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</span></a><a href = "#H_51bb"><span>Set up the NWB File
</span></a><a href = "#H_3b84"><span>Subject Information
</span></a><a href = "#H_47a8"><span>Time Series Data
</span></a><a href = "#H_336b"><span>Other Types of Time Series
</span></a><a href = "#H_8d44"><span>Behavior
</span></a><span> </span><a href = "#H_298a"><span>SpatialSeries and Position
</span></a><span> </span><a href = "#H_2d94"><span>Trials
</span></a><a href = "#H_098c"><span>Write
</span></a><a href = "#H_9060"><span>Read
</span></a><span> </span><a href = "#H_2a80"><span>Reading Data
</span></a><a href = "#H_0b60"><span>Next Steps</span></a></div></div><h2 class = 'S4' id = 'H_5755' ><span>Installing MatNWB</span></h2><div class = 'S5'><span>Use the code below within the brackets to install MatNWB from source. MatNWB works by automatically creating API classes based on the schema.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%{</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">!git clone https://github.com/NeurodataWithoutBorders/matnwb.git</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">addpath(genpath(pwd));</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre"><span style="color: #008013;">%}</span></span></div></div></div><h2 class = 'S4' id = 'H_51bb' ><span>Set up the NWB File</span></h2><div class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object with those and additional metadata using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span> command. For all MatNWB classes and functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value. Ellipses are used for clarity.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last, First'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S9'><span style="white-space: pre"><span >nwb</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="1F1393C7" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="832" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> NwbFile with properties:
nwb_version: '2.8.0'
file_create_date: []
identifier: 'Mouse5_Day3'
session_description: 'mouse in open exploration'
session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
timestamps_reference_time: []
acquisition: [0×1 types.untyped.Set]
analysis: [0×1 types.untyped.Set]
general: [0×1 types.untyped.Set]
general_data_collection: ''
general_devices: [0×1 types.untyped.Set]
general_experiment_description: ''
general_experimenter: 'Last, First'
general_extracellular_ephys: [0×1 types.untyped.Set]
general_extracellular_ephys_electrodes: []
general_institution: 'University of My Institution'
general_intracellular_ephys: [0×1 types.untyped.Set]
general_intracellular_ephys_experimental_conditions: []
general_intracellular_ephys_filtering: ''
general_intracellular_ephys_intracellular_recordings: []
general_intracellular_ephys_repetitions: []
general_intracellular_ephys_sequential_recordings: []
general_intracellular_ephys_simultaneous_recordings: []
general_intracellular_ephys_sweep_table: []
general_keywords: ''
general_lab: ''
general_notes: ''
general_optogenetics: [0×1 types.untyped.Set]
general_optophysiology: [0×1 types.untyped.Set]
general_pharmacology: ''
general_protocol: ''
general_related_publications: {'DOI:10.1016/j.neuron.2016.12.011'}
general_session_id: 'session_1234'
general_slices: ''
general_source_script: ''
general_source_script_file_name: ''
general_stimulus: ''
general_subject: []
general_surgery: ''
general_virus: ''
general_was_generated_by: ''
intervals: [0×1 types.untyped.Set]
intervals_epochs: []
intervals_invalid_times: []
intervals_trials: []
processing: [0×1 types.untyped.Set]
scratch: [0×1 types.untyped.Set]
stimulus_presentation: [0×1 types.untyped.Set]
stimulus_templates: [0×1 types.untyped.Set]
units: []
Warning: The following required properties are missing for instance for type "NwbFile":
timestamps_reference_time </div></div></div></div></div></div><h2 class = 'S4' id = 'H_3b84' ><span>Subject Information</span></h2><div class = 'S5'><span>You can also provide information about your subject in the NWB file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store information such as age, species, genotype, sex, and a freeform description. Then set </span><span style=' font-weight: bold; font-family: monospace;'>nwb.general_subject</span><span> to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object.</span></div><div class = 'S11'><img class = "imageNode" src = "data:image/png;base64,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" width = "159" height = "193" alt = "" style = "vertical-align: baseline; width: 159px; height: 193px;"></img></div><div class = 'S5'><span>Each of these fields is free-form, so any values will be valid, but here are our recommendations:</span></div><ul class = 'S12'><li class = 'S13'><span>For </span><span style=' font-family: monospace;'>age</span><span>, we recommend using the </span><a href = "https://en.wikipedia.org/wiki/ISO_8601#Durations"><span>ISO 8601 Duration format</span></a></li><li class = 'S13'><span>For </span><span style=' font-family: monospace;'>species</span><span>, we recommend using the formal latin binomal name (e.g. mouse -> </span><span style=' font-style: italic;'>Mus musculus</span><span>, human -> </span><span style=' font-style: italic;'>Homo sapiens</span><span>)</span></li><li class = 'S13'><span>For </span><span style=' font-family: monospace;'>sex</span><span>, we recommend using F (female), M (male), U (unknown), and O (other)</span></li></ul><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'001'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper outputs"><div class = 'S9'><span style="white-space: pre"><span >subject</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="D1A03FC1" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="197" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">subject = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> Subject with properties:
age: 'P90D'
age_reference: 'birth'
date_of_birth: []
description: 'mouse 5'
genotype: ''
sex: 'M'
species: 'Mus musculus'
strain: ''
subject_id: '001'
weight: ''
</div></div></div></div></div></div><div class = 'S14' id = 'H_FF7B59CD' ><span>Note: the DANDI archive requires all NWB files to have a subject object with subject_id specified, and strongly encourages specifying the other fields.</span></div><h2 class = 'S15' id = 'H_47a8' ><span>Time Series Data</span></h2><div class = 'S5'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> </span><span>is a common base class for measurements sampled over time, and provides fields for</span><span> </span><span style=' font-family: monospace;'>data</span><span> </span><span>and</span><span> </span><span style=' font-family: monospace;'>timestamps</span><span> </span><span>(regularly or irregularly sampled). You will also need to supply the</span><span> </span><span style=' font-family: monospace;'>name</span><span> </span><span>and</span><span> </span><span style=' font-family: monospace;'>unit</span><span> </span><span>of measurement (</span><a href = "https://en.wikipedia.org/wiki/International_System_of_Units"><span>SI unit</span></a><span>).</span></div><div class = 'S11'><img class = "imageNode" src = 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" width = "206" height = "221" alt = "" style = "vertical-align: baseline; width: 206px; height: 221px;"></img></div><div class = 'S5'><span>For instance, we can store a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> </span><span>data where recording started</span><span> </span><span style=' font-family: monospace;'>0.0</span><span> </span><span>seconds after</span><span> </span><span style=' font-family: monospace;'>start_time</span><span> </span><span>and sampled every second (1 Hz):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >time_series_with_rate = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example time series'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'data'</span><span >, linspace(0, 100, 10), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0);</span></span></div></div></div><div class = 'S14'><span>For irregularly sampled recordings, we need to provide the </span><span style=' font-family: monospace;'>timestamps</span><span> for the </span><span style=' font-family: monospace;'>data</span><span>:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >time_series_with_timestamps = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example time series'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'data'</span><span >, linspace(0, 100, 10), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'timestamps'</span><span >, linspace(0, 1, 10));</span></span></div></div></div><div class = 'S5'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> class serves as the foundation for all other time series types in the NWB format. Several specialized subclasses extend the functionality of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, each tailored to handle specific kinds of data. In the next section, we’ll explore one of these specialized types. For a full overview, please check out the </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format.html#type-hierarchy"><span>type hierarchy</span></a><span> in the NWB schema documentation.</span></div><h2 class = 'S4' id = 'H_336b' ><span>Other Types of Time Series </span></h2><div class = 'S5'><span>As mentioned previously, there are many subtypes of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> in MatNWB that are used to store different kinds of data. One example is </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AnnotationSeries</span></a><span>, a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> that stores text-based records about the experiment. Similar to our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> example above, we can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AnnotationSeries</span></a><span> object with text information about a stimulus and add it to the stimulus_presentation group in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span>. Below is an example where we create an AnnotationSeries object with annotations for airpuff stimuli and add it to the NWBFile.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6' id = 'H_341153F5' ><span style="white-space: pre"><span style="color: #008013;">% Create an AnnotationSeries object with annotations for airpuff stimuli</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >annotations = types.core.AnnotationSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Airpuff events delivered to the animal'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'Left Airpuff'</span><span >, </span><span style="color: #a709f5;">'Right Airpuff'</span><span >, </span><span style="color: #a709f5;">'Right Airpuff'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'timestamps'</span><span >, [1.0, 3.0, 8.0] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Add the AnnotationSeries to the NWBFile's stimulus group</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre"><span >nwb.stimulus_presentation.add(</span><span style="color: #a709f5;">'Airpuffs'</span><span >, annotations)</span></span></div></div></div><h2 class = 'S4' id = 'H_8d44' ><span>Behavior</span></h2><h3 class = 'S16' id = 'H_298a' ><span>SpatialSeries and Position</span></h3><div class = 'S5'><span>Many types of data have special data types in NWB. To store the spatial position of a subject, we will use the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> classes. </span></div><div class = 'S11'><img class = "imageNode" src = 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" width = "492" height = "309" alt = "" style = "vertical-align: baseline; width: 492px; height: 309px;"></img><span> </span></div><div class = 'S5'><span>Note: These diagrams follow a standard convention called "UML class diagram" to express the object-oriented relationships between NWB classes. For our purposes, all you need to know is that an open triangle means "extends" (i.e., is a specialized subtype of), and an open diamond means "is contained within." Learn more about class diagrams on </span><a href = "https://en.wikipedia.org/wiki/Class_diagram"><span>the wikipedia page</span></a><span>.</span></div><div class = 'S5'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, a common base class for measurements sampled over time, and provides fields for data and time (regularly or irregularly sampled). Here, we put a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object called </span><span style=' font-family: monospace;'>'SpatialSeries'</span><span> in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object. If the data is sampled at a regular interval, it is recommended to specify the </span><span style=' font-family: monospace;'>starting_time</span><span> and the sampling rate (</span><span style=' font-family: monospace;'>starting_time_rate</span><span>), although it is still possible to specify </span><span style=' font-family: monospace;'>timestamps</span><span> as in the </span><span style=' font-weight: bold; font-family: monospace;'>time_series_with_timestamps</span><span> example above.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% create SpatialSeries object</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >spatial_series_ts = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'data'</span><span >, [linspace(0,10,100); linspace(0,8,100)], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is bottom left corner'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'starting_time'</span><span >, 0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 200 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% create Position object and add SpatialSeries</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre"><span >position = types.core.Position(</span><span style="color: #a709f5;">'SpatialSeries'</span><span >, spatial_series_ts);</span></span></div></div></div><div class = 'S14'><span>NWB differentiates between raw, </span><span style=' font-style: italic;'>acquired </span><span>data, which should never change, and </span><span style=' font-style: italic;'>processed </span><span>data, which are the results of preprocessing algorithms and could change. Let's assume that the animal's position was computed from a video tracking algorithm, so it would be classified as processed data. Since processed data can be very diverse, NWB allows us to create processing modules, which are like folders, to store related processed data or data that comes from a single algorithm. </span></div><div class = 'S5'><span>Create a processing module called "behavior" for storing behavioral data in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> and add the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object to the module.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% create processing module</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >behavior_module = types.core.ProcessingModule(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'contains behavioral data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% add the Position object (that holds the SpatialSeries object) to the module </span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% and name the Position object "Position"</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >behavior_module.add(</span><span style="color: #a709f5;">'Position'</span><span >, position);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% add the processing module to the NWBFile object, and name the processing module "behavior"</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre"><span >nwb.processing.add(</span><span style="color: #a709f5;">'behavior'</span><span >, behavior_module);</span></span></div></div></div><h3 class = 'S16' id = 'H_2d94' ><span>Trials</span></h3><div class = 'S5' id = 'H_FF7B86AB' ><span>Trials are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-weight: bold; font-family: monospace;'>TimeIntervals</span></a><span> object which is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> objects are used to store tabular metadata throughout NWB, including for trials, electrodes, and sorted units. They offer flexibility for tabular data by allowing required columns, optional columns, and custom columns.