJIWWkfSEYhwfGCNY3zLXP2FOyA8nAhQUMELjSXY9cJAxQwNhZtuoJDWhYvKEqFBoF7/HRTyl0R2KkkhpHOkdo8nv5q5fHCcUSeJy6BMoO42n3+vk0++4kGgyzY1XbswZnK1hjta2BrJe5yrguqIQUy01TxUQ/ZarN9HVP85Xf9XFqcEYnW1+Nq9oAshJTRlPpdnfNUQynaE7FOXAiUG2rV3K7Y8ezbbrPTvsnTPNNtzVPwYIVjT5cvo0v8fFY4dfyWr9FxrVzEX+buCr6BrLzwCHjF2bgL8CviqE6JdS/rBaZVAo5gK75cVwNEX/aJx4KkM0leEzDx7irS8NcPNVF7LE5+QnLw6hScknvn+Q7//ZGxZkZ1MK+d73+QLnsb4xWgJedj14ULfhcztY11rPH/5uh/I8rpB8MwNrIGrrswiNJ9l599MMjiVIaBnWL2ugJxTl/EYfh86NEI6lkBLOb6qjJeAF9HehNxzn5b4xEimND371AOta6yd52xYSJlXAdYUdpTj0TRXk3Odx0tlWT53LRcDjxOXIbYumUJhIaSB0zdhILMU9+0/x8MFexmJpjg+Mk0xn2PXAQe64prBzpnVZ3Jxc+T0udt79dDZZwXf/dOH1+9XUYH4COAJcIqUct2z/mRDi68BTwCcBJWAqFhR2y4sv9/WhZSaOSaQydIdiDEUSXHHBch4/OojTSLv3XHeIKy48b+5+QA1hXWY1BxSv28FYLI3HpdvwNXhdSricBuZArGVk0dSOz3WHGInq6SHjaZBS0lLvweEATYLf7SQjYWm9vnQYrPfQ3uzH73WSTGdwOHXbS9MZy27AV8KkohRKyZBTSkD0fBtJ63VMoTAYcHPJmhaOD4wzMJYg4HURTegRF5PpDB63g2iysHNmflmHIgnWL2vgscOvMBJNATASXZj9fjUFzNcAu/OESwCklGNCiG+gazYVigWFnTbmopXNtNZ76B9PkMlI6utcdAR9tAS8NNbp6SNjKY1Gv5utHUFgcXkbFiPf1jKa0Gioc7FhWUN2eWox1890MQfikwMR29SOZju8YHljNj3keY1ern/jGi5Z00IokuTu/cd56sQwWkZPGGAO5j6Pvix+8/0vkEhnOBeO4XY5lE2lYlqUIjza9cP5fepUtsYAoUiKj12xnngqw9d+dZxEWrK8qY6PvHEtf//DQ0h0m+KWgJeDZ8KYtsZTZf7JT7dq9vsLiWoKmFNFgK9OCiFFDuVkEarhrD/zDqs2JpbU+PLjx1jVEqBtiZc/uXQt7UE/K5r83PbIYfZ3DZGRko3nNfCF978mmwVFxXRkkteyaWuZb8OnqJyJJcHxSakd89vh3usv4fmeYR470sfDL/Tyv6dC3HL1Jj7zzs3cfP8Lhh2mfl0zIPXeA6eREt1WNuinoc61qJ3ZFNOnVPvc/H7Yrk+1u06+ULiiyc/tjx4lkZb4PU5ueMt67nzsZWJJjWDAww1vWZ/ty80oCLt3bC5qz5yfbnWhLY9DdQXM3wLXCSG+IqWMWHcIIeqB64xjFIoFjWmHdmooQjKd4Ycv9HLHNRdxZjjKqcEo0aQeI1DLSKLJdPacxeRtWAizHpwOwcpmPzsuWoHHpc9dTe3asb4xJWhOE5/HyZb2Rnbv2JJj82rVHPePJogm02xY1sD9z50FJtpmPKURjqWMpcZUNhzLiYEIPcNRVjT6iCU1MhlJIpXhbDjG2XCUfG2PQlEq5ZpU2PWp7c3+rK1l/rWtQqG1H0qkMzzfE2b/8UGSqQxnR2I83zNMdyhGIqWhSTg1GMk5t73Zb1vWYL1nwS2LW6mmgHk7cD/wnBDiX4DDxnbTyacTeG8V769Q1ARWOzSrvU5LwMtgJEEspQuYy5smMj2UsgS0GLDWQ3PAzT//7GXC0RTNAQ/3fvgSvvz4sUWv5Z1J8m1es4GoAx5WLw3Yeve3BLzc9sgReoajIGF7ZwtmOBaf25ldq2r0u6lzOwkGPNy9/zhPnxiepO1RKKpFfp/aEvByy4MHbbWOkCvA5p/b1uCZWIOV0NZQx/lNXg6eGUGTksFIEr/HVbVVqPliPlXNTD4PCCFuAP4R+BITj0MAEeAGKeWD1bq/QlEr+IxYf7seOEg0qWWDqZ8ZjrIqGKAl4EUA129fPaVX7WLDunz7xLEBfnTwFT01W0rjkRd7lZbXhlIHn2LHWbU98ZRGPJ3B7MLttDuhSJINbQ3EUxo7t62hs60+OyBv72xh57Y12WDU8ZTGnoePkEzrXm89oah6doqqY9duu0OxnHbY1T9uG0fXLoTa9s6WbGzMzSsa+dDFqznWF8HrctJQ5+JI70hV+qf5ZD5V1TiYUsqvCCHuA94GrDE2m4HWR6p5b4WilgjWe9jzni1ZexvTwHx5U122o+hsy+188peATJu2xbiseN8zpzl8djQ7GEgJa1p8HB/wZsPrxFMasaS2qOoln1IHn6mOszr+CKDJp+dT7uofY0t7U0HtzprWQDaY+rUXryKeSlPndmXbq2lf3BH00ROKgoCVQX9ZGvr5or1R1B757dbaDpc3+XJskPPfifz+2DQnaQl46eof4xtPnaRvNI7TKbi0tZWtHUF+cqiPnlC0aBsvtz3PJ/OpqmfykVKGge9W+z4KRS0TS2r6MmIoyspgX3Yp5sYrN+YIndbj870fiy3nLGTMDtXjcuAU4HI6WOJzssTn5dqLO0ikMnz72W7ufOwYwYCHa17fniPULCZKHXxKiRF4y9WbOHR2hLt+cZxDvaMIAXsPnGb3joZJ9Wpm6lnR5Ms69gyMJTgditDk8xAMuLj+DWtZ4nfT2dbA7h1bKposzSftjWLuKSa8+TzOnHYIkjsfO4aWkRzrG+fnR/t468ZlRfvl9mY/ux86xLG+cQ73jlLncoAQXPP6duM8aej9pe35+e35xis3cjYcA2Q2Fm0+88l8SuUiz0N5XSuqQVf/OE90DRJLapw2lmI62+qzOZqt6fXsBlG75ZxanrnOFLGkRjyVIRhwE0tquF0OnA5BndvFfc+cZjSWxuvS7VoBftU1wL6XB3A7xaISwk1KHXzam/0EA+7sEl+h477/mzOMxFKktQwrg34GxnKdI+KpDN95todQJEkw4AEk3aEYPcNRli/xcS4c50woiiZhf1eIJr+bS9cvZfeOLWxpbyr7980n7Y1ibohZbNzN/rXQZER3btPbYWg8icspOHRuhNF4mj0PH2HfSwPZPqRQv9w/mkAAWkaSkVDnFAgE+7sGGBhLUp91fBvnvmdO5yQ0sKaO7A3Hufn+Fzh4biRry7zbJgTbfDKfmjEBUwjxc3Qx/feklGnj+1RIKeXlM1UGhaJWiafSjERTpDIZkukM8VS6oFdjvueuuX06y4rzEWuHvsTnQpN6B66lJdGExplQjCU+N2MJ3fM+ntTQNP2YZEYuGiHcSnmDjzB8Z+0jypnts87tJJLQODEQoaXeg9/j4lM/+C1PdIWQUuJ2OniVkdVHosfRlBJG40kAMlI3aUhnJHEjwUClz2U+aW8Us09OQgZj4mntRwu1uVhS4/ZHjxIaT5JMZ6hzO0ilMzl9SKH+um2JFy0jWd7opd7rZm1rPd9+tpuBsWROaDWQkxIadAR9BAMeQpEkfo+TgbEEiVQGLSM5NVi4/5ovSQlmUoO5Fsgw0VutRcW6VCgAqHO7aPA5iSYEfq+DOrdrkhapJeC1jfloCgqVLivOV6wd+rlwnIghSEokQkAw4MkG7z6/Ue+oz2vy8utT4UUjhNtRyuBTLIOJiTl4nhiI0Oh3097sI+Bx8XzPME8cGyIcSyGAJXUuBscTnLekjgafk+FImjq3g4Y6N21LNKKJjBF+S1DndhTVmJby2+aL9kYx+1j7jGhSw+91kkhlsl7jhUKamecFvC48LgcZCS6nyOlDCk1urr24g3gqw33PnOZcOE4irTEay2RDq5lpbIFJCQ3MIO51bictAS+fffgwh3pH0TKSwUgim3J1vjJjAqaUcnWx7wrFYmZFkw+/x00ilcTvcbOiyWfsmdAinQ3nxnzMz69tXc5ZDFg79I6gj3QGBiMJ0prkNSubuOVdm3i+Z5iv7z+F0yFIZyQ3XLqekdclONo3zlWbl9tm71CUngnlxis3cuDEII8d6WM0ljZCtNThdAocQugaTJeTvtE4g+MJLjhvCZesCaJlJB6Xg4DHxZs3LGXj8ka8RgT2OrdjWmWfL9obRfUo9E5b27WZkOFsOEo8leG2R44QiiSLOrX1jybYti7Im9YvY2Wzj00rGnP6X6vNPJCTvjaa0Kj3ukikMvg9ThLpDMub6nL6cDMixt37j3MurPdrVlvL67ev5oyxElDvdWVTrs5XlA2mQjELDEUSrAr6Wdbgpc7tZCiSgAg5WiQQOZ2jXX7txSQs2YUGOXR2hP/41QmiKY3dPzyE1+3k3Egsa7MUDHj42Ld/w0g0xTef6Wbv9SpWph2laALNZUPTZuxjV6zPRjq4tLOVU4MRPC4H8ZTGqaEoo9EUTxwf5MVzI9TXuVi+xKfHxjwGxwcj3PCW9XzuR4eJJvRQXepZKCqhmKOXXZ9hDfi/oa2B3nCcnx/toyMYyK4ETYRD0x3UHnrhHG1LvGxa0ZhzX6vN/LUXd0xoSxNajlBZKNOYz+Oks60el8Npa57S2dZA57L6BWMCMqsCphDCBewAgsAPpZSvzOb9FYq5Ij8kkV3A6s62+qKD/mL0oM3XVjX63USTGkf7RkkkNVxOBxuXLyGTkezctoYjvSOMRFMAjERT/OzIK8oppABTaQK7+sc4MRAxlvKS1Lmd2fa2e8dmzgxH9WDSPzxEdyiKlBKBbnMZ9HuJpzWiSY3jA+Mg4eb7X+DwuVE8hiZTPQtFJZQSAcH8/r8nh3jx7CgNXhdIiCTTDI4n+OxDh3E4xKSUjnVGW7e7dv598xUC+UJlIc1jMfOUhWYCUjUBUwjxBeAtUsrfMb4L4DHgUnSx/R+EEL8rpTxerTIoFLWAqXW0m9XadSaFBl0z5WQ8pdEblgt6gLZqaoHs//0eF8f6xwiN64OAyynp6h9ne6e+ZHXB8kYa/W5Goika/W4uv+A8jrwytmA0ArNFLKlx9/6TnBgcxykEm89vJBxNZuOM+jy6zZiZPGDzikbG42l6hqM4HIKl9R6iSQ2vy0EyZcQuBTxu/bvf6yz7WSwm7b2iMFOZd5jtxO9x8YnvH+RcOIZTCC6/sJW3bzqfr+8/yalYBLTJjoDFoiuY9+0Nx/F7naxo8mX775aAN6dvtwtHZH6fqvwLyQSkmhrMt6MLlCbvAt4EfAF4Hj27z03AR6tYBoViTrGLc5bfIZXambQEvPQMR7PC03w3AC+Etc7M0DehSIpgwM1oLE1Sy+Aycrf7PXpHH01kuPOxl2lb4mXv9ZdwpHeErR1BgvWeBaURmC26+sd46kSIRCpDIq3xm55h/uZbz2dDpwDsevAgz5wM4TY0ku1NPl7T3sRHLl3LulY9BJfTIfB7nXz6HRdy52MvE09pBP0e9tiEXykmQC5G7b3CnmJaPms7SaYzjMZS1HtdaBnJ2zedz1s3trHvpT7OhWNFHAHtoyuYNpi7HjhINKEvl99y9aZsLExrHz9hWuLmD17fkfUq93v1rG6l9EkLYUJVTQFzJXDM8v1dwEkp5U0AQohNwIeqeH+FYs6xLqv0huNZjY/VS9xusLTrXM6GYwT9HoJ+D4EFYABeCGud9VgM3rtDMTJSUud2kslALKV7lQ+OJ6g3vD/7RxNEk2muuPC87PUWkkag2pjtbiSWZiSWIpnOkJGSdDqDQGSz+fSNxhmLpfEYoWA8LgcBr4t0RtLkd+cI9i0BL2fDMdIZDYcQLPG5yhYgVfxLhZVC77S5yhNLaTiFwG9oD1vqPWxb24LP4+Tmqy7kwIlBljXU5TjxmOcXi64wFEmQSGdyQh8BOX38Qy+cpTccB2B/1xAv9Y3TPxpHCEEqnWHXAwe545qLirbfhTKhqqaA6QHSlu9vIVejeQJYXsX7KxRzjnU5xO91Ek1oxFMaI9EUS+s128HS7Fx6w3H8Hid73qNre/YeOKlneTAy+SzU5V5rna0M+jE1mKYneSajDwQCZzaUiN/jZCSWWrShiWYC66DmcgqW1LmIpTIk0xputwOP00HbEg9f/nkX0ZTGuZEY61rr8Xsc+L0uRmPpSSk7Te3OyYEI3aEoG5Y1EIqkJrX5qcw/VPxLRSnkr/Lc88cXc3JwPLuaYXXUyXfiAXOJ3FMwvWOh/ebSec9wlJ8e7ufciK4MQECjz03vSIyUYRoSNSZx65c1FNRSLpQJVTUFzB5gG/AfhrZyLXCLZX8bMF7F+1edcrL+KBYn1uUcM7NEbzhOo9+Nz+20HSzNwfbl/jGSqQy7HjzIn795HaFIinWt9YRjKUsqsoWHnSeoWX+3PXKYlJZBygxOp4N0OkMsmabR78pJyaYoHXOQi6e07KCWSGV4TXsT4ViK85vquPbiVYDkrl+c4Nenh7PesH+0bVU2xt+LZ0f42q+O84Ufv0SDz8WeHVsYiiSywdoREEtprG0NTGrzU5l/LDTnB8X0KCSYDUUSrGz201qv6SsdUuasZpQmuMkp+pLc/ebS+UMvnOWnh/vxuBysbPbzgde3s++lAQbGErxmRRMSPduPGds41xTIzc5ta7Je7QtlQlVNAfNbwGeEEG3AJmAU+JFl/2sB5eCjWPBYl3MKGYVb0Z1ZnCRTGX0JMqEBgmDAnc1F/p1nz7B5RdOCHWjzl8DWL2vgWN8YoUiKRp+broEMTiGIJtMc6xvjpb5xXtfRbKsdUxQm3941GHATiqRsvWIPnhnhUO8IsZRGLKXhdzuzwmVX/xhf+9UJnj01TCyVwedxsuuBg+x5z5bsQLl93VJ2bltlm2PZFAyW1mv4jDBe+eYfytRBAcWXjwtF6zCZSnDTl8hT2fSOdpr2/P3tzf6s4uDcSCxr+rRt7VLaGrx87VcnSaQztDZ4coTIY31jE5l9uoboCcVY0xrI/p6FMKGqpoB5G7od5nuAEWCnlDIMIIRoBN4N/FMV769Q1BzWQbKQ/aTPWBbf9eBBxmLprMfizm1r6AnFsqE0Fpsg1RLw4nU5GI2lWFLnJpJMk5GQ0CQOASOxFJtWLJm3s/25wKrRCUWSfOyKDdS5HdlBzedxZge5eEojk4GAx4nTIfjIpWsAPdj0CWMJXDgEqUyGeoeLaFJjKJKYNFDGktqkjCpTCQYKhUkxLeRUgpnd/nI8vO32Hzo7wqFzozT63NkEGVs7gtz+6NGc+JuhSIo6tyOnzZuZshBQ53bm/J6FMKGqmoAppUwAHzY++Yyh219Gq3V/hWI+EhpPZjNF7NmxJcdj8cYrN7KmNZDTuS0ET8NSMG2nokmNRp8bIQTP9wwjgbQmOb/Ry2euvpDNhk1VoZRwilxyQq8YHvnmxCc0nsw6pbU2eA0nHXA6BW/sXMrmFU3Zwd7n1gNHdwT9DIwlWL00kJPmtL3Zn2Mmkq99sga6jqcydPWPL4p0qIryme7ysVVws9OGTqU5NM1FggEvP37xFf7psaP0jSZxOgS/t+k8tne25rwXSIinNNbkmYbkB3c3swzN5uSq2uPHnGTykVJm0LWaCoUC/UV/8WyYT3z/IGMx3Q7t8+/dkuOxmK8NAubU03A2hVur1uLwK6M01blxCEFTnQPhcPCP79vC76wJLhjvy9kiG3rlwdzQKzARhsjjdhBJpnEIwcbzlhBLaVy/fc0kW7HtnS3s3LaGFU2+STEBTae1jJRISdbjP18Lv/fAqawZiDUIdi2wWCZztU6pYYry33+751dIG2o64FgnqtZrL/G5eb5nmMGxJJFkmsY6FxLBFRe0FXwv7CZMZvrf3TsaZr1tzUZfWVUB0wiufgWwHmghP7AUSCnlZ6tZBoWilrDr5MwX/cWzo5wLx6j3uhiJpugfS+bM1FsC3pxzTRsemH1Pw9kW5MwOu6tvnFQ6Q5PfQ1MgSWt9HRvPq+d1q1uAheN9OZsMRRIkUpNDr4zF0ggBiaQeu3KJT7c7W9sayKaMzHdiG4okdA1R/WQP8Zf7x7J5mte11mc1nKC3p/1dA5wajJJM64HZ84NgzyVq4lJbFAtTZPf+2z0/0DWLwYBnkvYw3zZ557ZVgMjaTB46N0JoPInbqYs0aQltDR62rV2aLV85NpRzsRw+G31lNTP5rAceADYyWbA0kYASMBWLgkKDlPmiN/ncOIUeQNyM2/bWjW2TlhbNDm9F09x5Gs62IGdq2m6+/wXOhGOcGoqwbW0L129fk+M0slC8L2cTuzqLJfUwRBkJfq+LW961iWDAYztgWsMRFXK8MJ3WvC4Hq1sC/OHvdrC9s3WShnMwksDtciAsQbBrQXOoJi7zg0Lvf/7z6+of475nurMe3B+7YkOOhrGrf5yTAxHcLgf7jw/SHYrSEfSxxOfi6RMhMhIyUuJCD5P2N5d3ctmGZTl29bVuQzkbfWU1NZhfAtYBnwR+DgxV8V4KRc1TaJCyvui/t7mNKy5YzkUrmxiKJGhBD9dyNhyb8Dg0Ory1rQHb9JOzwVwIckORBOmM5FXLGoinNK7fvpYt7blx7BaK9+VsYldnZ4xEAGa4l2gyzYpmX9E0psUcL0yntWhCY3lTXVa4tJ7rdAhWBQPsuGgFHpfIaoOKaQ5nS/hUE5f5QaH3P//5mdpIYJLzTSypsffASbpDUdIZidMh8LmdhCIp3rt1BWeGYwB4nAF+b9Myrn71ihlJeDHbE6nZ6CurKWBeCtwppfxiFe+hUMwbCg1S+S96LKmx64GDjCXSnAvrYS9aGzzZAL9I8Lmd9IbjPNcdyhmsq9VJ5V93LgQ5a/2taQ3Q2VZve1ytaw5qkfw6s/PqLta2phLAgvUe7vjARTn2w6Z9m/Xc1gYvTx4fIBRJ8UTXINde3FFQcLU6IRXKiDWT9VNJe68F7etiRHcUG8tZ3TCdc0zzjkLt1QxFtGFZA5FEmmC9h7QmCQbcNNW5GRxPMBZL0+h3z6hwORcmGNXuK6spYCaAk1W8vkIxryg2SJkveiypcfP9L/D0yRBupyAjYWm9RiiS4mNXrAcEd+8/zpmQvpx471Pd/Oxof9amqBqdVKHOb7YFuXx7PzVwV4/8us73dM1vW6UIYNY2bue529U/TncownefPZO1BwVhKwjEklrWCcnlEkQSabr6x9jS3lTVOimnvSu7zdknltS45cEX2X98ECRs72zh5qsutI1cYNdeY0mNeCrDEp+L3nCc1UsD3HzVBZwNR9l74DRferyLWFJjzdLAjKbrLbYCMJ8nKdUUMH8CbAf+vYr3UCjmFVMNUl394/z2bNgIZg2t9d5sxp8VTX7OhqOAg4SWIZ7SnSHscuLOpJ3YVPZns9kBTmXvp5g5rHVtjedXqG2VKoB19Y9xYiCCzxL3r73Zz33PnM6m2zODVXe21dsKAmeGo0QTGi6nYDiSIpPRBYDdOyYHcZ8ulbZvZbc5+5wZjtITipI0+sbuUIznukOTnoMZNsuqTbdmWjsdihjZpCQ+jzMbe7jO7cThEDgcIsdJbboUWgGY75OUagqYfwv8UgjxceBLUspkFe+lUMw77AcuiQNBvUfvyD7/3s0sa/RlOz8zoPX5jXX6NfJS71XDTqzY8udcdICmAX5+YGJF5UyVE7lYPL9Sr2Xu23vgND3D0ayGyRzsTTtMM1i11fQjP2yMuYQfSaTJyCivOq+hKskHptO+ld3m7NPe7Gdl0E+30b46gj62dgT52dH+nGgcVg9xkIQiKbxuPWtaJJlmOJJiacCbk62nUEaqmZhgT8TEHMeaonK+T1KqKWDuBwLAF4DPCyHOAVreMVJKua6KZVAoahK7PLQrmnyA4JK1zZwLJ+gI+njd6packEQep4ORWIpYUqM54Ob/XvmqbHDxM8PRqjj95C8nwYT93Gx3gFYDfDNWohq4p8dUqfdKieeXf63ecBy/18meHVtylhB1+7YkG9p0R62d2/RsQPFUJidN5daO4KRsK/bL6tUNUl1q+7YTMpTDmc5sr3Ds3rE5K6iZQqDV3OPAiSG6+scJeFz0hKJIoN7rIprQ8LocvNQXI5nO0BOOcunS1my5Cy2pz+QE+75nTudcy/r+BQMe4imNWFKbN22pmgJmN4WzxSsUixpz4DLz0J4ajBKKJg2HHi+73nnBpPA7wYCHl/rGWFLnYmWznzq3k5FYMpvlpppaxEL2czdeuXFWtTRWA/xYSmPntlXzprOtVaaTes/uWr3hOC/3jZFMZ9j1wEHuuOaiSQJrbzhu5D735ASvfu/WFVy0snlSey5UxmoHqS5FC1lMyFjsDmdzscKhBy+fHF2ivdnPLQ++yC+P9TMSTdPkd7Nt3VJcDrITm/e+dgVffPRlPK0Oklomp3+xe5blTLCnErQLXcvUbO49cJI7Hzs2r5bKq5kq8rJqXVuhmO+YA5eZhxZgJJqitV5jYCxB32iczraGbLwIHTEAACAASURBVKfk97gYjaVwOUTWJujcSIx7n+rm/t+cJZrQcgJlz8SgVkrmC7tc09XEOuBbA34rKmcqISp/YJ3Km9zvdZJMZ/C4HUSNY63nv/e1K/jar04STWp87uHDRJP6wtbTJ4c4F47x8MFexuJpUukMvWE5aYkyv4zVFOImli7HKBTOeb4vY1aTWqqbM8NRTg1GCEfTaJkMiXSGay9emU15arapjqCP7lCM85v0cEbFNIalmkHYBW43+y7z3sWijNS5HYQiKWDu67Ec5iRVpEKx2LEOXHsPnGZgLEGj343b5aBnOMq9T3Xzk0OvAIKBsQTHB8YZT+iZVTYsa+BtF7bx86MDOB2CaELD73GSSGdmLEd5Ic2DXSc4m1oatew485RTp7qX7kG6QzE6gj5279gyyZt8z44tOeGD2pv9hMaTHDgxxGNHejkXTmQdhqJJDb/XSWg8mQ2/NRZLc2ooQiyp0eh30xLwzvlzN4Ny22mPlK1lYWqpbsxVIACnw0Gd20md25XTf8WSGiDISMnzPaPc8ehLtiGwrP1rfrQFEJPMSHJWrCyB20HkRGYo1MZrqR7LoeoCphDiTcCVwDLgDinlUSFEPbAVeEFKGa52GRSKWkRfyplY4msJeHmuO8S9T3XjdAi6Q7GszmQ8nmI0niYj4eCZET73ni0ceUW3y1zeVJdjewnTD1dUSPMw1wM9qGXHalCOB/j+riHdRi0UtQ0NFKz3cMc1EzEvY0mND/z7kwyNJ0mmM1y0sinHYejGKzdyNhxj74GThCIp/B4nq1sCJLUMPrczGwpmrp77VFq4WngnapVaqhufx8lt7301N9//AqFoktUtk2PpmjbCTiEYi6WIpTTbsEH5/au+/H6Q/V1DWdvw3Ts2TzINOTkQyU6kzP494HXl3MOujddSPZZDNVNFOoH7gPejry1I4JvAUSCNnkbyi8A/VKsMCsV8wDpwbu9szXo8mjPcgbEEHpcTKdO4nAKvy8HJwfFJHY7pTDETOcrnaklSUeuIiZVikf1nEtY2sr9rgJFoCqdDoEnJWCKd4zAEUOd2cPNVF+rZqyxpUWtBW1OK9ki9E4WppboJ1nu484OvnTJhQG9Y0uh3Z0PE5Qdiz+9fQQ+JlEzr4ZF6QlFbW2bTljIUSU3SYE618lRL9Vgq1dRgfhJ4H3q4oh8DR8wdUsq4EOIHwDtQAqZCkcXOY7urf5yv/KKLx4/2IyU0+t1s7QgW7HBmYjllvs6YFdWls62e7euW0hOKsjLoL5hNycrWjiCNfjcj0RTnN/m45eoL2LyiqaB3eK21vVorj2J6FBPU8j3O7SJyFOpfO4I+PdOagJVBv60t85b2Rnbv2JLTv1v/P59jXtpRTQFzJ7BXSvnPQogWm/1H0AVMhUJhYJ3BAtnZcTKV4XdWBRmJpfjM1RcWzR5R6YBolw6y3MwlahBe2JhhYMp5zsF6D9/90zfwXHeIrR3BSWGLrNqgrv5x6twO2pv9Jbe9SttdOedZoyiYIbpUG1+YWPs9n8dZMPzUobMj9AxHOXR2hE0rdMGxkA1moesD2f/PxMpTrVFNAXM1cEeR/WGguYr3VyjmFfmxMc3lk2DATTDgIRRJsmnFkmzcy/xzpyscFpo9lzIQz/eME4rSqWSpLljv4YoLz5skoOXH+TOXD0ttQ5W2u0rOmyrGp2L+YtfHTdUn3vj933IuHMfpEPzepvP4zDsvpM7trHjyMV8deYpRTQFzDAgW2d8JDFTx/ooyWX3TwyUdd+rz76xySRYnVm2O1QDczENe53ba5uCeCeGukCNDqdeupXAkitojltRy7M/slsPjKY07HzsGlN6GKm13lZw3VYxPRe1TjiBZrI081x3SU5RKSUaTHOsbY9cDB7ORPCrpgxeiKYajitd+AvhDIcQkK3AhRDNwPfB4Fe+vUMwrzBks6PY8K4MT6R872xpob/Zz+6NH+fwjR9n90CEjpEZho/NK722dPZd67ZaAF6/bgZaRC2b2rZgZzAH8cw8fZn/XEFpG5rQlUyPa2dZg2waLUajdWu99rG8s+66Uel6he9nF+FTMD8x2WGr/WaiNxJIajT4PjX4XDiFwOx0sW1KXjedaaR8ME+/CQhAuoboazM+hC5k/B+4xtr1GCLEeuAk9jeTnq3h/hWJeYefgYxqb6xqejO2MuppOPfnXbgl4OdY3lmMAD3D7o0ez8ThvvHLjgukgFdPHHMDr3E4QEEtprLXJZ15KUPN8iml9psqwk39eIe2WdZtdjE9F7WJ9foXsfVsC3oIBzu3aiNmmtnY086b1raxs9rO2tb6syAeLxV69mpl8nhVCvA/4GvB1Y/MX0XuOfuD3pZSHq3V/hWI+km/f1t7sz7HLNG0xp+oIZ+Le+dc2w8f0huP0DEdZ2exneVMd73ttOycHItS59WDvZtxChQLy8pmvW5rNYlKonRYLam5HIZvQUuJXmmYgB8+Ec3Ka33L1JmCyV29+jM+FLBzMd4qltc2397XGEc5PHGB17rJO8kdjaTavaMy2qXKSFSwWe/WqBlqXUj4shFgNvA24AF24PAb8REqp1hYUiikw7b7iKQ0tI/n4lRtsDclnKkaa3czavLbp5RhLaYxEUwT9abr6xrnrl8fpNsJzXLKmhXhKK5peTbG4KGcCZM14cnIgQlf/+KS80qXSEvDidTmy2sZC2vfdDx3ixEAkm13IusRZKNmAsi+uTYppLIciCW68ciPPdYdo9Hm46xfHc/YVyyFebJJvvWel9r9meRfapKXqmXyklAngIeOjUCjKoCXgpWc4ykg0RaPfzYomf9W0g4Vm1tZ86F6Xg2TaQYPPxZlwDC0jcYYFna31xFMa0WSaOx87tuBn5oryKFUoM9P57T8+CBL2Hjg5KR1lKcSSmm62YaSivOEt67ntkcOcHIwyFEmwKhhgeVMd117cQf9oAp/bmZNdyBQ+gwF3Ni2mWg6vPfLDuhXSWJrmPeYy9hKfE5dDkEhnpjR1sAqEVofLSmNX2pkdLVSNpspFrlDUMEORBCub/Syt13LS5lWDQjNrMzRLz3CU8xt9NPhcfPbNW/jyz4/hdjk4PjBOPKVR53YSTWh4XA7lSa6oCJ/Hyc5tq+gORfG5nYQiqYrakdmWnQ5BIpXh+Z5h9ncNEU1qxFIaS+u9RlsXE8v3luxC5sQKhGENOmETuljs52qd/AmxOVmACa2kVXNu1Y4/fWKYFU0+Wg1BtNhzzBcIreYdlcSutGb1AcnZ8MKNwFFVAVMIcS3wl8B6wC7YupRSKiFXoShAu2Hn2BuO4/foYYqqea98Y/eu/nFODkTQpGQkmmJpvZdEKkOjz8Wa1gD9owkuWdNCNJEmmtI4F45lbTPNJUk1EC8+piOEdbY1sNZoW5U6rU2k/NNjVta5naQzEocAp0MXFnVhob7g8r2Zl1oPFZbMmXAtRG3TfCN/QmxOFsxn3hLw5mjOzTZxYiACQg8Bl0jZ24znt99CbSQ/jms55kH3PXM6e14w4M7agy4kTXk1c5HvAv4e6AOeBIardS+FYqHiM7yydz14kGhCX/ar1qBm58W+98BJukNRJNDgm8jN29nWMMmeyeN0sLLZzx/+bgdbO4I5XpVqIF48TNeJYSac1rLvzQMHGYun2fPwYRwCXE4Hl69fyp9cui4n20qhlKvBgCebFtPOrm8haZvmG5M1i/XZZ27XV1qjFFgduuxCW9m1X7vnbNVG7j1wsmTzIHPiXud2Eook+dgVG7IZrBZSP1lN7eFfAPuAt0spU1W8j0KxoBmKJEikMjgdouqDmrUjPdY3RiiSYl1rPeFYio9dvg6JYGuHnj/B9Cj3ugR+r5NXwnGCAQ9bO4IMRRJqIF6kzIQQVokjTb7WaSiSIJHOkEpnGIulWbM0gMMh+JNL19LZVl8wtJF1ggUSafyFiXiv0UThMEVqCX1mmKoe7SYiZ4ajJNKF+0o9H3gTu3c0FLx2qe03NJ7Mpj+tczsIRVLZc/RQW2CXNjKW1LITdwRsX7e0aGrJ+Uw1BcwlwHeUcKlQTI+5SiGma3Dc7O8aIoNkz49eor3Zx/3PneWPt6/JZjVJpDN43Q6S6cz/Y+/Oo9u67kPffzdGAuAIipRlUTMly2NsJ7XjKEmb1nHrxK0T5yavdVO/1Ol0b3PX8mrrNmlcr/vUpE6T+l73Na9jntPopnmd4tqNHSeuM9qKHDexE8uWZIuaKEqySBEECeIc4Bycs98fII5AECBBCociwN9nLQ8kQBxI3Njnt/f+7d9mJG3ywJMH+ditl7fcsWeiPhejvVabdTq/TK7piodJREOs625jfXf1Ze5qOX2prE37zGlaQ6MZvvT88Lz1XquVxqlW/kbMr95Z8Gpl3bxl8nlSiuYbwNTTflPTFu//m+95my/33H1j2VJ5mIf3HuP7R1NeALn79qu8919MvbDZsbYD03a466ZNLds2/AwwXwQ2+Pj6QqwKy3WEWLUZg7ds6+Pg6QyhoOLYuMF0zkZrsAoOpl1gOl8gGFBkcwWUUqBdTqaMOQn2rdqBirkuxpF3tWadSu8jHglx8Mwkl6/r4oXhFGdmzpAuL7ids11GUiZp08YquJRvAOrvLOYeL1Tvtfx9nEnnqh4fKDOcC1vqLHitlKLSa9bzd16r/RaPOy0eAjCcyjJpFOfOJg2b7x8b562Da+jvaKMtHOQTTxyYaUNwMmXMev/lAezWvgSD/YufqS9tEJqvnuxK4GeAeR/wZaXUl7XWL/p4HSFant+196rNvDzw5AGeHRonbdiAxtWanK3oiYd5+fQUdsElOxNgRoKK9rYwgYDy8tWkXuDqtdy/+1qzTrFIcG4lhO6YtxmtryPqFdyORwP86GSaXKFYESGZiMw6ZOCBJw/MWtasNrNV/j7i0WJVhfLlWtkkVJ8LmQWvTCkaGp32NtQstXi/aTnc/9h+9g6Ng4I3buqhIxYiYxZobwvx5994jWzO8WYzNyZjnJxpK6X+sPy1lzoAK76Pl70yXrsGe5dUxmu5+HmSz3eUUh8GnlNKPQccB5y5T9Mf9us9lNv80SeW4zJCNKXKGYN9R89x6PVpLLtYdsjVmm1rEpxKm6xJRBmdzlPQoBREQ0G297fz4bdtYWMy0bL5RGLlmu+mXWrbpQMC+tqj3ma0tZ1tPPT0YQCOnzMJBBSJSIhgQHHwzCQ3X3GJd8jAWMbi0u4YCmoua1Y7+ao8SJJNQvW5kCCsMjgFvei/88pZ5pEJg+GU6c1Kjk3l+fT73sCkaZHJFfjkEweB4mzmwTOT7L79am+2s1p/uNQB2MiEwcmUgWUX38dwylzRbcjPXeQ3Al8AwsDbZv6ppIFlCTCFELXNLrcR5umDo5ybzmPaLp2xELFIkO54hO1r2/nAmzbypeeHee7oOLbjkogG2daf4Kd3rpXAUlw0tW7apbbtzORitoWDrOtuY9dgH4DX7gf740znC2TMYl5daTMbVD/woJ73URkkXax86ma01CCsWjWMxfyd18rnrZyVvGp9F7FIkNS0xWe/NeS1jes3Jr3NRI020BNnQzLO8IQBmhV/AIDSWvvzwsVZy60UA8hntNbpJb5OuqurqyudXtKPe2QGs3GOf+rdjXgZtfBTVpZGtcWVqjRqz9kuDz39Go6ryVoFPvSWLdy0tZdTaYO8XdzQs747zqm0OVNgPbDic4Hq0FTtsdXb4oWoluNYfhrVwTOTXL8x6eVPlj/ftBxvZ3B5fuXhsxk++cRB70CBj7/78iXPGtWRgyltscEWk/d6+GyGTz15yPv6o7fu9M4jr5yVnK9dLfa6i/mzLGMO5gW1RT9zMK8B/ofW+is+XkMI0SClGQPTcrwj8jb3xvnpnf2YlsNnvznEj0fSOK7mzduS/MbbtnmjeCGWw0I37Mpzo8tP5inPf/zGodGqmz9ikSA3X3HJnGuVDjxoxMyj5CYvn/nOCa8WMML8+bzls5IL7XSvZyf8UgLQ4vvoWspfx7LzM8AcBSwfX18I4ZvzR+SZlsN9j+3nuaPjZPIFgkrxHwdGOZPOs31tu2xUEMuinht2+XGAe4fGOZky2dKX8JZMy3Px5tv8Ue1aUhWhuczXXio37ZSXEqo3/3OhfNqFHr/QAwmaQcDH134Y+KBSSo6CFKKJVB6R98JwCiPvEA6d7y60Lq6dlDrOEtNyOHw2M3OOsxCNU+2GXak0+2TaDihoCwdn7eBOJiJk8wWSiQjVNn/Md63SzGOrBQGtar72Ur5px7Jdr5RQST2/61JbA0gmwuRsd1a/V/54tVnvetpzs/Mz+HsWuI3iLvK/BI4xdxc5Wuvv+vgehBCLVLlEdP3GJN84NIrjao6dy9LbHmF8Ok/bzLGRpY5zNYzIxcVTzwaZhY8DPH8yz/ru2q9X7VpSv7K5zNdeqm3aWWzaQ2Vbe+jp12b1ewvNhK6GNubnJh+34luVF1IUyxTN+7com3xWHtnks3KT2RulsqMrT2b/5FcPkDELdLSF+MR7rvaS2mslxzeJpmqPq6ktllvMDbjyudXaZ6l0ULXXqzw2chkHT9IWG2S+9lIrB3OxLqTfu4htrF4rdpPPr/r42kIIH1VuRCh9ffhshrztEgkF5pxmIiVYhN8Ws0Gm1jGC5e1zvtcrf+zw2YzUr2xCC/1+G1FK6EL6vVZvY34WWv+CX68thLg45utMS8e0lcq8rIDRtxCz3HnDRpYyYyWDJ1HLQkvh9c66t2Ibkw04Qoi6zdeZmpbjnVxSKgMjQaZYCarlBy/GxThfXTSPWjOli8lLb8U21rAAUyn1dji/aaf09UJkk48QzaVWZyrH4ImVqhFtU+pXisVabLtrtTbWyBnMbwNaKRXTWlulr+d5vpp5vPnDdCFWgdJST28iyng2P2eU3YpLPKI1LKVt1lPUvZVmm1YLv35v1V53tfeJDdtFrpT6EMWAcY/WWiul/s96fm6hXE3ZRd68Ftht3lQ7JWFl75b0W2mp50w6x4mUwZpEhM1rEl5x4vLnNelNt6na42pui0u12B3opfYejwRnVUsof9ynHb/SFn3i1+9toaLuF5qfeRGtjF3kWuu/r/haNvkI0SJKSz1Zq8DptMlE1uLUpMnQ6PSsY8tabYlHtI7FtM2RCYMz6RyvjWawbJf7HtvPg++/1gsCJB2kOfn1e5vvdRuRn9ms/DzJRwjRIspPpQgqRbCU4DJvFowQzWmgJ048EsSaKcll5J1ZJ60sdEqLWJn8+r0t5XXlJJ8LoJR6C/BuYAfQCUwBrwJPaK33+XVdIUTjnT+1YpqH9x7hdDrPxmSMwf6OZljmEaIu5W35E++5mvse24+Rd1jX3TanJFer7fhdDRrxe6vW3y3ldVdDfmbDT/JRSnUC/x/wc1Rfv9fAE8Ava60zdbye5GA2KcnBbE1NcPrEUjRVe5S22Hi1ShldhCBS2uIK1ehl7SYYnF9QW/RjifxfgVuBvRRP83kjsH3mv78KfI/iGeX/5MO1hRA+K+UUxSLBVbHMI1aHam25vK0L0ej+rtXbV0MDTKXUzwI3Aw9qrd+utf6C1vpFrfWRmf9+QWv9NuB/Aj+rlHpnI68vhFhekosmWoW0ZbEQaSOL0+gczF8CTgC/v8Dzfh94H3An8B8Nfg9CiGUiuWiiVUhbFguRNrI4jV4ifyPwqF4gsVNr7QKPAm9q8PWFEA1iWg6Hz2YwLWfe57X6Mo9oTdXat7RlsRA/2ki9fW2zafQM5nqKO8Xr8SrwoQZfXwjRAKuhRptYvaR9i5WildtiowPMTmDBneEzMkD7hV5QdocL0XhSSFq0MmnfYqVo5bbY6CXyAIurvCyF3oVYgaols7fqMo5YfWpt1pA2LhphMe2olTcO+VFo/V1KqUvqeN4bfbi2EKIBKpPZoWXqXQpRdbNGKy9ViuWz2HbUyhuH/Agw75z5px5yzpwQK1T5GbqHz2ZadhlHrE6VZ0S38lKlWD5LaUe1zitvdo0OMN/R4NcTQqwAq+FYM7G6SRsXjSDt6LyGBpha6+808vWEECtDKy/jCAHSxkVjSDs6z48lciFEC2rVZRwhSqSNi0aQdlQku7iFEEIIIURDSYAphBBCCCEaSi1wquNFp5RyAdXV1VX18e7f+oflfUOibum//uWaj01OTg5rrTct49u5YAu1RdG8mq09SltsXdIWxUpxoW2xGQLMAsWZ1qmL/V5EQ002UycK0hZbXFO1R2mLLU3aolgpLqgtrvgAUwghhBBCNBfJwRRCCCGEEA0lAaYQQgghhGgoCTCFEEIIIURDSYAphBBCCCEaSgJMIYQQQgjRUBJgCiGEEEKIhpIAUwghhBBCNJQEmEIIIYQQoqEkwBRCCCGEEA0lAaYQQgghhGgoCTCFEEIIIURDSYAphBBCCCEaSgJMIYQQQgjRUBJgCiGEEEKIhpIAUwghhBBCNJQEmEIIIYQQoqEkwBRCCCGEEA0lAaYQQgghhGgoCTCFEEIIIURDSYAphBBCCCEaSgJMIYQQQgjRUBJgCiGEEEKIhpIAUwghhBBCNJQEmEIIIYQQoqEkwBRCCCGEEA0lAaYQQgghhGgoCTCFEEIIIURDSYAphBBCCCEaSgJMIYQQQgjRUBJgCiGEEEKIhpIAUwghhBBCNJQEmEIIIYQQoqEkwBRCCCGEEA0lAaYQQgghhGgoCTCFEEIIIURDSYAphBBCCCEaasUHmEqpE0qpExf7fQghbVGsFNIWxUohbVHUErrYb6AOXV1dXV2AvthvRDSUuthvYAmkLbauZmuP0hZbl7RFsVJcUFtc8TOYQgghhBCiuUiAKYQQQgghGkoCTCGEEEII0VASYAohhBBCiIaSALMJmZbD4bMZTMu52G9FCCHqJn2XEKvnc9AMu8hFGdNy2P34K4xO5envjHL/bVcSiwQv9tsSi7D5o0/U/dzjn3q3j+9EiOUjfZcQq+tz4MsMplKqSyl1g1Jq0zzP2aKUusuP67eykQmD0ak8AKNTeUYmjIv8joQQYmHSdwmxuj4HDQ8wlVIfA84C+4CjSqlvKaW2VXnqW4DPN/r6rW6gJ05/ZxSA/s4oAz3xi/yOhBBiYdJ3CbG6PgcNXSJXSv0s8EngNeBxYAPwXuCHSqnbtdbfaeT1VqNYJMj9t13JyITBQE+8ZafWhRCtRfouIVbX56DRM5i/BxwErtVa/57W+v8AfgIYB76qlLq5wddrKUtN/F0tCcNCiOWzlH6l1s+Uvg+wfW1HS99URXNLTVs8feB1UtPWvM+7kPtuLBJcFZ+DRm/yuQL4X1rrXOkbWusfK6VuBJ4G/l0p9R6t9VMNvm7Tqzfx17Qc7n/sZU6mDDYk49xz8w4++cQBDMthXXdb3QnDpuWsihGUEGLxlrIRITVtcd+j+2f1RQBDoxn27DtBKmst+FrSL4lGW0ybOjVh8ot/uw/DcuiOh/mX33wLyfZI1ddcykad1da+Gx1gdgGpym9qrc8ppd4BfAN4VCl1R4Ov2/SqJf5uX9sx53lDo9PsPXIOy3Y5kTI4nTZ49fVpIuGA9zrVfq7catrFJoRYvHr7oxLTcrjvsf08fyzl9UVDo9N86fkTHB3LcnLCYEd/x7yvJf2SaLTFtCnTcvjoIy/x+lSOgCoewf3CcIqbr7hkznMX+/lY7HtpFY1eIj8JXFbtAa31BPAzwCHg34BbG3ztplYt8bf6FLwGXfw/x9UYlkskFMCyXeKRYF0Jw6tpF5sQYvEWuxFhZMLAyDuEQwGMvEM0FAA0o1N5YuEgaMjZzryvJf2SaLTFtKmRCQM0hAMBXK2JR4JcvzE553mm5ZCzXZKJMFD/Rp3V2L4bPYO5D7gd+INqD2qtJ5RSP0NxufxOvFBJVCb+ArNGO/fespPxbJ713XF2DfYynDLp74xg2S7t0RDxSIBf3bVlweuUfzhSWbvld7EJIRavWn90+GzG+/+h0WlydoG2cIjB/nYGeuL0dUQ49LomHFLEo0HWdxeD1NGpPLsGe7nrpi0M9rfXnLUpBbWlPk/6JXGhFtOmBnriDCRjxS8UfOqOa+Ysj5fPQiYTEe65eTuD/cVcyvLlbygGlL2JKOPZfPG1Z97LmXSOeDRIbyLq2597pVBaNy7GU0r9AvA3wC9prb89z/O6KQaZ12mt550jVkqlu7q6utLpdMPeZzPYP5LmE08cLI7+gXgkSL7gesHmqbTBnn0nGMvkiYYCxKNBpszCgvmb5R+Ou27a5H04LgJ1MS56IRrVFqXQ+orUVO1xOfvF2f1GmIIL+46eY8oo0BUP89bBNey+/SpePjXJHz9+gK5YmEgowEdv3clAT3xROWerLUetBmmLC1hMO2nkcw+fzfCpJw95X3/01p1sX9sx5zMCirFMnpMTBht64l5Osmk5c/KUV3g7v6C22NAZTK31vwP/Xsfz0sCbGnntVmJaDnv2nWA4ZeC4mmvWd2FYDsGAYnQqz6m0ydmpHGOZPMGAYtK0SZs27dEQZ9I59g6NsWuwb07DLZ+iT2Ut2sLBld64hRAXWXm/cfycwWTOxrRcbNfFtBxOpgxePpXmc88cY3Q6x7np4ozlUmYgS7trhailcqPr7tuvmvc+tpg2tdBza82Iln9GhlOmF5WlsxaxUJDpfIGh0Qxt4eJEUeleXk/uZjOToyIvgvlGSabl8M1DZzk6lkVrjVVwKbgO0VAIw3KIhAL8+dOvkXdcTqdNb3Rk5AsYlsvpSZMvPjfMNw6NzhodydK4EGIxSv1UPBIiGg6QztqcnjQpOBqr4BAKBIiGFJbjsPvxA5yaMImGg6zvjnHXTVuacbZGNIHyja7DEwZDo9NcPdBV9bnV7rX1zGhWPqf868oalsV7q0MyESGVtdiYjAGKU2kTs+Dy6miGUEDx8N5j/NG7r1xwyb6VZvElwFxm8+0kS01bfOzfXuLHJ9NMGBauhp5YiFdOZxjojnM2k8OwHEyrQGcszI61Hbz/TQN8+9Ux8oViqsOlaTNbVgAAIABJREFU3bE5o6Py8iF9HdFZeSNC1FLvUr4s4zc303J45dQkZzM5rt3Qg2EV6E1EeeDJgxwdy3J2KsdAT4zj41msgkskHODydZ2899r1/OV3jvDi8CQaCKpiGZG+zijru2NzdpW3+myNWC7nN7oW/1s9za/aTCew4E7uynv0vbfs5DNPHZqTWgbwn8dSfO6ZIxiWW9wH8ZbNdMUjJBMRfv9ff4ztuBQcTUApRlIm49n8vEXWW22nuQSYy2xodJpjY1nawsFZQWBptP/9oykMqwCA1jBhFFAKtM4ylSvgaii4mlTWYjhlsLajjbFMnmy+gONqumJhxqbzbOtr93aiV3b0sjQuROtabM7ZH/7bfr728hkKriYaVFy3MUk8EuBHJydJZS0KriZrFVBAQEHOculrj5JsjzKdL5y/vSvFlr4En7j9asazeYy8M6vCRW8i6m0UqpwVkv5I1Guwv8Pb6LoxGfOCvUrVZjrbwoE5O7nL84QBvvbyGfaPTNITjzA6leeF4RSjU3kcV7P3yDmGUwYbk3EKrsPeoRRpwyIYCBALB3j59BSXdsVIJiKYlkNAKZTSaK1Jtke8tl5roDU0muHoWJZYRXzQrCTAXEbF3MpjDKcMULBr25qyXZmZ4oadsCJrFTNrQwFFwS2OfvIFFwCFRgEdbSE29yaYNC0OnpkibdoEFGitiYaDZHI2puUwns2TMQsEAor8IkoZCSGaz2Lr/u0dGuPV1zPkCy5ag+1ozkyaJCIhUtk8drHbIW8XC0+7GgIBiEdD7Lykg45oiGy+gNbQ3xHh//7F60m2R4hFgqzrbgMgHg3y8Xdd4c0CVc4KtcJMjVg+sUiQ3bdfPe8s4MiEQc4uzJnprMyh7E1Evc9LZyxMOptn39Fx8gVNJKh46/Y1bFnTTjQUYGw6Dxpi4SAnUwZZy8FxNRqwHZe2sOLctEU27xCLBLny0k564mEKrubagW4eeO8187bx0t6LkzPlkubLY26WwZkEmMtoZMIglbXZsbYD03a466ZN3kh+z74TnJo0CagAN23pZTJnM5wyyBdcogGIhEPEo1BwNJGQYrCvgzUdEe7/91cYz1q4WhNQUHDB1S5TgQIvDKe4fmOS05MmjuMSj4b4+LuvWNENUgiQ5fmlqrcAdCkQPZPOkTZmH4k3mskTj9i4ZSuPoQDsuKSDbN4hEQkxkbV48D9eZVtfOxuSMe64boB3XnGJV9alssxR5fsqzQot9D6FqKbWLGBlpZQbt/ZwOp33ZjprtUvH1ewdOkfGtMkVipM4lqs5lc7xG//7B8VZyXiEgZ42pkyHDTMzmOPTeZLx4q7xtV1tDI1OEwwolILfevs2ouEAoOYtz1VSjA8sdvR3kLMd7rppy4LVYFb64EwCzGVUPnra2pfwpvbLG1bWKhCLBNFAezTE5eviBJUCBUGlyNkOv3fLZXTFw7x2NsN3Xx0jGFA4heJIqljeWBMLB+mKRTg1sxFoTXuUWDjoLb8LIVpPvXX/RiYMzqRz5GyHzWsSbEjGGJ+2yORsNvYmmM4XGM/auE6xX3E1WLamrz1aHPTOFFSPhAJEQgGu39Qzp2ZgeRBQ+b6u35jkG4dGpealaKjKSin33LyDtnBg1kxftXZ5bCxLMKCIR4KY3mohBJRi0rBZ0x4l4ga4e9c27/VMy+E7r43y+EunsQqaaChAbzzCtFVgc2+CK9d3LSrwK/+MbOlLMNjfvuCfcaUPziTAXEaVo6dS4ytvWH3tUW+3+La+dj745o1cvzHJA08e9JKVSw23NxGlMx7BdvMEFMRCilAowNY1HfR1Rvnr7xwhmQjT1xH1zgGWjlyI1lWrj6nUm4hycsJg0rDpiofZc/eNpLJ578zwgZ4YU4bN2UyegqsxbZcjY9N86dffjKs1vYnorCXuhfqVau+rnvcpxGJUDmQWmjkstcOh0Wn27DvG65N5jo5N0xELk7MdehMR8gWHWDjovR4UU9r27DvByZTBcMpgW187B14v5l/2d0a55+Ydi27b9X4mmulAAt8DTKXUdmA70EuVop1a6z1+v4eVpNrUfnnDKu+413W3sWuwb+ZZeiadRM/6uWs3dPLKqQyTpsWOtZ1Yjsuv3LSJR144BUAqa3PPzdtpC5/PvSxPtBdCtJZ66v6NZ/MzKxuOt7Jx9UA3u2/v8G5wqazFR770Q14+NYmjIW3afPprB3noF68nFgly7y07vTScevqSyvclNS9Foy1l4BKLBLl6oMvL6+xNRDmVNr2TqtZ3x7zTeKC4C/3oWJaTE8XAEgWTpg26uOpo5B0++cQB72CUxSxh1/OZaKbBmW8BplJqLfAF4J2lb1V5mgZWVYBZS3nDqmw8h89mSGWLhdRTWdubEh+ZMJgyHS7tjjFtFbAcl619CW7auoZnh86VjeLOH2XVLLkbQgj/DMzUz62cBSnvh9ZHYvzhu67gN7/4AzK5YmrN+Ez/M9AT9wbClTV3hbiYljpwKf2caTl86fkTs+6T29uLr3f4bIbRqXzxhD0NdsFl17Y1fOBNG/jnHwyTytpEw8X0ET+LqTfL4MzPGczPUgwu/wr4JjDu47VaSmXjGeiJk0yEvbIMpZtB+VT5rm1rZh39WG2E00y5G0II/9Q7C3LV+i5+ckcfzw6dI6gUW9bEq27aKZWAWekzKqL1XegO6/nuk7PuuYO93HXTFm8Z/qr1XXNWIVf6Erbf/Aww3wn8tdb6Iz5eYxVRM1PA5yeC57tJVBvhNFPuhhDCX/Uux/3Je69haHQa0N4AtrwvSSYi7Nl3zDshTGYzxcXSiFW6+e6T9d5zm2UJ229+BpgB4Mc+vv6qUdplnoiGSGWtWSOqxZ6zWkpornX6gRBCwOyZoMqj+MpvtDnb4aGnDwOyMiIurkas0i00u1/vwKz0nGapWekHPwPMZ4A3+Pj6q0ajZx4r80tWW6MXQsyvnpmg8pw1WRkRK0Gj7pWNynFc7fse/Awwfwf4llLqm1rrL/t4nZbXyF1jkocphFjIYvqJZtrVKlrbSmuLq/1+62eA+VfANPDPSqnTwFHAqXiO1lr/jI/voWU0akQleZj+qff0GSFWusX2E82yq1W0vpXUFlf7/dbPAHMrxUS/4ZmvN/p4LVGnlTbCE0KsPNJPCHHhVvvnyLcAU2u92a/XFhdmJY3whBArk/QTQly41fw5ClzsNyCEEEIIIVrLchwV2QncTHHJHIq5mP+htc74fW0hhBBCCLH8fA0wlVK/BjwItHO+QrgGppVSv6O1/n/9vL4QQgghhFh+fp5F/gvA31Kcsfwj4JWZh64E/jvwt0qpUa31V/x6D0I0u3p3ph//1Lt9fidCCCFE/fycwfx94CBwo9Z6uuz731BKfR54DvgDQAJMIYQQQogW4ucmnzcAf18RXAIwk3/5BeSkHyGEEEKIluNngKkWeFwOwxZCCCGEaEF+Bpg/Bj6klEpUPqCUagc+NPMcIYQQQgjRQvwMMD8DXA68oJT6baXUO2b++QjwQ2DnzHOahmk5HD6bwbQqT7wUQgix0kifvfrI73zl8PMkn0dngsk/Bf6C80viCsgCH9FaP+bX9RvNtBx2P/6Kd6bo/bddueqOfRJCiGYhffbqI7/zlcXXOpha679USn0JeCewZebbpULrk35eez6m5Sz6bNCRCYPRqTwAo1N5RiaMVXv8kxBCrHSVffbQ6DRt4cCqPBN6tViJ9+mlxButwveTfLTWaeBf/L5OvaqNcIAFG8BAT5z+zqj3c72JKIfPZub8zGpuTEIIsVKU99nJRISH9x5lJGWSTER44I5rSLZHFv2a0r+vbJX36YGeuG/XKm8LUD2GqHdGtVXble8B5kozd1Sb4UvPD8/bAEzLYWg0wx3XDdAWDrK+O8Znnjo052dkel4IIVaGWCTI/bddyciEQdqw+e//+AITWRuAjz3yEg/94nWL6p9Ny+H+x/YznDLZmIyx+/arV1Ww0AzKf+d+/v2blsMf/ttLHBnNsmlNnLZQgFTWnnPfr2dGtZXjhoYFmEqpb1LMs/xZrXVh5uuFaK31zzTqPdSjcoQDat4GUOpU9g6Ng4Jd29bwgTcNcHQsSywc9ILUtnCQnO2suOl5IYQQGsfRuFoTUIqUYS26fx4azbB3aJx8weXYuSwvn5rkJ7YkZz2nlYOFZhGLBH2975qWw9defp2vvfw6BVfz2miGKy7ppCcRmXPfn29GtTQQaeW4oZEzmFsBl/P1L7dykWtdVhtJVo5wgHmn1EcmDI6fMzAsh2BAcXw8y+eeOcbJCQOt4cpLO3l473GmTJtkIkIyEfZGMtWm52V0K4QQjVfZt5YHe8lEhDdvS/Lc0QlCAcXm3oTXPy+01Fl6PG+7aGA6XwDgc88c4ar1XbP68ZWYA9isluteWc91Ss/pTUR54MmDvDg8Qb7gElQKrfF+rvK+X2tGtbJtLhQ3NKuGBZha683zfb3c5htJVo5w5ptSj0dCnJ40MW2HgIJgIIBhOWzra+fV1zO8ns5xwJxiR38HqazFPTfvqJlILqNbIYRovGp9a3mwV+qbf+NtGlAM9rdXDUJBz1rqNC2H+x7bj5F36OuIcMW6Tn54YoJYJEi+oOcEkPXkAMokw8KW615Zz3XKnxMKKF46NUnedlBAWzjImo4Iu2+/imPnprl+Y3LOz1ebUR0anebYWJa2cHDBuKGZtWwO5mJGkrWm1FPTFh995CVMy6EjGgQUlu0wlrPpioXRWtMZC5MyLHK2w5a+hNdxLeU9SccjhBCLV61vrQz2yoPK0uPlP3cyZVBwNcGAwklrhkYz/NW3j/D8sRSRUADHbeeO69cTCijyBZd13W1zAsiFcgBlkqE+fswEl/ZSlA8w6rlO+XPGMnlytkNAKfo6ovza27Zw61WX8tlvHWZ0Ks83Do0u+Ds1LYc9+44xnDK8tLv54oZmtqwBplIqBNwOJIGvaK1f9+taS9lNVrlUct+j+3llJI1hu0RCAdrCATpjYRLREK7WhIIBjo1nufLSTn7r7du4smK5ZDHvSToeIYRYmmp9a7VgLzVtcd+j+zEsh3XdbXzkHduJhoqrUn2dEV44kSZnOXQnIuRsF8NyiIQD5G2X4+NZHvvRafo6Itx105aaQcF8OYCyhF6fRu0Gn720fWDWXordt19V13VKzzmTzjFh2kRDARxXc9O2JL9842ZGJgzOpHOYtoOTnjurXWlkwiCVtdmxtoNsvsBPXda3pD9bM/AtwFRKfRp4h9b6J2a+VsDTwNso5mn+iVLqzVrrI35cv7xz6U1Evf+OZ/NV82t6E9FZO8PvvGETmVyBnKMpuC5d4RA3bO3ByLvEo0GMvMNgf3GZfNKw+fKLI1y5vmvB93XnDZsAzWB/h+TuCCFEA9SaOSwP9krL3c8fSxEJB3C05pNfPYBhOUTDAcy8S852CQYDXNoVoy0cpK8jStYqEAkG0BpM22EsY9EWLh6CVypVBwuXuoPlLaOzUixlZa4Ru8HLJ22ioQBjmTxWwQWKs9Wle+xC1ym9l71DY3zxuWEAcrbD3bu2EYsE6U1EOTlTqSAeCRKPzB9WlQes57J5Pr/3ON9+dZTdt18N1NeOmoWfM5g/RzGgLPl54O3Ap4EfUTzd56PAr/v1BmKRIAM9cXY//gpn0jlOThhs6ImzrrvNq39Z3gCNmaOljo5lydkFOmIh2sLFmcvB/g5+422DtIUDXjB6bCxLMFDc03QmnZs3KKxVf7NkNXY8QgjRKKVgsnRUYOVNemTCwMg7REIBLNtFAUa+uHkzbdi4WhMNFx/riIVY3x2j4DrkbJc1HREOnZkmY9p0xcPEI6Gy3M0woEhlrTmrT5XB1XKV0VkpLmRl7kJ2g5uWw96hMc6kcwQDCsNySLZHOJU2QcGGZNy7x9ZznVgkyK7BPr5xaJTRqbyXDmdaDi8Mp7iks41MroBhOXzyqwd48P3X1vxzxiJB7r1lJ4+8eJKD350kYxY4nTZ5+VSaR1481VKrmH4GmBuAw2Vf/zxwTGv9UQCl1JXAL/t4feD8zKBpO6SzFomZ0cXIhAHgzRoalkMoAD86mQYFX3p+mN9952V86PPPY1gOpydNcnZx1hLgzhs2MmkW+KPH9nPsXJaueJjeRHTB91G6ZmUwuto6HiGEWKyFZsPmC2h6E1GioQCXdLXR1x7l/p+/0sud25iMAYq2cBAFfPxdV3BkLMOzQylcV/P6VI4NPTHWdkRpCwc5eGbS68+HUyYKSERDs/r2Wu/F7zI6K4nfK3PV2kPp771yUuneW3ZyKl2sBhCdmYFezOxqtQo0pesMpwwcVxcnqvLnX7My57N0zc88dYih0WkyeYdYKAAKRjNWy61i+hlgRoBC2dfvYPaM5lFg3YVcoJ7GUZoZtFIulqs5lTbJWgV6E1FikaA3a9jXEeHsVJ5Js0BAwb4j47x9ez+XdLbhaM3ptMmDT73KhmSc0k7DaDjAQHccq8MlFg4yns3XPB2inpOAVlPHI4QQi1HPbFitgMa0HB548gA/HknjaM2m3jjJRIT7b7vSCwKSiQi7v/IKqazFnz11iLRhM2nYKAU98TB97VEKrqa/M8r1G5PebFYpOC3NYJYvmS8mN68V+bkyV6s9lP+9X9oV44Nv3sj1G5OMZ/Os7457qXCdsRBG3vE2bNUzY1h+jz58NsPoVJ7gTNkrFASVYl13G/FIiHv+8UVeOjWJKsv5LN9YlIiE6IoV29Vll3Rw09Zenh0aa6lVTD8DzJPATcDfzcxWbgXuL3u8H5he6ovXO/Venj/BPrALLm0zweD29vP5F5OGzX/9hx/iaI2joeC4PP7SCKfSJrbrEgoEaAsHZ+00tAoBOtpC5AvuvMdHlr+PavmerTAVLoQQfqpnNqxWQDMyYTCcMik4xdLMpZSmgZ64d5JbKAAvnZrCLrgcG8+C1oDGdeHySzp54I5rGM/mvVz+e2/Z6eX0l65R3veXcvMmDXvBFa5W5efKXK32UPn3fvm6Lu9+Gw0XZxgdV/PtV8dwXU2iLYTjavYOjbFrsK/u91je1gaSMa899Cai3PfYfr5/LIVpO7RHQ7NyPst/7q2Da7jrpk3enoxWW8X0M8D8R+CPlFL9wJXAFPDVssevA5a8wWexZYjK8yfKO57SiGT/SJpQMEA4GEDr4iYcx1Vs62tnbDpPdyyMozXt0RBDY9NM5wp0xcPsed+NGDMzogsFjaVrlUY+9bx3IYQQ9c2G1bpJ9yaidMfChEMBlIJ13TFydrFsTakvHs/aFByXgqsJBYqnhCiK/woEVDGnPxKvObFR2YePZ/Ns6InT1+54kxpLOf+82fm1MlerPVT+vZenMxh5h2gowIsn0xhWsdxQMF/g+HiWLz43XFeZofI/V+WSOVk4lTbJmAXCQYVpQzCg5uR81gok6/m7aqZyhn4GmA9QzMN8DzAJ3KW1TgMopbqAXwD+11JfvJ4l53ILjQ4G+zt462Avx88ZJNuLSyd/9tRBnj08znS+wERbmFBQsbazjXzBZUtfgkQkhGEVFgwaKxuEbOgRQojFqXeGp/ImXcp5yxdcrhno4lfevJnHfjTCg0+9RjQUoDMWZMp02JBs49x0nrRh094WZvOaOD8aniQWCWI5Lt88dBZQ3saRhSYHBmZy/6Sf90et9lD5916ezrCuu433XTfAn379ECMTJlbBZVNvnEQ0tODvtNbJgJX5tp2xICNpE9fVrGmP8KfvewNv3NRTc+NXre9V02zlDH0LMLXWeeDDM/9UylDMvzSW+vpLWXKeb3QQiwTZffvV3usVRyEOuYKLqyFXcFEFCCiF4nyuRanTqBU01moQrTYVLoQQflvKbFhptSsYUBQcTc4uMJaxeG00g2W7vHFTD/fcvB1QPJh9lVxncebrt39qkM/vPUYmX2BkwmD3V16ZWcUKs7k3UbXQeuV7lX7eX9XaQ7W/98qZxsH+dhKREPFIkI+/+wpvs1fp3j3fsaPVYozyFdXT6TxrEhH6O6LEwkHWdkbnbECqrCZTb9DYbOUML8pJPlprl+Ks5gXxY8k5Z7s88OQBTqZMTqQMEpEgVsElElS0t4Voj4bYNdg7p9Burc6kVoOQDT1CCOGf8hrH5YP/6zcmeeSFU1gzB2jkZ/LyK2e+rlrfxYMfuJa9Q2P8P986wul0DldrFIr3v2mAn965dlGbQsTyqVayar7joavtDq917Gi1GKN8gqnWpi+oHg+U/r/Wa5drttVPXwPMmeLqNwPbgV5mUlrKaK31H1/odRrxl14aWRwbyzKcMtjW145SMNAd5w0DEX7tbVvY1tcxp1B7uWqdSbM1CCGEaHaVM0XlG3JikSCfeM/V3hnjpZnIWpMEuwb7+JcfnOTAmeIKVigYYGMyITOSK9x8s46V9+pqu8Oh9rGjCx0RCtULptd6nXpjhGabFffzJJ/twKPATuYGliUauOAAsxF/6aWRRVs4CAosx2XXtjV84E0bvELrsUhwVpJ2PXkTzdYghBCi2ZVK1eRshzPpYnm60uk7AMn2CA++/9p5T/4piUWCPHDHG/jYIy+RMiw29ya8eshiZal1zvxiVjbrPXa0UmXbqXatWq+zmBihtI+jGWIKP2cw/wLYBvwB8E1g3MdrXfBSRHmj2rWtWDqgvGZWtRMaqo2O5ksEFkII4b/yUjUdsRAP7z3KlFlY8saIZHuEh37xuqa4qa9W1Watl7J6WLm/o/x33oj7eK1BTL2v3UwbffwMMN8GPKS1/jMfr9Ew1UYW8+V2VhsdlY6lbIZfvBBCtKpSqZo17cWah2fSuVkn7Sylr5aJgpWt8p48ns0vefWwNEu4Eu/nzbTRJ7DwU5YsDxzz8fUbrtSBlJc76O8sFsetHAFVe6xWAq8QQojlU9qw0x4NsWVNfOYENumrW1m1e3LlPX0xVmobmS8uWWn8nMH8OrAL+Bsfr+GrhQqiVj4mG3qEEOLiW2jThfTVrafR+x1Wahtppn0dfgaYvwN8Vyn1u8BfaK0tH6/lm4VqZ1buRGuWX7wQQrSy+TZdSF/dmhqZxrCS20izpGv4GWDuBRLAp4FPKaVOA07Fc7TWepuP72HZNcsvXgghVjPpq8VCpI1cGD8DzGGKZYiEEEIIIcQq4udRkT/l12sLIYQQQoiVy89d5EIIIYQQYhXyPcBUSr1dKfUJpdTfKaV2znyvfeb73X5fXwghhBBCLC/fAkylVFAp9U/At4A/BO4GLp15uEDxGMn/5tf1hRBCCCHExeHnJp8/AN5HsVzR14CDpQe01jml1L8B7wL+xMf3IIRoYZs/+kRdzzv+qXf7/E6EEEKU83OJ/C5gj9b6z4FzVR4/SPGsciGEEEII0UL8DDA3A/vmeTwN9Ph4fSGEEEIIcRH4GWBmgOQ8jw8CYz5ef0UwLYfDZzOYVmWNeSGEaD7SpwlRnXw2ZvMzB/NZ4INKqU9XPqCU6qG46edrPl7/ojMth92Pv+KdZXr/bVeuqOOmhBBiMaRPE6I6+WzM5ecM5ieB7cA3gdtmvvcGpdRvAi9QPEbyUz5e/6IbmTAYncoDMDqVZ2TC8B6TkY4QopmYlsPeoTHOpHPA3D5NiJVqOe63893vVys/T/L5gVLqfcDngM/PfPvPAAWMAu/VWh/w6/oXg2k5jEwYDPTEiUWCDPTE6e+MeiOagZ649zwZ6QghmkWpzzqTznFywmBDT5x13W1en7aY1ynvI4Xw23Ldb3sTUaLhAEbeWdJnoxX5uUSO1voJpdRm4J3A5RSDy8PA17XWLRXe12rE9992pdehAhw+myFnu3NGOtvXdlzMty+EEDWVZmeCAcWGnjgffPNGdg32LepGvZgbvQSiolHKZxbPpHPsHRpbdNtdiGk5fOapQxh5h3gkyL237JR2i88BJoDWOg88PvNPy6o2Pb59bQexSJDtaztmda7JRJhkIkIqa82a2QTpWIUQK0/5asy67rYlBZel5fVgQM07sJYVHnEhaq0klmbfv/jcMN84NNrQdlU+AMsXXMazeZLtkYa8djPzPcBcLWoth5eUB6CprM09N2+nLRycFUhKxyqakRQ7b32VqzFLmbmsd3m91mBdiIXMt5K4d2iMLz43vOAAZykWuv+vVr4GmEqpO4HfprjZp7fKU7TWuiWC3IU64N5ElGgogGEV8zPWd8cZz+ZnPUc6ViHESlVajVmsepfXSzNPvYloXTdrWe0RlUYmDM6kc5i2g5PWs1YSdw328Y1Do74EgfUMwFZje/UtuFNK3Qf8X8BZ4HvAhF/XWilqdcCl/IxMrgDAB2/cxH2P7cfIO/R1RLnrpk0M9nfIKEgI0XLqWV6vnHm695adjGfzs3LXexPRWd8rTzm666YtDPa3r5obt6iuNxHl5ITBpGHTFQ/Tm4h6jy1mFn4pweB8A7B6VyerXbeZA1M/Zw//G/Bt4Oe01raP11nxSqOqI+emyVsOH/7Cf2I7mkgowKGzUwynDLb2Jbj/tiu9D0BvIlp3o2rmBiiEaC2m5TA0mgGUF/QtdGOvXL0Zz+Zn5a5XLq/fecMmRqfyOK5m79A4J1MmW2b6UOkDV6/xbJ4NPXH62h3awsE5uZD1zMIvNVVtvvvwQquTxc/MNHv2HSOVtb3rAk2dNudngNkJ/PNqCS7na1wDPXHikSCW7RIIKKyCSygYwLQcwkFFLByc1egGeuJlo/MId920yVtSLx/FxyJBTMvh/sde5mTKYEMyzu7br2qqBiiEaC7z9XXF/mg/e4fGQcGubWvYfftVAORsl6HRDIP9HVX7yGqrN6Ubc852SGct4pEQjqsZTmVJJiIMpwxQ0FbRh4rVaWBmAFJrFbAUyOXsAm3hUNVZ76WkqqWmLe57dL+XAlcZCM63Oln62bHpPKcmTHas7ZhVR7OZ0+b8DDBfBDb4+PrLrlbHWm3EA8WGGo+EOHhmkt9+xyCZ/CEcR3NmyiQZj9IdD9MZCzJlOiQTYXK2613DG50zR+z/AAAgAElEQVQfOcfxc1lShsWlXTFOT5reKP7+265kaHSavUfOYdkuwxMGQ6PTXD3QdbH+ioQQLWyh2Z2RCYPhlEm+4OK4muPjWYZGM/zdM8d4dmiMSDDATduS/PINm4mGAwz2