{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction on Tidy3D working principles\n",
"\n",
"This notebook will walk through the basic working principles of the\n",
"Tidy3D front end, including setting up and post processing simulation\n",
"results.\n",
"\n",
"Note that this content serves as a reference for the details of\n",
"operating the front end, but a lot of content is covered indirectly in\n",
"the other [example\n",
"notebooks](https://www.flexcompute.com/tidy3d/examples/)."
],
"id": "af8712e2-4557-4c9c-b240-466502350b9d"
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# First, let's import the main packages we'll need\n",
"import matplotlib.pylab as plt\n",
"import numpy as np\n",
"import tidy3d as td"
],
"id": "02039b9f"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basics\n",
"\n",
"A Tidy3D simulation is essentially a python class that combines many\n",
"Tidy3D “components”, which store the specification for how we will run\n",
"the simulation on our servers.\n",
"\n",
"Here we will discuss some of the basics that apply to all of these\n",
"components.\n",
"\n",
"Topics covered will include:\n",
"\n",
"- Defining Tidy3D components.\n",
"\n",
"- Saving and loading from file.\n",
"\n",
"- Getting help / debugging.\n",
"\n",
"- logging.\n",
"\n",
"### Background\n",
"\n",
"All Tidy3d components are defined using the\n",
"[pydantic](https://pydantic-docs.helpmanual.io) package. This means that\n",
"each tidy3d object corresponds to a data structure that has rigidly\n",
"defined allowable types and values, which get validated automatically\n",
"whenever you initialize an object.\n",
"\n",
"This has several advantages, including.\n",
"\n",
"- Catching bugs in the tidy3d components as early as possible.\n",
"\n",
"- Automatically generated schema for tidy3d simulation specifications.\n",
"\n",
"- Simple and reliable IO, including exporting to various formats such as\n",
" dictionaries or json.\n",
"\n",
"### Defining Tidy3d Components\n",
"\n",
"It also requires a bit more work on the user side to define these\n",
"components, for example\n",
"\n",
"- All tidy3d components must use keyword arguments in their definitions,\n",
" eg. `Medium(2.0)` becomes `Medium(permittivity=2.0)` to be explicit.\n",
"\n",
"- Components are immutable to avoid complications from not validating\n",
" changes on components. This means modifying a component requires\n",
" calling `new_obj = old_obj.updated_copy(val=new_val, ...)`.\n",
"\n",
"However, these decisions were made to make the overall use of Tidy3D\n",
"more explicit and ensure that components are safely validated at all\n",
"times, which pays off in the long run.\n",
"\n",
"Here’s one example of what would happen if a user tried to pass fields\n",
"(in this case, the relative permittivity of a medium) without using\n",
"keyword arguments), followed by the correct way. We see that the\n",
"positional argument construction fails. This is good practice in general\n",
"to avoid bugs, but pydantic and Tidy3D require it."
],
"id": "97e12a8c-fb98-487d-bab6-e6ab2d1045a1"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# wrong way\n",
"try:\n",
" td.Medium(2.0)\n",
"except Exception as e:\n",
" td.log.info(e)\n",
"\n",
"# correct way\n",
"m = td.Medium(permittivity=2.0)"
],
"id": "33213548"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Saving and Loading Tidy3d Components\n",
"\n",
"All tidy3d components can be saved to file as json or yaml format using\n",
"the `instance.to_file(path)` and `class.from_file(path)` methods.\n",
"\n",
"For example, let’s save and load a\n",
"[td.Box](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html)\n",
"instance."
],
"id": "4c943484-c14a-4c64-991d-c64945850018"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"my_box = td.Box(center=(1, 2, 3), size=(2, 2, 3))\n",
"\n",
"my_box.to_file(\"data/box.json\")\n",
"\n",
"# note, `from_file` is a @classmethod so it can be called from `td.Box` or `my_box`.\n",
"your_box = td.Box.from_file(\"data/box.json\")\n",
"\n",
"print(my_box)\n",
"print(your_box)\n",
"print(my_box == your_box)"
],
"id": "243dbc3e"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, the component can be converted to a `dict` or `str` in json\n",
"format. Many other options exist and the [pydantic\n",
"documentation](https://docs.pydantic.dev/usage/exporting_models/) is a\n",
"good place to see what else is supported."
],
"id": "802139fa-8aa8-477c-b461-d8531e8b15d6"
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\"attrs\":{},\"type\":\"Box\",\"center\":[1.0,2.0,3.0],\"size\":[2.0,2.0,3.0]}\n",
"{'attrs': {}, 'type': 'Box', 'center': (1.0, 2.0, 3.0), 'size': (2.0, 2.0, 3.0)}"
]
}
],
"source": [
"print(my_box.json())\n",
"print(my_box.dict())"
],
"id": "5023392f"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting Help\n",
"\n",
"Sometimes you might want to get some information about a component\n",
"without needing to look at the documentation. For this, each tidy3d\n",
"component has a `.help()` method that will print out information about\n",
"the stored data inside of the component.\n",
"\n",
"Here’s an example."
],
"id": "b514c4aa-4fe5-41f6-94ae-f67f915aa4ce"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"╭─────────────────────────────── <class 'tidy3d.components.monitor.FieldMonitor'> ────────────────────────────────╮\n",
"│ class FieldMonitor(*, attrs: dict = <factory>, type: Literal['FieldMonitor'] = 'FieldMonitor', center: │\n",
"│ typing.Annotated[tuple[typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49e520>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49e840>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4d6e90>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49e520>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49e840>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4d6e90>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49e520>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49e840>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4d6e90>]], WithJsonSchema(json_schema={'type': 'array', 'minItems': 3, 'maxItems': 3, │\n",
"│ 'prefixItems': [{'type': 'number'}, {'type': 'number'}, {'type': 'number'}]}, mode=None)] = (0.0, 0.0, 0.0), │\n",
"│ size: typing.Annotated[tuple[typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49f060>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49f100>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4df450>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49f060>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49f100>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4df450>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49f060>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49f100>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4df450>]], WithJsonSchema(json_schema={'type': 'array', 'minItems': 3, 'maxItems': 3, │\n",
"│ 'prefixItems': [{'minimum': 0, 'type': 'number'}, {'minimum': 0, 'type': 'number'}, {'minimum': 0, 'type': │\n",
"│ 'number'}]}, mode=None)], name: typing.Annotated[str, MinLen(min_length=1)], interval_space: │\n",
"│ tuple[typing.Annotated[int, Gt(gt=0)], typing.Annotated[int, Gt(gt=0)], typing.Annotated[int, Gt(gt=0)]] = (1, │\n",
"│ 1, 1), colocate: bool = True, use_colocated_integration: bool = True, freqs: typing.Annotated[numpy.ndarray, │\n",
"│ BeforeValidator(func=<function _from_complex_dict at 0x7f980faed300>, │\n",
"│ json_schema_input_type=PydanticUndefined), BeforeValidator(func=<function array_alias.<locals>.<lambda> at │\n",
"│ 0x7f980f49d1c0>, json_schema_input_type=PydanticUndefined), PlainSerializer(func=<function _auto_serializer at │\n",
"│ 0x7f980faefe20>, return_type=PydanticUndefined, when_used='json'), WithJsonSchema(json_schema={'type': │\n",
"│ 'ArrayLike', 'x-array-dtype': '<f8', 'x-array-ndim': 1, 'x-array-shape': None, 'x-array-forbid_nan': True, │\n",
"│ 'x-array-scalar_to_1d': True, 'x-array-strict': False}, mode=None)], apodization: │\n",
"│ tidy3d.components.apodization.ApodizationSpec = <factory>, fields: tuple[typing.Literal['Ex', 'Ey', 'Ez', 'Hx', │\n",
"│ 'Hy', 'Hz'], ...] = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz']) -> None: │\n",
"│ │\n",
"│ :class:`~tidy3d.Monitor` that records electromagnetic fields in the frequency domain. │\n",
"│ │\n",
"│ bounding_box = <property object at 0x7f980f34bf60> │\n",
"│ bounds = <property object at 0x7f980f14cae0> │\n",
"│ frequency_range = <property object at 0x7f9802479e40> │\n",
"│ geometry = <property object at 0x7f9802429e40> │\n",
"│ model_computed_fields = {} │\n",
"│ model_config = { │\n",
"│ 'arbitrary_types_allowed': True, │\n",
"│ 'defer_build': True, │\n",
"│ 'validate_default': True, │\n",
"│ 'populate_by_name': True, │\n",
"│ 'ser_json_inf_nan': 'strings', │\n",
"│ 'extra': 'forbid', │\n",
"│ 'frozen': True, │\n",
"│ 'validate_by_alias': True, │\n",
"│ 'validate_by_name': True │\n",
"│ } │\n",
"│ model_extra = <property object at 0x7f980fd21fd0> │\n",
"│ model_fields = { │\n",
"│ 'attrs': FieldInfo( │\n",
"│ annotation=dict, │\n",
"│ required=False, │\n",
"│ default_factory=dict, │\n",
"│ title='Attributes', │\n",
"│ description=\"Dictionary storing arbitrary metadata for a Tidy3D object. This │\n",
"│ dictionary can be freely used by the user for storing data without affecting the │\n",
"│ operation of Tidy3D as it is not used internally. Note that, unlike regular Tidy3D │\n",
"│ fields, ``attrs`` are mutable. For example, the following is allowed for setting an │\n",
"│ ``attr`` ``obj.attrs['foo'] = bar``. Also note that Tidy3D will raise a ``TypeError`` │\n",
"│ if ``attrs`` contain objects that can not be serialized. One can check if ``attrs`` are │\n",
"│ serializable by calling ``obj.model_dump_json()``.\" │\n",
"│ ), │\n",
"│ 'type': FieldInfo( │\n",
"│ annotation=Literal['FieldMonitor'], │\n",
"│ required=False, │\n",
"│ default='FieldMonitor' │\n",
"│ ), │\n",
"│ 'center': FieldInfo( │\n",
"│ annotation=tuple[Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema]], │\n",
"│ required=False, │\n",
"│ default=(0.0, 0.0, 0.0), │\n",
"│ title='Center', │\n",
"│ description='Center of object in x, y, and z.', │\n",
"│ json_schema_extra={'units': 'um'}, │\n",
"│ metadata=[ │\n",
"│ WithJsonSchema( │\n",
"│ json_schema={ │\n",
"│ 'type': 'array', │\n",
"│ 'minItems': 3, │\n",
"│ 'maxItems': 3, │\n",
"│ 'prefixItems': [ │\n",
"│ {'type': 'number'}, │\n",
"│ {'type': 'number'}, │\n",
"│ {'type': 'number'} │\n",
"│ ] │\n",
"│ }, │\n",
"│ mode=None │\n",
"│ ) │\n",
"│ ] │\n",
"│ ), │\n",
"│ 'size': FieldInfo( │\n",
"│ annotation=tuple[Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema]], │\n",
"│ required=True, │\n",
"│ title='Size', │\n",
"│ description='Size in x, y, and z directions.', │\n",
"│ json_schema_extra={'units': 'um'}, │\n",
"│ metadata=[ │\n",
"│ WithJsonSchema( │\n",
"│ json_schema={ │\n",
"│ 'type': 'array', │\n",
"│ 'minItems': 3, │\n",
"│ 'maxItems': 3, │\n",
"│ 'prefixItems': [ │\n",
"│ {'minimum': 0, 'type': 'number'}, │\n",
"│ {'minimum': 0, 'type': 'number'}, │\n",
"│ {'minimum': 0, 'type': 'number'} │\n",
"│ ] │\n",
"│ }, │\n",
"│ mode=None │\n",
"│ ) │\n",
"│ ] │\n",
"│ ), │\n",
"│ 'name': FieldInfo( │\n",
"│ annotation=str, │\n",
"│ required=True, │\n",
"│ title='Name', │\n",
"│ description='Unique name for monitor.', │\n",
"│ metadata=[MinLen(min_length=1)] │\n",
"│ ), │\n",
"│ 'interval_space': FieldInfo( │\n",
"│ annotation=tuple[Annotated[int, Gt], Annotated[int, Gt], Annotated[int, Gt]], │\n",
"│ required=False, │\n",
"│ default=(1, 1, 1), │\n",
"│ title='Spatial Interval', │\n",
"│ description='Number of grid step intervals between monitor recordings. If equal │\n",
"│ to 1, there will be no downsampling. If greater than 1, the step will be applied, but │\n",
"│ the first and last point of the monitor grid are always included.' │\n",
"│ ), │\n",
"│ 'colocate': FieldInfo( │\n",
"│ annotation=bool, │\n",
"│ required=False, │\n",
"│ default=True, │\n",
"│ title='Colocate Fields', │\n",
"│ description='Toggle whether fields should be colocated to grid cell boundaries │\n",
"│ (i.e. primal grid nodes).' │\n",
"│ ), │\n",
"│ 'use_colocated_integration': FieldInfo( │\n",
"│ annotation=bool, │\n",
"│ required=False, │\n",
"│ default=True, │\n",
"│ title='Use Colocated Integration', │\n",
"│ description='Only takes effect when ``colocate=False``. If ``True``, flux, dot │\n",
"│ products, and overlap integrals still use fields interpolated to grid cell boundaries │\n",
"│ (colocated), even though the field data is stored at native Yee grid positions. │\n",
"│ Experimental feature that can give improved accuracy by avoiding interpolation of │\n",
"│ fields to Yee cell positions for integration.' │\n",
"│ ), │\n",
"│ 'freqs': FieldInfo( │\n",
"│ annotation=ndarray, │\n",
"│ required=True, │\n",
"│ title='Frequencies', │\n",
"│ description='Array or list of frequencies stored by the field monitor.', │\n",
"│ json_schema_extra={'units': 'Hz'}, │\n",
"│ metadata=[ │\n",
"│ BeforeValidator( │\n",
"│ func=<function _from_complex_dict at 0x7f980faed300>, │\n",
"│ json_schema_input_type=PydanticUndefined │\n",
"│ ), │\n",
"│ BeforeValidator( │\n",
"│ func=<function array_alias.<locals>.<lambda> at 0x7f980f49d1c0>, │\n",
"│ json_schema_input_type=PydanticUndefined │\n",
"│ ), │\n",
"│ PlainSerializer( │\n",
"│ func=<function _auto_serializer at 0x7f980faefe20>, │\n",
"│ return_type=PydanticUndefined, │\n",
"│ when_used='json' │\n",
"│ ), │\n",
"│ WithJsonSchema( │\n",
"│ json_schema={ │\n",
"│ 'type': 'ArrayLike', │\n",
"│ 'x-array-dtype': '<f8', │\n",
"│ 'x-array-ndim': 1, │\n",
"│ 'x-array-shape': None, │\n",
"│ 'x-array-forbid_nan': True, │\n",
"│ 'x-array-scalar_to_1d': True, │\n",
"│ 'x-array-strict': False │\n",
"│ }, │\n",
"│ mode=None │\n",
"│ ) │\n",
"│ ] │\n",
"│ ), │\n",
"│ 'apodization': FieldInfo( │\n",
"│ annotation=ApodizationSpec, │\n",
"│ required=False, │\n",
"│ default_factory=ApodizationSpec, │\n",
"│ title='Apodization Specification', │\n",
"│ description='Sets parameters of (optional) apodization. Apodization applies a │\n",
"│ windowing function to the Fourier transform of the time-domain fields into │\n",
"│ frequency-domain ones, and can be used to truncate the beginning and/or end of the time │\n",
"│ signal, for example to eliminate the source pulse when studying the eigenmodes of a │\n",
"│ system. Note: apodization affects the normalization of the frequency-domain fields.' │\n",
"│ ), │\n",
"│ 'fields': FieldInfo( │\n",
"│ annotation=tuple[Literal['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'], ...], │\n",
"│ required=False, │\n",
"│ default=['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'], │\n",
"│ title='Field Components', │\n",
"│ description='Collection of field components to store in the monitor.' │\n",
"│ ) │\n",
"│ } │\n",
"│ model_fields_set = <property object at 0x7f980fd22020> │\n",
"│ plot_params = <property object at 0x7f9802429df0> │\n",
"│ zero_dims = <property object at 0x7f980f14cb30> │\n",
"╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
""
]
}
}
],
"source": [
"monitor = td.FieldMonitor(size=(2, 2, 0), freqs=[200e12], name=\"monitor\")\n",
"\n",
"monitor.help()"
],
"id": "26f52a0d"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, one can use the standard `help(cls)` syntax to get a look\n",
"at the docstring of a component."
],
"id": "007520dd-7d64-4e2d-a4c9-9566c9d38b68"
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# help(td.FieldMonitor) # commented out for brevity"
],
"id": "5264d2ef"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logging\n",
"\n",
"Tidy3D logs information based on the following levels ranked from least\n",
"to most critical.\n",
"\n",
" \"DEBUG\"\n",
" \"INFO\"\n",
" \"WARNING\"\n",
" \"ERROR\"\n",
" \"CRITICAL\"\n",
"\n",
"The “logging level” controls what types of messages get shown. The\n",
"default logging level is `\"WARNING\"`, which means logging messages of\n",
"“warning” or more critical (“error”, “critical”) will be shown, whereas\n",
"“info” and “debug” messages will not. The “info” and “debug” messages\n",
"are sometimes useful for the user, but are mainly for debugging\n",
"purposes.\n",
"\n",
"The user can change this logging level through modifying the\n",
"`tidy3d.config.logging.level` value, for example\n",
"\n",
" tidy3d.config.logging.level = \"INFO\"\n",
"\n",
"will make it so that “info” messages are shown in addition to the\n",
"“warning”, “error”, and “critical” messages.\n",
"\n",
"## Core Components\n",
"\n",
"Here we will discuss the core components of the package (sources,\n",
"monitors, etc) and how they are defined.\n",
"\n",
"Topics covered will include:\n",
"\n",
"- Mediums.\n",
"\n",
"- Geometries.\n",
"\n",
"- Structures.\n",
"\n",
"- Sources.\n",
"\n",
"- Monitors.\n",
"\n",
"- Modes.\n",
"\n",
"- PML / Absorbing boundaries.\n",
"\n",
"- Simulations.\n",
"\n",
"### Mediums\n",
"\n",
"Mediums define the optical properties of the materials within the\n",
"simulation.\n",
"\n",
"#### PEC\n",
"\n",
"The simplest medium is `td.PEC`, which just signifies a perfect\n",
"electrical conductor (no E field allowed within)."
],
"id": "b05b44e4-d402-41d8-8169-b6d9429069fd"
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"pec_medium = td.PEC"
],
"id": "292d2c40"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Non-dispersive mediums\n",
"\n",
"Non-dispersive mediums are defined using the\n",
"[Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html)\n",
"object and can be specified by either `permittivity` and `conductivity`\n",
"(optional) values, or from `n`, `k` refractive index values."
],
"id": "56938eb9-be70-473b-90d7-ffda6f8b0016"
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"lossless_dielectric = td.Medium(permittivity=4.0)\n",
"lossy_dielectric = td.Medium(permittivity=4.0, conductivity=1.0)\n",
"lossy_dielectric_from_nk = td.Medium.from_nk(n=2.0, k=0.1, freq=150e12)"
],
"id": "b88e3b6d"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Anisotropic mediums\n",
"\n",
"Tidy3d supports fully anisotropic mediums, but in this example, we will\n",
"show how to model diagonally anisotropic mediums. See [this\n",
"tutorial](../notebooks/FullyAnisotropic.html) for the complete guide.\n",
"\n",
"Diagonally anisotropic mediums can be simply defined by specifying three\n",
"[Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html)\n",
"objects for the xx, yy, zz components of the permittivity / conductivity\n",
"tensor."
],
"id": "c5222c43-9baa-453a-8a52-04358663fbbd"
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"anisotropic_medium = td.AnisotropicMedium(\n",
" xx=lossless_dielectric, yy=lossy_dielectric, zz=lossy_dielectric_from_nk\n",
")"
],
"id": "bcd12f62"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dispersive mediums\n",
"\n",
"Dispersive mediums can be defined in three ways:\n",
"\n",
"- Imported from our\n",
" [material_library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html).\n",
"\n",
"- Defined directly by specifying the parameters in the various supplied\n",
" dispersive models.\n",
"\n",
"- Fitted to optical n-k data using the dispersion fitting tool plugin\n",
" (more info later)."
],
"id": "858e2713-3ab7-4a0f-aebf-0681f5af16dc"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"variants for silver include: ['Rakic1998BB', 'Rakic1998BB_IR', 'JohnsonChristy1972', 'RakicLorentzDrude1998', 'Yang2015Drude']"
]
}
],
"source": [
"# material library\n",
"silver = td.material_library[\"Ag\"]\n",
"silver_variants = silver.variants\n",
"print(\"variants for silver include: \", list(silver_variants.keys()))\n",
"silver = silver_variants[\"JohnsonChristy1972\"]\n",
"\n",
"# models\n",
"lorentz_model = td.Lorentz(eps_inf=2.0, coeffs=[(1, 2, 3), (4, 5, 6)])\n",
"sellmeier_model = td.Sellmeier(coeffs=[(1, 2), (3, 4)])"
],
"id": "bba6f814"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Medium Methods\n",
"\n",
"The complex-valued permittivity of a medium at a given frequency can be\n",
"sampled using the `.eps_model(freq)` method.\n",
"\n",
"And the n, k values can be plotted over a frequency range using the\n",
"`.plot(freqs)` method."
],
"id": "13d23966-56e1-429a-a788-85d660fcc8fe"
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"complex relative permittivity at freqs_hz = \n",
" [4.+359.50207169j 4.+358.42679132j 4.+357.35792415j ... 4. +90.01053372j\n",
" 4. +89.94297515j 4. +89.87551792j]\n"
]
},
{
"output_type": "display_data",
"metadata": {},
"data": {
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}
}
],
"source": [
"freqs_hz = 1e12 * np.linspace(50, 200, 1001)\n",
"print(f\"complex relative permittivity at freqs_hz = \\n\\t {lossy_dielectric.eps_model(freqs_hz)}\\n\")\n",
"\n",
"ax = lossy_dielectric_from_nk.plot(freqs_hz)"
],
"id": "4f31bca9"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Geometries\n",
"\n",
"The new version of Tidy3D introduces\n",
"[Geometry](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Geometry.html#tidy3d.Geometry)\n",
"objects, which do the heavy lifting for any components with some spatial\n",
"extent.\n",
"\n",
"There are 5 primitive and 2 compound geometry classes:\n",
"\n",
"- [Box](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html)\n",
"\n",
"- [Sphere](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Sphere.html)\n",
"\n",
"- [Cylinder](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Cylinder.html)\n",
"\n",
"- [Polyslab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html)\n",
"\n",
"- [TriangleMesh](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.TriangleMesh.html)\n",
"\n",
"- [GeometryGroup](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GeometryGroup.html)\n",
"\n",
"- [ClipOperation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ClipOperation.html)\n",
"\n",
"If your device is defined in GDS format, they may be loaded using the\n",
"`Geometry.from_gds()` static method. Please refer to the [tutorial\n",
"notebook on GDS importing](../notebooks/GDSImport.html) for more\n",
"details.\n",
"\n",
"All\n",
"[Geometry](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Geometry.html)\n",
"objects have many useful methods for checking intersections, bounds, and\n",
"other properties."
],
"id": "5daeda8e-8a88-40b3-8d7e-e83d263a5425"
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"True\n",
"False\n",
"True\n",
"((-1.0, -1.0, -1.0), (1.0, 1.0, 1.0))"
]
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"╭───────────────────────────────── <class 'tidy3d.components.geometry.base.Box'> ─────────────────────────────────╮\n",
"│ class Box(*, attrs: dict = <factory>, type: Literal['Box'] = 'Box', center: │\n",
"│ typing.Annotated[tuple[typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49e520>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49e840>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4d6e90>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49e520>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49e840>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4d6e90>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49e520>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49e840>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4d6e90>]], WithJsonSchema(json_schema={'type': 'array', 'minItems': 3, 'maxItems': 3, │\n",
"│ 'prefixItems': [{'type': 'number'}, {'type': 'number'}, {'type': 'number'}]}, mode=None)] = (0.0, 0.0, 0.0), │\n",
"│ size: typing.Annotated[tuple[typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49f060>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49f100>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4df450>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49f060>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49f100>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4df450>], typing.Annotated[object, BeforeValidator(func=<function │\n",
"│ traced_alias.<locals>._validate_box_or_container at 0x7f980f49f060>, json_schema_input_type=PydanticUndefined), │\n",
"│ PlainSerializer(func=<function traced_alias.<locals>._serialize_traced at 0x7f980f49f100>, │\n",
"│ return_type=PydanticUndefined, when_used='json'), <tidy3d.components.autograd.types._TracedAliasJsonSchema │\n",
"│ object at 0x7f980f4df450>]], WithJsonSchema(json_schema={'type': 'array', 'minItems': 3, 'maxItems': 3, │\n",
"│ 'prefixItems': [{'minimum': 0, 'type': 'number'}, {'minimum': 0, 'type': 'number'}, {'minimum': 0, 'type': │\n",
"│ 'number'}]}, mode=None)]) -> None: │\n",
"│ │\n",
"│ Rectangular prism. │\n",
"│ Also base class for :class:`.Simulation`, :class:`Monitor`, and :class:`Source`. │\n",
"│ │\n",
"│ bounding_box = <property object at 0x7f980f34bf60> │\n",
"│ bounds = <property object at 0x7f980f14cae0> │\n",
"│ geometry = <property object at 0x7f980f14c950> │\n",
"│ model_computed_fields = {} │\n",
"│ model_config = { │\n",
"│ 'arbitrary_types_allowed': True, │\n",
"│ 'defer_build': True, │\n",
"│ 'validate_default': True, │\n",
"│ 'populate_by_name': True, │\n",
"│ 'ser_json_inf_nan': 'strings', │\n",
"│ 'extra': 'forbid', │\n",
"│ 'frozen': True, │\n",
"│ 'validate_by_alias': True, │\n",
"│ 'validate_by_name': True │\n",
"│ } │\n",
"│ model_extra = <property object at 0x7f980fd21fd0> │\n",
"│ model_fields = { │\n",
"│ 'attrs': FieldInfo( │\n",
"│ annotation=dict, │\n",
"│ required=False, │\n",
"│ default_factory=dict, │\n",
"│ title='Attributes', │\n",
"│ description=\"Dictionary storing arbitrary metadata for a Tidy3D object. This │\n",
"│ dictionary can be freely used by the user for storing data without affecting the │\n",
"│ operation of Tidy3D as it is not used internally. Note that, unlike regular Tidy3D │\n",
"│ fields, ``attrs`` are mutable. For example, the following is allowed for setting an │\n",
"│ ``attr`` ``obj.attrs['foo'] = bar``. Also note that Tidy3D will raise a ``TypeError`` │\n",
"│ if ``attrs`` contain objects that can not be serialized. One can check if ``attrs`` are │\n",
"│ serializable by calling ``obj.model_dump_json()``.\" │\n",
"│ ), │\n",
"│ 'type': FieldInfo(annotation=Literal['Box'], required=False, default='Box'), │\n",
"│ 'center': FieldInfo( │\n",
"│ annotation=tuple[Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema]], │\n",
"│ required=False, │\n",
"│ default=(0.0, 0.0, 0.0), │\n",
"│ title='Center', │\n",
"│ description='Center of object in x, y, and z.', │\n",
"│ json_schema_extra={'units': 'um'}, │\n",
"│ metadata=[ │\n",
"│ WithJsonSchema( │\n",
"│ json_schema={ │\n",
"│ 'type': 'array', │\n",
"│ 'minItems': 3, │\n",
"│ 'maxItems': 3, │\n",
"│ 'prefixItems': [ │\n",
"│ {'type': 'number'}, │\n",
"│ {'type': 'number'}, │\n",
"│ {'type': 'number'} │\n",
"│ ] │\n",
"│ }, │\n",
"│ mode=None │\n",
"│ ) │\n",
"│ ] │\n",
"│ ), │\n",
"│ 'size': FieldInfo( │\n",
"│ annotation=tuple[Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema], Annotated[object, BeforeValidator, PlainSerializer, │\n",
"│ _TracedAliasJsonSchema]], │\n",
"│ required=True, │\n",
"│ title='Size', │\n",
"│ description='Size in x, y, and z directions.', │\n",
"│ json_schema_extra={'units': 'um'}, │\n",
"│ metadata=[ │\n",
"│ WithJsonSchema( │\n",
"│ json_schema={ │\n",
"│ 'type': 'array', │\n",
"│ 'minItems': 3, │\n",
"│ 'maxItems': 3, │\n",
"│ 'prefixItems': [ │\n",
"│ {'minimum': 0, 'type': 'number'}, │\n",
"│ {'minimum': 0, 'type': 'number'}, │\n",
"│ {'minimum': 0, 'type': 'number'} │\n",
"│ ] │\n",
"│ }, │\n",
"│ mode=None │\n",
"│ ) │\n",
"│ ] │\n",
"│ ) │\n",
"│ } │\n",
"│ model_fields_set = <property object at 0x7f980fd22020> │\n",
"│ plot_params = <property object at 0x7f980f36c8b0> │\n",
"│ zero_dims = <property object at 0x7f980f14cb30> │\n",
"╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
""
]
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n",
"False\n",
"[False False True False False]"
]
},
{
"output_type": "display_data",
"metadata": {},
"data": {
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u3bo6j7kyExQ/pKNie6bLcDLUF4g0fq9PlavyVL58h3wlVXUeGzFi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\ngg==\n"
}
}
],
"source": [
"s1 = td.Sphere(radius=1, center=(0, 0, 0))\n",
"s2 = td.Box(center=(1, 1, 1), size=(1, 1, 1))\n",
"\n",
"# do two geometric objects intersect?\n",
"print(s1.intersects(s2))\n",
"\n",
"# does the object intersect a plane?\n",
"print(s1.intersects_plane(z=10))\n",
"\n",
"# get polygons that intersect sphere at plane x=0\n",
"print(s1.intersects_plane(x=0))\n",
"\n",
"# get bounds (rmin, rmax) of geometry\n",
"print(s1.bounds)\n",
"\n",
"# get td.Box() for bounding box of geometry\n",
"print(s1.bounding_box.help())\n",
"\n",
"# evaluate whether point(s) are inside of geometry\n",
"print(s1.inside(x=0, y=1, z=1))\n",
"print(s1.inside(x=np.linspace(-1, 1, 5), y=np.zeros(5), z=np.ones(5)))\n",
"\n",
"# plot the geometry at a cross sectional plane\n",
"ax = s1.plot(y=0)"
],
"id": "7fe85af5"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note, because simulations, monitors, and sources all are defined\n",
"spatially, they inherit from\n",
"[Box](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html)\n",
"and contain these methods as well, which can come in handy when doing\n",
"validation.\n",
"\n",
"### Structures\n",
"\n",
"A\n",
"[Structure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html)\n",
"is a component that simply contains a\n",
"[Geometry](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Geometry.html#tidy3d.Geometry)\n",
"and a\n",
"[Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html#tidy3d.Medium).\n",
"It therefore defines an object in the simulation with some geometry and\n",
"optical properties, such as a waveguide or slab."
],
"id": "d4b1d351-d86a-4891-9d4b-6dc24b6fbde7"
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"dielectric_box = td.Structure(\n",
" geometry=td.Box(center=(0, 0, 0), size=(1, 1, 1)),\n",
" medium=td.Medium(permittivity=2.0),\n",
")"
],
"id": "33695aea"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sources\n",
"\n",
"Sources define how the electromagnetic current is injected into the\n",
"simulation to give rise to fields. There are several types of sources,\n",
"each generally defines some spatial or polarization properties of the\n",
"source and also contain a `source_time` field, which defines the time\n",
"dependence. As an example"
],
"id": "9d444fbb-93e2-4c5e-84a0-daabfea258a5"
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:05:40 UTC WARNING: The default value of 'current_amplitude_definition' for \n",
" 'UniformCurrentSource' will change from 'density' to 'total' in a \n",
" future release. To avoid this warning and ensure consistent \n",
" behavior, please explicitly set \n",
" current_amplitude_definition='density' or \n",
" current_amplitude_definition='total' when creating the source. \n",
""
]
}
}
],
"source": [
"# define time dependence\n",
"gaussian = td.GaussianPulse(freq0=150e12, fwidth=10e12)\n",
"\n",
"# z polarized dipole at origin\n",
"dipole = td.UniformCurrentSource(\n",
" center=(0, 0, 0), size=(0, 0, 0), source_time=gaussian, polarization=\"Ez\"\n",
")\n",
"\n",
"# z polarized plane wave propagating in -x\n",
"plane_wave = td.PlaneWave(\n",
" center=(0, 0, 0),\n",
" size=(0, td.inf, td.inf),\n",
" source_time=gaussian,\n",
" pol_angle=np.pi / 2,\n",
" direction=\"-\",\n",
")"
],
"id": "440405c1"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitors\n",
"\n",
"Since it would be impractical to return the entire time series of all of\n",
"the field data solved for by FDTD, “monitors” are specifications that\n",
"tell Tidy3D what data should be returned by the solver.\n",
"\n",
"The following monitors measure their corresponding values in the\n",
"frequency-domain\n",
"\n",
"- [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)\n",
"\n",
"- [FluxMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html)\n",
"\n",
"- [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
"\n",
"And the following measure their values in the time-domain\n",
"\n",
"- [FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html)\n",
"\n",
"- [FluxTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxTimeMonitor.html)\n",
"\n",
"There are also various monitors for field projections, diffraction\n",
"efficiency, and other values. The [API\n",
"reference](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/monitors.html)\n",
"lists each of the monitor types, for reference.\n",
"\n",
"Note: all monitors must be *named* (have a `name: str` field supplied).\n",
"The data returned by the server will be indexed by the monitor name and\n",
"accessed by the user using that name."