</span></div><div class = 'S11'><img class = "imageNode" src = 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" width = "372" height = "368" alt = "" style = "vertical-align: baseline; width: 372px; height: 368px;"></img></div><div class = 'S5'><span>The trials </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> can be thought of as a table with this structure:</span></div><div class = 'S11'><img class = "imageNode" src = "data:image/png;base64,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" width = "378" height = "193" alt = "" style = "vertical-align: baseline; width: 378px; height: 193px;"></img></div><div class = 'S5'><span>Trials are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-weight: bold; font-family: monospace;'>TimeIntervals</span></a><span> object which subclasses </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. Here, we are adding</span><span> </span><span style=' font-family: monospace;'>'correct'</span><span>, which will be a</span><span> logical array.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >trials = types.core.TimeIntervals( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'stop_time'</span><span >, </span><span style="color: #a709f5;">'correct'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'trial data and properties'</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 0.1, </span><span style="color: #a709f5;">'stop_time'</span><span >, 1.0, </span><span style="color: #a709f5;">'correct'</span><span >, false)</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 1.5, </span><span style="color: #a709f5;">'stop_time'</span><span >, 2.0, </span><span style="color: #a709f5;">'correct'</span><span >, true)</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 2.5, </span><span style="color: #a709f5;">'stop_time'</span><span >, 3.0, </span><span style="color: #a709f5;">'correct'</span><span >, false)</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper outputs"><div class = 'S9'><span style="white-space: pre"><span >trials.toTable() </span><span style="color: #008013;">% visualize the table</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="D6770968" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_7" widgetid="variableeditor_client_Document_7" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_7"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×4 table </span></div><div id="variableeditor_TableViewModel_7" widgetid="variableeditor_TableViewModel_7" class="table ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span> </span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>start_time</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>stop_time</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>correct</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0.1000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>0</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>1</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>0</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport"></div><div class="outputLayer activeOutputDecorationLayer doNotExport"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport"></div><div class="outputLayer navigationFocusLayer doNotExport" tabindex="-1"></div></div></div></div><div class="inlineWrapper"><div class = 'S17'><span style="white-space: pre"><span >nwb.intervals_trials = trials;</span></span></div></div><div class="inlineWrapper"><div class = 'S7'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% If you have multiple trials tables, you will need to use custom names for</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% each one:</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre"><span >nwb.intervals.add(</span><span style="color: #a709f5;">'custom_intervals_table_name'</span><span >, trials);</span></span></div></div></div><h2 class = 'S4' id = 'H_098c' ><span>Write</span></h2><div class = 'S5' id = 'H_66678991' ><span>Now, to write the NWB file that we have built so far:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S18'><span style="white-space: pre"><span >nwbExport(nwb, </span><span style="color: #a709f5;">'intro_tutorial.nwb'</span><span >)</span></span></div></div></div><div class = 'S14' id = 'H_2D49648F' ><span>We can use the </span><a href = "https://www.hdfgroup.org/downloads/hdfview/"><span>HDFView</span></a><span> application to inspect the resulting NWB file.</span></div><div class = 'S11'><img class = "imageNode" src = 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dWAakA1oBpQDcxbA2pg1MCogVENqAZUA6oB1YBqQDWgGlANrKYG1MCoWFdTrPrXjHn/NUP8il/VgGpANaAaUA2oBtZQA2pgTtjA3PuZ393ddfVt3XOf+9xJm4iOXk95ylM6f5C7Se1TyJzQz2vjvq8+cwurD13VgGpANaAaUA2oBlQD518Dkzcw3dVVx4eKKC6il/+Nl3f3/s17u3uv7u3uuLqj+/xP+fzuK7/0KydpZKxJQPMQNQx9G4k+1/CUc/dZp7BxXYoX8aIaUA2oBlQDqgHVwNJrYNIGxhoXPmE0Mmw7ho55IY8xZ5850Lx8x9X/1n3n1Uu677r6R5tG5r7ub119XfelV7d3d37LnQc89slt2FM2Eaecuy9PwusDWjWgGlANqAZUA6oB1cD6amCyBsY3LyiGzA7/HBKNyynmrp0PmpfvvHrR9unLt159e/eab/3M7n3/7M91r/v7n7ttaL766qu3jcxzvuw5gxoZbiLwJGaotFy1c2L/KefmdUhf34eRrpmumWpANaAaUA2oBlQDLTUwWQOTTXaKJoIbmFPMn3HBzYs1MPYE5v/9V1fdx7rN1+42xyOPXW3H339196aRedH29zH2ROZpn/u0Xk3E9itiv7vJWTiurh7dULM7SjjLlZ1PZI/mxjyZzObvO3e0Htn0QagaUA2oBlQDqgHVgGrgvGpglgbGNxA8HlJAtpHlo5aDmxbMXYuZ228/2LffvFhjgsN+/3LvU1/Qfddf/eLuf7n6vu59b9k1Mvd821V399XmNzIbv2E/5zM+Z3QTwU2CbyTY5/W+TYThfY7D+ewnUreaJ9M9HuO+c899DZX/vD78dD11PVUDqgHVgGpANbDOGpilgbFiQOPAckiRbDfE9r0kevXd2Noahsw9VYw9eXnR/3CrcUEDY79/+Y6/9D91P/XXv777qbuftn0S86GPXnXf9d/9l9sGxv5C2VOvntp77VETgabAZGsDYdi+XEdzH86nBmaqulKedX7o6rrpuqkGVAOqAdWAamBcDTQ1MFebBoCPVtLHNi82j22Ihx5Y5ykbGP+1MWtedk9WXtK9+Mv/cve+D++eurzzoavuna99avedX/IV++bl8/6Dz+v+/F/885M2MIfNxPG/QsaNVE3v2zzhekuO+1AQf+JPNaAaUA2oBlQDqoEl10C1gbHGZbvRtCcg9puKzYubmUzHSY9pHqJ/za9tetl/6g2wb17wlTB78vIPr75n+/Tlp99x1b3qRVfd3/0Lz+vsB/32F8nsyYt9beyVr37loP9HTIk3fGWLGxnmzOt9OYzmthLaHfnXxfy8Nu47N2pOUh+6qgHVgGpANaAaUA2oBs63BtoaGOtarpuXFmlNzRRFE22Go41uZjvlBth+oP/p//6nbw/7q2J48mKNy13PeG73ngd3jctdn/KN3bdvGhb85gXNyz0vu6f7zQ/8ZjfkL5FFvPmGxY+n4rA2N89rejav2U95/aaoX+U43w9OXVtdW9WAakA1oBpQDZyuBtoamB7NC57QTHFRo81wacPrfafcANv/nPKznv5Z26coz37Ws7s/+2l/dvv/ebGnLC96+n/f/dqmgXnl3VebxuYl28Oezljz8rSrp3XWvPziL/1i965fe9f2f275Z67+TK+GMOKNGwfjyY89dxj35bA2N8+rBuZ0b/wp3p/KoeunGlANqAZUA6oB1cApaqCtgdETmF4NhF3IOz71ju3Tk9/+7d/u3vmL7+x+/Md/fPvnkO13LfZExhqZl129cvtk5gc2f3Hsm66+efu1MTQv1sC89ntf233zp31z76+R+SaCmwY0JpENPpZzNzClJqbv3Kd4A2lOfXCrBlQDqgHVgGpANaAaOG4NtDUwwROY7LcvNXufC+w34ryxbtFPuQG2JzBP/7KnW+u3f/36r/169+offfX2ycwz/91nbv9nldy8vPBFL+x++f/65e3Tlx/+nh/unv9pz+/dvBi/njduVjI947Mvh35uzmu/g+Gx6bwe7+s7d5/aEva4HzTiW3yrBlQDqgHVgGpANTBVDbQ1MLYFpybGmhS/2WwZ9/1tTGkz3DLfKTfA9psXe3ry4//rj3c/9jP/tPvjR/9438jYUxlrVr70r31p91lXn7V/8sLNy5AnLygKzxs3CZme8dmXQz93lhd2Xg9skH3nxvlL6gNSNaAaUA2oBlQDqgHVwPnWQFsDc4LmxYqu72YYG1/IU2+A7fcsf+Xqr3T2g/6X3fUD3Y/9kx/bNzGm/OEf/mF357fcuf/Ni31tzJ683PmUOwf9eB9vVM8bNwmZDs687Muhn9vn82Nej/f1nRvnL3m+H1i6trq2qgHVgGpANaAaUA20NTC2295+3ec4T15QmH03w0vdAD/tc5+2/d3L3/t3/l73khfc3f3sT/xs9weP/oGxun1ZI2O/k5mieTHuWngrNQ7MY98momVuzl9aR9+5UTeS+mBTDagGVAOqAdWAakA1cL410NbAnKB5saLruxnmjbHpS9oAf+WXfuX2qYr9gP+b/pNv6u79m/duf+tifyr5Y3/yse6HX7L7zcuQP5vs36Al3rhhYN1zh3FfDv3cPEesb3o4e8IXHH3n9jxofL4fXLq2uraqAdWAakA1oBq43BpoamCO9ZsXX4h+Mxxtcku2JW6A7StjX/C0L9h+teyuz76rs//ZpT2Rsb9a9tznPrf3XzvznNm4xFvcROT/P5a+HPade23XL+Jbtsv9ANW117VXDagGVAOqAdXA8WuguYGp/XWxyD/2gpY2w6WNL3x9N99j19s3/vM/5fO3f4nMfvMyVfNiayjxpgbm+G+yvnUhvK6RakA1oBpQDagGVAOqgbwGqg3MKcmzjfjY45Trb53bnsq0YltwYznz8S1zAuNjx46RVzJ/E4sbcaMaUA2oBlQDqgHVwCXVwKIbmEu6EDpXffCoBlQDqgHVgGpANaAaUA2oBuo1oAZm85UrFYo4UA2oBlQDqgHVgGpANaAaUA2sowbUwKiBUQOnGlANqAZUA6oB1YBqQDWgGlhNDaiBUbGuplj1ryLr+FcRXSddJ9WAakA1oBpQDagG5qwBNTBqYNTAqAZUA6oB1YBqQDWgGlANqAZWUwNqYFSsqynWOTt55da/FKkGVAOqAdWAakA1oBpYRw2ogVEDowZGNaAaUA2oBlQDqgHVgGpANbCaGlADo2JdTbHqX0XW8a8iuk66TqoB1YBqQDWgGlANzFkDamBW3MC88IUv7HSIA9WAakA1oBpQDagGVAOnq4E5N+rKHTeCaQPzwAMPdDqWwUFWvPZhpZcYEANiQAyIATEgBsTAaRiwvVi2T9M+evw+OuO22MB8+MMf7nSclgMr/uziqYE5zYeVZhUDYkAMiAExIAbEgDFQa2C0jx6+jy7tgdXALLxJK108NTD68BQDYkAMiAExIAbEwOkYUAMzvEGpNXelPbAaGDUwp3vXa2YxIAbEgBgQA2JADKyYATUwamD0dbWgmSp1n3oCs+JPPC1dDIgBMSAGxIAYWD0DamDUwKiBUQOz+g8ynYAYEANiQAyIATFwOQyogVEDowZmpgbm6uqq46P0sWK46JXZDVvyRbnmsGVrMHt0zLGGWs41rLF2DsfyZ1xF82fYFnuGieZhG8eZHh0Znu1T6rymsXmH5hoa59fbNw/wkH3yZTGWw3x8+LzAtNpruZAPOM5rtiW/lrK+oesYGuevSd88ffF+vj7jY87VZ11rx6qBUQOjBmbiBsY+rKIPrMxuHyIRvmSv+cx/jFffdWf4OdeazVmyZ74517mE3H3OO8OaPfKxjXV/3q2+DMd21v08U42nnGNorqFxYznAvJA+X2Y3XOQzW6s9w9XW0BLHGNZ97iWMl7K+oesYGjeW+2POe8y5xvKypng1MCtqYOxNEB21vyawBL+tu7aO6NzYZvGch/Va7r7+Mb+BsXVlr8zX1275s5hs7jns2Roy+ynWna0ls59ijXNcmyE5S5z4fBnW7JHP2/wY+VvtGc7ywAeJ3HPIKecYmmto3Fg+MC+kz5fZDRf5Ihtyel9tPFWcnwd5lyKXsr6h6xgaN5b/Y857zLnG8rKm+LENjF0XPvw+0XzeNveY15PpU62hdH6lPfCgv0KWTZbZpzrJKfK0rLEFw2vpi+fYml66ePamyV62ptqLMdAhfWxmN1zkMxsfPh/igPF+5PR+jCERBzzGkJnd/OxjHbERxnB8RNian2P8HN6X+bM5OD7DmD16sR06cjA+skV+5PA+GyOHx7AdOuIxhmQ7dJaGsxckfLVxhsvsPh9wJuGDzHzm9xg/5ljWgWNpOsaMremIY2kxWS7YGQ+d54INeO/DHPBDAsfx0OFDLEv2leyRz8/tc/kYxrPOcS32GibzYz3mH4NBfJQDtjEY5GBesHbYOH+GB9ZLH4t4yAhvtiwOePbDBoncwJgdNsbADwlfhI98iItyMz7TEccSObMY2dsZGNrA4Br4faC329hjjj2ecw2l3KU98KQNjBFaWsixCY/ma1lfC4Zz98VzbE0vXbyxDUz29rTz8a/IBoz3+bHhvK1lXMP4vB7v/TbmF+NZjzA1v8UYxuNqY8TxnF6v5fD+KCdjWOe52G66H/u87Eceb4vGkQ3xkH0xHs9r9b7auBTLvmytmT2aN7Jl8dHcbLNcpXzI2yJb87TgahjzlzDeh/WzPdMjLGyQHGs2PwYuk8BDRrjM5+19xzZXLaYFU8th/rEYHw+eYIeEPVo3+zLd5/FjxHm7H0fze4yNI1s2R2T38cBgfu/nMeuIK9nM5/1+jDyS7QyMaWCyPaBdF/hYh+3Ycs41lHKX9sBHa2BsgTgy4uHPTqbkR0yGYTv00joyH+yYz8ass780D3yQiPOydPHW1MDwR4Gdc/RiO+vARjb4TEb+yNaKzWKzOTM821lHnsgGn19rhmU761Gemt/PiRxRHNtYR4zP1YLxMdGY82d+nivTkYf9nC+zIw5Yw/mDMcCxzeeGj+2s13J4bJQPtpr0ufwY8d5eG0dxPsYwkQ2xkIzJdMYaJjuAM8m52J7pyJn5zZ7ljOzIl/l4ngjj56than6fL5t/SB6OYZ3n6Kv7PH6MfN5eG0dxPsYwkQ2xkIxhHX7IzMd21i2uNPa+2jzwX5r8pV/6pfSUI9+QBsauhd/v+TEwkOw3Gw62Q4ePJXwmzQ4Jnf1er2Fq80T5OAa6x5X2wEdpYPyJ+7Et2NuGjGsx0TyerCGY2rw1f2nO0sVTA3P4GWM8+5fZosPjbOzj/bgWk+HZzjryRTb4TLKf9b4Y4IfmiOLYxjrmMsl21jOMj4nGHJv5ea5MRx72c77MjjjGsi2yt+TycbUY9rPOa8nsjPG6j/Fj4L29No7ifIxhIhtiIRmT6REWNkiONZsfA5dJ4CEjXObz9rFjzM15WIefZeZnO+tZ7BAMx7DOc/TVfR4/Rj5vr42jOB9jmMiGWEjGsA4/ZOZjO+sWVxp7X20e+C9JPvLII91XfdVXdbb38i+zmc8w/JqrgcH+1K4b9Gi/WPMjlnGm8xiYTJaw3jd2zGso7YEnbWD8om0Rkc3ba5ia3+fjk/exfsxY6IbJDsbUdPhN8rysMybSSxdvqQ2MvbE9f/xm9z4eA2e26MVYj/Fji49sLfYozmzRgXVGMX6uCBPZkLMlvhWDnNl8bGe9FMc407OjlMOvPxrDxvmREz4eRzasFTLDez/GkFmct/s1+Hg/Rry3Y5zJLK5mhz+SmAs+P87sHufHUVyE8TYbR8fQfIgzGc3F/prO8axzXIt9CMZisgPzZ3nZPzQH52YduU16O8aQHmt2HOxr1X1eP0Yeb6+NozgfYxhvs3F0RPlgg/S5Mjtwmczianb4L01GTUzWvBg3x2xg7BpHe8XMzljGsM6YTM/wLXaPqY15DaU98OAGxhbgD54Uul9oZM8wERY2kxzHeobxMYxjPcuVYRjPegueMZFeunhjGxhba/byPj/muJIPOMawDr+XLRiL8bjamOfxWM7nfX6MPGxnHX7OCZvH+TFwkOxnHX6TbGedMdAzP9tZL8UxjnXEeJlhvN2PfR4be0zr2OOQG3bImt37MWaZ5TKM9yHO2zHOZBZXs8MfScwFnx9ndo/z4yguwrCNdcSbZDvr3heNzYZXLRY4lhzDumH8OLN5exTnMX6cxRgOrxqm5rc8GYbtrGPuKBY4SMZ6vQVTi8lyeHttjHkYx3qr33AcxzpyQGY+b8c4k33zAX/JkpuYUvNiHKmBuflnlK0WeU+LMWTkYxv00h54cAOD5CyzhZk9OhAbxcFnMoqFDbgsh7f7MeJZ9sUw3vTs8HMwjn2sly5eqYGxN5Xlz14lXxRbwpd8mJ8xrMPv58wwjPcxLWOOz+aI7JHNz9eC8THRuLTGljlqmJo/W1MUxzbWs3NowWTzc84I43P7MWIiO3JnvsyOnIj30sfxmHWOi+ze5seldURYni/SfYwfI8bba+MozscYhm2sI76G8TF+nOWBvQ8+wrbYPMaPs7UwjnXgTbKd9QhT8/t8UY5WDGKzOeGHbMUBb9LH+DGw3l4bR3E+xs8f+VsxHof5M7ufy4+zuJKd57xEHU1M9LUx5uPYDYxd2+jgvWPkNxswrMNWkhk+myfCRzabM7Obr7QHnrSBiRZSWhjIqmFq/mjeLPeYXMjp5+OcrDO+pmdxpYvX0sBYXv8yW2RnXOSf0uZz1ca2No+JbB7jx7VzjHJmNm/P5vL22hhrNJzH+jmjcWTzeaIx21jn9UCH9Li+46F5LG7IXBbj47CGKCd8U8VwHtYxT7YGj/VjxHm7H/M8JT2K8zYbRzaft4bxfpwL8kT+GsbH+DFy+zywGz6KiewRLsrrcX7cEtOCGZLXx0TjyAa+onXB5+MybCsOeTPZkscwHufH0To9xo99TORvxXicjfGK8nqbH1us2bzdjzGH5I4Ba2LsKL3mamDs2pT2m9m+EnHez3bWPS4aZ/jM7nMAB8n+yAZ/aQ98sgaGF8w6Fm0Sdkj2sd/rjPOxfsxY6H0xjGcd+UxmdmAyf+ni1RoYvOEsNx+wszQ/v/wYPs6TYQzbgmMM8kNmuTkmwnibHyM/pPf7MXAmzecP2Fmazi+fMxr7vB7D+UxnvPdhXMN4P8/JOueDDpnhkBs4yAhvvsjubcgJiZyQER4+lh7X4qvFYE0sOS90zsM6/CYju7f5MceZDwfn7atnc3DuDOPnQkwfPOfgeOjmRz5IxNTGwHEOtkHHXJCws/RzlXzIU4sBjnNBj2KBj3wcBxxsLOGLcsDWguGcpiMWdj+GHdjSHIwt6dEcPm+G8Xl9HPtbcnA8dMuBWEjk5THr8HNsyRbFwmYSB+eQPoyBIQ1MbV9o1yfaH7Idfp9rDIZzst6SM8P72NqY85T2wLM3MLaQlsXWMH39IKAWBxxLH8M+6Ixh3fx9x1EM5ildvNYGZthb8tYH7ND4U8UZ/0NfY2L7zHmsefqs6Rywa+B1DWtULSybgWPX0JTzTZlr2VdJq7sUBsY0MPZ+wH4P0mxsZz3aL9b8nDfSYStJPwdjvW/smHOX9sCTNzA2sV88bGaPfFgs/Bmm5C/FID9khm31G45zsM45zB75EA9/hjFc6eLN3cBcyocPztOug15i4FwZ4M+bTD/Xc9d5jWNgqs/GoXmyemX7uDNUtBgYzsDQBibaL1pNww6Z2VD/wLGEj6X5kQuSY0p6De/nQa4sztv9GPGlPfCgBgaJJW/+5YWpOSldPDUwwz9wokh7A+klBsSAGBADhwxM9dk4VZ7D1WkkBk7LwNgGZup94znlK+2B1cBsOtIlX+zSxVMDM92Hlm6s03GpTGJADJwXA1N8Pk6R47xY1dmcCwNqYObbR5f2wGpg1MCcy2eIzkMMiAExIAbEgBgQA0dlQA2MGphFPwk51VOaUvepJzBH/YzSZGJADIgBMSAGxIAYOGBADYwaGDUwwdMgNTAHnxMaiAExIAbEgBgQA2JgMQyogVEDowZGDcxiPpC0EDEgBsSAGBADYkAM1BhQA6MGRg3MgAbG3jg6xIFqQDWgGlANqAZUA6qB09TAww8/3EWHfYvmVD9BOId5S99CKv6I3wJ1nJ6D6E0hW/xhIV7Ei2pANaAaUA2oBlQDS6gB7aHH76Gz65g2MFmA7PpQUA2oBlQDqgHVgGpANaAaUA2oBk5VA2pgksd+p7ogmlcfBqoB1YBqQDWgGlANqAZUA6qBvAbUwKiBCb+3qTdN/qYRN+JGNaAaUA2oBlQDqgHVwOlqQA2MGhg1MKoB1YBqQDWgGlANqAZUA6qB1dSAGhgV62qKVf/Scbp/6RD34l41oBpQDagGVAOqgaXUgBoYNTBqYFQDqgHVgGpANaAaUA2oBlQDq6mBsIF5wwM/3ekQB6qB49ZA679q6Loc97qIb/GtGlANqAZUA6qB09VAtD9KG5ja/3lUfjEgBqZjwD4YozdoZDOsXmJADIgBMSAGxIAYOHcGsv2RGphzv/I6v1UwkL1BSw3M+9///k7HZXEQ1YNs+k66akA1oBpQDSytBmzzNXZNliPbH6mBMXb0EgMnZiB7g0ZvfsPay5oXvS6HAbveUT3Ipo2LakA1oBpQDSytBuzuPHZNliPbH6mBMXb0EgMnZiB7g0ZvfjUwJ75YJ5peT9vW+7Qteh/Lpg2nakA1cM41YLfKsednObL9kRoYY0cvMXBiBrI3aPTmVwNz4ot1oun1BGadmx1dt3Vet+izVzZdS9VAew3YrXIsX5Yj2x8dvYF5ylOe0vFhi9NLDFw6A9kbNHrzq4G5zGrRRrj9xhm9b05l03Vb53U7Vb1oXtXLudSA3anHnovlyPZHR21grHHxr8jmMWsYX11dLWKZQ9cxNM6fdN88ffF+vj7jY87VZ12Gzd6g0ZtfDUxfds8Dr43wOjc2um7rvG7RZ69supaqgfYasDvvWL4sR7Y/OnoDw09fWnRb/BpeS9kcD13H0Lix1+aY8x5zrr68ZG/Q6M3fu4F5y9d0du44bvv+h2h5b+q+4eqLu+/7TTJB/eD3dM+5ek7sA2ak/Lk7Nuv6+p8fl+VgnYXzGTdLEG1zfUPX9EetcQ2e/YPdB4JMLSZthNtvnNH75lQ2Xbd1XrdT1YvmVb2cSw3Yfa10Ls973vOKfou1V7Y/OloDs33S8rubpxTXx9XVo5sN1c0Dfsi1PKFZyuZ46DqGxm2ra8R/jjnvMefqS0n2Bo3e/L0amO3GmZuQ93cv/+Kr7lYTM9eGvyHvpvH44i/6hu4bvviLXJPkY/24xG4fLOcZEmcxLQ2M4a66v/0zPF9/XRvhdW5sdN3Wed2iz17ZdC1VA+01YHe5jC9rXnBkGLPbK9sfLaCB6Q4aGTQukC0NjG1McWzPNvgP/NkmtuRHzBgMcvilsZ3zs93HRGMfi3hIHwN7Fgc8+2GD9DnMDhtjOEfkB9ZLYDmeMfCzLdKBY4mcEf4UtuwNGr2x2xuYXbNyY+O8fWLx/OsnB0M27i0M1fN+6OVP3zZSkLey+lg/voW8qfXBcvSQOItpbWCSp1y8hIpe2gg/8u4Xdc+mz8GrL7y3e3Dz4R/Vz+y2R/6P7muvntW9+Fc+Hs7/8Tf+j/vPa3sffsFLfvUWrhC7O8dnp3kHn9d2zq/tfuLhR26tI+Fuv/Ye/Jau2+A1J+tTvhPVvK5H9b2j2ry82rRbWnTd0biwjHBms1e2PxrcwDz55JPbxPyfyAZ/9gQGjUomaw0MNqWYx4/N7m1Dxi0xJYz3+fVG/siGuEz6GD9GnLf7seG8LRpHtmyOyO7jgTFpPu/nMeuIK9nM5/1+jDzHltkbNHpTNzcwB40Kn9Gmsfmi/+r6qcdu4/6yt7xs83WxHT/p05ltPvxjARogy7trlMDv1735LdsnDhjHXxGjNdiTmP1Xq2w9mGMj79g8oeHx9utmuzXf8Q3P3lzP53c/s2nFbn0Nrsf5dDvs933wZ4M5NqeVni+4tHg0MLtcP/Dyr9/X7Y5Hs986nz23B7nbns5kG+FoY//G5/2tpg15VF+jbYUmZNcAUBPyyLu6e55FTUwhdtS6SnlbG5gt7qr7qp/8/8Kbcra+7LpleNkvb6Ona65rfo41YHdKf16+afFjj7cc2f5oUANz3333dXfddVf3+OOPW+7ty3Sz3X///TAdyJsNjG1QH91/pWxIA5NtPtnOOi8Idkj2mc521hnHdtZbMRzDOsf31X0eP0Y+b6+NozgfY5jIhlhIxrAOP2TmYzvrFlcae19tHviPIbM3qH8z27hPA3OrMeCz4Ccz1xtsNBDbjTW+crbblO9+H8P6Jtfmq2m7zfgu135jvp/G4ff2a+WgaaFmZuv2sdE4+xqcYTcNQ/V8bCLOy7r3bcb78zUfXhbDDcxmXvyeZ/vVPTR5UW5qWg44R+6bMtwIXzcAfTfVUV1NZsuahWSt1oA96+pv7xquLHbsv3CX8m59DU9gSjkK6wuvWwE/2XXQHDc2T+JWjYJq4Hg1YHcxzzcaFrZHNvgtR7Y/6t3APPHEE9tG5fbbb983MWheYDOMf91sYOq/f7GmpvQEpmVDmmGwvszPdtYRZ5LtrLdiOIZ1ju+r+zx+jHzeXhtHcT7GMJENsZCMYR1+yMzHdtYtrjT2vto88B9DZm9QvIlZ9mlgnrN5QnHzR+bcMPjNddfZD+t3Xzsjn3tiYFxuN+tbezQHxQYE+q+N2Xi/+T9oLCzY5yqNvS85n+2aGMv6xpmd7zYO/7EYbmD4a2Kcj3XkPuTsFufIfVNGG+GDzX+yYd09ocFToMNG4aX33PoDD/xVrlLM8772Czfvs12eB56HvBv5NQ/sblbJRj9dqzU2z7xt99Ww69i733j3/itx+3W5vOEajYPrRmlbo5s6/eo3vHHzlbZgneBrm/e6gbme4wYvW/utHFjT4RripzPRdeP3s/TjbajEtbhWDRyvBuwu5vm2ZsXbbJzZLUe2P+rdwFiyxx577KCJsScvaF74qYxh8WppYKInMrUGBjcpLzGv2UsvH8djxGU52M464kx6O8aQHmt2HOxr1X1eP0Yeb6+NozgfYxhvs3F0RPlgg/S5Mjtwmczianb4jyGzN2j0Rm9uYK6/2tXvNzD+6cz1pjxrVDL7jaaDWbQNfVQX2NS7Df+NXCW/9yXns10OY1nfONPz8udx+gbmmfR7jPfc87nX77frr2m5Tb99hWu7+caG/Lrp2H2167C5wW9YfAw27we1yfOwjiZhI22zz2vdx/OTGazr+px2DUJwLm6OW2t0X0nD/A6/n9v8Wx83MJvabOBlF3eraTlYK+bdSDUwx9swHVxXugay6xqoBo5fA3a3HMu75cj2R4MaGEtoLzx58V8n23kP/+sbGHu6su0tgr9Mxl8nqzUwh7PcHGFTe9Ozs9T8hsowbGed5/J2jCEZ6/UWTC0my+HttTHmYRzrrX7DcRzryAGZ+bwd40z2zQf8MWX2Bo3e/O0NzOYMDr7KZGdkG3X/9SsaH2zceVPv48DOrkHo9RUyWxO+4oU0B80Wz4s1Z083vN+t8+B8uJnZxG25ib4uh5zEi5luvGyuAQ3M9TXYN5YHa7wxyd4QbYRt07z/+hU2bLRZ322qXbNom3PCbGuMxs0xm/l2jQ/yB40G1rSR4VrNHzyBQfNka7OnPNuvyDWsMZ+j8IcFtnm5gaE/QEBzes6iufZrpfOOrlv0vpbt+BsscS7OVQPz1YDdvMbyazmy/dGoBsYSWxNjT2Rqr6iB4UZl19DsvlbG9iENDDaytibWeY2wQ7LPx02FwRxZPvghW3HAm/Qxfgyst9fGUZyP8fNH/laMx2H+zO7n8uMsrmTnOefWszdo9Obv1cDYwrcbdWwy8fUwnNGuWdj9IH6H2W+s/ZOP7Ub7Vp7Dr3zdsiN++/94sSct+F3I9ZRmv9nwdB1/jczHHo5LDU7pfBwX2z8QcKsxOpxjg03Pl7kb0sD43GiikDeW4UaYN9jYNJMt2mRva4owftwac4CrNCG7OXZPR/zvdQ7zuEbjxtOZXXNxEIPz3sjM7puPg/fVlgs1MAecEKeyz7fBE7fi9pxrwO5kY8/PcmT7o9ENjCVvec3RwNi8fqPqxy0YHxONIxuft/fDF9nH2JA3ky25DeNxfmz5va029jEejzWznXX4ITNfZPc2P8bavN2PMfexZfYGjd78vRuYwSfDTxgGJ1HgRAyEDcxmo7n72tj1179s48nNyVanv/KFjSljBsZsn77UvuqF+a7l7omNXyutz633oCHhNTvc/n1y3fDc+Jobx7o17fjq38Ds4g6/Qnbjadhmruy67dfs16Px6E2PuFVjoBo4fQ3YrW/sdbAc2f7oJA1M9D+w3Nk2K6WvlJleegJjJ2Yv24Ti2Flu/hf+bMNa8iOmBeNnRizsfgy7Sc5fwnFMpEexnBtz+diWOI7J8B7DcyPGS8TAbmPW4c/sHuvHHGc+HJz3lHr2Bo3e/EdrYMKvep2Spcueu7QRPvwqFzUEm82wNQEH/4+YylfIrObaYnZPVLbvpWd+bfe1z3rmwQ/x+WtgXMd+rQdPZK4bjd0fCti9T/d+14SEa7TN/3Vzg/c44vd/cOD69y37NW3xAxqYGzzRn4e2dVwfpesGjOQtvsSFuFANnEcN2B177LW0HNn+6CQNDJoU38jAzrKlgbETnPNlN8KpXlPmmmpNynN6BrI3aPTmn72B2X91qu2rTadn7zJWcPEbYW40qEGI3iNLsl38dVvRtVpS3Wgt57GJv+TraHfmBx98cPBh3Nkr2x8dtYGxZmTIsT2DE/5nqqZjaB6Lqx0npEdTT8BA9gaNPvxmb2AmOB+lmJ6BS98I81fWovfFUm2Xft2Wel20LjUIqoF5a8DugmfRwEx/Oz9exqGNh1/hVHl8Xo3Xz4AamPVfw7nP4FI3wre+KhZ/RWvpm5BLvW5Lvy5a37ybV/Erfu2eqAZm7p1BJf8UjccUOSrLlHvFDKiBWfHFO9LStRFe54ZA122d100bcF031cC4GrBboxqYI20QNI0YOBUDamBOxfx65tVGeNzN9FSbEV23dV63U9WL5lW9nEsN2N1VDcx69hhaqRgYxIAamEG0XVSQbYR1iAPVgGpANaAaWHINoAGzG7QamIvapuhkL5EBNTCXeNV1zmJADIgBMSAGzocBa6zUwJzP9dSZiIEqA1M0ML/1W7/V6RAHqgHVgGpANaAaUA0cuwZso6MGprrdE0AMnBcDamB0szn2zUbzqeZUA6oB1YBqYKoasF2ZGpjz2pvqbMRAlQE1MLqJTHUTUR7VkmpANaAaUA0cuwZso6MGprrdE0AMnBcDamB0szn2zUbzqeZUA6oB1YBqYKoasF2ZGpjC3tT+fyp8FKCrcvX5/8R4bG28KiIudLFqYHQTmeomojyqJdWAakA1oBo4dg3Y9k0NTLKJ3W7UH3zF5u+y3Tr2tiTmEsy+gbmEcz63c1QDo5vNsW82mk81pxpQDagGVANT1YDty9TABLtT26S/5o5nHDQv1shENrNf0qb+ks41KI2zMKmB0U1kqpuI8qiWVAOqAdWAauDYNWCbMTUwwZZ0u0m/blisacHBT2NYN3/Lxt4w/gimP8C0+Hlu1jmW7awbxsZ8RHHsh45YxsPGGPabvYZhvPTpGVADo5vNsW82mk81pxpQDagGVANT1YDtjNTABPvD7SabvjrGzUqkWwNz2223bZuAIN3WhI2793v7kDHHsM5zsT3TgS/52Wf4IeNaDNYhOQ8DamB0E5nqJqI8qiXVgGpANaAaOHYN2O5IDUywR9xusCduYIJptibezLPOeNgh2Wc621lnHNsznfHQGWu20tj7ohwtGMRJzsOAGhjdbI59s9F8qjnVgGpANaAamKoGbHekBibYI2432QtqYLDEls1/X0yGz+b0eB6zjniTbGc9w7Bd+vQMTNHAfPCDH+x0iAPVgGpANaAaUA2oBo5dA7YzUgMT7A+3m+wZGhjLGx1YQra5r/k5jnXEmWQ76/CZDUcWB2zm93mBYzvr8Ed52Sd9WgamamCm+pcU5dG/yqkGVAOqAdWAakA10FID1izZSw1MsDfcbrInbmBaNu4ZBkvM/GxnHXEm2c46Y6Czn3Wfx489tiVfhIFNch4G1MDoJtFykxBGdaIaUA2oBlQDS6sBNTCFveF2I76gBgaNAaRfOttZZxzbWWcMdPazbv7S2Pta8kUY2CTnYUANjG5IS7shaT2qSdWAakA1oBpoqQE1MJW9oW3G8eeTa9L+AtlS/gqZnZZvJGzMtkwHJSU/+7K5kKfFD6zPC7vk9AyogdFNouUmIYzqRDWgGlANqAaWVgNqYBr2hbapRnNSk8CW0hrGH4Y3G78Yw3bo3u/jDVfCeDxjIx/mhWQM6+xHTtggI7z5MjviJKdjQA2MbkhLuyFpPapJ1YBqQDWgGmipATUwjftBbMRr0hqcU720+T8V8+ucVw2MbhItNwlhVCeqAdWAakA1sLQaUAOzzr1nuGo1MCEtMiYMqIHRDWlpNyStRzWpGlANqAZUAy01oAYm2dyt0awGZo1X7XRrVgOjm0TLTUIY1YlqQDWgGlANLK0G1MCcbv84+cxqYCan9KwTqoHRDWlpNyStRzWpGlANqAZUAy01oAbmrLeoOjkxkDOgBkY3iZabhDCqE9WAakA1oBpYWg2ogcn3d/KIgbNmQA2MbkhLuyFpPapJ1YBqQDWgGmipATUwZ71F1cmJgZwBNTC6SbTcJIRRnagGVAOqAdXA0mpADUy+v5NHDJw1A2pgdENa2g1J61FNqgZUA6oB1UBLDaiBOestqk5ODOQMqIHRTaLlJiGM6kQ1oBpQDagGllYDamDy/Z08YuCsGThGA/Pwww93OsTBmmsgumm/8IUv7HSIA9WAakA1MG8NRJ+/sKmBOestqk5ODOQMqIFRY7HmxuJYa8fNkqVtWvQSA2JADIiB+Riwz1n+3PW6GpgG7u3/r8JHQ8giIFP9f2GmyrMIUrSIPQNqYNTAHKsJWPM8/qZpYzQwaz4vrV3vf9WAamCpNWAbFTUw++3aMGW7eX/wFV1Hx942LOXRotR4HI3qVU6kBkY3r6XevJa0LjUwep8sqR61FtXjJdSAbarUwIzYWloD8Jo7nnHQvFgjE9nMvrSGYWnrGXEpFDoDA6dqYKwuSx/AkT+y+RyMYd3j+o6nzGVzWz4++q5nLN6fjx+PzV+L53OfYu4pcpTWrAZGG8ZSfcin+lANTF8DtuVRAzNi42c3RjQs1rTg4KcxrJt/G1OZ0zD+4BDkYAz7obMfOnwmOQ/bocMPLHKwHT7EYJxhGSd92QycWwPDNxGrTx6P0afKhfeMX0tm97ipxlOdT9/1ZOeZ2Vvzz30+amCm35y0XlvhxL1q4DJrwHZPamBG7CHtxsgNSk23Bua2227bNw7R1NucgYPtpvPY4LUxUjIOOiQwkLBDwm6SbZkOPPthk1w+A6doYKxW7IYEGd2cIl9ki2Jh64tHXCSnylXKU/JFaxpjO+ZcvM7SvCUf54j0MbFRPm9TA3OZGyhfBxqrDlQDx6sB20GpgRmxj7QbY61pYX9LA5MtZzvXtZN1xmf2DMN41g3PY9Y5F3T2sw6/5DoZWFoDY7XVcthNxHCQrOMGwzbkhA8SGIwhYUccS2BMZnbGQEdOjCMJDEvowNsYB2ws4WMJP9ugm890YCDhz3yIAw5xmYzyeCxjoEf5YWNMlAu4yGc270c+jx/TwGAOSJ977rE/Jz8+xvw49ynmniJHdM5Z3sxuOczHh8/LvkyfIsbnGDO2dY6JL8XOmbs0r3zHazqm5Np2VWpgRuwt7Q3HDUpNVwMzgmyFHp2BYzcw/gbmx/jwi+zeZuPIxjlKfsN5P8dCz3A+1o85PsvhMRhbriift9XGnA+6yVpci7+G4fmiOb3fYyy/n8NjMPa4lrHHROuBbUgDY/mjOTI75ppaRmuYeo4oX3aemT3KEdnmOp9sXdF8JWyEx3mUfMB4OSTG5+gznnO+OXP3OUdh19HQ2IZIDcyIbaG94WpNC/tbGxjLGx1Y6nZeDEh6e5SDMaxbGowhKfWN9XifH1sOHOyTvh4G1t7A+BuR1SNsrMNmku2sZxgfE40Rm+UrxSCWZZQnsrXm9bGlsfdhXWxnHf7aWrKYLD7CRzY/7xgMr4X1oQ0M52A9WyNjptKPORevuTRvycc5In1MbJQPNssb5W61cR7oXka5PMaPh8T4HH3Gc843Z+4+5yisGpgHH3ywyw6rD3tl+6OrqIAMvLSXveG4QanpLQ3MNmdwomxnnaFsZ70VgxhIjvM6Y1j3OBvX/FGMbKdnIHuDlt6f73//+w8Wbv8jqWiDBxtyWY1AZxnZW2w1TOS3ednOemlNHufHiM3sfl7gMxnliWyteX1saex9WCPbWYe/tpYsJouP8JHNzzsGw2thHbXM0v5l0F6Mg56tAX6TwLCEDpyNccDGEj6W8LMNuvlMBwYS/syHOOAQl8koj8cyBnqUHzbGRLmAi3xmy/zAZ/lh9ziMI+ljgMns8Ecyi4HdJHSLx5hllhcY9nMu2DNbFI8Yk/AjHpIx0tfRTJziOtnnq57AGAsDX/aGqzUt7F96A2M0bM+pgQ/GsR6F1vxRjGynZ0ANzM3NHG68/IFt9e3HZosOxrHuc7DP6xE2mgs2jofNS4/JxhbHPuhsZx1+k5m95kMOjmc98sPmc1tcdiAmyg1fJLlxgT62gcE8WCvGkH6NtfHQuFreaH0+BnND1vyGY0w0h8dgzHGwmcQR+b0NWJbAQMJXGwPH0sfAl9nhj2QWY3bv82Pk8/bSuORrzWe4KI+3IZ/krfoVF7e4sB2SGpiR+0R701lj0nLYXyCb6q+QRcu2teDFOmwm2c46MENsHMN6KSd8kstl4FgNjNVM7eAPbsPy2HRv82OPifxDMD4mGputdmTr4ThgICMf27wexRnG20tj78McbGcd/mieVh9wnJf1yA+bScayzhjWWzCMR9PCcsoGhucyPVtfZud4jymNvQ952M46/KU11nzIwXlZj/yw+dxRXCuGc5Zi/Bx+7PP4XOxviWX8VLl4XtZ5Ltghs7nZH8VncSU755Fev5dcCke2c1IDM8H+0d60aE5qEtjStIbxh+HNxnI7oP/AD5PPAb+XjIfupc/FfuSDrYQFRnL5DByzgSl96Fo9sd+PzedtfuwxkX8IxsdEY7OV7C1+Xi/rtVjGso64aF0ex2PWsxwtGI6FnsW1rDHCRHmzOdjOOnKUJDcu0NXAHL5nmb8WfhnDOvJENvOxnXXEtWIYX4rxc/ixz+Nzsb8llvFT5eJ5Wfdz8XwZrsXegonmlk3NC9eA7aDUwEy0j7Q3ZcthDc5SX7Z+vcQAGDhGA5PdzPiDynTGsQ6ct/lxlMNj/NjHYOxxfgycSRwRBj5Iw0Q4b48wlsPba2OeF/qQPEPn4Tkxr8+V2SMcsJzXcB7bd8z5Ih1NC8u5Gxicl5e8Pu/D2GOyseHZB53trMNvMrPXfMjB8axHfth8bovLDsREueFj6XEYQwLrx7CzzDCZnWO9nsWU7ObzB/JmcexHLGws4YskcNkcmR1xkrfuJ+Ji90N6NTDYrV24tA8PvcQAM7DUBsY+vKObHdtYx4c926CbxAGcl/BzTISJbIj1vtIYMZAei3V4u40Rk2HYDx1xnI/jWQcGsZkPOJYRlv3QOXcWk9ktB8djjNyQHgN7hi/ZuXGBPraBwflBtqyvBePzlcbeh/xsZx1+k5m95kMOjmc98sPmc0dxjPV47+Oxz4UxJLB+DDvLDJPZOdbrWUxkj2yWj+2s+7lasLV4n4PnaIllvPTLbmhsr6IGhndsF6zbh4deYoAZOEYDo5vQZd+EzuH6o2lhWWpg7JxLmzX2sQ6uIpvP2YLxMX7ckqMFg3WzzOL8GqJxZvP2bA62s27x2RHhzJbZ++QBNsoFXyazmMge2Swv21nnOWGHhK82jnA+JsLAJpnX5KVzY3sVNTC8Y7tQ3T5Q9BIDngE1MLp5XPpNsuX8uXGB3tLARBs5s7GddV6Lt9fGiK3hxvqzeWCHtHn8XOaL7BEOWOTD2GP7jjkf6z5PNt8QO+aJ5oAvk1lMZI9sWC/n9zges44Yb6uNszl9HPJL6j4U1YDtV9TA+F2bxmJADGwZUAOjG0d045DtsC7QtLCsNTDg0DZtfMAOWdrUleIsnv3QYUd+P47mQ2zm41zQIyx8LDl3FpPZLQ/HY8z5YQMu8nlbNM7WkNn9vCUc5mvBAAuZxZTs5uMDa0VOjIHxdh5D9/Mh1tuBN+kxJSzHST/8/LlUPmyTogZGm1UxIAZCBtTA6EZxqTfHPufNjQv01gamzzzC6v2oGlANqAZ2NWCbFjUw4dZNRjEgBtTA6Gapm2W9BtC0sFQDU+dNtSWOVAOqgaE1YDs0NTDap4oBMRAyoAZGN5ehN5dLiuPGBboaGL13Luk9oHNVvR+7BmzTogYm3LrJKAbEgBoY3ZSOfVNa43xoWliqgdF7Z421rDWrbtdSA7ZDUwOjfaoYEAMhA2pgdDNby83slOvkxgW6Ghi9d05Zk5pb9XfuNWCbFjUw4datzfi+b3tWVzve++Iv69700KtuHG0zCCUGTseAGhjdBM/9JjjF+aFpYYkG5nTvXs0sBsSAGDhvBtTAjLi+1rxkL/M98u63dtbARE1MFgc7/sSgjU3Hi3XY1i6PfU5D5hsSs/brogZGDcwUG/xzz8GNC3S7seoQB6oB1YBqYN4awGduJD/4wQ9ut2Hvf//79/+TWjM8+OCDgw+7n9kr2x9dRTc8Ay/tVWtgsN6oiYEvkqXNcskX5VqD7djnNGS+ITFr4L60xuwNWnp/2gcFv+wDJPpgke23xMtviQO9D1QDqgHVgGpgnhpQA8O7MaejgbHNLQ5A4LPxYx954saTGOAiWdosl3xRrjXYjn1OQ+YbErMG7ktrVAMzz4eqblbiVTWgGlANqAZUA/PWgBqYwg6PmxQPi3xmw+9hPN7Gtkn2B+zARxtpjgFuiMzyYE74OTdswLDPdPZDBwZjlvD5WLb31X1+G/OL/dDhx5glfCYzO2PWqquBmffDVTcv8asaUA2oBlQDqoF5akANTGH3iSYl2sSaLzpKDQymsnz84jHrhqmNOU9JL+Uxn/e3zB3FtMS1YkrnA59fgz8X7y/FwQfpY/0YuLVKNTDzfKjqZiVeVQOqAdWAakA1MG8NqIEp7D7RwBQgBy7DT9nAZBvmzH6wGBpkeNghKSRsaMwfYTkuwvgYP0Z8Zoffywyf2TneY2pjxHoc7GuUamDm/XDVzUv8qgZUA6oB1YBqYJ4aUANT2HmigbFNa3Zw+NoaGKw92pRHNsNnduSKMD7GjxGb2eH3MsNndo73mNoYsR4H+xrlMRqY6A8CyKa/frbEGtAmY55NxhJ5XWL9aU03Pxej2hFPN3k6Z06iGoBNDUxh54kGpgA5cM3RwNiGOToOJq4MapvuyB/NCRtPB5uXHuPHHo8x42q6xUQvb0duLzm2NcbjOMfadDUwl3UjOOeb3BTnhpui5Pk3MlYvDzzwgI4Fc2DXKHov6tpdTt1mNYC6UANT2HWigfEbXx5z+BwNDOcfqtc23ZE/svn5M4y318Y+b+vY50Uc21mH36S318Ycey66Ghg1MHaD0LHjADdFyctpYD784Q93OpbHgTWX2ebV7ObXdVvedZvympRqAJ/RamAKu1E0MAXIgetYDYzfbB8sIhhkeNghOTSymZ/trJdiPc6PEZvZ4fcyw7OddY739toYsR4H+xqlGhht3tW83KoB3BQl1cBMuRFTrv4b7dLmVQ1Mfz7XWIOlGsBntBqYws4TDYxtWrODw6duYCy33zD7Mc9f0n0cj1nnHN5eGyO2BdeCQb6SjPKwjXXO4+1+bFhv82POt0b9mA2McRdtljN7hB1jO9Y8fddo6xq7tig+svm1MYZ1j+s7njJX37lb8LY+HIzHTVFSDcwaN3zntObS5tXes+Y/p/PVudxsyko1gM9oNTCFnScamALkwDVHA2MT4GZrcswry1PKm8VgHeyHbj6f04+B4Rjk7CuRA3NAIg/7oWN+YKIxbBzD+LXrx25gjEfeMJoe2TxmivGx5umz1qnWFOWJbKW19cUfK1dpniE+f548xk1RUg2MNpQ3N5TH5KS0ebX3vRqY016fY9RCqQbwGa0GprALnauBKUwplxg4GgNqYG59fWjIZnhsDG+ex+SK8kS20hx98cfKVZqnry87R9hxU5RUA3OMDZrmyDfhpc2rve/VwOTcnUtdlWoAn9FqYArbRWtg+h4t/x+YwpS9XHbjrR29Ei4EXDsn8+s1noFjNzB247FrxxvP2hhYxkE3CZ1x3sbzIibC+BwRBjbkQUwkgUEMMGyHDp+X8LMEhm0l3fDmh2Sdc7EfGPjh4zF0YE36AxjEw8926PBBwj6VtLxRLthxU5RUA3Mum8C1nkdp82rvYTUwamDsc1oNzPh9oDKIgVUysOYGBptOu5lBh2QbNqzmY3+EiWxRjLdhDpYe48fRXBxf8vtcfhzFGsbjeFzzRzmxXs6T4WoY78/y8JxYs5fAtEieV43L+TcuuMZWG9oEL3cTrAZmudfmWE1xqQbwPlYDs8qtpxYtBsYzcIoGxjYOvGlk3ft4A8o41oGp2SK/n28MBuvwOUv2bD6OiXQf58fRGmqYyO/ztGB8TDQ2m7dnuYGdUtpcODgvboqS59/I2HVvbWCsVvpu2lpiWjCYt4aN/JEN+SAZwzr8Q+XYXKXNa+3a2dzRccxzGXv+0VprOSN/ZPO5GcO6x/Udj81VqgF8RquBGb8PVAYxsEoG1MCUmylsbu2DONJh85Lx7PN2P2ZsSfdxfmyx3ubHHhP5h2B8TDQ2m7dn8wM7l+R5cVOUVAPDmzWrER636C0xLRjMVcNG/siGfJHsi49ywDY2V2nzap8FpeYzm9vsmQ/rjuSxYqK52VZbR+SPbJzT633xPp7HY3OVagCf0WpgVrn11KLFwHgGTtXA8ObVPuR4c+rH8LGd9cgf2aIYw7Hd9OyIcsLmJedkn7f7MWOh19ZjuCiPt/mxj4v8QzA+BmPLHx3mx+H9sEfSY3kc4Us2izU/boqSamCwEbPaMB0S9ppswbdgeO4SPvJFttK6++LnzFXavNp7dUgDw1yW1u59Q3gZEuPn5THyQbIPeuSLbMBHsi8+ygHb2FylGsBntBqY8ftAZRADq2RADczh5t8+cEsbXfONwfhYP/ZzZ35v9+NonTVM5Pd5WjA+JhqbreXI5muJjTBZPthxU5RUA+M3YVYjsEXS/DjMH+Hhhw8yysc24CC9z+y1g9cELNtYh98kz8WYzM6x0BkLW5SbcaXNq723hzYw2TmU1hWtNcOzHTqfF2wm2V7TgYdkPOcs6RaDeODYxjr8wPv5eAwdWI6FDoxJ2IBnH+ulGsBntBqYVW49tWgxMJ6BUzYwdhPCBxlvNs3GY+hsZz3yR7YoxnBsZx05WjEZvmTP5kNM5vd2P/ZrjsbeFuUYgvEx0dhsJXurH7hWWTtH3BQl1cBgo8UbKqsfHkP3dhtHNuBNRhj2Q6/lyXCYA36MS/miNZXwPndpjPlrGPhLm1d7v0/ZwNTOsa8/OtfI5vPi3L30OD8GPrJ7m40jG+co+Q3n/RwLPcP5WD/m+FIN4DNaDcz4faAyiIFVMrDEBsZuTvahxptSG7ONdeBqNp8jmiey+bx+jPm99Dg/juaq5YDf5/LjKHcNY36P8eMsr8f5cRaH84n8mY1jhuh+bTzGTVFSDUy0AbNa4Q1WhIGfsazDX4otYfrk8lg/9muI/EMwPiYam61kL21e7X0/VQPT95xb8NF5tcaBF5Y+1o+Bjeze5sd+rZF/CMbHRGOzleylGsBntBqYVW49tWgxMJ6BUzcwpU2qfZDi8Diz+81rzQY/cmLs82Au4Ly/FBdhszyYx8f4MeJZRrHRutjGOuZgG3STOIDzEn6OiTCRDbHeZ2P4ICPMFDbkN8n5cFOUVANjtYENFktv92Ng2c46/CYzOzCZP7K32GqYyO/X2YLxMdG4do6lzau9Zy+lgWnlO+PYx/uxj4v8QzA+JhqbrWQv1QA+o9XAjN8HKoMYWCUDx2xgeKMove03IOLpuDzhpiipBqZlI1fafHE869i0lWKB6RMXYb3Nj/0aIv8QjI/B2PJHh/n9Udq8Tt3ARGtiLrxew+N8+ZyyGM7NeOiZP7K32GqYyO/PpwXjYzC22OjA+bIs1QA+o9XANGw9PeENIWcPMU70WjcDamCOu0FWQ7JsvnFTlLzsBsbf76MxNlrmg86S7axnGLabHs3pbRwTzeFtfox5kCfyD8H4mGiMOTNZ2rxO3cBka4CdeWEd/kh6nB9HMd5mMbWDY6I5vM2PLZ5trGe5WzA+bzTm/JFeqgF8RquBqexB7WJ1D77i4NjbKrHn7N5ycM4neAHnpgZm2RtqNTzHvT64KUqqgYk2VLDxBo51+E2ynfUMw3Yf732RP5rD2/zY54n8QzA+JhqbrWQvbV7HNDD+HP04WhdjWAc2Og+P82PEZvYoJ2IgfawfRzlqmMjv87RgfEw0NlvJXqoBfEargSlsVO1CveaOZxw0L9bMRDazG/5SXpd0rud6TdXAHHeDrIZk2Xzjpih5uQ1MtjnDZguScaxjQxbZEJth4PexsHvJONaB8zY/NhzbTOex95fyDo1DTi9Lm9ehDUx0ftE51s6l5o9yRjafhzko+TJcFONtfuzXZX6P8WMfg7HH+TFwJnFEGPhKNYDP6ItsYJ588smmPamRi4bFmhYc/okMxubfxjRkNxwOg0dx8Gc+xAGXTQt/Sx7k4Bjo8GFeHktfHwNqYJa9oVbDc9zrg5uipBoYbKIyafdE9uEeCTtkX4zho1jOA93j/NjnavUbDgfm8hJ+5IRkXGZDLGO9Xtq8tjQwmIOln4PHJZz5GGt6CQ/s0DjkR56S9HP4sc/V6jccjmx++JETkvGZDbGM9XqpBvAZfXENzHvf+97uHe94R9Nu00hGc9IirYG57bbbthe+NME2LwFwMcl0I8eQGMsXxfl5ahjgGcc6/JLrYkANzHE3yGpIls03boqSl9vA+E2Uxrf+tfyYXJQ2r7UG5pjr1Fzz1UepBvAZfVENjD15sebl9a9/ffenf/qn1d3mdpPufv9SamRaGphs48921nmRbGd9DgznhM5zsg6/5LoYUAOz7A21Gp7jXh/cFCXVwGhjOt/GtIXb0uZVDcxpr03L9ZsCU6oBfEZfVANj20trXN6xaWLsqDUx2026Gpj9rpybFtb3ACmrYkANzHE3yGpIls03boqSamCm2IApx/CNdmnzqgZmOK9rqslSDeAz+uIaGNthWuNiT2FqTcwpGxibOzqwQ84aCLZH8bC15AGWZS0OfsnlM6AGZtkbajU8x70+uClKqoFZ00bvHNda2ryqgVEDg89oNTCbZiZ72ca99JUx75v7K2S8zu3a2HCts531ALo1RZjIZmC2s57lln3ZDKiBOe4GWQ3JsvnGTVFSDcw5NgVrOic1MJfRpJRqslQD+Iy+uAZGXyGrNyJZc8J21pe9TdfqMgbUwCx7Q62G57jXBzdFSTUwpY2VfPNvrkubVz2BmZ//JdR4qQbwGX1RDUzfH/Hbxs826vZkpeWwv0B2zL9CFm1MfWPRd4xzruX2eSO8bMtmQA3McTfIakiWzTduipJqYJawgbvkNZQ2r2pg1MDgM/qiGhjbTvb5M8rYftpmHc1JTQKL2EwaDodhTPcv+DOfx9s4wyKXj4nwyIMYSNhZ+nwar4cBNTDL3lCr4Tnu9cFNUfJyGhjbKOtYJgf2+Re9F9HA6Lot87pNeV2yGkBdXFwDY9vL1v+RJW9FsYmvSWtwhrwsr15i4JgMqIE57gZZDcmy+cZNUfIyGhi9H5f9fsw2r7puy79uU16j0ufxRTYwx9wkts6lBqaVKeGmYuAYDUzpw0e+898o6hrrGqsGVAOqAdXAHDWgBmaq3eCIPGpeRpCn0MEMqIHRTWWOm4pyqq5UA6oB1YBqYO4aUAMzePs3PNB/DW14JkWKgeEMqIHRDWbuG4zyq8ZUA6oB1YBqYI4aUAMzfP+nSDGwagbUwOimMsdNRTlVV6oB1YBqQDUwdw2ogVn1FlSLFwPDGVADoxvM3DcY5VeNqQZUA6oB1cAcNaAGZvj+T5FiYNUMHKOB+a57Xt7pEAeqAdWAakA1oBpQDfStgVLjowZm1VtQLV4MDGfgWA3M8BUqUgyIATEgBsSAGLhEBqzZUQNziVde5ywGKgyogakQtBL3Rz7ykZWsVMsUA9MyoNqflk9kE69gQvKUDKiBOSX7mlsMLJgBNTALvjg9lqbNRg+yBD0rBlT781xO8ToPr8rajwE1MP34OkC/79ue1dWO9774y7o3PfSqG8dBIg3EwAIZUAOzwIsyYEnabAwgTSFnwYBqf57LKF7n4VVZ+zGgBqYfXwdoa16yl/keefdbO2tgoiYmi2v5n1a2YLL8JbvP68cWazbYIUs55VsvA2pg1nvteOXabDAb0i+JAdX+PFdbvM7Dq7L2Y0ANTD++DtC1BgbgqImBb4icq3Go5a35h5yLYpbLgBqY5V6bPivTZqMPW8KeEwOq/Xmupnidh1dl7ceAGph+fB2g0cDYxh4HAPDZ+LGPPHHjSQxwQ+RcjUQtb80/5FwUs1wG1MAs99r0WZk2G33YEvacGFDtz3M1xWt/Xsfsn8bE9l/peiLUwIy4Vtyk+DSRz2z4PYzHYxwVqtlwGK4Fg3yMRw4fz3boWVzk93MxxvuQ1zB6LZuBJTUwWb1k9mUze9zVlTYbeK+W5HFXq9nEwHQMlGo/mmXJnyfRe9Sfw7HWX+K1dQ2tODvHPljPSTa2nJwX40j6HIzJfGznedjeR58iR5/51oBVAzPiKqFJiYrZfNHRt4HxRYu5eNkRxvtrGMPXMFP4fQ5ep/RlMbC0Biaqnci2LBZPv5rSZsOvTnx6RjReMwN9at/Oc6n1n60rs899zUq8tq6pFTfXdfHz+3HGocf5cbTeCJPlz+xT5Mhyr9WuBmbElUMD05rC8H0amKxg2c46r4PtrGcYs3tcn7HHYh62sw6/5HIZUAOz3GvTZ2WlzYbPo/eoZ0TjNTMwpPaX9h6orafmn+P61Xitranm92vui/fx0djn9OMoxmwe58etmCx/Zo/mybCXYlcDM+JKo4GxwsoOTq8GRl8b43pYur60Bsb48h/ifgyMfz8y14gBxvvgZzt0xJQwwC5F1jYbvM7ovCKbxcDO0nSMOS90+EsYYCXFwFgGhtR+Vpuwo4ZtbZENduAgcS6IwRiyrx1xLDkH5vWS8aaz3/uycY1XXkeUw/t5DdA5jvGsZxjYkSuK8TY/Rg6WGcbbecw65xqiT5lryPxLi1EDM+KKoIFpTTFXA2NFHR1YV1b03j5mHM0PW20d8Esui4E1NjC+hsEo27O69BjEQrLfbH4M3NJkbbPB643OKbJZDOwmoSOXHzO+hIFPUgxMwUBr7ft69WNbi9m8PbNFa0cspMdkdszt8dG4lCPK4/F+HM1hthZes1ze7seYk+2ZDqxJxowZWx4cnD/KCX9pbu9DzBA5Za4h8y8tRg3MiCuCBgbFHklOP1cDw3NEelb03j5m7GP7rCPCynZ6BpbYwBgrXGuslxhjHOuIqdkiv18Lci1Ntmw2sObW82Qc68hjku2sZxi2SxcDUzDQWvu+Pv3Y1tJqy9bN8axnuX0ei/FHhPE2jFvn9DjEs2zhNcuT2Tm/6YzLdI4Zi7F4zuHXEI0xfynO+xAzRE6Za8j8S4tRAzPiiqCBaU1xzAaGC511Xqu3jxn7WMzDdtbhl1wuA2pgbn3lMavdzL6kq9qy2cB6s/Pxdh6zjjwm2c56hmG7dDEwBQMttd9amxEusmXrZizrhvfjLIe3+zg/Bj6yRzbDZ3bkMtnCq+F8Lj82TPZibKZz7FgMx4/Ja7Gci3XOO0SfMteQ+ZcWowZmxBVBA2NFlR2cvm8DY7G+YDEP540wJT98feOmxmMdkstkYKkNjLGFWoRkBs0WHcBkMfBDMi7KBxvwS5Wtmw1bP5+zPx/4IOH348humOwAXlIMTM1AS+231K+tK8JFNmCjeufzQywk+/roHM86ckQ285k9OxCbyRZeMQfn6LsWxHIc6/D7ubLz4ljWOY/XGcc647ydx6xzzBB9ylxD5l9ajBqYEVcEDUxriiENjOW2osWBsZ8T/qjAI1spD3L7uNoYOQ3nsfAht+TyGVhjAxPVna+9CFOzRf7lX8HdCls3G54nf37gABJ+P47sGQZYSTEwBwO12re6rB1YV1TDrTbL4bEYQ2KevpLjWUeeyGa+zI64kqzxyrGYB5J9prfYGcM652I764xh3WP8GFi2sw6/SW/nMescM0SfMteQ+ZcWowZmxBWZo4EZsRyFioFJGVhyA2Mnah/m/gPdj0EI21mP/JEtijFcZkeOJcghm41s3dH5RjaLZzvrnDuzM0a6GBjKQK32a/XHftaxnlab4ftgkT+Ly/x+Dj/O4lrsjKnxylisAZJ9prfYGcM652I7662YlphovVEc21jntQzRp8w1ZP6lxaiBGXFFrIHpe9T+PzAjlqNQMTApA2pgbv0Gxoj1Nw8/npT8CZMN2Wxk00fnbDZv92PL521+nM0puxgYykCp9lvrDzhIXkurzWL6YHmOLDay8xys+3wYe4wfA+dliVePtXEpb+ZjO+tRPvO3YHhtHp/l5ZghmGgen7N1PGWu1jmXjFMDs+Sro7WJgRMysPQGxqiJPtDN5g/GZjGe6gyH3B6/1HGfzUZ0zjivzAc7eMEYcSxbMIyXLgbGMFCq/VKd8pzAQUY+tpnOdQ4ddmCjfPBFEnlYehznZBzrUQz83peNS7xGMbyuzI81QBoOcZAcCxx8kCWM9/EYus8LO8saxq/Fjy2Xt/Ud83ouVVcDc6lXXuctBioMLKmBqSxV7gIDfTcbWSp/gwUus8MvKQZOxcBUtT/H+tf8vlkyr63Xak7+fW4/bl0j46bIwfnOQVcDcw5XUecgBmZgQA3MDKSeIOUUm43SzbPkO8HpakoxsGdgitrfJ5tQWft7Zqm89rlEc14Dn9uP+6wT2ClyINe5SDUw53IldR5iYGIG1MBMTOiJ0k2x2SjdPEu+E52yphUDWwamqP05qFz7e2apvPa5VnYN5rgOWd4xc42J7cPJ2rBqYNZ2xbReMXAkBtTAHInomacZu9mo3Txr/plPT+nFQMrA2NpPE49wnMP7ZYm8jrgkCl0pA2pgVnrhtGwxMDcDamDmZvg4+bXZOA7PmmV5DKj257km4nUeXpW1HwNqYPrxJbQYuBgG1MCcx6XWZuM8rqPOoj8Dqv3+nLVEiNcWloSZmwE1MHMzrPxiYKUMqIFZ6YVzy/79P+q6hx9+2Fk1FAPnz4Bqf55rLF7n4VVZ+zGgBqYfX0KLgYthQA3MeVxqbTbO4zrqLPozoNrvz1lLhHhtYUmYuRlQAzMRw/bDPD4mSnt2ac7hB4xnd1GSE1IDkxCzMrM2Gyu7YFruZAyo9iej8iCReD2gQ4MTMaAGZgLit5vyB1/RdXRwMxPpE0yrFGJgVgbUwMxK79GSa7NxNKo10cIYUO3Pc0HE6zy8Kms/BtTA9OPrBjpqXriRifRtzI1MMoiBZTGgBmZZ12PoarTZGMqc4tbOgGp/nisoXufhVVn7MaAGph9fN9BTNzBobkzi8JN6DPsRAwz7oNcwffzRPBzv/TyGznis0UuPQazHaTwdA2pgpuPylJm02Tgl+5r7lAyo9udhX7zOw6uy9mNADQzx9eSTT9Jop0Y2gF5zxzO6Pgeextx2221IcUNio84Ov1mPMIaPcJynBVPL4f0+Zx+/YT3ej31+jCOc+fSajgE1MNNxmWU6Rh23bjZsLTiy9bK9Ze1DMC0xtg5ea2sMr79F93n9uCXHKTFD1jsk5pTnWJp7SO3b+c/FwVx5SxzM4Wvl1ebue87gv2/cHOepnMtmQA3M9fW57777urvuuqt7/PHH91fMdLPdf//9exsrfZoXYK2JqTUwPAd0fjOzHvlhM8lY1iNMze/zcQ7oWY7In2HZzjpymMzsjJE+jgE1MOP4W0p0y2bDv5/82J+L+WsYi2nBRLm9zY99Xj/2+KHjufIOXc8x4s7pnIfUPjieg4c5cmK9x5StvNr59jlnj/XjY56j5lo+A2pgNtfoiSee2DYqt99++76JQfMCm2H8C01JX7m0BgbnlX1YsJ11xLHs48+wbGe9zzyMlT6MATUww3hbWlRtszHkPWYxWRyffwuG8aa3xHiMH/ucQ8dz5R26nmPEndM5D6198Dw1F1PnwzqPLWu88nr6nLPH+jHnlS4G1MBc18Bjjz120MTYkxc0L/xUxpdM3+bF8EtuYOwDIzr4vL2ffaaX/ObDi3XYTLKd9QzDdunTMbC0BsZqAUd0lvBFNQNbhoHf5/X2LN7igAUGuTCGhJ1jvC3CMh5+k7VXbbPRkoPnAB6SfV4HxiSOFkwJCx+k5TPdv+CPfIhhDOO8HT5IxEMC79eAMfyIh4TfS/h9nMfZuIRBHuAgEWNjvGBjCd9a5dS1bzyU+GEfdObObPwChqX32xh+9p1Sr/HKa/PnzD7WM1xm51jpl8mAGhh33fHkxX+dzMH2w3NrYPYn1kOpfcCwP9N5ur4YjpU+HQNLamC4JuwMh4xLMd4HFtnOeraGGsbH1fCRP7JhvZGsbTaQzySOKE9t7VFMlA/zAV8bA8eyFtPXj9x94gxbw1veCONtmB9yTG7kMMnzDM3J+dam12rfc1Q7P+bTx3ofcrE904GNcnIM406pt/CK9bWuP8NlduSXvFwG1MAE196aGHsi0/rq28Qs7QkMPiAg/XlnduD6+BnLOnKZZDvrGYbt0qdjYCkNTK0Gan5jZAiGY1hnhtnOOjCRDT6T7Gd9DIZjTa9tNmxeP7cfIyfbWYffywzDdtYt3o99zgjDMaxzbGYvYXwMj1nPcrRgOBZ6S1xfTAve5s9wWNuaZK32cS52zv6ADzLjJbNHcTWsxTCGdeRbgmzl1Z9Pae3ZuWb2Ui75LoMBNTATXefWJsaal1oD49+wtTFOoQVXw/T129wcw3q0LvazDqzPl42zWM4jfRwDS29gcHZZLbCddcSZZDvrNR9ycAzrkR82lhzD+hgMx5pe22y0zGt5PI7HpvsjijGbvXzszrr7L/vYzrrH8Jj1Ugz7oPvY0tj7ohw1jPn9YXlqcUMwLTlLec23tlet9kvn4/ny41Is+ziOdcawzhjWGXNqvQ+vreeQ4TL7qTnQ/KdnQA3MRNfA3mRoTmqy9IaEzyQOv0RgvN3GiBmDqeVgfzRPyc941vlcIjvnxHlyjPTpGbi0Bobrytcg15/XwbyPYTvHwM7zeX0MhmNNr202SutGrggT2YCHzDBsZ93i/Bi5WHoMj1mvxRjWH7UY+FvmacEgH8uWuL6YFrytIcPx+tai12q/dh7MBetZnGGiA3ifI8IyhnXkWILsw2vrOWS4zL4EHrSG0zKgBmZC/u2N1nKUptSbtcTOLZ94usXFXJoamFvMttRbC8YyMi7Tb82c4zMM202vbTZ4DRzLdtOzg2O8zjnYx3bWDePHHAfdY3jMOvAm2c56hvExftySowXD80NvieuLacH7c8R61iprtV87L+aM9Sgu87M90zlfC4bxp9D78MrnU1prhsvspVzyXQYDamAWdp31Zm27IOKpjacxqKU3MKgBSH+ubGedcZG91WZ5GMs6z+F1xmU6x/TFcKzptc0G5+fYzA5MzW+4DMN21ksxmDfCcA7Ws5gWzDHn4XVG88LP62Ydfh/LGNYzvI9n3Br1obWPc2XOWIffJOyQ7GN/SecYzsM6Y06t13jl9fU5B4/1Y84rXQyogVlYDegNW78g4qjO0RSIpTQwdi7+mo8dgx+fJ5orw/pYP85yMY71CF/zZ2uD3WTLZqN1Hs7rY9gH3TAe13eMXCz75qjhkbuGYz/riDfp7dHY2zgeOTzGj4HjWI/hMeutMYxboz6k9nGeEV/exmPWkcMk2zO9Bc+YU+stvGKNfM6wZdJj/TiLk/0yGVADs7DrrjfszQtinPBxEyHLHAwsqYGx86vVQMlvvugV2SMbYofOkcVFc2VYW0OEL9nN17rZKM1refwrWwvjgCnlBiaKYxvrPsaPDVua0/uBhd0kXpw704E1yRjYkR8+SPi9hN/HeZyNSxjkAS6L93aO8741jYfUPvjMzhP+iCP2Qbc8wEIiNzAsS3jEnVq28srnEq3Z8wE8+IhiZBMDYEANDJiQFANi4ICBpTUwB4vToJmBPpuN5qQCjmIg2rhxwpqfsdJzBlT7OTdjPOJ1DHuKnYoBNTBTMak8YuDMGFADcx4XVJuN5V3HWoNS8y/vjJa5ItX+PNdFvM7Dq7L2Y0ANTD++hBYDF8OAGpjzuNTabCzrOrY0Jy2YZZ3VMlej2p/nuojXeXhV1n4MqIHpx5fQYuBiGFADcx6XWpuN015Ha0b4aFmNGpgWluoY1X6doyEI8TqENcVMzYAamKkZVT4xcCYMqIE5jwupzcZ5XEedRX8GVPv9OWuJEK8tLAkzNwNqYOZmWPnFwEoZOGYD8/DDD3c6xIFqQDWgGlANqAZUA7UasG2VGpiVbi61bDEwNwPHbGDmPpdLzq9/Lb3kq3/Z567an+f6i9d5eFXWfgyogenH12xo/o70mO8/j4nlk5sqD+f0+jHm8HNq3M6AGph2rpaM1GZjyVdHa5uTAdX+POyK13l4VdZ+DKiB6cfXLOjtRv7BV3QdHWYrHdlCpmoKpsqTrVP25TOgBmb516hlhdpstLAkzDkyoNqf56qK13l4VdZ+DKiB6cfX5Ohto0CNCzcxmV5qLkq+PoufKk+fOYVdFgNqYJZ1PYauRpuNocwpbu0MqPbnuYLidR5elbUfA2pg+vE1OXquBsby4sgWDf92DQ4EWwljIeyHjlTIgTEk7JCwm0SOzMcYjpM+PQNqYKbn9BQZtdk4BeuacwkMqPbnuQridR5elbUfA2pg+vFVRD/55JM3/JENoNfc8Yyuz4EnMrfddhtS3JBoANhRagaA85iWPD7G5+rr9/ho7G2YU3J6BtTATM/pKTJqs3EK1jXnEhhQ7c9zFcTrPLwqaz8G1MD04ytF33fffd1dd93VPf7443uM6Wa7//779zZW+jQvwFoTU2tgeA7ovPFnHX6TbGc9w7CddY5l3TA8zvTWXIyTPj0DamAO67WVYa5ri/Hj1jxT4Vo3G7ZOHC1zt5zXEExLjK2P19oa03JejPF5/ZixS9SHrHdIzBLP3dY0pPbt/OfiYK68x+a/lddsXX15wDXpG5fNL/t5MKAGZoLr+MQTT2wbldtvv33fxKB5gc0w/oWmpK9UA+OZ1HgOBtTADGs+lnaTbdls+DX7sa8v89cwFtOCiXJ7mx/7vH7s8UPHc+Udup5jxJ3TOQ+pfXA8Bw9z5MR6jylbeI3WY+ePI/JHNs+ZH0cxsl0GA2pgJrrOjz322EETY09e0LzwUxk/XWvzsnnX779uNkUDgw8RL7G+7EPC2308xshjEjGQ8PGYdfg51uuMkT4PA2pgbtVuH4azWu6TY0psbbORrTez29rMV/Jj/S0YYCFbYjzGj5FrrJwr79h1zRl/Tuc8tPbB79RcTJ0P6zy2rPFaW08fHjzWj2tzyX++DKiBmfja4smL/zpZNk1LA2PNCw7DT9HAZOuBPfuQYDvriDPp7RhDAstj1uH3uTIM46VPx8BSGhhcd5PQcZaweXvkb8EgDpJjWIffJOwm/cF+HwMs2xkPv8kxr9pmo29+4CFLawPGJA6PjzAlLHyQlg85ODf8kQ8xjGGct8MHiXhI4Hl+1uFHPCRjWIffxzEGegmDPIaFnuHZDh1zrFVOXfvgMeMHdpbMndn5xTjo3m/jyMe4Y+s1Xmvr8Txk+AyX2bM8sp8nA2pgZriu1sTYE5nWV62J2Xx62SfY9pizgeEPBdb5PNjOeoaBPcKyjXXEmGQ764yRPg8DS2pgomvvbbWxsVTDlPzeB9a9fYpxLQfmbpG1zQbmMokjywus+Vkv4T2u7zjK3TdHDY85ajj2m85jy+HHkS2Kw/yQEaY1N3L4uYfm5Hxr02u17zmqnZ+/BjxmnfOwPdNLeI5h3Cn1Fl5L62s9pwyX2Utzynd+DKiBWcg1zZqYzR3R7oq7VV7rtScw/s3tx5bM22pj0MQ41uGPcrfafL7amOeUPj0DS2pg/Nn52oCf7azDzzLzsz3TS3k4xnA8Zj3L0YLh2Jpe22zYfH5OP8YcbGcdfi8zDNtZt3g/9jkjDMewzrGZvYTxMTxmPcvRguFY6C1xfTEteJs/w2Fta5K12se52Dn7Az7IjJfMHsXVsBbDGNaRbwmylddsra3nleEyezaf7OfJgBqYhVxXe0NaY+KPzaeZfaIdHKU3L3wmcWSnCD9iGBfZzO/tnAN6huP8EQY2zsMxfm72SZ+eATUwh/We1Z+3l8beh6vGdtbhN5nZGRPptc1GltfbS2Pz+aO0Zs7FeimGz60U432Iy+zwR3P7GB6znuWoYczvj2gdyM/5WIffxzKG9Qzv4xm3Rr1W+6Vz8nz5cSmWfRzHOmNYZwzrjDm1XuLV1uwPv97W88pwmd3Po/F5M6AGZkHX98abfvNBsPkkiI8FrVtLOU8Glt7A+PcLxnw1YIP0Ph5DNyxemQ6/ScbUxh6LPGxnHf4oL/tKemmzUcrL62Adc0U2+CAzDNtZL60HOSMM52C9FmNYf9Ri4G+ZpwWDfCxb4vpiWvC2hgzH61uLXqv92nkwF6xncYaJDuB9jgjLGNaRYwlySl5L55Odf2Yv5ZLv/BhQA7Pka4rmhdcY2dgvXQxMxMDSG5ghp8k3PtY5F9szPcObnWP82PuQh+2sw+/zsL2m1zYbLfMZJjtK87fm5hxZTAnDMaxnMS0Yi/U4HrM+dh6Oj+aFn+dkHX4fyxjWM7yPZ9wa9Vrt186JOWM9isv8bM90zteCYfwp9Cl5La2fuWBcZmeM9PNnQA3M+V9jnaEYGMTAGhuY2o2N/awzQWzP9Axvdo7xY+9DHrazDr/Pw/aaXttsDJ0vi+P1ZBi2s956nqUY78N62M46/NHcHsdj1rMcLRiOhd4S1xfTgo84wJrWKIfWPs6VOWMdfpOwQ7KP/SWdYzgP64w5tV7jtba+PuflsX5cm0v+82VADcz5XludmRgYxcCSGxg7MX8jq42HxNRymr8FwxeiLx6xPg72mmzZbPjcfhzN0YrxuL7jlrn75vR4zOHtpbH39cmRxXIOj/Fjw3pbaex9PBf0KCf71qYPqX2cY8SXt/GYdeQwyfZMb8Ez5tR6C6+lNTIPJZz5PNaPa/Hyny8DamDO99rqzMTAKAaW3sDYydnNDEd0svBB1jDeb3H+hVzwQTKObawDYzYcsEFGePNldsRlsnWzgfW0ztOCA6aUGxhef2Qr+SN8aU7LxX7osGdz8TysZ3jYkR8xkPB7Cb+P8zgblzDIA1wW7+0c531rGg+pffCZnSf8EUfsg255gIVEbmBYlvCIO7Vs5TVbp+eBcZHP88N46ZfLgBqYy732OnMxUGRgKQ1McZFyVhkYu9moTiBAbwaiTRonqfkZKz1nQLWfczPGI17HsKfYqRhQAzMVk8ojBs6MATUw53FBtdlY3nWsNSg1//LOaJkrUu3Pc13E6zy8Kms/BtTA9ONLaDFwMQyogTmPS63NxrKuY0tz0oJZ1lktczWq/Xmui3idh1dl7ceAGph+fAktBi6GATUw53Gptdk47XW0ZoSPltWogWlhqY5R7dc5GoIQr0NYU8zUDKiBmZpR5RMDZ8KAGpjzuJDabJzHddRZ9GdAtd+fs5YI8drCkjBzM6AGZm6GlV8MrJSBYzYwDz/8cKdDHKgGVAOqAdWAakA1UKsB21apgVnp5tIvm7+iMPbrBz4Xxn7OsWO/ztp47HyK78fAMRuYfisTug8D+tfSPmwJe04MqPbnuZridR5elbUfA2pg+vG1SPR24//gK7qODjQdmSydiG8kgM3s8I+Vc+cfu75Li1cDcx5XXJuN87iOOov+DKj2+3PWEiFeW1gSZm4G1MDMzfDM+bebfmpcuInJ9FqjUPKXfGNPdc7cY9d2ifFqYM7jqmuzcR7XUWfRnwHVfn/OWiLEawtLwszNgBqYuRmeOf9203/CBsbmxxGdKnyQjDGbveBjCfsWQP/xGHId/N+OgWO/9H4MqIHpx9dS0dpsLPXKaF1zM6Dan4dh8ToPr8rajwE1MP34mhX95JNP3sgf2QB6zR3P6PoceCJz2223IUUobfOfvdjHuuFrY4+p4Yf4azHZecl+kwE1MDc5WaNFm401XjWteQoGVPtTsHgzh3i9yYksx2dADczxOQ9nvO+++7q77rqre/zxx/d+0812//33722s9GlegLUmZmgDw80B67wmtrPOGOjeXxp7X5SjBYM4yToDamDqHK0Boc3GGq6S1jgHA6r9OVjtOvE6D6/K2o8BNTD9+JoF/cQTT2wbldtvv33fxKB5gc0w/oWmpK9saWCsGfAHz9/SLGQY5PH+0tj7ohwtGMRJ1hk4twbG14cf1xlZJ6J1s2F8+CM64xbeWjBR7jG2U8yJ9fq5/Ri4Nci+a2/Bt2Dm4Gbq2h+7xlPxMHbdPr6VVx83xfhcOJyCi0vPoQZmIRXw2GOPHTQx9uQFzQs/lfHLbW1eNjuT/dfNWhoYP48fZx8i3m5jPjhPhM38Hgsc21mH32RmZ4z0mwycWwPjz/BS6qJls5FxEdkjm+f2FONTrsvP7cen4ONYc7acawtmjvVOXftj13gqHsau28e38OpjphqfC4dT8XHJedTALOzq48mL/zpZtsyWBsaaFxyGP2YD49fNHz6sG6409j7kZTvr8Ed52Sc9Z0ANTM7Nmjy1zUb2vsE5er8fA3dqecp1+bn9+NTczDl/y7m2YOZY49S1P3aNp+Jh7Lp9fI1Xj59yfC4cTsnJpeZSA7PAK29NjD2RaX3Vmhg0LybVwLSyKtxSGpjshsV26CZx+CsYYTKsj13zuLbZAC+1cwRXLC0G8bCzjXX4gTeff3lMCRvFRjbkjHxmg780l8cAy3boyAnJdrNN9cIafD62Y26WjAcWfvPBBhx8LOFjfOZnjI9DDNun1KeqfV4T1mzSv9gHnTE+BhiWER5+9p1Sb+U1Wndk43OB3yS/2A4987PddOTycRhD+jiNl82AGphlX5/m1WVNzOada+/eXZ5rfYonMJYQHwpYZG3sY2r4sf5sXbBLlhlYWwMzVb2UWVmft7bZsDPy3JXO0mNtHNmQo+ZnHHSTURz7vV5aA/JxTJTf5xga15o7Wg9iWTIOerRWXm/ND6zH8Zh1zIs4jA3jcWPHyD1WHqP2sUZ/zpH9/2fvXWPtyK4zMQZQkPyfXwMMggES5GFkjATySxoksOEEwQSYQQIbTiYPq2FrWm7RD8q2rJZsSbZl2RNblix7rCjyq23PtMe2xtYr1sOUZIWCSKrVJNUUKTbZZPMlvh8STdM0Xzv3q8vv3nXW2btqn9r1Oud8BdTde6+19tq7vlpVe32n6pxrbWydtiitHHXbtnZj1ptw9fPmMbD0x8ljsfpcm5hdzE9MxnFZehvKVU4TARGYaZ6XhWeFCw/ExO8bdz/cEWf2pou0SW8nB1vuVs46dSwpRwmZ36zM1mlHPykd7WwZs7V61eMILBuBiR2FPfe2DlvfjvVfBVlTssFjBB5+p86WHjffhq2V2XrKT46N7RurWx+2bm2t3NZLbNDX+/Jt+k/JqV+09P58O+bP2tg6bWMy6lhaG1unHqWVp+opeysvqXcZ+/YY7JxSctpYva1T70trY+vebsx2E66xeTfJYnoco5c3tYmLtbP1mJ4ylcuFgAjMcp2v2tniIrX7RgNXf3yv9SSlEAhBBGY1oqAp2ag7ypyFv8kmpseYVm7rdj4pubVh3draOvUordzWS2y831ib/lNjUr9o6f35dsyftbF12sZk1LG0NrZOPUorT9VT9lZeUu8y9u0xLDIn28/WUz6sja2n7MeQN+Eam3eTLKbHsXl5U5t4WDtbj+kpU7lcCIjALNf5Wmy2JC+2V0xm9aoLgccIiMCsRig0JRtNR+kX/6Y2/FkbW7djWbmtp2ysPFa3PlBP7exr7SlDaeW2nrLxfWJt9k35Yx/oYzv7x0r6ZGltYr6sna2zn5e18QFf1o+vN/nkXErLLmPfHkNqXk3H5X0sap8ad2h5E67+ODG/JlkKC98v1k71JS6+j5XbvpSrXA4ERGCW4zxplkJgcAREYAaHvJcBm5KNpkH94t/Uhj9rY+t2LCu39ZSNlcfq1oetx2whS9lYua1bP17e1GZfb0d5SUmfLOnLt2PymI2V2Tr7o7RyW8+xSdnbvl3Vu4z9pnmn9FaeqtvjzbGx9mPUm3C1x8D5Nclieva1pbfzbWvLeo4NbHPt6FfluAiIwIyLv0YXApNFQASm31Pz6NGjMMR+5a8fhps3bybHwqJdNw+vb2rDl7WxdTuOldt6ysbKY3Xrw9atrZXbeokN+npfvk3/KTn1bcuY35jMzzVmY2W2budm5baeY5Njb/2U1LuM/aZ5N+lxHNbG1u0xWrmtW5ux621wjR2Lldm6PT4vb2qzr7WzdepjZa6d7dvvKiHvdQiIwNShI50QWGMEpkJgcAqwsNgNbSuzdW/Htrfxbdp1UdoFbux6U7KB+QGL2Dxjci/zbe8vpvc2qXaqb85cfd+mNn022UGfY0N/tvT9rK6kHvMbk2EMK7d1jm9ltk59zIe3K23bsUrqQ8a+P2bO28pTddqizLGx9mPUm3C1x8D55ci8jW97fFK+fT/fzvVD/6VlF2uKfMwjIAIzj4kkQkAIbCAwJQKDE4JFiDvbKLFBHtus3NZpG5NRt0i5yAL38OHDMOR++daDcOPGjcYxgYXfU/OEHXW2HpPF9LCLySHjHrOJ9YmNaWXWH+Ux39TFxqAP6liyj/cX03sb27e0XjcedHa384j18zLbl/WYD+p8f2trj7POPtbH+/Vt65v1vmKfc+c4LCm3JXScK8u29uw3dtmEqz9Oi4Gde8oO8piOfWM69knp2NeWtk+sn7Vtqi+yBiyytsg2jYAITBobaYTAWiMwNQIztZORs2DVLXoPHjwIQ+yXvnE/XL9+fZCxuj4eJBX0aeuUjVVOaS5jYbAM4y5z7E8ZX+G6fe+uu8fnrBFTW9eWaT4iMMt0tjRXITAgAiIwabDrFia/oKUSkfv374ch9otfvxeuXbs2yFhdHw+IQtc+u/A31Xl1cWyr5GOZY3/K50G4bt+7U/d3vw7UrRnplUaaOgREYOrQkU4IrDECIjDzJz+1CPnFyi9qPhm5d+9eGGq/cPPvwtWrVwcbr6vjAknoyleXfqY6ry6PcVV8LWvsTx1/4Tp7//b3d3//9+tDah2ZX3EkqUNABKYOHemEwBojIAIze/JTi45dnOzCZRc1m5D83d/9XbD73bt3Q5/71278bbhy5UqvY3QxfxADu3fhswsfdk6od+FTPvqNeeK7LLHP+S5LKVzvztzDcT+393h777drgl0rUuvJ7KqjVh0CIjB16EgnBNYYARGY2ZPvFxy7GKHOhYqLl13QSFhsgvK3f/u3we537twJfeznrv1NuHTpUi+++5ivfPYTB+uIq2K/n1haZ1ztPRt1e0/nfd7e+7kecH3w64ZfV2ZXHbXqEBCBqUNnjXT+U8bSQ/f+0O5z8/59u2nsmD1kfm/yk6uPjZfbdyi7IQnM17/+9TDlHf9Hxe74VS/u+II8d3zXBDte2eKOpx/YL1++XO0gE9wvXrwYuF+4cCFoFwaKAcWAYmC6McD7NUrex1Hy/s77Pe//KLkucJ1AyfUDpV1bUJ/yWjiVuSEPEoEZKhuc8DhVMv38b4Zgdp+4+3bd4aSS85S8zleurtS37+/bnEdKTv0qlUMSmCnj5j8hs5+g8VM1fsrGT97wSRw/mcOndPz0+2/+5m8C9tu3b1f7X//1Xwfut27dCn3sL1+6Fc6fP9+L7z7mK5/9xME64qrY7yeW1hlX3q9R8j7O+zrv8/bJjH29jOsE1w27lvh1Zspr4lTmJgIzlTMx0jyqhNwQF0tiUvW6JL5Oh0Ns0reFodSv7W/rsfk06WN9llEmArN51vzCwkWHixAXJZAXvkIA8sJXDbCocYHjgsdF0CaV3/jGN0If+6kLXw9nz57txXcf85XPfuJgHXFV7PcTS+uMq71n8z7O+zrv87jn8/6PtYDrAtYIrhdcP7ie+HVmGXOGoecsAjM04hMbr0rGByQw9vBJBFByt3rWqbNlkw62drN9Wfd6tn1fyutK+oz1pYw28EOZ9Ul9TMc+TTbWX2ldBGYTQbuwcLHh4sPFiOTFP3XhgoYFDosdFz8mk3wc718h6LL90vkb4cyZM3OvKXQ5hnzNvmIoPKaBh2K/n/OwTrjWveLF+zjv63wqw/s+iQzXBT6N4brBdYTril1rStfvdegvArNCZxnB77eYjDYfePKbwyI7n8i88pWvpItomUrAvTGTcSv3fX2btl5e1/a6mA9v49vsEyu9bawdk1lfi+rR1/ex/rqoi8CEYBcULjIosfBwEbLkhZ+6YeGypMUSFSyIfBea70bb96W7rh87cyWcPHly6zs5XfuXv+3vOwmLaWGh2O/nfKwTrvhOi7+ued/mfRwfWOAeT0JjiQxJDF8ri5EYu7bYNaeLdXyVfYjArMjZ/b3f+73wpje9qUqqeEhIsCB75plnKJopFyEvtAWJaSIwGATJtd9nBn9s42Vo5yTm3qap3TSO7895QG537yfWj31pG7OxMltnnxwf1raPughMnMDwUzNcX568YLHCp29YwLCYYVHD4oaFjl/u5xf68cVPfBGUX9j92te+FvrYD7/0tXDs2LFefPcxX/nsJw7WEVfFfj+xtM648n7NL/Hjy/sgOLi/82kN7v14KoN1AOsB1gUSGb5SxtfJuJ6QxIjA5GczIjD5WE3WEhcAiMp3fud3bpEYkhfKYOM3kpJFyxwC48dC2yfqvs0+KTn1Ob4W9ZFjnzMu52j92Xqu3o8V80FffZUiMPMEhotNirzwqQuIC0gLFjYQFpAVLHxIIvGF+nPnzlXfS8GrXadPn672l19+OfSxHzhyKrzwwgu9+O5jvvLZTxysI66K/X5iaZ1x5f0a9258txD3c9zXQWhAZkhksAaQxGBdIJGJPYnhugISIwKTn9GIwORjNWlLsHlLYmwdyVZqyyUvG+xj63WztgQGc7CJuK3b+Xk52rG9qY/X1/nwY9q+vm5tYz4pYz9rH5PRPlbSHqXXW10fdRGYbQLDT8ew0OB6ihEYPnnBwoVFDIsZFjaQFSx2SCBPnTpVvc710ksvhRMnToTjx4+HF198sdrxlKSPfe/Br4bnnnuuF999zFc++4mDdcRVsd9PLK0Lrl/96lcDd14/vF/j3o17OO7luK+D2OBeDyKDD67wAVaMxMQIDNYTrC1cZ0hi+ljXV8mnCMwqnc2NY8GFAPKCHfWmLYfAgLxwh/2QBCaW/OOYvLyu7XXExMptnfpUaW1tPceeNrafrVOfU7btl+MbNutOYLiIoMTCwk/JYuQFrwfgUza8OoCFC+Rl165dk9if+uFdYefOnZOYy1Qw0TymEZt9nwfFfj/nWbimccVTGZIYvFLmSUzOq2R27cldr9fRTgRmBc86E6zcQ2siMSQvKJsITFNSbfW2budq5baesoHc29m2rad8WBtbt/asW72tU4/Sym2dNlZm69R7H1bOeqof9aWlCMyjrcf5JDC8tvBFTLzLjE/TsCDx6Qu+74JXxvDkBcmZNiEgBISAEFgPBHDPxw+m4EkMXhvGk3j7BX9+ub+OxGCtEYHJixcRmDycVt4qRWK2yAsQePwkpukJTCqx9nK0YzILttdT5+V1ba+L+fA2vh3rk5L5vr6Nfl62aDvmg/PpqhSB2SQwfKzP18fsF/dJXvD0Be874xM3PH3Bp3AiMF1FovwIASEgBKaPAO75eLUMrwzjO4/4MAtP5Eli+J0Yfh8m9oV+rjckMdM/6vFmKAIzHvaTGhkJNIiJ30labOmT7diBwMbv3o5+rJ23QdvqWafc2tNfSse+LK2d7UuftLMldb6ss0n5XsQHbO0YMZ/eX2lbBGabwPjXx2JPX/jdF7w+gPehRWBKI1D9hYAQEALLgwDu+fjODL4TY5/C+F8nwwde9ilM7BfJRGCaz7sITDNGa2MxlyA/fuJiyctWvQNUhkjCO5jm2roQgZklMP71Mf/0BZ+y4cubeH0MC5gIzNpeOjpwISAE1hAB3PO/8pWvVF/sx1N4PoWxv0yG70nan1dOPYURgWkOIBGYZozW14IExiIQk1n9AnURmAXAGsFUBCZNYPjdF3ySxv/5wtfH8LOaeA9aBGaEoNWQQkAICIGREMA9Hz9Zj9fI8BQeawG+C8NfJcOHXPwfMfYpjP3nlvw1MhGY5pMoAtOMkSx6QkAEpidgO3IrAhMnMHx9jF/cx3vN/PUxLFZ4dQA/rSkC01Egyo0QEAJCYAkQwD3/0KFD1U/W8zUyvFKM78LYpzD2uzD8WWX7Gpn9Iv8SHPZoUxSBGQ16DSwCM+0YEIHZ/PlkLCb8Aj9/fYyvj+HpCxYj+/0XfIETn8DlEJin/9nfC4vs73vjP5l20Gh2QkAICIE1RQD3/IMHD4ajR4/OvEYWewoTe40MrynzCQxJzJpCmXXYIjBZMMlICKwfAiIwswTG//oYf3kMT1/4/Re884xXB/DPzhYhMC8f2Rdu3bhS7X/wzteEv/p3/6qqf/IPfzH8yXt+uKof+tyfBRGY9bsOdcRCQAgsBwK45z///PPhyJEj1T+55GtkqacwfI3Mfg9GBCb/XIvA5GMlSyGwVgisM4Hh+8f26QsJjP3+C18fw/df+P9fXn755eoVglwCA/Ly22/7nvCrr391tb/9f/2H4Z3f/01V/R3/538efu6f/ydV/Zd+4L8Kb/3ef1DV3//0P12rWNTBCgEhIASmjgDu+V/60pfC4cOHqw+xsBbglWJ8sIWf1+d3YfjPLUlgsKboezCLn10RmMUxW/se+HR5HfZ1P9EiMJtPYPj6mCUw/P4Lnr5gMSKBwZc28e4zfkozl8DgyQvIyxc/9W+qJyyf+eNfDWeOfal68oKnL6hDhlfN3vPD/2348p4PhXc+8U3rHp46fiEgBAZCAK975+wDTSd7GMwZhCK1Qdflq+y45z/33HPVF/mPHTtWrQX4NTL8MmXda2QiMKkzVC8XganHR9oIAutAXnCM676JwMwTGP8FfhIY/LMyfMKGT9vwC2R4BzqXwPzWT//P4W3f9x9VROV3f/Z/Ce/d9V0B5S//i2+pdspAYH72n//HAU9ffuZ7/n5lkxejHwmv27HxdOdEnvXoVh///rDjtZ8cfRqNE3jpV8I/3vGPu8N167jbnK82fRJH2MVxzRyLTX4L8KqbV50ucZgS5yOQk+Tn2OSP2I0lCMq//x/8h1ESU6drOzru+fv37w9f/vKXqw+xsBbgO5H4YMu+RsZfI+P3YPRF/naIi8C0w22te4nArMfpF4HJIzBYjDyBwTvQuQRm38d/P/ziE/8o/M7bv3fjycp/GT76gZ8Oh/6/Pw9/8AvfX+2oY//E7/9CeNfrvr2qow8ITd6Wk9zm2OSNNmu1qN8Xw3tf9R3dkYLZyUy4ZY97UcxwWG369AVH+lhOvvebw45v//VwvHjoKR1v8cFM3kEOOcmxGeNAY0QlJutibrjn79u3r/olMn6RP/U9GHz4JQJThroITBl+a9lbBGY9TrsITJzA4BfI+AV+vD4GAoOfyORPKONTt0UIDF8h46+Rgaxg+8Tvv6PaGW0vPv+ZrS/xo89KEpiNT9K/fRmevvCkdFXOHHeb5LxNn64m7/zUHktX8+zKj5u7mlEEcshJjk3U+QBCS1hsveuhQWD27t1b/RKZ/SK//x4MXjn234PB0339lPJiZ0QEZjG8ZL2BAAkME67c0oJHH1Mu7XzXsS4CMwyB+cj/8+bw8//7fxo+9H+/Kbzrh76jesKCeCsiMNUrNZuv7nzrr7135hWyTzxpXumpyAKSQS/bGH/Orv4qmLeP+63zgk/of+BjtNhMUt/z8fdsvKq1Ob9vefepx8pN3ZMbT6R27Pih8FFIzTEjmdr2A+XGU4FXbx/jlm6mz2M/UdtY/805bL6at1n/tfe+duu7AttznZ2bPx+YXf5xz/raOvbHT2DiWMXO5ebxbOGASeC1r+rpyEdn4iWNa+QcJI5l+/VFi5k/FnvOavB+6f+NxGuuXxyotkURyCEnOTaLjtulPYlL6pWyLsYSgekCxXwfIjD5WMnyMQIkHSQuFhj7qXCqDnv6yC1xc8y1pV2bPuyLct03EZhhCQy+rI/vu/zRu34ofPaDvxE+8Jb/qdpRx/7