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| author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-03-21 17:19:04 +0000 |
|---|---|---|
| committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-03-21 17:19:04 +0000 |
| commit | 310edf444908b09ea6d00c03baceb7925f3bb7a2 (patch) | |
| tree | 7064577c7fa7a851e2e930beb606ea8237b0bbd2 /docs | |
| parent | Releasing debian version 1.44.3-2. (diff) | |
| download | netdata-310edf444908b09ea6d00c03baceb7925f3bb7a2.tar.xz netdata-310edf444908b09ea6d00c03baceb7925f3bb7a2.zip | |
Merging upstream version 1.45.0.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'docs')
120 files changed, 5149 insertions, 739 deletions
diff --git a/docs/Running-behind-apache.md b/docs/Running-behind-apache.md index 045bb676..6fa119c6 100644 --- a/docs/Running-behind-apache.md +++ b/docs/Running-behind-apache.md @@ -342,7 +342,7 @@ If your apache server is not on localhost, you can set: *note: Netdata v1.9+ support `allow connections from`* -`allow connections from` accepts [Netdata simple patterns](https://github.com/netdata/netdata/blob/master/libnetdata/simple_pattern/README.md) to match against the connection IP address. +`allow connections from` accepts [Netdata simple patterns](https://github.com/netdata/netdata/blob/master/src/libnetdata/simple_pattern/README.md) to match against the connection IP address. ## Prevent the double access.log diff --git a/docs/Running-behind-h2o.md b/docs/Running-behind-h2o.md index deadc91c..7e4601ae 100644 --- a/docs/Running-behind-h2o.md +++ b/docs/Running-behind-h2o.md @@ -105,7 +105,7 @@ Using the above, you access Netdata on the backend servers, like this: ### Encrypt the communication between H2O and Netdata -In case Netdata's web server has been [configured to use TLS](https://github.com/netdata/netdata/blob/master/web/server/README.md#enabling-tls-support), it is +In case Netdata's web server has been [configured to use TLS](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#enabling-tls-support), it is necessary to specify inside the H2O configuration that the final destination is using TLS. To do this, change the `http://` on the `proxy.reverse.url` line in your H2O configuration with `https://` @@ -173,7 +173,7 @@ If your H2O server is not on localhost, you can set: *note: Netdata v1.9+ support `allow connections from`* -`allow connections from` accepts [Netdata simple patterns](https://github.com/netdata/netdata/blob/master/libnetdata/simple_pattern/README.md) to match against +`allow connections from` accepts [Netdata simple patterns](https://github.com/netdata/netdata/blob/master/src/libnetdata/simple_pattern/README.md) to match against the connection IP address. ## Prevent the double access.log diff --git a/docs/Running-behind-nginx.md b/docs/Running-behind-nginx.md index 842a9c32..2cda4ab2 100644 --- a/docs/Running-behind-nginx.md +++ b/docs/Running-behind-nginx.md @@ -164,7 +164,7 @@ Using the above, you access Netdata on the backend servers, like this: ### Encrypt the communication between Nginx and Netdata -In case Netdata's web server has been [configured to use TLS](https://github.com/netdata/netdata/blob/master/web/server/README.md#enabling-tls-support), it is +In case Netdata's web server has been [configured to use TLS](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#enabling-tls-support), it is necessary to specify inside the Nginx configuration that the final destination is using TLS. To do this, please, append the following parameters in your `nginx.conf` @@ -236,7 +236,7 @@ If your Nginx server is not on localhost, you can set: *note: Netdata v1.9+ support `allow connections from`* -`allow connections from` accepts [Netdata simple patterns](https://github.com/netdata/netdata/blob/master/libnetdata/simple_pattern/README.md) to match against the +`allow connections from` accepts [Netdata simple patterns](https://github.com/netdata/netdata/blob/master/src/libnetdata/simple_pattern/README.md) to match against the connection IP address. ## Prevent the double access.log diff --git a/docs/anonymous-statistics.md b/docs/anonymous-statistics.md index f84989e1..0db002f7 100644 --- a/docs/anonymous-statistics.md +++ b/docs/anonymous-statistics.md @@ -68,7 +68,7 @@ Starting with v1.21, we additionally collect information about: - Failures to build the dependencies required to use Cloud features. - Unavailability of Cloud features in an agent. -- Failures to connect to the Cloud in case the [connection process](https://github.com/netdata/netdata/blob/master/claim/README.md) has been completed. This includes error codes +- Failures to connect to the Cloud in case the [connection process](https://github.com/netdata/netdata/blob/master/src/claim/README.md) has been completed. This includes error codes to inform the Netdata team about the reason why the connection failed. To see exactly what and how is collected, you can review the script template `daemon/anonymous-statistics.sh.in`. The diff --git a/docs/category-overview-pages/accessing-netdata-dashboards.md b/docs/category-overview-pages/accessing-netdata-dashboards.md index 97df8b83..af7b0df8 100644 --- a/docs/category-overview-pages/accessing-netdata-dashboards.md +++ b/docs/category-overview-pages/accessing-netdata-dashboards.md @@ -35,4 +35,4 @@ Netdata starts a web server for its dashboard at port `19999`. Open up your web navigate to `http://NODE:19999`, replacing `NODE` with the IP address or hostname of your Agent. If installed on localhost, you can access it through `http://localhost:19999`. -Documentation for previous Agent dashboard can still be found [here](https://github.com/netdata/netdata/blob/master/web/gui/README.md).
\ No newline at end of file +Documentation for previous Agent dashboard can still be found [here](https://github.com/netdata/netdata/blob/master/src/web/gui/README.md).
\ No newline at end of file diff --git a/docs/category-overview-pages/developer-and-contributor-corner.md b/docs/category-overview-pages/developer-and-contributor-corner.md new file mode 100644 index 00000000..d4d86382 --- /dev/null +++ b/docs/category-overview-pages/developer-and-contributor-corner.md @@ -0,0 +1,3 @@ +# Developer and Contributor Corner + +In this section of our Documentation you will find more advanced information, suited for developers and contributors alike.
\ No newline at end of file diff --git a/docs/category-overview-pages/installation-overview.md b/docs/category-overview-pages/installation-overview.md index e60dd442..703ca26b 100644 --- a/docs/category-overview-pages/installation-overview.md +++ b/docs/category-overview-pages/installation-overview.md @@ -1,7 +1,7 @@ # Installation In this category you can find instructions on all the possible ways you can install Netdata on the -[supported platforms](https://github.com/netdata/netdata/blob/master/packaging/PLATFORM_SUPPORT.md). +[supported platforms](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/versions-and-platforms.md). If this is your first time using Netdata, we recommend that you first start with the [quick installation guide](https://github.com/netdata/netdata/edit/master/packaging/installer/README.md) and then diff --git a/docs/category-overview-pages/integrations-overview.md b/docs/category-overview-pages/integrations-overview.md index 6fa2f50a..afd4cf30 100644 --- a/docs/category-overview-pages/integrations-overview.md +++ b/docs/category-overview-pages/integrations-overview.md @@ -13,19 +13,19 @@ sidebar_position: 60 Netdata's ability to monitor out of the box every potentially useful aspect of a node's operation is unparalleled. But Netdata also provides out of the box, meaningful charts and alerts for hundreds of applications, with the ability to be easily extended to monitor anything. See the full list of Netdata's capabilities and how you can extend them in the -[supported collectors list](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md). +[supported collectors list](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md). Our out of the box alerts were created by expert professionals and have been validated on the field, countless times. Use them to trigger [alert notifications](https://github.com/netdata/netdata/blob/master/docs/monitor/enable-notifications.md) either centrally, via the [Cloud alert notifications](https://github.com/netdata/netdata/blob/master/docs/cloud/alerts-notifications/notifications.md) , or by configuring individual -[agent notifications](https://github.com/netdata/netdata/blob/master/health/notifications/README.md). +[agent notifications](https://github.com/netdata/netdata/blob/master/src/health/notifications/README.md). We designed Netdata with interoperability in mind. The Agent collects thousands of metrics every second, and then what you do with them is up to you. You can -[store metrics in the database engine](https://github.com/netdata/netdata/blob/master/database/README.md), +[store metrics in the database engine](https://github.com/netdata/netdata/blob/master/src/database/README.md), or send them to another time series database for long-term storage or further analysis using -Netdata's [exporting engine](https://github.com/netdata/netdata/edit/master/exporting/README.md). +Netdata's [exporting engine](https://github.com/netdata/netdata/edit/master/src/exporting/README.md). diff --git a/docs/category-overview-pages/machine-learning-and-assisted-troubleshooting.md b/docs/category-overview-pages/machine-learning-and-assisted-troubleshooting.md index 074051e3..9a0e4b38 100644 --- a/docs/category-overview-pages/machine-learning-and-assisted-troubleshooting.md +++ b/docs/category-overview-pages/machine-learning-and-assisted-troubleshooting.md @@ -1,3 +1,97 @@ -# Machine Learning and Assisted Troubleshooting Overview +# Machine Learning and Anomaly Detection -This section contains documentation regarding Netdata's troubleshooting and machine learning features.
\ No newline at end of file +Machine learning (ML) is a subfield of Artificial Intelligence (AI) that enables computers to learn and improve from experience without being explicitly programmed. + +In observability, machine learning can be used to detect patterns and anomalies in large datasets, enabling users to identify potential issues before they become critical. + +Machine Learning for observability is usually misunderstood, and frequently leads to unrealistic expectations. Check for example the [presentation Google gave at SreCON19](https://www.usenix.org/conference/srecon19emea/presentation/underwood) explaining that all ideas that Google SREs and DevOps came up with, about the use of Machine Learning in observability were bad, and as Todd notes they should feel bad about it. + +At Netdata we are approaching machine learning in a completely different way. Instead of trying to make machine learning do something it cannot achieve, we tried to understand if and what useful insights it can provide and eventually we turned it to an assistant that can improve troubleshooting, reduce mean time to resolution and in many case prevent issues from escalating. + +## Design Principles + +The following are the high level design principles of Machine Learning in Netdata: + +1. **Unsupervised** + + In other words: whatever machine learning can do, it should do it by itself, without any help or assistance from users. + +2. **Real-time** + + We understand that Machine Learning will have some impact on resource utilization, especially in CPU utilization, but it shouldn't prevent Netdata from being real-time and high-fidelity. + +3. **Integrated** + + Everything achieved with machine learning should be tightly integrated to the infrastructure exploration and troubleshooting practices we are used to. + +4. **Assist, Advice, Consult** + + If we can't be sure that a decision made by Machine Learning is 100% accurate, we should use this to assist and consult users in their journey. + + In other words, we don't want to wake up someone at 3 AM, just because a machine learning model detected something. + +## Machine Learning per Time-Series + +Given the samples recently collected for a time-series, Machine Learning is used to detect if a sample just collected is an outlier or not. + +Since the query combinations are infinite, Netdata detects anomalies at the time-series level, and then combines the anomaly rates of all time-series involved in each query, to provide the anomaly rate for the query. + +When a collected sample is an outlier, we set the Anomaly Bit of the collected sample and we store it together with the sample value in the time-series database. + +## Multiple Machine Learning Models per Time-Series to Eliminate Noise + +Unsupervised machine learning has some noise, random false positives. + +To remove this noise, Netdata trains multiple machine learning models for each time-series, covering more than the last 2 days, in total. + +Netdata uses all of the available ML models to detect anomalies. So, all machine learning models of a time-series need to agree that a collected sample is an outlier, for it to be marked as an anomaly. + +This process removes 99% of the false positives, offering reliable unsupervised anomaly detection. + +## Node Level Anomaly + +When a metric becomes anomalous, in many cases a lot other metrics get anomalous too. + +For example, an anomaly on a web server may also introduce unusual network bandwidth, cpu usage, memory consumption, disk I/O, context switches, interrupts, etc. If the web server is serving an API that has an application server and a database server we may see anomalies being propagated to them too. + +To represent the spread of an anomaly in a node, Netdata computes a **Node Level Anomaly**. This is the percentage of the metrics of a node being concurrently anomalous, vs the total number of metrics of that node. + +## Node Anomaly Events + +Netdata produces a "node anomaly event" when a the percentage of concurrently anomalous time-series is high enough and persists over time. + +This anomaly event signals that there was sufficient evidence among all the time-series that some strange behavior might have been detected in a more global sense across the node. + +## What is the Anomaly Bit? + +Each sample collected, carries an Anomaly Bit. This bit (true/false) is set when the collected sample found to be an outlier, based on the machine learning models available for it so far. + +This bit is embedded into the custom floating point number the Netdata database uses, so it does not introduce any overheads in memory or disk footprint. + +The query engine of Netdata uses this bit to compute anomaly rates while it executes normal time-series queries. This eliminates to need for additional queries for anomaly rates, as all `/api/v2` time-series query include anomaly rate information. + +## What is the Anomaly Rate (AR)? + +The Anomaly Rate of a query, is a percentage, representing the number of samples in the query found anomalous, vs the total number of samples participating in the query. + +## How it works - a more technical presentation + +For each time-series Netdata trains every 3 hours, a `k-means clustering` model, using the last 6 hours of samples collected for it. + +Rather than using raw samples of each time-series, the model works on a preprocessed "feature vector" of recent smoothed and differenced values. + +This enables the model to detect a wider range of potentially anomalous patterns as opposed to just point anomalies like big spikes or drops. + +Some of the types of anomalies Netdata detects are: + +1. **Point Anomalies** or **Strange Points**: Single points that represent very big or very small values, not seen before (in some statistical sense). +2. **Contextual Anomalies** or **Strange Patterns**: Not strange points in their own, but unexpected sequences of points, given the history of the time-series. +3. **Collective Anomalies** or **Strange Multivariate Patterns**: Neither strange points nor strange patterns, but in global sense something looks off. +4. **Concept Drifts** or **Strange Trends**: A slow and steady drift to a new state. +5. **Change Point Detection** or **Strange Step**: A shift occurred and gradually a new normal is established. + +For a visual representation, check this infographic: + + + +A more detailed explanation can be found on [this (informal) presentation](https://docs.google.com/presentation/d/18zkCvU3nKP-Bw_nQZuXTEa4PIVM6wppH3VUnAauq-RU/edit#slide=id.p). diff --git a/docs/category-overview-pages/reverse-proxies.md b/docs/category-overview-pages/reverse-proxies.md index 07c8b9bd..1b4d935a 100644 --- a/docs/category-overview-pages/reverse-proxies.md +++ b/docs/category-overview-pages/reverse-proxies.md @@ -3,7 +3,7 @@ If you need to access a Netdata agent's user interface or API in a production environment we recommend you put Netdata behind another web server and secure access to the dashboard via SSL, user authentication and firewall rules. -A dedicated web server also provides more robustness and capabilities than the Agent's [internal web server](https://github.com/netdata/netdata/blob/master/web/README.md). +A dedicated web server also provides more robustness and capabilities than the Agent's [internal web server](https://github.com/netdata/netdata/blob/master/src/web/README.md). We have documented running behind [nginx](https://github.com/netdata/netdata/blob/master/docs/Running-behind-nginx.md), diff --git a/docs/category-overview-pages/secure-nodes.md b/docs/category-overview-pages/secure-nodes.md index 33e205f0..99e27392 100644 --- a/docs/category-overview-pages/secure-nodes.md +++ b/docs/category-overview-pages/secure-nodes.md @@ -47,7 +47,7 @@ This is the _recommended method for those who have connected their nodes to Netd metrics using the War Room Overview, Nodes tab, and Cloud dashboards.
You can disable the local dashboard (and API) but retain the encrypted Agent-Cloud link
-([ACLK](https://github.com/netdata/netdata/blob/master/aclk/README.md)) that
+([ACLK](https://github.com/netdata/netdata/blob/master/src/aclk/README.md)) that
allows you to stream metrics on demand from your nodes via the Netdata Cloud interface. This change mitigates all
concerns about revealing metrics and system design to the internet at large, while keeping all the functionality you
need to view metrics and troubleshoot issues with Netdata Cloud.
@@ -60,7 +60,7 @@ static-threaded` setting, and change it to `none`. mode = none
```
-Save and close the editor, then [restart your Agent](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md)
+Save and close the editor, then [restart your Agent](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation)
using `sudo systemctl
restart netdata`. If you try to visit the local dashboard to `http://NODE:19999` again, the connection will fail because
that node no longer serves its local dashboard.
@@ -98,7 +98,7 @@ the internet using multiple hosting providers). ## Fine-grained access control
If you want to keep using the local dashboard, but don't want it exposed to the internet, you can restrict access with
-[access lists](https://github.com/netdata/netdata/blob/master/web/server/README.md#access-lists). This method also fully
+[access lists](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#access-lists). This method also fully
retains the ability to stream metrics
on-demand through Netdata Cloud.
@@ -107,7 +107,7 @@ static IP, only `localhost`, or connections from behind a management LAN. By default, this setting is `localhost *`. This setting allows connections from `localhost` in addition to _all_
connections, using the `*` wildcard. You can change this setting using Netdata's [simple
-patterns](https://github.com/netdata/netdata/blob/master/libnetdata/simple_pattern/README.md).
+patterns](https://github.com/netdata/netdata/blob/master/src/libnetdata/simple_pattern/README.md).
```conf
[web]
@@ -134,9 +134,9 @@ The `allow connections from` setting is global and restricts access to the dashb allow management from = localhost
```
-See the [web server](https://github.com/netdata/netdata/blob/master/web/server/README.md#access-lists) docs for additional details
+See the [web server](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#access-lists) docs for additional details
about access lists. You can take
-access lists one step further by [enabling SSL](https://github.com/netdata/netdata/blob/master/web/server/README.md#enabling-tls-support) to encrypt data from local
+access lists one step further by [enabling SSL](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#enabling-tls-support) to encrypt data from local
dashboard in transit. The connection to Netdata Cloud is always secured with TLS.
## Use an authenticating web server in proxy mode
diff --git a/docs/cloud/alerts-configuration-manager.md b/docs/cloud/alerts-configuration-manager.md new file mode 100644 index 00000000..cedae0eb --- /dev/null +++ b/docs/cloud/alerts-configuration-manager.md @@ -0,0 +1,71 @@ +# Creating Alerts with Netdata Alerts Configuration Manager + +The Netdata Alerts Configuration Manager enables users with [Business subscriptions](https://www.netdata.cloud/pricing/) to create alerts from the Netdata Dashboard with an intuitive user interface. + +## Using Alerts Configuration Manager + +1. Go to the **Metrics** tab and navigate to the chart you want to configure an alert for. + +2. Click the **Alert icon** on the top right corner of the chart. + <!----> + +3. The Alert Configuration Manager will open up with the default thresholds. You can modify the configuration as required and the alert definition on the right will be updated dynamically. + <!----> + +4. If you want more fine-grained control or access to more advanced settings, enable **Show advanced** + <!----> + +5. Copy the alert definition that is generated in the code box and add it to an existing [health configuration file](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#edit-health-configuration-files) or a new custom file under `<path to netdata install>/etc/netdata/health.d/` on a Parent Agent or a Standalone Child Agent. + <!----> + +6. Reload Netdata Alert Health checks `<path to netdata install>/usr/sbin/netdatacli reload-health` and the new alert is now configured. + + +## Alerts Configuration Manager Sections + +- **Alert Detection Method** + <!----> + An alert is triggered whenever a metric crosses a threshold: + - Based on a standard `threshold` + - Based on metric `variance` + - Based on the `anomaly rate` of the metric + + +- **Metrics Lookup, Filtering and Formula Section** + <!----> + + - **Metrics Lookup**: This is the **Evaluate** line of fields in the modal and it defines the parameters for the database lookup that is needed to get the value that will be compared against the alert definition. It corresponds to the [`lookup`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-lookup) line of the Alert configuration file. The Alerts Configuration Manager provides a default selection for the lookup and can be modified to suit your requirements. The parameters that can be modified are: + - METHOD (`avg`, `sum`, `min`, `max`, `cv`, `stddev`) + - COMPUTATION (sum of all dimensions or individually for each dimension) + - DIMENSIONS (All dimensions, or a selection of dimensions) + - DURATION (the period in time to run the lookup) + - OPTIONS (`absolute`, `unaligned`, `percentage`, `min2max`) + + - **Alert Filtering**: This functionality can be accessed through the **Show advanced** checkbox and it allows for filtering the alert health checks to be run only for specific components of the infrastructure. It helps in achieving a fine-grained configuration for any given alert. + - `HOSTS` / `NODES` - By default all hosts are selected. You can pick nodes from the dropdown list, or enter a wildcard matching a list of hosts that you want the alert health check to run on. This field corresponds to the [`hosts`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-hosts) line of the Alert configuration file. + - `INSTANCES` - All instances are selected by default. You can pick instances from the dropdown list, or enter a wildcard matching a list of instances that you want the alert health check to run on. This field corresponds to the [`charts`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-charts) line of the Alert configuration file. + - `CHART LABELS` - All chart labels are selected by default. You can pick a chart label from the dropdown list or enter a wildcard matching a list of chart labels that you want the alert health check to run on. This field corresponds to the [`chart labels`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-chart-labels) line of the Alert configuration file. + - `OS` - All Operating Systems are selected by default. You can choose which OS(s) an alert health check should run on. This field corresponds to the [`os`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-os) line of the Alert configuration file. + + - **Formula / Calculation**: This field is available through the **Show advanced** checkbox and it is used to define a formula to be run on top of the `lookup` value. The result of the lookup is available in the `$this` variable, and after the formula is run, the result is also stored in `$this` and can be accessed while setting the alert thresholds. This field corresponds to the [`calc`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-calc) line of the Alert configuration file. + +- **Alerting conditions** + <!----> + - **Warning and Critical Thresholds**: These fields are used to set the thresholds for the `Warning` and `Critical` alert states, while also having the option to set the condition for the alert to be raised if it is `above` or `below` the given threshold. If the advanced settings are selected, a **formula** option can also be used, to define a custom formula instead of a threshold. These fields correspond to the [`warn` and `crit`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-lines-warn-and-crit) lines of the Alert configuration file. + - **Recovery Thresholds**: This field is available through the **Show advanced** checkbox, and it is used to set the threshold that the metric value needs to meet to de-escalate from a given severity status, like `Critical to Warning` and from `Warning to Clear`. The logic is appended to the `warn` and `crit` lines of the Alert configuration file and resembles a one-line `IF-THEN-ELSE` clause. + - **Check Interval**: This field is used to define the frequency of the health check for the alert and corresponds to the [`every`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-every) line of the Alert configuration file. + - **Delay Notifications**: This field is available through the **Show advanced** checkbox and it is used to set delay parameters on notifications for an alert severity `escalation` or `de-escalation`. It corresponds to the [`delay`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-delay) line of the Alert configuration file. + + - **Agent Specific Options**: These options are only available on the `Netdata Agent` and not honored on `Netdata Cloud`. They can be accessed through the **Show advanced** checkbox. + <!----> + - **Repeat Notifications**: This field defines the repeat frequency for the alert notification when the alert is in either `warning` or `critical` status and corresponds to the [`repeat`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-repeat) line of the Alert configuration file. + - **Send to**: This field is used to define a user role to which the alert notifications will be sent. If set to `silent`, then the alert won't be sent to any role. It corresponds to the [`to`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-to) line of the Alert configuration file. + - **Custom Exec Script**: This field is used to define a custom script that will be executed when the alert is triggered (but needs to be carefully designed as it needs to call the `health_alarm_notify.sh` module) and corresponds to the [`exec`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-exec) line of the Alert configuration file. + +- **Alert Name, Description and Summary Section** + <!----> + + - **Alert Template Name**: This field uniquely identifies an alert and corresponds to the [`template`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-alarm-or-template) field of the Alert configuration file. + The Alerts Configuration Manager provides a default name for an Alert template but we recommend you modify this to have a meaningful name for your configured alert. + - **Alert Template Description**: This field provides a brief explanation of the alert and corresponds to the [`info`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-info) line of the Alert configuration file. + - **Alert Summary**: This field enables the users to create a custom title for the alert notification (via [Notification integrations](https://learn.netdata.cloud/docs/alerting/notifications/centralized-cloud-notifications)) and corresponds to the [`summary`](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-summary) line of the Alert configuration file. diff --git a/docs/cloud/alerts-notifications/manage-notification-methods.md b/docs/cloud/alerts-notifications/manage-notification-methods.md index f61b6bf6..87271bd6 100644 --- a/docs/cloud/alerts-notifications/manage-notification-methods.md +++ b/docs/cloud/alerts-notifications/manage-notification-methods.md @@ -66,7 +66,7 @@ Note: If an administrator has disabled a Personal [service level](https://github - If you're an administrator, Manager or Troubleshooter you'll also see the Rooms from a space you don't have access to on **All Rooms** tab and you can activate notifications for them by joining the room 1. On this modal you will be able to: 1. **Enable/Disable** the notification method for you, this applies accross all spaces and rooms - - Use the the toggle enable or disable the notification method + - Use the toggle enable or disable the notification method 1. **Define what notifications you want** to per space/room: All Alerts and unreachable, All Alerts, Critical only or No notifications 1. **Activate notifications** for a room you aren't a member of - From the **All Rooms** tab click on the Join button for the room(s) you want diff --git a/docs/cloud/alerts-notifications/notifications.md b/docs/cloud/alerts-notifications/notifications.md index cde30a2b..822fef01 100644 --- a/docs/cloud/alerts-notifications/notifications.md +++ b/docs/cloud/alerts-notifications/notifications.md @@ -27,7 +27,7 @@ Centralized alert notifications from Netdata Cloud is a independent process from Netdata](https://github.com/netdata/netdata/blob/master/docs/monitor/enable-notifications.md). You can enable one or the other, or both, based on your needs. However, the alerts you see in Netdata Cloud are based on those streamed from your Netdata-monitoring nodes. If you want to tweak or add new alert that you see in Netdata Cloud, and receive via centralized alert notifications, you must -[configure](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md) each node's alert watchdog. +[configure](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md) each node's alert watchdog. </Callout> @@ -104,8 +104,8 @@ if the node should be silenced for the entire space or just for specific rooms ( ### Scope definition for Alerts * **Alert name:** silencing a specific alert name silences all alert state transitions for that specific alert. -* **Alert context:** silencing a specific alert context will silence all alert state transitions for alerts targeting that chart context, for more details check [alert configuration docs](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#alert-line-on). -* **Alert role:** silencing a specific alert role will silence all the alert state transitions for alerts that are configured to be specific role recipients, for more details check [alert configuration docs](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#alert-line-to). +* **Alert context:** silencing a specific alert context will silence all alert state transitions for alerts targeting that chart context, for more details check [alert configuration docs](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-on). +* **Alert role:** silencing a specific alert role will silence all the alert state transitions for alerts that are configured to be specific role recipients, for more details check [alert configuration docs](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-to). Beside the above two main entities there are another two important settings that you can define on a silencing rule: * Who does the rule affect? **All user** in the space or **Myself** diff --git a/docs/cloud/cheatsheet.md b/docs/cloud/cheatsheet.md index a3d2f028..460b03ea 100644 --- a/docs/cloud/cheatsheet.md +++ b/docs/cloud/cheatsheet.md @@ -5,10 +5,10 @@ Below you will find some of the most common actions that one can take while usin ### Install Netdata ```bash -wget -O /tmp/netdata-kickstart.sh https://my-netdata.io/kickstart.sh && sh /tmp/netdata-kickstart.sh +wget -O /tmp/netdata-kickstart.sh https://get.netdata.cloud/kickstart.sh && sh /tmp/netdata-kickstart.sh # Or, if you have cURL but not wget (such as on macOS): -curl https://my-netdata.io/kickstart.sh > /tmp/netdata-kickstart.sh && sh /tmp/netdata-kickstart.sh +curl https://get.netdata.cloud/kickstart.sh > /tmp/netdata-kickstart.sh && sh /tmp/netdata-kickstart.sh ``` #### Connect a node to Netdata Cloud @@ -39,7 +39,7 @@ You are expected to use this method in all following configuration changes. ## Metrics collection & retention You can tweak your settings in the netdata.conf file. -📄 [Find your netdata.conf file](https://github.com/netdata/netdata/blob/master/daemon/config/README.md) +📄 [Find your netdata.conf file](https://github.com/netdata/netdata/blob/master/src/daemon/config/README.md) Open a new terminal and navigate to the netdata.conf file. Use the edit-config script to make changes: `sudo ./edit-config netdata.conf` diff --git a/docs/cloud/insights/anomaly-advisor.md b/docs/cloud/insights/anomaly-advisor.md index 4804dbc1..611ddc5e 100644 --- a/docs/cloud/insights/anomaly-advisor.md +++ b/docs/cloud/insights/anomaly-advisor.md @@ -32,9 +32,9 @@ To enable ML on your Netdata Agent, you need to edit the `[ml]` section in your enabled = yes ``` -At a minimum you just need to set `enabled = yes` to enable ML with default params. More details about configuration can be found in the [Netdata Agent ML docs](https://github.com/netdata/netdata/blob/master/ml/README.md#configuration). +At a minimum you just need to set `enabled = yes` to enable ML with default params. More details about configuration can be found in the [Netdata Agent ML docs](https://github.com/netdata/netdata/blob/master/src/ml/README.md#configuration). -When you have finished your configuration, restart Netdata with a command like `sudo systemctl restart netdata` for the config changes to take effect. You can find more info on restarting Netdata [here](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md). +When you have finished your configuration, restart Netdata with a command like `sudo systemctl restart netdata` for the config changes to take effect. You can find more info on restarting Netdata [here](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation). After a brief delay, you should see the number of `trained` dimensions start to increase on the "dimensions" chart of the "Anomaly Detection" menu on the Overview page. By default the `minimum num samples to train = 3600` parameter means at least 1 hour of data is required to train initial models, but you could set this to `900` if you want to train initial models quicker but on less data. Over time, they will retrain on up to `maximum num samples to train = 14400` (4 hours by default), but you could increase this is you wanted to train on more data. @@ -52,7 +52,7 @@ The aim here is to surface the most anomalous metrics in the space or room for t The "Anomaly Rate" chart shows the percentage of anomalous metrics over time per node. For example, in the following image, 3.21% of the metrics on the "ml-demo-ml-disabled" node were considered anomalous. This elevated anomaly rate could be a sign of something worth investigating. -**Note**: in this example the anomaly rates for this node are actually being calculated on the parent it streams to, you can run ml on the Agent itselt or on a parent the Agent stream to. Read more about the various configuration options in the [Agent docs](https://github.com/netdata/netdata/blob/master/ml/README.md). +**Note**: in this example the anomaly rates for this node are actually being calculated on the parent it streams to, you can run ml on the Agent itselt or on a parent the Agent stream to. Read more about the various configuration options in the [Agent docs](https://github.com/netdata/netdata/blob/master/src/ml/README.md).  @@ -77,7 +77,7 @@ On the lower right hand side of the page an index of anomaly rates is displayed - If you are interested in a subset of specific nodes then filtering to just those nodes before highlighting tends to give better results. This is because when you highlight a region, Netdata Cloud will ask the Agents for a ranking over all metrics so if you can filter this early to just the subset of nodes you are interested in, less 'averaging' will occur and so you might be a less noisy ranking. - Ideally try and highlight close to a spike or window of interest so that the resulting ranking can narrow in more easily on the timeline you are interested in. -You can read more detail on how anomaly detection in the Netdata Agent works in our [Agent docs](https://github.com/netdata/netdata/blob/master/ml/README.md). +You can read more detail on how anomaly detection in the Netdata Agent works in our [Agent docs](https://github.com/netdata/netdata/blob/master/src/ml/README.md). 🚧 **Note**: This functionality is still **under active development** and considered experimental. We dogfood it internally and among early adopters within the Netdata community to build the feature. If you would like to get involved and help us with feedback, you can reach us through any of the following channels: diff --git a/docs/cloud/insights/metric-correlations.md b/docs/cloud/insights/metric-correlations.md index c8ead9be..7dc50c59 100644 --- a/docs/cloud/insights/metric-correlations.md +++ b/docs/cloud/insights/metric-correlations.md @@ -53,9 +53,9 @@ Behind the scenes, Netdata will aggregate the raw data as needed such that arbit ### Data -Netdata is different from typical observability agents since, in addition to just collecting raw metric values, it will by default also assign an "[Anomaly Bit](https://github.com/netdata/netdata/tree/master/ml#anomaly-bit---100--anomalous-0--normal)" related to each collected metric each second. This bit will be 0 for "normal" and 1 for "anomalous". This means that each metric also natively has an "[Anomaly Rate](https://github.com/netdata/netdata/tree/master/ml#anomaly-rate---averageanomaly-bit)" associated with it and, as such, MC can be run against the raw metric values or their corresponding anomaly rates. +Netdata is different from typical observability agents since, in addition to just collecting raw metric values, it will by default also assign an "[Anomaly Bit](https://github.com/netdata/netdata/tree/master/src/ml#anomaly-bit---100--anomalous-0--normal)" related to each collected metric each second. This bit will be 0 for "normal" and 1 for "anomalous". This means that each metric also natively has an "[Anomaly Rate](https://github.com/netdata/netdata/tree/master/src/ml#anomaly-rate---averageanomaly-bit)" associated with it and, as such, MC can be run against the raw metric values or their corresponding anomaly rates. -**Note**: Read more [here](https://github.com/netdata/netdata/blob/master/ml/README.md) to learn more about the native anomaly detection features within netdata. +**Note**: Read more [here](https://github.com/netdata/netdata/blob/master/src/ml/README.md) to learn more about the native anomaly detection features within netdata. - `Metrics` - Run MC on the raw metric values. - `Anomaly Rate` - Run MC on the corresponding anomaly rate for each metric. diff --git a/docs/cloud/manage/organize-your-infrastrucutre-invite-your-team.md b/docs/cloud/manage/organize-your-infrastrucutre-invite-your-team.md index b36e0806..1f382855 100644 --- a/docs/cloud/manage/organize-your-infrastrucutre-invite-your-team.md +++ b/docs/cloud/manage/organize-your-infrastrucutre-invite-your-team.md @@ -56,7 +56,7 @@ will open a side tab in which you can: 2. _Edit the War Rooms*_, click on the **War rooms** tab to add or remove War Rooms. 3. _Connect nodes*_, click on **Nodes** tab. Copy the claiming script to your node and run it. See the - [connect to Cloud doc](https://github.com/netdata/netdata/blob/master/claim/README.md) for details. + [connect to Cloud doc](https://github.com/netdata/netdata/blob/master/src/claim/README.md) for details. 4. _Manage the users*_, click on **Users**. The [invitation doc](#invite-your-team) @@ -106,7 +106,7 @@ We recommend a few strategies for organizing your War Rooms. - **Incident response** You can also create new War Rooms as one of the first steps in your incident response process. - For example, you have a user-facing web app that relies on Apache Pulsar for a message queue, and one of your nodes using the [Pulsar collector](https://github.com/netdata/go.d.plugin/blob/master/modules/pulsar/README.md) begins reporting a suspiciously low messages rate. + For example, you have a user-facing web app that relies on Apache Pulsar for a message queue, and one of your nodes using the [Pulsar collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/pulsar/README.md) begins reporting a suspiciously low messages rate. You can create a War Room called `$year-$month-$day-pulsar-rate`, add all your Pulsar nodes in addition to nodes they connect to, and begin diagnosing the root cause in a War Room optimized for getting to resolution as fast as possible. ### Add War Rooms diff --git a/docs/cloud/manage/plans.md b/docs/cloud/manage/plans.md index f84adaa8..9b21575f 100644 --- a/docs/cloud/manage/plans.md +++ b/docs/cloud/manage/plans.md @@ -62,7 +62,7 @@ To achieve this you can check the [Update plan](https://github.com/netdata/netda > ⚠️ Downgrade or cancellation may affect users in your Space. Please check what roles are available on the [each plans](https://github.com/netdata/netdata/blob/master/docs/cloud/manage/plans.md#areas-impacted-by-plans). Users with unavailable roles on the new plan will immediately have restricted access to the Space. -> ⚠️ Any credit given to you will be available to use on future paid subscriptions with us. It will be available until the the **end of the following year**. +> ⚠️ Any credit given to you will be available to use on future paid subscriptions with us. It will be available until the **end of the following year**. ### Areas impacted by plans diff --git a/docs/cloud/manage/role-based-access.md b/docs/cloud/manage/role-based-access.md index a0b38774..4bfb65f2 100644 --- a/docs/cloud/manage/role-based-access.md +++ b/docs/cloud/manage/role-based-access.md @@ -33,6 +33,8 @@ In more detail, you can find on the following tables which functionalities are a | Delete Space | :heavy_check_mark: | - | - | - | - | - | | Change name | :heavy_check_mark: | - | - | - | - | - | | Change description | :heavy_check_mark: | - | - | - | - | - | +| Change slug | :heavy_check_mark: | - | - | - | - | - | +| Change preferred nodes | :heavy_check_mark: | - | - | - | - | - | ### Node Management diff --git a/docs/cloud/manage/themes.md b/docs/cloud/manage/themes.md index aaf193a8..0ca7425a 100644 --- a/docs/cloud/manage/themes.md +++ b/docs/cloud/manage/themes.md @@ -1,14 +1,15 @@ -# Choose your Netdata Cloud theme +# Choose your Netdata UI theme -The Dark theme is the default for all new Netdata Cloud accounts. +The Dark theme is the default in the Netdata UI. -To change your theme across Netdata Cloud, click on your profile picture, then **Profile**. Click on the **Settings** -tab, then choose your preferred theme: Light or Dark. +To change your theme across the Netdata UI, click on your profile picture, click on the **Settings** +tab, and then choose your preferred theme: **Light** or **Dark**. -**Light**: +**Dark**: + + - +**Light**: -**Dark (default)**: + - diff --git a/docs/cloud/netdata-functions.md b/docs/cloud/netdata-functions.md index caff9b35..f86096d4 100644 --- a/docs/cloud/netdata-functions.md +++ b/docs/cloud/netdata-functions.md @@ -26,22 +26,22 @@ function (i.e. code) to be run by a data collector when the function is invoked. For more details please check out documentation on how we use our internal collector to get this from the first collector that exposes -functions - [plugins.d](https://github.com/netdata/netdata/blob/master/collectors/plugins.d/README.md#function). +functions - [plugins.d](https://github.com/netdata/netdata/blob/master/src/collectors/plugins.d/README.md#function). #### What functions are currently available? -| Function | Description | Alternative to CLI tools | plugin - module | -|:-------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|:-----------------------------------------------------------------------------------------------------------| -| block-devices | Disk I/O activity for all block devices, offering insights into both data transfer volume and operation performance. | `iostat` | [proc](https://github.com/netdata/netdata/tree/master/collectors/proc.plugin#readme) | -| containers-vms | Insights into the resource utilization of containers and QEMU virtual machines: CPU usage, memory consumption, disk I/O, and network traffic. | `docker stats`, `systemd-cgtop` | [cgroups](https://github.com/netdata/netdata/tree/master/collectors/cgroups.plugin#readme) | -| ipmi-sensors | Readings and status of IPMI sensors. | `ipmi-sensors` | [freeipmi](https://github.com/netdata/netdata/tree/master/collectors/freeipmi.plugin#readme) | -| mount-points | Disk usage for each mount point, including used and available space, both in terms of percentage and actual bytes, as well as used and available inode counts. | `df` | [diskspace](https://github.com/netdata/netdata/tree/master/collectors/diskspace.plugin#readme) | -| network interfaces | Network traffic, packet drop rates, interface states, MTU, speed, and duplex mode for all network interfaces. | `bmon`, `bwm-ng` | [proc](https://github.com/netdata/netdata/tree/master/collectors/proc.plugin#readme) | -| processes | Real-time information about the system's resource usage, including CPU utilization, memory consumption, and disk IO for every running process. | `top`, `htop` | [apps](https://github.com/netdata/netdata/blob/master/collectors/apps.plugin/README.md) | -| systemd-journal | Viewing, exploring and analyzing systemd journal logs. | `journalctl` | [systemd-journal](https://github.com/netdata/netdata/tree/master/collectors/systemd-journal.plugin#readme) | -| systemd-list-units | Information about all systemd units, including their active state, description, whether or not they are enabled, and more. | `systemctl list-units` | [systemd-journal](https://github.com/netdata/netdata/tree/master/collectors/systemd-journal.plugin#readme) | -| systemd-services | System resource utilization for all running systemd services: CPU, memory, and disk IO. | `systemd-cgtop` | [cgroups](https://github.com/netdata/netdata/tree/master/collectors/cgroups.plugin#readme) | -| streaming | Comprehensive overview of all Netdata children instances, offering detailed information about their status, replication completion time, and many more. | | | +| Function | Description | Alternative to CLI tools | Require Cloud | plugin - module | +|:-------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|:--------------|:-----------------------------------------------------------------------------------------------------------| +| Block-devices | Disk I/O activity for all block devices, offering insights into both data transfer volume and operation performance. | `iostat` | no | [proc](https://github.com/netdata/netdata/tree/master/src/collectors/proc.plugin#readme) | +| Containers-vms | Insights into the resource utilization of containers and QEMU virtual machines: CPU usage, memory consumption, disk I/O, and network traffic. | `docker stats`, `systemd-cgtop` | no | [cgroups](https://github.com/netdata/netdata/tree/master/src/collectors/cgroups.plugin#readme) | +| Ipmi-sensors | Readings and status of IPMI sensors. | `ipmi-sensors` | no | [freeipmi](https://github.com/netdata/netdata/tree/master/src/collectors/freeipmi.plugin#readme) | +| Mount-points | Disk usage for each mount point, including used and available space, both in terms of percentage and actual bytes, as well as used and available inode counts. | `df` | no | [diskspace](https://github.com/netdata/netdata/tree/master/src/collectors/diskspace.plugin#readme) | +| Network-interfaces | Network traffic, packet drop rates, interface states, MTU, speed, and duplex mode for all network interfaces. | `bmon`, `bwm-ng` | no | [proc](https://github.com/netdata/netdata/tree/master/src/collectors/proc.plugin#readme) | +| Processes | Real-time information about the system's resource usage, including CPU utilization, memory consumption, and disk IO for every running process. | `top`, `htop` | yes | [apps](https://github.com/netdata/netdata/blob/master/src/collectors/apps.plugin/README.md) | +| Systemd-journal | Viewing, exploring and analyzing systemd journal logs. | `journalctl` | yes | [systemd-journal](https://github.com/netdata/netdata/tree/master/src/collectors/systemd-journal.plugin#readme) | +| Systemd-list-units | Information about all systemd units, including their active state, description, whether or not they are enabled, and more. | `systemctl list-units` | yes | [systemd-journal](https://github.com/netdata/netdata/tree/master/src/collectors/systemd-journal.plugin#readme) | +| Systemd-services | System resource utilization for all running systemd services: CPU, memory, and disk IO. | `systemd-cgtop` | no | [cgroups](https://github.com/netdata/netdata/tree/master/src/collectors/cgroups.plugin#readme) | +| Streaming | Comprehensive overview of all Netdata children instances, offering detailed information about their status, replication completion time, and many more. | | yes | | If you have ideas or requests for other functions: @@ -57,22 +57,21 @@ any children connected to it. If the parent node is the one connected to Netdata Cloud it is capable of triggering the call to the respective children node to run the function. -#### Why are they available only on Netdata Cloud? +#### Why are some functions only available on Netdata Cloud? Since these functions are able to execute routines on the node and due to the potential use cases that they can cover, our -concern is to ensure no sensitive -information or disruptive actions are exposed through the Agent's API. +concern is to ensure no sensitive information or disruptive actions are exposed through the Agent's API. With the communication between the Netdata Agent and Netdata Cloud being -through [ACLK](https://github.com/netdata/netdata/blob/master/aclk/README.md) this +through [ACLK](https://github.com/netdata/netdata/blob/master/src/aclk/README.md) this concern is addressed. ## Related Topics ### **Related Concepts** -- [ACLK](https://github.com/netdata/netdata/blob/master/aclk/README.md) -- [plugins.d](https://github.com/netdata/netdata/blob/master/collectors/plugins.d/README.md) +- [ACLK](https://github.com/netdata/netdata/blob/master/src/aclk/README.md) +- [plugins.d](https://github.com/netdata/netdata/blob/master/src/collectors/plugins.d/README.md) ### Related Tasks diff --git a/docs/cloud/runtime-troubleshooting-with-functions.md b/docs/cloud/runtime-troubleshooting-with-functions.md index 839b8c9e..193ba33c 100644 --- a/docs/cloud/runtime-troubleshooting-with-functions.md +++ b/docs/cloud/runtime-troubleshooting-with-functions.md @@ -31,4 +31,4 @@ The following is required to be able to run Functions from Netdata Cloud. - [Netdata Functions](https://github.com/netdata/netdata/blob/master/docs/cloud/netdata-functions.md) #### Related References documentation -- [External plugins overview](https://github.com/netdata/netdata/blob/master/collectors/plugins.d/README.md#function) +- [External plugins overview](https://github.com/netdata/netdata/blob/master/src/collectors/plugins.d/README.md#function) diff --git a/docs/cloud/visualize/interact-new-charts.md b/docs/cloud/visualize/interact-new-charts.md index 16db927a..57b4c253 100644 --- a/docs/cloud/visualize/interact-new-charts.md +++ b/docs/cloud/visualize/interact-new-charts.md @@ -42,8 +42,8 @@ With a quick glance you have immediate information available at your disposal: While Netdata's charts require no configuration and are easy to interact with, they have a lot of underlying complexity. To meaningfully organize charts out of the box based on what's happening in your nodes, Netdata uses the concepts of [dimensions](#dimensions), [contexts](#contexts), and [families](#families). Understanding how these work will help you more easily navigate the dashboard, -[write new alerts](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md), or play around -with the [API](https://github.com/netdata/netdata/blob/master/web/api/README.md). +[write new alerts](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md), or play around +with the [API](https://github.com/netdata/netdata/blob/master/src/web/api/README.md). ### Dimensions @@ -71,7 +71,7 @@ whereas anything after the `.` is specified either by the chart's developer or b By default, a chart's type affects where it fits in the menu, while its family creates submenus. -Netdata also relies on contexts for [alert configuration](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md) (the [`on` line](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#alert-line-on)). +Netdata also relies on contexts for [alert configuration](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md) (the [`on` line](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-on)). ### Families @@ -262,7 +262,7 @@ By default the aggregation applied is _average_ but the user can choose differen - Delta - Single or Double exponential smoothing -For more details on each, you can refer to our Agent's HTTP API details on [Data Queries - Data Grouping](https://github.com/netdata/netdata/blob/master/web/api/queries/README.md#data-grouping). +For more details on each, you can refer to our Agent's HTTP API details on [Data Queries - Data Grouping](https://github.com/netdata/netdata/blob/master/src/web/api/queries/README.md#data-grouping). ### Reset to defaults @@ -283,7 +283,7 @@ The Anomaly Rate ribbon on top of each chart visualizes the combined anomaly rat Hovering over the Anomaly Rate ribbon provides a histogram of the anomaly rates per presented dimension, for the specific point in time. -Anomaly Rate visualization does not make Netdata slower. Anomaly rate is saved in the the Netdata database, together with metric values, and due to the smart design of Netdata, it does not even incur a disk footprint penalty. +Anomaly Rate visualization does not make Netdata slower. Anomaly rate is saved in the Netdata database, together with metric values, and due to the smart design of Netdata, it does not even incur a disk footprint penalty. ## Hover over the chart diff --git a/docs/cloud/visualize/node-filter.md b/docs/cloud/visualize/node-filter.md index 0dd0ef5a..1b3979ff 100644 --- a/docs/cloud/visualize/node-filter.md +++ b/docs/cloud/visualize/node-filter.md @@ -6,7 +6,7 @@ Inside the filter, the nodes get categorized into three groups: | Group | Description | |---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Live | Nodes that are currently online, collecting and streaming metrics to Cloud. Live nodes display raised [Alert](https://github.com/netdata/netdata/blob/master/docs/monitor/view-active-alerts.md) counters, [Machine Learning](https://github.com/netdata/netdata/blob/master/ml/README.md) availability, and [Functions](https://github.com/netdata/netdata/blob/master/docs/cloud/netdata-functions.md) availability | +| Live | Nodes that are currently online, collecting and streaming metrics to Cloud. Live nodes display raised [Alert](https://github.com/netdata/netdata/blob/master/docs/monitor/view-active-alerts.md) counters, [Machine Learning](https://github.com/netdata/netdata/blob/master/src/ml/README.md) availability, and [Functions](https://github.com/netdata/netdata/blob/master/docs/cloud/netdata-functions.md) availability | | Stale | Nodes that are offline and not streaming metrics to Cloud. Only historical data can be presented from a parent node. For these nodes you can only see their ML status, as they are not online to provide more information | | Offline | Nodes that are offline, not streaming metrics to Cloud and not available in any parent node. Offline nodes are automatically deleted after 30 days and can also be deleted manually. | diff --git a/docs/cloud/visualize/nodes.md b/docs/cloud/visualize/nodes.md index 3ecf76ca..ee29d247 100644 --- a/docs/cloud/visualize/nodes.md +++ b/docs/cloud/visualize/nodes.md @@ -31,7 +31,7 @@ These customizations appear for anyone else with access to that War Room. ## See more metrics in Netdata Cloud If you want to add more metrics to your War Rooms and they don't show up when you add new metrics to Nodes, you likely -need to configure those nodes to collect from additional data sources. See our [collectors configuration reference](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md) +need to configure those nodes to collect from additional data sources. See our [collectors configuration reference](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md) to learn how to use dozens of pre-installed collectors that can instantly collect from your favorite services and applications. If you want to see up to 30 days of historical metrics in Cloud (and more on individual node dashboards), read about [changing how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md). Also, see our diff --git a/docs/cloud/visualize/overview.md b/docs/cloud/visualize/overview.md index 84638f05..a2935d62 100644 --- a/docs/cloud/visualize/overview.md +++ b/docs/cloud/visualize/overview.md @@ -31,7 +31,7 @@ monitor an Apache web server, the **Apache** section still appears and displays One difference between the Netdata Cloud menu and those found in local Agent dashboards is that the Overview condenses multiple services, families, or instances into single sections, sub-menus, and associated charts. -For services, let's say you have two concurrent jobs with the [web_log collector](https://github.com/netdata/go.d.plugin/blob/master/modules/weblog/README.md), one for Apache and another for Nginx. +For services, let's say you have two concurrent jobs with the [web_log collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/weblog/README.md), one for Apache and another for Nginx. A single-node or local dashboard shows two section, **web_log apache** and **web_log nginx**, whereas the Overview condenses these into a single **web_log** section containing composite charts from both jobs. diff --git a/docs/collect/application-metrics.md b/docs/collect/application-metrics.md index ec73cefe..f3c97ee1 100644 --- a/docs/collect/application-metrics.md +++ b/docs/collect/application-metrics.md @@ -15,7 +15,7 @@ web servers, databases, message brokers, email servers, search platforms, and mu pre-installed with every Netdata Agent and usually require zero configuration. Netdata also collects and visualizes resource utilization per application on Linux systems using `apps.plugin`. -[**apps.plugin**](https://github.com/netdata/netdata/blob/master/collectors/apps.plugin/README.md) looks at the Linux process tree every second, much like `top` or +[**apps.plugin**](https://github.com/netdata/netdata/blob/master/src/collectors/apps.plugin/README.md) looks at the Linux process tree every second, much like `top` or `ps fax`, and collects resource utilization information on every running process. By reading the process tree, Netdata shows CPU, disk, networking, processes, and eBPF for every application or Linux user. Unlike `top` or `ps fax`, Netdata adds a layer of meaningful visualization on top of the process tree metrics, such as grouping applications into useful @@ -24,28 +24,28 @@ charts under **Users**, and per-user group charts under **User Groups**. Our most popular application collectors: -- [Prometheus endpoints](https://github.com/netdata/go.d.plugin/blob/master/modules/prometheus/README.md): Gathers +- [Prometheus endpoints](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/prometheus/README.md): Gathers metrics from one or more Prometheus endpoints that use the OpenMetrics exposition format. Auto-detects more than 600 endpoints. -- [Web server logs (Apache, NGINX)](https://github.com/netdata/go.d.plugin/blob/master/modules/weblog/README.md): +- [Web server logs (Apache, NGINX)](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/weblog/README.md): Tail access logs and provide very detailed web server performance statistics. This module is able to parse 200k+ rows in less than half a second. -- [MySQL](https://github.com/netdata/go.d.plugin/blob/master/modules/mysql/README.md): Collect database global, +- [MySQL](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/mysql/README.md): Collect database global, replication, and per-user statistics. -- [Redis](https://github.com/netdata/go.d.plugin/blob/master/modules/redis/README.md): Monitor database status by +- [Redis](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/redis/README.md): Monitor database status by reading the server's response to the `INFO` command. -- [Apache](https://github.com/netdata/go.d.plugin/blob/master/modules/apache/README.md): Collect Apache web server +- [Apache](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/apache/README.md): Collect Apache web server performance metrics via the `server-status?auto` endpoint. -- [Nginx](https://github.com/netdata/go.d.plugin/blob/master/modules/nginx/README.md): Monitor web server status +- [Nginx](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/nginx/README.md): Monitor web server status information by gathering metrics via `ngx_http_stub_status_module`. -- [Postgres](https://github.com/netdata/go.d.plugin/blob/master/modules/postgres/README.md): Collect database health +- [Postgres](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/postgres/README.md): Collect database health and performance metrics. -- [ElasticSearch](https://github.com/netdata/go.d.plugin/blob/master/modules/elasticsearch/README.md): Collect search +- [ElasticSearch](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/elasticsearch/README.md): Collect search engine performance and health statistics. Optionally collects per-index metrics. -- [PHP-FPM](https://github.com/netdata/go.d.plugin/blob/master/modules/phpfpm/README.md): Collect application summary +- [PHP-FPM](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/phpfpm/README.md): Collect application summary and processes health metrics by scraping the status page (`/status?full`). -Our [supported collectors list](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md#service-and-application-collectors) shows all Netdata's +Our [supported collectors list](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md#service-and-application-collectors) shows all Netdata's application metrics collectors, including those for containers/k8s clusters. ## Collect metrics from applications running on Windows @@ -54,12 +54,12 @@ Netdata is fully capable of collecting and visualizing metrics from applications caveat is that you must [install Netdata](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md) on a separate system or a compatible VM because there is no native Windows version of the Netdata Agent. -Once you have Netdata running on that separate system, you can follow the [collectors configuration reference](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md) documentation to tell the collector to look for exposed metrics on the Windows system's IP +Once you have Netdata running on that separate system, you can follow the [collectors configuration reference](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md) documentation to tell the collector to look for exposed metrics on the Windows system's IP address or hostname, plus the applicable port. For example, you have a MySQL database with a root password of `my-secret-pw` running on a Windows system with the IP address 203.0.113.0. you can configure the [MySQL -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/mysql/README.md) to look at `203.0.113.0:3306`: +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/mysql/README.md) to look at `203.0.113.0:3306`: ```yml jobs: @@ -68,13 +68,13 @@ jobs: ``` This same logic applies to any application in our [supported collectors -list](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md#service-and-application-collectors) that can run on Windows. +list](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md#service-and-application-collectors) that can run on Windows. ## What's next? -If you haven't yet seen the [supported collectors list](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md) give it a once-over for any +If you haven't yet seen the [supported collectors list](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md) give it a once-over for any additional applications you may want to monitor using Netdata's native collectors, or the [generic Prometheus -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/prometheus/README.md). +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/prometheus/README.md). Collecting all the available metrics on your nodes, and across your entire infrastructure, is just one piece of the puzzle. Next, learn more about Netdata's famous real-time visualizations by [seeing an overview of your diff --git a/docs/collect/container-metrics.md b/docs/collect/container-metrics.md index b5ccca5a..aecf7eee 100644 --- a/docs/collect/container-metrics.md +++ b/docs/collect/container-metrics.md @@ -13,7 +13,7 @@ learn_rel_path: "Concepts" Thanks to close integration with Linux cgroups and the virtual files it maintains under `/sys/fs/cgroup`, Netdata can monitor the health, status, and resource utilization of many different types of Linux containers. -Netdata uses [cgroups.plugin](https://github.com/netdata/netdata/blob/master/collectors/cgroups.plugin/README.md) to poll `/sys/fs/cgroup` and convert the raw data +Netdata uses [cgroups.plugin](https://github.com/netdata/netdata/blob/master/src/collectors/cgroups.plugin/README.md) to poll `/sys/fs/cgroup` and convert the raw data into human-readable metrics and meaningful visualizations. Through cgroups, Netdata is compatible with **all Linux containers**, such as Docker, LXC, LXD, Libvirt, systemd-nspawn, and more. Read more about [Docker-specific monitoring](#collect-docker-metrics) below. @@ -25,23 +25,23 @@ services running inside of pods in your k8s cluster. Read more about [Kubernetes monitoring](#collect-kubernetes-metrics) below. A handful of additional collectors gather metrics from container-related services, such as -[dockerd](https://github.com/netdata/go.d.plugin/blob/master/modules/docker/README.md) or [Docker -Engine](https://github.com/netdata/go.d.plugin/blob/master/modules/docker_engine/README.md). You can find all +[dockerd](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/docker/README.md) or [Docker +Engine](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/docker_engine/README.md). You can find all container collectors in our supported collectors list under the -[containers/VMs](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md#containers-and-vms) and -[Kubernetes](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md#containers-and-vms) headings. +[containers/VMs](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md#containers-and-vms) and +[Kubernetes](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md#containers-and-vms) headings. ## Collect Docker metrics Netdata has robust Docker monitoring thanks to the aforementioned -[cgroups.plugin](https://github.com/netdata/netdata/blob/master/collectors/cgroups.plugin/README.md). By polling cgroups every second, Netdata can produce meaningful +[cgroups.plugin](https://github.com/netdata/netdata/blob/master/src/collectors/cgroups.plugin/README.md). By polling cgroups every second, Netdata can produce meaningful visualizations about the CPU, memory, disk, and network utilization of all running containers on the host system with zero configuration. Netdata also collects metrics from applications running inside of Docker containers. For example, if you create a MySQL database container using `docker run --name some-mysql -e MYSQL_ROOT_PASSWORD=my-secret-pw -d mysql:tag`, it exposes metrics on port 3306. You can configure the [MySQL -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/mysql/README.md) to look at `127.0.0.0:3306` for +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/mysql/README.md) to look at `127.0.0.0:3306` for MySQL metrics: ```yml @@ -51,11 +51,11 @@ jobs: ``` Netdata then collects metrics from the container itself, but also dozens [MySQL-specific -metrics](https://github.com/netdata/go.d.plugin/blob/master/modules/mysql/README.md#charts) as well. +metrics](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/mysql/README.md#charts) as well. ### Collect metrics from applications running in Docker containers -You could use this technique to monitor an entire infrastructure of Docker containers. The same [enable and configure](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md) procedures apply whether an application runs on the host system or inside +You could use this technique to monitor an entire infrastructure of Docker containers. The same [enable and configure](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md) procedures apply whether an application runs on the host system or inside a container. You may need to configure the target endpoint if it's not the application's default. Netdata can even [run in a Docker container](https://github.com/netdata/netdata/blob/master/packaging/docker/README.md) itself, and then collect metrics about the @@ -76,15 +76,15 @@ your k8s infrastructure. configuration files for [compatible applications](https://github.com/netdata/helmchart#service-discovery-and-supported-services) and any endpoints covered by our [generic Prometheus - collector](https://github.com/netdata/go.d.plugin/blob/master/modules/prometheus/README.md). With these + collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/prometheus/README.md). With these configuration files, Netdata collects metrics from any compatible applications as they run _inside_ a pod. Service discovery happens without manual intervention as pods are created, destroyed, or moved between nodes. -- A [Kubelet collector](https://github.com/netdata/go.d.plugin/blob/master/modules/k8s_kubelet/README.md), which runs +- A [Kubelet collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/k8s_kubelet/README.md), which runs on each node in a k8s cluster to monitor the number of pods/containers, the volume of operations on each container, and more. -- A [kube-proxy collector](https://github.com/netdata/go.d.plugin/blob/master/modules/k8s_kubeproxy/README.md), which +- A [kube-proxy collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/k8s_kubeproxy/README.md), which also runs on each node and monitors latency and the volume of HTTP requests to the proxy. -- A [cgroups collector](https://github.com/netdata/netdata/blob/master/collectors/cgroups.plugin/README.md), which collects CPU, memory, and bandwidth metrics for +- A [cgroups collector](https://github.com/netdata/netdata/blob/master/src/collectors/cgroups.plugin/README.md), which collects CPU, memory, and bandwidth metrics for each container running on your k8s cluster. For a holistic view of Netdata's Kubernetes monitoring capabilities, see our guide: [_Monitor a Kubernetes (k8s) cluster diff --git a/docs/collect/system-metrics.md b/docs/collect/system-metrics.md index daaf61d7..e4051b0f 100644 --- a/docs/collect/system-metrics.md +++ b/docs/collect/system-metrics.md @@ -17,35 +17,35 @@ To gather system metrics, Netdata uses roughly a dozen plugins, each of which ha specific metrics exposed by the host. The system metrics Netdata users interact with most for health monitoring and performance troubleshooting are collected and visualized by `proc.plugin`, `cgroups.plugin`, and `ebpf.plugin`. -[**proc.plugin**](https://github.com/netdata/netdata/blob/master/collectors/proc.plugin/README.md) gathers metrics from the `/proc` and `/sys` folders in Linux +[**proc.plugin**](https://github.com/netdata/netdata/blob/master/src/collectors/proc.plugin/README.md) gathers metrics from the `/proc` and `/sys` folders in Linux systems, along with a few other endpoints, and is responsible for the bulk of the system metrics collected and