F2+WQ6MZ3nfdgPe90muWbswjKRfL1ZyaMDg+rkHBJZ1Rfu+Wy/jnH5ysWpFDrD7zzZaXJmOeOTzGhGHRHg3y9h39/Ml7r5kTDCYTYYZTJhuTsZpBKmiv/d732H6+fyxFMKBwtJ4TCNZ6X6blcN9j+3n+WIpwKIDWmvGsxc5L2r3rNnPanJ8B5n3Al5VSX9Zav+jjdZbFfB3r0Og0x8ay3ih6aHSaLz1/gpGUyY9G0oQUWI7m6oEuutrC9HZ0eblDH7v1Ck6lDfbsO8FDT7/m5R/1d0Y5NpYFDRqYNGwSkRCThk1fu1M2wpl5Asz8V1f/AwghxAVaaHZnoCfOpd1t7D81ieNqzk3nGc3keerA6+RtF6Xgm4fGePbwOOFggBu3JgHN949OeDOeH7v18lk34tIOYPYV+8FTaRO74JLK2nTHw+y+/SpJERKeWsvgIxMGx8ezjGctbEeTLxT49qExXj6V5ie2nN+DbFoOU2YB19UUS3cz67H7H3uZvUfOgYZdg73cddNmMmYB03KwXZfj57Kzcj/ne18jEwZG3iESCpC3HWKREG3hgHfdC6nesBL4eZLPd5RSHwaeU0o9BxwHKs9p0lrrD/v1HhqpsmMdGs3QFg7Sm4iyZ98xb6lm17Y1gGZ0Ks+kaZOzHdCagguvnc2wbU07wYCiPRoilbUZz+ZpCwdJZS3vtcezeW92cs++Y4xlLLriYbpiYbJWgbZwcNZoZtdgrzfaKo2ohBCi0RbaiBiLBLl71xZOpkwUkIiGODY2jXY1SoGrYTpXIBQM0B4NcWQ0iwasggvA8fGstwEyHgnyifdcTbI94u0ADio1pw9c6u52sboM9MRJxmfXpsxaBT73zDGuWt/tpZzd9+h+fnhigkgoQCIamjWIGpkwOJkysOxiex1OmeRst5imEVIkQmE29ybqroNZWtIHcHRxcigSDJDKWnNqaTcjP3eR3wh8AQgDb5v5p5IGmiLALO9Yk4kwe/adIJW1vKOhdqztwLQdb0d4f2cUq+ASDijytiagAA3JRITOWNhb0ulNRDmVNrzC68lEpBiUAlcPdLH79qu9TT/VcjAB7znNOMIRQjSP2MwpJS8Mp7h+Y7JqfzPY38H2te1eEHrr1ZfyD88PMzaVxyo4dMYiTOUKBBRs608AmvHpPChIxiOkDZuh0WkcV3PfY/t58P3XzprJqdYHCrGQWCTIA3dcw73/+iN+eGICw3JIJiIYM4cA7BrsK84oWg6RcADLdolHg7MGUcWAMMbRc1mCAcWl3VH++QcnAUi0FYPLgSrL6vO584ZNgGZ9d5zPPHWoaZfDq/FzifzPAQu4HXhGa5328Vq+K+/gcrbLQ0+/BuCNtPMFl619CdZ3xxmZMLj3lp2cShv89XeOsu/oOcIBxbUbunngjmK+R6mjfODJAwynTC7tjvJff3Ib//SDYR56+vCcUh2xSNAbEVWOjOYrjySBpxCiUUol10an8jVPQ6m2rPfl39rFd14b47Efj3Dw9BSdsSBvGOjmj95d/Pmh0Qx52yVnF/joIy8zYViEAwEyZqHqTE6pD6zMh5N+Tswn2R7hs3e+kZdPpfncM8cwLIfTk6Z3us+9t+z0ZhTjkSCfuP3qWfmSxeoImk3JOMlEhP9y3Qb+4ltDtIWDbOtrX9QRqpUbdD926+VesNkqbdnPAPMa4H9orb/i4zWWVfmxj+V13UqBYG8iOmsEcucNm8jZDtes7yZnO3zkp3d4HeP2tR3sH0mzd2gcq+ByMmVw8+WXkMoWN92fSefOLxVFiw291pR7rdpZzVzeQAhx8VX2LfXusC0PBk3L4VTa5HtHxsjmHTSKyy/pRFMsK7O9vYPB/g52P/4KR8eyFFyXrlgY7Wo6YqF5C61X5sPtLgsIxOq00MRKLBLkJ7b0ctX67jmn+5TS02rdT4fOTnMiZXDZJR3kCy57njs+Kz1uMUeolm/QPZ7KMvWITcHVszYKNzs/A8xRijOYLafaCD3ZHuHw2cyszhe0dwZqMhFhfXfMe43UtMX3j43jzuRdoGBtR5sXuMYjQTJmgSNj01gFl/se3c+DH7h2wQLFpUBSTgUSQlyIan3LYg+DKL3GsbEswymDbX3thIIK03boiIW8zRClWsGOq1FKsSkZJxEN8vF3XVHzhl0tH076udVtMRMr1U73Ka8/DcUC/wM9xVXJkZTJsfFppswCr76e4boN3eQL7qz0uMUNbs5v0HUcTcqw6GwLt9T92s8A82Hgg0qpz2qtCz5e56KotixdLG8Q8aa8B/s7uPeWnd5M5GeeOsT9t12JaTm8/2++RzprYTkuO9Z2srUvwda+du64boDRTI5rN/Twya8ewCq4RMLFIybLG11plJaznaqBpJwKJIS4ELUGqfXsaj3fP7mMThU3MqLAclxu3JLEyDuz+sTSCSwTWYuAAo2L1kE++63DNYOEgZ44G5JxhicM0FQtKSNWl8VOrFTm9pZWIJOJCKC9oucfecd2jo9nmTQKBAJwaWcbv/LmjXzp+ZMYlsPWvsSiN9gO9nd4G3Qv7Y4SCgRn7cMwLcf32Xi/0+j8DDCfBW6juIv8L4FjzN1Fjtb6uz6+h4tAzwxKiv8ez+bJ2y7BgOJMOsfeoTHyBZdJw0YpRSQY4PZr13HbNet54MkDswoUf/xdV/DJJw54xVtLnWf5KC2ZiJBMhL0PQuk5zV7eQAhxcdUapC60q3V2/xT2NjDu2raGu27aBCgeevo1b1lyaHSas1Mma9qLs0dWQZMyJunviBIMqHmX4XfffpXkYArPUiZWSu25fAXyZMpAA+3REKNTeQ6emWRjMs7YdLE+9Vg2z5dfPIVhFVPY7r1l56LbXrH9Xj1rxrRUOaa0D6N8cNXoYHA50uj8DDCfLvv/zzG3QOPMvmpapkcYmTBIZW2vBFGpMZSWyU9OGHzxuWF6EmE6YiEyZoHuRITbrlnPeDbPcMr0ynWcTBkYVoEHP3DtnEZVPkpLZS3uuXkHbeHAnIZX+uCkpi32Do1x/cbkrOR4CT6FELUsdpBabVUllbW55+bttIWDs26iyUSYsYxFNBTg4b1HmcjanBjP4jjFckYA2Zmb93xBQiwSlIMlhGehQuvzfb83EfWC0w3JOOUzmNdvTPLIC6dIREIEA4q1nW2cTudoj4bI2+6CZYlqXbtysNYWDnj7MMpnYP0IBpcjjc7PAPNXfXztFana6Km8UHApmXjKLPDp972BSdOaFfR1x8KEQwGUgg3JeM0ab5XXqXbcValBxyMh7nr4+0waNl3xMHvuvpFUNs+efScYy+QX3EAkhFi96pmtPF8R4yAnUwbrumOzVlVKx9ymspa3ItOTiBANBxiftth/2mRHfwdb+9rRWpM2CwSU4obNyVm7eIWoR7U2WytAq/x+eeUW03JmleP61V2bcVxNvuDS1xGl4LqcSee8e3XpOhey4XahI1OhccHgcqTR+Vlo/Qt+vfZKVWv0VEom/vorZ738zKvWd81qgJ956hD5gss1A138+lu3cmXZ49WuU6sWXamUwsN7j3MmbRIMKNKGjQLShs1HH3kJq+ByIpVFobDn2UAkhBDVlPqZ0kDVdlyOjE1TcDTDEwafueMazILD5eu6+MxThziTznFkbBrDcoiGA6zrjGE7xd3iaMjZDlv6Enzmv7yBH52cYG1H27x9oBCLUdpEls0XmMhafPPQKD+9s3/OKXzj2bw3Y1jKx/z6K2cpzWYmE2HuumkL67tjPPDkgVnpcI3YcFsrhvAjGFyONDo/ZzBXpflH/BrH1UyZ9qwE3lIDDAYUBUfTFQ/P+8uubPyl4u6AV0rhlTNTxMJBwkFFLBwkZxfrdaIhFg7iOBrbKRaSrdxAJIQQtZRupEfHst5A1bAc8gWHRDiI47j85XeOEA4GitUwcgXSpk02ZxMKBcnbLmenTAqu5lw2z41bk9y9a6u3ErO+J7bwmxBiEXoTUU6kDE6nTQqO5o8ff4WnD54F3Fllhs6ncWQ4OpYlFg7OysdMZW3awoGZGfliOtxYppiCtrazrSEbbqvFEH4Fg36fEtSwAFMp9XY4v2mn9PVCWm+TT3UjEwZjGYtTkybHzmVnnVCxUAOsVYvOcTV7j5xjOGWwtS/BnTdsZHQqjwYctxjMRkMBPnXHVeQKLpev6+Kz3zrM6FSeN29L8vpknkzOJhRQVc9OXUj58picrCHE6lDqf2YNVCNBIiFFLBTEdl0OvZ4hEgpwSWcbo5kchuWQK7gMdsfpjIcJKEUwUCxXdPeuLQz2t0tOuPDNeDbPmvYI56bzuNohZxdn3GPh4JwyQ6blsGffCU7OVCe4cWuSUEB5KR/lp++9Ppnj+HiWz+89Tns0RGcsxETWJh4Jzrqn3nnDRkBVTWcrqbzPV37djEdGNnIG89uAVkrFtNZW6et5nt9ym3zmM9ATJx4JYtkukVDxeMnyEyrmS0yuVYvu2FjWm5EsjpwU/Z1RHFdzaXcbaxJRNq+J88bNvQCzThj622eO8vKpSfIFlxPjBn/8xCv8yXuvmdWwawWOpdMzSuekn5ww2DBzpqoUdBeidZlW8eZcyrF86/ZejLzLdL7ASyNpjLxDwXHpihd3judsh3CwOMNpFVxeHcvwM5evpT0SZCxj0dseIZmI1syPk6BTwIVvSh3oibO5N8HJlIHtuLSFA8WarIHiRrTyMkPFzboWO/o7yNmON7teWcqoMxbE1ZqsVeDFk2li4SDXbugkGiquCn7mqUPce8vOWYevVBZQL7/Xlj+v2s8t9s+9Ej4/jQww76YYMNozX6+6TT7ziUWCfOI9V3s1McvLDpUerzY6ma8WXSnIK42sBvvbq57Xa1rOrOvecd0Ar57JYDsarcF2NEdGs95xbX/37FGMfPEIrcrAsbJw8vruGJOGzZp2Z6bkSMbbMSo3BSFaR2V5tN/6ya0oFBrNfx5P8dJImnzBwXHBtAp0tIXY0d/BobNTFFwNClxXcypl8vs/exl/+8xRMrmCt/GnVLaodFOUk8gEzL9Bp94AKhYJ8rFbL2fKtBnL5OnriPJ7t1xGKmtRWeKqfEWxtJsc8EoZnUnnMG2HKbMY6oQCAWynQCIaYtJ0CAVcEjPljV4YTnnPd9J6Ti3r0p8rGi5OOpU+Ay8Mpy5oU89KOcmvYQGm1vrvK75edZt8FpJsj/Dg++eWHZrvg1Is3h5mOGXOKSTcFg5wz82XcfDM5KzNPqUkZbLF177v0f18/+g4gYDCtBweevo10kYeV2sUEA4G2LQmxsN7j7N36ByTpk1HNIhG0TcTOJYaeCngLRVOBoo5o+EgyUSYPftOzBSLLSZDz7ckIIRoHuWD3dcnczz09GGGxjI4LoSCkMkXyxwrYGMywbquNqbzBd68tZcXhycYn7YIzfQ1//u5Exw4PUVkZiapo61Ytg0gHgnNSgM6OlYc/F490H2x/ujiIqo2yVLvACQ1bXmbYcezeQqupicRIV9w2f2VV0ibNv2dEX7uynXctHUNyfaIt6JYrSZl6UCAScMm0RZka28767tihIOKrWvauaQrCihSWYv+ziiXr+vynt8RCzFp2KSmLcaz+VnlvIx8cY9EvuB6ZZHKTxha7KaelXKSn2zyWWaVM5X1jTTUTCynZv3MmXSOE6ksvYkoG5KnuXvXNgb72wHOj4xCxVIg01Zx6Wo6n6YtFCBX0LRHQmzuS/D7t1xGVzzCJ584gOMWR2u2o+mMhWgLB2c18PLTim7c0svduzZ7ZUhytstDT79WzA0dGudkymRLX0JmH4RoAaXP/vFzWU6lTaZMG8N2CCiYKd/rSRl5tvYnmDRtHFfz2V+6nqcOvM6Nm5P0dcZ48KlXiYQCWLZLRzTE777zMj70+ecxLIe7Hv4+e+6+kWQi7B08sWffCXbfLoXUV6NqexTqCaBS0xbv/5vvzSrRV3qdaDjASyOT5G2HH5wo8N1Xz7GmI8q//OZbvCCzvCZl6ZCU7liEZCJCVyzM6bTJy6cn0WiuurSb337HNq5aXxwElYLgkZn0sWS8wEja5E+/fohU1uLSrhjxSJB4NMDolMXGZIyP3XrFrJS0C9nUs1JO8vMtwFRKvQV4N7AD6ASmgFeBJ7TW+/y6brNZ6INSygdpm9nNNjQ6TVs4wOhUnqxV4PT/z96bR8dx3Xe+n+p9w9YgQFIAwQ2UKYmUFdljWWY0GTm0EltO6CWeySgeTeIkzzMvmhm9ZORYsaKTcJxYY0fnOYlPbCce+1nxKGM7UqQTK441ihVHoilrFMoSJZISQUIECELE0mgsvVV11X1/FKpY3egGGkQ3gQZ+n3O0oLtQVej+1b3f+7u/JZ0nNadz4sIM56fy9HcnuOMdfYym83a7KUPjjckMhmmhFBRNCxX0U7RMWiIhkrEQm9siJW3XQoEQ1/e0cf/PXUdWL5YEHQ+MzVG0TBQQ8OFuLSQTIXK6SXdrmLPjGdBwSz9IhrogNDfeZz+rm0xldYqWwlLg0+yNcpgPqvdptEdDvJnOM5LOcWZ8jmdPTxAJ+nnq5BgPfewmtrZHAAgHfPzKgR2cenOWnG6iAdNZg5Oj09x5806GUzkiQbuFnowjG5NKYqsWAXVsKMV01haIjk0555nOGvznb76IYSmUUqBpTGcNjg2lOHjtFuCSSHMcOX/29BkmswX0oqJo2fOpZSkyusmPh9N85ZlB7np3P/3dLa6d9s6HmA1cnMO0FJZllwucyRtYlj1HXtUeATTb+ZRoKfm7L9fe10onv7oLTE3TWoG/An4WdxO1hHs1TXsC+CWl1Gy9r99sLPWgOF6DI2cmQMFDRwe5973X0t0aZiZv4PfZH7Gp7AF+bMZuTem45aMhP1vawkxlDZRS+H0aOzpiTGZ1dmyKu7GgTtu1V0emuTibd7cLyoOQndjLqze3uN2KnIfg0tbCrLtVLn3QBaG5KY+73twawZpPLvRrFjs3xZnJG4zN2lvaQb/Gjq4YM9kihXnXpm5aBP2+kone2YL80g/O0hr10xINMpuzvU1OyM/Orviqe2GE1adcbNUioG7sS9IWC7oeTMemnBCyA7s3cXY8w+sXZwhouMfApbC1e27byz++fpE/fOIkqTmdfNHiuq2t+Hwa7dEgL51PA7ZQfHlkmk8/cZJdnl07p2b1vY++zPl0jgvTOaJBPzm9iM/vI68X8WtaQxZQayHrvBEezL8GDmL3Iv8fwMvY3stW4Hrg17B7lH8TeF8Drt9ULPWgREN+7rx5O0OpLNGgn1TGYDJTcAforx45y/lUjomMTjwUoLs1TDjoY1tHjE0Jk9B8LbqxWdvj8M5dHfxft/TT0x6tmCH+yIvnGZsp8OzAREkmWzjgI6ubbuxlzjDZ1RVfMOjbrdvaOXyoZdVXT4IgrJzyuOuAT3OrVPQmI/zSO3bwpX86w1w+RdCv0dsRwzJtz2Ys5KenPcqrozP4fVrJRO/dgpzJmXz2w/uZzhkl3c3WghdGWJssJaCSiRDf/vi73BhMb7c6x6HidLtz8hgcp4o3mW0qo7vxxT7N9tD3b05wz217OTM+y1eeGSSV1RmZyrkVXbxi0Yn9fMt8OaT/dGs/D/+fIWZzRS5M5xaEoa0n6iowNU37GWxx+aBS6p4Kh7wIfF3TtD8C/h9N096jlPrf9byHZmSpB6W/u4VdZSt5pwfvH37w+gVZ4wBb2yMlJQ9G0jnKs+XK20OWb9cfPTvJ4HiGoN/HTM4gmQhRNBUHdm9yi7sv1m1otVdPgiCsHO8ui/Ps97THODM+x9hsHgUUTUU8HEAvWrREghSKdjm23V0JPvrOPq7Z2lYyiZeft7s1zL6e9ooLbBlHNh71KrGTTITcLe9K53Zsy1vc3zsPDqeymJYiGbeF59u2d3D3wT2e0LBO9vW0L6jo4hWLXjvf1RXnbTuSvG1HckPUkK63B/PfAueATyxx3CeADwN3ABteYC7FYl5O7wDsFYzlxzsF2hejMx62C8pmdXo7Yjx1cpQ3JjPM5Iu0RYNsbY/y82/d7G6fC4KwMSgvFJ2a0/nEIy8xlTFojwX4ib4Ort7cQizs51Pvu9Zt6NDVEqYtGiKVKXCgv2vB2CUeSqGcRpTYqVZvstK5K5UpiocDxOZLDTpezhcGU2442f7eNg4f2l91jq5k55Xm7fVGvQXm24DHlFKLFVhHKWVpmvYYtrdTqIHlruS9x9fywOZ0k8989yQvjaQxTUVbJEjRUmzriHF6bI6r2iK8cC7FxZk8zw5MSGb4GmPHJ5+o6bg3Hri9wXcirCcqjR0AR89OcCGdx1KKnGHyn/dsYn9Pe0kGrBPC81+++SIoONDfyeFD+6sukAUB6l9iZ7F6k5XOXS4InXvyJrv+zt8c53uvvuk2NXnkPxwgmQgt2mN8I9q5r87n68HOFK+F14DeOl9fqEClB7bSMcOpLMWiXXx9OmcQC/uJhwN0JkL4fFpJ16ClvKGCIDQ/1caO7paIm2Do92ls64i7XckAN8ZyNJ1HNyz0osVQKifjhrAkjgcRqEtsoteGnXqTS53bEYTeFo2ObZ+fynJmfA7DtLCUYipjZ58LC6m3B7MVqDUzfBZI1Pn6QgVqKengLVOEgh2bYm5dLrv3aq5qjIkgCOuTamPHvp42fua6zZwZy7C7O86+nraKv+sdU8obRQhCJeodOuG14a3tEe65be+K4h57O2Ls7kowMGaXHuqIX8o+F0rRltjNXt7JNM0CPqqUeriGY38JeEgpteg3rGlauq2trS2dTtfrNjcktQRNO7XuQJGMhxcE5de5t2mlElZrmqVssdZt6o3Kcrbn6/1Z1nDtprLHKzkuVnvuy1+vdJx3TFksKbDWa24QxBYvk1pttR7XcUr63bCto6Re9DpjRbbYiDJF79M0bcvSh/G2BlxbqEItMSBOZnp5BwRvd4ONGEciCBuZas99LXHezpiyHNZKH2WhuVjMbuo9d0VDft6+Mym2ugSNEJh3zP9TC/Vznwp1o7wDgre7gSAIQjn1TMxYK32UheZiNexGbHVx6i0wb63z+YRVoFIHBEEQhGrUs/fxWumjLDQXq2E3YquLU1eBqZT6QT3PJ6wOi3VAEARBKKeeiRlSH1O4HFbDbsRWF6cRW+TCOqBSBwRBEIRq1DPOTeK9hcthNexGbLU6IjAFQbgiSJa9IAjCxqHehdYFQRAEQRCEDU5d62A2gvnamlpb2/JKXQhrm+np6SGl1PbVvo/lsJQttv+H/3llb0iomfSXfmnR95vNHmVcXL+ILQprhZXaYjMIzCK2p3Vmte9FqCvTzTSIgtjiOqep7FFscV0jtiisFVZki2teYAqCIAiCIAjNhcRgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV0RgCoIgCIIgCHVFBKYgCIIgCIJQV9a8wNQ07ZymaedW+z4EQWxRWCuILQprBbFFoRqB1b6BGmhra2trA9Rq34hQV7TVvoHLQGxx/dJs9ii2uH4RWxTWCiuyxTXvwRQEQRAEQRCaCxGYgiAIgiAIQl0RgSkIgiAIgiDUFRGYgiAIgiAIQl0RgbkEOd3k9MVZcrq52rciCGsOeT4EQdjoyDhYmWbIIl81crrJ4e+8ythMge7WMPe//zqiIf9q35YgrAnk+dhY7PjkEzUd98YDtzf4TgRh7SDjYHUa4sHUNK1b07Q9mqZpntd2apr2+5qm/ammaXd431urnJ/KMjZTAGBspsD5qewq35EgrB3k+RAEYaMj42B16iowNU3zaZr2F8AF4BTwqqZpOzRNuxE4Dvwu8BvAXwJ/r2nampb5vR0xulvDAHS3huntiK3yHQnC2kGeD0EQNjoyDlan3lvk/w74VeDvgCHgV4AvAUHgT4CvA1HgPwP/Hvj1+ffXJNGQn/vffx3np7L0dsTE7S0IHuT5EARhoyPjYHXqLTA/DjyplHo/gKZpJ4A/Br6plPodz3Ef0zRtH3AHa1hgNpKcbopBCk2DY6+d8TCTmYJrt9GQnz2bW1b79gRBEFxSczrHhlLc2JckmQg1/HoyDlam3gKzH/hvnp//HrvVUKXo8MeA36rz9etKo4J3JShYaCYcex1N5xmeyrKtI8bW9ojYrSAIa47UnM5HvvxDprMGbbEg3/74u66IyBQWUu8knxZgzvPz1Px/Rysc++b88WuWRgXvSlCw0Ew49po3TKazBjnDFLsVBGFNcmwoxXTWAGA6a3BsKLXKd7RxqbfAnAC6PT8bwD8DMxWO3Qyk63z9utKo4F0JChaaCcdeI0E/bbEg0aBf7FYQhDXJjX1J2mJBANpiQW7sS67yHW1c6r1F/irwVucHpdQM8C+qHLsPOFvn69cVJ3h3YGwOUHU/r8RgCs2A117LYzAdJKZYEIS1QDIR4tsff1fDYzBlzFuaegvMh4EblzpI0xRRkKQAACAASURBVLQk8EHgC3W+fkN4+PlzdY+XlKBgoZnw2mv5gC0xxYIgrCWSiRAHr93SsPPLmFcbdd0iV0o9pJS6u4ZDp4GtwKfqef1GUM94SWknJawHyu1YYooFQajGepz3ZMyrjVVpFamUMrFF5prHiT9zViqXG3cmKx5hPVDJjuv1jAiCsL5Yr/OejHm1Ib3Il6Be8ZKVVjyyRS40G9XsWGKKBUEoZ73Oe5JHURsN6UW+3nDiz1ZiRJI5LqwHqtlxPZ4RQRDWF+t53pMxb2k2lAdzNbO+ZMUjrAeiIT/33LbXzdAUOxYEoRobad6TrPKFbBiBuRZiQSRzXGh2crrJ5548xdhMgX84NbZuYqoEQWgMG2HeWwv6Yi2yYbbIr0Q2eE43OX5+muPn0+sqY07Y2HjtvdpzZNt+muPnp8X2BUFoKi4n072WcbHe12w2NowHc6VZX44RdcbDrgfHu1LJ6Sb3P/4KR85MgIID/Z0cPrRfVjFCU1O+Mr/ntr0LniPb9o/zzOkJTKX4yf5N/OEHrxfbFwRhzXM53kdnzBtK5ehLRrn3vdcuS19sFI9nwwWmpml7gD1AJ6CVv6+UeqjR9wDVY0EqxU14xeRkplAiKsMBH1ndxO/TSrLizk9lGU5l0Q0LgKFUbtkZcxLDIawVcrrJwNgcQ6kMo+m8a++TmYIbg3nN1jbOT2XJGxaDE1mmsgaWUhwZmGRgbJb9ve2r/WcIgiAA1efXSt7H3o7YolphYGyWIwOT6EWL4VSWkXR2WbGm1bLr15sGaJjA1DRtM/B14D3OSxUOU8AVEZiwMBak0ioC4PB3XmU0nWd4Ksu2jhixsJ9swRaVWd0kFvZTMKySlUpvR4xtyRhDU1lQ0JeMuu/VYjQbZUUjrH0cb/yzAxMUTYt4OMCOzjhb2yPuYsv7fHS1hGiL2kOJT9Pw+zQqP+7CRmDHJ5+o+dg3Hri9gXciCDaLza/lu5ud8XBVXeC89uGf6L00xGn2v2qNNc3pJnnDJBkPkcroJTtB600DNNKD+QVscflF4PvAZAOvdVlUi5sYmymQN0ymswabEnZ8RCzkp1C02Noe4Z7b9i7oxxwN+Tl8aJ/bt7y/u8XdOi/fYqzUy3m91gsTmo/zU1nemMyQyuhYSqFQfOTt23j33m7XTr3Px/iszqEbrgI0ZvIGOzrj9HcnVvvPEARBAJaeX+94Rx+g0d+dWFQXOP8NB30c2L2J4VSWbclYzeOdVw8k40HuPng1/d0JoiE/py/OrjsN0EiB+R7gS0qpuxp4jZqo5kGsFpfZ3RpmNK1oiwWJBv2uqBxJ57CdrtXPvb+3reR9r7GOpvPc9/hx1/u52CpqPdULE5qL3o4YyZjdb9ynafg1DcfuO+NhAj4N3bRIRAIYRYux2TyP//gCXS2hkgFTEARhLVDJS3n64mzFnIrFdIHzWn93C4cP7Vv2drajB/Sixak355jO6u451qMG0JRSSx91OSfWtBngvyql/nyF50m3tbW1pdPpy/r98hXDnTfvLJkAvQIRWBB76XgbYeHWeVdLGFCkMkZVl7b3+t74TYBPvnfvgi379RR/sQRNt4e6UltsJlJzOvc++hJjszrpnE5vR4yQ34dlKU6+OYNpKSIhH22REJMZnbdsbsHv0xbYdBPRVPa4Gra4nK3vWpEt8oqILTYAZ36NhQL8wRMnmC0U0QClIBSwC+o449diuRkrmZ9zusnv/M1LfO+VMUylCPk1btjWQW8y6m7FrzENsCJbbKQH8xngrQ08f004KwbTspMPhlM5dnbFXTHoxE1Ui39IJmxPjuO+zs1vDXYlTIZTWRSQCAequrS9yUXlq6XyFcpGqBcmNAfJRIjP/+KNHBkY5+tHzzEwNsfkXAGlwOfTiAT9GDmTLa1+tCzkDJNdXfF1seoWBGH9EQ356e2I8Vvf/jE/GkyRM0wiAR/xcIDdXQm2tkcWdCYr//2Vzs/RkJ+D12zl6VMTKCCrF0nnDEIzPlc/rCcN0EiB+ZvA05qmfV8p9UgDr7Mojtv57HgGNIgE/RXF4FIxGs55zPmt80jQv8CDWW1y9RrmRulqIDQ/0ZCfA/1dPHpshJxuOrHs9orfr5GIBIiHAhzYvYk7b97uxh0LgiCsRc5PZd2EXcOTwPjRd/ZxoL/rioxfN+/qpDMRIp01iAT9tEeD62ZLvJxGCswvAnPAtzRNuwCcBcoriiql1E9f7gVqcVk7HsSBsVkeOnquJGvLSy3xD3e8Yzug6GmPlWydL0cwipdSaCaiIT+f/sB+7n30JX48nEY3Lfb3tPOf3t3P7q6WiglrgiAIa5Hejhhb2yOYluKNyQw7OuP0JqN1F5d2mbdZnMQh77mTiRDf/vi73FJvWb24bsfQRgrMXdiZAUPzP/fV8+S1pPR7Bej+3nYOH2qpKgYr1cl0agHmDZNvvTBUEmu5J9HiXkMQ1is53WQyU+ATP3sNd371R+SLiuGpLFe1x9yktw0WOywIQpNQPjaVh6wtd4HsaAJvpZhKx9z/+HGODEyCBgd2b+LwoX0LRObBa7fU5W9ayzRMYCqldjTq3LD0lnY1AbqY99D7vrczT9FU+DTYu6V1QVHU9Va3ShAcvPZtmBZ53STo05jJGnzir19icDKDUhAJ+tietOtkyjMgCMJaoNr87J3nnRyLWs9XS7e+81NZhlI59KLddGU4la1byaFm0xxN24vc2dIGKm5pr7T3uLczj2kpTKXIGWbJtQbG5hgcz2BaasX9zQVhreF9hhQQC9vr0Vg4QN6w0A2LnG6SyhhMZnRG03l5BgRBWBOsVAN4yekmRwbGeWMyg25Y6EXL7dZXTm9HjL5klFDARyjoY1syVrf4ynr+TVeCK9EqshU4iL1lDnYs5v9WSs2u5LzVWj86rLSmlB2rEeXsRAa/T+Odu5J87MDOkgLqDx0dZCiVdd3g6zFIV9i4eJ+hLa0RPn7LTqbzJjdsa+fzT73O6EyOgKWYKxS5MJUlqxfpjIdX+7YFQRBKxq9kPETeMMnp5rI9fo7XcDSdZ2JOJxjwoVHarc+L3XRlf9UYzHr9Tc2QGNRQgalp2q8BDwIJLtVTUsCcpmm/qZT6Hys5f/mWdrVYi1pjFcprYgZ8sD0ZI5kI8bu3X1fiTj8/lSWVMbh6cws5w+TOm7evaVe1IFwOd7yjj4Jh8c0XhvjaD8/R3Rrm3Xu7ufe913D07AR6UfHosfMYpkUk6GcyU1jWtpMgCEIjuJTgO8dDRwf5/FOnL2tb2fEa+n0a25MxPvL2bfQlo4tWzbCbrrQveW5Hc9QaD3o5umY1aWQv8p8H/hzbY/m7wKvzb10H/CfgzzVNG1NK/e1Kr1VLrMVyz3HHO7aTyhi0RoMUTbVg4vSuJHZ1xenvrix0BaFZKF9gVWoQMDZTYGBsjoefP+c2L9jSFqlanUEQBGG1iIb8RII+UhkDWH4LRrtvuEUyHiSVMdjaHuHde7vrMrd7PaNO85Za4tibqRJNIz2YnwBOAjcppeY8r/+DpmlfA54DfhtYscCsRx/v8nOA8rjXg+Tn4828vccrZZ03UwCuIDgsXGD1uc/DbL5o7z9YsLU9Aij3vVTG4O6De4gE/bKoEgRhzXG528qlXQBD3H1wT821fmtxNDmaw9u8Zb30IHdopMB8K3C4TFwCoJSa1TTt69iezRVTj7iE8nP0d7dw//uv45WRNF95ZpAHn3ytZHVRyYDqIXQFYTVYuMDS6G4NM5rOc2E6x1XtUWIhP/fcttfucBUPMjiRpTMepKc9JtvigiCsScpLE9W6w+gdE1MZnUjQX/V3qu3+LOZoqtS8Zb3tAjVSYC7Vw7JuTdCXG5dQLg6dn++5bS8j6WzJrX/tyBv887kpt1epY6T3PXacrG6WiM5mC8AVBIeFC6wE97//Oo4MjPON54bw+zRm80W+8/IIP33NFoqWYiiVZSSt8ZnvnnTrvKXmdI4NpbixLymiUxCEhlJrSJozPy9nh7HW+dwpXzScyrItGePOm7e7wnQ0nef7p8YqxmyutCZnM9BIgfkS8Muapv2ZUirjfUPTtATwy/PH1IVa4xLKtwLvuW2v2x88GQ8CmhtPdsc7tpPVTUJBH7phEQ74SGd1/uh7r/HCuSn8Pg1TKddT2WwBuILgUM12b+xL8uixEdI5g+Mj05wYneEvnhmkqyWMZSksS7l13jrjYT78pR8yldHpiId45D+8S0SmIAgNYbkhacvdYax1Ph8Ym+PImQkKhsXZiQyHbuhxd3/OpbJ8+okTaFSum+nolpxuQqbi6ZuaRgrMzwGPAsc0TfsT4MT8606STz/woQZevyLlRnZsKOX+PJTKoQHxcMCNw+xqCZPRi7RFgsTCPv7oydc5N5khq5sULYs3JjIlpVmaKQBXEBYjp5t87slTZHWTvGEXWdeAbKFINBmzvfoabp23758a40I6hzVfM/bo2Uluv37rav8ZgiCsQ5YrGC9nh7G2+VyhFMwVigD85XNv8JkPXs+xoRRf/qezDI7bytGpm1l+vsvJ3WiWZOJGdvJ5TNO0u4D/Dvwpl7bENWytfpdS6vFGXb8a5UZ2Y1+Sfzg1xthMgb5kFK8Hs6c9Bih8mobfrzGVKRIN+rEUhPwaiXCQHZ1xRtLZdeneFjYOlQY5b3mOeChAPBwgp5u0x0N89hfeSipjx2r2dycAKBSL+AAL8Gsa3S3ivRQEoTEsVzDWa4exXNz1d7dwfU8bPxpMEQn6KBgWk5kCB/q7+N6rF+2Wuqp63czlCuVmSiZuaB1MpdSfaZr2MPAeYOf8y06h9elGXrsalYzM+zPg/r9T6zIRDlBwtshzBjft6kA3LApFRVdLiIeOnnNF6Vr+sgWhGpUGOe8A3puM8sCHr+fk6LQbX9nTEQVKy21sbgsTDwW5ekuc3V0tnL44KwsvQRDqTjXBuJh3b6U7jNXE3Wc+dD33PX6cbMHOy3CuffjQviV7ly9XKDdTMnHDO/kopdLAtxt9neVQbmTeOAivYXq/+K6WEEULpnMGkUCA3/+5axhJ2z1Hv/3CsFsjcC1/2YJQDcfWR9N5YiE/sVDATXzzeucdUenF6+nc0Zngo+/s48a+pBvbLAsvQRAaQflcnprTSxJwy8evlVJN3CUTIR78yA0LhK1dcL1tyb9hOZ7VZkombrjAbBaqrUycLz5vWHz+qdeJhwOkMjoj6RwPPz+0oEjqWv6yBaEa0fkSRPc9fpzZXJE7v/qjmgv/ege8re0RDvR3NdUqWxCE5ienm9z3+HGeH0wRCvowleK+x49TMKy6LXIXE3cr8Y4u53ebKZm4bgJT07TvY8dZ/oxSqjj/81IopdRP1+seVoIzIZqWYnA8w8DYHPt720q8m17DcopN+30a2zpifPSdfRzo71rTX7YgLMZkpkDBsNBNq2rh30rbT5UGvGZaZQuC0Pycn8qSLZiEAnbVFzsh0azr7mKlkLrVCANqlmTienowd2HH92uen+tW67LR9HbESMZDHDkzAQoeOjrolhTw1sl03O3AAq+NiEuhmVmq8G8lLz9cilkuDztpllW2IAhrj+VmSvfO77gAxMJ+PvW+a/nC06frvsj1Op2aJdlmtaibwFRK7Vjs57VONOTnzpu3M5TKEg36SWUM17irGZFMoMJ6YqnCv+Xb3gNjszz8/FDVAbZZVtmCIKwtLke8LZXAW+85WsKAlsa32jewlujvbmFXVxy/T3NXPJWMyMGZQEVcCusFx6aTidAC23Y8nMD8f7Wqz4YgCMLlsti8uxjlc3Ij5+jy8VDCgBZyRZN8NE0LAIeAJPC3Sqk3r+T1l6IesWS1uPWbpUiqsLpUs5PVsp9K8UcSZykIQr1phhhuJzHSaY3bqLG4mfWCplRjwiQ1TfsscKtS6l/M/6wBTwO3YMdpTgLvVEqdWeI86ba2trZ0Ot2Q+6yFWr/gWtz6lVpVbtAi7Uv1ql9zXElbTM3pJXXVHFtaa3E/zTz4ldFU9rga4+KOTz5R93O+8cDtdT/nOkBskbU/ttR7LK70966B8X5FttjILfKfBZ7x/PxzwL/EbiF5x/xrn2zg9etGrW72Wtz63mz1gbE57n30ZR747ikOf+dVux+psOHJ6Sb3PXac58+meP3iLKPpvGtLXhsbTec5MjC+qnYjYSKCIDSCyx1bcrrJ6YuzDR8XL3cbvxKOkCzXAvW8xmrQSIG5DTjt+fnngEGl1CeVUv8L+BKwJkoU1YtaYjKcbPXXx2Z5YzLLSyNpTEs1pfEIjeH8VJasbhIK+tCLFrGw37UlN9PbUgxPZfnGc0OyOBEEQaC6UGsE9YzBrCYkmz3Os5ExmCGg6Pn5VuApz89nga0NvP4Vp5asNW+2esjv48z4HHnDZGdX/LKNZ61vJQjLo6TcRsjPpyuUyzo2lOIbzw1JBylBEIR5rmRmdz17m+cNk2Q85LacdrRAs5d7a6TAHAZuBv5C07TrsOti3u95vxuYa+D1l009hFotpVmcbPWxmQIH+ju58+ad9HcnLuuaayBGQ6gzlQaVSrG7/3Bq7IoHwctiRhCEtUojknIXo569zZPxIHcfvHqBFmjmcm+NFJj/C/hdTdO6geuAGeDvPO//BLBogs+V5EoINa8xL3dVUu1BkFpc65PyQaU8dvfo2YkrniAmixlBENYyy/H41XM8u1yh6p2/UxnDfW29LOAbKTA/gx2H+QFgGrhTKZUG0DStDfh54P9t4PWXRaOFWiVjrvX8iz0IzVDOQVg5TuzuswMTTOcM/tt3TnDLnk1ut6krgSxmBEFY69Tq8avXeLYSoeqdv5PxEA8dHSSVMdbNAr5hAlMpVQB+df6fcmax4y/XTFZLo4VaNWOuZeWz2IPQ7DEaQm04sbuvvTlLwTDJGxZvTGSvqMiTxYwgCOuFxcaz5XgkVyJUvfN33jD5/FOnL+s8a5UrWmjdQSllYXs11wyNFmqVjLnWlc9SE3szx2gItdPf3cLu7gQD43OYlmIiU6AzHr5i15fFjCAI64Vq49lyPZIrXXh7e5uvtwV8QwXmfHH1g8AeoJOFRTuVUuq/NfIelkMjhVolYz59cbamlU8jJnZJ1lgdVvK5R0N+PnZgB+dTtuM/Hg4wmSmQTIQacatV70EWM4IgrAecbn3eMXm5Hsl6zc/rcQHfMIGpadoe4DFgL9WrwStgzQjMRuEVFV5DXc7Kp54Te60rNBGh9cX53EfTebv80Af2VxSHi33u/d0t9G9OrKtVriAIwmI0ai6qNBc687IzTteyS1Sv+Xm9LeAb6cH8U2A38NvA97FbQ64bLqd9ZDIeLClJtForllpWaKk5nfseO05WL21VKFSm1lja0XSe18dm0Q2L+x4/zoMfuWFBn/HyAc/5Xefc1bZ1ZDEgCMJ6Yzlb1kuNg+XvV5sL77ltr9uq93NPnlrRNTcyjRSYtwCfV0r9UQOvsSos1k8cqGjApqU4MjDJcCrHzq74qnoNl/Kc5nST+x4/zvODKUJBn/s3raeVVT1ZTixtLORHNyxCAR/Zgrngc31lJM0rIzO0R4OMzRR4ZSTNV54dJDWns2NTzM0a9/6OlA8SmpVa+5tLz/KNS61b1kuNg5Xm7bxhkYwH3cxtZy6czBQoGFbFRhbOHN0ZDzOSzvLQ0XNugfRmG3sbrTcaKTALwGADz79qlPeDvu+x4xSKFsl4CFAlZQYcMXd2PAMaRIJ+12B7O2IlBn/XrXv4gydONNRr6BjUXbfu4eToNDf2JRdc4/xUlmzBJBTwoRsWsZBftmIXYTkxO79yYAempSgULba2R0o+19Sczj1//TIX0jn8Po2D13bz5R+c5ZmBCfs66RwDY7Ps722/7Ouvd8SbIAjri1pDyZYaB6vN263RIB+6sYebd20C4PTFWTrj4YrXTM3p3Pf4cWZzRS5M5+iIhbgwnePq7pamG3uvhGOikQLze8AB4MsNvMaq4DX4WNhPtmDi92kMp7IoIBEOlBjb/e+/zvZEPTNIoWjR3RqmMx7myMA4o+k8fp/GaDrPJx95mZOjM67XcGBsjkjQR2c8XJeC2t4YwOGpLNs6YvzDqbEFhlXSqjB8qVWhUJlaBkDvZx8OaPzGrf3s62kr+VyPnp1gdDqPUlA0FTs64jz9+jgAllKYlsIbzuxdSa+37MPLQTy5grD+qDWUbKlxuNK8DfCjwUkupHP842tjFC3FhXSevmSUe997bcm8m9NN7nvM3tnz+TRM0yIZC4GipnbPqTmdY0MpbuxLurH3q7kgvhKOiUYKzN8E/knTtN8C/lQppTfwWlcUr8F3xsN87slTjM0U2JaM4fVgeo3t0RdHKBQtYmE/d926h889eapE6IWDPsZnCgTnvYbhgI+Hjg4yPqu7x6zUq+kYVM4wmc4adCVMRtN5jgyMc6C/yz3vesxmayS1fF5u/OXFWfSiBZzlrnf309/d4h7f3RLBp2kYygLg28fOA+DTNFojAW7alQTsQQmoGqaxUb8v8eQKwvqkluSXpcbhSvP24HgGFESDfgYnsgylsliWYjiVZSSdLdktOj+VJaubBAIaOd0iEfYTDweWbPec001eGUnziUeOM5szaIsF+fbH30U05F/VBfGVqGvcSIF5BIgDnwUe0DTtAmCWHaOUUrsbeA8Nw2vwXqMGXAN2XvNOfJNzOj8enmJspoDfp7GtI8ZH3t7LUyfHeHN2GqUUb9vezq/dspMv/eBsiRhc6aTpGJSZVrTFggQDPoansnzjuaEFnsz1ls3WaJb6vHo7YsTCfvSiRTDg4+WRaT79xEl2eeJxd3cleGtvG69cmCES0DCKFju7EvR2wC+8rYenT43z4JOvsbU9wh3v6CsRU5OZwob/vqQQvCBsbJYah8vn7YGxObd7Tmc8yEhaw7LU/EZRafGb3o4YXS0hXruoEQ74eNv2JL9+y84SJ0E5zq7KKyMzXEjnSIQDTGcNjg2l2N4ZX7AgLi+ZVH6uejp9roQjqZECcwi7DNG6p9yoy2Mr77ltL8l4kCMDk6DBUyfH3MDire0R+pJxZnIGV3e3kDdM7nq33fDeKwYjQf+KJ83yFdyxoRTfeG6oYiBzJSS+7fJwPrdPve9a/uCJE4zPFRiZyhEti8f93JOnCPp9dMSCbOuIcXE2TyIcoKslxNOnxvnnc1Nu+ARoIqbKEM+7IAi1Eg352d/bxuFD+9058TPfPclwKsu2ZIz+7oR7rDOG/5u39zGcyhEJ+skUikSC/kXHGce51B4N4tc0TEvRmQi5uQ/eMbwzHq7q0WxU+E+jHUmNbBX5rxp17rVO+VbdZKbAnTfvdA1zJmdw98GriQR9rjBwDG1nV9x1tXvFYK3bn0uJQG/XgM2tEbpawm4G3GIiZbFyS0J1ygeGT39gPyPpHF89coYL6QJ9ySi9HTEGxuYYHM8QCdqezI++s48b+5JMZgrkDYsHn3ytJOmqvzuxIcVUrfYtCILgpZax4/ChfYt29knGg2xLxmqaM6F0V+Vn9nVz8Jqt3Lyr043B9I7hi4X4XE74z1pwCK1Kq8j1TrW2kMlEiGzBbgfV0x5lMmMbTDXPi3eyrKVbS043uf/xV9wV2OFD+yoalpMJly2YdLWEuPvg1UuKxeWWWxJsygeGkXR2/h0fGlC07NJEDz8/xFAqCxrctDPJ5tao/f0n7MXA1vaIneSjwaduv9b9zDeSmJIknsZQa6kgQWhWKjlIetqjnBmf4yvPnHWretz//usWjKneuW8oleO/3nY17bFQTcKtlrhQ53qLhfgsN/xnrYyVDReYmqb9S+A2YDPwoFLqlKZpCeBG4GWlVLrR93ClKTcqgM89eYpswSQW8vNrP7nLFXjexB3Hs3j64uxlrToGxuY4cmYC3bAYmsoyMDbH/t62kmO8mXChgA9oIRL0LXmtxcotbSSRs1y8A0MyHuSho+cYSmUZnsqyuyvBjwYnGRibYzJTYHdXgpxhki2YfP6p10uSd+66dQ+H//ZVUhmdzz/1mlsPcyMhSTyCIFwO5Q6SM+MZLs4UyBsGs3mT1mgQ01IVx5TejlhJiNu3Xjhf1XlTiVp3VRYTo8sN/1krY2UjW0X6gYeBX8COllXAXwGngCJ2G8k/Av6wUfdwpSl3STtf6PHz0+72Z1Y3+f2/fZUTF2bmBd6lIuYrb+GoLkW9KvdfJcc7mXCh4Px2a7i2GpeOgQ+MzZYUlpXYv8XxDgx5w+LzT71ONOgHBdM5AxS0R4OMz+aZztkxuU6B39F0nvseP07BsNCAl86nsRSMVKiHuRa2QxrN5SbxbITPRhCE6ngdJBZ2lvhU1pjfRVKkswZvTGbojIcXjBfRkL8kxC2V0SsKtsXGmfL3qh27mBhdTvjPWkl4bKQH87eBD2OXK/p74KTzhlIqr2na3wDvY50IzGriMKebPHR00N3+3H9VG1ljocDL6WZJXcxFWziWeT/BFqk97TEO9HcylMrRl4zS0x7j+Pl0iSC857a9l2pchpZX49IOim7n8KEWmbCXgdc77Tz0B/o7+Tdv7+ObLwwxPqsTCfqwlCIc8NMaCZDKGIQDPsZmCkSDfk6+Oc1MzkTTwKcFKBiWe/61sh3SaKIhP/fcttetJVfL37hRPhtBEKrjdZB84fsD/J9zKQI+DVMpAn6N9miQHZvijKRzPPz8ufkdpxB33ryd/u4W+rsTbEvG3PCzSt3vak3Quee2vW5pw0rH1mNuXSsJj40UmHcCDyml/ljTtM4K75/EFpjrgmou6VdG0px6c44dnXFMpfi1W3by6Isj+DXNFXiAW4T7XCrDpniYHZsuGbG3oHbp9jbzHsU3XFHpFId16nydHc8wlMrS2xHFtBSTmYJbngFUXQxPPES1kdNNfrJ/E90tEXZ3JTgzPsfNuzdhGCaf//4sc/k8qazOn/ybnyAc9PPVI2c5fiFH0VT452thpnMGllJ884Uhruuxwx9qWZisB3K66Q7MlRoEeI+rJXBeEIT1xL8ZbgAAIABJREFUx2Lewf297dz/c9fxi39+FJQd6vWWzS0omHe8KEbTeTJ6kROjMwylsuzqinPXrXuYyRmYlqJoWbwykiYSDLi5C06d47xhcn7KKqktXT4GHRtKlXQUco6F+tY2XgsJj40UmDuABxd5Pw10NPD6V5TOeJhw0Od6Fns7YqTmdD7xyHG79Z+m8TP7utnX086+nvaSB+D0xVnX4PKGRcG0cGpweVc/4aCP2UKxxPs5nTX4x9fGFxSHdbbl/T6N6ZxBTjfpTITojIcB3FXacr06y1mNCZdIzel85Ms/ZDpr0BINsHdrKz94bZyiadES8QPz5YcUhIM+IkEfM7kiV3e3kNGLxMN+jg5MUrQUs/kiI+k8A2NzPPz8uZKC/eXtJ9cTtYjFSva5FraKBEFoPLX0Iz85Os2Ozji6aREN+vnN297iVnTJ6SbDU1km5nQKRZOdnXG7y96jL3PywoxbO/rZ0xMEAz4O7N7E4UP76IyHGZ7KMpXVKRgWpqncRXD5dvWNfUn+4dSYO247dajveMf20laW8+FRzTyvNlJgzgLJRd7vB8YbeP0rhuNZcZJ47rltL9GQnyMD48zmDBLhAKalOHjN1orZv974EE2DZCzkxnnkDZOz4xmiQbu1VUs4wO6uBBrwW+95C5/9+1PM5IsA+HwaBcMq2ZYvWorWSICr2qP4NY2RdI5I0Fdxoq7FE7nYakw8RJcobwt2bChFOmuvgFMZgxMXZtCLJqYFqUyRoB9ao0Fu2pWkv9v+LpwFS393gnfs6OTYG1MYlolhKs6Oz5E3iiUF+z/6zr6SjkzrjWrVGbw2W6lE2FrYKhIEoX5Um6scT2LOMDHTpUk73na9F6Zz7oLcW0Hl/FSWq9qi804ZxcD4HD+xrd3OWwj4yOomvvn663rB5OToDK+OTJPO6WxujTCVsYXp6bFZQgFfSbto7/3e//7rODIwXlKHGtSlVpahSy2om3lebaTAfBb4qKZpny1/Q9O0DuBj2LGZTY8zqfl9GoWixWSmQHK+mGpbLMh01qAzEeLmXZUiBaon0LiFX6eyoOBAfyd3H3wLf/B3J8gWTB78368xVyjSEg4wkzdQwDdfGOLO4E7GZ3V62qOYSpGMhTjx5gwoeOjoIPe+91q6W8OMpvPEwn43sLmWWLVqqzHxEF3C66102oJds7UNvWiSMywiQR9v2dLCm9M5TEuhaRAPB9naFuNjB3YCLFiwALTFQuSMPP6Axu6uBJFgwP0utrZH1rW4hMrVGcpttjMeJjw/ETje3LWwVSQIQn1YbK5yPInO2Ovs2EHpPF1tQd7bEaMlGgAFnYkwvR1Rfv2WXTzy4nn8Po1wQMPn1/jHUxO2kLxY5K6/+md2bWqx52kNQgE/lqVKEmgrVYk50N9VMnf2d18Sot4W1M08rzZSYP4Btsj8PvD/zb/2Vk3T9gCfxG4j+UADr193qq2aqmVsJRMhvv3xd5UkJVQrQVQpgeb8VJZURnc7/Nx5805SGZ3UnE4w4OPN6TxjMwV000742L0pQSpjkDfMkofsNw/u4U+fPkM06CeVMZjMFLjntr3c95idLPS5J08taD1YbcVUKXhYPESlHBtKMZ01ANy2YJtbo8TDAcAkHvbz72/ajm5Y/Hg4Td4o4vdp9HfH6GmPlcRUOguW3o4Yn/ngPr7wj2cxihZXtUc3ZLF170BdHnvqhAxkdZNY+NJOgiAI64fFQmUmMwW2dcToSphEgn7X2QOX5unzUzk04JqtbRXn4U8f2s+9j75MKquzozPOdT1tXNfT5o6zA2NznBvPcnpsloKpuDijY1qz7NqUwLAsIgE/7bHgggTaSsK40vjtzP0rjcFcCzSyk88LmqZ9GPgK8LX5l/8IO7hwDPigUupEo65fbxZbNS0mspKJEAev3VLino+F/Hz6A/srFk/3TqB5wyQZt7fLd3bF6WmP8pnvnuBcKst0ziAW8uPTFEG/j0LR4uxkhn919SYiQR/bOmJsSphEg37aYmF2dcVLBPD5qSyFouVxz2sLvJrVKPcIiYeoFK/nui0W5Jqtbfx4eApNs3vY+jSNdN4WoDs3xRlKZdnSGgF8fOa7Jxif1UtiKp0WYqPpPKPTWVrCQYqWvajYiJ+991nyfk6gLu0kGFbJ5CIIwvpgqYLkW9sjFd+LhvzcdesefvHPj5ItFLnzqz/i2x9/14IxIhryEwv7GZ22Ko6zTkb56fE5tPlSgFndZGQ6x85NcVrCgYrzezVh7PVslnsumzX20qGhhdaVUk9omrYDeA9wDba4PA18TymVXeRX1xxLJRgsNdE7sSGvj82iGxb3PX6cBz9yQ9UsWGcCDQd8/Mef2u2uoFIZg972KDndpK8jxtBUFt206IiF2JaMui0cvQ9ZJU9X+UPa351wvZqz+SL3PXa8qggWFsfrub5maxtfePo0o+k8oYCPzliInd1x2iIBzqWyTGV0+/uLhxhN51BAIhwo2cJxbC9TKPLmdIHpYJFUVq9YSH8jUGmr68a+JCPpHMl4kFTGaOptJUEQqrOcguRAya7hj4fTzObtHSNnd+ngtVtKzj8wNsePBlPohsVEplBxnP31W3ahFy2Oj6TJzMdnOtU+vGFyXqoJ45JE3vnwnmaPvXRoeCcfpVQB+M78P03LSguX9nbEiIX86IZlBwsXzKrGUy5G/f5BHvzIDe495AyTSNBH0O8jHvajAZoGOzoX9jF37rPSA1f+kA6MzTI+W2A4ncUoKv7L/3qRP/7FnxCRWQPl4ROO59pbIUAvWlhBxbFzU4xM5cgbJru64lxI55jK6GxpjdAS9TOTM0tiKp3vfTpn4Pdp+H1aSSH9jYb3WdzaHuHGvqS76k/GQ/zKu7YznTfJ6WZTr/4FQahMLQXJS3YNw34+9b5reerkKHrRwlSKq9qj3NiXXDB2540ihqk8Q6wqKRXojDWbWsL8zu3X8D+fG0ahGJ22yxTt7IqX6APv+SsJY6/zygnvcbLHm32RLL3Ia2SlsYbR+W1xb5H08jqXXu9ieF6ERuezyQbGZokE7TaTv/y15ykULc5MzLF7U4Jop5+cYXLnzdsBXFe7c+7PfPckw6ksW9ujBHy4Hh4necQ57qGj59ztd58GJy5ML+lp3Ujxf9VYLHzCEUMDY3PoRZPzUwYZ3SRbKOLz+cjPLzjenM4xkSlw865N3H1wF/3dLQtCMAbGZvnqkUEupPP0JaP0dzfvynYpFrMt77MYCwX4zssjnE/l5j/HPPf+zSvkdNNNsJIFkiCsL3K66dZy9o6VzntOBZbRdJ7XL86iFy3uffRlfJrG27Z3kM4Z/PbPXM1IOsdDRwdL5sRvvXAevw9QcNOuDnraYyWlAp3s7lRGZ3MiwthsnkyhSCTo54M39PCe67a491OpMUqlVpRueFrIFsJZvbgu5tWGCkxN0+4AfgPYA1RKoVZKqaYRuSuNd0smQjz4kRsWtIwqFyc53cS07A4DSik64gE3u9wwLTKFIplCEUsp3pic4+rNrXQmQiQ9sXpObFo44OPlkWmMosXZiQzbkzFao8EFdbbueMd2Uhmdt2xp4dWRaUxLEQ76mc0VS4rGOkiHlEssFj7hxP381rd+zGzeQDft35nKFQn6mI/xUfg1H8WiYjRttyOrlgT2hx+8ft2L+lpsKxry49M0PvzFIxSKFkVLccN868ysbqJB1S0wQRCal5xucv/jr3DkzIRbXeXwfEKNd+xIxkOEgz70okUo6KNoKfw+8Ps09m5J8PhLowynsgylsly9ucUtu5fK6Ozd3EreMPnYgd2MpLMlpQJjIT+FokUyHuKh587Z87FeZDZv8OBTr/PC0BSHD+0jp5vc/c0XefXCDOGyttBenA5ljhD9/FOvcefNO1fjo607jexFfh/w+8BF4IfAVKOu1UyUi9RycTIwNscXfzDAj4fTBAM+ejuiHLxmK48eOw/YHvugX8OwFBpgKQ1LKbIF0y1flDdMprMGmxImmUKRoqVQSmEpaIsFUYoFdba8Nbh+6i3dZPUi2YLJhemcWwjWO9EvFZO6kbybi4VP5HT7ezn15iyWVfp7Qb/twbSUwu9TtMeCFduQedkIST21FFRPzen82784yvisjqZBWzTIe67t5qev2cKdX/2Rm2B1Y99ipXiFjcyOTz5R03FvPHB7g+9EWA7np7IMp7Lo8+1yh1I5d4zwjh2pjM5//Knd+LVBZvNFLkznuKo9Sizk51+/vY8v/eAMkaAfNMjNhyt5y+5dSqw9yblUBtNU/OSeTn739n1MZgrkDZMHn3ydgN+HaYGmaZjzDU8Gxmb54g/O8OqFGXKG7VXwtoUunxsnMwW3/e+RgUmGUzl2dsWb3nHTSO/h/w38I/CzSimjgddpasrFCdhiMRSwV15diTA37+rk2fmSLC2RAL/38/v41GPHQdmxl3rRIuJZXYEtJKNBP10tYba0h3nu7BQBn0ZLJMjHDuygpz3GZ757wm0x6dTgcrYdetpjHBtKlRSC9U70iwUsl9fzbPaHZCkqhU94t2myBTtmNmPreDTAp2G/ptsr4c54mF+/ZSc/u2/ruv6saqGWeOejZydIZ3QUoBSE/D7ef33PgtJgsj0uCOuL3o4Y25J2gisK+pJRd4woHzt2dSX4j/+qn9Njs/zV80NuEk4k6HePO7B7k9tzvHwsPz+VZXy2gIaGYVpkC5e8BMl4mFjIz57uBOdSWYqmhc+nsS0ZAzSyBdP1XF53VWtJW+jy3RlvsxU0u4WlJPksTivwLRGXi1MpIccuuWKveD59yM7kvuvWPdz76MvM5ot8/9RFfurqbkbTOToTYdJZOxO5tyPq1s7qjIfdGloDY3OMz5wgEvQzldG5OJOnp91+COymBJp7P3YHIFt03n3wLcRCfmbzRVqigZLSRdVE1eHvvMrZ8QzDU1mu7m5ZFw9JLXg9i84WzhsTGVqjQTriAfZuaSVvFBmfKzA2o+P3wdWbW4kG/Zy6OEs6q/PDMxP87L6tq/yXrD5e2+qMhysK90jQj70RrvABv/O+a5jMFEoSrARBaE6WisE+fGhfxRjM8rHjc0+eYjSd51wqQ96w0DS4aWcnoBbUmfQWQXfEZSwUwFQKvWi3Zs4apruVPTzf9actFuSbv3AzqYxd7q+/O0FuPllnd1eClmjAncedpE/TUgyOZ9wM9WrNViTJpzovAtsaeP51Q/m2Z7ngPH5+mi88fZoTF2YIBX34NY3fuu1qpnNFfvuRl8jpJvFwgAc+dD3JRIhkIkRONyFjn8+p2/XGZIaJOZ1vPDfEo8dGyM7/nrct5ZGBSfSixVAqy0z+VbIFkzcmM+zYFOdzT55aUP+z0nZ/NOgHRcWMuo3AwNgczwyMM5Wx11a39G/iN27dzVd/OMiLQ2l0UxEJ+gn6ffz8DT1MHhmkLWqX11lMjG+ksANnVV+t733Ar7EpHiJr2LVev/l/hgkFfG4g/Xr/fITK1LrtLaxdao3BrlaizZmXTl+cZTSdJ5XVmckV2bkpjqXsOPfDf3uCHZtiVWI3g4BmV1SZyrK5JUIs5GfHfI3LbMEkNx+G1pUIUzAssnqR/m57i97bOrolcklcgu1hTcZDbvzoQ0cH3Xuo1Gyl2cexRgrM+4BHNE17RCn1YgOvs6Yp70ldC84DkprTue+x44zPFTg/lSPg19ANi1jIT097jM/+/YuMzRbwaRqapnFydJqejuiCB9TOFlfkDYucbgckm0rREgmUlEMYGJtznZmmpUjN6fh8GjndxChaS3ojvdsTB/o73Zqczf6QLB+FaSospfBpGtN5g7HZAi+8kcZUdmmhoM+Og/2n0xeZmCswkbG3asrFeKXyGLWEHawHMbpY3/uCYXHDtnb72UhleeXC9HynpMqB9IIgNAe1xGDXgtM2Mp010E2LkN/HuZTtZAE4n84xMDbL/t72kmsOpXLunp6dyxBmd1fCrbfreEXbYkF3q93bDMNUChSEAnYDlJF0rsRT+q/f3supN2dpjwYZn9UXJNGupzj7Rnby+YGmab8KPKdp2nPAG4C58DD1q426h9WmUk/qWkVmTje599GX+NHgFJGgD02zi0p3tYb59KH9TGbshyHo92GYtuh0EhoqTcxvzhQwTYvpnEH+4hydiRAPfeymknII/d0JDuzeVFLSaHxWL3mQlkpA2WitCyvR393CT+7p5NmBFAGfxo7OON0tEfwa+DQfvgDs7k7w797Zx9d+eI6rN7e4ZaaqZeovpwDvesnwr9T3/nuvvlkSwvGJv36JqayOqaBQtAgHfBvOYy40FkkGurKstOa0g9M2clPCJOT38Z5ru3nsxxeYmNOxlMK0FI5HxXvNq9rDZAsWWcN0cxmcusQAd7yjD9DoaY+6wtGtXX1xlkLRno93dyXoaglXLIM0mSkwPpsnEvTz5X86y/devcjhQ/uacpxejEZmkd8EfB0IArfM/1OOAtatwKzUk9qJDavmYXJeT2cNXjo/Tc6w3fHv2tXJL7y9l5t3bSKZCNlbiMmo+3sPfPj6BT1XnQd056YEPx5Ok9OLWAr2bmmhLRoiqxcXdCM6fGjfggLt3njOpR6A9bT6ulyiIT9/+MG3MjA2izMQnR2fIxz0EZkvlxENBnj8pVG388yurviCupaXU4C3Uo/uZvXoVYpP9sYNX0jn7JqtPh/KsoiH/PzaLbvW3SAtCBuJejkqvG0jk/Eg125t48XhKUan85iW4kD/Jvq7EyXXdGIgC8UiLZFAiRMGFibo7Elcauscnk/MDQd97NgU56Pv7GNza4TPP3Xajbk8enaSVEbn6u4WJuYKXJzJM5cvMpLOrcvObI3cIv9jQAcOAc8opdINvNaapLwnteNhrOZh8r4e8NllDxIhP5pPw++DR4+N8OzAhHu880CARjToL2mJ5X1AjwyME/Jp+EIB8oaJqSgp9O6lXCA6/y/ZuMvDiadxvtPB8Qx6UbGtI8ab0zkspUhldO4+eDWRoK/iQFresaY8KL2caj2614tHz26VqhMPBxifLfCVZ84wMWeHiLREQ7x1Wzu7uxKrfZuCIKyQejgqykXjF39whmQ8xJ/84g1EgoEF4VvRkJ9I0E8qo+P3aW5spXMf3q5szsLdGyfeGvXztu0dFIoWXS1hNrdGSMbDBHwaJ0Zn0ICnTo6SjIdIZfR5D6iOUbTWbWe2RgrM64HfU0r9bQOvsaapVjJlYGyOwfHMglIEXo9Voai4vqeN6ZxBWzRIVjfRTRMzrUo8Ug8/P+QKiqvaonZQ8Qf2lzw4N/YlaY+HmM4aJBMh7r/9Wq7raVv2ynA9xPVdKS6VKbJjVyNBP5pmx122x0NuyMFiMaqVVvKLCf1KPbrLC+Q3E45gPp/KgQa/93PXkYyHGE5liYcDTGYM9nS3kCkUiYb8mJZakIgmCMLGxSsawa6N2R4LLZlH4HTV8VZOKXkvbDd6+PYLQ24XsZmcyd0H9wAaDx0d5MEnX2d4KktbJEjRtNi7pXX+mF1Egva5y0sF1kqzzMWNFJhj2B7MDU15yRS7JeMgQ6ksaJQkdpQbsNMyKhYKlBSPjoUCnL44S94wGZspkNGLTMzpTOcMUHDv37xMayRYUodypbUB10tc35WgPCPRWbE69dZ62mNLhhx4B5BaV/LlHs9mFpdgC+bzqRz/fG4Kw7L4d//jR7xlSwt5w2QolXWTqPb3tlEwrKYPCRAEof4sJ6azvKuOd8EaDdmtmj/xyEtk9CIf+LMjBH12fcw93S3s7o67meSpjOE2PEnGQgQDPruU4LyQdMblw4f2L1soNtNc3EiB+VXgo5qmfUEpVWzgdZoKx/jKEzuc3qq379vKXz53jmzB5AtPn3Zd/G2RINGAn9ZYkD/4uxMUDLtAd2s0wInRGQpFk5yuSMZDjKRypEI6rdFgyYS7VPznUvddj8y+jUBpNwmDuw/uIRL0l3zei4n8yx1A1kOSldc2eztioIFhWfg0jYm5AlMDeQwLLAXJeJAtbVHuvGk733ll1P28OuPhknARQRA2LssdF0fSWSbndKLzO4wDY7NEgn5ioQC//LXneXMmj6aBskAF/RimYk4vAtp8PKZFMh7EtBQtUbuO5rVbWvn4T+1iX0/7gm355c6jzTQXN1JgPgu8HzuL/M+AQRZmkaOU+qcG3sOaw7uachI7vL1VDdPul7p3c6tr3F89Msipi7OYlqI9GmR3l72tmsrofOjGHi6k8+zsjPP6xRlMSzGZ1YkYfuLhwIIYvMsVL/XK7NsIlH9W3hVrLaxkAGnmJKtKtvnAh67nF//8KDP5InndIOcZQdJZO8HnsZcucO97r3EbDCynnJMgCOuX5e4E2TuM5xie7xJ0064Ot/C5YVpk8gY+TcM0LRR2rWcFbEqEGZ8tcN9jx90+5b9x627+8rkhnjs7yeh0nm+9MMy+nvYV/03NNBc3UmA+5fn/r7AwglWbf21Djf6VVlOnL866vVWdWF+nSLmdLZsnEvQxl7czyoensuzuSrC1PcLNuzbx7MAEYzMF3ra9g1TWIB6yv9ZKMXiXK17Wg3fsSrHSz6qZBpB6Us02H/+Nn+To2Um+/IMBjo/MXBpINNjTnSCV0ZnMFNziys2yuhcEoXFcjjPFSSS8utsOxTl4zVYePXYesMVKPBJE04oEA0F6O2LkdZOJTAHLUsRidrtmv08jlbFD1sZm8liWwrJUSc/0ldBMc3EjBeavNPDcTU25l6m8t+pNuzr42IHdbsup9mgQv6ahaRAPB9jRGS8Rj+WtsZwHynm/fNvxcsVLM3vHrjQr+ayaaQCpJ9VsM5kIcfv1W9nRGeNDX/whetFu+dYaDqCbFn2dsaq9iDeKOBcEoZTLcaZ4x49tyRjdLWG3lFxvR5QHPnQ9J0enuWZrG194+jRjMwWuuaqFO2/eSU97lM989yTDqSzbkjFu7EuyLXmxYs/0ldIsc3EjC61/vVHnXm9U663qtJwqFC3e2tuGAkyLBQkcXmOr1h/cu4rbiOKl2WiWAaSeLCasc7rJN350jhu2tTNwcRa/D3w+H8lYkHtu21uxF7HYtyBsXC5nsXmptNEcDx0d5EvzpY3uPrjHnZd7Ouz605XmWlDzOyxq0Z7pG4VGejCFClRLsHE66ZyfyrqvecvOKKiYLFJOtf7gULqK22jiRWgOqglrx44T4QD/P3vvHidJWR/8fp++TnfPtWd3lmVnZy/swsICkhVRXI1iAC+gGA2+edHDMcYcPQk5L0YxeIK8hugLUTmvuRuTaOQ1JIIaOIomiMbbusghC7KwLDB7m53dZWd2eq7dPX2pfs4f1dVb3VPd0z1TNd0z8/t+WHanu6bqqapfPfV7fteB3hiJlFlHLl8wO3bYk6ZWo3IuCEI5i1lsnp5KMzqdLbm724L+Ot3rOdrDARLJXOldu9KKpzeCawqmUupX4WzSjvXzfKymJJ9aMSFO/cOtbDSrzdRCVkD9PVHisWCp1pa4DIXliGWNGB43+wenswanJtP82oXrRKYFQXCk0cVmvc0q7NtZJQUr39cyL7lrwfwRoJVSEa111vq5xvarLsmnVkyI/btTE7Nl2Wh28/zCONteTxCWI5Y14sEnh3j2+DgFwMhr9h0dJ501Vp3rabVRbz9wQVgM9TarKOs9njP4zS/tLfUeX/z7euXgpoL5AUyFMVf8WZJ8KqgVE2L/Lhouz0arxzxfzfVub6+XSGYlq1ZYlljy/WsXnsNf/+gQetZAFWtk7htKlDUzEARBWAj1Nqvo74kSDfvJ5gv4/IpU1iCZzTObMAAlymUR1xRMrfU/VvwsST4V1IoJqZUNXsvUbhVov2/vkZJp3u56l6xaYblT6Y76q5t28Xv37yNrFOiJhtg1EG/2EAVBaBEW00ax3rjNSMjPp2+4hDse2s/0bJ7hiTTD42kUcN/eI9x1wyWiZCJJPktOrZiQWtngTlgv3iOjSYYSKc5f1zHH9S5ZtcJyx+6OyuYLoOGb/+dunj81ueDWp4IgrDzcaKNYb9xmvD3Eve+5jOHxFBOpLJ9/9EUiQX9Zgs9qxzMFUyn1WuA64HygE5gCXgAe0Vrv9eq4K4V6hNyKF2kL+kFBOmewdW1sjpVSsmqF5YzdHRUK+khlDVLZvLjFBUEoY6nbKFrv1nTWfPeKp7Ac1xVMpVQn8M/AW3DOKvmEUuoR4L1a62m3j7+asLu/d5+3hpuv3CTBxcKKw+6OSmUNx6xOQRCEZoWEiafQGS8smN8ArsbsRf4PwDOY1stO4FLgg5g9yr8OvM2D468aRKiF1YLdHSWyLgiCE818J4qncC6uKphKqTdjKpf3aq1vc9jkKeCrSqnPAx9RSl2jtf6+m2NYbYhQC6sFkXVBEOZD5onWwefy/v4rcAz4+DzbfRwYAm5y+fiCIAiCIAhCk3FbwXwl8JDWulaBdbTWBeAh4HKXjy8IgiAIgiA0GbcVzA2YmeL18ALQ7/LxBUEQBEEQhCbjtoLZCdSbGT4NtLt8fEEQBEEQBKHJuJ1F7qN2/3Gn7QUXWEz3AkFYLoicC61KI/3Sj95znYcjERaKzC/u4kWZorcppeqpgPxKD469KnGje4EgtDoi54IgeIXML+7jhYJ5E/Vnhzdi7RSqsNTdCwShGYic108j1jRBEGR+8QK3FcyrXN6fUAfN6l4gCEuJyLkgCF4h84v7uKpgaq1/7Ob+hPqQjj7CakDkXBAEr5D5xX28cJELTUC6FwirAZFzQRC8QuYXd5EsbkEQBEEQBMFVRMEUBEEQBEEQXEXN09Wx6SilCoDq6upq9lAEF5mcnBzSWm9q9jgaQWRx5bLc5HE+Wez+8D8t7YCEhpj44nurfrfSZFFYvixWFpeDgpnHtLRONXssgqtMLqdJFEQWVzjLSh5FFlc0IotCq7AoWWx5BVMQBEEQBEFYXkgMpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiBQavWVAAAgAElEQVQIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIriIKpiAIgiAIguAqomAKgiAIgiAIrtLyCqZS6phS6lizxyEIIotCqyCyKLQKIotCNQLNHkAddHV1dXUButkDEVxFNXsAC0BkceWy3ORRZHHlIrIotAqLksWWt2AKgiAIgiAIywtRMAVBEARBEARXEQVTEARBEARBcBVRMAVBEARBEARXEQWzxUhnDV46PU06azR7KEITkPsvrHbkGRCElcGSZ5ErpeLAlNY6v9THbnXSWYO7vvMcI1MZ+jrD3Hn9TiIhf7OHJSwRcv+F1Y48A0Krs/n2R+re9ug913k4ktZnSS2YSqnzgFHgHUt53OXC8HiKkakMACNTGYbHU00ekbCUyP0XVjvyDAjCysFVC6ZSatc8m2zErKu01dpWa73PzTEsZ/p7ovR1hkur9/6eaLOHJCwhcv+F1Y48A4KwcnDbRf4k8xdb1cCf2n4W/0eRSMjPndfvZHg8RX9PVFxDqwy5/8JqR54BQVg5eBGDOQN8qfh3Jb3ALcCDwAEPjr3siYT8bF/X0exhCE1C7r+w2pFnQBBWBm4rmNcBfw38F+C/aa3/1f5lMQbzFuDrWutvuXxsQRAEQRAEoQVwNclHa/09YCfwTeABpdS3lVIDbh5DEARBEARBaG1czyLXWqe01h8BrgT6gQNKqY8rpSSYRhAEQRAEYRXgWZkirfWTwOXAnwD/HXgKeB3zJwEJLY4UQl48cg2F1Y48A4KwsvG00LrW2gD+VCn1DeCLwFcQBXNZI4WQF49cQ2G1I8+AIKx8lqTQutb6kNb6GuDdwB8ATy/FcQX3kULIi0euobDakWdAEFY+S9oqsjKrXFh+SCHkxSPXUFjtyDMgCCufJe9FLixvpBDy4pFrKKx25BkQhJWPKJhCw0gh5MUj11BY7cgzIAgrmyWJwRQEQRAEQRBWD6JgCoIgCIIgCK4iCqYgCIIgCILgKqJgCoIgCIIgCK7ieZKPUmo7sB3oBVTl91rr+7wew2JIzGTZN5Rg10CceHuo2cOpSTprSFamsGCc5KfVZKrVxrMaWAnXvN5zWAnnKgitgmcKplJqHfBV4BrrI4fNNNCyCmZiJsuNf/tzJlM5uqJBHvzQa1tWyZTOGMJicJIfoKVkSmR86VkJ17zec1gJ5yoIrYSXLvK/xFQu/wa4EbjK4c+bPDz+otk3lGAylQNgMpVj31CiySOqjnTGEBaDk/y0mky12nhWAyvhmtd7DivhXAWhlfDSRX4N8EWt9S0eHsNTdg3E6YoGSxbMXQPxRe3PS/eLdMZY/jTTPVdNflpJpkTGl56luOZey3295yDyJQjuorTW3uxYqSngY1rrLy1yPxNdXV1dExMTLo2sMdyKwVwK98syix9yCploabyUxVZwz63yGMxlJY9LOS96KQNLJffLLAZTZLGF2Xz7I3Vve/Se6zwcyZKwKFn00kX+U+AVHu5/SYi3h7j6onMWHXtpd7+cmphlz+Ao6azhxhBLWJ0xWkEREBqjEfdcOmvw0unpJZGfVpOpVhvPasDLa+6GW7qe56HecxD5EgT38FLB/APg15VS7/bwGMsGy/1iFDTHx1N87fEh7vrOc64rCcLyxJIPoKZ7zrL43PO9gyI/wrKnXrmvhjwPgtC6eBmD+TfADPCAUuokcBiofPq11vrXPBxDyxAJ+bnz+p3sGRzla48P4fep0op9vn68LeK2EVyk8p5a8jHffXay+Eg/Z2E54DSP1Sv31ZDnQRBaFy8VzK2YZYiGij8PeHisZUEk5Gf3trX84OBI3YHkrRCbJ7hLtXtquedqIYkIwnKk1jxWj9xXQ54HQWhdPFMwtdabvdr3cqbRFXu1FbpYNZcvtawu893XxVp8BGEpqJRjryyN8jwIQuvieSefVscrRW2+jNx6J1enFXqlNeC2a3cwlszIBLtMqGZ1qWXlsctOs2mVxU2rjGO1U3kfnOS4lqVxsRnei7GANoLImyA0xlK0iuwErsZ0mYMZi/l9rfW018eeD6/cz410RVmIxeql09NlGemf+NYzTKRzDMQj3HXDJUs2+cmEuzAiIT+3XbujVP7KunZOlQZ2b1sLnJWdeCwEaBLJXFNCJpodsmHJXG8szOcePSihI03GLg/xWIibr9wE4GitdJL5xXbZWao5qNlyLwjLEU8VTKXUB4F7gXbO1lPSwIxS6g+01v/g1rEWMtF45bapVnqj8rP+nmhdk1blCt1uDQgHfTwzPEkuX+B4IsXgyDSX9Hcv+hzmQybchZPOGiXl6AcHR0rXzrqvpyZmS5UGfnBwhJuuGCjJzvFECg20hwOeJzU4PVPNTKqwy1w44COVNRpKlhPcx5IHo6DZc+gMQ4kUA/Eo8ViwtAiyvC5OMl+vPDltV+/8WY1G3hmSTCQIjeNZmSKl1DuALwGjwEcwO/tcU/z3CPAlpdTb3TjWQktVLKZERq3aa077dfqsVg24Wvu3rJq3v3UHv/O6rShLdVel/3mOtFVbONWunXVf3/eaATb2REvKE6iS7KzvjtAdDWIUtKdJDdWeqfmeGa9qdEL5dUtlDaJhf9VxCEuDJQ+zOQM0RIJ+RqczvPGCddx69faaiqT996H2fWx0/nTCLpuNvjMWW05JEFYjXlowPw48D7xaaz1j+/wHSqmvAI8Dfwh8e7EHWujqcqEB4vNZ7+z77Y2FS/uvPNZCYvHsx7CSfV69Jc6hkSTn9cXY1tfuOF633UiSvdk4dvdutWtXWWnAconfdu0ODo/O8Hc/PUwqZ9ARDnDbtTs8sxpXe6ZqPTNeW7XtMre+u01ij5tE5Xxy5/U7GRyZ4b69RxidznJ8PMWDTx5nfXdbKTyoNxYuWZ3Xd7eVZL7eOdhpTq31HDmN2S6bN12xqaF3hiQTCULjeKlgvgK4q0K5BEBrPa2U+irwSTcONJ+yU0vBsruf61XE6lFoLQWy8oVr367apGV3Ox0ZTTI4MsMl/V1VxxPw+YiG/AR8cw3S1sR6amKWaMjPp995yaK7EtUau+BMZazaey7vpy0YYFtfu6NM3nn9Tp47Mcnf/fQw9z76Ims7wkzN5vjPY+OEgj7O7+tgLJlx5V46Md8zNZsrMDgyzba+jjlyC964EZ1kzqvzF5xJzGS546H9JUXRWkRc0t/FXTdc4ljnt78nyucePViyOlcujOyL5ZdOT9eMR6+cU+tdZFTKJuhSOEo05Kc3Fnb8vcp3grjFBaF+vFQw5/PVutYE3Q2rSiPWl95YmHDQRypTvhqvpF5FtPKz/p4o8ViIPYfOgIb79h4pS96xT3rD4ykSySyxcIBEMjvnGMPjKU5NzPLiyDTZXIE7Ht7PvTde5opCKBNu/TjFqm1dGytZeJz48s+PlBTKZDYPGkIBH9lcgWjxZesV1Z6pdNbgzoefLcnm7m293HXDJYCpdFbG3nkxrkZlTpLR3CGdNbjj4f08cSRBKGguZu3zTbU6v5bs+32KVMZg31CC3dvWLmierpxTx5KZuuShcsG0rc9MOrrj4f2kMmZ8aOUxm51YJLhDvb3DV0Df8JbDSwXzl8D7lVJ/rbVO2r9QSrUD7y9u4wrVXjyLCSKv3C6dNRgcmea+vcdIZQyiIT+3XLW96mRjT9qotUp2Opebr9zEUCJFJOgnkcyV1b+0T3q3XLXd0fVkH0M05CebKxAKmEqxBKgvPdai4YXT0+hirNp88pjKGCWFMh4N0RkJEAsHiIb9fHoJqwXYGRyZ5oWXp8kUY9YOvjzDsycm+NZTJ0rW2Vuv3l5m2WwmkozmHnaZzOQKFLSeM6c5LUyqJa/Z70W98/RCQ3OcxjU8niKTK1RNFBseTzGcSDORzpHOGuwZHGXXQFyqFwhCnXipYH4O+BawTyn158CB4uc7gd8HtgHv8vD4QP0TUj1u9ru+8xyHR5McH09xfl8HqazBZx45QCZfqBqLOd8quRrb+jrYujY2ZzyVpWw+890DVV1P1hg+/c5LSmNY2xFiNlcgnTU8L+shq/xKNAGl8CkwCrqm9bu/J8r67jaAMoVyqa5ptVJb9+09xuhMhlQxqePMTIa//+kRMnnzRZ1IZmkL+ptSusjpukj2r3tYMpnNFzh8Zoa8obn7ewe4+cotZaEelYt9S7mr1Sa33nl6MaE5tapxOB0zGgrw9PCEmcBU5FuxE6QyUr1gJVKvpVOoHy87+TyklLoF+FPgLzjrEldAErhFa/2wV8e3WEgQea0XVSToBw2zOYN4e2jeyWYsmam5Sm503PZJMRryM53OkzUKpWM5xaTF20Pce+NlJevrFx570dPVt1iN5jI8nmJ0OsvLU7Nk8gWUomaSTrX7v1Qvs2oZuolklgvWdXBmxvxuTXuYTN502VsLraVM+Jqv6YAko7mHtWC+9etPkTM0R8aSHD4zw/FEmi3FcI9KebYr/7Xa5DaiOLoVmjPfMZ8/NUnIb4YCzOYMpjN5QgFf02RdEJYbntbB1Fr/tVLqfszyRFuKH1uF1ie9PLadeiekWtvZX1S7t/Vy85Vb2NAdKXOXOE02ZW7ycP1ucvt4KoPfrUkxGgpw85d/wWQqR1c0WHPfkZCftqCfRDILeGvNEavRXPp7okTDfjI5A59foXX1BYHFfHLrpZW4mmJmfXbh+k7sBd+bldFdadG/4+H9ZHLlHgVJRnOPsWQGn1KlGPRgQNFWJdzDaaFZ616kswbHxpL0xsJLdp9qPWO7BuJ0R4NMpHIAdEeCUr1AEBrA804+WusJ4EGvj+M11V5U8728Sm7yhxp3k0P1LPDt6zp46fQ0G3uirG03aAv6qyosleVxGo0JbRSxGpVjXf+PXnMB7x9+glTW4ORkuuz6N6osem0lrkfeAdczuhu9DpUWfSePgiSjLYzK9qTWHGKFboQDPqJhP1PpvONzXq04uhOJmSw3/u3PS4vlBz/02qZXCIi3h3jwQ69l31CCC9d3kcrmpXqBIDTAquxFvlDLj9OLqp6X11gyU4pRa8SiZwWWD4+nOTQ6MycL3IqJqqXIOSUFfea7B0rKrn01Drhi6RGr0VnKus8EfQzEo0xn8nSEA6VsWnBuI1qLpbAS1yPvTolwC73vC1Wab7piAFB1eRSE+qgsq2VZq+OxIO+5fIC2oL9Uc9euONo9LZULzd5YuOr93Xt4jLGZLH6fYjKVY99QgqsvOsf1c2pUNuPtIdfHIQirBdcUTKXUDzHjLN+stc4Xf54PrbX+NbfGUA/zvcRapSi53XJ5ZDRJspi1bs8Ctyty9uLDdvdNpSLy/KnJUkyo3aUYjwUBM0nDDYuYWI1M7Nd/Op1nKJEilTXIGgW++vNjxVaQjRV9huZYied7NhZrVW1UaU7MZPnEvz5DYiZLfzzCB3ZvEfelS1jlzdI5g6m06SLWwIFTU3NiLrev6zDrYxYTCe31Me0LzWr3N501eOz5U2TzBQytObc7wq6BuKvnU+YJKibNiRVSELzFTQvmVqDA2fqXW3Gx1qVb1HqJ1XpBLkbxbNSiZ1kuT03MApAvFPAr0FqztiM0JzjeKj5slQHZWLRs3nn9zjmKyK6BeCnQ3u5SHEqkUUBsCXpcrybs17+jLcDm3hiT6RwnxlNkjUJZ0edGlMWlthJb9S+PJ1JsjEe564aLXc/YbkRpTmcNPv6NX/KTl0ZRSvHsyUmOJ9JsX9cuSWUu0BsLc3w8xWQqR3tbAI1mMmUmFAYDvrL7m5jJcuvXn+K5k1OEA+X1Me0LzWr3d3g8xVTa4JWbephI57jz+gtdV/5K9YBPT5PNF7jjof3c+5659YCl+oUguIdrCqbWenOtn71gIZNBrZdYtRdkte4VjVCvRc++0j4+niIeDeHzKS5c38l0Js9/uXxgjsXVUkbTOYPJVI617UbZ+CsVEbvV03IpDsQj2C2Y4l50B6fr7Z9QJLN5IkF/qejzQpTFpbISp7MGPzx4mp++dIbZnMGxRMqxu9RirapOSnO1Z3xwZIanhyfIFzRaawJ+xWzW4NTErCyOXGAsmSnFdxtaYxQ0HaEgZ5IZcvkC/T0RemNh9g9P8pc/fJHnTk6RLpbziYadmwDUUxnj4g2dXLyh2/XzsZLssvkCoaCP6UyePYOjZQXf61lECYJQP8s2BnOh7rhalh+nF+R83Svcxt71YmNPlBsv7+ex50f4xZEx0PD1J4fYuaGr9PK1K6PndkXoigZpKyou1iTvVJfO+rlW0ka9yKq/Nk7XuzcW5sREGsvI36ohBZaMvXBqmtGZDD4FWaNQVhvQwg2rqv061H7GNQGfIuj3UdCaoN/Hqck06bzhWfLaasIe390ZCfL08XGm03k6IgE+du0FbF3bzucePciR0SRHx5KEipbLned2OjYBsM8RTnG9XlvjIyHTLX7HQ/uZzuQ5OZGeU/B9cGSGPYfOkM0VGBp3XkQJglA/S6pgKqUCwA1AHPi21vrlhe5rMe64ai9zp4nupdPTZR1VGm3Rt5is2PXdbVy5dY15volUsR3k2a4+lcro+14zwIXru3j+1CQXru9aUE25hbThk5qX9WMvPXX/E8eKSRTBOcWqF4tbSr8lYz6fwq8gHPATC/tpC87tew/uKsrVspCHx1PEY2FesbGb0akMkZAfDRQKumY1BWF+7HJz27U72DeUoCsSZDyZZTZnVqsIB33sG0pwamKWtqAfn09xbleEvs6wY2xjtfhMO0uxwIq3h7j3PZfVKPiuzwZ16dL/BEFYIJ4pmEqpzwJXaa1fVfxZAY8Br8eM0/wfSqnXaK0PLWT/C02cqafgemW3B3tZjt/avbnu/S9E+XJyqZ6amCWRMvuN28+1NxYmHPAxPWtaFi5c38Vf/sdLjrGYXil9UvNyYVjXzSho9gyO1SxWbaeaDFeWlHFL6bdkLBL0s6EnwppYmP54pHRMLxcT1bKQ7Rb7c7ra+Oi1F3Dvoy9UbZcq1Ed55vjZpL94LMTajlApi/y+vcd4eWqWo2eSbO6Nsfu8Ndx85aay9qD20mh3PFT0AAV8RXkfndOLfCnObb6C79v6Oti9rZehRJqBeIRtfbXnMfHcCEJtvLRgvgVTobR4O/CrwGeBpzG7+9wO/M5Cdr6QxJmFJPCkswav27aG7rYgD/3yBF/88eGq2eeV+7cCy2dzBqcmdENdfKw6l5UWSmtiTmfNMkPTs3mOjiUZiEf5xLeeQWvIGQUmUjliIbOoerXjujFBSs3L+qi81v09xd7kL0+joWqxajvVYoErZe+mKwZcUfotGUtlDToiAb727teQSGa5b+8R7n30RaJhP3/0tovK6gO6+dKtXGxZVrPZYqzxmvYwqazBvd9/oWa7VMGZyntlXyzak/4SySy3Xn0+bUEfs7kC9z76AodGZ8jkCqDgE281k3ISM1n2DI6WFrojU5nSAjgQUCQzeY6MJR17kXt9ntbz0Rnxc/WF67nlqu1lcgumvN11wyV1ye9iPDeimAqrBS8VzI3AS7af3w4c0VrfDqCU2gm8dzEHaMStUqtERrWJwl78NxLys7k3Rqgig7LW/u2ZmFannXpKvVi/O1ssH5RI5ljbEWJdZ2TO8bJGgVTW4KWRGYyCJhryMxCPkjUKnBhPkczmHWPS3CrXJDUvTeZbpDj19c4XDIyCJhQwCy/UUtCtWOBfHEng9ykMfXbBUil7oFxR+u0hGJlcgVQ2T1vQx+h0lhdPT5PJF/jNL+3lvLXtpQ4n9jqUbigQTlUSzu2O0BEJkMsXCPgU02lNKOAjkyuIe7xOKq2VVmcyS24qk/6smpeDI9OEi+FC4YAPv1KMJU3Ze/cX9zCezBEJ+Ti3K0pHW8BU/EM+FAq/T5E3TLfzQhY+C1XMSnNlvsC/PzvGfxw8Q2+xiPpCXfUL9dxISJGwmvBSwQwBedvPV1Fu0TwMrPfw+GXUKpFhnygGR2ZoC/ro74mybyjBZLFNWCp7NqnBctc5FRW2d8mxMjHXtBtEgn5OTKRLcXe1rKDDiTRHx5JsXhPjnM42PvyGrTzw5HBZD3HLdZnNm71xk5k8Ab+PjT1R3rxzHT6lyBqF4nFTc2oD2uvcGRXW1UYnwVZNUFkq5rte9hfcsyemePbEBG1BP48fHiedNWgL+rjx8n7etGOdo5JvWe8mkjnSOYOcUeDomWRp4VAp29v62l1R+qslvRlalxoHpLIG6ZxZtWDfUMKTcAn7M9oTDfHrv3Iujz0/wuOHxwj6zG4ym+IxcY83QGWIxtGxFGvbw/zRdWct0mBm64MuWbMtK+ArN/WQyRdK1/zfnj3F8HiaQkEzmYbZXIG2oJ+L1ndy1Y4+EqkcbQE/h0ZnmM0ZbFkbm7cMVb3hRk7bVnoL+jrDPHtiCkNrV4q5L9RzIyFFwmrCSwXzOHAl8HdFa+VW4E7b933AjIfHL6OeEhnxWIj79h4p9Ve+5artdBV70bYFfbx/92b6OsJs6I46Wmpuu3YHt33jlwwlUvzJIwf45HUXlXXaAV1zchkcmeal0zMcHUsymc6ZL3GlmEznynqID47McP8Tx0quyy++75V8+Gv/SSqT5/T0LL924Tk8dXycwZEUfZ0h7tt7jNHpTFmryd5YmGOJJOPJHD2x8j7mtay9q91S6UQ16/W+oQS7BuL090TpjPj5t2fHMAqa277xDJ+8/iIm0zlyRoFMXpHJF4BypdKKv7WsdsfHU7T5FbFQkM29sZK1rppsL/bFVblfgM89ehA0JUv5y1OzRIJ+4rEgCpjNGfiUoj8ecU3Z642FCfjguZNT+HyKR545xTPDk0zP5vEpxYaeyJzwEZHT2ljz3uHRJBoYHk9zZDTJZ757oNQpLDGT5W9+NMhEOkcmZxAO+ImE/EylDW69ejttQXMhPTgyzf/79EmMgqagwadgoCfKiYk0+44lePbEBEG/ny1rY+ze1lszoS2dNRgcmea+vcfKmj7U64GqZkW/8/qdPHtigo9/cz/TadOjtJBi7omZbOm5rraIqyV/ElIkrCa8VDD/BfikUqoP2AlMAd+1ff8rwIISfBaKk6XN/hKdzRl84THTqz8ylSGVzXPfB17NbQ/+ksNnZvjkw88WA9o3O052h0dn2Ht4jJxR4PCZJO+9YqCUiblrIE4k5K86uaSzBl/ec5TDozPMZPIEfL6Sy9teHN2uqFquy1OTac5b2046Z1pKT06kefr4FOPJLIlkls29UY6MJctaTZ6YSDObM5Wa2VyBExPpkmuxmuVKXDvOVF6vaCgwp6/yr27v45FnTqPRnJo0Cz53RYJmV598gft/McTeQ2ew3JLhoI9UxrDVNg0zEI+SzhnEQoE5CpxXVmT7fq2Y4FDAx3lr23nfawbYNRDnxESKL/30EL//z09jaM3a9jD3vPtSV+TDspwlUjkKGravaWc8mSNf0PiUGVqwtiNcplyKnM6PNe8Njkzzlz8c5Mlj4/h9iul0vqQc3fHwfh4/PMbkbB4fEAr42DXQQ78tAeau7zzH4dEkQ4kUazvCJDN5OtuCBPw+ZrJ58kaBwizEQn6SmTC3Xn0pG3oijmOy7t3h0STHx1Oc39dRVj2gHg9UNSt6JOTnVVt6+eaHX1uajxsNpXDql+7UKrWW/ElI0epi8+2P1LXd0Xuu83gkzcFLBfNuzDjMdwKTwM1a6wkApVQX8A7gf3p4/IbiCK3VcOUkNjyewtDajB0yNMcTKSo7r1ju8uPjKYyCGWNkFDTHx1N886lhRqYypaD2aoWkJ1I5s9Yl4PMpdqzroK8zxG/t3uJoSarWnaevM8zI9CzT6RwBn2I2m2c2b5DNFQgFfKVWk7M5M/7P71PF1ku67Ho5lWsS144zlfdnz+AoE8kshoaJpGnx6O+JEPApcgWNXykuObeT12yN89zJKabSOdrDgbLEilSxNahR0ERCZimYl6dmObcrQkdboCnJLJUltCyl7sREigMnpskaBRQwk8nz/KnJqopEI1gKRCwUMIup5wzG01kz/s+nuKy/m7t//VLAVIBni+56EDmdj0jIzyX93dz59p385pf2ksrkOTmZLrWdTWXMsKBCQeP3+wgHfFxzUR83Fps9WHNCJOhHAQPxKPFoiJtfs4nTM7N89t9SJJJZ8gVNNl/g0GiSu779HF/4zV9xlF3rXof8PnL5AslMnm3r2uc0iKjmgXKaCytDmebrLV7rnWEPmarmYq/HBb7aQ4qE1YNnCqbWOgP8dvFPJdOY8Zcpr46/2BJB9vidgXjEVCwVbIxHyzqv2LvhdEYCnNMVZjKVpycWZGNPlO88cwoon2ycYh0VkM0ZKKA7EuTm127i54fG+OKPD5VcP3ZuumIToEulQezjTmcNuqJBJlM5umMhPvvuV5iZtsU6dL2xMF/6ySF8CrQPXr01zobu6JzrVVmuqR7Xzmp1T9pfGheu7yJb0KW6gReu7yIeC/Hmi/sYHEmxeU2EcDAAaLraguQM05JsT6xY393GLVdt5zOPHGDzmhgKWNfRVkrqakYyi9PzYVrej3BiIlW0KkL3At2PTljZ9scTKV69pZerL+zjX544Trr4rPy3q7eb2b+lhJVQKTFOXJD1kcrmyzwgVrz2+u420tkOnjkxQdDvoysa5OhVVvUAACAASURBVPpLN5Qpd6V7szXOTVds4oEnj/Plnx8lHgvxqi097B0cK4aCaMgZPHNi0rGAeTprMJsr0Bnx84vDUwT8PuKxUNlCaj4PVKUiap+b63kHzPfO2DUQL82r1Vzs4gIXhLM0pZOP1rqAadX0jIUGU9snMUtZ+sRbL+LERApQZbFD9lJCAFPpPJ//jcuYTGfndYnbx2gUNC+cngYUBTSv2tLDtr6OknJ6amKWOx7aTyZfIB4LAbr0Ar3z+p1zlLpIyM+DH3otew+fYV1HG+d2R/j0DZeUXENjyQyJZI4d53SSzhl8YPcWxpKZmterHteOuCdNUtk8l23sZjKdK7rB82zoifDJ6y5m7+EzPPb8ae599AWGEinOX9dBLBwoxRCmswb7hhJcuL6Lp4+PMzpjWu8si7i1eGhWt5rK52PP4CjDiTSxcJBQoEBfR5i733Wpy8qvRgMBH1y2sYf/5/svll7yG7qjZc+6vaTOalvkLBR71x5rnrJiyj/+jV8SDfopABdvMJVCyyJoYt0bRVvQX4oVTySzvGvXBkamsmRyBgdOmX3K80aB2Vy+7Pj2eQMgHg3REwuRL+i6FlKVimdlmTeo7x1QK/ERzELtD36ototdXOCCcBZPFcxicfWrge1ALxS9sWfRWus/8eLYi11J1tuXtvI4FxfbOFrUCgS3yhANJdIABPw+8jmDbE6XlQyJhv2kMgZ+n+J4IoUG2sOBsoSfSqUuEvLzs8EzJatOvmBwciLDud0nuemKzcRjIRLJLFvXxkrxVPNdL3sogd3tZCEZkib9PVH6eyKE/L6yGNa7v/c8L7xstlzc1tcOCtI5g4F4lHWdbaV4w+FEmsNnZsjlDWayBboiQS7d0MXmNTFy+cKSdaupp/TSqYlZziTNuMxw0MevDPSUFBE3GB5PkUiaIQSJZI7nT02WemQH/b7Sy74yi15e7PVTTSk6MZHmlycmmMqYyVQnx2e54+H9ZHKFYr3VTYxOmwrl6LTZ6Scc8JXqtF65dQ0/GzzDqYlZ1nW1MZPO4/cpHnhymPPWdpQspZUL7XyhQCKVZfe23kVZABt9BziVlbOTLnoO5isSLy5wQTDxspPPduAhYAdzFUsLDXiiYC52JVlPX1rrBXzbtTvmlAGyj6NWIHg8FuJj157P3//0CP95bJxoyE8mb9bzc3L1bIxHsVswq2Wm25W9o2NJjp5JUtCwf3iS4cQsm9dEufXq88texvVYKAdHZsoy7e1WSnEPmTjFzP7w4Ag/HRwllyuQzpu9vHeft4b3XL6RB54c4guPvUQ46GM6neeF09Mkkll8CtpDfta2h/ng67fwradOLNm1rbf0kt+n6O+J8obta7hsYw87KxZYi6VajJ2VXW8V7a71DArz46wUmfHCVjJVWzHxzGqxOJvLlxSyjkiQ+584WlbwPt4eKiYSzfCFxw7yn8cmUUrx8mS5R+Y9l/eXXO0K2LGuk5xR4OYrtyzqXtb7DrDm8dlcobR4qVzEiXdGEBrHSwvmXwDnAX8I/BAY8/BYjixuJVm7L+1iJpxKl153NMSdb9/J7d98BqBUW84+/kqFpVrCj/VZWfmlaIjj/hS5bAFD6+JxzdJLlRmOTsqwXck9UswYPX9dxxwrpbiHzmK39t758H6ePzXNZDJHNOSnsy3A+1+7mTftWFey0AGlpAqjoAn6FQUN/oCPC87p4OIN3Vy8oXvJru181mh73deTE2l+NjjGoTNJdrpovQRnZf2mKzYxlEjy4JPDJWVnLJkRq5HLbOiO8or+Lkams/R1hLjz7ReXOvT0dYZpCwZKClnOKDA4kqI3FioreB8JmX3rjYIiGvKTLZbjsuoK7zl0hqFEioF4hI9dewFff3KIRDJXzFRvX/Q5zPcOqCw4v7YjXCqPZF/EiXdGEBrHSwXz9cAXtNaf9/AYnjFfX9rF1IqstMr0xsLc/b0DpHMGvbGgY4Zw5URp/7eTUmd/MUdDAe769nOcmcmQSOXm9DS3U62nteX6agv6S67drQ7FksU9VM7gyDR7BsfMOpdK0dfZxkA8wpVb15QVgT41MUs07Oej15h9taczeaJBPx98/daysAsvr6393s9njbbka8/gKF97fKik6Hnx4rVkKjGT5Y6H95PKGKztCFVVBoSFU7mgzOQ167vaSvVzK5MJoyE/htacns6QzuUZm8nMcW1bMZ5GQYOCT719J3//s8McGU2ChkjQTyJpuqXrbdXo1rnuGRzl1MQsfp8ikcyV6ntWHl+8M4LQOF4qmBngiIf79wx7ck81t1sjtSKdknDsE7WlhGTzBU5O+DgxkWoovq6aUmcpMHd95zky+QLrutr47G+8giNnZkpJSJXnXa2nteX6yuQKxVqgm0oZ7EItlPUfXZEA8ViITF7zuUcPlhXov+MhU3H6+58d5o+uu4jnT00uqFbfQnEqWF1ZqaCSSMjP7m1ry8rCuP3itSs8dzy0nyeOJAgFfEAHH732fEdlQFgYdhmwFpR+nyqF7FgWSWshfff3DjAybW7b3x3B7zPLSFW6tstkPGvK+G3X7uDERLos3MbutakW5+32uVqhFhuLSnAtWa+saSwIQm28VDD/HdgN/K2Hx3CdRlqS1VMr0lLw7PuDchf3UCJ91gGvSv+bM65qyUJWOzenydEeK5fKGNz7/RfI5AqlupyVcXX2LEp7T2ur17TEuTXGtr52Xr0lzqGRJH0dYfLF2qN2+dg3lCi9zE9NzPKZ7x6Yc48qLctuW3nsFnl71QK7zDrhZVhEmcIT9DGdyRMImO0pAz7zGamWgCRhGo1jl4FU1iAc9DGRynFudxuzOYN01ihdT/uiOBjwcWl/F3lDs2VtjG197WUdbyIhP3sPjzE6nSFWTE4cS2a4pL/L0WLptNhxe96xz4sbe6JlnaCcsLfKdJo7BUGYi5cK5h8AP1FKfRT4C6111sNjuUa9LcmsCWa+WpGVSttzJyb58p4jpLKmmw8Uo9OZoiUmwubeWCn2qNJdVTnhmq7159lz6Axo2L2tl7tuuKSqayca8pcF6Ve6MyuzKDd0R+YoD0tde3ElEPCZ/eI7IkECPkoWm95YeI4Vxeke9fdEufPh/Qwl0pzb3Uag6M5zM9mgTE7CteWkEq/CIsoUnoxBNOg3Q6EVPHdqis/+2wv0xyNzFoH1VH8QyjGrWhil6hJrO0LkC2aM+NPHJ7j30RfLFpmZXKG0DlYKfud1W0uZ18+emOTj3/wl0+k8HZEAl2zo5v87migmA/m5rL+nlKHtJDvzLXbclnd704BqSAymIDSOlwrmHiAGfBa4Ryl1EjAqttFa6/M8HEPD1NuSrFqHhkqFzK60dUSCfPEnh3h6aIJAQHFmOkwk5KczEmRTPDqnn7LdemO98E9NnC0VEg76GJ3OkM0V0MALL8/MyXavVng4HgvNsUqMJTNzsii3t3fIRLoIzCSeLLFwgKl0rqxGo5MVZddAnLu/d6AU+1sZQnHkTJKBeJSuSNDVF12tAtXNijerVAKuu+Rc/vNfniI5m2cqnSNnmM+E/Ro8d2KSH784ilHQVas/rHQateBWJrrcevX5AHzhsRfxK7N9ZDpnVNTjDfLqLb2cmkizMR4tJXfd9Z3neO7EFCcnZmkPBxhP5jj48lSxvJaPbF6TSGXLQkTs4xgcmWE2ly8puo0uduqlUcu7xGAKQuN4qWAOUZl6vQyoNvHUO8FUrsjtSpuhNZOpHAG/YjyZwzA07W1BYuHAnFX0HOtNsXyR3cKVyhh0tgVRCpIZg5HpWe7be2SOFbMyG90qNfSFx14qswo4FVwWFkel3NjLQjlZUUys9p2q9LP1T79P0RsLki/g+j2qVrWgWRbAymfx2ROTzGTy5Atmce9sXhMtyi2YCsrf/ewwk2kzKz8UMJsSrCYWUt2ivKpFrrQAMpPPNF3RIJGgv0zZSyRzfPgN55U1lbBChLoiQfw+hVHQ9MSC7DinkyePJcjlCwT8PmKhwBxl0bI8W96YV2/t4darz2dDd8SzxU4jlnepkCEIjeNlq8g3erVvr3GaeOqZYJwsB3alzerCA1DQKS44pwO/Uo7xP5XKh90tbk24azvC5AsG6zra0DrN9r4OEslczVW+VTbEKo1jn+hlEnWfWte0WhyvZfFMJLMMj6fY1tfO7vPWlNy+n3jrhZ7HwtqfgWbGNJY/i2eVRQVctL6TT7/zkrJFWSZXoCcaZDZX4NINXXOqP6x0FuLKdVo8V1q0K+eeeCzEA8WSQlZMon0/b965jqsvXFeqlmBZJh94crhqGaDjiRTZnFnG6OREhragb07mejPnpKVKQBKElUJTWkUuV2qteKtZDiqViHTWKLYLHGEqbcbR2ZVL+8u8Wvyj9flszuALj73Emo4wiXSWrFHgnK4ws7lCmeu7MkFkNmfQGQmW3Fv2iV7KDLmP/cW0f3gCe8vRypdWbyw8p4SVWdGgXKlsVna5k0VsqRTQtmCArmiAZKZAW0DxsWsvKLsO1mIOIBr28+kKS/5qYCGu3GqLIPtcUG3ugfKExpuuGKCypS5QClO4eEM3gyNmW9zKsljruyMcPpPE71Ol8JBWSNiqVrpNCq4LQm08VzCVUr8KXAusA+7VWh9USrUDu4BntNYTXo+hXhYzmdWyHNiViLMWgOCcTjpOL/Nq5Yes/VkvE7MrTD/3P3GczzxyoJTgAJR1DQLN6HSWY4kka2JmJyDBe6yC63sGx0DB7vPWlO7P4Mg09+09VrLsOFmrm/Uyq5TrwZGZsj7fS9nhZEN3hGgoSCaXpSMSYuva8kLcYoFf+DWod2HpNPfYE9Yqq2U4cf8TQwyPpzl6Jsnm3hj98Qi3XbuDgA82xaPE20N84q0XAXOVOXC/gkIt5pZu2yTJPoJQJ162ivQD9wO/genR0sA/AweBPGYbyc8D/8OrMTTCYrJP7X3F7TXdKnGKdbKXoJnNFRqavCpfJoMjM/ziyFhZe8u2oK+0T6uPeaGgGU/mWNMentelLrjD8HiKoUSa2ZxBztAcGp0p9ZE/PJrk+HiK8/s6yrrSWDFtRkFzeDTJ4Mg0l/R3L+m47YXgwwEfX95zmKl0vvTCX8rs2rFkhk3xKOs6wlX7sYsFfmmuQeXcU1kto5ocWNsdPDXFeCpHOmfGdO4bSpBI5uiMBMnkCuwbSrCus61icTPN/U8MLdmCq7IQuzkWLck+glAnXlow/xB4N2a5on8Dnre+0FrPKqX+FXgbLaJg1tN73KKayyQeC3Hr1durFuut7NrSGwvPyeC0sifr6bRjd7GazG1vaXeZbYxHyRcM9h5KkDUKDI+ned22ud14BPcx70OIJ4/m0WheOj3DZCrDyFSGSLH8zmzOYIutO1J/T5R4LFiyen55zxHee8UmwkHfkhW5j4T83HLVdm7/1jOMTGc4fXK2pAhbcrhUL1xJQmst7HNPZYkzqwyRHWshroCJdA6joJlK5wgHfI495td2hMsW7aCWbDFjzcvDiTRHx85aWrf1dax6K7kg1IuXCubNwH1a6z9TSvU6fP88poLZItTuPW5Ry2WSSGZpC/prJgDdctV2PvPIAVIZ011u75bj1KqsUpmtZWV1am/pZOU8NZEh6PeRNQrcfOUmmSSXgEjIz1t2nstPXhwDIOBTTM7aQhy29XLzlVvKQiYiIT83X7mF44k0Qb+Pnw+O8YPnR4iG/Lx++5o51QK8IJ01+MwjB3j+5BTBgA+tdZkivJRu6crEE3nJNwenUCKnEmeRkH/OQvzUxCwvnp4GzGego81sh2ol89hbjyaS2bKyXsCSLWYsS+uh0Rky+QJKUdbCd7VbyQWhHrxUMDcD99b4fgLo8fD4c6gVYzlf73ELyyVoFDRHRpPM5ox5J73Sang8TSqTpy3opy3oLyqWquz37ZapSmX23b/SX9PKGgn5S50xKl/A1oS4ra+dLWtjjExlSivy+a5TKwTaL0cqr9uVW3tZ0x5iIpUjGvJz2cZurtzay97DZ1jX0TYnMQLO3q8XXp7mzEyGgoaZTJ5Do8k5ZV68uEfD4ylSWYNQ0EcmV2DHOR185OrzS3UPrWzapXrhWuW0JNGiOdhbLIYDPj74+q1cvKGrZF0+NTFLNOTHpxQfffBpUhmD9d1tpYV4OmcWdPcrRV5r2oJBzivG0kZCZ1uPWvvZ0B0pC4NwYzFTz7PS3xMlXOwaFSmej1NIhiAI1fFSwZwG4jW+3waMenj8MuZLRrArZ/NNPPFYqFSv7YEnh/jEWy/ixEQKpxaPYL6kh8fT/OexcbJ5g3DAz65NPfT3RNjW117VKlMZ33Z6erZkWC0UNEOJ5BylZL4XsGUFstpLznedbrt2R9MTTZYjTvIWbw9x3wdeze3fega0Wch6Np/n8UMJ/D7FK/q7uftdl5a9xKz79U+PH+W5E5NkDU1BQ9B31qrjZaKNpTgYWnP0TBKfUnzzqWG2rm3n7u8935SOOdJVpXlYlr0XiuW09h0f53Xb1vCB3VtN78x3DzCdzvP+rzxhtpsM+ACYzeUJB31k8z5i4QAo8CvFus4wJybSJZmPhIp9yx/eX/LyVM5fi7nXjTwr0XCg2Pce1naYiUxSnkgQ6sfn4b5/BrxPKTVH61JK9QAfAP7Dw+OX4fRSqsSavOwWxJdOT5POGmXb3HzlJjb2RDl/nVl38sREivufGOILj73IXd95rmx7MF/SCsgZBfw+H2G/4g3nrymW9DirFH7u0YPc872DpX1Y8W1guoau3LqG3dt62bwmRiTk58Enhx2PV8+53v/EMb7w2Etzfn9wZJrDo0mMgmZkKsO+ocS8+xLmMjgywxHbdbSuWyqbJ+T3EQr4OHomyc9eGmMinWNkOsPjh8e44+H9c+5nJOTnXbs2ck53GwG/oi3gY11XW+n7ytZ6ewZH5+xjoVgK7m++aoBzOttKyQ57D4+x59CZUuyyuWBZGiqfC4nFXBqsGMpwwFeSr0zW4GcvneEzjxwohf5kjQKprJm8k82bHcceeHKYVMagoy3AP/7WFbxqUxy/X/HypNkcwi6vVitKe/cet3CaG53m+eHxFFPpPBet72QgHuU9l2+cMz8LglAbLxXMzwDbgR8C1xc/e4VS6kPAPsw2kvd4ePwyGn0pWStdpwllW18HW9fG8PtU1eBzO5GQn7vfdSnndLbRHvLTFQvx3MnpMgXPaeKzXu63v3VHyQJ21w2X8OE3bGVTPFZ1Ap7vXKspoOmswX17j3F8PMWLp6eJx4LsGojLy7xBzOt4hKFEihdHponHQmWJO9b1jMdChAO+Ut+eYMBsCer0Qo23h7j3Ny7jwvWdvGpLnGQmX9rO2qdR0KUECbdfgj964TQnJtKl8+nrCNUVs+wFlc+FWJO8x5oPv/DYi0TDAS7b2IkPSOcLzGQMU3azBtGwn0jQT3c0yAXndHDFlji/87qtJJJZ/D5FJl+goDW3vGkbm+Kx0iLdLvNeLiAq922VV6qc563t/D7F1rWxsmocstAWhPrwspPPk0qpdwN/D3yl+PHnMf3II8Cva60PeHX8ShrtxDNfXUu7W/vERGre7O8NPREe/r3XsW8oQXckxN/8+FDZvqtl4zp1VLlwfZfZMjJrsLYjPKen+HznWu1YVt/s8/s6mM0Z3HzllpbqpLFcMK9jjvPXdZDOGaVEKuv+2etc3v295zk8OsPpqQxb1sRY391W9YW6c0MXO8/tdJSRygSJxbqOK5+FyvPZ0B3lkv4uEjNZNq+JLnnHHMvq7ySXEjPsPvb5cCqd48bLN5HMFMjlC5yYNMtvbe6N8c5XnMvEbI7LNvaQyuZrJudsLcaCV86ZbiWOOcmBU3mlSuu/1fjCvl21cxAEoTqeFlrXWj+ilNoMXANciKlcvgT8u9Z6yZeAjXTiue3aHTUnlMpYR6fC6ZXE20NcfdE5c4oU15ONa9XpPDqW5MxMhv7uKNGQn3zBmNNT3OlcKyfb+fqtb1kbY1tfu+O+hNrYr+PWtTG29XWUCq1bSWRWBvhdN1xc1o6v1gu1lozYEyQW+xKs9SxsXRtjQ7cZzpHJFVjbEeYTb71oSRW5dNaYU5zekv2lLPy+0qnstGOfsy7b2E28PcQzw5P4/YrOtiCz+Ty3FeOLd2/rLaty4CS3teY7L2Mt7fu2l46zrP9W68vKMchCWxAaw/NOPlrrDPCd4p+WpdJiOZbMFJNhzLZm8/2OvXD6fFiB7PuGEuwaiNcVwG7FuqUyBumcwZpYmJxRYCIN7eFATYtVtcl2If3Whflxuo77hyfYMzhGNl/geCJVKppuvw/1ZKjWkhG37p/Ts2DJ64Xru9g3lCgVn87kC0uaXWvJcmVxekv2JQHIHZzmDLvX5nOPHiQxk8UoaC44p4NkJs/otFHqJT6USDt2M7Pj5cK1XjloxPq/kPEmZrKleV4y0IXVhvQiL1LNbWzvHGG5Nq2X90KLTKezBnd/70DRmvVyzXqGZ7v85EGD36fw+xQa2BiPArpm9yBoLOtWrJXuMPc6qrPrFFX6X1XqcfNWcwEu9v5VyrWlUFhWnnO7IpycTLOxmGFeTe68cFVbslyrOL24MhdPtTnD3mEqFg4Q8Cly+UKxiUOBM8kMaEq9xMG7kIVa+3VqalENN63/dhIzWW7825+Xis8/+KHXipIprCo8VTCVUjcBv4eZ7ONUbF1rrVtCyXWy/lgTKZjxOXc8tJ9MvlBmBVyIxWhwZNrRmgXVuwR1RoJcdG4nk6kc/fEIH9i9peR6rbSEViIv3eazra+d3eetKZX1scIPnKjHzWuvRxgN+fn0Oy9x7eVVLU5tNmcwmTJbjG7sifK+1wyU4tUWcg61cFIeKluyVitOL1b4+qhHQXOaM+zfWfdgQ3eEExNpbrpiE222TlNeyWk9Zeduu3YHdzzkXO6oEi/kZt9QgslUDoDJVI59QwmuvuicRe9XEJYLXvYivwP4Y+A08HNg3KtjuUWl9cc+kUbDflIZo8yFUi3JYH6crVnVugQZBc0vjoyxoSvC2o4wn7zOzChPZ41SjUp73JDTeTU6eUqiRGPMd73s8ZbzXdN6LM5WPcIXR6bJ5grc8fB+7r3xskXfK/t5zI1T03RFg0SCftZ3t1VVLus9h1pjqFQeoP6WrGKFn596FLRa8b6VCTDV9rVQOZ3veapHvk5MpEvd1eqRQbflZtdAnK5osGTB3DVQqyy0IKw8vLQe/i7wI+AtWuuch8fxjMpscXvBcau8xUIsNNWsWZWTJmj6OsMcGU2Chlg4UBbz5pXrWxIlGqPe61XvPajH4tzfYyZ5ZXMFQrbyRl4kRlQ+B/MlI9V7DtWoVkbrbLxz9ZasQn3UM3fMF+9rfWf39FTuayFyWs/zNJ982UuFoWD3eWuW3HMTbw/x4IdeKzGYwqrFSwWzE3hguSqXFvaJtFp5i0YtNNWsWZWT5ra+jlLXnfv2HpkTa+mV61sSJRrD7etVj8U5UnQ3Wh1PasVC1st8pbkaTUZaqMuxmlxLmId7uDl31NrXQuS0XuW3lnxVKxW21FiVQwRhNeKlgvkUsNHD/S85TuUtFjpBN5LFfUl/l2MbS6/izSRmszG8uF71WDvj7SHuvfEy1+6/2+exUJdjNbmW2Er3cHPumG9fjcppvXJYS76cSoUJgrC0KK296cChlHoD8E3gGq31U4vYz0RXV1fXxMSEe4NbJFZ8UL3uwmazkHjKJYjBrJ1G3YLUksV6r1erx7bWGl+rj32RLCt5bLV50W3ZcGN/lftYRvIrsugim29/pNlDmJej91zX7CFUY1Gy6GUnnx8rpX4beFwp9ThwFKjsXae11r/t1Ri8YLnFJy50vJIo0Rj1XK/lIDvVzmM5jF1oDl7Ihhvzj30fIr+CsPR41otcKfVq4KtAEHg98L8B73f4s6yoloDgNumswUunpx37Sdf6rpKlGq8wP8v5XrTq2BMzWR478DKJmWyzh7JqsWTDKGgOjyaLzSlMGpmrvKRV5VcQVjJexmD+GZAFbgB+qrVuTft5gyxFfGKt1XajK3GJp2wdlvO9aMWxSyHr1qC/J0o8FmTP4BgouG/vMe66wbQctorVsBXlVxBWOl4qmJcCn9Jaf9vDYyw5S1HIuVYWZaMZy1J4unVYzveiFccuhaxbg0jIz81XbuF4Ik1b0E8imZ1TWqrZ1ShaUX6FhbMc4iobod7zaeFYTUc8c5EDI5gWzBWHFdvj1SRlrbaBql00nL5r1niF+lnO96LVxm4VsgakkHWT2dbXzpa1Mfw+VZqXFjJXeUmrya8grHS8zCK/E3gH8BqtdX4R+2npDDWvaIVsXo+Ps6wyJcE9WVxG2awtT2Im61Yh62Ulj604L1Zr77lQWV/Fz4nI4jysNAtmvTTBgtmaWeTAz4DrMbPI/xo4wtwscrTWP/FwDMuWertoeIVkXXqDXFd3kULWrUO12r4LmavkORGE5Y+XCuZjtn//PVBpKlXFz2TWaEGkm483yHUVhPmR50QQlj9eKpi/5eG+l5TV6KqRrEtvWGnXdTU+G4L3LOQ5EVkUhNbCy0LrX/Vq30vJanXVREJ+brt2Rym+bTWcs5tUe9mtpGzW1fpsCCZeKnSNPicii4LQenhpwVwRrFZXTTpr8LlHDzIyleEHB0dkwm6A+V52K6VL0mp9NoSlUegaeU5EFgWh9XBNwVRK/SqcTdqxfp6PVk/yWWkuzXqRCXvhrJZrt1qfDaH1ZFxkURBaDzctmD8CtFIqorXOWj/X2H5ZJPmsJJdmI8iEvXBWy7Vbrc+G0HoyLrIoCK2HmwrmBzAVxlzx5xWT5LNSXJqNIBP2wllN1241PhtCa8q4yKIgtBauKZha63+s+HlFJPmsZmTCXjhy7YSVjsi4IAi18LJVpCAIgiAIgrAK8SyLXCn1WuA64HygE5gCXgAe0Vrv9eq4giAIgiAIQnNxXcFUSnUC/wy8Bec+lp9QSj0CvFdrPe328QVBEARBEITmCX3zwwAACtZJREFU4oUF8xvA1Zi9yP8BeAbTetkJXAp8ELNH+deBt3lwfEEQBEEQBKGJuKpgKqXejKlc3qu1vs1hk6eAryqlPg98RCl1jdb6+26OQRAEQRAEQWgubif5/FfgGPDxebb7ODAE3OTy8QVBEARBEIQm47aC+UrgIa11rQLraK0LwEPA5S4fXxAEQRAEQWgybiuYGzAzxevhBaDf5eMLgiAIgiAITcZtBbMTqDczfBpod/n4LU06a/DS6WnSWaPZQxGEBSNyLAjlyDMhCHNxO4vcR+3+407brwrSWYO7vvNcqXfvndfvbIn2aoLQCCLHglCOPBOC4IwXZYreppQ6p47tXunBsVuW4fEUI1MZAEamMgyPp6TNmrDsEDkWhHLkmRAEZ7xQMG+i/uzwRqydy5r+nih9neHSKre/J9rsIQlCw4gcC0I58ky4x+bbH2n2EFqaRq7P0Xuu83Ak9eG2gnmVy/tbMURCfu68fifD4yn6e6LiQhGWJSLHglCOPBOC4IyrCqbW+sdu7m+lEQn5xXUiLHtEjgWhHHkmBGEuXrjIBUEQBEEQhCZRrzvdS1f6qsniFgRBEARBEJYGsWAKgiAIguAZkryzOlHzdHVsOkqpAqC6urqaPRTBRSYnJ4e01puaPY5GEFlcuSw3eRRZXLmsRFns/vA/Ld2AhIaY+OJ7q363WFlcDgpmHtOVP9XssQiuMrmcJlEQWVzhLCt5FFlc0YgsCq3ComSx5RVMQRAEQRAEYXkhST6CIAiCIAiCq4iCKQiCIAiCILjKqlEwlVLvV0pppdQbmz0Wt1FKHVVK/ajZ4xDqQ2RRaBVEFoVWQuRxZbHsFEyl1BuLAmj9MZRS40qpZ5VSX1VKvUUppZo9ztWGUuoCpdTnlVI/VEpNFO/Np5o9Li8RWWxNlFLvUEp9RSl1UCmVVEqdVEo9ppR6S7PH5hUii62JUup/V0r9u1JqWCk1q5QaVUrtLSpSK7anpMjj8kAp9VbbPbrc9f0vtySf4srmP4B/Br4LKKADuAB4JzAAPAbcqLWesP2eHwgCWa11YYmH7SlKqTCgtdbZJo7h/cCXgUPAEPAm4I+11p9q1pi8RmRxLi0iiy9jZrQ+DLwAxIHfAnYAd2itP9OssXmFyOJcWkQW/wxYB/wSGAHageuAa4Ava61/u1lj8xKRx7m0gjzaUUrFgOeAXky5fJXW+klXD6K1XlZ/gDcCGviYw3d+4N7i999r9lhX0x/Ml3h38d+XF+/Bp5o9Lo/PWWSxBf8Ab3L4LIqpbGaBnmaP0YNzFllcRn+AR4ACcE6zx+LR+Yk8tvgf4H8Cw7Z7cbnbx1h2LvJaaK0NrfVHgZ8Bb1FKvc76zim2w/bZryml7lRKHVNKpZVSv1BKvaa4zRuUUj8rutpOKaU+6XRspdTlSql/VUqdUUpllFIvKKX+SCkVqNjuR8VYjHOVUv9cdBukim6U8yu2bVNKfaq4r1TR9bxfKfW5iu0cYzuUUu9USu0pjn2m+O8bHLY7WhzXDqXUI0qpaaXUpFLqG0qpc+q89gltW4mudkQW54xpKWXxhw6fpYDvYFpHLqhnPysFkcU5Y1oyWazBMUyr3qqrlC/yOGdMSy6PynSH/z5wKzDdyO82wopSMG38Q/Hveru434Nptv8z4I+BrcCjSql3At8Cfgp8DDgI3KWUep/9l5VS1wF7gPMxVwP/F7AXuAvTRVBJDPgJYAD/N/CXmCu+h1V5XM5fAf8deBz4CPBHwA8w3c81UUr9LvCvmJbFu4A/Kf77IaXU/+HwKxuAH2G6t28D7gfeBdw337GEmogsto4s9hf/Pr3I/SxXRBabJItKqS6l1Bql1Hal1C3AB4AXgcFG9rPCEHlsgjwWlem/Ax7VWn+j3t9bEM020y7ArPtGqpjebdvsKm7zTdtn7y9+9kaHz/YBIdvn7yh+nsNmNgZCwClgr+2zNuBlTEEMVIzjIw7H/FHxs49XbHtb8fM32z5LAN+t45ocBX5k+7kHmMGcvDptn3dixkhOU3Rn235fA++p2O9fFT+/oMF7tOpd5CKLrSGLtt9/RfEa/qTZciOyuPpkEXiy+Dsa0zX+KLC12XIj8rj65BH4QyAFbCn+/CnERd4QVsuqzjq3/xtdHnj70+Lfv9C2oNfiNk8A223bXoMZxP0VoLu4Sl2jlFqDGdwMcG3F8QrAn1d8Zrn17PueBHYqpS6u8zzsY4oBf661LrXvKv77zzEDeq+u+J2TWusH6hiT0Bgii02WRaXUWkwLRxr4YKO/v4IQWWyeLP5u8fg3Aw9ghmr0NPD7KxGRxyWWR6XUeZjW1j/RWh9pcLwNE5h/k2WJJbD19kY9bP9Baz2uzAoKTjdgHDPryuLC4t9frrH/dRU/n9Raz1Z8Nlb8277vW4H/BexXSh3GzMr7NvBtXTvDbkvx7+ccvrM+21rx+eHKDauMSWgMkUWTpsiiUioOfB84F7hOa/1iI7+/whBZNFlyWdRaP2H78X8ppe4GfqKUulRrfaje/awwRB5NllIev4h5vT5fx7aLZqUqmJcW/36hzu2NBj+3Y9Xyug14uso2JxvYb6k2mNb6YaXUZuBtwBswVzO/DfxUKXW1drfcQV1jEhpGZLFxXJHFonL5GGZ5ohu0Q/LPKkNksXG8mhe/CtyO6f51TEhZBYg8Ns6C5VEp9evFsX0A2KTOliGNF//uV0pNAIfnUYzrZqUqmFZtsUeW4FgvFf9Oaq0fc3vnWusE8DXga8qUiHuAjwM3AP9/e3fsGkUUxHH8N2gTSPAiCGKTxirponYSEP+CKBYGEa1E8Fq1ECxtRKwsLMRKC4uEU9AQECwUrUTRQrAMBERQi6R0LOblXI7cJheez9vj+4Fl7y6XfRNu2Jvszr590ufXNv/LmVE0G1dN97wH/xa5GIrmYqW4nJY07+7LucdoIHIxDMN+cSyt99e+a7SRj6FUPk6ldb+juItpfUDS9xwDjlQPppntMbPbko4rmm5fFxh2WTGB7vX0pdYb05iZTQy60fS3tKqveXTkvk9P63ZMK5LWJbWrY6fHbUVj8cqgMWHnyMWu4rloZpNpmzOSTrv785zbbxpysatoLprZXjPrd9qyndZvc43XFORjV+l94zNJZ7ZYNgvga+n5TlsWttXkI5izlWkIqncImFJcobdQIgh3Xzez85KWJH0xsweKq8JailNzpyTNK65KG8SEpDUz6yiS9ZuiZ+Oyor/kaU1MP83squLqsndm9jD96IKkw5IuufuvAeOpZWb79HeneSit58zsRnrccfePOcccIuRi/5iK56JipzyrmHpksne6Eklv3H1Uj+CTi/1jKp2L45JWzWxR0ifF9FgHFZ/HUcVRq0cZxxtG5GP/mIrmo7t/1RbTYlUuTnrpme/k0+QC82xafisq/VVJryQ9dvcXJQNx92UzO6boqTmnOMT8QzHVwB1JuymsNiTdlXRS0Tcxrph6oSPplrv39ov0xnTPzNYUPSc308sfFKcLl3YRz3YmFXN4VZ1IixSfz6gWmORifUylc/FIWm9+Lr0uanRbRMjF+phK5uKGoniYU1yh3FJMPfNZ0hVJ9919J/2DTUY+1sdUet9YVOPuRQ4AAIDhNlI9mAAAAPj/KDABAACQFQUmAAAAsqLABAAAQFYUmAAAAMiKAhMAAABZUWACAAAgKwpMAAAAZEWBCQAAgKwoMAEAAJDVH6zfsMmki8bwAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "P2 = np.array([[0.8, 0.2, 0.2, 0.5],\n",
+ " [0, 0.9, 0.3, 0.2],\n",
+ " [0, 0, 0.5, 0.2],\n",
+ " [0, 0, 0, 0.5]])\n",
+ "\n",
+ "P2 = symmetrize(P2)\n",
+ "A2 = sbm(csize, P2)\n",
+ "heatmap(A2, title='4-block SBM adjacency matrix A2')\n",
+ "X2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n",
+ "pairplot(X2, title='4-block adjacency spectral embedding A2')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot of Null Distribution\n",
+ "\n",
+ "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0. Thus, we reject the null hypothesis that the two graphs come from the same generating distributions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ldt = LatentDistributionTest()\n",
+ "p = ldt.fit(A, A2)\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(10, 6))\n",
+ "ax.hist(ldt.null_distribution_, 50)\n",
+ "ax.axvline(ldt.sample_T_statistic_, color='r')\n",
+ "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n",
+ "plt.show();"
+ ]
+ }
+ ],
+ "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": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/inference/latent_position_test.ipynb b/docs/tutorials/inference/latent_position_test.ipynb
new file mode 100644
index 000000000..4232d1391
--- /dev/null
+++ b/docs/tutorials/inference/latent_position_test.ipynb
@@ -0,0 +1,480 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Latent Position Two-Graph Testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "np.random.seed(88889999)\n",
+ "\n",
+ "from graspy.inference import LatentPositionTest\n",
+ "from graspy.embed import AdjacencySpectralEmbed\n",
+ "from graspy.simulations import sbm, rdpg\n",
+ "from graspy.utils import symmetrize\n",
+ "from graspy.plot import heatmap, pairplot\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Generate a stochastic block model graph to model as a random dot product graph\n",
+ "To start, we generate a binary stochastic block model graph (SBM). An SBM is composed of 'communities' or 'blocks,' where a node's block membership in a graph determines its probability of connection to the other nodes in the graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x127829400>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x127a0c7b8>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n_components = 4 # the number of embedding dimensions for ASE\n",
+ "P = np.array([[0.9, 0.11, 0.13, 0.2],\n",
+ " [0, 0.7, 0.1, 0.1], \n",
+ " [0, 0, 0.8, 0.1],\n",
+ " [0, 0, 0, 0.85]])\n",
+ "\n",
+ "P = symmetrize(P)\n",
+ "csize = [50] * 4\n",
+ "A = sbm(csize, P)\n",
+ "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n",
+ "heatmap(A, title='4-block SBM adjacency matrix')\n",
+ "pairplot(X, title='4-block adjacency spectral embedding')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. ASE(A) recovers an estimate of the **latent positions** of $A $. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n",
+ "\n",
+ "For an adjacency matrix $A \\in \\mathbb{R}^{n x n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n x n}$\n",
+ "\n",
+ "$P = XX^T$, where $X \\in \\mathbb{R}^{n x d} $ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n",
+ "\n",
+ "ASE is one way to obtain an estimate of the latent positions of a graph, $\\hat{X}$\n",
+ "\n",
+ "In the above embedding, we see 4 clusters of nodes corresponding to the 4 blocks that we prescribed. ASE recovers the fact that all of the nodes in a block have similar latent positions. So, RDPGs can also model an SBM graph."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sample new RDPGs from this latent position\n",