BX/+x8C9/8L+u6vh5ZXt9zcYqkrntZLB6bSf6XQ2b9Nn6rLfFX0+yvmzd+/Vt2L5uk4xUKrQ3SAdfOarIBr9DsanbJgmb7a2EfMZ2MxnetuW46GO+G7SRwFc2W9/foN1GGZPNvLb1eK58egR7EqvKru58oO8ixx2Z8+MxtrCaGd8ch52zOyYQ0E38LC6bxxXHdVM3i2vsWLbnO/sKWY1vN7fNI4A9fdk6tLZd49dCMVId61LOPtL0osNivk1bjk2Tjz71IjB9ojuObxGYcXBf6lGZ5DOBQrnIjoOnj9wSN8dcW9q16cO+KNd9G5LA4HH6lHa8FoYdj/rx/Ra8IoafwMRPJeNVMfwsJr6YiV+YwZc08ctj+OeVeO8Z/4kZ70Hv3LmzMYRw3fAVso8/8/MbvzL234Tffuv3hj9/3xvDr+/67mpHHfszP/+/hV/4/v+iquPXyXj9zQ2ykby/ikl/pbTJ3YagSm6ZRFlCwOTwsceo3dxo24KovRt723q+ttF/PhmenVM8yd5wtXHMr94iDJuut2wjusqiIjnE4XEJAvJYPjOXmGwuabZzNce90bfpfMyMNeN3/lj4NAr3t2rfeoqWGB8ukueGxGljvt/G76bMzj2Ja2SeGGf+WCzGfMq1MacNXJK+C/FO+t2EU38XRABx1rTl2DT56EtP8oLS1rseD/f83bt3hz179lRf5j9w4ED1k8pYF/CrZCdOnKjWCnwvBmsH1hCsJfiCP39mGWsN1hysPVyHprQ2TmUuOHciMF1H8Br4Y5LPBMqXgIAy1tlmSR+5JW6Ouba0a9OHfVGu+zYkgZka1qn/A4P3lCf/HZi6hHkmadx4MrH1hXmTtOJkJO0SZypp7/wmuleveG3NhUa+r33tyelmxt/sn0NgfKLLkVFuPrniU4lNzazMzsHWYWvaG3NLExh7DjbHmOlbicxxR45zs5cZrxKY9kyf2fFwPCAcLOd8zfTd1G7hao+xUs36nvO1Kdj+W+t706wt3v68bs95e3jV8hHIISc5NvkjdmcZIywxWRcj6hWyLlDM9yECk4+VLB8jwCQfZIQ7VKznlPSRKnEz5A4b1K0tdbak3spYz9HRhuW6n3ARmGFeITv0uT8L+CeV73vjPwnv+D/+s+rXxvCF/Wd/+V9UO+rYP/PH767+Xwzq6IPrLL4hed1OvDeTwMdPWvBJPJ/ObCSQ2z8DbBJeOE3axUdM2zu/ie4gTPNf3t88jq1P9GcSXu9309a+6rSdxG4SgC0/W3Nw/rfk25XZxH5Tvi2zc7B12Nn25jic28z5aHXcO9xTDj+ea9edS2D6qteF123s1feI0LVm7rPE1h7jRrfksdgnQ9UAj//M4jLre9uuFO9Zv3bOqfr22KptIoB1tGnLsWny0bW+jqjU6drOQwSmLXLt+onAtMNtrXsxwWcCZQkLgaEu1oaOPmIlboRWHiMhVs96rB91KL2eupScc1/XUgSmfwID0oL9rd/7D7b+Dwx+Khky/LQydtrgn12+/fv+YdX+jTf8dzUEZiNikbRuXEfYZ780vpnMVzokrq9+1dZPFuNT6kpevZKUtotfD2n7Wb/x3khSmeBvW2wmmJtf1N+c27aNTT4f99hIoLdfr+KrcfQG++0PRbb8zPTZ0OPYDXZb32OJyeYSfZuou/mZ/vZ8LH7cG8cTm/PMXHDMdvz0udn6cYPqnFuszLHMjGdxtWP4HyJI+KKYZcq3wWvrHMwck/lhgtgrdCm/Mz7s/G2dk1NJBHgvaSppP5US8wVRSW3QwaarTQSmKyTz/IjA5OEkK4MAE3+SlC4JDG4m9G/LlLzOJqcP+qfszCGvZVUEpn8Cw8ACOfnyng9V/6Sy+59R5ihTLTeSx63vX9g5rmhSuZFc4+nQxzaeecSP22KwLPXUOVyW+WueQqAcARGYcgwX8SACswhasq0QIGnwxIWEBka27tvQ0YcvU2QiJbf9vY1vW1tbT9lVB7vGf0RghiMw73/6n4Z3PvFN4We+5+8H/JNKfLEfP6+MHXXseG0MT15Qv/jykblrbPVCdTUJTPVEiq/xrd5J0xEJgbVFQARm2FMvAjMs3isxGpN/khRboh7bNx5zzMjpw5cpMuHlaMd268/3gS7WJ2YH23XfRGCGIzCMNfyMcuz6ScnYbzXLVSEw5hWu6r5lfolrNU+cjkoIrCUCIjDDnnYRmGHxXonRSBKQVGGzyRUPkLqqvbFog8BU+2N7+vBlikxYua3b/l7e1GZfb0c5j2VdSxGY4QnMusaajlsICAEhsOwIiMAMewZFYIbFeyVGY4JPkiICsxKnde4gRGBEYOaCQgIhIASEgBCIIiACE4WlN6EITG/Qrq5jS2BIXnC0rNvSPnlhHXr6iJX+iQjaVmbrtr+XN7XZ19tRvrpnMO/IRGBEYPIiRVZCQAgIASEgAjNsDIjADIv3SozmE3wQEmwsZ+p8dcyUTQQG/kEquLPNcdmmniXl3s63ac8y1g+ydd9EYERg1v0a0PELASEgBHIREIHJRaobOxGYbnBcKy8kBDxoEBLuVsYnLrGSPqZc8ljWtRSBEYFZ19jXcQsBISAEFkVABGZRxMrsRWDK8FvL3iQd/uBJYlhuERdr+PhJDH1MubTTXse6CIwIzDrGvY5ZCAgBIdAGARGYNqi17yMC0x67te05ZdLR5dzW9gQ/PnARGBGYdb8GdPxCQAgIgVwERGBykerGTgSmGxzXykuXJGHKvtbqpEYOVgRGBCYSFhIJASEgBIRABAERmAgoPYpEYHoEd1VdT5l0dDm3VT1/ucclAiMCkxsrshMCQkAIrDsCIjDDRoAIzLB4r8RoXZKEKftaiZNVcBAiMCIwBeGjrkJACAiBtUJABGbY0y0CMyzeGk0ILA0CIjAiMEsTrJqoEBACQmBkBERghj0BIjDD4q3RhMDSICACIwKzNMGqiQoBISAERkZABGbYEyACMyzeGk0ILA0CIjAiMEsTrJqoEBACQmBkBERghj0BIjDD4q3RhMDSICAC0y+B+cSTOzb+VdL8/spfPZWIkY+E1+14VXjXiZi6ThezX0QG368LH12ky1C2H//+sOO1n9wYDXOcxfIHPjbUJGLjzM8nfV5j/fuSDTmvhrFe+uXw6h2vTsRz0/HDd+paaOorvRDoBwERmH5wTXkVgUkhI3kjAkef/tbQtL/wju8KHzn1/rm90bkMRkdABKZfArN9gnOTsTq7Ot32SO1q8D0mgUkd24vh177j2x8nwCmbdkec16tuTK9De4NgVWSrzrvvV2fbRuf99zmvtmPlHJf3ndNHNkKgXwREYPrF13sXgfGIqJ2NAMhLaoPuGwc/FUBgYiQm1a9Uzk+0S/0sY/+uj10ERgRm8zpAsjhBArPxCf63bxGCMRLaujEjuuqJww81PMmK9Ov0ZhTx39u82o6Vc8AR3zndZCMEekRABKZHcCOuRWAioEiUh0ATgaGXGImhrssSCfy6bn0cuwjMeARm5vUyl6S/++Pv3nj1ZvN1qe3XklxCVyWlfKWqKWluumrgmwRmcxw7B7yqdfLX/tHW63B+TtZ2W7cx5swcd4TtV742x3jydd+25ZPk3D7BwJi+z+zrdRtPaF61I2yNidfNvu294fgGhcDrR33NaxNNdz4q4eZ8OOf5c4w+PGfbT2vm7ZrOV51+yHk1jWX1m9jwPG9itKmPnyfbN4R5jGaxrhB5fP7r0JFOCJQgIAJTgt7ifUVgFsdMPR4jQALDRccm0dTB9N7XHsw9iekDRDt+H/6n7LOPYx+SwHz9618PU9pv3rwZsN+4cSNcv349XLt2LVy9ejVcuXIlXLp0KVy4cCGcP38+nD17Npw5cyacOnUqHD9+PBw9ejQcOnQo7Nu3L+zcuTMzZGaTsdlOVof6RmJbJeEbVhUB4HcIvJ35fsBG4raVxM86z2zBtyUwZg5ICjfmtOW/apMwNc3XkJa5YzE+q++3mOOpZm3nBMHjsbYIgJ3D68LHKtKSOIbI2CQZ8xg3zYuQYj5+zpGkeutYaBvrV+eTutwy5r+veTWNZfSImy2izmOB3sTa3HkiZrRHmfYJkrN1Xm0X1YVARwjgnr979+6wZ8+esH///nDgwIFw+PDhal04duxYOHHiRLVWnD59ulo7sIZgLbl48WK4fPlytcZgrcGag7WH69CU1sapzAWnTASmo8BdRzeWpPjjj+kg4/dhvD3bTMRRsm51TfImPX2x5Bil/eiHfm1J3ymbJr315eu2L+uw4VhWRjllLK1P9oNsSAJj5zCF+qNHjwL2hw8fhgcPHoT79++He/fuhbt374Y7d+6E27dvh1u3blWkC4sMFh0Qm3PnzoWTJ0+GI0eOBHwal7eZpIsdHhODzXMUIymbhttJmfFRJXqb1w/P8XxyyIFySvi2yb9NHM24lSvbtnU332qOJBlOZ5PQOZ+btmEDny3SlLJ5bApb4LCdvPY4L445dwxQbBCFre/sbDQzz3HcbmugBSvzx97fvJrGMvrHMdt0TqPxDgSSWJq4JfFfEDGZC4FcBPQEJhepbuxEYLrBcS29kKRsJUkbSQI36GJ7DoGBP795mW/DPkfmbdD2spgvbxPr521y/cCOW8wHdXWl75c7P/i0fW0/EZiRCMxMcm+TXpPwVcFgPzk3upn+dVGTq4NvkwjOPFkw41bubNvWoTTzjcwxmZzOkQGLCY/Bj0X5RtlIYLqalxlzbs4bOnvMtj5DbNxxJO3sWIvUnX90tWPYevG8GsaKYMRXEe0rZNuvBZrzZPsm57z5aiNIEfzOkqNFMJOtEMhDQAQmD6eurERgukJyDf2QwOQeOuxzCIz3ZxNsq/Pypjb7Wjtbj+kpQ2ltbb2tTY4P67uu7n35dm5f208EZiQCg4SbnxZXyZl9AmNeYZpJ3GyyiLqxqzv5WTr4a0tgzDzm5mueiszpap7ybNhuf3mfB2CPnzKUm3OPvUK2ldDOjd1yXnZYm2BX8s1zsvUUqPYcm2NP2s0MtkDD49TnvBrGmsNo8zC2ycbm3NLn6TFOdRhtnNtXfcfrwute9R0tf655AWhluvYIiMAMGwIiMMPivVKjkcAg6U3t9oBFYCwas4TIaiyJsPK6uu/j27l9bT8RmJEIzOMnFTgXO2aSr82E0H65fSsh9slglZSb63Lu+wV1EeF1GLctgXlViM93Y4yZOZKkYWyf+JovaW8cx+yX9znXzWS3wuzx/ehb3v2JmS/xV5/umy/xdz0vzmSz9POxxweLzacJ8+d49ljr7GbHy20NOa+msaA3JGRrHeGrhZv6+HkyfWuwBCp4slf2CmUutrJbdwREYIaNABGYYfFeqdFIYHIPqoTA2MTE1u3YkNvN2vk67XwfyL2tbdf1Y98mG6u3vm2dNrkl+trNt6mzY9i61bMuAjMUgSHiq1baJLOrY9vwyadTrV32Ma/Wk1HHJALdnKftVxOTA0khBDpBQASmExiznYjAZEMlQ48ACYxNhH3d9ikhMNZPqo6x7ebbVsd6zCYmoz3LlI2V2zr72bJJb22b6t6Xb6N/TObl1kYERgSmKe7q9d0koPVjtNFOdV5tjmWV+3RwnvAKWTHhXWWMdWxdIiAC0yWazb5EYJoxkkUCARKYhHpO3DWBsck2Bmtqc0LWztZjespQWltbb2uT48P6rqt7X76NvjGZl1sbERgRmLqYa9Z1kIA2D9LCYqrzanEoK92l5DzxFT3/6t5KA6aDGxkBEZhhT4AIzLB4r9RoJDBIelO7PeC2BAY+bGIda+fKcvzEfPXVL9cv5lS35fjxNvRn5bYuAiMCwxhRKQSEgBAQAvUIiMDU49O1VgSma0TXyB8JTO4hlxAYjIHkmntsTJt8Wz37xPQxGfu26RfzV+cHYzXpOZ+60o/r2+xrx2IdOtqzhEwEppzA4H/FaBcGigHFgGJg9WNABAaZw3CbCMxwWK/cSH0QmJUDaYkPSASmnMBM5b8Wax5fr/7pqHAQDooBxUBfMSACM2zCIwIzLN4rNRoIzKJ70/+BWSmACg+GT0jqysIharuLwIjA9LXQy6+SSMWAYmDVYkAEpjal6FwpAtM5pHIoBFYDAREYEZhVSzB0PEqaFQOKgb5iQARm2NxHBGZYvDWaEFgaBERgRGD6WujlV0mkYkAxsGoxIAIzbHojAjMs3hpNCCwNAiIwIjCrlmDoeJQ0KwYUA33FgAjMsOmNCMyweGs0IbA0CIjAiMD0tdDLr5JIxYBiYNViQARm2PRGBGZYvDWaEFgaBERg+iEwzzzzTNAuDBQDigHFwHLGABbxGPkSgRk2vRGBGRZvjSYElgYBEZj+CMxTTz0VtAsDxYBiQDGwXDFw7ty5ag0XgRk/lRGBGf8caAZCYJIIiMD0S2BiC6Bkeq1GMaAYUAxMNwZEYKaTrojATOdcaCZCYFIIiMCIwCiRmm4ipXOjc6MYGD4GRGCmk6aIwEznXCzdTJ7+Z38vLLIv3QGu+YRFYERglCANnyAJc2GuGJhuDIjATCcxEoGZzrlYupmQvNiJQ8YtVac+VeI/z9vNt61uyPpU5jHUMYvADEdgXvGKV0S/FKpEZrqJjM6Nzo1iYP1iQARmqAykeRwRmGaMZJFAgASFRCa3TLjbEk+VKEx1XlvAdVwRgVk+AgPur6Rq/ZIqnXOdc8XAMDEgAtNxolHgTgSmALx172oJDLBgO1anjmUddlMlClOdVx2WJToRmOUiMCQvLJXQDJPQCGfhrBhYnxgQgSnJKrrtKwLTLZ5r5Q1khDsOnPWcMgYUCILfYWeJA+u0s35ispiePqwOdfZnST3btszR0SdL9Lcb/VkZbVmmbLze+/Y+27SHJDBTSwBu3rwZsN+4cSNcv349XLt2LVy9ejVcuXIlXLp0KVy4cCGcP38+nD17Npw5cyacOnUqHD9+PBw9ejQcOnQo7Nu3L+zcuTP6NAT/+wE/nWqPufQVMpKWX/z3dgTs1rfq65Nc6VzrXCsG+o2BOgKDe/7u3bvDnj17wv79+8OBAwfC4cOHq3Xh2LFj4cSJE9Vacfr06WrtwBqCteTixYvh8uXL1RqDtQZrDtYerkM6p/PnFDmNCEybzE59KgRAVLBZwlIJHstYR0lbX7c2rPtk3LZ9Qk8dS/iw9RyfXfbxvtrM1/fxPmPtlAzyttuQBKbtHPvq9+jRo4D94cOH4cGDB+H+/fvh3r174e7du+HOnTvh9u3b4datWxVRwCKDRQfEBovbyZMnw5EjRwL+qVls4WkiMCQjsb4xmbUXgZlf6GKYSSacFAOKgTYxUEdg9I8s+1qR435FYOK4SJqBAEnJ0ATGT62JsMT08GHltu79s51jk+M35sfKbJ1j5/i1tl3URWCGJzAkIyybFlhvJwKjpKwpZqRXjCgG2seACEwX2UU3PkRgusFxLb144kJCAzBs3be9zoPnE3jbtnX2a5LF9Ohr5bZOv77MscnxG/NjZbZu52Dltm5tuqyLwAxLYDwZ8W2fdMT0IjDtExOPr9rCUjGgGPAxIALTZZZR5ksEpgy/te5NImJL1GP7BluYkdcB55Nz27Z1+miSQZ/a6QOlt7E66mMy3w9tbraeK4v1QV8vR9vu9N9VKQIzLIHhQmlJCEKJcltCbu2oi8moU6lkTDGgGFAMlMWACExXGUa5HxGYcgzX1kOMuBAM6qo2si3uG4IZHTuYMpaoU+11kDfJYnr6qyt9v6Y2fVk7W4/pY7JYH9il5DEflJWUIjDjExgkG57EsB0jKzGZEpayhEX4CT/FgGKAMSACU5JVdNtXBKZbPNfKG4kISu4EgLqqTfKCcmOb0VWS2T8+UbdtW2evJllMj74pecqvt/ftWL+YTZMspm8zX86nbSkCMw0Cg4WTpIUlZDGyEpNx4VWpJEwxoBhQDJTFgAhM24yi+34iMN1jujYeSVpISGyb9SrzekxcWKd9CiifwNu2rbN/jszbNLXhu8nG62Pzidk0yaD3Nk3t2Hw5n7alCMx0CAySDkte0I6RlZhMCUtZwiL8hJ9iQDHAGBCBaZtRdN9PBKZ7TNfOIwkJSwCwVUfW5fYtXQ1SNmFP1dnd6ptksI3Zox91LOnLlr4vbW1JX7as8+HtOIb3aX2wT5ON77NIWwRmWgSGCyjLGFmJyWivUkmYYkAxoBgoiwERmEWyiH5tRWD6xXctvIOQcOcBVyTFEZcZIkNDlXMIkMDMKQYWiMCMR2A2aPbMl/djxCRXpoSlLGERfsJPMaAYYAyIwAyciNQMJwJTA45UiyFAEsNyi7BYNyQ1Vqb6DAIiMDNwjNIY9R9ZbpAXkBNLYnLJiu/HRVelEjDFgGJAMVAeAyIwoyzJ0UFFYKKwSCgExkNABGY87DnyWAQGpMWSFZIYK0MS4u2YmFDOtsryhEUYCkPFgGKAMSACw1Vy/FIEZvxzoBkIgRkERGBm4BilMQaBySUrtPNkhW2QHS62KpV4KQYUA4qB7mJABGaUJTk6qAhMFBYJhYAQ0HdgxvsOjCchJCckL0hIYMM2S/+kRolLd4mLsBSWigHFgAjMdHIjEZjpnAvNRAhMCgERmOkQGEtWmESRrJC8QE4ZbVQq4VIMKAYUA93FgAjMdNIUEZjpnAvNRAhMCgERmGkRGJATm4jEyEpMZvuo3l0iIyyFpWJg/WJABGY6aYoIzHTOhWYiBCaFgAjMsATmFa94RehqV2K1fomVzrnOuWKg/xgQgZlOmiICM51zoZkIgUkhIAIzHIFR4tF/4iGMhbFiQDFQGgMiMNNJU0RgpnMuNBMhMCkERGBEYEoXe/VXwqgYUAysUgyIwEwnTRGBKTgXR5/+1tC0v/CO7wofOfX+ub1gWHUVAoMgIAIjArNKiYeORYm0YkAxUBoDIjCDpB9Zg4jAZMEUNwJ5SW3QfePgpwIITIzEpPpNQT6V/0NCLKYwnzZz8H18m8c31VIEpj8Cg0VQuzBQDCgGFAPLFwNYs2NEaNeuXWHv3r3h4MGD4ciRI+HEiRPh9OnT1b3+woUL4fLly+Hq1avhxo0bVf9bt26F27dvhzt37oS7d++Ge/fuhfv374cHDx6Ehw8fBv4/sqnmCGPPSwSm4Aw0ERi6jpEY6lQ2I7BsiT+PaFnnzfmLwPRDYIivSiEgBISAEFhOBERgxj9vIjAF54AEBokqd7qjDu17X3sw9ySGdiqbEVhWIrCs8+YZEYHph8DEFj7J9GqLYkAxoBhY7hjQExhmD8OUIjAFOFuS4t3EdJDx+zDe3rZJhlhaHevUpZJkq4/Z1Omb7DkHlrS3PqlrU3o/9G99eRurQ93qY/29je1Pe/qgLW28nm2rZ1+W3kfMljKW9EsfbFPfdykCIwKjhGq5EyqdP50/xcBwMSAC03dWMutfBGYWj4VaJCmxBBO62N5EYGJJqpct2sZB2T62zgO2Mlv3fVPtpj4cp6mM+YnJrJ8mfWrOKR/wV+ezSU+/dT5y59Tkg2P1UQ5JYKa2yN68eTNgx7vK169fD9euXaveXb5y5Uq4dOlSwPvM58+fD2fPng1nzpwJp06dCsePHw9Hjx4Nhw4dCvv27Qs7d+6Mvic9tWPVfIZLcIS1sFYMrG4M4J6/e/fusGfPnrB///5w4MCBcPjw4WpdOHbsWPWdGKwV+F4M1g6sIVhLLl68uPX9GKw1WHOw9nAdUszMxwxyHhGYgsyPBCbXBezbEBjr3ye01Fm5rVNvy0X0KVsrt/VFxrG2qOf4KbHheE0+Ynors3X6jM3f29m2rad85NjYvl3XhyQwXc+91B+/PIkvUuILlfhiJb5giS9a4guX+OIlvoCJhQWLDBYdEBt8IffkyZPVFzjxaZwWnvmFR5gIE8WAYmAVY0BPYEpX3sX6i8AshteMNQkMEs3UbjuIwFg05us5CXuJDUdM+ajT2z62zj4ovbyu7XX0Y+W2Tn1sHKvrsi4Co1fIVjHJ0DEpeVYMKAb6iAERmC4zkGZfIjDNGCUtSGCSBk6RQ2DQBYmr3a0bK/f1OjurQ72pL+1hF9us3NatbUpubWw9ZW/lqKd278vaeZ1t+7odjzors3XqUXp5Xdvr6MfKbZ362DhW12VdBEYEpo9FXj6VPCoGFAOrGAMiMF1mIM2+RGCaMUpakMAg0UzttnMugbF9ULeJrK17u7p2Uz+rT9Wt/0VtbN9U3fq0NlZu69amqW772XqsX0xvZbZu+3t5Xdvr6MfKbZ16lCm5temiLgIjArOKSYaOScmzYkAx0EcMiMB0kXnk+xCBycdqzpIEZk6REPRJYJqS2kX01tbW7WFZua2nbKw8Vc/xk2MT82/72bq1pZxlTAdZTB+TezvbtvXScWz/LusiMCIwfSzy8qnkUTGgGFjFGBCB6TIDafYlAtOMUdKCBAbJaGq3nXMITCyx9bJF25iD7WPrnJ+V2brvm9OO+aSsqYyNHZNZP0162DbZWL2tcxwrQ922Y/5jsqY+TfrYXCjroxSBEYFZxSRDx6TkWTGgGOgjBkRg+shE0j5FYNLYNGpIYBoNHxvkEBiYMkFmGfNPnU96aWv1MZs6fZM9x2AZs4cuJWe/VGnnlvLjbbwvq0/Nw9rY/jF7K2M91T/li/28nn6sHPWYfZ3c9y9ti8BMg8AwPlDmLLq5djm++rJZhjn2dezyq+RZMbCaMSACU5p1LNZfBGYxvGas+yIwM4OoMTkEkHytwyYCMz6B8Ym+b8cSoRybWL8hZcswxyHx0FirmdDqvK7XeRWBGTYzEoEpwBsEZtG96f/AFExnsl2RrDTtk518ZGI4lnXYRGBEYPpKwERg1iux6yuO5FdxNKUYEIEZNjMSgRkWb422AgiIwMwvmiA72F588cWZM/zSSy9V/9wR/+AxtuMGNMVtCv/IMpXkp+RcyKlHyZ06W1JnS6tH3epQ93pvE9PHbFK+Uv0ln7/mhIkwUQxMKwZEYIZdzUVghsVbo60AAki+1mHTE5hxn8CkkvyUnMkM9N6mqW37xuopWY7fmI2X0b/KaSVkOh86H4qB/BgQgRk2MxKBGRZvjSYElgYBEZjlJTCxpCOHNFgbW1/En+1n69ZHSm5tVM9PnISVsFIMjB8DIjDDpjciMMPirdGEwNIgIALTD4HBa3Wp3SYhqSSfcpR+R3/qra86ubWzfW3d2rCe0lu5rbMfSi9P4SF5OlaEjbBRDIwXA/Z+xroIzLDpjQjMsHhrNCGwNAiIwPRHYLjg1ZU+yadtSt6k9/3Qju30g9Lr63TWlnaQsW7LlNzaqD7+J8o6BzoHioH5GABxjOEiAjNseiMCMyzeGk0ILA0CIjCrS2BSBCIl52Jt9bZOvS9TNim576/2fPIkTISJYmDcGBCBmUYaIwIzjfOgWQiBySEgAjMugUGS4hN9344lMikbK7d16yMlp43V2zr1KK3c1lM2Vq76uImZ8Bf+ioHmGBCBmUa6IgIzjfOgWQiBySEgArO8BMYTh6Y2kxZrZ+sxPWTexrdTNjE7jqGyOYESRsJIMTBeDIjATCNdEYEpOA85/8TyhXd8V+A/r7RlwbDqKgQGQUAEZnwCgyQFyT73nKSF5IB92PZ9rZ51jkdbyllSbkvqUFq5rXubOlvbT/XxkjRhL+wVA/EYEIEZJAVpHEQEphGitAEITGqD7hsHPxVAYGIkJtVvCnIkF1PdupxbW1++n29PFbtF5yUCMw0CoyQinkQIF+GiGFAMjBEDIjCLZhP92IvAFODaRGDoOkZiqFO5GAJdkoW2vtr2W+xIx7cWgRGBGSM50JhKShUDioEpx4AIzPj5CWYgAlNwHkhgkNBypzvq0L73tQdzT2Jop3IxBLokD219te232JGOby0CIwIz5SRCc1OSqxhQDIwRAyIw4+cnmIEITMF5sCTFu4npIOP3YLy9bZMMsbQ61qlLJdNWH7Op0zfZcw4saW99UrdoaX3QL3x4ea6OfVnG/FhfsEttsb6wtf1Zp631FZPF9PRhdWPURWBEYMZIDjSmklLFgGJgyjEgAjNGRjI/pgjMPCbZEpIUJqY28YQutjcRGOuDE/GyRdvwY/vYemwMr89pN9lwnLrS+4CtlzW16d/aoW7bMRvKckrvy7b9WNSxhH9b53he5tu0G7IUgRGBmXISobkpyVUMKAbGiAERmCEzkfRYIjBpbBo1JDCNho8NYN+GwFj/qcTWym3d9mV9EX3K1sptnWOgTMmtja3n2OfY+LFTfVJyO6dY3fezbVtn3yZZTO+Pgb6GLIckMGMsQnVj3rx5M2C/ceNGuH79erh27Vq4evVquHLlSrh06VK4cOFCOH/+fDh79mw4c+ZMOHXqVDh+/Hg4evRoOHToUNi3b1/YuXNn9Fe5Uotf3XykU6KmGFAMKAamEQOpezju+bt37w579uwJ+/fvDwcOHAiHDx+u1oVjx46FEydOVGvF6dOnq7UDawjWkosXL4bLly9XawzWGqw5WHu4Dum8z5935EIiMAUZIQkMEtDUbt2LwFg05uupRN5a5tjA3trZehtfto/37duxsZpkMb336+cwRHtIAjPE8SwyxqNHjwL2hw8fhgcPHoT79++He/fuhbt374Y7d+6E27dvh1u3blUEBYsMFh0Qm3PnzoWTJ0+GI0eOhF27donAbPwMtBZfYaAYUAysUgykCAzu+Xv37g0HDx6s1gAQFpAVrAsgKiQpICfAA2sI1hKsKVhbsMZgrcGag7WH69Aia9c62YrAFJxtEphcFzkEBr6Q0Nrd+rdyX6+zszrUm/rSHnaxzcpt3dqm5NbG19HH7jF9TGb7sE47tGNbSh6ztTLfz7ZtnX2aZNCndvoYoxSBEYFZpaRDx6IkWjGgGOgiBkRgxshI5scUgZnHJFtCApNKPn3imktg/ASsH1v3dnXtpn5Wn6pb/4va2L6L1O046NfUpm9rZ+vUx3xZXV3d+7NtW6ePJllMz75jliIwIjBdLPbyoaRRMaAYWKUYEIEZMzPZHlsEZhuLhWskMLkd+yQwTUnwInpra+v2OK3c1lM2Vr5I3ftuatO3tbN16lGm5NYmVvf9bNvW2bdJFtOjb0pOv32XIjDjEJhnnnkmrPKOuG1KZr70pS8F7cJAMaAYaBMDOfeYpntQnV4Epu/sI8+/CEweTlErEhgkmqnddswhMLGk1csWbWMOto+tc35WZuu+b0475pOyutKPmzNWrI/vl2NTNy+v8/5s29bZL0fmbXybvoYsRWDGIzB1i+ey6xDDTceApEWbEBACQmBRBHjvaLrHlOhFYBY9K/3Yi8AU4EoCk+sih8DAF5JXu8f8L6KPJcO2v9f7tp+Tn0/Mnn28bVO7bl7s68fzfaj3JfvbkjZWllO3/VJ1+rH6JhlsY/bsN2QpAiMCU7LIp/oihlM6ypmEhHBzw1q7MFAMKAZyYiBUT25z7jG817QpRWCA8PibCEzBOeiLwBRMSV2FQGcIiMCIwLRZ3Jv6IECbbERgcpI12SipVwzMxoAITGcJwBI4EoEpOEkgMIvuTf8HpmA6k+3Kpwp15diTr5sbdWPPcejxRWBEYJqIRhs94ripnwiMEtPZxFR4CI+cGMgjME888UTjPajuHqUnMENnI/HxRGDiuEgqBNYeAREYEZi6RbytDhdWU18RmJxkTTZK6hUDszHQTGBAXrg33YdSehGYaaRHIjDTOA+ahRCYHAIiMCIwqQW8RI5Ab+ovAqPEdDYxFR7CIycG6gkMiYstm+5FMb0IzDTSFRGYaZwHzUIITA4BERgRmNjiXSpDoDf5EIHJSdZko6ReMTAbA2kC40mLbzfdk6xeBGYa6YoIzDTOg2YhBCaHgAiMCIxdtLuqI9CbfInAKDGdTUyFh/DIiYE0gWm65yyiF4GZRroiAjON86BZCIHJISACIwKzyKKea4tAb7IVgclJ1mSjpF4xMBsDIjCTSyR6nJAITI/gyrUQWGYERGBEYJqIRhs9rommfiIwSkxnE1PhITxyYkAEZplzjkXnLgKzKGKyFwJrgoAIjAhME9Foo8fl09RPBCYnWZONknrFwGwMiMCsSXpSHaYIzDqdbR2rEFgAAREYEZgmotFGjxBs6icCo8R0NjEVHsIjJwbSBAZf2o/dd1LymC1l+g7MAolEj6YiMD2CK9dCYJkREIERgeGC3WWJa6LJnwhMTrImGyX1ioHZGKgnMJ6s8JfImu5HXi8CM43MRgSmg/PA/9TOsgOXo7vAsfjNHl9M7+0XbXufvr2oP9mXISACIwLjF+4u2ojKJj8iMEpMZxNT4SE8cmKgmcCQxJC8sN10T7J6EZiy3KKr3iIwhUhWSfbzvxmC2bdkhb6n1H0IMjHEGFPCdOpzEYEZh8BMPS66mJ9NBmJ1EZicZE02SuoVA7MxkCYwuM9Y0sJ67P7TJBOB6WIVKPchAlOAIRLuDzz5zTPkBUQmJoN8mRP0IeY+xBgFp3vtuorAjENgmhZP6b/e+ARHGAkjxYBioK8YEIGZRjokAhM5D48ePYpI50VVwv2YsIC0cLdPY2wd+pwkHTZ2tyOzf0rvbWln5bZOPf1Sx7bVsw4b6mlPmbVJ6bwN27akP+uDMtrFdE02Xg9f2uIIiMCIwPSVAMivkkvFgGJgWWNABCaeMwwtFYFxiL/wwgvhs5/9rJPGm1Xya14ds2QlVgeBeeUrXxlN/jlCLKG2MtRtG/18OyZb1Mbbl7R939Sxers27UX7xLDi/Na9FIERgVnWBEPzVnKsGFAM9BUDIjDTyI5EYMx5wJMXkJdnn302PHz40Gji1SpZHoDA2NF9gk6dlds69Sit3NZjNl5f1/Y6+kvJU3pvb9u2zv4ordzWF7Wx9qpvIiACIwLTVwIgv0ouFQOKgWWNARGYaWRJIjDuPIC4fHaDxGBvIjFVwrxiBIZweDJQ1/Y6+mgqfb+6ttfRt5XbOvUordzWrY3q8wgMSWCmtpDdvHkzYL9x40a4fv16uHbtWrh69Wq4cuVKuHTpUrhw4UI4f/58OHv2bDhz5kw4depUOH78eDh69Gg4dOhQ2LdvX9i5c2f0uxqpxW9qGGg+SjAVA4oBxcB8DKTu4bjn7969O+zZsyfs378/HDhwIBw+fLhaF44dOxZOnDhRrRWnT5+u1g6sIVhLLl68GC5fvlytMVhrsOZg7eE6pHMwfw6QsYjAzOdtFXHBU5gmElMlwx0TGEwHfu1up5hKwK3c9vV1+rL2lNnS6+vaXmf9sA6b2E49Su/Htm091SfHhuPAlrv1p/o2AkMSmO1Rp1HD01js+BDjwYMH4f79++HevXvh7t274c6dO+H27dvh1q1bFUHBIoNFB8Tm3Llz4eTJk+HIkSNh165dIjAbv/yjxVcYKAYUA6sUAykCg3v+3r17w8GDB6s1AIQFZAXrAogKSQrICfDAGoK1BGsK1hasMVhrsOZg7eE6NI1VcXqzEIGJnBMEzpgExk/JJua2bu2s3Natja032Xh9Xdvr7Diop/ReXtf2Oo5h5bZOfd34tEn1o35dSxEYEZhVSjp0LEqiFQOKgS5iQARmGlmRCIw7DyAvnx3xFTI3nappE2xbt7ZWbuttbbyPurbXcUzKWVLO0svr2l4X85Fjw362TPWzNutYF4ERgelisZcPJY2KAcXAKsWACMw0MiIRGHMeFv0SP7oi+eXPJzeV+AUy/QrZNuCeOHTd5kjWr63H9JSpDEEERgRmlZIOHYuSaMWAYqCLGBCBmUaGJALjzsMiP6PMrkiKSU6aStqyb6yEjd2tDeTYUnpvSzsrt3Xq6Ze6Rdvol/LldbSjHCU3O66tW73tTznKmH1Mzv4srQ/VtxEQgRGB6WKxlw8ljYoBxcAqxYAIzHaeMGZNBCaCPp7ELLoxGW4qQXBKNvjXJgSGQEAEZhwCgy98TnF/5plnwlNPPTXJuU0RLz8n4VcW18RvlRJhHUs/xA7rY5/YisAMkYE