visualized by Netdata. It collects CPU, memory, disks, load, networking, mount points, and more with zero configuration. It even allows Netdata to monitor its own resource utilization! -[**cgroups.plugin**](https://github.com/netdata/netdata/blob/master/collectors/cgroups.plugin/README.md) collects rich metrics about containers and virtual machines +[**cgroups.plugin**](https://github.com/netdata/netdata/blob/master/src/collectors/cgroups.plugin/README.md) collects rich metrics about containers and virtual machines using the virtual files under `/sys/fs/cgroup`. By reading cgroups, Netdata can instantly collect resource utilization metrics for systemd services, all containers (Docker, LXC, LXD, Libvirt, systemd-nspawn), and more. Learn more in the [collecting container metrics](https://github.com/netdata/netdata/blob/master/docs/collect/container-metrics.md) doc. -[**ebpf.plugin**](https://github.com/netdata/netdata/blob/master/collectors/ebpf.plugin/README.md): Netdata's extended Berkeley Packet Filter (eBPF) collector +[**ebpf.plugin**](https://github.com/netdata/netdata/blob/master/src/collectors/ebpf.plugin/README.md): Netdata's extended Berkeley Packet Filter (eBPF) collector monitors Linux kernel-level metrics for file descriptors, virtual filesystem IO, and process management. You can use our eBPF collector to analyze how and when a process accesses files, when it makes system calls, whether it leaks memory or creating zombie processes, and more. While the above plugins and associated collectors are the most important for system metrics, there are many others. You -can find all system collectors in our [supported collectors list](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md#system-collectors). +can find all system collectors in our [supported collectors list](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md#system-collectors). ## Collect Windows system metrics Netdata is also capable of monitoring Windows systems. The [Windows -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/windows/README.md) integrates with +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/windows/README.md) integrates with [windows_exporter](https://github.com/prometheus-community/windows_exporter), a small Go-based binary that you can run on Windows systems. The Windows collector then gathers metrics from an endpoint created by windows_exporter, for more -details see [the requirements](https://github.com/netdata/go.d.plugin/blob/master/modules/windows/README.md#requirements). +details see [the requirements](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/windows/README.md#requirements). -Next, [configure](https://github.com/netdata/go.d.plugin/blob/master/modules/windows/README.md#configuration) the Windows +Next, [configure](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/windows/README.md#configuration) the Windows collector to point to the URL and port of your exposed endpoint. Restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system. You'll start seeing Windows system metrics, such as CPU +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system. You'll start seeing Windows system metrics, such as CPU utilization, memory, bandwidth per NIC, number of processes, and much more. For information about collecting metrics from applications _running on Windows systems_, see the [application metrics diff --git a/docs/configure/common-changes.md b/docs/configure/common-changes.md index 1c6f6f5a..2d1757fe 100644 --- a/docs/configure/common-changes.md +++ b/docs/configure/common-changes.md @@ -22,8 +22,8 @@ directory. The Netdata Agent's [local dashboard](https://github.com/netdata/netdata/blob/master/docs/category-overview-pages/accessing-netdata-dashboards.md), accessible at `http://NODE:19999` is highly configurable. If -you use Netdata Cloud -for [infrastructure monitoring](https://github.com/netdata/netdata/blob/master/docs/quickstart/infrastructure.md), you +you use [Netdata Cloud](https://github.com/netdata/netdata/blob/master/docs/netdata-cloud/README.md) +for infrastructure monitoring, you will see many of these changes reflected in those visualizations due to the way Netdata Cloud proxies metric data and metadata to your browser. @@ -38,7 +38,7 @@ to help you determine the exact settings for your desired retention period. ### Reduce the data collection frequency Change `update every` in -the [`[global]` section](https://github.com/netdata/netdata/blob/master/daemon/config/README.md#global-section-options) +the [`[global]` section](https://github.com/netdata/netdata/blob/master/src/daemon/config/README.md#global-section-options) of `netdata.conf` so that it is greater than `1`. An `update every` of `5` means the Netdata Agent enforces a _minimum_ collection frequency of 5 seconds. @@ -51,13 +51,13 @@ of 5 seconds. Every collector and plugin has its own `update every` setting, which you can also change in the `go.d.conf`, `python.d.conf` or `charts.d.conf` files, or in individual collector configuration files. If the `update every` for an individual collector is less than the global, the Netdata Agent uses the global setting. See -the [enable or configure a collector](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md#enable-and-disable-a-specific-collection-module) +the [enable or configure a collector](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md#enable-and-disable-a-specific-collection-module) doc for details. ### Disable a collector or plugin Turn off entire plugins in -the [`[plugins]` section](https://github.com/netdata/netdata/blob/master/daemon/config/README.md#plugins-section-options) +the [`[plugins]` section](https://github.com/netdata/netdata/blob/master/src/daemon/config/README.md#plugins-section-options) of `netdata.conf`. @@ -83,9 +83,9 @@ sudo ./edit-config health.d/example-alert.conf Or, append your new alert to an existing file by editing a relevant existing file in the `health.d/` directory. -Read more about [configuring alerts](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md) to +Read more about [configuring alerts](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md) to get started, and see -the [health monitoring reference](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md) for a full listing +the [health monitoring reference](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md) for a full listing of options available in health entities. ### Configure a specific alert @@ -116,7 +116,7 @@ template: disk_fill_rate ### Turn of all alerts and notifications Set `enabled` to `no` in -the [`[health]`](https://github.com/netdata/netdata/blob/master/daemon/config/README.md#health-section-options) +the [`[health]`](https://github.com/netdata/netdata/blob/master/src/daemon/config/README.md#health-section-options) section of `netdata.conf`. ### Enable alert notifications @@ -131,7 +131,7 @@ click on the link to your preferred notification method to find documentation fo While the Netdata Agent is both [open and secure by design](https://www.netdata.cloud/blog/netdata-agent-dashboard/), we recommend every user take some action to administer and secure their nodes. -Learn more about the available options in the [security design documentation](https://github.com/netdata/netdata/blob/master/docs/netdata-security.md). +Learn more about the available options in the [security design documentation](https://github.com/netdata/netdata/blob/master/docs/security-and-privacy-design/README.md). ## Reduce resource usage diff --git a/docs/configure/nodes.md b/docs/configure/nodes.md index 8fdd1070..99e00223 100644 --- a/docs/configure/nodes.md +++ b/docs/configure/nodes.md @@ -31,20 +31,20 @@ these files in your own Netdata config directory, as the next section describes exist. - `netdata.conf` is the main configuration file. This is where you'll find most configuration options. Read descriptions - for each in the [daemon config](https://github.com/netdata/netdata/blob/master/daemon/config/README.md) doc. + for each in the [daemon config](https://github.com/netdata/netdata/blob/master/src/daemon/config/README.md) doc. - `edit-config` is a shell script used for [editing configuration files](#use-edit-config-to-edit-configuration-files). - Various configuration files ending in `.conf` for [configuring plugins or - collectors](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md) behave. Examples: `go.d.conf`, + collectors](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md) behave. Examples: `go.d.conf`, `python.d.conf`, and `ebpf.d.conf`. - Various directories ending in `.d`, which contain other configuration files, each ending in `.conf`, for [configuring - specific collectors](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md). + specific collectors](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md). - `apps_groups.conf` is a configuration file for changing how applications/processes are grouped when viewing the - **Application** charts from [`apps.plugin`](https://github.com/netdata/netdata/blob/master/collectors/apps.plugin/README.md) or - [`ebpf.plugin`](https://github.com/netdata/netdata/blob/master/collectors/ebpf.plugin/README.md). -- `health.d/` is a directory that contains [health configuration files](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md). + **Application** charts from [`apps.plugin`](https://github.com/netdata/netdata/blob/master/src/collectors/apps.plugin/README.md) or + [`ebpf.plugin`](https://github.com/netdata/netdata/blob/master/src/collectors/ebpf.plugin/README.md). +- `health.d/` is a directory that contains [health configuration files](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md). - `health_alarm_notify.conf` enables and configures [alert notifications](https://github.com/netdata/netdata/blob/master/docs/monitor/enable-notifications.md). -- `statsd.d/` is a directory for configuring Netdata's [statsd collector](https://github.com/netdata/netdata/blob/master/collectors/statsd.plugin/README.md). -- `stream.conf` configures [parent-child streaming](https://github.com/netdata/netdata/blob/master/streaming/README.md) between separate nodes running the Agent. +- `statsd.d/` is a directory for configuring Netdata's [statsd collector](https://github.com/netdata/netdata/blob/master/src/collectors/statsd.plugin/README.md). +- `stream.conf` configures [parent-child streaming](https://github.com/netdata/netdata/blob/master/src/streaming/README.md) between separate nodes running the Agent. - `.environment` is a hidden file that describes the environment in which the Netdata Agent is installed, including the `PATH` and any installation options. Useful for [reinstalling](https://github.com/netdata/netdata/blob/master/packaging/installer/REINSTALL.md) or [uninstalling](https://github.com/netdata/netdata/blob/master/packaging/installer/UNINSTALL.md) the Agent. @@ -98,7 +98,7 @@ method for `edit-config` to write into the config directory. Use your `$EDITOR`, > defaulted to `vim` or `nano`. Use `export EDITOR=` to change this temporarily, or edit your shell configuration file > to change to permanently. -After you make your changes, you need to [restart the Agent](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) with `sudo systemctl +After you make your changes, you need to [restart the Agent](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) with `sudo systemctl restart netdata` or the appropriate method for your system. Here's an example of editing the node's hostname, which appears in both the local dashboard and in Netdata Cloud. diff --git a/docs/contributing/style-guide.md b/docs/contributing/style-guide.md index b9dd63b8..2487f2eb 100644 --- a/docs/contributing/style-guide.md +++ b/docs/contributing/style-guide.md @@ -305,7 +305,7 @@ Don't include full paths, beginning from the system's root (`/`), as these might | | | |-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Not recommended | Use `edit-config` to edit Netdata's configuration: `sudo /etc/netdata/edit-config netdata.conf`. | -| **Recommended** | Use `edit-config` to edit Netdata's configuration by first navigating to your [Netdata config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#the-netdata-config-directory), which is typically at `/etc/netdata`, then running `sudo edit-config netdata.conf`. | +| **Recommended** | Use `edit-config` to edit Netdata's configuration by first navigating to your [Netdata config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory), which is typically at `/etc/netdata`, then running `sudo edit-config netdata.conf`. | ### `sudo` @@ -366,7 +366,7 @@ In Markdown, use the standard image syntax, `![]()`, and place the alt text betw using our logo: ```markdown - + ``` Reference in-product text, code samples, and terminal output with actual text content, not screen captures or other @@ -466,14 +466,14 @@ The following tables describe the standard spelling, capitalization, and usage o | Term | Definition | |-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **claimed node** | A node that you've proved ownership of by completing the [connecting to Cloud process](https://github.com/netdata/netdata/blob/master/claim/README.md). The claimed node will then appear in your Space and any War Rooms you added it to. | +| **claimed node** | A node that you've proved ownership of by completing the [connecting to Cloud process](https://github.com/netdata/netdata/blob/master/src/claim/README.md). The claimed node will then appear in your Space and any War Rooms you added it to. | | **Netdata** | The company behind the open-source Netdata Agent and the Netdata Cloud web application. Never use _netdata_ or _NetData_. <br /><br />In general, focus on the user's goals, actions, and solutions rather than what the company provides. For example, write _Learn more about enabling alert notifications on your preferred platforms_ instead of _Netdata sends alert notifications to your preferred platforms_. | | **Netdata Agent** | The free and open source [monitoring agent](https://github.com/netdata/netdata) that you can install on all of your distributed systems, whether they're physical, virtual, containerized, ephemeral, and more. The Agent monitors systems running Linux, Docker, Kubernetes, macOS, FreeBSD, and more, and collects metrics from hundreds of popular services and applications. | | **Netdata Cloud** | The web application hosted at [https://app.netdata.cloud](https://app.netdata.cloud) that helps you monitor an entire infrastructure of distributed systems in real time. <br /><br />Never use _Cloud_ without the preceding _Netdata_ to avoid ambiguity. | | **Netdata community forum** | The Discourse-powered forum for feature requests, Netdata Cloud technical support, and conversations about Netdata's monitoring and troubleshooting products. | | **node** | A system on which the Netdata Agent is installed. The system can be physical, virtual, in a Docker container, and more. Depending on your infrastructure, you may have one, dozens, or hundreds of nodes. Some nodes are _ephemeral_, in that they're created/destroyed automatically by an orchestrator service. | | **Space** | The highest level container within Netdata Cloud for a user to organize their team members and nodes within their infrastructure. A Space likely represents an entire organization or a large team. <br /><br />_Space_ is always capitalized. | -| **unreachable node** | A connected node with a disrupted [Agent-Cloud link](https://github.com/netdata/netdata/blob/master/aclk/README.md). Unreachable could mean the node no longer exists or is experiencing network connectivity issues with Cloud. | +| **unreachable node** | A connected node with a disrupted [Agent-Cloud link](https://github.com/netdata/netdata/blob/master/src/aclk/README.md). Unreachable could mean the node no longer exists or is experiencing network connectivity issues with Cloud. | | **visited node** | A node which has had its Agent dashboard directly visited by a user. A list of these is maintained on a per-user basis. | | **War Room** | A smaller grouping of nodes where users can view key metrics in real-time and monitor the health of many nodes with their alert status. War Rooms can be used to organize nodes in any way that makes sense for your infrastructure, such as by a service, purpose, physical location, and more. <br /><br />_War Room_ is always capitalized. | diff --git a/docs/dashboard/customize.md b/docs/dashboard/customize.md index 301f0bd6..afcf9216 100644 --- a/docs/dashboard/customize.md +++ b/docs/dashboard/customize.md @@ -4,7 +4,7 @@ > > This document is only applicable to the v1 version of the dashboard and doesn't affect the [Netdata Dashboard](https://github.com/netdata/netdata/blob/master/docs/category-overview-pages/accessing-netdata-dashboards.md). -While the [Netdata dashboard](https://github.com/netdata/netdata/blob/master/web/gui/README.md) comes preconfigured with hundreds of charts and +While the [Netdata dashboard](https://github.com/netdata/netdata/blob/master/src/web/gui/README.md) comes preconfigured with hundreds of charts and thousands of metrics, you may want to alter your experience based on a particular use case or preferences. ## Dashboard settings diff --git a/docs/dashboard/import-export-print-snapshot.md b/docs/dashboard/import-export-print-snapshot.md index 5a05f51e..4a5ca499 100644 --- a/docs/dashboard/import-export-print-snapshot.md +++ b/docs/dashboard/import-export-print-snapshot.md @@ -14,6 +14,10 @@ learn_rel_path: "Operations" # Import, export, and print a snapshot +>❗This feature is only available on v1 dashboards, it hasn't been port-forwarded to v2. +> For more information on accessing dashboards check [this documentation](https://github.com/netdata/netdata/blob/master/docs/category-overview-pages/accessing-netdata-dashboards.md). + + Netdata can export snapshots of the contents of your dashboard at a given time, which you can then import into any other node running Netdata. Or, you can create a print-ready version of your dashboard to save to PDF or actually print to paper. diff --git a/docs/deployment-guides/README.md b/docs/deployment-guides/README.md new file mode 100644 index 00000000..18f57885 --- /dev/null +++ b/docs/deployment-guides/README.md @@ -0,0 +1,25 @@ +# Deployment Guides + +Netdata can be used to monitor all kinds of infrastructure, from stand-alone tiny IoT devices to complex hybrid setups combining on-premise and cloud infrastructure, mixing bare-metal servers, virtual machines and containers. + +There are 3 components to structure your Netdata ecosystem: + +1. **Netdata Agents** + + To monitor the physical or virtual nodes of your infrastructure, including all applications and containers running on them. + + Netdata Agents are Open-Source, licensed under GPL v3+. + +2. **Netdata Parents** + + To create [observability centralization points](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/README.md) within your infrastructure, to offload Netdata Agents functions from your production systems, to provide high-availability of your data, increased data retention and isolation of your nodes. + + Netdata Parents are implemented using the Netdata Agent software. Any Netdata Agent can be an Agent for a node and a Parent for other Agents, at the same time. + + It is recommended to set up multiple Netdata Parents. They will all seamlessly be integrated by Netdata Cloud into one monitoring solution. + +3. **Netdata Cloud** + + Our SaaS, combining all your infrastructure, all your Netdata Agents and Parents, into one uniform, distributed, scalable, monitoring database, offering advanced data slicing and dicing capabilities, custom dashboards, advanced troubleshooting tools, user management, centralized management of alerts, and more. + +The Netdata Agent is a highly modular software piece, providing data collection via numerous plugins, an in-house crafted time-series database, a query engine, health monitoring and alerts, machine learning and anomaly detection, metrics exporting to third party systems. diff --git a/docs/category-overview-pages/deployment-strategies.md b/docs/deployment-guides/deployment-strategies.md index 69daaf9f..3be2edbc 100644 --- a/docs/category-overview-pages/deployment-strategies.md +++ b/docs/deployment-guides/deployment-strategies.md @@ -1,34 +1,5 @@ # Deployment strategies -Netdata can be used to monitor all kinds of infrastructure, from stand-alone tiny IoT devices to complex hybrid setups -combining on-premise and cloud infrastructure, mixing bare-metal servers, virtual machines and containers. - -There are 3 components to structure your Netdata ecosystem: - -1. **Netdata Agents** - To monitor the physical or virtual nodes of your infrastructure, including all applications and containers running on them. - - Netdata Agents are Open-Source, licensed under GPL v3+. - -2. **Netdata Parents** - To create data centralization points within your infrastructure, to offload Netdata Agents functions from your production - systems, to provide high-availability of your data, increased data retention and isolation of your nodes. - - Netdata Parents are implemented using the Netdata Agent software. Any Netdata Agent can be an Agent for a node and a Parent - for other Agents, at the same time. - - It is recommended to set up multiple Netdata Parents. They will all seamlessly be integrated by Netdata Cloud into one monitoring solution. - - -3. **Netdata Cloud** - Our SaaS, combining all your infrastructure, all your Netdata Agents and Parents, into one uniform, distributed, - scalable, monitoring database, offering advanced data slicing and dicing capabilities, custom dashboards, advanced troubleshooting - tools, user management, centralized management of alerts, and more. - - -The Netdata Agent is a highly modular software piece, providing data collection via numerous plugins, an in-house crafted time-series -database, a query engine, health monitoring and alerts, machine learning and anomaly detection, metrics exporting to third party systems. - ## Deployment Options Overview @@ -39,7 +10,7 @@ This section provides a quick overview of a few common deployment options. The n To help our users have a complete experience of Netdata when they install it for the first time, a Netdata Agent with default configuration is a complete monitoring solution out of the box, having all these features enabled and available. -The Agent will act as a _stand-alone_ Agent by default, and this is great to start out with for small setups and home labs. By [connecting each Agent to Cloud](https://github.com/netdata/netdata/blob/master/claim/README.md), you can see an overview of all your nodes, with aggregated charts and centralized alerting, without setting up a Parent. +The Agent will act as a _stand-alone_ Agent by default, and this is great to start out with for small setups and home labs. By [connecting each Agent to Cloud](https://github.com/netdata/netdata/blob/master/src/claim/README.md), you can see an overview of all your nodes, with aggregated charts and centralized alerting, without setting up a Parent.  @@ -71,13 +42,13 @@ The stand-alone setup is configured out of the box with reasonable defaults, but ### Parent – Child Deployment -For setups involving Child and Parent Agents, the Agents need to be configured for [_streaming_](https://github.com/netdata/netdata/blob/master/streaming/README.md), through the configuration file `stream.conf`. This will instruct the Child to stream data to the Parent and the Parent to accept streaming connections for one or more Child Agents. To secure this connection, both need set up a shared API key (to replace the string `API_KEY` in the examples below). Additionally, the Child is configured with one or more addresses of Parent Agents (`PARENT_IP_ADDRESS`). +For setups involving Child and Parent Agents, the Agents need to be configured for [_streaming_](https://github.com/netdata/netdata/blob/master/src/streaming/README.md), through the configuration file `stream.conf`. This will instruct the Child to stream data to the Parent and the Parent to accept streaming connections for one or more Child Agents. To secure this connection, both need set up a shared API key (to replace the string `API_KEY` in the examples below). Additionally, the Child is configured with one or more addresses of Parent Agents (`PARENT_IP_ADDRESS`). An API key is a key created with `uuidgen` and is used for authentication and/or customization in the Parent side. I.e. a Child will stream using the API key, and a Parent is configured to accept connections from Child, but can also apply different options for children by using multiple different API keys. The easiest setup uses just one API key for all Child Agents. #### Child config -As mentioned above, the recommendation is to not claim the Child to Cloud directly during your setup, avoiding establishing an [ACLK](https://github.com/netdata/netdata/blob/master/aclk/README.md) connection. +As mentioned above, the recommendation is to not claim the Child to Cloud directly during your setup, avoiding establishing an [ACLK](https://github.com/netdata/netdata/blob/master/src/aclk/README.md) connection. To reduce the footprint of the Netdata Agent on your production system, some capabilities can be switched OFF on the Child and kept ON on the Parent. In this example, Machine Learning and Alerting are disabled in the Child, so that the Parent can take the load. We also use RAM instead of disk to store metrics with limited retention, covering temporary network issues. @@ -130,7 +101,7 @@ Set the following parameters: #### Parent config -For the Parent, besides setting up streaming, the example will also provide an example configuration of multiple [tiers](https://github.com/netdata/netdata/blob/master/database/engine/README.md#tiering) of metrics [storage](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md), for 10 children, with about 2k metrics each. +For the Parent, besides setting up streaming, the example will also provide an example configuration of multiple [tiers](https://github.com/netdata/netdata/blob/master/src/database/engine/README.md#tiering) of metrics [storage](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md), for 10 children, with about 2k metrics each. - 1s granularity at tier 0 for 1 week - 1m granularity at tier 1 for 1 month @@ -237,12 +208,12 @@ On both Netdata Parent and all Child Agents, edit `stream.conf` with `/etc/netda We strongly recommend the following configuration changes for production deployments: -1. Understand Netdata's [security and privacy design](https://github.com/netdata/netdata/blob/master/docs/netdata-security.md) and +1. Understand Netdata's [security and privacy design](https://github.com/netdata/netdata/blob/master/docs/security-and-privacy-design/README.md) and [secure your nodes](https://github.com/netdata/netdata/blob/master/docs/category-overview-pages/secure-nodes.md) - + To safeguard your infrastructure and comply with your organization's security policies. -2. Set up [streaming and replication](https://github.com/netdata/netdata/blob/master/streaming/README.md) to: +2. Set up [streaming and replication](https://github.com/netdata/netdata/blob/master/src/streaming/README.md) to: - Offload Netdata Agents running on production systems and free system resources for the production applications running on them. - Isolate production systems from the rest of the world and improve security. @@ -262,7 +233,7 @@ We also suggest that you: 2. [Change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md) To control Netdata's memory use, when you have a lot of ephemeral metrics. - + 3. [Use host labels](https://github.com/netdata/netdata/blob/master/docs/guides/using-host-labels.md) - + To organize systems, metrics, and alerts. diff --git a/docs/deployment-guides/deployment-with-centralization-points.md b/docs/deployment-guides/deployment-with-centralization-points.md new file mode 100644 index 00000000..b3e2b40d --- /dev/null +++ b/docs/deployment-guides/deployment-with-centralization-points.md @@ -0,0 +1,121 @@ +# Deployment with Centralization Points + +An observability centralization point can centralize both metrics and logs. The sending systems are called Children, while the receiving systems are called a Parents. + +When metrics and logs are centralized, the Children are never queried for metrics and logs. The Netdata Parents have all the data needed to satisfy queries. + +- **Metrics** are centralized by Netdata, with a feature we call **Streaming**. The Parents listen for incoming connections and permit access only to Children that connect to it with the right API key. Children are configured to push their metrics to the Parents and they initiate the connections to do so. + +- **Logs** are centralized with methodologies provided by `systemd-journald`. This involves installing `systemd-journal-remote` on both the Parent and the Children, and configuring the keys required for this communication. + +| Feature | How it works | +|:---------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:| +| Unified infrastructure dashboards for metrics | Yes, at Netdata Cloud | +| Unified infrastructure dashboards for logs | All logs are accessible via the same dashboard at Netdata Cloud, although they are unified per Netdata Parent | +| Centrally configured alerts | Yes, at Netdata Parents | +| Centrally dispatched alert notifications | Yes, at Netdata Cloud | +| Data are exclusively on-prem | Yes, Netdata Cloud queries Netdata Agents to satisfy dashboard queries. | + +A configuration with 2 observability centralization points, looks like this: + +```mermaid +flowchart LR + WEB[["One unified + dashboard + for all nodes"]] + NC(["<b>Netdata Cloud</b> + decides which agents + need to be queried"]) + SA1["Netdata at AWS + A1"] + SA2["Netdata at AWS + A2"] + SAN["Netdata at AWS + AN"] + PA["<b>Netdata Parent A</b> + at AWS + having all metrics & logs + for all Ax nodes"] + SB1["Netdata On-Prem + B1"] + SB2["Netdata On-Prem + B2"] + SBN["Netdata On-Prem + BN"] + PB["<b>Netdata Parent B</b> + On-Prem + having all metrics & logs + for all Bx nodes"] + WEB -->|query| NC -->|query| PA & PB + PA ---|stream| SA1 & SA2 & SAN + PB ---|stream| SB1 & SB2 & SBN +``` + +Netdata Cloud queries the Netdata Parents to provide aggregated dashboard views. + +For alerts, the dispatch of notifications looks like in the following chart: + +```mermaid +flowchart LR + NC(["<b>Netdata Cloud</b> + applies silencing + & user settings"]) + SA1["Netdata at AWS + A1"] + SA2["Netdata at AWS + A2"] + SAN["Netdata at AWS + AN"] + PA["<b>Netdata Parent A</b> + at AWS + having all metrics & logs + for all Ax nodes"] + SB1["Netdata On-Prem + B1"] + SB2["Netdata On-Prem + B2"] + SBN["Netdata On-Prem + BN"] + PB["<b>Netdata Parent B</b> + On-Prem + having all metrics & logs + for all Bx nodes"] + EMAIL{{"<b>e-mail</b> + notifications"}} + MOBILEAPP{{"<b>Netdata Mobile App</b> + notifications"}} + SLACK{{"<b>Slack</b> + notifications"}} + OTHER{{"Other + notifications"}} + PA & PB -->|alert transitions| NC -->|notification| EMAIL & MOBILEAPP & SLACK & OTHER + SA1 & SA2 & SAN ---|stream| PA + SB1 & SB2 & SBN ---|stream| PB +``` + +### Configuration steps for deploying Netdata with Observability Centralization Points + +For Metrics: + +- Install Netdata agents on all systems and the Netdata Parents. + +- Configure `stream.conf` at the Netdata Parents to enable streaming access with an API key. + +- Configure `stream.conf` at the Netdata Children to enable streaming to the configured Netdata Parents. + +For Logs: + +- Install `systemd-journal-remote` on all systems and the Netdata Parents. + +- Configure `systemd-journal-remote` at the Netdata Parents to enable logs reception. + +- Configure `systemd-journal-upload` at the Netdata Children to enable transmission of their logs to the Netdata Parents. + +Optionally: + +- Disable ML, health checks and dashboard access at Netdata Children to save resources and avoid duplicate notifications. + +When using Netdata Cloud: + +- Optionally: disable dashboard access on all Netdata agents (including Netdata Parents). +- Optionally: disable alert notifications on all Netdata agents (including Netdata Parents). diff --git a/docs/deployment-guides/standalone-deployment.md b/docs/deployment-guides/standalone-deployment.md new file mode 100644 index 00000000..5baef805 --- /dev/null +++ b/docs/deployment-guides/standalone-deployment.md @@ -0,0 +1,139 @@ +# Standalone Deployment + +To help our users have a complete experience of Netdata when they install it for the first time, a Netdata Agent with default configuration is a complete monitoring solution out of the box, having all its features enabled and available. + +So, each Netdata agent acts as a standalone monitoring system by default. + +## Standalone agents, without Netdata Cloud + +| Feature | How it works | +|:---------------------------------------------:|:----------------------------------------------------:| +| Unified infrastructure dashboards for metrics | No, each Netdata agent provides its own dashboard | +| Unified infrastructure dashboards for logs | No, each Netdata agent exposes its own logs | +| Centrally configured alerts | No, each Netdata has its own alerts configuration | +| Centrally dispatched alert notifications | No, each Netdata agent sends notifications by itself | +| Data are exclusively on-prem | Yes | + +When using Standalone Netdata agents, each of them offers an API and a dashboard, at its own unique URL, that looks like `http://agent-ip:19999`. + +So, each of the Netdata agents has to be accessed individually and independently of the others: + +```mermaid +flowchart LR + WEB[["Multiple + Independent + Dashboards"]] + S1["Standalone + Netdata + 1"] + S2["Standalone + Netdata + 2"] + SN["Standalone + Netdata + N"] + WEB -->|URL 1| S1 + WEB -->|URL 2| S2 + WEB -->|URL N| SN +``` + +The same is true for alert notifications. Each of the Netdata agents runs its own alerts and sends notifications by itself, according to its configuration: + +```mermaid +flowchart LR + S1["Standalone + Netdata + 1"] + S2["Standalone + Netdata + 2"] + SN["Standalone + Netdata + N"] + EMAIL{{"<b>e-mail</b> + notifications"}} + SLACK{{"<b>Slack</b> + notifications"}} + OTHER{{"Other + notifications"}} + S1 & S2 & SN .-> SLACK + S1 & S2 & SN ---> EMAIL + S1 & S2 & SN ==> OTHER +``` + +### Configuration steps for standalone Netdata agents without Netdata Cloud + +No special configuration needed. + +- Install Netdata agents on all your systems, then access each of them via its own unique URL, that looks like `http://agent-ip:19999/`. + +## Standalone agents, with Netdata Cloud + +| Feature | How it works | +|:---------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------:| +| Unified infrastructure dashboards for metrics | Yes, via Netdata Cloud, all charts aggregate metrics from all servers. | +| Unified infrastructure dashboards for logs | All logs are accessible via the same dashboard at Netdata Cloud, although they are not unified (ie. logs from different servers are not multiplexed into a single view) | +| Centrally configured alerts | No, each Netdata has its own alerts configuration | +| Centrally dispatched alert notifications | Yes, via Netdata Cloud | +| Data are exclusively on-prem | Yes, Netdata Cloud queries Netdata Agents to satisfy dashboard queries. | + +By [connecting all Netdata agents to Netdata Cloud](https://github.com/netdata/netdata/blob/master/src/claim/README.md), you can have a unified infrastructure view of all your nodes, with aggregated charts, without configuring [observability centralization points](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/README.md). + +```mermaid +flowchart LR + WEB[["One unified + dashboard + for all nodes"]] + NC(["<b>Netdata Cloud</b> + decides which agents + need to be queried"]) + S1["Standalone + Netdata + 1"] + S2["Standalone + Netdata + 2"] + SN["Standalone + Netdata + N"] + WEB -->|queries| NC + NC -->|queries| S1 & S2 & SN +``` + +Similarly for alerts, Netdata Cloud receives all alert transitions from all agents, decides which notifications should be sent and how, applies silencing rules, maintenance windows and based on each Netdata Cloud space and user settings, dispatches notifications: + +```mermaid +flowchart LR + EMAIL{{"<b>e-mail</b> + notifications"}} + MOBILEAPP{{"<b>Netdata Mobile App</b> + notifications"}} + SLACK{{"<b>Slack</b> + notifications"}} + OTHER{{"Other + notifications"}} + NC(["<b>Netdata Cloud</b> + applies silencing + & user settings"]) + S1["Standalone + Netdata + 1"] + S2["Standalone + Netdata + 2"] + SN["Standalone + Netdata + N"] + NC -->|notification| EMAIL & MOBILEAPP & SLACK & OTHER + S1 & S2 & SN -->|alert transition| NC +``` + +> Note that alerts are still triggered by Netdata agents. Netdata Cloud takes care of the notifications only. + +### Configuration steps for standalone Netdata agents with Netdata Cloud + +- Install Netdata agents using the commands given by Netdata Cloud, so that they will be automatically added to your Netdata Cloud space. Otherwise, install Netdata agents and then claim them via the command line or their dashboard. + +- Optionally: disable their direct dashboard access to secure them. + +- Optionally: disable their alert notifications to avoid receiving email notifications directly from them (email notifications are automatically enabled when a working MTA is found on the systems Netdata agents are installed). diff --git a/docs/diagrams/build.sh b/docs/diagrams/build.sh new file mode 100755 index 00000000..9b3963e5 --- /dev/null +++ b/docs/diagrams/build.