],
"id": "a83dd835-124f-445b-9673-b5dc6201356f"
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# measures Ex, Ey, Hz fields on the plane at frequency 150THz\n",
"mon1 = td.FieldMonitor(\n",
" center=(1, 0, 0.2),\n",
" size=(td.inf, td.inf, 0),\n",
" fields=[\"Ex\", \"Ey\", \"Hz\"],\n",
" freqs=[150e12],\n",
" name=\"fields_at_150THz\",\n",
")\n",
"\n",
"# measures time dependence of flux through a plane every 5 time steps between a window of (start, stop)\n",
"mon2 = td.FluxTimeMonitor(\n",
" center=(1, 0, 0.2),\n",
" size=(td.inf, td.inf, 0),\n",
" start=1e-13,\n",
" stop=3e-13,\n",
" interval=5,\n",
" name=\"flux_over_time\",\n",
")"
],
"id": "2eb929f1"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modes\n",
"\n",
"Modes (for sources and monitors) are specified using a\n",
"[ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html)\n",
"object. This component contains all of the specifications needed for the\n",
"mode solver to know which modes to inject or measure in the\n",
"[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)\n",
"and\n",
"[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
"objects, such as the number of modes, mode index, or a target mode\n",
"effective index.\n",
"\n",
"For example:"
],
"id": "bb43d2ca-6adc-4266-a6f2-c95d157e97a7"
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# default mode solver spec (returns first mode)\n",
"fundamental = td.ModeSpec()\n",
"\n",
"# tell the mode solver to return 4 modes\n",
"first_4_modes = td.ModeSpec(num_modes=4)\n",
"\n",
"# have mode solver return 4 modes around the target effective index\n",
"complicated = td.ModeSpec(num_modes=4, target_neff=2.0)"
],
"id": "05921df5"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the mode specifications, we can make modal sources or monitors\n",
"similar to before."
],
"id": "2bf9d4fa-4084-481e-b2d7-6a856e9ecd2f"
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# inject the fundamental mode\n",
"mode_source = td.ModeSource(\n",
" center=(0, 0, -1),\n",
" size=(td.inf, td.inf, 0),\n",
" source_time=gaussian,\n",
" mode_spec=fundamental,\n",
" mode_index=0,\n",
" direction=\"+\",\n",
")\n",
"\n",
"# do modal decomposition and return amplitude data for the first 4 modes\n",
"mode_mon = td.ModeMonitor(\n",
" center=(0, 0, +1),\n",
" size=(td.inf, td.inf, 0),\n",
" freqs=freqs_hz,\n",
" mode_spec=first_4_modes,\n",
" name=\"modes\",\n",
")"
],
"id": "ab368c4a"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The\n",
"[ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html)\n",
"is designed to help users come up with the correct\n",
"[ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html)\n",
"for their problem, at which point it can be used directly in\n",
"[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)\n",
"and\n",
"[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
"objects without setting it explicitly using a\n",
"[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
"method. For more details, refer to the [mode solver tutorial\n",
"notebook](../notebooks/ModeSolver.html).\n",
"\n",
"### Absorbing Boundaries\n",
"\n",
"Absorbing boundaries are defined as one of three types of boundaries\n",
"\n",
"- [td.PML()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PML.html)\n",
" defines a standard PML, with an adjustable number of layers.\n",
"\n",
"- [td.StablePML()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.StablePML.html)\n",
" defines a PML with a ‘stable’ profile, which can reduce divergence at\n",
" the expense of more layers.\n",
"\n",
"- [td.Absorber()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html)\n",
" defines adiabatically increasing conductivity values at the edges of\n",
" the simulation, which can dramatically improve stability of\n",
" simulations involving dispersive materials, again at the expense of\n",
" more layers.\n",
"\n",
"As before, these layers *add* to the simulation size defined in\n",
"[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html).\n",
"\n",
"Also as before, it is important to extend any structures all the way\n",
"through the PML if they are meant to be touching the simulation boundary\n",
"on that side.\n",
"\n",
"To define a sequence of PML layers on the x, y, z sides of the\n",
"simulation, one may use the convenience functions provided in\n",
"[td.BoundarySpec()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.BoundarySpec.html)\n",
"and\n",
"[td.Boundary()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Boundary.html),\n",
"as shown below.\n",
"\n",
"Default PML boundaries are always used on each side of the simulation,\n",
"so if a boundary is not specified along a dimension, the simulation will\n",
"have PML on that boundary."
],
"id": "4c5a1819-58bf-4baa-80fd-09020ba0e439"
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# standard absorber on x, PML with 20 layers on y, no PML on z (periodic BC)\n",
"boundary_spec = td.BoundarySpec(\n",
" x=td.Boundary.absorber(), y=td.Boundary.pml(num_layers=20), z=td.Boundary.periodic()\n",
")"
],
"id": "c5431c43"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulations\n",
"\n",
"Finally, the\n",
"[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
"objects contain all of the specifications needed to run the Tidy3D\n",
"simulation and contain all of the previous components.\n",
"\n",
"A few notes:\n",
"\n",
"- [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
" accepts an optional `medium` parameter, specifying the background\n",
" medium (air by default).\n",
"\n",
"- Each simulation must define the discretization using a `grid_spec`,\n",
" which specifies how the grid is to be generated along each of the\n",
" three directions. These are discussed in more detail\n",
" [here](../notebooks/AutoGrid.html)."
],
"id": "dad086f8-f027-4e44-a582-ea7769142876"
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" WARNING: The default value of 'current_amplitude_definition' for \n",
" 'UniformCurrentSource' will change from 'density' to 'total' in a \n",
" future release. To avoid this warning and ensure consistent \n",
" behavior, please explicitly set \n",
" current_amplitude_definition='density' or \n",
" current_amplitude_definition='total' when creating the source. \n",
""
]
}
}
],
"source": [
"sim = td.Simulation(\n",
" size=(4, 4, 4),\n",
" grid_spec=td.GridSpec.uniform(dl=0.2),\n",
" run_time=1e-12,\n",
" boundary_spec=td.BoundarySpec(\n",
" x=td.Boundary.periodic(), y=td.Boundary.pml(), z=td.Boundary.periodic()\n",
" ),\n",
" structures=[dielectric_box],\n",
" sources=[dipole],\n",
" monitors=[mon1, mon2],\n",
")"
],
"id": "869b774d"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A defined\n",
"[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
"also provides several useful methods in addition to the ones inherited\n",
"from\n",
"[Box](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html)."
],
"id": "7503271c-9a7f-49cd-b53d-438be5be521c"
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
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Watu2rZSeni6lpqZKrVu3li688ELp5ptvlj777DOnF3oqmYe3lCzLbDZLixYt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\njCFDhmDOnDno06eP5mVEE9Hf/8wf3EFrb/k5YUtE0S7a+39Rx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/6PiEhMovf/euYPx5OV\nRH/9veGELWmBE7akCvfbFKrdaYd7Z631oMX8YucnIqIaFovF6CbUwvGP+QFx8xNx/0/f978Rt4H2\nhv0fEZGY9O7/w+VkJZno458exz/DOb8nnLAlLXDCllRRXV2NuLg45Xe1Ou1wH6xkWg1azC92fiIi\n+kdsbGytCYJwwPGP+QFx85PYuP8n9vtfr/zhdrCeiEh0eo5/4XSykozjv3b5w/HK4oZwwpa0YG54\nESL/xMbGIiUlRfm9uLgYdrs96PIiZbCSJSYmMj/zK7+Hmp+IiCIDxz/mFzk/iY37f2K///XIT0RE\n4Yfjn7b5w/1kJdFffyKt8QpbUpVaZ9pEamfN/MwP8EpbIiLRcPxjfkDc/CQ20bd/5hc7PxGRqETv\n/7XMH45XFtflLX+gIvX1l/EKW9ICJ2xJdaEOWpHeWTM/8wOhf2ghIqLIwvGP+QFx85PYRN/+mV/s\n/EREohK9/w/1+F+05TebzbDb7UG//uF+ZbEnnLAlLXDCljQR7KAd6YOVjPmZH+CkLRGRaDj+MT8g\nbn4Sm+jbP/OLnZ+ISFSi9//BHv+L9vzBvP6RcGWxJ5w4JbVxwpY0E2inHS2DlYz5mR/4J3dlZSUS\nEhIMbBEREemB4x/zA+LmJ7GJvv0zf2AHraMtPxGRqDj+BXalabTmt9lsAMR6/XmFLWmBE7akKX8H\n7WjqrN0xP/MD/+SuqqoysDVERKQXjn/MD4ibn8Qm+vbP/P5N2kZrfiIiUYne/3P8T4TL5VJ+FyU/\nJ2xJC2ajG0DRLzExESkpKcrvxcXFsNvtyu/R1lnXxfzM756fiIjEwPGP+UXOT2JraPuX/yaLtu1f\n9Pd/3fzylUayaM9PRCQqT+Ofu2jv/0Uf/wEIn59IDbzClnTh60wrETpr5md+q9VqdDOIiEhnHP+Y\nHxA3P4nN1/ZvNv9z3ni0bv/e8otysJL9HxGRmLzdHtj9b0D09v/erjSt+3M05wfEGf95hS1pwSRJ\nkmR0IyhyFRUVIS0tDVOmTEGXLl3QqlUrn8vX3UF1Fy2dtS/ML27+vLw8WCwWnH766cjMzDS6OURE\nESUrKwu5ubkoLy9HixYtjG5OwEQe/wDm1zq/w+HA1q1bMXfuXBQWFiI1NTWk8oh8cd//a968uV/r\nuE/QAjXbbFxcXK0DmoEoKChAenq67usGu76cX84tCyS/Ee1Wq26n01krt8yf/EblNplMKCsrQ2Ji\nYlBfa7N27VoMHjw44PXUWD9S647UdhtZd6S228i6I7XdoaxvsVhgt9vRuHFjBDMFEEo/bDab6419\ngP/jX6SOffK6dT//yBrKH2mfc7ytG2j+YOqurq6G3W7XdT9I/iwMALt27TLsrorFxcU444wzAID7\ngFGGV9iSruqeaSMT4WAdwPyi54+NZZdLRCQi0cc/5hc7P0WvlJSUBk/YBeqftGCz2ZCRkRH09p+T\nk4Pu3bvrvm6w68v5bTYbkpKSAAT+/jei3WrWLeeW+ZvfqNx5eXkAgL59+wZ1su2MGTMwe/bsgNdT\nY/1IrTtS221k3ZHabiPrjtR2h7J+VlYWsrOzkZqaGtSJr6H0w3a7Hbm5ubXGgEDGv0gd++R1PZ20\n6U9+o8dsteoONH8wdcsnrhqFV9iSFjh7QEREREREREREREREROQHTtiSFjhhS7rydks4+W/RfpUB\n84ud3+l0Gt0EIiIygOjjH/OLnZ/EJvr2z/x2j7dEFCU/EZGoOP4xvwj5OWFLWvB8M3EiDdTtrFNS\nUmrd5724uFj5IvpoxPxi53c6nUF9/xEREUU20cc/5hc7P4lN9O2/bn6XyyV0ftFefyIiUbH/h9D5\n+foThYZX2JIuPHXW7mfTyI9F25k2MuZn/srKSn6HLRGRYDj+Mb/I+UlsvrZ/l8ul/D1at39P+UtL\nS+t9p7VI+dn/ERFFP08nK4nU/9e9s4Ro459o4z+vsCUt8Apb0lxDnXViYmJUn2nD/Mzvnp+IiMTA\n8Y/5Rc5PYmto+5f/Jou27V/0939DB+ujPT8Rkag8jX/uor3/F338B+pfWRzt+eUJW6P+UXTi5V6k\nKX921oF/zqiJtjNtmJ/53fNbLBbjGkNERLrh+Mf8IucnsYm+/TO/5yuL64rW/EREouL4x/y+riyW\nRVt+XmFLWuCELWnG38FKFm2dNvMzv3t+q9XKWyITEQmA4x/zi5yfxCb69s/8YucnIhKV6P1/Q3eW\nqCva8wf7+kciTtiSFjh7QJoItLOWRcugxfzMXze/zWYzrkFERKQLjn/ML3J+Epvo2z/zi52fiEhU\novf//t5Zoq5ozh/K6x9pOGFLWuB32JLqgu2sZZF+T3vmZ/5Q8hMRUWTi+Mf8IucnsYm+/TO/2PmJ\niEQlev/P/IFdWVxX3fxmszmi8hNphVfYkqrUmqyK1DONmJ/5OVlLRCQejn/ML3J+Epvo2z/za5uf\niIjCE8c/sce/YK8sritSX38Zr7AlLXDCllTjdDpVnayKtE5b7ck65hc7PxERRQaOf8wvcn4SG/f/\nxH7/65WfiIjCi+j9v9b55StNRRv/5a+SC/fPP+44YUta4IQtqSImJgYOhwPx8fEA1JusipSdVq0m\n65hf7PxERPQPp9NpdBPq4fjH/CLnJ7Fx/0/f93+40St/uB+0JiISDcc/sT//a5nf5XIpv4drfk84\ncUpq44QtqcJisSg/qz1ZFe6DltaTdcwvdn4iIqpRVVUVVpO2HP+YX+T8RNz/K1Z+1yt/uGD/R0Qk\nJr37/3A7aUev/OF6pakexz9TUlIiavznFbakBU7Ykqq0mqwK1502vSbrmF/s/EREVKOysjIsdto5\n/jG/yPmJ3Im4/Rvx/g+Xg9Z693/hetCaiEg0on/+1TN/OF5pKvrr7w0nbEkLJkmSJKMbQZGrqKgI\naWlpuPfee3HaaaehTZs2mtYXThNkRrSF+SM3f15eHhISEpCRkYHMzEwdWkhEFD2ysrKwd+9e2O12\nWK3WiOr/I7XOcGoL8/uu0+FwYOvWrZg7dy4KCwuRmpqqaXtIbO77f8nJyUGVUVBQgPT0dL+XN5vN\nys8ulyvg9UOpuy6n04m4uDilLXrUbTab4XA4EBcXF3CdodZdd926r4XWdZeVlaFJkyYB1xlq3SaT\nCWVlZUhMTERVVVXA669duxaDBw8OeD011o/UuiO13UbWHantNrLuSG13KOtbLBbY7XY0btwYwUwB\nyP1oMP2/+/rBCGXMDbXuuusGkj/UzxqhPOdq1V2XP21Ru25/6qyurobdbtd1P0j+LAwAhw4dMmzf\nq6ioCG3btgUA7gNGGV5hS6qoqqpCbKz2m1O4nGlj1IFD5hc7PxGRyNxvvyla/y/6+Mf84ZGfyF1V\nVRVSUlLQqlWrgNfNyclB9+7d/V6+7nsPQEDrh1J33TbYbDYkJSUF9f4Ppe7c3FwkJSUBCK7vCbZu\ned22bdsG3f+FWndGRobudefl5QEA+vbtG9TJtjNmzMDs2bMDXk+N9SO17khtt5F1R2q7jaw7Utsd\nyvpZWVnIzs5GamoqWrRoEfD6OTk5tU7QCnQMCnUMkMc+I+ru3r17UJ//Q6k3XOqWBdIGI+qWT1w1\nCq+wJS1wwpZUUV1drVtdRh+0MnqyjvnFzk9EJKrY2FhYrVbld9H6f9HHP+bnpC2FFyP3//S+PXA4\nvP+Nvj2i0fnd28D+j4hIH3a7vdZVjkYc/zLyO03DYfwHuP8TzuM/J2xJC5ywpYhkVKdt9GAlY36x\n8xMRiSo2NhZJSUnC9v+ij3/M7zm/+9XnRNEqXN7/LpfLsM//Rhy0DoeD9UBkHLQlIoom4f75V4+2\niJyfr79/OGFLWuCELUUsvTvtcBmsZMwvdn4iIlGJ3v8zP/MDtfO73yqOKJrJ27/NZgNgzPu/tLRU\nk7r8wf4vvA/aEhFFC9H7/3A5WUnG8T98x39O2JIWOGFLEU2vTjvcBisZ84udn4hIVKL3/8zP/MA/\nuUtLS3mVrQqcTid+/fVX/PTTTzh8+DCOHTsGh8OB5s2b47zzzsO4ceOQmpqqap0bNmzAihUrcODA\nAZjNZnTo0AFjx47Feeedp2sZkUSv2wNHyvuf+Ytr/V0tTqdT1fKIiCJFON1Zwh3HP/0mLSMpP/eB\ntFNVVYXt27cjOzsb2dnZOHbsGABg5MiRuO+++1Sty+Vy4bvvvkNWVhZyc3NhtVrRuXNnXH311ejR\no4eqdUUCTthSxNN60ArXwVrG/GLnJyISlej9P/MzP/BPbvfvd6bALViwAA8++KBy5WZdCxcuxD33\n3IOZM2fioYceCrm+wsJC3Hzzzfjqq6/qPTZ79mxMmDABb775ps9tTo0yIpmWtweOtPe/1vnD5WC9\nTI/8lZWVqpRFRBRJwu3OEnXx87/2+cPpyuK6wvFOQ9F6he3tt9+ODz74ABUVFfUey8jIULWuw4cP\nY8KECfjpp5/qPfb000/jrrvuwksvvYTYWHGmMcVJSlFNq0Er3AdrGfOLnZ+ISFSi9//Mz/xAzcQd\nhebw4cPKZG3fvn0xevRodOvWDVarFdu2bcP8+fNx/PhxTJs2DSdPnsRzzz0XdF0OhwOXX345NmzY\nAJPJhJtuugmjRo1CdXU1Pv30U3zyySf48MMPUVZWhhUrVtQ6cKZmGZGO73/98ofTwXqZXvmJiETB\n8S+8T1aS8fNPeN1pKFonbHfv3o3q6mqcc8456Nu3L8455xzccccdqtdTUlKCESNGICcnB7Gxsbjj\njjtw0WOF1qcAAFjYSURBVEUXwW634/3338d3332HefPmoaKiAgsXLlS9/nDFCVuKGmoPWpEyWMmY\nX+z8RESiEr3/Z37mdzqdYTmhEklMJhMyMzPx1FNPoXfv3rUeu+KKK3DXXXdhyJAh2L59O55//nlc\nc8019Zbz12uvvYYNGzYAAF5//XXceeedymPXXXcdZs6ciSeffBJfffUVPvjgA9x4442alBEN+P5n\nfkC7/EREomD/HxknK8k4/ofPnYaidcL2rbfeQrt27RAfHw8AqKio0GTCdubMmcjJyQEALF++HJdf\nfrny2M0334zJkyfjzTffxJtvvokJEybg4osvVr0N4Sj6TrUloSUmJiIlJUX5vbi4GHa7PeByIm2w\nkjG/2PmJiEQlev/P/GLnT0hI4O07Q3T//ffjq6++8joJm56ejv/+978AAEmS8MEHHwRVT3V1tXJ1\nbv/+/WtNtMoeffRRdOnSBQDw7LPPalJGNBH9/c/82uTnbeaJSBTs/5k/Eq4srqtufqPIE7ZG/dNK\n165dlclarRQXFyv7V+PHj681WSt75ZVXkJaWBiD692ncccKWok6og1akDtYy5hc7PxGRqETv/5lf\n7PxVVVVGNyGipaamNrjM+eefj7i4OADAnj17gqpn/fr1OHHiBABg4sSJHpeJiYnBTTfdpNSzY8cO\n1cuINqK//5lf/fwifU8aEYmL/X/05Q+Ep/yRJDExEcnJyYa2IVonbPXw1VdfKScde9unadSoEa6+\n+moAwNq1a3Hy5End2mckTthSVAp20I70wVrG/GLnJyISlej9P/OLnZ+05XQ64XQ6ASDo74Rdt26d\n8vOFF17odbmLLrpI+Xn9+vWqlxGNRH//M7/Y+YmIAhUt/Z/o/X/d/GazWaj8vNNQ5JL3acxmMwYN\nGuR1OXmfprq6Ghs3btSlbUbjhC1FrUAH7WgZrGTML3Z+IiJRid7/M7/Y+Uk733//PVwuFwCgR48e\nQZUhf0dTTEwMOnXq5HW5rl27Kj/v2rVL9TKilejvf+YP7EqjaMtPROSvaOv/OP6Jnd/IOw3xCtvg\nyfs0p512Gho3bux1ORH3aXifF4pq/n4Re7QNVjLmFzs/EZGoRO//mV/s/KQ+l8uFJ598EkDNgZkJ\nEyYEVU5ubi4AoGXLloiJifG6XLNmzRAXFweHw4EjR46oXkZDioqKvD5WWFio/OxyueBwOAIqG6g5\nQz6Y9fxZ32KxICkpCaWlpQBq2ut0OpGQkFBr3fLycmUZAEhOTobFYmmwXaG0XY91veWvu34g+bV8\nvdRet25+k8mEkpIS5fWXNZTf5XLB5XKhsrLS5/vBm4qKiqDWU2P9SK07UtttZN2R2m4j647Udoey\nfmVlpdKnBTP+RcoY4Gn88/Q5xd/8Ro33wa4v5y8rK4PL5ar3+UfWUP5Iyy3f/QaAcmKlnkpKSgyb\nOC0pKVF+bugkNX++/kVv8j5N69atfS7n/nig+zSRyiRJkmR0IyhyFRUVIS0tDVOmTEGXLl3QqlUr\no5vkka8DciIcrGN+4/Pn5eUhISEBGRkZyMzMVL18IqJolpWVhdzcXJSXl6NFixZ+rxcO/b+RmF+M\n/A6HA1u3bsXcuXNRWFgYljvkke6ZZ57BY489BgC45ZZbsHjx4qDKOeecc/D777+jc+fO+PPPP30u\nm5qaiuLiYgwfPhzffPONqmU0xJ8DT1OmTEFSUlKtA2XhxGKxwGq1Kr9XVlYqV2D4eixaMH9o+S0W\nC8rKypCbm4usrCx9Gk1EpIHMzExkZGQgKSkJJpOJ/T/ze3wsklksFrzwwgvYt28f2rdvr3l98lxI\nJAl1+q+iokKZ/L/tttuwaNGikNvUrFkznDx5EhdffDF++OEHr8vZ7XYkJSUBAG699Va8/fbbIdcd\n7niFLQnB25UWdX+O5IN1vjC/2PmJiEQlev/P/GLnJ3V88cUXeOKJJwAAHTt2xKuvvhp0WfJBsdjY\nhnfDLRYLANS70kCNMtSwYMEC1cskIiIidfGkEyIKR/7u08j7M4A2+zThiBO2JAxfB+2A6D9Yx/xi\n5yciEpXo/T/zi52fQvPTTz/h2muvhcvlQtOmTfHVV1/5/I6lhjRq1AhAzVnqDSkvLwdQ/1beapTR\nEPfbHtfldDpx5MgRNGnSBMnJyTCbzQGVHYri4mJ06NABALBv375a39dGpBZuZ6QHbmekB25npAcj\ntzOXy6Xc4rlNmza61NmkSROfn5X1VlxcrPtncjU0atQIJSUlDe7TyPszQOD7NJGKE7YklLoH7WSi\nHKxjfrHzExGJSvT+n/nFzi+qjRs3YubMmR4f69KlS4NXym7duhWZmZkoLy9HkyZN8PXXX6Nbt24h\ntUk+gNXQ90xVVVXBbrcDqDkopHYZDWnottrNmjULqDwtpKSk8PbfpDluZ6QHbmekB25npAcjtrP0\n9HRd6zObzWH1XgqntgQiJSUFx44da3Cfxv3xQPdpIhUnbImIiIiIiCiq5OXl4euvv/b42MmTJ32u\nu2vXLlxyySUoKSlBYmIisrKy0Lt375Db1KlTJ3z77bcoKipCaWkpkpOTPS53+PBh5bumOnfurHoZ\nRERERERERunUqRNycnJw8OBBn8u5Py7KPg0nbH2orKzE1q1bceTIERw7dgySJKFly5bo37+/JpfZ\nl5aW4vvvv0dubi6sVis6d+6MCy+8EDExMbqWEc3sdrvHMzfkv0X7VRbML3Z+IiJRid7/M7/Y+UV1\n/vnnY9WqVR4f83V29t9//41hw4bh5MmTiI+PxxdffIGBAweq0qaePXsqP//2228YPHiwx+V+/fVX\nj+uoVQYREREREZFRevbsiS+//BKlpaX466+/0KlTJ4/LibhPwwlbD5YtW4b58+fjl19+8Xof7WHD\nhmHu3Lk444wzQq7P4XDgySefxMsvv1yvvlatWmHevHkYN26c5mVEu7oH6+reTizaD9oxv9j5iYhE\nJXr/z/xi5xdZ8+bNMXLkyIDWOXToEIYOHYpjx44hLi4On332GYYMGaJamzIzM5Wfs7KyvE62ZmVl\nAQDi4uIwfPhw1csgIiIiIiIySmZmJp599lkANfst9957r8fl5H2a5s2b47zzztOtfUaKrG8j1smq\nVauwdu1aVFRUoFGjRhgxYgSmTp2Ku+66C3379gUAfPfddzj33HPx448/hlSXy+XChAkTMGvWLFRU\nVOCss87C1KlTccsttyA9PR3Hjh3D+PHjsWjRIk3LiHaeDtYlJiYiMTGx1pehFxcXK9/1FE2YX+z8\nRESiEr3/Z36x81Ngjh8/jqFDh+LgwYOIjY3F//73v4AnfBvSpk0bZQL47bffRn5+fr1l9u/fj48/\n/hgAcOWVV9a75bEaZRARERERERmlf//+6NatGwDg1VdfRXl5eb1lNm/ejDVr1gAAbrzxRpjNYkxl\nipEyCB07dsSCBQtw8uRJrF69Gq+99hrmzZuHTZs24YsvvkB8fDzKy8sxYcIElJWVBV3PG2+8gU8/\n/RQAMH36dGzfvh2vvfYaFi9ejL/++gvnn38+AOBf//oX/vrrL83KiGbeDtbJov2gHfOLnZ+ISFSi\n9//ML3Z+CkxhYSGGDx+Ov/76CzExMVi6dCkuv/zygMt5+OGHMXLkSIwcOdLrnZpmzZqFmJgYFBYW\nYuzYsTh+/Ljy2IEDB3DllVeivLwcVqsVTz31lGZlEBERERERaWHSpEkYOXIkxo8f7/Fxk8mE2bNn\nAwD27duH6667DiUlJcrjf/zxB66++mpIkoTU1FTMmDFDl3aHA5MkSZLRjQg3v/zyC3r37o24uDiv\ny7zwwgt46KGHAADvvvsubrrppoDrcTgcaNu2LfLy8nDeeedh06ZNMJlMtZY5ePAgunTpAofDgRtv\nvBFLlixRvYxQFBUVIS0tDVOmTEGXLl3QqlUr1cpWQ0MH64JdNlIwf/jkz8vLQ0JCAjIyMmrdyo6I\niBqWlZWF3NxclJeXo0WLFg0uH079vxGYX6z8DocDW7duxdy5c1FYWIjU1FSjmxRxJk+ejDfffBMA\n0KxZM/Tu3dvn8m3atFGWdzds2DB8//33AICysjIkJSV5XP+NN97A1KlTIUkSrFYrevfujerqavz2\n229wOp2IiYnBBx98gGuuucZrG9QoI9LI+54AuK2TZridkR64nZEeuJ2RHridRZ+vv/4aL7/8svK7\ny+XCt99+CwDIyMhAjx49lMcSEhLw2Wef1SujW7du2LNnD5o0aVJrf7uuRx99VLk1clJSEs4++2zY\n7Xb8/vvvkCQJ8fHxWLVqldevgYlG/A5bD/r379/gMuPHj1cmbH/99degJmx/+OEH5OXlAQDuuuuu\nehOtANCuXTtcdtllWL58OT777DNUVFQgPj5e1TKiVaAH4OTHouU7zZhf7PxERKISvf9nfrHzU3BO\nnTql/Jyfn4+vv/7a5/Jdu3YNqb4777wTnTp1wsMPP4wtW7Zg48aNymPnn38+XnjhBQwYMEDzMiJN\namoqeL45aY3bGemB2xnpgdsZ6YHbWfQ5fPiw1/2h3Nxc5ObmKr+Hut/8zDPP4KyzzsLMmTORk5OD\nDRs2AADMZjOGDRuGl19+udYEsQg4YRsk9wlPT/fY9sc333yj/Dx8+HCvyw0fPhzLly+HzWbDxo0b\ncfHFF6taRjQK9mqJaDlox/xi5yciEpXo/T/zi52fgjdt2jTccMMNfi/v7crZ2bNn4+TJkwBqzjb3\nZfjw4Rg+fDhyc3Nx8OBBmEwmtG/fPqA7FqlRBhERERERkWzkyJFYtWqVX8vGxnqeXly0aBFsNhss\nFkuDZVxzzTW45pprsH//fhw+fBhWqxUdO3ZE06ZNA2p3tOCEbZA2b96s/Hz66acHVcaOHTsAAE2b\nNvW5U92rV69a67hPtqpRRrQJ9dZ2kX7QjvnFzk9EJCrR+3/mFzu/PzvC5F3Pnj3Rs2fPkMs599xz\nA14nIyMDGRkZIdWrRhlERERERERq7FsMGjQo4HXat2+P9u3bh1RvNOCEbZBeeukl5efLLrssq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IiIiIiIiIVLF9+3bMmjULADB37lykpKRoUk9iYqLyc0VFhc9l5VvtAUBSUpKqZVB4KSsrw6hR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\nzZ8/XwIgdez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}
}
],
"source": [
"# get permittivity at yee cell centers in a volume defined by a box.\n",
"sim.epsilon(td.Box(size=(1, 0, 1)), \"centers\")\n",
"\n",
"# get a `td.Grid` containing all information about spatial locations in the FDTD / yee grid\n",
"print(sim.grid.centers)\n",
"\n",
"# plot the simulation cross section\n",
"f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
"\n",
"# plot the structures, PML, sources, mediums\n",
"ax1 = sim.plot(x=0, ax=ax1)\n",
"\n",
"# same thing but plot structure in grayscale using permittivity value\n",
"ax1 = sim.plot_eps(x=0, ax=ax2)\n",
"\n",
"# add the FDTD grid boundaries\n",
"ax2 = sim.plot_grid(x=0, ax=ax2)"
],
"id": "dac4c54d"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulation Submission\n",
"\n",
"Here we discuss the process for submitting, managing, monitoring, and\n",
"loading simulations from our server.\n",
"\n",
"Topics covered will include:\n",
"\n",
"- [tidy3d.web](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/submit_simulations.html)\n",
" functions.\n",
"\n",
"- working with\n",
" [tidy3d.web.Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.Job.html#tidy3d.web.Job)\n",
" and\n",
" [tidy3d.web.Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.Batch.html#tidy3d.web.Batch)\n",
" convenience containers."
],
"id": "d635d20c-fcf9-4757-b262-cf00c2260156"
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import tidy3d.web as web"
],
"id": "0d8f4c80"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Web interface\n",
"\n",
"The web interface provides a number of basic functions, but usually, the\n",
"most convenient way to run a single simulation in one line is with\n",
"[sim_data =\n",
"web.run(sim)](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.run.html#tidy3d.web.run),\n",
"which simply performs all of the necessary steps under the hood.\n",
"\n",
"The output of the simulation is a separate data object called a\n",
"[SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html),\n",
"which provides an interface for post processing and visualizing the\n",
"data."
],
"id": "42460fbd-b146-4573-b329-15f7c38a89a0"
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:05:45 UTC Created task 'web_demo' with resource_id \n",
" 'fdve-888cb136-8df5-41c1-87c4-63311dc84842' and task_type 'FDTD'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-888cb136-8df\n",
" 5-41c1-87c4-63311dc84842'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Task folder: 'default'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:05:46 UTC Estimated FlexCredit cost: 0.025. This assumes the FDTD solver runs\n",
" for the full simulation time; if early shutoff is reached, the \n",
" billed cost can be lower. Use 'web.real_cost(task_id)' to get the \n",
" billed FlexCredit cost after a simulation run. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:05:48 UTC status = queued \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" To cancel the simulation, use 'web.abort(task_id)' or \n",
" 'web.delete(task_id)' or abort/delete the task in the web UI. \n",
" Terminating the Python script will not stop the job running on the \n",
" cloud. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:06:00 UTC status = preprocess \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:06:05 UTC starting up solver \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" running solver \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:06:06 UTC status = success \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" View simulation result at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-888cb136-8df\n",
" 5-41c1-87c4-63311dc84842'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"08:06:07 UTC Loading results from data/data.hdf5 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" WARNING: Simulation final field decay value of 0.023 is greater \n",
" than the simulation shutoff threshold of 1e-05. Consider running \n",
" the simulation again with a larger 'run_time' duration for more \n",
" accurate results. \n",
""
]
}
}
],
"source": [
"sim_data = web.run(sim, task_name=\"web_demo\", path=\"data/data.hdf5\", verbose=True)"
],
"id": "ca5906b8"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Note: the warning here tells us that we should probably increase the\n",
"> `run_time` of our `Simulation` to ensure the fields properly decay.\n",
"> Since this notebook is just a demonstration and we don’t care about\n",
"> the results, we can ignore it in this case.\n",
"\n",
"### Containers and Batching\n",
"\n",
"Since `Tidy3D` version `2.10`,\n",
"[web.run](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.run.html#tidy3d.web.run)\n",
"is also capable of running any nested combination of lists, tuples, and\n",
"dictionaries of simulations and returning them in the same structure.\n",
"The legacy function `web.run_async` will be deprecated.\n",
"\n",
"The convenience containers\n",
"[Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.Job.html#tidy3d.web.Job)\n",
"and\n",
"[Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.Batch.html#tidy3d.web.Batch)\n",
"allow one to manage single and multiple tasks in a more “object\n",
"oriented” fashion.\n",
"\n",
"They follow the same basic API as the `web.` functions, except\n",
"[Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.Batch.html#tidy3d.web.Batch)\n",
"objects accept a dictionary of simulations and return generators that\n",
"can be iterated through to give\n",
"[SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html)\n",
"for each task, rather than returning it one by one. This cuts down on\n",
"memory for several large jobs.\n",
"\n",
"While we won’t cover all of the details here, for more information, see\n",
"the [tutorial on the Web\n",
"API](https://www.flexcompute.com/tidy3d/examples/notebooks/WebAPI/) or\n",
"look at the examples in the other notebooks.\n",
"\n",
"## Output Data\n",
"\n",
"Here we will discuss working with output data from a simulation.\n",
"\n",
"Topics covered will include:\n",
"\n",
"- [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html)\n",
" objects.\n",
"\n",
"- Obtaining information about a completed FDTD simulation.\n",
"\n",
"- Selecting data by monitor or field value.\n",
"\n",
"- Post-processing and visualizing data.\n",
"\n",
"### Simulation Data\n",
"\n",
"As mentioned, tidy3d data for a single task is stored in a\n",
"[SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html)\n",
"object.\n",
"\n",
"In addition to storing the data for each of the individual monitors in\n",
"the simulation, it has its own useful functionality."