0jyEC04zRpCxEYCZ1OlZ6MiIw4xGYKQYWE0gk5toWR0D4LY6Z7UH8+kxM5bsfQjE0roibPscUgbFX5nh1EZjxsG81sghMK9jUqQUCIjDjEhj/Cf7YbSaQJDBjz2fZxhd+egLTZ1It39vkC8tdn3iIwLRIKHroIgLTA6h9uhSB6RNd+bYIiMCIwFiSoAS8mwRcBLAdjoy/PhNT+d4mAcuMBdaxPucvAmMzhfHqIjDjYa+RhcCkERCBEYERgWmXbFvcWGcCLgLTDlPi12diKt8iMDkxIAIzjdRFBGYa50GzEAKTQ0AERgSGyTdKJpBKwMsScOFXhl9Ogimb1SAibc8jFtO2fXP6icBMI10RgZnGedAshMDkEBCBEYERgWmXbFvcWBcBLMOS+OUkmLIRgamLgSeeeKKI4IjATCNdEYGZxnnQLITA5BAQgRGBYfKNkgkk6tisTvXm5Fz4NWNUF0fEry4xlW69iQvPP+5PrPsS5IW71+W2RWCA8PibCMz450AzEAKTREAERgTGJpRMICHDZnVTquOHTrhPaV7Lgh8wA35Twg5zIX65Sabs1pfM4P4UO/8kLraM2TXJRGCA8PibCMz450AzEAKTREAERgTGJrFMICHDZnVTqfvE27fHnOey4DdF8ofzRvyakkvp15e48Nzj/sQ6S09afJt2OaUIDBAefxOBGf8caAZCYJIIiMCIwNiEnwkkZNisbip1T1h8e8x5LgN+xGdKuHFOxC8nwZTNepMY3J98DJCwWHlMZvWpuggMEB5/E4Hp4BzwEyuWHbisdYFx7ObbVtdVfYgxupqr/HSDgAiMCAyTR5RMIFHHZnVTqOMeFZtHSh6z7VM2dfzssU8FMzsn4pdKKiVfb9Jizz/uT7aNOsiKl9XJY7aUicAA4fE3EZjCc1Al9s//Zghm35IV+k51r/ynlD3Jxxizp0OR20wERGBEYGIJJGTYrG4KddyjYvNIyWO2fcqYgGOMKeJnj30qmNk5ET8mkSpFWFIxgOsrpetCLgJT3cJG/yMCU3AKcJP/wJPfPENeQGRiMshh38XWlZ9F5jLGmIvMT7bdIyACIwITSyCnmoDjHmXny3pKTv1QJRNwjIdtqHHbjDMVzOzciV8XCah8rDb5wfXV5zkWgQHC428iMJFz8OjRo4h0XlQl9Y8JC0gLd/s0xtahr/rMu5qRwMbuVFoZ69Chzo11q7c66ilDGZN5ubWx9To/sLO7tVV9+giIwIjAxBJIyLBZ3RTquNfE5pGSx2z7lDEBxxhTxM8e+1Qws3Mifn0mpvK9GsQG11ef51IEprqFjf5HBMadghdeeCF89rOfddJ4Ezd5S1Ca6iAwr3zlK5OEAaNUPt1wXlbXhs7qWWcZG8Pq7NBWnqrn2NPG+qBM5XQREIERgYklkFNNwHF/sfNlPSWnfqiSCTjGwzbUuG3GmQpmdu7Er8/EVL5FYHJiQARmGnmLCIw5D3jyAvLy7LPPhocPHxpNvFol5Oa7L30RGD96Na4R2rat06RJFtOjr5Wn6hyjzt7aqL48CIjAiMDEEkjIsFndFOq4R8XmkZLHbPuUMQHHGFPEzx77VDCzcyJ+OQmmbFaDiLQ9j7i+2vbN6ScCU93CRv8jAuNOAYjLZzdIDPYmElMl9SIwWwhWeDxu2fqWgSpLhYAIjAhMLIGccgLuE2/ftsczdJ0J+JTxIyZTwo1zIn45CaZsRGCef/750HZvih8RmGmkMiIwkfMA4oKnME0kpkrSOyYwmA782t1PsRrXCG3b1mnSJIvp0dfKU3WO4e3ZRj/u1lb16SMwJIFpWjCG1t+8eTNgv3HjRrh+/Xq4du1auHr1arhy5Uq4dOlSuHDhQjh//nw4e/ZsOHPmTDh16lQ4fvx4OHr0aDh06FDYt29f2LlzZ/RTwNTix2OcaoLLBHKq88O8cK9hwhtrW93Q9WXAj5h4HCkfsyR+vE5UrjdJqTv/WF3bkhf0q/MNXeoejnv+7t27w549e8L+/fvDgQMHwuHDh6t14dixY+HEiRPVWnH69Olq7cAagrXk4sWL4fLly9Uag7UGaw7WHq5DTfNZRz3OsQgMUHDb2ATGTWeGSECHxcVutm3rtGmSxfToa+WpOsfw9lbOuvVBmcrpIjAkgZkaCnidFDvuBQ8ePAj3798P9+7dC3fv3g137twJt2/fDrdu3aoWOiwyWHRAbJDgnTx5Mhw5ciTs2rUruhCmFj8uQvCBbcxkMTY2E8ipzo9zxn2GO2VTKJcFP2AF/KaAmZ0D8eN1olIEJhUDuH+OQWBwz9+7d284ePBgtQaAsICsII5BVEhSQE4wd6whWEuwpmBtwRqDtQZrDtYerkM4Hm3zCIjAOEwQNJ8d8RUyN52q6RP/urbXwUGTLKb3/ayNrVcTfPwnJadNk552KqeBgAiMCEwsgYQMm9Wpfq4RDybgwq8Zq1g8Eb9U0iq5CA1jAPcnERigsNqbCIw5v4t+iR9dkZTz55ObSvwC2RC/QmYOqarGiIOXxdpWZus8bjsO9NbG1mkXk1GncnoIiMCIwNhEkgmkEvCyBFz4leHHJFWlCEsqBrCaisBML6foekYiMA7RRX5GmV2RmJOcNJW0Zd9YCRu7p2wohy03W28j47joa33ZuvWbsmd/6mP96UflNBEQgRGBEYFpl2xb3FgXASzDkvilklbJRWgYA1hRRWCmmVd0OSsRmAiaeBKz6GYT9bo6CI42IbAMCIjAiMAw+UbJBBJ1bFanenNyLvyaMaqLI+LHJFWlCEsqBnB/EoEBCqu9icCs9vnV0QmB1giIwIjA2ISSCSRk2KxO9ebkXPg1Y1QXR8QvlbRKLkLDGMD9SQQGKKz2JgKz2udXRycEWiMgAiMCYxNKJpCQYbM61ZuTc+HXjFFdHBE/JqkqRVhSMYD7kwgMUFjtTQRmtc+vjk4ItEZABGYcAoNETbswUAzEYyCVtEouQsMYwKInAtN66V+ajiIwS3OqNFEhMCwCIjDjEJinnnoqaBcGioF4DDBJVSnCkooBrJQiMMPmC2OMJgIzBuoaUwgsAQIiMOMQmCl/8v5Xf/VXSxC5052i8Cs7N8AvlbRKLkLDGECUicCUXWvL0FsEZhnOkuYoBEZAQARGBMaTKSXgZRei8CvHj0mqShGWVAwgykRgyq61ZegtArMMZ0lzFAIjICACIwIjAtPthScCU4annsCItKRIi5UjykRgyq61ZegtArMMZ0lzFAIjICACIwIjAtPthScCU4anCIwIjCUqqTqiTASm7Fpbht4iMMtwljRHITACAiIwIjAiMN1eeCIwZXiKwIjApEiLlSPKRGDKrrVl6C0CU3CWjj79raFpf+Ed3xU+cur9c3vBsOoqBAZBQARGBEYEpttLTQSmDE8RGBEYS1RSdUSZCEzZtbYMvUVgCs4SyEtqg+4bBz8VQGBiJCbVD/IdO3bMqH17RjlgYyrzGPCQa4cCHquMiQjMahAYxilKT0gWbfsE3PquvViMsk0f032pq8Kv7PSJwIjApEiLlSPKRGDKrrVl6C0CU3CWmggMXcdIDHWxEgv8FLepzmsMrNYBCxGY5ScwiFNLUnzb6nLqNgH314Bvx65Lb+PbsT6rJBN+ZWdTBEYExhKVVB1RJgJTdq0tQ28RmIKzRAKDRZg73VGH9r2vPZh7EkO7WDnVRX2q84ph2LdsHbAQgRGB8aRGCXjZnUX4leOXSlolF7lhDCDKRGDKrrVl6C0CU3CWLEnxbmI6yPh9GG+PNkmQLSmnPRNn2lBOO+qtnHX2SdlYvbXx8lwd58TS9kvJKGfJsdH2G3Usvb6pzfnE+lNGG/qyctapQ0mZ70cdS69fpJ/vC5/c6vzApklPPyhFYJabwOBcewKCdkoes/UyJuDwEdtSctimdCl5zP+yy4Rf2RkEfkxSVYqwpGIAUSYCU3atLUNvEZiCs0SSggWYO91BF9vrCAz7+gXdtv041LGED1vP8dllH++rzXx9H+8z1k7JIE9tsXFg6/Hz7ZhNTOb7tR0v1s/7zh0fdtxiPqhDKQIjAiMCY6+I8roITBmGIjAiLSnSYuWIMhGYsmttGXqLwBScJRKYXBew74LA+PFiiaiV2brta+W2bm1sPccG9tbO1umrSRbT5/il/9wyNk5M5sfOaXMO1p+tx/SUef+xfova5Piw46MuAiMCIwLjr4qytghMOX42UVVdhCYWA4gyEZiya20ZeovAFJwlEhgkh6nduheB2UQjlkxbma1b/Kzc1q3NIvWYj5gMPr28qc15WDtbj+kpQ2ltbX1RG9rn+KAtSxEYERgRGF4N3ZQiMGU46gmMCEuMsHgZokwEpuxaW4beIjAFZ4kEJtfFmAQGCWxst3P3eqtDPZYE+z5ss2+qD/UsrZ2tU4/Sy9G2u7XNqXt/HMP6tHXr0/e1dr7Ofr5P7nixfuxb55s62vp5sW3tbF0ERgRGBMZeEeV1EZgyDEVgRGA8WYm1EWUiMGXX2jL0FoEpOEskMEwEY6V1PyaBsfPIreN47NbUpq21s/WYPiaL9YFdSh7zQVldGfMXk8V8eDvfzukDm7b9fN8mP0362HyHJDCxRWhM2c2bNwP2GzduhOvXr4dr166Fq1evhitXroRLly6FCxcuhPPnz4ezZ8+GM2fOhFOnToXjx4+Ho0ePhkOHDoV9+/aFnTt3Rr9w/NJLL0XlPF5PGtq2cc5jfVPymK2XKQGPXSn5MuGXj1XMUgRGBIb3yboSsTMGgcE9f/fu3WHPnj1h//794cCBA+Hw4cPVunDs2LFw4sSJaq04ffp0tXZgDcFacvHixXD58uVqjcFagzUHaw/XobpjXVcdzrEIDFBouZHA5HafGoFpSmq9vqlNHKydrcf0MVmsD+xS8pgPyurKmL+YDD68vKnNca2drcf0lKG0trbe1ibHh/WN+pAExo89dvvRo0cB+8OHD8ODBw/C/fv3w71798Ldu3fDnTt3wu3bt8OtW7cqIoJFBosOiM25c+fCyZMnw5EjR8KuXbuiRGUoAgPygfNuSYhvW11OnQk4zo+PKd+OnUNv49uxPqskE35lZ1MERgQmJ2FHlOXYtbVJ3cNxz9+7d284ePBgtQaAsICsYF0AUSFJATnB2FhDsJZgTcHagjUGaw3WHKw9XIfKrprV7S0CU3BuSWCwCKd2634sAoM5+EShqd22j+/nx/F6tLFZO9Rt2+tj7ZQM8tTmx6Cdl/s27HJk3sa3c8frqp/349ucD0sRGBEYT2qUgPPqaFcKv3a4sZcIjAhMDulAvOTYtbURgeEVOW4pAlOAPwlMrotcAgN/NrlM1Tmu1TfJYBuz55jU19lwjFgf9vOl72Pb9EOZ7Ys629SzpI4l5bllyi/602fKpk7Ovn4eqT5N46X6xeQcO6ZrGsfPVwRm+QkMCIiNCU9IFm3bBDwnnmJxaOfjY27V28Kv7AyLwIjA5JAORFmOXVsbEZiy67ir3iIwBUj2SWAKprX0XWNJz9If1BIegAjMahCYRUlKnb1PwJcwrEedsvArg18ERgQmh3QgynLs2tqIwJRdx131FoEpQBIEZtE95//AFExpJbqWEhj7CW+qvhJAdXgQHie4FoERgfFkRgl42UUn/Mrxa5t0qt/6kB9EWZ/nWwSm7DruqrcITFdIyk9nCJQSmM4msuaORGBEYERgur0JiMCU4aknMOtDQkoICKKspH9TXxGYsuu4q94iMF0hKT+dISAC0xmURY5EYERgRGCKLqG5ziIwc5AsJBCBEYFpIhfQY8uxa2sjArPQZdubsQhMb9DKsRBYbgREYERgRGC6vYZFYMrwFIERgckhHYiyHLu2NiIwZddxV71FYLpCUn6EwIohIAIjAiMC0+1FLQJThqcIjAhMDulAlOXYtbURgSm7jrvqLQLTFZLyIwRWDAERmHEIDJI07cJAMRCPgbZJp/qtD/nBUtzn+RaBmUayIwIzjfOgWQiBySEgAjMOgfFPPabURlKtrT0Cwq89dugJ/PpMTOV7NUgOYqXPcykCA4TH30Rgxj8HmoEQmCQCIjAiMJ48KQEvu1SFXzl+fSam8i0CkxMDIjBl13FXvUVgukJSfoTAiiEgAiMCIwLT7UUtAlOGp57ArAbByCEJJTaIspL+TX1FYMqu4656i8B0haT8CIEVQ0AERgRGBKbbi1oEpgxPERgRmCZyAT22HLu2NiIwZddxV71FYLpCUn6EwIohIAIjAiMC0+1FLQJThqcIjAhMDulAlOXYtbURgSm7jrvqLQLTFZLyIwRWDAERGBEYEZhuL2oRmDI8RWBEYHJIB6Isx66tjQhM2XXcVW8RmK6QlB8hsGIIiMCsDoHZsWNH8GSkTdsn4PDLPTf8aY8yteXY2L4pXzl+cmzsWCX1LvDD+Knj5dxyjmlIG86rtBSBEYHJIR2Isxy7tjYiMKVXcjf9RWC6wVFehMDKISACs/wExiapbQiL72MTcJ9E+3bsgvA2vo0+Xubb3i/0MRsv8+02Y/mxF213gV/qeDkXf5y+nXvcvp9v5/rhvLooRWBEYHJIB2Itx66tjQhMF1dzuQ8RmHIM5UEIrCQCIjDLT2BIQJB8sl5SliTgsQQYF46XN7X9xQZ736fEb8yXH7NtuwQ/O2ZqjjnyIW3snLuoi8CIwOSQDsRajl1bGxGYLq7mch8iMOUYyoMQWEkERGBEYDzZYQKekwT7iyKnT46N9Ut7ltT5dkyeY8N+XZUl+Nk5lMw9p2+JjZ1n13URGBGYHNKBuMuxa2sjAtP1ld3OnwhMO9zUSwisPAIiMCIwXRKY1AVjk2Vbt/YxuZXZOvr5Nn15eVOb/boqhyIwOC7uOXP3OKT6WDvWOQ7bqb5dyEVgRGBySAdiLceurY0ITBdXc7kPEZhyDOVBCKwkAiIwIjB9Exif9Po2L6yY3MpsHX18u8kP7FN92LeLcggC44/Dt3kcPOaUnnYsvR37U4/S21hdF3URGBGYHNKBWMuxa2sjAtPF1VzuQwSmHEN5EAIriYAIjAhMnwQmluzGZLi4vHzRNi/Qtv3Yv7QcgsDE5uiP29u00af6pOR+zDZtERgRmBzSgdjKsWtrIwLT5urtvo8ITPeYyqMQWAkEhiQwbReSvvrdvHkzYL9x40a4fv16uHbtWrh69Wq4cuVKuHTpUrhw4UI4f/58OHv2bDhz5kw4depUOH78eDh69Gg4dOhQ2LdvX9i5c2d0EU0tfjwWTxq6aCOp7MLPFBLwWILsZb7NC9LKbZ16lCm5tWlbnwJ+qbmnjrsreWrcReQiMCIwvE/WlYipOn2pLnUPxz1/9+7dYc+ePWH//v3hwIED4fDhw9W6cOzYsXDixIlqrTh9+nS1dmANwVpy8eLFcPny5WqNwVqDNQdrD9eh0vmuYn+cYxEYoKBNCAiBOQSGJDBzg48sePToUcD+8OHD8ODBg3D//v1w7969cPfu3XDnzp1w+/btcOvWrWqRxCKDRQfE5ty5c+HkyZPhyJEjYdeuXdFFNLX4cZHpgmh4H0hCvaxNu4sEPJUQ45SndFaOempn2Fh7yrz/HBvbt4t6F/j547DzyjmmHBv6TNm2mQN9lpQiMCIwvE/WlYixOn2pLnUPxz1/79694eDBg9UaAMICsoJ1AUSFJAXkBHPAGoK1BGsK1hasMVhrsOZg7eE6VHLNrHJfEZhVPrs6NiFQgIAIjAiMJzlMwBFWPrn17VjotbFp0ydnfim/KXnseBaVleLH8VJzzJHn2MTw49gsc/3QvotSBEYEJod8INZy7NraiMB0cTWX+xCBKcdQHoTASiIgAiMC0yWBSSW8/uLxdr7t7dGO2XiZb8f6xWxi47WV9U1gco/JH2dTO3W8bful/DXJRWBEYHJIB+Iox66tjQhM05U6jF4EZhicNYoQWDoERGBEYOoIDAIaCSz3WIDbBJd2sdL3tTZeF2vbcaw+x0+Tjffd1Lbj+7olMNAtOjb9+TlQzrLJL+zqbKzO1zkGS6unzJbQ262pbW19XQRGBCaHdCBucuza2ojA+CtznLYIzDi4a1QhMHkERGBWh8B4ItK27RPwyQfxxCYo/MpOiAiMCEwO6UCU5di1tRGBKbuOu+otAtMVkvIjBFYMAREYERhPdJSAl13kwq8cv7ZJp/qtD/lBlPV5vkVgyq7jrnqLwHSFpPwIgRVDQARGBEYEptuLWgSmDE89gVkfElJCQBBlJf2b+orAlF3HXfUWgekKSfkRAiuGgAiMCIwITLcXtQhMGZ4iMCIwTeQCemw5dm1tRGDKruOueovAdIWk/AiBFUNABEYERgSm24taBKYMTxEYEZgc0oEoy7FrayMCU3Ydd9VbBKYrJOVHCKwYAiIwIjAiMN1e1CIwZXiKwIjA5JAORFmOXVsbEZiy67ir3iIwXSEpP0JgxRAQgRmHwCBJ0y4MFAPxGGibdKrf+pAfLMV9nm8RmGkkOyIw0zgPmoUQmBwCIjDjEBj/1GNKbSTV2tojIPzaY4eewK/PxFS+V4PkIFb6PJciMEB4/E0EZvxzoBkIgUkiIAIjAuPJkxLwsktV+JXj12diKt8iMDkxIAJTdh131VsEpisk5UcIrBgCIjAiMCIw3V7UIjBleOoJzGoQjBySUGKDKCvp39RXBKbsOu6qtwhMV0jKjxBYMQREYERgRGC6vahFYMrwFIERgWkiF9Bjy7FrayMCU3Ydd9VbBKYrJOVHCKwYAiIwIjAiMN1e1CIwZXiKwIjA5JAORFmOXVsbEZiy67ir3iIwXSEpP0JgxRAQgRGBEYHp9qIWgSnDUwRGBCaHdCDKcuza2ojAlF3HXfUWgekKSfkRAiuGgAjMahCYHTt2BO6ekCza9gk4/aJss6X65fjNscGcUmNwvrl+aF9SdoFfznxLbWx/X/fHb/Ve13VbBEYEJod0IO5y7NraiMB0fWW38ycC0w439RICK4+ACMzyExgkl5ak+LbV5dRtAg5fdvNtq4vVYR/r42W+DV9e5tu0gTym43y8zrdp11VZip+fn2/zuO1829jE+lifrHs736ZdV6UIjAhMDulAvOXYtbURgenqii7zIwJThp96C4GVRUAEZrkJDJLJGClJyWO2XlaagNuLBfOIJbxe1tSmT2/XtZz+SsoS/HKOr28be+w5Y1n7LuoiMCIwOaQDsZZj19ZGBKaLq7nchwhMOYbyIARWEgERGBGYFIEpTV7ZnyUvIN+OyXNs2A/lova2b9d1Epg2c8rpU2JjjzXlJ8cmp6/1s0hdBEYEJod0IKZy7NraiMAsctX2ZysC0x+28iwElhoBEZjlJjCefLCNBJP1RcuSBJwXg01wbR163471idml+sVsvU/05U5dX2UX+Nm51R13yo59eMxsx+zrbKw96zFf1HVRisCIwOSQDsRajl1bGxGYLq7mch8iMOUYyoMQWEkERGBWj8AgwVyUtFj7LhJwm+TaOi4i3+aFFZNDxp12sTLWF3axvinbmN82sq7wi809NR9/TLG+bWw4Hv15H9R3WYrAiMDkkA7EXI5dWxsRmC6v6va+RGDaY6eeQmClERiSwLRdSPrqd/PmzYD9xo0b4fr16+HatWvh6tWr4cqVK+HSpUvhwoUL4fz58+Hs2bPhzJkz4dSpU+H48ePh6NGj4dChQ2Hfvn1h586d0UU0tfjxWCxh6LKOBLPUX2kC7pPcpjYvsCY7r2c/lCndonLrs229FD8/buoYaBfTx2Swt3Jbpy9vY+Wsp/pRX1qKwIjA8D5ZVyLO6vSlutQ9HPf83bt3hz179oT9+/eHAwcOhMOHD1frwrFjx8KJEyeqteL06dPV2oE1BGvJxYsXw+XLl6s1BmsN1hysPVyHSue7iv1xjkVggII2ISAE5hAYksDMDT6y4NGjRwH7w4cPw4MHD8L9+/fDvXv3wt27d8OdO3fC7du3w61bt6pFEosMFh0Qm3PnzoWTJ0+GI0eOhF27dkUX0dTix0WmlGTE+iOxjMkXlZUk4LHk1st8m2Fg5bZOPcq+5XastvUS/FJjDnncqbHs3HJsrP0idREYERjeJ+tKxFSdvlSXuofjnr93795w8ODBag0AYQFZwboAokKSAnKCOWANwVqCNQVrC9YYrDVYc7D2cB1a5BpZJ1sRmHU62zpWIbAAAiIwq0FgkFAuSlRS9iUJOOaR2hmWqeTXym2d/VD2Lbdjta2X4hcbN3bcMRn7pnRWbuvsh9LKbT1lY+Vd1EVgRGByyAdiLceurY0ITBdXc7kPEZhyDOVBCKwkAiIwy09gkGSmyEgbORNwBLxPYH0756KI9fGypjbH8XZdy+mvpCzBL/f4Unacd0pv5bbOfiit3NZTNlbeRV0ERgQmh3Qg1nLs2tqIwHRxNZf7EIEpx1AehMBKIiACs9wEBglmG5JS16ckAY9dJLEk2Mt8G368zLftWIvo6mytz7b1Uvz8/JraqXnm9OvKJjWHNnIRGBGYHNKB2Mqxa2sjAtPm6u2+jwhM95jKoxBYCQREYJafwCAJje11JKVOZxNwBLn1HQt6nwR7m5S+yW/O2BwrNYbVczzKbOn7N7VtX1/vAj/O1c8DY1mdr/u5WL3Xsd2FDXzYraltbX1dBEYEJod0IG5y7NraiMD4K3OctgjMOLhrVCEweQREYJabwNQRkbY6n4BPPognNkHhV3ZCRGBEYHJIB6Isx66tjQhM2XXcVW8RmK6QlB8hsGIIiMCIwHiiowS87CIXfuX4tU061W99yA+irM/zLQJTdh131VsEpisk5UcIrBgCIjAiMCIw3V7UIjBleOoJzPqQkBICgigr6d/UVwSm7DruqrcITFdIyo8QWDEERGBEYERgur2oRWDK8BSBEYFpIhfQY8uxa2sjAlN2HXfVWwSmKyTlRwisGAIiMCIwIjDdXtQiMGV4isCIwOSQDkRZjl1bGxGYsuu4q94iMF0hKT9CYMUQEIERgRGB6faiFoEpw1MERgQmh3QgynLs2tqIwJRdx131FoHpCkn5EQIrhoAIzDgEBkmadmGgGIjHQNukU/3Wh/xgKe7zfIvATCPZEYGZxnnQLITA5BAQgRmHwPinHlNqI6nW1h4B4dceO/QEfn0mpvK9GiQHsdLnuRSBAcLjbyIw458DzUAITBIBERgRGE+elICXXarCrxy/PhNT+RaByYkBEZiy67ir3iIwXSEpP0JgxRAQgRGBEYHp9qIWgSnDU09gVoNg5JCEEhtEWUn/pr4iMGXXcVe9RWC6QlJ+hMCKISACIwIjAtPtRS0CU4anCIwITBO5gB5bjl1bGxGYsuu4q94iMF0hKT9CYMUQEIERgRGB6faiFoEpw1MERgQmh3QgynLs2tqIwJRdx131FoHpCkn5EQIrhoAIjAiMCEy3F7UITBmeIjAiMDmkA1GWY9fWRgSm7DruqrcITFdIyo8QWDEEhiQwbRcS2+/5558PXe1f+tKXAvbnnnuu2r/4xS+G/fv3h3379oUvfOEL4fOf/3zYs2dP+NznPlf9MtJnPvOZ8KlPfSr8xV/8Rfjwhz8c/vRP/zS85jWviS6iqcWPx+JJQ2l7x44dgXupL5+A0y/K3C23T5PPHD85NnbeTWNa2zb1LvCz46bmm3PcdTZW5+t2fNSt3uu6bovAiMDwPllXIu7q9KW61D0c93zcY5999tnwwQ9+MHzoQx8KH/vYx6p14ZOf/GT4y7/8y/DpT3+6WjOwdmANwVqCNWXv3r3VGoO1BjvWHq5DXaxrpcc8tf44xyIwQEGbEBACcwiIwCw/gUFyaUmLb1tdTt0m4PBlN9+2Ota9jW/DDjLu7OdL38+36cf2i9l4fZONtW9TL8XPjom5xubrZb4NH17m23YcW/d2TW3bt4u6CIwITE4ijVjLsWtrIwIzfhziHIvAAAVtQkAIzCEgArPcBAbJZYyUpOQxWy8rScB9ssuA60O+qE/MBX1S/TjX0rIEPz92bL6p+Vu5rVufKXnKJmWfkls/besiMOMnjm2T/iH7Ib76HE8EZvw4xDkWgQEK2oSAEJhDQARGBCZFYFJJakqO4KrTzQVfjX3Kj5XbuvXdJE/prY+SOglMapyU3I9JO5bU+3ZMnmPDfrb0/Xybtik59SWlCMz4iWOfxKAr34ixrnzF/IjAjB+HOMciMEBBmxAQAnMITInAPPHEE4F7bEGBrIv3hOmD7x6vwndgLAlBcmnbi9ZLEnAmtii5zwWdEdDeiGqrOfYxGyuz9drBWipL8OOQdo62HtND5m18O9WPcpa+n2+n7CjvohSBGT9xTN1/pyRHrPU5HxGYfuKQazzKpvOHcywCAxS0CQEhMIfAVAhM7k2N5KOLcpUIDBJN7osSFm9fkoBzDjbQUkkwbOp01keubcqfldu6H6OLdgl+HN/O0dapRwk5dyunzsvq5Cld3dgx/13IRGD6SRybksVl0yPW+pyzCEx/cZi73uMci8AABW1CQAjMITAFApN7M8Ni1QVxoY9VIjCWhCDptO1F6yUJ+KIJb8reB2qOXcrGy33bj1XaLsEPY/v5+XaJTcwXjzel83K0vYw+uihFYPpLHPtM+If2jVjrc0wRmH7jMGfdxzkWgQEK2oSAEJhDYGwCk3MTs4sUyUcX5aoSGBAWJJiLEhfalyTgqcR2UbkN1FTfHJtY35jM+iqtd42fn69vc74xOWTcYRezqetvdbl+2KdtKQLTb+Jo76fLXEd89Tl/EZj+47Bp/cc5FoEBCtqEgBCYQ2BsAoMFqOkmZhepLogLfawCgUFSSeJhy5Tc2qTqXSfgCLpU4pySM1Cb9HW+qYOP2M4xui5L8YvN1eJg63buKXmOTU7fHD/Wpm1dBKb/xNHeU5e1jvjqc+4iMP3GYc66j3MsAgMUtAkBITCHwBQIDBahnJsZ7Eg+uihFYJ6Jkh8m4AgWn9j6tg+olH5ReWxsP1auje+Xmou3a9suwS82pp+vb7OPlds69Sj7ltux2tZFYPpNHPtM+of0jfjqczwRmP7iMHe9xzluS2BKYwNjp/KjHTHnMNYmBITAcAikLtC66/PFF1+cmSBu9OfOnUvuuAFhi/m0spybWhfEhT5WgcDgKQqSUvs0xbetLqdemoD7JNm3bfCkdCl5Tl9rE6vn+I71y5WV4ufHic3Xy3wbPrzMt+04i+jqbK3PtnURmP4SR3u/XfY64qvPYxCB6ScOc9Z5nlecYxEYoKBNCAiBOQSmRGB406orST66KFeFwJDEILHEnkNS6mxsAo6Aod9U4hqTN/VhIMb6+jGtL2vv5bZN/7HS+qDey5ra7Bcru8DP+vVzoQ5y7pT5kvqUD9rn6OmLfWzp+ze1bV9fF4HpJ3Gsu68uow5x0+e8RWDGj0Oc46UjMF0EZRcJjnw83+krO6uOZxdxO6QP3BxEYJb7H1nWEZG2Op+AI0605SMg/PKxilmKwIyfOA65DrUdC7HTtm9OPxGY8eMQ51gE5nkl4qtOHqZwfDk3xSnZ4OYgAiMC44mOEnBcGe034dceO/QUgRk/cZzSOpWaC2IlpetCLgIzfhziHIvAiMDoScoAMdDFTXNIH7g5iMCIwIjA4ErobhOBKcNSBGb8xHHIdajtWIiytn1z+onAjB+HOMciMAMkr1N4AqA5jPukLeemOCUb3ByWjcB0id/NmzcD9hs3boTr16+Ha9euhatXr4YrV66ES5cuhQsXLoTz58+Hs2fPhjNnzoRTp06F48ePh6NHj4ZDhw6Fffv2hZ07d0YX0dTix/l70jClthJwXBntN+HXHjv0FIEZP3HkfWrKJWKlz/ml7uG45+/evTvs2bMn7N+/Pxw4cCAcPny4WheOHTsWTpw4Ua0Vp0+frtYOrCFYSy5evBguX75crTFYa7DmYO3hOtTnsSyrb5zjlSMwOb9ioGR+3GR+HfGfwk0i59rgPHFzGJLAYLwpbY8ePQrYHz58GB48eBDu378f7t27F+7evRvu3LkTbt++HW7dulUtklhksOiA2OAX106ePBmOHDkSdu3aFV1EU4sfsZ8SYfFzUQJeFqXCrxw/XicqRWZSMYAoS+m6kKfu4bjn7927Nxw8eLBaA0BYQFawLoCokKSAnGAeWEOwlmBNwdqCNQZrDdYcrD1ch8qumtXtvVIEJjdBW8cEWsc8Lmnr4qbZhY/cawS3vKEIDG5CU9t/7hffE7j/7DvfHbC//Rd+Nbz9He8Kb/v5Xwlv/blfDj/zs/9X+Om3/8vwlrf9Unj6re8MP/XT7wg/+fTPhl1vfGv4kTc8Hf77/+F/jC6iqcWP59eThim1lYCXJQPCrxw/XicqRWBSMYAoS+m6kKfu4bjnP/n6N4TX/+gbN9aAN4ddP/kz4cff9LZqXXjjW34+vGljjcBa8eaNNeMtG2vHT2+sIT+zsZa8dWNNedvG2oI1husN1x+UU1sfpzSfun/TgPOEDf/egecdbdZZIi9i3ZYpOXyk8qPa/wNjndt6bmKGPkrmx03m1xF/G6tj13OulboLNDZ/XMzYFv0/MHU3nzF1eDWMr4fhE7SXX3652vF0BZ+q4TjxSgBeGfvKV74SXnjhhep1gS9+8Yvh85//fPj0pz8dnnzyyehNMbX4EVckudqFgWIgHgO8TlSKwKRiAGtRSteFPHUPxz3/wx/+cPjEJz4RPvOZz1SvkuGJDNYF/DQ/nsxgrcCagbXjq1/9arWWYE3B2oJXkbHWYM3Bq8lch8ZcC5d57EUIjCcrzJNi8YL46ozAcCA/gdjAkK1jAq1jHpe0pWJxLHnTNVN3gcbmLALTHYGZ0hMXPxc9QcCV0X4Tfu2xQ0/gF7v/SCYyY2MAsWLbXddFYNL/nHpKhGdRAkMOUZIf6QmMflhg5Uhm1zfQEn9NFyd8Y0t9whAbWwRGBKYKGv2pRUAEphaeRqUIjIhKbP3xMgSSl3XZFoFZLQKD2LB5EeupmEF8pfKjVgTGTyA1MOR6GjHu04h1xL8uHofU8cJEWTdu3QUa6ycCIwKDmNFWj4AITD0+TVoRGBGY2PrjZYgjL+uyLQKzegQG8dFFftSawOROYB0TaB3zuKSty5tnW1+5Fyf8Y0t9whAbXwRGBKYKGv2pRUAEphaeRqUIjAhMbP3xMgSSl3XZFoFZTQKDGGn6cBc22FL5URGByQlSJfPjJvPriH9OXE7Jpu4Cjc1TBEYEprqr608tAiIwtfA0KkVgRGBi64+XIZC8rMu2CMzqEpicOEF8icDouy5r8zpfzkUxJZu6CzQ2zzoCg5v9su34VRjs+OeU+MUx/6tj+OdkX/7yl6tflcE/K8MvzOCfV+IXyPDrM/gVmte+9rXRRTS1+BFX/8X5KbWVgOPKaL8Jv/bYoacIjAgM75N1JWKlTl+qS93Dcc//4Ac/GD760Y9WawB+jfJzn/tctS584QtfqP655XPPPVf9YiV+kQxrCNYS+6tk+HVLrDdYe7gOLdv6OaX5IhaAJ895F7EBHyIwIjAiMBuPI3lhTamsu0Bj80wRmCn9Iskic+HPV+KnLIf+GeU+CcuOHTtCiX+fgMMfd8RMzkZ7lHVbjp6+Un6or/OVY5Pyv6i8b/zssfi6n6vVex3bXdnQX2kpAjPN9SK2JowpQ5z1OX6KwOhnlKf3ZAaxIAIjsrE2ZKOPV9z6vJn24RsXfeoThth4KQIDP8u48T8g478h478i478j478k478l478m478n478oA4ubN2+Ga9euhUuXLlX/cRm/53/kyJGA/8ocwyq1+NG2hGDU9WUyWmfTpLMJOPzZzbetjnVv49uwg4w7+/nS9/Nt+rH92tpYH6X1vvGLHWNszt7Ot9HHy3w71yY2fluZCIwIDO+TdSXiq05fqkvdw3HPx/99wdMVrAF4goIPwPDh2YULF8Lly5fD1atXw40bN6r5YQ3BWoI1BWsL1hisNVhzsPZwHWp7vajfJgIrRWBKg1f