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env bash +# SPDX-License-Identifier: GPL-3.0-or-later + +path=$(dirname "$0") +cd "${path}" || exit 1 + +if [ ! -f "plantuml.jar" ] +then + echo >&2 "Please download 'plantuml.jar' from http://plantuml.com/ and put it the same folder with me." + exit 1 +fi + +for x in *.puml +do + [ "${x}" = "config.puml" ] && continue + + echo >&2 "Working on ${x}..." + java -jar plantuml.jar -tpng "${x}" + java -jar plantuml.jar -tsvg "${x}" + # java -jar plantuml.jar -ttxt "${x}" +done diff --git a/docs/diagrams/config.puml b/docs/diagrams/config.puml new file mode 100644 index 00000000..0ce0932f --- /dev/null +++ b/docs/diagrams/config.puml @@ -0,0 +1,46 @@ + +skinparam handwritten true +skinparam monochrome true +skinparam roundcorner 15 + +skinparam sequence { + ArrowThickness 3 + + DividerFontColor Black + DividerFontName Comic Sans MS + DividerFontSize 15 + DividerFontStyle Italic + + DelayFontColor Black + DelayFontName Comic Sans MS + DelayFontSize 15 + DelayFontStyle Italic + + TitleFontColor Black + TitleFontName Comic Sans MS + TitleFontStyle Italic + TitleFontSize 25 + + ArrowColor DeepSkyBlue + ArrowFontColor Black + ArrowFontName Comic Sans MS + ArrowFontStyle Regular + ArrowFontSize 19 + + ActorBorderColor DeepSkyBlue + + LifeLineBorderColor blue + LifeLineBackgroundColor #A9DCDF + + ParticipantBorderColor DeepSkyBlue + ParticipantBackgroundColor LightBlue + ParticipantFontName Comic Sans MS + ParticipantFontSize 20 + ParticipantFontColor Black + + ActorBackgroundColor aqua + ActorFontColor Black + ActorFontSize 20 + ActorFontName Comic Sans MS +} + diff --git a/docs/diagrams/data_structures/netdata_config.svg b/docs/diagrams/data_structures/netdata_config.svg new file mode 100644 index 00000000..5b2ed8da --- /dev/null +++ b/docs/diagrams/data_structures/netdata_config.svg @@ -0,0 +1,2 @@ +<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"> +<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1" width="905px" height="511px" viewBox="-0.5 -0.5 905 511" style="background-color: rgb(255, 255, 255);"><defs><clipPath id="mx-clip-7-34-132-26-0"><rect x="7" y="34" width="132" height="26"/></clipPath><clipPath id="mx-clip-7-60-132-26-0"><rect x="7" y="60" width="132" height="26"/></clipPath><clipPath id="mx-clip-7-86-132-26-0"><rect x="7" y="86" width="132" height="26"/></clipPath><clipPath 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use_count</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1247-269-172-26-0)" font-size="12px"><text x="1248.5" y="281.5">avl_tree_lock rrdvar_root_index</text></g><path d="M 3 173 L 3 147 L 253 147 L 253 173" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 173 L 3 667 L 253 667 L 253 173" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 173 L 253 173" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="127.5" y="164.5">RRDHOST</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-178-242-26-0)" font-size="12px"><text x="8.5" y="190.5">avl avl</text></g><path d="M 3 225 L 3 199 L 253 199 L 253 225" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 225 L 253 225" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="127.5" y="215.5">... host information</text></g><path d="M 3 251 L 3 225 L 253 225 L 253 251" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 251 L 253 251" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="127.5" y="241.5">... streaming</text></g><path d="M 3 277 L 3 251 L 253 251 L 253 277" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 277 L 253 277" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="127.5" y="267.5">... health monitoring options</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-282-242-26-0)" font-size="12px"><text x="8.5" y="294.5">RRDCALC *alarms</text></g><path d="M 3 329 L 3 303 L 253 303 L 253 329" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 329 L 3 433 L 253 433 L 253 329" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 329 L 253 329" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="127.5" y="320.5">... health monitoring options</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-334-242-26-0)" font-size="12px"><text x="8.5" y="346.5">ALARM_LOG health_log</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-360-242-26-0)" font-size="12px"><text x="8.5" y="372.5">uint32_t health_last_processed_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-386-242-26-0)" font-size="12px"><text x="8.5" y="398.5">uint32_t health_max_unique_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-412-242-26-0)" font-size="12px"><text x="8.5" y="424.5">uint32_t health_max_alarm_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-438-242-26-0)" font-size="12px"><text x="8.5" y="450.5">RRDCALCTEMPLATE *templates</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-464-242-26-0)" font-size="12px"><text x="8.5" y="476.5">RRDSET *rrdset_root</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-490-242-26-0)" font-size="12px"><text x="8.5" y="502.5">netdata_rwlock_t rrdhost_rwlock</text></g><path d="M 3 537 L 3 511 L 253 511 L 253 537" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 537 L 3 667 L 253 667 L 253 537" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 3 537 L 253 537" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="127.5" y="528.5">... indexes</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-542-242-26-0)" font-size="12px"><text x="8.5" y="554.5">avl_tree_lock rrdset_root_index</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-568-242-26-0)" font-size="12px"><text x="8.5" y="580.5">avl_tree_lock rrdset_root_index_name</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-594-242-26-0)" font-size="12px"><text x="8.5" y="606.5">avl_tree_lock rrdfamily_root_index</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-620-242-26-0)" font-size="12px"><text x="8.5" y="632.5">avl_tree_lock rrdvar_root_index</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-7-646-242-26-0)" font-size="12px"><text x="8.5" y="658.5">struct rrdhost *next</text></g><path d="M 253 654 L 273 654 L 273 631 C 276.9 631 276.9 625 273 625 L 273 625 L 273 605 C 276.9 605 276.9 599 273 599 L 273 599 L 273 553 C 276.9 553 276.9 547 273 547 L 273 547 L 273 127 L 128 127 L 128 140.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 128 145.88 L 124.5 138.88 L 128 140.63 L 131.5 138.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1613 472 L 1613 447 L 1783 447 L 1783 472" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1613 472 L 1613 654 L 1783 654 L 1783 472" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1613 472 L 1783 472" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="1697.5" y="464">RRDVAR</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1617-477-162-26-0)" font-size="12px"><text x="1618.5" y="489.5">avl avl</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1617-503-162-26-0)" font-size="12px"><text x="1618.5" y="515.5">char *name</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1617-529-162-26-0)" font-size="12px"><text x="1618.5" y="541.5">uint32_t hash</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1617-555-162-26-0)" font-size="12px"><text x="1618.5" y="567.5">RRDVAR_TYPE type</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1617-581-162-26-0)" font-size="12px"><text x="1618.5" y="593.5">RRDVAR_OPTIONS options</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1617-607-162-26-0)" font-size="12px"><text x="1618.5" y="619.5">void *value</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1617-633-162-26-0)" font-size="12px"><text x="1618.5" y="645.5">time_t last_updated</text></g><path d="M 1313 550 L 1313 524 L 1533 524 L 1533 550" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1313 550 L 1313 914 L 1533 914 L 1533 550" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1313 550 L 1533 550" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="1422.5" y="541.5">RRDSETVAR</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-555-212-26-0)" font-size="12px"><text x="1318.5" y="567.5">char *variable</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-581-212-26-0)" font-size="12px"><text x="1318.5" y="593.5">uint32_t hash</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-607-212-26-0)" font-size="12px"><text x="1318.5" y="619.5">char *key_fullid</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-633-212-26-0)" font-size="12px"><text x="1318.5" y="645.5">char *key_fullname</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-659-212-26-0)" font-size="12px"><text x="1318.5" y="671.5">RRDVAR_TYPE type</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-685-212-26-0)" font-size="12px"><text x="1318.5" y="697.5">void *value</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-711-212-26-0)" font-size="12px"><text x="1318.5" y="723.5">RRDVAR_OPTIONS options</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-737-212-26-0)" font-size="12px"><text x="1318.5" y="749.5">RRDVAR *var_local</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-763-212-26-0)" font-size="12px"><text x="1318.5" y="775.5">RRDVAR *var_family</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-789-212-26-0)" font-size="12px"><text x="1318.5" y="801.5">RRDVAR *var_host</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-815-212-26-0)" font-size="12px"><text x="1318.5" y="827.5">RRDVAR *var_family_name</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-841-212-26-0)" font-size="12px"><text x="1318.5" y="853.5">RRDVAR *var_host_name</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-867-212-26-0)" font-size="12px"><text x="1318.5" y="879.5">struct rrdset *rrdset</text></g><path d="M 1533 901 L 1553 901 L 1553 504 L 1423 504 L 1423 517.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1423 522.88 L 1419.5 515.88 L 1423 517.63 L 1426.5 515.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-1317-893-212-26-0)" font-size="12px"><text x="1318.5" y="905.5">struct rrdsetvar *next</text></g><path d="M 963 725 L 963 699 L 1243 699 L 1243 725" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 963 725 L 963 1375 L 1243 1375 L 1243 725" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 963 725 L 1243 725" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="1102.5" y="716.5">RRDDIMVAR</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-730-272-26-0)" font-size="12px"><text x="968.5" y="742.5">char *prefix</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-756-272-26-0)" font-size="12px"><text x="968.5" y="768.5">char *suffix</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-782-272-26-0)" font-size="12px"><text x="968.5" y="794.5">char *key_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-808-272-26-0)" font-size="12px"><text x="968.5" y="820.5">char *key_name</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-834-272-26-0)" font-size="12px"><text x="968.5" y="846.5">char *key_contextid</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-860-272-26-0)" font-size="12px"><text x="968.5" y="872.5">char *key_contexname</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-886-272-26-0)" font-size="12px"><text x="968.5" y="898.5">char *key_fullidid</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-912-272-26-0)" font-size="12px"><text x="968.5" y="924.5">char *key_fullidname</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-938-272-26-0)" font-size="12px"><text x="968.5" y="950.5">char *key_fullnameid</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-964-272-26-0)" font-size="12px"><text x="968.5" y="976.5">char *key_fullnamename</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-990-272-26-0)" font-size="12px"><text x="968.5" y="1002.5">RRDVAR_TYPE type</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1016-272-26-0)" font-size="12px"><text x="968.5" y="1028.5">void *value</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1042-272-26-0)" font-size="12px"><text x="968.5" y="1054.5">RRDVAR_OPTIONS options</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1068-272-26-0)" font-size="12px"><text x="968.5" y="1080.5">RRDVAR *var_local_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1094-272-26-0)" font-size="12px"><text x="968.5" y="1106.5">RRDVAR *var_local_name</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1120-272-26-0)" font-size="12px"><text x="968.5" y="1132.5">RRDVAR *var_family_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1146-272-26-0)" font-size="12px"><text x="968.5" y="1158.5">RRDVAR *var_family_name</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1172-272-26-0)" font-size="12px"><text x="968.5" y="1184.5">RRDVAR *var_family_contextid</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1198-272-26-0)" font-size="12px"><text x="968.5" y="1210.5">RRDVAR *var_family_contextname</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1224-272-26-0)" font-size="12px"><text x="968.5" y="1236.5">RRDVAR *var_host_chartidid</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1250-272-26-0)" font-size="12px"><text x="968.5" y="1262.5">RRDVAR *var_host_chartidname</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1276-272-26-0)" font-size="12px"><text x="968.5" y="1288.5">RRDVAR *var_host_chartnameid</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1302-272-26-0)" font-size="12px"><text x="968.5" y="1314.5">RRDVAR *var_host_chartnamename</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1328-272-26-0)" font-size="12px"><text x="968.5" y="1340.5">struct rrddim *rrddim</text></g><path d="M 1243 1362 L 1263 1362 L 1263 679 L 1103 679 L 1103 692.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1103 697.88 L 1099.5 690.88 L 1103 692.63 L 1106.5 690.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-967-1354-272-26-0)" font-size="12px"><text x="968.5" y="1366.5">struct rrddimvar *next</text></g><path d="M 843 881 L 903 881 L 903 679 L 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890 361 C 890 357.1 896 357.1 896 361 L 896 361 L 1000 361 C 1000 357.1 1006 357.1 1006 361 L 1006 361 L 1698 361 L 1698 440.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1698 445.88 L 1694.5 438.88 L 1698 440.63 L 1701.5 438.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(1174.5,355.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="48" height="11" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 11px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;background-color:#ffffff;">avl tree of</div></div></foreignObject><text x="24" y="11" fill="#000000" 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stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 63 810 L 63 966 L 283 966 L 283 810" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 63 810 L 283 810" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="172.5" y="801.5">ALARM_LOG</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-67-815-212-26-0)" font-size="12px"><text x="68.5" y="827.5">uint32_t next_log_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-67-841-212-26-0)" font-size="12px"><text x="68.5" y="853.5">uint32_t next_alarm_id</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-67-867-212-26-0)" font-size="12px"><text x="68.5" y="879.5">unsigned int count</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-67-893-212-26-0)" font-size="12px"><text x="68.5" y="905.5">unsigned int max</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-67-919-212-26-0)" font-size="12px"><text x="68.5" y="931.5">ALARM_ENTRY *alarms</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-67-945-212-26-0)" font-size="12px"><text x="68.5" y="957.5">netdata_rwlock_t alarm_log_rwlock</text></g><path d="M 283 927 L 413 927 L 413 1030.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 413 1035.88 L 409.5 1028.88 L 413 1030.63 L 416.5 1028.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(374.5,921.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="57" height="11" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 11px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;background-color:#ffffff;">linked list of</div></div></foreignObject><text x="29" y="11" fill="#000000" text-anchor="middle" font-size="11px" font-family="Helvetica">linked list of</text></switch></g><path d="M 343 1063 L 343 1037 L 483 1037 L 483 1063" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 343 1063 L 343 1115 L 483 1115 L 483 1063" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 343 1063 L 483 1063" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="412.5" y="1054.5">ALARM_ENTRY *</text></g><path d="M 483 1102 L 503 1102 L 503 1017 L 413 1017 L 413 1030.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 413 1035.88 L 409.5 1028.88 L 413 1030.63 L 416.5 1028.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-347-1068-132-26-0)" font-size="12px"><text x="348.5" y="1080.5">...</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-347-1094-132-26-0)" font-size="12px"><text x="348.5" y="1106.5">struct alarm_entry *next</text></g><path d="M 253 342 L 270 342 C 270 338.1 276 338.1 276 342 L 276 342 L 305 342 C 305 338.1 311 338.1 311 342 L 311 342 L 333 342 L 333 599 C 336.9 599 336.9 605 333 605 L 333 605 L 333 625 C 336.9 625 336.9 631 333 631 L 333 631 L 333 710 L 173 710 L 173 777.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 173 782.88 L 169.5 775.88 L 173 777.63 L 176.5 775.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 33 1086 L 33 1060 L 253 1060 L 253 1086" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 33 1086 L 33 1424 L 253 1424 L 253 1086" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 33 1086 L 253 1086" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="142.5" y="1077.5">RRDCALC</text></g><path d="M 33 1112 L 33 1086 L 253 1086 L 253 1112" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 33 1112 L 253 1112" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="142.5" y="1102.5">...</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-37-1117-212-26-0)" font-size="12px"><text x="38.5" y="1129.5">EVAL_EXPRESSION *calculation</text></g><g fill="#000000" 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1053.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 143 1058.88 L 139.5 1051.88 L 143 1053.63 L 146.5 1051.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 253 1385 L 273 1385 L 273 1040 L 143 1040 L 143 1053.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 143 1058.88 L 139.5 1051.88 L 143 1053.63 L 146.5 1051.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 253 1411 L 273 1411 L 273 1040 L 143 1040 L 143 1053.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 143 1058.88 L 139.5 1051.88 L 143 1053.63 L 146.5 1051.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 583 1060 L 583 1034 L 833 1034 L 833 1060" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 583 1060 L 583 1190 L 833 1190 L 833 1060" fill="#ffffff" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 583 1060 L 833 1060" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" text-anchor="middle" font-size="12px"><text x="707.5" y="1051.5">RRDCALCTEMPLATE *</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-587-1065-242-26-0)" font-size="12px"><text x="588.5" y="1077.5">...</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-587-1091-242-26-0)" font-size="12px"><text x="588.5" y="1103.5">EVAL_EXPRESSION *calculation</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-587-1117-242-26-0)" font-size="12px"><text x="588.5" y="1129.5">EVAL_EXPRESSION *warning</text></g><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-587-1143-242-26-0)" font-size="12px"><text x="588.5" y="1155.5">EVAL_EXPRESSION *critical</text></g><path d="M 833 1177 L 853 1177 L 853 1014 L 708 1014 L 708 1027.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 708 1032.88 L 704.5 1025.88 L 708 1027.63 L 711.5 1025.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" clip-path="url(#mx-clip-587-1169-242-26-0)" font-size="12px"><text x="588.5" y="1181.5">struct rrdcalctemplate *next</text></g><path d="M 253 290 L 270 290 C 270 286.1 276 286.1 276 290 L 276 290 L 293 290 L 293 339 C 296.9 339 296.9 345 293 345 L 293 345 L 293 547 C 296.9 547 296.9 553 293 553 L 293 553 L 293 573 C 296.9 573 296.9 579 293 579 L 293 579 L 293 599 C 296.9 599 296.9 605 293 605 L 293 605 L 293 625 C 296.9 625 296.9 631 293 631 L 293 631 L 293 682 L 143 682 L 143 1053.63" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 143 1058.88 L 139.5 1051.88 L 143 1053.63 L 146.5 1051.88 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(216.5,676.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="57" height="11" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 11px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;background-color:#ffffff;">linked list of</div></div></foreignObject><text x="29" y="11" fill="#000000" text-anchor="middle" font-size="11px" font-family="Helvetica">linked list of</text></switch></g><path d="M 253 1333 L 270 1333 C 270 1329.1 276 1329.1 276 1333 L 276 1333 L 323 1333 L 323 930 C 326.9 930 326.9 924 323 924 L 323 924 L 323 713 C 326.9 713 326.9 707 323 707 L 323 707 L 323 631 C 326.9 631 326.9 625 323 625 L 323 625 L 323 605 C 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WmnKlWwzty0zjAInvVwNrpm1WYuFrkyZK6sV846aeBWjmQzeQwGmmfgyGC/+sGgMoeLQxsVz9a3eLCflavP9ArbIShXPHxg3Ira6oxQGfnK4C2XwseRmoOKU/B232qwX5Viuw21PjtejZK0H+yM1DEOPRD0qD1bT1Wos4OFNVvDqXmFm5xIYBxgVEFObn0fTLfUFBc8NtJ2O29bnhbcnla6ywoWfoLPOpQrxszlps9QMQ6Tn8pVy8Y8Ibe5rsOWRveQ/1IzaTBfri1sB2+5Eo4UltW+MmMwgo3Uuzvm1jtzWufRtVdA6ZMWejLeLWpPx2JVYM62Ghc/sirUTRCuPEW19lgWsqtNh3J3FXujc3jSEZ1vHiefw6J6bRlGWXtG2BMzbVQMB6pKte4UDM3f9Qx2Ycrl9Y4bDWA+v1dC81h99YQZg1kbzZnC2G29RWKebzZsqTpUsNHERn+v7aopanny2Kbg1Q6KnBCry7x7SJfS2B3svKCI0WA896i27I2+KrjBxu5QjczjaY8ajlW443KYMrOu/zZ8pVO22U5lnDiljrdhy15h6lx7JOUApv+WEXL3o3Pux+xZ9+PQxqbPeuVje+eG2g/UotR6mpbG69pT+/Jtg3uELDcdE+SReuPMIMu+Y5aPp14sZbfa76TmEba9cUiYvNfeaSztjnqa0ti6jE39uFNzaTC91qtLNpSvhjlO75a5PoSpH4Vt62i4n4pMqpzTO9iub5h68SfA0WNUWhQqrZm3hpa+w+mLa098FXofONp2pW5wfQ2k4eAJG6qu9GKYk9fDtGM2iz22nbSdiOgslsSre/DNXnX7M1WC1uYju2dtYNapRC2Xc+9uY0692Lx6UM7YmFh7mx9JzOver522UJ6ZfPF0tZO9rgrp1rjLwt4mxLAIGGyuV58dog7o87KCZwSEvW3PAuBgczqvLN4T6Uw0rgS1yJWGxjOId2PyGAxLsd1CSTKPczbh3WWDjM+y7Vd/omMjnV291WvwTD99UDduyWVccVE7LzUziVPwdt+KPttqN8+HlisM9VmUEhe5lXjdKxenvv+TeWtpNUzsj5XBW8iEsWB+bRqfMxK54Q4EtAeTcEadq77fxnxVGT5jsj0fVNt2mar82vLUSaoANV8qP7a8cpK+TU9ofqry5+3ApXqeMFcWB68+t9JoI/LfcHqOCX+soQY0nC/bj7UnqLRt3CZezpgMLVwb+8ow89jd+GOuReXRL8qtjM1lZD+LsEJVbNn9sbi3nPUut/Sxjrlq3jqF8kYJTf/maFKaVhaqU+aV27fvsSIjt5+0rjI9DwNV3mqkwR4YbzVKqe8bFjYutWdE6zK0PkEvXP5u6D3WOMmrfrm8/nFDLyP1gTqNLXuZz7crtrupmGWhcHmsNIhZsTshK6vhsnXYeouZr+sTd8wzsC/qj+yhZhlO7UN1OoenfYKV/S/jsba1yG3f9dn1RnzlhNgJ0xpKl7X7y/gm3k6NWknYeJrTdqNOujdvSd+QhvN4apIGYhXuuBy+zCysdoiQsnOddZt11zBlr/p1robVvd10bb26djzzheZgj/XCi4DnVVnpxkanz/rl49rj90hk207w0+Vx8z21kCakcbsG67o97bcdDtNZpO+w1cPsyFiEubbv5m2KW0C49sbbw+S99rqTKv8aGqjLhNNQ3G3j5Q6ZfCzEPmpEediqS4bLVxs+TocIbZcMWz8Kk5fb8406/VTtXd1HKk2vd4dBiaE65kmnvvTzBOMZJv+VfUAewVVvfWHScJ39RdWVQh+DqNVtx2weDdXRIoMjoJjv90h62imyyQ6+txjfyMf4xjbGd0/OxoyZwzC/3l0FogPj21zUfjBrDLaOj6wOI8Y3trXL+E5+BrM7f4LRu+lsiBVMv7GN8a1LrpRADD33kPHdS1GSxzO+sY3xjW2Mb2xjfGMb4xvbYj6+cmeGcTswuhkebSd3KT1ju/WYj+iwL/GN2NtQExEREdUntwzqgfHmrWeIqAVI5cp83j8REcUY5vFEREREFKvkGdM9m+GOkpOfwaQ6d9eMfewsJiKiKBHuWeXUHskzm2qfTU9Eze7NKRhtPu+fiGLL1Bi6qpj2EvN4IiIiIopZ0zFh1Gn7fgedaVdh+Kj2VXdiZzERERERERERERERERERUTvEzmIiIiIiIiIiIiIiIiIionaIncVERERERERERERERERERO0QO4uJiIiIiIiIiIiIiIiIiNohdhYTEREREREREREREREREbVD7CwmIiIiIiIiIiIiIiIiImqH2FlMRERERERERERERERERNQOsbOYiIiIiIiIiIiIiIiIiKgdYmcxEREREREREREREREREVE7xM5iIiIiIiIiIiIiIiIiIqJ2iJ3FRERERERERERERERERETtkCOgmO/3aPio08x3revb2e+b7wzykx0OR/A1dJzT6dSvMohw81ivwpourGl2oePsy9nXZb06nQ61rcbpeVrbwjkfmO+ajvE12Jezr8t6ZXyppTD91k1rwpourGl2oePsy9nXZb0y/VJLYXxjG+Mb2xjf2BaN8d2X39xWonFbMf1GPqbf1sH0Sy0hVuPrcrnQv29vXH3ZRRg29GBzbPPZsm07nnvpNcz5cgG8Xq85NvIw/ca29hJfaU30BAJI8vvQxVuD/auq1FCJnjXVyPF7keTz6yst/WoodzmR53RjoycOy+MTsCI+HtvcHpQ7XaiRNkdZYZRg/rx32kP+HDVXFksjvwx+vz/4KuTVPgifz6ff25explnjZR5hn0fGWe/t40NfreVCx1mf1X/URMZ2C79N7YNgfIkii5EuwqcZ+yCYfomIiIiIqD2QqodRB1HvdEXEqIzoj/qdNd2sq8hnczDUvqsVZi69DvVZBjVe/5WPxlT9wf4dRLR35CT0rl064Y7J1+OIww7Bjp25zT507JCDyZMmYuQRw4LfSUTNL1EdFw+qrMDvivIxJXcnbt+1HZcV5OK48lL0qalGqs8Pl5pPUqC8yufeavyxarrMJ/NPyd2hl5f1JOpjMLUV5s/NI2quLJaGfhnkDAH5yXJ1mtUJIIGR99YVa/LZ+t+y3ss06WywgmiNt6/L/irkvbVut9sdnN9ircOax/p+eT1iNK9cayzGt/F45l5sY/pl+m0I02/kY3xjG+Mb2xjf2BaN8d2X39xWonFbMf1GvqhMv7M/QEBVa6RzVuoi8s8i1ZUan6o7eVXdR9VLXB4X3Hqy1GPkjZqmqzROtayMscZbjM96FrnOSc3scKj6kcxskmk1NcYJug6HCx6PE07bd+g6k/ynlnE6jHFMv9QSYi2+VhvDyaOPxz1T/oL/vPs+Xvn32+bU5tOrZ3c8cOct+H7xz/jLbfcEvzfSMP3GtliOr1Olp/1qqnBWUREOqipHms8Htzltb8j1pcUuF36OT8Q76Rn41RMPv+24HImYP++d9pA/R82VxcLqiJBAWB0FQsZZHQhWh4KwgmXNb3UW2JcPHWd9hzVOSEeETLOvTwZrHnm1vtdahpqO8SWKXky/1BQSiXADEREREVE0CzhUfUXVPSoqqrFtey6+nLcY/5j5Jm695wlcee3dOPN3f8ZJZ1+J0377J1xyxW246dbH8PiMl/Hehwuwau12lJZWGHUXNcCvSshGNcdg9EKraepFDeWV1diwdSc+/WIR/v6v1zHl7idw2cS7MV59x8ln/RHjfns1Lv3jbbj59r9ixjOv4ePPvsW6jdtQWl6ulvfVWTURNU5yUpJuW1i85BfsysvH+FPH4B9PPAyvz4fSsjLcPeUveigpLcOOnbuwbcfOOsN2Na6wqFi3VeTlF+h5ZLmq6mrszM3Dhk1bUFxSirTUFP19VlsHEe0baXPK9nrxu6IC3L1zG44rL0HWPnYUC1le1iNXJMt6Zf3yPWzjan3Mn/ddVF1ZLKzGfzurA6Ch8TU1NVi+fDnWr1+PrVu3orCwEP/3f/+n55fBms9a95o1a/Duu+/qjokzzjgDvXv31tP3xFpeOiZ45VrjMb6NxzP3YhvTL9NvQ2Ip/cqpA8nmPmOnoo1KFSevEfKoE43xPfO0seiQk2V+alk1NV5dyM5VBfat23dgy9btKK+oqJc3RCrGd/cYX4pk0RjfffnNbSUatxXTb+SLyvQ7+z2sXr8Nj09/Aj8tLcTmLRuwafM6+EpV+dfpgyclGXHueNT4a1BdXAL4nXAlOJCe0QV99++JPp2TceQxIzHmhGPQv083xMd71FrleGpco+zz+7Bx8y7MnfcdvvjsQ6zZVIG1a7ZiZ/5W+IrlcT7lcKclqe9IQrVfla2LK/RFyIh3oXOnbPTs2xt9u3tw/XV/xuGHHaDXyvRLLSHW4mu1KUgZe8qN1+KeaX/FnC/n45brr9FXs11+7V90J8PfHrlPnzBy1Q23YMeOnfCHlIdlPRecexZOP+UkPPLk3/HdD4v1+jrkZOOehx5HUmICnp0+TT8b88pJk82lIg/Tb2yLtfhKh+6BlRU4t6hQX00c14L11GqVxpcmJOKNtEz9GolPtmX+zPy5IVHVWWxdHSYkePZx0nEQ2mEh03766Sfcd999WLjwW5SUlKKiokLf7rSysrLO8vIq5P2nn36KP/zhD3o9r776Kk444QQ9Xj5b67Z/v7UJ7d/LzojGY3wbj4Wx2Mb0y/TbkFhKv2cXFuLIynJVKDP2Cet8S/n7UWoa5qWk6s+NLpxEiGiM7/m/OQPXTbxCp4+WJoVwr9eLqqpqncZ35eXh64Xf4/2PPtOdi1ZajVSM7+4xvhTJojG++/Kb20o0bium38gXjfH99rP3sHNXIUaOvAhr121GQud4HDtiOIYdNBBdO2chPT0DcXHx6rhZg9KyImzfUYCVqzfiw3d/RFHhQqRn7Yesrh1QmbsVY087Czf/36Xo36uzXveu/FK88NI7eGr6v+BOSUJ1ZQU2r10Pd1I3nDLucBy0f1907ZqJNPUdHncC/L5qVc8qwbYduVi+Sn3H59+hdOMG+BJzsGrJ22q9XaSixPRLLSLW4mu1KYw/ZQxuu2lSvc6IP153s74SrTGdEceNPBKnnXwinn7uRaxdvwG/GX8aunftjH+++Boy0tPYWUxtLpbiKx3Fx5aV4qLCPHRSx96Wrx0b7Vk73B68kJGNr5JTIq7DmPkz8+eGGC3wEU6CLcGyOgSscVYHgnRESAOVRabJFWpyddrw4cPx1ltv6R0gPj4e3bt3x6BBg4IdCfIqnRP/+c9/9NVt1vplvKxHBnm/atUqzJo1C9XV1cHfYi0vZJzMK6xXahzZXowvUXSS9MD0S43VR8VobHEh0muqkaH2CxlyVGFdho41NTiprAxZtv2FWtZnX8zDytVrzU8ty6nSYZzHg9SUZH1W5uCB++Gy35+PV/75N9z056vRr08veNz7egMosmN8ye7UKw7Gnx4fVW+4/MHj0OfgDuZcdSWnxeH3t48Iu9zFd49AfDJjGtGkrFRcBN+G9aj+5CNUPP8PlD/5OCr+8Qyq/vcefKtWwl+QD1XYMhcgouYgzyLu2DETzzx/O1589S788u27+O+rj2HqHddg4pW/wwW/PRXnnDUa5507Fldc/FvcfvNVmPnMPVi2XG4T/QlOPecMVBevRVx2D7z7/tsYNPC3eOaf/8Fr78zGIYf8DrfdPh2ujGRUlW5Gt4OH4q13n8fq5e/gjZmP4N47/oSrL1ffce5pmCDfcc4puOKSCbjj/ybihX/eh2XfvIVZ77+A/752P/r37mL+YmpLUsMNHSiyyaOwhHGiexW+/u4HfDr3S6xetx47d+Vi4aIf8fn8b1BQUKjnCyVtEp/P+xo33X6v7ogQb836ANOfeU6fQC9tGT6zzYOI9o2k1qPLSnBZYS46t1JHsZDvke+7XH2vfD9rTa2D+fO+i7rbUFvsnRJ2Mn7ZsmW48cYbMWfOHN1RMXLkCIwbdzoOP/xwDBgwAFlZWcGODPHtt99iwoQJ6Nq1K95++238/PPP+P3vf6/nkSvXDjroILX8OGzfvh2vvPKKWt9Ic0nj++y/Qd7Ld/LKtcZjfBuPZ+7FNqZfpt+GxEL6TVH7ydV5uzCwshJefdPpWhIl+SyR/zAtHW+nZ8roqBKN8ZX0cOGEs3HFxRfojr62ImlTng3z/kefYtaHn+rnwkQaxnfvMb6R4fSrh+LAkd3hranbMRjwOfDWE99h3ZL6cZHO4gvvGIm07ARzjMHpdKCyrAbP3DQXVWWRf4JPNMZ3X36zCFSUo2bBfFR/PQ++NauBcCdiqTzC1bU7PEceBc+xx8OZsW/H3mjcVtGSftuz6Ey/7+tXXb5Vf4xyrqqX6HfGZzuZVsuPqhovNm7ajg8/+hzvfTwbS1blAmXbEPBkqjK0E/36dMXYow/A2WeNx8D9+iEpwWyCljqYeece/Wxj+2oV6zfov3qaeh9QpW/1fvgo1o9anxEgl4qpy+EyYxaAL+CDT983XBh7j/EafWItvlb7wvDDhuLRqXfip6XL8NHsuebU5iEdHAMH9MdvTj8VH8/5HFMfnl6vXSNS8Pgb22IhvnJ0lCuKpaNYniHcVvLcbvwzIyeirjBm/tx07SV/jsrOYgmEfJZGMGu8FZzc3FyMGTMGv/yyVN/a5+6778ZFF12kOyCs+aWTwXoVEydOxLPPPotjjjkaH374Eb744gvdGeFyOfHyy6+o8cfoK+Ckk+IPf7gIzz//r+COYH2vvMpOI+uU90eeeLqe3tqiMbEzvo3HwlhsY/pl+m1ILKTfI0pKcHFhHhx+FRNn3WYxYe1FEqf7O3TD+jiXOSY6RGt8u3XpjCceuhc9unc1x7SdGlWB++Gnn/HgY3/Tt/WJJIzvvmN825Z0Fh9wVDfUVNdtonDAtdvO4gtuH4HUzHhzjEHy6fLiKvzz1i/ZWdxC9uU3B9TxtuKF5+D98XsEqqv1OEd8Aly9esGRlg5/aSn8WzcjUFysp8Hthqt3HyRddQ2cnYxb3e6NaNxW0ZJ+27PYSb/2Zjd7KTh0vNw9yai7yOdPv/gWF//hZmR37aim+LFuxRY8+vgNuOyCs+DxqLqN06WXql+y3h2j29q+DNNvy5N6qJD6qdvhQYajEzoEkpDmdyPFF0B8wKfqSVXwBypQjgpscQNbnaUod6q6k7GCYD03WsRifOVuOpmZGXjo7ltx0OD9UVlVZU5pHhJjOcmzSh2/r/nLrfhlxa/mlMjD429si/b4Sr45pLIC1+Xt1Ff4trXtbg8ez+6InxIS6xz52wrz56ZrL/mz0RofJaxGfyEdC0YBulZBQQHOPfdc3WnQrVt3vPfee7j++uuRlpaGFStW4OabJ+Pggw9Gx44d9fKyPumUkGdgihtuuFHfCtXqUJBymHRIeNSOcMstt+hxX375Vb0OECG/RdYprN9ITcP4EkUvpl+SLasH2Rfsn9WQ7vXjhIpquFVIpKNYU7Fyp2fCpfYB+PzBeeP8AYzP3w6PGT9qWdJp9877HwbTS1uS2xQfcdgheGLavRjQr48u7NO+YXxpX7g8LrisPNtGnjkfCfsU1RcoLUXFi8+h5tuvEfB64VRlroTfX4zUvz2D5Cl3IvHqa5Fy861IfXwGEv/0Z7j69NXL+VavQtlj0+DfuUN/JqLmZpV0Q/PU0PHq1RGA0+3CLyvX4v5770JKhhs7N+ajcNsu9OjVEQ/cdSve/2iOyosdcPrr1rkaR7qJQ38HtSy1vdVhMzGQhL7OARjuOhbD3Qein7MrOrtSkeRKQoKaJ029dnWl4KBAGs6qTMTFxUk4sRTI8QbgUnUkJ4+9bU6eb9m7Z3f9iBW5pemHn85p1uGjz+Zi6fKVSExIwKjjjtbfyTYMoqaTx5tNKCrQzyiOBPI7JhQX8LFrLYj5c/OIqs5i6SQQVieCPSDSGfDaa6/h+++/R2pqKm699VZ9xVmpqjA//vjjOPXUU/Hoo4/pTonCwsI6HQry/ExZd+/evfW4Pn364IYbbsBNN92EXr1663n69++vpxUVFdX5HdarjJNXmZeNJ3uH8SWKXky/FJfoQ0JyDRJSvMarOXgSfDiwsgb9q0rgV7uFjoDaJ9xZWej3yqvofuetcLiNqyJEpYr/Af4ABpU371mA1LB3P/wEa9dvND+1vR7duuLeW2/CwQcOrpOX0N5hfGlv+Wp88HqNY7Kd0cnA2EWaQE0NKl+eiZpvvtZXC8eNOhHJN01B3Ihj4F38IyrfeA2VL/0Lla+9jJr5X8E9cBCSb7kD8aeeDsTHw791CyqenYGAKk8RURuRgrIali5fjWuv/TM2bMpDWlIcnv/XXXjzzafQp0cGnO6uuP/+B/Hp3PkqK1Z1H9ZvIl4c4tHJ1R8Hxg1Ht7jBakw1Nvo2YrF3Bb7xLcOCwC+Y7VqHT5zr8D/XDsx17cJP7jJUO/0YXpOAC4udGFVWgxTGuk1Z5dYe3bohOSkJL7z2H0x7fAY++uxzrNuwCY/P+CceffLvWPLLCj3I+4fU9HDDg399Sg/hPt877XHkFxTqK+OodVml292VdEPHW/M2ND+1Pjmx5pTSYhxYVRExcZHfcWBlhf5dPPGn+TF/bj5Gq3qEsxr5rQ4Ei9XoLx0BO3fuxOuvv47KykqMHj0av/vd7/SVZA8//DCmTp2KLVs2o2fPnnjooYcwb948vZyQdXbq1Em/rly5Uq+zX79+uPnmm3WHhHRQyLR169bp+Tt3Nm7NJb/H+k3WDimv7IhoOsaXKHox/ZJs4YSEGhx2dD6GH7+r3jBiTC6KsqpQIXFRIXBUVyN1+DCkHjMSZT9/j6J1WwCX2cmv/wJVar8plFvrmZ+pZZWUlOKfL74KbwSd5dqnV09MvPwPyMmKvudXRxrGl/aWSx4HEObQaRxnzQ8UMeS20zU/fC+FL8QdezwSzj0fKCtB2SMPoOL5Z1H1v/dQ/fkcVH3yISpe+hfKH34A/k0bkXDG2Yg/+VTdwexdswbVC1RZjGUmojag6lPqvw2bd+DySy5GXoEf2dldMeOph3HamKNx5LAD8Pe/P4qBgwYid5cTV19xBRYvW6/Sq8qQmWQjltsRj94Jw9AjYSjinEnYVL0UP/q+x3LfSmxxbMdORwnynVUodtbo1+3OMiyLq8achDL8J7kCc+MrEO9w4/hyP04p8yFe6lPmuqltWG0f5RWV+u5np59yEv581WXo368POnbIwWW/Pw+XXvhbZKlyrsRK2iGsQci4wQP3w+RJE5GTnaXHH3/0Ufjt2eMRF+fR67e3XdjfU8uSLS1tVX1698Lo448xRoaQeSRuSUmJwc/WQJFhv5oqnF5ahLgISzvye+R3ye+jlsH8ed9FRWexdaWYZNjWrU2lkULGy6uMk1ubfvvtt/qWpNOnT0diYiJmzpyJBx98QAfuxhv/oq9au/HGGzFs2DC9DuFWleLx48fr99OmPYSiokL9XpaxOhmKi4tx//3363GyrLzKIDuILC+sjgj7ctQ4jC9R9GL6pYAjgE69y5CUXgmXp6buEOeFJ64KSYeXYlFyCpwZ6cj6zVlwxsWh4K3/Ytczz6Lyg1nwV9foZxZLdBJcTnySkIiNah5WuFqPPEv2p6XLzE9tT9Lq0IMOwOV/uEDfvpj2DePbPiSlxiE9OxGpWQlIz6k71FT6UFJQifLi6jpDUV454hNc9eaXISHJjeoqf71lZPBW+5CcFh92Ofn+4CMHqFUEKitRs2ghAhXlcGZnI+6ksQgUFqB02gPwrVmNgNcHZ4eO+rbU8lxih5TbNm1E2cP3w7d2DeJPOQ2urt0Abw1qvl0AP68uJmp9qjBcWlWBe+55GnllWdi+zYk77rwBww87QE007pbUp3sHTH/yTmR36AFn8v44e/x12Lw9V01lqTkSuRGP/nFHItPVDRU1+Vhc/TXW+9egMlCuwu3TUTMG+z9jnE+VlYpdwNdJDvwrpQqL45OxRdW79FKs07Ypq53Bqco6Ut6RZ1jq8mwgoNtFpN1DPlttJXa6XUK9jht7IkYdOxIHHzBIj5eOiNPHnoSM9HR9K1Wv2bZCLUdSUYbXi67VVeipojJ65JG4fuIVePmpR/GKGi4583R0VeUrmR4cUpLx+wln44PXZ+KzN17Ea888jgfu+D9cdP45GHnE4Rii4tktOxPdVLmrznKNGLqpQW5RzKtO901iwI+zVDk2PULTkPwu+X1JjHOLYP687+pvmQhkNfJL478VTGucDNLg9Oabb+rpJ5xwArp27YodO3bgqaeeUvO79HMy5bansuz8+fPx4IMP6nUIWV6md+nSBT/88COuuOJKfPPNN6iurkZ5eTm+/PJL/PGPf8RPP/2EHj16YOLEieaSRkOXfKew/xZqGmvbMb5E0cdKG0y/7Vdyeg269S1T27u2sCvbWm9vVVCvVJWezr0yUX3m4fAO2h/FX81H4edfwa/i6EpJ0fPLkhIdebL0ancc5qaly2hqRUVFxfh07peoUBXiSCIF9YbO6qbGY3zbhyNO64vf3XoUzr/5SFxw20g9/O7WEfp1/bJcvDx1AV594Js6w3+f/B79D+mM86ccFVxGBvl88sUH4ZMXltRb5uX7FuD1hxdi3JVD6ixjLXfmNYciJT3e/FXUGgLFRfAtN04IcR80RHcIV7w8E4GiQjjUsTbhN+ci+ba7kfrAI0i5cyoSLvgDnDkddCdzxQvP6VtYx40+SS/vW79OdzQTUeuRuorcUnrOl9/gi6++Ru7OfFx93bk4edQIXUa217MG9uyEm2+6BN7KCvgcFXh+5kuoqq4x1kERwoE4JKB/wnCkuzsjr3o9VtUsQLkvX0+TSO0pWroOa77f4XHhrXQfvk72wKvqWIx127LaGWpqvPB6fdiVl49duXkoK69AeUWFfi/jKisbvnrwX6/8Gw8/8TQW/rBY15unTX8af53xLAoK1HFbJXqn/KEWIzmqR8VxwvbteHzlr3g0dyduv/wPOP83Z6DffgPgSkhA58JCTF/6Ex5ftgx/Xb4cM3p0xtOTJ+GqS3+v1+FOSkK/Af11p9I1l1+Mv95/J/6mhr9PvQNPdcrC40uX6nU3eljxKy7ZshVxAX90dNZEIEk1A6qqcFBVuTEiQsnv619VqX8vNS/mz/su6vIfKRRJoKzCspBx0oEgxo4dq6dLp8PatWuRlZWFCy+8EGlpafp5mZdccgnuueeeOoVtuRpNbmmapDL6WbNm4ZxzztHjhg8fjvPPPx9vv/22vnT96quvxiGHHBLc8YRuDFdkPfbfRHuH8aWWJjG1D9R8mH7bp14Di5GSomJvtllY29rv96kPPhzQ9zj07T4E25LW49PVyxGvKl2Ij5MA6fksDhWnavX6ZmYWyhivVidnSM75cj42bNpijokMctbn73/7G3TIyTbH0N5gfNuHxBQP0nMSkd4hAclpHmNIj0NmpxQEfAEU7aqoNxQXVCIxNQ6pmbZl1CCfU7MSUV5cFXa5ipIapKnp9mVkkE5iWdbpZhmrNQVKS+E378ASN/JY+Favgm/Dern1i+4ElttMB3btQMU/noZ38Q+IO+Y4xI0bD4cqQ/lzd8G77Bd4RhwDh0cdn30++Nes1usiopamCtDqP8kx8wqKcc3EJ1BcUoHjTzgcN/zpAiQmxOmytVV3daj3Lpcb48Ydjd9MOBn5eSV47s0v8OPyNWodai3sQ2xzuj6sXju4eyPN1RnF3l3Y5F2C6kCFvmP43gXJuKJYrjYWHlWuy1DHdf2IHz2GWpPV5tC5YwfU1NTghVffwJWTJqty9mbk5xdgyj0P4tZ75c5oRboMHkraJ6TDYvYX8/TjYuTzug0b8f3in1Gt1peRlq7bOKxHyFjtGtS8JP0kqlimqKFzWSE8atsH1PuAKgdJTOJUqutSXoRuafHo8fhD6PrUE+g84ii4VdlKBR6Bd95B4I47gDvvRODhh4GCAsT985/o8MzT6OyrQtfSPHQt3KGHLmroVLQr+FmGDsX5dT5n1VQh2e+Dk/n4XpO88ZDKcqQ1cOWnpKj1qqz7bVIyPklJxX9VWpuVmo5tHo8xw17aqfaJ99R6/quGj9V6v1brXxsXj5oG0q78Pvmd8nupeTF/3ncO9T/d6D1z+KjTzHet65vP3tPBsQdAgi+FZmt8t27d9HMxP//8cxx11FGYNm0a7rrrLv0czC+++EI/93LkyJFYtGiRnr+qqkq/WsvLDmTcFvVBlb/no6ysXI1XB47EJGRkZGDKlCm6I0PmlUva5fut5WWQ32L9JnHkiafr19a2cM4H5rumY3wZ39Yk21duAREq9JmKDc0XSvYeyeglThIzO1mHxE5e7dOtcfLZuoVyW2L6ZfptSKSlX0tO53IcekwevDVGgUzf6sXhQk5GN7XNnejZdQg2b/8Jm7YvQ3xcElb9nIErvixBj4AP1aoiFHfgAfBXVMK/eg0CKmbfJ2fgBRXTShWqaCs2x0p8Tx0zGndMvi6YXiJBpcoT/vrUs/jvBx/rNN0WGN+Ww/g2n1MvPwiHjO6DyrK6Z0onJMfjzUcXYuWi7eaYWvHJHoy/+hD0Hpyl8u3a7S/H0dKiKrx2/9coyq1/RXp8shuX33csktLj4ffVnqgl5DbVcgWydCpHgmiMb1N/c83X81E+4wkJHNL+9QpqvvocFS/NhCMhAUnXXg9HWjrKbvs/BGrktCwg6ao/wTVwMMruvxv+XTsRP/4sJJzzW5ROuQm+zZsQd9rpSDzvQj1vY0XLtrKLpPRL4cV2+pU8V1Vq1LHPp8rFj894HXfe/iAye/bV9d8B3Ttgv/490blLBlJTE3W+XFFeo+pVhVi9djNWrt+CgpJKOMq34+yzz8aTj9wClzq8S1m8qZh+m4/UihMc6RiUeBycDjdWln2KskBJsz1aWtZ/SV45DqwqxSMdsrHV0/R4t5ZYzZ+7d+2Cpx97AJmZGSotrjPH1qqurtHPttwbsmyPbl1Vmk/Bk39/Hv9+e5ZO+21VRt6daI6v1IQ8qvz6xy2bcfquHUjJTlFlp68Q37mzPilHtneNPEpt8mTg2WfhGDRIdyRLLKSj2P/aa3D8eRIcxbWP7QgMGYrA9u1w7jDK2xIxKSFLmrUGuTOAy4ylfJbp8knaQspTcvBldg4e6dkTleoYULd03fqiMb6ZPh9uyd2Ogyvr10EKXW68kp6J2cmpqJAYm+PF2cWFuLIg1/zUdK+mZ+GljKzgOiW2CWp/GVlRhj8U5KGDr247t/g5IRH353RGQSPau1sC8+emay/5c+S0FO2GzowVa+PLq4yTxn/r83XXXYe7774b/fr109OkQ+L222/HX/7yF+Tk5Oj55NmXMo8MocGUDoYrrrgCn376KZ555u944IH7ce+9U9X7Z/DRRx/h8ssv1wV2q9PBWt76LfIq0+zrpMaR7SesbWdtU8Y3tiUmJuCgwfvrQZ4DMOTAQdh/QD9zqkG2fZbK4GWaNV+44cBBA9GrZ3ckqXUK65YQsrwMErs0lZnvv19/HH3k4Tjx+GP0rWKOOOwQ9OvTK7gcNZ1sX2GlDSvNMP3GPqcrgF4DC+VO00GyjffrNRJXjn8Rxw+7DEt+/RDb8lbpjmKHy4Gu/UoxK6MjvorLwVcJmaqAnInFaepzYkd8rYbPkpNVwV2tx1wftb7ZX3yln28bSRLi43Hk4YchOSnJHEN7i/GlRlOHdzmGUpSwTnpUZSZ5HnGgqkrO3lPv1eekZH2LaqujWNSoz46kRH3lsVatpsn8Vjqsqp2XiFqOLvMGHNi4ZRf+9te/o1OfAaiq8aKkvBrffb8er//7YzzxxAu4774nMHXqdDz62FN48cX/4qt5y5FbICfyOBGX1h3Pv/hv/Lx8rf5MbUti2sndHx5HAjZXLUGJ+udXh9Pmqt/I3Zh8gRqUOR1ws9bU6qRstHNXLp7510tYvnIVkhIS6w0ZaWl1PmdmZGD/Af3Rq0f3OuPDDbJsfkEhXvn32/jg49n6O9mO0fKc+fnwzJ0rG9scA3gGDQLeew+O/ffX43XbUkUF/E8/Xa+jWDh+WgyH2VFskRzZKk3Lmq2OYiHvZJrMYx9Pe6+LtwbdbeVdi5SSv0xMxqfJaSiXtkFjdJCM2xcVKj+2r1PeS4f0l0kpmJuSqk8SCNVN/U75vdR8mD83j6i4svjb2e/rjS9BtzoghM6ozZ9vvbfmsToGrHHyKp0J9uWFfR3yau9QCF2nvFrjhf177OuR97xyrfEY38aLhTN/pCNXtqQ8C+QPvzvXGKk59NW910+5C6vWrAvGa0Df3rjrlhtVBt7wM0wlNHJFsmTa//3gI3zwyWzUVBsH3eSUZH2LS+kkloOAPNzeKadcK/J98nwnuVL1iWf+ia/mf62mtV2Vi+mX6bchkXjmnmzqXgfmY9ABFaiqUsVvva2B9NROOPfE+7Fo+X9RUVmM5evmqnh4EacKVxvXe3DQxwMxrEoVmH1eJB/USyXEAGpWbdNnzn6WmIe5qWXBM2yjSSzFd9ghQ/DofXcgMSFyTqTJzcvH1Tfcom8f1BYY35bF+DaP5ryyWO4UUVZcg1emzueVxW2gqb/Z+9NilD3ygD44pz31D3h/+RkVzz2