],
"id": "b7b44e0b-536c-4237-9cec-3b63ae87fc43"
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[08:06:00] USER: Simulation domain Nx, Ny, Nz: [20, 44, 20] \n",
" USER: Applied symmetries: (0, 0, 0) \n",
" USER: Number of computational grid points: 1.8400e+04. \n",
" USER: Subpixel averaging method: SubpixelSpec() \n",
" USER: Number of time steps: 2.6240e+03 \n",
" USER: Automatic shutoff factor: 1.00e-05 \n",
" USER: Time step (s): 3.8131e-16 \n",
" USER: \n",
" \n",
" USER: Compute source modes time (s): 0.2872 \n",
" USER: Rest of setup time (s): 0.2048 \n",
"[08:06:01] USER: Compute monitor modes time (s): 0.0002 \n",
" USER: Solver time (s): 0.1970 \n",
" USER: Time-stepping speed (cells/s): 2.64e+08 \n",
" USER: Loading data for monitor fields_at_150THz \n",
" USER: Loading data for monitor flux_over_time \n",
"[08:06:02] USER: Post-processing time (s): 0.2354 \n",
"\n",
" ====== SOLVER LOG ====== \n",
"\n",
"Processing grid and structures...\n",
"Building FDTD update coefficients...\n",
"Solver setup time (s): 0.0113\n",
"\n",
"Running solver for 2624 time steps...\n",
"- Time step 104 / time 3.97e-14s ( 4 % done), field decay: 1.00e+00\n",
"- Time step 209 / time 7.97e-14s ( 8 % done), field decay: 1.00e+00\n",
"- Time step 314 / time 1.20e-13s ( 12 % done), field decay: 1.00e+00\n",
"- Time step 419 / time 1.60e-13s ( 16 % done), field decay: 6.48e-01\n",
"- Time step 524 / time 2.00e-13s ( 20 % done), field decay: 6.48e-01\n",
"- Time step 629 / time 2.40e-13s ( 24 % done), field decay: 6.48e-01\n",
"- Time step 734 / time 2.80e-13s ( 28 % done), field decay: 6.48e-01\n",
"- Time step 839 / time 3.20e-13s ( 32 % done), field decay: 3.08e-01\n",
"- Time step 944 / time 3.60e-13s ( 36 % done), field decay: 3.08e-01\n",
"- Time step 1049 / time 4.00e-13s ( 40 % done), field decay: 3.08e-01\n",
"- Time step 1154 / time 4.40e-13s ( 44 % done), field decay: 3.08e-01\n",
"- Time step 1259 / time 4.80e-13s ( 48 % done), field decay: 1.81e-01\n",
"- Time step 1364 / time 5.20e-13s ( 52 % done), field decay: 1.81e-01\n",
"- Time step 1469 / time 5.60e-13s ( 56 % done), field decay: 1.81e-01\n",
"- Time step 1574 / time 6.00e-13s ( 60 % done), field decay: 1.81e-01\n",
"- Time step 1679 / time 6.40e-13s ( 64 % done), field decay: 9.43e-02\n",
"- Time step 1784 / time 6.80e-13s ( 68 % done), field decay: 9.43e-02\n",
"- Time step 1889 / time 7.20e-13s ( 72 % done), field decay: 9.43e-02\n",
"- Time step 1994 / time 7.60e-13s ( 76 % done), field decay: 9.43e-02\n",
"- Time step 2099 / time 8.00e-13s ( 80 % done), field decay: 5.14e-02\n",
"- Time step 2204 / time 8.40e-13s ( 84 % done), field decay: 5.14e-02\n",
"- Time step 2309 / time 8.80e-13s ( 88 % done), field decay: 5.14e-02\n",
"- Time step 2414 / time 9.20e-13s ( 92 % done), field decay: 2.30e-02\n",
"- Time step 2519 / time 9.61e-13s ( 96 % done), field decay: 2.30e-02\n",
"- Time step 2623 / time 1.00e-12s (100 % done), field decay: 2.30e-02\n",
"Time-stepping time (s): 0.1839\n",
"Data write time (s): 0.0019\n"
]
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/plain": [
"{'background_structure': Structure(geometry=Box(center=(0.0, 0.0, 0.0), size=(inf, inf, inf)), name=None, background_permittivity=None, background_medium=None, priority=None,medium=Medium()),\n",
" 'simulation_bounds': ((np.float64(-2.0),\n",
" np.float64(-4.3999999999999995),\n",
" np.float64(-2.0)),\n",
" (np.float64(2.0), np.float64(4.399999999999997), np.float64(2.0))),\n",
" 'medium_map': {Medium(): 0, Medium(permittivity=2.0): 1},\n",
" 'mediums': [Medium(), Medium(permittivity=2.0)]}"
]
}
}
],
"source": [
"# print the log, which is stored as an attribute rather than as its own file\n",
"print(sim_data.log)\n",
"\n",
"# get a copy of the original Simulation, so it also doesn't need to be stored separately\n",
"simulation = sim_data.simulation\n",
"\n",
"# access related simulation metadata through the current APIs\n",
"simulation_metadata = {\n",
" \"background_structure\": simulation.scene.background_structure,\n",
" \"simulation_bounds\": simulation.simulation_bounds,\n",
" \"medium_map\": simulation.scene.medium_map,\n",
" \"mediums\": simulation.scene.mediums,\n",
"}\n",
"simulation_metadata"
],
"id": "d6654f0e"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor Data\n",
"\n",
"Data for each monitor is stored as its corresponding MonitorData\n",
"instance.\n",
"\n",
"The monitor data can be found using the `monitor.name` by indexing into\n",
"the\n",
"[SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html)\n",
"using square brackets.\n",
"\n",
"The data are stored as [xarray](http://xarray.pydata.org/en/stable/)\n",
"objects, which means they work similarly to numpy arrays but provide\n",
"many additional useful features. For more details refer to the [tutorial\n",
"on data visualization](../notebooks/VizData.html)."
],
"id": "7e742ddf-5122-4d23-bbf7-7bb3478abe52"
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"image/png": 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BAAAA8EaRCqFmdkWZmVR+QtMlVvcDJKmrl0wqAAAAAMWj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\nG5o7WjVhxFjXzzGLVOa4n2QPT2/p2JX3WoB8bJ1UARV5+oPT6n5pWcf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2nFWS+7HFa5NQsyfgLBvZjPp9w3worppDJ/d42os3dgljrut8UoUrm9qWzPpXL/Xq0sII8q48jp\nZpHqxbyfI/WN8+UWHw/YZ5rOPub9lmOeeX2pZTEZoFxRpAJ2c+2kirrfeLuJeRSp/HRSSfZcqo15\n5si3tu7wfJcWpTFXyzMFMe5n717ICU6PldZJVR3L00nVYN6weY++FBKa3ve4dyaV2UVSV+2+Klc+\ntkyq3aOL3fEeJXK6taqisbxjkBnmu7Ytu5epLnXUL2PqOLNIRXj63sSp6GMWSDt7S8+1e3Hda7rz\nuYdcH/fTsdB3Le4F84xixv38dCqY/HZSmW8U5M2kKnDcz76yn/eon9Q30plbyNrR1uz4u3tr6w41\nd+wpNNRW1Wj/MZNsx0l9v7cu//AXLfv+88ozgXTzovxt27XT9lqh0CKV+bPSWNtgK7yUOu5nRgbk\nFuXNDmZ/nVTWf6PZjSU5jPttWVPQKoVmsckpOF1yyqVyL1KZxe18C0VI0tyZ1iLV4lUv+fp3PGeE\npr992ts0ffx+lg6zZCqph156Ku+5gMFGkQrYLahMKsm+EltGTazasUXZyT5G67VXJ9X1D/1Nh3/z\ndB3+zdP1+//c6v9C4dtAjPt5ZVKZP5/5Mqnsq/vlC043Oqny3LCZXV/5ij35MqnMLpJiQ9Mlh06q\njhZJhd/E5jLzLzI3BbYi1VuKK1KZqwGywt/exWlRALMQW+riC+l0Wj/41y8t+8xunF1d7b5WIu02\nnt91VU5FqsJX9/OzepbJXNygrbvDMUjYviiCdzfmmAKD0808qukey8hnxCJRWzHM6YbWHNc7cJ9p\n2dEoJ+865HhLR0YildTjyxflvR4Mfesc8gpbOgosUnVbi0INNfW2rsZSg9PtK/vteR40Ojyn8/1e\n8jPuN2HEWNsKfxuMFU69bGm1vtHrNvlgfq8KKVLN8PF7Y9bkAyxZUltat+vNHZvyfp6ZTTZn2tsk\nSafOnmfZ/8CLC/KeCxhsFKmA3dw6qYopUpn5QRnjh4/xveqXbYU/l0yqu59/RFfc8Ssld49f/ey+\nPxW0Egj8yTfu1x+dVF6ZVPk6u8zH3X4mM+yZVHk6qYwimRnOacqXSWXvpCpl3G+EZTsz7mfPrPFf\npDJfELd2tqmju1NL1y637C+2k2rKWGvXBON+exfz57u+uk51VdZCrDkOWKhHXnlGz7z+gmXf9z/8\nJcsCBOl0Wu0+ikldcR/jfjWFd1KZN75+xv0ilRHbc9XpTYFCn99mYT7f7zz7yn75O6kke3i6U8ez\nLY/KZdQvo6KiQqe87VjLvodfpjsiDNY5FCv8jMvlsndSOYz7ldhJZRv3y/m7HI1ELUXudDqd9zVU\na6e1sOY07ldRUVFSeLotM8/l95O96yzYIlVVNKbDpr7Vsm/Rau9cqmQqacvHfPv0QyRJ7zWKVI8v\nXxTIitRAf6JIBezmVowqNDjd61x+3jXOmGQWqRzG/V7buEpfvumHln2tnW2+c0fgn1lUMQWRSWUW\nqXI7qczuvHi+IpUZap63k6qw0ZdCQtP7vr53JpV5g17ncFPsl1tw+q5u+wtzv8wVyFo627R49UuW\n8cGpY/e1dUD6td+YfSwF7E3NWz1XDMLQ4tRJVcy4nJtEMqEr7viVZd9JB8/VCQcdaRuX83ND6yeT\nylx9s6O7mHE/f38TbblUDv8G8yY3X6fkqPrhllU7d3W1ez7n7ON++W82JdkWQXEsUpkr++0zI+95\n32kUqf7z6rO+uuQwtJmh6ZIso6J+2LIZa+vthZcSM6nMXEuzKGwuRpJv5M8c93ObSig2l6o3Ec++\noSX1FbzcVuEzx/22u3yv2ro6LN1Z0cqIrWvazVEzZlu2F+cJT1+x6Q1LLt+o+uGaNm6yJOltkw/Q\nvqMmZB/rjvfosVef9XUdwGChSAXs5tZJVUxwultBYPxwf6HpkrTPKOvN7kZj3K+5o1Uf/80ltpsJ\nSZY/tAiGOZ5mKrWTKplK2rJKLMHpBWdSmeN+3j/HhQSBSk6ZVIWN++XLpKrPszKXF7fgdHNlv8LG\n/czg9F16+vVg8qikvv/Wud2T6XTaV3s/hgZ7J1WtrchTSibVbc/eZ+kYqKio0Hc+dJEk99B/L8UF\np/sZ97N2V/jppJLsuVStXfZ/g/lGgTkmaKqsrPTdQdoT77U9H6fuvgHMZ8Lw/EWq//oMTc915IxD\nLaNNO9ua9eK65R6fgb1BEJ1U9udKg22lyu0Bj/uNNopUjUZ4er4ilT043blI5ZRL5Yc5sjdm2EjX\n+wLbuJ/L92qVMSI8Zey+rtEipqNsK/y96Hn8c+ao3/S3Zd/4qqio0KmzT7Q8zsgfyh1FKmC3WCTi\nvD/A4HSz7d+Lbdwvp0iVTCX1uT99zzGbQJKa21m5I2h5x/1KzKQyx/NqYtWWzpoqo6MvX2eXfdzP\nu5OqkCBQyWE0Mc/qgWYRy5ZJ1RtkJtUIy/bO9hbHcYJ8N7G5bMHpnW2BhaZnMPK397J1UtXU2X7G\nix336+jp0lX3/sGy7yNHvVdv3XemJHvHQjFFKn/B6fmLVGYYs3lj7MZ8/u3qtN/Q2p7fPorQfsPT\n127faFnNdNLI8b5/R9k6qYzvQSKZ0Oub11r2HeijSBWLRHXiwUdZ9j388tO+rgnB29C0RRfdcJk+\n9ftvFTRiVqg3nTKpCl7dz77K7ZhhIy2dhU0drXnfDPNiz6Ty7qTK93vJfNzsrszIDQmX+jqM/DC7\nPL1+N/kd9ytm1C9jzrRZlv8eK7es8+xwf261tUj19mmHWLbfe5h15O/hl58u6b8v0N8oUgG7uQen\nF95J5ZVJ5dfohhGWG4P27s7sC4sr7/6dZ0hqE0WqwOX7Y17quJ8ZVGwWdcwiU6GdVPk6nUbVD1e0\nck+hdldXu2OXXvZ6zZB3h2Bly+P5MqmM5ePNvJ5C1FXVWJ47vYm4Ono6i7qJzTDftd2wc4teenOF\nZd8xbzm8iKvdY+rYwlf4S6VSenblUr2w5pWCVjHCwDJH4Zw6qYod9/v9f26xFH9qYtW65AOfyW43\nFrGSlvn7yKmTqqHG7KTyvv50Om0LJ/c7Am/LhHPopDLfKPDTKWmO87iFp5uh6dPG++uikvJnUr2x\nbb3l9+HYxlG2G3o373zbcZZtilSD5ys3/VD/Wjxf/37hUX36D9/pt9/HTp1Uu7raC8oibTP+3g6r\nbVCkMmLrLMz3ZpUXc/EV82fa9nvJofCckUwlbb+3zO7KDHsm1Vpf/y22tPgvUpljym7jfqu2rLVs\nF1Kkaqip16zJMy37njcKUZbHHDqpch05/RBLN9uurnbbG21AOaFIBezmHpxeeCeV27hfIZ1UFRUV\nmmi8A7uxaavuWfIf/XL+TZ6fm1nNDMExizJm0ajUcT+vPKq+r1fouF9hnVSVlZW2GzZzNCeXfSXC\n0jKpnDJ7ilVRUWEb+dvZ3moPVnZ5kevEvEl+fPkiy03BzAn7F1SEdmJb4W9b/iLVJbdcpTN+9jmd\n+pNP6f/d/duSvj76j9PCAPbgdPeisJvtu3bqV/P/Ztn3v+84S5NG7enEdQr9z8c+7udjdb9u704q\nM/OptqrGdg435g2tGaIsFT7uJ/kLT0+lUvrnwgcs+6b7zKOSnDKprDfDttD0ffJ3UWW8Y9bRlo7b\nV9a/rs0O+ZXoX6lUSs+8vjS7/d9Nq/PmOhajs7fb1o0o9RWA/RSfM9psnVR9z0Pzb1gp4em2cT+j\nw9lP4TnDXoCuc33NPnHEWMsbUB09nba4DCf2Lk/3ArrTip1OhTBz3M9vjl3GkdOtI38LXUb+tu/a\naXlTK1oZ0aH7H2Q5JlIZ0XsOOd6yj5E/lDOKVMBubp1UQQanm9kU+ewzyjry959XntGX/nKFZd+4\nxtE64+3vsuyjkyp45rifeXNT6rhfd57xGrNYmq9IZXYq+ekILGR1H3swe6GZVOa4X3BFKsn+grip\nvcXhhXnxmVRm0eGYt5Q26ic5jft5F6lWblmrvz55V3b7l/Nv0ovrXiv5OhA8pyKsvZMq/7ic6Wf3\n3WD5vFH1w/WF93zMckxxRar8nVSFBr87jfr5Xe3WNu7n1ElVRKfkGKMwv8OhMH/lPb/TA8usN3OH\nGytveTHfbDI7qf5rhqb7GPXLGN0wQnOmzrLse+SVZ3x/PoLR1NFq62RyKiaVav2Oza6PFZJLZT5X\nMkVgM2vJa9W6fGzjfsZzrZBOKvPfZubs5XJa4c/PyJ/ZNeb1plPfuPaeAns8mXAMr19pjPvNnDAl\n73XksoWnu6zw9/wb1lX9Zk0+wHHxmfeauVTLFrAaOMoWRSpgtyCD011X9yugk0qy51L96K5fW97h\njkWi+tNn/p/tRS3B6cEziz5mkaqYG0yv85vjc/ZOKu9MKnsIu48ila2Tyv0Fqi2TKm9wutlJZQan\nG6v7lVikMnOpmtpbAs2kMpWaRyXZg5jzFan+llOgkvreTf/ObT9n7K8MmXlTzsHphXVSrd76pqVI\nKUlfOfVC282fGVDc6qPjoiteTHC6d5HKHpruvHKWE3+dVNbfweY4ohPz97g57nfrM//WdQ/+xbLv\nwH2m6/QjTsl77gxzwZTNRpHK1kk1Kf/KfrlOMVb5e/jlpwr6/E3N2/S/v/+2zr7uS1pi3OzCH6cx\nUbNjLghuOaSSPVjci/mGTWY01tZJVeS4X0dPl+W1alU0Zisa257THp1UfkPTMw7Yxxz5y58RVsi4\nX9/j3jmeiWRCa7att+ybPn6/vNeR68gZ1lypl9b91/HvxHOrX7Jsv90Y9cs4/sA5lt+L23c18ZxH\n2aJIBezm2klVxLifW9dKoZ1Uk4zl7M2bzx+f/TW9ffohGlVvHW0qdDli5Gd2/pgr1ZTaSWV2LpjF\nUbPwaQajm3rzjCc6MV+Ueb2Lmm880WSOwHYnei0/z06ZPaVwCk8vdIn6XCPq3d+5lUrPo5KkKWOs\nnVTrd25WPJlwPLart1u3PXu/bf/zb7ysfy1+sORrQbDsq/uVHpz+r8XzLe+C7z9mki6Y9yHbcbal\n3gMKTq+rthauOnu7lEqlbMdlmJ2ZZuemFz+dVOa4n69MKltw+p5iw9Mrlujrf7/SenzjKP31oqvz\nFuVzjaxvtPw+7+jptFxrqUWqdxpFqidfe84zTzBXOp3Wp//wbd37wn/0+PJFOvdXF9OJXYTcn5uM\nra32VRxL5RSantFSwOs+8/XKnk4q6/Oh2E4qM49qdMMIW9ekbXU/z04q67/NLHCZbJ1UPopUhS7q\nYHadmblUb+7YZPn7PbZxVN7XEabxw8dYYgASqaReWPOq7ThbHtU05yJVdaxKp8yy/r6478XHC7om\n9J+NTVu1uXkbbzTuRpEK2K2/O6nqqmt9vbOby+ykynX+8R/U+cd/UJI00sjfoZMqeGYm1egGo0gV\ncCaVmQFj3hTF8477mZlU+X+OzRX+3MJA+85vZlJ5F6mikaglmD2dTltewJnjfnU+s2rcmJlUTe0t\najczqUoY98t14D7TfQcde6mvqbPkgiVSSW1s2uJ47L0vPOq6otMVd1xfcpA/gmV2+dRX1zpkUhVW\npDJ/Nv735LMc//YU1UnlY9wvUhlRbc7zPp1Oq8tjFVT7OI2/0HTJqZMqf3C6r9X9zOD03cWG1Vvf\n1Cd/903L76iaWLX+8rmrNHn0RN/XLfWNH5ld1Jluqo7uTkt3TGVFpd4ycUpB5z9o0gxNynmt0BXv\n0dMrXvD1uU+//oLlBrelc5euf+ivBX19OHdSmR1zQXAKTc8oZIW/XbZ8xr7nSiEj/17yrewnORTP\nu91/L9k7qbyLPQdMtK7w56eTqtAilW1FZKOgV2oeVcZRM6y5VItXWUf+euK9Wrbuv5Z9b59u7cDK\n9d7Z1lX+HnhxAUWRMnHtA3/WYd/8gGZ8+WS9+/99IvQLYVCkAnarcsmeKqaTyqmwNaGA/I0MtyLV\nnGlv0w//5+LsttNoE4JljteZL7pKXt0vT2eS2elX8Op+vjqpzDyKAjKpfHQWeOVS2cf97DfFhbA/\nJ1q1y5bD4b9I1VBTp0hOkS3XsQeU3kWVYV/hz/md878+cafrOba27tC1D/zF9XEMPKdMqvoac9yv\nsCKV2XkwweWmyhyNMbsSnNiD052fj3XGGy+dHmPPZleG2YngJV+uViqVUntPMeN+5up+zWpqb9X5\n13/VdtN/3ce/p8ON/Ce/zFyqrbtHi/5rZOVMHbev6/faTUVFhW3k75FX/N3c/Pqhv9n23fDYP225\nWfDmNBbXH5lUnuN+BXRS2RYZqOkrGBXyGsDLTuM1qPk8k5w6qdw7PM3Hhtd7j/vZV/hbk7cQU2iR\nyv69sv4MlJpHlWEWqRYZ4ekvr19hedNw0qgJ2seYwsh18qyjLfcob+7YpOUbVxV1bQjWyt2rQXb0\ndGrZuteUTrt3JocBRSpgN/fV/QrvpHIqbBWz8tekUfY/NOMaR+uPn/6xpSgwqn6E5RjG/YJnFmWc\ngtNLeTfKvlqe0Ullro5njMvlO5+v4HTznUGP1f0KzaRyOib3HE7jUKVwCk43O6kKGferqKhwXfL6\n2ABC0zPsK/yttx2zfMNKPWe0939wzjst27/7zy2On4vBYf58N9TU2zqp8mU6mcwMF7fctEZzVM5j\nrCbDb5GqkPD0YMf9rP+Gzt4uy+/D2qoa17/puWwrmrbu0Cd/d6neMJ473zz9s/rAESf7vl6T+fc/\n02XzmnFzeGABK/vlsudSPZ3379FrG1fr0Vefte3vivfoFw/8uajrCCvnTKqB7aQqJDjdfP5kOqns\nmVTurwG82Fb2GzbCdozZSeXV4Wl2UuXLiNxn5DhLkbq9u1Prd7qHzseTCds1j8vT6Wk+bhYqVxlF\nqhkTiuukMlf4e/6NV5TI6fC05VG5jPplNNTU64SD3m7Zd9/Sx4u6toxVW9bp9Ks/o9mXnKY//Oe2\nks4VZkF13+0tKFIBu7llUvnJ8rF/jkMnlfFOqh/7jZlkuTnIBKWb5xrVYG19ppMqeOZ4W2Ntg+Vn\nI5FK2go3hchX9KmsrLSMy0lyzSuS7J1WxWRSebX6m+OP+cb9+o6xF9oyunqsN8VBZ1KVGpwuOb8w\nrqio0NFvOazg63MzdZzZSWUPT7/JCMs+/sA5+tl537Rk3vUm4rrs9msDuy6UxmlhAFumU4FFKvNG\n0+xMyDCLq14BxRn2cT/n57f5PDXHGnOZN3GFBKfbO6ms//ZiRv0kh2J2R6ueXbnUsu9/5p6qL77n\nAp9X6sy2wt/uvKLXSljZL9dxBxxhGb3c2LTFtmqg6TcP/931sb89eZdnQQRWTkWqoDup0um03nTp\nrJUKe3PSvrpf0ON+1u+HmeEpORXP3X8vmQW4fMHpFRUVOsgo+HqNTpn//UYPG+l6T5CRLx5h1e6u\nmIxii1TTx+9n+f519HRaOp9seVQuoem5bKv8lZBLtal5m/7n2i9o0apl2tK6Xd/95zV64rXFRZ8v\nrFo6dll+DmORqPYbs88gXtHgo0gF7BaNOI/y5PtD5cRptCrfuzJO6qtr9c3TP6tIZUT11XX61Scu\nd5w1H2EGp3fuYlnZgJlFmepYla0Tp5SRP/Om0KnoU1VALpVZVPPT6WTvKnB/F9Vcnc9XJ5XZDWYZ\n9wu2k8pPcHohmVSS87LXb913pkYaz79SmJ1U5rhfR3enbl/0gGXf+cefofqaOn33QxdZ9j/00lOO\nnRIYeE7jftXRKssIaTyZyDvGm8u+LLtzp5+tC8lPJ5W5up9LEbrBtsKf/3G/QrqLbcvVG4W2YgvQ\n1bEqz66MuTMP00/PvbTgUX2T+cbSlmwnVTBFqtqqGh134BzLPq+b8s3N23Tncw9Zz5Hz3ziRSupn\n//5jUdcSRtsdgtODzqTavqvJM/PNbydVPJmwdEpWVFRkuzrNwvH2XU2eiyG42dnWYtl2zKSyFc+9\ngtOt/za3gnyudx1yvGX77ucfdj3WNurnYxTZa9wvnU7bxv2K7YqpqKjQUcbr/kUrl2W/znOrrUWq\nt09zz6PKePehx6uyYk8J4LWNq4vqvG7p2KWP/vLL2tS8zbL/+//6JfcgBVq99U3L9tRxk311A+/N\nKFIBu7kVo4oLTnfOpCrGp08+Wyt+/pBe+en9On2O87LXsUjU8iI+nU47LtGN4jkVZcwiRykr/Nky\nqRw6F8zcNK8V/orppDILqdvbmlxfaNjGE6P5O6nsmVS5437BZlKN9hOcnmeFINMIhxyMYwNY1S+X\nbdzP6KS66/mHLd0qYxtH6T2HniBJ+tCR77a1+n/v/7N33mFulFcXP2rb+9per3s3rhQbmx4bG4yx\nDQRTAwESIEAogZBAPlKogUAoSSAJafQSAsFAQjU1VBtjirEN7r17ey+a749Fsua+M6OZ0WhXuzq/\n5+HBMxqNXq00o3nPnHvuv37nSPggyUFmNeVm5XwzOXTvprLtpBIijJ1W9U02gtOBjvcRi3W5n5eZ\nVPr37qazXwTZ4S/C0N4D8MBFv3HUyc8MVaTaA03T8JUUqVyW+wHAMROO0C2/biFS/e3Np3RO3GF9\nBuK2M3+q2+aZRa/g623xA6cJsMfASbWnttLS7ewUqzwqwH4mlZpHlQu/v2M6mJ2RpbuWbAu3K/lS\ndlDK/fLsOKmsyv307y2ekwoAThDXy4vXfoEtJo1IIhlxEWSjAyPUeIR957e9dVW6MWeFMjGgpG/c\nfZoxReZSre0QqTbt3a573eyMLIwdEL87aGleEQ4ZeYBu3UufveNoTE2tzTjvz9fga5GrBwDLt6zG\nM4vYZdgJq4XzbqRL511PgiIVId9gFoocclHuZ5RJ5abcL0JeVm7cMFWGpycXJSg8mKHcra+1KHWJ\nv38xKTRyUonvogxHt3rMTiZVVihTNxlsD7ebtiP3JpPKPDjdcydVbZWaw+HQSWXUUejw0d7lUQFq\ncPrG3Vt1d7IfFoHp3zlsXvR74fP58Oszrta5Ptbs3Ih/vPUvT8dInGPkpOr4vwgeF1lQZoTDYQOR\nyvj7nJ+Vq/tO1Dc36DJNjHCdSdVkLFI1t7boJm1+n98wp8aMvMwcy/fgttwPMOs8lo9HL71L6RLq\nFiMn1a6avaio33d+zc7IwuDe/V2/hsylWrLuS0OBobaxHo+KkuGLZ34Hpx4yWxc4HdbCuOM/f3U9\nnnRid02lsk7TNMMyQLdIkUoVn+2JVEpnP3GsSIeQUSh8PPba6O6nuiPrTF1b8lxn5GqWDO7VDwcN\nHadb98KSNwy33VnjLDQdUEWq2HI/mUc1vGxQVAh0w9QRB+iWF6/5HJqmYYnIozpwyFjb1R/HHzhN\nt/zE+y9Y5nbF0h5ux6UPXI+PRIh7LL95/n7bv2eEeVRGUKQi5Bt8Pp/hyd1NJpWR+8pNcLoT5MV0\nZb2xuEDcId0oRuV+suTECaroY0eksir3E+O1+T22ujsYi5JJZZJZo9vGIpNKOkg8z6Sqr0ZbjCss\nIxhy7JCQkwK/z49DRnqXRwUARbkFOjGsua0FO74pRfhs40p8sWlfq2mfz4ezjjhR9/yJg/bDWYef\noFt314v/UFwspHOR4k1EnJKOQatyuVjqmht0wdi5mTmmpQF+v1+ZiMqJqsR2dz+b5X7yPNK7oMT0\nxpARfr/fYFK77z0kUsrbS5Q4Bf0B/OOi37jOkDFCOql3VO3Gii360PTR5cMc/U0k/Yr7YNyAkdHl\nsBbGWwblvo+++5zu71WaX4xTD5mNgD+Aa0/4gW7bFz99C59tXOl6TOlAOBxWMpgieBmeLjPCJg4a\nrVu2K1IpTipxXKm5VC5Eqrr4IlUoENSdV8Ja2NSJWVUvMqnidPeLIBuKPGdS8rdDOKnsVD6U5hfp\nSuYq62uiNzO9yqOKMH7gKN3falfNXmzYvUVpoBIvND2W2ft/S7e8ducmzLjlu3juY/OySKBDfP3F\nU/coYesHDB6rm/tsr9qNv7z+hO3xpDtq0P6QrhlICkGRKkVpb2/HSy+9hEsvvRQnnngiTjvtNPz8\n5z/HF198Ef/JxDVGF/nugtPV57gt97OLzMWhk8pbFBEpmIF8Ueoiy8kc7d9GJpUUVaxEqpZWKarF\nF5EAg441JheoRs6yeJhlUmmaZhgsnQiyBFbipBwogiwxmDBotOVruMWsw9+jwkU1fewhhsGaPzvx\nYt246poacOvzf/Z8nMQ+auZax/dbfs8bmu3debabR7Xvcdkdz7rkT4pUZo0R7Hb3k6HCTkLTI1jl\nUsnA9jwHTRGOEG7IO876mbIuUcqEk2pn9R4s37Jat85tHlUsxxh0+Yulpa0Vf3vzn7p15087NToB\nPv6AaZg4aD/d47c//5eEx9WTqWqo1d0