9dRNd9RjAZS8C86haRERgntl6alOSgMcSYMRZH/Icnzk2mF+XW9/4pY7JHkPKxssXbXMM34/yLkoRGK29OWsvYi3Hrq2NCEwXV/NwPiZDYIY7ZI0kBNYbgTYEZlUQ4ydfq/IEBkklnq6wbHrSktIzAU8lqSk54iKl60puYy/HZ46N9dlFvQ/87LxSx5RjY/vaeqpvysbad10XgRGBySEdiLscu7Y2IjBdX9nL5y+VHyV/hQwdtAsDxcBwMZB7g1+1c/InH/xI4P7Hf/rh8G//5EPV/uy//bPwb/7o34V//eyfhj/8138Sfv8P/zg88wd/FH73mWfDb/3OH4b3f+D3wr9632+F9/zab4bv+d7viy6iqcWPWKfIQ1s5Ek32tXXKFilLEnAsUT7p9W27jKV0lKPkbvul6uyX0lOea0f7RcoS/DgvHjPbdnzK6mysPevsl2rH5Oyz6Fj01aYUgRGB4X2yrkRs1elLdal7OO75v/hL7wq/8q73hve8933hN37zA+F97/+dal34wG//fvjt3/3DjbXi31RrBtaOP9hYQ7CWYE3B2vJHf/zn1TqDNQc716BVW19X5XhicRQlMDFDyXQzUwwoBvqIAXyvBTveVb5+/Xr1HRe8u3zlypXquy54n/n8+fMBX/bHF/1PnTpV/WrM0aNHw6FDh6pfJNu5c2d0EU0tfjyORQhFji0STNrZOmWLlCUJOBPW3IQXdrGN/a2uyTaltz5Qz7Xz/XLbJfjlHHeODedK29gxx2ToZ+XsT39eb+Vd1UVgdL/nfbKuRLzV6Ut1qXs47vm7d+8Oe/bsqX5xDL9QiV8Zw7qAXxfDd2KwVuB7MVg7sIZgLbl48eLW92PwfUqsOVh7uA6Vzlf9h7tuRGAm+stUugiGuwiE9bhYc+FYdgKDJNMSFN+2upx6SQKOpMImwLE2ZNy8bVt5U79cPe1KyhL8cvDIsYnN3/fzbfaxclunHmVKbm3a1kVgxr0vLsu6hPjqc64iMIrDVHyJwIjA9HrzSQWe5LopMQZWgcAgkfSkJCbzNnXtvhNwm9imEuFF5SU+bd8u6kPiZ+ebwixlk7K3cltP+bHyLuoiMLpH8x5dVyLW6vSlOhEYxWEqhkRgRGB6vfmkAk9y3ZQYA6tCYJBkxvY6klKnGzIBXzRBtva2bhPnmDwms326rPeNX+pYrNzW7bFZua2X2Ni+XdRFYHSP5j26rkSs1elLdSIwisNUDInAiMD0evNJBZ7kuikxBlaBwMSICBLTmDxXxgQcCYJPcn0bNnZL6fuQ5/pM2dl5d1nvG7/U8Vi5rdtj8/JF2/Tl+1HeRSkCo3s079F1JWKtTl+qE4FRHKZiSARGBKbXm08q8CTXTYkxIALzTJTolCTgSCp8cuvbsOG2iC5m62VNbY7bZzkEfjnHOaRNl3iKwOgezXt0XYmYq9OX6kRgFIepGBKBEYHp9eaTCjzJdVNiDIjANBMYJAlIhLmj7TefKENP+5jO9s/R05ftZ+vUx3xZna97H4u0ra2vWwIDnR3X21Lv5U192I92vj/b1KNMbV3YeP9N7dRcIBeB0T2a9+i6ErFSpy/VicAoDlMxJAIjAtPrzScVeJLrpsQYWFUCk/uqWMrOJ+BIFLTlIyD88rGKWYrA6B7Ne3Rdidip05fqRGAUh6kYEoERgen15pMKPMl1U2IMiMDkPYGJJZmSpREQgUljk6MRgdE9mvfouhKxVKcv1YnAKA5TMSQCIwLT680nFXiS66bEGBCBEYHJSagXtRGBWRSxWXsRGN2jeY+uKxE1dfpSnQiM4jAVQ1EC85a3vCVoFwaKAcXAUDHw5je/OXB/+umnA/Y3velN4ad+6qfCG9/4xvCTP/mT4Sd+4ifCj//4j4c3vOEN4cd+7MfCj/zIj4TXv/714cknnwzf/d3fHV1EU4sfb4ip17emIFcCPptQL9oSfosiNmsvAqPEkffJuhJRU6cv1aXu4bjnv+Y1rwk/+IM/WK0BTz31VNi5c2e1Lvzoj/5otUZgrcCagbUDawjWEqwpWFuwc63h2jPUeqdxFs+tYnGUJDCztzK1hIAQEAL9IPDo0aOA/eHDh+HBgwfh/v374d69e+Hu3bvhzp074fbt2+HWrVvVIomnNdeuXQuXLl0K586dCydPngxHjhwJu3btii6iqcWPN8MpEJXUHJSAl8Wb8CvHj9eJSpGZVAwgylK6LuSpezju+Xv37g0HDx6s1oATJ06E06dPV+vChQsXwuXLl8PVq1fDjRs3qvlhDcFagjUFawvWGKw1WHOw9nAdKrtq1LsPBED4YrFUS2CQIGgXBooBxUCfMXD27NmA/cyZM9UC9PLLLwfsICdYlF588cVw7P9v78pa5ai6aP6Gv8ef4LsGBEFFP+fhzSeffBAURBRxnvDBi6hBjFNi1JjcaIyJA0YF5xFERJzO52q/Rda3Pbvq1NC53X1XQd2zz95r71N1et/aZ3VVd7/3Xjl58mQ5ceJEOX78eNne3i6HDx8uBw8eLPv371+8A1e7wGXFj9iMPKyC3gvwaaXQ8zd9/vh/4tYEJssBZFlmm0OfXcNx531ra6vs27evvPDCC+XAgQMLQoO6cOTIkQWxQa1AzUDtOHXq1KKWoKagtpw+fXpRZ0B6UHtYh5ZZ63Zb7DlyAzFMYEzGTEadAyuZAywcO0FgsMj17jlwDtRzYI4FqGNsNvmZY5HalSMmMOv7BuocuWEC44XrSi5cd9u7ET7f+oV4JwnMKtxpyY7BdxBQusZvnr/xcwdPzF/XwtK2zSYmra8vcqUVOwZnAlOvm+uwnpgjNxDDd2BMYkxinAMrmQMmMP4WMhSpuTcTmGkzagJjgtJCOJBlLbixGBMYExgTGC9eV3Lxug7vIvgYl3sBNYExgZm21K57m8DU56VVawJjAtNCOpBPLbixGBOY5dbfZa5v5sgNxFg6gdmzZ8/gBfIYn7kme+zYY/3mOu6zFWeZ58nYbKee01xxuo5jzjE0FuTa3nUsY2w6Jvz7+mPGyHxqY8VzBonZic/AZI9vrYLeC3CUrvGb52/83MHTBMYEpoV0IFdacGMxJjD/rtdZra3VdmJjHa7pMwyxWZv5zZEbiGECE+7AZBOevUDUj/Wjv9sz/4xzzeVccc7Wa6PHq7KOn+kVM0Vednw9Nh1LZWJAXqA3gfn/OzFegKN0jd88f+PnDp4mMCYwLaQDudKCG4sxgTmzZmLN7GprNRZ46Gs21akcxxhj68uNvXv39uYOYpjAmMAMvkMWE3iuPv8R2E6NO1ecqcfR6q/Hq3L077JF7ND+MmPHY+FYbKOdj5DBjq+0xFcoY/fXKL+Ea7e3kTNgAjNy4v7nZgJjAtNCOpAuLbixGBOYs0dgUJuzOj1Uj1hduQHywr0rNxBjdgKDk+FeO2natOXCRXWUW2zE9LWMqS19VEeZtpYWPsBpm8WJGI1PH2LURrkPM8ReG0f9o137lBXPY4xtxNBXcdRpSz/FqUw7faIN/S5MtBPLOLW49NGWeOpiHNoZr2anjTHoE1vFEVuLRz/a2FJPX7a0s6Ve8dQpRu3Q92GIVyx12pLA6B0Y+IDAcHy0td+Bof2cc86pFtGs+PGCOdejYjyOWjt2jNoCHPGHbHo8md9cGI2fHWfLWBpnihznb8zYrT7Z+fL4W+LMheGYU1sTGBMYXie7WuRZl32qLbuG6+/A6P9O9jswitHfgYEetYd1iLUJ+igzBvVoa7qaXePRTh1jsK92tam9S09/benLlra+fobr08Oe5QaJi7ZZniDGrASmdsKqU5kniTbq+/r0jTjqa22GjfrYr8WKOvWBrH1ga/2oy3BdY7X4xHFiP8YYYgc24mM/xmc/4rQPWfv0Qat7H6YlTozB+NSzpZ4t9WyjvqsffYCNuthnvIiNuL7+VP+++LD3YeIx1PrQsXBEAoP4+kOW6OsPWaK/Kj9kiWMZS1QyP12AIz73RVVo+AO8brEPW9TFfismjjNHHI05Ro7zpzFqx6d2yBET+8RAX7MxXrTFPuMQX+vXdLU4GmOqbAJjApMtKlWPPNP+3HIfgcH/gf6QJfrxhyyh0x+yRJ8/ZAm5hcAAh3qldaymI0Zx1Cme9pqOeLZDMRHPsbQdE7srLuNpW8uNSFpiP+YPYsxGYGonUJsUPQnK0Tf2iYttKy76aT/GiH3FZrL6qKx41atMTE0Hm+pVpp9i+uyKVX+VsxjEqF1l2tGqXuUMM8anJW4LRo9JZfVVuetYI47xVK8y7TFmrU+s+qtMe/TNMMRHe1c/2moxWjDxGDUO/HUHiYkEpvYIGXzw68rb29sF77TtFgKDizg2nH/rFrF9fcbtw0U7/djCXsNEXezTf652CoHJjm0Z+paYLZi55o1xTGBMYOJistZHvtT0c+m6CAz+L/bt2/d/BAZ1Afpjx44tagVk3L1XAsO7+3hUGfYWAsP6xRZ+lNmqTmXa0ape5QwTfWp99c3sOlYmM47aNV6mp19sa7lBwqL5UdPRjhgmMCHZ4gsRJ77WVx+VFat6lYmp6WBTvcr00zazq15l9aU8xJ5hVa8yx0Ab9dpXOfOZC6PxVdb4KsdjV5vKrbGIi77o13bi43Fk+hhXcbUYEa99lTWO6lXOMLVxFUsZ5AXxTGD6P8SPeWrZMpzqVdaYqlc5w9T00S/26ZPpaZ/SksBkY2R6jJnZ5tLrebXEbMFozDlkExgTGC4iu1rkWpd9qm1ZBGboHRjWK7b4n6TMVnUq045W9SpnmOhT66tvZtexMplx1K7xMj39YlvLDZCVWk5kesQ46wQGJ1rb9QTjZHCi+vw0Rk2u+cexYr8WJ+rUR2XFqV5lYmo62FSvMv20hT3bu3BqgxxjqF2PQeUpGI7JGK1x43GyPyQOx6avtoxDjLa0AV+TqYs+is8w0UdxKg+J1XdejBtjal9l4tGqXuUME30UpzIfIUNMfogfcssdGOCwr8JnYPAoGI8HbfZoWKueC3BcwLkhbsuW4TK9xlSMyhmGesWqDHvs13yom6vl/I0dO/rFvh5nZqMeLXf1y2T6ZXbqW3HED2lNYExgagvNqENORd2c/XUmMPyfjy3rH/SUtY362Ac2i0mbxqvpGJNtho929tlGP+3PkRuIcVYJTHZiUd/X50REHPW1NsNGfezXYqku4mOfWNWrXLNTh1axKiuGcp+duNj2+ak9kzXmVIz6D42b4TN9y1jwJY4t42lfZdrVN8rE1PxqOuLZZphMX/OL2K5+tLXEG4Ihlu1YAoPjXKVHyHA8Sk5iX20tMhfguIBzQ8yWLcNlesaM9tjPcNArVuVoY4wuvWLGypy/eCyMl+lpRwsMd9VHOYtV8+3DZvbWMSNubN8ExgSmhYggv1pwYzHrTGBY47IW/+s1W9TH/hifGIN9tjEm9WxpZ58t9bV2jtxADBOYkCgtk68vSMTHPrGqV7lmpw6tYlWuYfrs6qNy5keM2jOZWLRTMeo/Z1zG0vgq0x7PgfoaVnUq0yfGqmFadRozxlVbLV5mj9iufrQxpupVph2t6lWONvXZFAJTIyWYg5q+RccFOC7g3BCvZctwmR4xa7aaroaNuL4+zyHiqJ+j5fxlY2R6jh3tsU8c2sw2VM+YmV+rnbgprQmMCUwL6UCOteDGYpZFYPA/tq6fgdH6SRnnQxltX5+YiOuKQVuXDzFo58gNxJiNwOCg4sGjrzqV9WSivq9P34ijvtZm2KiP/Vos1UU8+jVdlw9tfX7A9WGG2mPM6N9lr2EjPuurr8rE13Sw6d6HiXb6ql5l2tHW9C26iOnrDxlLj49yX/xoj+NF+9z92nHGMeIx0Sf7DAz81+VbyDJCgnPIbH16LsAXVeB/fxCvZctwy9DXYkZd7PMcMj3tU1rOXzZGpseYmW3Zej3foWOp7xyyCYwJTAvpQK614MZiugjM1tbW4n91yreQ4bFl/K/xjTTWKehYo1Qeoot+ff0sdotfHybaa+fJ8dnWfOhHTFc7R24gxqwEhieAk+MJsuXJ0KYt/YjJ+urDuGzVN5OjP33Z0i/2qa+1Ecs+Wu7Rj5ioR58+UzB9MdReG6fLrniV9Vxqeo3J86RPxLMffYjXtgvDOIqHHPUag3KG64tFP42jPnFs4hWT6SKGfY5Vi81YGabmozqVW8ar4XkM6k9ZW/jGHcWj9iF+xa3678DUCAmOv6Zv0XEBjgs4N8Rr2TJcTV/TcYzMpnrI2T4kDrFztZw/PVaNnemByWzL0A+JmWH1vOaSTWBMYFpIB/KtBTcW00dg8C1k+L/gPvR3YEBgUHvoj7allhEXa5v2GYexa7aoo0/Ux/EYk20LPmKysYiLY/bpaWc7R24gxuwEhge4G9rai1jT7Ya5GHqOnCe2Q/03Bb/bzn/I+fKdrxqB0TswNQKzSp+BqZESzENN36LjAhwXcG6I17pFbOwjTk0X40dM7Ed8Fjf6xX4tzhSdzl8cK/bjOJl9GfrWmBkuHvtcfRMYE5gW0oF8a8GNxbQQGL0DkxGY+DXK/BYyEhjWoU1Zc+A8cM3YyfOZIzcQwwTm7xdzzhdypxNjznNZZizP07x5t8zXaqdis3CsM4EBIUGuKzGJfbW1yLoAx0UcG2K2bhHb18/ijvGLPogddbGfjT9Wr/MXx4r92hgRE/vqM8RWw0ZdX1/HXpZsAmMC00I6kH8tuLEYE5j1XUMgN44ePTp6R85gM4ExgZmVwLUsdlGEW3DGrO8Fao7XblMIDEkM8h57C0npwugCfHEV//tPXNhSj7Zm47H02RTXh9UxM7kWg8fIsWq+0a+vX4tBXZw/jhtjEl/T9/l0+dKGtiVOF0ZtUY7jDOkrNsomMCYwLaQDedOCG4sxgVnf9QFywwRmZvIxx6ILRWSOOJsUIxbWTTo3n8vyLqKbRGC6CMlQW1yAoxh4a58Bz1/7XNWQJjAmMC2kA7nTghuLMYFZXu1d9roGuWECs4IEZtkvvOOv7z+tX7thr12NwODrLWs/ZHnixIly/Pjxsr29XfCs86p8BmYoOWnBewGO8jd+8/yNnzt4msCYwLSQDuRKC24sxgRmWD1dpfUHcsMExgTGd3ucAxubAyYw91YfN/MCHOVv/Ob5Gz938DSBMYFpIR3IlRbcWIwJjAmMPwPjBfDGLoBX6R0HH8vwi60JjAkMFkFzbyYw02bUBMYEpoV0IMtacGMxJjDDa+qqrEOQG74DY/Jh8uEc2NgcMIExgUGhm3szgZk2oyYwJjAtpANZ1oIbizGBMYHxHRgvgDd2Abwq7zb4OMZdaE1gTGCmLbXr3iYw9Xlp1ZrAmMC0kA7kUwtuLMYEZlxdXYX1CHLDd2BMPkw+nAMbmwM7SWCwSPPuOXAO1HNg7KLTfruH/JjArC/BWDbJMYHxwnVjF67L/udx/PW4sO4kgWn5NrCdwmBR7W38DHj+xs8dPDF/JiK7h4iMfa2RK2N9W/x8B2Y96nhtvYXc8B0YkxiTGOfAxuaACYwfIUOhm3szgZk2oyYwJi8tBANZ1oIbizGBMYHxZ2C8AN7YBXCN+Vu3Phc9ExgTmGlL7bq3CUx9Xlq1JjAmMC2kA/nUghuLMYFZn1oe113IDd+BMfkw+XAObGwOmMCYwKDQzb2ZwEybURMYE5gW0oEsa8GNxZjAmMD4DowXwBu7AI6s3/31uuCZwJjATFtq171NYOrz0qo1gTGBaSEdyKcW3FiMCcx61XNdfyE3fAfG5MPkwzmwsTlgAmMCg0I392YCM21GTWBMYFpIB7KsBTcWYwJjAuM7MF4Ab+wCWBm/5fW72G0SgdmzZ0/hPvXby2oLcMQesvFYuvxaMDpmVyziMszQsRhvTBvnb8zYXT5qi3I8XrV32RQHOW5qj7a5+yYwJjAtpAN514IbizGBWb+aznUYcsN3YEw+TD6cAxubA5tCYLC4VNIS+2prkXUBPmbhCh/dYh+2qIt99Sd+LCb6xX4ca2o/zp/Gaxk7YmJf46kccUP7GkvlvjiKnUM2gTGBaSEdyLUW3FiMCYwJjO/AeAG8sQtgsn2363mh2wQCg8VljZRk+ho26nQBzgVpXMRSH9sMp3qV1T/TAwNblz3DZD6ZXo9nrKzzF8eJ/ThGZs/06q8YlYdiMnyLXjFjZRMYE5gW0oH8asGNxZjArGddx3psjtxADBMYExgTGOfASubAJhOYSEqG9HUBvqgEf//JFsS0s81wqleZfmj79JldfSMm9jlepqd9Ssv5y8bI9Hoecfwun5pfhle9ynE89jNMpqfflNYExgSmhXQgxyJu7969/9IBk+mjv/ZNYExgTGC8eF3JxavvmqzvxWmu126TCAwWlNyHkJUalgtwXYROXbC2+NcwqlM5O7YaJupiX2PNIXP+snEyPcbObJmexxvtsV/DEYOWO3FsiWGfbaanfUprAmMCo0Qik5Fj0QaiEslKTRf9an0TmPVdI9Ryo/Yad+kQwwTGBMYExjmwkjmwKQQGi0klIrGvthaZC3BcwLlNWbC2+GYY1avM40KrepUjBrbMrtipMucvGyvTY9zMlukznwyvesjar8WKdmCwZfp/rNP+msCYwHQtLGlDllFmS7JCEhP7xLW0JjAmMCYwXryu5OJ1rnfxHWd9L3KbQmBqpAQLzJq+RccFuC5Dxy5YW/wyTNTHPo4v6mK/FaPnOlXm/NWOpXY8cbzoh37UqU9mi/oYJ9oZM+pr/aij7xytCYwJTAvBQK7VcEpaKNdwfToTmPWt7Vlu9L3makcMExgTGBMY58BK5oAJzL1VksMFOC7g3MYsWFt8MkxNH3Wxj2ONutifcj707Ws5f1PGhi93jJfFarG1xuF51cZiDNrY0mfO1gTGBEYXkpmMnMtsJC5oM0yf3gTGBMYExovXlVy8+s7J+l6c5nrtTGCWR2BaFrhdGNiynYvlzK5xVaYf2kyvmLHyHAQmjp0db6aP/uwrXmXa0Wb6oRjFD5FNYExg+sgF7Ni6cFPIC+KawKzvGqGWG1k+ZHrEMIExgTGBcQ6sZA6YwCyHwCxrATwmbuaT6VG0pm4kMIgTx4n9OFZmX4a+JWYLJp7D1L4JjAlMFzGhDXlGeRmtCczmEZhIVtCPOuYS8ssExovXlVy8zvUuvuOs70VuEwgMPtOCRaZ+tiX21dYi6wIcF3Fs2UL2H+uZvy24FsyZiGekFr8aJupi/8wI80g6f3Gs2K+NGDGxrz5DbDVs1MU+xoq62NfjmUM2gTGB4SKyq0Wuddmn2kxg1re213KDZIWEJfZjviCGCYwJjAmMc2Alc2BTCAxJDBaW2FtIShdGF+C4iGPrWrSqjcdQa/+J9E+sml3jEKttnx3YDKPjaUzK0a+vT79aG+dv6NiI2efDceNxUs+2Jc4cmHgcfX0eX601gTGBiYvJWh+5U9PPpTOB2SwCg7xQ0kI5yxfklwmMF68ruXj1nZP1vTjN9dptEoHpIiRDbXEBjgu5t/YZ8Py1z1UNaQJjApMtKlWP3NH+3LIJzPquEbpyg8QFbVfOIIYJjAmMCYxzYCVzwASm/TMwuJh7a5sBE5i2ecpQJjAmMF0LS9qQP5SX0ZrAbCaBQa70kRdgsJnAePG6kovXud7Fd5z1vcjtJIFZRsF1TC/8nAPOAefAPDlgArO+tR3kY+r/gQmMyYvJi3NgZXPABGaeQj+1UNjfr4NzwDmwajnQSmBeeeWV8tprr5XDhw+XI0eOlGPHjpXjx4+XEydOlJMnT5ZTp06V999/v3z44Yflo48+KqdPny4ff/xx+eSTT8qnn35aWIf8Zuh8hMkExgvPlV14+h99vn/03TyXLBwoIigmKCrYUWRQbFB03nvvvUURQjFCUdre3l4UqoMHD5b9+/eXiy66qPpOT1b8Vq1I+3i8cHQOOAecA//Ogewajmv+1tZWefbZZxc1AATm0KFD5Y033jCBWZF1swnMirwQu3mB6XM3UVlmDpjA/LtoeyHjOXEOOAecA/0/ZNlFYN5+++3yzjvv+A7MDq2jTWB2aOKXuWBzbBMC58CZHDCB8ULNCzXngHPAOVDLAd+BOVMr123dsKMEBoN78wx4BjwDy5yBv/76q2D/888/yx9//FF+//338ttvv5Vff/21/PLLL+Xnn38uP/300+IRsR9//LF899135auvvlo8WonHzPBY2RVXXOFHyP7+xpbaAsA6z4tzwDmwrjmQERhc8/GZF3zW5d133y0ffPDB4tFjLPK/+OKL8vXXX5dvv/22/PDDD4vrImoIaglqCmoLagxqDWoOag/r0DJrnWOPm4HB30IGB++eA+eAc+Bs5MCNN95YuN9www0F+/XXX1+uu+66cu2115ZrrrmmXH311eWqq64qV155ZfnPf/5TLrvssnLJJZcsPv9y7rnnVhfvKH7ePQfOAeeAc2B9c6BGvnDNv+CCC8qFF164qAEXX3xxufTSSxd14fLLL1/UCNQK1AzUDtQQ1BLUFNQW7Kw1rD1no9Z5jHFrqloO7KkprfO7Nc4B58DZygHcVcGOd8q+//77xR0WvHP2zTffLN5F+/LLL8vnn3++uOOCD/p//PcH/PHhfnyzzFtvvbX40CYK19k6Xo/j/w3ngHPAObCzOYBrPr7ABV/kgm8ew50Yft4FX/wCwopvGsMXw+CuDGoIagnu3vPuDO7mo+ag9rAO+XXd2dd1yPybwPixCy/8nAM7mgMsHCYw61M4hhQZY/26OgecA3PngBKYN998c/FmFr8ymQQGb3bhTS8TmM3MPxMYL153dPE690XN8dbvQjWUwOAdNby7hu/1x7fM4N0334FZv9fd/6t+zZwDzoGxOYBr/osvvrj46mT87gtqAT4Lg7qAz8Pg85EZgcHdfdzl9x2Y9c4/ExgTGBMY58CO5kAfgcEtf3wo87PPPlv82BgIDIoTfhsGjwygeJnArHchGruIsZ9fd+fA7swBXPNfeumlxQf5jx49uvh9MDxWjLqAR4z5+Bi+5RK1AzUEtQS7Ccxm5IwJjBevO7p4dfHZjAvJ1NcRJAaPkPExMn4GBoUmEhg8EoDihHfZ8MgAftTSBMZ5NDUH7e8ccg6sTw7gmo8fr8QPV/LzL7j7oo+P4c2uSGDw+RcSGH7+hZ+B8eu/Pq8/Xqs9jz/+ePHuOXAOOAd2Mgcee+yxwv3RRx8tjzzySHn44YfLQw89VB588MHywAMPlPvvv7/cd9995d577y333HNPueuuu8odd9xRbrvttnLeeeeZiPvNGOeAc8A5sEtyANf8W265pdx6663l9ttvX9SCO++8c1EX7r777kWNQK1AzUDtQA1BLcGOuoIdNUbrDGysM6wxN910k9fIK8oT9oCd8ht9jh8/vmCyeCQDz5VjB7v17jlwDjgHlpkDr7/+esGO7/XHfujQofLqq68uvmHmwIED5eWXX148LoBnnl944YXy/PPPl3379pWnn366bG1tlfPPP9+758A54BxwDuyiHMCbXU888UR58skny1NPPVWeeeaZRV147rnnFjUC31KGmoFHzVBDcMcGO2oKd+ixA4cdtQX+iIXacvPNN3sNvEM8gDwEX9IAXoJHBfHEBb59FI+P/xfyijGGBgqGuwAAAABJRU5ErkJggg==" width = "483" height = "413" alt = "" style = "vertical-align: baseline; width: 483px; height: 413px;"></img></div><h2 class = 'S4' id = 'H_9060' ><span>Read</span></h2><div class = 'S5'><span>We can then read the file back in using MatNWB and inspect its contents. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S19'><span style="white-space: pre"><span >read_nwbfile = nwbRead(</span><span style="color: #a709f5;">'intro_tutorial.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >)</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="EDDA4B70" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="788" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">read_nwbfile = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> NwbFile with properties:
nwb_version: '2.8.0'
file_create_date: [1×1 types.untyped.DataStub]
identifier: 'Mouse5_Day3'
session_description: 'mouse in open exploration'
session_start_time: [1×1 types.untyped.DataStub]
timestamps_reference_time: [1×1 types.untyped.DataStub]
acquisition: [0×1 types.untyped.Set]
analysis: [0×1 types.untyped.Set]
general: [0×1 types.untyped.Set]
general_data_collection: ''
general_devices: [0×1 types.untyped.Set]
general_experiment_description: ''
general_experimenter: [1×1 types.untyped.DataStub]
general_extracellular_ephys: [0×1 types.untyped.Set]
general_extracellular_ephys_electrodes: []
general_institution: 'University of My Institution'
general_intracellular_ephys: [0×1 types.untyped.Set]
general_intracellular_ephys_experimental_conditions: []
general_intracellular_ephys_filtering: ''
general_intracellular_ephys_intracellular_recordings: []
general_intracellular_ephys_repetitions: []
general_intracellular_ephys_sequential_recordings: []
general_intracellular_ephys_simultaneous_recordings: []
general_intracellular_ephys_sweep_table: []
general_keywords: ''
general_lab: ''
general_notes: ''
general_optogenetics: [0×1 types.untyped.Set]
general_optophysiology: [0×1 types.untyped.Set]
general_pharmacology: ''
general_protocol: ''
general_related_publications: [1×1 types.untyped.DataStub]
general_session_id: 'session_1234'
general_slices: ''
general_source_script: ''
general_source_script_file_name: ''
general_stimulus: ''
general_subject: [1×1 types.core.Subject]
general_surgery: ''
general_virus: ''
general_was_generated_by: [1×1 types.untyped.DataStub]
intervals: [1×1 types.untyped.Set]
intervals_epochs: []
intervals_invalid_times: []
intervals_trials: [1×1 types.core.TimeIntervals]
processing: [1×1 types.untyped.Set]
scratch: [0×1 types.untyped.Set]
stimulus_presentation: [1×1 types.untyped.Set]
stimulus_templates: [0×1 types.untyped.Set]
units: []
</div></div></div></div></div></div><div class = 'S14'><span>We can print the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> data traversing the hierarchy of objects. The processing module called </span><span style=' font-family: monospace;'>'behavior'</span><span> contains our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object named </span><span style=' font-family: monospace;'>'Position'</span><span>. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object contains our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object named </span><span style=' font-family: monospace;'>'SpatialSeries'</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S19'><span style="white-space: pre"><span >read_spatial_series = read_nwbfile.processing.behavior.Position.SpatialSeries</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="4DBF56E6" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="300" data-hashorizontaloverflow="false" style="max-height: 311px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">read_spatial_series = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> SpatialSeries with properties:
reference_frame: '(0,0) is bottom left corner'
starting_time_unit: 'seconds'
timestamps_interval: 1
timestamps_unit: 'seconds'
data: [1×1 types.untyped.DataStub]
data_unit: 'meters'
comments: 'no comments'
control: []
control_description: ''
data_continuity: ''
data_conversion: 1
data_offset: 0
data_resolution: -1
description: 'no description'
starting_time: 0
starting_time_rate: 200
timestamps: []