+ "Given the estimate of X, we now sample two new RDPGs from the same latent position above"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x12c535390>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x12c6287b8>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x12bf77a20>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x1279c46d8>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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aVLuUl6ERJlmXfzKqG/9NqB7k3wXwaHSHDC9DK8ywPn4QOkIXcXcY5UGNY/vUx56GSpX4Iypz0/kA9uxo+17jqI8/BFX00DJUDq7Xogrn/QCAB3ec489R3USvrOfn0e2x9czpZMwvmPL57vXnR7fmyjT+3YpqIXESqpDudaa0ZQB8x+H7XgXVA/qk+tzuqL/L8+rz3N6hLdPzz2aeVke1I/5DPde/Q+XLsytaIeV1+U1QLXD+Wv/7NqpF1r2usynHtka10Li+PuelqJzIj0G1RmmP7xGoHI7/Ul8zy1EtTB7TKvfoutzldbkr6+vn39EIKe65Npd0zRmq0Oh3oFqYrUT1e/sFgP/X0YbU82cAPMnhO+zse2C8k+t7l8ax9VE59C6vx3o+gH/tKluXPxCVae+OZv/oCWvu+l5ROTffhSqfSns+vlO3v6PtfPAf/2n+E2PUTZ6kUGoF4GgATzHGLM07GjIWRORPAH5rjOnymZhJRORCVGnpXcOECSERoA2SEDKV2sdofVSms7lARJ6Kygn8M7nHQgipoM8KIaST2i/nH1GZUH4wUHz01IuUzVD5XV0NLlYIKQYuVggh0zgU1T3ivajS9s86/wFgJ1QRey81CR1rCSH90GeFEEIIIUUTrKysXLHCAMDrV99yqKgTh99aZYSftDv5f9u+2vX7yvS1O20cNu1qjbOvrM05DLVHSC66ft8l0Pd71xrrtHMvdU5CiXVe2u3atrfa4sWpcynNNXSwJYQQQkjRBJuB9pcl1bvlPVSKPlzK9tXvG49LX6HjCSFW37l3brO6gwwhZE66dv997Q315aI0dpUP/V5t2hvLNWQzTpf5czlvG1Uo5TzGVlhc2/YdD5WVtFBZIYQQQkjRZFVWXP1QhrDZ5fWNq28sPuqGryKSqq/Sd6OhaO8+iR1j8U3Q6ivWdZbDT6ZJiLqnpQq163ThOzdDzwj6rJQFlRVCGnChQsjsk8OcT8LgYoUQQgghRaNmBmqjbeLxJZaJZxYoXUWIaZrJ4ZgZIn/HNGVNC9MNlddTmmsmlObUq+1YG9qXBrNmMqWD7TigskIIIYSQoommrDQpVbnwSahW6rn4oO08SMZFrKRkY75mYjnWpmhHo89QR1uXUPkc11lfcAeVlbKhskIIIYSQoik2KZwW2uHSsdvrK9NFSJ8uxAgdDCHFbjQkDNM1QVsOxpruPYXPhEu6/VkKqR7zdx56XbjOAZWVtFBZIYQQQkjRJPFZaTNLfh999NltXer3RQuEtFfqLiolpftnlPJdxUr2RcJwTb45rX7O79U1UWeqaCcmhSsLKiuEEEIIKRouVgghhBBSNOpmIBcTT8zEcT7jsHEinQXTVekyfUzZmYmz/Ejxplyf9mMl2nMJwdUiRQK5PsdhjfZ8y2jjm0xxWp2u+/4RZhnNQAmhskIIIYSQosniYNtFTuUipG9XdagkhWYWdvw5mDXVJAfau22XUONpdW3Hk9OReJYcwn3HEpJcTlMxo4NtWqisEEIIIaRoilFWJsRSHvradfks9vhitG3TZ+mUFBar9cLAWHW0oILkhvZ89SUWdOnHNTR4qO3QBHmhCfemzUFMNa0LKitpobJCCCGEkKIJVlZWrlhxjwa0VAFfJcSlvVT+IzlUExti+QukYIwRQ6G7zgla6d5DzjO2z8msEqrK2dR18cso4VUBoan0U75WoDkORgOlhcoKIQ1KWkwSQgipKM5npY2WKuGSu6AUYis/KXNIdBF7Z+9i7/bt36adnLvZaWNI2Y6vn1is8cRsZ6j9JrH8WmL3mZtSfL/os5KW0SgrpS4oyPzBazEeuR2JCSFlMprFCiGEEELmEzUH2xxOqtpOuF19tfu0aV871b8NIU7HzXqzIBPPOi5mJZvPJozF/KLlbJkCn2R1Wk7VsV4ZUIqzq9a4bH5PXcdoBkoLlRVCCCGEDCIiG4rIB0XkJyJyk4gYEdnFof6WIvJ9EblZRK4Tkc+LyPpWdUt1sPVRLkoLEdZ2kI3VHpkNfENAU6okQ2pOKQ63oZTiBOqKVgI5befelAkKbb+HeVRW6oXJTwBcAuAqAE8A8BRjzFKLug8CcDaAGwB8AsCaAN4EYBmAHY0xd/TVXyVg3IQQQgiZH34FYH1jzLUi8hwAJzjUfTuAxQC2NcZcDgAichaAkwHsBeBzfZWLVVbauPqskOmE2rBLSAEfK2Fbs+0QvwDXnWCqOdXaocbaOedWRHIQkkLfNxFgG5+U9a59+iR8c2k31NfHdU7nUVlp0lis2CorVwL4kTFmz9bx3wK4zBjzjL76VFYIaTCPD0tCyHwhIjcMlTHGrKPY38YAHgDglx0fnwVgt8E2xqKs9OGrsNjUK129maYCuEQijZkcO0GiR6lp+3Mm7usiZ3RNaNTZUJnQ33BKtbVJamVF81n7X/jDjUNlhhYrLsqKiGwP4BcA9jTGfLn12aEA3gxgFWPMndPaoLJCSINSF6WEEKKFpmpiyeL6720dn61slLl5WgMMXSaEEEJITFbUfxd1fLZaq0wn6magFGYTm9DlCVomnhzmoBzvBrIhtuzqm+gqVp8lkCN0s0np8xOCb8h3+7PcTt8+TvLT2rdtx6fvWNjMl6Zjcuq3LmuagTTG7mgG2hjAnwG80Rjz0dZnxwHYzRizQV8bNAMRQgghhbNwxLFHxpjLReRqANt3fLwDgHOG2lBLtz8hNN17SFmXVMq+pHDKHdq59e0gfNqPiXbK7zGrOj6p17XJ7cw45GQ5ZgUnx/fpSsowaQ1yqK22faR2sH3VAj1l5VN3xVVWRGQzADDGXNo49mlU+VQe0cizsiuAUwD8qzHms339UVkhhBBCiBUi8s76Pycr371EZCcANxhjPlkf+1H9d0mj6sEAXgjgJyIyyWD7ZgDnAjh2sF8tnxWXl0Dl9Ptw9WvJgYZaYqO+aKkdvqTa5cW0w4fMqauvRBvtENB2OymujxJCjX2/B5dkZC7j8FXeXO6zIcpW7oSHGmkZfJNANkmtrLx24UPVlJWP33mZ99hFZNo4/mCMWVKXWQYAk/9v1H0UgI8C2AnA7QC+A+ANxpirh/qlskIIIYQQK4wxgwud9iKlcfxCALv79BstGqiJlj+FTztaqk7fLjY1NnPsWqZEn4FSfQByRObk9jPy2XmX9J2F4hsN5KOWhM5/15hj9alxXYRGY9n0FeP6nVdlJRejUVZKM9UQQgghqRhzNJAGTApHCCGEkKJJ+m6gEMfRUPNNn8wZC61Q42ntarbjg4+D7Bj6ciG2yWMWTSpNUjrzalOqs3FKXNJI2LQR2/FX07SV2gx0wCp6ZqDD/kYzECGEEEKUWSijW1+oopYUTjtJmotDq28yOBfnK5dwOR+1I6Xi09dnCbu6UpxKp32WInQzdAcYUj9W4jdfSro2Q9EOk+67b6RQTm0cWX3VEds6Nu35jG/IQTm1svKm+2yqpqx8+I7fj27lQ58VQgghhBSNerr9PlxWzTkSx+Xou2sM0+ZJa1yzsEMtjVna/U9IEaJdqi9SG+2Ee77tzMJ1ppXYMfdcpFZW3rKqnrJyyO1UVgghhBBCVImWFG4MeVF8UpynJLbiU/ruTCuNuRY5fDm009E3Gdqh+vrmuNRJSe6d+BCxxqcVbdPXto3aYaMY+ySFi3WfGBoXlZW0MBqIEEIIKRxGAyXMsxJKLN8NFw97bVKck815ulD6DnWWKPWVA1popZYndqRSv0pTRW1wve5SKytvX7SZ2rP24NsuHd3KZzQ+K6Vf6ITEZF6u/1JMsISQsqAZiBBCCCmceX83UPBiJZVpJuUYXJ25SqJrfD5j7pOLtZ30YlNK2KNPn76J6DTGlcIEleP7SNGXZlr30D5T3rNCEsDZ1otFqfcvUjEaMxAhhBBC5hM1B1uf1by2I2uMxGo5dymxw8Bj7uhKV1smhKbSH2qv67Ou9kPmy2WHWprDbugrAzTH0EdKxaerT63rw+WeEitE2Cc5aIrQaldSO9getPhhag62B624ZHRGJSorhBBCCCmapC8ynGBTNlRNmLaS1m7Xt8y0Os16TLNfBmNRici4iPUiyb52fPxatBVYl3Zd7rMx1bmudqispIXRQIQQQkjhzLsZZK6TwoVQ0lh8SfEiMa2XloW0p1XfV03TePGdza5Ra3es9b36RoZo+wqFjMf3up2Q49UKJB7N7/gIsyypOvEeRWXlP0aorIxmscYf8PzB73z+4HdOCOmCZiBCCCGkcPhuoIRmIBupfGxmlTG9R2iWEnDlCHktLYlVHzmSksVu12cMKccxJjNQSEhvLFOna/82SQxjmvNSO9i+f/XN1cxA77j1d6Nb+YzGDEQIIYSQ+WRUDrbalPCqgHbd0Pq+4dLtvm125H3taBOyQ7IJC8/tsDtUP4YDqtaclkCq7yFFO6HkcA7OiY1SH+NcUisrH1xDT1l56y1UVgghhBBCVEmSFE4DV+Uhlg9MyqRwNu1O68ulvVBy7LZjhsXGStHtm4pfu6+hMinOVwsX/wXbuqFjaVKCwtJFSapJKYqUK1RW0sJoIEIajM3BmxAyHzAaSNlnJZbyoFV/TNE7sUkZSVMa83KePoxJWcnRVwilqwgpxhdL0Uqt4KVWVj605sPVlJU333zx6FY+VFYIIYSQwlk4uuWFLnSwJYQQQkjRZAldTmEqKglf01PsOfANYx2j5D4hJEQ4hwNlKXOt5aQ95EhcupkkBTkSHrbbD+3D5f1epXznrk7uqc1AH72vnhnoDTfRDEQIIYQQZehgW0hSuFRKSkoH277+S1CMxqiixNp1aodCa+9Qczi7pjxP1/FM67N0p16XlPApEwGGXv82qRNyJjN0qWvbfmpl5WNrPUJNWXndX387upUPfVYIIYQQUjTFKCvzRkjiuFBVJodKUppCUxqp5kcrWZrmDnWecPHlcG1vWrvziEtiR19V6QizLKk68cm19ZSVV99IZYUQQgghRJWk6fZzJoPz6adJ7Igcl/lrEjKuFOnjY/uP+NYrNSJBi5L8BErB5jvXfu1BTmxe8Dftc9t2Q/ze+q4zl/a1+7TtO7XPyrwrK6OJBhrzTYOMB15n8we/czIGGA1UmM9KymgZmzwa0+q4RCa4jidnlJILKfwWQuz42sqAq7+BxthdfRJKSmUeI5rFp0wIY1LcfFSJ0Ot2FhQ3X1IrK59eZwu1Z+0rb7hodCsf+qwUwjz+2IfQWrRpL/5cpfJUaPep1d4sXNuzcA42lJBSgZAuRmMGIoQQQuaVeX83UJa3Lqcg1CSTk5yOxGNmXiXpUs7bx1GxBIfW3KYemzmIPU8+CeSan9k420+r0/dZKSbPrrGmDl0+cl09M9ArrqcZiBBCCCFElWJfZBhStolW0rUQpcYmTM51PBp95t6JT9ByDHTpS9vhM5a6UWoYcWnjKmFOfIn1+/RRO0L7smkvNG1A7N9a11i6+kztYHvUenrKysuvo7JCCCGEEKKKmrJiowKU4s/SpnT/lljzNmvhiqnCbHP7OLRxsf1rjz1FgrDSfV76iO1XEXP+h9poYuPXYjuWXLiOh8pKWhgNREiDUm6chBDShEnhRpRu36WdHBE1Ngm9uuoNlW3X0Uo2V1oyN5t2UtirS1ywxBhfyG46pY9Du05MXw6Ndvva7oukGarrWtZlXD5l++rnjqpK4TuWWlk55n5bqikr+1z7m9GtfOizQoolhzmuxIUKIYTMOzQDEUIIIYXDpHAJQ5dzOrKGhjXHwse043ouPnJzTkp4J8wYGMv3qUXK79wlUVssE5avWSOkLy2zkkviuNJMsLZzkdoMdNz6emagva6hGYgQQgghRJWkDrax6Avjc3F6jTmeGO13oZWsLtYOZ8whwhNcHKeb5XM6LOZQJXKoEaWgoW70Xf85HeBjJnHzCVe3GYv27whIn27/S/d/pJqysufVv6ayQgghhBCiibqykjKBmUu9WVBU+vqcEOr7QuaPUn0JYvtetOu41otFigRtNikBbK6LaX36UpKaNnRdpPZZmXdlhdFAhBBCSOEwGijDiwy16VM0QqNtSogYcsHlfHMnfMtBThWhhHkrTUVwwWb37zvHPmnoY3+fpSQCdPmtpEi4N63v1KRWVr76AD1l5Z+uGp+yQp8VQhrkvgHkgAstAAAgAElEQVSS9IxtQ0LIPEIzECGEEFI4fDfQDJiBbBijo21IO1phydrJokLJkQgt5btkfBLiuThHuowr5txq9xHrukh5/ZdgJvRlLGPXNOulNgN9fYNHqT1rX3DlhaNb+dAMRAghhJBBRGSRiBwiIstFZIWInCEiu1rWfZqILBWRa0XkehE5XUReZN33rCsrPqG9pTrY+r4yIHa6fe30+NpKgTYp3u7q076Ng7lLaKqWk2pfmdBrp4TrovTXQ5TonNpkDE7fXd9famXlhAfqKSvP/YufsiIiXwbwfACHA7gEwD4AtgewszHm9J56zwLwLQA/B/CV+vCLATwRwH7GmKOG+qbPCiGEEEJ6EZEdUC0wDjDGHF4fOxbABQAOAfDknuqvAnAFgF2NMbfVdT8D4PcA9gYwuFjJoqzkSJ6mhc1OtVRi+Ti44PIStBg7e62+bNv1bTtnWvoS1ArbcZQyVsDt2u4r01fPJ0TbRXHzVWFsXhWg/fsM6VNDzZk3ZUVEDgXwegDrGWNubhx/G4D3A9jYGHPFlLqnAVjbGPPo1vELAPzZGPOMof6prBBCCCGFoxkNJCI3DJUxxqzTOrQdgIuaC5WaswAIgG1RqSdd/BTA20TkvQCOqY/tA+DhAA6wGnNKZUVbldBKBherbIp2htrv6qOEXWgXJe2SS0bb70NjDKUxq5E5oePKcR1oJe7z6Tuk3S5y+qx8a8NHqykr//iXC28cKtNerNQqyOXGmN1bxx8J4EL0+J6IyBoAPgfghagWNgBwM4AXGmO+bzNmKiuEEELIHNGhmtiwGMBtHcdXNj6fxm0ALgbwNQAnAFgI4BUAjheRXY0xvxjqnIsVQgghpHAW5E8KtwLAoo7jqzU+n8YnAOwA4HHGmLsAQESOR6XIHI4qKqgXtbcukzByh+8NSbU20qirOc7HFBYSiuvSvms9l3Z8Q9D7+gghpcmjBPNKqU7VLn2UZp6aBUpPCvedjbZSe9Y+a/n5Pg62JwPYwBizdev4rgBOAbCHMeakjnqrArgFwMHGmHe1PvsYgP8HYLEx5m99/TMpHCGEEEKGOAfAFiKyZuv4jvXfc6fUux8qK87Cjs/uU382uHgajbISGgpXeriuTf3QpFg+oX6+ztAaqolLna56sZyYQ/t0UVZ8v3ONa1sr/NSmD+3QWW365qKLWVY+XO9nJVyLMX4zqZWV7z54a7Vn7TP/dJ6PsrIjgDNwzzwri1DlWbnSGLNTfWwTAKsbYy6q/38hgGtQRQptY4y5oz6+JoDfALjBGLPVUP/0WSGEEEJIL8aYM0XkawAOFZENAVwK4KUAHoIqDHnCsQB2Rq2WGGPuFJEPA3gfgNNF5AuoVJaXA3gQgDfZ9F+8slJaqJ5LXyn6jhUG6OJnEZoIykad8ElipaUAhSo0NnOq4TPUZN58G+btfEtj3vyegPlTVgBARFYD8F4ALwGwLoDzALzdGHNKo8xSVOn3pVV3TwCvQ5VbZVFd9xBjzAk2fVNZIYQQQgpnwcLs0UAwxqwE8Ob637Qyu0w5/iUAX/Ltu3hlJTelrOKBeP4CzTZddvQ+aEXb9LXtovi4tKeNr7JS0rXYhbYvjZa6OqGE+XMlRyK1VN+VzbVdYqRVamXlpCXbqD1r/37ZuflXPo4wGogQQgghRZNEWSlJnXBlrGnKfT3229hErmhFwMTKP6L9aoQY0UDT2nMZ36ww1vtFzN1/CXPio8rYlo81Du0+mxxhliVVJ76/2XZqysozLj2bygohWsQyv5By4XdOCOmCixVCCCGEFE1SB9sSpEwtZuFc+qRan0R0NmHENk6lXWNK6fjbxqdvLZOWax8+lHotc1x5+yyhby20nKyb7aQ2A/3g4Y9RMwPtfvH/0gxECCGEEKLJqPOsxArvtGnHV3HQTrcfUtamnk368z7VJGRX1tW3jfrSN95pZXzTvNvMl8b3F9OXo9Qd87yPq5Rw9dD7WYr711A7WvNX6jU5D4x6sUIIIYTMAyUkhcvJqBcr7VWuVkid9g7ANVTV57x8ytqOJ6TdPp+VoTZtQoRd+u6q16dYuKgZPonofEOY521nP2/Y+HflxHUM2vevEhRskh76rBBCCCGkaIpNtx9rlRuaLC2l3VULm/EMpXvvKjtBK726S9I1G2yik7oISS9ucw59fdpEHpWeGMznHGIlX3RtM/b36TOGlPQpgqXcz3yIcQ6p0+2f8qjHqT1rn3bhL0ZnU6KyQoolR4KwMd+QCSFkVuFihRBCCCFFU6wZiITh+iZTF3zMZ111UyknoY6sLiYjF+dI7b6HxqFRNie+76aZEOv8SjPbxOojNJFa6Un0XFNhpDYD/XjrHdSetU897yyagQghhBBCNKGyYklsh98cO2DfkN42LuG6XfXafbrUDXWijZ3Gv6/tPvWldEfbMeIS8qqVnt2HUpSusSoiqaCykpZR51khhBBC5gGZ86RwM6usxF5ta+90cyfgGlIUbFSAvno+4Z42+PqjhIyjL5w7lkKTe5cdwhj8Kkpn1s8vFb5+TyX4rPxkux3VnrVPOfvM0a186LNCSAM+DAghpDxmVlkpnZQ7JZdoFBufCR9/D5vkctrE9EPR6Cs0Mko7yZ+LL8e8LepSzsUszHFupTgFqZWVpds/Xu1Zu8svT6eyQgghhBCiCRcrhBBCCCkamoFGTkjoc8xwWJvQTx8H1nYZV/NJrBDtdns2SeFcxhFTSi/J7KD1Th+tccQy8di0HToXPnW12plVp+rmeR1hliU1pZy64xPUnrVPPvPnNAMRQgghhGiipqyU5myWM6nTGPEJvfVJrtVVP1TdmPaZdtkJKc7ThhKu6TErISUpSkOEjLU0Z9eQ78q1nkt7ruNK7WA778oKk8IRQgghhSMLRre+UGXmfVbGtHtqkyOx3TR8kyiFKjQuhKTQD/EJ0OhLO3SZjIeS7lEx/NhcVO5Yrx+JUTa1svKzxz9R7Vn7pNNPG93Khz4rhBBCCCmamVJWSrPNhpIiImHC0AvcYqWub5bpG6dLsrqu+i7tlIRLEjdfSvCHKEldyIVL1JhvGc5zhc11W1q6/dN2epLas/aJ//MzKiuEEEIIIZpkVVZyKyFj3lH6vpCrWcdFeZhWflp7Q0rN0GdDffoSS33xademna42tW307Tqu4wpBS01IMQ7NfprYRJuVQM7vwXVOXF434QuVlbQwGiiQnIss9qlPSQ+HJqWaqWaBUue21GuR5EEWjm59oQrNQIQQQggpmmhmoNKkzFLk5qG++/r3TeVu0+cQfX2GtqNRtqteyh2zVuhyrHBrbbR+K2N0+BzjmCdo3C/GeN5a5Ey3//Odn6xmBnrCT08dnUxDMxAhhBBSOPNuBioudDllIrRpTlyhL/4qbedh47Q2IWcor4vjb/t432ez4JPgmqzLRUn0Gc+YKdVhd1bvPxpohBqHtNdVNrWD7elP3VntWfv4H/90dCsf+qwQQgghpGiKU1Zc8AktHVPIcUhd31C/acRQMEJ9Xobq+ig1Nn3FDL/WmBMtbJLyDdUbKusyDpsQ+Zw+NKX56eUk91xoKFulJYU78+lPUXvW7njyT6isEEIIIYRoklRZCUlkZlsvNTl2eVr221CfFZtxafuYuOzo+wjxj9FOpufblxal+zrEGl9M/7WuPkLa00pypnFeMe95Ln3alI+VQBGgspKa0UQDlXojJbMFr7P5g985GQPzHg1EMxAhhBBCimbUDrYTSpexx0RIMrKYodA+Dp4uphTf8U0bl42Joa+dvvGmktxj9klH1mHGdJ6uzqljYMisnTop3FnPeKras3aH7/94dDLNaMxAhBBCyLyyYMHo1heqzISyMiZsHOW0nOls++7Cx9HWVTFwUWiG+vZtx0X1sCHUwTbEwdlmPNo73piOjy51S1BXc6gJpSkYJSlwsUntYPvLPXZVe9Zu/70fjW7lQ58VQgghhBQNlZVAtJPMhbYT2r+ND0YbXz+NoXZs1Imu+u06vgpGLH8UF2Ughf+ICyHJtmzq5f4d2BCSUsCn3ZiUrFykxnUuUisrv/qHp6s9ax/77ZOprBBCCCGEaKKurMSyWWq2GYtYO66U2JzDhFC/Fhf1xUVxsBmfi9rRR0i0k4vi4xL9RNwJUYw472nJEQ3XBZWVtIwmGog3BEJIDELD6glJwQImhSOEEEIIKZeZd7ANlQw15GEbM1DusEcXbEwVOXerKU08IWVC52ve3hHUxbyYb8ZsYo4137kT0aU2A539nN3UnrXbnfjD0ck0ozEDEUIIIfPKvL8bKKuy4qI4lLZrKDUluc/bRrtwUQxc2rZxiLUZnws2yopP8rpp5Yfqhya9i+nEPtTu2MKmtfrKrXxqJ4gk4aRWVs55/u5qysq2//2D0a186LNCCCGEkKKJZgay2YmMYXcwbSfTNXab8Frb9ofqTSPWbtumbLNvH18Om/nymcvQsOvQVPgaClFfGykVPZ8dfoqkcD7qUF/ZkJD0rr58FLIUiQBjJ/mbNZrnfoRZlrRvWTjf2sJ8nz0hhBBCiieLz0rsXULMtku159tgoxBo7Sh98OnbN9Knr0+NOfBVVkJ9hUpn6Dcy5l17yrGXdq/RepVEKnwjNFvKSlK/j3Nf9PdqPivbHH/S6HxWGA1ESINSbv6EENJk3pPCFZ9uv48Qe7BtGW2mqRuuESca0VOuykFoev1pZXzHM9SnTSSH7/xrM+uKigZjUhM02m22HXLfSHF/LCF6KgZ9c5A6Guj8f95DTVnZ6svfG93Khz4rIyTHj3/MN5xZgPM/f/A7zwtfw1AWXKwQQgghhSMLRO2f9xhEFonIISKyXERWiMgZIrKrQ/09ReQsEblFRK4TkZ+KyA5WdUtNtx8iQZY2njFIokO7CJtkbr4OuzbmG412+8p04WPu0gpvdgm/HjOlmXZSkjOJZKlm8lhohWjndLC94F+eqfasffQXv+s1dhH5MoDnAzgcwCUA9gGwPYCdjTGnD9R9H4C3ADgOwM8BrAFgGwAnGmO+NdQ3HWwJIYSQwlmQOc9KrYC8GMABxpjD62PHArgAwCEAntxT9wkA3g7g+caYE7z6T6ms5NhJzNLuQBsftaOrfozw4ZCyfWOfVs/FGdd3XNPanTbWkPZyUtrvMqeC0Vcn1rgmlKJK244hdByh7djMX05l5dd7/4OasvLIY7/tPHYRORTA6wGsZ4y5uXH8bQDeD2BjY8wVU+p+FcASY8yOIrIAwOrNNmygzwohhBBChtgOwEUdi4yzAAiAbXvq7grgFyJyMIAbAdwkIstE5F9sOy/WZyUFWjskjXa1cU161C6r7T/SV689Hl8fGO2xusybCy5KlM35djGW67TNGPy7xkIpyTdLShExwTW1Qwmhy7/Z99l6ysox375xqIwxZp3m/4vIBQAuN8bs3jr+SAAXAtjPGHNUux0RWRfAdQCuBXAngHfX//8qADsBeJ6NaYg+K4QQQggZYjGA2zqOr2x83sWa9d/7Afg7Y8yZACAiJ6By0v0PAIOLlZlVVmKpJu2yTUJSiNskgtLGx68ktD2t5Gu+PiYaPiFafiSh4xuj8uCzw9Vqd9Z95kpUynLhkkTPd97GrKxsefS3fHxWfJWV9QFcDeAyY8ymrc8OA/A6AGsN+bBQWSGEEEIKp4C3Ll8BYMOO45Njy6fUuw6VInNlx2dXovJ3WRtA72Il+9kTQgghpHjOAbCFiKzZOr5j/ffcrkrGmLvquht3fPwgVH4s1w11XowZaCjRWKi0qR066GsGGirbV1/7HR9DSY/6jtuW8XW+jdWXxnhsriHf77z0UOXSTB8uaP3ehxzDc6ExDtfzzHk9hJjtNcxBqc1AF+33HDUz0BafPdHHDLQjgDNwzzwri1DlWbnSGLNTfWwTVKHJFzXqvhHAhwHsZow5uT62FiqflYuMMVNztEygGYgQQggpHFmQ1xBijDlTRL4G4FAR2RDApQBeCuAhqDLZTjgWwM6ozDsTPg1gPwD/XfupXA/g5QDWAfA2m/6zJIWbEBr25rOq105AFDPJk+84XOu64rPj0kogNzSWIYb6skkg19e/tqrjG7qcilJDjVOOq5R7wTRK/Y5s8FVPYgVWNEmtrPz2Fc9TU1YeceQ3fNPtrwbgvQBeAmBdAOcBeLsx5pRGmaWo0u9Lq+4DAXwIwDNRRQ79qq57qk3fVFYIIYQQMogxZiWAN9f/ppXZZcrxvwDYy7fvYnxWYjEWG7tv6HJOZcVFLbFRxnzacw1ddsEnoZSWb860sTT7iB3SHnM369NXX5/avm3t9mPgM8+lKyNjGacvzesjdbr9373yBWrP2s0//fWkY9eA0UCEEEIIKZqZV1a0cFEBxkJoNFCs6BuXPl19TEL7H+qzT/HRmoOucYydMf+exjx2F2IkVmvWbZJDbSo9GmjelRX6rBDSYNYfOISQcVJAUriszPfZE0IIIaR4soQua4UR55Rffft2SdCmTUj4sKv062NCscEnsZ1rO0N1XNu3mYt22VCn6nkxTYyZVM68Y77PtseQchxDfaY2A13y6hepmYEe9snjaQYihBBCiC7zbgZKulhpr05DnahSJJWbhu/qflo93/ZczsElJXyfetCnkA21E+p42rcbTZWIrg/fkGqbOfUhR3hsaDjyhJzp2V3aCR37tLJaaoLWfTZlSHssSlCJiB/zvVQjhBBCSPFE81kJTXLWrutKyO7OJjRVu28tfHc/LtgkSwvxWdH2DfFtu6+vNj4hxi6h2a471ZTJzYYo0d8gZf+uY/C5T3aVT3kfCvGjivFdDfXle39s1kvts/L7A/ZU81nZ9LAvjc5nhcoKIYQQQoqm+KRwpdgYSxlHbEKSnPkqKyHt9fUR6vehrU65RCBp+UNoE+t30KdqpvShiRVpNZb7R25Fygbt+XfxdWtCZSUtjAYihBBCCkcWLsw9hKwUr6z4ksNmX9ruKVU6ahffi772fO3w2n46LtEZLr4qPpFMNuMi4cTypyjNb4TcTeh8pVZWLnvTXmrP2od++LjRKSv0WZlRtENzY4X69hHTSTikT60yLvABFI+QBSwZJznuZyQMmoEIIYSQwmFSuBETmjBOq692nyWkQ49tEukq02yvLxmc7ThsEqv5OuG20U7G1sTl/Gz6H5vc72sm1O5/Wh8x+tYI5bUxnYaOQZvSxzfBd26b1/IRZpn6uMh05nupRgghhJDiUXOwTeXMOQZC041rnLOLs6prGRtiJV+b4OKAGhoibOP82oePk/Cs+1GM+fftgnbIbMp7S47Q8aE2tNpxbaOrXmoH2z++bV81B9tNPnA0HWwJIYQQQjSZ2dBlG+Ztdzch1D/DJSxZo/2+Pm0UEd/+J7ioHjZz4dJOzGRpY6WUxGU+SojL2Es5z1Bm9XqlspKWUTvYEqLNrN1QCSGzwbxHAyV9kWH7M5ddqC8hSbv6+g9NRGaTfCp2VJHNzs1XufBRN3wTx7kQktDOZS5C/VGG2m/30SbEt6GUBdtYIkt8KWHM2vexUokx16mVlT+98+VqysqD33fU6JSV+V6qkaIpNREdIYSQtNAMRAghhBQOzUCFOthqheiFyn+ppGgXk4x2eHizzVjp622SuIWajoaSbPX1EdPpeFqbvuGsJZgPCJkVfH9Pqc1Af37Xv6o9ax/07s/QDEQIIYQQokmxykqb3ImNchBLqXFJOKbloOyjOIQ68GqdQ1+dUAdi7XZKQCtdvDZav6cJKZOljfk+NlaGnjmplZXL3/1vas/ajd/1X1RWCCGEEEI0GY2yEgONnVbOHWKM/n1SgKck5XhKO/dpaIVzx/ZXSl1/qB3t83ZNjlj6deVCqI/hhDHdi6mspIXRQIQQQkjhMBqoUGWltCiInDt6n5TdLhEws0qpvhNaaCfMa+ObKMymTReVw+X8tF+1MCFG5Nws+96lVH9ztZdaWVn+vleqPWs3euenR6esjGapliNBGCGEEELyQzMQIYQQUjg0AymZgULeLupKjpC/2I6nvu9Scml7LBIyscPmfUTtz7TNGb5h3C7thYapD4Wna5/D0HimjatUSrt/pBpPaaHLV3zgVWpmoA3f9imagQghhBBCNCnWwTYFsZJElZDsyfctvTZlbdoIUYdmwblaa0dt4yTc10cKFaEEtJxwXZIExnLmDSXktxZTER8aQ8o+NUitrFx56GvUnrUbHPgJKiuEEEIIIZpE81nRprTVN5OTlUep8xQrZDxFKK9t+01iqTlaryvw6TNH6LJvigIXUiqesVXkUJ++CbbtUFlJC6OBCCGEkMKRBfNtCCneZyWlopIi6iZHQqMSUuiXqnqMGe1ooJTJ12K34xpVlCMyapZ9QlJEg8Zq35bUyspVH36d2rP2AW/62OiUldEs1fiQI4TEgPcWQsqneGWlCxcvftcb0bR6KfOqhJRtlp+3m/C8nXdo3pFZjRSK7afjgo3apY2vqmEzvlg+KqERdzl+86mVlasPO0DtWXv/Aw6jspKD3O+ICMGlLy5USCgu19AYKWmh0iTHeLRC7dtltK+hlO9YIuNlJhYrhBBCCJldkoQu+8ifKZIVaagQKdNpa5u2xsTYksKlQMvBdlqd3ISoJK5zkYMQ82+oyuHibO9zb7cJ9S497cPQ9XGEWZbUlHLNx9+oZgZa/7UfoRmIEEIIIUSTaA622kqIa/r4kD5KSfPuQmwHtdAEVaFO0Vrh16WEPWqile7dJcRXezwuDsEx/W5cru2QkG9XxpZCPxSt++5QwETfZ0N9p3awnXdlhUnhCCGEkMJhUrgRhi6T2SZmmHibsdjNbfwF+vDxN4gRyhyiSti011emjZYSot1OyqSSLoxFZQ6lVGXl2k++We1Ze79Xf2h0ysp8L9UIIYQQUjxJfFbapEyGZBMxEXvHqxUh5UJuNSGEmAnz2vVKiizz7TMUF0XFxn/EN1ldTnKMSyMKsfTf8hjwncvUysr1n36rmrKy7is/SGWFEC14I85LaQuKWPA6I6R8uFghhBBCSNEkSQrnglYSH613YqQyF4SGF4aOMyT0OeV7Qih/342Wk+qEEHNQKLNmHrJxJB4K74/hYO4Srl6CE69vuHos83GT1GagG458u5oZaJ1XHEwzECGEEEKIJlmUlRQJ3qb1mXO30NV/LHViXsi9E8yJtrLSh0/IsU+ytNDXAaSciwmxdv99fcS6F4zp9xT7fjg0F1RW0sKkcIQQQkjhyML5NoSoLVZcVrcpQkzH8tKsUnYvscK4Ndrva8fVr0VjHLn9i7SxUU1sxtyuF6q6TPusz5cmJNV/V32b78hXzXG5DlJdK76/mRzXdOy+Svl9kor5XqoRQgghpHiKiwaa0BeRENtua0OonTrnbqXUnVLKcZRyfiFo+Y/YJFAcGoNrOy7X4FD7XfSpJbb1+sbiO57SooFyksN3UZPUPit/Pfo/1HxW1tr3PaPzWRmNslJaSCOJD79zQgghwIgWK4QQQgjJh4gsEpFDRGS5iKwQkTNEZFePdr4nIkZEDreuM+a3Lo9Ryo+dfE2LFH1qm/VKeD9PKX2HmIhCzS2xw4d9TTxd9W3b6DNl+aLh5K3tsF4aJY8vtRnopmMPUnvW3nfvg7zGLiJfBvB8AIcDuATAPgC2B7CzMeZ0yzaeCeCrANYA8DFjzOtt6lFZIYQQQkgvIrIDgBcDONAYc6Ax5kgATwXwRwCHWLaxKoDDABzq2r9a6HLK5GZjdrr0SZo3q7un9nhCx5fj/Eqa0xQ+Pn2qhI2D7ZD64uJw21XPZg5s2umr0w6b1wrRHsv1a+Owq+XUa6NoxQzqsPlsjngBgDsAfHZywBizUkSOAvB+EdnQGHPFQBuvA7AYwIcBvNulcyaFI4QQQgqngKRw2wG4yBhzc+v4WQAEwLYApi5WROSBAP4dwKuMMbeKuFmi1EOX+8i5Ko0VCh0jLHBsKfhTjqGEvly/81hh/SUQ6rOi5d8Sy0/GJ+TbtW0ynRS+Ob6pHFL7rNz8xffo+ay85F03DpUxxqzT/H8RuQDA5caY3VvHHwngQgD7GWOOmtaeiBwN4NEAdjDGGBExcPBZobJCCCGEkCEWA7it4/jKxued1P4ue6NyxPVadI06GignNiv1CTar+bEnSEpFCUpSaaT0VdFIsNbVTqiCoVVmqK5vfdu2Q9BSAqe1a9OGloJh01dshuYttbJyy5ffp/asXeOf3+k8dl9lRSp7z88B/MkY86LGcSorhBBCCFHlCgAbdhyfHFs+pd5zAewA4O0isqT12Vr1sSuNMSv6Os/usUMIIYSQ4jkHwBYismbr+I7133On1NsE1VrjxwAua/wDgH3r/955qPPizUCU/cuntJC/Ut99pD1PKR1Ybdq1cVQPGYf2u4F850bbKViLkGvat26OPkPQvDekNgPd+tUPqD1rV/+nt/mYgXYEcAaAA4wxh9fHFgG4AJUyslN9bBMAqxtjLqr/fzMAW3U0eQKA7wA4CsDPjTFX9fVPMxAhhBBCejHGnCkiXwNwqIhsCOBSAC8F8BBUmWwnHItKKZG63qV12XtQhy5faow50ab/US5WSt05T0PLwU0z7G5oXFrzV9quaYJPuHronMQKXe7b6Yc4xLoqB9PKuyaOG2rXVbEJCaXuquvjHGwzzhzXV4o+p/WRI9XDWJ4ZBbM3gPfWf9cFcB6APYwxp8XueJSLFUIIIWSuWLAw9whgjFkJ4M31v2lldrFsy8kUVYzPytgSobmglTjOZ45C+4yJxvcY8zw1kgaG9mlTNiVaSdJslLwQnxztUGPf5Hc5UhOkSKRmW1djHNPazn3fSO6z8rVD9XxWXnhg0rFrwGggQgghhBRNsLKyvywxQLzVt4sd2DX5mo19OuWuesKQj0kMj/2S1KrcCfJCInN8xxkrGmiCdtI0rSRuE3yVFZ++fdprojXvqVRW4kdpSeFu/e8P6ykrz38TlRVCxkwO0wohhJB+svqs2ESjNAlRVGzG4dJOzJ1NDtmRRq8AAByjSURBVF+JEEqwkZdCn0Kg/X26+E70EbL7d1VqNHxMtP1RutrWmlubvkonljJbil+d7++RykpaRhMNNJYfNiGzQo7fHJWt+YP3djtkYf5ooJzQDEQIIYSQoikmdNmF2OYCbXmyFLmzj1TmrhRz4SJb24wlh/OsS7sTYppH2riYeLrqtdFysO3r08e04+Jg6xt2PY3c942cZtnYvxkNM21qM9CKEw9Te9Yufs4BNAMRQgghRJkCksLlRE1ZKSG016Wua/2cao6NA/C0uq7t2ISQl6oOjQXtBIgxHGt9ysYOj/ZVboZ2zq7pDKb15aqmlOhIn+P+mPveMhYH2xXf+riesvLs145OWaHPCiGEEEKKZpTKihZjHLMPOZKchaClJM06LvPURyzVRCvst095CFFoXNoIHVefihOCTXu+qq1PmS5S/UZjzm1Xu8mVle98Sk9ZedarqKwQQgghhGiiHg00L2qFL1q23Qkpk9UNMQbbc6odbwq0fE18iBVR09dHzARtQ2Noop2MryR8EgOG9uXiuxVzPK5QWUkLo4EIaVDKjZAQQprIgvk2hMz32RNCCCGkeJImhRtzeJsWNJOFYRN2moNYyfNCQ5ZThRPbjmvo3UB9ZggtB1ubsi7t+7Y3S9erFi7h3CGpGDRIbQZa+b1Pq5mBVtvjlTQDEUIIIUQZJoWLE7ocGlLXLlPaDqCPkFDhrnqhScTmPdy3lN2sNtrKiku7vsRSLvqucZv0+JplbQm5FlPcF30dYKfVyaE+hrQzdN9Irqx8/0g9ZeUZr6CyQgghhBBlqKzE8Vlx3c3G2imkbFc7fXaIIuI7/z592bTrkrbcpT3bz0LKavftQmiCPJf5j6UUpAg1jp3ozbU9m/HMkso3S9j+lpMrKz88Sk9Z2e3lo1NWGA1ECCGEkKJJGg0UylAEwRh2KrF34F2MYV5SM7ZXEHThs/u3UQL76qVMLpcjisenHS31hcTH9zfcVS+1snLbKUerPWsXPW1fKiuxSJm1khBCCCHlMJrFCiGEEELmk2LMQKVJ7LZ0ObKWfi6xQgiblHru2sR26rVpz4a+lAI+qmXou4EmhJqeXEKXtZ1nteafjJPkZqAfH6tnBnrq3jQDEUIIIYRoUoyyQspCO7HdGMkZ9q7Vngsp3mo81IdrKgANB1jXBHIhZaaVb9Yp5bejlc5AO6VDDopwsJ1zZYVJ4QghhJDSYVI4KisTcvgflLrL0EpoV9p5heDrm5PKV8XmFQspw5ND29PwqWnW105+p51ifhZ/MzHQfg2AL8mVlaVf1FNWdvmX0Skr9FkhhBBCSNFkVVbG4ONQ0m4n5g69hPPrQ8Ono1m/pO/VF+1ImpAoF9d6Lu3lUHxSKFElXnu+94R5VKVTKyu3/+wrasrKqk96MZUVQgghhBBN6LMSAdqp7dCOgElZvyRyZnd2jZIZKttXzyUlv2/0jos/ylD7Q5QeDTSN3IqszTxNK6OZE4rKSloYDUQImWv4Kg8yChbMtyFkvs+eEEIIIcVTjBloSLZzDVPTdqQMkR5j9DWtzoTcUnJOE492O7EIldNdTB8+6fFD34ScAg1H2BgJ37rqtevm/G2MmVRm9qHvKrkZ6LTj9cxAT3wRzUCEEEIIUYZJ4eIoK7P2cruUqdfHRspzKHW+Sgjd9H1JofaL+UJePGjTns1rCnzP2yacO8Txt6tMm1LunSUoxb6/p5AU/7Zlkysrp/+3nrLy+OePTlmhzwohhBBCiibYDGSTuMlntxIyhhTk3OGXZvd2STMeOvZQX6QSlJnSVKGUTFMauo67KD0u32vfvWpaO64+K1qJ9nKicU/2bcelTlfZvu9Ps++UyJybgaisEEIIIaRoiokGKp3YO3KXnYiv/TwWNjbj0ihhfKHflY+q2YVNBJ6PUuaisvahdZ42+PiRlKKalBARGBrJqK0U+8xJqT4rd5x5otqz9j47Pmd0PiuMBiKEEEJKh0nhCCGEEELKJdgMtL8sMUDakM2h9pt92MiAJZgEcpPqu8k9xxpSeSnvRtHGJ/TYNxGdi+mprz2NcOm+cdiMKxQt00eIqa4Us40Poe3Z1O8qk9wM9Itv6ZmBHvdsmoEIIYQQosu8RwMlSQqXM/lRKTv6saO9Owvt06ZejmRWNu1o7QQnxHKwtSnj6tQYS5VwSRwXmkwvVIGaNi4bSrifxVQWtX9zE3yVo776qZWVv/3qu2rKyiqPfebolBX6rBBCCCGkaJKELpewG5hQir9BrIRLOZKl+dp8Y+MTHttXPuU5uMxpFyFhtS6Kp2vossv4XBRZF2x8OlKES2vcA3LfU0sZRw6SKytnf19PWdnuGVRWCBkz83jTJYSQ0lFXVrRttC74RgPNOrGVjxyJ6HLimuwshU9PCC4KgZaaEKsvreRwLvj2meo3EppwMiSBX9exWM+I1OoolZW0MBqIEEIIKR0mhSOEEEIIKZfi3g1UQuiaT/td5AzrC3UadOmzFMfTmO8BCSGl+XMaKc03IdiYD0LaDGnHpR+f+0Mpv6NU7cQ0mcYOZAAymIHO/aGeGWib3WgGIoQQQoguspBJ4YIayPHW5RJ2IK5OlrGYl9DBUs8z1PnQpx2buhrqgY16VQqxkrlpl9FSKEv9PUwoLRVAjD5TKyt3XvAjtWftwkfvOjplhT4rhBBCCCmarOn2XQhtr/SdSB+lj32MO6Qcqc590rz7JoWbhq/6oq04tPEZS7NeiF9LDD+ZdtspwvuZpsEejedTcmXl10v1lJVH7kJlhRBCCCFEEzVlJXbiK+4S7sZ1VzCLczjmKJ5YEW+h+CTt8k2EFqLq2KhDLmqJbxRPqDIVEg2UQlm0mVvtPm3GkuM+1jV2KitpYTQQIYQQUjoLGA0U1MC0dPuAXox7m1gREyl20CF9zoLfjkuUQF+ZWSdk5+tKqjwkrmjndglRRJr1YkUDpSRWZNo8kVxZuehnesrKFk8anbISzWeFF3o85mVu5+U8SfmUsoCLzbycJ/FDRBaJyCEislxEVojIGSKyq0W954nIV0XkMhG5VUQuEpEPicjatn3TDEQIIYQUjpTxbqBjADwfwOEALgGwD4CTRGRnY8zpPfWOBLAcwHEA/ghgKwCvBfD3IrK9MWblUMfBZqD9ZYkBZncX7PLGUKJDTudZ3xBTn1cZ9PVr41RaiqOhRtkY9TWJGXocEm49JiVk2jVd+isgpnGEWZbUlHLXxaepmYEWPPyJzmMXkR0AnAngAGPM4fWx1QBcAGC5MebJPXV3McYsbR3bG8DnAexrjDlmcMyuAyaEEELI3PECAHcA+OzkQK2IHAVgJxHZcFrF9kKl5oT6r9VqM0lSuAmlhoCmxMdxctZVnVk8r3l0Ep7F7zEmIa/ysMEnSV/MJHhahCg0muNLrqxccoaeg+3mj79xqIwxZp3m/4vIyQA2MMZs3Tq+K4BTAOxhjDnJdgwisjmAiwEcaIz50FB5KiuEEEIIGWJDAFd0HJ8c28ixvbcAuBPAN2wKqznY9u2qtENU26ti3yRWLkmZQn0S2mW0E4P17ZBK3+n6JCPro4Qd/hj9NEJ/K9rfTazkebGUShtFJMarGlzG41LGJ4leitcdhPTpkk5iTP5ArrRVE0sWA7it4/jKxudWiMieAF4O4APGmEtt6jAaiBBCCCkdyW4IWQFgUcfx1RqfDyIiT0Ll5/JdAP9u27laNNCEnBEcNn4gvlEVOZK5la4m2Oy4fHaWOVShFH2OLRGXVmr4MZHyvIbS2YdGyfT1E5LYznUMIZFuLmVsxu6b5K+L5D4rl56lFw202Q4+0UDBPisisg2Apah8VZ5qjLnFesyuA87FLEtypJscD8JZe/gSQu4NnydenANgCxFZs3V8x/rvuX2VRWQzAN8HcBWAZ7osVIARLVYIIYSQuUUW6P3z4+sA7gNgv/8bksgiAPsCOM0Ys7w+tomIbHGPoYs8EMAPAdwFYHdjzDXOpx/r3UDaDoY2knQTbadNLTT6jOnI59K/Tx/a5hbX9sbyncd2wNbsQ5PcjuElmIEm9JlSXMq44NqeTfDEtDqlqBu+iSFTvxvort//Us8MtOn2XmMXkeMBPAfAYQAuBfBSAI8D8BRjzGl1maUAdjbGSKPeOQC2AXAogPNbzV46kP0WAB1sCSGEEGLH3gDeW/9dF8B5qHxVThuot03998COzz4PYHCxEi0pXCixwgrHEFIaq68Qh+ImLipOrJ19qMNuiANw6HhCyTH2ElWYsePi4BniNNv320uJRjK3vvK+zrO+40j+1uXL/lcvKdxDH8O3LhNCCCGEaJJUWQldUdu2b1svJBzZhVC/Cu1x+ia0i03p/jYxSXUt2oyhtLmJTS6/J5dkZDa+ISHKRR+l+piEhFRrnEvq0OV5V1bos0IIIYSUTv6kcFlRU1ZCIh6a9Vx8HMbkZ6CBVmK7sZxvH7F2txptDvVVyvy7JFnUUAFcxpCqvms/zb607lUuu3wb3xUfur7P0hSVCbHG5dpucmVl2Tl6ysqSbUenrIxmqVbaD4bMJqUsJAghhNwNzUCEEEJI6cjoxBBVsoQuz6vpoon2HJQwT6Um9Mo9rvY4Sk9IVxqzcA5dxEoKpx2uazNGm3fwuIzBZ3w25nEbE6ctyc1AfzhXzwz0kG1Gt/IZjRmIEEIIIfNJNGXFJj2+dsK3Jlrp9lMlaAvd/c/q7nNCDkfKCVrOpC59zQKzcH4xHYi1HWtD1AgtxcEnLcVY/RGTKyt/Ol9PWXnwVlRWCCGEEEI0mZt0+yn60Egfb1O/FB8MH1IqbqGUNh4XXPx36EsznZih1T6+HNPKdvWVUrEYu0rSxDaMm8pKWhgNRAghhBSOYVK49On2J/ikxLetF9KnTXu+fi4+yaJK3326UKoqlELxmbfvmvQzdC8I9UvxSfQWqtTY9Dlm5ac5P6lfZPi3P1+opqys8qBHjU5Zme+lGiEtuFgghJDyyJpuv4vQlPLT2mvi81Kwobpd7bgqRyG+Ki628VIeyLOgJmirJbGjz5pt21wXIblrtFS0Uq4T7Tw+IVE7rpTkU+I7Fu1zCG0vtc/K3y7/jZ6ysvGWVFa00b5R2bST46bo0mfumzYZpoSHQhfaD9NSf0/aaM1FDkodlwva5zALczJvFL9YIYQQQsh8o+5g62sCmUaoI6tNOzbjcgnzzGkacEnulCMZXh8uifJKMQ2MGc5hXmKbgVI4u9okq4vdnksgg007tueQ3Ay0/Ld6ZqCNHkEzECGEEEKIJklClzV2cL67Ay2n3GntxUyT76LihIyBzEZq+HlhzOHvNoQmhXPpo69uLIfWdp++55AbKitpYVI4QgghpHSYFC59un2bXayPb0hulcOHUP8Yn75K2o3GINSvyKVMbHzHEBKW3NWGVnhySeHcLsRQc7STpNm0Y/M9+rTnM/YU4dTaPjVNkisrV/xOT1nZcPPRKSvzvVQjhBBCSPGoJ4Wb4OqhHVtN6BtXSDuuvg5DKbZtyqSYN6JHSd9NDn+PUn1MchOirKRMkObyWagaox2tZINvn6mVlTv+cqmasnKfB25GZYUQQgghRBMuVgghhBBSNEmSwoUmNZtWZoKWg63NuPraDwmldnFmDDVlxZqb0sYVi5iO3FpovMcmdzi3jclUo91QtBNhTnAN6dUwobgmk/Qxj2ubdHxDvdu4PnuSm4GuvEzPDLTBQ2kGIoQQQgjRZDRJ4fra8w1DLTWMMmRcE0LDr2OpJaU6B+dWEdrjGIvK5IuP8uP7G0553bu0O/SbjRUybNtXTsff2A7EGonoqKykhUnhCCGEkNKR0a0vVMmaFC63OjEvu1gNbHa8TbSSiI2VMYQIx1YWtcqW7hMVquy64OOfEVo2hipREr4qTnJl5aplesrKA5aMbuVDnxVCCCGEFE0WZUWbUpSaWSTHeZfiPzKNGH4VQ31pqQquYw9hXn8zXcyCKtGneuUYR5/PT4pxJVdWrv6jnrJy/02orBBCCCGEaMLFCiGEEEKKJpoZyNfB0Cak0aXd2ImkQp0abcwGPkmeXM+3hPBOF1I6XeY83xLm2pYxjRUIT+amdU9pt5fTPOQbFu7Sdizzja+ZyicFBpDeDHT7NX9WMwOtuv6DaAYihBBCCNEkq4Ottvpi21ebkORkWiGqoY6PY1EKZoExhV/HDAl2HUOKvlLi4rzcV38aoanv+8q7KNgu7bn0GZrQLreDMpWVtDApHCGEEFI6C+bbEDJToctNSk1K5mMHDgl5bdafpUR5Wj4E2r4IKZWMHMnSSkpH7zueUq5xDWWlr55WCv3QPmOR+1ySKyvXLddTVtbbaHTKynwv1QhpkfsBRggh5N6oKyu+SbFi7aKG+mm2p53Qq69/rcihkPa1xlkKY47aGeN8D5HiWiw1lb5P26FRMrGiB7VUHZs+QyKkXOZLIwIpubJy/V/0lJV1H0hlJRa5nakIIYQQkofRLFYIIYQQMp+M0sE2lfSr1Udoe6EhktPaiVWnr50mOediqF1NSjJVdFGCiVOrrBYpzHpD4cjapo+UpBiXS+JQrbDpJsnNQDdcpWcGWucBNAMRQgghhGgy6qRwNnV9nPNihjfbOM3G3nXG6jOl4uOCdsK9FNdHrPZzU7pKklJ9jK2EpAgn9kkKNytQWUkLk8IRQgghpSPzbQgJVlb2lyUG0A/t094NpSwTylAfpSoYvpQ6Lm1KUxVK8KUZQx8a2HwPfWirEyHh0qHJ6nzqlkhyZeXGa/SUlbXXH52yMt9LNUIIIYQUj5rPipatPefuc4JLsiKXdm3rteunSG2eg1LGMa/My/z7JFec1kZXPW2fFV/1xaUdrSiZkCRwocqKzWsF2vgqQV31Uisrt/31OjVlZdFa61FZIYQQQgjRJEk00Jj8Rab1OSGFyqGxEyR2lDK3OXPg+ODjU9ZX3qVM7u9qGr5KaojPSqgKk8oXpq+sTfm+slp9us5FcmXlphv0lJX7rkNlJRZjd8YihAxT6kKExCPHvZ3Pk/ExmsUKIYQQQuaTLEnhXML4XNJVu6a2tnGU8wkjDk0iNnRevu2NxcRQCtrzpj2nORMezktYcswxuPzOtQkx/2iaUobacynTVy/UkbiL5Gagm2/UMwOtuTbNQIQQQgghmhSnrKSkhHGEjiFHenZtZ+hZcige89jbhKowsUPvY7Tt0r9P6GxXOykZctTN7cuR0+HXFSoraWG6fUIIIaR0mG4/34sMc1PCLtjHT4YQTUpKPliaapKirwk+Piuuvhw+Yb4lKUBa7Wkk3kuurNxyk56yssZ9R6eszPdSjRBCCCHFU5yyorWz0Y6GGPPrBEroOwU5kwbO4pz6JnOLPSe51RctcvuHNNFSMmIpIk1CIpjGnG5/5a23qD1rV1t9DSorhBBCCJk9RGSRiBwiIstFZIWInCEiu1rW3VhEjheRG0TkryJyoog81LZvLlYIIYQQYsMxAA4A8AUArwNwF4CTROTxfZVEZE0APwHwJADvB/AuAI8BsFRE1rXpWN0MpJ3wTSu5VopxlYDv+HKEDw+Z6sb8vqTQcNZSzysnY56bvmu6TV8ySe0kbjnxvRenOofizECKLherLV7sPHYR2QHAmQAOMMYcXh9bDcAFAJYbY57cU/dAAB8E8FhjzNn1sS3qugcbY/5jqH8qK4QQQggZ4gUA7gDw2ckBY8xKAEcB2ElENhyoe8ZkoVLXvQjAjwC8yKbzpMpKH0NJikpxaNVKpa9VVltxcGmv9J1uqeOLdU02sVGrtNG+trXRSuaWcjxtUqbkn9a3luKjOQ6Nsq6MWVlZvPrqNw6VMcas0/x/ETkZwAbGmK1bx3cFcAqAPYwxJ7XbEZEFAG4FcKQx5rWtz94L4B0A1jTG3No3HiaFI4QQQgrHx3TTww0edTYEcHnH8SvqvxtNqbcegEWNcu26Urd9aV/nSZSVWDsurbTvpe3AY8/XhByJ6GYh1LWvvdKupQmhKkJJv6MU55KSUlPfu9D3W451fiHhyBpzm1pZyY2IXArgQmPMs1vHN0W10HiNMeaTHfUeDOCPAN5ojPlo67OXoTIjbWWMuaCvf/qsEEIIIWSIFagUkjarNT6fVg+edf+PYGVlf1liAP0kbqHqgk8St9DdY0kRNbkpdVyzRElqhy2ljadNSr8WH5+VUIb60ooc8o16Cumr77uK4Q80h8pKVp8VKiuEEEIIGeIcAFvUOVOa7Fj/PberkjHmLgDnA9i+4+MdAfxuaKECcLFCCCGEkGG+DuA+APabHBCRRQD2BXCaMWZ5fWyTOodKu+7fich2jbqPAPBUAF+z6VzNwTa2s6uNY6ZvGZfxTRtvk9iOoqWGccckpC9fB9sxmlk0KeUdPKXPsVa6hj6mmTNczTZayeWm9ZnTOdjXhOU7t/NmBgIAETkewHMAHIbKqfalAB4H4CnGmNPqMksB7GyMkUa9+wI4G8AaAD4C4G8A3oAqEmhbY8y1Q30zdJkQQgghNuwN4L3133UBnIfKV+W0vkrGmJtEZBdUi5x/R2XV+QmA19ssVIDMb10O3bmVnoSqDx8H4FJwUb8022/3EdqP63hC+yztGhwrMa8zbfXXpv82pYcuhzrhpji/FH3Mo7KSE/qsEEIIIaRosior2qRIfd9uP3fKbhe4sydNxqzu+eCjiIQmubQZjw2xfFZiKRAuSeFsFFSb+jb3Yp8Q5mljVc4oSwagskIIIYSQoileWdFSLkpTFbR3Z7HbiZliPkcyPZ/xdDFvvlY2lDpmH3+UmOeioWaEprXXSqjmgs/vyncM2u0069NnJS1UVghpUKpTIyGEzDNJlJVYOxqXHCrtOk1S7nBT7dxi5MgIyUNT2i7bBZ/rrKu+TZ1ZmK82MX3JQuqWFlXU1057nKVH0rik22+i4cdjO66QvgEqK6mhskKsoOJAUpA7mVhJcC4IuRsuVgghhBBSNOpmoHlOCV86pc2Fi0kstrksx5yUns4+5vhKuBZTjMEnwaGvWcPG3DLUdg7TTMx2prXbxLcPmoHSQmWFEEIIIUWj/iLDPkJ2bqU4jGoxbwm5tMnx3WklCAup01VPW5HKoY76hoenug5i3n/aaDqB2mJz7dg4AGuPJ5bK1PV90sG2bKisEEIIIaRokiaF80mP7NKeb5lpdVzr+bTnslvRPgebRFDaoeN99W2I9Z27tBMjQd4s+OSk/G5mARf/Ea0Q5hShz7aU5t/i2g6VlbRQWSGEEEJI0QQrK/vLEgOE7chtCfHzCFVxbNrVVjBiRWmUtrstVVUodVzaxPafSuGbljJpo3ZfE3IqDS5RMn1zkTM3jM19Vqs9gC8yTM1olJUSZEtCCCGEpGc0ixVCCCGEzCfFv3XZBi2nxhKccX1xGXts80azba1x2UjmISaxLrSui2nt+qJlHox9zWiZhGMnC9SaN992SkI7yVxffZ90F67jCjFPDZ0nHWzTQmWFEEIIIUWTVVnRdrxzdaKNnTBL25E4h4JUijNoKeMIobTQXu2+Yjnqhl6vOdQXl3FNw1fBiKkmhIwrJ6EOwF31qaykhcoKIYQQQoqmWGWldP+RmGGxJSkf2iHfMdW0EJ8SmwRcNu2VFiZdWp/kblyUFds6XfXHon7EgKHLswOVFUIazPONnRBCSoWLFUIIIYQUTbAZiBBCCCEkJlRWCCGEEFI0XKwQQgghpGi4WCGEEEJI0XCxQgghhJCi4WKFEEIIIUXDxQohhBBCioaLFUIIIYQUDRcrhBBCCCkaLlYIIYQQUjRcrBBCCCGkaLhYIYQQQkjRcLFCCCGEkKLhYoUQQgghRcPFCiGEEEKKhosVQgghhBTN/wf1ww8vOKIUdgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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lqMIdL0dlKvgb3D5s8tGoIh2uQfVD/hVUL84TcftQ0dUATuzo842oVtSrWttXoRUejFtDGR+HSpX4NSpz0xkA9u5o+3bjqLffC1X00GpUfhGXo/oBegeAe3Yc4/dRJaO6uD4/D2iPreeczsb8zDn796r3H9U6V67x73pUE4njUIV0bzynLQfgywHXe11Ukv1x9bHdVF/L0+vj3MmznfZ42/98ztM6qH7Mz62v6a8AHILK5He7NlCtxI+sr9119bXeac6917XtXqh8Oy6tz+8PUKkq8+7Hrep75tf1PXMxKr+XPWLuLdShyz3X8ejWthUAXgzgf+vxzq7TG+a08c26nX0C3wG367tvvKgmz+2w7A0AvKu+hmtRmcheDWCPdtm6/PNRKbY3NvtHT1hz13VFlW/GAXhRR9kP1fueEXI++I//tP6Jc+omT2KUWgE4CsBjnHMnjjsaMhVE5Huocnt0+UwsJCLyVVSh91s659aMPR5Clh36rBBChtgSlelsKagdWfcC8DFOVAixAX1WCCGdiMieAJ6MKkIo+RMJ1pHq8xDbozIV3ojbJ80jhIwEJyuEkHm8BlXo+b+g8n9ZdF6Eygn3lwCe68LC4QkhGaHPCiGEEEJMk6ysrF2zpvoC2gbbDxXNxmHXV9njtcYwa69JStvN9mbtaI05pZ2u4/ShfQzLRvNczzsHPmUWjfY9uCzHPWMZr7kPud4Xudtt0tXHeuuvr5kdnAxAB1tCCCGEmCbZDHSArHKAvroRQgmVYszjy0XqiqSkwjKvr9TVbMgx9JXVaiemvViG+gh9Dubty7nyTWnbmkKY67z5KgUx7WmMsa+9mOcqdlyh55/KSlmorBBCCCHENGrKyoyQlVeXL8e8On1ltNHyWYlVY+adJ+3jH3PVPjZWx2d1XLnRUq1yjiN3XyGK24w+f7iYMcSW8eljRkhfsX1rK7Fd7VFZKctklJVFMr8QQggZj2VbDCwCk5msEEIIIWQ5UTcDzRjDfEPC4OpimoxhKsrt/BnqQKk9jlJ0mSFiTOBTCQMu2YePo+68/THQDFQWKiuEEEIIMU02ZaXJ0Cw3lZxJ4RYh2V2pkFKihzVH25TxTDFZmtb573OSL3UuQoMdhtrRcuptj8UKvsdEZaUsVFYIIYQQYpoioctaSacsoZ2SP3UcfX2nKCtWQkpz9xmy4vXxnbCeiCsn1lShNqlqQMonFqwltMt1LmLGoNVeV9tayRqbUFkpC5UVQhpY/YElhJBlpojPyozcvit9fWr7s5RUUZgUTo+UJFhjRK6UTDcegtVxpWLhvs2pmuRUGnzbmzGlz310QWWlLFRWCGlg3fxICCHLCCcrhBBCCDFN0W8DtdEKEU41k/h8syjEkXVe3ZQx+o4hpr1mm2PIw/Pab2LdfBDCmA6eqeMqGXaq9Z2YlD7HaC/ncZYynYzhSBzyjDAp3PSgskIIIYQQ0xR1sO3DUshyqhKSEk5cEqvKSizaX1odgxA1oeQ1Szm3Uzr/ltBWJ1LbWRaYFM4mVFYIIYQQYppRQpdLKA4hYacafiS+7eRWVEKOLyVFdmj9MbCqAGlTMow45DnKpfhYuRdT/XdS6pf0rYl5r5VM3+/b9lDZNlRWbEFlhRBCCCGmMeOz0qakT0dfX5Z8S7SindrbY7GuXGirAF11c0eqWDvH1j5CV0phyOH3MfQ8xyQuHOpn3v2qrWRbQ8tnq8kRbjWVlYJQWSGEEEKIaUbNs+KDVo4SK32m+O2EjCs0emSMfBA+q9g2MVEyoaviXBE5qWioGlbyteSs32zDt52hPktE1JSI7ko5t7nT7zfRVpVyXDP6rJRlMsqKhZBmK/Bc5MPquR1jXBacVhe1T0JIGJOZrBBCCCFkOTHrYNsmNkS4XWcMs5JVSpouYk0yy05ICnyrDtM5r7lGSv6Sydf6ypR8VjTOW2qfWmW1CO2TZqCyUFkhhBBCyCAisoWIvFNEvi0i14iIE5HdA+pvLyJfE5FrReQKEfmoiGzmVXcqykoT7XC7XCn1Y9rTbDOkz3lMYYWT0k4J5cJa+LEmPgnaQtQEjf5T2vPpx0c1mVdWq4zPuGIdWIcoGbpvmWVUVuqJybcBnAvgEgB/CuAxzrkTPereA8CPAVwF4AMANgTwCgCrAezinLupr/66CeMmhBBCyPLwIwCbOecuF5GnAvhcQN3XAlgfwA7OuQsAQER+AOB4APsA+Pe+ypNUVmaUSNim0cfYfjIhSeFyKw0hK1SrPhh9fc2gCkNC8UlVEPJchiS51PKTCRlnSj+hfWrRHPuyJ4VrTFZ8lZWLAXzTObd3a/vPAJzvnHtCX30qK4QQQsgSISJXDZVxzm2s2N9WAO4G4H86dv8AwJ6DbaQqK2vXrLlNAzGz+Xn1fPFJp+5TP3UlEkIun5eY4w4l90omxGbv007oCtUSUxvvELmUuzFW19r+GmP6Ro3pm9ZkzIR71qOBNK0Y/4pfXT1UZmiyEqKsiMhOAH4IYG/n3Cda+w4B8EoA6zrnbp7XBpUVQogZlj2dACEl0FRNPFm//ntDx761jTLXzmuAocuEEEIIycma+u/Kjn3rtcp0MmkH2xmxzlwWQpbHxFryrxJJsRbBrLIIxzAVrJxrn+9opYRUW0M7gVyOhHRTNgNpOAcHmoG2AvAbAP/onHtva9+xAPZ0zm3e1wbNQIQQQohx1plw7JFz7gIRuRTATh27dwbwk6E2zCkrKc64IQqLb98pjqtazre5nHljHU9LrTatJJ+y+skAK6v+Nn3jKpHSP0f7Pn1bVf9KhCP7lLV+nkIpray8eIXeb+2HbsmrrIjIfQDAOXdeY9vhqPKp3K+RZ2UPACcA+Fvn3L/19UdlhRBCCCFeiMjr6v+czYL3EZFdAVzlnPtgve2b9d9VjapvB/AsAN8WkVkG21cCOA3AMYP9jqGspIQKz6s3r502semph8bXtS81pDqGEBVm7FBLjXZDr6d15ahrn+9YUo83pE5XXY3zlVMFCDnOVDUhxMckpa8c97/vGPr6zBWa3qwXojznSGBZWll56Tr3VlNW3n/z+dFjF5F54/iVc25VXWY1AMz+v1H3TwC8F8CuAG4E8GUAL3fOXTrUL5UVQhosguM0IYTkwjk3ONFpT1Ia288CsFdMv+Z8VrTJ4ceSMgYff5uhFUOsymRpBW0FrVVeSnslyJUgL1QpCLm3tdDwudC6/0tGzIWov7FqsIYvWapKXUIh7iqzrMrKWFBZIYQQQowz5WggDZgUjhBCCCGmMWcG0gpd9pGXc5uBYp1dS/lNLEoIYQy5x7yoZrNcxF4PC07e2mg5xmqbOEPrD7UTa/6JGU9Iu1aTwh24rp4Z6NDf0wxECCGEEGXWkcnNL1SZzGTFJ1FbSCh0c19f/SH6FBvtJG5aaK9WrCkHPisjH8fYkg58IXWnpmT5rNZLhLr6tKvlKJor7DrV8XdIaUhVtkLqh7znQsOS550vn/H5BmUc4VYPtkX0oM8KIYQQQkwzqs+Kj4+JdqK2ef03GTs5nIbi44PVlbl2MqucK94xsKD4kFuxpn6F+OuVvIcW7X4r7bPyqjtuo+azcvCNv5ycTYnKCiGEEEJMo6as5PazCCU2EqddJ5fKkZqsTnsMU1v1WIseWSRSz9GynWMtZSUmQV5oUr6Y9mLqa907mtE7XXW66vleTyorZZmMgy0hhBCyrCx7NFCysrJ2zRoH+M1GY3KfjJ1/xIpSlIMxVs6pqerHWPVr5Y4IIWa1nhoZElI29TpOmZLHl+uah9QJYdGvfZPSysprV95HTVl5+w3nTW7mk81nRftHfpEnDWRaWHCatMIy/CiNhdVrTsgY0AxECCGEGGfZvw2UzQyUuirwacfHMTbGtOCTgC7WgVf7PMWQGspYSuodI8lW6rhSTVkhDoYhppmpMMYxhDzvYxHjeLpIaKcz8OljyE3hCLe66PThn9fTMwO9ZS3NQIQQQgghqowaumzNkTU2udxUSXVkDSkT0qdP2djxaLQXkspdczwahIw9xmHTt551SigXKSqaT7t9xDiYjxnK7qOaxz5z886XNWXljev/sZqy8sY151JZIYQQQgjRZJR0+7lUihK+IX2roKmpLjlDhBeZRVUT+li2+8H68cYqn6XUR62yM0LVpiFVNFUlAqislIbRQIQQQohxlt0MUlRZyRUplNpujK9KCf+W3H4yU1BWLEYp5RhDSrK6WB8H7T5jxjcjtswY5L7PSt5D2lgYQyixYy6dFO7NisrK6yeorCz7ZI0YZmpmNUIIIXmgGYgQQggxDr8NZMwMVDJEOCXc2sfBdsqJ33ywKtNbZUyJPMXk02SMkNd231YSx+XqYwzn13ad2Hq5TYqhfeSktBnobRtsq2YG+qfrfzG5mQ/NQIQQQggxzSihyzNKhP2WTPTWlzwsd98x5FRftBNBtSmxWrREzk8PaLej9TmBMcjlmKzFovetdX+kOIL71i2trLzzTnrKyquvo7JCCCGEEKKKGWVlRq7kbVpl++qEKCvz6sTW1wqT1kYrEZQFxSdE6dFWc3zusymGulrDQqi8Vtr9kD5y3guW7jPNRHlUVsrCaCBCCCHEOIwGUlZWLPhiNMmdHt9qNJAPqVEaFlZKOdBeXS/6+ZqxLMdJbDHW5y9KKyvv2vC+asrKK6/9+eRmPlRWCGlgdWJJCFlu1pnc9EIXOtgSQgghxDTJZqC1a9Y4IG5FmmqSCUky19dP7vBmLVNRTmda7a+UDrUX02ZOtBNUdZWZssnIQsKyEu1pOYRrM2ZCx3nOuL7jGRp7qBknxTk49hns2lbaDPTeO+uZgV5+Dc1AhBBCCFGGDrYjhi53MW+2nNOXwFqKfw21xUe1srZ6n6EVuhmTnKxJiGqSSsoqfcy05TmVMqsqkxYaKep97ts+la9v+xjh2ynPZV97IXWtJoV7313up/Zb+w+/+9nkZj70WSGEEEKIaUb1WWmSom5o+3L4rERSx5FLzQk5hpxJpzSI9ftYFpblXEzxOK2POcWnLFZNy+XjU9L/qXkMR7jVRdWJD26kp6y85GoqK4QQQgghqpjzWRmTXApNajRQbhXGB6tRPFqU8OUYIz27VnsWlIIS0VQWjnOGlsqRqoRone/c51brOK1GAy27ssJoIEIaWPiRIoSQNowGmqCyEqM0xMbVxxCrhIR46g+1EVO+iaUVpi9aUURD7fu2kytnR6rtP3cukZT2m/VSr2fua57rfovt0wKp0TsxfY11LkorK4dvvJ3ab+2LrjpncjMf+qx0UCKEuU0u00DJPq0yhvlsjHvIh5BxWX0OfMY1psl0yn0SYhWagQghhBDjLPu3gcx/dVnbpNJVZkaJMac41OY6t6SfseVmC1g4B2OOoYQJsGRZLeVV29TW7lvbrOTjPOvbZ2kz0Ic30TMDvfBKmoEIIYQQQlQp4mCbK/Q2hFS1Q6NOKCGOhlp9tdv1cR60sOr2ZczwYQur7Vi0+pjSvTKPUudCOxS3i5ikcCFj6Gsn9jxauYdKKytHbqqnrOx3BZUVQgghhBBVRvFZKZEIzYKa44OPWpIaqj2vnpVVu1ZfVlZclhk7ud9UVTmfJH+hiQCHFJAc52TeeGLDza29Q3zR8IWhslIWRgMRQgghxmFSuBGTwoWu/q2rJPNITdQWYoOOHdcYlErspdWutRViyjHE1iuRFG5GLvUl9z0V2/YYSeZKMsbzk+veAcp/yPDou26vpqy84PKzJzfzmYzPylQnKiQeXnNCCCEAzUCEEEKIeZgUboLfBophKg63TXwc71LMZwxDvT0lQze1CQkX7Svj00cJ092Y4eCp5DIzWjOJlTr/VkxZTUo72B67mZ4ZaJ/LaAYihBBCCFElWVlZu2aNA8IcR6ekbsSgnYAuti/ful3ErPJyJrEqFepqcQWnifbxaSk1IW0s+jXKiXZ6/Kk4voe8q3zHUFpZ+fgf3V9NWdn70p9SWSGEEEII0WQhfFaWRbHpIzVxHPEnJH25ZXKle9cmV0hvX18zrCQo1PL3aJPqazKmUhOj7Gr6BVFZKQujgQghhBDjMBrImLJiyacjJ30+GCljDTleqypA6kqrr+6YialmjDmuEknhctH3rMzzO+jal9pnSr0pqFWl+yxxTkLuHd/6pZXcsfa5AAAgAElEQVSVT95NT1n5y0ump6zQZ4WYxepkihAybawtXskwNAMRQgghxuG3gYybgayacWbEfvcnxhE2xGTUVybGDNEl1ZZMKJVSp6v+jFzJrEqaIbQdIMc0+YydTK+N9r1d8vj6UgC0y1hRMee998dOYNl1Lkt/G+gzm/+J2m/tMy8+a3IzH5qBCCGEEDKIiKwUkYNF5EIRWSMip4jIHp51HyciJ4rI5SJypYicLCLP9u67hLLSt7pIqROTRExboYl1jC2pGPmk7Sc6jBGymuo8ONRuVztjhuaWHJcPJfpMUdi0Q5en7KQdiwUH28/dXU9Zedpv45QVEfkEgGcAOAzAuQBeAGAnALs5507uqfdnAL4I4PsA/rPe/BwAjwSwv3PuyKG+6bNCCCGEkF5EZGdUE4wDnXOH1duOAXAmgIMBPLqn+osBXARgD+fcDXXdjwD4JYDnARicrJjzWSH5yJUCP5ahlWCfUmB9lRa6mh1D9Qo5p7nuB+2wbuv3RSzavhszUn1qLChZYylby6asiMghAF4GYFPn3LWN7a8B8DYAWznnLppT9yQAGznnHtDafiaA3zjnnjDUP5UVQgghxDia0UAictVQGefcxq1NOwI4pzlRqfkBAAGwAyr1pIvvAHiNiLwFwNH1thcAuC+AA73GbF1Z0UqW1te2zyo4JuompIw2msmPSuOzwp+hkTa7FFYiGyz3aSVp2hho+YZo9O27L6VsyXGl1JlHaWXli1s8QO239i9+e9bVQ2Xak5VaBbnAObdXa/v9AZyFHt8TEbkTgH8H8CxUExsAuBbAs5xzX/MZ82SUFauhy4QQQsiU6FBNfFgfwA0d29c29s/jBgA/B/BpAJ8DsA6AFwL4lIjs4Zz74VDnk5msEEIIIcvKivGTwq0BsLJj+3qN/fP4AICdATzMOXcLAIjIp1ApMoehigrqJdkMtHbNGjrYtpii3D+mecpSe319WFH3UhxZF9W0sujHZwFtk4wVYsde2gz05S0fqPZb+2cXnhHjYHs8gM2dcw9qbd8DwAkAnuScO66j3h0BXAfg7c65N7T2vQ/A/wOwvnPu9339MykcIYQQQob4CYDtRGTD1vZd6r+nzal3V1RWnHU69t2h3jc4eRpVWcmZDlrL+Sp3cq1c+PTZl0K/zRhOwjkYSpjl47CbYywhIaU+ZUKu+Rhoh2prtTdmsjuSnyk72H7lng9SU1ae/H+nxygruwA4BbfNs7ISVZ6Vi51zu9bbtgawgXPunPr/1wFwGapIoQc7526qt28I4GwAVznnHjjUP31WCCGEENKLc+5UEfk0gENEZAsA5wF4PoB7oQpDnnEMgN1QqyXOuZtF5N0A3grgZBH5GCqVZT8A9wDwCp/+J+2zYm3VMpUQ4ZRQ7a4yPiHG7bqhZXOrOSF2+D5Fqi/sNCQBWuhYSzHlsGlr74t5WBtn7oSFY6t9U/FZGVtZAQARWQ/AWwD8NYBNAJwO4LXOuRMaZU5ElX5fWnX3BvAPqHKrrKzrHuyc+5xP31RWCCGEEOOsWGf0aCA459YCeGX9b16Z3eds/ziAj8f2PWllpSQhSkFq+yFppFPUnFT1pGvsQ+2F1ItNuBfi75GqMoWQq70ucvla5Vr1x0aaWFMhQhjjEwuLSMinH6bss3Lcqger/dY+cfVp4898AmE0ECGEEEJMU1RZWTbb84xUm2zu1WwfIav3VMUgxN+ja3zzVqqx4/LxR+kaR7tMil9LyDjHYGx/gxByvzemdC5SyHmcU/qNKK2sfO0+O6opK08478dUVgjRYpFf+IQQQvzhZIUQQgghpikaDaQV0mvJlOJDqiNkyvE26/f1leLUG2oWGULbfBNbpr1P+8u0PozxBd4QSiZfS32Gc6cUCL32Gs7POe+FeX3k7NPSvd1F8/od4VYX7VsMRAONCZUVQgghhJgm2cH2AFnlgPTVf5uYFWpomvyYUNcQUlc/uVN/x4T8dZWJUQpi68fQd1/4OL32jTPmXGidA+urUC3GCJceYzy5sKrKWWfovJV2sP36fR+i5mC718//d3IyDZPCEUIIIcaxkBRuTNRCl3MlMJsxtjoRkno9N7Ghsz7ttOvEXjOfFPMx1ziX70tsCHSKf0yqwrIIq+QQZcunnZi61rCm3ISE3Fv1scpxTksrK8dv/1A1ZeXxZ/9ocjMf+qwQ0iC3SYoQQkg4S5duX3vGP4UESUPtN0lJhNbXR4gCEUJo1NMYykWIopKyUvXxt9GKZNJSQlIjdHKptalK8dD1XBQ0FC3tCCnf8QwpqENjKa2snPAnD1P7rX3cWT+kskIIIYRYhgrq9OBkhRBCCCGmWSgzkDWnrhKkyMuxDqPz6vS1HSKVhxDabmrCuaH+Q0xrffUsmA9izUo+bY5xfFrPitV3i8a5DTWrxoxraLtvmZj6Maa7eeVLm4G+9aCd1X5rH3v6D2gGIoQQQgjRRF1Z0Q5hnhKWwpt9iFVPQo4zt8oRG+4c40isrSb0of38xDpIh7Rr6d4uQS7FgZQl9jpQWSkLk8IRQgghxln2bwNNxmdl7BWcVqilJVKVldB68+rGhOIOlR9qP0QBDCFEQYrxXRmqn4J1VXQqzxUpw9j3Q2ll5ds77qL2W/uYH586uZkPfVYIIYQQYprJKCtjM/YsPgcxKeL7yqb2OSaxytGYfVpQN8ZWPK0ylffFVMaphebxllZWTtzpEWq/tbv/z8lUVgiZMlYnU4QQssxwskIIIYQQ00wmGihHsqKQvrS+WWFJbvUJRx6qN6+uj7NrSCK0mG+QxIQh950TbdUlZ3taSdza5L5/Sz7n2miPvcR7I7XdMa5NyrvA+j3Ux4oljwaiskIIIYQQ05h3sF2EGbEWsV+YTUmA1kXuMrHOvT7J6lL6iC3rE7o8pERZCV1Oba9Uun2ramYqqSkUFul9WuJ+7dtX2sH2u7v8qdpv7aNP/f7kZJrJmIEIIYSQZUVWTG5+oYp5ZYX0o2UT1yDW38NHCUkZT6qy0lcnZOwhKkmMXd4qi6ZyaH9Ww7rakfLBwK5tU0462DyuI9zqorOH7z3ikWq/tY86+aTJzXzos0IIIYQQ0yyUsqK1gkv16i9pS9Vot4+QVPWh0Tch7Qy1G9qOVpp9bYXGx39nypFpU3k2rPU5D+3r6dPe2PeQFUr7rJy066PUfmsf+d/fo7JCyJRhUjhCCLHHqMpK7Cw+1wfrtGypoV7lKYREV6T+EA/1Eeob4hMl00ZbwdCKbAodx1DZPkKiunwo6Utg3SdhqG5X/djn3ZJC04eFcY6df8dCNNCyKyuTiQay/kAvOjz/hBAyHsKkcIQQQgghdpmMstJkngybI3HWkMkjNOFSioQZ0r7PvpDzFWsCiUmEFtpOu0x7X1+fJZLfhSS9iznP2maqrnOb4mxpxSEzxFTqcw/NtsUe01TUyjHMLj6MaUoEgCPc6uL9LzOTnKwQQgghy8Sym4HUHGxzh9damc33YT2RV4hTqQ+pDrZD7YY4V/u2E4KPA3Du6KGURHJ9ZfvI5QTeNZ4Yx/WudsbEejh2yfDmEu9rK78JpR1sT37sbmoOto/41ncmN/OhzwohhBBCTFM0dHnsULPQuqH1Q9M1h7Yf0mdssrQYYsKTU/uakbNP7bFrt5tbxRwjEV3OZy+kbMn2YurH+sxZJ+X9mHq/hp6/0srKqY9/jJqyssvx36ayQgghhBCiifl0+yUjCRZhZdJHLv+KEGWlz98jJInbvDZCx9zF0OrOx5cmp19LiVWjJaaaSC62bSt+H6XOe+g7PlckZei4qKyUZTLRQFN8yZI0xg5NJIQQKyx7NBDNQIQQQggxjfnQ5VSsjssaIY5t89SHVAfDvvZC+vRRR2KS/KU688aG5w6NawxiEwpONbVBbPI7C9cqlpSw8lzXudl27nNrzQz0gyc8Vs0MtPPXvjU5mWYyZiBCCCFkWVmxYnLzC1XMONhaX4H4hA5qtN/VpnYYZVefKcQ6q4akodfo26edEuHOPmpQbDspWE9uZpXYZ67UeRmjT21CUzGUeDZKKyv/86Q91JSVnb76zcnNfOizQgghhBDTmFFW5jFG6OCU0VJWQkKOfeq3t3eNIyThVUj6+ZxqTojiFnIu+sa3SKHLixqWnAurY05NeqfVV0qf1pPC/ejPH6/2W/vQLx1PZYUQQgghRBNzykrM6tOnbOysfhHt+CnRMn1lQvuK8c1J9WfR8oeZ117qfRYyhj61ycLK20risdz+Zj59LhvW7sUQfK8dlZWyMBqIEEIIMc4KJoUjhBBCCLGLGTNQru9klJSiU9oZO6QxhHlyemrStHl1Q+unONWFjjPk20AhTstjhC5PmZJml1LfpglpN0fbseMAFuPetJYU7sdP3VPNDLTj578xOZmGZiBCCCHEOMv+baBRlJWcCdVmpKobGu2ModSkpkFPSYqWqiZojmXeuIb69mknNbV+iLIyb/9QH2NixbF2EQlxquY5zktpZeUnz9hLTVnZ4b++PrmZD31WCCGEEGIadWXFgv+HT7u+bVsKXdZO692nrMQkOWuS4nMRq1z4hP8OHV8JNUc7EV0IPmpaSCi09vPkc0+GtDfUR5uQ8GZtBcmCElLSByanWhjzHutTMy34rJz27CeqKSsP/tRxVFYIIYQQQjRRU1ZKKhCWvPGnjJaK0EdMBMy8/X1lu/rQVmh82oldtWuNvTTaK3Er0S0xhCoFKarLmO8zK9do7N8BKitlYTQQIQ1KTOAIISSUZU8KVzQayJq6UWo8PvlHxl4hzYjxNYm1PcfkFulqf0iVCPX3iLFh+/TpcwztfWPnWcl9f1r3cZsi2qr0vDZS2+lrewrtllZWzvirJ6kpKw/8xFcnN/OhzwqZNNpKyFTMLyWgykQIsQInK4QQQohxZIWo/Yseg8hKETlYRC4UkTUicoqI7BFQf28R+YGIXCciV4jId0RkZ6+61pPCpcp3FswsWmGn7TaaaIWx9vURQ0xSuJj2rbbXbDMmVHnMZHBjOFJacd4ck1zvDc3xWKfEmEubgc587pPVzEAP+I+vRI1dRD4B4BkADgNwLoAXANgJwG7OuZMH6r4VwKsAHAvg+wDuBODBAD7vnPviUN90sCWEEEKMs2KdcQ0htQLyHAAHOucOq7cdA+BMAAcDeHRP3T8F8FoAz3DOfS6qfysfMhyDqSVs0iYkAVdIAjktfJxgferHlo0JRw4hxMG2xCo7NcGa9SRiuUNdY8/F1MKSqX5VlFZWfvq8P1f7rb3/MV8KHruIHALgZQA2dc5d29j+GgBvA7CVc+6iOXU/CWCVc24XEVkBYINmGz7QZ4UQQgghQ+wI4JyOScYPAAiAHXrq7gHghyLydgBXA7hGRFaLyHN9OzevrMSuLDUSLvnW18ZCeGFMyGxIKve+vmLDdrVDoee1E3sv+vQfcgxWV7VjJ+tKwYKqYSHRWuw4SiiBqQrgvLqh7ZZWVs7e9yl6ysrRX7p6qIxzbuPm/4vImQAucM7t1dp+fwBnAdjfOXdkux0R2QTAFQAuB3AzgDfV//9iALsCeLqPaYg+K4Q0YLguIYR0sj6AGzq2r23s72LD+u9dATzcOXcqAIjI51A56b4ewOBkZZR0+xZWEH3kWF1o9JlrhdIkxh8l1WdFO1qmr4yWj0kbn/MVotCk5nuJuX59lHxmh/oqoXxaU1vnETquMfxjfFSNKTJlZWX7o74Y47MSq6xsBuBSAOc757Zp7TsUwD8AuMuQDwuVFUKIGahsEdKNjBwNBOAiAFt0bJ9tu3BOvStQKTIXd+y7GJW/y0YAeicrox89IYQQQszzEwDbiciGre271H9P66rknLulrrtVx+57oPJjuWKo82Qz0AGyygH6UrJ2MrjY9ny+6+Lbt2/9GDSczvoI/Y6NdjhzinkkNcw59ZxqOxvnMgOWuCctmYBT3wm5ztcMrXBpn+SPIc9wqHPqlB2u+yhtBjpn/6eqmYG2+7fPx5iBdgFwCm6bZ2UlqjwrFzvndq23bY0qNPmcRt1/BPBuAHs6546vt90Flc/KOc65uTlaZtAMRAghhBhHVoxrCHHOnSoinwZwiIhsAeA8AM8HcC9UmWxnHANgN1TmnRmHA9gfwH/VfipXAtgPwMYAXuPTv/mvLlubYceEqlpwSuyr0yRkNRXbx7w+23V81ByfvvraaZfxSX7XR6xj7NDxjeHgObZTqfUwYq3xWXvHlSJnkr95+zTVtNLKys9e+HQ1ZeV+H/5sbLr99QC8BcBfA9gEwOkAXuucO6FR5kRU6felVffuAN4F4MmoIod+VNf9rk/fVFYIIYQQMohzbi2AV9b/5pXZfc723wLYJ7bvUUKXfSi5srFKbh+fWF+OEMUh1V8khhj/lpAQ6NTj9cFHWVkW5YHMZ2z1ax7a48p5nLH3aWll5RcveqaasrLt4Z8pOnYNGA1ECCGEENOomYG0Z7tjzp59bKklVo0hbWutcH18VuapB12rn5AkaanEJLQrkYguBasRNSH3UgksnJOSLOrxzjuunBFbi3YOFxX6rBBCCCHGMZAUblSW++gJIYQQYp5kZSXFvJIqv8WYSULD5eaZFmKTwvkkLBs6rthEUDEOoqnmDC0TTJuucxsjIWubjHzMNj4hlzF9k2mREl4bE64b25cPKUnhtJxnQ96LNP1MD5qBCCGEEOMsuxkoebJiNWxyHiEJwoa2DfURu0Lybb+L1JVDyOq/r6yPYjHkzKulysSuuGbbtJyNfcYY4uDMVeKthKiFVs7XvHGkPrsh7eU6FyVD2vkcLAfLPVUjhBBCiHmKpttfRGL9R9r7Ymy9fX2F+EN0kRqCq52YbahuV30tX5WYccaGWobABIxxWPDT0ByHhfZyq1ehPnja784uSieF++WBe6v91m5z6MeZFI4QQgghRJNR0+1bTRXdZJFWlG1ypttPUQq0ImpKJKRLUVt8ksvlJOSczqvbxMIzor3aTu2zZHtjHJ+PohIS9TQllY/KSlkYDUQIIYQYR9ZZZ+whjMrC+6xYmYVbJDT/y7yyfW375HzQUjdCSI12mlfHZ1yx0VO+7aeSMy9HTC4Qn3bGfM6noBBrw/dqeWXl/Ffso/Zbe+93Hzs5ZYU+K2TS5HTkS2ERXuJjHEOsCcoCi3DNfbB6/sliQzMQIYQQYpxlTwq38GagXGg7wWmnnNZyPI1tZ157fWXa7fuQakoJ6aPPfOMzrpA+QsbV1U/OkM1QQsP72yyqYuHzLphh/RyEvANyv/v6+tA8j6XNQKtf9Xy139pVB3+UZiBCCCGEEE3UlZWSYXclnLw0+vBZKZVw0iulPKTW1x5fSN9aZX3qhYRmp662p+wQaWHsU3KizRW+HRvavqih4qWVlV+/Zl81ZWXrdxxFZYUQQgghRJMiSeEshBVOMVndGL4EKWpGruRwqfVik1nNK+tbb6gvbV+fRSA0+WC7Xuo5KfmusqAYhWA9YVvp9zaVlbIwGogQQggxDqOBRogGik2nHpK2OXVfSBmLxPrJxKz2u7aHRDhoRR759J0SdRObHG6ovdC+pnpPNlmEY8hFLp+MZpsaviux9UugkTRw6L1xhFtdVJ34v9ftp6as3POtR05OWZnMVI2JiMZlbFmXEELI8kIzECGEEGIcmoEKmoH6JP2Q76jkJjW5VkzdMdBWLmIdbFNCl/uSr4WYW3zG5UPfva1h0upqJ5ZSYawx7fa1rZ1ELMdzSjPXfKydm1iTUWkH29+84W/VzED3eNNHaAYihBBCCNEkm7IyxuontE9LSkhJp7UQB+euMcWEBvfh44w7r06oI3HIWObVy+lg60OulOQW2hvqQ6MfK899itOnFVWiTWzwRIlxaFNaWbngTX+npqxs9YZ/pbJCCCGEEKKJmQ8ZWkmh7NtOavtT89+xupLrw0qCr5LJ/VLQSAwY2+eM2CR4ucecQ1m0kIZe+z1WglK/FUPvZCorZWE0ECGEEGIcRgMZUVZmLEIyrJgkZ9rH6+PLYZWpXOexiYku6iM1kV9IssCYJJA51ZOhPkJ9mVKUlZx+emQ+oe+d0srKhW99kdpv7ZavO3xyyspkpmpMEEYIyQF/5AmxD81AhBBCiHFoBhrx20BNcjsfpiR3C62fmxzSb6kEYaFJv9osUphnE437NNYM4WNuSUmi12faCRlvap+a7fvu6xvDFO7LIRbBwTaW0magi97xYjUz0Bav+RDNQIQQQgghmphzsNUmd1rwsWf3PixC8ikL47EwhibanwoIwcd5tiQhjr/afaW208bafRZCqkJYStkN7aerXmll5eJD/l7tt3bzgz5AZYUQQgghRJOFV1aWlUULaZzyajMXJVL7a7eXazwzxowa1ApdjmWMzwiU8nXTItZ3iMrK+DAaiBBCCDGOrFhuQ4gZZaVUSvLcq5kcfeRCS33xSehVok+ffSlY+wBbiIoQo2jk8PsopYTk9Flp9xF7TuexbEnhYo8h9ydVhiitrFzy7n9QU1bu9or3TU5ZWe6pGiGEEELMY0ZZmeGTmnsMSsziU3JQ+KgaseOKQdPrPgdjr+ZS0IpCya3MlCBkXCEp80tEWlnPTVJS4bH+XFn4kOGlhx6o9lv7RwceSmVFC0sTlRJo/QhM+XyNMfapTVRSmfLYtbA24cpNyeNNmagQ0ofZyQohhBBCCKBoBrK+6gwxv4SkhC/p4NmHT5/Wr5F1tJyptRJn5Uq3H9KGD7Hp9rUcWOedp5A+SzgbhzC2c+m8+qnPSIhjfopJPfV4AeAIt7qoKeWy9/+jmhlos5e+h2YgQgghhBBNzDnYakM1IT9WnHNL9ZHDOTdVbUkh1kk1ZQy5nXz7lJAQJ9quNqboYDuP1ERyGs+qlfDr0OeztIPtsisrTApHCCGEGIdJ4UZUVkJm1FMONbWCNV+aofFo2alDxxXTXggllaM+QnxW5pUN9aWZN84Udaevvb4yIVjzWSHjU1pZufyDr1RTVu76kndNTllZ7qkaIYQQQsxj3melpD1zkVK4W7EDa7GICpl2Iq3QFX+IWjVUN3VfqnIRo8zkijIKHcdUyPl+tPh8Dz2fpZWVKw9/tdpv7SYveieVlVxYuokJWQbGSNbFBGHLB6858WEykxVCCCGELCdLkxQuF1rmlpzf1sj9XZB2ndB6IeRKhhWbzGrM+z5X2G+7Tlc9Hwfb0ASMMWi1U6rdkL5iw4hj6lt5f8ccg1ayxnZ7Q22UNgNd9eHXqpmBNn7h22kGIoQQQgjRJJuyEjrbtTKzB/oVAp/QzZj2+vBZsc4bX19fFs51F5buhSbWnAhjHDxjk6WlOMbGOs/6hEsP1Y2tH0Kqg3MMXfdSSAj6ULu+40vp0+c3wuc4xwpSoLJSFiaFI4QQQowj6yy3ISRb6HKs4jDl1X8pRSCnb0iKzXjKas4YlAhd9ln1pyRki1UpUlSOkBDrrr60wpy1EtnNQ0vlmCJTOJbSysrVR75OTVnZaL+3Tk5ZWe6pGiGEEELMM0o0UKytshSLmlBtRu5InZx9WOizbxy5VsA+KmQT7VV/rjT27TGkKhjtOrH1UhWfRUwKp03sO97HN6oEpZWV3x31ejVl5S77vpnKCiGEEEKIJpysEEIIIWQQEVkpIgeLyIUiskZEThGRPSLa+aqIOBE5zLfOKNFAPsmitMKcY0wgOb59od1OSHsp0nZsuLRG0rWxHYlDrlVuCTo05DjkWmuFz6c4rmo5sMaYmLXMaT5YS9+g/RyEENtebjN2yXMQgpFooKMBPAPAYQDOBfACAMeJyG7OuZN9GhCRJwN4dGjHJo6eEEIIIXYRkZ0BPAfAQc65g5xzHwbwWAC/BnCwZxt3BHAogEOC+091sD1AVjmgrFIw5gpkiimsyWISsir2oU89CXFq1Hb81VY1fMY3I2ffU3kv5H6PWT0n1pLCXXPMG9UcbO/8vDcGj11EDgHwMgCbOueubWx/DYC3AdjKOXfRQBuvBPBSAPcDcB2A9znnXubTP5PCEUIIIcYxYAbaEcA5zYlKzQ8ACIAdAMydrIjI3QH8M4AXO+euFwmbL6lNVnIpIl11YkItY0MvfXxo5vWZGpoaMj7ixyIrZKkqQEzStFBfE+1U9yHEHg8Q7o81BrnC50sktOuql1I35r1v/fnWRESuGirjnNu4tWkLABd0FJ1NULYcaPIdAH4G4GODA+yAygohhBBChlgfwA0d29c29ndS+7s8D8BuLtL3ZJSkcIsAVY50ckdGlfzAmQU/Ku0VfmxitXb9MZSHUH8bnwirefVTPznQHoN2xGHI/dqniJR458WMK7X92PNe2mfluk+8Vc1n5U5/9boYn5UzAVzgnNurtf3+AM4CsL9z7siOegLg+wD+zzn37MZ2B/qsEEKIH1bMOIQY5yJUpqA2s20Xzqn3NAA7A3itiKxq7btLve1i59yavs5H99ghhBBCiHl+AmA7EdmwtX2X+u9pc+ptjWqu8S0A5zf+AcC+9X/vNtR5tq8uN/GR54ekuBwS5LKZrqziI/22mXIoaBep3wtq101J9OaTHCuEkHHl/KZPyLslpl0fc4uPQ3LJezv1Hahheg09Tivv7dJmoOs/+Q41M9AGf/maGDPQLgBOAXCgc+6wettKAGeiUkZ2rbdtDWAD59w59f/fB8ADO5r8HIAvAzgSwPedc5f09U8zECGEEEJ6cc6dKiKfBnCIiGwB4DwAzwdwL1SZbGccg0opkbreeXXZ21CHLp/nnPu8T/9FlJXchKR7zzkbz+UwOmPslcQQqSskH6WgZPLBobqp40ltJ2TV3tVnStm++jFKRYjy0LWvjY+jaEyCur525vUf0uZQuzFoO9hafw+VYtmUFQAQkfUAvAXAXwPYBMDpAF7rnDuhUeZEVFE/vX3QwZYQQghZNFasM/YI4JxbC+CV9b95ZXb3bCtowrQQysoYxCRg8i0/BiE+Q1NecU1prEDY6j00FDdkZR/id+aDzz0UE04c0vfY7ZRSVoeZ0PIAAB1YSURBVEq07XOfpipZKeTwpSmurHz6ED1l5VkHFR27BowGIoQQQohpsikrqemWtdMjl/B5yOUHoeXLEbL68SE1ekFr7PPKxozFt7x1YqKBShLjHxNz7Zv1Ulb4Oc7RItxni0Csf2NxZeW/3q2nrDzjFVRWCJky/AEhhBB7qCsrVn0CSqSKTiHEl6BZJkSJGmq/q46lczQWue38TVJVuRRColpiFYeQyK+YdlPaSG0vNs/NvDo5o4FKoq2Wj/Fu6rqeR7jVVFYKwmigCcIJBCnBGOahMfok48Jr7oesM3400JjQDEQIIYQQ05gPXU6VELXTSY+JVri01jGVSK2t0efQOJrtjRWiPeTk52MCtLpC9QmB7ivb3qd9nGP02ddHTnOjhgP9GMkaY83kvu2Hjg8obwZa8/lD1X5r13/qgTQDEUIIIUQZA0nhxmRUZWVsx68UYp0jU1YOMWVJXqyoVDFohPCGOHaH4jO+MVQlrdDnIeVO6zkv8b7wUflSFBEtNJWg0qHLa774fj1l5SkvnZyyQp8VQgghhJhmaUKXu9BK4jYjd5K6VN8crTDn2CRK7fY1VouL4IvUJOT8t8tYVRdS6/n4TGi039WHT58hfcQoLPO2tbF6Tw9BZcWPNV/+kJ6y8mcvprJCCCGEEKLJqOn26Xuh72FvPaIgtf8xEkFZuze1o4DG8A0JURO0xpCilqQqK0NtWqEv6qy9fdmhslIWRgMRQpYaqyHfhDSRFcttCFnuoyeEEEKIedTMQFphd1al93mMHV64yI53TSyYgXIlwPJp0ydZXSpjmoNC+sxhkglBK4w7l6k2JS2CT7tjhEJbpLQZaO1XD1czA633pBfRDEQIIYQQZZY8KZzaZKU9A9Z2qNRamWivcLRm/rHJwHxWebkddUs40Wo7afuMWfueHmo/tax2CG5Op9KUhGolVKYYxg7BndenZXWiTYxSPKZqRcpBZYUQQgixzpIrK+Y/ZNhFyKrRwkzYaoj2GIntUklNS587jF4rzXhMAr++PkOUjNg688qHJDGMTTI3r/1QSio1XP1Pm+I+K984Us9nZc/9JuezwmggQhrwRU8IIfaYlLKS4n1v9UeoxGooJoV+SLtWz61VtH18YrGUpt/H/yBWSYpV4Yb60jr/8/pe5ueq71zkisoLba+0snLDCUep/daufNy+VFZywcRNhBBCyHIymckKIYQQQpYT82agnKYGS2YMbQdPC8c0xJTGapEY816os3G7bN810/6WTwolEsdpfauITJPiZqBvHaNnBnrs82gGIoQQQgjRxLyyogVX8cuHlWte4kvYGpRIfT+vfs6kc0N9hIZfL9JXl0s6+Fs43i5iP1NAZaUsTApHCCGEWGfJk8It/GTF+qw+hpxJ5mI+3qed7lrrmuVM/2+xXR9iFYIY1aWrzzbaY+jrY97K2UeFCfXjCUmQNzTOnJR4Rqy/exfhMwXLAH1WCCGEEGKaosrKIqocJSlx/lI+3qc1Lh+/A5++fCJXSqwIY8beR4rPREiK/2bZefu0VQ7fsZemTzUJUXz6zr811cUH7XvbAlaPRdZZbjMQlRVCCCGEmEYtGsjqbLSPKY7ZAtbPW8xn5nP0oY3FLM4+ikMXMceS4jfTVzZ1XLHk/qjmlBh6nkp+jNSX0tFAN37vP9Wige74qOcwGoiQRWeMSYPFiQrJy7JNVMgAK5bbELLcR08IIYQQ85hPCqcdFhvb/9RWOcsoJYcw1evaJFditTHp+7pu7BeRc4dUd7UXE5LdNwYL92nJZ2YKz2dxM9BJn9IzAz3y2TQDEUIIIUSZJU8Kp66saCUR055Z9yVu0u5jhs/H47TDWUM+WBcyjjFWeVZWlmM60+Zc/Wu3k9JeKiHOtzNiPso41EfI+FLeBdrvs5zvwpA2LT1r1tLt33jyf+kpK494xuSUFfqsEEIIIcQ0RXxWxrQ/aikObazZUlOPU7PvKfQV004JdS4E7Q/qpaoJ8xKExfqa+OwLKRNCaip9n3OQcs9Yue+svQc18D23pZWVm079vJqycoddnkplhRBCCCFEE/PRQCHkmPGXWsFojX0KXvQalDhO6+dSy68iRQGJVRy69rWxEN2UM9JqTF+OecpP7KcpYvosQc4+qayUhdFAhBBCiHWYFI4QQgghxC5FzUAxoXkzUp0ac8mBJUxP1s0RM8YwZS2aI7FG+KkvQ/dXrOkj1Vl2Xp3YcYU4/vqEE/ukKBgaQ1df1tF6Ln1MgSnveJ96IeNr0ixb3Az0wy/qmYEe9hSagQghhBCiizAp3PQdbMdIaFQyyZy1FZiFZHqx7WuM2crqOCaU1ye8toRTaUo4s89KPKdzbsxKPDV0OcQxOWRcfXWsvXfmkZpML6YvADjCrS6qTvz+R19R+61d96FPnpyyQp8VQgghhJjGnLJiVU0IISZBm1Ya7jGwlizNhzHCMUspUn37UhWHEP+MeWPRHI/PGHL55MS0U0J1XSSF13K4c2mfld//+Gt6ysqOT6CyQgghhBCiyUIoKyEe+331fcrGMLYd2LrK0Sb2ek7lOHOlXs9JihLSd63GIDUiSUu1yqWghii7JZ+ZqTyfvlBZKQujgQghhBDrMCkcIYQQQohdks1AB8gqB+R3MNSWzlPHU5LcZiqfvnOGhWvdFyn1p2xWmqGdhC009FUjHDmkDd/yvn0392mZilIT0JHbE2Piz5FAtLgZ6LRv6JmBHrwnzUCEEEII0UXWYVK4pAbGSAqnHS46hnIRs/Jq1x2qP9SOFiHn2JoqNBUHQ59rHqs45EZ7XNYcdruw8Fz2kfu+10p9b5nSysrNZ35T7bd2nQfsMTllhT4rhBBCCDFNEWXFJynTlGbUpSmx+i+lMIQmyAspm8MubZmpKCuxxKTbz92377hSlFOrTDH5Y06KKys/PVFPWbn/7lRWCCGEEEI0maTPCrGFdb+PRVXwxlBPUqNbUpLKjZn8LucxpETFaSk22gnpusYzT20KVWo0fL9io/+ax1f6Q4bLrqwwGogQQgixzgpGAyU10KeszFuBxM7eY6J2xsxRMgY5bePW7dPWx6eNdu6U2HZKKh8paI8zNReLD7nyhOT+2GGomplLLZlXNravJsV9Vs75np6yst2jJqesZPNZ0X5xWX8RLjo8/6SLZZkYjsGyPHNjmjNJGCKyUkQOFpELRWSNiJwiInt41Hu6iHxSRM4XketF5BwReZeIbOTbN81AhBBCiHHExreBjgbwDACHATgXwAsAHCciuznnTu6p92EAFwI4FsCvATwQwEsBPFFEdnLOrR3qWN0MlCovWv26bq4vovqUnTGl480t645xPWLabbYd4txn4TMAWpJ5aoIwSwnMUvvQCrsOSTqXK6y72XbKeKYacl/awfaWn5+kZgZacd9HBo9dRHYGcCqAA51zh9Xb1gNwJoALnXOP7qm7u3PuxNa25wH4KIB9nXNHD445dMCEEEIIWTqeCeAmAP8221ArIkcC2FVEtphXsT1Rqflc/ddrhprNwTY1WVdM3b72pmSjtDBmq+HI1sZl4VqRaeKTLHNGiOIWQ8jnSFLb1laDtFUm33NbXFk59xQ9B9ttH3H1UBnn3MbN/xeR4wFs7px7UGv7HgBOAPAk59xxvmMQkW0B/BzAQc65dw2Vp7JCCCGEkCG2AHBRx/bZti0D23sVgJsBfNansJqyov3BrlTbZ0gfKX4CJe3dXYyZAGrKpKg5i5pkrk3opxFCFIK+dnzr9LWTmvRLOzFbSAK0kNV/qOqR4sdS4jMH1mmeg9Khy5rKyoo/fniMz8p5AM5yzj2ltX0bAOcB+Hvn3Ac929obwH8AeIdz7rU+dRgNRAghhFhHRjeErAGwsmP7eo39g4jIo1D5uXwFwD/7dq4+WfGZuYesuHxm8yWUEJ+Vks++efiMS2sVr7WyHNN/JLXvEBt9qjKoscoOvWba52sefe3FPrsx58AHn3dBTB8+dWLut9DEe7HvXl9KPO8lEwwO9dX1PDXLHuFW5xucTS5CZQpqM9t24VADIvJgAF8EcDqAv3TO3ezb+ehTNUIssWyytjXGMKstsimPdMNrHsVPAGwnIhu2tu9S/z2tr7KI3AfA1wBcAuDJzrnrQjrnZIUQQgixjqzQ+xfHZwDcAcD+fxiSyEoA+wI4yTl3Yb1taxHZ7jZDF7k7gG8AuAXAXs65y4IP38pXl4ckxhDTkU8/vn2VSrxVMvnXmA62y+KcGspUnJ5zmTi1+ophjOfdh5zhxDHkNE9pj6u9b0afyTS0j+Khy7/8Hz0H2212ihq7iHwKwFMBHIrKqfb5AB4G4DHOuZPqMicC2M05J416PwHwYACHADij1ex5A9lvAdDBlhBCCCF+PA/AW+q/m6DyPXnSbKLSw4Prvwd17PsogMHJirmkcCGkhpSGOC+GtNvXhkba8lAVRjvR3phor7aXNSx5SuS+5qFltcYT4wStkd5+LGJCtH3a0zq+kHMKjPDV5fP/Vy8p3L0fwq8uE0IIIYRoku1Dhk3GXKGWVBW0Qqr76rfLTnn1P4ZPzlR8Q/pYhGOYMtrKyjxC1ZyYd0tq8k0rvjPN7X37YsdrwWdl2ZUV+qwQQggh1hk/KdyoZFNWUtNddzFm9Eru5FrWsaLmjOm/MEZ7ObGaADCEXNdvhlYCxdSxhERJtuuERu/4tJNCjO9KqmqSw3+nuLKy+id6ysqqHSanrExmqmbtJUkIIYSQMtAMRAghhFhHJieGqGImKVwIId/98XHCCkkqlArNSvPJ7XDbJDUEvd2OhUR7Y5osSD8h32/qo+9+y2XaSTWzpJhgLIdoFzcD/eo0PTPQvR48uZnPZMxAhBBCCFlOijrYajNmsrPY4xxjhbtIacunohCEqnwpfeRSVGKd3cdI3JcrUZsWIUpIrFJgWYXoIkbd8S2faxxNiisr/3eGnrJyzwdSWSGEEEII0WSSPivapCRzy8lUVIRFgKHZ8+um1l/U+zdGKSuRYl5LPYlVPjT67mtfo22fhHtD/VBZKQujgQhpsKg/rISQaeOYFK68spK64opJ3ORDSDs5P3ynsSIdQykI7ZNqFZkiuRPRdZGSFC6krxCfGp/2uupZ8TlJpbSy8vvfnKWmrKx7jz+ZnLKy3FM1QgghhJhHTVnRSrPvo1ikflhrTPt7zHHGlG2X1xh7X1+5sZZLpNR4rCh3KXWa9WL8A6z4kM3rO/Ya5VYCYpSVrvoh/jFaviUp/jKpfftSXFm54Gw9ZWWr7amsaKP98FuV/0PGVcKRj8zH59zmdDBMYYz7okSSPwtYfbeMgdVrRKaL+ckKIYQQQpabUcxAIenPfdJUt7f3tTfU9rx2xkhAFxJSnVpmDCw5u1oxM1k6JzmZ8nFqm4RnpKbALxXW7GNqtqKs5AypLm4GuvBnemagLe9HMxAhhBBCiCZmk8Jpp9iOVX40nHFTwwtjfFRKONqOyZRX5iFM8Th97sWpHE/seGMTjQ2h5dDqoxiHvMeG2vWpE9tnSB9a5x8A1lt/fSorBWFSOEIIIcQ6TAqX/0OGVkNoS63yQnx0muV9EkGllCnJ1FbUXcT4WvWV0RpPieSKPm1YvRdz33up6suYxPjr+bRn4dhyMKqyctEv9JSVLbadnLKy3FM1QgghhJgnm8+KVqKkkCigsSM5tPqaZ7e1okiRxSIlum5s1USbXM+eT1SjhRw42j41U8T3GEpHA9302/PUlJU73P0+VFZyMeWbnxBCCCHxTGayQgghhJDlZDKhy7Fl5tXpqqfd3lD5eXWsOiT7th/bhzVTnU/Cq0UwffiQ8mxohS5bMV/mMg3l+kRDn2N4SMqDkDLahDynffWmHLp808Xn65mBNr83zUCEEEIIIZqMoqxYc4gdk6kcpzU1ITQc3AIWrrW169jG+vg0yZV0LSRxnJb6MhXH2phz3SxPZWU8mBSOEEIIsY5Mbn6hSpGkcCFo+WtohyBq2NpT+vftI+RzAj5lQ/pMDVfPFaodikZSvpT2p0RJhSv0sxWafaY+K31tx+Dj6+ZTP1cK/VimotAAI4QuX7JaT1m526rJzXzos0JIgym8JAkhZNko4rMSs1rvImX1VGI1O7UVc19qbSvHYG08QF6lLFckWMwYSiRCm1enq0zOeyGXmuajzLTb9fE1GYMQ/5YQJUrr2EJ9c1JVnOLKyqW/1lNW/mhrKiu5sPRjRcpg4QVNFh/eZ8sHf0+mx2QmK4QQQghZTtTMQFrhyCFJ07SSsOUm1dyS6uSXKxFXyW8W5TIBhIRlahPy7Z0prgRLXbOcfYyZoDAknLiLGNNHaJLKmCSXWoztjFvaDHTjZb9RMwPdcbN70AxECCGEEKKJeQfbEg6xIX1ph0BrOSGOmWgv9vpOUS3QoES69hCHx3Ydn75ixulTv4Tzcsn7LuT8t9FysM3pqDumupHLUdcXKitlYVI4QgghxDorltsQYvZDhn3ErJBK2M+nphRoJTdLaaPZjtaqXStZXUiffWXmUdI/pt1PX19WwvwtKIHaaIbONttotqOldvg8T13jGOp7bF+TNiHvjea24srKFRfqKSubbjk5ZWW5p2qEEEIIMY85ZUU7SmaGzyo7JMogVySMZptDfU1JCfKJmNBO6DXUt2Y7Pr5MIeMZU7mY10+JvqxSMtFbSoJHLUXQmrISk3Bv6DhLf8jwxit/q6esbHJ3KiuEEEIIIZpwskIIIYQQ04xqBhpDHrYqSacmjhuDEuYRi+329dWFtZD9dp/aIfvW74uYPkPfG9rOrhYICb/WcvwtmWTOZxyjOthedYmeGWjju9EMRAghhBCiSbKycoCscsB01IBYUlaWVhw8c2NFtcrp/NwmxqlXO7HgFNFIjhjblxWGFIdYJ9BcxKpEISHQQ2301St5LgAqK6VhUjhCCCHEOrLchhB1n5WQhEGxIXVj2ti1V3szYvwauurlXKHmwvr4ZuQc51TOwbJRMkxcO9zXSir8XP47Y/sDFVdWrr5MT1nZaLPJKSvLPVUjhBBCiHlG+ZBhSVu01RWr1XERfTSudUjCwikwtfs/hz9WikpiIbFa174ZPtGNJRPlheB7nKWTwt3wuyvUlJWVd9mUygohhBBCiCajONiGKCEh6dS79qfmSxgitr2S/jGlsBIN5ENJdU/DZ2vsyKFc6uiyoBUB1seYPhx9fbb3pSoqIan923WmmM/qDyy5g+1kjt78jUQIISSYsR2AyTSYzGSFEEIIIcuJmoNtLmfXkmGBucgZdt3VR2hdrb4tpEUvVX+onVCH2FyJ7HycBbUStM27B7WcLbXvrzFC0fvORUks9e1zzUNMPfPabhIaJm4h3f4N116t52C74UZ0sCWEEEII0SRbUjitFe+UwpzHWGWHrOBiKKGajJGUL1e7UwqnD1l9aqt8WvdtbkVKCx+1asyw5D5KjidGSVmWpHDLrqww3T4hhBBinSWPBiqSFE6DkBBm3zJDZfvqj70qXlYsnP8phWinEBvmmfsa9Z3/VH8vbZXJt9+clEh9r422wpJDsSmurFx3jZ6ycqc7T05ZWe6pGiGEEELMox4NNCPE3uxbPgXtqKIQO3yOMkNorz5T+yTTxoKi5UPJcZZI9DaEtVT1M0LGlSMayLfvFEorK2uvv05NWVlvgztRWSGEEELI4iEiK0XkYBG5UETWiMgpIrKHZ92tRORTInKViPxORD4vIvf27ZuTFUIIIYT4cDSAAwF8DMA/ALgFwHEi8oi+SiKyIYBvA3gUgLcBeAOAhwA4UUQ28el4Mg62sWiZbWaUCLXMFVI6FUKcoFMdprWw4HgaSilHWG1nVy3Gvh4pJoq+xGUp7WuZlbRNMjnbm9fOUJniZiDF39qYL0aLyM4ATgVwoHPusHrbegDOBHChc+7RPXUPAvBOAA91zv243rZdXfftzrnXD/VPZYUQQgghQzwTwE0A/m22wTm3FsCRAHYVkS0G6p4ym6jUdc8B8E0Az/bp3LyyMkZKbO3+U5NrWU94FTOG0HGEJAu0ft60xt4Xhpo7qVnMOPvKx57jXEkkYwh1ak9RPnI5jpZ02C3hPJvzfE1ZWVl/gw2uHirjnNu4+f8icjyAzZ1zD2pt3wPACQCe5Jw7rt2OiKwAcD2ADzvnXtra9xYA/wRgQ+fc9X3jYVI4QgghxDgxppseroqoswWACzq2X1T/3XJOvU0BrGyUa9eVuu3z+jpPVlYOkFUOsJdYLcQOGbo69K0TQuqq2FLK+mbbPkmoUkKrS5631PoWVv2x979Pe8tOrP+aD6WSuIX6cgyV7arnowiOrZr4UFpZGRsROQ/AWc65p7S2b4NqovH3zrkPdtS7J4BfA/hH59x7W/v+BpUZ6YHOuTP7+qfPCiGEEEKGWINKIWmzXmP/vHqIrPsHzPus9KFts8/lm9BE27aea2U/xZWz1TGPMS5tX6sZFnxDmkzt3Pqc0/b2oX0pZVLJpW5oKSwhCm8oS6isjOqzQmWFEEIIIUP8BMB2dc6UJrvUf0/rquScuwXAGQB26ti9C4BfDE1UAE5WCCGEEDLMZwDcAcD+sw0ishLAvgBOcs5dWG/bus6h0q77cBHZsVH3fgAeC+DTPp2rfxsoxCRTIgHUGOaWkDpjjk+rPSvfY7FimgDSkwVaOJYxQu5zJFnUqJvjXVUqdFk70VtoG0P1fMxdOcObY1k2MxAAiMinADwVwKGonGqfD+BhAB7jnDupLnMigN2cc9Kod2cAPwZwJwDvAfB7AC9HFQm0g3Pu8qG+GbpMCCGEEB+eB+At9d9NAJyOylflpL5KzrlrRGR3VJOcf0Zl1fk2gJf5TFSADA62JUNBU9vTVkssrIa10Q41LkGKopVTEdG636yqcSSMsUJup4SWspLDEXgZlZUxoc8KIYQQQkxTNHQ5l99HSP0SfjIh4ylVtwRjn1tiA+v3aQg5/VqGVvk5Ut+n+I/kIrTPlISTPv34/lYoZ5QlA1BZIYQQQohpsikrY6SPb2J1VZfbJ6fkuUhRykLrafTdV79vXCWUgiEfGqv3cyyLelwhDD27Pn5KsZE58+qnRuZY8TGJUVRCoc9KWaisENJgmX88CSHEKtmigZqMmWdFu8+YSJMSuSNS86Jo5PkYW52IITUXTsiKd4yU8H19574mse37+FOMSeqzMRVypdLX7ism3X5oX119UFkpC5UVQogZpvijTtIYM9CBTAdOVgghhBBimkl/dVmbKZoqLKRpt3reCK+NFiXT7aemjc9lvsnlYBvqjKtxXBrt0QxUFiorhBBCCDGNOWUl10owV6irT6hf6LhKqSVdq0VrKeFzUfKzDhrtpF6rktcj5DkocR2mcp5SKekQa6nvVEKSzDWhslIWKiuEEEIIMY2aspIaAjpUZqyw2Bi1ZFlJTTeutfKdmmqSSmpK+JRx8RMLfuQIndUoOybWxhk6HiorZaGyQgghhBDTJCsrB8iq2zQQm3xKQ8HwWeWFKjQa9vdYb/dc6oQ2Vv1TrI4rBu3PFPi0U0I1sRq95pPYUTv5o5bCENOeVnr72DT7M7TPSUwEE31WbEJlhRBCCCGm4WSFEEIIIaZRMwNZldqnaAqwPuYYJ+hcY2gSYvqzfo5nxHyXyKe9vnqx4b9jhiz3oRG6nDMpXKwZIjdjmJOG2s3RdkifzW3rrb8+zUAFobJCCCGEENNk++qytTDPMUJe2+12tZ3rOPuwoiaEOCRbS+g1Zp8+lFI5tB1/Q9sodf5zKivz8EkUaYWQZJnz6vqU1RpXaD9d9ehgWxYqK4QQQggxjbl0+zNKKCExdvgZYyhHqcm/Usjpj2LBB8YqJRIeWji3sWraGGg9u/PUr75zoR3e3De+eeP0adcnVDim/VBSzht9VmxBZYUQQgghpuFkhRBCCCGmSTYDEUIIIYTkhMoKIYQQQkzDyQohhBBCTMPJCiGEEEJMw8kKIYQQQkzDyQohhBBCTMPJCiGEEEJMw8kKIYQQQkzDyQohhBBCTMPJCiGEEEJMw8kKIYQQQkzDyQohhBBCTMPJCiGEEEJMw8kKIYQQQkzDyQohhBBCTMPJCiGEEEJM8/8BjpwhElNKLdQAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "A1 = rdpg(X,\n",
+ " loops=False,\n",
+ " rescale=False,\n",
+ " directed=False)\n",
+ "A2 = rdpg(X,\n",
+ " loops=False,\n",
+ " rescale=False,\n",
+ " directed=False)\n",
+ "\n",
+ "Xhat1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n",
+ "Xhat2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n",
+ "\n",
+ "heatmap(A1, title='Sampled RDPG 1 adjacency matrix')\n",
+ "heatmap(A2, title='Sampled RDPG 2 adjacency matrix')\n",
+ "pairplot(Xhat1, title='Sampled RDPG 1 adjacency spectral embedding')\n",
+ "pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Qualitatively, both of the simulated RDPGs above match the behavior we would expect, with 4 clear blocks and the corresponding 4 clusters in the embedded space. But, can we say they were generated from the same latent positions?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Latent position test where null is true\n",
+ "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance. In this case, we will use the `LatentPositionTest` in `GraSPy` because we know the true alignment between the vertices of the two graphs we are testing. In other words, node $i$ in graph 1 can be thought of as equivalent to node $i$ in graph 2 because of the way we generated these graphs. \n",
+ "\n",
+ "In other words, we are testing $$ H_0: X_1 = X_2 R$$$$ H_a: X_1 \\neq X_2 R$$\n",
+ "\n",
+ "and want to see that the p-value for the latent position test is high (fail to reject the null)\n",
+ "\n",
+ "Here, R is an orthogonal rotation matrix found from solving the [orthogonal procrustes problem](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.linalg.orthogonal_procrustes.html) (Note: this constraint can be relaxed for other versions of semipar)\n",
+ "\n",
+ "Note that LatentPositionTest.fit() may take several minutes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.8325"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "p = 0.8325\n"
+ ]
+ }
+ ],
+ "source": [
+ "lpt = LatentPositionTest(n_bootstraps=200, n_components=n_components)\n",
+ "lpt.fit(A1, A2)\n",
+ "print('p = {}'.format(lpt.p_value_))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see that the corresponding p-value is high, indicating that the observed differences between latent positions of Sampled RDPG 1 and 2 are likely due to chance"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Matched test where the null is false"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we distort the latent position of one of the sampled graphs by adding noise. The matched test should have a low p-value, indicating that we should reject the null hypothesis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x12dc56438>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x12e084470>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x12bf6e860>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x12e0a4080>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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jSne8BpWr4FW4d9rkU1BlOtyI6of8+6i+OE/BvVNFFwM4pWPM/VHtqNdvHV8frfRg3J3K+HRUqsRfULmbzgOwc4fte82jPv4gVNlDi1HFRVyD6gfowwAe2HGOv0JVjOrK+vo8sj23nms6mfOLpry+Q/36ka1r1UwBvgXVQuJEVGrFalNsGQDfc3i/l0Ul2Z9Yn9vt9Xt5bn2eW1jaeS+qmKaJjX+i+hHaF5apz6iUkn1RuV1uRZWKfjAql9+9rjWqnfgR9Xt3c/1ebzHl3us69iBUsR3/qK/vmahUlWn347r1PfOX+p65ElXcy3Y+9xbq1OWe9/Go1rEFAF6PakF4S+N92m+KjZ/UdnZx/A6419h980W1eG6nZa8A4KP1e7gUlYvsnagWr/doW7d/BSrF9rbm+OhJa+56X1HVmzEAXtfR9rP1azu6XA/+4z+tf2KMusuTFEqtABwJ4KnGmFPyzoaMBRH5JaraHl0xEzOJiPwAVer9OsaYJbnnQ8i8w5gVQsgQ66Bync0FdSDrDgC+zIUKIWXAmBVCSCcisj2A56DKEAp+RELpSPV4iI1RuQpvw72L5hFCMsHFCiFkGvuiSj3/L1TxL7PO61AF4f4JwMuMWzo8ISQijFkhhBBCSNEEKytLlyypnoC2wsZDTf/FobdU1d6bfbqO9R2fNSbnCYSdq8+1taHLXulM5tw13/ZrfW1Dxta0OQ/4Xjft92/MTLu3u471Xa9pbbTtubbR6KPB8osWaVYHJwMwwJYQQgghRRPsBtpT1jdA/+rbhXlRUkqgS82x2Z2UsIuNOYch2zFUE43r3zevrj4x1SRbuzYqwCxRmnLko3a43mc+Y44BKitpobJCSAMukgkhpDyKV1ZsdoI+sQnTjrnOLwZDY/Rdk755hcRnxNzxurx/Y2HWFYJZJ8V95/PZnWajr03fmD6E3tta19ZFyYvxHUNlJS2jUVa4450/+J4TQggBRrRYIYQQQsh8ouYGaqOVikviMc/uibG6l7oIdYNq2Ztm16aN6zxiEXvM0tyDsQN3+2xr3xe+KdVDdqdBN1BaqKwQQgghpGiiKStNxpyOnHPuWmP7FIUbY9CrT0pviF2bPr5juowxLypASkq4x13ViVlUq1Kk3NuM2QWVlbRQWSGEEEJI0SRRVibY+BFd0nVtxrK1P4/M2m651MJ2JezSu/ApBUDykKMoXMjjOULHzoltLA2VlbRQWSGEEEJI0QQrK1oPMvTBJRo8dIxSFZmQ+ZW6o/HJXOlr4zJ2qB2XsUq7/rEILc/uM9YYr612nJhPvIfWZy4068ynTZOQMW37UVlJC5UVQhqM8UeOEEJmHS5WCCGEEFI0xQTYhrgxXAJutd1CvudQgnspNBU3dgCeK6me+FpqWnJpT/R1Qft5MVr3QCw3labNLttN+77F12zG8b0/Q+fQZTflc5wAuoFSQ2WFEEIIIUWTNMDWZqUfS2GJmSY9VvrOM8UONza5dlyp7GkFJJegNOQo955yPj7kKGYYaid0zFiFHTXm0IbKSlqorBBCCCGkaNRiVmweXmZDCSnHLorDmJUXrZ2Sq6IVMlYsStpRp0K7DHquuYyVEq6hj6KqXeLfJrW9a459yqLm/Ka1obKSFiorhBBCCCmapNlALuTMliklZiX2NdAqPhUL7SyG3GgV9tKYQ5/NEhSuXOOXTmhsWex7sKTvjy40C+9RWUkLlRVCCCGEFI16NpC2GtBXKlp7DK36Lz5j+rYJGcu3hHUJO++Y9Ue0d5LT7lubDLXYtThcx9Can88Ot7Rdu1asVttezDgLl3tRm9LHcn0/qaykZTTKypgDWWeBUn4gYsP7LC+8/oSQLkazWCGEEELIfJK13L4LpQS9xiKH62nW1JKSUnFLQUsOj+maCKGE9y30vGMVb/Nx5cYMavdJNdZyZfmUYOgbE6AbKDVUVgghhBAyiIisLSIfEZGficiNImJEZFuH/huLyA9F5CYRuVZEviQia1r11VZWYge/2o6RKvV5jIpPip2vhp3QwNjSUi59yuOntDfNrk1b1/axrnMJSosvIcHaTbQVh2l9XPtpjB2KjVJjyzwqK/XC5GcALgZwFYAnAniqMeYUi74PAHAWgOsBfBrASgDeBmAxgK2MMbf39V82YN6EEEIImR9+A2BNY8w1IvJ8AN9y6PsuAIsAbGaMuQwARORMACcB2AXAF/s6Z1FWbFI2bXDZXbjYs5lXTOUmhyrUJnYKbR+zUAzOd/c5ZmWgdFKpOjHHcJ0HYKf++qrVqdLe+5T1XOn986isNGksVmyVlSsB/MQYs3Pr+O8BXGqMeWZffyorhBBCyBwhItcPtTHGrKY43roA1gLwfx0vnwlg+0EbOcrt+8ahuNib9lqKInOl4VIUTiOjxre/yxgpY0tSMu096ru2pccm2I4Ra6wS3s+cyqSLspIb7SKLMUmtrGg+2ua/8ecbhtoMLVZclBUR2QLArwHsbIz5Wuu1gwG8HcCyxpg7p9mgskJIg1ldsI6FUn40CZllNFUTSxbVf2/teG1po81N0wwwdZkQQgghMVlS/13Y8dryrTadFJ+67EKf3JljXqW7lzTS92IyhoBFHzuluthKp8R7VBPtZw35jG3znVCqu9cmCNc3Lb9tFxi3G+hwszh47o5uoHUB/A3AW40xn2i9dgyA7Y0x9+uzQTcQIYQQUjjLjDj3yBhzmYj8A8AWHS9vCeDsIRtJlRWX0s4uuwyt1GVtQlOrh3YHWvPrIkfpdBdiBWbGTDlOUXK9RFKqQlpFv0q//11ImfZrM4/Srpev2qKhTrjw+gV6yspn74qrrIjIQwDAGHNJ49hhqOqpPLxRZ2U7ACcDeLUx5gt941FZIYQQQogVIvKe+j8nK7ddRGRrANcbYz5TH/tJ/Xf9RtcDAbwYwM9EZFLB9u0AzgFw9OC4Y3mQoa9drTFLUGq00C6L7rubjVWS3yfGxDc1OGc8SwnKluvYLqmzOWImXN7P0Osfcp+WXtjORc0ppYCiq73UMSt7LfNgNWXlU3de6j13EZk2jz8bY9av2ywGgMn/N/o+AsAnAGwN4DYA3wPwFmPMP4bGpbJCSIPSJGpCCCkJY8zgQqe9SGkcvwDADj7jqi9WXBQIrbL7fTt7rXgPHzsuO/GUio3NDsLlR9v3B15jYdBlo31NbXZuoeegpdxp99HOMOn7HNgoUtPGLCWeIcf4LjF4PsXS+saKRZ/K2v7/UOXTtt+0+ZFxQGWFEEIIKZwxZwNpwKJwhBBCCCmaLM8G6iJETvd1zaRyvfiOqZXGbds3NyWkU46h+FqOZ6OkDDwNsVOKO8kHn3vRNS15KMjV5n1t4mOnr61NEO60sUNxvW6pA2z3XlYvwPaQO/wDbHNBNxAhhBBSOMvI6NYXqsxUuX1XUs3VNZDYZcfsElSnNaYNMctcaxC6A9QmtLy4bV+X3XrMVG2bsUq3E6o2aV9LF7QDT7UVSq3SBzGVmtTKytvus4GasvKx2/80upUPY1YIIYQQUjTFxKxMw0cV8B1jgvZYWjErMdWT3AWWYhDrPGMWsUp53XLGCE3QuhYl3G9dpIyJClFic6qGY4gT6yK1svKO5fSUlYNuo7JCCCGEEKJK8cpKH7EKco0h3kaD0mIwUtob6xz6cN2hhpyPjboXOgcbFUC7hHsJpIgZinUv+2QV5VYRfa8FlZW0MBuIEEIIKRxmAxWirIRE32srIdp1UbRqu9jY6fONx9rRjDmGI8aOq4SxSHxKqAtk06bv8xmahZYy9iukRozLmDbXCwAON4uTrh7etfAhasrKgbdeMrqVz2hiVubFNWNDqdcilqRMCJldfMsZxB6TG4eyoBuIEEIIKZx5fzZQEjeQSwpuaYSkKuc8v74gxNJdDqUXCssdvFna9YlNzmBQH3tdNku71qUFueawE2ovdYDte5fXcwMdsJRuIEIIIYQQVYoJsI2FdnpzDoVFW6kpZXc3IVbxr1jBg7mVFR9mocBaDkKvRY6icH32Q8rs57wvtMv3a7wPqZWV/Rc9VO23dv8lF1NZIYQQQgjRJOuDDG3iKrQILVXv09bGThexrkXpMSulzouQJqWmLIf08T2nEtSWFFmIXWNQWUkLs4EIIYSQwpl3N0hxMSs5VA4fUsSjxD6/HIWgtLHdBWmOFaOoW+yHvaW8TqHkePCdy5htcsZPkXzqTmpl5QOKysr7RqiszPtijRBSEDk2HmMooUDIvEM3ECGEEFI4fDZQRjeQ9jN4cqD13J/QMfueYWFjJwSXp+H62ovZz5WYz0KKjUvhsr7nS2m5wlzSbUspXFZ6kUWbZ5PZFOp0KebZ9/0z5FqL4Tp1+V50cf0126Z2A31ohQ3V3EDvvuWPo1v50A1ECCGEkKIpLsC2BFIqPiUECccgZilzTbs+xAhWLWl3rv0U29B5xLJvM3aKRzWkVHNCisK52M+tkE1Dcw6plZWPrKinrLzzZiorhBBCCCGqUFkZYJbK7E+z3xyjhBgM7ZgV7d2/7wPrSthZjomSYpC07qHS05Fd51fSe9TXL0Z8EZWVtDAbiBBCCCkcZgPNkLJis5v1VSliKyyhWUUh83MllQqTo+Cb71jaD0oLzXRwUXyG5uDaL5YdEo9S4pTGRGpl5aMrPUztt/btN/1hdCsfKiuEEEJI4SwzuuWFLgywJYQQQkjRzJQbqA/fYDGN4FYbe7nHbLedZ3l3lskh4WsVCLNxjQ2NY0vos4A0xrK5FjbtU77nsZ+R1dc2RRBuc4zDzeKkWscnVtZzA73lRrqBCCGEEKIMA2znRFnpIkdBtmlj2uwWY80lBSE7phRjhpIyjVv7+kzIoaaNWcmLVfjQRikIVaI05tlnWzvY23Y+03BVqdp9uuacOsD2k6s8XO239k3//P3oVj6MWSGEEEJI0RT/IENf9SOWajKWgm8uc4hByO5pzLttbcaoMhE7ht6TlOrErBH7sQJAemXlM6vqKStvuIHKCiGEEEKIKnMTs6KtiEzoezBZinmF9M+trPjsLEvYUaYsz14KvkXDbPv4kvORD7FjVlKO2UUsNSfW/ZH6MRhUVtLCbCBCCCGkcJgNFKisLF2yxABpYi9KivOIEYGuoZKEzqtNjhoNTUp5SKLmWC7X1PU+0XpEwJA9m7ZUl/I8HiKGilASJWSxAemVlcNW20hNWXnd9ReNbuUzmpiVMX+4bCj9/HLMr/Rr0of2otGGUu2N+X2ckHODRAihG4gQQggpnnl/NpB6gG1KV03O4FRfbMbUnpdLITobckrJsyBjl8KQm6qUa5zSpZLTxaP1OIFS3UAlBF5ruppTu4E+t7qeG+g119ENRAghhBCiSpLU5ZCU3glaReH6Sij7xBloB8rajKVVmj9GCXcyPkp470uYQyn4pon7BENrqx3aDzJ0SedukkL1Sq2sHLGGnrKy+7VUVgghhBBCVJmbonBd+CgqOeJbJmjHsBA/fFNyfXadJaRah7ZNYScWWg+S1I4j8U0zn6ZY5HgfUhbBi3F+VFbSwmwgQgghpHBYFG5GlRWXrJsJfbuoEoiVJdS0GXuX4/J+kLg7Xu3308dOjhLzPmOUWuAu5sMOx6h6adrtImfMylH33Vjtt/aV11w4upUPY1aIFSUt2gjRhPc2IeVDNxAhhBBSOCwKN+KicKFozDWmy8gl/TrEPpkNtF0VMV0fWsG8pbsqQohx/Ut5rs4skNoNdMyaem6gXa6mG4gQQgghRJUsAbY51ZfSgmdtnnibojR/qem008bUSikNmUvfWNol3HOrHCFKSMwgUG07GnOwnUeOsv0+bX3UGK37NZaqpjG/1MrKV/9tEzVlZed//I7KCiGEEEKIJnMXs+JattnF3rQdiGsZ/9B5+Iw5FkrfQZfyoLicO3sShvb7kiPtupRU75j3OJWVtDAbiBBCCCkcZgMFKitLlywxQLlKig8uKkcpMTDaykqor9jmEe054xfGoiqMZZ6ulPYAvdR2UxJamj90zKGsxhQqTIxHSaRWVr6+lp6y8pKrxqesMGaFEEIIIUVDNxAhhBBSOHw20Iw+G0iL0gvHhYyZ81krMYk9dozU5Xa/UBdWrCDNMbtDYmHzTLGxXLfcKfKx0ZxDajfQ8fd7hNpv7YuuvGB0Kx+6gQjGYh6kAAAgAElEQVQhhBAyiIgsFJGDRORyEVkiIqeLyHaWfZ8uIqeIyDUicp2InCYiO9mOHewGil24LKb92OXsfW1oqDk2AW5D/Zp9u9r07U5yp+7mJvf5xyrEpT2mSyB3KU8RbtvpsldCoGhf/wltOzEDnceiLpVKIdlARwHYEcChAC4G8EoAJ4rINsaY06Z1EpHnAvgOgF8B2K8+/FIAXxeRlY0xRwwNzJgVQgghhPQiIluiWmDsbYw5tD52NIDzARwE4Ck93V8P4AoA2xljbq37fh7AnwDsCmBwsTKaonA5HhjY9VrpRe/6cNmhllBSP7cdbWKkT3b1aWKT0l56ufcclFzmPXQOtna0yg9oxO9oKXg2Nmwf5ZE6ZuVb99eLWXnB391jVkTkYABvBrCGMeamxvF9AXwIwLrGmCum9D0VwKrGmEe2jp8P4G/GmGcOjU9lhRBCCCkczWwgEbl+qI0xZrXWoc0BXNRcqNScCUAAbIZKPeni5wD2FZEDULmSgMqF9DAAe1vNeYzZQD5l7EOVkNiKilbZfpexYhZoC3n4WWm77DallBK3wUYRzPHeaMWjjOWeccEnjmda+2n9XfqkxOd+zXUPpFZWvrP2I9V+a//j7xfcMNSmvVipVZDLjDE7tI5vAuACAHtMiz0RkRUBfBHAi1EtbADgJgAvNsb80GbOVFYIIYSQOaJDNbFhEYBbO44vbbw+jVsB/AHAcQC+BWAZAK8BcKyIbGeM+fXQ4FysEEIIIYWzIH9RuCUAFnYcX77x+jQ+DWBLAI8zxtwFACJyLCpF5lAATxoaXO3ZQGNkFiTkkEDM0KDlMQcm28zPJyDZZUyf+dnOZ9r8tANGXdGee05iB6rneGaOlpuwdFepxvxSu4G+t86j1H5rn3v5eT4BticBuJ8x5tGt49sBOBnAs40xJ3b0Ww7AzQAONMbs13rtkwD+H4BFxpg7+sZnUThCCCGEDHE2gI1EZKXW8a3qv+dM6XdfVF6cZTpeu0/92uDiSf2pyzkLNsUsce4zD+20wljz7Bqjfdxlnl12UuKjSmgHL7vaHrr+KVKOUzB0/cd4Tn2Urqz4UOq8bBhzuf3vP/DRasrKc/56ro+yshWA03HPOisLUdVZudIYs3V9bD0AKxhjLqr/fxkAV6PKFNrUGHN7fXwlABcCuN4Y86ih8RmzQgghhJBejDFniMhxAA4WkbUBXALgFQAehCoNecLRALZBrZYYY+4UkY8B+CCA00Tky6hUlt0BPADA22zGV1uspCwsVXrao0sRJRc7MVUrlx2vTblxF2zeR5v3PEQV8u1j0z5EtfJFY0cfU8FrU0qBQu33KKR/jHtRg7GpKU3GPPdC2BXAAfXf1QGciypW5dS+TsaYD4nIpQDehKrc/sK67wuNMd+yGZjKCiGEEFI4Cwp4OJAxZimAt9f/prXZdsrxrwL4qu/YSbKBYu3ybF6bMC8r6tD4EZ9YnxyEZhf5xKy4ZAzZFNxzmWepMStahQXHHLOSYu6xxpjX70kNUsesnLj+pmoxK89afE7+lY8jzAYihBBCSNEUV2fFZ4c6C7uBHL5n15oiWoqARh+XGi+haGUOaSgsXf1Lu/99y8VPyHn/zxKhdWBiqzm+Kq5PXF3IPJs07aVWVn74kM3VfmufeclZVFZiMYtfJmOilGJuZLbJ8Tnndwsh5TOaxQohhBBC5hO1py77pBGPWYbNIcHnSCnVCtjVDip1mVfMYOMSgoy1KDUgNqe7q5THFExjzN+hMUlxL6d2A/3oYY9RcwPt8Iff0g1ECCGEEKJJknL7JRWoSkmKYnUuxdJsitW5BED2qQo2hdCGVAnXc+jq124TEnxbWrp0CWjNa8yfcxdcg9rHxljOxfe+bfY73CymspIQFoUjhBBCCqeEonA5yZq6HHO3WKqfeyxopQ/7jOGiJviqQza4KEhd83GxY2u/aWfM91ebWTynsROSYhxaXHIMxe9Sx6yctPFj1ZSVZ1z4m9GtfBizQgghhJCiKU5ZiZXxolWAKKRtaWipADZFp3za9o0VM5ZmaD6+MUh9doZij1LcZyWoG6HnGXoOvtmMbULmHmIjJqVnRtngosQOnUtqZeXkRzxOTVl5+gW/prJCCCGElEzpiypyb7hYIYQQQkjRZM0GSuHq8RnDpc+srNBjB7TZtPUJ2O2bt49Ly6ati7uqbx6+wcFjkdx9CD0n3+fgaI2vgW/geuz7IoX7J3ZQr81vRAn3QBfzng1EZYUQQgghRVPMU5d9AjF9guBcy7O72teYVwguRdJcroVvGnHIvEKLubnYcQmw7Ruri5BUapdddswU0FRprK6UHpQam9ICr2O956WdJ5A+wPanj95SLcD2aeeeOTqZhkXhCCGEkMKROXcDFaOskHSEFifziTHRUhOGxukiZrl9n8JxfWipOSQ9rrv/EhSLMVKKypdaWfnZ5lup/dY+9awzRrfyYcwKIYQQQopmrt1AsXcrfbEhOXdIPvEZrkrEtFgCLZXDd14hhI6Vcq4uTLsnY8QJDN3/obFR2sT8vGpnHeZUHFKObRODN9RnjMgy860tzPfZE0IIIaR4uFghhBBCSNEwwHZE2MidWs826SPEtk26dOgcYrtbbIpj+RZ88xlLixzPIyJ+z6YpzcU8S2OX+mygX2z1RLXf2qec8SsG2BJCCCGEaDLzykpI4JdrPx9ypi9qKRC+BdGGFAZfFUYjPbmJS1qyzY439LpT5bgbn6JwKVWAUtJs54EUBTqbbQ83i6msJGSus4EIIYSQMSALRre+UEVdWZnVXUEJ55VCWYld1r6vfx8h8R6+52kTa6KllrTtTSjhviNpSKkCh9xXMZVnnzR6FzVzzEXhfvmEJ6kpK08+7dTRrXwYs0IIIYSQoskSs9K3yk1VqM13jNIf1GVzbV3wVUhCysaXUjwtx4MHXdDKChsbY8hSGtv1Ly27qA+NeYUqUkD6mJVTt36ymrLypP/9JZUVQgghhBBNRp0NFLrC1qpb4uIfdSlDHxvXWA6XeiE2pdKH7NqONQ1tdanLRknz6sInW8aGUnfdYyTkeyj390aqe7BENS11zMq8KyvMBgqkFJdFbHIWeyKEkHlHlhnd+kIVuoEIIYQQUjRZUpdD22i5UmJLq9rl8bXRSun1ZSjgNGbAbuzzdE2jDHUjaZAidTZVcKRLgHPKz16J7gziR2o30K+2eYqaG+iJP//F6GQauoEIIYSQwpl3N1CxAbY5dz1aAbspzkGruJMPPgHFPnb7xtKOa3FR8mznozHX0lJKx/j5HMuYvpQUhJubWN/FOVOXT3vaNmq/tU/46c9Ht/JhzAohhBBCiqZYZWVCKXEfseahVYwpNN5gwjQVwPXaahVSm9bWZUztVOhSspRKil2xsde0GVuFnAU1wbXEfCo7Ka5n3+fUZ64x5p46ZuWMZzxV7bd2q5N+RmWFEEIIIUQTNWWlNN9uafOJRayYFRe/t28mi0YGjM2uvau9y47LJWPIJcuob6xQOyGkKLaYYyxt5uU7ZkIs9StUHUo5jyZUVtLCbCBCCCGkcOY9G4huIEIIIYQUTXFPXdYi1jNR2vbHLAH7BtiGBLL6uoGmza/Pnk0K9NBxzXlpBWePLZA71I52sOW010Pb+KLlyh3rd9FYzyG1G+jMZz5NzQ205Q9/OjqZhm4gQgghpHAWLBjd+kKVJMqKy47X1oZNn5jYqBI5Uvxc2mgVhbMZU0tZaY+du3Ccz1haKo4PLipCjPs3lu2xqEQpyKHUlHYNXBhLgO3/PXs7NWVlix/8ZHQrH8asEEIIIaRoii8KF8qYV/w++O7sh4pF2cR92I5layc0zVlbzbEZq328j9zK4tg+G6XPNzT2ovTzc2GscShNhs4htbLym39/htpv7WO/exKVFUIIIYQQTYKVlT1lfQPkzQ6waTvBplCYa38XSto9+agdzTZtO9qF1fr6a2fW9Nl2uYe68IkVmqWicCl22SnjT2Ldi2RcUFlJC7OBCGnAHxVCSIksYFE4QgghhJBySZq6nDPorLR04hJwed5PzCDVNjZBuX1z8XX/2BJaIK+rr0/Qck5SPCcm5TOGxkKO85yla6sZqJ7aDXTW87dXcwNtfsKPRyfT0A1ECCGEFM68Pxsoa+pyKeltJewcclyLGKXlY405NJcu2ymK1tmkLmuU9g8tkz9mtB9XkGI+2nbnXWUq5beiSWpl5ewdd1BTVjb7xo9Gt/JhzAohhBBCiiZa6rJNGfTclDYfDWLFEsTERZ1wSQMOKUjn2qZvnhqpyqWWvnf9nNsoWj7jh5QxcN21+8QgpUzjDqH0+TXJrTalVlbO2elZasrKpseeSGWFEEIIIUSTmS+338blQW59bWYB153gLOCy402V9WQz1jzHrJC7sfl8llDq3+V7tpQ4I1cVjMpKWpgNREgDLgAIISUy70XhspTb9416D3lYXI4foTHtgLXqjmjadR1Tu3aKT8yLllJjYyfH/T+me3oILQU1RZzYWL7rJsTKlLIdI2Q+tvdFamXlvP98tpqy8qiv/WB0K5/RxKzMmjuClEmpRdlmYXFQKry2pAveF2UxmsUKIYQQMq/IAlH75z0HkYUicpCIXC4iS0TkdBHZzqH/ziJypojcLCLXisjPRWRLq75aAbY5CiX1tXFJL9Sah0aflPim9Nr00UoNntbW1/6QHdeUYxdc0q77+pZ+XxE7Sn8fQ9yNuZMUYv8eAcDhZnFSV8r5L3uOmhvokV/5vtfcReRrAHYEcCiAiwG8EsAWALYxxpw20PeDAN4B4BgAvwKwIoBNAZxgjPnO0NgMsCWEEEIKZ8EyeR0htQLyUgB7G2MOrY8dDeB8AAcBeEpP3ycCeBeAHY0x3/Iaf95Sl23IvSvIwZBi4RqAaoNPSmOosjLtNRd1KPRa2Ow+YwcslkJIUTfbfjnR2uHnUGG0C/jlIOacUwfY/m7Xf1f7rd3k6O86z11EDgbwZgBrGGNuahzfF8CHAKxrjLliSt+vA1jfGLOViCwAsELThg2MWSGEEELIEJsDuKhjkXEmAAGwWU/f7QD8WkQOBHADgBtFZLGIvMx28CTKSqoVeeiuuM/mmHYTtrgoDkP9NCmtIF3K+bjE5ozlnpwXpdJGKRvTd43LnCeUMvcJMR9fkVpZuXC35+kpK0d994ahNsaY1Zr/LyLnA7jMGLND6/gmAC4AsIcx5oi2HRFZHcC1AK4BcCeA99f//3oAWwN4oY1riDErhBBCCBliEYBbO44vbbzexUr13/sCeLwx5gwAEJFvoQrSfR+AwcXKTMWshO7gcj8YKych2U59+MSW9GXk5FRdfHeYPoXjfBSVWVMwYn8eZ+16kbSMWVnZ+Mjv+MSs+CorawL4B4BLjTEbtF47BMCbAKwyFMNCZYUUSyluIDLbcKFCxoBkzgYCcAWAtTuOT45dPqXftagUmSs7XrsSVbzLqgB6FyvZz54QQgghxXM2gI1EZKXW8a3qv+d0dTLG3FX3Xbfj5QegimO5dmjwrEXhSpFhQ55HZOPe8B07ZH6uwbNDKoaWa8a36FqoO2loTN9n8WilcffNoz32rLkgNdAu2lhCqrCWvSY+gb8uY6W4XrHHsrWf2g100R7PV3MDbfSFE3zcQFsBOB33rLOyEFWdlSuNMVvXx9ZDlZp8UaPvWwF8DMD2xpiT6mOroIpZucgYM7VGywS6gQghhJDCkQV5HSHGmDNE5DgAB4vI2gAuAfAKAA9CVcl2wtEAtkHl3plwGIA9AHyjjlO5DsDuAFYDsK/N+MUE2A6Vx/ddRc/Sij8mNo8naGPTNmSnG1rwrWteGiXvfVPk++bpo8j4FOsqRc2cF2bxOyE3Nt9VsYos5iy3//vXvFBNWXn4577pW25/eQAHAHg5gNUBnAvgXcaYkxttTkFVfl9afe8P4KMAnoMqc+g3dd9f2IxNZYUQQgghgxhjlgJ4e/1vWpttpxz/O4BdfMcuRlnRJmYxoKZ911iTknYrNnEtfdjsbHzVEdu2vkXrQuZn8376XL++sVIUhRuTiknCmNf3SvNzlDpm5Y+ve5Hab+2Ghx2fdO4aMBuIEEIIIUUzs8pKTsaSSWATvxD6YL7QYmkuY4agFT8SYsPW/rzuimcdLWVrQt/nMsV8tO2UBpWVtDBmhZAGLERHCCmRAorCZWW+z54QQgghxRPsBtpT1jeAXcGx0gICY+Pz1NKh9rZjuLp4NFJ6h8aY1i8ktbfvPvO9/i7z1QrC1QrQJeMh9rOPtO1pFbBM4VYaaqORwp/aDXTxG3ZScwM99DPH0g1ECCGEEF3m3Q2UNMA21mqe+OFaUG1aW181TbvcfrutyzjaAbt9tn3GGmOAbcpU6NgqRYxieqUXwnRRBPvu7Vjf97k/B6mVlUve9FI1ZeUhn/yf0Skr871UI4QQQkjxFJu67OP79I3/CNkV2OwyQknh6x0qtuZb8G2a/S47NkpN3zhaao6GymITSxM6ZunKShclpfVrxD6EzsHXzgRXexrKRaw4O9v55VRUmueeutz+n/beWe23doNDvkplhZAxM8YFACGEzDpZlJUx+SVTEcMn7jLmNFwyYGz62xSFs5lf6L3jowqFxM3Y2tG6Bqk+N1r3bczdeipixmmEnF/u75aQOee4x23tpo5ZmXdlhdlAhDSY9UUxIWScyDLL5J5CVoqNWemjhF3ULBAjdsKm5LeLvSG7fW1cxvKNT9GqTxOSeTEmSopJCI3DInqUGufXR2pl5dK37aL2W/vgjx0zOmWFMStEjRhpv6RiXn4oc9xDNtd2Xq5/Dvi9QWygG4gQQggpnHkvCqe2WEkpTbsEV/b18SmD3te3pLRMVzSCI21SjV3s+bZ1KVbnU/Su2Wfa9bcZ08YNEbNYnca9o1UgzHcuIam8XW1jBYO227qOZZPeXwKxg16bpHQZlXq954n5XqoRQgghpHiKCbD1KdwUWiDJZoyxEpoW64JWkGpIgGyzv5YaYXMvuhSrm2a3y7ZverSL3Vj3u8/nKebOedpYvvPzKS7nMg8tpcbXzjR7sXD9rhpS5/rsuNgbInWA7V/23U0twHa9Dx/JAFtCCCGEEE2yKitavuxSlZHS5uVbJM0W35gV7fRmHwXJRS3RVmy6bLrE3aQg570cSx3SLkSXohCaT/8cReFKQ0uFb0JlJS3MBiKkAdMoCSElMu/ZQMXErLQpTZXQwibbY0gZ0FKZUviB++YeS83RzqDxyRzSGqPP7qx9NgixIbbabts3tbLy1/fsrvZb+8APHjE6ZWW+l2qEEEIIKR66gQghhJDCoRuoUDeQCzkk8ljPsnC1qTUPn6DS0MDYkAJtffZsx7fFZcw+t16IndAA21A7sdrOAjaB6zk+0217WjZLCdjV/u5ztZPaDfS3/V6t9lv7gPd/nm4gQgghhBBN1JSVseymXHcF2oFaqa6TVlE4G6XAN7jUJZDYd67T7PioOdqBu76MTd0Ivc9iU4pSkBKN7zWN/j42Yt/bpQbYXvb+16opK+vu999UVgghhBBCNEkSs6KdaqbVttTdZwnzmVDSNSLdaKdLjwkXtdBGKfN53IGNgqpdgC4lIXE3zX7a3/G5v5uorKSF2UCEEEJI4TAbKJKyklIp8C2ENqEE1aBUZYX0U9KOV+uxAj5tu+ahlS02zb4vseykyEzro4TMI21SzG/a/dqnxgPA4WZxUnXi8g++Tk1ZWec9h41OWZnvpRohhBBCioduIEIIIaRw6AbKmLrs65KJJQ3GKsYUa55N27bSZYz5dFG6vOxDaecUWqxuGr6F7Ybm1zdWVx+XwEybednI/T6uId9A3VLuoyFC7/vSPjdtfMMIUruBrvjw69XcQGvv+1m6gQghhBBCNJmJcvvk3oTuUHOUsC59B6aF1k61j1hpybOU7uyiRqaYjwslBHb3je/aRsOeDZqFOlOnLl958BvVfmvvt8+nqawQQgghhGgymtRlrRLPs75rt6H0a5FjfqWkjoem4bexKUoWoiL4qhIlpCHHvja+8yghDb4UYhX19P2854xZmXdlhdlAhDQo7cuaEEIAQBbMtyNkJh5kmCIWw6VwkM2YJexkSlET2viqCyWdgzaumTku2UA2Jetdslu0C8hpj93u00dM1aX0bMahcVzHmjY/V3vaZft9SR2zctXH3qSmrKz1tk+OTlmZ76UaIYQQQoonajZQ6ApYuz6IjzpiY29CyQ/das6hySypETljXXLU5nGt6+HT1gVfdcRG2YlNjiynUIWh3S9l5p1PXZtS8f1eTK2s/OOQvdWUlX/b+xAqKxNCP/SxFyqhuJxfqWmepX+JEDt8Fiop6RuzpIVKKYQW1oyNzZilfufZUNr9QCroBiKEEEJI0SQNsPVpk0JKtAlUjC3zu8jjvpKrtlpVArmL32kxNJ8+F4Fv4GnOAm+xA3a12vric35abpy+oOWheyem8jwW96xt39RuoKs/9VY1N9Cae32cbiBCCCGEEE2yltvPvfrOgXbaXY7CSD6UpmTMAj5puqEKSwnF3GKSoxicDSUEcvvG0pSYoq0xPyoraWFROEIIIaRwWBSODzIMQmsHYeNXLiXt2SfexsZOSNvc5EybtsElhkJrTJcxfDOZYsWz2Izjk8mU4v4o4ftiVmm+x6nL7V/zmber/dbe9w0fHZ2yMt9LNUIIIYQUT9IHGZZeJC60rVb8yISx+KdT4pK5Vfq5hGJzLVzwVS6m9Y8V1xJ6ni5j9Y3pEwcU8zOtkVVkU2gwRsbQGD+jqWNWrjvsnWrKyuqv+wiVlViUFohHyKwzL5+5Mf5QzhK8/sSG0SxWCCGEEDKfJAmwjZ1qllPuzzGvMcqn85imHhsbd4QLfe4zbZUldoqwq9tmyE4Ml5ZLgLltn9D+vt8tPoXocqRCa34np3YDXf+5d6m5gVZ7zYF0AxFCCCGEaDJ3qculqRKhK32f8v0pH2FQwjUeI6GBj9Nei1maP6SNS3CvjeLj0sZVgXCZ+1iCvX3nFyv41mbM3AkDVFbSwqJwhBBCSOHIMvPtCEmautzHkEKglabcRe6yzcSfFEX5SiVU5YjdNoWdIftdY4SOHXK9xnJvhRL6eYod7zfGmJUbjniPmrKy6u4fHJ2yMt9LNUJazMuPCSGEjIniYlZcosp97NmMNS9ZRS6kVB5CCwra2JsFctZBcYmlcbUz9JpLPImtHRdl1+Z7Q6O0v1Ycm1aGj0ubWf3MNUmtrPzzyPep/daustsHqKzEYlZveDIdvuckBfNS/I6QMTOaxQohhBBC8iEiC0XkIBG5XESWiMjpIrKdh50fiIgRkUOt+5QSYOuCxjMwtOxq9be5XqHphVrXK5VbqrR7KObYoYWyhuzlCGT1sRNT5fBJ+faxb2undOWQrtd+UruBbjx6fzU30Mq77u81dxH5GoAdARwK4GIArwSwBYBtjDGnWdp4DoCvA1gRwCeNMW+26UdlhRBCCCG9iMiWAF4KYB9jzD7GmM8BeBqAvwA4yNLGcgAOAXCw8/hayop2YGybUlfqpc6LpCWHUhOqfrkUS5sWTOoaGO6jXNgEWWulXducpxY+weM+hQC1SKFOa9vRmENzHs1jh5vFc6WsiMjBAN4MYA1jzE2N4/sC+BCAdY0xVwzYeDuAvQA8HMDNcFBWWBSOEEIIKZwCisJtDuCi5kKl5kwAAmAzAFMXKyJyfwDvBfB6Y8wtIm7rpWjKSqmUliKcMk4mVqxPDkJ3eaWd19Cu2CZFOHf8SN+cY40Zi9C0ZJcx2nZzl5GfNtYErTFTKjUu2I6ZOmblpq98QE9Zefl+Nwy1Mcas1vx/ETkfwGXGmB1axzcBcAGAPYwxR0yzJyJHAngkgC2NMUZEDKisEEIIIUSRRQBu7Ti+tPF6J3W8y66oAnG9Fl3FFYWLRWk76Ql9RaxykjMzatZIVTq8FFViglbhuHbfLnxUJpdCbzFjVnIQUmgv92c6pBBdSHxQu19qZeXmr31Q7bd2xf98j0/MipeyIpW/51cA/mqM2alxnMoKIYTYUtoij5BCuQLA2h3HJ8cun9LvBQC2BPAuEVm/9doq9bErjTFL+gbPHrFDCCGEkOI5G8BGIrJS6/hW9d9zpvRbD9Va46cALm38A4Dd6v/eZmjwLEXhUrgISnBDpHDxaJ9nCdetjxxBiKXdrykLl/nYjl2szrf4mo07w8eeSxp3n72UxCr45nKeoUUup9nts635WU7tBrrl6x9WcwOt8JJ9fdxAWwE4HcDexphD62MLAZyPShnZuj62HoAVjDEX1f//EACP6jD5LQDfA3AEgF8ZY67qG59uIEIIIYT0Yow5Q0SOA3CwiKwN4BIArwDwIFSVbCccjUopkbrfJXXbe1CnLl9ijDnBZvzRlNv3VSlKTY8rAZtrmlOdcL2HSk39nDam72ckdun8vvsiNB15aCceQzUZeq9dA4BdroVPEKgNKRVVrWD7CWP8nu26BvOmrACAiCwP4AAALwewOoBzAbzLGHNyo80pqLJ+esdggC0hhBAyayxYJvcMYIxZCuDt9b9pbba1tOW0YCo+dTlFCu3YlJXQeKCxnW8TLXVizPioG119XdI7Q2JNXNJiu/qFKCxafWziW1zPs6T7NTR+JKTQZMpro6kSpS63f8txB+spKy/eJ+ncNWA2ECGEEEKKJku5fZudmwu+/tahQlAuY9v2Cx2j3dfmHEJ8xqWqMPOsuLkUOXNpE1vJsLHjq06EZBVpxQC5XtvS7qs2pceA5SZ5zMo3PqanrOz4NiorhBBCCCGaZI1Z6YvC1979x1r5u8QAuI4fe86uMQku9SpikUL9io2W+uWSSeNbU6TdP1YNlZj1X9pj+Cg0WllK02zGJDTGjXRDZSUtzAYiVvDLjKQg1kKlD97bZAzIMvmzgXJCNxAhhBBCiibYDbSnrH8PA9pFirTtakH59G7GHKyaAxs3hEugqXagaBexA2ptAuG1SHm9JqQs4qZhN0MClBwAABuoSURBVKbtlAUmY4UVAOndQEtOOETNDbTo+XvTDUQIIYQQZQooCpeT0ZTb98Wn6NSslZWed1Ujd4nzFLvgtn2NUvVaBdv6+k/QUlZSBveGFpULIUXq/lDfvv7ahd5S/J64XpPkysp3PqWnrDxvr9EpK4xZIYQQQkjRFF9uPzdDq+0YpaKnjZlit2IzdkkKTYqifDnwLZI2jVLjWmKpOFop1Vq4pE1rF5CLWc4+1fdFid8/yZWV731WT1l57uuprBBCCCGEaFK8suKyog5dfY8xwt5l/FJ2JD7kKP1d+nXTzqCJVX4+Rjn7CS5xYrEVFdf3ocQCbaHxNynOwUXNiZmpSGUlLaPJBir1B4MQQgiJjSyYb0fIfJ89IYQQQoqneDdQKLHSiUuQbMeEjxwb87lLNuR8j0OKw3W9ZoOPrO4qvYe4YrRdPFrP79FK384BCzqOxw209AeHqf3WLv/s19ENRAghhBBl5rwo3GgWK7476Bhlll3tao3Z7uM7jxxBcH277GltYu72bPqn3EnaXK822mm/fW1CFBFXhWbIju/70h6ry960ebgGnuZIj7bBpRSDy72ordCkDKh3CcYl+RjNYoUQQgiZW+ZcWVF7kOEYV6OpyuOnjKsoZXeQwxfuslsPLTPuYsfmPdIqfz4NmxgTF0LTkmOnSdsoee3joWP69p9XfO/52N8btiSPWfnxEXoxK9vvPrqYFWYDEUIIIaRosmQDhZZ/Dtm15yjQlrsoXGxKUXPmjVLjIrTwiW/JUYhOy3aKOCxtYhfSLPk7JbWycuvJR6opKwufvhuVFUIIIYQQTbhYIYQQQkjRFFMUrgTZr4Q5kLvxfdbHvKBdFK5NaBE2raBem4BYn+Bll9TlvrF82/Ie9qOU74DkbqCfHq3nBnrarnQDEUIIIYRoUkydlZBVslYqaO6VOhmmhIBpX0rZEU5jWtE0m7Zd+CoXNnZs+zT7hRR866PPvovt2EXTbNuXNGYXpX5+SFyKWawQQgghZAosCldGzIoPpe9USyHVdaLKkYdYKcw2iohrau80FcImtsPlNReV1TWmJqSoXMzHamgUFsz9eShlHjYkj1k55St6MSvbvowxK4QQQgghmmRVVsa0Eyf65NxFpRw75wPctAuoxS6z7xvfEpINlItY8SNjUid8KOX8Uisrt/3yf9SUleWe/FIqK4QQQgghmow6ZiUFpazic5Fb/Ur1qHib89R+TETfmL6ZF0Pz01JEtB8W6Rv3EVKK3/eeilnKf2z4PPok9GGT2o9m8bVDZSUtzAYiVuRcqKRkFn5ACCEzyIL5doTM99kTQgghpHjoBvIkZoCbS3nweSe3PBwLm/urjeu9OGTHpZy87bxcxgx5yrJW2rXWIwNsx09NaZ8Dn6DxXHNP7gY69Vg9N9CTdqIbiBBCCCHKsChcHGUlZUEjX7saY+YOQNUmpLR5aedf6rwmaJdBb+ISEOsThGujOMRUJXwImV9ooLONnViKrC8ugbEh9pu4qMmhj2GYNqYtyZWV076hp6w8YcfRKSuMWSGEEEJI0agrKy5FmWKSw6/pk3ZXWnGy0tWIEoihiISmW0/DRTVxKWvvWx6/JHKfw1hi0vgd1U1qZeX2M05QU1bus9XzqawQQgghhGjCcvuWxC44pjV27NgfX9ul+eNz4FIUzsWeFi5Fu1z6x5pn17x8xxyKVYmhsAwpK66KzyzEmfmQ61yorKSF2UCEkLlmFn6wyRzAonCEEEIIIeXConCeuKTUuT53ZqgNd4J3MwZXYiw3XqzgT9+05L7+Q220CtH52AlNXda6BtNeb7bRDjwNfU5Pe645v6NSfxckdwP9+jt6bqDHPY9uIEIIIYToIiwKR2XFBZudktYYWruVUspTp6YvjX7CmK+Fi7KiXSLeZ+wufObjW0p/Wh+Xsvt9bbra+yo87TFjB9n7fsdofJ7GoI5OaM71cLM4qTpxx2++r/Zbu+xjnzM6ZYUxK4QQQggpmuKVFa0V/wSb3UrM+JHYJax95mIzvk1huyaxz2dMxaN80Col7oJWATktfBSH0Dlrj9lHCd8BLmh954Xa05rXkJ2h78fUMSt3nPVDPWVl82dSWSGEEEII0WQ0ykqTWLEhITZC7WiToxBdk5xK0Rj95kO+f9eMlZBYEO3y86HzCkUjfsS1j4uK40NpJfBTUsp8qKykhdlAhDTI/QVICCGdsCgcIYQQQki5jMYN5FK0KNX4JVDC/FzcBqXI1rFTQUPt5Qiw7cMmGDQknVm7iFsOXN1dqZ7lk9vFOQ3tgN2Y8+hqk9wNdM6P9dxAm25PNxAhhBBCdJFlWBQuyMC8FYWLhfbup9TdVB8lqERjIjTAdkj90lIrbNJ+XQqz5QzSbWKT6pqiiGQJlBrwG3NeqZWVO8//idpv7TKP3G50ygpjVgghhBBSNDOhrGiVow+NIShJGXApbNck5dxjX6/QeKeS3nPXonzahIzpW/o+5Lxyp0u35zELCktJ328xsT3P5MrK707RU1Y22ZbKCiGEEEKIJqNRVmLEYITsnEv10RJ9SisL3rYXixjqxLRYFRuVr4SMH19sSrinUAJ9KDWjr69tiowhKitpYTYQIYQQUjoLmA0UZGCirPisbkvJWNGOX5hQmhJSqkKQY6zS1KoS6qq47OhT1HTRHsslfiTW+eV+lEMp9/sskFxZueiXesrKRk8enbISLWZlzJItKQPeQ3kpJUWYEFIGIrJQRA4SkctFZImInC4i21n0e6GIfF1ELhWRW0TkIhH5qIisajs23UCEEEJI4UgZzwY6CsCOAA4FcDGAVwI4UUS2Mcac1tPvcwAuB3AMgL8AeBSAvQA8S0S2MMYsHRpYPcA2RRqxj70JMYOwXBijS8aH0ufXRksy900L174vXMaOReg1Le0e0i5KFqv4XWl2QuzGTEX3Pb/DzeKkrpS7/nCqmhtowcOe5Dx3EdkSwBkA9jbGHFofWx7A+QAuN8Y8pafvtsaYU1rHdgXwJQC7GWOOGpyz64QJIYQQMne8CMDtAL4wOVArIkcA2FpE1p7Wsb1QqflW/ddqlTjzqcspH4RoE4w4bzvLaX37+vvsXH3nk4NS3s8cD4QbK32l9LVU5FgF8lywUZ61lRqtMV2ul8a1Ta6sXHy6XoDthk+4YaiNMWa15v+LyEkA7meMeXTr+HYATgbwbGPMibZzEJENAfwBwD7GmI8OtaeyQgghhJAh1gZwRcfxybF1HO29A8CdAL5p0zhLzEqsgj+uJeZT7QD74hdSFDSKTWjZ/pQ7/JAU4dDCXi7EspPi/k8Zd2Yztq9SN0ToPT50Xikfo+A7VuxU7/Y4rmO5FCF0ZczKyoKHPt4nZuUSABcYY57XOr4BgEsAvNEY8xlLWzsD+AqADxtj3mXTh9lAhBBCSOlIdkfIEgALO44v33h9EBF5Mqo4l+8DeK/t4MVlA4UQuuNNUa46luIQq60NoTvXEhSkmIqDxntTWmzO2NS/UGLetz4ZQ31z0FAebMcMGavPXumZTEAGZeWSM/WUlYds6aOsBMesiMimAE5BFavyNGPMzdZzdp0wIbPMvPz4EkKII2cD2EhEVmod36r+e05fZxF5CIAfArgKwHNcFioAFyuEEEJI+cgCvX9+HA/gPgD2+NeURBYC2A3AqcaYy+tj64nIRveYusj9AfwYwF0AdjDGXO18+incQKlcMDZuoCYaY7na0CjWlbtwmY29sQRvxrY7ZkpxPeUMWo55X0xzedgEhru4bWzoc5f4uKdKw+X8bM8puRvoT/+n5wbaYAuvuYvIsQCeD+AQVEG1rwDwOABPNcacWrc5BcA2xhhp9DsbwKYADgZwXsvsJQPVbwEwwJYQQgghduwK4ID67+oAzkUVq3LqQL9N67/7dLz2JQCDi5WoT11uU1IJcF9SFJkrYZcfsgtt9it9NxtrHq7B3jmv01iCenPQt8t2uV6hSohWCu5Qv1ClxmZeKZWaWQqwvfPS3+oVhXvwY/jUZUIIIYQQTYott6+lYJSeVpuiKJyLb3wWSVHEzWUes3rdZ/38tPEpCa8Vs5KjAN00cqg5rgVEu6CykhbGrBBCCCGlk78oXFaKV1aa5FA5NPrbrPjHVMJdYw6+8yjhHHxJEe/kYm/ofo2R6TZkO8bnPkRZjJnJWErZ+K5xmpRWSj9Hcbkukisri8/WU1bW32x0yspolmpj/HEihBBCSDh0AxFCCCGlI6MTQ1QppiicC7FSSmPhGszlkl4YMh8Xmd71uoVI7b5B0bPgVoo9H6Yju5Eijb5NyqJwPm6SFIXoYvTTJrkb6M/n6LmBHrTp6FY+o3EDEUIIIWQ+yRpg61JELDTwLjS4sbQdeInYBAk3KaFQnHahwhzB0Nr3b2jAaAkB3Nqf8xgK3pCyEkNxKEGVSBEc7JMW7kpyZeWv5+kpKw98FJUVQgghhBBNgpWVPWV9A+SJH/HZFYfGj8QqSe6rPJSwmy2FEhSyFDFbWmin9JZ6nm1ilSjwtaONi3KhHQPjQ8q4G5+201h+0SIqKwlhNhAhhBBSOIZF4dLHrLjsykJKSA+N6UNfsaHYSkiOnetYdsspiZlRU9L1ZuZQGkpQWHzs9tmOGZvjwiwVhbvjbxeoKSvLPuARo1NW5nupRgghhJDiyRqzAsTLUsipgGjZc4nJiVGmvCS0s3ZCCYlhcmmTu15R7FgtG3ulxJhojZUjEyeknkrueialklxZuexCPWVl3Y2prLjg8iHQ+sC4BuFqkPPLieijEZxn28ZlTG20ztOFMd+3NteL3wWE+EE3ECGEEEKKJku5fRuXhY3sr5W6rCWHu8yjb15D9lwl85AUVaIHr7sfoYXeSkXbJVNSUbi+AFutObikXdt8h7oqialTl++4/Pd6bqB1Hk43ECGEEEKIJtkDbG2xWQm7BiPaKA4+Qa6x01lDg4VL242WNh8fQt+jkLF8+jb7x75vm/jE6eS8L2LOIXYBNC07LuUkbNTzWQnYTR5gO+fKCovCEUIIIaXDonA6MSs2O8tYakd7HFt8fKnaMSuh/vhY6dzaqcKxivLloLRiab7XZCgOK+V75dO2SchctdLNQ8btsmujTrjGBk6zZzO/rj451SEXezFUneTKyhV/1FNW1t5wdMrKfC/VCCGEEFI8Wcrt96Htj+9rM9S2r7/WbtFGWfGZZyg5FIx5GbOPlIXfYikfNnYmjPG9jnUt2oQqGDaEKsWzEn/SxdD1T62s3P73S9R+a+9z/4dQWSGEEEII0YSLFUIIIYQUTTFPXbbt0+zn8xyOmOmZPqmgsWT/JimDBnOS87prUcK8tANYfcf2KXg4JoaSCWy+W0JThEMTGbQL27m09WkTGmibsyjc7VdequcGut+D6QYihBBCCNEkaVG42MWsZk0paBO6i/S5XloBmj7zy4FL2nqzvVZhwbGgpb6UpoYNpXNrjh8SnOqbnhyihGgH2uYsEqcRvJw8wHbOlRUWhSOEEEJKR0a3vlAlWlG4Llx26doF0WKRW80p4Rp0UdK8cr9HsdBSFn0+ezb2uuaipa522dYkZYmCdpsJMVOXY8XAtPs00Yob0cZ3PsmVlasW6ykra60/upUPY1YIIYQQUjRqbiCbHUhfm/ZrofamobX7DJmDjd0x0XUOsXe8LuNoxfj49A0ZfwitezFlDIbGWK42Ypf9943lKP2zb6O0TTs/l/MOVVhCVaFp3ymlKD//Ys6fDTTfZ08IIYSQ4uFihRBCCCFFo5a63MZVGtUo9OaSihvaxqZtaUWsUqYhu6Ad4FkaGqnLvs9ssZHIY6Ptes1BytRlF3eLTYq8azr+tLFd5qWFr0tmKIB4jKnLt139N7UA2+XWfAADbAkhhBBCNCnuqcul41MSe16YlxTh3HY05uA6j1jpzTmCXkPaNtG6H9r2SgnsdFFmtMru+xS7027b7jOtH5WVtLAoHCGEEFI6C+bbEZK03H5plLDTzYmWEuKbwql93WMVHAstvjbW+6z090yjf6wxY5WoL5USVCGbOBRNtSi5snLt5XrKyhrrjE5Zme+lGiGEEEKKJ2m5/T5SZYS4Zu/42J5m19W2dqyEzxx87Y1NTdCOt5kX9aWJ9v1vaz8FKbOButBWLmIpITFiQ1Jjq4YlV1au+7uesrL6/amsEDJmxrxYIISQWYWLFUIIIYQUTbTU5ZjuFhdiF0LLLfe7FJbq6ztLikJI+mpMuV/bxelzb7sUCOtLy7fp32fHBi33ZakuthwF1bRTjYf6uPYbE8ndQNdfpecGWm0tuoEIIYQQQjRJUhQuVqEmF0otNZ+SsZznWObZxZiCQFPN1TcFOvb8cqdNj0VxCA3GDekfqzCdBlRW0sKicIQQQkjpyHw7QrIWhbPZCboU6+rrV1pZ8Fh2fK9XSYxlnn2kiAcqQbF0ZWgepapDMQgpXBbKtDFixpq4xE/FRuM8kysrN1ytp6ysuubolJX5XqoRQgghpHjUi8JNSJGNor2zTLFLs8kM0VBWYmWyEH9KUN58xokxVgn3Ve459GWitYnVJtSuiwpkkxVaetG7JqmVlVv/ea2asrJwlTWorBBCCCGEaJK0zkpoHIlGHIpN/ReXMVKoQ7l3gBqUqviM8drGvme07+nS4ne07Gidp4sq4RJjop3FE6vkv41Nm7a2ZfJtGRozubJy4/V6ysrKq1FZicVY0vwImRXGtIAj44Xf7cSG0SxWCCGEEDKfZCm334dNmXANe65txkIpjzJIxZgfGeBTut7Vdqlp66XNZ0LK74uh90irtENKQoJ8h/r5jKVF11jJ3UA33aDnBlppVbqBCCGEEEI0yVoUrkmqdN0cO/GYqlDIfGLtDF1tl7rL1sIlMHzWr8WEHOdpEyiaUomahViNWEG3PkHHNsVBNedAZSUtLLdPCCGElA7L7cd/kGEq5qlU97wyC+/ZmNOubXaxJaglpd0fNsUfY6UIuxCz3L7PPFLOwXXM5MrKzTfqKSsrrjw6ZWW+l2qEEEIIKZ4kyorL7qe0nVtObGIdfK7JvF3HWae0z8yY76+c2XRjiWFxVV9Czk/r2sS4xqmVlaW33KymrCy/wopUVmIxlg8yIYQQMouIyEIROUhELheRJSJyuohsZ9l3XRE5VkSuF5F/isgJIvJg27FHs1ghhBBCSFaOArA3gC8DeBOAuwCcKCJP6OskIisB+BmAJwP4EID9ADwGwCkisrrNwMUE2Po898c3ZXlojL7nGo05cDf2Nfbt5+MmnKA1r9zvmcZzr7rshdjJXXBP+5pMs5/7czpWd1AoPmnKtu01xhwiuRtIMZll+UWLnOcuIlsCOAPA3saYQ+tjywM4H8Dlxpin9PTdB8BHADzWGHNWfWyjuu+Bxpj3DY1PZYUQQgghQ7wIwO0AvjA5YIxZCuAIAFuLyNoDfU+fLFTqvhcB+AmAnWwGT1Juf0KsXV7MonCpdl+5d7GxGIsipa3SuapzLoqPdlB1rBLzPt8Bvp9zlwKFNm1D74dpY9v2G0KrANoYKUWBGrOysmiFFW4YamOMWa35/yJyEoD7GWMe3Tq+HYCTATzbGHNi246ILABwC4DPGWP2ar12AIB3A1jJGHNL33xYFI4QQggpHB/XTQ/Xe/RZG8BlHcevqP+uM6XfGgAWNtq1+0pt+5K+wdWVldDdsfZOvM/vrbFDdX3YmEab0HMgZaSo+qoULnZKuD9KVw21VeCQcWMzpEqkTEvuGjPV2K5jdo2VWlnJjYhcAuACY8zzWsc3QLXQeKMx5jMd/R4I4C8A3mqM+UTrtVehciM9yhhzft/4jFkhhBBCyBBLUCkkbZZvvD6tHzz7/gv1Bxn6qB1D7dv9Ymdy+KocLrZzxnDEmoNNnECT0kukk/KJfe9oFdzrU0VdsFETfLJsYioZsWNM+uzHHHsOlZWsMSujUVZyB1MRQgghc8zZADaqa6Y02ar+e05XJ2PMXQDOA7BFx8tbAfjj0EIFGNFihRBCCCHZOB7AfQDsMTkgIgsB7AbgVGPM5fWx9eoaKu2+jxeRzRt9Hw7gaQCOsxk8SepySAqiC6UG8vm6QGbdPTIL5xfLBTnmazJhlq5NjO+WkGJwNmnXtv00SHkOQ/Y05mHDvLmBAEBEjgXwfACHoAqqfQWAxwF4qjHm1LrNKQC2McZIo9/KAM4CsCKAjwO4A8BbUGUCbWaMuWZobKYuE0IIIcSGXQEcUP9dHcC5qGJVTu3rZIy5UUS2RbXIeS8qr87PALzZZqECFFRuf4JGmmdKfIvM9fUp6ZxjpnW6lFNPVXpdawzXdPVpY8e8F1jwMB1aAbYloBUk3GVDS/mZZmfM5fbnHcasEEIIIaRossSsxMI1TiZHanAshaAkNWaM2Ox8x3xtUxZrDME31i1krFyq1dD9ZVMIUDs+xeb+11KEYqsoXa9pxslQWUkLlRVCCCGEFE1x5fanEarUhJYmj1l4LmReLm1z7Ey120wIPQet619C4T4yToZ28n0qsK8a4aPQ9M2rPT9tUqgvvm2prKSFygohhBBCiiZLNlAslUPL3x2a4ePSt8+eVjZKGy2FYCwxHTFVuVJjkLSUmZDsPN/6QjlJOS+fmJDQNrFjXWIrLLZj+MQsutpVfgoyGYDKSiBjTT8kZAje2/NHaYvHWPDeHh9crBBCCCGkaIorCpcSjcDVFGW4tdpO6+vaf2xBpSnS6G1SI33saM8rxZhjJocbKEcQaYi91GXtY9izOYehNgywTQuVFUIIIYQUTXGpyznSYfuC/mIFu5YeaNgmRrEuDVUoRZG/FAzN3eb6d5FCCUxF6Z+jUDWtTYrU5Vh9UuDyvc2icOOHygohhBBCiqbYcvuuqbex7WhRwg4wBWOJlShhDikZk7JS+j2kpSJPI4ayMhZynKfrmFRW0kJlhRBCCCFFkyQbyKZwlk22Tbt/juwRjeJprv1dxi69NHypO3uXecUsMmfTNqRQm+88Utkt9f7wxaWgoA05lRWbYnMTUsbUaNu1bUtlJS1UVghpMAs/kIQQMmtwsUIIIYSQoklaFC7EhWLj1hhT8S+NsVMwxgDUVEX1yD3Rck+NhdgBtr6kTE+2SQ3WHlMDFoUbH1RWCCGEEFI0y6YczOVpoJP/71MwcgTwhe4Sc+76Z32nO8Hl/Eq5FrPw3oxNyQqdi0vBvZLOuwtfdSNEFekLHrchRJmx6aNROI7oQWWFEEIIIUUzsw8yLCl9cp7SMUPs5Ei7tplXjrYuaKX3a8VgjE1hcUX7/tfGRcGe9npfmzGj9bgCAFh+0SLGrCSEygohDWbxC5oQQsYOFyuEEEIIKZpgNxAhhBBCSEyorBBCCCGkaLhYIYQQQkjRcLFCCCGEkKLhYoUQQgghRcPFCiGEEEKKhosVQgghhBQNFyuEEEIIKRouVgghhBBSNFysEEIIIaRouFghhBBCSNFwsUIIIYSQouFihRBCCCFFw8UKIYQQQoqGixVCCCGEFA0XK4QQQggpmv8PvAHBgbBTU8QAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 20 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "A3 = rdpg(X,\n",
+ " loops=False,\n",
+ " rescale=False,\n",
+ " directed=False)\n",
+ "A4 = rdpg(X + np.random.normal(0.05, 0.02, size=(X.shape)),\n",
+ " loops=False,\n",
+ " rescale=False,\n",
+ " directed=False)\n",
+ "\n",
+ "Xhat3 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A3)\n",
+ "Xhat4 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A4)\n",
+ "\n",
+ "heatmap(A3, title='Sampled RDPG 3 adjacency matrix')\n",
+ "heatmap(A4, title='Sampled RDPG 4 (distorted) adjacency matrix')\n",
+ "pairplot(Xhat3, title='Sampled RDPG 3 adjacency spectral embedding')\n",
+ "pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0175"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "p = 0.0175\n"
+ ]
+ }
+ ],
+ "source": [
+ "lpt = LatentPositionTest(n_bootstraps=200, n_components=n_components)\n",
+ "lpt.fit(A3, A4)\n",
+ "print('p = {}'.format(lpt.p_value_))"
+ ]
+ }
+ ],
+ "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": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/inference/nonpar.ipynb b/docs/tutorials/inference/nonpar.ipynb
deleted file mode 100644
index 59cb2dc6b..000000000
--- a/docs/tutorials/inference/nonpar.ipynb
+++ /dev/null
@@ -1,188 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Nonparametric Two-Graph Testing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "np.random.seed(8888)\n",
- "\n",
- "from graspy.inference import NonparametricTest\n",
- "from graspy.embed import AdjacencySpectralEmbed\n",
- "from graspy.simulations import sbm, rdpg\n",
- "from graspy.utils import symmetrize\n",
- "from graspy.plot import heatmap, pairplot\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Generate a stochastic block model graph\n",
- "\n",
- "We generate a stochastic block model graph (SBM), which is shown below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "n_components = 4 # the number of embedding dimensions for ASE\n",
- "P = np.array([[0.9, 0.11, 0.13, 0.2],\n",
- " [0, 0.7, 0.1, 0.1],\n",
- " [0, 0, 0.8, 0.1],\n",
- " [0, 0, 0, 0.85]])\n",
- "\n",
- "P = symmetrize(P)\n",
- "csize = [50] * 4\n",
- "A = sbm(csize, P)\n",
- "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n",
- "heatmap(A, title='4-block SBM adjacency matrix')\n",
- "pairplot(X, title='4-block adjacency spectral embedding')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Nonparametric test where null is true\n",
- "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between SBM 1 and 2 (up to a rotation) are no greater than those differences observed by chance.\n",
- "\n",
- "In other words, we are testing\n",
- "\n",
- "\\begin{align*}\n",
- "H_0:&X_1 = X_2\\\\\n",
- "H_\\alpha:& X_1 \\neq X_2\n",
- "\\end{align*}\n",
- "\n",
- "and want to see that the p-value for the nonparametric test is high (fail to reject the null)\n",
- "\n",
- "We generate a second SBM in the same way, and run a Nonparametric test on it, generating a distance between the two graphs as well as a null distribution of distances between permutations of the graph. We can see this below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "A1 = sbm(csize, P)\n",
- "heatmap(A1, title='4-block SBM adjacency matrix A1')\n",
- "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n",
- "pairplot(X1, title='4-block adjacency spectral embedding A1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Plot of Null Distribution\n",
- "\n",
- "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0.94. Thus, we cannot reject the null hypothesis that the two graphs come from the same generating distributions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nonpar = NonparametricTest()\n",
- "p = nonpar.fit(A, A1)\n",
- "\n",
- "fig, ax = plt.subplots(figsize=(10, 6))\n",
- "ax.hist(nonpar.null_distribution_, 50)\n",
- "ax.axvline(nonpar.sample_T_statistic_, color='r')\n",
- "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Nonparametric test where null is false\n",
- "\n",
- "We generate a seconds SBM with different block probabilities, and run a Nonparametric test comaring the previous graph with the new one."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "P2 = np.array([[0.8, 0.2, 0.2, 0.5],\n",
- " [0, 0.9, 0.3, 0.2],\n",
- " [0, 0, 0.5, 0.2],\n",
- " [0, 0, 0, 0.5]])\n",
- "\n",
- "P2 = symmetrize(P2)\n",
- "A2 = sbm(csize, P2)\n",
- "heatmap(A2, title='4-block SBM adjacency matrix A2')\n",
- "X2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n",
- "pairplot(X2, title='4-block adjacency spectral embedding A2')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Plot of Null Distribution\n",
- "\n",
- "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0. Thus, we reject the null hypothesis that the two graphs come from the same generating distributions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nonpar = NonparametricTest()\n",
- "p = nonpar.fit(A, A2)\n",
- "\n",
- "fig, ax = plt.subplots(figsize=(10, 6))\n",
- "ax.hist(nonpar.null_distribution_, 50)\n",
- "ax.axvline(nonpar.sample_T_statistic_, color='r')\n",
- "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n",
- "plt.show()"
- ]
- }
- ],
- "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": "3.7.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/tutorials/inference/semipar.ipynb b/docs/tutorials/inference/semipar.ipynb
deleted file mode 100644
index 2ca652061..000000000
--- a/docs/tutorials/inference/semipar.ipynb
+++ /dev/null
@@ -1,390 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Semiparametric Two-Graph Testing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "np.random.seed(88889999)\n",
- "\n",
- "from graspy.inference import SemiparametricTest\n",
- "from graspy.embed import AdjacencySpectralEmbed\n",
- "from graspy.simulations import sbm, rdpg\n",
- "from graspy.utils import symmetrize\n",
- "from graspy.plot import heatmap, pairplot\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Generate a stochastic block model graph to model as a random dot product graph\n",
- "To start, we generate a binary stochastic block model graph (SBM). An SBM is composed of 'communities' or 'blocks,' where a node's block membership in a graph determines its probability of connection to the other nodes in the graph."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<seaborn.axisgrid.PairGrid at 0x254c28370b8>"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 20 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "n_components = 4 # the number of embedding dimensions for ASE\n",
- "P = np.array([[0.9, 0.11, 0.13, 0.2],\n",
- " [0, 0.7, 0.1, 0.1], \n",
- " [0, 0, 0.8, 0.1],\n",
- " [0, 0, 0, 0.85]])\n",
- "\n",
- "P = symmetrize(P)\n",
- "csize = [50] * 4\n",
- "A = sbm(csize, P)\n",
- "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n",
- "heatmap(A, title='4-block SBM adjacency matrix')\n",
- "pairplot(X, title='4-block adjacency spectral embedding')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. ASE(A) recovers an estimate of the **latent positions** of $A $. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n",
- "\n",
- "For an adjacency matrix $A \\in \\mathbb{R}^{n x n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n x n}$\n",
- "\n",
- "$P = XX^T$, where $X \\in \\mathbb{R}^{n x d} $ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n",
- "\n",
- "ASE is one way to obtain an estimate of the latent positions of a graph, $\\hat{X}$\n",
- "\n",
- "In the above embedding, we see 4 clusters of nodes corresponding to the 4 blocks that we prescribed. ASE recovers the fact that all of the nodes in a block have similar latent positions. So, RDPGs can also model an SBM graph."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Sample new RDPGs from this latent position\n",
- "Given the estimate of X, we now sample two new RDPGs from the same latent position above"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<seaborn.axisgrid.PairGrid at 0x254c828d860>"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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H7nCkWfpDpZ58BNVu8ipUduarUe1SXoxGmGRd/omobvw3oHqQnwZga3SHDK9AK8ywPn4IOkIXsTqM8pDGsf3qY09BpUr8DpW56SIAz+9o+w7jqI/fD1X00ApUDq5XowrnfQeA+3Sc4w9Q3USvrOdn6/bYeuZ0Mua9p3z+tPrz41pzZRp/N6FaSHwdVUj34iltGQBfc/i+10D1gP46quiav9ff5QX1eT7KoS3T82czT+ug2hFfVs/1r1D58jwZrZDyuvx9US1wrq/HfCqqRdYdrrMpxx6KaqFxbV1/KSon8uNRrVHa43sQKofjP9XXzOWoFiaPaJXbui73x7rcn+vr5/+hEVLcc20u6ZozVKHRb0K1MLsZ1e/txwD+vaMNqefPAHiCw3fY2ffAeCfX9w6NYxuhcui9vB7rhQBe2lW2Ln8wKtPe35v9oyesuet7ReXcfBuAnTvKfq1uf1vb+eAf/zT/xBh1kycplFoBOA7AjsaYpXlHQ8aCiPwewCXGmC6fiZlERC5GlZbeNUyYEBIB2iAJIVOpfYw2QqWKzQUi8iRUTuhH5x4LIaSCPiuEkE5qv5zdUZlQvjlQfPTUi5TNUPld/QVVGDMhpAC4WCGETOPdqO4R/4sqbf+s8z8AHo8qYm9fc8ccPISQTNBnhRBCCCFFE6ys3LxqlQGA/1onOM/X7fjATVVG+Em7k/+37atdv69MX7vTxmHTrtY4+8ranMNQe4Tkouv3XQJ9v3etsU4791LnJJRY56Xdrm17ay9alDqX0lxDB1tCCCGEFE2wGehAWVK9W95DpejDpWxf/b7xuPQVOp4QYvWde+c2qzvIEELmpGv339feUF8uSmNX+dDv1aa9sVxDNuN0mT+X87ZRhVLOY2yFxbVt3/FQWUkLlRVCCCGEFE1WZcXVD2UIm11e37j6xuKjbvgqIqn6Kn03Gor27pPYMRbfBK2+Yl1nOfxkmoSoe1qqULtOF75zM/SMoM9KWVBZIaQBFyqEzD45zPkkDC5WCCGEEFI0amagNtomHl9imXhmgdJVhJimmRyOmSHyd0xT1rQw3VB5PaW5ZkJpTr3ajrWhfWkwayZTOtiOAyorhBBCCCmaaMpKk1KVC5+EaqWeiw/azoNkXMRKSjbmayaWY22KdjT6DHW0dQmVz3Gd9QV3UFkpGyorhBBCCCmaYpPCaaEdLh27vb4yXYT06UKM0MEQUuxGQ8IwXRO05WCs6d5T+Ey4pNufpZDqMX/nodeF6xxQWUkLlRVCCCGEFE0Sn5U2s+T30Uef3dalfl+0QEh7pe6iUlK6f0Yp31WsZF8kDNfkm9Pq5/xeXRN1pop2YlK4sqCyQgghhJCi4WKFEEIIIUWjbgZyMfHETBznMw4bJ9JZMF2VLtPHlJ2ZOMuPFG/K9Wk/VqI9lxBcLVIkkOtzHNZoz7eMNr7JFKfV6brvH2VW0AyUECorhBBCCCmaLA62XeRULkL6dlWHSlJoZmHHn4NZU01yoL3bdgk1nlbXdjw5HYlnySHcdywhyeU0FTM62KaFygohhBBCiqYYZWVCLOWhr12Xz2KPL0bbNn2WTklhsVovDIxVRwsqSG5oz1dfYkGXflxDg4faDk2QF5pwb9ocxFTTuqCykhYqK4QQQggpmmBl5eZVq27XgJYq4KuEuLSXyn8kh2piQyx/gRSMMWIodNc5QSvde8h5xvY5mVVCVTmbui5+GSW8KiA0lX7K1wo0x8FooLRQWSGkQUmLSUIIIRXF+ay00VIlXHIXlEJs5SdlDokuYu/sXezdvv3btJNzNzttDCnb8fUTizWemO0Mtd8kll9L7D5zU4rvF31W0jIaZaXUBQWZP3gtxiO3IzEhpExGs1ghhBBCyHyi5mCbw0lV2wm3q692nzbta6f6tyHE6bhZbxZk4lnHxaxk89mEsZhftJwtU+CTrE7LqTrWKwNKcXbVGpfN76nrGM1AaaGyQgghhJBBROTeIvJBEfm+iPxVRIyI7OBQ/5Ei8h0RuVFErhWRz4vIvazqlupg66NclBYirO0gG6s9Mhv4hoCmVEmG1JxSHG5DKcUJ1BWtBHLazr0pExTafg/zqKzUC5MTAfwfgFsA7AZgR2PMUou6WwD4EYAfA3gPgHUBvB2VaLKNMeavffXXCBk4IYQQQuaGs4wxGwOAiDwT1WLFlrcCuAHArsaYG+s2LgJwMYD/APCuvsrFKittXH1WyHRCbdglpICPlbCt2XaIX4DrTjDVnGrtUGPtnHMrIjkISaHvmwiwjU/Ketc+fRK+ubQb6uvjOqfzqKw0qRcrJ8NCWRGRNQGsBPBxY8yrWp+dA2BNY8xj+tqgskJIg3l8WBJC5gsRuW6ojDFmsWKXmwJYBOCijs8uALDvUAOjUVb68FVYbOqVrt5MUwFcIpHGTI6dINGj1LT9ORP3dZEzuiY06myoTOhvOKXa2iS1sqL5rP0YLls5VGZoseKorGwH4BwAzzLGnNT67O0A3ghgHWPMqmltUFkhpEGpi1JCCNFCWTVx6trzM4YuE0IIISQqV9f/btjx2QYAVhljbu5rQN0MlMJsYhO6PEHLxJPDHJTj3UA2xJZdfRNdxeqzBHKEbjYpfX5C8A35bn+W2+nbx0l+Wvu27fj0HQub+dJ0TE791mVNM5DG2BUdbL8P4E5DDrZUVgghhJDCWSh6f6kxxvwdwGkA9hKRdSbHReSBAB4L4MtDbail258Qmu49pKxLKmVfUjjlDu3c+nYQPu3HRDvl95hVHZ/U69rkdmYccrIcs4KT4/t0JWWYtAY51FbbPlI72P7HAj1l5SO3+SsrIrJ3/Z+PBnAwgENQ5Uq50Rjz9brMCgAwxixp1NsSVVK48wC8F6uTwq0J4OHGmBv6+qWDLSGEEEJs+WLr/w+p/70MwJJplYwxy0RkR1TJ374E4O8AvgXgv4cWKoCiz4rLS6By+n24+rXkQEMtsVFftNQOX1Lt8mLa4UPm1NVXoo12CGi7nRTXRwmhxr7fg0syMpdx+CpvLvfZEGUrd8JDjbQMvkkgm6RWVl618P5qysoR/7h0dAnt6LNCCCGEkKKJFg3URMufwqcdLVWnbxebGps5di1Tos9AqT4AOSJzcvsZ+ey8S/rOQvGNBvJRS0Lnv2vMsfrUuC5Co7Fs+opx/VJZSctofFZKM9UQQgghqcgRxVMSNAMRQgghpGiSvhsoxHE01HzTJ3PGQivUeFq7mu344OMgO4a+XIht8phFk0qTlM682pTqbJwSlzQSNm3EdvzVNG2lNgMdtIaeGej9t9IMRAghhBBlFsro1heqqCWF006S5uLQ6psMzsX5yiVczkftSKn49PVZwq6uFKfSaZ+lCN0M3QGG1I+V+M2Xkq7NULTDpPvuGymUUxtHVl91xLaOTXs+4xtyUE6trLxmzU3VlJX3/v23o1v50GeFEEIIIUWjnm6/D5dVc47EcTn67hrDtHnSGtcs7FBLY5Z2/xNShGiX6ovURjvhnm87s3CdaSV2zD0XqZWV191JT1l519+orBBCCCGEqBItKdwY8qL4pDhPSWzFp/TdmVYacy1y+HJop6NvMrRD9fXNcamTktw78SFijU8r2qavbRu1w0Yx9kkKF+s+MTQuKitpYTQQIYQQUjiMBkqYZyWUWL4bLh722qQ4J5vzdKH0HeosUeorB7TQSi1P7EilfpWmitrget2lVlbeuNZmas/aw275zehWPqPxWSn9QickJvNy/ZdigiWElAXNQIQQQkjhzPu7gYIXK6lMMynH4OrMVRJd4/MZc59crO2kF5tSwh59+vRNRKcxrhQmqBzfR4q+NNO6h/aZ8p4VkgDOtl4sSr1/kYrRmIEIIYQQMp+oOdj6rOa1HVljJFbLuUuJHQYec0dXutoyITSV/lB7XZ91tR8yXy471NIcdkNfGaA5hj5SKj5dfWpdHy73lFghwj7JQVOEVruS2sH2kEUPUHOwPWTVr0dnVKKyQgghhJCiSfoiwwk2ZUPVhGkrae12fctMq9OsxzT7ZTAWlYiMi1gvkuxrx8evRVuBdWnX5T4bU53raofKSloYDUQIIYQUzrybQeY6KVwIJY3FlxQvEtN6aVlIe1r1fdU0jRff2ewatXbHWt+rb2SItq9QyHh8r9sJOV6tQOLR/I6PMiuSqhOHKior/zNCZWU0izX+gOcPfufzB79zQkgXNAMRQgghhcN3AyU0A9lI5WMzq4zpPUKzlIArR8hraUms+siRlCx2uz5jSDmOMZmBQkJ6Y5k6Xfu3SWIY05yX2sH27etsrmYGetNNvxrdymc0ZiBCCCGEzCejcrDVpoRXBbTrhtb3DZdu922zI+9rR5uQHZJNWHhuh92h+jEcULXmtARSfQ8p2gklh3NwTmyU+hjnklpZeee6esrK62+kskIIIYQQokqSpHAauCoPsXxgUiaFs2l3Wl8u7YWSY7cdMyw2Vopu31T82n0NlUlxvlq4+C/Y1g0dS5MSFJYuSlJNSlGkXKGykhZGAxHSYGwO3oSQ+YDRQMo+K7GUB636Y4reiU3KSJrSmJfz9GFMykqOvkIoXUVIMb5YilZqBS+1svKe9R6opqy89q/LR7fyobJCCCGEFM7C0S0vdKGDLSGEEEKKJkvocgpTUUn4mp5iz4FvGOsYJfcJISHCORwoS5lrLSftIUfi0s0kKciR8LDdfmgfLu/3KuU7d3VyT20Get+d9cxAr76BZiBCCCGEKEMH20KSwqVSUlI62Pb1X4JiNEYVJdauUzsUWnuHmsPZNeV5uo5nWp+lO/W6pIRPmQgw9Pq3SZ2QM5mhS13b9lMrKx9c/0Fqysp/Xn/J6FY+9FkhhBBCSNEUo6zMGyGJ40JVmRwqSWkKTWmkmh+tZGmaO9R5wsWXw7W9ae3OIy6JHX1VpaPMiqTqxIfvoqesvGIllRVCCCGEEFWSptvPmQzOp58msSNyXOavSci4UqSPj+0/4luv1IgELUryEygFm+9c+7UHObF5wd+0z23bDfF767vOXNrX7tO279Q+K/OurIwmGmjMNw0yHnidzR/8zskYYDRQYT4rKaNlbPJoTKvjEpngOp6cUUoupPBbCLHjaysDrv4GGmN39UkoKZV5jGgWnzIhjElx81ElQq/bWVDcfEmtrBy5+MFqz9qXX/fL0a186LNSCPP4Yx9Ca9GmvfhzlcpTod2nVnuzcG3PwjnYUEJKBUK6GI0ZiBBCCJlX5v3dQFneupyCUJNMTnI6Eo+ZeZWkSzlvH0fFEhxac5t6bOYg9jz5JJBrfmbjbD+tTt9npZg8u8aaOnT56LvqmYFedi3NQIQQQgghqhT7IsOQsk20kq6FKDU2YXKu49HoM/dOfIKWY6BLX9oOn7HUjVLDiEsbVwlz4kus36eP2hHal017oWkDYv/WusbS1WdqB9tjN9BTVg64hsoKIYQQQogqasqKjQpQij9Lm9L9W2LN26yFK6YKs83t49DGxfavPfYUCcJK93npI7ZfRcz5H2qjiY1fi+1YcuE6HioraWE0ECENSrlxEkJIEyaFG1G6fZd2ckTU2CT06qo3VLZdRyvZXGnJ3GzaSWGvLnHBEmN8IbvplD4O7ToxfTk02u1ruy+SZqiua1mXcfmU7aufO6oqhe9YamXl+A23UFNW9rv6F6Nb+dBnhRRLDnNciQsVQgiZd2gGIoQQQgqHSeEShi7ndGQNDWuOhY9px/VcfOTmnJTwTpgxMJbvU4uU37lLorZYJixfs0ZIX1pmJZfEcaWZYG3nIrUZ6ISN9MxAL7qKZiBCCCGEEFWSOtjGoi+Mz8XpNeZ4YrTfhVayulg7nDGHCE9wcZxuls/psJhDlcihRpSChrrRd/3ndICPmcTNJ1zdZizavyMgfbr9z95tSzVl5fl/WUZlhRBCCCFEE3VlJWUCM5d6s6Co9PU5IdT3hcwfpfoSxPa9aNdxrReLFAnabFIC2FwX0/r0pSQ1bei6SO2zMu/KCqOBCCGEkMJhNFCGFxlq06dohEbblBAx5ILL+eZO+JaDnCpCCfNWmorggs3u33eOfdLQx/4+S0kE6PJbSZFwb1rfqUmtrHxhYz1l5TlXjk9Zoc8KIQ1y3wBJesa2ISFkHqEZiBBCCCkcvhtoBsxANozR0TakHa2wZO1fAVn8AAAgAElEQVRkUaHkSISW8l0yPgnxXJwjXcYVc261+4h1XaS8/kswE/oylrFrmvVSm4FOuvtWas/avf988ehWPjQDEUIIIWQQEVlPRI4QkStEZJWInC8iu1nW3UtEfiAi19Z/54rIs637nnVlxSe0t1QHW99XBsROt6+dHl9bKdAmxdtdfdq3cTB3CU3VclLtKxN67ZRwXZT+eogSnVObjMHpu+v7S62snHwPPWVljz/5KSsicgaARwA4GMClAPYD8AIAuxpjTu+pty+A4wF8CcCx9eEDAOwF4ABjzCeG+qbPCiGEEEJ6EZGdATwFwJ7GmJPrY2cC2BTA4QCmLlYA7A/gMgDPNsbcVtf9JoDfAtgHwOBiJYuykiN5mhY2O9VSieXj4ILLS9Bi7Oy1+rJt17ftnGnpS1ArbMdRylgBt2u7r0xfPZ8QbRfFzVeFsXlVgPbvM6RPDTVn3pQVETkGwLMAbDBZcNTHXwrgaABbGWOWTal7BoBNjDFbt45fBOCPxpinDfVPZYUQQggpHM1oIBG5bqiMMWZx69DWAJY1Fyo1FzQ/n9LchwF8WUTeBOBjAATAywA8CMBrrMacUlnRViW0ksHFKpuinaH2u/ooYRfaRUm75JLR9vvQGENpzGpkTui4clwHWon7fPoOabeLnD4rX91kazVlZfc/XbxyqEx7sSIiywEsN8bs0jq+OYDlAP7dGHPktPZE5BkAPg1g0u6NAF5ojDnFZsxUVgghhJA5okM1sa7q85mIPBXAZwF8DpWT7UJUjrmfE5G9jTGnDXXMxQohhBBSOAvyJ4W7GsCGHcc3qP+9pquSiAiATwL4rjHmwMZH3xCRewP4EIDBxYraW5dJGLnD94akWhtp1NUc52MKCwnFdWnftZ5LO74h6H19hJDS5FGCeaVUp2qXPkozT80CpSeF+9o9H6L2rN3l8gt9HGw/jirUeMOWg+1LAByDKQ62InIPAFcAeLMx5u2tz96DymdlkTHm5r7+mRSOEEIIIUOcjMrfZNfW8X0AXDItEgjAtQBuBvCYjs/+BcDVQwsVYERmoNBQuNLDdVMkxfIJ9bNxhvZVTaYpDi51+sqEOFb2YaOaaDlO+6owLnPgEmYb+psZSkCnFTrrMgYb+uYiJSUoKq4KksZ9Nsb9f2zIwuxmoNMBnAngWBHZEFVSuH0BPB7A7pNCIrIUwPbGGAEAY8wtInIUgP+q1ZmTUPms7FPXfbNN56NZrBBCCCEkD8YYIyLPBHBY/bcYVajynsaYUweqvwbAL1GFK+8N4DZUEUQvAvAZm/6L91kpLVTPpa8UfccKA3TxswhNBGWjTvgksdIKRQ/dSdvMqYbPUJNZ2Em6MG/nWxrz5vcEpPdZOe0+D1V71j7j9xdkl2lcobJCCCGEFM6C/GagrBSvrOSmlFU8EC+hU7NNlx29D1rRNn1tuyg+Lu1p46uslHQtdqGdnE5LXZ1Qwvy5kiORWqrvyubaLjHSKrWy8vUlD1N71v7rip+PbuXDaCBCCCGEFE0SZaUkdcKVsaYp9/XYb2MTjRLqV+FSxoaQPCt97dlERvnOxbT2XMY3K4z1fhFz91/CnPioMrblY41Du88mR5kVSdWJb2y2jZqy8vTf/JTKCiFa5AgNJXnhd04I6YKLFUIIIYQUTVIH2xKkTC1m4Vz6pFqfRHQ2YcQ2TqVdY0rp+NvGp28tk5ZrHz6Uei1zXHn7LKFvLbScrJvtpDYDffOBj1AzAz1t+f/RDEQIIYQQosmo86zECu/0TY9voziEpPqOmVbaRy3pqzs5pp363kZ96RvvtDK+ad5t5kvj+4vpy1Hqjnnex1VKuHro/SxHWvx2O1rzV+o1OQ+MerFCCCGEzAPznhRu1IuV9ipXK6ROewfgGqrqc14+ZW3HE9Jun8/KUJs2IcIufXfV61MsXNQMn0R0viHM87aznzds/Lty4joG7ftXCQo2SQ99VgghhBBSNMWm24+1yg1NlpbS7qqFzXiG0r13lZ2glV7dJemaDTbRSV2EpBe3OYe+Pm0ij0pPDOZzDrGSL7q2Gfv79BlDSvoUwVLuZz7EOIfU6fa/vdWj1Z61T7n4x6OzKVFZIcWSI0HYmG/IhBAyq3CxQgghhJCiKdYMRMJwfZOpCz7ms666qZSTUEdWF5ORi3Okdt9D49AomxPfd9NMiHV+pZltYvURmkit9CR6rqkwUpuBvvvQx6g9a590wY9oBiKEEEII0YTKiiWxHX5z7IB9Q3rbuITrdtVr9+lSN9SJNnYa/762+9SX0h1tx4hLyKtWenYfSlG6xqqIpILKSlpGnWeFEEIImQdkzpPCzayyEnu1rb3TzZ2Aa0hRsFEB+ur5hHva4OuPEjKOvnDuWApN7l12CGPwqyidWT+/VPj6PZXgs3LmNtuqPWt3/OkPR7fyoc8KIQ34MCCEkPKYWWWldFLulFyiUWx8Jnz8PWySy2kT0w9Fo6/QyCjtJH8uvhzztqhLORezMMe5leIUpFZWlj7qsWrP2h3OP5fKCiGEEEKIJlysEEIIIaRoaAYaOSGhzzHDYW1CP30cWNtlXM0nsUK02+3ZJIVzGUdMKb0ks4PWO320xhHLxGPTduhc+NTVamdWnaqb53WUWZHUlHLWttupPWuf+MMf0AxECCGEEKKJmrJSmrNZzqROY8Qn9NYnuVZX/VB1Y9pn2mUnpDhPG0q4pseshJSkKA0RMtbSnF1DvivXei7tuY4rtYPtvCsrTApHCCGEFI4sGN36QpWZ91kZ0+6pTY7EdtPwTaIUqtC4EJJCP8QnQKMv7dBlMh5KukfF8GNzUbljvX4kRtnUysrZj32c2rP2CeeeM7qVD31WCCGEEFI0M6WslGabDSVFRMKEoRe4xUpd3