rPgOJl10J14D9UXrHLcYzjFXaT771Ln3cLntsGgLFxUg49zzEjxuPkhuugX/XLsT/ZgISzvyNufbGiZZtZRcJ6dcouRqsFGgvuzbEWi7cXPZ1yj6xp3VZrHJ0Y+dvDbGdflV9SP6qvPeFf3+ASbc+jvSUBFSqaqyKmo6HyooV9ccKif4s9Sip76g6rcMPj6rb7tq+C5OuPg/33XUt4pwuc77GY/ptPolIwqDEk+B1VGJlxVeoDpQHw9ccJLQJap+Rq9aK5IQfNaLuUThyxGJ8haRNab+QCxI8jbh9be8e3fHXB+7Ch5/NxbMzXzHHNkzaMopLSlQdO7JP3Irm+EqLoFxZfPn6tThzxxYku1QqnXQdXFOmqEScYJx4Z7ZP6SuN5X11NQJ//zscd94Fh/noj+Zg5Q8lqR14ZfE+Oqu4EJcV5MJtfrZUqDT796yO+hbR4fLjsaXFuC5vp/mp6Z7LzMZ/0jLNT3UdW1aK6/J3IsncnyxyrfFzmTl4Jy3DGNHKmD8bmD/Xt2+nTrQSqazIIB07VsVJXq0OAuu9kFfZKaxlhLxKp4F1m1n7eGs5eW9fj7Dmk+VkncL6fplP1imv1jj7KzWebDMZGN/2QbZ6xw45uPwPv0N6WpoeUlNSdEaenZWJP158ob6SVLaybP8Vq9bg2+8XIykxCYmJiWGH9LRU5ORk6yuHb77uT7j8ot/p7zpkyIF4+e9P4A/nn4t+fXojIz0NcXFx+oAhVzLpZdX39uzRDYP33w++mir4eGZXk0iakIHpt/2REGz5OQt5eaowZpYmZFxB8TZ8+8sbWLj0DRSWbsLwA89Ft06DUVnmQtIPXVRhOQ1OFRtPtR9JPifiK30IVHvhVpW1Y9S0bK8RT2o7K1erfHfRjzrNRIqc7Kx6d5+gvcP4UmPI7mE/7lJkc2ZnG1cJq8B5lyyGq1dvOFQZV66AqZ73FRxuF1LufQBJV1yNlKkPwdm5C6q/+hyBkmJ9NbGrdy/4tm7VHcXC1a2HfqWWJ/USq34i5dSGyqr28fK8RKkvuT2qzhSyjLyXcrDcxSk5WcVWFdLsawydX4QbZ9ndNNpXqn6i8tmKyip88dkH6JjsRHWlG6oWo6dKPizn4vjUPL6AOfjlltUqf5ZDuEO9cXhR4/Oid+9ueOvVf2D79jy1HPPutuNAurML4lyJKKjejGp/hSQic1p4MtWaw/4+HLn6UPq0Kp0OFKg8QNWsEWc70Ytah5ShpR2isLAIu3bl7naQq9yq5O4dilyksGPnLv08zHDzWkNeXr6+3Sm1jGB6U8fQjBFHIO2Be+FatQquqffCoY6b0lGsp6tjqQxBKo6OJUvgKCs1R9jIMqosFhgyBIHbb4dv+HA5wJsT9yxcut9dXkDh7V9VWa+j2OBATRtt0IbuLCG/U34vNS/mz/suaq4sDu00EPIqn2WQCpE1Ta4w/P777/WzMMvLy8POY3UiWOsKN0+SqjiPGjUKQ4cO1Z0RMs6az1rOGies8bKOI0aP0+NaWzSeGcL4Nl60n/kj286jCmS3XH8NTjlplD5gypYuLS1DSkqyjlt5RSXumfZXfLXgW9noermsrEwcfeTwYIeinUNVlIYeeADGjDpWN5oIWc/Nd96Pqy79vb7NtEW+f/Xa9diybbt+RmJCXBw6deqo55GH3v/zhVfDPuC+tTD9GvMw/dYXyWfudexeiYMOl8bl2rRz2OCz8OOK/6LGW4OE+GR0zhmM0h0dMeATDzotU4Uradyq8SF9aD/4KqtRtmYbAi4H3KokvTC5BK9nNN+Zuq0lluIraUVu1T/lxj8jVeXNkeKd9z/CA489aX5qXYxvy2N8912zPrNYriwuqsar9y3glcVtoKm/2V+Qj7JHHoR/4wa4DzgIyTfejMq33kDVB7Mk0SP+lHFIOL/2GcTVc2ej4qV/6efueY4agcQrJqLy9VdQ/cmHcKSkIuWuqXB26mzO3TjRsq3s2jL9yomrvXt0Q2JCvP4sdaSNW1QdpbISHTtk49c16/V4IXdG6tm9KzZtUeUl9e+AgQN0mVo6jf0BP9Zv2oriklJdr5K7MQ3o2ytYL5L609oNm7Fjl/Esvn69e6qw16jv2qY/C+lYzs7IUPWwCnTumINlv67R42V9nTt10MeNrdv3/oqbfRHz6VfFb+PWXEy46M/4dXkheg+pxtaN8Qj4VL1HytZq29dn5NVyGkBVlROZvUvh25WG8vyNePnFp1W9eLharGnXhTD9Ng+JSV/PkciO645VFV8j379Fja09tobjUv86+RORGPCgwlGDnc5SfcVZLWMfcKv67NBKL3qrPGJOapK+7elJxaXIUeNnpaYi1x1uX2lbsRbfvSH55/779cdzTz6CN9/9AI899aweZ7VTRLOoPf76/Yj3+3DkSaNx/Z+u1I++s+IR7ByWzyH5b0AdTx2bNyPw+9/D8dVX5lg1/sgj4T/6GDhPHK3+x4YD6emozsuD6+FH4PrnP+AoKDDnrM++F8iVxV9nZeOvPXrAp766yulCpfV72kA0xvfprRvRJ8xtqCvUMfFv2R0wJ7n1ryw+rqwUk8JcWSzWeeJwddee5qfWxfyZ+XNDoqKz+JvP3qvNuM2gydBQ58I333yDCy64AJtVJi6VKGs59Z8t4MZ65BZrxjjjIGCtT16lgtWnTx88++yzOPbYY/V0+R7798qr/b1ME7zNaeMxvo0XzZm5ESMHhh1yMO69dbK+kli2p5zJ8/eZL+OWG64NPmv4ywXfYurDj+uO43AdxKGGHnQAHrp7ir5KWb5D4ii3tOyQkx2MXWlZGZ55/iW97oqKCn2Gtkt9vzTUHDBooJ7ny/nf7KEq17KYfpl+GxKphTGJjMvtxwGH5yG7c4UESW9v6SxetOwd/Vn++b1xSJ4zGL/pfbRKdw7kf7EUVbnFyBo+sE5nsfyLV3F6Jm0dliW2XcVob8RafJMSE/HofXfisKEHmWPa3rKVv+Liq683P7UuxrflMb77jrehDi8a49vk36zKVJVv/htV/3tf32Y68fKr4D7oYFS89hJq5s7WV7ekTLkTrv4D9G2nSyZfh4AqG8s8SRP/DP+OHSh/8jH48/LgGXE0Ei+5Qq+nKaJmW9m01W92u104fMhB2KnqK9t37tInq0q9RMrG0mg9sH8fzJ33rZ5XyloJiQk47OADsHjpcj1u6AH745eVq1Fd40XHnCwMUZ//++Fn+gSg0cccia8XLUZJaZmu16SnpuKgQQOw4Lsfdd3qkAMHYdB+/fDmex8Hr6aQuzTt1683lixbiVNHH4cvv/4OhUXFurw9bOiBWP7rGuQX1n02Y2uJ6fSr4i6VoNlf/ojzf38zCnc6cNfcFfj8xWx8/0lnuONVHUaqwo7a+pA839i4oliVuf1OuFOLcdjlufju0e4q43bhst+dg7umXGJcUa7W3VhMv/tOtrcnEIf94o9BgjsVK8o/R1mgUNeFwpHoOOHCwYE+6OXIhKu6VNeDtvq2Y0F8HmrMOqyQWlGSOkZfWlSDwSrd/yvJgbUJcfi/3AKk+Hz4e0YafkxObOCb2k4sxXdvde/aBUcfNRx//uOlKq3Px0uvv4mNm7boixeiXbTGd1RBPs5V5Z4By39EWlqaOVbZU55ppkf/ou+Bs88Ghh0GXHghnCNHIpCZaVxJrObRt62WeeUY+8ILcEyeDKiyl0XWIt9kT6/yuSitA4o9CcjzuOFXYz7IyVFDNvxNyMubUzTG9z+b1yI1pF4i2qqzWCJ3fFkJrs3fFbazuFgdtyf06GN+al3Mn5k/NyQqWmKthn55tTodpNFf3gt5tW5DKvNdc8012LBhg+5kknmNwZhmqF2fMcr4bI0T0kAilbVVq1bh2muvDU63vtdY1pjfem/vpKDGs29Lxjd2yfaTjtnTxpyobwct21Fi+PZ7H2L+N4uwdNkKPZ+MHzF8GA4cvH8wxrLcyaOPx9gTT6gznHLSCTjt5NGYcNbpSElJ0ctLjGQ5q6NY4inDq//5L/77wcfIyy9AWbk8Y7Vav+aqz5/P+xpftHFHcbSSbWylCyvtMP22HxIin9eJTesk/RnPA3d5jKKFJ86lP8fFe7BrSQYGbVQVn/cWovSXjcg56RCk7t8dnq5ZiOuoKmgqNkJum+ev8WNMSSdVmK5720RqXXJ10cxX30B1TeTcYqdLp47mO9pXjC9RjHG7EXfUSDhzchCoKEfVe+8gUF6GhHFnwJGRqa8grvl2vp5VnmcsHcVwuZFw1rlqjAOV77ypO4odaemIG3lskzuKqWky09Nls2O9NEhVVqGyolLf/s56/pm9TG2VYa2ykqamVVZX66uQ123crDsG5UTcvr26607knbn5qFTrkvUat9TLR//expUrXlUGl07hE489Sl/NbFGlZlW+9uH7n5aib+8eugNbOpGlwayoJMwtN2mfWRFdt34lqou3YNiplUjtlocTr12P9IxSOFW52Ki7WHOqV+kolmY8nxuVheUYduVWxHfLRcfDSpGoJm3b+LO+PaLeZ6h1qU3ucsTD40qE118FX0BOtNp9HDo4stHdmQO/txqq5oSAtxL9kYZeNYlq0QAyfcBhajVJKrZlqg48O9GFD+LdWJyciF1uF15JTca76v3SRDU/tbimtCXIhRAHDhqIfzzxMK6feIU++ebkUcfh+b89ilPHjDbnahy2YTSvJHX83D8OiLfarEpLEVj5K7wbNsBvnkQVlhkH57DD4F36MxxvvQXn2Wcj0KEDHHFxupNY38JazaevUJZxV14J/9tvI9C1KwIOlcZlNXotxmuF040Kl/oxiquyDGlV5ehfUoi+lRVI8da9xwDtWVKYjuLGKHC6sCw+AT8lJOFHNSxSw7dJyViQlIKvklPweXIqvlSv8vkbNf67xCT8oPJdmV+Wy3U1fMtxKV/pc77CSPbv+eIoahzmz80nKjqL7Q391qtVgRIyzuqYkArT4sWL9Xs7o6xsdD7I8vZgWgXpuuOMzzIsW7YsOI/9Nwjre+3jrFdqHPs2ZXxjk2xn2ZaDBg7AUcMP0+9lO27euh2fffEVSlXh7It5X+sORBnvifPg6ksvQlJSoi5ApaWl4u4pf8E9IcPdt/wFd958A0Yfd3SwQ7KouBivvzULFWpfschzoNas36Dfy3Oh5Pvlu6zfIQcK66pmahq9/Zh+2zXZooW7ErD8++5Ysbgvln3fB/nrM/DLot7688+LuiFzRQa61cTBmRSP8vU7sX3WN0gZ3BN97r0Q3S4aDYfHpdO6CipqnH70CiRjQLlxW0ZqO4t+/Alzvphnfmp7aamp5jtqDowv7Y7k7TxmRhdnr96IP+Ns3dHrW78O/i2b1ftE/Xxi4d9mXFnu27ZVvzozM+DIyECguEh3IIu4USfCPfgA/Z5ajtzqWTpg/ao+Inc70unNmKTJbaflimGZr5MaOmRnIcG8XbWdlHXlJFzpGJYOQrlrRF5BoU67up5jzidXBcvtpIVc1bxJ1cG2bN+J/fr2DpaxvT6vXkY6muXW2BnpqThw/wG6Q1vqTdT8rNrNpo07UVaYhJzeFXDHVSMxqwDnP7AJNVXSUSBz2ZvtJKoO+LxVOOSSAsR336k+1SC5RwW8pQ7kFxTrE8FsVSdqRXKqq9PpVum6Bj7z2dPhSHhUrRepAY9KfCpeeoxB0mFqwK3HHF3uwThVJzq40qsjvyzBhffS41BlBviH5ET8LyNN1Z/q5iHU/CSvlJNy5KKFM08bu8fh9FPH4MTjj9G3+c9X+fL7H32Gn5Yu0+1WcmefcMuEG04fexJ6du9m/gpqDgGn8Rgz4S0the+D/wEjjsKOseNQ/NPPus1QHUj19HrM5eLMuxrK8dZ6xrEsF1xWxlvTTzgBvnf+C9+o0VA7gE6rPocT2+OSsSQpC/nuOD0uubocaaV5SKgoUvkAO4r3xt52cv2YmIQ7OnbBPR06456OnTFVvd6f3QkP5nTCNPX6SHZHPGx+fkC93pfTGfd2MOaX5eYn7e7RTg3nzlHRKRcFmD83r6jZLyWDtRos5NVq/LcyeGuaPH/HPq85WalNnLJsKGM9tfNYyxsCwY4o63tlsL47eACwvVLTWNvUes/4xh7Zdlf84Xe641e2oQxff/c9duzYpc/i+errhbpxQ8dPzTtwQD+cPe4UfVa7LCuNKfKq35uvwc+qQCYPr5erg++47xE8//LrwQfOy/ckqQPAwP79jCqY+izfYQ0yXa9Pz017w4qD9d5KR1ZasaYx/cYm2aI+XxZKcs9AwTZjKCw6LPi+dMf5WB2fgRKnUeGRClT6If0kkNj1/CfI+99CqESop8k4eZfnqMKWhMi4fWl7Jo3Dr731rr4VZSQIPkOKmgXjS7sjeTuPmdEn7uhjkXjxFfq5xc6u3RCoqkIgd5eepq8wVpyZWfo1UFyipzvS0uAZeigSz78Q8ePO0FcpU8tKSU6qd/t2O+kclucZy+2oZejZrYu+nbQlJysDw4cehBOPG4HhhxyEbxYt1nm6lIl9ar2hKbdK1YvsZWy55fXK1evQqUOO/i12cveejZu34YyTR2Nnbh7yC9rm9tPtggqUFIE3bs2DF4lI7+JT6U+Vl71OdB2yCSf8vhDVJV44dUClhKzqM3DBocrSWT0rkTNsK/xe9dnpQ1KHalTnupBfVI2qmmrm321En6Kh6jN7YswRQJVawikdRYHa/CCgxlWrPULmWRrvxXJXFdZYd25SIxODq3cgXr3P9LEtozVIW8JRw4fhhj9diRuv+eMeh79ccxXOOeM0JCYkYN2GjXj4iafxyZwvdD59wjEjwi4Tbrhp0tU498xxiI+P4wUOzcyTkgLn0qVw33uPfq5w1ao1+PWW24w6ye62tUwzB3v9RfLd/K++worJt6Am33hOsYyTeVyHHQrXKy8BV1yBCqcLP7mS8LPPhXKV/qn5NFyy2r1qFadSFZcyFSu5ZbU8K7pKDTJeHgngNV/ls4yX6TKfzC/Lyfi9sbe/l+pi/ty8oqJVRmeuapAKjnQKyXt5lSDKDmHvAJB5rPlllEyT4pZaS3A+Y5wxzb6sNU8oeX6XVL5kPuv7rfVYnRP2adQ01vZkfGOTtT3HjT0Rhxx8oDkWKCgs0lc0ydn0cvbzlm3b9dk8mrmdJ5w9Xp+lY90iTcdFvUomPPerBTjnoitx7h/+iDMvuAzjz78EN995H777YbG+qvjrhYuMeJvff/45Z+j1dczJRkpysj4oJCclIicrE0cMO1SfVWR9BzWeFV+m3/ZJtqja0oh394XX64TPCz34He7g+5oaH4oT9sf3SWVwZ6Wgy9kjVCyA7e8swK5PF6Nw8XoEVByt+CY6PZiTWIJclezrR5xa2/oNm/Tz3OWkmrZWVlpmvqPmwvhSQ2SXkBP1KMqo8pdnxEgk33SL7hSu/uoL+HN3wZGYhLijjbKu58gRalomAtVVqHrnTXluBJKuvQ5xp4yDI5539WgN8uzguLjwtyyU8uraDZux8Icl+Hz+Qj3IraG37qh9ll5uXgG+/XEJvvvxZ72u0rJynY9Lmdjjdhula1u5N159lzx+wE5uLy23sD7koMGqzGzMK8vIOrZs24GSsjKsWLU2bPmbmoeqpaDG68O23Dwg2YPEFL8KgtRdVLnY78Nhp29AZvdq9d6KZQBupw811T70HK3SdXyljplEyJOkxtcEUFwSQGWlnDRdG39qJWqT+wOV8Pqq4HYlwuWQ2/k3HAeZshMFKHGUw+E02jtU5FGs3m7wGMffdXEBvJGu5vM4kajS4m+LanDr9gJ0ra5Bukqr1+7Iw307dmFgZRUj3grkzgzSKbBm/XrM+vATfPDJZw0O73/8Kb5Z9KM+Yb57t64487STcdjQg3VevfzX1WGXsQ+y/gULF+lHssldI5yOqGi+jxqVRZXAVVcBK1cCeSoPVuT4Wfjlt1hz173G7aj3dPxT0/XVxF4vqrfvwKpb78Disedg0/Rn8d3436Bi40YEVDqVQXcqy+2qZ8xA/Irl6P3y8+h00W8R39W4+ws1jzKncSFJJNld3hyJvzdaMX9uPlHzfyOVFHtjv9UJEMo+zngrnQbG/LfddhtuvfVW/WrNJ+uzxk+ZMiXYwWGvXMl7+/fJ7xD2eUKXoaaR7cf4xibZtnLW+vm/OVNXZK1xa9dvwKFDDsKlF56nh0su+C0yM9L1PLKlZZtnZWbgpBOOVRXimmDsLKVlZdilKtbSybxzV67O5K0Yyd/X356FrarAZpHOYXlo/d8euQ93T7kRt/7lz7jj5hvw6H134rH778TII4YxxntJ4sL02z7JVvc4suByy3PwHDrtyav1V8JSrSpanbv0hvf4kXAO6Yrcr35BwcJVcMR54E5KgNNqLFUxcjtc+Nmdh29T+Wy8SCEn33w85wsURcDVp9tsDeXUPBhfsrjj3PDYBvm8u6u9vdXGcZcikJR5pMykjr8OjzrWHjwUCRdcBNf+g43JiYlIvORKPd7ZubO+DTWksYplpVazWdVfslU9Jy6+7hVFVqpKTIwPxsMqw9rLsvoEHzVIJ3FJaSn69e6hO3l35eVjQN9eeh5rbo/aB7p37YxNW7abZTPjW+RVOourqqowaEA/o7yspxisuzRRy5Jtrk/MUfF1utU211UZOYkSSO5chEFHFcinYPL0+9xIzKpE+gC5ak1H1BjU24DTrxaXZc0Y67/UWmS7e+FDpa9Y1WniEO+Qq/YbjoJMqVT/vvMtw3LnDqx2FmGZqwCfuLejxFFtW9IMfkDtI+q9w+VBkjXRGUCBGueLnqbdmPDL8l/xyJPP4KHHZ+DBvz4Vdpg2/Wm89uY7KCuv0HeLuP5PV2LUsSN1ep/9+Vd6nt0tL+t/5Y23zW+kluCfOROOqycCubn6syPghzspGRv/PhMFX3+jx+nMOAzdSaym+dUxdNe772HJhAux4cl/6EeByDrKf/wFP447B7tmfYBAQQF85mPyZBnXgAHImnAODn7+WRz4wtNwd+tipXLaR3mq/BtNou33RgPmz/suqjqLrQ4Bq6JkdQrIq0yT8dZgqM3U5QyDu+66C3feeSduv/12Pc6aTzojZNrdd9+tOyOMdRjfaR+EtYy82n+L/T01nWw/xjc2ybY7QWW6vXp2140YaoQe/7d/zNS3mr70wt/q4fKLzsdZ48bq6VY85L08jzgjIx01XuNWTJJ5SwOJnLljj481yDSZb/Xa9Zj+9D9RXFyi55NB9qVOHTrg8EOH4vijj8KII4ahb59ecKv9QvYN2juy3Zl+2y+HZ4iq3MTrCo5EQm19Pd7nD8DjceLEE/qjf9+O+G6DE//5fj7iSqrhiKub3qRhy6EWq4QXH6aVg9eyRZaffv4FP/xkPM+yLa1eu858R82J8Y19vhofsrqmoEvfjHpDx+6pKNpVjq2r87F5VT7W/rwTK77bhm1r8rFzYxGyOodfrs+B2ebaKVJJp3D8uPFIuvpafXtqO/eQoXp8wtkT4OzQ0RxLraWgsBjFpaUYc9xI9O3VQzdU9evdE6kpycFyrVXG1c8/VOXY+Li4YHlWl63VIHWr5b+uxX79+qBDThbWbdyiy8unnXS8fkaxnLB75GFD9G2nN23Zpkto9jqU+H7JL/pOSylJScHvFg1d+UzNKyAdgBIONfhVAVguItZxUuMDzhocMCofNcU18MSpsrITKNkSQN9RhXCnVKhFZEEzZuaLS71REdbvjb/UmvwBH8oDRXA5XEhwpqkxRpR2p9RRg1WOLfjJtRU/ewr057pUPqD+VqnK0jupLvw1KxFr490odjnxD5V2H+uo0r76XJt6aV9JHil3swsdLC617UW4+SySn/68bCXufvAx3PfoE7hn2l/1IJ8/+2JecB4Rug7J44U8rq2O0PnUQPuoUuWl6lgqkehcU44hFQXoVpCL3MefDN5KWgXKeDXJcVli79+4CSuu+wt+vuo6FC5ZAYd+9rAar6Y54hNQvmErVvx5MhYedwq2v/wqfD//jOp58+D/z38QeOQRBG6/HSmPP47sCp4o31w2ehq6rbccG9smlzS+N/x3N/x7qSHMn1ue8X8Y4SRAOiNWGbJUfuSzvJdxQgIkn2W8MUgnhQS1tsPAmibvrcFidRLZx8mtTcPNK+uwdgwZL8/0EfZxMg81nhUXxjc2ZasKjHTMlpWVo7S0DMUlpfjg49lYvnIV3v/4M+zYmavHWUNRcUlwkLN8crKz8NuzxqNEpqtxJbIO9Sq3TmsoFjJeGk++XPAtLpl4Pd778FNs3rJNrb8ENd4afRWyLC+/SR5m/+OSpfjw07nBZanxZHtJumD6bY8ccLsGwuPqpCpMcjKHiov6l5iciN49MtCvb0eccGwfrF6bj49nr1bpNgWL0npjpacUcS63vqK4GqpiFudCnMetYu3G4tQq7PCo/cf8BooM8qiAf774KspVntxWJO3+8NNS8xM1J8Y39tVUe3HcOQNx6X3H1hn+cNdI/PamI7Hqxx144a4FePner/HGw9/hv0/+oD9/9NzPOOXyIXq+0GXPuf5wJKR4sLvnrlIEkLJSSooUqMwRJlU2CjueWoXked//9AuWLFuJvr2645CDBqFTh2xVR/Hquo48TzhIl2l9WLlmnb4bhNRh1m7YpOszQvLw+d/9gKSEBF3/mfft9/hlxSocuP8AHDRogK5fzf/uR3jVNLFh81bdUW2Rdc/+6musWrdRl9BlkHl/Xv6rfk8tR9VMVF3Jic7yHPGSGlSVuY0ysPoTkLMo1WvHfkVIzpBbURuNki53DbL3L1HT9KmWepw06/nKXXDHB5CaFI+EBHb0t5WASq8F/m06jWe6u8Ll2HMsrHTn1zE13ocjHVDFLgdy3S441Prl+cVFqq6dpz771DRqHtJ2ILcjTkhMqDfEeTy6TcGt6q1y57qEBJXebNPj4+P1dIvcYUtuVSqPW7MGuaOP3BnPTtpM7OuR9cr67eQEnnDfZ7V10N6TNCdRc6l0lV1dhv2qijHgv2/CNXo0qt9+R89jsTqKi775Ft+fdBqK/vUKOlRWoGtNOXpUFOCAsjw9DC/dhREVeThi21oMW/EDul55OVxDhyL+uOPgnDABmDwZmDoVzldehmebcTIXU/G+WxGfAKMVsC7Jibuo8pJbxbg1yffJ94Y7EsjvlN9Ljcf8uXWoMkbjU8rwUaeZ71rXt7PfD3Y+7OnnlqqKT6Y8g8mcz9gRpAPBpa9Ms9Zzxx136OlCrlqTAMoy99xzt6pk1W/0kJ1IlrN2rNDfYf9tsq4jRo/T71vbwjkfmO+ajvFlfFtCclIS9t+vf3DbyjOx1m3YhNy8fH2GvEyTzLchspx0+smr1Rkp72X5DZs268+h8bLIfPKtcua9nLHftUtnpKel6u+TxhRpjNmVm4stW7frz/qWbm2I6ZfptyFtFd+G6HQFt0rDJ6jtmxFMO5Lixp7QGzdMGomv563F1CcWory8RhXcVCHJ4YI3UIxO5QvQq0JuyyYNYF0Rp1a2efVW+BwB/BpfgHxXhW5oiTaxFN9wJB1NuuoynH/OmcG01poKCgtx9Q1T9CMM2gLj27IY3+Zx6uUH4ZDRfVBZVmWO2TNVusJbT3yHdUt2mWNqJafF4bxbjkBGByPPbqzy4mq8+sA3KNrVdicg2EVjfPflN+8NKSfJSZQpKcnmmKaLxm3V1ulXclspQel8V8Wgbgm29cj3h5afI0Usp185iVa2/W13T8dDd72EcZM9GPOXb1XdV+0PqlzsCKg6kC8eHzy8P756rRsSU/3I7FOCIX9ereq31nP+jfrutnl9sP6Fzjj0yM6Y+a/pyEhPUetufCMl02/zkSu794s/FqnuHPxaMR9F/u3mlOYzqqQMnb0+/C8tBYXmVVSRKBrj27ljB0w4e7xuPwrVpXMnDB44QD/ubOnylfquDXZV1dX6wgi5a0+wfhymXG3ltzJN7gBx7pnj0FG92vn8Kr2np+Oo4Ydh67YdWPLLsnrrkhOM5nw1H/O+XmiOaV3Rmn5P27UL127ZgrSi7XCp7bxbw4cDc+fqu7WoAAQ7iytnzYLjgt/DXV4K5x6OnxK1PR1h7fP41H5VnJKNF7r2wL/VPicnirSFaIzvoKoK3L5rO7LME+TsNrnjMDMzC0sSElHhdIXtVG4u0sKd6Pfh4MoKXFyQjx7eamOCTb7LhakdumBZG3UYM39m/tyQqOkstn6m1Zkgr9LhEzq+rKwMGRkZerx0ShnTjYDJdBmMcUYHk7yVzgNrunQYWfPIq0U6I6wOC3mV7xPWvDLOWpadEU3D+DZeNMbX2mYW+WzdSlreWx3ADbFiEEoKaY3p3JXlhfU9+lZuJulEtn6P3msasb6WxPTL9NuQtorv7qnt69gfKQmHqDRkPFdLmquysxJx3NG9sGFDAVLTE/HNd5tRUFCBpKQEVPqWYX9vIjomdENNpReZ3VKRlODBtnWFOj2v96/AFqxSazHOrI8msRff+nr36oHH778bXbt0Mse0ni/mf6NvDSR3mWgLjG/LYnybR0t0Fl9w+wikZsabYxqHncX7bl9+896Qx73cdNs9OHn08TjlpFHm2KaJxm0VLflzexbL6dfqLH72uddx0/X3YcjpWTjvoQXwu6SRW+pAarqqu/zy0f7459W9kZwN9DspF30nrELAK/VXlYMHVD1Hzbn6P/uh6Jt0VQbvh6f+9igSE5p2RQvTb/NKd3ZC//iRqPSXYEXVZ+YjdpqndiNRvWvLDnRWdafHszOxTJ5xHqGiMb5PTrtXP7asKenHIm0P8miXKfc8hKKiot22V8ktSqXN5I+XXIjfnXuWbq/aG3JHvpPOPM/81LqiNf2G6yyW1qU8VxyqnG5kBryI91brtBbo3h346CNg//3hsMXIv3oNHCeeCGxYr5fdHdkLvA4nalwelDpc+tH0VSr+1Wqo8CSiQ3U5OldbJwCxs3hfZPi8uDV3Bw6qrF8HkTgUqxhudnuwXQ271FCgPhe5nDou5Wq7W3HxyqAiK8v41eaXV4mCU72RV7nxv1w1HKeGeDVIx3CqOqan+/zI9PnQwVuDzmroroY0aQ8NkxdIp/UDOZ31b2gLzJ/rx8TS3vPnqLqyWH6q1UEgO4bVISDjrR2lpKREd0YY8xnJ2fpftJYV9nVZwo2zWLczFdLpIN9nn1d+i4yzfgs7IxqP8W28aC2MRZLQ/SSSMP0a7OuyhBtnYfptO7KV3Z7RcDuy1DZX21pt44AqTVdU1cDjcSE7K0EV5rpj67Yi/Lp6HVKq1mMwDkaVrxLeaj+69+moKl0BbFufD4cqedeof0sc81GFEr2HRJP2kD/LrX32tcC8N+SWm4/P+Afeef+jYJ7R2hjflsP4Np/m7ixOy07EBVOORFJ6056nxc7ifdeU3yxXBJdXmNtaHYtTkpKRmJiAUjW+whwv5aKU5CRdDpJbEKenpuoGDElzSUmJ6pjtwUVXXotxY09Sw4n61sYet0ffkccqx+1JNGyrUNGSP7dnsZx+rfrNZ3O+x+8umaxKwQ5MmfULkjpv1+VpVapW6Q/Y+nNfPDy+L5Kz43DweVvRafRq+L2SLl1QtS74yhPwzbSBqrKVht+fcxruvfsKuF1160N7wvTbfGSrO+FBn/jDkO3pjR1VK7CpZqmKlDy2Z1/J2gPYr7Ia3aursSAlGVVyErYxMeJEY3ylfWNXbp4+ibG0tNwcu2dV1VW4Z8pNSIiPx5/+MkXfynRPnRFyq9Jbrr9Gn6h1y90PYOPmLfrY21hXXfp7HHn4oUy/TdRQZ/H6+DSsSUzXn+MCfnSsLkd8cgK6/f1vcJ95pj55RwTkQoSCAtRccAG8n80xOn9dHvicLqgpKDXv6lDsdKPKbZzMUanGyd3TZI+w7xXyvb0ri9C3stgYobCzeO9J5+35Rfk4t6hAX93bWLKNJY5yS39pXQx+VhEKqM0vMZMoyBMiXOqTRFiuKJfvkNuXW58bS77jP+mZeC09S3dOtwXmzw3Hq73nz0YOFiXshV17Y5KMtz7L+9r56o63u+WWWzB58mT83//9n5pmjKudJ/wOI+uSwd4RYY2Tz9Yr7R37tpNtabG2rfW+dj7Gl5rOiqkM1HzsacO+ba20Y72vnY/pNxZINLy+FVIyVttYoqoGVbJITnQjzu1AYUElZs9dA5/PiyMGJeLIPkNR5auWUrfMKX80HRu1gngkoHOgdwNRprYmnQezv5iPvPwCc0zrkFsTz/vmO52OqeUwvhTKV9P2j+ig3ZPn0U5/5p+45a77cdu9D2Ly7ffiny+9iqKiYjzx9D/0uDvvfxhT7r5fj1+6bAVuv/chPP/S63r85Dum4m/PPq8fFSIWL1mKqdMexy133oe7HngEK1ev0eOJqAWYdZT+/buhV99OKNnixpaVaaqELM10tXlvYlqV+uiAyw140uQEEN0srZb3wu8IoCo3CY7CFFRW7sLIYwfDbasPUeuTyPlRg23VK1DpK0GHuH7o7B6gItaUrotwJKbGfrEqIQ5z0lJRGcEdxdFKngdfVVWNTZu3Yvmvqxo9rF2/UZ8AKSev701MpCNi9dr1Ydcdbli/cRPyCwvNpak5uFXKlRQlnX/VDic2xadgVVUAm39da1wZaisT+1NSsHn/g7AopSO+T+2EH5Nz8HNiJpYmZmB9Qpoe8uOSUO506VseS4ejpF97io2qDpkoUaOOfT8mJOkriJtCd/yqId7vR7IaUlU6TldDls+LbK8XOWqQV/ks42W6zCfzy3JN6SgW8vvkd8rvpcZj/tw6oipvsq4Os1iNSvaCsHE1W93Q13YUyCejw+Cee+7B1KlTceedd8ocMsHWSFU/sdq/o3Z9xqvF+mwfR43H+BJFL6bf9klvef9OwLtVR9bYumo7m++dLmnYisOKVWuweekWpCakY/iYAUjJSIS/JvQ5MipGaoW9XAOQ4exojqNIIx0Hc79aYH5qeZJvPPfi6/oMUmp5jC9RdJGTLb76+lvccO0fcetfJmHUsUfjqwXfYunyFWr8Qvzf9dfglhuuxemnjMGcL+Zjw6bNWLNuPY4/ZgRuVtOum3g5vpj3DdZv3KzX161rF1x71WW44+YbsWNnLpavWKXHE1Hz0+VoVfjNyU7DwJ7Z6NLLje8/TIOvMk6Xo60aTlyiD/GpLjgSfHAlqfqU36mvbnKqVylx5/6aguT0OKS5ynHAoP56HLUtiUBZoAhrK7+G3+tD1/jB6OE50Ky3yoN7rOjunswltz0Vh1SoelK1XH9uRZhxbmlW+0FjB9c+PD/afsVauHXbB70fqf9Cn8lJzUNSlpW65P4NVSt+RaCiMvhIO7kdtTM+Hs6B+6FGfZarT6WzUAbpaLaz1qWipgfpjrbmCXdrYto3skVXxyfg5/gkY0SEkt+3Su1D3AP2Xri8cXcD8+fGi6pfLvcLNzobjMYlq2PCCoa8GuNqOwWMwpgao179fhlnvffrwbrNnn3ecLzmbU7t67Nud2p9tn4b7R3Glyh6Mf22T0bhtgbVvjWI80jHsKvO4HQ7dYex27sRWTU5WLVoK5bO24DBR/XCfof2QN+hHZDZKVmvRYcv4Nex6149QFXM3FLGoggj8Zn56hsoaIUzJeUZmq+/9a7uCJF8gFoe40t2Lo/Kx3dz/KW2V15eDp/Xh0H7DUDPHt1x6smj8Zdrr0JVtTRfAnn5hbrTV9KYT6U5mV/s178venbvhgP2H6jSvRelZcaz8jp1zEEvtZ5ePbsjIyMNPn/oiV1E1Jwki5Xbxh91/HEIJFfi1zlZKNiUaU41ytoOpxeuOK9RVnaqehGqdRlZ1a6AikQULM7Ahu25OOn0CejauZNZqKZIUBIowK/VX6LKX45O8QMx2HMMMhyZqp5j1HOtbmN7xIzPxhiP2gG6qqru+YU+TCipxLEV8ozMuvNTbGlUmZjl5hYSLmUFULhqNarLStVkY7p0GsuQfuAglYEbj2rRty9Wg7warV61rGhZ42Qeajnlavu+k56OIrM9MdLI75LfV2HerpyiR3vJn6Nmz5TGfntQrM/yap8mr9Z7GW9nn/euu+7E7bffjltvvTW4nlDWeoR0OljzyHjpeLDWZY2zpodbF+2efVsK67O82qfJq/U+dDvb52V8iVqPPa0I67O82qfJq/U+NB3Z52X6jS6ylf2BXJSVf4GKynl1hsqKeSgu/RQdnHHw+BPgSXCiorQC3338q47bb/58FA4fO1DFRWJjFEn86l92QiekBbIleHocRRa5TfHLb7zToidhSKfll/O/wQuv/cccQ62F8SULb0Md+SQ+xjHUKN+kpaSgb+9e+mz2QMAPORlDhjXrNmDcKWOQkpqi57PIsVcibC9XEVHrkbvqSHP2mOOOgKu4AqnpSVj8UQ4cAbcuBkvK9nodqCx2qeOy5MsytzwxUf11+VC4KgeVu1IQyN+Oyy48E/EeVZ5mfSdCGPmqdBivqfwaBTWbkelIx6C44ejnGoyujk5IdqTCDY+Ks3llonqN9zuQ4wX2r47HqHI3flsah/38SVjpceDLBAeqVXiZYxM1Pzk9TpWq6nTm+uU20pu2In/2XKOspctdxvTUvn0Ql5lVr/NXalChaVQ+h9asGuo0drRgHay9+NUTj/dS0vXtxCOJPJ9Yfpf8PqJIFTWdxVKBtQZhZc7y2eoYsDM+182erWXl9cEHH8TDDz+MadOm6XF7IuuzL299tn+vfR5qGtlu1iCs7SqfGV+iyCbpwhqElW7kM9Nv+xBQVSt/YAcC/i11h8BWVdDYiV2+raiGPGPNAaeqcMkSq5dswb8fXYC5//7JWIdUilSMnOpfcU0+yhx5Ejg9jSLPZ3O/xK9r1pmfmt+SX5bjb8/+C4VFxeYYak2ML1F08LjlGZgO/Rwu8eOSpbjjvmnBz1dddhGu/eOlGHfKSdiwcbNxrFWsIlFJaSmcDifi4owrY4iodemSrtOBPr264vKrL8K29evw67zOKN7SAXDJVCcqSxPh96q0W+6Et0yleTVaOpn9lclYP6cjyvKKcN6F5+LQoYPUJPlHkUMy2wDKAoVYW/UdVtZ8j0pfJXq6uuFA98EY4RqCY3z74Uj/QBzl7YmTqjphTHU3nF7dESdXp+MAfwYK1f4xK6kK/0n3YGO8J6QWTUR7q8oZhwp3vB7K1SAdw/L8WblNdHCQGYvLsHzSzfDPnq2Xs8pSri5dkDRoABxVlXWXacIgJ4n4ne7g75ChSg1ONY72nt/hwIcpaVganxAxeab8jqXxifp3ye8jilRR0Vlsb/S3bisq46z38mp1SFiDwegcsDoIasfL5/qdCRZrGacqlNnJvPI9od8r7NOs76PGscfB2rbG9md8iSKdPZ1ZacdIX0y/7Y1s2XCDqEAJdji2SGT1lcPy6vY4seC9ZdiwbKeaUcVKQqpi5HJ5sBErUA1vcHmKPDtz8zDni3n6VsLNSTo4Pp/3NW689R5s2badabaNML4keBvqyCe3nO2Qk41b7roPt937IJ557kX41TG1f9/eyMnOwl0PPIL7HpmO6U//E1tVmnPpE7aA626+DbdNfQi3qmU6dshG544ddIexPFbEEufxwO1iYyVRy3PouwFcfslZyMjsjJ3rnPhyZg+4vAlqmhPlBYnq1aPvJFBVqN47vHD4A9gytxN2LEpCYmo6Lrv4PHjcqv6j5lS1JVkpRRifqt3s8m/Fz755+NH7M3bWbIbXW4IkdxI6uVLQ25mGHHc2MqXjKODFKkc5PowrxGuplfhR7Qrlqv7LyBLtu68zMnDtfvvhssOOxBWHjQgOUw46BDMG9sczIcOzAwfg2X598fLSleYaTCrf3njaKfjHwQfoeUKXa+xwx4FD6vwOGf40cCA+UuU7diruvXxVpn0jPRM7bM+bbUvyO+T3yO8iimRGq3qEk4b+0MYkq/FfGB0HRieAvFqdBULmk8HOWlXoeCHrsJYJ+crgNPtvsdZhn2aNo8YJ3aZCxjG+RJEvNM0IGcf0SxYjIn7sxAZUOauNGEgYAn7ExcuzjVVs9Fxy+zUnNtesRZ5ju16KIpfcRvjzeQuwbfsOc8y+27x1G57918u49+HHg8/PpLbB+JLgbagjX4/u3TD5uj9h9HHH4Ihhh+KcM8fhxmuvRr8+vTF50p9wzFFH4NAhB2Hs6ONx06SJyMoynoV6603X4ajDD9Pjb77uGv384ksvPA+HHzJETxfn/eZMHDLkQPMTEbW0Lh1zMO3RPyMuqQLz3+iI72f1VRmxB9tWSGexqkc5nCjeFqeK1UkoWtkBa//XCakdvThvwjEYduh+qjQtJ2QaxWyKPMbR1AFvwItc/xYsxgrMd6zAAt9SfO1bjtmODXp4370N7yUW4sPkUiyL9+lOYsGjceuTemtjh+YiJ+mFW799UH/MuWlv5Hs8WJ2UhHVqWJ2UGBzWJidifXJSvUHGy7wfLlmKhT/8hB+XLtPDD+pzVUaGntbQso0ZZFn77zCGJBSo30l7T/LMpQmJeCEjG3lt3EEr3/+i+h3ye5iXN49weWNDQ3NpL/lzVHQWW2SjWx0B0hGhgxAiMTERBxwwWL+3dxoYjM/hlmuIrOOQQw6p0/Fh7QDW+3Cv1HT2bcf4EkUXe9pg+qVQstXLUYLN/l/hccbrqx78aqwv4NOvAf3MxACqUIGtrlVs5IoSGzdtwbsffGx+2nvScfjGf9/D9bfchX+/PQslJaXmFGpLjG90kg7e5uSrMY6xFLkOPmAQxo09CaefMganjhmNAX37GOMPHBwcL6+DBg5ATnY2jh5xJDp17IDTTj5RTztg0EC4XC4cf8wI9OndSy8rRhwxDL179jA/EVFLsuop48Ych2OPOACduvvw3iOdsfbbnvjl2xSkdAzAmeBA4aYkVGzNwq/vdkZKaiLifFW44vILkRgfr9bhUoNem14nRSKrLiqvAVT7K5HvrMQOZzm2O8v0kOesRpnTZ87JumtbkjTZ2EFUV9fo171RVV2l1yMn6YWuO3SQ9o+APwCvt3nLfO2JkQIbP0iuukHVjeSuLLfc/WBw+OdLr4WdvzkG2ndyj6yvklPwjzbsMJbvle//Uv2O5r1nV/sWLm9saBDMnxsvSjqL6zb2C7k6zeqQsHcAyLinnpqBvn376ltpGVeyGfM4HE79KuOs5eoO0BVl67PchkvWM3369ODVcNb3Wr/F+g3yKssa4/Ws1GiML1H0Yvqlxgpgm2Mtcmt2wOPxwKP2gbi4eOO9xw2n24UtjvWoAK84jBZSWH5r1v+wactWc8zuSRqU2xqXl1cgNy8fq9eux0v/fguXXfMXPPrk31UFfHOz3/aY9h7jG51cKi9tLnIbanlkQJOxhStiDRzQD3dMvt78RESRRDp701PicdetNyPVkaveJ+HVW/thx6/JcCeojNXlh7/Ig8XP9EUgNx1leUvw6itPoH/vbqoiZK6Eooo8e1oOmvLPOHgaAw+jbUcVZ5Gi0t6o447GOWec1qQhOzMTDmnfMNe1R+q7/H4j2qecOApnnjY27HrDDaecNAq9eqi0T61CTp30+nwoLipGUWFhcCgpLjFmoIgltc95yan4Z0YOtrtb77nv8j3yfc+p75XvZy143zF/bh2OgLTsNNLwUaeZ71rXt7Pfr9PoL6+7I/MsWbIEX331FYqKioIdCRar48Bal9GBYHRGyI5gdTikpaXhyCOPxKGHHqrnF9b8Ql5lXmt5a7yMO2L0OP2+tS2c84H5rukYX8aX2hbTL9NvQ2Ip/cYjBUmBZPNTrYAjgHJHMaoDleaY6NKe43vk4YfioMH7m58aJhXsiopK5BcUYOu2HbrzsKQ0Ok4OYHwZ34ZEUnwPOrYbuvbJgM/X+KuBAwEHFs/diLyt9a/4Tkhy47AxvZGY0rTb4FWW1eC7j9ejqjwymkWiMb778pvbSjRuq2jPn9uD9pZ+pc4itZZvvluCP/3p/7Cz0IsaV5y03KmxRp0m0Q0kqzLznXfdit/+5mQ1zqgHNaEJtB6mX2oJ0RjfP15yIS6c8BvEx6t010RerxcffjYXf33qHygrLw+2QYQjaVbaMc4aNxbXXHkJEhPkGeVNt3TZClx6zY3mp9bF9BvbYi2+cl3xgZUVmFBcoF/jdpM+91W1St8/xyfhP+kZ+tbTkdhRzPyZ+XNDoqKz+JvP3tOv0kFgNf4LqwNAXq2OBWu8sM9vzWu9F9Y4ebWWt0+T57XZP4vQddqXs6/7yBNP16+tLRoTO+PbeCyMxTam39o0Zo2TV3s6tKYx/UYvIwrhNbpAEoEY39jG+Ma2WImvy+3cfSYbhvQ/+Lz+sPmvrErWaVz51ATmOiNFNMZ3X35zW4nGbcX8OfK1x/RrPSt+gXQYX30byn0OFFXW6M7grNQE5G/5FQ88cC8uPP90eDxyV6Z9v6yY6ZdaQjTGNyE+HgcfOAgdO+SYY2rJYx/kcQ8//7ICH3zymTm2VmVlFb5fvASFRcW6/aEx5PsOG3owMjPTzTHQzyf3+X3o1aM7Lv7dBN3h8NZ7/wu2Z1jk0S7LVq7Crtw8c0zrYvqNbbEYX0lBWV4vTiktxumlRUj3Nf+tgotcLryXko4PU9KQ73ZHbBsX8+c9a6/5876XKluB1dAvnQChjf9COg1CryyzOg2s+WWcfTl5tea3Oh2scTJdxsnyQsbLemS8tU4hr9b3yjTr1VoPNY5sL/u2tWJhbVPGlyhySXqwpx0rrVlphumXQkk0GhqIiGjvSQetPGO4KYO3gY5iIeNlerjldjtEUEcxEVG0car6iqrxYMSwIXjqqfuQ5NiK1DifvqK4dNdSTH/yEVx04VnweOQRPVHRpEcUNaqrq7Hw+8V4/6PPgsOnc7/EnC/mY+mylfrqtPUbN+rPsz+fV2e+zz7/CgWFRcH2h8aorKrC/G+/q7Oe/306B3O/XIDPv/paz7N+42bM+3qh/r7/fTw7ON+XC75FXl6+noeI9kxSpjxD+NX0TNzZsQu+TEpBvsu1z1f+yvKyni+SUvV6Zf3yPWzjal7Mn1tHVJUsrYZ+ebWCK6/2TgdrnDWfTLPmtToRrHEyyDir08GaT1gdEELGW+u2z299r7CmW99LTWdtZ3m1tqG8WtvZ2sb2+axYCisW1jgZ7PGy5hOyvDW/jLfWbZ/f+l5hTbe+l4jqstKRvFppRF6tdGSlIft8VloVVlqzxslgT4/WfEKWt+aX8da67fNb3yus6db3EhERERERRSqXww1VK8KI4Qfh1dfewOEH9cLAnp3wwgsv4dyzT4bLWVu/IqLmI1f2S9qSQdoUOmRn4epLL8L/Xf8njBt7IuI8Hhx+6FBMvm4iJk+aiD69euoTPGTY1zRpfe9lvz9Pr19uuSoOHXIQbrzmj/jLn6/CkIMOCM4nrDsREFHj+VX6WRGXgMdzOuHBnM54Mz0TSxISm9RxbHUQL0lI0svLeh7P6aDXK+un5sf8uXUYreoRTrax02nsCMZgvJeNL1eOyavx2ZhfxlnzCitQLlftuNrl667TmCbvjXXLq7W8TLeuVAtdj/VeWJ+pcWRzWdvfGKx4Mr5EkU6Sg5W+jMFKr0y/REREREREe0XXdYAD9u+NZ595HC+9+CiOP2aYbsTjFcVELcc6eV39QXZWJk4adRxGHTsSBw3eH263Gz27d8PJatypY0ahb++e+upB6RRojhPTpb1i9LFH6/UfefihelzXLp0w6jhjnHR+WL+vOb6PqD0rV+nt54REvJqehftzOuHeDp3xXGYOvkhKwTpPHEpcTsiNqiWlyat8Xq/Gy3SZT+a/P6ejXl7WU8Fjc4sL5n1qYP7cMqLimcXUeHxmRGxjfGMb4xvbGN/YxvjGNsY3tjG+sY3xjW2Mb2xjfGMb4xvbGN/YxvjGNsY3tjG+sW1f4stTHoiIiIiIiIiIiIiIiIiI2iF2FhMRERERERERERERERERtUPsLCYiIiIiIiIiIiIiIiIiaofYWUxERERERERERERERERE1A6xs5iIiIiIiIiIiIiIiIiIqB1iZzERERERERERERERERERUTvEzmIiIiIiIiIiIiIiIiIionaIncVERERERERERERERERERO0QO4uJiIiIiIiIiIiIiIiIiNohdhYTEREREREREREREREREbVD7CwmIiIiIiIiIiIiIiIiImqH2FlMRERERERERERERERERNQOsbOYiIiIiIiIiIiIiIiIiKgdYmcxEREREREREREREREREVE7xM5iIiIiIiIiIiIiIiIiIqJ2iJ3FRERERERERERERERERETtEDuLiYiIiIiIiIiIiIiIiIjaIXYWExERERERERERERERERG1Q+wsJiIiIiIiIiIiIiIiIiJqh9hZTERERERERERERERERETUDrGzmIiIiIiIiIiIiIiIiIioHWJnMRERERERERERERERERFRO8TOYiIiIiIiIiIiIiIiIiKidoidxURERERERERERERERERE7RA7i4mIiIiIiIiIiIiIiIiI2iF2FhMRERERERERERERERERtUPsLCYiIiIiIiIiIiIiIiIiaofYWUxERERERERERERERERE1A6xs5iIiIiIiIiIiIiIiIiIqB1yBBTzPRERERERERERERERERERtRO8spiIiIiIiIiIiIiIiIiIqB1iZzERERERERERERERERERUTvEzmIiIiIiIiIiIiIiIiIionaIncVERERERERERERERERERO0QO4uJiIiIiIiIiIiIiIiIiNohdhYTEREREREREREREREREbVD7CwmIiIiIiIiIiIiIiIiImqH2FlMRERERERERERERERERNQOsbOYiIiIiIiIiIiIiIiIiKgdYmcxEREREREREREREREREVE7xM5iIiIiIiIiIiIiIiIiIqJ2iJ3FRERERERERERERERERETtEDuLiYiIiIiIiIiIiIiIiIjaIXYWExERERERERERERERERG1Q46AYr7fo+GjTjPfUaRaOOcD813TMb6Rj/GNbYxvbGN8YxvjG9sY39jG+MY2xje2Mb6xjfGNbYxvbGN8YxvjG9sY39jG+Ma2fYkvrywmIiIiIiIiIiIiIiIiImqH2FlMRERERERERERERERERNQOsbOYiIiIiIiIiIiIiIiIiKgdYmcxEREREREREREREREREVE7xM5iIiIiIiIiIiIiIiIiIqJ2iJ3FRERERERERERERERERETtEDuLiYiIiIiIiIiIiIiIiIjaIXYWExERERERERERERERERG1Q+wspuhwzv1447GzzQ+taPIzmN0W30vK2Zgx835cYH5qUSrOb0w239NeaMVYNQXTb2RieiNqPXud3lS+PusZ3GZ+ahVtVdajEJPwxqwIPKZTM2F8o5stb95D/n7bzFbOw2nvtWjZuA2O59QyrLqt9UrRwUrf+xC3Cx57g/VnIqJW1fQ6U6zk1VHZWXzbzA+wcI41sOAbHaSSYotbazZSnHM/Zgf3FzXMnGROaIRpV2H0DW+bH6jJVIE4uN3V0HqZZmilWH2OxM7MSNJmsdoXe4gr02/LCMlTW6Oxgsf9VtZm+YEqkDflGE111E0nb2DGOeaEJrjgsWfM5d7GxPFXYaoeS5EnpFzdrPnidEwYPwWvmJ8stfvX3u1b1NIau0+Ejy9FgEbVWRvIm23LskMhwoWUsZq1HK3Wzfi3kpaM456wjrtPpDE/OuJWWy965YYJmDBNv6WWIMdQ+zFXfbafwKr3GXsbdqOO1xTxGox7SJnaFvtw9e02y1NiSQOxsG9ba3jjpbrHXxn0dm/L47JpT3l1bVtLZIvSK4vLsGjGaRg+Sg0z8nGCmXDl7Nk3zIRr7RTBhGwlbtnh1Hw6Y5dx1s5km25l+g2uw5Zx6HW1wQ4YnTZhlsRMhnlZmGRmBLWJ39b4FEzkoQ1SxrYPzQisSlFwHwg9WG/8xPheGS6erkao9ci8VhyDyxuf9SDxth8swswf9rdTHSWLnzO3/XPIPdraTqrga23nYKxqx4WmqdrCme2gbaVHtR9IOmxcQ6ksb80nMbXStDLY2p/ab0dUi8cqmP/WTS+16cja9mG+Mxjn2vXdNvMyDOs5BJP05zDLNDn91s4n/1/1834KsuWpc7MuM7dpmJgr9eNrMWImy+4pFg0d96nlNC4/UJ+DsZD4G/PVj2eY/aDeMVyWH4PePcfY8hpqGls6GTUBE980R2vh88g6nQsqJpOG9sCwiUbcaq9Kq13WSI+2tG7Lo/Vna13KHtN1cJna/QRZY8zf1H6PxY1nK1ePMjqPwtWFwsXGXl6ujZO13SVOIXmsWsd4WPn+68BFRnwaH+Pa76aWFLJP6DRuy3N1fl0bX/v+8sbk2nTNWLWh0DqrLZ+20nRt3mxRsRsHPK+X+wQ4ujb9HmyVZcPk+/b11cs3qEXVlrFOC+k8sper1PsGj7G1eW1wnMR2bA/0Hmt+tpZRZascWYSaXb04qm1e2y5Rm6fW1ovClMXqLBNmum09bww2R6lYG3Xc8PsL67C7obbdpf1XY7oZt+dxXvCYF67+YtldeUfGh8tPa2NoCsYtTL2oXuwl7rZ6kewnVtqvt1/V/S315gv9HdREZ2Nk1mrMWp2Fkbb9Is9WV16UNUjH0V6+Dh9TKz4y7Q289ZLtKkTJwxmrCBK+/6J+fdvYP2rzlDH18g/ae9L5Ktt1+uIyrP/I2MYTfn+Vsf0/2hQ8DltlKWseKQ/n9R9W5xi4p3y83rFg5iBjhBI+bw9N44qVV0t+r5ap066hptW2tUR2Hh39t6F+cxFWIgt99IcewDL7TjEJB1sNGypxn2EGPjsrX1empq/uj0mDl+vps/L764z/thFWRes5rMwKv44LHjsPA1cbO+QS+U5qumnLsT6rk96+Z2TNN7bvqPnIuUgKPCrBHZ1vZrbWONFDJabzgBclI+hhm+c55A6unUfvA7pD2KanKmSZiThYCc7KQq5a1/AZPyF7sCROW6xVprN+XshZ96HzS0HT/tvHRV4CjyxvY/5qIKevpKFByDUPsNPzR+rM9LaZI4Pj5madFzzA9lEZ9aV4HcPHTwFU2suZZ8xjT9O9Iek49Cz7HhhvxnzhnMswLMsc3QBjHWq9HwEntPuGkpaLlZH/2jox7OlIOgHVgTLcdworRlZ+PfXiT7C+9CdM19/XCUtkHTIdRmG9jsamX3M+yWPCHT+ovqnLNiG7s0ozk8fUifmlko7CxNcgBatBOmYT1u4pFiFnYNc57lPL211+MB3vBivOPZCTPx8TESae9faDv2LGYNsx/GgpQL+NiS/+hBJpKA89hlMjJavKh3XcsxqfDBeEySMvGJGFlTqeZjlq2lWYtVEqwHU7muvl+fcOQ45ZDp6eP0gdA+zlNqtMNgkj5Xgg42asRs6IumWvuuW42rJesIy+OAsHM9/dA1s5J9hgWL8utPvy8qbaBg51fFy/+JOwV5PfNjgLi9630qVc2fgJ1qm41y/DK2FjbP9uajkh+8SbUzBa5bkHP3a/EYt6VxTX7i8Ya9SxautF1CZ61tZZdZm7XttEePPfN2N7Tidk6zHCiq/k+yN1eT38+kLzDWppqUMvM+Ncv1Oqvgby8dC6jUrvz5uNqROmqWXGwmjofjEf2Sl6RdTMwsXRapdYF6YubC9PGeWn0GXClNWa3PYYvg2UDBeM6I88WzufdES8u1bKqA2UabTdl2l1ftp/jFnurp/v1hO2/SM09g3Ui8LVt0VofnBOaFndmI12w3b8XThxSO2xVG1LLJuCqTcsb0Sbrxl/FbP69WYpPxt5+AydF0zAb34/HzDLXLpeFixrU6tpKO52wf4LYatvW/WvrCGYJJ9VWn7lhqtCTtimRmtMLPbWHtp/67QTWycULZPjs6V+3t5Qu7WlXruGva0FkZ1Hx9gzizdhib7cexNy8+V1uipQmTubKiwb04C81Yt04eCV7flYv8zIjNfl6wWwDv2NRB7sXAq3jnysXGA0XksjOe0DVaFFvrUNVdysDoD8HWYBrvZWadlDx2BYSj62WhlvipkhS6x6Wh0H1j4QwnaWdrDjIX815su63tyBPD1iOt5VCdyIdQ+j88Ou3vxKMDMbg97BgwftSR+Vtqw4Sjo0tnVtbKdebDZUq4Pu+KHJyNte21kkZ0uHxshKx3XZr654DouMJN6g4DrWqhmzGlMRax+aO1ZW/muXZ+UB0qipCtbhv7M2RlZ+bffK9qxgA+n4nuZIu8amX2u+Bo4f1LALOgO5a80PtnQUGl/Re6za5qX56phr2m0sKFKES5uvLMg3Gk0mD1IFaDMfDRPPuvvBeuTAOs6/ja35WejKRoxmYD/Tue4JVOHySIndQF3ZvQwD8+vnzbVC8vzbFyG3v9FAOql/vpFO65XJVLzNeepU9uzpOkxZz15Gpz2xlXOCnYChdSFlt+XltzFxWZYxXcWpd6PLP2+r72uoDG+LcdjvppYTZp9Qee4SVUZD6Emwmm1/2RimnEStz1ZnldvY1W+bCEelR4ndOfdj9sT+WPliaH4gZWfjpK/w6wuTb1CLqr0ite7JWQ0Kl5eGq9vYbVxulAPkJMtSPYaaWbg42tsl6teFazsZ5OoiSZPCWiZ8fbapbY+swzaWdfXYpHGq7NNQu6S2pzKtvS5TP98NJ7R+vMe2DFND9e16+YFK9/XK6rR79jt7zPgpmK/KCQbDdFpW9dqetRclZAdPFlH5cr6Z39riH75Nyyh3D8N8M8+Q9CrrlKtTGac20UDcG2arb+uytpwEYHyWDsPGnQRGYTU5FvUFj7sqvWaHtj3vrs3RVpYKtlnLSQJ6jKiftzfUbm3ZbbtGhOfR0d9ZfM4wDFQFqGCDcx2T0DXfLMB9hEZcoaAyaBUko5JtdS6FW0cWBlpnk9HemTwIvc1Gwuxgw5QqMFuxDHa8TgreTkff5sN+ZZpcUWjGavqMMM9uajIVf3XQtjKnelezhRPMzNRvCFbMKTxJX1bhtrZj4ILOWWYHY+244O3V8iXGz5lXnRmCt5WY8Ryeb0yM6rAX5NX+Fq7Rpa8aGawotFetG6tgHnCOdWumcN+5e/qqJ7PQNmujOXJPdpt+m3r8aL9uG9wjuF8EK8S2dFQ/vua+YT8buil56W6P+9T89pAfSEE3axhmDLY1SIWJZ939oLdalXWcPxtdVeXYKmhTywiXR14wAphrxml3V6zVy/PlDHvzqobp1pXloWUySaf55n7QUGUvTFmPWsAeysu3dbauWFOD/eoVm6nL8m11H7lt1huYMaSBMrxds5fVqckmP4MTVFrkld3RKFzbRHi60+MiuWo4fOejNGjlrm38+qit2Dqp7FeJ701eauvUoLZRvy5c28kwXY0Lfa5h+Prs7toew+0vrMPuzisLViPbvFW/vr2pKqOWGJMaLtPssUwbvi5j5LvmhxCh9ePGt2WEr2/XE66sTnthEs6w3bbcnqZqb0OthrDl53BtWsadImbZriScukyt87FhyNntibvUpmz9F/WpmJpXGEueMn1xwyeJUMsLHnfVUK9Pp5Ftjtl7uMtObd7e9HbroAjPo6O0s9h22f/ELMwNnk0f4pxN2IrzjPkadVad3GrROqPrPAzMGoIzZtRfxys3vI6V1pllY60CBe2Z7dZocislOaC+OQXPW1fzzhmJXJ1gp2PCPPMqBxm3bFNtQlXzz5UzJWfCNs9lmFTnNjFhBM8gUUODDZKqUDi0MfOZ6vx29Rv2eEuS9qn29kyXIWee0YAx9eL5yLHOqs2arytK9nEn5C/HumBm+TYmzoPad55BH5X2co825lk48bK9uq2SNHga+cdIZNsaSWrP/AXmNrkTOja0Sazs6Ujl57kLwn9ng+RMe5VWa+OqflNW1p5vJb6n9Nvk40c7Y8tTT8g3Gjvk2BiMucrjdaNImPgGTftEH0tnj1jUiLy0kcd9ajaNzQ902Sm/P4bBuoIlTNqqtx98VecYnjPP1vCp9i0+M3Fv2W9Dbbsl4thngHB5pEqPB5sxqL2yWNZRe9KPqJfnD1kEDDY+G2fC2sttZplMrmTKMvOJi/rru8PUbbTeTVmPGsn+uI26MasVJjbmFMPZ6NrfumJNDcHbWYeYdpV+Pr3xXXK7YpUn3G5P61YZ3m5P303NL2SfmPyMeTyeblxB3lB8KUKFaZsIKU9L/i7POT1jaLLt6lMr/6/dH8brq5j2vD5qO0YsbTFSx05D0/LS3mMl/mqZj6ROZiwzjLehbnV16kVmXbhOOXpi7aOcLOHqs7tvewyzv7AOu3tSJ5HHEcr2kWHiEKRa4xsq0zRUplV5qJUuB662HuMRmu/qkWb6NoWpHzfYlhFSLwpb3w4rtKxujqYmueCxkXWvTpy2HDBPNtiT+vXmSXjD3Lf0La31c0vVjLLOof3VftA+2yAjV5j+i3BUnvtu8BhtbyOhiBKujSqcOseIMXVuQx2atzep3TrIamuJ7DzaEVDM93skPd4UQlXCZ3f+pHFXobYC2dH2VruP7zn3Y0bfKZhoJnC5egYXR9ZVEIxvbGN8YxvjG9sY39jG+Ma29hvfSZjx2CZMNOsxFzz2DEYuiL1nbTH9xjbGN7YxvrGN8Y1tsRvfszFj5jDMv7juiXKR2IbYkph+m4NcmdoJ70bgyfCMb2xjfBsvGvP2fYlvjD2zmGjfDAze317OFrGeO0FEREREFGPOAXKsK5bUwKtPiIiIiIhagzzaJcyzVYmI2hA7i/fVtKsi5qpi2kdvTsHoUbX3t2/ouW1ERERERFFPbiE+3lb25S3+iYiIiPbgbUwMuapYTG1HVxVTc1D7kSqHs0+BKLK1t7ydncVERERERERERERERERERO0QO4uJiIiIiIiIiIiIiIiIiNohdhYTEREREREREREREREREbVD7CwmIiIiIiIiIiIiIiIiImqH2FlMRERERERERERERERERNQOsbOYiIiIiIiIiIiIiIiIiKgdYmcxEREREREREREREREREVE7xM5iIiIiIiIiIiIiIiIiIqJ2iJ3FRERERERERERERERERETtEDuLiYiIiIiIiIiIiIiIiIjaIXYWExERERERERERERERERG1Q46AYr7fo+GjTjPfRR6Xy4X+fXvj6ssuwrChB5tjm8+Wbdvx3EuvYc6XC+D1es2xkWfhnA/Md00XyfElA+Mb2xjf2Mb4xjbGN7YxvrEtGuO7L785lFQGpUpY6C3FtyW/4ojU/ZDpToHD4TBmaCbRuK2YfiMf4xvbGN/YxvjGNsY3tjG+sY3xjW3RGN99+c1tJRq3VUxcWSwNGV27dMIdk6/HEYcdgh07c5t96NghB5MnTcTII4YFv5OIiIiIqD2xysDyGjpY4yl6BKSrOBBAgbcEf936AR7c+j6e3P6R7jj2q/F6OhERERERERHFtKjvLJYGKTkTfvDA/fSVxW+99z9cc9OtzT7ccvcD+url008ZY34zEREREVH74DQ7hKXcLe979eiKEUcMwwnHjsChQw5Aty6ddFlZpts7jyly6a5gFa98bwnu3vwW5pWsRIrTo18f2jYLRb4yPZ0dxkRERERERESxLWaeWZyclKQbpRYv+QW78vIx/tQx+McTD8Pr86G0rAx3T/mLHkpKy7Bj5y5s27GzzrBdjSssKtYNInn5BXoeWa6quho7c/OwYdMWFJeUIi01RX+fzEeRSxoxhx50AGa9PhOvPTcD/3nh7xhy4GA2XMYIxje2Mb6xjfElij6SPj1xHvTu2QOXX/Q7vPTsE/j3v57GY1NvxgO3X48Zj9yl0vPf8MLTj+PCCWejV8/uuuNY0jtFJt0JrIZCbxme2PYhlpZtRJLTDW/Aj0SHBz9XbMJzu+aixF/BDmMiIiIiIiKiGBczncV+v1+/JiTEIy7Og+5du+hbR3fp1BGZGRnorF47qc+pZmdvOL8ZfyqmP3QPhhx0gL7t2rV/vBS3T74OaampurHL7XKZc1I0SExM0DGXW5R37dJZfxbskIgNjG9sY3xjG+NLFFkk7Vnpz3pvH9dJlaOvvvQiPDntXlx2/ij0y/oevm33wrdpkhr+DN/m6+EqeAL9O6zCny4dh0fvvQOXXvhbZGVlBNdBkcPo/AUKvKX42/aPML/kV8Q73KgO+PR0L/xIUJ8Xla3Fa/kLUOqvlMuQ2WFMREREREREFKNirrM4lFzV0Fibt27Dxs1b9ZXFYuWqtVi/YROqq6v1Z4os4Roz5VVfxaJfnfpzfFwcZKp8puhhj60M1jjGNzbYYyuDNY7xjQ322MpgjWN8iSKPpEXrKlNhfy9ysrNw5+Tr8NuzxyPb8R4Ca0+Ef+PlQO6DcBS9bAyFrwI77oR/wwUIrBuHzsmL8Ifzz8YDd07RJ27qtE8RwegoliuKS/DI9vfxVclK3THsQ926lE/N51F58/fl6/Fm8UKUBqp0VzE7jImaz6hjR+L912di1qvP43//flEP8v6D11/Ah2+8hPde+5eeLu/ff22m/qzfq3HWe5lXlpH31vLyai1vrUuWt9Yl4+Sz9d5aV+jy/5n5jPlLqXXwWElEFE1C2zus90RE0SpmWmfdbrd+9fl8qKyswtff/YBP536J1evWY+euXCxc9CM+n/8NCgoK9XyhpOHk83lf46bb78Xa9Rv0uLdmfYDpzzyHiooKOJ1O+Pz+Oo1n1LasxkxrsMbJVeFy8sDPy1bg+il34bpb7sSNt92Dn5evqDOvnRzQ2ZAZWaxY2WMmr4xvbLBiZY+ZvDK+scGKlT1m8sr4EkUWOakyKzMDp489CY8+cCde/seT+MeTD+NPV1yC/fr3QU5WJqY/dDeG7J+KwPpzEdh8BRw1G+BQ5WKHwyUJVA2qOuHwqZc49dELR9UvcGw8D44dt+PgQT1w83UTkZ2dyXQcAVQuq/8V+yrw3K7PsaR8E1JV3GrMK4otEikZ5JbU8SrOa6p24uvKX1Ed8Oqu4vo5NRHtjYS4eKSkJCM+Pg6ZmelIS0/V7+U1PSNN3zEtNSUFmVkZiE+IQ1Jion6flJSIhPh4ZKhlUlKN5WV+uYuax+Ous3xyShKyVB4s88n05LQkuOKd+nN2TpaeLvPJulLT6i4vd22jliPH0qTMjsjsMxhJ2V3gjk/U5V59bKUYZ9Rv3C6HSnMepCS4g0NSvFu3b3pcTjWP7A7cH4gilTMuAXB71DsHHJ54OD1x+j1FP29NDYryd6Ewd6d+7/f5UFVZjqK8ndi1eTlyN32D/E1fIG/zdyjO3YCqinIE/OEvZCSKJo5AuJbZBgwfdZr5LnJIwUn+F4YfNhSPTr0TPy1dho9mzzWnNg/pgB44oD9+c/qp+HjO55j68PTg90aahXM+MN81XSTGtyFSsL7x2qtw3MgjdSd+bl4+Xnr9Tdx4zR91Z0SCqjA/9Y8X8MdLLkRZWTmcqqB93yNPYPHPv+jl7bGTWEplWyrD8txqEYmxFYwv49sQxpfxjRSMb9NFU3zbq1iLr6QtMfzQIbj09+fjoF594Vu5E/6dJXq8q082clP8KKquwn49k+HffBUcpZ/phm1VMtbzhBVQFWTpRA6ocrLcujjrcri63IVZn/yI+x/7mzFLBKbhaIxvU3+zdBLLf0W+MryQ9yUWlK6C3+dFma/KOCHW/OeCE26XGx6nG4muOKS4EtExIR29kjrgqOQB2M/VGR41l2MvGsKiZVvZRUr6tdJsJImUtBzN8R11zAj92KukpCQUFhbqDqLU1FSUlJToNoi0tDR9l7Py8nKkp6frE+6Ki4v1PHKyjywjy8arspK8j4tTaTYlRc8jy2dkZKCysgKb8rbgx60/Y+mWxcgr2KHTb0p6Dvp37IcT9j8eXdM7obq8Wq/T+v6amhrEJyTghHHnmr+2bcTa8TdIpemMHv1w5O+ugSshBZVlpSjcsg7rv52Dgg0rVf5cI4nMnDl2xWx8G6DrNm51nPXEITVeOoxVmUuydzVepkm+Kh8l8vKuxutHZY0XVTU+lNfIcTy6OiLaW3zbm/aYfkVcRkdV3YlDZe4m9DrkeCQ4/Fi1eB6GjjoDm9f8ip3rlsHTpR9Qmoea0kIjTUdhft7e0295abHuAE7LzNbv83duR/8+WRg+zIlDDtmCvn2LVJmpAjt2AMuXA7PeT8aaDV2R3bkPXOYFjZEsGuO7L7+5rUTjtor6K4slw5WG6TXrNmDl6jU4/NAhuOnPVzfrcPN1f8KEM8fB6/Pinfc+DH4vtQ3rAJ2WlorsrEw9ZKSn6WdeduiQgw7ZWfo51clJifrWiV06d9KDnHEdjsTy3LPGYebTj+vnXFPbYnxjG+Mb2xhfoujRq2c33D3lJhzoyUHZre+h/IFPUPHUl3oou/N/SP9wLfbv0ROB7XfD6CiW9L2bjmKhbykvZWSvepsER9Gr8ObNxNiTjsOJJxyjZ6G2IfmpdBQ/mztH31rao6qBcuWwdOzr6TpuRj4uYySSxhSgxu9FtaoH7fIXY5VvO6r0FcasC7UmiV+kDbTvpHNY0lxVZaW+ulA6a627mknHr3T4fvvtt9i0abN+L/NLB7JeTi0vVx3LMtKxm5iYqN9XqnXJiXbSafzLsmV4a/77WP/pmzjsqVfx5/vewrRpc/DwtNm46b53MWrGf7Hqw7fxz29expqCDbrTWb5f1pOQkMDHcbUgiV9W9z7wpGVLxou4pBRk9j4Qh06YiB7Hng2nR67qtnJhihWZSR50zkxCdrILcS4H3KpspetPkq/65Zhs5q3q1amOs/FuB9IS3OiYnoTO6fHqc9Q34RJFGSMfdjrVcTGjoy7/HDhwP4w76UQ9vl96ALdefT5+M34cbr1kPLLjauBX5eZzTz8D3bt2VfM7kNKhhz6u63WZ7SUU+VxuD9KzOiBv+1YEKnIx/dE/4j//vgEPPliBiy7ahaOPrsCQIcCYMcC11wIvv1iG8aeswboVS1BTXWWuhSj6xERJQ65U6t2zO/r16aVvOf3hp3Oadfjos7lYunwlElWFadRxR+vvtBrEqe3ISQJywHWryqxLHbilaUuiIpVbYdwiUW7rY1Smw8VM1hGnKuYnjzoeHXOy8ejUO3TnhoyntsX4xjbGN7YxvkSRzeN24/qrr0B6tQtlf/sCgY2FcARUuov36MHh9cMZ50bA5Yej9BO1hHQYNPZqFpVG9a2pvbqx01HwIuL8m3HhuWfrE0fCpXdqWdKwVeKvwKsFC/Br1XbjGcUBuZpYTzUHiZzxT54jr58tr6uKARX5AKr8NahUQ1mgEr96t5m3pDYbtanFHXzAIPz5qstww5+ubHC43hyuu/py/bq7eexDuPn2NBxz1BE8HjcDq1xUXlGhbzEtncByFbHk0dJx6/V68eZbb+OUk07A2BOPw6V/uAj/d9ONuOOOO/HYXx9X097BrPfew3vvv4cFC77G12r4bPZsfPjRR5j5wov44c23cPazb+LEP96JPi++hazl6xGfVwhPQTEyV6xB79dnYexlt+DS6R9iyRdv49MVc1BRWaF/h3QWV1ZU6t9HzU+fdOEPINE8YVJyU7/fhxof0Puw43DoedchLr2DcTxVuTFFrypnEnZ4+sDriEOlCrDcylTKRxZ5J2UjaxDWq55L3qtjtkMt41X7TLkrEzUO3iKeqFWo5CdlY1dcPI485Twcd8wx+M2YEfjDmcdj6GHDccCBQ5CSGI/LLjofLqcTBw49AieccCIuHHOImu9YnDTqeAw5bhwCkr71ySHM06NFnIp5/s5t6JgBvP3Wgzjvt2PQtavczWWtmlr3BGoVenTpAvz1MT+uvXo7CrYtVmWo0jp5PVG0iJnbUJ952lhMufFaTJs+A2/P+h8OPnAwBg7oh3c/+FifhXvSqOP0/J/O+QJeKZyFYW2KYMHM9rl71y74++MPYfPWbbhy0uTg90aaWL5NhFSajdsfSlnZj6ysTHTIydbvK6uqsH3HLvTp3VNnxtJJsW7DRvTr01ufaS3LrV23QVe4VfD0OmS/kBh269IZrz33FNweec6EOgio/eeRJ57W76155ZZfkYDxZXwbwvgyvm2J8Y3t+FJsxVc6eAbvvx8evvd2JH6+AVX/+gYOl5F+LZL+Eq8cCdeoRDh+PVAlN5XezDS3Z9L5YZa11XpkXeh4F8qSJ+K2qQ/hu+8X6xM9I0k0xrexv1m2v1xR/HL+PPxcsRkuFUfp9K3wVqHCV4Uqnxc+/cxiiYkD2a4kdIxPxUZvCVJdCUhwxSHVnYgO8WnondgB/RI6olwtm+JPwOD4bkiQZ1U3stEr0rdVOJGSfs887WTceM1VxklWZlrUaSuEXAn6y/L16NQxDV3VMdSa1yLLWMdNmWYNdtZ6ZXzod1jj/qvq2I899azxDLcwv6M1RXN8Rx87Ut+GWjqGS0tLdeexXCEsV/dKnJKTk/Hmf97EC3+7C8+/dDPK8ktQWFiMbTtKkF9QiuLCShSWlakylA8+NVRV16iyVwBffbMOd54/ASc+PROudetQk54O35VXwXX+b+HKytL5uX/Hdnjfex/u6dPhLimBNzsbX99yOVYfNgC/G34+qlT5TW6jOHr8b81f2zZi6fgbKrvPQBx7xf+husYPn4pbdZVXxV29VvskRCgtyMXGbz5G4apFKq5yApYs1bbprbnFcnxFsTMbWxMHo8yRgv4Vi5AVyEWXjCRYFwhLfupVWXJxtSqfWSflOZxIdKv6lcs4ZUse2SPpeltxJYq9cViVdCSc6rjdvXIpUn356ggcuftErMe3vYvl+ErJSFJWZvf9VFmnCiU7NuD8c87EhLPP1I/Vqq7x4pmvdiIvvwIP/m4QkpISsD23GH957VccvV86fnNYjjqmO/Sx9Ol/voBP5n6J7F6DVF7vReGmVcH1R7L2nH7LS0tQnL8LZUXb8OD9EzBuXD9kZTlUeS1NTb1HDdv0fOEUFgIzZwLPz0xHaVUvZHXsovJx4+TASBKN8Q33m+dvLcGnG4rMT23nlD4ZOKJzivmpViRtq8aKmc7i8aeMwW03TcJdDz6GuV/Ox1/+fBXGnngC/njdzSgoKMQT0+7V8/3pL7di167cOpVaXUlWr/vvNwDjxp6If73yb+zKzcPxRx+lKtodVGX4I+SoStWz06dhy7bturM4UsViZi6Nmp44D/728H3o2b2bGhPAN9/9qGN06phRuiKdl1+AF19/U5/pLnGWhpS//+tlXH3ZH/StuCTGjzz5jD57Xg7scjbXy2+8hRdfexNHDDsUT6r9w9onNm/ZiulPP6fXUVRcjF9Xr9XP1YyEhk3Gl/FtCOPL+LYFxrdWLMaXasVSfCXdjj7+aEy54c+oueMjBJZthyO+7nOVAqpgnDTlZLgHrEBg/WlwuJLU2PAnWzZMTvKQjgu/Wv9gBPp/i6effxGv/PvtiEizdtEY38b8ZslzpaP4qV2fYV3VTiQ4jSuK5TbS0llc7qvSt5f2B3wqugHEBVwYm9ANx3c8HLMr1+HrkjXIdicjwR2HLE8KOsVnYHByNzhV+IqrK+HxOXBUyn5IdMarupTUpnYvkrdVQyIl/Z4+9iTcNOlqfccNOW5KbGWQ90KOt7vyK/Hx3A3YsjUXfbpW4ewzjkVhUSnyi9V+UFSDwuIq1NT49TMwhcftVMdTJzLS4pGe7kGnbA8y0lP1OmV9Qt5b32F9r7y+8/5H7CxuBiced7Ruw5BbUMsVxXJFr9x+Wm5LLVs1IT4Bq9asxnEjjsCqxU8iPSUJ+pEAISf4wGqEVNOWr9yCVR/uxOl//xcCGzag6ogj4H79NXh69TbmMeMrJ+bJ99b8+iu8U6bA8+67cHTsiPfvm4jEYUfiqF7D9W848YzzjOXaSCwdf0O5PPEYeelkpPfcT9+y0t5ZLCcASHrzVVdj3YqFKJj/X5Wpq+NwG6e35hbL8S10d8SGxENRo46tAXX8zanaiD7Vi5GRnISMBGMeOWm2qLwa+aU16pMRW3lWscshea3aR1SekJngQrXaH/LLfdgW1x9b4wfq/cCDKgyoWIQkX4FeLhLFcnypfcS3a5/9MPHi8/HxnHmoyj4Ql5x8CIb2zdLpdMEvW1UZCOiQUI3OHbOweVsudlZ4kJnswaH7dVHJ1I+vlm7Ha7O/R2/nFhwzcgQefuJpbNuyyVx7ZGuv6bcwbyfK8tbj+uvScPLJG9C9ezISE32qfCZ3z8tSc0gZbL2eNxw5TFdUAFu2AA8+5MbsL/uiQ9e+Or/31lRj55YNer6O3Xrp59e3lWiMb+hvLq3x4YxZv2LOxrbvLD6hRzo+OHMgEkMeFxEp26opQmoZ0UsqVsIpt3VQg1Sk5UonSaVyhq5UhOSzXNEUSle21at0FI86dqS+zZf47dnjdcU8Iz1dV4IbuiKZWp40QCUnJSE9LVU/m0luY5iQGI/MjHT9PMyUlGSVcXv0LUqzMjP0MzHjVUU7LTUF6Wq6zCOf5TmZaampSFYFdPks5Cxu3SCiYiwNqD26dcUjU2/HA3feghmPPoCX//E39O3TS0+jlsH4xjbGN7YxvkTRJ9jUXOM139TlSPTA1S0dKPvcHLM3pOFTrUuSZ/Uq+H3b0LVzR11mZ5ptWRJfqbsU+8vxYt5XWFO1HXEOlx6vBzVN7wPmH3lJCLhwakJXHJ8zFINTB+LS7BNwVEo/HUD5V+P3ocxfhVJflYqfE3FOFwq8ZVhUusZ4hrFaJ7Ucq8NWyHup07pcTl33lW0vHcXPv7YCXy7chTWbA/hmaSLuevQHTHnoZzz4t8V4+sWf8erbv+A/7y3Hfz9cqQd5/9o7K9W0pXjoqZ9x09Qfcc9fF+HtD1Zh87YyM3swOod1HVv9BHlv/y20b6SNQhQVF+kykHyW99JhnKTKVsUlxejcuRMcnhTs2lUKh8ul0nYcAjW+ukNltR7KC4rxj38txaj/faY7iqX1wvnrrwgUFOrvkSCuUp9/WrIEjz78CLbv2I5An96If+klVJ11FgJbt2L8LTOwav1i/cxjKYdRy/HVVGHVVx8gPt6tctn6JG174UL/Iceg85irgMyegCukYVnlxwG41fgU+N1d4PP0Ua9qPneqpF5zJmpt5c40bE440OgoDqi6jDqGFrmzVUxc+ipFef6pPm6qITnOpdOmPh5LHgv//7N3HoBRFN8f/14v6ZdeCb33JkWaiCg2RPnZC3bE7s+C2LErigW7YuFn+SP2hhRFQEB6rwESSO/1+v3nzd4lRwgYWsrlfeJwt7uzu+e+fTNv5s28kXOM5WxzuwM5pVYUVTpk6OkCXSuRzS1Oc8EOA3YYB8CmCZL3ZBjmZEB2jhphiW0Rm5CEZIsZ3du3wqWXXokVrhhkFFTC5XILWxjoEKvHaR2jEREWgn/Wb0VSXBSGdo1DYgRFgYGM9rErrwol5ta4eMJEJERHICUuGrEpbRAa35ru5L0n05jIutbpgN1ahYKcTFQUZODz/w3HpEl5wvZyYdGiUixdWoGSErKJ0kVSnL1kIm3eLJrLFXKzGpK9MOHQvj0w/SknenTeg7KSfNmvVZS7D2cO343zxu5G7oEdyrIEzHFBLc8/M8qwPrcCKqcL6vwiqHMLGjblFUBVWSV/z4a8CizKKJXfmzsB4yyWnckCh2jV0ijMvIJCObOpQgiN1gCi77TPaj3yIuM0o5hG+axau14otwovzHwLr8x6V85MJmXnjq2Gx78zgta+pO3qgQGiAqeBABSe1DcIwP84fadEAwfEFzK9ZX5fEjulTPfuz0DGwUx5DkHnUKFDiUKkkkODjHS6BnNyoWftg+UbeNCz9sHyDTzoWftg+TJM88CnQ/nCJrY77NC0ixatWZ3ibLA54XEp9rTaYoY6wgRYt9JZIp1AQ1alhcddAY1tn3SE+MoE5tRA5Sl1PpOjeE7BUqyvSocOGnhoUepDUNwI5FSOUBkwxhiP0yN7o2NoZ9FAVEMryvFQjVHkoT/INYptLjvKXEqDmMpovVqLLEcxttgyUOmuUu7NNBDKs6ZZiJnZFXhyxj/4adE+bN+dL9PqjTlYuGQ70nb8jj1bf0X2vgUozVkIR+lyuCtWykTfy/OXIXv/QqRtX4g1qxbh90Wr8Mp7a3DTA39ixrvrkH6QOsaEZL2i5UEBJxe5BIcgOChY9lnQNn2nQeoUupIcxjS7OCo6EnmFxXAjWEhDcTArUHhaoXtCLz32g1i2ahOuSG4N8+rVsPfsCdcvv0IVGQnPuHGwzZ8vy4ZHH3scn332GVavWIazx56LvXv3QW00wvjOO3D07QtPdiZ6L9mCr9d+L8NgM6eWrK1rsW/1X8JmPvIatGXlNkTEpyB64CVwtx0OV2QnuMNai8+OcIV1gccgtnVdRWqH1NTuGHb6GbA624jXg6KCMA2NXWXCPmNPVKnMQuWEXSXqS42oSKOcmaI+daPK4ZY6TjMSK+0uFFk9KFeHokQThTK1BZWaMLm+cW00oh4Oc2ZJW05BXEdjwG5jf3Gvw0NfMgxz/KiFjXvnrTfhnttuQWhoKDqnhOHV083o2ypI2EZZ+OGPXfhp6T6s2ZIu+yVOH9gLpeXlWLUpHb8sF2npLuTk5uOsruG4d2gQkuPCEBVpwdR7p+Cmqy6t9mEwjYfDZkVRXjYy9+5EfsZu5GVsh6cqH++/NwZ9+izEW2+V4ZFHAOrC6t0bCA/3nui1wX//XTlGjuG6EK8FDhwAkpNcKMjcgYKsnTitbzoGD/bgphs96NQuD3a71ZubOVYqHC58s7sQxTYnVEUlcKdnwp2R1bApPUsYcnmAqNOLxO/4eleBnO3c3Am4MNRvvjdbhrukDuRg0bjJysmVjaLo6CiZv3YI6vrSpWMHvPrcE9idtheT751afd+mRiCFiaBnTLPRYqKE7IQ9HBsdDZNoyFJHFIUdpQqZZqmRPB12BwqKihEfGyOPa9RqZOfmIU5sU6ObHBdZOTnyWoojQrwL+cq70LpVCm6ZdBUSaNS2uKd8P0TyVd70PlHIVF+HWmPC8mX5HgmWL8u3IWH5Hk4gybc+kMPvRBu5J+MaDUUgyJd0iKD1xO+4+TqMOH0ItDY33AeL4diTC8fmbLh35sBzsASa01IR/FBfuPeMgspJYbaUc48PDTxuG1QpX+OnFTq89No7omFsb3Sd9ac5yreu30xlLLVPytxV+LjwL2yuyoBaqJhHiE8tZEjlrdPjgs3tgM3lQKmzCmaPGiMNMTjd0hedwzrLPOQe/qxgMf4o240gjR4asY9SiM6EFGMUOprjZQjbUlulaKjb4HKWo7epFTqGdvC+KXW/L03pWdWXpqK/1NalZZZokBXN8qVnTDLIK6zCPU8sw1+rcpSMfqjgxKiee3HHreNhsVhk5A+qZ2nGDEHvCl3DbrfJNvL2nXuxfsMWbNi0C6u3mOHSdcaw/qF4/9WLEBNNPWSU3y2vMe+HXzgM9Ulg1OmD5ZrFZpMJhUVFckYxzegtKyuT9SN1UNttNgwcNARvPjEOg0aMhsrjEOVyDtyuMqDwC3gchVCbu0Nd+gVm/TwKk38sg3PlSthmfwTz1dfAtW0b3KNEWS5kheXLceUjj6C8tBy56RuxJ6cSv/zyC3r17CXXTbb973/QXXUVKoYNwwN3DsRzZzzMYahPMWQLmSNjMeT6++HSBAudcgqdVMJQ0ztA6xnbHcp3p8uD/QXFqCwohFathaugFJqcTPEOKEs+0JqI0REmoa+h2LyL+sFKobXvAly1pj01IQJOvsLW2mPojUJdonj+LupwlbPBE6xbEe/YI7YVu9es16DcbUKOJhGF2ng4NEGirhZls8hPDmWtoxxBrmLEOPcjyF0TXlOUwEooamNnsWFX8quF3J0ZSK7aLOr6ptVJHej6W2+8A6bJoUAyCxQCUb5qjVbqlFvYt8OHj8C1V1+L5JgQYX+pkbYvA0nxcbJv47H31iGz1IUbz22LS0Z3FWW0kKswz2Z/twFf/pGOTglGPHRtH9lHkiNsrHatU1Baace+zCK8+tqr2LZtC7QGk4wwQeV3UyRQ9Zeed372ARkOetjpPXDJJaOFHdQBU6Y8L9IITJz4M9LSduPdd4H+/YHzzqvbIfz338DAgVSPK9sUepoC5Hm7tFBaCjzySDd8/XWaeDeqcOMNVBR4MHIk0KcP8OCDwPwl/REcHq2c0MA0R/n6/+bN+ZUYM287ssuswNbd8NhEndgYUPSl9q3hCTYj0qjFH5d0Rbcok/dg03hWx0qzdxb7SEqIx1sznkVERLh06NaGZh5Rx/bxQOdS6MuQkGC8/s6H+HLe97LDjZ3Fpw5fh+a1V0zEFZeMF3WuCi5RoPueOTWqKAt1WBCUnzpOqIL25aHQbL7jBG3XPk4zXKjypk4OmiHhgzoxt+7YJR0Rq9dugE1sNwV5s3xZvkeC5cvybShYvnUTKPKtL+RwJNsrVzR+M7NzxFtwbA57OStRtKaSkxJkB/WetH1Slk3Vedzc5Ut6SBEABg3oiwfvvR1OQwje31ECm9DDs5NNSAwyIkSnglntgmpPvsivhjYpC+6954nGAnUyn0jji5zFTtGIWouvf9uFmW+9L/SfZtQ0vt76aI7yrf2bRQkqy8JKjx0/lK7BPxVpcIjnTI5hKkLJCUxuYJ+zuMplh1rIf7guEqeF90DX8O5SLx0eJ+YWLsN3pZsRpw2RZTqdS9c2U4e30YJuwcnQedQod1hRXJ6PdroI9LX0kb9B/haRl945+vOnqTyrY6GplM8+ZzEtq+SrQ8mZ9OwrP+D1t34QOTyyrSvrQ/Gfw+mEUxWD0NBgvP/iMIwePUy5kPc9IflQVno3xL/KEe/+ouJCLFywEB9/9gOWr9PitecuxFVXXCiPU2Jn8cnj7DNG4oG7J8tZxLSUB9lTtHYxOY9prVJyGpM91Lt3X3zy6mD079sfKPwesO2BytRGfJ8PhJ0mZGCCzvkH5v52JSY8/DnsMdGwbtyAP5avkNcYHRcPy913Qp+bi1uFTn8vymWL2YH0vCq0SmmFx598EuMvvADOPeK6PXpAExmFtz58AKM7jsWV197h/bWNQyDo79Hw6WJst/7occH1qKpS1i2u7Swm3XOQ3ouUcTAftowMqLKypT4ehscmqu0SUfdWiPK7EipXvvdA0yPQ5FusicHeoAHSzqGQ0qL2RaTjANrYN1Ahq2QSONVGrDePgketF40muyiFDy1H6Vya2diuYjki3AVyn39Ze9DQEdn6DmKfsNvENp3foWoVQlxK3qZCoOvvETHq4Y6MgKtbW7jbJMFD9bNWA3WJ0MnsPGhWboIqv1gZTOCP0GePsMndsZHyPHd8NDWuoRJtJMqv2SLK6JwCse3wntC4BJp8SZfM0ckYOOZihBRuRh7isNPQBfcN1CM2XIeCwiK0SUmETZTJD723AVnFDtx2fjv8Z0xXWUZTef721+vw8aJ09E4x4+Gre4p63YW9+zMRG23B7hwb3l7vQPvyfxBvsiI3uCNW//QprKUF8vymRrOWLz1PIQ8Z5lkIlkL/0z6rtRL5B/eid/dkTJ8+WdhXHVFRUYX77nsVERFGPPWUsL80szF3rhM0L6KNMLVSUigSjDJTWJhn1QjTTTqH164Fbr8daN0aeOklUZ/HSrUVdTjw55998cYbuxAZWYqXX5YqLh3PdL033wRefTMV0UldvFdsWJqjfH2/mbTl9kV78eaGHKhyhf5kZMn9jYUqPBSeNsnynbuuazQ+GNNWlidEYz+r48E7/qF5QwUydVa+/dGn2LZjF8xG02EpPDT0kG1aM7FT+3ZolZx0yP66Ep1bWFSMOV/Ow0+/LZT3bIoFeSBC8jEaDHIkPK1jSWtfUjKbhWxEKe3bpmO0fUgev+O+7drHaXQ+yZI6x6gxVmW14sY7/osLr7geN9/1AJat+Ec6JljepwaWb2DD8g1sWL4tD5IFJXI40rrRb770NF5/cTrOG3smjEajPPZv+PLEREdh8g3X4L2ZL+K155/E/XdOls5nsumYkws9U+pMHnH6aXjsgbvxd6kWV/yZide2lWHWjnJMXJyPcb9l4rolOXh6YzG+0gRjb6xoCatEa1bOQDyRWSoa0ZgzAlH3A8Z2GNC3J/4z4QJYLOEs65OIdNKK/8hRvKhyC3Y7cqFXaYXk3NUNVUKWluIft0gWlR7D9dEYGN4dncI6y6gPFGr684Kl+KFkMyxqk5yRTDOKRUELpyiH7W4Xqtx2WEXSarRwOyrRyRCLnuE9lN8gsLpsqPCGquby+dRAj5V0+udffsPaVb/i7pu64vXnzsHsNy7BG8+djTeePxsvPzkaD9wUgTNPc+CHH39FenqGdBqWlJSivJzWXSuV22Vl5d7PMrk+LX2SKEeMGInnn3kAj903BH8tWSgjhigqyzI9mVAnMkGDp8jBTzqj1+mlU4iir+i0OpQJueTk5CLBMwcq61aoKteJolkHVdVuyFnmhrZQO/bBYwUiK9XwuJzQdu+OlTs2IjkpCX379MbS4iIUv/MubMJuezo7G61zMrH/YBFKS0uRkZ6GQYNOky+WOj4ezrAwoKAAHexmaYsxpxalnPQgb/t6rPntexQeocqlbOREJswGLTSFRbIc8Iiy2CPKb4+cuegGnDkAzTB1boPGnd6kHcWBhkvYTYX6JDiFmKSkRP0Z5C5Cqm2LIkABOSyMOg10or42u0rEfqfIp+T3iC8ejV6ZYSzsp3jrLoS7CxBm1iE61AAdxbL2EmtPQ6gzV9pq9A65ocVBfQexXZOHaXjIye9OiIbj/JGwX3E2XIN6wh0ZLmeciYYzPIkxcPXpAselY+Hq39V7loLHoINzUA84rjoP9v+I4+JcT2oCPClxcLdLgeu0HrBddjacZ58OdxiHHT8VkB6W5x1E8bYluOqyibjyvOG4sncoWiVEIik+Fm1aJSExIQ6tWyXi0at74PHL2yDcYMcXv23Cr0t34LNf1qN9rAbPXtkW91zaA4nxMUhJjEdyYixSUxLQJiEcV/UJxw2XnINLJ1yIwg2/o6qJOoqbKy5hA9mqaF1pF8pLipC+exuslUp0jTKxnZ+xE7ffej7mzJkuHcVWaxnefff/EBNjwZNPnivq1QXyGibRFO7VC4iLk6ciLw/YIopyf8hRTGGmycFssQzBpk0qpKUpTmVi40Y6bw26dCkFrWscElIzQ5mcyYkyAIWwu5ljZmtBJeZs9w6cKSjy7m08PMWlUJVXyu9zdxXKdZSbM2SFNHuoYKXG1W8L/8QDjz2N2x945F/TMy+9JjufFy1ZVufx2umuhx7Dh3O+QHntlcuZUwp1YFAjiBKNpPdPtPbliW7Tdf07K39d8Ac2bdkmBwf49jalmS+BBss3sGH5BjYs35YFzS4kuUy86Hw89sA96Nqpg5QRhRen2W6Tb7wGRpPRm7tuyFFMEqNZyRR285ILz60eXHD2mFF47vGH0LObMrKW7secPCgs/LWX/we7rS5MWVGA7SUe6DUqmLTiOQu5FDo8WFVgx0e7q/Df1YW4Y0UJbLqOUIWOl2thUqfl8eDxaKCOmw5N/FR8vn0f/pdWgCuuuhQvPDENqSnJ9RpgwBwdctJSW6jCY8Piqq1Ic+RBK5p4tN//8dJX0iqaux+m1qOvNhyDwnugW3h38S7oxTEV5uQtxtdFa0FbpKvkKCYZucWWGy4569jqcqDK7UBJRS7iPCZ0De0k8invh1Mc/7tqBz7K/wPF9nL5G7gD7GSjyFVx/JbgtVem4b/33YHLLp2I0047DYMGDUb//v1x7rizcc+992DmjAfwyLR7EBMTLUMah4WF+X2GeFOoTDSD1Wazi/JXi/DwcJEicP2kSzF+/DnIzqYR+8oMSOqAY04O5BAmHaG1ia1Wq3TKBwUHyf1VVVUwB5mRmZmFUKMT8VEGeNRBUDkL4TGkwO0sFUqtl0lly0CVQyvkp9TDaosFWXsyhSxVyMrORlZWFsb/9368d/XVsEZH4QdxzxF2Zb3ky6+8FvHe3lCVqNc9ok6mkNVBTmGTGdlZ3FC4nXYUrvsd2zesxL4SK6qsNEBDBY2QoU7U1aT39EmzjAvziuGyRMBtEO+EVshMvC8VJRXw2ITMbbuglhFBmIbGoTKhVBMlhOmWstOKurKVdYuoUWtmgUaF6BBuFjoralWLU5SrVEXKepIGZHoQ5CiAVtSlJlQgyrFfDuQKNmhh0mkQFWyAVrwPBOWJduyFVpyj7HGhXBeLErW4P9NoeOKi4JgwGq4OreQADhkWVdSZvjathwY6i/fDHRoE19lD4ernnVEoyl7HuSPgGtEf7vAQ+Q7RuR6HqCMoeb+LFwLunh1gv/JceMxHb3cxx44+NAoduvbC2BGDhMzU6NMhFhMGJgoZVqG4pFSIUljRQpZkG0dbzGibEoUwYUfd/d5feGPeP7jvwxWiPo1BTGQQIsPNity9si8sKoVZ68LF4nqdW8eQymP8OWegY49+MIaLba8mMydGaVEBqirLUVZSiHDjOvTulgGnaJPQ+sQVRQfx1pv34r77rhQ2LtlAX2Hjxltx8GAW7r77cmi1X4t9+1FZCXTrJtpLYYpDmCguBrZtU777INFGRECcG4TzzgvDe+95ZNhqUTVLh7GommWeq65SrlUbYarB5VQcjEz9obbsC6uz5FrFKBXtTWsjhZ+uTWau+HEelNldeHlttvydzZWA6YmjRhY1XIuLS+SaS0dLNAuZQh0StLZiTm4e8vIL6szrSwUFhaBw1EzDQPKkqjIrO6dBOoxp4IDVasO3P/1avU1OCO7gOjWwfAMblm9gw/JtWfg6N0JDguVM4LtuvUE2in3OfN9ggYkXnoenHv6vnDFc29FP3ynp9Dr079MTn737Gvr26lH9/tAnNbppjepnHn0QI08fLGczsSPxxKHnTroybPBAWBKT8cDqClDAwtqPlvoeNd6d1BFJzuQlOaIBFnWbuIivM6q+8hD5SEdVoVAlzkJFyLV45reNmPTBn3ji+7W44sOFiEluhfvvuhWRkRaW8wkgnrKUb7nbhoUVm5HtKoGeZhnJ3mcv4vEq4aCpaxowiuZfH1UIeod0RJewLuL5a2B3O/Fh9ny8m/c3TDSjSWQnuUhnsbe5SOWu0+0SeR0orypGjDsIvSN6QUNh3UR+l8eFfc4c7HMXIc1ViI9zF6PEUVH9G5kTw/cM6YM6K6mc/c/Ei5CSnCydvHScom2Eh4chISFBbhcWFAk5qhEVFSWjd5DjUJa3Iu3ZswdTbr8DRUXFMgQyfVZWVoHCVkdEKD1alM9g0OPssWcjLi5e1r9UpvjKbubEoRnF9Exphm9wUJDcLi4uljKltYspJPU/q9ZgwkgrtCEWqCpWipdAlM32g9B4SoRO64DypaIAd8IUEg5rsEFquys/HytWb5QO6FdemQm3eGeSkxJx+/TpGFtUAppr+pF4R841mTBh/HjlxRK4y8qgLimBx2hCvsGGCu8sCaaBcDngWfcrsosLsTGvBKvT85GRV4b80iqUVgk9La7AgV0Z8JCj361CeX4xitIzUJSXD1dlhngN9tBFROIytzEo1sTCoabILLSlQqinEEa34rgnPY8I0iPYoJOD9cgJHOHKgt5jlccoUTyQtrZ16FG5EO0rV0KPKliCdNDSgAChowadBsFGcjQTHoS78hFsz4FHLcoBcZxmtRZoE+RR5iQhninhMZvgtoTWmTzk3BWy8Yj603HZ2fCIOtSngSqqc4XuIq8QmooqaVNR6Gmyy9x2B1zD+iszkS8cBU9PCiuu2Ex0HtlXKlFeqwpLoKHz6LeIRGF1VbGRcI4dKp3HzMlDbQ5F+849MLB/X2FnqWETMtIL/dt1wIYn3l+Dae+txebt6cgvLMaO3fuwL/0Atu/JQGyEUbajEiMM2LQ9DfsyDiI9IxO5+YVYvWkvps/egGlvr0R6vkPaYnRdihzSr08vtG7fBTDQTHEut08GGg1NOhClacU2vPC8FeUVKlSWlyP34F5MumYszjlnkLCxVouct8DheBGzZuXivPNGC/t5h9g3V+ofzRFMSqpWf4nYLWxn6ovy7vAiTDV06JCPMWN+lusX+5zLlJ/WJQ4PV8JTDxXqWhsK3uJ2NRFHZzNiTXY5ft9fDDiFvVNccohQdEJvI4ONMOu968QLDEKHLcI+DjJoEWLUyeO1U0SQQXxSOnQ/De7Sa5Vr+hLlpX218QibW1VWLr8vFL9vRWbznTUeUDULKTVVrv+WCJ+z2Ed9zqU8TMOye+8+2TFxyp+9qAXWbtiEHbuogcU0FCzfwIblG9iwfAMfchaRU6Bb5454/KF7cdnFFyr7azkKyGlBDBnYHzOfexIjhopGmFYx0GUHmPik9acvv3g8nn/iYYSFhkq5yk4PP+g6NAP20Qfuxq03XCMdzwQ7E08MmhE+buwYrMi1Y0tJ/QY+UgjFr/eVo0rbDp7o+0ChpD0yXCrJ4mjNB6VD06NvB3XKZ6gMmoAnflqNl/7cjBCTHmaTDn/tz8ekTxcjOrk1LptwvrSxa78LTD0Rcip1V+Kr4r+x1XpQziim50ly8ume8i9l9SBSY8aEsB4YEN5VOoo1Kq2cLTwn7098UrASsRqT7Kqic6T+0zfx6WsHOTxumMVr0EOXiO5hXeU1CZpRvMueiQJVFYxqPUK1Rvxj24/XD/6EYluZNxdzIvh0hBzCy5Yvx59LllT3jVDHI9XHMmyxTod9+/YiJCQEYWGhyM3NQ1raXjlAmvCpWlxcLMaNO0fI1S1nE5MzOSrKApPJJB3H6Rn7xbk5WLz4DzmLOScnGwsWLEBmVuapr/dbEt5HSTrne6o+WdNzpuU+lvz1B26bKLTRUQ512d9Q69XQqrZBpXOirNKJPbvTsXS9Hv/7pQQ7C3OgCguHe/NmXHTeGLRu3RpO8c6MOetM3HzLzTCI621y2DFSXJ8cxp+Le/UX24TU9X37oS2vEC9ILLaqy6qjBjANgyyzK4qhzdohN1ziHcitrMLB4nIcyC9F5s4MuErKoaq0ijpZyMupyE6rtSM8qFB+ZxoJoT+FunjZaS2dgcJWDrVnCatIsbvIQRwq7CCa4KLWqBFk0MiZxyHuYqn7VBbbYUCJNhYaUafqvU5m2RdJ1xTXpzIhxKiBRuvVS3FOhDNT3FvYZVRuuBwo10bBqTYox5kTh56rgBy6ri5t60zOrm3hNpvgHDcMnhCz0EsnCRQqUbdqF62C/tMfYZj9PbSffA/1ohWgUKWag7nQrt0G9eKVcLVJhLtDK7irrPJedE/t5j3Q/O9neZ7ho++gnfMjNOnZotxX2lduqw2eDqlybWPmZKGCNTsNP8+djXseehzzfl+JL/7Owq70HOQViTK4xI20XCey84pQWlYh9THKEiHarmEosTuF3QwcqHIgMTYK8TFRUk9LSsuRmVOEbXlOpBW7UVxSgc17cvDFimx8M3857rz/Efw692NYc9K8v4E5UYzmIFleDhzoRFaWsHUKLagUOhdrMeD668cKO5lmDz8s0h5h19JAy/447TTqc3iNTqdAANLB6+3eqIZmEO/cSRMOvTv8oLWNySHsDzmNqcukXz+KBgQMHuw94EW8PnLmMQ/APDZsLje+3lWIvCqnLEs9ZYcOamwbG4L/3TYCFw9sLW0qYmDbaHxww+m4dFBb3HZmF3m8dnr96kH46Kbhh+1/7tIB6JMadci+j28ejqcu7oeO8bWmi0vndam0A/KtTvyQViR+r8+6b160yLcyMT6uOsxhREQ4OrRrA4Nv+AfTpNifcVCG+ziVkMGen1+AF19/W24rnWxMQ8DyDWxYvoENyzfwoac9ZGA/6Sju36eXbHj5Ul3Q/tSUJEy99w7pWJZ5xX4KqfmEuMY1l18i7S3afyQHMDWYgsxmTLzwXBmqmkJW0+9gh/HxE2UJR7s2rbC52Ca26vccq0RDLFxPa4Z7oI27H+rUeVCFXSo7Jj0u6syqGa1bg0YeU+laQZ38EcoMQ3HFxwvx4epdcj1Fn/4aDFosP5CPLzfsxUUXnIfUVklyP3NsUCdViasCb+b+jrWV++QsYHrCitb5UL7Tv2pxsJPajI7mJHQK7QyDRlln/Iu8P/Fp/t9yxrF0/nroCuQoVstrKlfwwCVSkNOJcaHd0DW4vTxOkLN5fcUe7LEr62Ka1HpoVRoEaQxYZk3D4/u/RLG9VP5e5vjxPb+pD0/DJx9/jLfffg/33Xdf9X7pgBB6RoNu7vvvVPz40y8ICgrCk9Ofw8JFf6CwsFCue+twOKXzd+/evfjss8/gFOeFhATDaDTIsplmLc//fQGuuOIqTH/6eTz33PO45JJL5DVnvPIqrrt2knRKMycHu8MuZRgSGoKKigo5GIBChFM4appVnJuXh/LM3xEd48KmPSX4ZrELj72lw2UPmDD85mSMvqcjLn+mLx7+ZBQ+XHA21ucGwz7+AmiyszFy8WLZkf31vLlwiOvmCvmnpLYW74kGB8U9Lxf3L1SrYLjmGth++Vn+DvdPP0IlflNO1/Yod9kQZ6HQmExDQWWwx+WALS9dVKnkABQ7vfaP82A+PFaqx70IXffIaAEeBJuKRFa/Y0yD41IZUKkKFkJUykeaJRzsLvGuPyz0TK8V/yrlNZW1NOtJI/KEOChSEx0jPMjTKDOD5bbQyeJKuyinlaFZdJ5WvBcUltpHhCsHWhd1loua2+OCW2OCVRWkHGROCFdyHFzd28PVsyM8ISaoKqrqTLrVW+GJj4anXTI8docyWEDISvfDn9D+s0XODCbHsbqwFDqxrZ/9HXRf/QbdghXQ7NwHdGwtdVm8CFBptdCu3ATNz39Bsz9TDgxRCb1Xp2dBO/d3oNTrqRL38Ih3yGOpI7Ytc8yQvDQ6PWLb90Jq6zbSR3CwXNjIq3OFDmvRLikE15zZClMuaCsHb+QVVSE9uxxZ+ZVCfLGY+9A5eHbyKPz48Dmi7RqC9JxypGUUo6jMCb3Wg7suaIdJo1OQFBMCq0OFd//JQ3GVCm1TW6FV6/aIbtcDaiF7+h3MiaHTG2AOCcNfy8OxYqUKOVll0KlzcNNNFyI5eavI8Z5IJTLv3LnA+PGni7bpX2Jrt9xHvltjHRHeIyOBgwfFmcqph0AzhI8EOZlp9nFysneHF/EaIVcuO8/l9bGQXeHA17sL4XSLWjE7T3mQfgTpdRjTPQntYmlygrIvJsyEkV0TkGwJwso9efh+bToKym0y39p9BXL7980H8dP6dCzZno2uSRZEizKf9v+5PUsOBKG8dMsfxL6d2SW4amg7vHrVIOUG/pSUyXWU6fd9u6cIuZXNM0Jxs3cWH0thSh0iNDvmvddexN2Tb5QzLc4aNRwfvvEyzhlzhjdX/eBC/NRDozFLSkqRnZt3yp43dazQtWe+/YEMqUoVP9MwsHwDG5ZvYMPyDVzomZPDgcJhXnvFRLw0/VEkJSZA6x1eezR50zFyVASZTbjh6stlQ7t718747N3XMXzoIHkNunZ93hnKR6GqKWR1v949ZQjr+pzHHA45gshZW2C1i2fo3XkEPKIR5HB58J9UMx7uFYm/8/Px+M5d2IkesCa9B1e7f6AKuxgelbimRzQjKMmWmFqei6DhcLT9E5sr2uCSDxbgt12ZSme2uLEvSYeEyPrLpv2ocLgxaEB/scUcK6XuKswuWIKtVZkwqKhb2q20icU/JOdqUdMXIZsuHj3a62IRpVNm7FM46U9yF+D1vD9hFJncSle0PNcnK1qvTeVRurcjhKwvjeiPHiE0I1mUByIfhZ7ebctEmq1AWYNPyNak1kGv1srzQzUGrLMfwIsHvkOh49QOLgp0qPz748/F+H3+b7j55pvx1JOP46+/lmDFyhXSwUtORmrbUr4nHp+Gz//3GZ588hkU5GfKcNUUmppCHOfk5Ehnr8lkprEfqKqskvWtP06nG6FhUfjs04/wxBOP48CBA3j9tRmY/dEHyMjYL1K6NydzotBMcJJZRXmFXLeYtsmZT7I0Go246657sX5bIVqf48ZpV6gw7ZM+sEc/jTGXzMMd93+NaY/OxtSHXsPdd0/HlNseRZ/eI/FtvwFQh4UDb78N7NgpZ4dv2bIV48dfiA3r1uKNN99At269cNqtU2Be8pcot4OAq65G5QfvQ/3aa7IiWHd6Z1zcc5xcN5lpBIRyitfC+9UNR3oOhRUQBUFN9x05IdU6I/SaAug8hfIcpvGoUAXBozUrVa6oA7WOMhg8VaIOdYv6UKiZ18FL+k5hhMkJLHIi2FMMrcdBB8SmC5VaC0o00TIvQY7ikiqRV1TEcnCQyBekJ5tceUFoFrLZJepXcU/CJewxq8osvzMngLCbYTbCHRYsk4ds6RAh41pJypvs2lbxMhIAoTLoodm8G5qd+6Ujwx0ZDtuk8bBdJ9K1F8A+4UzYLj0btmvE98vPhTMuSl6DrqUuLIF21Sa5bjydWw21n6qsUO/Pqp5dTO8ShR5nTg4kvSBzMKbeezv+e+dk3HbRYLx2cQo8jirERxoxcUw3XDq2G8qcWlz45HzM+CUNV7/5N7LK3OjXORld2iVigPjcllGOu95ajalfbsCEab/AHByG/5zZDZed1RWWENHG1tnw6aWtcO0FQzH1vjtw07WXCfuMvJMsy5MB6QX1IxiDE5GdrcLT023o2iUYl1zSRxylWcU1oYH79gVWrVohqtcVYssu1yWua+YwQfMLKTha+nGYwHSutyulGjK9d+wQ+3UR3j1Mffh8RwF2F4uysKgEHm/UpLqg8NGpUSEyRYcoA6SdokxdvDUTb/6+Fb9uOCDzfbUyTW5//NcuvLNoO2aLz4IyK9Jyy+T+z5fvQblVcfiu3ZePWQu24bGv12D+poMY2yNJhrf2R64tn1Mgv28vrMIXO5TB1M2NZu0spkKA1tAbO3okLhw39l/TeeeMwegRp4vGsRGFRcX48dcF2LB5q+zY7Nure53n1JXOG3smUpISvb+COVWQY8AujCSSEyGN45MIXZ/eoc/nfos/l62QhQfTcLB8AxuWb2DD8g1M6JnTk05MiMN/77gV1191mZSFU8iajv3bzG7KS+TlF2D2/75Ct86dZEjp+NgY7E7bhzfem43PvpyHisrK6rxHgu4n/pEhq597fKoMYU2hrJljp7ikDG4hwwiD7qjdECRencgwrWc4nukXgS/S9+OW7WswKyMNF61divu3bsBfFZGoSHgP6lTR2I5+GB5DF9k/7fFooQq/EqpWn2N5lgM3f7kUK9LzYRb3lNeWXTDKPQj6HdlllahyutClQ/uTXoa0BDZU7sOqijQYyVEshaDsJy1WNFn51+zRYKghBkPCu6F7RA9vWFkV/ijbhNkFKxGvNkvnfW3obLVIHnGRELcKF4b2Qt/wntWydHmcSHflIsdTBq1aI3+DWySjRgejWgedWiuvGyS+LyjfhqXFNJqfORHWr9sgnbyr16zH7wsW4sYbb0JcbKzsFKP1hn1rF3fp0gUdO3bEq6++jKefno6wsHCZhyJqxcfHIzyctjVyEA45kHNzc0WZXKODNpsV7du1kveiPBERFkRHK84Lg8Eo8h7qXGaOH1nXCRxOh+yTIDmRM192aonP7j26Yerjb+KPpevx5PTn0bVTF/TvN1wWppmZu7Bz5yasWfMnNm9aidy8TJSVFOHdb3/G/rPGQF1UhPz/TIQ28yAumXiJfD/IAX3LzTdj08a1ePONmYjs1Qvqb76RDmPDTTdDK86pmHAhDvTrhG6xXUT9z7PIGxoPddEZQ+Gh2aR2J5w5RcoaxVSiU1nvReVyQo9ChBgL6UXy7mUaC6ua1hz1IupZndsKlXeWMTmLtfSPF7eoWO1ORZYmVzlMjnx41N61iFVqFGrjfVW6xOYQeb0yJkczXcst8vswe2oGY1E4axetYcycGKKdotmxD7ola5S0dB20y9dDnZUvnba+hKIyJQpAlEXOKCZogIfqQI4iMyrTh/WBJzkO7uQYeFLiDk0JiqOY8pJ9pcopgMrmHTzgD7WbRD512RE8WcwJQTJwO+xI27AU73/0sVyL2BJqQsfWcSgsKhRqqRF1tFraSrTmaa/kICSH6NAvPghhJsULSAP3SJQGnQet4ozoaDGhfWowjKItRHYTDQIj/a4sL0X7lBiEBBuxc88+zP7kUxzcukqGL+f20MkjLDIaf6/phBdmxCAm9jRERuaKvYe2RS6/HEhL+wU//LAe2dnAwoV1zyr2QeGmaWbxyQiwQ/dbv0ENQ1DN4CDm3/lmt3fJDQr3fBSuGtoeC6eOw6KHz8FjF/WpHrB1IiRGmGVI69HdEmUI6gOFFaiS9lktSsuq64Pv9hTJz+ZGs3YWU0E6aEA/3HPbTbh3ys3/mu6bcgsuvmCc7Gzcuz8dL772FuYv+lM2zEaePrjOc+pK/73zVlxy4bmisaznDuxTiKywhVH09fc/IW1fugyTdrKQI+iF7P7v2x/x2jsfysY4hzdtWFi+gQ3LN7Bh+QYmJNdePbrhjRefxllnDIdepxO20tu45+EnqteMPpIspPNXyI0G473x7kdok5qC22++DsmJCfjr75W466HHpEzfnf0Z7pv2FDKzc+R5tWe0+UM2FnWkm01GTLryUhnKmjk2SKYFhUUoEiklSA+Hn0OoNlqVBy/0t+CWzmF4a/9OTN+3FeUuJ3RqNYqFHs7LO4hrNqzCpevX4JeiVtDE3QdNm2+hin8Rqqg7oEl8Dt9uLsFVHy3G9qwiYV/7nJaKLAn5QT9BfFY6XHKEb2ioXwcrU296mFqhj0h2DzmW6mrSiQctHriuwo4IaNEqOBVGjVHqFDmBU/TRiNSaUeUW51dLikSj/Ml+bTrfbsP5wV0xMKKXdDTTO0VrFG+070e+qlI6t+j+VAZQ0qm0ct1ichYbRaryuDAitDOGhCtLADHHz4iRw1FWVopRI4fhjttvw223TUabNm3kc3c47NLxS/LIzMzEgQMHcfnll2P6U9OV8llC0R+EtL36SJDzkPaXl5crO46A7xRyOGqow5M5KdCMcNIpcuDTjGKbzYaIiAg5II9CUT889SFce81VSE5KRFxcPPan78CsV6bgnVdvwW+Lvsfnn7+ByqpKZBzYjBWrvsX/Zj+GqryteCk6Bs6zzkL0tu0Y+fDDcP72Gzzi2r7wqFKg4rvHaoVt7VrZ+0mliG3IIDxzZQ8MbD1Q7CpBaFio8kOZBkNtNMPcujtQZoMzpxCeKptXXj49Fl/VGqgrMmBW7VP2C1kyjYsDfrOLRJ2o91hFyerdFPLz8xXLGag1EvMgxpEuzyFUbjvK9HGwq0xymyC/ssOpRFwi6MOgcipfBGpRzxI0y9TjUcEFchb73ZA5cajsFDa0qqgU6pyCmiS25WGtnx3mckNFkQDEOe6kWHg6tJazgdWijqYw0xSqmmafeWyi/Kew1SRHuj790QAdqrOr6+1DccUp0WGYkwvJQKM3oONpZ+Gm66+Vyypl5ZdjxaZ0xERHywEA1GZVC0XOL7VibUY5MkodWJVTgSqnos1kf5EobVYX0jOrsKvQij17yoXuuqHWaJT+ElFeG8xhWLszBwUlVejcoS0m33wzUnsNg1p79EG9zLFBgyKj4lOhNSagY0daUHiRSMo6/z5obeIbb3Thxx+tePBBYNSoo4eUpmArZAIL8+wwSLzC/JZBQP4NYXph1Spg3YZwmIJCvHuZ+jChnUX5En70EPw/rkvHje8vwQ3v/YV3F21Hlf3IfU715eIBbTDvrtH48MZhaBMTgrs/W1HndVVhIdVRHy5u7/29zYxm7SwmtFqNdNru2bcP3/8yHz/NX3DE9ONvv2PF6nWy4UUhFS8cd5YMcUgdn9t27q7zHP9E11++ajWMBgPMJtMROmaYkw11MD7/6puy8UydHdSgPl58HVmlojH+grjmm+/NlhVyTScK09CwfAMblm9gw/INDKhxS7bU+WePwYtPPYLYmGjszziAm+68H/N++Bmr1qzHNbfehZ9+WyBlTY1ln6zpkxJ1bG/ZtgOPPfsyzj37TJw5cphseH8573s88vSLyM3Ll+dRQ3nN+o146IlnsXXHTlBI86PJmK5BeXQ6rQxpzRwb9PxIPvMXL8HpsRokmWntYO9BLyRKDTx4uX8kzk4x4vldW/D83p1wKm0c2aHpO0WlVmFlaREu3bIcw5f9gW+zbCgKvRbq+Cfw+foc3PjFX7BSr6Y4lzq9qDvU5yj2QdcgNOKTnJTl5TxL4ngI05gxKXoE2hvjpMOYnjfhe9pyWwiu0KjClsI8rM5fjTxbnjxGculiSsGjiRciVGeGQ3Y018hJiky0c4xCr8cFd8GgyP6Ko1j8OeHCsoodWGXdT5cX+9XKzGKxRR3gtF4xzS42qHWwi33dg1LwQOIFsOjY6XQikB736tkbN9x4AyZdfxMeeuhhvPHmm7BabdWhgsmRSwOsbrplCs4973y88cbrcLo1+N/nXyoylRLz4ZEz1Oi6UVGRcr1cWs9YlhluJ+x2pSfMIwoM6nCj2TRy5qtGLaNNMCcHeqb0zG1CjuTsp21rlVV+GgwGuZ/6KsipHxoWhEqbG3FR4WibEIUwcyhCQkJRUpwLh8uJdu3bY0CnZIzt3x6ezNW4MyYWBZddBl1hEbQXXADngAGwXnklqh56CFWPPQbbddfBPug0GG6+GTpRt5dNuBAf3zEOl/Qejw4x7eU96f5MA0FKahDlce9xKLXpYcsrgMdZqxOS9NOogbZyP3SlG6Rj8VC9ZpoqpOdHIsydB4OjUIavFjnFHzkOaziihMmAYxoGetaivaLOL4I6Pbs6qUrKpIBUrpq2DDmE3amJ1MCS+TU/LIb24++gnfMTND/+KfdLRDkv9d6vHeROjIY7JEg55oPyiGs6e3YA2reSTmZZFlTZ5PWZk4SQQ35mOu6b9iTuffhxvPD5Qkz5MQs2lxYHcivwyffr8NF36xEXosGCZ8fjgfM74NNbByPCqMZvK/dgyfq9mP9PGjqlhuHd+07HC1f2wf89ew6KC/LxwbfrxPnrkV/igBUGXPzlPrz++e+4+8FH8dQLL6MkP0u8YtR+OnI5wRwvHtmP4FuTuDbCNJIO4uHDlfWFjwSFp84TzShau5gcw7XJyAD+/hvIz6/7OEHFCI3NXLMGeO45HVS6JHHvmigRzL9zaadIdIgwwh0RClXwkZdc2JdXhkVbM2XakF4Ip18ZfbzM33RAOoivfedP9Hr4G3z9z17vkRpoYJAnSnEQd7aYcJHPud3MCBhv55ZtO/HS62/j+Vdn4blX3qwzvTDzLXw+9xtUVFYhOtKCu2+7CaOGDZEN5YV//CXzHO18uv6cr+Z578g0FGQCb9y8FU88NwOVVcq6Wr7O6X/Dl48a2dQZTR0oW7bvxK33TMW8H36Rnds8Y61xYfkGNizfwIbl2/yhzqsosokm34i7Jt+AoCAzFi9ZhqlPPidlS01WykMyeum1tzHr/Y9lGGkpO7GPPqusVimz3xb+gTtunoT+fXrhQGYWXn79Hbz1wSey49sHnUPs3J2G+x99Gt/++Ku0y3zvQm0oP9lpG7dsk7+JOTbouZIMf12wGJqyEkztEQ6baCw5vR5jeuSpwcAbp0VidLIB07ZvxKyMPQg+wsxBpzjBoFYjTLSqt1aW4dadG3Dt2pXIFTIusTmh0ZFDUSD+IXnWdhQr+k66D3SIC0eISYdNW7fLd4w5NuiZRWiCMSVmDHqYkqUT1/e85b/eR6rVqrHDZMWirO1YlfMPSpwl4hCFlXejmykVjyWNR7wxQoaR1ojz6RL01pgcLpxh7oC+Eb3ke0RyFdqIBaUb8EPJelhdDu+sZhW08r4e8V7RaolAkMYg9qtxWnA7PJ40AZE60aD3/jbm+PA9vztuv0OGDz592FD06dNbhjUkZzE5FmlAdGFhIR6e+l+MO2eslNubr7+CoUMGSZ2rwYPExAQ8+NCDCAkJkefqDXoUFRUJ/XShf/8B4j5TZL4uXTrj2WeflmeFh0dgxsszEB/PyzGdLGRYSkFFZQUo+hltl1eWyzX+SS40a5j0ldYzDgkOgd1WhnJEIrHHBKg1OowceQFGDD8fcTHJCA0OlnaYy+VBfFQYFi75BT+PPRt/330fbK1Sodm3D/qv/g+G55+H4cknoftsDvR70mBv2xqbn7gXL13XD4NOOw+tgpOl/pKzuMrKaxafWoRek4PQFA5XSg+4+o6HyxAHZ3k5PCZvPawRpaooxz3iQ2XPgy57JbS5y0WFTGv1HaLYTCOiFnVwNaI+tauM1dKhstjlN1JP1rXe7wSta5zs2IFgdwmCPGVItG6B3uPVPZFXR6+AeA/oOgR92Dw1dppbLi9B1xG1t8oDjYcHETQkKpodnJmrCEbgEW1aZ/f2cPbpLAd8aLemQbM3E6qKKrg7pspBsAR9qvOKoKJBIeI7ne0KC4Fz1AC5TrI0yAQekxHOXh3hHH0a3DabnLGm0uug3rIb6lx2Fp8MpI4Ku7YwYzuKCgrkTODT20fjybNSEGLWYm9WBf5vaRbe+22/0GU39Bo7YiJ0iLUYsC89Gze+PB/TP/xTfmbllCAyVIvE2CBEh+tF2a3DewvSMWdJJg7kVyLCpMZrY5PQNTkEZRWVKMzPQ/GBXfC4OAz1qYAGPFZWUl9E3VN+9XolvPSIEd4dtRBmNb77DnhamMKJwvwls426M2gWsT9hYcq1Jk0CHnkE+PxzYNMmxcFcWqo4kTduBGbNAu65V4+CsnawxCZU6zlTP+LMelzcPlJZ2iE+Wpad/lDUMgoN7evrIKj/wUoRzYR97MPuUvIdpnJih02UyQ5hT/ug+pvybsooxFcr9+KHdenIKqL+MG8Gf2hWsVEvf98FbSMQG9Q8BwMc+lSbMNRAps6I2skHhdQi6srngwreTVt3yE7tp19+DU++8IpMtL3gz6XVeYja16BZN4SvQVdN7XwiMScXkglJ5a/lK3HzXQ/glwWLpVOi+thREkHvRHFxiZwZfueDj+LWux/Evv3pcr+v05ppPKSsxCfLNzCRshKfLN/ARMpKfLJ8my8d27XBC08+jPHnni1sGDXeev9jPDPjdezZq8waJOPaJy+rzYZvfvgFd9z/KNIzDko50TEajEf2z83XXYX2bVvLGcYPPv4Mfvj1d1itVnmN2tB5+QWFeGXWu5j51vsoF41lwpeXjtN3ugctGfLQE89iw2Ze8/R4oOe4X8hrztxvMSZOh+f6BkMjzNVSuwuxJhVe7h+B0+OMuG3TanyZdUDatkfTPpIQNZ/0Go10Ev5ZWoDX9+/AJb3aINkSLGXnmz2svD/iUzSyfLKld8UpGmFjOybB4HHhj2V/y/3McSAec7gmCJdFDEEnfTyc0uHrbd751E586oSsii1m7KgshY1mogkZkL4L6aC7uTXuiT8HQToTHOJ8cQQmhwPnh3TFgIhecqYw5XPCgW8L/8ZH+ctgcznkdWwUwlpcR6NW1kGm+6vFn16tRW9TCq6PHgmLNkT+Tm4hnRi+cphmnHbv3h3njhuHoUOGym0KZUxtVXIUh4WFyf3kXBRiRkxMDFJTU+V3f4xGE7p26Spns9K1w0LD5HWys3OkI7lbt24yX6jYP3jwYDmgS6/Xye90bebk4PDO4Ca5UWQVkkF4WDhs4pNme4eGhkrloZDQyUnJKC4qRo+ePWGJ1KNt+wSYDBUoKt2Bbt3bindBC4eQE+FyuoU+6rB502Y8sX4dpg4fibeuuho/X3YpVk+4GKsvuQTLHrgBn704GV++cgfyLxiLyaddj/ZR7eRvsTvsMix2cDAvE3CqIPsGehPcbfrC3e88oNNoqEKSRHmqhc5gFuW2QToR1Y5yqHI2wVDwN/TZS6Au3i0KBFFLe8sEpmkQ5C7xfhMI28ahNsKjUvoOqc/av+NaK4wwg85v5qgg3JGNdpUrRVoBi8PPCyHkbNBRHBYFGc5WXEstZ5UrVKpqIneoRJ2s9XD0hwZFtFnVew9ATSIWei2dwUJuzjMHwXHN+bCfOxy2S8+C89KxcCfHCfUVtpaQo6q8Evp5C5V1kEmfvTrt6tQazmsvgH38KHHuMNivPR+uswbDYzTI66u1WqiyCqBbuEJkPsIURuaYIR0LTuqEKyfdiqem3ocRp3XD6O5RSIqNQFR4MFIi9OgYZ0BSXCSiIiMQEhwEm82OwuJyRIUZYNZrER2iR15hMfIKiqRtZRQyi48OR49YPdpEaBFjCUGbpEiM7hGFcSNPw6vPPYGzL7kO+qgUpU5gTjpkG+3cmSG++S0V4AeZTTk5hzt/bTbg00+BK64A/voL6NoVuOYaZRayyQSkpwPPPAPs36+oLs1Kzs0FliyhWcPAzJnApZeqhN0MDB0KDBkCjL/IgDffSUC5qz9iklKr/UxM/dGL+pOcsNEmLTzU1qk1u3h7VgmGTf8R7/+xw1ekYvHWTJz94q/4+K9dyg7BrxsyRL6fsDPbr+4W5JZace07S/Dw/6327gF255TJvO8t3lHdn1EnWlGvh4v6WMiVft/4dhYYqNOlGSLqpKP9nx7KgFHjvN8aFlIgGl2rlaEDDmXsGSNw1+QbMX/hn5jx5rtKCe8Hhc6izs1j+N+U0Dp9h91PXKJTh3Z4+5Xn8OOvCzDz7ferG2Q+6H7UuGusTu5Vi37yfjt2Gku+xwJ1MJIkw0UjdtiQgejVrStapSQhNCRYdpbQGE2aKUEN7bz8AqTtz8DqtRvwz9r1Ui4+jvV9aCqwfFm+R4Ll2/Rh+bJ8j0RjyZeW4nj4vjsQHxuDXCGTtz/8BPMXLZEyOJId45NzsGggTzjvHBlSesL55+CcMWdI2f6xdAVee+cDGXb6WOjTszum/fdOxMVEy/eF7l9ZWYVPvpgrE5l3dN/Gej+au/76OiBuvu5KXHzBOOy26fDK5iI80ScEatGIuWXTP1hRUoggrfaojuK6IIexAWp803sotqXl4davlsrGEoUkJshvLL95RUeNrMFt4zD7stPx/dff4r3Zc+Tva0zdb47y9f1mcuTSo6vwWLHEugP77XmocNpQ5ChHmdMKh1tpq0jddXnQxxqFDpYIdArvhBhDjJQHOXg3Vu3BYwe/RbhHgxstgzEkaoC4MkUPABweJ34pWY2PC1YiVKWDSWtEgsmC3qGpiNGGotxRhaKqcph1BkSaQ6H1qNFBHY8gFa2RTM0z5f1r7Gd1PDSV+peWCbjvjlugE7pFZaTL5ca27TuRlJiI/IIcsV+PyMhIORu0PpC+VVbZYdDrlNlNKo+cmbx37x4kJaXIGccku6oqK5YuW4a2bVqjTZu2cuYq6et3P8+XbW8KSX3UjpMGoDnLd/TwobLuo74HGmxHsjXoDcIuUpb3IHmSXHypbduOWL9+DVqntvJewYuQyccff4Zv3puOXh1SpEw+/H0bVq1ahYSEBCm3nNxcZGQcQEZ2Dn766XtYuodjyiW3ISE8TsqUBncpy1IYZH1OAwTo3Rh9waXemzQOgaC//45QNkMwXEYaHFDTeayxlwslLBUKG7gOoUCRr0ttxDrTCFFvKjWeTthWHcqXwOSm9eBVCDXrYBGJyl5y+DocLmSVVB0y47guyLEcG2qSn7ImFbpaXOlAcYUSIt6l0mKjeRScom6mwQXUb9quYjmCXYXyeGPTMvRXIMpK+zlD4enXFW6nqCfFLg85cql+FWUqzQgmpzJtSzlWWqH/6jeoD+bC1aEVXBeeAXd1dB7lnaDZwzKkNUVoonOF7KlMVhWVQf/5z1AVHurkaAwCTb6GUAsSRbv4wjOHYcigAYgT32mJjuycXOj0ehntKiUpXtaZeflFKK+owJ7Mclz34iJ0SQ7ClowKzH9+PDzuKiTERiGCHEeCtPQDMIq6nerZhARR54p96QcO4veFf+DXZWtRkJ8HRxWFNG9ce6o2gSDfwtxM2MuysGN7a5hMK7x7D2XlSmDaNOCBB4CUFGU28dtvS5XDlVcCcXEAjZOkFBRE/SBAmRAXOYZfelmFCRd5cPXVwPTpwKuvKvnGjAHWbYqDWbS3KGoPzXDWiXeAyv+mQnOUr+83X/dbGmZvzYVK6KEnI0spIxubUPFitBUvkCinr+gUhY/PaieDwzT2szoemoWzmDoOJ150vgwdXZv4uFh06dgemaLRs3nbDiGIQ0dmUAf0T78txIZNW6obsr4OM398j4GOxUZH4ZILz0WM+PSHwk1EhIVh0IC+yMzKwcYtWw+7FlUki/5ahqV/r/LuaVgCrbKuC3rmlKgxS5W1ThhR0rkvZ32rZEFMTnxaa4saxrLTWXyShI/hdW+SsHxZvkeC5dv0YfmyfI9EY8n3+ScexvAhp+GfdRvwwSefY72wlcihVN+Of5J1v9495cxkWnf2i3nf4+vvfpKdzMfiPJDvjfhMTIzHDVddjjNGDJUzlz/49HM5a506rNkZceLQcyY9o84PcjwN6tcLe6x23LtjA9aUFkJPeuvNe6yQU2NKcnvc0aYDFm/NwNb8UmzIK8DO9CJkl1tRZldkGKTTYFz3VDx+Tl9kbN6M6S/NRHFJaaPrf3OUr/9vJocx/VfhseGPqm3YXJWBcqcVJY7KamexLJtFqrBbEVNsx+DI1hgcPxgxhigpG5ohvL0qXeibDZ2CWkNZoxhyRvH80vX4unAN7C6h22KfmZzFxgj0CG2FFH0kqsQ5xVUVsuxvGxSLdto46KEMulW0W6EpPKtjpanob42zWCueM9W1avw8fxWy87Xo2DYErZJMMBqMQhfrp0ukcr/MXw2bOxrRUSGirnYjOMggrutGOIVQ86iRVyD0eM0isV+DG66/Wqmzha7Tu/TtT7+xs/gkcPbokXjgrskylHhERISs70rLShEaHAKNkHVxcTHMJjOMJiOKiovQs0cffPv9N+jTs6f3Cl6ETL6a+zU+fWUa+nVKEddx4LMle7F69WpEWg7tQyH53Xf/gxg4vB8uOfcSOYPZZrMhPDxcyNOJktISObuYbLfsnBxceMUN3jMbh0DQXx/07Alfneerl+uE8jaybjUEASNfIa+txiGo0EQIuTmh1miRUrkO0c4D8rBBq0ZcuFkcUywtchhQWMuCMvsh4S79IQdxhFkPs15T/a5QEZ9VahM6rpyTr03EXnN/YYjRwFs1TJ4KdKpaDq27aaw3Hkj6+6+Idq9DlKvuPl3gMenhEeWpqDSVY8I+kl4DIUDNwVxolq6FJu2gMjNYyNbVPgWuMYPhsoRCJWRMoayrEe+KSitsMiFz7c790P65GqrcwiZRPgSifD3C/j1t4EDcP2US0nId+CvTjgmdQxAXYURZeSWSE+NAy5+u3LAX+QVFQnxumINDhZjUqKIleYT9bXV4kJIUg16dEqXe7k0/iNjoSOzMKMTve+0YlqxFbLAH0199F5s2b4LK3TQHBDV3+dptVuRmbEZ8dC6eeaYTzjpru/fI4WzdCsybp8wwNhiAYcMAMrWEaYSQEGVGsT8lJeL5rAIefFCDfQcsuPaqAiTEu7F2LUXlAfan67A/uxeCwqK9ZzQ9mqN8fb95c0ElTv9yC0rKrMCe/fDIUOONiCjHVW1bwRMWjBCdGksmdkOvGGXWc2M/q+OhWTiLX3/hKbn+HXVAHCv0v7d2wyZMffJ5Gb7paI1Z6kChRhHNurj8kvGyQXw8lJSW4cwLG2cEbiBV1iQP4t9kJsO81AHJnhJdha5wDK96k4XlWwPL91BYvk0flm8NLN9DaSz5WiLC0So5CfszDkiH3ZFmEx8JkjENCEhJSpQ20649e+UggOOBOsEo0ZqNbVJTkJOXj4JCWj9TWRe5sQkk/aWws61bJWHG04/j1byDePdA2gk5ignS9AujEvFS114oqtyOSFMybB4jyiodKKyyYfHBLGQcKMWwLgkYmZqA7Vu24ckXX0Vubt4xv3enguYo39q/mRzGpCqVHht+KluHtZX7YHPaYXUr4ShJv2g+C607XOGwIa7YjkHRrXB63HBE6i1Sz3yhqb3/SUfxb6Xr8F3xRjhdDlhd1BntkTOLYw3h6BKShA6meDhcTuRXlSBGE4q+wW2hh47azOJuSl3ho6k8q2Ohqeivv7OYOiRJ1vszcvHIc0vgVofjjCHxGNIvWuh2hLcNSxJUoLz+5Sg5m0nvMrPy8MxL/4cCaye5X0Mz1+g9EWIjh0RZSQEidOvx8vP3ICoqSt6XZjTT9WmdenYWnzhnjjhdRvigUO4Op0P2d9B3F61dKI7TsybZybpQ48HgUUMw48kZGD36DEWmvmcv5PbDDz/h7WfuxYAurVBcZcWP/2Rj7drVCKkVSppsszPOGI27H74LowaOEtuK3Kn+pk95f7dL3tMp9o0Zf7n3zMYhUOpferY+PfT/3lg0hd9ABIR8xbMkXczWtUWGqZsoQB2y/otw5yK1cr0oU2k9fw/CzTpEBBmqdY2gkNJlVQ4UW8U+ryOZHIvBOjUswUZZa0tEfjqjuMqJkgqbUsKLOnuXoS+KdfFQiXt6RN0cVbUHre0b6WiTIFD0t97oNHALG9s5sBtcrYRcvG1hcv5qsgug2bwH6m27hbFmo854ecyHxxIGV88OMgy1JypCcS47XfDY7dCmHYR6005o9mcB1pqIXI1NwMlX6Jk5Kgm9h42DKnMNygytYY/vh5v6GYXuArl5hYiPjRJaqcETH27C/lIH7r6wAyaO7iInl5Ed/d53G/DB73vRN9GE+67sBpvVhtz8QiTERWNfvhNz1luhP/AHgpy5cCb0w4aFc2EV9pZ4A5Tf0IRozvKlSQoZuzbgyktzMHiwB/PmmTB7dtVhTl9/qBuDVlQTprawv5TkVeHDOHAAWLMGuO8+cZ62s6wG9NiFmGiaia6DR9cRYZFJR+wPawo0R/n6/+YbF6Th/U25UOXkwXMgx7u3cVDRQFuaVSxehJt7xGDWqDayPUU0hWd1rDTdt9aPAX17y3Xtpvz3YVx76931TpddPxm70/YhRjRuKcRWfaDQ0zSjmBpmtD7eFTdOqfPaR0or/lmLsNAQ79WY44WMZwq7RWGwfIZ0XVDnBBnbdSVfB3NQkFl+Hu06TMPC8g1sWL6BDcs3cCAH8bqNm4/LUUyQjGmtJlrfeOeuPcd1DR/0HtD5FZWV2LJ9pwxjTe8K7WdOLuTcoTVHtVoN0iqVWb0n+pRJ8p3CQqFyV2Lu1lvw0fr/YMHuh7C/7HPEBh/ENd0i8MIF/dE31IjZH8/B5HsfQk5O7gm9M8yhUDcyFaVmlR5jg3sgRRcJG7yhEJUs4pP0zAONyJgW5MbfB9OwNGsJ8m35shz2dxQ7xN8f5VuwqGIXDNAIGdNRcb5ILo8bDo8LNrdDbCtHYvRh6GVuDQOFwhQ3Vbq1mZONr74k3YmJCkGXNlXYvmM7Zr6/GtfcswR3PfY33puzBX+vPoDde4uQk29DQZENxaXO6pRbYBf77bDaDRg0aAAKiq3Ym1Eq8hdjZ1oRdqQVY8fODOTu/RF3TJ6A6GhaHoC6DZTIIczJw1fP6Q16GWaatg1Gg+x0pm0a3EMyp9nGjyx/FAeqMlFYWCrPpf3UCSmT+B4bGyP302zEqio7goNDpL1WncebaPbyjl07MXfPPFRaK+W5dB9f6Gl5f/E7aFt3tF5V5piggR7dunTCGcOHyoFxNNCOtuXgSpH+DSnvOvLVtb9vr+7o1b2rd6sGykfrbA4e0E/Ktq7rMceB11YNd+VA66ryVroelKossKqDpBOY9Lyowo5KmzIoROYQ+zRqFSJDjEi1GJEUYUZCuAkpEUbEhtO68+JCXhnRvzaHC2VCt5W7qVCsiUK5PlZxFNN7IK4X40yXR5lGQshIvXs/9HN+gunlT6D/4BuZjK99Dv0nP0CzejNUFdbDHMUEhZXWLv4Hhne/hv79edC/M1c5940voJv7OzQ79jcpR3FAIuRSmX8Ay75+C0v//gexmjw8Nz4J/bqmoGO71kiMp0F5SQgLFvop7F+qbymSB9WdBH13O+wIEvs9Qu8t4aFoI/KTo7h9m1YY3D0Jj421QFtxEMv+Xo1V896BrbRpOoqbO1n709CrWzYee8yD00+nPVVYv14eOgwSnzB5pHOYxteJKlo6la1WZe3i2ggTSYahXrcOGDlSqH1FBiJjU2GIGIac0oEIihmB8OgUaXMxp457+sQj1iwEFR0FlbCjGwsp59goWV/HiN9zY7fYakdxc6VZvLnUUKLOyIwDmdi2c1e9U9q+dLlesWyEea91LNAaAuRsruvadaV96RkoLC72ns0cL7LBIz4fumcKnnz4v3L7eDom6BwKf/rGi09j4oXncedGE4HlG9iwfAMblm9g4XPUnbDDTjSsyXF8shy77EBsOII0SqjgE4G0l2SWoDcgo2w97O5K2Fyl2F+yDCsPvI8vtk7Bl5tvRlYprVG+Dl/N+16WHczJh0poerahajOuiBiCbsYkkDv3kKctNkjD1Fo1toTY8UfmLqzIXoFCO4U0VLI4xd/Cso34q2IXzRGGy+sQJuhfWn+eZh7aRHKK71GaYPQxtYFZY5B5lJqCOZWQ85YcgVNuvhAP3ZKEhNDdyN7/Nz7/v0WY+txSXHrrbzj/2h8w/rrvMPGmH3DF5Jo04frv5P5xV32Hx2ZsxtbtB7B/fzr2phdg7/58HEjfDU3lIrzw9PXo27evn76evHKeUfANvKNw06GhoXK7sLBQOm+Dg4NlZLS8qjzc+vetWJO3DhFdInD3Y3ehy4Bu6Dm0D0aMGI5x556HK6+6Cq+88ip2ZxZh4ZqdWL5pHxwOO+bN/Rr/++x/eO+9D/Dkk9NxzTXXYsCwgaiyVGKXehemrXkExVXFKCsrq75/QUGB/KRQ1GWlZd5fypwoF44bi3sm34j42BhotBr0690DZ40aLicp1KfEpDx15fPtp/fIp6t0r9EjTz9kn/wuPi0REZh40XkIMpMzUjnGnBx0niqEufNFBatEBHCqddhv6Aq3SuurXpFbZkeFzaV0LovnT/locAjJQqejNcu1Uk60bI9EHKe8VocbeeU2kVe5klNcM0/XWtTBvqpbg1D7QQR5Gn8dW8aLzQ51Vp5MqsoqMpa9B/4Fl0s5LztfJlVjh1dtaZBCqTSISO6AM0aNQmV5iRw8VVphw87MSuw9kIcqqxW3TuiBu89vh07JYUg/mIXCohJkiM/eHaNxx/j2uOrcrigS+/ZmFuBAgQ0VVTa55IMQMEafdS6CYlNl4c121amB1gcm5y85fiMjRb14IfDTT8rM4do4HMC2bcp3UlNyEpNrh2YO15W/okJJdHz8eNGeNpfLgQJ6gxGhEZHQanmgXUOQHKLHOa3DhboKRYohZ633QEMTGgSP2SgdxOPE7+loES9dM6fZ9c5SQXosSRkFfXzo/BS8rmv7J2loi/9qr5nM1B96htRYokSGs8lkkiNf/Z8pORRkg4qedx34jlc7HkQ+o9GA8PAwKScftUfw0vYh53mhbV9e32870r2Zo8PyDWxYvoENy5c5GuQoZpoXVqsdFE62tTbIu+f4oVmqZpUGqXozCir3yH1qlRZatUEm0spSeyYKbDlC38NlFB/a56/3zMlDlIr0D0LVJlxhGYJupiQ5C1g+c0q+xy4+afmd7SF2LMzciqXZf6HYUSxnFH9XvAI/lW4Wuu2n374Pse2bXWx122GGHu208TDSjGLvH9MwUD0YFBSMi8afj0/evQvvv3I+rp+gRYeo5SjL+h3Ze37G9nXzsHr5z/hj8cLqtHrlH9i54Rtk7ZyHovRfkBz0O8I0G1FZVoiKvNWI1q/Cs09cj+HDh8t6taZqpe8s35OJb3AUOYerZxbrDVLPaKYSDQj4ZOunKHaVwWP1ILRDKOKvjoN5fBB043QoPaMKmf2zsDFuEzZGbIS9VzD2RLhxMNKFg8ZMXPvoJNzw9E144L0H8eGqD7AmfA1wjgYdb+kEfZgBW0u2Y8qSO8T1i+X96Pcoa18rM5tpqQnmxCCdSYiPlQ7apSv+weK/lqOivEKG+Pbp0+DT+stl1xR9U2Fgvz4Y2Le3/E62brs2rXH7zZPENc6H2Wyq3h8cFISrL7sEN113pYyOR9Cs4g7t2qBH1y6YOP48IU+DfL/GjBqOe6bcjC6d2gvZkiPSVxkwJwta0MNiPwCtyqPUhKKepDWM9+m7SZl5VCKH0K28ciuKypXpatSWoryybhXvBCWC9tExWtu4sMKB3FKreGdqZJajb4NSbbS8h3wf4ESCQ9hg1ZU8ExCwPBsF0j+Xw45X3ngbb7z7EaqsVdidWYYnVjuxM8+N1OQEnN4nBYO7RaFruwRpT6/duA1hoaHo2TEeg7pG4bQeyaLsj8MfaTa8vdqOjJxSOTDriWdfwnsffgCP0yr1njk1WGLisXadEd9+q6hR+/bKesSbNnkz+EGzimfMAB5+GHjmGeDFF4EnnwRmzRJtWCWYSzV0LZpV/PvvQEoKEC2KYbMZMnIX07AE6zSY0D4S4QYtEBkGdWqSSIkNnpAQKypsjfwdF7WzIEj8rubOoT2vTJ3UqwDnQv6EkB3+4pPCM/k7BHzfpVPAm2g2mq+BRJ+Eb5ug4ySP6mPij9bmkt8pn7gGjc4kufq2xRflPIE8LrZ9x8hAp7MpXCMl5UrMscDyDWxYvoENy5dhAo+8/HxUVVnRxxIl9elErFiH0NcQnR7J5mAUVu0nrfYeORSNStFj5tQjy1aVGmFqM66xDENrQwwcbhfUtQRNTl/qjN5ituLP9G1YkvMn/q/gL3xTvEGGnvbIP4VDylchc5vHiUh1EDppEqATeZWagmkohAjoX6m/JB2LJQJnjBqOF5+bht9+eANLf38EH826GtOnjsD9t7XDTf8xy3TvDVG4T6TH/jsYn7x7HX795l78/N17ePf1exGt+QOtog7i6ccnYeTIYfIesnzwe2+U+zEnC3LIks1DSwNUVlZKh21QcJDcb7Va4RZKu6dsj3zurkrFueh2eqoTQXaTLlwPU5IZkadFI/qMOLS+sA3aTmiHrpN6oPNVXeV25OAYGGJM0BjUMFiMUrupnMi15+Gh5VORVrRX/hbf/en3kLOaOXHat22DiPBwjDvrDIw4fbC0X/3p0/PQsNG9e3RFT+/26YMH4plHHpBOh+FDTsOsl59BcJBZhh1///UXpWO5fdvWeHfmC4iPi5EDwfwhe/mOWybhiokXITQkGFNumiQHa5KsmZMLhRYOd+ch1J4Jj1qxeVQeFwoNrZCpaye3CY/bg+JKOzJoiQDx6RAyU2m0Qpc1StuG1g0X4imusOFgYQVKK23VA7dordSD+o7INHSWUT6UglqNKFcmzC6eVRxQsI42EjQhzI3S7H3IzjqI/Xml2LB9L95/bxZGYDdSI0m3PVJvNxy045d/DqCyohLDBvVBRlYevl+ViR05DshlX9Qq9I7TIqJoPV59bSZWbd6NnEIrCnKzUVmYrdyOOSVodTqERnfD/Q+a8PLLQLZ43OTU3VjHku40e5hmCX/8RQfMfDMRTz+tw6+/At27A2Fh3kwCUklyHpPDmZzFl11GdjFkyGqXk53FjcGIpFB0jzLDI+pOtyUc7siIBk8ek2Ir94oOwqiUsIBoEdfdm8MwDQg1YMgMumvyjfjfB2/is3dew+UXXwiDX8x5GmV93tln4qM3Z+CLj97Cow/crazhJI75Oi3atG6F155/Sh5/97UX0bNbF7lflt4C6VgQiZwOL01/BJdNuEA2kmKiIvHiU9PwxYez8NaMZ9G/d0+5n/K+/sJ0XHrR+Zhy8yT87z3x2959HRMuaNyF+psbLN/AhuUb2LB8GSbwIJ0uKi7B9l270cMShjYmWnfr+LGJMqBjcDBCtE4UVO2CXiNa4n64PU4YNeFICO6GvLx86YRgGgYqg8M1Qbgt+kx0NyfLkNS+AUDSoeyVvFavxZZgO74t2oUfijdC71HDJf58rmIll7e8Fp80U7mrMREXhfZHkJw9fiJvEHMskExFNSjrQt8nIetruc+D0NAwdOvaA+MvPA9TptyGR6ZNw6uvvCjT9KeexNNPT8c999yDiy6agMGDhyAmJhqnDeiE228bi5kz7sI555xdXS/7rkm3qfmu3JM5cWgWMcm0tKS0Ogw0haQmO0pv1mP639Oxo2SX1DGX1UkvgPdMf/zl4k0eNzyibPY46VMc8yW3B6Y4Ezx+I0fIYUxrIT+yYhqKbEXV9w8JCUF5ebk3F3Mi/LNmPXJF/ffCzLfw+dxvpe3sD2371rwkaJvkSaGiaUbwnLnf4KXX3sL0F2fK5dkuv2S8dBzv2bsfk+99CA8+9gxWr9uAG666HOs3bcGWbTuxbuNmfDnveznjeNjg0/Do0y/g8WdfxhPPvSzv5bPRmZOMkFuybRuC3KVCX5V2lMftQLahIw7qO8GmEvqn5ITb7ZLrGOeUWJFVWI7s4kpkF1Uiq6gCmUVVKO++nNQAAP/0SURBVBTH7KTDlFnIy6XS44Chs7hWO/GSeBfS1OhgcpUj1rpb1M+HvlcMwxw/vhKywubG829+hHVr/0GyqQgW0czJLygWqQjLdhdg4fYiJCUlyPo7LjYG327Iw7r9yvG8gkIkWrSIdB3E2vXrMOuj/8EqbGgJl8GnnLDIaBjDeuCNt+Nx/wNGzJ2rkjOM/SFH8ebNQFm5FvHJbRAUEo5hw1z47jvgwQdrnMUUCCYvD9i+HfjxRyX8tMmkHEtOFueXFCkbTIMSpFPjyUHJuKt3XKOnB/snwKwNDDcrO4uZRicq0oKP33oVZ44cJhs1e/btx603XIN+vXpUN2LunXIz/nv7LcjLL8DK1WvRo2tnfP3Z++jQvq3syDjrjBF459UXYDYZsWr1OtkAmvH0YzAZDfI7NbYo3/hzx2LuJ+8iNSUZ6QczMWrYEHw5+23ExcRg9fqNMozpC09NkyGbaMRux/ZtMFn8lgvOOQubt+3A9p270bZ1qvxNTP1g+QY2LN/AhuXLMIEH6RyFQfz6+58RrTfijMg475FjhxoSDnGtzkHBKLUfQLk9Vzngh9vjQp/4K6FxheKftRtk+EvfDBnm1ENlNTmMr44chh6mFLnGsB4aOReYQohT+asRktTq9Sg0CLmI/8gh7PvzfadPvUorHc59za3l9WhtZDrGnHoqq6qQX1CIrOxsZOfkIic3r/p7dk6O+J4jP5Xv2XJb+aQ8OcjNy5MpJzdX7svMyvJ+Up5clJQU4ZJLLkHPXr1kfe67jnL9Wkncu6quRdyYY8bjdRup1Cp4RFkqbSJyMIlPcu4OjRuKjmHtYXPZ4LK5pLOXzC+1UQVDhA7GKD2CWwUjpHUwQtuGVKeQNqEIbh0CY6wBFNSBrkd3MyeYoDEdOqvV4XIg2hCFSd2vR7A+WN7fB5UPzIlDdS5Bdq8QhvzuDz1nn11N0Iw0kpdOp4UlIhzXXf4fOdjyjZeelrOI7XaHDC+9Lz1DyS+uSY7j6OhIua3W0NIviuzofJolnpObL9+27Jw88TvYqXgq0XuqkFq1FibxqSJ9EvKhpRuydW2w2zQQZVoKGS7kTe+CkLtTyNvmdMMqUpXDBasQj//8cHozStSR2GEcgDx9avX7RLW1VpQN7Wz/wOip8O5jGOZk4Cupiw7uQlluOvTB4cjWpOCpV2Zh+apVIoMb6Us+R/+IHJSWVQjbqRAatQd99buwe9k88R347feFeOrlt1Ea3kXWrZUFB1FRkKVcuI66gDm5UL1KawjHJHeHU9sboWEGpPp1FxUUADt2AN98A+iCOghbTNTFIgUFKU5lMoFomWkaN0chrNPSgFdeAYQJjfh4QJjU0oFMiWw4pnEYmhiCF4a1avR0Rkqo9xc1f1Si4VDvEmrAqMaZsbP012+kcTvlvw8jSzRQj4XZb70Cs9GE2x94RM5mOFrnFI20N5qMeOjuKbJz+9pb78a2nbu8jaujU/vcxnpWqxb95P127DTWb6bO/qsvvRjX3XYPtu3YJfdRGKY3XpyO7bv24L3ZczDz+SflSNx5P/wsj8fHxmDO+29g7YbNePql1/Dhmy/jYGY27p32hBxtS1A4J3Ig0Ey4lWvWoWP7tujRrYt0Vkyb/oKo0MvkTLTyigrc8cCj1WsM3HbjtbhK/J5LJ03GB2+8JEf2Xia+06iwxobly/I9Eixflu+pgOVbf5qjfJn6E2jy9XVKv/LM44js2B6Xb1iBbKftkM7J+kBd0XaXG//XdQC6hFXg191PoLAqTV6fwiMaNEHoHjMeA5MmYfmKdXjsmZdhFa3uY2h+NAjNUb7H+ptJHiWuSryTtwBbqw7CI+RW6bbB6XHBJeRBf1qVGka1HmaNEWF6E8xaowxfXeG0wuayo1R8tjfG4t74cxGhDZZyPhZncXN5Vv5w+dz0ac7yHXX6YDxy/10yDHVRURF0Oh1CgoPluoa0pm1YWBjKbOX4ftcP+GPLH9hv3Q+72QGVhgZwiBLYp36iSPU5nv2RZb0o2K0FVul8NsYYZdQA0lsaJGIxWDA25SxM7HyJyEQdo2rl/uXlMgqE0WTCiHEXe6/WODR3/SUZkGP3wzdn4KXX38a6DZukpCgqT4e2bTDzrfelbavX6/CusKsp/62TrkZpeRm+++k3PPbAPfju59+wc89e5YICGrRx7eUT5RIs73/yuZTbfy46H4MG9MXt/50m36kqqxUvvfa2tLGfefRB3HD7fSgpLZPrGV93xX/w7IzXhb1d7ud4bBwCuXwu1sRgv7kPHB6NsHvcUl09pJNqHcIc2XKNYb2rQmiiE1o521DRYY/I6VJp4FZpYVebka1NRbE+CR5RH0uFFnW2ShzXwYb2VathdjXdGW1c/wY2LUO+UnOh0eigDw5DVUk+uvcbgpTW7fHT/83G6UOG4IarL8eXX3+LKy+biEenP49du3dhwjVTsPrvxUjftQ0mcZ61vLiOWrppE0jyrSgrQVzoMnzyCZCYSIO3gJ07gQMHgGmPmmBTnwaDUXxaq5C25R98O68cgwYBGRnKGsX79wPPv6DB7r20VrxH2Eh2+SkKY+jFea06dBV1cvNacqk5yvdEfnNj0RyfFfXxNCtkp0Q908nCt/bi0ZL4x5ubOVYG9u2Nb378BTt27pYNHZo9Rk6EjVu2ydG31OghR8OiJUtF4auV8sjNL5CNqY6isRMVGYHwsFDM/t9XoDV6SB6Uh86XiO/jzztbOjh27U7D1Cefk6PzacZc69QUmIxGPDn1Pjz/xMN4TqSO7dvJRlOv7l1kZ+aSZSulI4J+F12XElN/WL6BDcs3sGH5Mkxg8/6nnyOEOpS7dEeQmjoz69+NQY0Iyv9g6/YYKHS2KEuNce2ex+g2UzEk5TaMSL0X53Z4Ft0tl+K335fghZmzmqSjuKVA5S/NML4p+gz0MrWCHS5oaAYjHZNJKT8Vt7FHDhygQbb0R7OK7R4X+gW1lo5iizZEXs93DsMwxwc5h0mXyDkcFBQkwz/TgDmdTo9gsV1RXg49dLi8y3/w8nkv4rUxr2JKt8noHdEL4bowWZ7anDZYXTSgw3ZYcjiFpnucCI4KQnRCFBJM8RgRNxzXtL0K0/tNx/tnvosrO14BtV0Ns8kkfw/dn5YFCQ4OluswMicHtVolbWUfWmG7+urDFf+sRbfOneS6whRiumOHtvh71RpUVlZh2crVmHD+OAwbPFDa5ZER4fIcuhbZvz7IcUxyI5at+AcD+vTGuWNHyygENAOZBlxeJGxucmps3rodFUK2je0oDnTCXbloU7kKwZ4SUclqFUcxydztRIkuDtuNA7DDPBhppgHIMHTBAUMnHDR0wAFjF+w198VO02A5m7hIlwAKZQ3pUBbX0Ohh9pSiNc1edhUrN2MY5hShlNMulwPW0gJZZ+/YtRO/LV4o96cVOfHEm3Pw8/zf8cTbX6DYpZf9Jj/+9jOyaAqqON9aUeK9CtNY6PUGbN0ZhQceUGHGDOCHH4B9+5QZwyUlWilXghzGscntcN99aixYgOpZxS+8aERucUe07dIL7br3Rafepymp10C5r7k5ihnmaDQ7ZzEZ1PVNBIXoOV5sdqVDS3aU/EsiQ5vCQnFIn2OHGjl2h3eNHr9nSjPGHE6nrGhd9HzFPiq+fZWszWaDWpxL51NnFY1+9ofye79g6/adePvDT9G2dStcdvGF8hg10MixQKGZ2qS2QquUZLRulSzXyCRnx4HMLHlvmtkmLyN+A70LR5udzhwOyzewYfkGNixfhglMfLpMEQM++OwLDAgKwxzR4E3UG6Ueab0N5rqgxgMdpRnF97ZqhyltOuLr73/C3VMfx5tvfoXS9HjEq89AlHswtqypwKNPv4KXXn8XhYXFNbrPNDjSsSv+I4fxddEj0CcoFVUeBwwqxXlBMq2Rure8F9+00MDqcaKPORU3xY6WM4qVS9XkZhjmOBFlLQ2+s9ntwrZyC5vLIQfVkI1lF8lqs8t9ZIu5bG7EmxNwXpvz8OSAJ/D6kNfxyYiP8c6Qt/HqgBl4a/AsvDnoDfl9lvj84PT38cbg1/HBsPfxyZkfY9aQWXhDnDO130OY2GEiukZ0gREmGfVFuadi21mtNnlvmtlMv4s5MagsJRn/vngJCotqZoBS2GhaX5js6B279+D9T/4nHcGU3v/4f/I4yeCn3xbItYfJDu7csb1cE5Mg+3nTlu3yO9nB23bsxqIly+T2kuUrMfPt92W0H7LZn3vlTezPOIhOHdrJPF//8LOUNXPqCXEVom3lasQ50qBS64TOi7aRxw24HfLdsKkMKFWHI1uXgixdG2Tq2iNbk4wSVSSsKrLJRF3rcVFISFleiAYWYmxpaF+5EqGuAlETs13FMA2FtI1FcpTmw1VwQO7L2rUee7f8I/Vz55q/UHiAopMCjtx9cFQogznoHKZx0RmMiE/thTVbB2LmrHa4464w3HKrDg9P06HSHgetd7AVEREdj7yS1njscTU+/BCY+rAa2QWtEJ3QSoapJuSgWfHdt80wgUSzCEP91y/foLKqEp988bXsgD4WLptwoTCw3DIcT259wlAbjXjgrttw9pkj8cnnc2WHNI3SrA+0tsy5Z52Bbl06Nctp5o31m2+/6Tqcf85ZuPOBR7Gdwn6LfYMH9MP0R+7HR3O+xP70A3j60QdliKa53/0ku6YSE+Mx66VnsHNPGqa/+JoMR7pvfwYefup5+Y6QnGnG25r1m2SY0wV/LsV7H8/BlRMvws2TrsI7H36Kz7/+Dp+8/SqKS0rleRWVVdLpP3zIaXJtTmpA/fD5bHz/y3zMePNd+X40tiOC5cvyPRIsX5bvqYDlW3+ao3yZ+hOo8qWGLjUFKHzljddcjiyXGy/v3Yb5BXkQWifz+BzHTpGPvqmEqrUJCsbTrTqjf3g4Pvu/efjw0y9kHl+zwnddH01BR49Gc5Tv8f5mkgv9FTnL8VLmD1hfmQ6jSivDUdNMY71aC5PGgGCdCSFaE6rcdiRqI3BD5EiEa4PEOyD+vO/EsdLcnhXB5XPTpznLlwbVUfjh4xp8cXxqKAoB72c9oYgvjUmg6K9/vej7Xruu9JWt/vt8+B/zP9+3j/Df77+PqCt/U6AllM8eUbeSg7hEE4VMfUdY9RbpxBfSUdRRyuNwmcj1jr1o1CoYHUVIdOxEqDNf7Gk6MjwaXP8GNizfWmWssqNJlbEnQqDL1y3KYZKVxi/qhw8ahFWYl42K0mIEhYTBEhMfcI7h5ijfE/nNjUVzfFbNwll883VX4sqJE2Aw6L176g+N4vxlwWK88uZ7qKisPGqhTYU8NdjGnzsWU266Toa/PB4orM+kKfd6txqW5qjs8XGxmPnsEwgLC5UjbmmdpLGjR2DV2vXSyU9Mu+9OGUbpr79XIeNgJs4YPhQGvR73TXsSW7bvlDK745brZYglWiezS8f2cuTtxVfdKNfT/HPZCtmRSYU7reEzZGA/3HH/I0iMj8PU++5AxoFMGf6pa+cO6N6lE+59+Els27kb33z2Pn74bYF0hAQHB6GsrFz+nsaC5cvyPRIsX5bvqYDlW3+ao3yZ+tMS5Hv6oAGYcME49OnZDRtLy/BPeTE2FpegyKY4CkxaPTqHh6JbcCgGRUQhM20vvv3xF/y+6C85K42o3RHdFDum66I5yvdEfjPJQ3EYl+HN7PlYWb4Hem9IanIWGzUGhOpMMGh06GxIwEXhAxCuCZadYD6ZHg/N8Vlx+dz0YfkGNoEi39qDpuqqHykPUdfgKt8xOkLnHOl837l11cfKnrqv31i0NP11Q4MyTQTKNZEo10XBqjLD4dEpIar9UHtc0IncBk+5DDUd5sxHiKsIahx/5MTGgMvnwIblG9iwfAOb5ijfE/nNjUVzfFbNwllsNBjQo1tnxERHeffU0KNrZ5wz5gwZguen+Qu8e2ugMEpr1m+Us5Pq+79K9+vbqwciIsK8e8jwplCbLrRKTsK1l0+UDmEK31O7w4Q6q7fu2IW8/ALvnoalOSo7NWpiY2Mw4bxzMKBfbznzbOnfq/Ddz7+hlDr/hdwMQiY023v0iNMRFhqKjVu24qtvfsT+9AzvVYA+QmYXXzAOyYkJcm2eb378FX//s0aud/nboj/x1/KVMh/J9+7JN0onxg+//i4dFxT6tHWrFOTk5eGHX37HkmUrZEPsucenyu/zxfmn9e+DP5b+La/RWLB8Wb5HguXL8j0VsHzrT3OUL1N/WoJ8KbQ7rZHZp1c3nDN6FPr16QWV0EmHnP1Cs1rU0Amd37xlG77/9Tf8s3YjSoR9TbNjjqE50SRpjvI9kd9MkMzor9hZgdezf5EOY7NaJ9s8Zo0ROq0O/YPa4D8RgxCiNuJEZhT7aI7Pisvnpg/LN7Bh+QY2LVe+KjhVWrhEcqv0cKiMsEKZrKJWuWFyl0PrsUJDUT88TpG7ea4vzfob2LB8AxuWb2DTHOV7Ir+5sWiOz6pZOIv9R0j6oFnGGrVGdk7fM+Um/PL7Irz+zkey46PKavXmOnlotVrodTqkpiRj9luv4MdfF8h1YGhNZF9YTcI3YrOxRmtyYR7YsHwDG5ZvYMPyDWxYvoFNS5Ev2dw+C5YiCbRJTUZ0VBR0ei3yC4rkIJDcvAJpb/vyHkNTosnSHOV7Ir/Zh3QXC/nRDONnDn6LHdZshKgNNHIA3c3JuDXmTIRpzN72zYk5ionm+Ky4fG76sHwDG5ZvYMPyDWxYvoENyzewYfkGNs1RvifymxuL5vismkXAdXK8yk4KkWjWQ3SkBbdOuhoP3n2bDH1JTtz+fXrh/rsm4/47J8sZSNSBRelER8D77nv9VZfK61NIbKJPz+64d8rNuO+OW9Cze9fqfERTCuvDMAzDMAzDMPXB34YtKy/Hhs3bsOCPv/DL/MX4Z8166SgmyOalvIHgKG7JkAOY/izaUExNHI8BwW1R4XGgt7kVbokejXBNkNLGEX8MwzAMwzAMwzAMwwQuzWZ1buqMkh1SIkVaInDmqOEYNWyIXL+QZv2mJCXiLLHvnDGj0CY1Rc50OFmdWNRJcsawofL6FOqSSIiPxajhyj5yTvt+38m4H8MwDMMwDMM0Bv427dESExjIwa4iRWhDMCX2LEyOGYUbRIrQBrOjmGEYhmEYhmEYhmFaCM0iDDVTfzhMRGDD8g1sWL6BDcs3sGH5BjYs38CG5RvYsHwDG5ZvYMPyDWxYvoENyzewYfkGNizfwIblG9iciHybzcxihmEYhmEYhmEYhmEYhmEYhmEYhmEY5uTBzmKGYRiGYRiGYRiGYRiGYRiGYRiGYZgWCDuLGYZhGIZhGIZhGIZhGIZhGIZhGIZhWiDsLGYYhmEYhmEYhmEYhmEYhmEYhmEYhmmBsLOYYRiGYRiGYRiGYRiGYRiGYRiGYRimBcLOYoZhGIZhGIZhGIZhGIZhGIZhGIZhmBYIO4sZhmEYhmEYhmEYhmEYhmEYhmEYhmFaIOwsZhiGYRiGYRiGYRiGYRiGYRiGYRiGaYGws5hhGIZhGIZhGIZhGIZhGIZhGIZhGKYFws5ihmEYhmEYhmEYhmEYhmEYhmEYhmGYFgg7ixmGYRiGYRiGYRiGYRiGYRiGYRiGYVog7CxmGIZhGIZhGIZhGIZhGIZhGIZhGIZpgbCzmGEYhmEYhmEYhmEYhmEYhmEYhmEYpgXCzmKGYRiGYRiGYRiGYRiGYRiGYRiGYZgWCDuLGYZhGIZhGIZhGIZhGIZhGIZhGIZhWiDsLGYYhmEYhmEYhmEYhmEYhmEYhmEYhmmBsLOYYRiGYRiGYRiGYRiGYRiGYRiGYRimBcLOYoZhGIZhGIZhGIZhGIZhGIZhGIZhmBYIO4sZhmEYhmEYhmEYhmEYhmEYhmEYhmFaIOwsZhiGYRiGYRiGYRiGYRiGYRiGYRiGaYGws5hhGIZhGIZhGIZhGIZhGIZhGIZhGKYFws5ihmEYhmEYhmEYhmEYhmEYhmEYhmGYFgg7ixmGYRiGYRiGYRiGYRiGYRiGYRiGYVog7CxmGIZhGIZhGIZhGIZhGIZhGIZhGIZpgbCzmGEYhmEYhmEYhmEYhmEYhmEYhmEYpgXCzmKGYRiGYRiGYRiGYRiGYRiGYRiGYZgWCDuLGYZhGIZhGIZhGIZhGIZhGIZhGIZhWiDsLGYYhmEYhmEYhmEYhmEYhmEYhmEYhmmBsLOYYRiGYRiGYRiGYRiGYRiGYRiGYRimBaLyCLzfGYZhGIZhGIZhGIZhGIZhGIZhGIZhmBYCzyxmGIZhGIZhGIZhGIZhGIZhGIZhGIZpgbCzmGEYhmEYhmEYhmEYhmEYhmEYhmEYpgXCzmKGYRiGYRiGYRiGYRiGYRiGYRiGYZgWCDuLGYZhGIZhGIZhGIZhGIZhGIZhGIZhWiDsLGYYhmEYhmEYhmEYhmEYhmEYhmEYhmmBsLOYYRiGYRiGYRiGYRiGYRiGYRiGYRimBcLOYoZhGIZhGIZhGIZhGIZhGIZhGIZhmBYIO4sZhmEYhmEYhmEYhmEYhmEYhmEYhmFaIOwsZhiGYRiGYRiGYRiGYRiGYRiGYRiGaYGws5hhGIZhGIZhGIZhGIZhGIZhGIZhGKYFws5ihmEYhmEYhmEYhmEYhmEYhmEYhmGYFgg7ixmGYRiGYRiGYRiGYRiGYRiGYRiGYVog7CxmGIZhGIZhGIZhGIZhGIZhGIZhGIZpgbCzmGEYhmEYhmEYhmEYhmEYhmEYhmEYpgWi8gi83/+VAaPGeb8xTZVVi37yfjt2WL5NH5ZvYMPyDWxYvoENyzewYfkGNizfwIblG9iwfAMblm9gw/INbFi+gQ3LN7Bh+QY2LN/A5kTkyzOLGYZhGIZhGIZhGIZhGIZhGIZhGIZhWiDsLGYYhmEYhmEYhmEYhmEYhmEYhmEYhmmBsLOYYRiGYRiGYRiGYRiGYRiGYRiGYRimBcLOYoZhGIZhGIZhGIZhGIZhGIZhGIZhmBYIO4sZhmEYhmEYhmEYhmEYhmEYhmEYhmFaIOwsZhiGYRiGYRiGYRiGYRiGYRiGYRiGaYGws5hhGIZhGIZhGIZhGIZhGIZhGIZhGKYFws5ihmEYhmEYhmEYhmEYhmEYhmEYhmGYFgg7i5lmxp346vtncIV362hcMeMrfHW/d8PHxc9g4aK3Mc27eUpoiHswTIvjIsz6vp56df/bWDjjIu8GwzANAutdgCLK3tn1s7umzWbbp0kidPMwe5hhGIZpBGr6MrjObIkco/zZtm7iHK1vsv72c32os2+TOQUcvc/ppJbbrN8NTxN45qzLgcTJLeebEgHrLJ42+yesWiSSrLypwPduy+Qt4EVBUbPvJ6XQkI4+7756OiWZw6EC0P9Z12x7n7OXajkt+gqzLvbu9FJzTElKgToTE8+fijkyx7Ez7VwLFo+6BdP95Uxp9p1KBnon/OUutpX71nqH/PP4X4uuM3cqzphViB5c8R+K33NSnqkwrn3Pzd+5Xvt5+lNLZ6srWTrHP6/Y/sr3/Ou6Xl26z5xEamR7XM+2lnwUuc3D5POF7io5mCbBCcr5mAhcQ+yE8deX2mXmceJf/9a7MfPCLTjjnnnejWOhto12uD3wb1wx4+06zmnI97P5MW12zXOWNtopt3lZh08qh9g9yrteb109ydStf0xj4q/fBJXpNeWg//tSU05yu7e5QHWmf2e1Uoce0iaS9WitupXl2zgIG+1QG+TE+jKYZsaJyv+ItrUou0+Szc8cAydBn/1tJrafmgC1+xGPp89JvBc1Nvhx6qZfn2Vj2fOBSG172L9MZf0LFGrbxScC160+AtZZPP3acfg+PQPf+yrvwg2YOWocBog0c70FPbwFcNn6D+Q+SlRoXDHYgh2zlO0BnwAXcAfjcTHnnoniOWdg9SyqaC/CEMvu6uf/IcYohbKoVM/HfO/z/wK4uraCV4jzvbKYtQGRXUhpazobaVTXV94O7dodINTZfIHFu6uaO5FQOL+64i+olv0HWG3pXH3vfYUWTKpT7uJ98v4/DFhqwZ2yEBG/5+p2Ne/M1s7Kb5k7FRvRjxvl/kgn+gbsE8994gti+/7OwK/e5zZqG3oc7Xn6UaOzHyB/6L85FY58vX3V956PgnYsq5OLaDiJ57ovfb7XGKMK3NtZ5at8hf77OrDqMoj95ezTz+qRnGSQi+/y/OrOr5p7fNVF7mBOOccp5+p9Pv3166w+7Dwlz7TZ16NfSk/cyZ2dhyMaPVJXRPla5t1V4+wlnSG51NSvdIxkcWgef+5Ej+q6eRw2dlGO+9e5/nJSdFHkEY1cZZCOuB/l9emjV+7+g8YOv6df/fprIToOVsrpmnNqyvrD9onfcGevZPSbfGh9MG12Z2yU1/wAO9qNqXU/phrx/CbhCwyQ9rLQxWod828sWXCBV57VdXK1jtZVhvuVBd7rsQ6fKuhZK+/6xBf8dZ300O9ZCz1W5OV3vJaMDtHnf9XjuvXvcJ31u0/1+8Q0NNQuXmy5VMpk2uwhyBd2Mdni02aPqbHFRbt3EsuoGUId29Qm8urt1TQweiImz6VjdbVdmcbl0LK5x9HsqrrK6doOBZHfVy/77Dul3KXz67avD29DMQ3H0eTvb4ORDEU9+tThtvXCGY+JY2OQmjKG69VGp0aedbdzauxnn75W20yzDrWf6mxnVetwXX2bzKmius/Jr7ytlqHAp7ey7KU8Y5OROpaOk9766aZ/eVuH/V3TZyX2nQt8KOvr+cBQLptPGb7+Cn9d9LZnasu5uq/ksHZS3fKsqVPr2se63LDUYf/Uoc+HyriW/h7BhlLO97VziVrlfIDQ4sNQh/S6vpawLaLAoG2h/HOnYmKdI/mYY8bSE3d6lWzOPbfIBuy0Lhas/nGmNwM1dOdjb7XCEUFeWYg0WTR8r/Xl9ZEMbFUqVJ+zr6bT4wvkW4KUbD4ujkWU9+tR2XpLdYfKEXlhG/ZZYnHFxf3QsXCZt0FO+2/Bh8u93y0WtPZ+ZQ6H5BUpjCpF92ZiIsn3aM/zMOZh2W4gqo13kwp1qcv0vvREJO07pusxp4orZlyKqKVKZ9XMwiGiEhUV79BC7wCSD5Df5XCDuKZsvl6ce/jozlRsk9f7vrAdhghdpXt03K04mDdS2cA0OPWTs/++ZYi6+hm8PCPW69QT8oR3YIBoPCkdnCLPuXdi+rXzsa98A2bybIx/RxjCCbJuFOlX0aC9X9SvS+mTDl6EBGzAd2m188gzFajusyjlKTWCp1/r0z9fnUtyGlJdRyq6WEtHRf2X/4nI6zfQq3rQmBw0VDNwSyEZ5/vKb/F+fChtrztFY2qZ8hu97wq9P4ftE+X69+k0uMzXOU5chL0/1vzuqGD5halNG9Fo6rLtCLNW/PDJs9rxfvQy3L8sIAfFBeL9Yh0+NUybfSnwyeF1ZG18dSbp+0ih13XJqCaf93q19ViULZP89U+UzWRXVesf6jgu7LAob908s7Dz0W1r5pQy/VohE9GuGln4hbesvBM9LKI+oAGcBA3q/DHjMHuMaQ6Iev6TQow8gs0s8bVdvZtMU+DodtXeOsrpmokN3rpXyLXAQu0eYd8VZiA/jhyLwu4p3CbOP9y+Jmq3oZjGorb8Z+K73RavTEiGyzB5g8yo4K2Tz7jnCaHvG1CWPh8DDusfYxqHI7RzattR/jbT5Nrtl8PLg6P2bTKnHP/ydnW6z17yycnbBy1spw/XV8iJKBNfoLr4UN2sy/6uq89q2Y/e9tHFsUo/JnNqOaT/oO4+q+rB84e1kw6V52H1dB37WJcblitmdJYDY0kGSr9kXfpcW8aH6u8V9eijlBzW7xUYtBxnsc9ZKdKdvQqx0dswrpm9plTSNCNWbs+iBpf/jFXm+CFHsKJkpKiHjsLwZx7mVHf0ElR4K+fJmafVo3J8ZHjlKBpGhXKHwLePHIkVck81bSyHVLyRfs6ojrsP7bie/uNudJQd08fOnLm+TlcLErgBdkTmiAr6DF9BS4MCfKN1alHzPP8FKtTl9UQSBXWBd3dtfNej0X+K/Mcgcvdq7rw+pRQiP035RmWsnFke7CuTabbZ4QMr/KM+bOxyeJmxb6tigO8t9Cl/IXYsV2Q7fWuG/GQamnrKuTDHq29K6K57sy3VjsLzU+QBIUN49wnD2itrpp7MzUGUr3wb622EvrANkM6efogqFOVdXXl8zCW5KLq32EL1pG/krK9+Jb2rGajj08VDKNyNZVSfi/soZbGwA7ZalHdhck+kyo5Nf/xmP/lmt4nGMgp9uizqeVGntq5rn3frUHz2hGj80Tv0678701oeQeg3FNjh10F9RHzyFM81s9Br2/xLGV5dx4r3K1J2XjMnnZQx6CFk09HXWD0K1XqaJpTXq391yegQfT5MjwXinvIcoVepdTmeah+fuxr57RR7+852hd73iGkUqAOyXLSNDit//RA2MtvDzRTRzoWQL5e3zYl/t6tql9NzlheioxxMfz06kj0nbSFlADuy5wv97ocrKHqXuMacOuxr4vA2FNM4HC5/km8URdfxyvAQqm0xpulxhHZOXXbUEamrPDhK3yZzyplzzzaln/KQto5PJv590EfmcPu7rj4rb7uVZj5Obocdn/DA2gbnsHbtTGyEt00zFtW6Wd/21OH7WJcbktbCJM701pdzsgsV++kwfa5bxj7qsqHq7KM8pnK++dBynMV+YagPmwHjR3VMe2/I3KM2qJl6cmd1SB0lPLVi/EzfWhNqkkbjyVA7J+xcTT50hpQ/L2zDDu9XwheGmkYU+SrsamiE2O52uOBI4WyFAZ9KDo+5q7HDL4S1DEvgc3qyQX9UKFSPEqbBG8rW26l4xOd5GBdhSDtUO6fq5CjXqwlDrYSgZ04lluoOkOq1QWiGmXz+H2CmDFd/olj8yhOmcainnKsdDKJuEPooo0x4BwZ9ny4PYFqcb3TnfMXJyRwBqjt9zlwvon6K9A22+NXXCCVjuDNmDbYgn+q7OvN4oYaqt5xUlvQ4fOATGeBHLXvroEamItVzJkRktQ2WjCjRuN4rvtW1r05kmCBfeF7vPsaPCqz+ZComy9mGNQMCqh3wdY5svwgJlkKl8fUvZXh1HTvrA+9Mceakkz4fE+8R9mrhEO/gVj9nPulHXWHOyKHkHXBxXDKqHpgn5F5XZ1bt4+TA8I6un1k9Y4ppeERdQeGJz5+IxRhTbX9v9J9ZSGX/YQNzmeaBMivmw/O/wI52R4iO5Wu7ejeZpsWR7Kra5fQVg4HF3jJ2BzmGqeNZlPsXnCsukEbfgSFxyrXqsq+Zpkm1/GmAlZDrrC6Hd1wzTZvjaecciZry4Ch9m8yp5/7Y6tmJR/Mh1Itq+7vuPisZxvxqCkXtm2nONCiHtWtpCUtfX0mtKGzEv7SnDt/Hutyw1PRfXRFnQUE29X/V1uejy7il91EGrLOYYo+fn5KM84+p0UthC72zHGm0QJ2hj5n6QJWdsgbA25h2cQa+W+odaSfSnZZlSsftCxTu2Te7l8Lo1a4Y/cJQ08iNpf8+worCHMI7iufOXrXDO8xEpuXwdQt9Ydlqx5efcw+FiPAfLHBomMyZ8t2gUAXKLHS5v8s2xTi8+Bn0AM9WPQQ5Uq4nUntdrzzr5cuQP9T73BYNQb7sdDzC8/Tj0PDE/2ZM/fv1mFOBMpuP1nugDmypS15Z32kpxDIaIFBdJlyPO+uYxV8jZ1GWizP+zWime+zwzl6iEXxMQ3A8cvbfJ/R+awb2bi2sLutHWiwYOeMx7M0GJsk8fqP2aMTnEQePtFREGbfUO8KRRiLT0g4UktCnP0MtiPSue0QjIfv5ZvYdIY/CaiyWUUC8MqkOWVpTB9ZHJw/lIiS0843YFekw28yvfvX9f9Cgrerf4a0j6tonzyd7wd9pfhFmDaVyoOa6tet4xofQSdFAOl/OIKfRzt5RtFe38x4XVEfn8UViOVIZLp630FH/smDV5OurQ3exDp8afPWfDBtfXZ4OQaTfjIeaEe7A4nvmHVlGR+MQ/RNyr57R7NW/Oo+vFo1p5T48s7gh8WtDff8evv6+Jjyxr61EZaKvDSTzUSclh4lvJvjVmbPfFbaYfztqNzpWr+Hvl6+67co0NIcve+ajRj512VV1ltPLgR7yHN/MYpFP2MypZHeL82kmjM/Wq6kPfPY1D6ptDI5N/uTwb4d+2Pbvjilv+4tpWOqW57+1c/zxb7P4fz/8fTh63yZz0hC6pMj00PbiFXFCF3020hGjYyqkjvU77qebddnfh/dZ3YkLSL7Vs1uPfi/mWPC3h2vrpU//6mjXXpyBTFxaLTvf4J36tKfq2se6fKrxt4tpCbua9o3P/3SYPj9Vt4x9+nu4DXWEPsoAReUReL//K+RRZ5o29CIfLy1CvtJhacHiEx0ZdjRO4T1YvoENyzewYfkGNoEu32mz3waq1y8+Vu7ErBkZmOwdbUszz4csv+UYHc6NC+tvYMPyDWxYvoENyzewYfkGNizfwKZlyPfE2zkn1s5qPAJZvtNmPIO993gH0V38DL4avBoTvTKuF/e/ja9wS7OOcMXlsx8BIM/atCT5nrA+N0NORL4tJww1wxBzp+KMU+koJhriHgzDMAzTXLgYiPKNohaJZxgyDMMwDMMwDNPs4XZOAHIRYGlXM1t8cjtlKSeGYZohrM/HCs8sDjDoxT9eWL5NH5ZvYMPyDWxYvoENyzewYfkGNizfwIblG9iwfAMblm9gw/INbFi+gQ3LN7Bh+QY2LN/A5kTkyzOLGYZhGIZhGIZhGIZhGIZhGIZhGIZhWiDsLGYYhmEYhmEYhmEYhmEYhmEYhmEYhmmBsLOYYRiGYRiGYRiGYRiGYRiGYRiGYRimBcLOYoZhGIZhGIZhGIZhGIZhGIZhGIZhmBYIO4sZhmEYhmEYhmEYhmEYhmEYhmEYhmFaIOwsZhiGYRiGYRiGYRiGYRiGYRiGYRiGaYGws5hhGIZhGIZhGIZhGIZhGIZhGIZhGKYFws5ihmEYhmEYhmEYhmEYhmEYhmEYhmGYFgg7ixmGYRiGYRiGYRiGYRiGYRiGYRiGYVog7CxmGIZhGIZhGIZhGIZhGIZhGIZhGIZpgbCzmGEYhmEYhmEYhmEYhmEYhmEYhmEYpgWi8gi83/+VAaPGeb81bzwaDRBsgiouBsjOA8orAJfbe7R5s2rRT95vx06gyDeQYfkGNizfwIblG9iwfAMblm9g0xzleyK/ubFojs+K9bfpw/INbFi+gQ3LN7Bh+QY2LN/AhuVbByoVPB4VSu1BMGls0Gvs3gPND5ZvYHMi8m0ZM4uFMkvU4n83JAhIiQeS4+E26OBOioWnVQI8ocHiuDefLz/DMAzDMIfhcbvgcTm9W6eehr4fwzAMwzAMwzAMwzAMwxDkKC62heBgRZx3D8MEHi3DWUyTp/U6eJJioWqdBB3NKKbZxW43NHo9DAnxUKUmi+PxgEGv5GcYhmEY5nC8daS9sgzuU+3AFffyeNywV5SKDR7IxTAMwzAMwzAMwzAMwzQgKmVGcVZlDFy8qisTwAT+263TwhMVAU/7VtAlxCEoMRHB8fEIS01FWOvWCG3VCubYWAQnxovjsTKfJ9oCjziPZxgzDMMwTC1E3ahSqaEzBcNWVnRKHcbklnZUlkGt00NFg7wYhmEYhmEYhmFaGNQ7WZ24r5JhGKZhEOWtB2qU2IKRXpEAl4cdxUxgE7hvuEYDT3gIkJoEbetkmOPiZNIYjaBlmlVqNdRaxSHsoRnGYr8vD+VXifM8ESHK+sYMwzAMw9Qg6k61VgdDcLic9etyHN9aLUfr6KC62V5ZKuprLXTGYO9ehmEaE42wiyMtFvnJMAzDMAzDNBBqFSyWCMRER8Go18Ptcsm+TablwYMFAhuWb9OCQk8X2UJwoDJemc2gOlK5q4JaY/B+Z5jmS2A6i01GeFLioWqVCGNyAoLi46EPCfnXApeOUz7Kb0pOhColER5xDQSZvTkYhmEYhvGh1mhhCAqF214l1xX+N1TiT6sPQmLbM8Sn4gA+rG72dno4rBXQaHXQmYJop9zHMEzj0aVjB0yfdj/ee+1F+UnbTGBBJa2bwv8rmwzDMAzDNDC1ey31Bj169+iGMwafhu7du6Fjlw4YMmgAunfqALVoR9GSPUzLgORN74ecAPUv/dtM84Pl2wQRcpChp6tiRBtJyESKpQ7ZqNQwhsYhIbkV3Loo706mMSDVqTN5jzP/TmA5i/U6uBNiZChpfUIcQpKTYQyPUEJX0ptRT2jWsSE8XJ5voNDU7VLgSRSf4voMwzAMw3gRdatKrYFaZ4C9ouSoDmNq8GgNZnTudz0SO16ClI4ToNGRI1g5Vo347rRVyq+U3+c8ZhofEhOJLKy9G/FDjz/FDHDDYBFy5QhOTR6z2YS2bVJx15QpmDr1aazJ64lr3wDm7+mGu+57EvfffTc6tW+LIDMPrAwE8ssqsHjrXqTnF3v3MM0V7hBpeVAns16nk58MwzRPpPYKHSa7KiYqEgP69MaI4UOhCjJiafFB/JCxBb9m7cavmTthsgSjdXI8rJUV0rnEBC7UVqayXavTIiUlBZ06dmSHYgDB8m2CiGfvH3ra7RayOFI5q9YjPLoVzhpgRmgQoHGVeQ8wjUGwwXVYMupc0GiE/Fil6kXz76YjBfauS4zWSdC3SkJQXBxMUVFQi8bS8RpNdB6FqTaL6wSL69F10SYFnmhxH1rPmGEYhmEYWQ9r9Ebp2HVUlMJTxxrG1NAxmiPRuf/NsCQNh91aBY86DAltzxP7o725CBUcVRVwOR3QGRVHMl2faRoYYzxoc7ELbS9xIWm0+7hTq7PdaH+ZC5bO3LHVVNHr9Rg+dBDuvu0WTLhqGjYUD8SkV0vw8e8lyCt24ceV5bj1jTKsyu2DC658DPfePhlnDB8Ko4FDbzVX3EIdN2Xk4ZX5q7Fs1wHY7Daxl3W0uUE1plrILVlrg0XjlNtciwYuZF/55BsbHYmU+EgkJ8ZV7+eOZoZpJng8MAgbKjk5EX26d0affr2Q2CEFe11l+CV9G5YVHUCFRoWQSAuiY2MQEhOFFaW5iE6Ih16rhvM4lwRimjayLBfJIOzyTp06Yfz4i/Cf/1yCisoKuZ8HCTRvWL5Nl5rQ03GKk/gwc8q7QxuMtm1a4bbzNbA7gJ178uBxUxuKaQz0WjceHr8Xj008ND14YQYmjczE6K6FiAoR9SWbx0eleTuLqfAMDYIqNRGalASYU5Jhjo2FLsgsZwefFMQ9tCYTzDExCBKGm5pCU7dOgic8VB5jTpwBfXrCIAxfk04Ds157TMmk16BPz26IFPKo6/ipSCaeYX5MsHwDG5Yv40OjM0BjCoKjsuyQGcbUXanVm9G+99UIjx0Il8Mp62iHwwaVNhzRKWdApw+ReZ3WSmGLu6E3h568epw5Kaj1HrQ+34XQ1jTK2QO3w338yemGIcKN1he6EdyKQ+c1NUYMHYRXn30Co8+7Ff+3qQee+9qN+WurUGX3wKBTQadVyU+7E/hd7J/+pRWfrumCQWNuwYvTn2CncTPF6XTgYGEZ9ueVIS2vGGU2GvjDbZ3mhE9a8Tonplmy8FhYBnRq7mgMVMhKoo7kSEsEurRJQkVBJnZsWo+SnAPo2iYR8dGR8jhbUwzTtPCvWUlHg4OCMHTQAIwbdTqi2qRgl6MUS3L2YlVRFrJUDqiCzTCL9rLObITd7caZ3Xpi4sDB0IUGY1dRLjq1aw9bVaXi0GACAhmO2OssbNe2DW64/jpMvPQyjBo1AuvWrkNmZhYP52vGsHybOEI2NaGnhRUltmXJLT8F0nfshDG8DYb2icMztxpRUOrB4jVlcFo5OlOjIcSj03rQM6UcfVuVobdfGtqxCP85LRcPX5yBV6/ZhXN75Ssi9Z7KHIpKFE71LoMGjBrn/dZ4kCA9alGoUod/TCRUkRFynWFjhDfctDCeqhX4ZEKPSa2Gx+WCtagI9rIyeAqLocopAOx2qNxNY32vVYt+8n47dhpLvh/NfA5lFRX44MPZqKysFI+5fk1at5B11y6dce64c/DKzNfhdB4+m+1UYBPy3pdb4t1qWFi+px6W77HB8q0/zVG+x4yoK10OmwwjrQ8Kk2sak6O4U9/r5YxichSTdV1RmoXC3J2Koa1Sizq0FGkbZ8NeWQp9cJio68lya16mWyDLV4gI8UNdSDoTcFn/fW3q+qIxalCyx4Ndc7RwO7w7myiBLF/qsAgNDZEj2s8Zcw6Corvik4WV+GtTFQx6tXQOu8nOlaawtMSljrrEPlJTOm53elBpdaN3OyNuOz8Uqqp0zJv3BTZv3YrS0jK5Dm5TpjnK91h/c2F5BTKLShAfHorQIBO0ao1sV/mosNkwZ/lmvL5wPS7s3RaTR/dBXFiIUh57cYm6m/LtyytCdGiwvNax0FyelT/Npf4lKYkWMpJ0DrwSsRdhaheMouxeYzPhieIUFLvUcB8i8cChJci3NlRuGwx6JMZGw2Urx+4d26UtrdMpgyJlB3THTlDpzMjKK4DNZm/y5fCRaJbyPcqyLE0WUSc0Bi1Cf4XuKTMI1XC6FD01m4wICwtF29RWCLaEIT0/D5sKM1HmsMMYHASdXg+1HIxtFHVxONJys+U1Qo1mjOrSHdedPhKzFvyKX/5aggG6EPy54h8ZlUmjbVpREFti+XwiyPdEfOoNBqSkJGPsmDHo1r07cvIKERMZhv379mHGzNdFmW6T5Xxjw/I9Nli+TRwhH5pRTI5iCj1NZTftOwSxada40CZegwsHA9efA6zc4sIzc9zwVKZ5MzUPAlG+QUYXvrp9E4x6IQ9vu4c0ib75a5TBoMLXf0di9p/xKK3UHnIsUDgR+davV78J4RFGE2Kj4ElNgi4xXgk5HRmpzECqS5FPFnRdMvLEfeh+dF99ciI8bRKBuGi4RWONOT6eee4FHDhwENdde41cG89ut8vGLiWHw3FYov2Uh5xN4y+8AN9+9710UtWV92Qmui/dkzpMmfrD8g1sWL7MIYi6sjokdWUpjMYQdB6ghJ52CRlQA0maaiq1lIXH4xLJjdycTGi0sQiNaSvyqHlWcRPDHO9BzABP/RzFQrxqnZBfPcwxul6wMKMs3ciFwbrZGESEh+O8c87Gf++5H4PH3od5G1vjrneK8Pc2K4LNGukIdrmUTk4NRZHQqzBxWDBCzNTpSSqvgt3hgUatQrBJg837bJjyRh4+/DMcvUbehTtv/y8mjj8fifFx3jsyjcWWzCI88H9L8eyPfyM9rwgeUSeS1lFy0GBYu6gnZU4qomkAgEvIXuTxdlbRZ35ZOd77Yx1u/ngB/t6dVX2MaVyUmtWDLvoqPBuRDovGJW2eCqG7PcS+e8MyEa5RXMX1KJqZJoqUnyhz9VotkuOiER8Zgqx9O7Fr+zZ53Oco9kH7D6ZtR1SwAbFR4aIM18jz+R049aiqChslaW3FMqmqig479m+JOflQHUlL60RHWtC6VRK6dGqPlPhYdGiTih59eyA8ORpbSvPw7Y71WF2aDbfZiJDoSOhFm1ql1ci2kl60iXokpYirqXDfmHNxzdARWLV3D7YcyEBGUQGsISbk2qyIskTAIZePYJobslz2JrPJhD59euOmGybhrjtuR5yQ/f4DWbIMp4EGH382p8k4Epn6wfJtPhw99DTZTx5YzG6M6KHGE1eJMvlSFfZlefDBzw6gKt2bj2kq0Bh3DSXxnT5pmR5KhNPuxvj+ebjv3HSYDa5asmaaXW+sOzkOKnIWa9RQiwYPhYhucEShQffVGo1w03SbmEgIq897kDlWaJbC1/Pm4cDBg7j7rrtgNptl5UjJv2KlRNB+cvyMHjUSLlGhbtu+Q85m9B2vfY4v1aauPL7kO+779H1njh2Wb2DD8mXqghzGBnOIqKfDEBLVEy6HMKDle1ETctjjpndEjYKc3cg7sAlJXS9BbOpZ0BuUmWost6YBTTSJ7Alh99Sv0epxqFC6T9hrtvrJT6X2ILqfsKuCvTuYBoPCTb8540XEdb0Mr/wejxfnFuGfXTape3q/2cTkJCZ1pO2IYDWuPCMY0WEaeYz203H6TuU7nUeO43VpdrzyTQleXRCP4LaXYMZzz+C8sWd678w0BvFhJiRHhGD+5nR8sWIbiiqrUFBWgY3p2fhlwy58unwT/t6dKeW/M6sQ81bvxO+b07DlQC6KKytRZXdg/pZ9+HLVTkQHm5EQHszldBPAJ4G2OhseCs9ErNoGBw0EELKhTi270M3BhjI8Fp7OIambMdRpQ9KLjopEx9aJKC3IRNqObSgpKTnEjvZB27SfBmTuT9uFopwDaJsQidY02F0c5yF5pxaVqA91oi6kT1oQnp43bfu+034tyegkf3c4nKisslOFfVgeujd9+n+nY5SHtpmTh08b7dYqdGnfFqNHDkevbt0QlxiPDp07YmduOhambcW6snxkaZ2AqFO1ZhM03gkoZE9Z7XY5MGRC/0HoGJ8IS1AwrC4X1uxLw46sA/h54xrcOuos9E9ti31uKyyWCNG2dohzeXmX5gRFifD1nfTs2QN3330H7rnrTnTt2g1bd6fLgX3tWyfBoNfiszn/w8GDmd4zmeYAy7cZIWRVZ+hpL1q1E/HhwKgeHkyfpMLo3mpUVGjw6QIN8gsrhYwbJlIicxRqxHUIJEqX0EHSR0qUjcweUaUKeZbg1tEHFCObqUbahs0KjVoI1CUbQNbiYlRkZsEtDKmGxC3uX5GdjYqsLPk7aJuc10phwhwfKswRleO+vWm48orL5HOt3eglfPuCg4Nxxpmj8c13P8hZir4KuK5zCDrmjy8/jbqn94m++z59yZ/a28yxwvINbFi+TA0kR50hCN2G3oOEzpdh+c8zhAzs1fL1iVml1sBaWYLcjI3o0Pt8ISgX1FoLEttdUL2G8ZHeCabhMEZ7YOl6hI4nIR6ageiDwkqn/6rBjtla7PpKA42+JqShzFeHOGnQgDnWhfBOrKcNSbs2rXHrjTdj6v90eGFuBbILybYWtqwQg6/I9Nc/chTTdoXVg6JyD4pFopDU/sVrjY6LT7FfI2zjglI3ZnxdjinvAVdcfjW6d+kk8zANT7IlFMM6JiEi2IAfN+zF92t34d3F6/Dw3CV48tu/8fP6/SizOtA53gKbw40f16Zh2rylePybpZi9ZBMWbt2Lr1ftgMqjwukdE9E+LsJ7ZaaxII0jh3A7nRXPRexHgoYitwhdFftp5Dx9U3vcsIl9fQ2VeDQiE8EqUdeSgjLNAl+5GhERjovOOxsP3nETSgrzUFRYJAdfkkP4aPjOrywvx6b1a1FVnIPObZIRHBJUp5OZOTkE6zy4prewnbyVJD3lB4e5EROs1Ke02+lSIh6drO9OlwetU1Nw1123o0P7dqIcpwgDIoPA4XQLWQNWl3hftCY5uJfOo/N9eZgTx6dNOr0OemEDxUVHolPnLqgS7V1zUJCc3a/X6REdFiFk4oA+JBitYuOkM/g/A4egV0obxIdbkCDS3WedB6NOhz6tWmNndibO7dkXP25cg96tUhFkMGDF7p2iLl+DjIJ8uENM0AaZoBJtY+qbZJo2VO6Sw4IwCV3s3r0bHnrwfkx94H507tQJafsP4K9VG2QEieSEGPlebdi0BRs2bJTlNvdtNG1Yvs0MISuyl0tsITL0tNvtK8kFUhZqGLUedElU47Zz1Xj3LqBDsg4enRpvfOPE3orOaDNkCgxhyeJa7BdqVI6iOho/udAsYyEo+b2qChjdu1Suc8ySq+HorYsmCq1NLNcnFoprLytF2cGDqMoXRhLNXDqF0PWrCgpQlpEBe0mJfA/pd9AMZ25znzjUaJnz+ZcIFob0oEGDpBOJ8FWW9OlL7dq2QXlZGdav31Adcquuxq4vPx0jxxI1qin0R6eOHTByxHBcNGECLr/8Mlx91ZXy89zzz8fQIYOQmtpKVtQUvtZ3vu9aLOzjg+Ub2LB8GYKetdEcic79ldDT8a36o12Ps7B60SxUVuRKBzG9GxSSurI0G2mbfkFKx2Gy84Rwu53waEIR12acuE603FfXu8E0DCotEH+6C1qTWzp1a+MoU6M0TQOXML9UwuqmsNLlmYq8Kg6qYS1SnMwuURyUpgmdFPlliOpakCM5aaQHxkjWz4aiU4e2mL9RgzW7rAgx1zj1lTWJD4XCTFMndJBRhcQoLSKDVUiI1MCoV8Eh9tPx2tB1fB3QFM56e4YdXy/3oHPH9tUdKEzDQjOURnZphZGdkuEUderbi9fju3V7kBwZiknDuuOOs3rj3rH9cN/YAbj37H5iuw+uGtxZvB8GfLZ8K15fsBYHi8sxqF08zu3VFhFBjRDdiamGtIi6t3rpK/FE+AEZetpBzh8//fI5hWlPhQsYpC/FHeG5CFKdWEjq4z2PqT9k+1AKNpswfMjA/2fvPgCjqPI/gH+376Zn03sCofcuRaUJKIKCir13TkU9y1lOT8/u6cmp/O0dK6KiKCpNeu+EHkJ63fRk+/7f781uCCEgJSDZ/D534+7Ozs6G/c1r8+a9wf1Tb8GEMSMQYTbj4Ycewk033YTUtDSlTnWMnUNU584+cAD7dm6D2aRFYkyEqFcbG76L49pyKBk2re4EN3PXMl8dl8rME31O6LnT6UJ6egfRJroaj/7zCXTp1l0cG27Y7C6ER0bj3OGjcP+0v+G5Z59CWvv0Qz7PTh6NEAwLD0eHtBT07doRSXGRqKuugNVaJ9MW3WM8LjIKLlE36pCQCmtBKc5t1wFd4xMxQMSjTLSNbzp7uLwXMU01XWWtF89Hos5qw6YDWeif2g755eXYlJ0l61E0g8QPG9aI7eqg1uthNWgRFhYCu83qbfOyMw2lOV+6CwoOxtnDhuLee+7C3++7B7179kRJaRmWr92KfdkFGNirM2JFHu12u1BXb8VPP81FfZ1VnhNhZyaOb+tE2aXFFoa8+kZTT9OjWNRqD0JNbgzq5MbDV3hw+4Vq0Z7Sy4utvvy9Hn/sTUBEcn/oA8KRMuBWBER0FB/msrU1oA5jj4gVtaVMWifO7lIBrdYbf9b6OospbibRSApJSoIhNFSuczudsFVUoDo3F7bKSrmuRYkvtYvKW3VuHqyigkbfRwwhIQhOTJT3MGYnz9eZ8823szHp4omIiY4+pPHrK3xpmy5dumDdunXyua9Abqrxe9SQjoqMxNQ7bseT/3oSk266A64e52Ghvgveq0zG/0pi8UlNCtaF9EL44Am49Z778MjDD+Kcc86WfwMV2r7vZyeG4+vfOL5M/MLQ6gPQoc91CIsZBJfDKTsYY0UFumPfi7Bh0dvYuuIDlGx7DWU738X+Ld+KbS9CYHCkjLdCHEcUL20YopJH8Qjjv5i5sxsR3UW6dfnic5CzVoWsn9TY960K+Yu1h3Umq6myTY86NUo3asR2Yttv1KjNU0YgN0b714sqXfxwl+ygZqeWXqdDWmo7rM90w6hX7h8u05hIhzRRDlHyTKCm3oXEKC0euMyMD++Pwqu3hSM2XIP/3GLGJw9E4YFLw+X71WK7xvl20wl3gk1qbDngRlxcAjTei4TY6SdyWGUUmkieyeYQ3De2L/5+/kDcMrwPLujVAUPSE9E/LQ5DOyRhYt8OuGNkPzx0wSDcObKXPDlNnwsLNCI6OIDb0n8h329PU0/fG1KAKJp6muIqS+KDebFHpYaHRqnI9KyCU6TRkcYKPGwuPGQ7crR4Uqezr+OZNP5k4/WsZVCsKD/t2D4Nf7vlelw1eSIiwkNlnVbJm1UYMGAAHn3kEdxw3XUwmUzyHofHgi7Oq6+vx55dO5CftQfRIUZ0Tk9V6mFiv+z0op+cFvr9fXXh43mu9Rba9DzQpMfyVeuxPzMTnTt3xssvPo9Jl1yCiy6+CB+8/w7++cTj6NixI7744ivs2LETWm2rOxV4xqG0SGVjanISRg07C306pMBhrcb8HXvxc0kR1uZlY8nihQgwGmGzO0T7OBKR4WFylq1+SSkoEduFGowYmJYOp9uFvHILnvzsc5j0emTk54l1HvnZ3PIyPD/3O9gcdizZlSFzXeUYAMb36ivK5UCUuB2IT0yCzVqv/HHsjKHk26K0FAGjC9qHDB6Mfzz8IO6441Z06dxJXsizb382fvljrbyVy5D+3RASEiS2dyMgIBA///IL9mVmiR15d8jOKBzfVkzErsIWiiIrXcjjK5BpvVp2FMeHqTC6twav3iry2iEGiGazjPeC9cCslZGI6zQcal0AXE4XtIZQJPa+CoaQBGU/7PQ7zp9d6TCmGVqAHil1CDI4ORl6tboaoqgSicSok0tgbCwCEhOhMhrgFhkzTblSU1iImpwcOOrq5Zzk1K1LC3VZUNB9gZcHxBEW37U8tK1TVLaqc3JRmZ8Pp1O556LKYEBQQgIC4+KgERU5OdJZ+Qg7CZTpUqfO/v1ZWLFiJW6+8XrRiNHK9YQKX0KN3Nj4eGzYtAV68fuTptv4HqlRTVdg33zrrZj25LPYoGuHcd8XoO//ZeCu7/fj3dXF+HlfNX4Vy/fby/G/5YW49qt96P/+LvxrG3DWhCtx30P/QHR0lNwnX+l14ji+/o3j27ZRDLWGAHTudzPC44eK31qUlaKxJKIpf3tTQDiG9k/F//45Gv974S68+uxd6NerE9xOKxy2WrGdKEdVGrEjamiJElscAjwl9V+LRvnGj3TDZT940YckwuCyAvu+06Bqn1o8V8Fe5X2vGW6nG7YKkT/Ygbpi6lzWoL5YpMUm4XTWuxDeWYWQNCX9s1OHRrcEhEQjs8AJk0ElRyjZ7B5cNDgI798XhahQtaxXG3Uq3DspHLMej8KV5wYgLFAtT3JQUqTkTa+vEuvp/fvEdgadOB5E2jcHq/HG1AhcNzoE1fVKvqvVqlBQ7kJkdLIcKcdOvyqrDT9s2IvFu/LQNzkKD1wwEBf37Yj0aDP0Wo084U0nuyivlY+imUhTYHaJj8Llg7vhwfMHol1UCJbvzsfv2w/A5r14lp0+VIeKjopAZIQZvWOC8Z/2dYiNCIU7MEx2CGtUouz1buvR6OR6V2A4PAGhcInFLRa7WIYH2/B0RAHMRg2ixL6iaJ+RZsTGRCEgMMC7ByWbpkUn9mvWOJGms6G3oRaDDdXorq9HstaGcA1Pa92SKB3SfYmvvOQiPHT3bWifmiTqw04ZezrhTI+BgcoU0lS/Gj1mDJ599jkMGTJUvuerQx8NpXHa1m6zYfeO7cjfvxs9OqQgyhwm98tdiKcHdfQ2ngKaymKikfVn5X0KpzKKmNpHhz4ndPGPDz13Wisx94dv5L2qY2Jj8eijj+DJJ59AWFgYXv3vdEy77yFs3LxNpFgN3CojImMTERsbIw4KjvqJoPTaq2cPpMZHYdnGtfh481r8IIKTPsKGKyfU4B/X1mD9xlVYs2aFiJ1GxKUeXTq0h1avQ/euvbEnJxuosWHe1k04v2dfVFvrMXXCBYgKCkFZdRVmzP8FD371KewiD8irsDTktE7RRo4MDkH/tHYYmtYRfVPboU7tFnW7QFGvM8DpUGb4Yn8tymvpGCHBwUEYOvgsPPPvp3DftLvkBQZ0YbXNbse6TTvxx5qt6N4xBf16dIROqxWfo7qzFps2bsKPP/0s8grlokx25uD4tmIiblRjLreGIrc+TtSzvOtFDKgNpFN70CnOg2tHATPuBjon6aF2uOVsaPsLXJi5SLSlE4dDZ4pQ8mXxOYqf1mRG6llTYQhJVMpV7/HBTpMTSkJKjJLNVgSbKB3Kl21eq6sViuqx9xFwedxISohH9/4D0K5TR9hFZhsbH4duiYloJxq6KeFhSIuPR5KoKEeFigay+Ey1qJDXu91ICAsVlbo4pMTFIrnRQq+TRKPZITL1nPwCGKpr0DM5GSNExn/OgAHo368vkrt1k/cXkUeRb2EthhrCP8z5EUEhoejdu7fs4PEVnPRIjR1qRJWXlckCummhSq9pCQgw4ZJLJuPKu/6O+Y4UjP5wKx7+NQu7Sq1QGXVQBYhFTx0U4kPiP/I5LWJ9pRP4LqMcIz7egY+yDbj5rvswbMhg5QvYSeH4+jeOb9tDcWw89bTLe0sI6iyuKs9DZc4CuIp/hM6Zg7lz5+LhRx7Dv595Hj27xmP8YD3O7m5Hh8gcBHsyUFtZQh8UxwCNCOcpqf8qNLo3qo8bhjDvCh/x83vcauT8rkXNATWofz+8iwdJY5XGU3N8U0xH9BZpXfzfZhGNrO+0cDuoAeXdyEflQlQ/DzTGQ/MF1rJoFJpTHYnKWqVBRCertSKWq3dakVPixLM3RGBc/wA8flU4bhkXhPxSN975uRq3vV6GN36slvt45xfx+n9lePPHKuSJ928V2z19XTguGBCAF26KQGmVG39sqUegQTkZTt9TJb6v1hMBo9Eo98FOr+p6G/aXVCAtMhi3j+iDfmnxMHov2vozdH/Eszsl446RfaDXqbF45wHklB3lKhHW4ii7NOp1OGfoYNw/7Q488NjDqP/bm6i6/HHYeo44pMOWnnkMgbD3Px811z+Hyr9/jMr7PkDl/R+hbsLdqAmJwVm6CjycUIdLxw3Hk489iPvvuQMXnDcC8TGRch/0fVqxp576OtwZXIiXwrNxf3AexhstONdQhesCivBGZBb+EZLTMK01Ozk068OEsaNw983XYPS5Q+RoYapHU55Nnbg0IriyslKuN4g0SXkpzb4TGhqCO++8A/fffz/69u0rT0DTrViOBXUal5aUYOvGdfBYq9ClfTICg4M4nqeBr6NXaQ9RG0i+lBdrEd/7vjJUXqwlXlPnhHLh1uFRqrO5sXDJauzcscO7RqEVcR573ij8/d6/4fZbrsd111yOq6+8BFdOmYw+fftBreFpXU5Ep/T2CDRpMW/PRnh6ApdfWgdjoAbtIyqQItLp+vyzYEtpj19+/w379++VaZiqyx1SU+Byu9A3uQPqc/KwryAPOhGjnfl5sNRU44W53yGrrFhmxDTNtEvkA0adHiFGEwa374QpA4fghrOHY3K/QVieuQc9k1JhCApCmb0eCfFxsNXXef9C9legtCnTp1gio6Jw3uhReOD+e3HHHbfJTsR6a73MowuKy7BoxSZkFxTj3EE90KVDCn2kIf8tLS3DF19+LV/78gf21+P4tn70e9PU0wXWaFHIupSgiJUU1xCTG2d1VuPhKcC9U7TyNkxUy6XZ0sqtbrw8y4NqQy+YQqmT2QEVXfRFgfXQPeMd0OiDeErqVkSGXlS3tCoXwgNP7a1tW5NWeAmhkotqRWKsEk9v7t0bb1w0AY9PuBCO2FjcNm4cXr/tFrxx841489JL8MaFF+D/JozHW5dMwotim86REdhmteKmPn3Ee+PxplhmNFro9X0DBiB3w0Zc2bkT3v3bVEy/5Sa8dPkUvHzF5XjtyivwrtjvnWcNQo34ft+Vn6zlUAZtFTH69tvZGDp0iHztW+iepcnJySgtK5NXafm2b9zhRNuEh4fjvmnTkBPXH1fPzsUbawqQSydFdRp55T3l540X0vS1R9TkreL5R5uKccl3+Tj34ik4Z9hQ5U12wji+/o3j27aIyDaZelpUmFVqeQzs2fANIhy/4O83dsFTD1yG+++bhmuuuQY333gDEhLi5LTil00ag5uvnYB/3HslnnjgciSFlfiKeYFmDBH74ympTzt9kK9ztyEYEv32lXtFQ2mnEoPgVDdSJ3hgCPE0O1U1ofUaoxspF3hg7qac+KwrUMGyld6ULw8Sr8M6uBGc7H3NTomoyCjY1NGH3GuYqrP55S489pEFK3fY8MRVYTivjxHr9zpwwyvF+Oj3amQcsDc0eU0GNTKy7Zi5qAZXvVCM3zdaMbKXEf8Un6PPP/ZxObKKnXK/Pm4R/gprMNJSUrxr2OkUFxaCv48diBenDEePpGhoZXCOLT+lrWj08ZD0BPx78hD8bVQftItuejUJO9Xovnb7f/8e8bkboQ4Kgyc0CvptS2Ba/q13CwXFS11XAePCzxD09Qvihag/0ejjkEjosrZAU7Rf1Jfc6GnJQPof7yEgIhJ/LF2JL2bNQWbmgYaj4qIAC54Ky8FFgRVY4QjDU5VJeLUqAa9VxeGV6gTUuZWR6PU8tvik0e8YYQ7DlMkTkJSYAJuoR9EIYur4pc5hX+cvdRpTBzKtoxl45EWYGo18Th3Fo0aNRI+ePREdLfJ4UdduXMc+Evo87Z8uRogODUBMhJn+IO+77FSikcK+GDV9JBQGnZbSGVBtdcFSZZVLnZ1Oaivv0eLyUKdFDK6+6ko89dS/0KVrV/l5qo+vXb0azz73PB574t947fW38N4Hn+KjT7/Eh598gVdeexNzf/4VbiePRD0elDq0Wh06JIVhXsZ63HvjPtw/dCmys2tR69bjzZ9jYTDeguenPYde6WmoE2n5599+Ee1YoKCkDO3SUkR6D0dQWDhKKipRV1SGf33/Nf7YnYHvNqxBtqVUHgcOka4pb0gxR+HyQUNFGT4BfZNTkWspQ0JwGFbs2YV6hx2z162khIwSOBEh0i/9fTTrIju9KFZ0sTTFTm8wYPz54/DYIw/hhuuuQfu0NErcIv92weV0Y1PGXixetRkOpxPnDOylXKglkj7tg6YnppHo8xcswoGcHG9+/Od5OTu1OL5+QvzeR5p62qB14exuGrx8kwqTh+kQSBPfibdEaEV56sRbP3iweVcl9EbRBhLtWhW9QRvQPsTn6bVH5L3KlNRX8pTUp9sJ/NR0AZd8VHtg0Cnnqpj4PbyPrY7MSimTFg0outpdTSORxHOantpkNMIqGjwrM3Zg+fYMbD+QDbXYtl9qCp4YPRKxoiJFV1cG6PUyo99TWobssjLsLyzE/vwC7M7Nw7CePfDYNVchMToKFXV04/J9WLpvLzJLS+WVnPVOF4zir+AsvWUphadIpCKmO3bsgFGnRUJ8vFxHqGHcMb09CnJzZWPZhz5HmTCto3vx/P3Rx/Fhtg6P/ZyFfCfgokJd7tv7gWNAm9JCn91f78LFn+/F8PEXoX37dvJ9dvw4vv6N49u2UNwOnXpaWU/xzt/8Bh64Nhn/uP96OcVlbW0NigoLkJeXKy8IWLduPfbs3Suf05RsVVXVsm5nDtWjvioPLnuN2J834OKBp6Q+jcRPmzDKDR1NoOKLgZfL7kHFDtE2sovYmzyIH+aBxuA6bLum6H2Vyo3YszzQhdBFAED5bjr5efhIFlGdQ+J5LqgNR98nO3E9unVGSaVHjk7ykdNfirRL00hnFjhg0tPUakBEsBp9042QI53E/w065TNG8T6h9T3b6ZEer5Pb0+cs1U7ZmUH7U6bMVLZ1ilUHSjzo0rmzfM1OnWV7DmDetj3YX2yBy+NCWU0Nlu/Owp4iC3QaNWpsVizeuV9u41sWZWTiQKkF5bW1WLUvR66rqKmXZSmh6dpqbU5U1tpgEetL6T15zLiwp6BEbr9EfEe9KOt9I+NYy6AURFNM99LX4NnQTIToREZJ6Ur8zjTeoeFMR1PU5i3KhGnpN+KF2MZph3XIZHj0gUpmGxiMxGkvoXLLWqxcMF/e65L2KGpcOM9YjvvCChGocmJObRjeq4xAmUsHm0cFumykWDzfZTegwGOSHVXs5FE9uLpKGbFPF04Sqjv76tGN0TrKZ6urqxEQECC3rxHpvKSkFGtWr0ZUdDS69+guO5t9J7eb8u2DOqUHDRok1y1eslQpD9gp5yuCKTS+ew8Tek3vURzclN48ekTHxOHC88fgyX/+A/9+6nEMHTwIOlMIHDDA6qSZltQot5RgyeJFeOrxhzF16t9w4403YeKECbjpljvw7bffy9l7HNYqqN310KvsMGpcCNCJclresI0dL5oFq6rKjt0qI+76aiDWb3oIZ8XfjSCHQ5SLov4UFobuor7z6tS7oQ8JQUFZMX77+SeYTEZYyisw/NwhMIj0qRfpb3/GDlRZyuUIYkIDUpLNkeib0g53jTofE/v2R3p0LPaVFqPcWoetudn474KfsXr/Xny7fjUyS4rFceOGPSQAtXCK9lQIT0V9GlE9l+rUlIebRV583ujReOn5Z+QF0pGRkXCJA4Iu0KJcuLi0Ar8vXYuMPdlIjI3E6GF9ERYaKPdB6Z62oQt49mcdwNJly2WG0Fz+zU4fjq+fEAGgOu5hU083UEGvcSA21I6EeKt4LcplERqXU3xKxGhTpgqLN1nhqi9EwdZvYKsvkzVmiWJIAaa6NR0L4jVPSf0X4KTUYg7WSls534ko32OeqGzd+cMcTPv8C1z/xpv42/sfyJMXKaLhNDEpSVSU6+W22YWFmPjW27jr7Xdx2//ewB1i24e/+gpjBw6QmbhFNMDu/vFH/OPX3/DwvN9w/3c/4Nbvf8DnW7eJRjtV4PlobClNC0kaebh1ewZGDD9XXhVLKCadO3dCUUmJ3N63UMFNkR8woD8uuulO3D+/CK+tKoLToIXTLRrCJxEm+iztO6fWgZfWVODiy69W3mDHhePr3zi+bQuVn4dMPe10ynV0QtJhr5EnSWbN/gHT7n8Us76djS1btiA3Lx/FxSWwWMow6eKL8K+nnsEbb76J77+bLaenXrx4EdolheLy8yIwYYgOWtsu2WHMU1KfXjSyN6K7SHcO5YRVYx6HGrVFyu9uigYCk5U0fCyow9gU60ZYR2X72lw16ksP/yzd49gUCcQNcctprlnL69q1J/YUKFNuURqjtCTDKB4jQzW45fxgVNW5MXdNHeLC1Xjx5nBMuzgM0WEapMTq5D46xOvk6zvHh+J/d0Qg3qzG3LV18nPXjw5EYqTYTuxPScPUIeEBnQvfV+BAj+595D7YqfPqz+vx0OdLsXRPrnjlQXZZDZ75cS3+8+t67C2pEq+r8dycNXKbf36zAk/MWoEX5q7Fmv1FyCmvxoz5m+R7uRXVVMArOxX72VdSjn9+twL3ffEHFmw/AIeLLgzw4JV56+T2L/+0FvV2hzypxloG/ZI0HfTZ+ko8FpoLtW8EoCgXJTrbdYR8WOUR+biIhXHNT9BY8sRzNVwxqagdfT3UdhvsZ02Ex2BCx++fwS1BhTCplP2kaG24NaQEteLjFrcW39crI9UITcXnm3R6SX0I1tTxPchbUkhIiMwzKyoq5AV11BGs1+tFOlNOPhJ5MY5A01DTe7W1tXJ6apqu2mw2y8etot5VWFiEpORktEtLk+t8o5Pp8zTqmEYl0wjU+Ph4bNm6FZmZmfKCT3Z6UDh9SbfpOaWAwGCkpXfG9ddfhxefewrvvPM2nnv+OVxx5ZW49LLL8N/XXsWrLz+PKZdMxMQLx2LsmFG4aPJlorGlR67I4zdv3iKXiopKBAfqERJkkHk1fY1D1LN8S+N7HrPjU1dXK9Mk3c7BWm+XU9Hec/1VuEe0Z41aHTIO7Idb/L7Dzj4H5/fsCbXLg4zt27Bo0SLU1NZBZXdi9IhzRGxC0T+9C2py8+QI4rM7dsFl/QdjTPdeGNmlOxLCzbCJtDtz5VLsLynCnI1rYXXYkVlchIraGoSKtE0DYehv0Ys8oUjjQUhoEJwO2xHLBtYyZNtXLHQuIzEpCRdPnIBHHn4QN1x/DaKjokSca+QIb/E2bKJutCVjH/5YtQnllTXo0SkVg3p3kbcfoDgdrDap5D2pP5v5Oaqqqg7LG9jpw/H1L/RTHzb1tA8FyONCrd2IRVuA2b/uhNu2V8THTZPzyPfbxavRMckIjzYIjtp8ZK95H3WVOeItb7ca1bmJt8PYLdpIypTUt/CU1KzV8R7V/ocydHtkJNqlpiI+Lg7fb96K/KIiefKis9EAe73SeUFTBJSLiplHNLJUouFE72tiYxFE0y8JNPXLJks5DKKRHq3Tyh+suMwiKoT1nNRbCHUk0dRZyslK30lL5XHDxo3o26+vvFqatqErn0NFw7a0pFS+TwuhBm+aaAifffEVuOunHMzdXwWVVi07iloC7ccpjqmvd1qwopCv0jweHF//xvFte0Tkmkw97ZTr6Ip2a205arK/wfWXDcJ1114Dk0GDESPOldMinnuu8jhhwoW46aYbcfmUybhw/Hhcd/31uF4sV1xxBW668Tpcc9UluPaqSTi7X6IIns37rR5R4eYpqU81bQAQd86RRwrXl4n0Wqk8D+vobTwdRzql+xeHpisNKbfVg7pimqL68B5h6jCO7g8ExLZQJsAaBAUHISoqEVkFIj151xFfp27PNANSojTIyHHiqZnleObLSjla+KrhgfjiH1G4cIByv+EhXfX44uEoXDc6SN7L6bUfqvDEJ+VYscOOyFAtuibr5bHROK/3eFTILXEhLCpJ/h3s1KFZ1TxqURZ7r4qngYIqFb2mPFzhEa9pm3G9knHZoI64qE97pEeHyc/QQFF6j/hOZFE+r1HRCDbRPhJtpg1ZhSiqqsWBsgpsyi6W29PnKIXzya+W4YvVIH0VpoUWIFSt1Lck9bHeY1RErqIIQbNehig8RYXIgfpR18I6YDzqh1yCkC+fhb6iAOcHVOKe4DyxtQcDDDWIUDvksZPlCkCJSyfXN44qPV9iC8UqW/Ah69nJ2bhpkxxdTOcyqI5NI4t1Oh1CQ0PlOqpPU4duUFCQ7Eyme6DSI3Ua5+fnw1JRiUsvm4J27dujpLgYWVlZcn27du2Qmpoq90f76de3L9LFNpn79mHXrl3y3AY7/ajcbfxII4x1Wg2iIs3o0ikdGvE8ODhY3heTHDhwAIWFhfKYiE9IwMLFy/Dj3N+wZs061FRVYMjggeLYCJZTUxt0apn3U/lOC2s59Gs6nS5U1trROVKUm6INVFNdLS/guHbcOERrNPh1+1ZUVFWKurIak8aOQW2gqC+FhWPT+nXI2p+JfVnZ6JCahA5pyWiflIo0tQmpWiPsLifO79kHwSYTlu/ZiZkrl+DTFUuwqzAfy3bvQI139g4afUxpmY4d3/EjAg1noFFOb01/E838wVoelcK00O9O5zmuuPwyPPaPB3HJ5IsRFxsj2lHit6fzI+JIofiXlVdh0YqNyNiTJY4bNwb364ZuHVNFOlbqVAfryUpcv571HXbv2SvXsdOP4+uHRAwOm3paRvkgeuUWbaDKqhp8vqwD1mw/AJd1PVxOuyhLPYgOV+Hvk10ICo0VH9fCUVuAvI2foq4iGyp5UoSIvFgG/eCU1Bqekvr04Z+3xfhtZzEJEwnVRYkxNARRXTrLRhWx1NbKTmFCGXagqNjttNmxXzS8cuLjoRKNsT1FxaKu5UZMWBjmXnoJLurQAVWigV0iEjv9aHwMtpzrrrlaFLqx8jnFgxYqUKnyWyTiYCkvx4B+fWWDmTr+7Q6nXOfbngpq+vwt9z2If/yWh/VlNlEIy9Nj0DYJFL1uvFADqjF6SUvT7WgdfVedR43HF+fRpuwYcXz9G8e3baHYNp16Wpne0A1DQCDK81fhgrPj0blDCooKC+Vo4oztGcjYuQ/rNmzBkiVL8Ou8eZj97TdYtPgP8XqpHMmybdtWzP3pJ3z99Zf48svPxeNX+GPZGrFvnfyOBqL+zVNSnzp0D+LAeE2zncXUqetxikaUTfm9jVGHb9OYOCSapQ9RHqnbylFF8VNeN6XSuhDS3k1tLdaCEkR+qzFFoKRK6XTSaKgzl2Z+oK5AIDVGC71OhdU77bA5PPh+RTWmvlGGqjoX4iM00GmB/DKXSPOQr2vqXXjw/XJ8Or8KDpcHyzNsMOmBLila2bAmlP/6vqesyokaqwo9unSS77FTa1dBKX7fth+rM/NhtXtHojbRLYGmuozCwPax6BJ3cATp0Ri0amzPL0WupRq/bc+S5TZNb330XIEdLzoROVRfiSfCcxGodiu35Djuk/8it6WpTjOWwzT/E3j0RqhsVlRf+zQMO1ZAu2cdPBotXCKdjg+qwt+D89BdVw+6pTl9f45DC5u7+YyYLjk59LITdrJy8wuwYuUqFBUXyw5Byj9pqmmaYppGB9PIY6pj04gkutiSZu1xOh1Ys3YtVq5cierKCjjsNowbdz7Ov/BCBIjP1Is6OM3wQvW1IUOH4qyzzkKF+PzyFSvkaGPaHy20L/o+dvrQVNPKoqQxmoaY7ke8a/c+zPxiFv47/f/w+ptvyXhTbJYv+hUvPPtvEWM74kS7qluXjlQxQ01VOWZ/9yM+m/kVHNYamSo5lKdexr596GEMg0a0Z1esWS9j1L5dO4wND0Pu0mVYuWq1vJija9euCE5Oxp5SC7SiIjXvl58hUhs2ZezCWf37wS2OgZ5de6Fs134MTEjFGwvm4Zs1K7E2cw+25ByQHcjU1pIhFf9pnE6pLqe0hWg2F7GNQQ+7USs7pZwiL+ADoQWJ35nOI2tF3hwTG4NLL5mE5595CpdOnoyAwEDQdMT0cysLnedwY/e+bPy+dB3KK6sRHBSAEUN6ITUpVqb7pm1Yer1j924sW7ZMvub8+DTj+PolD9Qot4YcYerpg6iOHaSuRLSxFPUVhZjx+0CUlhXCVr8JTkcNPE432qWacNdEQB+kXMDlqCtB3qYvYKstFvFtVFem2HtHGFOe3TAldTBNSX3occFaUNMkRWlVeTjqwg7nt6fg9KIS1ivcjNjwcCRGRWPGuLGINptRVVuLuavXQq9XGl+JsbH49onH8MPUOzDntlvwzcUX4c5uXfHdzl1YIha6IX2nxAQ8cP44zL78MkwfNwZdkpM4gbegOhGTa6+7FpdNuQwhwcEiA6eTkEqnEz1ftGAhhg8/V14xnZyaAoulXDaOfWJionHX3+7EowtzsDBHue9lz0gjRqUEy0eaD1WU1YIHvaKMmNQxDJd3MeOcxCCE6w8mAZmfi+80adUYmhCIyzqH49JO4RgYGyDWcbxPFMfXv3F825ZDpp52OMTvrpy4phELhfsWItKYj/2Zmfhl3i9Yu34TRo4ai4wdu/HUc6/jpdc+RVZWNgKDgtCpcxdce8012LR5K77+ehb278+CjUYoq+neelqoNVpMPH8YwrAVtVWF4puVGCpTUjvgUfOU1KeCWu85Yuesx/XnVWm1Tg1DmLId9eVrA44eF7X3vrfNob+D7psMDVfhW9LAfr2xJ9+NevvBkUYHRxypRL1ZDZGNYmeOXdSBAXOwBoXlbmQVKa3rWctqcfVLJfjwt1r5uqjCgwKLCzHhOtgdQGa+MgVxVAiNejwYX/oOWTaI5xv2OtG/Ty/lDXZK/bYtC8/OWYkPl2xFRZ1vpoZDzViwGc/NWY2Pl2eg2qZMU3tUIogxwQFyGr3V+wqwcEcOusSHwzsQmbUgGqcwwFgLk3giqkONUtRxorQXEATTHzOhy94hVoid0RSl1jpf6SrTZq1I5ucFVKKnvlq+prK11qOGWzz6pp5mp1ZocBCsNhvWr1uHNatWydHB1OlD+advNh8aTUwdyXabFXv27MGK5StQkJ8PjVYrF+oULi+3IDoiEhMuuggDBgyUo6N279qFYrG/vLw8OaKYRkH60P6poyk2JgZ6UQdjpx7FKiWtA7r36odQc7ScCYlm4Xn8sYfkbXvCgo0wh9B9cSvhcNjlqPB9OcVYuHQNvp09W3b0t2/fDtW1SruKppqmKacPlunsVKO0qhZxSBJp6dvffoWltFSkPxXOHjIY5UmJ+L8ffpAd/Xp1GdTVVahr1w7rdmzH9i0b8M1334n6khq1dXUYOeQsebEGzRrwy6IFWLJzO3IspZQJyymmG6MmD+XNTds+vpe0vk7Ux9PapcEm8gye6ePk+X5vrchbKc1NuexSPPrIw7hk0sVy5D9dzCO2kDGgvJTQaNNla7di/dY9Iq26EBcdgSH9uiEyIhw0dXHT+NGFujSV8Y8//oTaGmWKc3Z6cHz9G9Wfi61mCsLBjLJZakSYKkWdi2ZZsyErtxqfL+8Ijz0P1ppFIj/dD5doS409y4iR/QKh0QWIz3jklNR5G7+QdS8lqt7Y0nd5O4xpSmqamS95wA3ikWfXOtXop9frRZrWe6DV/fmiF9vR5EsOl/dEGB0mx7r4qSOcEmz9UqOiMH3yxXjt4ol4c+KFGNutm+z4/WbpUmwsKmrIvENF5j+4Z0/079IZfUXlra+oVCWaw0F3ZHt60WK8+Nvv2JadDbuoBEaFhmJwhw54cewYXNazO0QWIk+IsZPz49yf8dZbb8spGh577BHZOKLGjyywRSNq4+YtMEdGylGJKaLSbSktlldo0vvUYKZpTN/NBL7aVCwSOo18cuO+sxMw8/Ju8hEuD4L1arw+vh3mXNcT713SBW9N6oQvr+yGH6/tjoHxJiWdiwK8X5wJi67rjK+v6o63J3XGO5M7Y/Y1PfDNFV3kqAl2/Di+/o3j23boRMW2Y7+bEB43RMTbDbWaplZSQaPVobxkJ/olF+JfD12N2267Fbffdhvuu/dvuPG6Kbjn7jvw+IO3oUNyMK644nKcc8656NWrtzwhdtagfvJe1WPHjsUF5487ZJl44Wg8/+SdiDNlimPGBbVGJ+rb4jvF4oH4fn0kYlLOkyOMucHVMtx26vz3vmiCOou1gTRCTXntqD68CknbpF4IJI52ov2lThGzw+PirPM+ETSicn40bpt438VptyX17NoZizcr043SaF86qUzpmFAyouf0Mi1Wh9vGh+KNuyLw1t1m9EgTLSihuBLYX2hHQbnSedwhXovpd5gx/U4z7pscjrQ45QCRncPiUatR9kmUq+yB5dut6NGjF4x8b8xTrltCBC7o3V60XxJh1DffAXRu50SM69kOZ7WPg0GU24ekSvHC99qXz1I048OCEBsahHlb92N/UQXO7pQImygXlEizlkKjdt+tjsWPteHQU3oVMTg41d3xoRET6uoKqKtKKfMVidQF28DxcJvjZOKn8AaJXX9ZG4UcV4A8SUAx5+vtTq+zt36P/kFuhISFobi0FJs2b8bajZtk3Zo6kmgEKnXyZmVnY9XqNdiXmSnPU/jyWTpJTegchdPlhMNmR1pqCi6eNAkDBg5UOpxFPZw6pnxo37TfsX274cEkDwwuvmVLS6KppRujspCmiX7qqafwxuuv4eWXnsdr//0PunbviVHnjUG3rt1kzCj9UQd+aVEB6uvqYbFYsHLVGgQYNHjjzXfkTD10b2saBecSZS6Vu9xRfHrRSDKLpQJRYeHYJ9LlW++/L9Mi3QoPVdVYcOAA7r5nEtavnQ6npRzudWtg1+pRUmfD7FnfYOnSJcjLK0D75ET06t5NTlO9becuOKtrD+kkpmPBl8abOqwNJDbz6LQwBQTBZq0T7x+hYs+OGf3GCQnxoo17Cx5+8AGcP24sIry32fLVow/GwYOde7OwcOVGFBSVyfc7tU+SHYmhIUG0s8NiSWV7YHAQFi9egh0Zuw6th7FTjuPr36gIjjZaRN74591fpdZQuDy0nQouawV+3xCBdXvDYdLb4LFuhq16PjQqG64dp0FUbKLyIcEUlkCFu/dVY95oi2NCrTHCWlUg6mDKLVHZKUBJT/zkS7ZqsGCjCgs3ao5pWbBBLZcKUzhU6UlQiTR9rIt3zg+/8+eppZWqt9uxo6QUB8rKkJFfgDlbt+KxH+bg/S3b4NTTfdSUTHyvqMBd/cxz+PunM3H/F1/J5YfNW+SV3AaxzTe792Dol1/j5k8+w1uL/0BpdY3IKPSY0L0HijRaKKddmq+4sWOjFhVhukL6089m4pNPP8OtN9+EswYNlIUzxaikpERe/ZyalopIs1lOy0XrqfHUp28fVMT1xHsrC6HWa2VDiQSL5xEBOvlI1/fc2Ccat/aLR3SgHrnlddiUU456hxvp4QbUUHtYxLqz2YBvr+qBASkR0IlGXEZhJf44UA6rQzS0nS5RWDQ/fR87Oo6vf+P4th0BQVGw2jwoytmIojxlKc7bhIID68W6HWjfPkXEVi1j6/ZOk0knI+12B+qtNqzbmo3/vfEWPvnkY9x19zTce++9+Ozzb/HV198hNzcXVrENLfX1VrnQc7qwwKi1IXvHz8jbuwyFWetRsH+dXPL3rUJxYQ4Mphj5XXRcsZNTX6SCo1r8liINNUVXRBtCNdAFKem0fCfgsh9aOaZtdEEuJAxXwRStvD6EeFmxS6l6ao2i4SzaWM565VhpijpEagtEDsDnuFpMdFQkouNSsCnTCr2W0urBEyABBjUSozSINys124cuDcbdE4LQJVEvO5Wr6ihdA7eODcT798fiH5eFyNcVNUqA0mN1uGVsAJ65Lky+jo/Uyv0FGdXiO2iN0jGtE/vadsAGozEAYaHeOcnZKTM4PQ63nNsbk/qmI9RIl8IqGqfw83uk4NIBHXBOh0TZ/mn8XpXVioq6Glhqa0S56+1AEhsEGHTonhgh2ltOJIQFoktCxKEfZC2mzqPCjKoYLLKGKun1GKehbpz7ekTZrLaJcnXABXDGpECXuUk0dAPgCotCzYR7oBbtZprR4/3aWHxdJ+IqT5RBjiaOUTugpwu0DtkjO1UCijPRZd50nFO6Be1CjNBr1CgvLcHChQuxc9cuUS9yYOXKVdi+davsJPZduE7HBi1UL/fl61Q/1ojP04nq2poa9O0/AFddfpmobyv5Nm1DncR90hLxn2HtcHvFCqRmrxfvcMHbkijN0sgmhQouGGAKCEZ6hw6IiIyU96Pu2LEjnnvmKeTm5MgZeHQifbpF2qfw0oxML7/8Mv7972dQWED3FXeL+lcNnnv2GXw3+1sEivKb/TUo9ZVZLIgT6ciQnIzps77FT3O+wp49q6Aur4DNrcLXi/bhpddzYSuvxMBOaRgnymNtXBysCQl4+8P3kZ2Xj2Vr1qNX106IjYrCmB4D4MotgL1WubpS6aQ6jgJWq0WQVoewkCB4RH7hu4CEnZj4hHjcdOMNIr09jVHDh8uLdqhxopSIFBd6JhK4yoOq6losXb1FjjZ10wV0lL92bY++PTvJvJiaV94suwHFly6w37t3H74Rxw9d5KPEnJ0OHF//R2VmuLEKiaYCaI46DZIbta4QFNRFijJbKVerKyvx9vxuKKnUidg6xWFQDUfFL6LtU4xrRnugD22HkIQBiO95Gdx2mzen9h4EdBKDYi2OE7porCJvHXLWvg+P6xhmcWInRvzctXYV7nhJgxue1otFd0zLjf/Wy2V3cm+ozusL1eg+MF17IQJunnzUJfDWS6Hv160h5P7Eb2uW2aWlmPLDHDwy5yc8+NNcPCYaWAtzchEeGAh1agpUoiFFbE4n5tbVYUNFBdYWF8sl1zstRL2oWCXpdBgmPlMktnl93Xp8tmqlyGDUCA0MQL/gYNjFdk0LBHZ86LemzoVA8Ttv3boNb8z4P0yedDHGjx8v36POhq3btiNWVKoDQ8NQVFwi14eGhODiK67Bu2sLUaVSN3Q0NRUojvIB8cEwiAx6d3E1xn66HSM+24XR72/CTd/tRm6FFUZRsl/TNwZxwQZY6uy454edGPHxDkz+YicmfLIVjy84oFySxI4bx9e/cXzbjipLFor2zUF9VR5qq8pQV2WBpWgP6vIXomNULr768gvMmPEmXnzpJTz3/At4/oUX8fJ/XsVvv/+Ol16ZgTBzPNLbt0NiYhJiY2Nw55134LNP3sX4C8bg3vsexH/+8x989dWXmDnzM/zjkUfw0ccfY/78+ejWtSNuv3ooEkKKYSneL767GFXl+aivKUN9ZYaoxO+Tfx8dV+zkWMtUKFot0u0RTjyqtW4EJSi/c22eGvWFNNL70EoQdRBTB/BhHcVis7pC6mRWtjfGUAPrCN+jEw2q3R5U7uMKVkuKiYpEQYUepZUumSUqI309mDohFP93dySeuzECUWEaOF1AcYUT/ze3Gs9+USnvWfz4xxUorxV5vVGFUb308rGizo1/zazEPf9nwSMfleP1H6twoEQ5KR5sUuPhKWZ89Pco8RguL7amDgxSLT5XXG1CakqSfM1OEZEnmgx6mGlqUpNe/P4ivVLng/dtep+Wp39Yjbs/XYT7v/wDczbt854gU9771/fLccO783Dze/Pw65b9Yh3Nq6T8j0Yix4cHY0yPNATRdLYyD/a1izjtthTqsK31aPBWVTSW2UKhF21T6WhX0lCC0x4cua9yOuAyJ6B2wt8Q+Ms7CP76eZGJl0PlcMDeeyTKR1yHT6oj8HlVOKxuNfY4lM9SFNsZnAhQHeW7WIsSKQg2hx3By77C0GXv4JyKDMQEGaHR6ZGxfTsWL1wIjVaN6Ogo2VEsO34p0YnFI57T0sD3vkibNA21Qa+HW62VJ7VJUnQEpvaMxyOmHKTtmA+nqNfRhQWsZd09WCVi6JttQ41BA3riuuuvk7Gqq6uT7SgSn5CAK6+6SnYe9+zZTc7c4xuVvHrNOqxbtx4GnVZmtVSe0gVYMts9BlTeN+dI69mxoZ/f5XTDZHchoKYOpe3a44HnnsCv82cBNhvScrIQLsrfnTXVsLZLRUy0EW9MfwiXDBkKbWQkautq8evc7+VtHXZlZqFTuzSEiPbyoMR0uPKL5UxOCoq1EuzD2zveGIpjQq3VQudwIcYQDLvdhvT0jvIe5txGOjGUzkaNGIGx542CWvzOdfV1cp38zeVPqvyuThGnffvz5GjTnIJima5MJgMG9e6MDu0SRUHuOnJaE/urqa7FpzO/kLM8sNOH49uGiDwwzFCJGGOJaAP78kPf40GUV1bYw1FQFyXqT2oRWhsyc+rx5rzeqLNp5aGhVdvhqtmEiX234IIBbhhCkpT6l+/YkTuiupf4v6iP0y3WynPXIX/TTHpDeZ+dMnoNEBqiRYiodx3vorLW00EAj120m+rr4aqtO+pySJ3bz/hta0CeChGJlZKqQTwmiMp2hEYjr6xFQCBE7VvZjjqNw8IQICrigeI5LRqxfbBoiCeEhGCpqMAfsFpFw9kNu9Ol7FegTMQqKnXcnGo51FCijqUDB7Lx4suv4Jyzh+Lsc8+V67LEuri4eAQGh4rCtlr+/ldfdQUWFbiw/ECVyJi9O2kGna+2e09aBxnFcWDUwGVzYm+lHT/vq0SdSN/BWhWGJoeKjF+FX3aX4Zsd5SK+HjjEe9stdmwvsYpj5ihfwv4Ux9e/cXz9H53mqirbi4qiP6ATRadalJsleRn498OX4N//vAv/efFp3HTTzbj7rrtw19/uwiWTL0FqSjImXHghOrWPxz/uuUSsuwhWmwN/LN+Mn376BRFmM668cgrS0pJRXFaLG264AbfcciuuuvIK9OnbF5MmTcKNN1yH66+9An+7/RrYaktEhVsHvSEIBh1V0CziDxOBZi2meL0aFfs8UNONa5vSehDeRbxn8IgKNFCwgk5OH2NNSKVB0Vo1HDU08klUvTrSiKbmRzvQdNY5v4s6m43TbUtKTIhDjkXkoSLJUD4s/i+Tz7rdNrwxpwK3Ty/GQ+9ZUFol4iLqwku32/DLulrszbPDqFOJNKdCrdWDOautqKx1I0CvQniQChnZdizfXo+Fm6yyE9pS7cY/Py7Hvf9Xgic/LceiTfXy+6gN7TuZsi0HSIiLk89ZywsQ7ZhAvQE6kV9SCUqdSkbRejbolZEP9DpAp0eQwYDKOgdKq+tRKhq9dQ6HnHbYJNpN9PmqOqc4HmworbGj1u6U8aPprA06NVIiQvHvSYNx1eCu0KtFO0psH6BVpitnLYtiWOrS4b8VsVjoVO7VLxNwUyKuqvpaWPteIJbz5DZ08sqjN6Hy5hehzdoGw6YFUBXtR9B3ryntYVs9bGNvwvqoPrCLChmVqJsdwfCI96j6laCux1kGUVdraAWzU0lFeSWNHhdp0FVVBvO6OTh3yVs4112I8LBQVFRVo6SkFDabXdaxQkPD4PSegFZOclO+rox4ohGFBpMJ7VJTZB25vKxU3j/VYDThqgFd8XRkOc4pWAVdeT4c1El8glOcs6P7cL0HpXVK+rE7nfhj6Wq89c6HuGPqPbj3nnvw4AMP4u/3/10ujz36GB6662b897U3UVtvQ41VqeNq1R7xWY/Ih90yXdJ6R6MqFD33NpcaWB2iLSTW19nF52zKc58jrWfHj251V19di3TRRkVgIPZEdsTs3/bBExeDAX10+Oi1ETBCDU9EBGITe4k2zzj884G/I06vAyIikZV1ACtWrBDxE/m1SLvREWYkJqYgRh8Aq0jvTVE6V+pwSsDVOpGPa5X8OsDqxKCAaOhtNIOLCSHBwXI2Al/ewI7fnDk/YuFi0fYV9SrvTy7R9N70u1pFXrxi3Tas2bILVivNwKJCpDkUwwf1RGJ8tNjmYN7cFMWM7lu9ZOlS7N61mzZsiCs7PTi+bQeFyWyoQJyxWLyg8xfNx40iVOEMQ05tjGgza6By1mBVhgkbs2LkMSJD6KmDy5qJ689Zjhj1GtRX5ou9efcnO4pFHi3qXTpdAKoLN6Nw6yy4HMptoNgpJEJgd6nlxXY+vpgdy9IUpe1mF+/7/uwYz/C1PiJp0n+8zw8uUuPI+rYRj773C5wunJWaig+vvRq/XzwRd/fqhbFpaXhs8CDcMHSI7BTJLivD5uoaaMWBwhl+y6Df0Zf4qCH7yn+nY8S55yI+Pg4W0biNiQyXoxfLyizo1KkjUrv0wL3zDsAmGsNHC0GtR4Vf91Wgot6B5DAT5t/cG29enI4RqcEI04kkID5s0muQEKJcRb+poBoOKtm9xwk