</div></div></div></div></div></div><h3 class = 'S16' id = 'H_2a80' ><span>Reading Data</span></h3><div class = 'S5'><span>Counter to normal MATLAB workflow, data arrays are read passively from the file. Calling </span><span style=' font-weight: bold; font-family: monospace;'>read_spatial_series.data</span><span> does not read the data values, but presents a </span><span style=' font-weight: bold; font-family: monospace;'>DataStub</span><span> object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="0213EC81" prevent-scroll="true" data-testid="output_5" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="123" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> DataStub with properties:
filename: 'intro_tutorial.nwb'
path: '/processing/behavior/Position/SpatialSeries/data'
dims: [2 100]
ndims: 2
dataType: 'double'
</div></div></div></div></div></div><div class = 'S14'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access all the data in the matrix using the </span><span style=' font-weight: bold; font-family: monospace;'>load</span><span> method with no arguments. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data.load</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="F964EB7D" prevent-scroll="true" data-testid="output_6" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2×100</span></div></div><div class="valueContainer" data-layout="{"columnWidth":73,"totalColumns":"100","totalRows":"2","charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 1024px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> 0 0.1010 0.2020 0.3030 0.4040 0.5051 0.6061 0.7071 0.8081 0.9091 1.0101 1.1111 1.2121 1.3131 1.4141 1.5152 1.6162 1.7172 1.8182 1.9192 2.0202 2.1212 2.2222 2.3232 2.4242 2.5253 2.6263 2.7273 2.8283 2.9293 3.0303 3.1313 3.2323 3.3333 3.4343 3.5354 3.6364 3.7374 3.8384 3.9394 4.0404 4.1414 4.2424 4.3434 4.4444 4.5455 4.6465 4.7475 4.8485 4.9495
0 0.0808 0.1616 0.2424 0.3232 0.4040 0.4848 0.5657 0.6465 0.7273 0.8081 0.8889 0.9697 1.0505 1.1313 1.2121 1.2929 1.3737 1.4545 1.5354 1.6162 1.6970 1.7778 1.8586 1.9394 2.0202 2.1010 2.1818 2.2626 2.3434 2.4242 2.5051 2.5859 2.6667 2.7475 2.8283 2.9091 2.9899 3.0707 3.1515 3.2323 3.3131 3.3939 3.4747 3.5556 3.6364 3.7172 3.7980 3.8788 3.9596
</div><div class="horizontalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" tabindex="-1" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div class = 'S14'><span>If you only need a section of the data, you can read only that section by indexing the </span><span style=' font-weight: bold; font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data(:, 1:10)</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="D2E3B9B4" prevent-scroll="true" data-testid="output_7" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2×10</span></div></div><div class="valueContainer" data-layout="{"columnWidth":73,"totalColumns":"10","totalRows":"2","charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 732px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> 0 0.1010 0.2020 0.3030 0.4040 0.5051 0.6061 0.7071 0.8081 0.9091
0 0.0808 0.1616 0.2424 0.3232 0.4040 0.4848 0.5657 0.6465 0.7273
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" tabindex="-1" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h2 class = 'S4' id = 'H_0b60' ><span>Next Steps</span></h2><div class = 'S5'><span>This concludes the introductory tutorial. Please proceed to one of the specialized tutorials, which are designed to follow this one.</span></div><ul class = 'S12'><li class = 'S13'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li class = 'S13'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li><li class = 'S13'><a href = "./ophys.html"><span style=' text-decoration: underline;'>Optical physiology</span></a></li></ul><div class = 'S5'><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></div><div class = 'S5'></div>
<br>
<!--
##### SOURCE BEGIN #####
%% Introduction to MatNWB
%% Installing MatNWB
% Use the code below within the brackets to install MatNWB from source. MatNWB
% works by automatically creating API classes based on the schema.
%{
!git clone https://github.com/NeurodataWithoutBorders/matnwb.git
addpath(genpath(pwd));
%}
%% Set up the NWB File
% An NWB file represents a single session of an experiment. Each file must have
% a session_description, identifier, and session start time. Create a new <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html
% |*NWBFile*|> object with those and additional metadata using the <https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html
% |*NwbFile*|> command. For all MatNWB classes and functions, we use the Matlab
% method of entering keyword argument pairs, where arguments are entered as name
% followed by value. Ellipses are used for clarity.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'general_experimenter', 'Last, First', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
%% Subject Information
% You can also provide information about your subject in the NWB file. Create
% a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html
% |*Subject*|> object to store information such as age, species, genotype, sex,
% and a freeform description. Then set |*nwb.general_subject*| to the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html
% |*Subject*|> object.
%
%
%
% Each of these fields is free-form, so any values will be valid, but here are
% our recommendations:
%%
% * For |age|, we recommend using the <https://en.wikipedia.org/wiki/ISO_8601#Durations
% ISO 8601 Duration format>
% * For |species|, we recommend using the formal latin binomal name (e.g. mouse
% -> _Mus musculus_, human -> _Homo sapiens_)
% * For |sex|, we recommend using F (female), M (male), U (unknown), and O (other)
subject = types.core.Subject( ...
'subject_id', '001', ...
'age', 'P90D', ...
'description', 'mouse 5', ...
'species', 'Mus musculus', ...
'sex', 'M' ...
);
nwb.general_subject = subject;
subject
%%
% Note: the DANDI archive requires all NWB files to have a subject object with
% subject_id specified, and strongly encourages specifying the other fields.
%% Time Series Data
% <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|> is a common base class for measurements sampled over time, and
% provides fields for |data| and |timestamps| (regularly or irregularly sampled).
% You will also need to supply the |name| and |unit| of measurement (<https://en.wikipedia.org/wiki/International_System_of_Units
% SI unit>).
%
%
%
% For instance, we can store a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|> data where recording started |0.0| seconds after |start_time|
% and sampled every second (1 Hz):
time_series_with_rate = types.core.TimeSeries( ...
'description', 'an example time series', ...
'data', linspace(0, 100, 10), ...
'data_unit', 'm', ...
'starting_time', 0.0, ...
'starting_time_rate', 1.0);
%%
% For irregularly sampled recordings, we need to provide the |timestamps| for
% the |data|:
time_series_with_timestamps = types.core.TimeSeries( ...
'description', 'an example time series', ...
'data', linspace(0, 100, 10), ...
'data_unit', 'm', ...
'timestamps', linspace(0, 1, 10));
%%
% The <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|> class serves as the foundation for all other time series types
% in the NWB format. Several specialized subclasses extend the functionality of
% <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|>, each tailored to handle specific kinds of data. In the next
% section, we’ll explore one of these specialized types. For a full overview,
% please check out the <https://nwb-schema.readthedocs.io/en/latest/format.html#type-hierarchy
% type hierarchy> in the NWB schema documentation.
%% Other Types of Time Series
% As mentioned previously, there are many subtypes of <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|> in MatNWB that are used to store different kinds of data. One
% example is <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html
% |*AnnotationSeries*|>, a subclass of <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|> that stores text-based records about the experiment. Similar
% to our <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|> example above, we can create an <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html
% |*AnnotationSeries*|> object with text information about a stimulus and add
% it to the stimulus_presentation group in the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html
% |*NWBFile*|>. Below is an example where we create an AnnotationSeries object
% with annotations for airpuff stimuli and add it to the NWBFile.
% Create an AnnotationSeries object with annotations for airpuff stimuli
annotations = types.core.AnnotationSeries( ...
'description', 'Airpuff events delivered to the animal', ...
'data', {'Left Airpuff', 'Right Airpuff', 'Right Airpuff'}, ...
'timestamps', [1.0, 3.0, 8.0] ...
);
% Add the AnnotationSeries to the NWBFile's stimulus group
nwb.stimulus_presentation.add('Airpuffs', annotations)
%% Behavior
% SpatialSeries and Position
% Many types of data have special data types in NWB. To store the spatial position
% of a subject, we will use the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html
% |*SpatialSeries*|> and <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html
% |*Position*|> classes.
%
%
%
% Note: These diagrams follow a standard convention called "UML class diagram"
% to express the object-oriented relationships between NWB classes. For our purposes,
% all you need to know is that an open triangle means "extends" (i.e., is a specialized
% subtype of), and an open diamond means "is contained within." Learn more about
% class diagrams on <https://en.wikipedia.org/wiki/Class_diagram the wikipedia
% page>.
%
% <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html
% |*SpatialSeries*|> is a subclass of <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html
% |*TimeSeries*|>, a common base class for measurements sampled over time, and
% provides fields for data and time (regularly or irregularly sampled). Here,
% we put a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html
% |*SpatialSeries*|> object called |'SpatialSeries'| in a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html
% |*Position*|> object. If the data is sampled at a regular interval, it is recommended
% to specify the |starting_time| and the sampling rate (|starting_time_rate|),
% although it is still possible to specify |timestamps| as in the |*time_series_with_timestamps*|
% example above.
% create SpatialSeries object
spatial_series_ts = types.core.SpatialSeries( ...
'data', [linspace(0,10,100); linspace(0,8,100)], ...
'reference_frame', '(0,0) is bottom left corner', ...
'starting_time', 0, ...
'starting_time_rate', 200 ...
);
% create Position object and add SpatialSeries
position = types.core.Position('SpatialSeries', spatial_series_ts);
%%
% NWB differentiates between raw, _acquired_ data, which should never change,
% and _processed_ data, which are the results of preprocessing algorithms and
% could change. Let's assume that the animal's position was computed from a video
% tracking algorithm, so it would be classified as processed data. Since processed
% data can be very diverse, NWB allows us to create processing modules, which
% are like folders, to store related processed data or data that comes from a
% single algorithm.
%
% Create a processing module called "behavior" for storing behavioral data in
% the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html
% |*NWBFile*|> and add the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html
% |*Position*|> object to the module.
% create processing module
behavior_module = types.core.ProcessingModule('description', 'contains behavioral data');
% add the Position object (that holds the SpatialSeries object) to the module
% and name the Position object "Position"
behavior_module.add('Position', position);
% add the processing module to the NWBFile object, and name the processing module "behavior"
nwb.processing.add('behavior', behavior_module);
% Trials
% Trials are stored in a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html
% |*TimeIntervals*|> object which is a subclass of <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html
% |*DynamicTable*|>. <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html
% |*DynamicTable*|> objects are used to store tabular metadata throughout NWB,
% including for trials, electrodes, and sorted units. They offer flexibility for
% tabular data by allowing required columns, optional columns, and custom columns.
%
%
%
% The trials <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html
% |*DynamicTable*|> can be thought of as a table with this structure:
%
%
%
% Trials are stored in a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html
% |*TimeIntervals*|> object which subclasses <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html
% |*DynamicTable*|>. Here, we are adding |'correct'|, which will be a logical
% array.
trials = types.core.TimeIntervals( ...
'colnames', {'start_time', 'stop_time', 'correct'}, ...
'description', 'trial data and properties');
trials.addRow('start_time', 0.1, 'stop_time', 1.0, 'correct', false)
trials.addRow('start_time', 1.5, 'stop_time', 2.0, 'correct', true)
trials.addRow('start_time', 2.5, 'stop_time', 3.0, 'correct', false)
trials.toTable() % visualize the table
nwb.intervals_trials = trials;
% If you have multiple trials tables, you will need to use custom names for
% each one:
nwb.intervals.add('custom_intervals_table_name', trials);
%% Write
% Now, to write the NWB file that we have built so far:
nwbExport(nwb, 'intro_tutorial.nwb')
%%
% We can use the <https://www.hdfgroup.org/downloads/hdfview/ HDFView> application
% to inspect the resulting NWB file.
%
%
%% Read
% We can then read the file back in using MatNWB and inspect its contents.
read_nwbfile = nwbRead('intro_tutorial.nwb', 'ignorecache')
%%
% We can print the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html
% |*SpatialSeries*|> data traversing the hierarchy of objects. The processing
% module called |'behavior'| contains our <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html
% |*Position*|> object named |'Position'|. The <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html
% |*Position*|> object contains our <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html
% |*SpatialSeries*|> object named |'SpatialSeries'|.
read_spatial_series = read_nwbfile.processing.behavior.Position.SpatialSeries
% Reading Data
% Counter to normal MATLAB workflow, data arrays are read passively from the
% file. Calling |*read_spatial_series.data*| does not read the data values, but
% presents a |*DataStub*| object that can be indexed to read data.
read_spatial_series.data
%%
% This allows you to conveniently work with datasets that are too large to fit
% in RAM all at once. Access all the data in the matrix using the |*load*| method
% with no arguments.
read_spatial_series.data.load
%%
% If you only need a section of the data, you can read only that section by
% indexing the |*DataStub*| object like a normal array in MATLAB. This will just
% read the selected region from disk into RAM. This technique is particularly
% useful if you are dealing with a large dataset that is too big to fit entirely
% into your available RAM.
read_spatial_series.data(:, 1:10)
%% Next Steps
% This concludes the introductory tutorial. Please proceed to one of the specialized
% tutorials, which are designed to follow this one.
%%
% * <./ecephys.mlx Extracellular electrophysiology>
% * <./icephys.mlx Intracellular electrophysiology>
% * <./ophys.mlx Optical physiology>
%%
% See the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html
% API documentation> to learn what data types are available.
%
%
##### SOURCE END #####
-->
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