yzTN06XZHVd9V3aKQmXJG6+lOAPUZK6kAuXqDHfMpznCpvrtrR0++c8/glqz9rHff9sKiuEEEIIIZpkVVZyKyFj3lH6vpCrWcdFeZhWflp7Q0rN0GdDffoSS33xademna42tW307Tqu4wpBS01IMQ7NfprYRJuVQM7vwXVOXF434QuVlbQwGiiQnIss9qlPSQ+HJqWaqWaBUue21GuR5EEWjm59oQrNQIQQQggpmmhmoNKkzFLk5qG++/r3TeVu0+cQfX2GtqNRtqteyh2zVuhyrHBrbbR+K2N0+BzjmCdo3C/GeN5a5Ey3/4Ptn6hmBtrue2eNTqahGYgQQggpnHk3AxUXupwyEdo0J67QF3+VtvOwcVqbkDOU18Xxt32877NZ8ElwTdbloiT6jGfMlOqwO6v3Hw00Qo1D2usqm9rB9twnba/2rH3sd783upUPfVYIIYQQUjTFKSsu+ISWjinkOKSub6jfNGIoGKE+L0N1fZQam75ihl9rzIkWNkn5huoNlXUZh02IfE4fmtL89HKSey40lK3SksL98Kk7qj1rtz3jTCorhBBCCCGaJFVWQhKZ2dZLTY5dnpb9NtRnxWZc2j4mLjv6PkL8Y7ST6fn2pUXpvg6xxhfTf62rj5D2tJKcaZxXzHueS5825WMlUASorKRmNNFApd5IyWzB62z+4HdOxsC8RwPRDEQIIYSQohm1g+2E0mXsMRGSjCxmKLSPg6eLKcV3fNPGZWNi6Gunb7ypJPeYfdKRdZgxnaerc+oYGDJrp04K96OnP0ntWfuYb3x3dDLNaMxAhBBCyLyyYMHo1heqzISyMiZsHOW0nOls++7Cx9HWVTFwUWiG+vZtx0X1sCHUwTbEwdlmPNo73piOjy51S1BXc6gJpSkYJSlwsUntYHv+zk9We9Y+6vTvjG7lQ58VQgghhBQNlZVAtJPMhbYT2r+ND0YbXz+NoXZs1Imu+u06vgpGLH8UF2Ughf+ICyHJtmzq5f4d2BCSUsCn3ZiUrFykxnUuUisrP9n1qWrP2keeegaVFUIIIYQQTdSVlVg2S802YxFrx5USm3OYEOrX4qK+uCgONuNzUTv6CIl2clF8XKKfiDshihHnPS05ouG6oLKSltFEA/GGQAiJQWhYPSEpWMCkcIQQQggh5TLzDrahkqGGPGxjBsod9uiCjaki5241pYknpEzofM3bO4K6mBfzzZhNzLHmO3ciutRmoJ8+cye1Z+02p3xrdDLNaMxAhBBCyLwy7+8GyqqsuCgOpe0aSk1J7vO20S5cFAOXtm0cYm3G54KNsuKTvG5a+aH6oUnvYjqxD7U7trBprb5yK5/aCSJJOKmVlZ/t9TQ1ZeXhX/rm6FY+9FkhhBBCSNFEMwPZ7ETGsDuYtpPpGrtNeK1t+0P1phFrt21Tttm3jy+HzXz5zGVo2HVoKnwNhaivjZSKns8OP0VSOB91qK9sSEh6V18+ClmKRICxk/zNGs1zP8qsSNq3LJxvbWG+z54QQgghxZPFZyX2LiFm26Xa822wUQi0dpQ++PTtG+nT16fGHPgqK6G+QqUz9BsZ86495dhLu9dovUoiFb4Rmi1lJanfx8+f/a9qPisPO/Hro/NZYTQQIQ1KufkTQkiTeU8KV3y6/T5C7MG2ZbSZpm64RpxoRE+5Kgeh6fWnlfEdz1CfNpEcvvOvzawrKhqMSU3QaLfZdsh9I8X9sYToqRj0zUHqaKALn7ezmrLykM+dPrqVD31WRkiOH/+YbzizAOd//uB3nhe+hqEsuFghhBBCCkcWiNqf9xhE1hORI0TkChFZJSLni8hulnVFRF4mIj8RkZtE5DoROU9EtrOqX2q6/RAJsrTxjEESHdpF2CRz83XYtTHfaLTbV6YLH3OXVnizS/j1mCnNtJOSnEkkSzWTx0IrRDung+1FL3iG2rN268+c5jV2ETkDwCMAHAzgUgD7AXgBgF2NMacP1D0WwF4A3g3gBwDWBfBIAOcaY84Y6psOtoQQQkjhLMicZ0VEdgbwFAB7GmNOro+dCWBTAIcDmLpYEZG9UC1sHm+MObfx0WnW/adUVnLsJGZpd6CNj9rRVT9G+HBI2b6xT6vn4ozrO65p7U4ba0h7OSntd5lTweirE2tcE0pRpW3HEDqO0HZs5i+nsrJsn13VlJUtP3Wq89hF5BgAzwKwgTHmtsbxlwI4GsBWxphlU+ouBWCMMTv6jZg+K4QQQggZZmsAy5oLlZoLGp/fARFZE8C/ALhQRA4TkT+LyK0icrGI7GvbebE+KynQ2iFptKuNa9Kjdllt/5G+eu3x+PrAaI/VZd5ccFGibM63i7Fcp23G4N81FkpJvllSiogJrqkdSghd/sX+u+kpK8efunKojDFmcfP/RWQ5gOXGmF1axzcHsBzAvxtjjmy3IyL3AHAFgOsB/AHAIQCuA3AAgOcAeJkx5pih8dBnhRBCCCE29C2Ypn02seCsDWBnY8xlACAi30bl7/I/AAYXKzOrrMRSTdplm4SkELdJBKWNj19JaHtaydd8fUw0fEK0/EhCxzdG5cFnh6vV7qz7zJWolOXCJYme77yNWVnZ4riv+visnIvK72S71vFtAZwH4DnGmBM76i0CcCOAC40xD2t9diiA/wfg7saYK/v6p7JCCCGEFE4Bb12+GMBeIrKg5bfykPrfi7oqGWNWicivp7Q5WTS1/WDuQPazJ4QQQkjxnAxgMYBdW8f3AXDJtEigmi8D2EJElkwOiIgA+FcAvzXGXDXUeTHKylCiMVeJzuYdMO1jOaXarr61JEybMkOOaL7miHZ9LefUvnZs+nAZR5+JwSXk2OY7Lz1UOfR6izX22HPSZW60+X2OzSRjY1Z1Db+O7ejrMu9ddVKZ3WeA0wGcCeBYEdkQVVK4fQE8HsDuk0J1mPL2xpimqek9qJLHfUNE3orVDraPBPBcm86LWawQQgghpBtZkNcQYowxIvJMAIfVf4sBLEOVJO7UgbpXi8gTUC1aPgpgEYALAexhjDnFpv8sSeEmhIa9+azqtRMQxUzy5DsO17qu+Oy4tBLIDY1liKG+bBLI9fWvHeLtG7qcilJDjVOOq5R7wTRK/Y5s8A16iBVY0SS1g+0lL9tTzcH2QUd/mW9dJoQQQgjRZGZDlyeUFl44NIbmOGKPPVRZcVFLbJQxn/ZcQ5dd8EkoFdrn0FiafcQOaY+5m/Xpq69PrTmJFVrd11epidV8GMs4fWleH6nT7f/q5XurPWs3P/IkKiuEEEIIIZrMvLKihYsKMBb6dqhDx4c+cynjUqdv/rVUjaEx9/XZp/hozUHXOMbOmH9PYx67CzESqzXrNsmhNrm2l9pnZd6VFUYDEdJg1h84hJBxUkBSuKzM99kTQgghpHiyhC5rhRHnlF99+7ZJIpbDcdIlpDekvVCzjc28aYcch7ZvMxftsqFO1fNimhgzqZx5x3yfbY8h5TiG+kxtBvr1K56tZgZ6wIdPpBmIEEIIIbrMuxko6WLFJt14m9BU1rGcwnxX99Pq+bbncg42aoKNetCnkA21E+p46pL6PlYiuj58Q6pt5tSHHOGxoeHIE1KmZw9pJ3Ts08pqqQla99mUIe2xKEElIn7M91KNEEIIIcUTzWclNMlZu64rIbs7m9BU7b618N39uGCTLC3EZ0XbN8S37b6+2viEGLuEZrvuVFMmNxuiRH+DlP27jsHnPtlVPuV9KMSPKsZ3NdSX7/2xWS+1z8pvD3q+ms/Kpu//7Oh8VqisEEIIIaRoik8KV4qNsZRxxCYkyZmvshLSXl8foX4f2uqUSwSSlj+ENrF+B32qZkofmliRVmO5f+RWpGzQnn8XX7cmVFbSwmggQgghpHBk4cLcQ8hK8cqKLzls9qXtnlKlo3bxvehrz9cOr+2n4xKd4eKr4hPJZDMuEk4sf4rS/EbIakLnK7WyculrXqT2rL3/e08YnbJCn5UZRTs0N1aobx8xnYRD+tQq4wIfQPEIWcCScZLjfkbCoBmIEEIIKRwmhRsxoQnjtPpq91lCOvTYJpGuMs32+pLB2Y7DJrGarxNuG+1kbE1czs+m/7HJ/b5mQu3+p/URo2+NUF4b02noGLQpfXwTfOe2eS0fZVaoj4tMZ76XaoQQQggpHjUH21TOnGMgNN24xjm7OKu6lrEhVvK1CS4OqKEhwjbOr334OAnPuh/FmH/fLmiHzKa8t+QIHR9qQ6sd1za66qV2sP3dG/ZXc7C97zuOo4MtIYQQQogmMxu6bMO87e4mhPpnuIQla7Tf16eNIuLb/wQX1cNmLlzaiZksbayUkrjMRwlxGXsp5xnKrF6vVFbSMmoHW0K0mbUbKiFkNpj3aKCkLzJsf+ayC/UlJGlXX/+hichskk/Fjiqy2bn5Khc+6oZv4jgXQhLaucxFqD/KUPvtPtqE+DaUsmAbS2SJLyWMWfs+Viox5jq1svL7Nx+gpqzc523Hjk5Zme+lGimaUhPREUIISQvNQIQQQkjh0AxUqIOtVoheqPyXSop2Mcloh4c324yVvt4miVuo6WgoyVZfHzGdjqe16RvOWoL5gJBZwff3lNoM9Ie3vFTtWXvvtx5DMxAhhBBCiCbFKittcic2ykEspcYl4ZiWg7KP4hDqwKt1Dn11Qh2ItdspAa108dpo/Z4mpEyWNub72FgZeuakVlb++NZ/U3vW3ustH6OyQgghhBCiyWiUlRho7LRy7hBj9O+TAjwlKcdT2rlPQyucO7a/Uur6Q+1on7drcsTSrysXQn0MJ4zpXkxlJS2MBiKEEEIKh9FAhSorpUVB5NzR+6TsdomAmVVK9Z3QQjthXhvfRGE2bbqoHC7np/2qhQkxIudm2fcupfqbq73Uysrlb3u52rP2nm8+cnTKymiWajkShBFCCCEkPzQDEUIIIYVDM5CSGSjk7aKu5Aj5i+146vsuJZe2xyIhEzts3kfU/kzbnOEbxu3SXmiY+lB4uvY5DI1n2rhKpbT7R6rxlBa6fMU7/kPNDLTJGz5CMxAhhBBCiCbFOtimIFaSqBKSPfm+pdemrE0bIerQLDhXa+2obZyE+/pIoSKUgJYTrkuSwFjOvKGE/NZiKuJDY0jZpwaplZU/v/uVas/aux/8ISorhBBCCCGaRPNZ0aa01TeTk5VHqfMUK2Q8RSivbftNYqk5Wq8r8OkzR+iyb4oCF1IqnrFV5FCfvgm27VBZSQujgQghhJDCkQXzbQgp3mclpaKSIuomR0KjElLol6p6jBntaKCUyddit+MaVZQjMmqWfUJSRIPGat+W1MrKle/9T7Vn7cav+eDolJXRLNX4kCOExID3FkLKp3hlpQsXL37XG9G0einzqoSUbZaft5vwvJ13aN6RWY0Uiu2n44KN2qWNr6phM75YPiqhEXc5fvOplZW/vP8gtWft3Q56P5WVHOR+R0QILn1xoUJCcbmGxkhJC5UmOcajFWrfLqN9DaV8xxIZLzOxWCGEEELI7JIkdNlH/kyRrEhDhUiZTlvbtDUmxpYULgVaDrbT6uQmRCVxnYschJh/Q1UOF2d7n3u7Tah36Wkfhq6Po8yKpKaUq474bzUz0EavOpxmIEIIIYQQTaI52GorIa7p40P6KCXNuwuxHdRCE1SFOkVrhV+XEvaoiVa6d5cQX+3xuDgEx/S7cbm2Q0K+XRlbCv1QtO67QwETfZ8N9Z3awXbelRUmhSOEEEIKh0nhRhi6TGabmGHibcZiN7fxF+jDx98gRihziCph015fmTZaSoh2OymTSrowFpU5lFKVlas//Fq1Z+2Gr3jP6JSV+V6qEUIIIaR4kvistEmZDMkmYiL2jlcrQsqF3GpCCDET5rXrlRRZ5ttnKC6Kio3/iG+yupzkGJdGFGLpv+Ux4DuXqZWVa498vZqycteXv5PKCiFa8Eacl9IWFLHgdUZI+XCxQgghhJCiSZIUzgWtJD5a78RIZS4IDS8MHWdI6HPK94RQ/l6NlpPqhBBzUCizZh6ycSQeCu+P4WDuEq5eghOvb7h6LPNxk9RmoOuOfqOaGWjxyw6jGYgQQgghRJMsykqKBG/T+sy5W+jqP5Y6MS/k3gnmRFtZ6cMn5NgnWVro6wBSzsWEWLv/vj5i3QvG9HuKfT8cmgsqK2lhUjhCCCGkcGThfBtC1BYrLqvbFCGmY3lpVim7l1hh3Brt97Xj6teiMY7c/kXa2KgmNmNu1wtVXaZ91udLE5Lqv6u+zXfkq+a4XAeprhXf30yOazp2X6X8PknFfC/VCCGEEFI8xUUDTeiLSIhtt7Uh1E6dc7dS6k4p5ThKOb8QtPxHbBIoDo3BtR2Xa3Co/S761BLben1j8R1PadFAOcnhu6hJap+V64/7HzWflfX3P3R0PiujUVZKC2kk8eF3TgghBBjRYoUQQggh+RCR9UTkCBG5QkRWicj5IrKbYxsiIt8VESMiH7CuN+a3Lo9Ryo+dfE2LFH1qm/VKeD9PKX2HmIhCzS2xw4d9TTxd9W3b6DNl+aLh5K3tsF4aJY8vtRnohk8dovasvfM+h3iNXUTOAPAIAAcDuBTAfgBeAGBXY8zplm28DMAhADYB8EFjzH/Z1GPoMiGEEEJ6EZGdATwFwJ7GmJPrY2cC2BTA4QAGFysici8A7wZwAICTXPpXW6ykTG42ZqdLn6R5s7p7ao8ndHw5zq+kOU3h49OnStg42A6pLy4Ot131bObApp2+Ou2wea0Q7bFcvzYOu1pOvTaKVsygDpvP5og9AKwE8JXJAWOMEZFPAjhaRLY0xiwbaONIAGcZY74k4ibuUFkhhBBCCqeApHBbA1hmjLmtdfyC5ufTKovI8wDsCGBLn87VQ5f7yLkqjRUKHSMscGwp+FOOoYS+XL/zWGH9JRDqs6Ll3xLLT8Yn5Nu1bTKdFL45vqkcUvus/PUzh+r5rLzwLSuHyhhjFjf/X0SWA1hujNmldXxzAMsB/Lsx5siutkRkI1QLmbcZY46ojxk4+KxkX6oRQgghZBT0LZj6PjsClUPuh307HnU0UE5sVuoTbFbzY0+QlIoSlKTSSOmropFgraudUAVDq8xQXd/6tm2HoKUETmvXpg0tBcOmr9gMzVtqZeXGz71N7Vm77vPe7Dx2ETkXlZvKdq3j2wI4D8BzjDEndtR7KoBvAHgSgJ83ProWwFEA3gDgr8aYW/v6p7JCCCGEkCEuBrCFiLTXDQ+p/71oSr2tUK01lqJaoEz+AODA+r+fMtQ5HWwJIYQQMsTJqEKOd0UjIgjAPgAu6YkEOgnAzzqOnwngS6hMQxd0fH47ijcDUfYvn9JC/kp995H2PKV0YLVp18ZRPWQc2u8G8p0bbadgLUKuad+6OfoMQfPekNoMdNMX3qH2rF3nOW/wMQMJgO8AeChWJ4XbF9ViZXdjzKl1uaUAtjfG9Pbh6mBLZYUQQgghvdQ5VZ4J4LD6bzGqCJ89JwuVmIxysVLqznkaWg5ummF3Q+PSmr/Sdk0TfMLVQ+ckVuhy304/xCHWVTmYVt41cdxQu66KTUgodVddH+dgm3HmuL5S9DmtjxypHsbyzCgVY8z1AF5R/00rs4NlW07qzigXK4QQQshcsWBh7hFkpRiflbElQnNBK3GczxyF9hkTje8x5nlqJA0M7dOmbEq0kqTZKHkhPjnaoca+ye9ypCZIkUjNtq7GOKa1nfu+kdxn5Yvv1vNZedbBSceuAUOXCSGEEFI0wcrKgbLEAPFW3y52YNfkazb26ZS76glDPiYxPPZLUqtyJ8gLiczxHWesaKAJ2knTtJK4TfBVVnz69mmvida8p1JZiR+lJYW76Uvv1VNW9noNlRVCxkwO0wohhJB+svqs2ESjNAlRVGzG4dJOzJ1NDl+JEEqwkZdCn0Kg/X26+E70EbL7d1VqNHxMtP1RutrWmlubvkonljJbil+d7++RykpaRhMNNJYfNiGzQo7fHJWt+YP3djtk4XxHA9EMRAghhJCiKSZ02YXY5gJtebIUubOPVOauFHPhIlvbjCWH86xLuxNimkfauJh4uuq10XKw7evTx7Tj4mDrG3Y9jdz3jZxm2di/GQ0zbWoz0KpT3q/2rF30zINoBiKEEEKIMkwKp6OslFKTSskAABvsSURBVBDa61LXtX5ONcfGAXhaXdd2bELIS1WHxoJ2AsQYjrU+ZWOHR/sqN0M7Z9d0BtP6clVTSnSkz3F/zH1vGYuD7aqvHqGnrOz2qtEpK/RZIYQQQkjRjFJZ0WKMY/YhR5KzELSUpFnHZZ76iKWaaIX99ikPIQqNSxuh4+pTcUKwac9XtfUp00Wq32jMue1qN7my8rWP6Ckru/wHlRVCCCGEEE3Uo4HmRa3wRcu2OyFlsrohxmB7TrXjTYGWr4kPsSJq+vqImaBtaAxNtJPxlYRPYsDQvlx8t2KOxxUqK2lhNBAhDUq5ERJCSBNZMN+GkPk+e0IIIYQUT9KkcGMOb9OCZrIwbMJOcxAreV5oyHKqcGLbcQ29G6jPDKHlYGtT1qV93/Zm6XrVwiWcOyQVgwapzUA3n36kmhlo7Z1fTjMQIYQQQpRhUrg4ocuhIXXtMqXtAPoICRXuqheaRGzew31L2c1qo62suLTrSyzlou8at0mPr1nWlpBrMcV90dcBdlqdHOpjSDtD943kyso3jtZTVp7+MiorhBBCCFGGykocnxXX3WysnULKdrXTZ4coIr7z79OXTbsuactd2rP9LKSsdt8uhCbIc5n/WEpBilDj2IneXNuzGc8sqXyzhO1vObmy8q1j9ZSVnQ4YnbLCaCBCCCGEFE3SaKBQhiIIxrBTib0D72IM85Kasb2CoAuf3b+NEthXL2VyuRxRPD7taKkvJD6+v+GueqmVlVu+fZzas3atp+xPZSUWKbNWEkIIIaQcRrNYIYQQQsh8UowZqDSJ3ZYuR9bSzyVWCGGTUs9dm9hOvTbt2dCXUsBHtQx9N9CEUNOTS+iytvOs1vyTcZLcDPTdT+mZgZ60D81AhBBCCCGaFKOskLLQTmw3RnKGvWu150KKtxoP9eGaCkDDAdY1gVxImWnlm3VK+e1opTPQTumQgyIcbOdcWWFSOEIIIaR0mBSOysqEHP4Hpe4ytBLalXZeIfj65qTyVbF5xULK8OTQ9jR8apr1tZPfaaeYn8XfTAy0XwPgS3JlZeln9JSVHV4wOmWFPiuEEEIIKZqsysoYfBxK2u3E3KGXcH59aPh0NOuX9L36oh1JExLl4lrPpb0cik8KJarEa8/3njCPqnRqZeVvZ39eTVm50xOeS2WFEEIIIUQT+qxEgHZqO7QjYFLWL4mc2Z1do2SGyvbVc0nJ7xu94+KPMtT+EKVHA00jtyJrM0/TymjmhKKykhZGAxFC5hq+yoOMggXzbQiZ77MnhBBCSPEUYwYaku1cw9S0HSlDpMcYfU2rMyG3lJzTxKPdTixC5XQX04dPevzQNyGnQMMRNkbCt6567bo5fxtjJpWZfei7Sm4GOudEPTPQ455NMxAhhBBClGFSuDjKyqy93C5l6vWxkfIcSp2vEkI3fV9SqP1ivpAXD9q0Z/OaAt/ztgnnDnH87SrTppR7ZwlKse/vKSTFv23Z5MrKuV/SU1Yeu9folBX6rBBCCCGkaILNQDaJm3x2KyFjSEHOHX5pdm+XNOOhYw/1RSpBmSlNFUrJNKWh67iL0uPyvfbdq6a14+qzopVoLyca92TfdlzqdJXt+/40+06JzLkZiMoKIYQQQoqmmGig0om9I3fZifjaz2NhYzMujRLGF/pd+aiaXdhE4PkoZS4qax9a52mDjx9JKapJCRGBoZGM2kqxz5yU6rPy9x+eovasXXPbZ47OZ4XRQIQQQkjpMCkcIYQQQki5BJuBDpQlBkgbsjnUfrMPGxmwBJNAblJ9N7nnWEMqL+XdKNr4hB77JqJzMT31tacRLt03DptxhaJl+ggx1ZVitvEhtD2b+l1lkpuBfvxVPTPQo3ejGYgQQgghusx7NFCSpHA5kx+VsqMfO9q7s9A+berlSGZl047WTnBCLAdbmzKuTo2xVAmXxHGhyfRCFahp47KhhPtZTGVR+zc3wVc56qufWlm59SenqSkrazzyGaNTVuizQgghhJCiSRK6XMJuYEIp/gaxEi7lSJbma/ONjU94bF/5lOfgMqddhITVuiierqHLLuNzUWRdsPHpSBEurXEPyH1PLWUcOUiurPz0G3rKyjZPp7JCyJiZx5suIYSUjrqyom2jdcE3GmjWia185EhElxPXZGcpfHpCcFEItNSEWH1pJYdzwbfPVL+R0ISTIQn8uo7FekakVkeprKSF0UCEEEJI6TApHCGEEEJIuRT3bqASQtd82u8iZ1hfqNOgS5+lOJ7GfA9ICCnNn9NIab4JwcZ8ENJmSDsu/fjcH0r5HaVqJ6bJNHYgA5DBDPTzb+mZgR62E81AhBBCCNFFFjIpXFADOd66XMIOxNXJMhbzEjpY6nmGOh/6tGNTV0M9sFGvSiFWMjftMloKZam/hwmlpQKI0WdqZeUfF31H7Vm7cOsnj05Zoc8KIYQQQooma7p9F0LbK30n0kfpYx/jDilHqnOfNO++SeGm4au+aCsObXzG0qwX4tcSw0+m3XaK8H6mabBH4/mUXFlZtlRPWdlyByorhBBCCCGaqCkrsRNfcZewGtddwSzO4ZijeGJFvIXik7TLNxFaiKpjow65qCW+UTyhylRINFAKZdFmbrX7tBlLjvtY19iprKSF0UCEEEJI6SxgNFBQA9PS7QN6Me5tYkVMpNhBh/Q5C347LlECfWVmnZCdryup8pC4op3bJUQRadaLFQ2UkliRafNEcmXll2frKSsPfsLolJVoPiu80OMxL3M7L+dJyqeUBVxs5uU8iR8isp6IHCEiV4jIKhE5X0R2s6j3EhH5qohcVtf7Vd3O3Wz7poMtIYQQUjiyYIHaXwAnA3gBgDcDeAaAZQBOFpGdB+q9FcD1AN4A4OkA3gfg2QB+LCKLbToONgMdKEsMMLu7YJc3hhIdcjrP+oaY+rzKoK9fG6fSUhwNNcrGqK9JzNDjkHDrMSkh067p0l8BMY2jzIqkppTblp+jZgZa8MDHOY+9XpCcBmBPY8zJ9TEBcDaADY0xU78IEdnYGHNl69j2AJYCeJUx5kODY3YdMCGEEELmjj0ArATwlckBU6kdnwTwYBHZclrF9kKl5sf1v/e26TxJUrgJpYaApsTHcXLWVZ1ZPK95dBKexe8xJiGv8rDBJ0lfzCR4WoQoNJrjS66s/Po8PQfbzR+7cqiMMeZ25hkRObc6bLZrHd8WwHkAnmOMOdF2DCKyC4BTAexjjDlhqDyVFUIIIYQMsSGAazqOX9P43AoR2QDAEQB+BcBqgaOWZ6VvV6UdotpeFfsmsXJJyhTqk9Auo50YrG+HVPpO1ycZWR8l7PDH6KcR+lvR/m5iJc+LpVTaKCIxXtXgMh6XMj5J9FK87iCkT5d0EmPyB3KlrZq4VPX87J+IyDoATgGwAYAnGmNusanHpHCEEEJI6Uh2Q8jV6FZPNqj/7VJdboeILALwVQDbAHiaMeYC287VooEm5IzgsPED8Y2qyJHMrXQ1wWbH5bOzzKEKpehzbIm4tFLDj4mU5zWUzj40Sqavn5DEdq5jCIl0cyljM3bfJH9dJPdZ+c2P9KKBNnuMTzTQxwHshSry57bG8ZcAOAbAVsaYZT3110a1UHkcgJ2NMd9zGrPrgHMxy5Ic6SbHg3DWHr6EkDvC54kXJwNYDGDX1vF9AFwysFBZC5Xp5wkAdnddqAA0AxFCCCHlk98MdDqAMwEcKyIbArgUwL4AHg9g90khEVkKYHtjTFO9OQnA0wAcCuCvIvIvjc/+Yoz5zVDn0d4NpO1gaCNJN9F22tRCo8+Yjnwu/fv0oW1ucW1vLN95bAdszT40ye0YXoIZaEKfKcWljAuu7dkET0yrU4q64ZsYMvW7gW777fl6ZqBNH+U1dhFZH8BhAPZGpbIsA3CoMeaURpmlaC1WRKRv7J80xuw31DeVFUIIIYQMYoy5HsAr6r9pZXboOBa8sIuWFC6UWGGFYwgpjdVXiENxExcVJ9bOPtRhN8QBOHQ8oeQYe4kqzNhxcfAMcZrt++2lRCOZW195X+dZ33Ekf+vypf+nlxTu/o/gW5cJIYQQQjRJqqyErqht27etFxKO7EKoX4X2OH0T2sWmdH+bmKS6Fm3GUNrcxCaX35NLMjIb35AQ5aKPUn1MQkKqNc4ldejyvCsr9FkhhBBCSid/NFBW1JSVkIiHZj0XH4cx+RlooJXYbizn20es3a1Gm0N9lTL/LkkWNVQAlzGkqu/aT7MvrXuVyy7fxnfFh67vszRFZUKscbm2m1xZWfEzPWVlycNHp6yMZqlW2g+GzCalLCQIIYSshmYgQgghpHRkdGKIKllCl+fVdNFEew5KmKdSE3rlHld7HKUnpCuNWTiHLmIlhdMO17UZo807eFzG4DM+G/O4jYnTluRmoMt+rmcGut/DRrfyGY0ZiBBCCCHzSTRlxSY9vnbCtyZa6fZTJWgL3f3P6u5zQg5HyglazqQufc0Cs3B+MR2ItR1rQ9QILcXBJy3FWP0Rkysrv79QT1m5z0OorBBCCCGEaDI36fZT9KGRPt6mfik+GD6kVNxCKW08Lrj479CXZjoxQ6t9fDmmle3qK6ViMXaVpIltGDeVlbQwGogQQggpHMOkcOnT7U/wSYlvWy+kT5v2fP1cfJJFlb77dKFUVSiF4jNv3zXpZ+heEOqX4pPoLVSpselzzMpPc35Sv8jw1j9crKasrHHvrUanrMz3Uo2QFlwsEEJIeWRNt99FaEr5ae018Xkp2FDdrnZclaMQXxUX23gpD+RZUBO01ZLY0WfNtm2ui5DcNVoqWinXiXYen5CoHVdK8inxHYv2OYS2l9pn5dY//kJPWbnXFlRWtNG+Udm0k+Om6NJn7ps2GaaEh0IX2g/TUn9P2mjNRQ5KHZcL2ucwC3MybxS/WCGEEELIfKPuYOtrAplGqCOrTTs243IJ88xpGnBJ7pQjGV4fLonySjENjBnOYV5im4FSOLvaJKuL3Z5LIINNO7bnkNwMdPklemagez6IZiBCCCGEEE2ShC5r7OB8dwdaTrnT2ouZJt9FxQkZA5mN1PDzwpjD320ITQrn0kdf3VgOre0+fc8hN1RW0sKkcIQQQkjpMClc+nT7NrtYH9+Q3CqHD6H+MT59lbQbjUGoX5FLmdj4jiEkLLmrDa3w5JLCuV2IoeZoJ0mzacfme/Rpz2fsKcKptX1qmiRXVq74lZ6yssnmo1NW5nupRgghhJDiUU8KN8HVQzu2mtA3rpB2XH0dhlJs25RJMW9Ej5K+mxz+HqX6mOQmRFlJmSDN5bNQNUY7WskG3z5TKyt//9Nv1JSVNe+xGZUVQgghhBBNuFghhBBCSNEkSQoXmtRsWpkJWg62NuPqaz8klNrFmTHUlBVrbkobVyxiOnJrofEem9zh3DYmU412Q9FOhDnBNaRXw4TimkzSxzyubdLxDfVu4/rsSW4G+vOlemagu9+fZiBCCCGEEE1GkxSurz3fMNRSwyhDxjUhNPw6llpSqnNwbhWhPY6xqEy++Cg/vr/hlNe9S7tDv9lYIcO2feV0/I3tQKyRiI7KSlqYFI4QQggpHRnd+kKVrEnhcqsT87KL1cBmx9tEK4nYWBlDiHBsZVGrbOk+UaHKrgs+/hmhZWOoEiXhq+IkV1auXKGnrGy8ZHQrH/qsEEIIIaRosigr2pSi1MwiOc67FP+RacTwqxjqS0tVcB17CPP6m+liFlSJPtUrxzj6fH5SjCu5svKX3+kpK3e7L5UVQgghhBBNuFghhBBCSNFEMwP5OhjahDS6tBs7kVSoU6ON2cAnyZPr+ZYQ3ulCSqfLnOdbwlzbMqaxAuHJ3LTuKe32cpqHfMPCXdqOZb7xNVP5pMAA0puB/nbVH9TMQHfa6N40AxFCCCGEaJLVwVZbfbHtq01IcjKtENVQx8exKAWzwJjCr2OGBLuOIUVfKXFxXu6rP43Q1Pd95V0UbJf2XPoMTWiX20GZykpamBSOEEIIKZ0F820ImanQ5SalJiXzsQOHhLw2689SojwtHwJtX4SUSkaOZGklpaP3HU8p17iGstJXTyuFfmifsch9LsmVlWsu11NWNrjn6JSV+V6qEdIi9wOMEELIHVFXVnyTYsXaRQ3102xPO6FXX/9akUMh7WuNsxTGHLUzxvkeIsW1WGoqfZ+2Q6NkYkUPaqk6Nn2GREi5zJdGBFJyZeXaP+kpK3e9B5WVWOR2piKEEEJIHkazWCGEEELIfDJKB9tU0q9WH6HthYZITmsnVp2+dprknIuhdjUpyVTRRQkmTq2yWqQw6w2FI2ubPlKSYlwuiUO1wqabJDcDXXelnhlo8cY0AxFCCCGEaDLqpHA2dX2c82KGN9s4zcbedcbqM6Xi44J2wr0U10es9nNTukqSUn2MrYSkCCf2SQo3K1BZSQuTwhFCCCGlI/NtCAlWVg6UJQbQD+3T3g2lLBPKUB+lKhi+lDoubUpTFUrwpRlDHxrYfA99aKsTIeHSocnqfOqWSHJlZeVVesrKXTYanbIy30s1QgghhBSPms+Klq095+5zgkuyIpd2beu166dIbZ6DUsYxr8zL/PskV5zWRlc9bZ8VX/XFpR2tKJmQJHChyorNawXa+CpBXfVSKyu3XH+NmrKy1vobUFkhhBBCCNEkSTTQmPxFpvU5IYXKobETJHaUMrc5c+D44ONT1lfepUzu72oavkpqiM9KqAqTyhemr6xN+b6yWn26zkVyZeWG6/SUlTsvprISi7E7YxFChil1IULikePezufJ+BjNYoUQQggh80mWpHAuYXwu6apdU1vbOMr5hBGHJhEbOi/f9sZiYigF7XnTntOcCQ/nJSw55hhcfufahJh/NE0pQ+25lOmrF+pI3EVyM9BfV+qZgda7C81AhBBCCCGaFKespKSEcYSOIUd6dm1n6FlyKB7z2NuEqjCxQ+9jtO3Sv0/obFc7KRly1M3ty5HT4dcVKitpYbp9QgghpHSYbj/fiwxzU8Iu2MdPhhBNSko+WJpqkqKvCT4+K66+HD5hviUpQFrtaSTeS66s3HiDnrKy7p1Hp6zM91KNEEIIIcVTnLKitbPRjoYY8+sESug7BTmTBs7inPomc4s9J7nVFy1y+4c00VIyYikiTUIimMacbv/mm25Ue9auvc66VFYIIYQQMnuIyHoicoSIXCEiq0TkfBHZzbLuZiJyioisFJEbROR0EdnStm8uVgghhBBiw8kAXgDgzQCeAWAZgJNFZOe+SiKyMYCzASwBsC+A5wHYAMD3ROTeNh2rm4G0E75pJddKMa4S8B1fjvDhIVPdmN+XFBrOWup55WTMc9N3TbfpSyapncQtJ7734lTnUJwZSNHlYu1Fi5zHXi9ITgOwpzHm5PqYoFqEbGiMmfrFiMi7AbwSwGbGmMvrYxsCuBTAZ4wxLx/qn8oKIYQQQobYA8BKAF+ZHDCV2vFJAA8eMOnsAeCMyUKlrns1gFMB7GnTeVJlpY+hJEWlOLRqpdLXKqutOLi0V/pOt9Txxbomm9ioVdpoX9vaaCVzSzmeNilT8k/rW0vx0RyHRllXxqysLFpnnZVDZYwxi5v/LyLnVofNdq3j2wI4D8BzjDEnttsRkUUAbgTwTmPMG1ufvQ7AOwHc3RhzZd94mBSOEEIIKRwf000P13nU2RDA8o7j1zQ+7+KuAKRRblrd3sVKEmUl1o5LK+17aTvw2PM1IUciulkIde1rr7RraUKoilDS7yjFuaSk1NT3LvT9lmOdX0g4ssbcplZWciMiywFcYozZtXV8c1SLmJcbY47qqHdPAH8E8BpjzOGtz14K4GgAWxhjftnXP31WCCGEEDLE1ehWTzao/+1STgDgWgDGs+4/CVZWDpQlBtBP4haqLvgkcQvdPZYUUZObUsc1S5SkdthS2njapPRr8fFZCWWoL63IId+op5C++r6rGP5Ac6isfBzAXqgif25rHH8JgGMAbGWMWTal7q8BLDPG7NY6/mkAOxljNh7qn8oKIYQQQoY4GcBiALu2ju+DyjzUuVBp1H2qiNxjckBENqjb+rJN51ysEEIIIWSI0wGcCeBYEXmxiOwoIscDeDyA104KichSEWmbbN6LKuz5dBHZXUSegSpny60ADrPpXM3BNrazq41jpm8Zl/FNG2+T2I6ipYZxxySkL18H2zGaWTQp5R08pc+xVrqGPqaZM1zNNlrJ5ab1mdM52NeE5Tu382YGAgARWR/V4mJvVCrLMgCHGmNOaZRZCmB7Y4y06m6OatGyIyqh5GxUTrcX2/TN0GVCCCGEDGKMuR7AK+q/aWV2mHL8VwB29+0761uXQ3dupSeh6sPHAbgUXNQvzfbbfYT24zqe0D5LuwbHSszrTFv9tem/Temhy6FOuCnOL0Uf86is5IQ+K4QQQggpmqzKijYpUt+328+dstsF7uxJkzGrez74KCKhSS5txmNDLJ+VWAqES1I4GwXVpr7NvdgnhHnaWJUzypIBqKwQQgghpGiKV1a0lIvSVAXt3VnsdmKmmM+RTM9nPF3Mm6+VDaWO2ccfJea5aKgZoWnttRKqueDzu/Idg3Y7zfr0WUkLlRVCGpTq1EgIIfNMEmUl1o7GJYdKu06TlDvcVDu3GDkyQvLQlLbLdsHnOuuqb1NnFuarTUxfspC6pUUV9bXTHmfpkTQu6fabaPjx2I4rpG+AykpqqKwQK6g4kBTkTiZWEpwLQlbDxQohhBBCikbdDDTPKeFLp7S5cDGJxTaX5ZiT0tPZxxxfCddiijH4JDj0NWvYmFuG2s5hmonZzrR2m/j2QTNQWqisEEIIIaRo1F9k2EfIzq0Uh1Et5i0hlzY5vjutBGEhdbrqaStSOdRR3/DwVNdBzPtPG00nUFtsrh0bB2Dt8cRSmbq+TzrYlg2VFUIIIYQUTdKkcD7pkV3a8y0zrY5rPZ/2XHYr2udgkwhKO3S8r74Nsb5zl3ZiJMibBZ+clN/NLODiP6IVwpwi9NmW0vxbXNuhspIWKiuEEEIIKZpgZeVAWWKAsB25LSF+HqEqjk272gpGrCiN0na3paoKpY5Lm9j+Uyl801ImbdTua0JOpcElSqZvLnLmhrG5z2q1B/BFhqkZjbJSgmxJCCGEkPSMZrFCCCGEkPmk+Lcu26Dl1FiCM64vLmOPbd5otq01LhvJPMQk1oXWdTGtXV+0zIOxrxktk3DsZIFa8+bbTkloJ5nrq++T7sJ1XCHmqaHzpINtWqisEEIIIaRosior2o53rk60sRNmaTsS51CQSnEGLWUcIZQW2qvdVyxH3dDrNYf64jKuafgqGDHVhJBx5STUAbirPpWVtFBZIYQQQkjRFKuslO4/EjMstiTlQzvkO6aaFuJTYpOAy6a90sKkS+uTrMZFWbGt01V/LOpHDBi6PDtQWSGkwTzf2AkhpFS4WCGEEEJI0QSbgQghhBBCYkJlhRBCCCFFw8UKIYQQQoqGixVCCCGEFA0XK4QQQggpGi5WCCGEEFI0XKwQQgghpGi4WCGEEEJI0XCxQgghhJCi4WKFEEIIIUXDxQohhBBCioaLFUIIIYQUDRcrhBBCCCkaLlYIIYQQUjRcrBBCCCGkaLhYIYQQQkjR/H+jZawFL0wgcAAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 720x720 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 20 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 20 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "A1 = rdpg(X,\n",
- " loops=False,\n",
- " rescale=False,\n",
- " directed=False)\n",
- "A2 = rdpg(X,\n",
- " loops=False,\n",
- " rescale=False,\n",
- " directed=False)\n",
- "\n",
- "Xhat1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n",
- "Xhat2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n",
- "\n",
- "heatmap(A1, title='Sampled RDPG 1 adjacency matrix')\n",
- "heatmap(A2, title='Sampled RDPG 2 adjacency matrix')\n",
- "pairplot(Xhat1, title='Sampled RDPG 1 adjacency spectral embedding')\n",
- "pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Qualitatively, both of the simulated RDPGs above match the behavior we would expect, with 4 clear blocks and the corresponding 4 clusters in the embedded space. But, can we say they were generated from the same latent positions?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Semiparametric test where null is true\n",
- "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance\n",
- "\n",
- "In other words, we are testing $$ H_0: X_1 = X_2 R$$$$ H_a: X_1 \\neq X_2 R$$\n",
- "\n",
- "and want to see that the p-value for the semiparametric test is high (fail to reject the null)\n",
- "\n",
- "Here, R is an orthogonal rotation matrix found from solving the [orthogonal procrustes problem](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.linalg.orthogonal_procrustes.html) (Note: this constraint can be relaxed for other versions of semipar)\n",
- "\n",
- "Note that the SemiparametricTest.fit() may take several minutes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "p = 0.8325\n"
- ]
- }
- ],
- "source": [
- "spt = SemiparametricTest(n_bootstraps=200, n_components=n_components)\n",
- "spt.fit(A1, A2)\n",
- "print('p = {}'.format(spt.p_value_))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We see that the corresponding p-value is high, indicating that the observed differences between latent positions of Sampled RDPG 1 and 2 are likely due to chance"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Semiparametric test where the null is false"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, we distort the latent position of one of the sampled graphs by adding noise. The semiparametric test should have a low p-value, indicating that we should reject the null hypothesis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<seaborn.axisgrid.PairGrid at 0x254dba1e5f8>"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 2 Axes>"
- ]
- },
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- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 20 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 20 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "A3 = rdpg(X,\n",
- " loops=False,\n",
- " rescale=False,\n",
- " directed=False)\n",
- "A4 = rdpg(X + np.random.normal(0.1, 0.1, size=(X.shape)),\n",
- " loops=False,\n",
- " rescale=False,\n",
- " directed=False)\n",
- "\n",
- "Xhat3 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A3)\n",
- "Xhat4 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A4)\n",
- "\n",
- "heatmap(A3, title='Sampled RDPG 3 adjacency matrix')\n",
- "heatmap(A4, title='Sampled RDPG 4 (distorted) adjacency matrix')\n",
- "pairplot(Xhat3, title='Sampled RDPG 3 adjacency spectral embedding')\n",
- "pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "p = 0.0025\n"
- ]
- }
- ],
- "source": [
- "spt = SemiparametricTest(n_bootstraps=200, n_components=n_components)\n",
- "spt.fit(A3, A4)\n",
- "print('p = {}'.format(spt.p_value_))"
- ]
- }
- ],
- "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": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/graspy/inference/__init__.py b/graspy/inference/__init__.py
index a0becca8e..05a8feb12 100644
--- a/graspy/inference/__init__.py
+++ b/graspy/inference/__init__.py
@@ -1,4 +1,4 @@
-from .semipar import SemiparametricTest
-from .nonpar import NonparametricTest
+from .latent_position_test import LatentPositionTest
+from .latent_distribution_test import LatentDistributionTest
-__all__ = ["SemiparametricTest", "NonparametricTest"]
+__all__ = ["LatentPositionTest", "LatentDistributionTest"]
diff --git a/graspy/inference/nonpar.py b/graspy/inference/latent_distribution_test.py
similarity index 92%
rename from graspy/inference/nonpar.py
rename to graspy/inference/latent_distribution_test.py
index 77612f58a..ea4ee3550 100644
--- a/graspy/inference/nonpar.py
+++ b/graspy/inference/latent_distribution_test.py
@@ -19,11 +19,13 @@
from .base import BaseInference
-class NonparametricTest(BaseInference):
+class LatentDistributionTest(BaseInference):
"""
- Two sample hypothesis test for the nonparamatric problem of determining
- whether two random dot product graphs have the same latent positions [2]_.
-
+ Two-sample hypothesis test for the problem of determining whether two random
+ dot product graphs have the same distributions of latent positions [2]_.
+
+ This test can operate on two graphs where there is no known matching between
+ the vertices of the two graphs, or even when the number of vertices is different.
Currently, testing is only supported for undirected graphs.
Parameters
@@ -46,7 +48,7 @@ class NonparametricTest(BaseInference):
input graphs.
p_ : float
- The overall p value from the nonparametric test.
+ The overall p value from the test.
null_distribution_ : ndarray, shape (n_bootstraps, )
The distribution of T statistics generated under the null.
@@ -81,7 +83,7 @@ def __init__(self, n_components=None, n_bootstraps=200, bandwidth=None):
def _gaussian_covariance(self, X, Y):
diffs = np.expand_dims(X, 1) - np.expand_dims(Y, 0)
- if self.bandwidth == None:
+ if self.bandwidth is None:
self.bandwidth = 0.5
return np.exp(-0.5 * np.sum(diffs ** 2, axis=2) / self.bandwidth ** 2)
diff --git a/graspy/inference/semipar.py b/graspy/inference/latent_position_test.py
similarity index 93%
rename from graspy/inference/semipar.py
rename to graspy/inference/latent_position_test.py
index aaa3705b3..9ca4c6d56 100644
--- a/graspy/inference/semipar.py
+++ b/graspy/inference/latent_position_test.py
@@ -14,7 +14,6 @@
import numpy as np
from scipy.linalg import orthogonal_procrustes
-from scipy.spatial import procrustes
from ..embed import AdjacencySpectralEmbed, OmnibusEmbed, select_dimension
from ..simulations import rdpg
@@ -22,12 +21,15 @@
from .base import BaseInference
-class SemiparametricTest(BaseInference):
+class LatentPositionTest(BaseInference):
r"""
- Two sample hypothesis test for the semiparametric problem of determining
- whether two random dot product graphs have the same latent positions [1]_.
+ Two-sample hypothesis test for the problem of determining whether two random
+ dot product graphs have the same latent positions [1]_.
- Currently, the function only supports undirected graphs
+ This this test assumes that the two input graphs are vertex aligned, that is,
+ there is a known mapping between vertices in the two graphs and the input graphs
+ have their vertices sorted in the same order. Currently, the function only
+ supports undirected graphs.
Parameters
----------
@@ -78,12 +80,11 @@ class SemiparametricTest(BaseInference):
The p value estimated from the null distributions from sample 1 and sample 2.
p_ : float
- The overall p value from the semiparametric test; this is the max of p_value_1_
- and p_value_2_
+ The overall p value from the test; this is the max of p_value_1_ and p_value_2_
Examples
--------
- >>> spt = SemiparametricTest(n_components=2, test_case='rotation')
+ >>> spt = LatentPositionTest(n_components=2, test_case='rotation')
>>> p = spt.fit(A1, A2)
See also
diff --git a/tests/test_nonpar.py b/tests/test_latentdistributiontest.py
similarity index 70%
rename from tests/test_nonpar.py
rename to tests/test_latentdistributiontest.py
index dc78b64af..4d7e8f143 100644
--- a/tests/test_nonpar.py
+++ b/tests/test_latentdistributiontest.py
@@ -6,12 +6,11 @@
import numpy as np
-from graspy.embed import AdjacencySpectralEmbed, LaplacianSpectralEmbed
-from graspy.inference import NonparametricTest
+from graspy.inference import LatentDistributionTest
from graspy.simulations import er_np, sbm
-class TestNonparametricTest(unittest.TestCase):
+class TestLatentDistributionTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
np.random.seed(123456)
@@ -19,28 +18,28 @@ def setUpClass(cls):
cls.A2 = er_np(20, 0.3)
def test_fit_p_ase_works(self):
- npt = NonparametricTest()
+ npt = LatentDistributionTest()
p = npt.fit(self.A1, self.A2)
def test_bad_kwargs(self):
with self.assertRaises(ValueError):
- NonparametricTest(n_components=-100)
+ LatentDistributionTest(n_components=-100)
with self.assertRaises(ValueError):
- NonparametricTest(n_bootstraps=-100)
+ LatentDistributionTest(n_bootstraps=-100)
with self.assertRaises(TypeError):
- NonparametricTest(n_bootstraps=0.5)
+ LatentDistributionTest(n_bootstraps=0.5)
with self.assertRaises(TypeError):
- NonparametricTest(n_components=0.5)
+ LatentDistributionTest(n_components=0.5)
with self.assertRaises(TypeError):
- NonparametricTest(bandwidth="oops")
+ LatentDistributionTest(bandwidth="oops")
def test_n_bootstraps(self):
- npt = NonparametricTest(n_bootstraps=234, n_components=None)
+ npt = LatentDistributionTest(n_bootstraps=234, n_components=None)
npt.fit(self.A1, self.A2)
self.assertEqual(npt.null_distribution_.shape[0], 234)
def test_bad_matrix_inputs(self):
- npt = NonparametricTest()
+ npt = LatentDistributionTest()
bad_matrix = [[1, 2]]
with self.assertRaises(TypeError):
@@ -51,7 +50,7 @@ def test_directed_inputs(self):
A = er_np(100, 0.3, directed=True)
B = er_np(100, 0.3, directed=True)
- npt = NonparametricTest()
+ npt = LatentDistributionTest()
with self.assertRaises(NotImplementedError):
npt.fit(A, B)
@@ -60,7 +59,7 @@ def test_different_sizes(self):
A = er_np(50, 0.3)
B = er_np(100, 0.3)
- npt = NonparametricTest()
+ npt = LatentDistributionTest()
with self.assertRaises(ValueError):
npt.fit(A, B)
@@ -74,8 +73,8 @@ def test_SBM_epsilon(self):
A2 = sbm(2 * [b_size], B1)
A3 = sbm(2 * [b_size], B2)
- npt_null = NonparametricTest(n_components=2, n_bootstraps=100)
- npt_alt = NonparametricTest(n_components=2, n_bootstraps=100)
+ npt_null = LatentDistributionTest(n_components=2, n_bootstraps=100)
+ npt_alt = LatentDistributionTest(n_components=2, n_bootstraps=100)
p_null = npt_null.fit(A1, A2)
p_alt = npt_alt.fit(A1, A3)
self.assertTrue(p_null > 0.05)
diff --git a/tests/test_semipar.py b/tests/test_latentpositiontest.py
similarity index 74%
rename from tests/test_semipar.py
rename to tests/test_latentpositiontest.py
index 52b8144b1..60e7b0748 100644
--- a/tests/test_semipar.py
+++ b/tests/test_latentpositiontest.py
@@ -4,13 +4,12 @@
import unittest
import numpy as np
-from graspy.inference import SemiparametricTest
-from graspy.embed import AdjacencySpectralEmbed, LaplacianSpectralEmbed
+from graspy.inference import LatentPositionTest
from graspy.simulations import er_np, sbm
from graspy.utils import *
-class TestSemiparametricTest(unittest.TestCase):
+class TestLatentPositionTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
np.random.seed(1234556)
@@ -18,42 +17,42 @@ def setUpClass(cls):
cls.A2 = er_np(20, 0.3)
def test_fit_p_ase_works(self):
- spt = SemiparametricTest()
+ spt = LatentPositionTest()
p = spt.fit(self.A1, self.A2)
pass
def test_fit_p_omni_works(self):
- spt = SemiparametricTest(embedding="omnibus")
+ spt = LatentPositionTest(embedding="omnibus")
p = spt.fit(self.A1, self.A2)
pass
def test_bad_kwargs(self):
with self.assertRaises(ValueError):
- SemiparametricTest(n_components=-100)
+ LatentPositionTest(n_components=-100)
with self.assertRaises(ValueError):
- SemiparametricTest(n_components=-100)
+ LatentPositionTest(n_components=-100)
with self.assertRaises(ValueError):
- SemiparametricTest(test_case="oops")
+ LatentPositionTest(test_case="oops")
with self.assertRaises(ValueError):
- SemiparametricTest(n_bootstraps=-100)
+ LatentPositionTest(n_bootstraps=-100)
with self.assertRaises(ValueError):
- SemiparametricTest(embedding="oops")
+ LatentPositionTest(embedding="oops")
with self.assertRaises(TypeError):
- SemiparametricTest(n_bootstraps=0.5)
+ LatentPositionTest(n_bootstraps=0.5)
with self.assertRaises(TypeError):
- SemiparametricTest(n_components=0.5)
+ LatentPositionTest(n_components=0.5)
with self.assertRaises(TypeError):
- SemiparametricTest(embedding=6)
+ LatentPositionTest(embedding=6)
with self.assertRaises(TypeError):
- SemiparametricTest(test_case=6)
+ LatentPositionTest(test_case=6)
def test_n_bootstraps(self):
- spt = SemiparametricTest(n_bootstraps=234, n_components=None)
+ spt = LatentPositionTest(n_bootstraps=234, n_components=None)
spt.fit(self.A1, self.A2)
self.assertEqual(spt.null_distribution_1_.shape[0], 234)
def test_bad_matrix_inputs(self):
- spt = SemiparametricTest()
+ spt = LatentPositionTest()
A1 = self.A1.copy()
A1[2, 0] = 1 # make asymmetric
with self.assertRaises(NotImplementedError): # TODO : remove when we implement
@@ -72,7 +71,7 @@ def test_rotation_norm(self):
rotation = np.array([[0, 1], [-1, 0]])
points2 = np.dot(points1, rotation)
- spt = SemiparametricTest(embedding="ase", test_case="rotation")
+ spt = LatentPositionTest(embedding="ase", test_case="rotation")
n = spt._difference_norm(points1, points2)
self.assertAlmostEqual(n, 0)
@@ -86,7 +85,7 @@ def test_diagonal_rotation_norm(self):
diagonal = np.array([[2, 0, 0], [0, 3, 0], [0, 0, 2]])
points2 = np.dot(diagonal, points2)
- spt = SemiparametricTest(embedding="ase", test_case="diagonal-rotation")
+ spt = LatentPositionTest(embedding="ase", test_case="diagonal-rotation")
n = spt._difference_norm(points1, points2)
self.assertAlmostEqual(n, 0)
@@ -99,7 +98,7 @@ def test_scalar_rotation_norm(self):
# scaled
points2 = 2 * points2
- spt = SemiparametricTest(embedding="ase", test_case="scalar-rotation")
+ spt = LatentPositionTest(embedding="ase", test_case="scalar-rotation")
n = spt._difference_norm(points1, points2)
self.assertAlmostEqual(n, 0)
@@ -113,8 +112,8 @@ def test_SBM_epsilon(self):
A2 = sbm(2 * [b_size], B1)
A3 = sbm(2 * [b_size], B2)
- spt_null = SemiparametricTest(n_components=2, n_bootstraps=10)
- spt_alt = SemiparametricTest(n_components=2, n_bootstraps=10)
+ spt_null = LatentPositionTest(n_components=2, n_bootstraps=10)
+ spt_alt = LatentPositionTest(n_components=2, n_bootstraps=10)
p_null = spt_null.fit(A1, A2)
p_alt = spt_alt.fit(A1, A3)
self.assertTrue(p_null > 0.05)
|
django-json-api__django-rest-framework-json-api-768 | Tag new version for Django 3.0/DRF 3.11/Python 3.8 support
Is there any chance we will see a new version in pip any time soon now that #752 is merged? Thanks!
| [
{