AicXL8PSNIp9w/8FjdMt2M6lUR7H+OkbmMbl5D2p3P1Wk\nAoDCbHleMi75qxYCXLzufhHmHXS0zoH5xaavsE5k/wDuOo8G/AFFfItkk6322BUTCgQxedh43bpF\naz43CE2Pn0cVoX9JGeYcOE23rqaxDhf/45e4/KGbTDNW//DKw3jwnWd060b3G4Z/XvE7XHD0abr1\n9776KG+Q2UQp9ysf0iXjSCUoUqUgGzduxBFHHIE5c+bgT3/6E1544QU8/fTTuPXWW7H//vvjhz/8\nIVpbmTGSDIycVG6C041Kq5LvpCrSLe81KdMi7pBB5FmhTDWTKhEnlcH+JaFAIuV+9sRWuxeozW1S\nVHNe7hcpcWxqbUZY22fzzwiGXB13EnlMxOJGXBpQWq5bPmq/g022TAyjDn81jXV4VgQdn3vUtw2f\n3yu/GD+Ze4Fu3T8/+C+Wrl/u7UCJLaxEWFXkseeksptHFcFpLpUU5e2W+5llasnQdDuTQInVe1Ay\nqRyUC591xIm4Zt4PcOzEI/C3C2/Fdw6f53hs8cjJyNKNvy3cjvdXfaLbxhuRSp9L9faKRbpcpAUf\nv6YL9M7OyMJ535ofXfb5fLjupEt0+3hrxUf4YNXShMfWU7Eq6fMyPH2T4qTSi4l2suYAg9JY8VvY\nO8FyP03TFJHKyEkFAAU55sJzLIoobyM4HQDKi/vgEFEq9/wnryvb7ZDB6TYbHZnd1JOumJEeuGKm\nDNfnUr25/EPFjTlpqF7IisddZ1+H6eMOUdY//dFLmHHLOVi6/kvd+n9+8F/c9vz9unXlRb3xxGX3\noCi3AD+afR5KYm6YNzQ34rf//ZujMaUjLW2tSpOcEWWDumg0qQNFqhSjqqoKs2bNwkcffYRQKIQr\nr7wSCxYswGOPPYbjjjsOAPDnP/8ZF198cRePtGdilD/lrtxP/5z8rFwlYNZrSnL1Nfos9/MWxTkU\nylDu1ickUkknlVFwuvheWQWnq+V+9krb5AWq2QW4HK8dp5bcJlIyKHMLEi31i2CVJyPvHtthzoHT\noxeb/Yr74AczTnc7NEuGiFyaDbu34t+LXtG5W8qLemPG+MNM9/G9aacoF8a/+Nfdrjo1kcSQImxm\nMCMqwka6akVIlpNKcSHFmdDKbqPmIpW9cj8pdjsJTY+gdinc9x7cdvcDOm5E/XjO9/HID+/EvElH\nOx6XXWQu1YerPtUteyFSHTBkjM61Ut1Qi4+/ya3RNA1/XqgvvznzsLnKefJbY6bgUFHG/Jvn79eV\nl5J9SJdgLDs9LffTT2LHDRipKw9taG601SSjVjqpxLFSVqAXkJ06qeqaGnTjyM7IMv1NVzr8GYSn\na5qmdP6z66QCgJMOliV/qkglnVR9C+1lyMqOyJHvglq6lXjp8FQRnv7ip2/rfldG9h3sOEOvKLcA\nj196N24+9Srl+nLjnq2Y99uLcM9LD6A93I7Xl32Aqx+7TbdNYU4+nrzi9+hfUgag43fmanGD7PH3\nXlC6mBI9G3Zv0TUpKi/q7crt39OgSJViXH/99fj666/h8/mwYMEC3HPPPTjppJNw1lln4eWXX46K\nUw888ABef1090ZLECPn17o2AP+AqI0KKXX1tdApJFAanJxcjkUo6qczs0XZQuvsZOJOk+Nlq2d3P\nrZPK3gWqkbMsHmaZVPWiVEdO3N1SauGkcnMBUJJXiNeuewhv/uJRfHDT0+jtYqJtB1nut27XZjzy\nrr7U76wjTrRsTxwKBHHLaVfp1i1dvxzPLO5ZHXf21Fbikf8twOI1n3f1UEwxC00HDMr9Wux195Od\nsOI5A6XzIF73VzWTKrFyv11C7LbrVIjFakKbSHB6ZyFL/uU5f78EOvtFCPgDmDHuUN26SMnfm8s/\nxFfb9k0W/T4/Lpr5HWUfPp8P/3ei/kbo4rVf4E2DfCuyr8TLCOnQcUtTa7POleXz+TCwtNyg62X8\nXCop6MpzhxKcXu0s/F3mc1n9Disd/gycVHVNDboJfHYo01Ge5JwDp+uu47/etk4RTVSnp73zk6yQ\n2Fm9F02tzdi0V+96G+aBK2bS0PG69yE7L08eZr/ULxa/348LZ5yOV372AEb3G6Z7rD3cjttf+CtO\n+O1FuPBv1+leMzOYgYcv+S32E88556hvY3jM+w1rYdz87H2uxpYuJEPU7AlQpEohKisrcf/9HTbK\nU089FXPmzFG2ueuuu1Ba2nHyvO2225THSWIEA3pByo2LClAn7G5KG5yiilR0UnmJnFBkBFWRSk6U\nEtm/YbmfzeD0cDistL62+13uU6i/Mygv3iLI4HRb3f1MMqm8zqOKYFXu58RpEUtHi+eRtkQ5t8gO\nf2+vWKS7qPb7/PiOCEc34ltjpyrhqL9e8CdFgOiuNLQ0YcYt38U1T9yOE+68CC9++lZXD8kQq++3\n/K6biTyS6kbppIpT7pcbfzIYoT3cbluEliKVzIaK4CbzRWI1oVUyqZLsXHaDVYffXvnFiivDLbLL\n3+vL3gMA/Gnh47r1cw+ajsEGmXZAR8t7uZ/fPH8/nZgGWJX7eZVJJbuu9SsuQ0YwhGLFQW9DpBLX\nKfKGjRRedtU4ew+yo2SvfPPvtR0nlXqui9/ZT//6xUpp/vMxAept7W2K0GjX6WnUaGb9rs061+GA\nkr7IidOd2w65WTkYP3CU6eMHD7cfmm7E2AEj8crPHsD3p52iPPbJ+i911w0+nw9/Ov9GHDLyAGXb\nUCCIX377Mt26N5d/iHdWLEpofD0ZtbPfkK4ZSIpBkSqF+O9//4uWlo4Lw+9973uG2+Tk5OD00ztK\nTN555x3s2eNdKCNR86fcilSTho1HMOaOx/Sxas2316gXKxSpYnni/Rcw9upZOOL603Ud0uwiBaEs\nj8v9pFPLMDhdijwmmVRyfUYwpAsPtUJeoJqVMqjjdZ9J1dginSbeTDAtRaoUdFpEkE4qWUJ17MQj\n0K+4j6193XDKFbrvzc7qPUr7+e7KW8s/1E0Cn138msXWXUeDDE2PEVDk5MUs00ni3Emlf9wqv0aW\n8maHMk3PH3KCa7e7n5vgdEsnVQLlfp1FuYVINbb/CM9eZ9rYqbrrj9U7NuL5Ja/j/a/1GVg/PPZs\ny/387ISLdMvLNq/Cf1NUCO5KOiOTauNuvTMnIi6qTqr4uVSqk0p/rEiHsNmNKjP21Ig8qgLjPCpA\nFZ6lIAUYlDbb7OwXy4mTZ+qWn1vyelRI2lNbqROVSnILbTu15HlsV81erE5CHlWEqSKXKpbJDjr7\nmZGdkYVbz/gJHr30LtOwewC49fSrMefA6aaPz9r/SKXz8Y3/vldxf5EO5HeGTqoOKFKlEP/73/8A\ndFgvDz/8cNPtjjrqKAAdHQA/+OCDThlbuiDLZ9yKVKV5RXjqR3/AiZNn4qfzLlQ6XiSD7ljut3bn\nJvzjrafx5eZVSX2d2sZ6/Pypu1FRX401Ozfitufuj/8kgRR+Mg2cVJ6W+9nIpDJzUsn1Tr7H6gWq\nsRBu1O0wHmaZVKrTJPG7jkCcTKokdOXzit4FJZZusnNMAtONGNy7P74//VTduvtefbRHuKnWfdP1\nMIJVNkxXYlXuZzd4XOLUSaW4kCwms402Q9MB+04wOdF100jEykmliFQpKEJbvWcv8qgiFGTnKRPE\nqx+7Vbd82KiDcIDoDicZP3CUMrm/44W/ok24dNMdq3K/nVXe3EiWeVSDe3XkFsrj3o6TSu3uJ51U\n6jWAkzwyJ+V+UniW4jsAVIn3JJ9jh+MPnKa7Dtqwewu+2PQ1ALUk04nLU267q3pvUku3po40FqmK\ncgoS7iAYyzETDsdbv3jMMFT9R8edi+8ZuK1i8fl8uOGUK3TrVmxdg3999LJnY+xJrJGd/ShSAaBI\nlVKsXLkSANCvXz/k55tfcI4ePTr67xUrViR9XOmE4qQKuBOpAODw0ZPwlwtuwdVzzk9qaVCE7lbu\nt3HPNsy85bv4+VN3YfZvvo/PNzp3N9llzc6Nukn5KvGDYAc7mVQJlfvJIHID0Ud2jTQXqaSTyn5+\ng5JHYXKXuFkR1eILS+aZVOaT+ESwujhOxUlsBJ/Pp5T8RRhYWo5pY6Y62t8PjzlLJzTsqtmLR/63\nwOIZ3QPZkt3OBK0rkKVoycikys9y5qSSQcSxqHlU5se27UwqL4LTLZxUdXFKmFKB8iJz9+N+Hjqp\ngI5JZizyO/jDY6xdVBGumfcDXQ7Omp0b8fSinpVrlyhWTqqqhhpPbghsUkSqDidVsSh9s+OkkjfT\n5A2bwpx83fVHY0uTaRmvEWq5n8dOKgeh6dHXyc7DjHH6RiPPfVPyp4SmO8iQNSr3k4KDlyLVlOHG\nuVOTho2H3+/tlL5PYWk0VD1yvXTRzDPxsxPtNe46YPAYzJ8yS7fuN8/fb7ukPV3QNM1A2BzSNYNJ\nMShSpRBbt3b8CPXv399yu9jHt23bZrGlSmVlpel/VVVVjsfc05BOKqNuf6lKbNtXAKhwUe5XVV+D\nax6/Haf//gq8neT68Zc/ezt6x761vS2peTLyIsTpRaOmaYbOIVlSkki5n+peMHJS6b+fZiKV2tnP\n/ve4KKdAd8exvrlBCTYHjEW7eJhlUsmJuVflfpYiVQqWA8UiO/xF+O6RJzm+GO1dUILvxbSZB4D7\nXntU6arY3ZAtm1O1xFmWwMV+v71zUsURqcRk1tJJ5USkEtlPsrQR6MjIkxN5u8HEsThyUqXg8V1m\nMfn10kkFqCJVLKP7DcOM8YeaPh7L8LJBOP3Q43Xr7vzv37v9ucNLrEQqoMNdkygb94hyv94RJ5X+\nuJauIyPiOal8Pp9yfMpyXSv21IpyPyuRyo6TShGpnGVSRTjxYL0r8IUlryMcDquh6Q4EdKXcr3qv\nQb6QdyJV74JSDOszUFlvJl4lSiRU/fM7XsTXdy/Ejaf8yHZ0BAD87MSLlbiB+19/wuIZ6ceumr26\n36+czGzL0vB0giJVClFX13FyzsmxnqDFPh55jl1KSkpM/xs2bFj8HfRwvMqk6gqKRWlTVX2N45DT\nu178Bx55dwHeWbkY37v/WlsXPG6RFzLJnGDKixCnIpVR+Zzf71fElETK/exkUqlOKuNMqpZW904q\nn8+nlPwZXaA2yWBlW+V+xplUySv3KzJ9LBWdFrHIXCoACPoDOPOwua7298Njz9a5dnbXVOCR/z3r\nenypwIbdW3TLVfU1jspSOgsn3f3s3mVWM6niBKfn2M+kapSZVCad/QB7TqqK+mq0xWSRFGTnWQpf\nZljlakmnRyo6Jc0mHj6fD6PKh3r6WsPKBhlOZgHgkpnfcTTR/PGc83XXQlsrduDO//wt4TH2FGR5\nm/zd2VGdeC6VWu7X4aQqElmkVXa6+zXKTCpV4Fa71tkvW5TXdlbZRopIZeDwlE0eilw4qQDgmAlH\n6M47Wyt3Ysm6ZdghcsOsGhxI1JD5vVizc5NunZeZVAAwdcQByjov8qisyMnIcuVgG1hajgtnnK5b\n98fXHvOsoUBPQMmjKhvs6Pzck6FIlUK0tnZMhIPCLSEJhfZdLESC1ok3dGeRKhQI6i7Mw1rY0Dpt\nRWywamNLEz7buNKz8UnkJMuJndwp8gexsaXJ0WTWKIgcUO/WJ7u7n/w+SseU2Xon7ZoB9e6gFPk0\nTVPLE20Fp5tlUpk7TRLBSqSSYbGphlG53+wDpikCol165RcrXXvue/XRbmu9b2lrxbbKXbp1beF2\n1+eRd1YswrG3nouT7/4hvt623oshRpFORH1wujsnVU2T/vwZz0klRSyjyWAEKeJblavLY1WW7gJq\nVpibUj/A2g0mnVSpKEL3yi+G36dedg/tPcCT7l8SIzdV38LeOFmU4MRjQElfxYl5/+tPumpA0tPQ\nNE1xUk0cNFq3nGh4uqZpipNq0DeZVFKwsSVSyWPF4LfQyCFkl70OnFRS+DDqOupFuR/QIajPmnik\nbt1zSxaqTR0cuDxzs3J0Nxpa2lp15/CC7DzPunZGmDJCn0sV8AdwwJCxnr6Gl1xx3Lm6a7GG5kbc\n8cJfu25AKQbzqMyhSJVC5OZ2/FA0NVm7PBob950A8/KcBQhWVFSY/rdu3Trng+5hhILdV6QCEs+l\n2il+rLdV7kx0SKbIi6lESuXiYegEEqKQFWppW8ekTcmkSiQ4XRF94nf3a7UZnO6k3A8wvjsYS1u4\nHWFtn0sv6A8opbJGmGZSWThNEsEyOD0FJ7GxDDFwQZzrIDDdiEuOOUt3Qb2nthIPv9M93VRb9m7X\nfQcjuHFktra34YcP3oAvNn2ND1YtxbVP3uHFEKOo32/zcj/7Tir9xE2WwkmkiGWVXSPPjc4yqVSR\nUCmncVHqBxi8hxihTbpY87K8Ebq9JBgIGgp0Xpf6RThmwhHKuguOPs3Vdc1P512I/sVl0eWwFsaP\nH70VrWkeol7VUKP7G+Rm5mBob/25O9Hw9D21lTrhIzczJ1rKLp1Utrr7SSeVwW+hDARPyEmVZ99J\nVW0YnC6dVO7K/QDgpMnH6JZf+OQNbK3QX+f2ddjUwUp0H9HXe1fM4aMO0ondk4aO8+yaKRkUZOfh\nJ3Mv0K17/P0XcPsLf+0W3f7+u/RNHPrLU3D87edjtYs823gkM2i/u0ORKoUoKioC0JEbZUXs44WF\n5pMwI4qLi03/i7x+OqN090sgOL0rkJNyJx3+WtvblDtg0qngJfJiKplOKqO7gLKkxQrppIqUtkmh\nwyi7yS52uvtJETUZwemAQRiouECVoelGgpoRpplUFpP4RCjMzteF/saSik6LWMb0H66bTA4vG4TD\nR09KaJ+leUU4f5q+098fFz7WLd1UG0T5SwQ34elb9m7XnfsWrfnM0/JjJ+V+doPTZfB5vI5Xigsp\nicHp0qUqJ7hunVSKG+ybCW1za4vuXBj0BzqlWYkbjEqJ9uuXHJFqyoj9dTeu8rJyHHUGjSUvKxe3\nf+da3bovN6/C/QvTO19Guqh6F5Qowduye5xTNsk8ql79osKH6qSyI1KJTCoDJ5XaQCU5mVSqk0o9\nL1WLG5oFcVyjVkwfd4hOGNtTW4n3vl6i28Zp51HpOovFyzyqCIN69cM18y5EZjADZYW9lC56qch3\njzxJ+Vvc89IDOOu+HytB+6lEVX0NrnzkFqzfvQVL1y/HZQ/e6PlrqBlmQzx/je4KRaoUYsSIju4u\nmzZtstwu9vGRI0cmdUzpRncu9wOAYhGe7mSiZSTkJNNJJe+YJZLnFA95Jx9wlktlFhKek5mtu0vW\n2Nrs+s6y0i3PRiaVFM/2rVcztJyghqbqL8SbXISmG21nlkmV7VEmld/vR3Gu8V3XVAxWjqU0rwjX\nnXQJskOZ6F1Qgj99/yZP7shefMx3dCLg3tpKPPTOvxPeb2cjO/tFqHIhLsnzpKZp+Gj1Z26GZYgM\nE48VdtTgdHvnJSWTKp6TSnEseBOcHgwEdeeqsBZWmkDICa6cANtFdVJ1vAej0PRUzfQw6hw2doC3\nnf0iZARDuPvs69Arvxil+cX40/dvNMwfssvMCYfh5IOP1a27879/x9qd1tesPRlFpMovVvOcEiz3\nU/Oo9jXVUDKp4oj0mqYZHC92MqnsiVThcFi5OVqaX2S6vRqcrp6XpPDmNpMK6LgGmX3At3Tr5M0+\n6SKLh9X2XudRRbjy+O9hxZ2vYOltz+OgoeOT8hpeEgoE8ZvvXIOguGn49opFOPbX52Lp+uVdNDJr\n3l6xSHcD/fONK7Fiy2pPX0NmUo0sH+Lp/rszFKlSiIkTO7oz1NTUYPVq84Pg448/Vp5DvEGKVN2p\nux+glvvtdVDuZ2Tn3l6ZeOCnGfLuWFKdVDXqe3MmUhnnL/l8PsVN5UZs0zRNmdjJ0jhALdszK/cz\nG69dpNNBfjfU/Cy7IpVZJlVynFSAeS5Vqpf7AcDFM7+DFXe9ii9ufxH7D97Pk32W5hXh/OnCTfXa\nYwm5ACVV9TX4cPWnSS0F8tJJZXSe/GDVUsf7McPq++0mOD0cDiuOg3jiQ05mts5V2NTabFryrGRS\nWQSnR/Ydi/wuyRsgTieBEXIzc3RlLo0tTWhpazUo9UvdY9vISTUmSU4qADjugKPw2W/+gy/veAnH\nijweN9x02lW6TsLNbS34yWO3OW7SYkVbextuf+EvmPnrc3Djv+9N6ZKg3bVGTir9Z5xoJpUUqQb1\n7hf9t3RIyusqSZO4kRYKBBWHMwC1eYpNN1h1Y62uSUJeVo6lq1Ep92usU5yY8lzntrtfBFnyJ3Eq\nols6qZJYupWblWPqFE9Fjhg9Cf+68l4lo2tr5U6ceOdFeOidf9vOio10jDWrJvCKN5Z/qKx7etEr\nnu2/vqkBWyt2RJf9Pr9hHmm6QpEqhZgzZ0703//9739Nt3vxxRcBAGVlZZg8eXLSx5VOdPtyv1z3\n5X5GTqqtyXRSiQuPZGVShcNhk3I/+yKV4hwK7rvoUnKpXISnS0dUKBA0vPhQnVT2MqmcOqniWf3t\ndCI0wiyTSi338y5fwVSkSsBR0JlkZ2R57gq5eOZ3dJk9FXVVeNAjN9XqHRtw4P+dgG/fdQmOvvls\n5bviFWZOKjdlekbPSa5IFVPuJ1xKdsr96psbdXlcOZnZyg0Wic/nU9xUsuwnguKkClk7G1U3mP49\nKMHELsv9fD6f4qaqaaxTzrmpLEDLvJvsUCYG9+5vsrU3BANBz84hvfKLcdNpV+rWfbj6Uzz+/gue\n7F/TNFz31F2456UH8eXmVfjzwsfx7OLXPNl3MtgjnFS9CkoS6oxnhAxNj3VSSadwPJFeCrr52XmG\n3w15DSDzSs1QS/2sQ8MzQxnIjrl+aA+3K+cPKby5DU6PcMR+k02vC4pyChyXCsfLpCL7OGzUQVh4\n3cOYMlxvsGhtb8PPnvwtLn/oJjSYXJvvrqnAM4texqUPXI/x1xyPCdccjxFXHo1jbz0PP338N3j0\n3efw2caVnl1zhMNhvGkgUi1Y/JpnwvnaXXoX6uBe/RzfVO7JUKRKIaZOnYoxY8YAAP7whz+goUG9\ns71o0SK8/fbbAIBzzjkHfj8/Qi9Ryv1C3UykynNf7rfTwG20PUmZVJqmKeUmXjo5YpHtzyM4clK1\nyfK2fd8L2RnHjZNKhqabXSRlKJlUZuV+iWVSqXdRpUjlzqllN5NKdjxLhFKT8PRUnsgmm5K8Qlww\n/TTduj8tfNyTY/COF/4WPbZW79iAFz55I+F9GiHdBRHsdLeSGIn5K7au8SyXyqp7pdIdz4aTSnZt\njZdHFd1OTO6MQooBKK7O7DhOKvU96N+vLBVyW+4HGOVS1RqW+6Uq0mUzqt+wbuWGAID5U47D0eMO\n1a276d/3enK9cP/rT+CR/y3QrXv9y/cT3m+yUMv9SlAuPuMd1XscdROWSEF+cK9YJ5U8pmstX6vG\npqCrNE+xKbTtra3SLVvlUUXHYOCmisXLcj+g4zp/7kHTDR8rMyjHjYeZMzToD2AIXTEKfYt6498/\n/hMumnGG8tgzi17GnNvPx7qdm9DW3oZFaz7Db56/H8feeh4mXHM8LnvwRvx78avR3+yWtlZ8sekr\nPPruc/jp47/Bcbd9DyOuPBrH/PocXP3orXj4f8+6Pi99sflrJacXAHZU79Z1Qk8EhqZbQ4UjhfD5\nfLjjjo6uQhs2bMDpp5+Oiop9P4CffvopTjvtNGiahpKSElx77bVmuyIukU6qUHdzUind/apsP9co\nc6C2qd6VMyge9c0Nyp0IaUP3CrOLKyciVYvipNontnjR4U8JZjeZFEqxyTw4Pbnd/aycZVbIcr99\nmVRiEp+VXCdVRjCU9nerLpp5puKmeuDtZxLaZ0tbK95e8ZFu3ecbVya0TyOMWrJHqKxzLlJVGpT7\neZlLpTipsmKD04WTyoZI5bSzn9l2ZrlUsqmEVSYVEL9DoTwHSxHcCUYd/rpTud+BQ8bplg8fdVAX\njcQ9Pp8Pt3/nGl2ZZ21TPf7vn3cmJMa8+OlbuOnZ+5T1S9Z96Xqfycao3K8wJ193o6mhuTGhOAM1\nOH2fkyorlKl7rdb2NstziLw+KTARdHvlF+tKayvra2w5VPaIv0epiWMpFiN3ZASjG5p2z3dWmJX8\nlbk4N5k5qYb0HhDX4ZquhAJB3Hjqlfjrhb9WbnKs3LoWx9x6Hsb+5DiceOfF+N3LD+GLTV/Z3ndr\nexuWbV6Fx99/Adc+cQem/nI+Pl77heMxvvHlB6aPeVXyJ/OoRiQpw6y7QpEqxZg7dy5+9atfAego\n+Rs4cCAOPfRQTJw4EZMmTcKmTZuQnZ2NBQsWoLTU/YUeMUb+oDid3Hc1SnC6gwmbWbhnMkr+zO7g\nJyM83SzwM5HufrECh1ru5/xi1L6TSv99NBWpWhN1Uukt+ntqK9EWIyC6z6TSbxcRu6S928tyP6OL\n5FSexHYWxbmFuPDo03Xr/rzw8YSOwcVrPlcmQcs9DhkFOtwLZhOxSjdOKhPHlFclf/VN5uV+UgBq\nbGmKm+/jtLOf2XbSkRU7hlgSFqmU4HR3mVSA6qSqbqjtVuV+o/sNxa9OvhyDevXDrIlH4ofHnt3V\nQ3LFwNJyXHfixbp1r3z+P/x36Vuu9rd0/XJc9sANhiLX1ood2JFgrlOyMOru5/P5lO/49ip3bo7m\n1hZsi3muz+fDgNK+um2KREaTVYc/WeJr9lsY8AcUF5QU5Ixw0tkvgpE7MkJja7PuOicUCCol0m6Y\nOmJ/9C1U8+Hc5OX1KTQuaaQrJj4nTJqBl3/2DyVgvr65wbIDLaA6881oaWvFb//zN8djsxKpXvr0\nbU+6Iksn1Uh+Z3RQpEpBbrzxRjzzzDMYP348Ghoa8NFHH2HZsmXw+/2YPXs2li5diqOOOqqrh9kj\nCfq7e3C6yKSqr7L9XLPMgWSU/JmFeybDtWWWB9HYmkAmVYyIJC/y3Ezy7YSmd7yuPSdVot39MoIh\nXb6Zpmm6NsFKJlWccqDodiaZVLLMzMtyPyMnVSIdrnoSF804Uzehr6ivTshNtXDZe8q6FVvXJOSu\nMMKs1A9wl0lVYdJg4sPVnzrelxHSKZgTc+c44A/oMlmA+C5Pr5xUskOg2evHO76tyv3qhYskFAia\ndty0g1F5U534+6ZyuR8A/PDYs7D4lmfx8A9/a2sSn6p8b9opmCQ6i1331J2Oj8FNe7bhnD/9RPkd\njGXJumWuxphsZOfbiKtGdnF0m0u1pWKH7vxZXtRbuYlVmKs/JqxKnuU1ltVvodLl18Z72Cvc+7ZE\nKgN3ZAR5rivMyfckXy3gD+CESTOU9UbdN+NhJrpTpLLHqPKheOVnD+DEyTPjbjtx0H64cvZ5eP4n\n92PN79/EsjtewhOX34OfnXARjj/gW+hf0tfwee99/YlhNq0Ze+uq8OmGFbp1sb9b9c0NePXz/9ne\nnxlrdm7QLY8o43cmFopUKcr8+fOxbNkybNq0Ce+//z6WLFmC3bt346WXXsJ++3nT5YmoyMyf7uak\nSqTcz+wCZFsSnFRmd/rkZM4L5J3OCM66+8mg8BgnVbZ+guam3E91JhlPCqXTzyyTSq6363SKpbcM\nTo35fsi/h907WmaZVFbB0okihVsAyM/KMdgy/SjKLcCFIhfizwsfdy0WL1ymZsdUN9RiS0z3Gi/Y\nYBKaDsRvwW6E2Xly+ZbVnuRSxWsMkC2Dx+OEp7t1UsksFzMnlTwfxXMuyPLc2ON5t0FoeiKTTDvB\n6XRKdg4BfwB3ffc63e/S7poK3Pjve23vo7qhFmf/8WrFgSOdFala8ieD0yMuZJk9tqPKnUildPbr\npYbsF0snlcU5ULpTrARdtctv/En+nhr951hqQ6QqtHBSqXlUiXX2i+Wkg1VRpKzAuUhVmldkeE6j\n4GCf3Kwc3H/+zbjltKsQjMnoK8krwvwps3Df967HsjtewmvXPYSfnXgxpo44AKFAEL0LSnD0uENx\n5fHfwwMX345Pbn0Oy+98Bf+84ve6v39YC+OFT163PZ63l3+kE4fHDxyFMw6bp9vm6UUvJ/COO5oE\nrNu5WbeO5X56KFKlOAMHDsRhhx2GSZMmobi4+95x6y4omVTdTqSSTioH5X4mFyDbkuCkMruD35lO\nKrMOIkYoQeFWmVRuuvvZFKncd/dzLlLJbIZd1fsuxpvaXAani/cVEdPkpDzXQxHJyEnFSew+fnD0\n6bq76ZX1NXjg7acd72ftzk1Yt2uz4WMrPC7527B7i+lj7rr7GZ8nNU3DotWfO96fRBVhc8Sydbmc\nRHFSZdt0UslyP9NMKmflfjli/LHOKXn+le4MpxiV+ykdy3h8dxr79RuGK447V7funx/8F/9buTju\nc1vb23DBX/8Pq7av162/4rhz8ZO5F+jWLV2feiKVpmlqJtU33exkF8cd1e7KFa1C0yMYuQvNUI8V\nc4HbTZdCV+V+Fk4qrzv7xXLgkHEYWFquW+cmOD0YCBrGCkihlVjj8/lwwdGn4/0b/4V7z7ser/zf\ng/jyjpfwx+/fiFOmzlZiKMwozSvCtLFTccZhc3Xrn/3YfpdQ2dVvxrhDcerU43Tr3lmxWLkJ44Qt\ne3fookRK8ooMb6qmMxSpCIlBOlW6W3C6mklVHTffBOhQ9M0cR8kQqczs6LJswwtMM6maHZT7WXTL\nk4KHKyeVzKQyKa+Rzr7WJJX7AQZW/5juj3K8UnwywyiTStM0ZVLuReZEBCORKtXLgTqTotwCg2yq\nJxznLbxu4KKK8KXHIpV1uZ93TirAm1wqtbufXtSRIk+88HTFSZVjM5PKdnC6EKlC1sdjnkW5nzz/\nWrVrt4Ohk0oJTqdTsjO54rhzMap8qG7duX/6KS7463V4bslCwxJ4TdNwzeO3492vlujWnzBpBn52\nwkWYPGyCbv3nG78yLW/vKmoa63Rjys7Iit5gKRN5RztdO6nMQ9MjFOXKTCorJ5X9TpiyC6csbTRi\nb51zkcrKSaWGpntXqu/z+XDRzDOjy/lZuZg2dqqrfRmd14aXDXI9tnRmcO/+OPWQ2Thg8JiEOtif\ndLA+HH/p+uWK6GtEe7gdby3XN4A5evyhGDtgJMb0Hx5dF9bCWPDxQtfjk6HpzKNSoUhFSAz7Dx6j\nWz5gcPcqrcwIhnQX6GEtbFrSEcue2kqENWMxy23gpxVmgYjJcFLFiiuxOHFStbSKbnm6cr/EM6lk\neY2Z6COdfebd/cyD3u0iL7pi6/llkLzM1DFDimzNrS1oaGnS2aozgxmKozERjO5wMpNKzw9mnKH7\nm1Q11OClT992tA+jUr8IXjuprC40q+prbAnzETRNs3RffbA6MZGqubVF17U06A8oorEUreKdm9w6\nqaRIZXYeVp1UcTKphCgUK7J5GZoO2AtOp1Oyc8kMZeDu716nK3lqbG3Gf5e+iYv//kuM+8lsnPfn\na/DMopej37l7X30ET37wH91+Jg+bgN+f+0v4/X70LylDeUzJXHNbC77cvMrzsYfDYTzx/gs47XeX\n44f/+JWjDE6j0PQIMttou8vg90174jupZBlvVb1FcLp0UlmV+8kuvy6cVHbK/aycVMks9wOA86ed\nit+e9TN871un4Nkf/8n1tYE8r/UuKFHEQ9K5DCjpi0NGHKBbt8CGm+qzDSt1zVQKc/Kj2XunTJ2t\n2/aZBEr+mEcVH4pUhMRwzITDccVx52LioNG4cvZ5mDH+sK4ekmOkc8SovbrEysa9taJ7Z1KZd/dz\nUO7XZh5srpT7eRKcbuakMg4el8jMKC+cVLF/RyWTyqYIFgwEdXkDYS2sTLi9zKMCWO5nh8KcfJx9\nxIm6df/88L+2n1/TWIePLELGv9zscbmfhZMqrIUdHYN1TQ2KiBQ72U40l0op9cvKUfJLZKMA2UhA\nknwnlT1nZwSlXDGmm6EMq0203M/IScVyv65n8rAJuHD66YaPNbe14JXP/4fLHrwR435yHE6++4e4\n9bk/67YZ3Ks/HrrkDl1p6SThpvrE41yqLzevwrzf/gA/fvRW/O+rj/Hsx6/h18/9yfbzzUr9ADWT\nym1wuuKk6p2Yk0p297M6VvqI8qpklftZOamMgtO9xOfz4btHnoTbzvwJJgwa7Xo/shSNgkNqIN1U\nz378WtxGLm+IUr9pY6dGb5yefPCxut/vLzZ9ja+36cuV7SI7+zFoX4UiFSExBANBXHfSJXjtuofx\nsxMv9tTR0VmU5Bbpls3aq8diFYiZDCdVZ3X30zTNvLtfAsHpsaKMUu7nKpNKBp2bZVLZc1JJ8cpV\nJpW8i1qTuEjVsa3+vUkBQIZIJ0pORpby9+QkVkXmN7z/9SfKBMmMt1csQlu4Pbo8qFc/+H37Li82\n7tnq2bFd39SgczAE/AGd4wJwlkslz499Cksxtv+I6HKiuVTxSv0AICdTX06XLCeVdAmYOamkszNe\nJpVVdz/ppEq03M/QSeXAHUKSxw2nXIFfnnwZ+hX3Md2mtb1NKaEtyinA45fdrQgak4aO0y0vWe9N\nh7/axnr88l/34Nhbz8MnIuvqHRtZWhEsnVQyk8qFk0rTNKW02TiTyoFIJY4VK+eQmkllnb/THm5X\nzr1GN4kkSlaepZPKW5HKK6T4zjyq1GDepBm6G6Ortq/Hyq1rLJ/zxpcf6JaPHndo9N/lxX1wxOhJ\nusf/vfgVV2OTItXIvkNNtkxfKFIR0sMoztNfsJi1V4/FysZd19RgOplxS7VJcLrXmVR1TQ2mYpQT\nkapJ6Wa3T/RIRrmfWXmNFJvMy/1EeaILJ1Vvpdxv33dEOr/sdvcDVEFLfj+9dlL5fD4ljJKTWJVR\n5UNxkJgU/uvDF209V5b6zTlwOoaXDdStW7HVGzfVpr164WxASV9lcuskl0rmUZXkFeGwUQfp1iWS\nS2Wnc6WVyGOEV04qs7Igp8HpVsHv8iaBzLlxinF3P/3fiyJ01+D3+3HpsWdjya+fw0vX/h0/POYs\nDDIQVWIJBYL4x0W3GboIZC5Voh3+NE3Dc0sW4ogbTsff3nzKMOJgd02FbUHdSqQyCh2P5+CQVNRX\n65oQZGdkoVe+Gh5dLJ1UDrr7WbmKZblfvJDoirpq3XssyilQcl6NkOV+tbrg9OQ6qbzikBEH6paP\nGnNwF42ExFKaV4SjxkzRrbMKUN9dsxefb1ypW3f0uEN0y7Lk79+LX3UUMRBh9U5mUsWDIhUhPQyZ\nwWMVChxhRxwbt9fh6Z3lpLKyp8uSFiusnENelPspQeQmok9GUH/BZypSKeV+bpxU5qGpSjfCOOVA\num2FSLVXfD+9FqkA9W4uy/2MOeNQvZvqqQ9fjHvx1R5uV+48HjPhcIwbMEq3bvkW67uXdtkg8qiG\n9O6vNoxw4KSS2xbnFqgiVQK5VA2Kk0r97slGAQ1xmjooTiqbEzc1kypZIlWMk0q4L6T47RTpGqlu\nqFFuDPD47lr8fj8OGjoev5p/ORbd/G+8dt1D+NFx5xoGSd/13etwuHAmRJgwaLRO5NhascOVIwkA\n1u3chDP+8CNc/Pdfxi1bs+oeGsseUe4XKyDlZuXorg1a29uU37p4qJ39+iulwoDD7n6N9p1Ustxv\nd02F5e+Bm1I/QC33i72JKZ1UqSpSHT3uEFx30iWYOmJ//HTehZh9wLe6ekjkG06ecqxu+bmPF5p+\nj99asUi3vP/gMcpv1pwDp+lyWLdW7MBHaz5zNKaKumrsjTleMoMZGFDa19E+0gGKVIT0MOSEzY6T\nKt5F27ZKb3OpzC6ivM6kkqUmsTS2us+kinUmeVHuJ51UZqKP6qQyyaTyorufgZMqcpdUKU8M2hep\npAAnRQLZ6cwLpHBbQCeVIScdfIyuNHJLxY64LqJPN6zQCeGFOfk4ePhEjBswUrfdco9Cj+UEcnDv\n/momS4JOqkNGHuBZLpWaSRXfSdXQ4rC7n82wX2UyaDM43az8OILqBEtecLp8r9UNanc/OiVTB5/P\nh4mD9sP/nXQJ3rvhKbz1y8fxk7kX4MzD5uHJy3+H0w453vS5WaFMJSdIlufFo6m1GXf852+YdvNZ\nhqV8A0vLFQfDul2bbe1byVsToo4MT3eaS7VJ6exn7EqTJXBWTtIaB8dKdkaWTsRqC7dbCm3yMbsi\nlXRSxYrn8oZmqopUfr8fVxx3Lp7/yV9w9ZzzEYgpMSNdy3H7H6UTlbZU7MCSdcalw1alfhHysnIV\nEdJpgPqaHRt0y8PKBvI7YwBFKkJ6GLK0yc7kSl5sSaHASccbO5hNjrx2Usn3FYuTcj+lfC7kdbmf\nu0wqKUZFULr7uXBSFWTn6cbR2NocLT2QmVeJZFIlu9wPAPbrN0y3zNp/Ywqy83C8uPiKF6C+8Iv3\ndMvTxx6CUCCIcQOFSOWRk0rmZA3pNUAV5p04qer0k6Di3EIU5xZ6lksVGyIO2MukksKWpEaUS9t1\nUimTwYZawxIk6TKN56SSvxeR8be1tynuChkw7BT5XjuC01nu1x3w+XwY0384fjL3Atxzzs8xXZTR\nGDF5qPuSv9b2Nnz7rktw94v/UH7DQ4EgfnTcuXjn+ifxrTFTdY/ZdVIpwemKSKXPynPqAlPzqNTQ\ndEDteOfESRXvWFEd1ebXVNJZVppfZLnvCFbiuTzXSSclIfHIy8rFMROP0K0zKvlra2/DOyv0QvZM\nk+ZZ86cep1v+zydvOppTrBGlfiPKhth+bjpBkYqQHoYsbbJT7ifv8E0ctJ9ueavXIpVJForXmVTW\n5X4JZFJ5Xe5nt7ufEINa7WZSORCRIvh8PuWiO/L3lJNYZyKVzKSq0i1LV4YXXDjjDEwctB+C/gC+\nP+0UTEygi09P58zD5+mWX1z6lmUmncyjOmbC4QCgOKm+2rYWbTFd9NyilMD07ucok0VSUV+lW46c\nP73KpVKD09XvtxR5GixEqnA4rHwedtumZ4UydYJ1a3ubEtIeDofVjDynTqpvRKO9dVU6Eawkt9CV\nqzOWnIwsXRBuU2uzrXB60j2ZNGy8bvkTEweEEQu/eA+fblihrD989CS88YtH8X8nXYKcjCwM7aPP\nz7PrpLLKpAKAssLEOvxJkWpQb2MnVaGD7n7y3BHPdShzqazegxSkS906qWLE8+4SnE5Sm5MPnqVb\n/s/SN3VdfQFg6frlumOnJLcQBwwZY7i/b42Zojvea5vqlWshK1azs58tKFIR0sPwotzvwCFjdcue\nl/uZZKHIu3yJ4pVIpWQw6br76SdodU0NjkMUZSaVzG2KIENIzbv7JV7uB5h3+LPbjdAI+d6k0y8Z\nE8wBJX3x2nUPYcuf3setZ/zEMNeDdHD4qEkYULIvG6GxtRkvLHndcNstFTuwIqZTjt/nx/Rv7PF9\nCkp15R5Nrc22J39WSJfDkN6qk8pRdz9xfiz5ZsLnVS6VneB0MyeS2f5iA59zMrNthRNHUHKpxCSw\nqU09tv1+60tFeQ6MiEby/CsnvG7w+XyWzrG8rJy44yXdBxme/vnGr0x/9yTPLVmoW+5dUII/fu8G\nPHPlfRhVvs9NO7TPAN12tp1UUqTKty7321HlVKSS5X7GTipZAlvTWGd4DRIOh5UbgXGdVErZv7mT\nam9tlW7ZKOTdCCPxPNKcpbuU+5HUZvq4Q3Tfnb21lXj3q49128hSv2ljp5qW4AUDQXx7sj7ryknJ\nnyz3Y2i6MfwlJ6SHIZ1U8SZs4XBYufA4YLD+7oGX5X4tba2mApHXTirLTKpEgtNjLqgC/oBSDhOv\nXEdi20klyvaa21oMy3WUcj8XTipAzdiIfE9kRpeZqGaEfA9SJMjJoAuiK/H7/Tjt0Dm6dU9+YFzy\n97q4c3jw8AnRcmOfz6fmUm1JrMNfe7gdm/du160b3KtfgplUxi3TvcqlsuPykd95KyeVFPgLsuy5\nqCLI7niy9FoNTY8vQJt195Mt6+X5xC1WGVx5BsH0pPvSr7gP+sY4kprbWmydR+qbGpRS5H/84DbM\nn3qccpNimAsnlaZpBqWsekGnb6EUqRyW+ynB6cZOqmAgqBObNE0zdL/WNzfqrheyM7IQjCNwS2HZ\n6ppqr3RSiWtRK4zcVED3CU4nqU1mKANzDpymW/fsYn3J35vLP9QtzzAp9YtwyiH6kr83v/xQOSeY\nsYZOKltQpCKkh1EqMqnidZSpqK9GW7g9upyflat04fHSSWXW2Q/o7O5+ToLTpeijn7jJu5FOc6ns\ndsvz+/26UhcAus8ugrzT7NZJZXaBqnY7dBCcHq+7n0GwNOlcThci1Sfrv8Sq7euV7aS9feY3pX4R\nvBaptlbs1H3fS/OLkZeVm1gmldLdrzD6fy9yqaRgnWNQ7mcm8hihdvZzJlIVZFs7qZyGpgPGTjBN\n05SW9V44qQDrDK78bO/LhUnX4fP5XJX8vfrFu1E3DgD0L+mruLIiDCjpq/td3V1TEfc3vK6pQXdz\nKSuUqTgKlUyqavsiVUtbq9JVeWBpuen2ilBvcJ0lowjslAnLBirW5X6y26G9cj/AuMOfvKHp9/mR\nl4Q4AJIeyJK/lz97J/r92lm9B8timrv4fD5Mi5OZN2HgaIzsOyS63BZuxwufvBF3HM2tLYpLcngf\ntfMpoUhFSI/D6YRtl7jo6FvUC/2Ky3TrtlbuMnTsuKG6wTzfxvtMKm+C05tbrUUfmevgNJfKrpMK\nADKEyNMiBCNAFb1kV0C7mF2gyvEmEpzeGd39iDMG9+qnlLs99eGLuuX65ka899US3bpjJujDSccP\nHKVbXr45MZFKZrQM+ab8JaFMKoPufhEOHXmg7rEPXZT8uSn3s+ruVyOEfLud/SJIUUs6FVQnlXVo\nOtBxPow9J7aH29Hc1qJMaGUIs1ssnVQMTe9xTBYilZ3wdFnqd+KkGaZloMFAEIOES2n9LuuSPynA\n9sovVhxaanC6/XK/rRU7dGW9fQt7Wx6LMqtJHteAQR6VjWNFHrNW11Ruu/sBRtdQdQalfnks5SWu\nOXTUgboIi/rmhuiNtjeXf6Tb9sAhY+M6AX0+H049ZLZunZ2Sv/W7t+iO7f7FZcjNovhqBI92QnoY\nUqSqqq+xzEhSSjIKe6E4t0AnlDQ0N1qGJzvBSeeZRPGqu1+88jY5MXLqpGpURB8LkSoQv8OfXJfp\nOpNKdvbpuFNqtxuhEfJvx3K/1OSMw+bqlp/+6GVd8Pn7Xy/ROQwHlpZjdLm+a+JYj51UG0T5y5De\nEZFKZlLZF6lUJ9U+wcuL8HR75X76yWdjs/m5qUaW+zksf5GdwOT+ZD6eHZEKMHaD7arWOyuk6O0W\nqw5f7OzX85AOKLP28RGq6mvwlph0nnTwMZbPGdJbn0u1Pk4uVbzOfoCa6egkOF3JozIJTY9gp+RZ\nXpfEC00H1PcgxblY3AanA2oZX3VDrXJDk539SCIE/AGcOGmmbt2Cb0r+ZB5VvFK/CCdP0buzlq5f\njrU7N1k+R+ZRsdTPHIpUhPQwMkMZum5L7eF2S4Fph7zbXVAKn8+HfsV9dOul9dwtVp1nmttabIei\nxqO5tUX3Wn6fH37fvlNea3ub0t3Dal+xxCv3c1q2qEwMrUQqITjJ/CkAaFGcX+6cVDJjI+K6U4PT\n3WdSsTNXajLnwOm68pVdNXvx1opF0WWl1G/84YqTYETZIN3nvatmr+UkJx5Kt6tvnFRuM6k0TVNE\n0ti7p4cIJ9WXW1Y7cmkBar6U0R1TuU4eE7EoEzenTiqxvdyfGycVoJYx1jc1YFeNdFJ5U+4nc7Vi\nsTPxJt2LCYNG65oDbKnYYSn4vPTZ27rf9mF9BmLCQOuOrjKXan2cXCopwNoRqXbXVNjucGp2rjND\nCjhG11lunFR9HDippEjlxEklz0s1jXXKDU3mUZFEkWL1G8s/wN66KrwTc20DAEd/0wAmHgNK+iqO\n62cWvWL5HOZR2YciFSE9kJI8+92uZLlfJDekXBGpvMmliufIqmvypuRPBnz2LihBTqZ+wiXFITNU\nkUovtCgildNMKtlRyyKsWBWpVFFPClfed/cTzi8HIli80kDZzp50DbmZ2Thh0gzdun9+E6CuaZoS\nmn7MRH0eFdBRRjO63zDduuVb1ijb2cXMSSVLXaoba9FukNUmaWhp0h17GcGQrvSuJM8gl2rNZ47G\nXN9ko9xPCEENFi5PJTjd4cRN6e7XGK/cz55L0shJJSe0RhN5N1hl6bDcr+eRFcrEhEF6kcnKTfXc\nx/pSv5MOPjZuR9chfRJzUhm5BDOCIZ2bKKyFlY6AZtgNTY+glPsZiOny5lm+DYHbyA1m3KylVScq\n+Xw+pQzbCiMnlRTanAryhEgOHDJW55psaWvFjc/8QXfNXppfjP0H7Wd7n7Lk78n3/2N5o3rNTiFS\nlVGkMoMiFSE9ENnhT7oFYjHLDZG5VF51+IvnRHBaKmeGLPXrU1CiuALslvzJtuxSlMlT8hScCW1S\nLLPMpLIhUinlfh5194tMOtVMKifB6dbbSiGRdB1nHKov+Xvti3ext64Ky7esxvaYTlXZGVlKaVyE\n8QP1JX9fxoSTOmWjmDhGRKpgIKgTLjRNs8y+i6DkUeUWKZPZREv+6pr15zMjEVauk8JWLDUeO6nU\nTCr756JYpEjV0Nyo3CjwzkllEZxOkapHMnmovuTvE5Ncqt01e/He15/o1skSHyOcOqmk2GQmwCod\n/myW/CnlfnGcVNJNahSrUCudVDZchwXZebrrncaWJsMbibLrakleEQKiyUu814nF2EnFcj+SGD6f\nD98++Fjdun999JJu+ehxhzrKPpt70NG638kd1bvx6+f+ZLr9auGkGiliEsg+KFIR0gMpybXf4U/e\n7Y6EffYToZ9bPRKp4jmprEpdnCDFtz6FvRSRyiqgOBbFOaQ4qfSTTKfZWk6CyGXpnmG5n0dOql4F\nJbpJe0VdFVrb29RyP5tuCyB+aSCdVKnDwcMn6jp9tra3YcHiV7Fwmb61+7fGTDEVM2Qu1QqXuVSa\nphk4qfbdEVVL/uJ3+FPyqPLUSVCiIpW94HT756VEnVTScSFFL3kusp9JJc6BTfXKjQKvRCorJxXL\n/XomBw0bp1v+ZL2xSPWfpW/pQonH9h+B0f3iTwKHykyqOMHpaic7Y5FKcSLZDE/fJJtE9I5X7qc/\nro1y+ZTuflnxBW6fz2dQ8rfvPTS2NOEPrzyMWbedp9vGSakfYOak0p/rinJZ7kcS59tx8ulm2Cz1\ni1CQnYcLjj5Nt+6hd/6ND1d/qmyraZpS7jeS5X6mUKQipAfipNxPEXO+sa33KxFOqiqvMqnMg9MB\n53lOZhg5xNw6qaRbSTqpEi33c9TdTwhO0jUVDoeVrC0n5XixhAJBxZW3u6ZCcZZlOSn3i7MtM6lS\nB5/Ph9MPnaNb988PX1TyqI6ZoJb6RRg/QN/h70uXIlVlfY3uuMoOZepKbNyEp0uHqfyuA4nnUtkS\nqTJUF5JZN9WEnVRxyv1kqaFdkSpPCPXbq3brzmvZoUxlG7dYOalY7tczkeHpn2/8ytBF/PwSWepn\nPSGNMLC0HMEY58+umr2Wrm7bTqoi6aTabbhdLEaCfDwnVXFOfCeV7AwqHeBmGJX9h8Nh/Oujl3DE\n9afj1uf+rLirZBONeBg5qWqYSUWSwKjyoUrn4Qh+nx/fGjvF8T6vnnO+4sa8+tFblTnGjqrduhvx\n+Vm5njUU6YlQpCKkByInbLKsJRZVzPkmk6pIZFJVeJNJJdsKS+o8clIZlZpkh5yLVOFwWLkYlkKR\nWu7nVKSy3y1PduprFWNraVfHGi+Pw4oyGZ5eszduRpcV8bbNoUiVUpw6dbau4cCXm1dh6frlum2s\nOuGMHTBCt7x25yZHnTUjbBClfoN699d9r2X+iR2RSu3sV6hsk2guVb2YvBk5BTOCId0EuS3cbtpA\nItHuflLU8io4XYpvslyqT2GvhM5DsRRks9wv3ehfXIa+hfvc3U2tzUq30C0VO7Bozee6dXZK/YCO\nkuGBpeW6dVIoisW+SKW/jtpRFV+kqmpQBfl4eW5KcLqN7n4FtkUq/TXAi5++hVm3nYcrHroJWw2y\nSof2HoCfnXCRrX1HsOOkokhFvEKW/EWYPGy84XVAPLIzsnDXd6/TrVu3azPuevEfunWrDTr7efW7\n2BOhSEVID8RuJpWmaaYlGf2Fk8qr7n5yUhQ7AQaS56TqU1CqhAA32AhOV0LNQ5nKj4qcGDnN1VJL\nbMxFqlCcTKp4gppTpNV/W8VOXSh1wB9AMKbzUjziZVLRSZValBf3sbyzOHHQftESYSMKsvMwKCb0\ntz3cjq+3r3c8jk0io2WIcBaoTqr45X6yDFo6UCMkUvInu/sZibA+n09Zb1byl3B3P2UyqJ/Muhep\n9OLbOlEuJfPtEoHd/dIPn8+HScPG69Z9IsLTX1jyhm75oKHjMDhOmVwsQ5VcKvOSPxmc3tuk3M9N\nJpXMo5KCvBFSpPequx+gdvn9x1tPY5lBtmBRTgFuOOUKvP2rJzAspkzcDtJJVdtYr2ZSWYjThDjh\nxMnG4rXdrn5GHDryQJx71Mm6dX9e+AQ+3/hVdFnmUY3oO8T166UDFKkI6YHYLferbqjViTA5mdnR\nkox+srtf1S7TEhQnyAsPaYf3LpPKwEnlotzPjmtIKfdzILRpmqYEp1sJObJcTopoMj9LZlg5RVqR\nN+3VX0DbDVbetz0zqbobMkA9FqtSvwjj+ieeSyWdVLL8pciGk0BSKcv9TO6gJiJSyXI/s5I3o+54\nRiTqpFI7IVo7qewe31Jk27BbdVJ5hZWjguePnstkKVIJR+dzstRvsr1SvwhDlQ5/5uHpbsv97GRS\nOe3sBxg7kSTy5pmd7n6A6qSSZARDuGjmmfjw5qdx8czvuGrUIkWq6sZaRUCnk4p4xYCSvjhkxAHK\neitXuB1+8e1LdXOn9nA7rnrklmgEB/OonEGRipAeiN1yP0XIKSiN3rEryilAdswEpaG50fDCxyny\nwkN2EfTKSbXbqNzPjUgVp7MfoN69d1Lu19repgt6DfgDCFk4k+J191M6+3nspJKOFqcXpPEyqdjd\nL/WYtf+RiggU4ZiJR8R9/jgPOvxtEEHC0iFRLIT5ChtOKjuZVID7XKrW9jbd+cPv85uKPoqTykSk\n8tpJFS84PcdluZ90UsWb6DrBsrsfnVQ9FplLtSTGSbVu5yZ8sWmfY8Hn82HeQUc72r/d8PT6pgbd\ntUNGMGQa5l9WqHeZ2smkkr+x8fKoAIPGER46qawaHpw4eSbevf6fuPGUH7kqk4ogS3hrGurU4HSK\nVMRDZF5dWWEv06wqu+Rn5+L271yrW7di6xr88dVHAQBrdm7QPTaijCKVFRSpCOmBlMpyP5MJ1U5x\nwRR7MeLz+ZISni7v3MuyQq8yqaQA18dlcLrMizIqn5NhvU7K/ZROeXGcC2p3P1Hup4w3MSdVWYH+\nAlW2mo7njJJYiVpZoUxHbatJ55AVysTJU9QMhz4FpZg4cHTc549TOvytcTwG6S6Q3a5kcLAtJ5WN\nTCrAfS6VFJpyM7NNy3akyCMDzCMk6qQyCigOh/eJ5Gq5nz0nlXQwSUesl+Gwlt39mEnVY5kwaLTu\nBs7mvdujcQXPLXldt+0hIw5AuXCDx0Mt9zN2Usm8y94xN/ckSnC6jUyqjVKQt+GkUpykBjcUaxv1\nx6TVcRTL/oPHKOumjtgfL137d/zlglsclVSaIUt4qxtrFVGtMNf4Rgkhbjhx8kzd79JZR5zoST7U\nMRMOx/wps3Tr7n7pAXy9bb3ipBpBJ5UlFKkI6YHIcj+7TirpmpHh6VsrPBCpxMVT/yQ4qdrD7YZ2\nfOkKsCNStRhkUkkSKfdrahXlNXEmhRlBvcsqnpMq0Uyq3oX6MoZNQqRy2jnQqpSRoempyxmHqSV/\nMyccBr8//mWEFKmWb1ntuHRYnbhJJ5UMTrfjpKrSLZtlUgHuSv7Uzn7mpWiyw58MXAc6xDHppLI7\n0YwQCgR1x1lYC+tuDDS2yHw8m06qLOtj18tyv6xQpul5jSJVzyUrlKk4HZasWwZN07Dg49d06+12\n9YtFcVLtNnZSKdcWJnlUANArv1h346WyvkZxK0pWbl2rWx5kQ6TKy8rRvU5Dc6NybVDbVCeeY+9Y\nGTdgJK6cfR7ys3JxwOCxePDi2/Hc1ffjoKHj4z/ZJkYOT3mjgU4q4iXFuYX45xW/w/nTT8VNp16J\nK2ef59m+bzrtKpTmF0eXW9pacflDN2B7jEgd8AcwRJxziB6KVIT0QOyX++nzEfoKa7rMpUrUSRUO\nh5W7Y9Kt5UUm1d7aKl0JXVFOAbJCmcjOTFImVQLlfnJSmBVH9FGdVC1iWZT7uciHiEVa/dVyP+8y\nqRianrpMGDha5yYCgJnj4+dRAR3t3WPFlNqmekXstKKptVl3cefz+ZROXEU5+nOeUbmLxK6TCnAr\nUonOfhZCjjw3GTmp6psbdOe17IwsVyK0kksVc+PAbSZVvGPXSyeVz+czFedkp1XSszAq+ftq21pd\n16yAP4C5Dkv9AGBgr346oWdn9R5DsVgJTS8oVraJHYtsGiBvDsayp7YSSzfos7Ym2HCr+nw+g1wq\n/TlQ3jxzUhr7sxMvxqp7Xscr//cAZh/wLc87kmWHMnUuuea2FkUMtOrqSYgbxg4YiV+ffjV+MOOM\nhG/oxlKaV4Rfn/5j3bovNn2tWx7Su7+nr9kToUhFSA9E5rNU1tcYOhdkZz/ppFLC0w3aDTuhtqle\nN47czBylTMcLJ5XS2e+b96U6qWx097MhUiVS7ifvqsZ3UsXr7he/PNEJcnLpdhIbwcp5RSdV6uLz\n+XD9/Muj3TgPGjoOx9rIo4o8d6x0UznIpdq0Z5vuvNGvuEw5DkuEk8qso6nVNrJMOhaZS7V8yxrl\n2JOoTirz73e8cjnAKI/K3aRNyX+JuXHgtrtfXqb1hNfLTCrAPJcqj8HpPRqlw9/6L/Hcx/rA9KPG\nHGx5LJsRCgQV8Vtm4QH2Q9MjyFyqnRYlf298+YHuXDdh4CjbZYtSfK6slyKVdGE6E3S9FqbkvqWb\nSuaBWnX1JCTVOHHSTMyaeKTp48yjig9FKkJ6IFmhTN2Evz3crjiYADXEU7pmZKj5tsrEnFRGQZjy\nbp4XmVRqZ7+OCZK7TCrRec8oON2g3M9uOZPTTCr5+vHL/RJ1UllPLp1nUpm/PzqpUptvjZ2Kj25+\nBv/60R/w/E/+gqBFwL9k3AC9C2u5gw5/siW7zKMCjDJZ4jupZLi6FPdjKckr1J0fw1pY5+4yQrow\ncizL/YSTqlk9N6l5VO4mbUr+S8x5WZ7vvCr3swpfdoORkyozmJGwc5SkNtJJ9fnGr/CsLPVz2NUv\nFtnhb91ONZfKSbkfoOZSWZ03XvviPd3yMRaTXIkMT489rlvb29AYc2z7fD6lxLirsWoCkZ+Vy7xK\n0q3w+Xz4zZk/NS1BZx5VfChSEdJDke3UjUr+pJNKChLyDl6iTqqaBnWSJR0EdR44qXbV6J1UkQlS\ndijx7n5GIlJmKEPnWGoLt8fNnYigOKniiFShOJlUaoZWYpO23Mwcy4mq4+5+LPfr1gzq1Q9HjZli\n2YHSiHED9FkyjkQqkQ1jFCSsuEfjOKkaW5p0x3/QH4ibZySbPGyt2GG5fSJOqoYWtbtfop39os9z\nUO5nW6SyeG9+nx+l+UX2B2gDIycVS/16Pv2Ly3SxBE2tzbpmHpnBDMw+4Fuu9y9zqTYY5FKp5X7x\nRCrhpBJO7wjNrS14e8Ui3Tq7blVAPSZinVRKqV9Wrq08wc7EqpzPqqMnIalKeXEfXD//csPHRvQd\n0rmD6Yak1hmKEOIZsp26UZCwvFiStvT+MpOqMn5nGitkaHphTkGSnFSi3K8g4qTSC0BuMqnMyufc\nlvwpTq04oo90RkkRraXV2+B0n89n6YJwKlJZiWapdmeXeIdReLpdNiid/dSw0YLsPF05Sm1TPVrb\n20z3KUthivMK45az9C/uq1uOJ9obdfczI0dmUjWrIlWinf2iz8v2XqSyconJ8GgvMHJSMTS95+Pz\n+ZSSv1hmjD/UcTOBWIaJDn/rDDr87RFOql7xRKpCex3+Plr9qa7M12731AgyOiH2uJY5mal4rFiV\n80mXGCHdhbOOOBGHj56krB9FkSouFKkI6aHITlV7DZwFO2ucOam2Vu503JUrFlmCU5STr2SIeJFJ\ntatafxEZdVK5KPeTIpCZKOO2w58SnB633E8vOrUq5X5SVEu8/EUGv8biZSYVnVQ9l9H9huqEis17\ntyuitRnxOvsBHQHFMpNFdoeKRe3sVxR3HP1KZLdTa5FKCU63KvcT330jkco7J5X+efpMKmfnowhW\nx67MOvQCWd4JAPlZzKNKByZZdJU7MYFSPwAY0lsvUhl1+HNa7ldm00n12jJR6jfhcEdup0JZ8qxz\nUunPHfkJCHnJwmpMdFKR7orP58OdZ/0M2TG/pZnBDIwsH9J1g+omUKQipIciy/2kk6quqV43EcoM\nZigXAkU5BTphp7GlyVbWixkyF6sgOy8pTirTcj9PgtONJ21qhz9770PuX7q9JKqTylqkkqKWG6za\nxzvt7meZScVJZo8lK5SpZDCs2LrG1nOlk2qwQSYV4CyXyklnvwgDhJMqvkjlpNxP/5h8LgBUd4mT\nyq5IZX7setnZL4JRHlduCrpDiPdMNnFS5WRmY+YEex1HzRgmMqnWGzipdjkMTlecVAYilaZpSh6V\nk1I/QHUbxZ7/VCdV6v3WWonubgV5QlKBoX0G4nfn/hKZwQwE/AH8ZN4FCTk+0wVnoRKEkG6DzGiR\nzoEdVWoHPFnu4vP50K+4D9bu3BRdt71yl60JnRFV9TI4vUCZWHiRSSWD0/uYBae3Oi/3MytXk+V+\n8qLQDKeZVBlKJpUs9+tsJ5V3mVQyPJr0LMYPGImvt62LLi/fvAqHiq55knA4jE0yON3ASQV0nPNi\nnQ9WuVR7xfmw1CI0PYLMpNoSJ5Oqrkk6qSzK/TLiO6lqPHJSSceZTqRyG5xu8d68Dk0HjDsbpuLE\nm3jPxMH7IRQIKuW8syYembAbd2Cvfgj4A2gPtwPocD3VNzfq9iszqeKJsDKTyqjc76tt63TZWlmh\nTBw5ZoqjsVsd193BSWUlukuXGCHdjRMnz8TM8YehsbUZvfKLu3o43QI6qQjpocjyFdluXc2jMp5I\n9FNK/tx3+JOZKoW5+cgOZerKgJrbWuK2do+HeSaVi3I/6aQyEX3yxASpvpMyqeJ390vcSWWZSeVQ\nBLNydlnl2pDuz1gllyq+k2pn9R6dO7Aop8A0n8SRk6rOuZOqf4lwUsXJpFLK/SxEFDm5bjA4N3nm\npJKTWV25n/517QrHGcEQgia5U1Yit1uMnFSpmLNDvCcrlInxA0cp60+aPDPhfYcCQQwsLdetiw1P\nr29u1AnIGcFQ3FI02d1P3iAEgIWi1O/w0ZMc37SxdFLJ4PQUbDJg6aRiuR/pAeRm5VCgcgBFKkJ6\nKPHK/XYpeVRmIpXePbA9AZGqSganfxN2LAUe6UBwgqZpBl0L3WdSNbXZE5GUTCq7IpXDDBgpOsXr\n7udURDKit8Wd4iyb5UARgoGg6WSWmVQ9m/EuwtM3iDyqQQad/SLIHD4pzOseE+dDO5lU0km1zWG5\nn8ydsnpMClyA6qRyWy4gJ4ORrquapinnRLvHt8/nMxXhrMqF3WLkpGJ3v/Rh8rAJuuXCnHxMG3uI\nJ/uWHf5iS/6U0PT84rgNF4pyCnS/w/XNDUpjlYUJlvp1vI7I5Iu53jKKWkg1rER3+d4IIT0filSE\n9FDilfupTipjIUI6qeJ1tLLCqLsf4L4zntlrxDovsjOyoiKYzFdpaHbT3c/b4HQpgiUuUgknVcgL\nJ5W5SOVGBDPLpbKaxJPuj3RSfb1tHdosOvABRp39jEv9gESdVPHLSUrzinTHZ21TvTL5i0XNpLII\nTs+Q3f3Uc5PS3c/lRNPMSSVdnRnBkKOufLIJRoTOyqSikyp9OHK/ybrluQdOd9xp1oyhSi7VPieV\nLPXrFSc0HfimQ66Fm2pPbSWWrP9S9/gxE5yLVFbB6fLGn7zmSgXopCKExEKRipAeSqlwBsjuftJy\nbtdJtS0BJ5UqUnVclKhOKvcilcyjKivsFb3T6cZJJUUf80wqd24wxUkVx+KvilR6EU0t90v8wt2q\n3M9pdz/A3I1GJ1XPpndBie671NzWgjUxeXdGbNojRaoBJluqwrxVJpWb7n6RjL5YtlrkUjUo3f0s\ngtNlubCBk0rp7udy4qZm13RMZqVIZTePKoKZyGwlcrvFOJMq9SbeJDkcM+EIfPvgY+Hz+TC2/whc\n9+0ferbvoUqHv31OKqWzn81SViU8PSaX6o0vP9B1TR4/cJRynrGDLPerjhHpVSdV6h0r1plUFKkI\nSTcoUhHSQ5GTLqXcT+Y2mQgR5UXSSeWlSNVx4SEnF4mU+6md/fZNkNxlUtks91O6+7kNTneWSSVF\nKbXcz4PufhZOCDd3r83eI0Wqns84WfK3eZXl9ok4qSotu/vpH7PbDEINTzd3lkonlRSyY1GcVAbn\nJq9KduTzImWEsttpdsiZSGXmFEtGcLqRk4rlfumDz+fDn8+/CWvueQNv/vIx5aZcIgztoxep1sU6\nqdyKVDI8vXqfSCW7+rlxUQGq+Bx7jlO7+6XesUInFSEkFopUhPRQ5KRLKfcTmVTyTl+E/iV6kWp7\nlZciVaTcTz+5sSvwGCHzqPoU7HtfrjKpbAanuy73c9jdT4pOrVKkatUve1ECUZpfBL/P+OfClZPK\n5G/Icr+ejyJSxcmlig0tBoBBJp39ADWTqrLOXKRSu/sVWY4jggxPtyp/rnfQ3U8KPIbd/Rq9cVLJ\n50UC2RCxYnYAAHkKSURBVOX5UJZHxyM3y/j99U6Ck8oooyYVJ94kuVg1I3CLLPfbYFHu19tGuR8A\n9C2UHf46bqa1tLXi7RWLdI+5yaMCDI7rhtqoQ6u7d/eTNyAIIT0filSE9FBkxkplXbXOUi6dVGYl\nGYqTqmKnbj9OkN2pInfO1Ewq904qtdwvxkklBJXG1maEw2HL/TVLZ5KJKCPfg12hTWZexRN9QnEy\nqeR4vSj3C/gDph1J3IhgGXRSpS3jBjoTqTYmlEllXu4nnaV2MqkAtfx5qwMnlWUmVaZeQJfPBfYF\nnEdw66TKz8rVhT3XNTWgrb3NdWh6BKPjNz8r13GXMjsYTbIpUhEvGFharsti21G9O3o8unVSyUyq\nSCboh6uW6kp7+xSUYv9B+7kad3ZGlu76obW9LSp2dwcnldX5jE4qQtIPilSE9FCyM7J0zqG2cLvu\nQkWKOWblfoU5+TqHS2Nrs2UgsRmapqlOqlxjJ1VCmVRKud++9+X3+xURqEmIOhJVRLJX7mf3PShO\nqjgTQ+lCkqKUFK28KPcDzEVMNyKVmZPKahJPegbSSfXlltWmondNY52uC19GMIRyUTYTi8ykqqo3\nP0/Jzn92MqkAYECJFKnMM6lUkcqiu1+G/rFG8VxN06IB5xHcilR+v18t+WusN3BSJV7u1ycJLiqg\nQ8yX53J5DibEDRnBEAYIx+TGbxydu2ukU9ve91vNpOq4Tnltmb7Ub+aEw+D3u5+aqUJ9xzVXjXB2\np2J3P5b7EUJioUhFSA9GTrwiJX8NLU260pGgP4ASk0wWn8+Hfh7kUjW1NusElIxgKOps8jSTShHf\n9BeRsoQlXsmfzKSSweURlHI/myKVzIGJ1y0vXnc/dbzedDzqbXIxnhV0Xu5nJvQlw3FBUothfQbq\nHI17ayuxS0z8IkgXlXQ4SIrFBK3CRKRqbm3RuRf8PlW0MaO/IyeVLPczF2GVUuTWZrSH26PLDc2N\nuuXsUGZCpbyqSFWbcHC6kQiXjM5+EeR7oMhNvGKYkkvVEZ4unVS93GZSVe2CpmlKHtWxE450OlQd\nhbnGTRHkTbNUzG/Lycw2Pb9TpCIk/aBIRUgPpjRP5lJ1uAfUUr9elnfv1I5W5hMzM6SLqiA7L1py\n4mUm1U4ZCF8gRSpnuVQymNx2uZ/LTKp4OTBSdFLL/WR3P6+cVMZOO6clQYBFd78k5IuQ1CLgD2C/\n/sN1695ZudhwWxmaPtgijwpQu1uZOamUUr+8QtvuBZlJtcUjJ5Xf77c8NykuqgQnbWp+TZ0HTir1\n/SUjNH3fvvXndrulV4TEQ3YRXR91UrnMpFKC0/fg6+3rsXnv9ui6zGAGjhxzsJvhRpFCfSQ8XWm6\nkILlfj6fz9BNlZ2R5dl1DCGk+0CRipAejBqe3jE5U4ScOCUZ5cWJh6dXCZEq1pbuZSaVdFLJiYxj\nkSrJ5X5OM6kygkHdshSp1O5+3jip+phMAF2V+5m8R2ZSpQfjB47SLV/58C34/csPKflwG/fYz6MC\nOoTv2Dvx9c0NyvEBqCJVic08KgDoJ8r9dlTt1jmcIrSH2x2LPvL739AcI1J5lEdl9vyaxlqlo6DT\n7n45RuV+SXRSnXPUydF/zxh/GAaWlifttUh6YRaergSn282kEtchO6v34LUv3tWtO3z0pIR/A43C\n0wGgTtw0S9XSWCPx3ahJAiGk50ORipAejFLuV18FwChc3PputwwL3ubCSaWE/sa0EPc0k0oR4PTv\nTZaUOS33s93dz20mVVyRSjqprDOpMkLe3IE0yyxzI4KZPcepc4N0T6aNnapbDmth3Pb8/fjun36i\ny4qSIlU8J5XP51MmaUZuqr0u86iAjvNHbGl0W7hdEcYB9bySk5kd160lu1vGlgvWyKYTCU7c5MSv\nqr5WKT126pKU53EgeZlUAPDdI0/CK//3IB6/7G48fMkdSXsdkn4MNSj3a2xp0t1AC/oDtgWUvKxc\n3fHR0taKf334km4bt139YlHcpA010DRNcVKlYnc/wFh8L2RnP0LSEopUhPRgjDr8AcDOKmdOKlnu\nt61qt+OxSCdV7CTLq0yqhpYmnThklLXlvNzPXnc/5T24LPcz238EJZOqvU23rJb7eeWkMsmkcuGk\nMnpOdijTMm+I9ByOP2AafjrvQl2HOQB448sPcMyt52Lp+i8BABt2b9E9Hs9JBUA53ivq1Q5/lXWy\ns59xHp8Z/WV4eqUq2jsp9Ysgw9NjnU3VDd6EpkefLybXNY21Bs6vxLv7JVOkAoADBo/BjPGHIRgI\nxt+YEJsMFeV+G3ZvUfOo8kschZzLpg9rdm7ULR8zwQORykB8bmptRluM2zMUCHrmsPaawhz1vEYn\nFSHpCUUqQnowZsHpsgNe30LzjlmAgUhlMCmLhyxX0Zf7eZNJtdsgNF1eRKoilV4kkjS1SpHKJPQ7\nM1s36W5sbUarEJAM9y/dC3FEKvn6La3SSSXL/ZLb3S/eeI0w+hvmMI8qbfD5fLh6zvl44rJ7FFFp\na8UOnHjnxfj7m0+pmVRi4miEnVyqiKM0QkmeU5Eqfi5VfZP90PR92wgnVVPynFQy+6W6oU45F3nR\n3a8sieV+hCSLQb36we/bd+2wvWo3NsXkRwFA74JiR/sss7jOGj9wlCJ+u0G6jqoaapR8zPyYPNBU\noyBbPa8VGAhXhJCeD0UqQnowarlfJJPKOrdJIkWq7S66+1U3mmeqyEwqOcGzixqarpaoJZpJZSb6\n+Hw+A0dYfLFNKfeL414IBeJlUiXJSWXyHYnn/DJ8jkFHQOZRpR/Txx2ChT9/BJOHTdCtb21vwy/+\ndY8uVBjomDjGQ3GPdoKTaluFej6sUzr72XBSZerPTZ3upGoVJYpedPdLYnA6IckiIxjCgFK9GL1k\n7Re6Zbud/SL0LTI/Fo6ZcLijfZlhVO4nb/rJ65RUwshJxXI/QtITilSE9GCU0heT7n7xJhJKJlVl\nR/tkJ6hOqn2TJK+cVHbEN6cileJMshBllFwqGyV/Sne/eE4qITo1t7XoPgtFVEugTX0svU0cEe6C\n0w2cVBkUqdKR/iVlWHD1n3HRjDMstysr7GVLNCkS57xKIydVAplUANC/WD953Vpp4KRyUe4nnUjJ\nzKTqrO5+yS73IyRZDBO5VIvWfq5bttvZL4JV9qcXpX6AUblfjdrZL0VD0wHjrCyW+xGSnlCkIqQH\nI8tYouV+Dp1UBdl5uglUU2uz4eTPChmcHnt3zKtMKlnGaHRRKHNWHDupLESZPBcd/tRMKmvRx+/3\nIyiym2LzJqSTyqtyv9zMbMNgZK8yqeikSl9CgSBuPPVK/OOi20zv8tvJowLUTn1GTiqZU+XUSdWv\nRO8s3WrQSKK+SYpU8cv9pAin7+7nrZOqMFuKVDXKudBpKW+uOD+EAkEU0wVBuilDRHnxkrXLdMtO\nBViZSRXdT0EpDhg8xtngTJBOquqGWuU6RDrXUwl5XgISF+QJId0TilSE9GCkQyAiLO2s1gefx+vu\n5/P50K9Yf4HlNJdKDU437+7n1kklu2wZtYd26qRqEk6qLIvyOTcd/pxmUgFASIanxwhp0vnlVbkf\nYPw98SqTiiIVmXPgdLx23UMYN2Ck8li8zn4RbGVSfSPWRyh1mEk1QGRSbTXKpBLlfrJznxFym4aW\nfUKX904qkUnV6L2TqneBs2BpQlIJ6aSSv+fOnVTGItXMCYd5dpxI11GloZMqdTOejPKn6KQiJD3h\n1QMhPRjpEKioq0Jza4vOBeX3+dErP34AqFHJnxNkuV+hrtwvOZlUxk4qZ8HpTpxUarmf9ftoa2/T\nuaB8Pp/Svc8IteRvn3tKjtfO/uwiS/4C/oCrrlpGmVR2JvGk5zO0z0D895q/4azDT9Ctn2kzs0We\n8wwzqRJ0UvUX50JDJ5Wrcj8ZnL5vH55nUgnHQk1DrUFwujMBenjZYF0m2GGjDnI/QEK6GOmkkhjd\nBLPCLJPKq1I/QN+QBui47lKD07uXk0rm5xFC0gP27CWkB1NsUO63q0Z1GwVE+ZgR5Ql2+LPq7pcV\nykTAH0D7N4JNc1sLWtpaHQss0kllJ5OqwcJJ1dbeFh0TEF+UkWJbvHI/KShlhTJtdd2Rf5dY91Sz\nLPfzKJMKUP+ebvdNJxWxIjsjC3d99zrM2v8ovPL5O5gyfH/MO+hoW8+VTqpkZFL1KSxF0B+ICswV\n9dVoaGnSles1KMHpNsr9OtVJJcr9GuvQK8HufhnBEB665A7c89KD6JVfjF+dfHlCYySkK5FOKolT\nkcropllmMANHjZniaD9WFBqU+ykiVVbqOqmMgtPppCIkPaFIRUgPJicjC9mhTDR+k3vUFm7H2p2b\ndNvEy6OK4LWTKtbW7fP5kJeVo9umrqnBcWt4pbufkZMqJEWqRmWbCE5dSfnZzsoWG2VnP5uijypS\ntcb8O3lOqj7CSWVV+miFcSZV/Ek8SS+OnXgEjp3ozGUgm0XYclI5PM8E/AH0LeqNLTFlftsqdmJE\n38HRZcVJleW83C92H55nUinB6Wp3P6ciFQBMHXEA/nnF7xMaGyGpwKBe/eD3+RHWwoaP9/IgOP3w\n0ZM8vUFTmC3LeGuVzsqp7KSSDk+AmVSEpCss9yOkhyNdAiu2rNEt220R3q9IZlIlWu6nv+MnXUhu\ncqmkSyzR4HTpSoonIjnt7idD0+3mO5mJVOFwGK3tbfptAx6KVELQzHJYDhTByEmVnel8QkyIJF4m\nVWt7my6jxefzubpTHy+XSopUebaC04WTqjnWSaUXqRJ2UonJbE1DbcLB6YT0JDKCIQwo7Wv6uFMn\nVWYoQ7keO8ZmGbNdgoGg7jpE0zSlHDmVRSojJ5W8ViSEpAcUqQjp4Ug30lfb1uqW+9oVqUqkk8ph\nuZ8sVxGTJBmeXu9QpGprb8PemEBkn89nMzjdPJOqWXbeM8hSikUp92u2zqQyKvezgwxDj7inWtr1\nolooEPQ0uFg6qWQ2ll2M/o50UhEviJdJVSWWi3LybZU7S/qL8+FWcT6Uwenuyv32iUayO2qiTqqc\nzGxdl9DG1mbFreXGSUVIT2KoSS5VwB9QXJt2mDhodPTfoUAQx0480vXYzJAC9ua923XLZh1UUwEj\nJxXL/QhJTyhSEdLDkZM2KVLZdVKVF+kzqbY7cFK1tbehLiYM3efzKZMsN53xYtldUwFN06LLJXlF\nCBnkRznp7tfcZj80HVCFtjqnTiqbziTppIo4vlqE88vLzn6A6kzLdOm0YCYVSRbFcZxUexPMo4rQ\nXziptlRIkcp5uZ88BiK5VpqmodrjDl0+n08JJN4lyqUpUpF0Z6hJLlVpXpGrG0DXnnARhpcNQlFO\nAW469UpF7PYCeQ6UIlUqd/czclIxOJ2Q9ISZVIT0cOQkbNX2Dbplu5lU8mJqe9VuaJpmK+i7Rog1\nBdl5ygWeIvA47PC3U5b6FRi/LyciVZODzn6Ac6FNdtOK59Tat51epGr9RpxSOhF6mEcFAMPLBumW\n3V5gG5VN5nBCTDwgNzMHoUAwWvba2NqMxpam6HGfaGe/CPK7L52lsZ35AHvdK9Vyv6Zv/t+oa+CQ\nHcr0pCFCYXYeKmLcpxXib0ORiqQ7Zk4qp6V+EQ4cMhb/u/5JAHDl4LSDLI+TpcjS8Z1K5GbmoKyw\nVzRftH9xGbJZdkxIWkInFSE9HDkJk+6dsgJ7Tqr8rFxdyUpTa7MyqTGjukHvZjC6k5doJpV0ARjl\nUQGqGGLppFLK8eI4qbKdvQclON2mkypkkkklnVRedvYDgIGl5bhg+mkAOkoKLj32bFf7MXRSZbHc\njySOz+ezzKWKFWUAtRzaLrKRhJpJ5bzcT7qtIvtQXFQeOQvi7YeTQ5LumDmpZOm7EwL+QNIEKkDN\n5WuLEbiB1HZS+Xw+XD//cmSHMpGdkYVfzr/M1o1QQkjPg04qQno48SZhZUX2RCqfz4d+xX2weseG\n6LptFTtRaqNcpkrkqRhlDKiZVA6dVNV6J5XZnU5H5X5CRIpXPiedVHVxRCq5f7uTQpkFFSlLVDv7\neStSAcAtp/8YP5p9LnIysl0LS0Zlgiz3I15RnFuI3TUV0eXK+mqUF3eUK1fWeeOkGiAzqUS5X4Ms\n93PjpPrm3KR0RvUo+Dhe1gudVCTdGdrH2EnVy6WTqjOId1yncnA6AJw8ZRbmHnQ0AG+7ExNCuhd0\nUhHSw4mXuWLmODKiX7HIpaqyl0slQ3+NurUkmkm106aTSu3uZx6c7tSZ1FXd/VrbOkqbZDfCZF3g\n9S4oTcj5ZBS4zuB04hUyk6Uy1klVn5xMqm2Vu3SZeO6C0/WiUEToqmmUIpVHTqo4+3HbvZOQnsKg\n0n7w+9SpUu/84i4YjT2kk0qSysHpETKCIQpUhKQ5FKkI6eFYOanMOuCZoZa42OvwJ51URuGYiWZS\n7RJOKrOsrYQyqeI5qRyW+ymZVDbL86RDKuKkUjKpPC738wpmUpFkYtXhr0IGp7t0UhVk5+kme02t\nzbruokpwug0nlRSy6qMilcj0Mzh/usHoPBwhFAgaNp4gJJ3IDGUYZi+6zaTqDGR3P0mqO6kIIQSg\nSEVIj8dqEmbWAc8M1Um129bzZLmK0UVUwplUIjjdrGuhs+5+0ukUr7ufs3I/99399J/ZvkwqWe6X\nmnciDcv9mElFPKIzMqkAoJ9FyZ8bkUoKtVEnlTx/euakMhepWOpHSAfDDHKpUlmkKjZwqsfSHZxU\nhBBCkYqQHo5VOUtfB6V+gCpSyY5WZlQ3xi/3SzyTSpb7GTupZJctJ8HpRuJKLM7L/WQwu12RSi+W\nRcQptdwvNZ1UhsHpzKQiHqE4qWIaN3jV3Q/o6DwVS2x4unSC2hFh5bmpvrkRmqYZBKd746SyKguy\ney4ipKczxKDDX+/81BWpjK6vImRnZCFIhyQhpBtAkYqQHk6xhVOgj4mQY4Ys99tWaS+TSnFSGdzB\nTzSTSi33S9xJJUWkeM4kpWSxuQHhcNhi/95kUpk5qTJT1EmVZSCeydBoQtyiZFLVWZT7ucykAoAB\nIpcq4qQKh8NoaNE7qeyUs2YEQzpna1gLo7mthU4qQrqQbuekshCfU7mzHyGExEKRipAejtUkzElo\nOoBoh6wIrkUqG939nGRSaZpmu9wvFAgiGNP+uS3cjtb2NsNtZfe9eBlPwUBQN7nTNE2ZrMaiilR2\nM6lMRKrWzglOTxQ6qUgyke6oqmQ5qUS535bKDidVY2uzLkQ9K5Rp270g3VQNzU1Jc1JZZddQpCKk\nA6MOf6ksUlkd1yz1I4R0FyhSEdLDycnIMnXolBU4c1LJ8pbtoqOVGW4yqeLlOcVSUV+tE5rys3It\nnQt23VSyu19WML7TyUnJnwxOT9RJ1axkUqVmuV8wENQJpCW5hZwUE88oypFOqthMKumk8k6k2lbR\nIdo3iM5+UniyQoq19c0NXeSkYrkfIYBa7uf3+RNyYCYbS5GKoemEkG4CRSpC0gCziZiZ28iM/Oxc\nneOpua1F19HKjGQ7qXaJPKp4ZYx2RSop+tjpluekw5/MvLIfnG7c3U+Kaqla7gcAP5l7AQL+AAL+\nAH4y70L4/fw5It6glPt946Rqa29T8vGsSmPioXQ7/cZJ5SY0PYIse02mk0qKebFQNCakgyG9B+hE\nqTH9hyMQ48ZONazcoXRSEUK6C0zPIyQNKMkrMizN61vkTKQCOiZmq7avjy5vq9yJXvnFls+pbtBP\nsowmR/LiyYlItVPkUfWJ4xCzK1LJTCo7IpUTR5jbTCopPrWaZVKlcPjxd488CbMP+BY0TUvp0gnS\n/ZA5fJESv6qGWp3zszAnP6EQ4f4mmVSqSGW/c6Uanp5EJ5WF2MXgdEI6yAiGcPuZ1+C6p+5EZjAD\nN516ZVcPyZK8rBwE/AG0h9uVx/KZSUUI6SZQpCIkDTBzCzjNpAI6OvzFilRbK3Zi4qD9LJ9THZMJ\nAxhPjqSTyklwutrZz/p92XZSOcykApyV+zUq+7c3MQwJkao5Wu7XPTKpIsQTNwlxgxTBq+o7zj9e\n5lEBQHlRb/h8vqjwtatmL1raWpXOpI6cVDKTqkV1UhV6lUllIXbRSUXIPuZNOhrzJh3d1cOwhc/n\nQ2FOPioMXO4s9yOEdBdYX0FIGmCWnxDPcWTEoNJ+uuW1OzfFfY4s9yuykUklJ3pWyND0srjlfnox\nyFykkt3yXIhUVk4qkUmV7dJJ1RIt93M+XkJ6GkZOKk3TlElbInlUQIcIXFawTxDXNA3bq3YrTiop\nwFshBa2G5kbFSVXQCU4qilSEdF+MrrEAoCCLTipCSPeAIhUhaYCZYyBedpMRI/sO0S2v3rHBcntN\n09RMFYNJVlYoU5fz0NzWomQsmbFLlvs5dFI1NNsLTrdV7qdkUpmLbc1tboPT9eOIBqcLUS0jlNpO\nKkKSQXYoUyfQtrS1oqGlSRegDiTupALU8PStFTtQ35yAk0rJpGpMmpMqK5Rpes6xajxBCEltikzc\n8/L6hBBCUhWKVISkAUZOquLcAle5IyPLh+iW44lU9c0NumyE7FCmodjj8/lc51Kp5X7OMqkaTDOp\nRDmeLSeVCIB30t3PdnC6WXc/We5HJxVJP3w+nyJAVdZXo6K+SrfOiw5dSnh6xU7FSZXjKJNKf24y\nyqTyMlfGrMMfM6kI6b6YdfgroEhFCOkmUKQiJA0wKmtx2tkvguKk2r5BF0YsqVI6+5l3lMp1mUsl\nnVSeZVKJ8jk7EzdH5X4ug9PNRCq13I9OKpKeSCdBVX0NKur0mVQlCXT2i2DspHLf3U9uu6e2Em0x\nIr+V+8kNZq4sWRJNCOk+FJtcZ7G7HyGku0CRipA0oMSgrKXMRR4V0BGcHtutqrapHjuqdptur3Sm\nyjXPU5EXUPU2RSrppOpT4FVwuovufkq5n5VI5Xz/gJFI9U0mVWv3Ck4nJFnIZhGV9dVKcLoXTqoB\nUqSq3JlYuZ/Ydrs4t5o5n9xilm/FTCpCui9mNwPZ3Y8Q0l2gSEVIGmA0GXPrpPL5fBjRd7BunVXJ\nn+KksrhIctvhb2dnBae76O5XlxQnlX4c+7r7tVhuR0i6oIpUqpPKm0yqvrrlrRU70SCdVAkEp8sb\nAF7lUe3bH0UqQnoaZplUdFIRQroLFKkISQOMyv36FrkTqQBgVPlQ3fIqC5GqpkGG/tp3UtnJpKpr\nqtdNCjODGZavARg5qZoNt5Mikh3RRyn3s8qkEvu3W2Ijy/hao+V+rZbbEZIuGGVSqU6qxEUqJZOq\ncifqm9yX+8ng9G2Vu3TLXnX2i0CRipCeh2l3PzqpCCHdBIpUhKQBRo6BsjglcVaMMsilMqOqQd9R\ny0pAcpNJtVPkUfUuKIHP57N8juxc1dhqr7tflh0nlSj3q2s2F9qU4HSbTqqQzUyqDJvlg4T0NIwz\nqap065LR3W/L3h2oa9aft3IdBadbO6kKPHZSmQanM5OKkG6L2XVWHp1UhJBuAkUqQtIA43I/d5lU\ngEF4uoWTqloJTneSSRXfSaV29osvvrkNTrfT3U9eBFp191P2b1NUkuOI7Eft7kcnFUlPpABVkaRM\nqtK8Ip24XN/cgO2VemEpkeD03bUVuuVCj51UZo6L7BCdVIR0V2S5cwR29yOEdBcoUhGSBuRkZCnC\nhh0xx4yR5UN0y5YiVaMs9zPvqOUmk2pb5U7dcnlxn7jPkRMw80wqvdPJVnC6g/fg1kklxafW9jYA\nqpMqK0g3BElP5CStqr4Ge2V3Pw/K/Xw+n+KmWrV9vW45kXI/2TnVcycVy/0I6XGYB6dTpCKEdA8o\nUhGSBvh8PvQqKNGtKy/q7Xp/g3v10wklu2sqUFVfY7httSz3swxOd55JJTNb+tsRqWwGp6vd9+KL\nPkomlYlIFQ6HFSeV6+D0b8YpyxMzQnRSkfREOqn21lUp5ygvyv0AoL9BLlUsTsr9crOsBS2vnVRm\n52O7+XiEkNTDKDjd5/M5OhcRQkhXQpGKkDRh3kFHR/+9/+AxGNSrn+t9BQNBDO0zULfOzE3lpNzP\nnZNKL1LZcVLJ3Bez4HTZ3c9OJpXdcr8mA4EqXpZWhIxgULccEaekqMbufiRdkZO0jbu3IqyFo8t5\nWTmelcP2E04qiTzfWG6bYb0tnVSEkHgYlfHmZ+XavsYghJCuJhh/E0JIT+CXJ1+KUeVDUd1Qg7OO\nODHhi5WRfYfg623rosurd2zAwcMnKttJkarIotzPTSaVLPeT3baMcJtJZWdSK+30tU310DRN+Xu7\nKSXcNw79tpEyP1nux+5+JF2RLqmNe7bqlr3Io4ognVQSZ8Hp1uJQp2VS0UlFSLfF6GZgPjv7EUK6\nERSpCEkTAv4AvnP4PM/2J8PTV5l0+JMilZUTwAsnVbwJI2BfpGpxUe6XGcxAKBCM5kS1trehua1F\nKeVzm0cFqGJZSzSTSgan00lF0hOZSSWPDa9K/QCgf0lfy8fjlfDFkhNH0PK6hTydVIT0PLIzspAV\nykRTzM0weROQEEJSGZb7EUJcMcpmeLoMTje7cw+4zKSqkE4qbzKpNE1TSvLsOJN8Pp+aS2VQ8tfU\n6qFI9Y2YJssT2d2PpCtGmSyxeBGaHmFAnHI/Z8Hp1uKQ1+V+5plUFKkI6c5I1zo7+xFCuhMUqQgh\nrpBOKlORql46qexnUtXFcVI1tDShIqatfNAfQJ/CUsvnAPacVK3tbbrOWqFAEAF/IO6+ASBPXAzK\nIGXAQKRyUF6jiFTfuESkW4TlfiRdyQplWgotJR46qeKVGDsLTrfe1utyPzqpCOmZFObqj215E5AQ\nQlIZilSEEFcMLxuky1navHe7odgju/tZO6mkSGXtpNouSv3KinrbEpJUkUoNTpeuJCeZUYNK9aH0\nt7/wF6WVvNI50EFpnty2ua0FmqYpmVQZDsZMSE/DSojyMpPKKjg9Ixhy5GjMjtNAobOcVE6cnYSQ\n1ENea3ldKkwIIcmEIhUhxBXZGVkYWFoeXdY0DWt3btJt09zagsYYx1DAH7B0FShlcnGcVDI0vb+N\nUj/AnpNKOp3s5FFFOPvIE3XLby3/CAuXvWe5fydBxX6/H0EhxrWF29FMJxUhUaxK/rzMpMrJyDIV\nvZyU+gEd5cJWLiavnVTBQFA5Jwf8AYQCjCwlpDsjy/1kUxdCCEllKFIRQlwTr+SvplFf6leYnWfp\nEnDqpNqq5FHFD00HVJGqoaVR2UZ29nMi+Jw4aSYOGXmgbt0v//U7nTCVSHA6AIQMcqkUJxWD00ka\nYyVEeZlJBZjnUsULQjfCStjy2kkFAIVin9kZ1m4uQkjqIzv8MTidENKdoEhFCHFNvA5/VaKzn1Fb\n5FjU4HRrJ9X2Kn25X7lrJ5Va7qd09nMg+Ph8Pvz69B/D79t3it24Zyv+8vqT0eVEgtONxtPc1qpm\nUrHcj6QxneWkAoD+xcYd/pw6qQAgJ8P4OZnBjKSU4RUIdxbzqAjp/sgOpyz3I4R0JyhSEUJcE6/D\nX02DvrNfYY51x62sUKYuU6qlrVXJhoplm8ikslvulyUEnqbWZoTDYd06xUnlcHI4bsBInHvUybp1\nv3v5oaj7K5HMK0ANT29ubVZEqowAy/1I+mKVSVXqYSYVAPQrMT73uBGpzJ6TDBcVoGbXUKQipPtz\n1JgpuuUj9zu4i0ZCCCHOoUhFCHFNvHK/KhGaLstKJD6fT7Gk1zWbl/y5Lffz+/3IFqJTo3A2KcHm\nLlxJ15xwoW6i3NjShJufvc/w9Zw6JKRIJf9OoUAQfj9P8SR9sXRSeVzuZ+6kcl7ul2MiUnmdRxVB\nil8MTSek+3P0uENxzzm/wPwps/CXC27BpGHju3pIhBBiG85gCCGukSLVup2b0NbeFl2uaZROqviT\nLNmC3SqXSjqprLpsSeKFp0unU5YLkao4txA/O/Fi3brnlizEB6uWokm8XpaD4HTAQKQSfyfmUZF0\nRwYHx2LlsnJDf5Nzj7tyP2MnU7KcVCz3I6Tn4fP5cOZhc/HH79+IEyfP7OrhEEKIIyhSEUJcU5Rb\ngN4FJdHl1vY2bNyzLbpcVa93UllNGiMoTiqLXCq33f0AOyKV6O7nUvQ564gTMGHgKN26nz91F+qb\n9WHt0tkVDylCyb8TO/uRdMfKLeW1k2pAiXeZVGbuq2Q5qdTgdIpUhBBCCOk6KFIRQhJCKfmLCU+v\nFsHpdpwAaoc/Y5Gqrqle59TKCIZQmlccd/8R4opUslOeyxDygD+AX59+tW7dyq1r8ci7C3TrnGZe\nSSdVbWO9eJxOKpLeFJuI4jmZ2Z6XtJk6qbLclPt1rpNKZgVmO3R1EkIIIYR4SbCrB0D0XHrppdi4\ncWPc7aZPn46rr7467naEJJuRfYfgg1VLo8urd2zAcTgKAFDtotxPOqlqG43L/bZWiM5+RX0cZTDJ\niZjs8NfcKjrlJSD6TBmxP06ZehyeWfRKdN2mGMcZ4Ka7n3UmVUaITiqS3hTnGYtUXnf2A4DeBSUI\n+gNoC7fr1pvlS1nR6U4q0fXLrNyQEEIIIaQzoEiVYrzzzjtYvnx53O2KioqSPxhCbDBSdPj7evv6\n6L+rZXC6jUmWkknVbOykkqV+/RyU+gF2nFQy2DwxZ9Ivvn0pXv7sf6g3CYJ3KlKF4jipWO5H0p2i\nHGMxqtTjUj+gwzFZXtwHm/du1613E5ye3clOKhkwn+NizIQQQgghXkGRKkWZMWMGrrzyStPHBwwY\n0HmDIcSCURYd/mS5nxsnVV2jsUi1vUqEpnstUnnQ3S+WvkW9cdXx38MtC/5o+LjT4HTp7JJlkSz3\nI+lOSSc6qQCgf0lfA5HKTXB653b3mz72EIQCQbR+0/Ri5vjDkvI6hBBCCCF2oEiVogwYMABz587t\n6mEQEpdR5UN1y2t2bISmafD5fKhu0Jf7FdkQqZRMKhPn0dYK6aSy39kPiC9SNUmRKph4TsuFR5+O\nJ95/Aet2bVYec+rUUjKpFJGKTiqS3sispQgleUVJeb0BBrlU7oLTjZ+TLCdVn8JSvHTtP7Dg49ew\n/+Ax7ARGCCGEkC6FwemEkIQoK+ylcz/VNzdgW2WHy0mW+xXYEqlkJpVZuV/3clJF9nHzaVcZPuZ5\nuZ/HwdCEdDcygiFF9AaA4tz4XUbdYCSUuyn3M8uxSpaTCgAmDBqNX82/nAIVIYQQQrocilSEkITw\n+XxKLlWk5E8Gp3vppJKZVGbdtcyQ4cDxMqm8yniaMf4wHDPhcGV9wsHpTcykIkRSZOCmSpaTyugc\n1B2cVIQQQgghqQRFKkJIwoyUuVTbNwAwyqSK72Cwm0mlBqcnWu6nF6Va2kR3Pw+dSTedeqVSjmfk\n+LBCZk6x3I8QlRKDkPSkZVIV91XWdYdMKkIIIYSQVIKZVCnK6tWrcc0112DDhg3w+XwoKyvDIYcc\ngnnz5iE/nxeqJLVQRKodGxAOh1EjnFQF2XoBygg7TipN07BVKfdLUKRqjZNJ5UG5X4ShfQbiquO/\nh9tf+CuADrfHwcMnOtqHkknVyOB0QiRGTqrSznRSORSfASCnk7v7EUIIIYSkEhSpUpQPPvgAH3zw\ngW7dvffei8LCQtx000244oorXO23srLS9LGqqipX+yRklCj3W7VjA2qb6qFpWnRdXlYOgoH4pxw7\nmVTVDbVoaG6MLmeHMh3nzMTPpBLlfh6KVABw5ezvYVBpP6zZuRFnHDZPGU88pEhVL8Q8lvsRAhQb\nOqmSk0k1oMTISeVcpDJ7Dp1UhBBCCEkHKFKlGD6fDwcffDCmTZuGIUOGoG/fvqisrMS7776Lp556\nCtXV1fjRj36EDRs24O6773a8/5KSkiSMmqQ7Rk6qKhelfoDqpJLiC2BQ6ldSBp/PZ2v/EbIz9OV7\nDXGC07M8dib5fD7Mn3qc6+fHdVJ5LKoR0h3pzEyq/Oxc5Gfl6kpvzULQraCTihBCCCHpDEWqFOOV\nV15B//79lfXnn38+fv7zn+P444/HmjVrcM8992DOnDmYMWNGF4ySED2DevVDZjADzW0dws7e2kps\n2LVZt01htr0JlsykMnJSqaV+zjr7ATacVG2y3C+1uuUxk4qQ+BhmUhms84qjxhyMFz99G0DHeWmg\ngbsqHkZOqsxghuPmCoQQQggh3RGKVC44//zzsXPnzvgbWrBgwQKEQuok0kigijBy5EgsWLAABxxw\nANrb23HPPfc4FqkqKipMH6uqqsKwYcMc7Y8QAAj4AxhWNhArt66Nrluy/kvdNoU2OvsB9jKptguR\nqrwocZGqodk6kyrVRB+1u58s96OTipDOdFIBwK9Pvxo5mTmobazD1XPOh9/vvD+N7DwK0EVFCCGE\nkPSBIpUL3njjDWzcuDGhfbS3txuKVPEYP348DjvsMLz77rt45513HD+/uLjY8XMIscPIvkP0ItW6\nZbrH7YtU8bv7yXI/o8DieMRzUrUIJ1VWipXPhYRI1SQytFJNVCOkK5CuqaxQpqEI5BV9i3rj3vN+\nldA+jEoEmUdFCCGEkHSBIpULHnjgATQ0qO4OJ2RkuJ/wjh49Gu+++y7q6upQV1eHvDzeYSVdj8yl\n+mRd8pxUiXb2A+wEp6d2uV+8YHR29yNEdVIZlf+lGkYiVb6NzqiEEEIIIT0BilQuOProo7v09SMC\nmc/nQ2Zmak2cSfoyUnT4q2ms0y0X2RSpskKZCPoDaAu3AwBa2lrR3Nqi666nBKcnI5NKEalSS/SJ\nJ0KlmvOLkK6gVJT2JbPUzytCgSAygiG0tLVG19kV+QkhhBBCujvOwxJIlxIOh/HBBx8AAAYPHuyq\nZJCQZDBKOKkkBTYnWT6fTy35E26qbRVSpHLupMoR3f0aW/TlcjKTKtUynuKV87HcjxBgwqDR6F2w\nr6vtzPGHdeFo7JMr3FQFNhtPEEIIIYR0d+ik6mb88Y9/xIYNGwAAJ510UpeOhZBYhpUNgt/nR1gL\nGz7uxAmQl5WDqoaa6HJdY33UEaFpGrZX7dZt70kmVavs7qcXrVLPScVyP0LikREM4T8//SseeOsZ\n9Cvpg/Onn9bVQ7JFTkY2Kuv3nQPppCKEEEJIukCRKoX429/+hry8PMyePRtFRUW6x2pra3HXXXfh\nlltuAQCUlJTg2muv7YJREmJMVigTg3r1w4bdWwwft1vuBxiEp8c4qfbWVelCwvOycly5DByX+6WY\n6BNPpIqXWUVIujCk9wDcdNqVXT0MR+Rk6s9PdFIRQgghJF2gSJVCvPHGG3jqqacQCARQXl6Ovn37\norS0FBUVFfjiiy/Q3NwxMe/duzdeeOEF9O3bt4tHTIiekX0Hm4pUBQ66U+WL8PTamA5/ah6VcxcV\nYEOkismDAVLRSWU9nowUGy8hxD45mfpzIJ1UhBBCCEkXKFKlEBdddBGysrLw6quvYsuWLdiyRT/Z\nLysrw1lnnYVrr70Wffo4D4omJNmM7DsEC5e9b/hYQk6qpn1Oqu1KZz93x0J8J5W+3C/Vgsjjd/ej\nk4qQ7kpOBp1UhBBCCElPKFKlENOnT8f06dOhaRp27NiB7du3Y8+ePcjOzsaAAQMwdOjQrh4iIZaM\nLDf/jhaKVvBWqCLVPifV1qSJVM3QNA0+nw+AUXe/1OqkGYpb7pdaohohxD65WXRSEUIIISQ9oUiV\ngvh8PpSXl6O8vLyrh0KII6w6/BXm2HcC5IkJWqxI5VW5XygQRNAfQFu4HQDQHm5Ha3tb1IHU3Jba\nmVTxxkMnFSHdlwMGj8HrMa7UA4eM7cLREEIIIYR0HhSpCCGeMbJ8iOljTpwA+cJJVRsrUlV4I1IB\nHW6q2H03tjQhIxhCOBxGi8ikSjXRJ35wemqJaoQQ+/xgxhnYtGcbVm5dg3OPOhlDeg/o6iERQggh\nhHQKFKkIIZ5RkJ2HssJe2Fm9R7c+IxhCloNyOVnqEptJtU2U+/V3We4HGItUhTn5hi6qSBlgqhCv\n3C/VRDVCiH0KsvPwh/N+1dXDIIQQQgjpdPxdPQBCSM9ipEHJX2FOviORRyn3a94nUm2V5X4l7p1U\nOZnZuuVIeLp0UaVaZz8gfnB6qmVoEUIIIYQQQkg8KFIRQjxllEHJn5POfoBa7lfX2OF2CofD2FG1\nW/dYYuV+eiEnIlKpoempJ1JlMJOKEEIIIYQQ0sOgSEUI8RQjJ1VBtjORysxJtbu2Aq3tbdH1RTkF\nyBVuKCcYdfgDgKbWZt36VMx3ip9JRZGKEEIIIYQQ0r2gSEUI8ZSR5UOVdU7bp+fJ4PRvnFQyj6q8\nuLfD0elRRapvnFQik8pJnlZnEU+kykhB9xchhBBCCCGEWEGRihDiKaMMnFROy/2kk6r+GyfVNplH\nlUCpH6CKVA0m5X6pWDpHJxUhhBBCCCGkp0GRihDiKb0LShTnVEGCmVRRJ1WF7OyXoEgVkiJVI4Du\nkUkVrwQxXmYVIYQQQgghhKQaFKkIIZ7i8/mUXKpEnVSRTCqls19xH+cDjEENTu/IopLlfqnYKc/v\n9yPoD5g+noruL0IIIYQQQgixgiIVIcRzFJEqt8DR82UmVV00k0qIVCXelvuZdffLSkEnFQCELISo\nVAx7J4QQQgghhBArKFIRQjxn3qSjo//2+XyYNvYQR883c1LJ4HSvM6nMRKpUFXzMxhX0B+D38/RO\nCCGEEEII6V4Eu3oAhJCex/Sxh+DvP7gVH63+DMftfxT26zfM0fOzQpkI+gNoC7cDAFraWtHc2qI4\nqfonXO5nLFI1tTXr1qdiJhVgXtLHzn6EEEIIIYSQ7ghFKkKI5/h8Psw96GjMPejo+BubPD8vKxdV\nDTXRddUNtdhRtUe3XXmCIlWOTSdVqoaQm4lUWSk6XkIIIYQQQgixgvUghJCURJb8rd21CWEtHF0u\nyStCVoKB5mpweodI1dLWqlufqplUpk4qilSEEEIIIYSQbghFKkJISiLD01dtX69bTrTUDzAq9/um\nu19rdyn3Mx5XRoid/QghhBBCCCHdD4pUhJCUJF84qaRIlWhoOmCRSdVNgtPNnFSZFl3/CCGEEEII\nISRVoUhFCElJpJPq622dJ1I1twmRKsGywmTBcj9CCCGEEEJIT4IiFSEkJYlX7tevJBnlfsbB6d0v\nk4pOKkIIIYQQQkj3gyIVISQlkcHpu2r26pb7e+KkMg5Ol5lUqSr6mJUhpmp5IiGEEEIIIYRYQZGK\nEJKS5AsnlSQp5X7fiFPNortf9yv3S01RjRBCCCGEEEKsoEhFCElJcoWTStLPg+5+ORnZuuXuVu4X\nMhOpUnS8hBBCCCGEEGIFRSpCSEpi5aTy+XzoW9Q74dcwz6TSl/ulavmcWRc/dvcjhBBCCCGEdEco\nUhFCUhKZSRVL7/wST0ra7Hf3S02RyqyLX6qKaoQQQgghhBBiBUUqQkhKkp9t7qTyIo8KMApO73BQ\nNbVKkaqbZVKlqKhGCCGEEEIIIVZQpCKEpCS5meZOqv4liedRAUCWEJ+aWpvRHm5XMqlStXyOwemE\nEEIIIYSQngRFKkJIStIZTiqfz6eU/DW1NKOl25T7mWVSpeZ4CSGEEEIIIcQKilSEkJQkz8JJ5UVn\nvwhSpGpoaepG5X7GYhSdVIQQQgghhJDuCEUqQkhK0hlOKsA4PF0JTk9RZ5JZGaKZeEUIIYQQQggh\nqQxFKkJISmKdSeWdSJVjEJ4uM6myUrTcL2Ra7kcnFSGEEEIIIaT7QZGKEJKSdJmTqrUJza3NunWp\nWu5HJxUhhBBCCCGkJ0GRihCSkmQGMxD0B5T1fp8ffQpKPHsdw3I/4aRK1Ywn00yqUGqOlxBCCCGE\nEEKsoEhFCElJfD6foZuqb1EvBANBz17HOJOqVbeu+5X7peZ4CSGEEEIIIcQKilSEkJTFKJfKy1I/\nAMgOGTmpune5X2aKimqEEEIIIYQQYgVFKkJIymLkpOpX3MfT18gWwem1jfVoC7dHl/0+v2HZYSpg\nVoaYquWJhBBCCCGEEGIFRSpCSMqS1xlOKlHuV91Qq1vODGXA5/N5+ppeYZZJxXI/QgghhBBCSHeE\nIhUhJGXJy1KdVP2TLFJVNdTollM1jwqgk4oQQgghhBDSs6BIRQhJWYxEqn4lXpf76UWqynq9SGXm\nVkoFzMQoOqkIIYQQQggh3RGKVISQlCUvKzXK/VIVMzGKTipCCCGEEEJId4QiFSEkZck3clIlOTi9\nSjipUtmVZFrul8LCGiGEEEIIIYSYQZGKEJKy5AonVSgQRO/8Ek9fIyczW7csy/1SOZMqZFruRycV\nIYQQQgghpPtBkYoQkrJIJ1Xfot7w+709bcULTs8M6Z1WqYSZGJXKOVqEEEIIIYQQYgZFKkJIyiIz\nqbzu7AcA2SEhUinB6anrSjITo1J5zIQQQgghhBBiBkUqQkjK0reol255SJ8Bnr+GEpze2H2C083E\nqFQuUSSEEEIIIYQQMyhSEUJSlqPGTMHIvkMAdGRHnXPktz1/DRmcrmmabjkrmLrlfqbB6Sz3I4QQ\nQgghhHRDgl09AEIIMSMrlIlX/u9BfLp+OYaVDfK8sx+gOqkk3dFJxXI/QgghhBBCSHeEIhUhJKXJ\nzczGEftNTtr+u7NIlWnimDJbTwghhBBCCCGpDMv9CCFpTU43Fqn8fj+C/oBuXdAf8LwDIiGEEEII\nIYR0BpzJEELSmrhOqhR3JYVEaV9GCotqhBBCCCGEEGIFRSpCSFrTncv9AFVEy2QeFSGEEEIIIaSb\nQpGKEJLWZGVYd+9LdSeVDElnZz9CCCGEEEJId4UiFSEkrQkFgggFzHtIZIWsRayuRin3o5OKEEII\nIYQQ0k2hSEUISXusSv5Sv9wvJJZTe7yEEEIIIYQQYgZFKkJI2tOdRSpZ3pfq4yWEEEIIIYQQMyhS\nEULSHiuRKtUzntRMKpb7EUIIIYQQQronFKkIIWlPtkV4elaKO5OkKMVyP0IIIYQQQkh3hSIVISTt\nsSz3S3HRh04qQgghhBBCSE+BIhUhJO3pzplUUkTLSPHxEkIIIYQQQogZFKkIIWmPtUhlXgqYCqjl\nfnRSEUIIIYQQQronFKkIIWlPjoVIleqZVCGl3C+1x0sIIYQQQgghZlCkIoSkPdbd/VLbmSSdU6k+\nXkIIIYQQQggxgyIVISTtsQ5OT/VyP71zKtWD3gkhhBBCCCHEDIpUhJC0x0qkSvVyP3b3I4QQQggh\nhPQUKFIRQtKe7tzdTwlOT/HxEkIIIYQQQogZFKkIIWlPdoZ5SV+qiz6qkyq1x0sIIYQQQgghZlCk\nIoSkPdZOqtTOpOpXXKZb7l/cp4tGQgghhBBCCCGJQZGKEJL25FgGp6d2xtOJk2diTP/hAIAJA0dh\nzkHTu3hEhBBCCCGEEOKOYFcPgBBCuhozJ1XQH0DAH+jk0TijV34xXvnZg9hdW4E+BaUMTieEEEII\nIYR0WyhSEULSnuyQsUiV6qV+ETJDGRhQ0rerh0EIIYQQQgghCcFyP0JI2mMWnJ7qoemEEEIIIYQQ\n0pOgSEUISXvMyv2yKFIRQgghhBBCSKdBkYoQkvaYiVSZQYpUhBBCCCGEENJZUKQihKQ9ZiJVBp1U\nhBBCCCGEENJpUKQihKQ9dFIRQgghhBBCSNdDkYoQkvYwk4oQQgghhBBCuh6KVISQtIfd/QghhBBC\nCCGk66FIRQhJe7JCmfD5fMr6zJCxeEUIIYQQQgghxHsoUhFC0h6fz2dY8sdMKkIIIYQQQgjpPChS\nEUIIjHOpMoOhLhgJIYQQQgghhKQnFKkIIQRAtkFpH8v9CCGEEEIIIaTzoEhFCCEwcVIxOJ0QQggh\nhBBCOg2KVIQQAopUhBBCCCGEENLVUKQihBAAOQYiVRaD0wkhhBBCCCGk06BIRQghMHNSMZOKEEII\nIYQQQjoLilSEEAIgO0MVpDLY3Y8QQgghhBBCOg2KVIQQAmMnVRYzqQghhBBCCCGk06BIRQghYLkf\nIYQQQgghhHQ1FKkIIQTs7kcIIYQQQgghXQ1FKkIIgYlIxe5+hBBCCCGEENJpUKQihBAYB6czk4oQ\nQgghhBBCOg+KVIQQAmMnVQadVIQQQgghhBDSaVCkIoQQMJOKEEIIIYQQQroailSEEAJjkYrlfoQQ\nQgghhBDSeVCkIoQQANkhIyeVmlNFCCGEEEIIISQ5UKQihBCwux8hhBBCCCGEdDUUqQghBMbd/TKC\noS4YCSGEEEIIIYSkJ8GuHkBPZO3atVi8eDEWL16M7du3AwCOPvpo/OAHP3C0n8WLF+PFF1/Eli1b\nkJmZiZEjR+KUU07BwIEDkzFsQtKanMxsZV0Wy/0IIYQQQgghpNOgSOUhV155JR577DHs3btXeSwv\nL8+2SLVr1y6cd955ePnll5XHfvrTn+K6667DDTfcAL+fRjhCvILd/QghhBBCCCGka6FI5SFLlizB\n3r17MXToUEyZMgUHHXQQrr32Wkf7qK+vx+zZs7F06VL4/X585zvfwbe+9S3U19fjiSeewOLFi3Hz\nzTejvr4ed911V5LeCSHpB0UqQgghhBBCCOlaKFJ5yO9//3sMHjwYvXr1AgA0NTU5Fql+/etfY+nS\npQCAhx9+GGeffXb0scsuuwzf/e538eSTT+Luu+/GKaecgkMPPdS7N0BIGpNjkEnF4HRCCCGEEEII\n6TxYL+YhkyZNigpUbqitrcXvf/97AMDcuXN1AhUABAIB3H///SgoKAAA3Hzzze4HSwjRIZ1UmcEM\n+Hy+LhoNIYQQQgghhKQfFKlSiBdffBENDQ0AgAsvvNBwm4KCApx22mkAgNdffx1VVVWdNTxCejR5\nWbkYN2BkdPng4RO7cDSEEEIIIYQQkn5QpEoh/ve//wEAfD4fjjrqKNPtpk2bBgBobW3FBx980BlD\nIyQt+NuFv8aJk2filKnH4ffn/rKrh0MIIYQQQgghaQUzqVKI5cuXAwDKy8tRVFRkut1+++0X/feK\nFStw/PHHJ3tohKQFw8oG4S8X3NLVwyCEEEIIIYSQtIQiVQqxZcsWAMCAAQMst4t9fPPmzY5eo7Ky\n0vQxlg4SQgghhBBCCCGkq6BIlULU1tYCAHJzcy23i328rq7O0WuUlJQ4HxghhBBCCCGEEEJIkmEm\nVQrR0tICAAiFQpbbZWRkRP/d3Nyc1DERQgghhBBCCCGEdAZp56S6/PLLsXv37oT28eijj8YVktyQ\nk5OD6upqNDU1WW7X2NgY/Xc815WkoqLC9LGqqioMGzbM0f4IIYQQQgghhBBCvCDtRKr//Oc/2Lhx\nY0L7eOihh5IiUhUWFmL79u1xs6Gqq6uj/y4oKHD0GsXFxW6GRgghhBBCCCGEEJJU0k6kuvfee1Ff\nX5/QPmLL7bxk+PDh+Oqrr7Bp0ybL7WIfHzFiRFLGQgghhBBCCCGEENKZpJ1INW/evK4egikTJkzA\niy++iKqqKmzYsAFDhgwx3G7p0qW65xBCCCGEEEIIIYR0dxicnkLMnj07+u+XX37ZdLtXXnkFQEen\nvilTpiR9XIQQQgghhBBCCCHJhiJVCnHEEUdg6NChAIA//OEP0W5/sSxbtgyvvvoqAODMM89EMJh2\nZjhCCCGEEEIIIYT0QChSpRB+vx+33norAOCrr77C+eefr+v0t379epx22mkIh8PIy8vDz3/+864a\nKiGEEEIIIYQQQoin0IbjIW+99Rb+8pe/RJfD4bDusTPOOCO6nJWVhYceekjZxxlnnIH3338f9913\nHx577DG88sorOOSQQ1BfX4/33nsPra2tCAaD+Oc//4ny8vKkvh9CCCGEEEIIIYSQzsKnaZrW1YPo\nKfz973/HhRdeaGvb3Nxc1NXVmT7+l7/8BTfffDO2bt2qW3/wwQfj3nvvxdSpUxMaqxGVlZUoKSkB\nAFRUVKC4uNjz1yCEEEIIIYQQQkjPwGsdgSKVh6xbtw6LFy+2tW0oFML8+fMttwmHw/j888+xefNm\nZGZmYtSoUdHMqmRAkYoQQgghhBBCCCF2oUhFkgZFKkIIIYQQQgghhNjFax2BwemEEEIIIYQQQggh\npMuhSEUIIYQQQgghhBBCuhyKVIQQQgghhBBCCCGky6FIRQghhBBCCCGEEEK6HIpUhBBCCCGEEEII\nIaTLoUhFCCGEEEIIIYQQQrocilSEEEIIIYQQQgghpMuhSEUIIYQQQgghhBBCuhyKVIQQQgghhBBC\nCCGky6FIRQghhBBCCCGEEEK6HIpUhBBCCCGEEEIIIaTLoUhFCCGEEEIIIYQQQrocilSEEEIIIYQQ\nQgghpMuhSEUIIYQQQgghhBBCupxgVw+ApA7hcDj676qqqq4bCCGEEEIIIYQQQlKeWO0gVlNwC0Uq\nEqWmpib672HDhnXhSAghhBBCCCGEENKdqKmpQWlpaUL7YLkfIYQQQgghhBBCCOlyfJqmaV09CJIa\ntLW1YfPmzQCAgoIC+P3dQ8OsqqqKOr/WrVuHoqKirh0Q8Qx+tj0bfr49F362PRd+tj0bfr49F362\nPRt+vj2X7vDZhsPhaFXWwIEDEQwmVrDHcj8SJRgMYujQoV09jIQoKipCcXFxVw+DJAF+tj0bfr49\nF362PRd+tj0bfr49F362PRt+vj2XVP5sEy3xi6V7WGUIIYQQQgghhBBCSI+GIhUhhBBCCCGEEEII\n6XIoUhFCCCGEEEIIIYSQLociFSGEEEIIIYQQQgjpcihSEUIIIYQQQgghhJAuhyIVIYQQQgghhBBC\nCOlyfJqmaV09CEIIIYQQQgghhBCS3tBJRQghhBBCCCGEEEK6HIpUhBBCCCGEEEIIIaTLoUhFCCGE\nEEIIIYQQQrocilSEEEIIIYQQQgghpMuhSEUIIYQQQgghhBBCuhyKVIQQQgghhBBCCCGky6FIRQgh\nhBBCCCGEEEK6HIpUhBBCCCGEEEIIIaTLoUhFCCGEEEIIIYQQQrqcYFcPgPR8mpqa8Omnn2LRokVY\nunQpGhoaAADXX389JkyY4OlrNTQ04Pnnn8dHH32EPXv2oFevXpgyZQpOOukk5Obmdto+0ok1a9Zg\n8eLFWLRoEbZu3QoAOOKII3DllVd6sv9wOIwvvvgCH3zwAbZs2YLt27ejvb0dZWVlmDp1KubOnYus\nrCzLfVx33XVYtWpV3NeaMmUKrrnmGk/G3ROoqqrCxx9/jEWLFuHLL79EW1sbAOCxxx6L+ze3wxtv\nvIE///nPtrZ98MEHkZ+fb7nNrl278PTTT2P58uWoqalBeXk5Zs6ciZkzZyIQCCQ83p5EW1sbli1b\nhsWLF+Pjjz9GVVUVAOCCCy7Acccdl/D+f/Ob32DJkiW2t8/Ozsajjz6qWxcOh3HaaafZev65556L\nefPmORpjT2XVqlV49913sXHjRmzfvh0NDQ3o1asXJk2ahHnz5qG4uNjT1/vqq6/w7LPPYu3atWhv\nb8fgwYNx4okn4qCDDurUfaQDVVVVePfdd7F8+XJs374du3fvRl5eHkaOHIljjz0W+++/f0q8zldf\nfYVf/OIXtl7rlltuwX777efFsLs17e3t+PTTT/HRRx9h69at2L59OzRNQ3l5OQ499FDMnj0bGRkZ\nCb/Oj370o+i1mhXTp0/HpZdearmNpml4//338fLLL2PLli3IzMzEqFGjcOqpp2Lw4MEJj7UnsXXr\nVrzzzjtYs2YNtm/fjurqahQVFWHixImYM2cOBg4cmND+d+zYgcsuu8zRc0455RScccYZunX/+c9/\n8PDDD9t6/r/+9S/4/fS7AB2/u++88w42btyIbdu2oaWlBaWlpdh///0xb9489O7d27PXamlpwcsv\nv4x33nkHu3btQlFREQ444ADMnz/f9u+7F/tIOhohSWLPnj3apEmTtFAopAFQ/lu4cKGnr7dw4UKt\nX79+hq/Vt29f7ZVXXumUfaQL11xzjVZSUmL4tzr99NM9eY0//OEPWnFxseFrRP4rLS3VHn74Ycv9\nTJ061XIfkf9OPPFET8bd3Xnvvfe00aNHaz6fz/DvVFtb68nr/O1vf7P1uQDQdu/ebbmv++67T8vO\nzjZ87oEHHqh99dVXnoy5JzBjxgzTv9W9997ryWvMmTPH9mcLQCsvL1f20draavv5v/3tbz0Zd3fm\n+eef18rLyy3/Tjk5Odqvf/1rrb29PeHXa2pq0i6++GLT1zr11FO1qqqqpO8jHWhpadEOOuggze/3\nW36+xx57rLZp06Yuf513333X9rH77rvvuh5vT+GWW27RCgoKLP9Offv21Z555pmEX2v06NG2Ppdz\nzz3Xcj/btm3TZsyYYfjcQCCg/eIXv9DC4XDC4+3uLFu2TBsxYoTl3zoQCGgXX3yxVl9f7/p1Vq9e\n7eg3F4B2++23K/v57W9/a/v5ra2tifxpegQffPCBNnDgQMu/UygU0q6++mqtpaUl4df7+OOPtVGj\nRhm+TmFhofb44493yj46AzqpSNJobGzEJ598gqysLEyaNAlTpkxBTU0NHnroIc9f67333sPcuXPR\n3NyMPn364JJLLsGIESOwZs0a3H///dixYwdOOOEELFy4EEcddVTS9pFOfP7556ioqMDw4cMxZcoU\nTJkyBVdffTXC4bBnr/H111+jsrISAHDAAQfguOOOw6hRoxAMBrFkyRI89NBD2Lt3L84991xUVFTE\ndW8dcsghuPrqq00f79+/v2dj785s374dX3/9NQoLCzF58mRMnToVH374Id56662kvebDDz+MnJwc\n08cLCgpMH7vvvvtw+eWXAwDGjRuH73//++jVqxeWLFmCv//97/j0008xY8YMLFq0iJ8xgI8++git\nra3Yf//9MWXKFBQVFeG3v/2tp6/xf//3fzjvvPMst3n77bfxxz/+EQDiOqbOO+88zJkzx/TxiRMn\nOh5jT2PTpk3Yvn07AGD48OGYO3cuxo0bh5ycHKxcuRIPPPAAtm/fjp///OfYvHmzbRejGeeeey6e\neuopAMCxxx6L+fPnIxAI4JVXXsEzzzyDp59+Gnv27MGrr76KUCiUtH2kA+3t7Vi6dCkAIDc3F8cd\ndxwOP/xwlJWVYc+ePXjuuefw1ltv4bXXXsMRRxyBDz/8EP369UuJ17n11lsxcuRI08fHjBnjeJw9\njZUrV6KmpgYAcPDBB2PWrFkYPnw4fD4fFi1ahEceeQQ7duzAKaecggcffDDuudUOM2bMwMUXX2z6\n+JAhQ0wfq6mpwaxZs7Bs2TIEAgGcc845OOqoo1BfX4/HHnsMH330EW655RY0NjbizjvvTHis3Zk9\ne/ZgzZo1AICysjLMmTMHkydPRlFRETZu3IhHHnkEK1euxP3334+vv/7a9bmuvLwcTz/9dNztfvKT\nn2Djxo3w+Xw45ZRTLLeNtz861IGdO3di69atmDRpEqZOnYpBgwahX79+qKurw+rVq/HII49g7969\nuOuuu7BhwwY888wzrl9r5cqVOOaYY1BVVYXCwkJcdNFFmDBhArZs2YK//vWvWL9+Pc4++2wEg0HT\nayov9tFpdLVKRnoujY2N2scff6xTjh988MGoWuuVk6q1tTV6l6J///7a5s2bdY9v3bo1qnIPGzbM\nUMn2Yh/pxueff67t2bNHty4QCGiAd06qSy+9VJs9e7b20UcfGT6+YcMGbfDgwRoALSMjQ1u3bp3h\ndhEn1fz58z0ZV09n+/bt2sqVK3V3QS+66KLosZsMJ1VlZaWrfWzYsEHLzMzUAGjTpk3TGhsbdY+/\n9957UTfnKaec4sGouz8fffSR1tDQEF3+8MMPo5+DV04qO5xwwgnR1/3ss8+Ux2OdVPfcc0+njau7\ncu+992qTJ0/WXn75ZUMHQ2VlpXbIIYdE/6avv/6669d6+umno/u5+uqrlcfvvvvu6OO/+93vkraP\ndKGxsVErLCzUbrvtNtNzZawD4owzzujS14l1Un344YeuxpJOnH322drJJ5+sffrpp4aPf/XVV1pZ\nWZkGQMvLy9N27Njh+rUiTqrzzz/f9T6uvvrq6Of79NNP6x5rb2/XzjzzzOjjZtdv6cJbb72lDR06\nVHvkkUcM5w4tLS26v1cyz3Xr1q2LOuSnTZtmuE3s8U3is3PnTm3nzp2mj9fV1WnTp0/35Hf3iCOO\n0ABo+fn52vLly3WPVVZWahMnTtQAaMXFxabnby/20VnwG0g6lWSIVI8++mh0n0888YThNk899VR0\nm4ceeigp+yDei1R2LsQWLFgQ/VxuvfVWw20oUiVOqopUl156qQZA8/v92urVqw23ueqqq6Kvs3Ll\nygRG3DPpCpFq165dUfHwwAMPNNyGIpUzdu7cGbe8ZuXKldG/6TnnnOP6tfbff38NgDZ48GDTmzZT\npkzRAGhlZWVaW1tbUvaRLrS3t2u7du2Ku93hhx8evWnj5jzt1etQpHKGnWud2OvnP/3pT65fK1GR\nqrKyUsvKytIAaCeffLLhNlVVVVp+fr4GQJs7d67rsfYEKisrtaamJsttamtrtaKiIg2AdtBBByVt\nLNdff330O2QWk0GRyns+/vjj6N/0uuuuc7WPt956K7qPO+64w3Cb999/P7rNLbfckpR9dCZMOyPd\nnogdtaioyNS6evLJJ6OkpES3vdf7IN5TVlYWd5uZM2dG/71ixYpkDoekGJqm4dlnnwUATJs2DSNG\njDDc7oILLoj+OxGrNfGOxx9/HK2trQCA733ve108mp5Bnz594PP5LLfZb7/9oiWvbs+Xa9asweef\nfw6go1zPrDTl/PPPB9BRDvHuu+96vo90wu/32wrejfwetrS0YO3atSn7OkRPd7rW+c9//oOmpiYA\n+t/WWAoLC6PX0q+99lq0lDEdKSoqQmZmpuU2eXl5mDp1KoDkfbaapuGRRx4BAOTn58ct9SPeMWjQ\noOi/29vbXe0jMu/0+/34/ve/b7jNYYcdFi2ftprrJrKPzoQiFen2/O9//wPQcWCZXegGg0Ecfvjh\nuu293gfpGiITXaDjR5ikD6tWrYpm8EybNs10u7Fjx6JPnz4AgHfeeaczhkbiEMkmzMjIwFlnndW1\ng0kzIudMt+fL2GPI6riLfUwed17sg6h01u8hf3c7n1T5m0eufwOBAI488kjT7SLHbktLCz788MPO\nGFq3JvbzTQbvvPMO1q9fDwA4/fTTLTNAibd8/PHH0X9PmjTJ1T4ix9348eNRWlpqul3kuPv8889R\nXV3t+T46Ewank27Ntm3boq3T44Vv7rfffvjPf/6D2tpabN68Odru1Yt9kK7jjTfeiP473ue3efNm\n3HjjjVi3bh00TUOfPn0wZcoUzJkzB7m5uckeKrHg73//O9asWYOamhoUFhZi7NixmDVrFkaNGmX6\nnOXLl0f/He+zHz16NHbt2kW3XQrw+eefR100J554YtShasXixYvx05/+FNu2bUNmZiYGDBiAI488\nEjNmzGALbAcsX74cu3btAuA+sNrucTdixAgEg0G0tbUpx50X+yAqkd/DUCiE4cOHp8TrPPfcc3j8\n8cexd+9e5OXlYfjw4Zg5c6bryVq64uRaxw5r167Fr371K2zYsAE+nw99+/bFIYccgtmzZyMrK8v0\neZFjd8CAAcjLyzPdbr/99ov+e8WKFZg1a1bCY+6p1NfXY/HixQD0fzcviW1aZde9fO+992LZsmWo\nq6tDUVERxo8fj9mzZ2Po0KFJGWNP5J133sEPf/hDAMDkyZMxf/58x/toa2vDqlWrANibp0ZYsWIF\nDj30UM/20dlQpCLdmq1bt0b/Ha9rV+zjW7ZsiQpMXuyDdA3t7e349a9/DaDjrt6pp55quf3ixYuj\nFwKxFBcX44YbbsAVV1yRlHGS+Pz0pz9V1vl8Pnz729/Gn//856gTKhY3x+62bdsQDocpbHQhDz74\nYPTfdi+Wn3zyScP1w4YNw/33349jjjnGk7H1dG644Ybov88880xX+4gcd4FAwLJMye/3o2/fvtiy\nZQu2bNni+T6InldffRWLFi0CAMydOxf5+fkp8Tq333674fojjjgC//jHPyxvRJAOmpubo3/HzMxM\nfPvb3054n2+//TbefvttZX3v3r1x2223RUttJZFj1+n1MjHnzjvvRF1dHQD352Ur6uvr8e9//xsA\nMGrUKBx22GG2nmd0Tez3+3HmmWfivvvuQ1FRkZfD7Pb885//jEZKVFVVYd26dVi/fj1CoRDOPvts\n/PGPf3R17bl79260tLQAcH/cebGPzoYiFenW1NbWRv8dzwkT+3jkx8CrfZCu4cYbb8Rnn30GALjw\nwgstL3bHjBmDo48+GkOHDkV5eTmqq6vx/vvv45lnnkFlZSV+9KMfYc2aNfjDH/7QSaMnQEdew7HH\nHouxY8di8ODB8Pl8+Oqrr/DUU09h/fr1ePbZZ/Hpp5/iww8/VCazbo/d+vr6pE3giDVtbW144okn\nAAD9+vXDsccea7m93+/HoYceisMOOwyDBg1CSUkJtm/fjtdeew2vvfYa1q1bh1mzZuHJJ5/E6aef\n3hlvodvyxBNPRC+gp0+fjuOPP97VfiLHXU5OTtwMrMhxJ38vvdgH2ceePXvwgx/8AECHiHHLLbd0\n+etkZ2fj6KOPxgEHHIDBgwcjOzsb69atw4IFC/DZZ5/hvffew5QpU/Duu+9iwoQJSRlvT+GnP/0p\n1qxZAwC46qqr0K9fP9f78vl8mDhxIqZNm4YhQ4agrKwMFRUVePfdd7FgwQLs3r0bF1xwATZu3Iib\nbrpJeX7k2OX1sjd8/PHHuO222wAAgwcPxmWXXeb5azzzzDPRz8DOjaHS0lLMmjUL++23HwYNGgRN\n07B8+XI8+eST2Lp1Kx5//HF8+umneP/99ylUxfDll19GxcBYTj/9dFx77bUoKChwtd90netSpCLd\nmogqDHRkRlmRkZER/Xdzc7On+yCdz7///e/oBfJ+++2HO++803TbJ5980tCefMkll+CGG27A3Llz\n8fXXX+Pee+/FrFmzMGfOnKSNm+xjzpw5OOuss5Cdna08dtNNN+Gqq67CH//4R6xfvx4XX3wxFixY\noNsmkWOXIlXX8OKLL2L37t0AgHPOOQeBQMB020AggLVr12LIkCHKY1dffTUWLlyIk08+GXV1dfj+\n97+PI488MqHJW0/mk08+iYYcl5SU4OGHH3a9r8hxF++YA/Ydd/L30ot9kA5aWlpw6qmnYtOmTQCA\nO+64A2PHju3S1xk/fjy2bt2K4uJi5bFf/epXuOeee/DjH/8Y1dXVOPPMM/H5559bngvSmQceeAD3\n3nsvgI48mxtvvDGh/b300kuG10OXXXYZvvzyS8yZMwebNm3CzTffjFmzZkWzWCPYPXZ5vRyf7du3\n4+STT0ZzczOCwSCeeOKJpGRFRUr9AoEAzjnnHMttzzzzTFxxxRW6zy/CzTffjIsuugiPPPIIVqxY\ngauuukrnjE53zjzzTBxwwAEAOsSdZcuW4YknnsBjjz2GJ598Evfccw8uv/xyx/tN17ku6x1Ityb2\nZB7vQGpsbIz+O1Yl9mIfpHP53//+h7PPPjuaK/XCCy9Yfh5W9fMjRozA888/Hw3Mv+uuuzwfLzGm\nvLzcUKACOrJO7rvvPhx99NEAOnJN1q1bp9uGx273w0kuhs/nMxSoIhxzzDH44x//CABoaGjA/fff\n78UQexyrV6/G8ccfj8bGRmRlZWHBggUJlapHjjs7F6+R404ec17sgwDhcBjnnntutGzrBz/4QVLK\n1p2+TlFRkaFAFeGqq67CJZdcAqAj4+jVV1/1dLw9hRdffBEXXXQRgI4OYc8995yheOAEq+uh8ePH\n49lnn426G42uh+weu/zNtaaqqgrHHXdctJzq/vvvt12G54QNGzZEm07MmjUr7o2c/v37m37HsrKy\n8OCDD2Ly5MkAgEceeQR79uzxdsDdmHHjxuGUU07BKaecgvPOOw933XUXVq9ejblz56K9vR1XXHGF\ncrPVDuk616VIRbo1hYWF0X9Hws/NiO1QEGu59GIfpPNYsmQJ5s2bh6amJhQVFeG1117DyJEjE9rn\n6NGjMWPGDADAe++9h7a2Ni+GSjwgEjgJQMnPcHPshkIhU2GMJJc9e/bgxRdfBAAcfvjhnmTRnHXW\nWdHvgVG+SrqzefNmHHPMMdi1axdCoRCefvppHHXUUQntM/L3bmxs1N2dNSJy3MnfSy/2QTrcwP/8\n5z8BdJSU/PnPf+42r3PppZdG/81jV+Wdd97Bqaeeira2NpSVlWHhwoUYMGBA0l930qRJmDp1KgDj\nzyVy7PJ62T319fU4/vjj8cUXXwDoyKQyywBLlIcffjjaDdJuBqQVfr8fF198MYAO8Zrdzq3Jy8vD\nY489Fj1u7rjjDsf7SNe5LkUq0q0ZPnx49I7Pxo0bLbeNfXzEiBGe7oN0Dl9++SWOO+441NTUIC8v\nDy+//DL2339/T/Y9evRoAB1tgPfu3evJPkniRD4XoMMaH0tsVym7x24yO14Ra5544olom20vLpaB\njvKFyLlYfj/SnZ07d2LmzJnYuHEjAoEAHnvsMcydOzfh/UaOIU3ToqVfRjQ2NkZLO+XvpRf7SHeu\nvvpq/PWvfwUAzJs3D48++mhSGkIk63Wszu3pzuLFizFv3jw0NjaipKQECxcu7NSA+chnU1lZiaam\nJt1jkWOX18vuaGpqwgknnIAPP/wQQEczi6uvvjopr6VpGh555BEAHTlTJ5xwgif75bHrjMLCQnzr\nW98C0HGjPSIa2qW0tDSa/eX2uPNiH50NRSrSrcnLy4uWg3z66aeW2y5duhQAMHDgQJ2i7MU+SPJZ\ntWoVZs6cib179yInJwcvvvgiDjnkEM/2H2t/TdROT7zD6nOJDdu1OnZbWlqi7esZ0Nt1REr9cnJy\ncNppp3m238h3hMftPvbu3YuZM2di1apV8Pl8eOCBBzz7m0+cODH6b6vjLvJ7CajHnRf7SGd++ctf\n4u677wYAHHvssXj66aejJevd5XX4m2vM559/juOOOw61tbUoLCzEq6++2unf/chn4/f7lc87cuzu\n3bsXmzdvNt0Hj12V1tZWzJ8/H2+++SYA4Nprr8X111+ftNd79913ozEJ3/nOdzw7znjsOifiSGpv\nb0d7e7vj50eOIbvz1MzMTEXY9mIfnQlFKtLtmT17NoCOg2rnzp2G2+zevRuffPIJABh2NPJiHyR5\nbNiwATNmzMDOnTuRmZmJ5557LuGSFcl7770HoONug1WWBulcIp8LoN7RKSsrw0EHHQQAePnll033\n8eabb0bvBvPY7RqWLVsWvTA65ZRTPAuu37t3L7766isAvFsfoaamBrNmzcKXX34JoCPrJF5YrhNm\nzJgRnZi89NJLptvFHpPyuPNiH+nK7bffHm0aMm3aNDz33HPIzMzsdq9jdW5PV1auXIljjjkGlZWV\nyMvLw0svvRTN/+kswuEwPvjgAwDAkCFDlED7yPUyYO/Y7dOnT6e/h1Skvb0d3/nOd6J/syuuuAK/\n+c1vkvqaTjIgncBj1zlff/01gI7rVjsNQySR427btm34/PPPDbdpbm7GW2+9BQCYOXOmIjB7sY9O\nRSOkE3nwwQc1ABoAbeHChZ7s86OPPoru89prrzXc5rrrrotu8/777ydlH0TTAoGABkA7/fTTPdvn\n1q1btWHDhmkAtFAopP33v//1bN8RHnvssehne95553m+/57ARRddFP0b1dbWdspr7t69WxswYIAG\nQMvNzdWqq6uVbX73u99Fx7VgwQLD/cyYMUMDoBUUFGgVFRVJHnX348MPP4z+De+9996kvMZVV10V\nfY23337bs/1efPHF0f3+/e9/92y/3ZW6ujrt8MMPj/5N/vCHPyTldU455RQNgJadna1t3LhRebyy\nslIrKyvTAGiHHnpo0vaRbtx7773Rz/awww5L2rk42a/T3NysTZ06Nfoay5Yt83T/3ZG1a9dq/fr1\n0wBoOTk5np4nnXDfffdFP5crr7xSebytrU0bMmSIBkAbM2aM1tzcrGzzySefaD6fTwOgXXXVVZ0x\n7JQmHA5r3/3ud6N/14suuijpr1lfX6/l5+drALT999/fs/1u2bJFKy0t1QBopaWlhp8/0fPWW28l\nPMdYv369FgqFNADaWWedZbjNn/70p+jrPPXUU0nZR2dCkYp0Kk5FqvXr12vz58/X5s+fr/3lL38x\n3e7b3/62BkALBoPao48+qnvs8ccf14LBoAZAO+GEE5K6j3THqUh1zTXXaPPnz9dOO+00w8d3796t\njRkzJvq5/Pvf/3Y8pscff1z717/+pdXU1CiPNTY2anfffbeWmZkZFULWrl3r+DXSAaci1WuvvRY9\ndt966y3l8TVr1mh33nmntmrVKsPnf/DBB9rYsWOjr3nLLbcYbtfU1KQNHTpUA6D17t1bW7RoUfSx\n1tZW7Zprronu49Zbb7X3ZtMMpyJVS0tL9LP91a9+FXf71tbWqNgwbNgwLRwO2xrXb3/7W+2NN97Q\nWltblcd27dql+06OGTNGa2lpsbXfnkpTU5M2c+bM6N/kjjvucLWf3//+99HPd/v27YbbrFy5UsvI\nyNAAaJMnT9a2bdsWfayyslI79thjNQCaz+cznWx7sY904sEHH4xO/A8++GBD0T4eX375ZfSzfeyx\nx5LyOvX19doNN9ygLV261PDxr776Sps+fXr0e3r22Wc7fh89jc2bN0eFn8zMTO21115ztZ/LLrtM\nmz9/vva9733P8PEHHnhAW7BggVZfX688VldXp91yyy3Ra92ioiLT4//xxx/XfX4NDQ3Rx77++mtt\nxIgRGgCtsLBQ27Vrl6v30pO45JJLdCKF3d/AWBYsWBA9dhcvXhx3+4cffjj6mr///e9tvcYXX3yh\n/f73v9fWr19v+Pibb76pDR8+PLrf++67z8lb6JHccsst2rvvvqu1t7crj7W0tGgPP/ywVlRUpAHQ\nsrKyDK95a2pqop/tbbfdZvpal19+efRvf+edd+q+R6+++qqWl5enAdAmTZpk+h3zYh+dhU/THKZ3\nEeKAm266Kdq9AugIY1uyZAkA4Mgjj0SfPn2ij82cOTPaMSLCZ599hgMPPBAAcNFFF5m2GK+srMTh\nhx+OlStXAugI9RsxYgTWrl0bLQUZPXo0Pvjg/9u796go6/wP4O9hAEW5KAMIIl4KBEKTCkxE01JE\nZHVdUc5uaVbiimtmLqyXs+sp3FXzsqln03INPeW6lXkDL6nIJKGikGJhuiwgchHQQRRBBWX4/v7g\nx7OMMyODzvgovl/nPKfhuXyfz2c4I9Pn+V6Ow9nZ2WJtPE2OHTuG1atX6+zbuXMnhBDo0aOHtDpM\ns+3bt+u1ERQUhFOnTkGpVBpcUe93v/udtKKQoTbv1bdvXyxdulRnX0xMDBITE2FrawsvLy+4u7tD\npVKhuroap0+fRk1NDYCm8eLffvstRo0a1XryT4EpU6boLEObnZ0tzW0wfvx4nSEAM2fOlFZHbPbZ\nZ59JS4xv3rwZb731ls7xEydOICQkBADg4eEBDw8PuLu7QwiB3Nxc6V4AMH36dGzYsEFa4OBeP//8\nM4YOHYobN25AoVAgODgYLi4uOHPmDMrKygAA48aNw65duywysfCTJjExUWcYVVVVldS9OzAwUGdy\neX9/f/z1r3/Vub6urk5aIXHYsGGtrsy1Z88eacLWxYsXY9GiRSbF6efnh9zcXNjb20ufXQcHB1RU\nVCA7O1uahN3b2xspKSnS3IJPq4SEBHz44YcAmiZqHTly5H3P79y5M7744gu9/ZMnT8bWrVsBAHl5\neUaHc2zZsgVTp06FEAIdO3ZESEgIrK2tkZGRgdraWgDARx99hPnz5xuNwRxtPA2Ki4vxzDPPSHOZ\nvPbaa60OS4+Pj9ebt/HIkSN49dVXATTNh3PvcCNz3Of69evSNSqVCp6ennB3d4etrS0KCwtx7tw5\naeLgsLAwJCUlPfUrrkZERODAgQMAgD59+kjD2I154YUX8Oc//1lvf/N3VpVKhcrKSr3jEydOxI4d\nO9ChQwf07NkT7u7ucHZ2RlVVFU6fPo2bN28CaPq9JScnY/DgwUZjePfdd7Fu3Trp/ODgYNTW1iIj\nIwNarRY2NjbYt28fwsLCTH4f2qMdO3Zg4sSJAJoW+hg3blyr30P++c9/6v1/xkcffYSFCxcCAHbt\n2oXx48fft40RI0ZArVbD1tYWZWVlUKlUrcZ64MABaUhY8+fW3d0dWq0W586d01nkIi4uDqtWrWq1\nzfbO3d0dly9fhpOTE3r06IFu3brBwcEB165dw08//SStlGdvb49t27bpDJdtVllZCVdXVwBAZGQk\n9u7da/BedXV1GDlyJI4dOwagaThuQEAASktLpeF77u7uyMjIMPp9yBxtPDIyFsjoKdA8zMaUbdq0\naXrXZ2dnm9w9trq6WsyYMUN06tRJp107OzsRExMjrl+/3mq85mjjafHVV1+Z/Ls19k/NSy+9JAAI\npVJp8HhkZGSb7vHyyy/rtfH999+L119/Xbi6uhq8xtHRUUyfPt3gcJOnWefOnU1+3zdu3Kh3/aef\nfiod37x5s95xjUYj5s+fL55//nmDbVpZWYmhQ4eKpKQkk+ItKCgQY8aMEVZWVjrtuLi4iCVLloiG\nhoaHfUvajVmzZpn8uw0NDdW7/vbt29LxYcOGtXq/CRMmSL/TtnzONm/eLMaMGaP373Hz1qNHD/Hh\nhx+K2tratqTfbsXFxbXp30snJyeD7bzxxhvSOXl5efe9p1qtFoGBgXpt+/j4iO3bt5sUtznaaO/O\nnz/fpt8tAPHtt9/qtdNy2ImhqQ3McZ87d+6IxYsXi8GDB0u9q+/dnn/+ebFx40bZn9Q/LloO0TVl\nCw8PN9hOcy8XlUpl8Ph3330noqOjhbOzs8F2u3btKt59912jPajutWHDBmlI/r1/N4z1pHvatBxB\nYupWUlKi186yZcuk48amNmhWVFQk9YaMiooyOdZLly6JP/7xjzq92FtuSqVSjBgx4oF7+rVH69ev\nF+Hh4cLOzs7oZyo2NlYUFxcbbUOj0UjnR0ZG3vd+dXV1Yv78+cLJyUnnPjY2NmLSpEk6PZIt2caj\nwJ5UZFHp6elGJyK/V58+ffDSSy/p7KuurkZKSgqApmVvm3tV3U9tbS1+/vlnXL16FSqVCv3792/z\nJL3maKO9Ky0txYkTJ0w+v/lJUktqtRpVVVWwsrLChAkT9I4fO3asTcvbqlQq6SnxvcT/L3VeVlYG\njUaDjh07wtPTE/7+/uxdY8Du3bsN9m4zJCgoSO+Jy4ULF6QVQoKDg9GrVy+j11dVVaGkpATl5eVo\naGiASqVCv379Hugzd+XKFZw/fx41NTXw8PDAgAEDHmiSyvbszJkzyM/PN+lcFxcXDB8+XGdfY2Mj\ndu7cCQBwdXWVllY2Zs+ePaivr4e9vT1Gjx7d5njv3LmDixcvory8HNevX4eDgwOeeeYZ+Z/yPWZy\ncnKkyVlNYWtra3BJ8qysLGkJ6oiICHTu3LnVtgoKClBQUIDGxkb06tUL/v7+pgduxjbaq5qaGhw8\neLBN14SEhMDT01Nnn0ajQVpaGoCmnor9+vWzyH1atldcXIzy8nLcunULXbt2hb+/P1xcXNp0j/bu\nyJEjBns+GePu7o4hQ4bo7T9w4ABqa2vRoUMHjB071uj1Wq1W+r1UVlbCzs4OXl5e8PX1Ndpj2Rgh\nBHJyclBaWiqtBubl5dWmNtqzoqIiZGVltemayMhIvd6Fubm5yMnJAQAMHjwY3bt3N3p9cXExMjMz\nATT1umvZO9pUlZWVKCkpQUVFBRobG+Hi4oJ+/fqZ9PfgaVRfX4+ioiLpe0rnzp3Rs2dP+Pj4tPqZ\nunPnDpKTkwE0jSwIDQ016X4//fQTLl++jC5duiAgIKDNo3zM0YYlsUhFRERERERERESyY/cBIiIi\nIiIiIiKSHYtUREREREREREQkOxapiIiIiIiIiIhIdixSERERERERERGR7FikIiIiIiIiIiIi2bFI\nRUREREREREREsmORioiIiIiIiIiIZMciFRERERERERERyY5FKiIiIiIiIiIikh2LVERERERERERE\nJDsWqYiIiIiIiIiISHYsUhERERERERERkeys5Q6AiIiIiIiIiOhR0mq1EEJAoVBAqVTKHY5FPIk5\nskhFRERERERERO1WXV0dsrOzkZmZiZMnTyIzMxMFBQUAAE9PT5SWlsoc4cMzR44VFRVITU1Famoq\nsrOzUV5eDo1Gg44dO6J379545ZVXMH36dLz44osWy0MhhBAWa52IiIiIiIiISEZ/+9vfsGjRIoPH\n2kuRyhw5BgUF4dSpU/c9R6FQID4+HsuXL4dCoXigWO+HPamIiIiIzODChQtYunQpAGDRokXo1auX\nzBFZ1uHDh/H111/r7V+2bBlcXV1liMj8NBoNFi5cqLc/Ojoao0aNkiEiIiJ6EHZ2dhg0aBAGDhwo\nbdOnT0daWprcoZmNOXL08vLCwIEDMWzYMDz33HPw9PSEg4MDysrKkJqaiqVLl6KgoAArV66Eh4cH\n5s6da/Y82JOKiIiIyAxOnDiBkJAQAEBWVhaCgoJkjsiy1qxZY/DLaV5eHry9vWWIyPzy8/Ph4+Oj\nt3/lypWIj4+XISIiIjKX4cOHIy0trd30pDLE3DlevXoV/v7+0Gg0Fnvf2JOKiIiIyIjz58/j73//\nOwBg8eLF6N69u8wRPX6srKywYcMG6Wc3NzcZozEvNzc3bNy4Ufp5xowZaGxslDEiIiIi+ahUKowd\nOxabNm3CpUuXcO3aNXTt2tWs92CRioiIiMiIS5cuITExEQDw/vvv37dI9eyzz0oFjd69ez+K8B4L\nCoUCMTExcodhEY6Ojjq5xcbGyhgNERE9DkpKSrB27VocOnQIhYWFuH37NlxdXTF48GDExsYiLCxM\n7hAtqnmVQBsbGzg4OJi9fRapiIiIiMzA1dW13RZriIiICEhMTMSsWbNQX1+vs7+iogI7d+7Ezp07\nERMTg88++0wq5rQn1dXV2Lt3LwAgLCwM1tbmLylZmb1FIiIiIiIiIqJ25F//+hdiYmJQX1+PESNG\nICUlBTU1NdBqtcjNzcUf/vAHAMDnn3+ODz/8UN5gzaChoQENDQ2oq6vDhQsXsGXLFoSEhKC8vBxe\nXl5Yu3atRe7LnlRERERE96ivr8esWbNQVlYm7fvggw/05l1oOU9Va6v7nTlzBp988gkAYPny5VCp\nVLh69Sq++eYb5OXlwd7eHqGhoRg9erRePEePHkVaWhrKy8vRvXt3REVFwdfXt9U8Ghsbcfz4caSn\np6O8vBxWVlbo2bMnRo0ahX79+rXtTTGDwsJCHDhwAPn5+dLwCA8PDwwaNAiBgYH3vdYcuQghcOLE\nCRw9ehRlZWUQQsDLywsBAQEYOXKkRZ4IExHRk6+qqgozZ84EAEyePBlffvklFAqFdLxv375Yt24d\nXF1dkZCQgBUrVmDmzJlP9FyWNjY2evtcXFwwb948xMfHW24lX0FEREREOmpqagSAVrecnBzpmoyM\nDGl/VlaWXpu7du2SjhcWFop///vfws7OTq/N0aNHi7q6OiGEENXV1WLUqFF651hbW4u1a9feNwe1\nWi2ee+45o7GPGzdOVFZWPvB7tHr1agFAKJXKVs+tr68X06ZNEwqFwmg8fn5+oqioyGK5pKamioCA\nAKNtuLq6ioMHD963DaVSKQCIlStXtpozERE93oYNGyYACE9Pz1bPXb58uQAg7OzsxLVr14yeV1dX\nJ5ycnAQA8cknn5gx2gfTlhzvpVQqhVKp1PnbbWVlJcLDw8Xx48ctEG0TPi4iIiIiukfHjh2xceNG\nnD9/Hh9//DEAICEhQe+JqKen5wO1n5KSghkzZiA0NBS/+tWv0KlTJ6SmpiIpKQkHDhzA7NmzsX79\neoSHhyMnJwexsbEICAjA1atXkZiYiJKSEsyZMweBgYF45ZVX9Nrftm0b3njjDTQ0NMDNzQ2TJk2C\nn58ftFotMjIysGPHDiQnJ2Po0KE4efKkRSY+bWn27NnSBPQhISEICwuDm5sbbty4gdLSUpw8eRKn\nTp1CVVUVevbsafZctmzZgrfffhtarRa2trYYP348goOD0aFDB5SUlODs2bNISUnBhQsXLPo+EBHR\nk+nQoUMAgCFDhqBLly4AmnrntvwvAFhbW2PAgAH44Ycf8OOPP+q109jY+NCrxCqVSp1eXJbS0NAA\noCm/iooKHD58GAkJCTh48CCOHDmCrVu3Iioqyvw3tlj5i4iIiOgJl5KSYrDXlCFt6Unl4OAgli5d\nqndOfHy8ACBsbGzE7NmzRffu3UVeXp7OOZcvXxZubm4CgIiIiNBrIy8vT3Tq1EkAEL/+9a9FTU2N\n3jnHjx8X9vb2AoB47733WnsbDDK1J9WNGzeEjY2NACDi4uKMnvef//xHVFVVmT2Xs2fPig4dOggA\nwtfXV+/9bHblyhVx4cKF++bCnlRERO1HW3oZ9ejRw6Qe1i23sWPH6rUzY8aMNrdz77Zr1y6L5GiK\na9euCT8/PwFA2NvbP1SPbGM4cToRERHRI/biiy9i4cKFevsXLFgApVKJu3fv4h//+AfWr18Pb29v\nnXPc3Nzw+9//HgCQmpqKu3fv6hxfunQpbt26hV69euGrr76Cvb293n1CQkKQkJAAoGmC19u3b5sr\nNT1FRUVSjNHR0UbP8/X11Zvzyxy5LFmyBPX19bC1tUVycrLe+9nM1dUVffr0aVNuRET0dLh+/ToA\nQKFQQKlUmrRptVq9dky99n6blZV8ZZwuXbpIf3Nra2uxa9cus9+DRSoiIiKiR2zatGkG96tUKqmI\n4uLigrFjxxo8b+DAgQCAO3fu6AxRa2xsxM6dOwEAb775Juzs7IzGMGHCBADArVu3kJmZ2fYkTNRy\niOT+/ftNvs4cudy9exfJycnSOX379m1T7ERERACkoeRTpkyRVr1rbdu3b59eO+vWrTP5emPbuHHj\nHnX6OoKDg6XX+fn5Zm+fc1IRERERPWIDBgwweszFxQW5ubno37+/0aelLVfUaX66CwC5ubmorq4G\nABw7dgyxsbEA9OfNEEJIc00AQGlp6YMlYgJnZ2cMHToU6enpSEhIgFqtxqRJkzB8+HAEBAQYzdEc\nueTm5uLmzZsAgOHDh5s9NyIiejp4e3ujvLwcp0+fljsU2bXswd2xY0ezt88iFREREdEj5ujoaPSY\ntbW1yecAul8Wr1y5Ir1Wq9VQq9UmxVNbW2vSeQ9q8+bNiIiIQF5eHtLT05Geng6gadjA8OHDMXny\nZPzmN7/RKViZI5fKykrptYeHx8OmQURET6nw8HCkp6fj7NmzyMrK0ulN9LT54YcfpNd+fn5mb59F\nKiIiIqJ2ouVqP++99x769+9v0nVDhgyxVEgAgGeffRZnz57Ftm3bkJSUhLS0NGg0Gly/fh27d+/G\n7t27ERISgn379knzUpkjl5bFPENzgxAREZkiNjYWK1aswI0bN/DWW28hLS0NLi4ucof1yJWWlkpz\nUjk5OSEiIsLs92CRioiIiKidcHd3l177+fkhJiZGxmh02draYvLkyZg8eTIA4L///S/UajU2bdqE\nrKwsZGRkIC4uDps2bQJgnlxatnHx4sWHS4CIiJ5oLYeGA/8bNm7oWMuHHEDTnJGJiYmIjo7GuXPn\nEBgYiHnz5iEiIgKenp64ffs2KioqUFpaCrVajf3792P79u3w9fW1XEIGPEyOX3zxBb788kuMHz8e\nwcHB6NatG9zc3NDY2IjCwkLs378fq1atwtWrVwEAq1evhpOTk9lzYJGKiIiIyAg5V9B5ED4+PnBz\nc8OVK1ewZ88ezJw5U+6QjOrbty/69u2L6dOnY+jQocjIyMDevXul4+bIxdvbG926dcPly5exf/9+\nzJ0715wpEBHRE6K0tBReXl4Gj126dAk2NjY6+9LT0/V6GU+cOBHbt2/HtGnTcOnSJcyZMwdz5syx\nWMxt9bA5NjQ0mDS83tHREWvWrMHbb7/98EEb8GR98yIiIiJ6hOzt7aXXN27ckDES0ygUCkydOhUA\n8N133yE1NVXmiFqnVCoRGBgIAKivr5f2myuXN998EwCQmpr6RLwfRERkfgqFAkql0uSt5ZDzliZM\nmICLFy9i1apVGDFiBDw8PGBnZwc3Nzf0798fkZGRWLVqFc6ePfvIe1E9bI5Tp07F0aNHsWjRIrz6\n6qvw8fGBg4MD7O3t0bNnT4wZMwYff/wx8vPzLVagAtiTioiIiMgob29v6bVarcbgwYNljMY0CxYs\nwDfffIPi4mJMmDAB69atw+uvv67XK6yxsRGZmZnIzs62aI+rX375BZmZmRg9erTByctzcnKwY8cO\nAMCgQYPMnsu8efOwdetWlJWVISoqComJiYiKitI5R6vV4vDhw+jSpQtefvllc6RNRESPEU9PT73h\nbg/KyckJcXFxiIuLM0t75vKwOVpbWyM0NBShoaFmjOoB4pD17kRERESPMWdnZ7z22mtQq9X44IMP\noFar0bt3b6lIsnjxYnTv3l3mKHU5Oztj7969iIyMRElJCaZMmYI//elPGDhwILp27YqqqipUVFQg\nPz8f165dg6+vr0WLVIWFhXjnnXcANA3h8/T0hIeHhzTHRVZWFoQQcHR0xIoVK8yei4uLC5KSkjBm\nzBhoNBpMnDgRvXv3RlBQEDp06ICSkhKcP38eGo0Gn376KYtUREREMmKRioiIiOg+Pv/8c0RHR+PH\nH3/E999/r3Ps/ffff+yKVADQv39/nD59GkuWLEFiYiIqKiqQnJysc07zE9Po6GiLxtKvXz+88847\nOHToEPLy8pCXl6dzXKlUIjw8HKtWrYK/v79FcgkKCsKpU6fwl7/8BV9//TUuXryoM5G6tbU1Ro8e\nrdeTi4iIiB4thWg53TsRERERGZSbm4tffvkF1dXV0Gq1AICoqCh07doVAKDRaJCUlAQAGD9+vN7S\n1EVFRUhJSQEA/Pa3v9WZ76qlffv2oby8HL1798bIkSMNnlNZWYndu3cDAMaOHYtu3boZjbuhoQGn\nT59GcXExbt26BRcXF3Tr1g0+Pj5wdHQ0/Q24x5o1azB37lwolUqThxcUFBSguLgY5eXlaGxshLu7\nO1544QWoVCqTrjdHLjdv3sTJkydRUVEBa2tr9OjRA76+vibFYG1tDa1Wi5UrVyI+Pt6k+xEREZHp\nWKQiIiIiojZrLlIpFAppOB8ALFu2DK6urjJGZj4ajQYLFy6Uft60aROEECxSERERWQiH+xERERHR\nAxNCIDExUfp5wYIF7aZIVV1drZMbERERWRaLVERERETUZmFhYdi4caPefjc3NxmisQw3NzeDOXJy\ndSIiIsvgcD8iIiIiIiIiIpKdldwBEBERERERERERsUhFRERERERERESyY5GKiIiIiIiIiIhkxyIV\nERERERERERHJjkUqIiIiIiIiIiKSHYtUREREREREREQkOxapiIiIiIiIiIhIdixSERERERERERGR\n7FikIiIiIiIiIiIi2bFIRUREREREREREsmORioiIiIiIiIiIZMciFRERERERERERyY5FKiIiIiIi\nIiIikh2LVEREREREREREJDsWqYiIiIiIiIiISHYsUhERERERERERkexYpCIiIiIiIiIiItmxSEVE\nRERERERERLJjkYqIiIiIiIiIiGTHIhUREREREREREcmORSoiIiIiIiIiIpLd/wEIIlbrIwAruAAA\nAABJRU5ErkJggg==\n"
}
}
],
"source": [
"flux_data = sim_data[\"flux_over_time\"].flux\n",
"flux_data.plot()\n",
"plt.title(\"flux over time\")\n",
"plt.show()"
],
"id": "2ef1d634"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For field data, we can further index by `fields` specified in the\n",
"monitor, as follows."
],
"id": "7697e570-6971-46e3-9d45-8e7223a2ed6d"
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"image/png": 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}
}
],
"source": [
"Ey = sim_data[\"fields_at_150THz\"].Ey\n",
"\n",
"Ey.real.plot(x=\"x\", y=\"y\", robust=True)\n",
"plt.title(\"real{Ey(x, y)}\")\n",
"plt.show()"
],
"id": "ddbef2e1"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally,\n",
"[SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html)\n",
"provides a method for potting field data with structure overlay, similar\n",
"to `sim.viz_fields2D()`."
],
"id": "680099cb-a68f-4d16-84bd-a4eb8bfac5b5"
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"image/png": 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jRhg2bJjufdasWTNdjsYuJC3r812aHMtz4aSlN28poFQq8cEHH2DChAnYvHkz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\nRU5ErkJggg==\n"
}
}
],
"source": [
"sim_data.plot_field(\"fields_at_150THz\", \"Ey\", val=\"real\")\n",
"plt.show()"
],
"id": "bc949472"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plugins\n",
"\n",
"Here we will discuss the plugins that support and extend functionalities\n",
"of Tidy3D, including:\n",
"\n",
"- [Dispersion fitting tool](../notebooks/Fitting.html).\n",
"\n",
"- [Mode solver](../notebooks/ModeSolver.html).\n",
"\n",
"- [Near field to far field\n",
" transformations](../notebooks/FieldProjections.html).\n",
"\n",
"These plugins are designed to import and make use of Tidy3D components\n",
"described above, but the Tidy3D components have no dependence on the\n",
"plugins by design. In this sense, they can be considered “external”\n",
"packages that are useful for defining simulation components.\n",
"\n",
"The components from these plugins can be imported as\n",
"\n",
" from tidy3d.plugins.{plugin_name} import {component}\n",
"\n",
"for example\n",
"\n",
" from tidy3d.plugins.mode import ModeSolver\n",
"\n",
"We won’t go into the details in this notebook as each of the plugins has\n",
"its own example tutorial notebook, but here is a summary:\n",
"\n",
"### Dispersion Fitting\n",
"\n",
"We provide a [tool for fitting optical data to create dispersive\n",
"mediums](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.dispersion.DispersionFitter.html).\n",
"\n",
"Given a file or arrays containing wavelength, n, and (optionally) k\n",
"data, this tool will fit the data to a pole-residue model with some\n",
"constraints and parameters.\n",
"\n",
"After fitting, the user can visualize and inspect the results.\n",
"\n",
"This process can be repeated until the user is satisfied, at which point\n",
"the tool can return a [dispersive\n",
"medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PoleResidue.html)\n",
"for use in the Simulation.\n",
"\n",
"For more details, see the corresponding\n",
"[tutorial](../notebooks/Fitting.html).\n",
"\n",
"### Mode Solver\n",
"\n",
"The `mode` plugin provides a [mode\n",
"solver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html),\n",
"which is mainly useful for coming up with\n",
"[ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html)\n",
"objects for a given source or monitor.\n",
"\n",
"The tool takes a reference simulation containing some waveguide or other\n",
"structure definitions to feed to the core solver. It also requires a\n",
"[Box](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html)\n",
"specifying the plane on which to solve the modes for.\n",
"\n",
"Then, the user can iteratively send different\n",
"[ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html)\n",
"objects, solve for the results, visualize, and repeat the process until\n",
"satisfied.\n",
"\n",
"The resulting\n",
"[ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html)\n",
"can be saved directly, or used in conjunction with the mode solver\n",
"settings to return a\n",
"[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)\n",
"or\n",
"[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html).\n",
"\n",
"For more details, see the corresponding\n",
"[tutorial](../notebooks/ModeSolver.html).\n",
"\n",
"### Scattering Matrix\n",
"\n",
"The `smatrix` plugin provides a wrapper for computing S-matrix\n",
"components using Tidy3D simulations from the [Component\n",
"Modeler](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.ComponentModeler.html)\n",
"plugin.\n",
"\n",
"This tool allows users to connect “ports” to their simulation and\n",
"post-processes the results into a datastructure that can be queried for\n",
"S-matrix data.\n",
"\n",
"For more details, see the corresponding\n",
"[tutorial](../notebooks/SMatrix.html).\n",
"\n",
"### Simple Inverse Design\n",
"\n",
"The goal of invdes is to provide a simpler interface for setting up most\n",
"practical inverse design problems. It wraps tidy3d’s native automatic\n",
"differentiation support to perform the gradient calculations, but allows\n",
"the user to focus on the important aspects of their design without\n",
"getting into the details of autograd.\n",
"\n",
"For more details, see the corresponding\n",
"[tutorial](../notebooks/Autograd0Quickstart.html).\n",
"\n",
"### Adjoint\n",
"\n",
"The native autograd integration allows users to write functions\n",
"involving a Tidy3D `Simulation` run and take efficient gradients of\n",
"those functions. Regular `tidy3d` components and `web.run()` are\n",
"differentiable without any change of syntax.\n",
"\n",
"For more details, see the corresponding\n",
"[tutorial](../notebooks/Autograd1Intro.html).\n",
"\n",
"### Resonance Finder\n",
"\n",
"The `resonance` plugin provides an interface for analyzing time series\n",
"data using the technique of “harmonic inversion” and using that data to\n",
"find resonances in the simulation.\n",
"\n",
"For more details, see the corresponding\n",
"[tutorial](../notebooks/Bandstructure.html).\n",
"\n",
"### PolySlab\n",
"\n",
"The `polyslab` plugin extends the normal `td.PolySlab` functionality to\n",
"provide more features needed for self-intersecting and complex\n",
"geometries.\n",
"\n",
"For more details, see the corresponding\n",
"[tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/SelfIntersectingPolyslab/).\n",
"\n",
"## Conclusion\n",
"\n",
"We hope this gives a useful overview of the main changes in the revamped\n",
"version of Tidy3D.\n",
"\n",
"We highly recommend you check out the various tutorials in our [examples\n",
"page](https://www.flexcompute.com/tidy3d/examples/). If you are new to\n",
"the finite-difference time-domain (FDTD) method, we highly recommend\n",
"going through our [FDTD101](https://www.flexcompute.com/fdtd101/)\n",
"tutorials.\n",
"\n",
"FDTD simulations can diverge due to various reasons. If you run into any\n",
"simulation divergence issues, please follow the steps outlined in our\n",
"[troubleshooting\n",
"guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/)\n",
"to resolve it."
],
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