"content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.0.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n",
"path": "rest_framework_json_api/__init__.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.1.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n",
"path": "rest_framework_json_api/__init__.py"
}
] | diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7b2b34f9..f2dc90b7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
Note that in line with [Django REST Framework policy](http://www.django-rest-framework.org/topics/release-notes/),
any parts of the framework not mentioned in the documentation should generally be considered private API, and may be subject to change.
-## [Unreleased]
+## [3.1.0] - 2020-02-08
### Added
@@ -18,10 +18,10 @@ any parts of the framework not mentioned in the documentation should generally b
### Fixed
-* Ensure that `409 Conflict` is returned when processing a `PATCH` request in which the resource object’s type and id do not match the server’s endpoint properly as outlined in [JSON:API](https://jsonapi.org/format/#crud-updating-responses-409) spec.
+* Ensured that `409 Conflict` is returned when processing a `PATCH` request in which the resource object’s type and id do not match the server’s endpoint as outlined in [JSON:API](https://jsonapi.org/format/#crud-updating-responses-409) spec.
* Properly return parser error when primary data is of invalid type
-* Pass instance to child serializer when `PolymorphicModelSerializer` inits it in `to_internal_value`
-* Handle serialization of related resources on inherited polymorphic models that are absent on the base model
+* Pass instance to child serializers when using `PolymorphicModelSerializer`
+* Properly resolve related resource type when using `PolymorphicModelSerializer`
## [3.0.0] - 2019-10-14
diff --git a/rest_framework_json_api/__init__.py b/rest_framework_json_api/__init__.py
index 619fd5ad..a15ece29 100644
--- a/rest_framework_json_api/__init__.py
+++ b/rest_framework_json_api/__init__.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
__title__ = 'djangorestframework-jsonapi'
-__version__ = '3.0.0'
+__version__ = '3.1.0'
__author__ = ''
__license__ = 'BSD'
__copyright__ = ''
|
certbot__certbot-7766 | Required pyparsing version
I've been experimenting with writing tests using the oldest allowed versions of our Python dependencies. `setup.py` for `letsencrypt-nginx` says it requires `pyparsing>=1.5.5` but when I pin version 1.5.5, I encounter problems. You can see Travis logs of the issue [here](https://travis-ci.org/letsencrypt/letsencrypt/jobs/100739657) and [here](https://travis-ci.org/letsencrypt/letsencrypt/jobs/100739658).
We should determine what version we require and update `setup.py` accordingly.
| [
{
"content": "import sys\n\nfrom setuptools import find_packages\nfrom setuptools import setup\nfrom setuptools.command.test import test as TestCommand\n\nversion = '1.3.0.dev0'\n\n# Remember to update local-oldest-requirements.txt when changing the minimum\n# acme/certbot version.\ninstall_requires = [\n 'a... | [
{
"content": "import sys\n\nfrom setuptools import find_packages\nfrom setuptools import setup\nfrom setuptools.command.test import test as TestCommand\n\nversion = '1.3.0.dev0'\n\n# Remember to update local-oldest-requirements.txt when changing the minimum\n# acme/certbot version.\ninstall_requires = [\n 'a... | diff --git a/certbot-nginx/setup.py b/certbot-nginx/setup.py
index 3b75a34247d..b180fe06a9e 100644
--- a/certbot-nginx/setup.py
+++ b/certbot-nginx/setup.py
@@ -13,7 +13,7 @@
'certbot>=1.1.0',
'mock',
'PyOpenSSL',
- 'pyparsing>=1.5.5', # Python3 support; perhaps unnecessary?
+ 'pyparsing>=1.5.5', # Python3 support
'setuptools',
'zope.interface',
]
diff --git a/tools/oldest_constraints.txt b/tools/oldest_constraints.txt
index 6154b497a4e..85d05879627 100644
--- a/tools/oldest_constraints.txt
+++ b/tools/oldest_constraints.txt
@@ -13,7 +13,6 @@ ply==3.4
pyasn1==0.1.9
pycparser==2.14
pyOpenSSL==0.13.1
-pyparsing==1.5.6
pyRFC3339==1.0
python-augeas==0.5.0
oauth2client==4.0.0
|
ansible-collections__community.aws-989 | Invalid import path for BotoCoreError in redshift_info module
### Summary
In case of any AWS related error (like missing permissions) the module will throw a gigantic python stack trace with error summary as:
```
line 304, in find_clusters
NameError: name 'BotoCoreError' is not defined
```
This is due to an invalid import path that is present in the module https://github.com/ansible-collections/community.aws/blob/main/plugins/modules/redshift_info.py#L280
Instead of `from botocore.exception` it should be `from botocore.exceptions`. Once that is done, ansible no longer hides the real error with the stack trace.
### Issue Type
Bug Report
### Component Name
redshift_info
### Ansible Version
```console (paste below)
$ ansible --version
ansible 2.10.8
config file = None
configured module search path = ['/home/wojtek/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules']
ansible python module location = /usr/local/lib/python3.6/dist-packages/ansible
executable location = /usr/local/bin/ansible
python version = 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0]
```
### Collection Versions
Non-relevant
### AWS SDK versions
```console (paste below)
$ pip show boto boto3 botocore
Name: boto
Version: 2.49.0
Summary: Amazon Web Services Library
Home-page: https://github.com/boto/boto/
Author: Mitch Garnaat
Author-email: mitch@garnaat.com
License: MIT
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires:
---
Name: boto3
Version: 1.20.54
Summary: The AWS SDK for Python
Home-page: https://github.com/boto/boto3
Author: Amazon Web Services
Author-email: None
License: Apache License 2.0
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires: jmespath, s3transfer, botocore
---
Name: botocore
Version: 1.23.54
Summary: Low-level, data-driven core of boto 3.
Home-page: https://github.com/boto/botocore
Author: Amazon Web Services
Author-email: None
License: Apache License 2.0
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires: jmespath, urllib3, python-dateutil
```
### Configuration
```console (paste below)
$ ansible-config dump --only-changed
```
### OS / Environment
Ubuntu 20.04
### Steps to Reproduce
Run the module without DescribeClusters permission.
### Expected Results
AWS API error on missing permissions is shown.
### Actual Results
Python stack trace ending with
```
line 304, in find_clusters
NameError: name 'BotoCoreError' is not defined
```
### Code of Conduct
- [X] I agree to follow the Ansible Code of Conduct
| [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: re... | [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: re... | diff --git a/changelogs/fragments/970-redshift_info-boto-import.yml b/changelogs/fragments/970-redshift_info-boto-import.yml
new file mode 100644
index 00000000000..568c6cdf605
--- /dev/null
+++ b/changelogs/fragments/970-redshift_info-boto-import.yml
@@ -0,0 +1,2 @@
+bugfixes:
+ - redshift_info - fix invalid import path for botocore exceptions (https://github.com/ansible-collections/community.aws/issues/968).
diff --git a/plugins/modules/redshift_info.py b/plugins/modules/redshift_info.py
index bc4cb021840..9a6784ec506 100644
--- a/plugins/modules/redshift_info.py
+++ b/plugins/modules/redshift_info.py
@@ -278,7 +278,7 @@
import re
try:
- from botocore.exception import BotoCoreError, ClientError
+ from botocore.exceptions import BotoCoreError, ClientError
except ImportError:
pass # caught by AnsibleAWSModule
|
ansible-collections__community.aws-970 | Invalid import path for BotoCoreError in redshift_info module
### Summary
In case of any AWS related error (like missing permissions) the module will throw a gigantic python stack trace with error summary as:
```
line 304, in find_clusters
NameError: name 'BotoCoreError' is not defined
```
This is due to an invalid import path that is present in the module https://github.com/ansible-collections/community.aws/blob/main/plugins/modules/redshift_info.py#L280
Instead of `from botocore.exception` it should be `from botocore.exceptions`. Once that is done, ansible no longer hides the real error with the stack trace.
### Issue Type
Bug Report
### Component Name
redshift_info
### Ansible Version
```console (paste below)
$ ansible --version
ansible 2.10.8
config file = None
configured module search path = ['/home/wojtek/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules']
ansible python module location = /usr/local/lib/python3.6/dist-packages/ansible
executable location = /usr/local/bin/ansible
python version = 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0]
```
### Collection Versions
Non-relevant
### AWS SDK versions
```console (paste below)
$ pip show boto boto3 botocore
Name: boto
Version: 2.49.0
Summary: Amazon Web Services Library
Home-page: https://github.com/boto/boto/
Author: Mitch Garnaat
Author-email: mitch@garnaat.com
License: MIT
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires:
---
Name: boto3
Version: 1.20.54
Summary: The AWS SDK for Python
Home-page: https://github.com/boto/boto3
Author: Amazon Web Services
Author-email: None
License: Apache License 2.0
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires: jmespath, s3transfer, botocore
---
Name: botocore
Version: 1.23.54
Summary: Low-level, data-driven core of boto 3.
Home-page: https://github.com/boto/botocore
Author: Amazon Web Services
Author-email: None
License: Apache License 2.0
Location: /home/wojtek/.local/lib/python3.6/site-packages
Requires: jmespath, urllib3, python-dateutil
```
### Configuration
```console (paste below)
$ ansible-config dump --only-changed
```
### OS / Environment
Ubuntu 20.04
### Steps to Reproduce
Run the module without DescribeClusters permission.
### Expected Results
AWS API error on missing permissions is shown.
### Actual Results
Python stack trace ending with
```
line 304, in find_clusters
NameError: name 'BotoCoreError' is not defined
```
### Code of Conduct
- [X] I agree to follow the Ansible Code of Conduct
| [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: re... | [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: re... | diff --git a/changelogs/fragments/970-redshift_info-boto-import.yml b/changelogs/fragments/970-redshift_info-boto-import.yml
new file mode 100644
index 00000000000..568c6cdf605
--- /dev/null
+++ b/changelogs/fragments/970-redshift_info-boto-import.yml
@@ -0,0 +1,2 @@
+bugfixes:
+ - redshift_info - fix invalid import path for botocore exceptions (https://github.com/ansible-collections/community.aws/issues/968).
diff --git a/plugins/modules/redshift_info.py b/plugins/modules/redshift_info.py
index b79b28b3074..a6a8a578a37 100644
--- a/plugins/modules/redshift_info.py
+++ b/plugins/modules/redshift_info.py
@@ -277,7 +277,7 @@
import re
try:
- from botocore.exception import BotoCoreError, ClientError
+ from botocore.exceptions import BotoCoreError, ClientError
except ImportError:
pass # caught by AnsibleAWSModule
|
sherlock-project__sherlock-77 | Version Number System
I do not think that the version number system should use the date. There are multiple systems out there, but they are all flavors of major.minor.maintenance. This allows the version number to have some meaning to other people.
https://github.com/sdushantha/sherlock/blob/e2c4dbf1ef69db80a9c6ebf591be874686e04301/sherlock.py#L20
| [
{
"content": "#! /usr/bin/env python3\n\n\"\"\"\nSherlock: Find Usernames Across Social Networks Module\n\nThis module contains the main logic to search for usernames at social\nnetworks.\n\"\"\"\n\nimport csv\nimport json\nimport os\nimport platform\nimport re\nfrom argparse import ArgumentParser, RawDescripti... | [
{
"content": "#! /usr/bin/env python3\n\n\"\"\"\nSherlock: Find Usernames Across Social Networks Module\n\nThis module contains the main logic to search for usernames at social\nnetworks.\n\"\"\"\n\nimport csv\nimport json\nimport os\nimport platform\nimport re\nfrom argparse import ArgumentParser, RawDescripti... | diff --git a/sherlock.py b/sherlock.py
index d9302cb20..93ea02967 100644
--- a/sherlock.py
+++ b/sherlock.py
@@ -21,7 +21,7 @@
from torrequest import TorRequest
module_name = "Sherlock: Find Usernames Across Social Networks"
-__version__ = "2018.01.04"
+__version__ = "0.1.0"
amount=0
# TODO: fix tumblr
|
streamlink__streamlink-3952 | Add lxml dependency
### Checklist
- [X] This is a feature request and not a different kind of issue
- [X] [I have read the contribution guidelines](https://github.com/streamlink/streamlink/blob/master/CONTRIBUTING.md#contributing-to-streamlink)
- [X] [I have checked the list of open and recently closed plugin requests](https://github.com/streamlink/streamlink/issues?q=is%3Aissue+label%3A%22feature+request%22)
### Description
Streamlink should finally switch to a proper HTML/XML parser for extracting data instead of using cheap regex workarounds which don't work properly. I've already commented on this issue last year:
https://github.com/streamlink/streamlink/issues/3241#issuecomment-706486239
The reason why I'm suggesting this again right now is that I was trying to fix the deutschewelle plugin (https://dw.com) yesterday and ran into issues with the `itertags` utility method, which is based on simple regexes for iterating HTML nodes and their attributes+body. `itertags` for example does not work with nested nodes, which makes adding ridiculous custom regexes necessary. Just take a look at this madness:
https://github.com/streamlink/streamlink/blob/3668770d608f0fab54d40a46acd6720a97f63775/src/streamlink/plugins/deutschewelle.py#L18-L29
With `lxml` (https://lxml.de/), HTML page contents can be parsed and the data extracted via XPath queries and/or the respective API methods. The methods are similar to python's native `xml.etree.ElementTree`, which itself is considered too slow and unsafe in certain cases. I am by no means an expert regarding python's standard library though, so if someone has better insight here, please share. In regards to packaging, this lib is available on basically every packaging system and adding it as a dependency here only has benefits.
I'd suggest that we add `lxml` as a dependency now and start using it for extracting data from HTML documents. The validation schema methods could be improved for this as well. There's also the `parse_xml` utility method, which is currently based on the native module.
Comments?
| [
{
"content": "#!/usr/bin/env python\nimport codecs\nfrom os import environ, path\nfrom sys import argv, path as sys_path\n\nfrom setuptools import find_packages, setup\n\nimport versioneer\n\n\ndata_files = []\ndeps = [\n \"requests>=2.26.0,<3.0\",\n \"isodate\",\n \"websocket-client>=0.58.0\",\n # ... | [
{
"content": "#!/usr/bin/env python\nimport codecs\nfrom os import environ, path\nfrom sys import argv, path as sys_path\n\nfrom setuptools import find_packages, setup\n\nimport versioneer\n\n\ndata_files = []\ndeps = [\n \"requests>=2.26.0,<3.0\",\n \"isodate\",\n \"lxml>=4.6.3\",\n \"websocket-cli... | diff --git a/docs/install.rst b/docs/install.rst
index b6f528def71..08f56fb7722 100644
--- a/docs/install.rst
+++ b/docs/install.rst
@@ -289,6 +289,7 @@ Name Notes
`iso-639`_ Used for localization settings, provides language information
`iso3166`_ Used for localization settings, provides country information
`isodate`_ Used for MPEG-DASH streams
+`lxml`_ Used for processing HTML and XML data
`PySocks`_ Used for SOCKS Proxies
`websocket-client`_ At least version **0.58.0**. (used for some plugins)
@@ -321,6 +322,7 @@ With these two environment variables it is possible to use `pycrypto`_ instead o
.. _iso-639: https://pypi.org/project/iso-639/
.. _iso3166: https://pypi.org/project/iso3166/
.. _isodate: https://pypi.org/project/isodate/
+.. _lxml: https://lxml.de/
.. _PySocks: https://github.com/Anorov/PySocks
.. _websocket-client: https://pypi.org/project/websocket-client/
diff --git a/script/makeinstaller.sh b/script/makeinstaller.sh
index f9921218cb2..6c8a0d07cd5 100755
--- a/script/makeinstaller.sh
+++ b/script/makeinstaller.sh
@@ -96,6 +96,7 @@ pypi_wheels=certifi==2021.5.30
idna==3.2
iso3166==1.0.1
isodate==0.6.0
+ lxml==4.6.3
pycryptodome==3.10.1
PySocks==1.7.1
requests==2.26.0
diff --git a/setup.py b/setup.py
index 37e627c90a6..6f79f045db8 100644
--- a/setup.py
+++ b/setup.py
@@ -12,6 +12,7 @@
deps = [
"requests>=2.26.0,<3.0",
"isodate",
+ "lxml>=4.6.3",
"websocket-client>=0.58.0",
# Support for SOCKS proxies
"PySocks!=1.5.7,>=1.5.6",
|
HypothesisWorks__hypothesis-3148 | clarification on `note`
https://hypothesis.readthedocs.io/en/latest/details.html#hypothesis.note states
`Report this value in the final execution.`
From my test, `note` wasn't printed on successful run and was printed on falsified run.
Please help me understand this functionality
| [
{
"content": "# This file is part of Hypothesis, which may be found at\n# https://github.com/HypothesisWorks/hypothesis/\n#\n# Most of this work is copyright (C) 2013-2021 David R. MacIver\n# (david@drmaciver.com), but it contains contributions by others. See\n# CONTRIBUTING.rst for a full list of people who ma... | [
{
"content": "# This file is part of Hypothesis, which may be found at\n# https://github.com/HypothesisWorks/hypothesis/\n#\n# Most of this work is copyright (C) 2013-2021 David R. MacIver\n# (david@drmaciver.com), but it contains contributions by others. See\n# CONTRIBUTING.rst for a full list of people who ma... | diff --git a/hypothesis-python/RELEASE.rst b/hypothesis-python/RELEASE.rst
new file mode 100644
index 0000000000..ff8b57ab04
--- /dev/null
+++ b/hypothesis-python/RELEASE.rst
@@ -0,0 +1,4 @@
+RELEASE_TYPE: patch
+
+This patch updates documentation of :func:`~hypothesis.note`
+(:issue:`3147`).
\ No newline at end of file
diff --git a/hypothesis-python/docs/details.rst b/hypothesis-python/docs/details.rst
index a3ba31def2..30bddc07d3 100644
--- a/hypothesis-python/docs/details.rst
+++ b/hypothesis-python/docs/details.rst
@@ -43,7 +43,7 @@ intermediate steps of your test. That's where the ``note`` function comes in:
Shuffle: [1, 0]
ls != ls2
-The note is printed in the final run of the test in order to include any
+The note is printed for the minimal failing example of the test in order to include any
additional information you might need in your test.
diff --git a/hypothesis-python/src/hypothesis/control.py b/hypothesis-python/src/hypothesis/control.py
index 3132e8c76b..e042136eda 100644
--- a/hypothesis-python/src/hypothesis/control.py
+++ b/hypothesis-python/src/hypothesis/control.py
@@ -116,7 +116,7 @@ def should_note():
def note(value: str) -> None:
- """Report this value in the final execution."""
+ """Report this value for the minimal failing example."""
if should_note():
report(value)
|
LMFDB__lmfdb-5795 | Half integeral weight page visible on prod
https://www.lmfdb.org/ModularForm/GL2/Q/holomorphic/half/ should redirect to beta, but it doesn't since the whitelist thinks it's inside CMFs.
| [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom lmfdb.app import app\nfrom lmfdb.logger import make_logger\nfrom flask import Blueprint\n\nhiwf_page = Blueprint(\"hiwf\", __name__, template_folder='templates', static_folder=\"static\")\nhiwf_logger = make_logger(hiwf_page)\n\n\n@hiwf_page.context_processor\ndef b... | [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom lmfdb.app import app\nfrom lmfdb.logger import make_logger\nfrom flask import Blueprint\n\nhiwf_page = Blueprint(\"hiwf\", __name__, template_folder='templates', static_folder=\"static\")\nhiwf_logger = make_logger(hiwf_page)\n\n\n@hiwf_page.context_processor\ndef b... | diff --git a/lmfdb/half_integral_weight_forms/__init__.py b/lmfdb/half_integral_weight_forms/__init__.py
index 048237c441..0ef40b41c7 100644
--- a/lmfdb/half_integral_weight_forms/__init__.py
+++ b/lmfdb/half_integral_weight_forms/__init__.py
@@ -15,4 +15,4 @@ def body_class():
from . import half_integral_form
assert half_integral_form
-app.register_blueprint(hiwf_page, url_prefix="/ModularForm/GL2/Q/holomorphic/half")
+app.register_blueprint(hiwf_page, url_prefix="/ModularForm/GL2/Q/holomorphic_half")
|
nltk__nltk-3205 | `corpus_bleu` function does not catch all the expections when calling `weights[0][0]`
In your codes https://github.com/nltk/nltk/blob/e2d368e00ef806121aaa39f6e5f90d9f8243631b/nltk/translate/bleu_score.py#L201
I pass in `weights = array([0.25, 0.25, 0.25, 0.25])` and find this error:
```
File "/home/cyzhao/miniconda3/envs/prompt/lib/python3.11/site-packages/nltk/translate/bleu_score.py", line 200, in corpus_bleu
weights[0][0]
~~~~~~~~~~^^^
IndexError: invalid index to scalar variable.
"""
```
I then find out the reason why.
Not all exceptions are completely caught. The `weights` passed in by the framework are `array([0.25, 0.25, 0.25, 0.25])`, and for `ndarray` the error is `IndexError: invalid index to scalar variable`. Hence, these codes haven't caught all the exceptions, leading to the situation where one must pass a tuple `(0.25, 0.25, 0.25, 0.25)` to be caught by this try-except block.
| [
{
"content": "# Natural Language Toolkit: BLEU Score\n#\n# Copyright (C) 2001-2023 NLTK Project\n# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim\n# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan\n# URL: <https://www.nltk.org/>\n# For license information, see LICENSE.TXT\n\n\"\"... | [
{
"content": "# Natural Language Toolkit: BLEU Score\n#\n# Copyright (C) 2001-2023 NLTK Project\n# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim\n# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan\n# URL: <https://www.nltk.org/>\n# For license information, see LICENSE.TXT\n\n\"\"... | diff --git a/nltk/test/unit/translate/test_bleu.py b/nltk/test/unit/translate/test_bleu.py
index ac3565e74b..990b76406a 100644
--- a/nltk/test/unit/translate/test_bleu.py
+++ b/nltk/test/unit/translate/test_bleu.py
@@ -5,6 +5,8 @@
import io
import unittest
+import numpy as np
+
from nltk.data import find
from nltk.translate.bleu_score import (
SmoothingFunction,
@@ -217,6 +219,14 @@ def test_reference_or_hypothesis_shorter_than_fourgrams(self):
except AttributeError:
pass # unittest.TestCase.assertWarns is only supported in Python >= 3.2.
+ def test_numpy_weights(self):
+ # Test case where there's 0 matches
+ references = ["The candidate has no alignment to any of the references".split()]
+ hypothesis = "John loves Mary".split()
+
+ weights = np.array([0.25] * 4)
+ assert sentence_bleu(references, hypothesis, weights) == 0
+
class TestBLEUvsMteval13a(unittest.TestCase):
def test_corpus_bleu(self):
diff --git a/nltk/translate/bleu_score.py b/nltk/translate/bleu_score.py
index f6c232e1cb..da445bc3ec 100644
--- a/nltk/translate/bleu_score.py
+++ b/nltk/translate/bleu_score.py
@@ -198,7 +198,7 @@ def corpus_bleu(
try:
weights[0][0]
- except TypeError:
+ except:
weights = [weights]
max_weight_length = max(len(weight) for weight in weights)
|
microsoft__botbuilder-python-1507 | Python 81.skills-skilldialog throwing error: [on_turn_error] unhandled error: Cannot deserialize content-type: text/plain
## Sample information
1. Sample type: \samples\
2. Sample language: python
3. Sample name: 81.skills-skilldialog
## Describe the bug
When you run the sample as per the instructions, the skill bot is throwing the following error:
======== Running on http://localhost:39783 ========
(Press CTRL+C to quit)
[on_turn_error] unhandled error: Cannot deserialize content-type: text/plain
Traceback (most recent call last):
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/bot_adapter.py", line 128, in run_pipeline
context, callback
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/middleware_set.py", line 69, in receive_activity_with_status
return await self.receive_activity_internal(context, callback)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/middleware_set.py", line 79, in receive_activity_internal
return await callback(context)
File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/bots/skill_bot.py", line 21, in on_turn
self._conversation_state.create_property("DialogState"),
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/dialog_extensions.py", line 68, in run_dialog
result = await dialog_context.begin_dialog(dialog.id)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/dialog_context.py", line 91, in begin_dialog
return await dialog.begin_dialog(self, options)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/component_dialog.py", line 67, in begin_dialog
turn_result = await self.on_begin_dialog(inner_dc, options)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/component_dialog.py", line 221, in on_begin_dialog
return await inner_dc.begin_dialog(self.initial_dialog_id, options)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/dialog_context.py", line 91, in begin_dialog
return await dialog.begin_dialog(self, options)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/waterfall_dialog.py", line 65, in begin_dialog
return await self.run_step(dialog_context, 0, DialogReason.BeginCalled, None)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/waterfall_dialog.py", line 156, in run_step
return await self.on_step(step_context)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/waterfall_dialog.py", line 132, in on_step
return await self._steps[step_context.index](step_context)
File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/dialogs/activity_router_dialog.py", line 50, in process_activity
return await self._on_event_activity(step_context)
File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/dialogs/activity_router_dialog.py", line 77, in _on_event_activity
return await self._begin_get_weather(step_context)
File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/dialogs/activity_router_dialog.py", line 156, in _begin_get_weather
get_weather_message, get_weather_message, InputHints.ignoring_input,
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 174, in send_activity
result = await self.send_activities([activity_or_text])
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 226, in send_activities
return await self._emit(self._on_send_activities, output, logic())
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 304, in _emit
return await logic
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 221, in logic
responses = await self.adapter.send_activities(self, output)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/bot_framework_adapter.py", line 729, in send_activities
raise error
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/bot_framework_adapter.py", line 715, in send_activities
activity.conversation.id, activity.reply_to_id, activity
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botframework/connector/aio/operations_async/_conversations_operations_async.py", line 529, in reply_to_activity
request, stream=False, **operation_config
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/async_client.py", line 115, in async_send
pipeline_response = await self.config.pipeline.run(request, **kwargs)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_abc.py", line 159, in run
return await first_node.send(pipeline_request, **kwargs) # type: ignore
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_abc.py", line 79, in send
response = await self.next.send(request, **kwargs) # type: ignore
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_requests.py", line 106, in send
return await self.next.send(request, **kwargs)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_abc.py", line 84, in send
self._policy.on_response(request, response, **kwargs)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/universal.py", line 252, in on_response
http_response.headers
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/universal.py", line 226, in deserialize_from_http_generics
return cls.deserialize_from_text(body_bytes, content_type)
File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/universal.py", line 203, in deserialize_from_text
raise DeserializationError("Cannot deserialize content-type: {}".format(content_type))
msrest.exceptions.DeserializationError: Cannot deserialize content-type: text/plain
## To Reproduce
Steps to reproduce the behavior:
1. Run the root & skill bots as per the instructions from the sample readme
2. Start the bot framework emulator & connect
3. Choose the DialogSkillBot
4. Enter activity 3
## Expected behavior
Error not returned
| [
{
"content": "# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport os\nfrom setuptools import setup\n\nVERSION = os.environ[\"packageVersion\"] if \"packageVersion\" in os.environ else \"4.12.0\"\nREQUIRES = [\n \"botbuilder-schema==4.12.0\",\n \"botfram... | [
{
"content": "# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport os\nfrom setuptools import setup\n\nVERSION = os.environ[\"packageVersion\"] if \"packageVersion\" in os.environ else \"4.12.0\"\nREQUIRES = [\n \"botbuilder-schema==4.12.0\",\n \"botfram... | diff --git a/libraries/botbuilder-integration-aiohttp/setup.py b/libraries/botbuilder-integration-aiohttp/setup.py
index f45e67ec8..e1e06e54b 100644
--- a/libraries/botbuilder-integration-aiohttp/setup.py
+++ b/libraries/botbuilder-integration-aiohttp/setup.py
@@ -9,6 +9,7 @@
"botbuilder-schema==4.12.0",
"botframework-connector==4.12.0",
"botbuilder-core==4.12.0",
+ "yarl<=1.4.2",
"aiohttp==3.6.2",
]
|
Gallopsled__pwntools-200 | pwnlib.util.fiddling.hexdump() no longer has colors
The colors used to be so pretty but now they are gone because of this piece of code: https://github.com/Gallopsled/pwntools/blob/master/pwnlib/util/fiddling.py#L460
It looks like it was added as a quick-fix to overcome some issue, but I can't figure out which. Thoughts, @zachriggle?
| [
{
"content": "# -*- coding: utf-8 -*-\nimport re, base64, random, string, sys\nfrom . import packing, lists\nfrom .cyclic import cyclic_find\nfrom ..context import context\nfrom ..term import text\n\ndef unhex(s):\n \"\"\"unhex(s) -> str\n\n Hex-decodes a string.\n\n Example:\n\n >>> unhex(\"74... | [
{
"content": "# -*- coding: utf-8 -*-\nimport re, base64, random, string, sys\nfrom . import packing, lists\nfrom .cyclic import cyclic_find\nfrom ..context import context\nfrom ..term import text\n\ndef unhex(s):\n \"\"\"unhex(s) -> str\n\n Hex-decodes a string.\n\n Example:\n\n >>> unhex(\"74... | diff --git a/pwnlib/util/fiddling.py b/pwnlib/util/fiddling.py
index 2a6fc8925..320d42d72 100644
--- a/pwnlib/util/fiddling.py
+++ b/pwnlib/util/fiddling.py
@@ -457,7 +457,7 @@ def _hexiichar(c):
'ff': text.green,
}
-if 1 or not sys.stdout.isatty():
+if not sys.stdout.isatty():
default_style = {
'marker': lambda x:x,
'nonprintable': lambda x:x,
|
pytorch__vision-3472 | Investigate inconsistent casting inside functional_tensor.py
The operators in [functional_tensor.py](https://github.com/pytorch/vision/blob/9e71fdafd871e3de9e72a6022291b49100945e29/torchvision/transforms/functional_tensor.py) perform casting in two ways:
- Using the `tensor.to(dtype=dtype)` PyTorch method
- Using the `convert_image_dtype()` Transformation method
The first method does direct casting from one type to the other. The latter method has more complex logic that handles corner-cases and performs rescaling. Sometimes both are used on the same operator, for example:
https://github.com/pytorch/vision/blob/9e71fdafd871e3de9e72a6022291b49100945e29/torchvision/transforms/functional_tensor.py#L397-L406
We should investigate if the use of the two different approaches across operators is justified and fix any potential inconsistencies.
cc @vfdev-5
| [
{
"content": "import warnings\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad\nfrom torch.jit.annotations import BroadcastingList2\nfrom typing import Optional, Tuple, List\n\n\ndef _is_tensor_a_torch_image(x: Tensor) -> bool:\n re... | [
{
"content": "import warnings\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad\nfrom torch.jit.annotations import BroadcastingList2\nfrom typing import Optional, Tuple, List\n\n\ndef _is_tensor_a_torch_image(x: Tensor) -> bool:\n re... | diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index 69445e6a231..d20d24a8413 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -227,7 +227,6 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
result = (gain * result ** gamma).clamp(0, 1)
result = convert_image_dtype(result, dtype)
- result = result.to(dtype)
return result
|
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