10TjCJ4/j6984vm2DR/yiNMLYkv8brDUl6JpqQMeO6d6LBdRy6qaAABOCggIQHh4mGlX1yMzcj3btUtC7dw98/sUsPPHKr/CEno23P1uMURfeghdfehWxMdHo2a09fvrpJzmtOd3j2mm3i/264HI5YbNZcdZZZ6F9olGE1g29phJOR6H4g5STaKzlUAdt4TI1nNZmUo5IW8HtXAhOoZNUoiG1U4X934t6kJNOMIv07+0I9JGv5SoNcn8HSjdQmgVM0R5E9qYN6L1DUSd1wTINbJZm3mQnxeFwoleaFiYD5dUinDKvVGHptnr8scUGuxPIKXHg57VWRIdqcN2oIOhEuqY4nt3dgCCjCvM32fDAO6WYvaIeRr0KZ3U2yDBqNSpMOScQ8WY1Vu60Y2+BSL9i9+v32vDHVppKke5TL1aIA4c6nTvHa1Bdc/hJUNYyPr9zAlY9eS2uOKuLaKto0TMpFt/fMxmf3nYheiVHo2tiFH647xKsfOJauax68josevgq3HxOb3RJiMY7N50vP+9blj52Fa4e0h09k2Mx997L8MJlI5ESZUZ6bBTMQYFIj4mU231998WICAqSHdKsZYnUgxqPBq+VmbGxXJSNtIJQPUqkY8qTZZtUo0PdmBtkpzF18NIH6wdfBFdUEoK/fFaW4x5DAIwb50OzZ534rHJSZfiIs6HVK3WpDTZR36oJhl5k2Wqx/Z3BBeigs4LuaC2+7ZCFcLhbDp1c9IgYyo4j8ctSfDxVpYie/x5GZc5HtzADTCIulZWV2LsvU44WTkpKgpY6hyn+3ro4xSQqMgIx4aHIzDqAElH/pimnE9QOPNNeg6sKFyEof6coCFxKXVk5gOTfwFrW+Z1UCDHIs1NICVfhloEqXNe9BoGuUqxctxmFO5Yia8tSmMuWomz3UmzZvgtjUmtw12CgT7wSG63Kg6tEvemus4BeccCk7ip0jFTqYlSej+2oQn8a4CZe211Ah0hgZLoKvcXn7xhEiwe9xeeo3O8UBQxMAm4ZANzc3yPaV8rn2Imh9GYR6THVFAy1zQZPVDRsEy+Gp2MXRMWMxKjRb+L9555BYFUNckotounikdOOXzlkKFBegaS0dCyaPw87du5GeVW1SM+JCA4ORpeU9kB2AVy1SgcDfY8PPdeoNfL2AXqnGykONYaZojEiKhUeuvBLLFGx8SgsLPB+gp0ISnvVNTX49LPPsWTpMugNepGOKd2JclS8l51XhHmL1+BAXrG8CI/aw4mxkRg2oAeCAgMoBz8kbo3R5+k9mnVtwcLFcIrPynyYnTYc3zaGYiJiFm6sPHRK6ubiIuJVQ1NS10fArTIgMsKEtCiLrFtRyNWiTCZq1CPcRINhXEoHpbejmDqj1WotKvLWImfte3A5qC3M8T8txM+sCzrFt7o6Qrr3J6Kp0dooQWkIjfeJL1a+f5DSRDoCmUkoZObeJM0GiEZaiNGAOlHZG9ChA24bPRKPTBiP6885GyEBAcgqKcUHq9cgSXyQpqFmLYNi4StUSWmJKFgXzMcN118nr56maWqpI4IK9EEDBuC5VSVwOui+qHLzo5qTUYqHf92LP7LKYdSqcefARPxyfS98OCkdacFaaMW6AJqvQCip5elBTgWOr3/j+LYNvkZOVVkWLHl/oGNqODQaLYyizDSZAhAgykhaTCYTiouLsVQ0vF5/4w1kHcjBC//9EJ/OzUV6z7Hy5EZgaALcujh89M067M3Mw+jRo5GYkIj583/HKlHG/j5/AVasWIn5Cxbhm1k/iP28hcJSK7SaKricJfLvYKdGzQE1yndSjerwBKo1qpByvhuB8UpHvWW7Grs/V6Nsmxr2ajVc3tuaEnpdnqHBvq/VKFqlpFFjhAepE93QmkSjuenZSfF1NFV18bpjyBjYcVu7YRPyM9fg7omh1AaW+S+dODbq1WJRnpO3f67E2j12jO5txJ0TQpAYqUWv9gYUVbjxjniPpqD+ZH41Mgtd6JuuR7tYLa4cHoSLzgpERrYT07+vkKOXaX800pj2T8/p+5xi/Z3jRQOuZqv4e7bI72OnB01Huz4rHx8v2yLaMpVKx9Ixoi3pJFlGXine/WOTeCw+PP2yU45yxnq1AXk6s0hPInGJxRUcCVdEEtxmUaaaE1E3/Eo4o5OhsluhcjvhTO2J+vNuRMC896Cx5MOjppNZHtCd1VYuXgy3Wpl5a/Dgs9Cvby+4xXqXR4W3qmPwa30oaB6tAPFVT4Vl4/KAEnTT1yNK7UC8xo7uBhvGmcoRrnE2U1qw4yJiQHXkA8HJ0JsCofbe086XTilOus2L0GfxWxhRvB5pIihGUdeiziBLWRnCwsMQFREBnU6P8LAwREZFweZwIis3X84EQ9ufa81C7xUfw7BhHuqpU0tnaNg/XeCh1+uRGd4epTXKNHysZQxLAYJEGRug82BUOw/W5riwo8iJ+4e60c7swQUdPLh7sCgWrW6U1Xrwt6F61NncWLTHhfM7An3j3Lh9II0G9mDRPjcq6j0waYFuMSpRB1dBr/bgrCQ3xqZTPi/iqFXhmt40ilmZWGlVlgvrsl0YJd43wI5zxN8zsQuwuUCF7QUuXNkLCDN6/1h23KhtVG+1Qm9zoR1VdamCRfclFenJbjDKsvO8USMxrmN7rF23VqTZQnkR3hWXXor67j1QoTegR9fu+PH7b2GxWJCXX4jE+BhEhkegf/suqMrOEXVrEUy1yIi9F1nrxVckawLQ3xSO4aZIpOsC4KmqQpHID1xuF6Jj41FUkI/snGzRTqYRk5yeTxTFz1pfj/fe/wizZ38Pl8uF6rp6rN2yCys3bEed1SYvrKSZV7p2SMVZ/bvBQKPGBd95kWaJ92gGiM8+/wLFJSWiTiWCyk47jm8bJPLDQ6akPiyOSn5J2aYdoTDHxOGusRuQGFEvN6VI0uO+ohC8/GM/7LWNhj4wGi6HzRt38aDRoipvTaOpp49yrLCW4y3qdKIeLdtJwmHhZcdE+fVaEW/s4RQpN0hE/Y+du/HlshX4bctW0XDVYO2evfhquXi9fRtSRYbuaFIxogZRJ6MR8zduxGdz5+KnVavE64MNJfpvuEaDD7duw4Qvv8bbCxZiweYtWL1rN37ftBmvzfsV47/9Dhty85TkTp/jo++k0CgyWhp3NBF6vnLlKnnF9MgR58opt9z2evk6tH03zNteBo1BuW+PD523avya0C4dHhXe3VSKy2ZmYNSHW/DGqmzoxPFxfscI3D00Ac56B+rpMlwhKvDglAXs5HF8/RvHt+2hxo7b44bGVYiS4hy8++57+OXnn7Fm9SqsW7sGv//+O15++RV89933eO65Z/HiC8/BozJgU2YAQqI6iRg7xSL24aaLBVQIDInG9iwPHnv8KcTFx2HSpMnyOJj7ywK88t+38PXXXyP7QCZqqiswenhPaDxl4n2eM+9Uog5fGgXscR5eTaQpovUhHqRfDgS3UxJsTY4KOb9osOtDNfZ+qUPmd1rs/kwrX2f9pEbFHiVvCIz3oMOVHpii3XI/TalF/at0o6i71RzMS1jLqayqxitvvI3uUVk4r69Jns+kkx+N8296oKkuH/3Qgr0FTlw1PBBv3R2B1GgNPvqtGpszlWkxd+fZ8caPoqEdpsGbf4vAnRcGo7TKjSc+qYCl2gWtOHQoHdN+af/ye8T+R/UxYXgnC1747xsor6iQ38lOj2qrHct25+H9Jdvw8fLN2FdUBqfL6X336BwOF7blFuPdxRvwxaqdWJNZCBvd3JqdVqGhIbj79hsxqF9PURa7RL3KjvpzL0fl/R+g4oGPUPH3D1E74W6obfVwh8Wi8pZXUX2DKIMNgbAOmoj6sy6GyuWQHZHfa9IRPHgcBVe2g2n68RuvvgzT7rwJqanJqPWo8XpVPF6vTsB+pxEJWgduDy/DSxG5eDv6AN6MzsbdwfmiTa5Cjds3xpidKPr9qqpr8UK+CT8kjYYnoROMvqKQ8lHx4NHp4a6pQODWhThr1Sc4r3I7EmJjZEdVaWkZampqkBwfC7s4LoqLilFdVQVzuBmDtbUYuO5LxKyZDZRkw6XSKPHy5ft2K3TmOCxMH48XS8KQU1LO8WxhdNGs1aHCN9tU2F6swYJMNTbmAz2jRH1I/G9rkQfzRF0pJkiFxFBgRa4WB2p0WJUL9IjXosqmQmq4qGtVqrG/XLSxsj3oFi3KcFGfbhehwq5SNcJNQHSQWAJFehZl8IoDHmwsUEGr0yBRlNXhRiDIpJMjj5dlARniM9tKNKi0AubAVnda8IxCJ6XzRVu3S0CIkpgpbYl6D92eqa6uXs6udeEFF8CWloqvZn8v60VdOndEXJQZ6+prEZmYiIioKCxZtgw2kX4t5RUICQpCSmIyuop2Um1xCew2GyLsHpwbGo+Lkzqio04Eu6xCXpxbb3PAYAqAOTIaUTGxcsr5X3+eC71YpzcYZV2MnTgqI+0OO76bMweffPYFFi3fiH1ZuXLGHPplqfO/f89O6NYpVU75Tz/30X5yir9Wo8W8X37Fli1blQoz+8twfNseit+hU1I3pgSXbv/QLsaDaWM346yOxXC6VfKCLJpN69dNyXjym7NxQH0VwpLPhkajoxMZcqYfjS4QNYWbkL/1m0ZTT/MxcLpQ9GhgitYYIF9z8jsxra5WSIGn++/IDFgkxG8yM/HwylX4cMdOGEXGPTcrCw+sWIn3d+wCXSDp6wT2oeq4WavFZ5u34PavZuGNlavl66bX+ZjVagTW1+N1sd2lv/6G8+f8hEt/m49PxX7TROOc7mvs46G/x/ucHb9bbroR5503GjHR0dCKWBCKLy1OpxMff/IZxowZI0eq1VRXoVevXlhXZUBh/eEnuMQh0GzBTfGlCz0tDjfW5Nfi7m/3iGOgADrxfT1iglAv1udU2uS2I9ubYRItLNqeTpzIfcp32Ing+Po3jm8bJIKkE7/xbTfdgCeffAJnDxsiR7Tk5eXhQHY2AgMCkZNfipiYaMTGxMDldCEz1wpDUIw8ud20wKSOY7XWhJzyMDz3wn/x7axvUFCQj1tuvAqzZ32Kz2d+Jr/nn//8J1549mnces0UUXnnk1qnGnUAF28Q6chEqelQNKJQF+RC+iVuRPV3Qxcs0qlI0rZKlfxceYYa1QfU8rXHqYI2wIOwzm60u8wFQ7ioM9GVIU1QQ7wmz42ybeL7uFJ1ylTX1OLFV/+LK4ZYkRJD97jzduSKR9/JRJpqje5b/ND7FuzMcSA2nO5z7MbFQwLx4s2RuG9yGF4Sj7efHwKHaDXHiPcPFLnwjw/Lsb/QLj8v9+PdL6Hvof1M7m/FC6+8iorKKrmenT5hgUZc0Ls9+qZE45fN+zFjwQYs25ODijq64EuZHu+Qhf4n1pdW1WJ+Rib+99saLNuVj34psRjVNRVGuk8iO63qqisxe87PeOSplzDtH0/hHw8+gv0PX4bI56cg7PkrEP785TA/OxnmpybCsOlXBM35H8L+cy3Mz0xC6P/dDX3uDtg0erxam4JPSk1477NvxH7+hXvFvu55+F945F8v4PNZP6CyolLUvVSwekTbujYEd1ra4W+laXi+PAHv1MTgveoYPFySgKli3UJrKOweLpNbSklVHT7dXoTHVH2wJX0UEBSm3MdYLCJByrgQd0UxApd+hZErP8CAACdCRKW33m5HaWU1amrrYNJr0cvowPk7vkPCnP9AVZSlXOzn/bxvn+rAUGR3HoUnAs7GDFG3LqysPez8CTt5Oo1K/K5ArZ1ONNMaFeocgEGvEWWvGsXVKnnvYIPIVlPDPZg2xI1/nOvByHZAldWD99d5kFsJPDlahT4JaljqANH8gV7lwKBEYHexAztL3BiS5EFnsxMilDCqHXj4HA8u6Eiz5tEtIJS6Fv0ddFGYi856i7+DHpprf7FjR2VmZVUVIkwBCHGLwMo0pMLWklIUFhfL+lX3lDSEhIXjtXVrsGP3bjkzU6RY6kW76Z1dO0RQMrF761Lk52bDKYJCIx5V4n/dOnRGcrUDY4PjMS4xHSa7A7t27EJtbQ3CzVGIiIpBSEgoNKJMLszPxby5P2HO99/J+86HhEfKkcXs5FGMqU27Zt0GeQEAdTBRPclkMmDkkD5IS4qV6ehYOuZpm6wD2fh53q/yWOEc96/H8W1jRFzoMrzDpqQm4j3RiEV8qA2TBmTinB75qLepxCoPnB4tvl6aivdWjIUx9UqExvaUx47scBaPNPV0Zd5aZK99D27ZUczRP93oF1drtdAF0lTUysUd7Pi1upYdBd5aVgZbpagtCzHiIOhiMiJOr5evqeO3m8mEBO/r5rjFwRJtMKBHWCiSxGepmtyUSyR0WpLEdoMDAjA0UFmi9To5WtmX5G2iUlgn/h524vIL8pGYmIiLL5qAu/92By6/4nJ07dZNVKA1MuPds3cvtm7Zgg4dO8IuKsf9Bg/FiqwKuDVqeZVuY/RKfOQQ9LpHtAlj00IQa1IjQKdCx9gAdItSrjTZVlyDcpsL8/daYBcV86FJIXhwaAJSg7RIDNRgXPtQjBCfpZuD+U6AsmPH8fVvHN+2ySjK2MR4ajSpkNauHQYPHoKLJ03G5MmTcfY552Di+FHyXsY0ReLK1athsYZ4T1Y0CXAjal0AVm8uRkFRKR555B9o3749yspK5T2wfUtdXS3S0lJgpLNp7JQrXK5BfYloTNE8hk1Qh6/G5EbyOCc6Xu1C/Ag3zN3cCEpWRg7TY3hXN2KHutD+MjfaX+qEIVTUn5qkex+3S4WCJRo46ZY+7JQqKirGN998in9cakCgQSVPGhPZ2PU+Un55oMiJ/86uQr3dg82ZTnmP4gsHmXDz2CD5GBakluvp/f/7uQpbMq2HdBD7HinkNC3mw5casXj+t9i3P0uuZ6ebCmEmA0LE4vaosHBHDl6auwavz1+LHzbuxsYDhcgqrUB+eTX2l5Rj7f48zFq7A6/9tg4v/7IWW3MtsqPB4RIxdziOkpuzU8UuGrFZ2bmibLTIJbe8BjPKI7C5XOSvdSI+laVQV4mltgJqaw3UxQcA8ZrW66qKUVdSiLerYrDAGgqHqJPlFxQ17Mu3lJSUwVKhtLOVrgrle7c5AvBLXQh+qA7Bz7XB2O00wSFPtXHdqiXJ7juR/+7PzsNzu+vxdtAg7I7vD21IeMOJG8pbKf3RNNKuAxlIX/g2Rh9YhO4GO0KCApEWrMc5ZZvRdcWnwM41otIWIOrdNJuPkmqpSNeJdXlJfTEzdDD+lQlkZGbLSDZ3XoSdPJpC2qTzIDHEA4fTLQc/RIimTGU9DYJQfnXRNEKVDcipVOHVpW48vQB4coEK326jd1X4eosHH22g+xa7RZqkEcZA/2Q9ogKBrAo1ft2rRp8EFTpEa7EmF+gSb5Adzf9Z6sHn6+0orRXNoSYzQniLf9YCVGoNqsorkag30gvRsBH12sgIvPH55zJNx0ZFiPaLHnkiLd738Sf4bu7n0IXnwGx0o1MnAy66ogDWUDW++fpLWOvqZIexXq/D3t07USXy5trKKhTTjCzie8IiIkX7qz3M4eHIy8vB0iWL8dN3s/Hzz3NFO6oIoZExYpto8XkDZSjev5CdLOrAr6utRnGxctG60ajH0P7dEWUOlfE6lnMNtA86B/LzL7+iurqa61JnEI5vGyTqRYdMSS1Qfh0d4sKYPuW4+py9IqgeiKwb+ZYgPP9tP3y+9XKEpI6BISQWTifd1IXqwnS+RJl6OldOPS335H1kfwV9YDC0JlFB8uKi8Pi0us5iii9VrutKSuCmabPEa+rU9V0B2/T1YcRquhc5ve+gjJxee99qDm1HU177lsb7dTudqKf7D4hHduIWLvoDX3zxJd5+93188NGnyM3JwaUXT8RT/3oCPXp0lw3bxUuWwiRy6ISkJKjDYrA6v0bGrimKZdNMIFBk+ncOiMUnU7pi0c29Mf/qTph3Yy/0iQ9FaZ0dH2wshUOnwSdbSrCjoAIhRh0eOjcF82/pg0Vi+fTSLnhudKqs8Psa2ezYcXz9G8e3baIOIbpfj06nQ3291TsdeZ18dDjsKC8vR0x0DGw2G35buBERsV1E2f0nZaWIj85kxtIVW2C1WpGcnIRVq1YhKChIjlqne+nR/ZBpxBONZmSnHnXcZv+qPmIHr5xKWvzfFOVGwtlutL8E6HSNG11uVB7bXwokDHcjOFnEizZtLuELap0aFTtUqNjT6qqlrRLVZZcsW4H1K77DK7dGyI5cJQdW+LJKvRa4dnQA/thqw9Q3SnDjqyW4bXoJbv9fqXy89uUS3Pl6Kb5dbsU1IwJlZ7Lvs9TQ9j2K7AKv3mrGgYzf8cv8hfJ+YOz0s4t206KMA1i5rwAGrQY3DO2GJHMQvlu/B//7bSNe/mUNnp+7Cs/NXYkXflqF//y8Bv+bvxG/Z+xH76RITB3VR2wfgvVZxfhp0z6U1/qmVmN/FUpuuS4Dni5PwHpboCiTtfCoNbIy5VaJR40yalElylAaZfZqdSJ+rQ+Hw3Ps00b7tqNHWqgD2bf41rOWRfVVWmwOF5bmVOI/hSa8F3426tr1g8atzGhGv76MCd2P1G6Dds86dFv6EfrvW4Q+y95H+KZf4a4ub+gk9m2vdTngTOiML5PG4dmCQPySQxcE0e1BDl4Mz1oejSy2u1S4qpcK/RJUuKSnGh0jgC1FGgTolboPzdqzoxiod4iyt48KSWHAmPYeOS1173hgYJIKqWId3b6WCttdxR7c2N+DcitQadcg0wKIZhDamYH8SnH8OFVyWuq0CBUu72NAjxiqv6tlmSyaTtBq1PB+NWsBVN8pK7Ug2WCCjmZAEjESDRd8um0b3v/4M5TX1MFWZ4PLrYLFtQ16w2N4etI2DE4pxb5iA55ZPAIBSQNgCgrETz/MwtZN6/Djd19jy9qlGNC3Ly4aMxLXXjYZUyZNQFhICOb9NAfvvfM2/li4CPsPZMMBDSJjExEeGYvA4FBodXpZFrCWRbNKFublyIsD1mYX4f25S1BeXQ+jvD/10XNRymdNAYFYv34j1q5bJzsWaR07c3B82x7KJhtPSR1sAgak1+OhiRsQEWwVsQdW7zTj6e9GIRNXIbbzKOgMoeJYETVhEV8Plam6gIapp90OUShLHPu/jPjpaXRxdEKsiCm1hSh9cpF4PFpf9dDpEpVcUbsVGa/Tbv/roi2+1yW+n6bUpClkVHSVJhcEJ4Qq1r6Frr6aP38B/vmvpzF79ne49uorcfttt6KyshK5eXkIFBXuZfn1KKoWragjhV6sL661I7vCKh8pVnvL6pBVViMaY1qkRYVCr9VgQ341rvpmBzYV1Mrvzq2w4/yZOzBzcyGKah0wiEadQTTaymptWCMa6hrxmiN8/Di+/o3j2zbRvbEWLl4iGkLr5XTjSuVLLZe8/EJ8+/1vqKiswfIVq7E7x4GAoAjx+1MEjhR4hVqjx55cOx7757/Fft34+LMvsHrNamQdyMK+zH1YuGgxPhTrrDZxbHCZe1pUZ6lRvV/Uc0QaOhLqTHY5aKSMU4TYBZVWefSI1/TekTqbCY1UtFlEGl5AJ9a8K9kp5xDp9pMvZqFw3x+4bVyATE90EQjlp778sl9HE/q0M+C7FbWwOTyornNjd64D63bZ5WN5tUt2PM9aUo32cXqM6KXM+ED7oRMqtC86SXLViEDYy7fgzXc/kvfSZH+NHEsVluzORXmNFRf2SsNVQ7rj4QuG4KUpIzC+VwrCAvTILhXb7MpFZkklIoMDMGVgR7w8ZTjuHTsQk/p1wqUDOsq8fKnYZk9huXfP7K9W6dHi3+WJ2GALpMJYpt+G7mDxus6twlPlSVhmD4VTlMOc1bYelMfWWO34dW8xHiuLwfKuF8ETHi/jq6IRqSK+dPkc3ScPtjoE5e2CrrxAfk6u9y50b1uVMRibuozHYzVpmJWRj4p6B+gWA6zlWUU1aK938rnFWUBpHTBvjwdvrhZlawKQGKrGi0tovQpL9wP5NSJOotwUTRc8swiosKpwaTe6l7Ay2jgu2IOzU4GkUA+eXEj1beBApQqrspWRxyLcMtDz90IcKyo4PCpkWdxYtF+FCzoCdeLvee4PoNymwh4LjV6m02puObJ4a7EKNcrdfNhJot80UqNBgO88pahr1Zkj8Ni8OXjjg8tx56StSAuqhNnowo/72uOydychIz8Zl7TriL/3PRdX9OyDtMRkWMot+HHuj6L9XIh+Z50Lm8uFL3/4Ca++8YacZWnbpg1Ys24dbE4XgiOiERETj6DQcOiNJtGWkvOcs1OEklpBfjbq62pkZ9GSrEJMe3kGFv8hErQIOdV7m+0gFKvoPHZhQSE++XQmHDRDS3Pbsb8Ux7cNEnGimnG4oQKpgXnolWzFIxPXID7SCrtTg2+WJeOfcybBZr4YwbE95HGhHCm0iHazWg3LgaU89fQZyOoxICQhFSp58awMtXxkf04lMrBjPpIHjhzvffbX8dD9scyh8ISFQGcOhzE0FFojTfVyjBGnjEAk5uq8PNirq6ELDEJIUuLxZeRiW6fVKqegdpSVQ1VRBY+lEipRIPzV1iyc6312/P6q+LaLCRPhOxg/GQvx2iEq2WFhYbhw/AXokN5eXhnSuXNnXPDxZvy2twIemqvpCHGLCtAgXK9Bud2FkjoXREsYoSYtUkMN8n6m9U43skTLq9Iq3mty7GjEPpNC9Ygx6uBRq0TjzYaCatGYFttRJmOoL0ePNW95tz69OL4Kju/hOL4Kju9pIn5nl9MBk0GHpPhYGE1G6Gl0i/hfcVk96tWdYFTXQqMzwGNKh0Zr8H6OKtcqVFpyUZyzWTSqDp1OWimLxeK2IchQBqtdVLjdLpjE/kldvVUcS3pZjp9p/Cq+TYR2cqPDFAofDWdpWRTLgmVq5C9Vw9Pyu28x/hrfsNAQPHDfg5i3MxW/baiDQaechKak2ClRh3H9TXjtO2VKWl8ncFN0veQt54dgS6YNG/baGraj6a2HdTPhkj75eOP//ouCgiLvJ848rTG+x/s3L92dixd/WoHOceG4c2QfpEVHyPJbTrtnc6BS5K+z1+/Cu39sxYW92+GGYd2REB6CAL3Ic+VpFA9Kqqrx2Ypt+GbdHjx0/lmY1K/jIXWAP9NafqvGzvT8ubFEjQ33hRair6EWNpEG6RqfWrcaM6piscga5rcdxW0hvjKdUcYsHvskhGOsthS9qvZAU1sBB62mbUQ9zJCQDvuBDDnym2KtcjmgiojHnsBkzHNGYW1hjTyJTfs5rvMff6HWGF9tbYn3mXJvYVG0ykfqDKZ7ExPR1GkWRcW3DYXd91lfuGhUsC/X9e3bhzahYtp3fR9N6ELntWk/omiWi++zPrQNrXcFRnnXnF7+ln5TExOQJ9qtK+vpvqfKuv7x5bh20E7kl4fim83tYLDGICk4DO3NekS71LJNZbXa5MyH1BlMsynRPaXNoi2dW5CPfbv3oLqmBpddOlmOcHz7nbdFW9qAkIgo2UF1POXw6eZv8aXfmvLO3gOHIdcTgLIDu6Aqy4Fep0evnj0w8cLxSE5JlhfQHhIXkc7sosL81jvvYv36DcqqVpIHHw3H14vje5jWUr9qzBQcgVvG5mLKsCwUlxvw6bKeWJ4zBOHJw6A1hIq6FV2op8SVLtKTHcXZq1C8/TuRj7e+GZf8Pr7eJGqvq0FdST7coqz1kVFskkRDb54Cfef28Ngd0EfHQE0zdBwBpW9NYABss36Hc0PGYfs6E5xMfM+8s61/QuUQmXKxBerMHDizc1GbX4Da4mI5JbTIrSli3i1bGO2XCg5RKNAU2LUFhXBk5wH7c4DisjOio7i1okKWphj1LTStKd0jgqYbpSlMv/76Gyz6YymSUlJRXWvFkkIRa2MAVHojVAZTs0upU489dRr5KNcFBKJKZcCWSmB1mVs+VsEAFe2nyWfdYt0BqxZryt1Ya3Ejz66T6+g9aI+cWbDmcXz9G8e3jRJx12h18j6G+3KLsH13FjZk7MbGjL0oLCmCy7oX+rCeUAfT/avF704jWpQPyv8eCZXgWp1JHD8q2MVn1BqtqKQZYHNBLtT5fCZ2FPu7qn1qVOxyQ2Ns+dEK9koPyjaf2R3F/qyyqhqff/kp/jbOjW4pIq053LKjlxpAO7IdePXbKrGVkm59o4UbL0SrUeHj36uwYa8yapi2o/t1xYRp8eDFKnz33ZcoLCyW77G/Trf4cLxw2TA8cuFgJEeFKzEU6+lER7DJgPBAI4xaJY1rxXt0b+NAgx50jzYKNT1GBgfh1uF98M7152FwelzDMcDODL4pqXfaROxEKKn5+p+KeL/uKG4rKE+m+NHjprxy/C/fgDfNw1HceQQMNJKQgt2YeE3rHe3740PzuXi5wISVuRVyOnrfftip49IHw2UQi3hUmw4+usWjRjzSIrfxbefdlha3eC63CRCfMSrr6JFe03raxyH7bvJZ0GvvQs99+6HnjT/re5TrxcJaxoG8XAwwR+Lc6CjZU+9xOLEtLxCv/DoQ6zNG4JLEs3B5ajy6a0XDpqgMldXVqKqthzk2Dr179UJSanu4nR5kZGQgOjwEJcXFsNkduPv2W0RbW4d33nsPVqcbgSFhZ3xHsT/LydoHe8E+eEpz4RZxtttsWL1mLf47/XX8Pn8h9HRewluflrmtqGutXbsWW7duk3UvzoPPbBzftsetj8TY/hW4ZPABrN0dgke+GoN1lkmI7DAWGkOIiKky7TRUatlRTFNPV+SubbUdxW2CNxnqA4IQEp8Grd6krBAonfqKT3rkovRQrW5k8WGCRCYdZYYmLBSG8HDog4OPfiJZ/HNPZGSxx+2Go6YG1nILXJXVUFEHcXWd990zR2u8MiQ67OBNx4/m3GFDYEzqhKcWZnnX/DW09nok71/gfXV6cXxPPY7v8eH4Hju/vXJPVppF7cpbjvrK4ODQRITHj4ZHTaPSRKXa4x0mIV41N7KYymGNTgeDpgZOR6F3bevht/H1Mpg96HKjAxqqYx9zzfEoxCGj0Wmw7xsVyrad+RcA+Ht8hw8bjFtvvRd3/l81LFV0jy0676F0Evo6iemxKdqG0q7yqGxLz0MDVHjnnjB8MfNt/Djvd+/WZ67WGN+T+ZubQ51IX63egZd+WYPLB3XGHSP6IDL42Mr4Y9Uaf6vWkH59fOc5zGon7g/JxazaKGxyKDFsiWz7TNVW4tuYr4PIqNdjcvswjK3ZAWNpFnQJ6XDm7oYjKBIbY/vi3b21qKi3ye2P47TPGaUtxrct8bf4UsoMNOoxpG9P1Oj0yFfZYHbrkRQYCEdNNYoqKuFwOuQI4fj4RJjN4SgsKZWzcuXlZGPl8qWoENsYg4Jx83VXwxxuFvUqNdZu3IiffpiDgOAQhJgjYQwIasgHzmT+nn4PyVvFc19EBg8aiCsuvwzhIr4umo683ooXX34FOTm5oq7s9m7V+nF8Ob5H0prKX40uED27hOHZyxdi/pZEvL2gF8ztxyAkuivcLhoYKCJPM+TRLVGp3atRy47ivI2fKjtopdpM/YoSrkjGdBtZa6UF9uoKEVe6nZZvHh5FyE3ekcUOJ/SR0VAbDDL9+8raps81ASa/HVnc+juLKfMWCVUVEgSPOQxasehDQ6ALCGy+05iCexydxbKTuK4ODppy2lIBiEVVWa3MuXcGajOJvY3i+Po3jq9/a0vxpUoU3b84OCwVYbHnwKM++jTUdKWmRqeHXl0Dl6vYu13r0hbia+7mQfIYN3ShJ18bdtmB4jUq5C3WtIpRxW0hvpddPAGdBlyBZ76shV20i5urRv8ZGlGsFmn/gckBKNv7PT776hvvO2e21hjfk/mbm0PXAizekYX/zluDKwd3xSX9OsCgpxk5fKfGTl5r/K1aXfnrfTSp3LCLVy5P49Mg/qktxbcx30lseuwcHYYxYXZ0DgRyqmz43RqKbaU1qLe27o5i0lbj21b4Y3y1Gg06t0uETqOGXquFRry2i7yYRgIbAwJgMgXI51ZrHSzFhdiekYGS0lLYbA4YAwNhNAXJGbuSYqPl6OJNW7Ygt6AAgSHhCAgJFWWz8egDZM4gbTH9ynaweIyNi8W4sWNw9rCh+OTTz7Fo8eJWnx83xfHl+B5Ja4mvSqVDh3YhuHPMFizZ2R6/bgiCrd6B9JH/FPmsRqlXU0zliGJlRqbWPPV0Y221fuVy2OGy1cHjqIPd7oLbZpPpNujai6FNipPb6M2R0BhNsk9QJW+hSKcoD39um7sE7ox9ct2Z5mTi2zpqGEcjAqqmjtvyKqiycuHcnyOniK4rKhbBVxpHJ4I+57LbUVdcjNrCQjjEviEWFd2b+AztKGaMMcbOBFTZog7g6ooslOfPh0al1J5UKpriVNaulCsz6ZV4TlNP04ji1tpR3FZYdqiw52s1SreoUJuPE14sGSpkztK0mo7itmLOz7+hrnA1nr4uAiGBKui1x7+EBKjxwKXhCHZuxuwff/LumbUGdM/KHklRuHdsfwztkAiDni7yObF2FPvrUGlLS51HDWcb6Chuy3wnpOlxV1E53t5vxxM7HXgtR4P1eRZYbcqtAfzpxDVjrQHdF3x/Tj4ioxMQaI6GNiQM4ZHRiIkXrwODkZO1H7/8+D1mffEFFiz6AyVlFTAGhSEyLhGh5igYTQHQijI4u6AIy9ZtQFlVDcKj4sR7kTDS7ZVaSUdxWyXbweKxsKAQMz//Es89/yKWLVvmdx2JbRXH17+EiLx5UIcivLVgAH5YrkdtpUXE1wOVh0adChRTtUZ2FPPU0/5BS4NUgsNgCI9FUFQ8guNTEJyQCu26vRAHgVwcn86F9d1ZsL0/G9Z3ZjX73Pbet/DszRbHiHfHfqT1jyxujl4Pd2wE1GHBMISFQx8aCrXWO9Wl+OcecWQxLSKDp/sf26uqYLOUw1NVDRSVQmVVGltnurZ6ZUhbwfH1bxxf/9YW4+u7YKvxlNRUmaosz0dx9ibxvrpVTz3dGKdf/9ZW4hsm6sz33nkr+vbu7l1z/Pbuz8PL0/+HgsIi75ozX2uM78n8zUdCjUJqE1HefSq6iVvjb8X585mP46ug+4pTGqZ76vnTZXccX//mj/GlMtTpsMuLZTt364rQoBDYrXU4kJWFjG1b5G0fgkLD5X1PDdQxrNVBrdEo5yQboddut0uUxzRjk1haYSdxW0+/vg5E36O/4fhyfI+kNcRXrQtEaloiqmtqUVJYDLiVfh+1PgQdRzwOlUYnXiktIn+ZerqxNl+/EqGV0RVJ1/9S78nF1z8vSROVL3VuEZCVD2tuHmoL8mGrqFTuHSAy8aZ8BwVl7rbKStQVFMAmPufJzodK7EdloznqGWOMMXY8fI2mmqp8VBQugUpUwKkxJd6Q0/pQRzFNPe10iso5Y+wvVyHqwS/893Xc9cBjJ7w8+q+nW1VHMTuIWknU4XR4a4kxdqZzi7oV1bt4fhbG/lqUDjVanZzdYcO69Vg4fz5++WUedu/Zi4AQM2KS0hAWGYvA4FDodHrZCdxcRxO1mTQarexI5tHErZMvrs3Fl7V+HN/WTAW3oxZ5eRYUF5Q0dBQ3RaOMqaOYpp4u3DrLu5b5BZFsKely6j2cf9Y4KNo0f3hVDVQH8uHely07gOsKCmGvrW3SYeyRJ0ScYj1NN12XXwBnZjYgPifvTexyKftjjDHG2HGjxlPjKampI0Kt0YmiWM1TTzN2BrLabMjOzTvhhT7PGGOMMdaW0YjhgMAQGINDERWXgPDoOISER8BgMEGtVisdwIecm2SMMXZ6KP08ttoSqDzNt109In/W6Xnqadb2+PXlaTLpu9zyfsbqvQfgzM1DXV4+anJz4ayrkyeqnVYravJyUUudxLkFcju6LzH4vsSMMcZYi2joMK7MRVnOT9CgHgFGmzL1NHcUM8YYY4wxxvyNHB2skSON6VF2DnMHMWOMnfE0ag0sB5bJqae5o5i1JW1nLhOXG6qCUiArF46CYnniWqXVQOX2iNfKenV+sdK5zBhjjLEW5ZueqboyDzVlK+G0t+57FDPGGGOMMcYYY4wx/6HSaFDOU0+zNqrtdBbTSWrqIK6thyq3EKqsPDlNNbLFY24BUFOnjET2nsxmjDHGWMvyjTC21lWIR57BgzHGGGOMMcYYY4ydGegexUXbZ/OIYtYmqTy+oT7HYODI8d5n7Ey1ZuFc77Pjx/E983F8/RvH179xfP0bx9e/cXz9G8fXv3F8/RvH179xfP0bx9e/cXz9G8fXv3F8/RvH17+dTHzbzshixhhjjDHGGGOMMcYYY4wxxhhjDbizmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg1QewfucMcYYY4wxxhhjjDHGGGOMMcZYG8EjixljjDHGGGOMMcYYY4wxxhhjrA3izmLGGGOMMcYYY4wxxhhjjDHGGGuDuLOYMcYYY4wxxhhjjDHGGGOMMcbaIO4sZowxxhhjjDHGGGOMMcYYY4yxNog7ixljjDHGGGOMMcYYY4wxxhhjrA3izmLGGGOMMcYYY4wxxhhjjDHGGGuDuLOYMcYYY4wxxhhjjDHGGGOMMcbaIO4sZowxxhhjjDHGGGOMMcYYY4yxNog7ixljjDHGGGOMMcYYY4wxxhhjrA3izmLGGGOMMcYYY4wxxhhjjDHGGGuDuLOYMcYYY4wxxhhjjDHGGGOMMcbaIO4sZowxxhhjjDHGGGOMMcYYY4yxNog7ixljjDHGGGOMMcYYY4wxxhhjrA3izmLGGGOMMcYYY4wxxhhjjDHGGGuDVB7B+/xPDRw53vuMnanWLJzrfXb8OL5nPo6vf+P4+jeOr3/j+Po3jq9/4/j6N46vf+P4+jeOr3/j+Po3jq9/4/j6N46vf+P4+reTiS+PLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMcYYY4wxxhhjjDHGGGOMsTaIO4sZY4wxxhhjjDHGGGOMMcYYY6wN4s5ixhhjjDHGGGOMMcYYY4wxxhhrg7izmDHGGGOMMcYYY4wxxhhjjDHG2iDuLGaMMeYHJmPGR8/hau8rxtjpNA1fz+H0xxhjrdalz+HrVyd7XzDGzhwH61iPf/QWHldWsjbi6le/xpqFX2PGQ89hAde1GWOMMXaKtb7O4ofeEpWlxpVkUXleOBdfP+R92ZJEo3mB2Pca77LA14Busn4NV9pOscmYMafR7y/ISvNRfvfHPzoYn4PHhnKsHLZeHFON981ONyW+B+N0aKdfc7FUGk20TskLaJvGMVQa0k32K9OtaGhd6n3NTlrzv7viYIyazztlXCg/b5yOxWtaf+h+m8njZTlw8Ljg9HvmOeKx0bj89MX+SGUtOwWOlN8eWj42xIfTmt9oXJau+Wiad+3JOTyfP3gcKa+V463he3319+byAXbSGsejJX5X2t8haZ7yAzp2ms0XjpS3sFPukDKU67mtUpN6kC+PfvyjQ+PZUJc6lrK5uW2a/R4l7Tbeh8xLOG/+azQb2+mYMvFRzFS2YK3FsaTTI7j61bca0n6aGVg3YwqmvvQoRvFxcOodIT8+JiLmh5yzOE7Nnj8Rmq/DU9598NwLO04nkT6PXZN2UEO5euQ685GOgTbhdMSkSfpu+A65vlF6Eq99F1AeNSan5ThiR3Qy+fWfaOv5cascWZy1yYIR3qA8/tFQILtWPj8kM/ZlxJTIRcNKOYAOBu+QAMttm/ksyf4NA0eOl8si880HM/RG6+dY0jGUG+anlmUzdpmvUCrNIqY3mS3Ikm9MO2Q0k9KonoaeOBifLV2VuD/+0RhgnrJu4Mj3UTqMK1dnDEsO0Fw8ROE78ZBYKidNZt4/BdM35YiG0x14Rm6YgzLf8dFgNqZO9MVZpO/rzFg0UjS0ZnnfZi2gud9doDSavhfTvXH7AFcoeecs0cidsVnk4e9jykvKplkWM25qUql65gbKb5X9Uh5fOmN8w/Y+WQ1p+TeUpfc/mGcLVLD78uqGxnZDRY5PpJ4ezR8bVw8xY5eIp4zdJ8BFvtgfqaxlLa/Z/HY6pnjTU1bNZiXtek9I/Vlak+nqoy7eNY0r1o3SWqOGlBJbahS/Jcpv33a+TsaDnzl8P40/0+QCEnZ0TcrS6Zahyu93SB35eH/7yRhqbpzPjxHb0nEkjiGRnkfdP5s2kvU33zbTN5nRU3z2iPkAO3FNyt2By3xl66Gxa2jsHpYmqbwV23nbR7TdzPuXH5LmH+9qxrqfpsvnzeYLR6rLsVOI6rfpB9PTyOWIvE5pFzXEk9rM4vjwnUw5/Bg4mPYbNHN8sFOrTNSNlRi+j3XmLn+ajo5WNvtUN+xzfEOe3Oz3HLGdzf4Kh8eW8vFG56eITKOU3za62K8FT5KyltFsLJuec2yUP8v8VsR2Wu8k9J8q8uYZov6WHCieN4qx3N5b1tJx0PjcJWsRzeWTzdaNfbH0xWVcElLHeePYKK6+2B5SLh+WdpurV4vVIsbN1uHZSTuW9Hnwoi2KlzfdUWy9MTty/4PvczmY440d1c2nyc8JzdaZj3AMtCEnH5NmykSRhigmDf1BRzjvVJaNhn6mg/48Js3Wx2QZrfwdvv0fzEOUv4NeH3zPe87ykOOnSR4jHcO/r405PL8WvxvlteK3k22eprGQebP3t6L35ryLbxt19Cpx4fy4dU5DXfgoPqCgiANghOVL/GDxrn9oDCKXeRNqw0kSIEI0eD6gQHpPUlECa+hMnJeDrGWPYuYRPtvYMxk5iIj1rk8e03DA0d/AHVCn3vIb6ASISPTU6XfDDu/a6fhhr9nbWU9xXY6p9NSsxIcyh2duoA5F8Z55M35o6HCajeXic8rxwP56FvzwycGLQHwan5gkz9zwpYhw87bI46NpY2k2ptJ+F94s0revY5m1pOZ+96uHpKOM8lXva+rc/yHz8DxVyrijoWO4sWdov6Jh3FL56+Nd4a2oi/1e2LQSyE6F5tOk2XvCQ1TIZj2KKb4OpUYOKWvZKdB8fnvcREW7oXMqA0iVK6dhKL5U6lIz9iJyCMVRNGqGWbyV7fdR2tV7TJjNKP1E2a7T1C7YQu/Ps6CT9zMXmZcr+2nU+XHwM5sR0ZXT8bFqWpbOXLG34ffz1ZFlHChvpLg2/u19+WVzv725F6ZRXVgcSzPvv+M48uo/zwfY8Wla7uKlHQdPWPhiJ9LfrvQxojF8hDSJJFEm0zrfyY7p2NJwQSzVo/di+VFj3EJ5Czt2l/ZHJ4to+zTEpXHMvPG8YToeHwIlndMxYKbYNj4GmpbVRzo+2OnXqJNILBOTvauPUXDvm72f9Z1YPbLm29nsjEQnN7vuEOnzDux/NUapP4llDv78IgP2F2vmnOPBC+i8+e1Ld2BOdq0ymniq7zm9/5tyEYe8+NqCnq8+p+TVPNr4NGimXSLK38i9SifFdEsXzMCj+GBTrew4oovcG8d1XbbvPGSjctl7QXzD5ymPbqZefbQ6PGthzaTPZzIsiGwn3rs0BqXZFsRTnNqZUZYxHVe/esUh21/kPbestK2aGaQi6uZZ5hhvneoIdeYTblv5qeOMSbPpSkiFUmY2PR98yHkny29KP1PTPoLjjkkz9ejG7WuRfx+trdRw/KBJHiP+LVe/2qXRv+/g33qkf1+b5W37jrofmNG1USzoAo3DytBbcckyePuGlE5imV+38fy41d6zeOb9X6JUZLAfNDrBdHUsUJrpfZFpEcEVhbFQtnedrEDNLPT1Kk/HDyJhycbTuCSZORzps0fU6GqUhlFz7BSbjikiEaNJp9/MFaKwoJPLD3URlS+RcGfRVE1KbOhKobZ6hU2rIzLtRRhzxLSkzAZwM26SHQnNoQsH0g+/0ENUHFBTy51Pp8wRfncv3xV00y48cp76zE970alpp6Ko/EWIuB0pL6ardpUTYGMQ4c3jj+aZDGCid3uZT7DT4PBjgy4ckGUnVZJFPBpGOLHT60/y28aOltZ89SvZ+JVrcoB078npqb0QIdcJQd7KtsjD+yebkUbrLN6Op1lFKMveoZTrov5VRo8i/csrrqUcUd9r5jP0mp20hhjSb+o7gZHsuyByDFJ96w777Wn2joON1SN2RvgaWmKZ1tuCLaLxxfnAaeaLnYhZvsWsnFRpLk2KtEbxkWnO22SiEzLyAg6qYzfqjD5ivnAceQs71XzxBPYj/WC8RbVYshR549bMFLfNHh/sVIpo6NgVv7nFWybC10mkLHOy5Urp8DQ4rWG0iS/9HRxZfPCkdfPfQ5pvZ7PT7+htnCRMHJeEam8daWah2du+Of6LCdip1zSWaOacI53L6iQvCrkZnSxN430EoqzdIupXjctl1nIOyyeba5fMWodSb5tnWrrlsIvpZt6/Q174fmg5erBcbnwxEI0kj2x3jPVq1mKOJX3KR+ovGALk/wR53pkGIfji2LAPb78CaWhb/ZnD6sx8DJx8TJqmK/lJZB3j+T+aVQnjGvcf/HlMGv/NDXlyM/XoMl8eIuI+6oYj/z0H2+aH5zF0W4J8b15D/Vu+Y+5Y/33+qNl6bUPbVxwD8LV3GrWDm5ahL+0AqNOXLgKS5TCnxVbbWSwT7Q1NK0fmhsxAdhA1FOhNTcZQ+K4M803L9OeffbxrEsoKefTDX+qlOw6bjlZmoub+mOErIGhaAe+VOjSd7ZxsyhDoSvteDVd7yWMgvVHBw84Iz9zwGzBsjMjQva8zLOjvHdVEsaSZAI6GLiKhUTM9va99V3V9MJHWNzNdMmsRTX93eYXVMKXzV3YKzNiMauWt5onC+oO96bhIpGHFZGXa8IlTjnjS+eB0Lwen1vNpXHFKMytnRR+P9V1RJo4xvhr3tGl6bDRMG0SVZHFcNHcxAJe1p0fT/PZIjpbWInwjF33kKDfvxXQivg0dur6prUe+j+kNtw84uoiGY4Mq+Rbs975ix69xWUrkKNSmjUq6QMfXedRwQaSI1ydHOhEpylfvdGDKrSFEY91Xj26s0TTUviuejyUfYMencbkrPdRFnmQ5NHaTEW+2KCcZjjVNvvSbHInaUMf2Olq+cKx5C2sBog20yzy0Uf22uRHg1OaxeGdXeR/rLN7VDaNbRFpuOsLhBPJsdnIOTqMnlqOcRPQ5PA3SbQCU14e1lRs56vc0185mp93R8lfqbKK03HC7Hhrl4r2goPHFBOzMcHgsDz/nSB0ei+Q2vpkfjsFDNMPibzzzwynSXD55WLtEtHngHfE4vWGmw0YeimkYAdj8iL+DFwNNn0G36Gq+Xt1cHf7I57nZ8TiW9Kmcax6Di8TL/fQc/RHfqF3asA8Rw8YD2Zol6uapDRfqKQ6tMx9j28qPnXxMmqYrufKIDj/vJOpS84ARF4qdS38eE+VvFvXrmkYXgzRTj27IQy59Tt4PublzlodoNo/xdnYKV8ea+ZyZ8Kf154b2TqN28GFl6HRsQRfMGGJG6Qr6TTk/bsWdxYeTo42Hea/qoA6iI2bWooDvfXAaaWVY+RE+m9x4uulGmU2j9dPSvcPU2V9kNpZb0kUR4bs6eh0Wyas/fHFTprGVBXHDVT80LfHBq6yPZ5oudiopV7Wn+spKmoYJjdLqOKWApdGqyr18mo4any2nnY6QV1aLDH7hUJTKE920nqY65fieGo1/d8Hb+esbTUYjDINpPV3IIZ6nivTWtANY5sGyAkX35jg4bbgv3R7TKCVzL2UUK02/6U3TI0SFgPa5vxC4Sf49PLL49Gp8bIg4/LTce5U1HRc01aE3Fkcqa9kp1CS/PV6HpPMxyjTUsuPCG8vr0hEh6lqPy+8xe7e7GdMOm5q8GbTvhnLcl4+zE9akLJ1mXt6Qxhquxp2ajl00rdIhv72IV6MG0SEuzcEPDXH17ZPKXXEsiPR85NHCR8kH2IlrWu42bss0jO6+GZ32/ibK1uNJk03r2MfiJPMWdhx89Vtv3JvNL5Vb7yijD69AJ3E8XPRQ42NAfCYjRx4DEb2pA+oE8mzG2GklZ2US7dr4OkvDKKoRZjNG8EwdZ7RmzzmuAHp689uDI4tpdNwRZsejacjlZ6djaobIq70ntNkp1Gy7ZB3QVYll45HFqeOU801Xx4q6U0PZfPg5KN8tt+Tnp4qy99/N1avFhs3U4Q9eRJLUMLMAndNmJ6f5PoHZyBe/c4R3tGG+qEP50ukh20+9udHApMYaxUjsc/phbZ5GdeZm21bKVm3V8cbksHTV3LnfPzvvJNLcIto/PT/mmCjnvEbItN5MPbpxHiLavqUi3z/8nGVTh+cxh/z7+Pg4BofGQp5jPkIZSjNR9vfl5UeKexvKj1Uewfv8T1FPvV+49DnMaPcopnoT1uMfvQXI+9q2fnRgnqjWHF/qPLyocMoxZZY0oiX+p2buIdEKtNX4thUcX//G8fVvHF//5pfxpSubh6zjewYL/pt+J2PGR/2x/Ia2fcEF58/+jePr3zi+/o3j69/aanwff/U57L/fW/dqifr2Q29hQexvjTomzgycfv0bx9e/cXwbOZ489gzNj5s6mfj61cji49GpYYTpXEw8rqvl2RlHJFTfffCOxX4LjnylJmOMMcYYY4wxxhhjjB2XyYC58Sxr6d6pTU8C3ae1mdnZGGOMnSSaAXOcGbuONZ9uA/lx2xxZ7Mf4yhD/xvH1bxxf/8bx9W8cX//G8fVvHF//xvH1bxxf/8bx9W8cX//G8fVvHF//xvH1bxxf/3Yy8W2zI4sZY4wxxhhjjDHGGGOMMeZnaBrw476f+2TMmHP8s1HS7RF59DdjrLXjzmLGGGOMMcYYY4wxxhhjjJ2BqBN3Lhb8aefvNHz90TTv8xMxG1Mn3uG9XeWf7evg+zPvn4Ipx3h7xMaok5lGAdLy5/82xhg7tbizmDHGGGOMMcYYY4wxxhhjZ55L+yNy72/YZe6Pq72rGo/mvfrVtzDjUupQHoPU5DFY4+vkNY/BAtkZ6xstrHQ6yw7aOc959yXWffQWvvZ2Rj8unj8ut2u0L7q36SGduk3ef0h8/iFad3BU8uMfzVX+PvGer0P40NHHkzHUvBfTR46XU/t+gDHi30Drp+Fr7/YN/w7av/i71iyciZ+P6TsO/Tcxxtix4M5ixhhjjDHGGGOMMcYYY4ydca4eAmy5fzqmZphx0SEdro3NxtRPNqM6+zcMvGG6XBNhtuCDkeMxfZMZPWWn6hhELlM6ZwcuM+MmX0eq2YzST8Zj1P2zlddN9vW4+H7az8CR73s7rA//LvmZZVC+B5MRj8344aVp+HqYxdsh/D5Ku/o6qL3MvTDN2yk88/47MHUWdQAPRekM5W+cbuni7UAGUrFDrLsaFxzrdxz2b2KMsaPjzmLGGGOMMcYYY4wxxhhjjJ1hJmNoei9MpE7VcUlI7eodbXsMyvauw0zxOLPQIl9fHQuUZsqnQKZYZ05Snlv2Yvks5Wlz9iNd6dRdeDP6m70rm/PSDoD+PhoJbVG+G0HeDmH6bLIZaXJDQlNe+zqFh4r3v/Z2DAei/1RllPC03kmIbCc3RlaGt1P6WL/jT/5NjDHWFHcWM8YYY4wxxhhjjDHGGGPszPLQGHTa+77sVKVlDrrIaZipAzgiVhkZnGY+Wg9uY+aGzle0E5+x5HhfHA11Vlswxztyd53S73wE07FF/H0zhphRusI7ordmc8Oo3+kzfPdDJtPwtXcqbLrn8fRN8P5ttVjnG1k84/1m7oV8PN/BGGPHjjuLGWOMMcYYY4wxxhhjjDF2BpmMGcPM2OXrFBWeyQBG0PTRL+1AWe+b5QjcEeZGPbjJY454n96Z93+J0mHKqN01wyz44M+maJb7ApbvNSsjmxdegU7mXgenwm7mu+jv659u8Y7qnY4py8wNo36nXddoGupLc/BDw3tzMc28XHYMP3PDckT6RhZPvaJhGurGjvk7GGPsOKg8gvf5n6IrWtiZjQqSE8XxPfNxfP0bx9e/cXz9G8fXv3F8/RvH179xfP0bx9e/cXz9G8fXv3F8/RvH179xfP0bx9e/nUx8eWQxY4wxxhhjjDHGGGOMMcYYY4y1QdxZzBhjjDHGGGOMMcYYY4wxxhhjbRB3FjPGGGOMMcYYY4wxxhhjjDHGWBvEncWMMcYYY6xZGo0GnTq0x2svPIVl875r8eWrD/8PY0aeA61W6/1GxhhjjDEmqTRQGUOhTR8Fw5Cp0HYeC5UpXKznU3mMMcYYY6xlcQ2TMcYYY4wdRqVSIT4uBk88dB8G9euDouLSFl+ioyLx0LSpGDqof8N3MsYYY4y1dSpDMDTJg6DrcxW0qYOhCjBDmzgA+r5Xi9dDoTKGeLdkjLUGHrfb+0zh8XjgsNtQW1WBirLihoVeOx122sC7JWOMMXZ6qEThdMylz8CR473PWj+1GgiIcKO+Qg2Xw7vSD6xZONf77Pj5U3z9FcfXv3F8/RvH17/5W3yp05aqiGNHDcfTjz6Ab374CTO/mu19t+WkJCfi+ScfwfpNW/HA4083fO+ZhtOvf+P4+jeOr3/j+Pq3NhlflRqamC7Qpg1TRhGrm5l9xeOCp74SjgMr4MnfKupOLu8brQunX//mb/H1eNyoqSxHVbkFdls93C6XbLvoDSYYTCYEhYTBGBgk1injshw2K8qKC+RnqPOXttfodNDq9GLRob6mBi5a73YhJjZGfoYUFRZBrdFAbwxAZGw8wiJjGvZ5JuH06984vv6trcZXDk7weND4jJNOq4XL7RZ5sXJBj2/4Qmu+XOdk4ttmRxYHRrnReZwdkenOg0cBY4wxxhhrEBgQICvUm7ZsR0mZBRMvGIN3//cynC4Xampr8dSjD8iluqYWRcUlKCgqPmQpFOsqKqtkB3CZpVxuQ5+z2e0oLi3DgZw8VFXXICQ4SH7fmdhRzJg/Mmg9iDA50S7UijFpVbi2WzEe6J/TsFzfvRgTO1jQI8qO2AAbAnVukRd4P8wYY6zFqdRaqIJjoOsxGbpuF0EVGNV8RzGh6akDzNB3Hg9drylQh8QDGr6lB2OngtPpgKU4H9k7N8Nda0Gvrh1w+eSLMfW2m3HbTdfjgjEjEBVigrW8APl7tqIgaw/y9u9GUdZOtE+MwQ3XXoVnnnoSr7z8Ih66/155MW7vbp3xjwfuw++//oI9mQewbuNWrF6/WT7S62++/gqXTboIsFZj//aNqLKUys5mxhhjx4/OaVFTVi7iPxqxGHQaxEeZcd6w/hg1uC/MIYHQadTyfZVabO/7TBtrBLfJzuLASDfaD3dCo1cheZAL5jSn9x3GGGOMMebju7rSaDRAr9chMT5OTh0dFxON8LAwxIrHGPE62NvZ25xLJl6A6S8+jV49usHt8eDu22/CPx+6FyHBwVCLirdWo/FuyRg71WKC3BiVVI57e2fi5WFb8M45q/FU75W4q/M6XJaytWGZ2mkdHuu+Cm8NWYLXh2/CYwN2YlK7IiSH+NGUTIwxdkZQQR0cDW374dD3vhKa6M50ltL73p8Q9Sh1ZHvo+lwJbYdRUIXE0UrlPcbYSbPW1yJnTwZSYsz432v/xbc//Ix/vjQDoy6/DV2HT0b/cVdgym0P4J3Pv8c33/2Ejz78AFOn3omLzz8Pn37yCd54fybOmXQjQjsNgSaxN5IHjMO19/wT09/5BA889BCSOnTHR/M3Y9obc/C36d/Lx1dmr8TuumBcdNO9+PHneXj04QdhqypBXtZu2K313r+MMcbYsTLotDBqgUAdEG5UIcKkRqjOhTCjGnqNBsGBRoSJdWEGt1hUCBXbBIjtdeq2d76qzXUWa40epAxxwhCsDC9XqTxIG+JBYMSh945gfz21WiTckGB5AppOJjPGGGPs9PJ1FjelOY4Kc25+AbJz8+XIYrJrTyayDuTAbrfL14yxU08nWn0XtivH9HNW47EBGzApcQ+6BhUhSF0HlUjnHqfq8MWtggYOJBksGBWdhXv7bMV/xedv7ZGPAB23nRhj7KSpNdCkngVdzyny/sQqw5Evvjsalc4ETWJ/6HteBm27c3iUMWMtgEYUF2TtxfnnjcQHH30MW3g6HvroD9z9+g946pP5ePnrJXjq0wV4+N1fcNur3+KhT5bi5z023HjTLXjy389hdZEat7/2vdz2tW+X4fXvV+DVWUvxyHvzcOsrszB1+vfILSnH9WP7oUNSFHbnloqlBH9szsTHv67DEx/+hkc+XY4Boy/G/N9/R2pCrByxTH8XY4yxI/P1ItFI4WA9YPDYEBPsxsBUJ8zhYYikARCxcTAajai31sNSZkFIUDBiY2IQYQ6DOSQIHaOBxHANTGo7Ag1a2U/VFrSpzmKKaVx3F0Jj6RYvHuXA8Xig0TnlCGOdkac+PFPQSegrJk/E/178N958+Vn8/e470K93TwQEmLxbMMYYY+xU02qVk40ulwtWqw0r127A74uWYO/+LBSXlGLNuo1YvHwVyssr5HZN0bTSi5etxIP//Dcysw7Idd/OmYvpb72P+vp6WeGm+8Pw9NOMnRo0xVZamAuvDMvAE31WIlVfgQCXXbaB4O3vVcEDtar5hd5TucW24v96hwOJmirc2nETvh67GoPiamHUcKcxY4wdN60e6oh0GAbeBF36KKhMYXKU8MmgyRJVxhBo250N/cBboY7uRKMlvO8yxo5XlaUE3Tql49nnnseHC3fimc8WoKiiVk5PqtNpodGooRePWq1yEW1pZR00om3jcLpw/9u/4PP5G2FzuOT7tC2NTqPnOp0GVrF+9Y4c3PLKt/hw3nrccv4A/O2iwXK6U7XYP7WRaMkuKsddr8/Bhtw6zPz8S3ROT0NRdia3nRhjrBly6mi5AMFGHYJ1LgQaNOjf3oA7RqrQKTkcMdGxiIqIgNlsRmhYuHI/eY0W4eFmRJgjEB8bi5ioGHRJDUevZA3O7awR+/EgSOsReb5G9ifK5STrbWeqNtVZHBzvQkwXN1wO70kNKltFXOk0SFC0G5Ed+P4PZwq6d+Go4Wfjj+Wr5JKWkoSHpt2Jv91yg3ieLEcbGw0Gv02YjDHG2JkgNz8fNpsd48eMxgVjRsqTFyvXrse5Q8/C6BFnY9O27dizL1M+v2DsKFw4bvQxL2NHD8clF41HeGgocvLy5fdxuc5Yy9FrPBifWoJXBq3GoMgsuJ2y4SNaPkoncEOH8FGSHb3X9H2PE4jSl+OlActxR88DCBKNcMYYY8dApYHanAZd5wug63ExVEEx3jdaljowAvquF0HXZTw0kR3ECh5pzNjx8HjcKC/Mw1VXXY01mRZ8uWgjQgKViy/cbs8hi0csTpcbceZgXHNeHzz/+SJs3lcAk0Enb8Ejt/H27Tb+nEGvhUmvw6w/tuD+GT9iSLcUTJ04GHaHS3ZE0GepbaTTavDvT3/Hb1vz8ewzz8Btr0N9TZWyQ8YYYw1MRgM6pCQgPiIERpUD5vBwxMTEok6bgILaMDmSNChQC2dALKpDO6E2oh+qwvvIx5qIvqgLTofbFAmNKQT1niAkRungMcYjwmyGOcSEYK0HKXFRSEmM89vpqVWe47gcaeDI8d5nrVP3STYER9LoGPFPFgUz1KL4pUciCmCVRo2MH7WoLmq9fehrFs71Pjt+Z1J8aUqA/zzzBN756DNs2LQVYWGhOGfIIFx12SQ4HU5YKipQXlGJ7Tt2Yd3Gzdi3/wAcTv+/97S/xJc1j+Pr3zi+/s0f40u3gAgX5fGLTz2GHl07w2qzed9pGVQF1et0sNntuOuBx7B9527vO2ceTr/+zV/je1P3Qlybvg0BHmXKd+oilh3FR+kc/jPUcvTthx5dopG8pDAFT63tDqvjzJwWkdOvf+P4+jd/iq/KEAhtu+FQR3WESh/oXXvqeRz1cJfthXPvInisZ1YHE6df/9aa41tdUQZXTRl+/PlXPPjhEuSVHTntUL2qssaKZ28eB4OOOnYXyNHH1CF8LGS9TFSw2sVF4LW7JuKrRZvx0a/rDumI0GmU0cYz7rkI991+HXbuzUJSehfvu38NTr/+jePr3/w1vkP69kBCbCSKCotQVFyEiPBw6PUGwO2AOjAcxeaB0AUlISgsClq9HgG6gw1jyrKtDjesViuc9jqgaB0CSzcCDhvc0KC2rhY1tbVITkxEuDkCazZnICuv0PvpM8vJxLdNjCymkxlxvR0IiVHB6RSR93YUu12iPKa52SQPqBxOGuDkiy7PAFXVNdiyfQfGjRouRxHTVJff/TQPz7w8HUtWrMb+A9myojRpwvl44V+P4a7bb0JqcmLDdJmMMcYYOzl0NTuVre3TUmQ5/MvvC1t0mTd/Ebbt2AWT0YiR5w6T38kjixk7eTSimDqKb++yESaP0oHbEh3FhD5Po5EJPWpdToxMyMSTA7bxCGPGGGuKMk19IDQJfaEfdLt47HNaO4qJvJ9xbA/oz7od2uSB8u9hjB2dpbgAnTp3gcWmQX5ZlZxd6UioUzgmPAhj+nfAL2t2yTbUsXYUE7oQj/KKXTklePqT+bh8eC90SY6WF9b60Jgnm8OJwvIaJMTHw26zet9hjDFGOXSAQQeT1gOHzYbgoEA51bRBr4dDGwhr8mjUdp+KxM7nomP7VKTHBqJjtAEdorTiUVk6i6VbnBFdEkORkhCL0K4TUNvrHthj+ou6lBGhwcGIiYqCyWSE1VqPuKhweVGPv53DahOdxcZwIK6rOFjsbnmShNB/HTRAptE5DafNjaAoIDzV2bAd+2s4nU58+8NckQBNuOiCsXLkEd0vccPmrXjzvY/wn9ffxr+efwUPP/kcvv3xZ/Ts1gVPPHw/xo8dJTMExhhjjJ04X4U3KSEBgQEB+PiLb/DSazMwb/5i7D+Qg9dmvIdXRFm8ZftOudDzF8X7zS0v/PdNuTT3+t8vvQZLeYUcucwYO3l0Hey45FI5opgGFPvaNE2nm/adf3R7VA0LrfMtjdeTRucrJdqX3Jaa5nYPzok9gKk9sxDAHcaMnXqNEjM9U6t1UGv0hy5q5Z5qkp+dxGotVBQH6qTtMRm6TmOh0gd43zkBHrfIa2uVxxOk0hqg7TAa+p6XQB3fSxQYBu87jLGm7DYbevXsibzSSlEXOnq6sztdGNY9FdV1NmzPKjyhLJfqVHQv42Vb92P+hr24ZnRfubJp3e14OqEZY6yt0GlVMMCBvfsyUVRUBKfIl3U6PeyRPeDofg1COw5H+5gAdIwAUsJUSDOr5WNCmBbJ4cqSZNYi0fteeoQanaOA7kmhCOkxEe4uU+AIiINOo0Z9vRU5OTk4kLkPRo0oH5o2lFs5v+8sVot/YWR7J7QmN7wXwTfwuOhEiO+FWMS2HrgRne4R9WZuUP3V8vILMPvHXzDynKHo16endy2QEBeLvr16wOV2Y2/mfnw563s8+dx/sC1jJ2686nJMveV6hIWGeLdmjDHG2Ilyi7KW1IkKscFgwITzz8M9d9yM9PZpiI6KxM3XXoGbrrkcZnO4PClNV8D7FkLrunbqiIemTUVkhFmuHz5sMC6fPBF6vU7uv/FV842fM8aOX3KoCzd03COnnvZ1FDc90ehLZr6OYLXWA7VBrFeJxq8nQHxKLV/LRS1aR406jBsnUdovfQc9alwuTEjOxLDEau+7jLEWJxKbKSgKYTGdENNuGOI6jkBSzwlI7jURqb0nH7Ik95wo34tJPxdRyQMREpUOQ0C4SNM8E9dpYQiGrsdk6DuNgzo8RWSmJ35fO4+9Fo6d82Bb9zHsGT/BYzuJfFbk8+qwZNl5re91KdQBZu8bjDEful+xw25DQkICqmqtsoP2aJ20GrUaI/t2wI7sEtTU2+VrGonsW46HXqfFx7+uQ6fkaLRPiIDDefAiPGonBRj1sJSXe9cwxljbRjks3T4tQKtCcKAJoWFhspPYYa1BVcw5UHecgJS4OLQX1Z3UcJXsDE42axETQosGcaFqRAUrS7RY4sS6+FANEsI0SBTvJYSo0TFSbJecDne3q1AV0h1Ohw0BAYEICQ2BSVSrg0z+dfGd33cW64M8iGgvCnU610lHkO9siSjo7bWqgxdm0mqxjk54BMW7EBR19CvH2KlHU7ds2LQFK9eux6QLz0dQoDJiWKPRYMqkCejaqYN8Tfcqzs7NkyOOP/lyFgYP6I9H/34PUpISZYbBGGOMsRPju70Dneige2/RTB86WifKaCqPdd7XdGuIpuiEBp1WuXDcaHnhF80CQqijeMK48xAWGirLeqeLRyIy1hJ0Ihne13MXEgIr5Gtq2TSuCovkJtfJ5+INu0i/Oc4QfHegK+5eNgKXLRqHSxaMwqRF5+POpSPx3o7e2FYRj2q1UXYkez8q9+ND+6fX1IbSux14vNdGxAV532SMnRiRsCi5abQG6ANCER7XDYndxqPDwOvQrv9VSBLPY9oPQVTKAIRGdURQWBICgmMREOJdgmMQHNEOYTFdEJM2CHEdz0Vy9wlo3/9qpPWdgoTO5yEksj30xmCoNTrxdcr3sZajjewAtTlFPNF71xw/j70OrryNsK9+RzxugKfOAnfBFthWvQNnzlp4bDW0lbLxcZKjnsNToBLHDzu9KK35Fp/G6xqvZ38RSlaickPtG2qrHC2VUXvHoFWjQ2IENu/Ll1NFU2dxWKARgQadHN32Z3wXywabDOjVLhYpMWHIK6nAhMFdYbM7G+pyOtH2CjbpYK2vh95gVFYyxlgbRrlniMgXw41ORIQFITwsHGqtDpVx5yG443AkmfVIoRmHQzRIDNPIDuKoIBUiAiGXcJP6kMUcoLxH20QHi+1DqfNYjXZiH53ixf57ToA1rLs8LxYWEorQkBDoPHZ5oY+/8PvO4rAUFwxBsqSXncFHrXl5r/iiE6KxPRyiAi1fsr8QdQQvXbEaifFxSEn6f/buAkCu4v4D+Hd999wlJ7lc3D0EJwlOcClepAWKlP6hpUaN0mKFFmipUCilULS4uwVJQoi7nruv239+s7t3m8vF7eT7KS/79tled3bmzbzfm3kFellldQ1effMdtLZtfUet1+vDa2+/h3v//Hfk5ebge1deigK1HxEREe2ZWM9ivz+gL3bUNzahvqERTpcbLrdbz8syj0ee7dGzfz35DO554K+Yv2ixviB99/1/xR8f+geam1v0xQ/e2EW0b5xQ0oyZ2ZsRDur+vup/kYuPQq5DxpaFzUZscmXgwSXjcP3Hh+KOhSX4qsqO8lag0RlEdVsYX1fb8I9l+fjB55Pw6/lT8UVTIQJGsz5G7HgxkoVluUx2kxs3jlup77KmvdfTM7B29lysna2P2dXtYnZ3e9o9sW9Xho+WYG9m8VQUjD4BpVPO18HdtLxRsCVl6W1CQT+Cfo+avGo+oJdJOEPfpCWT3iYQ3SYyhcNBGE1mOFLzkV4wAUXjTkHJ5HMwaMQcpOWPhcWRrtI4enmIab3XwhIk3sMLSuGAB6Ga5Qgsf0n3KA57ndE1UX43AmvfhX/5CwhWLUVYpe+eUecEoyU6TweClKPpackYNqQImenJkgJ6uc1qQW52BlKS92KoctqnDEYjvF4vLGaTbqtsr1iUdckJNh3ITXJY8dMLZuHRH52D+68/Aw/ccDpuv+IEFGaldLapehIIhnDRnMn403Wn4rtzD8GVJ8/AiKJsDC/M1uuElO1pSXYkGPyoqKhAYkqqXk5ENJDJsNDJFh9mjbPi2NEuWM0heHKmI2XYYShKi/QMzkqM9BpOdRiQaldltl3N203qvapzWw1Isqll0SnFoY6n1qWpbSRwLEHj7GSTnnISDSjIsMEx6gT4kwpht1lQnJuAOWMNSDCFYFJ/S3/Qr4PFRksY2cMD+qSqW0zyEK84QZ9aHIpbJrOyaSCE9CL1Y2Hv4l5h/abNqGtowOiRw/WdfYFAAJ/P/xpbysqjW3Tx+XyY99UC3P/Xf+oAswxJnZqSHF1LREREuyN2YSMvJxt+vx///u+zuOrGW7ClvAJNTc342W134ue/vQutrfI8r7joUZTUwSSg/P7Hn6G9vUO/37SlDF8vXgafOp7cjSnDW8u5XTAYQbRnUmzAhSNWIxSM5CEduo1mp0hTKBo8Vg3iZc35+MWCyfjf+lzUduy4OdjmBT6vSsGvvxqFF7eMhMEaOZaIz/KRUHFYtaOAKXm1GJfVLbhBu03KQ92O7aanZfF2tj6m+3ZywXt7N+/I8p6Ou73taffJt2u1pyFv2DEYPOks5A0/BsmZQ2A2O9R3H0I46NeTbClB3cgkAYxont9qXr+o19h2kXweOU4gehz1ebZUpOaOQOGoEzBEfaYEqI1mu2yo19OeM8h3uAffY7i9Fv5lL8G3+g0EmzaqBdvplajSMtRchsDqt+Bf+jzCrZXRFbtB/1CY1gdSQW4mLj3vDFx09um44sJzMWXccBw6ZSy+c/G5uPKic/G9yy/EEdPH6w4ssfwsZC42DS7MxmnHjcIJRw5FbmaSXkb7lnz3FqsNm7dsQWpipAdvfHrEk9tzzKbIsNPnz54Eu9WC3zz+Hv784md44t1FSEtKwK+/fRysZlOP51EZYWnysHycP2si3l+0Hh1uP57+YDFe/XxldIvIuVaGoy7ISkF1RZlqU7UjMZnBYiIiiyGI4bnq1ZoAn98ArzEVxsKZyE6xIC9Jeg8bkJEowWEJFBuRaDUiQU0O1Sa2mQ1IsBj1q1z18gTC8KpJONQ2KWr7JP0KpCVIwNmE3CQD8rNSYR09FxZTEKOzO5CXFsKEwiDUYfrFOVnVYXe9Bjtj9inRub4hvSSIMScF4XdHK9ixYHFI7rgFWsqNSM5TJ3aLeiOroj2LZb3ZZkLtGmDjR9Y+1Vaa/8Hr0bnd11vTVyplZ592sg4W//EvD6PDufOLTzI05gmzj8b1V12ON9/7EI88/pTuAdXX9cf0pS5M3/6tL6bvV2+9gqDRh3Z/AMtXN+DxJ7/C6rX1+MWP5+DomaUwyfPXDGaYwgZVoVCnT6N6q065jYEAvv/a27jhiKk4JCM30rFC1umTrZxUY1O3Hhfx51t9SpYqm5qR52VGT9E7c7C+q/6af+XGq7/edwfS09OwfuOm6NIuPp9fP3t4T8i+RQWDkJychAf//iieeeEVfc7fjarpAdMX03dv/uaDhfl3z80pasbPpy9CQjDyrOL4a5qxLCU9iiVQfMOn0+DxR4JFu+s746vx7WHLYQ371XGlVO/6LPkcHUhWC57YOAZ/Wz4YARnZ6SDri+krF4azs7Pw3W9fiNEjhnfeuCNlZG19Pf7+ryf06Ay3/fxH+sZY/d2rf+TG2vc++hRPPPuCvolW2kTnnH4KDpsxTd/c43S59HHGjhqB7152EbIyIs+Sl3L8o8++wNr1G1EwKA9PPvsigtFHBMjfYrfbdXvs2GOO1J8hHA47/vjQwzjm8EPxyH+eQk1dvV5+oPX1/KuDuSaL7jmcM+QwfbFLegVLACKStQz6WZnytUfOkZHXnkXyW/dtYseS5RGdM3pO6nBqB5jMVrjaa1Cz4TM4m8rV9uo30LXTQdFX09dcNAPm4XNUBtrF3sWhAAKbPkVg8xfqO9+TTgsq/YrVZw49Wg8xvSvkdxFc/5H6zHnRJQdefzj/7iqH1azK9PNUuZsFn9erv//GhnqMGDEMbe0utLe163ybkpqKJ597EWs2RjpH6LyrJpvaf+6cobj2yhlISLTq5V6nB0+/sgbPvrICTa1uveHBzbFb68vpW75+FUYPH4Lf3v9PfP/BV1RWNuqyeFthZKUk4F8//hbueuojvL1gDYJqu199+zgcPb4E97/wGX5y4Wxc/8BLWFfZoLbfuvz2eP348QWzMKY4C/f9bx6+fcI0PPTiPJx55Djdq/lX/34HGckJ+tnJ3z/rcKByCX5+6y8wfNIh0SMcPAMp/w5ETN/+ra+nr5SkFtW2TbUBeVnJmDvBh8r2NCzLuBIFIyZgSBpQmG5CmiMSKJbAr91qgMVogNpNt2+kndrsDqLDE4DDYoLNYkSsc7AM6iBBZCFBZKc3hFYP0OIKoao1hPWNQPXSdzDe/QLyE9vw6qIANjWE0e7Tu6jqc9+tP0e/gv5Hbp5NLwqpRq6qgklDKb5XsXpvVL+MUEAaXtFlMZKWavKrH0FKPmBP601VrYFJMpj0QMpIS8PwoUOiS3dMLm58NO8LPSz18bOOwhGHTtcXS4iIaNe1Bf34ZlU97n/oU9zy85fw6VeVaGj14Y8PvoVvltQgLA3msKoN6VNsGMZQEEFjCK+u2YDV9S6EvWEYOkfwiJ1P5b1MPVRBoseJXLWUE3R0kgh05/50oMgFq7r6BvztX//BqjXrkGB3bDOlpaRs9T5dnatHDR+GwUWFWy3vaZJ9m5pb8OQzL+D1t9/Xn9kbA8VEvZ3NHMa03CYkhLYNAMeylLSHNrWn465vRvcYKJbhEw1IQjCQrjZOVQ1l1fLuwVOrc/Fu1RCEwka1/XbyqyrDJ+U2IsUabS3TbnM4HLj6sovhcrl1QFYesyOTDOG/dPkq/Pzm7yM3Jxs2qxWP/Odp/Omv/9TrHvzHozj2mCMwY+okXYaXlhSrdtAM2GxWXPfdy/Qz5osKB+Fnav/5Xy/W+8hx777/ITz1/EuqDE/VAeRYj2E5howacepJx2HyhHF4+PH/dv4tt99zP77+Zine/egTtHXIs1Npt6jvVr5la0KGHmZ60LBZKt9ZOoeVlnXy3RfmJGJUSTrsVumVplep15A+X247RfK8pJuqlcHfUQ1/6xYE2qtVnS124UNu8JA6mHxC5G+I/BNW+d8LW0ImisechNzSw2AyOyKraL+TNIUlEcZEGWp89791Y2ImjNakyA+AepVYag4dXIhBuflwOp362lRTYyOSk1OwpawSa9ashtPlRIezAz6PBylJkYf/y74OuwmHTh2EK741HnOPG4aA1w93mwtbNjZh1QYzDj3kCFx63okoyktna2kfSk7LQHVVFTISzHqYaSljt0eCw7K6ud2lg/qpSXb8+aXPce39L+H9bzbAFwgiN2P7Ix56fAH9m1ixuQY//vtr2FzbjKzURGyoatRDrMqxZf2ciSVYvPgb2JNSonsSEQ1Qqq5rVm1Om8UCiy0V8yqGYHVoGuy5w5GdaEB6ghHJtsjQ0zLsdIKat5sMqkyNBIo7VLm7ps6tyu8gBqVakZ9qUeW9KTI8tZrSHSY4LAZY1T52s0H3SE61A0lyTPWanxxC+pBJKA+NwMKaQniQpreVGviOzhd9Qb8NFtuSw0grVg0sX0i6T0vbJ0Je1YncpBIwMopTt4q4vFWTXAC3JQIJGbFGFR1M8pziVWvX46LzzkJ2ZkZ06Y7JxZWnnn8Zm8srcObck5C1i/sREVHEo//+ELf+6lm88NJaNHVY9fMqgwYLtlQZ8deHP0J1vTwDLxouMIR0R4jyDieeWrISHcYEGEOqstTtNBsMhiDXQYOBsB56WG7ukcqU9JoKqYpaKBzUy+RmL+nUpE/h0XO4Xq+208vk9KxfI41zudN7R8+Cot0n6eJXafT2+x/jx7/6HW748S92Ov3+Dw/odPjgk3k9ru8+/eCnv8KjTz69S6OG0J4LhnyobfsamxreQk3rfPV++8+Ypr4nyRLEyPRWVQ5HCsv4clfmpU+hz2zBC5sGY2PL1iMByMBKRqSjtmY4aquHob6uRM2XqmkEQsF8tefWzUWn34iXNhagBXZ93Eh/xYjOz1VF8XBHM7ISooU37bbU1GSkp0d6ly1buQpLl6/U0zdLluugrtvj0UHgDqdLPxZAHhPQ2tYOt9uj9pVgv1xcDuP6716OLxcswk9/cycOnzkdUydPwIlzjtE3Aj334qv6eLHjtrS2RT89Qi6kxC52nHzcbGRlZeLc0+fiyksv0NOF554Js9mMBYuW6HYX7R65RmFJSNPPDk7LGaHykso4Og9JMFfyU2RY8DOPGY4LTxgNf9AolzH08siGkjbxU2xdhLvmazQueQIta1+DS4Yz1usi2+nApBK/fSe1Tp5jmz14OgpHHQejhc9QPRCkN7C5eDosE8+DueRQlQF3cdQWVTc3F02HWe1nGnwIDOaeb/Tpib5VoKffAO0RydN6ir6PkVyX6LAhPdWCysoK1NfXoqKyXJXZrZBnlNfUVGP0qNHIzs5BVmaWSlKzvgFHjmW1mnHx2VPxqx8chfNPH42hxWm655PZbILbn4bBpbNQ32BHaekEXHvFUchJT4x8KO01R1KyHu65ubYCeRnJ6jwbGW2jJ9K+lfNlol1Gpoy0TWXY6JrmdnS4vSiva9XPLZbl3RlNBqzcXKODw2mJdthVmtusJqQk2NDY6tQ38/lUe2zs4GwkmkOYv3Ah0rJyo3sTEQ08cp41SqDYbIDNbofDZtOPAvDnTEJuugMptshziBPVJIFeq0wSJFbNWqlb+1SZvbK6A3kpZqQnWLCorAVvLKnV0+cbmtDgDOjqkWxrUWW0DhhbDHCYZQhrdU63ydDUBtXmSoM/ZQQ84UQkOBy6/SWPIujr+m+wODEEi6onS4NK/4piNbboayhsQNC3/f/7cuEkGAjBnhJprNHBJcOovfnuB8hIS8X5Z5++y72Em1ta8OSzL2BQfh4OP2Razw1iOoDC8KYCLUNU/otvx6pkcWcY0DGo670rW21Xauiahhjhi9xgq+/x6CgywjnIiLDKxh35Rvi7tYsCDgPaitXyBHXCUFNbsQGB2GdGfwbeVAPaiwwImSPvae/4HCH1Xca3gMLw20MIylD/8s4Yhi8h1DWp7WP36wTVfrKd3iZuuZD0ke3DqjIQsIURMm3dygrY1GfEfa76VSDL5IVN90alvXH7L/+C1cvXwu9rVe/aYTL4YFAn1mDYhgXL6/DA399Gh1POqSpt1BZyIfPjDZuxvrENIb9Kl7BKN5U2kpzRkLIOJFZX1aK6uk6XyTJJcNjlcmHDhk3qs4IIh4xoae7AhnVlWPzNaqxbW6b2qYfX61OTB1s2l2HTxs1q+81qvgIN9U36QnnsojbtO/KdSvq0tLSivr5hh5MEH7zqfC2cThdq6+r184p72jY2NTY26eGoaf8IBN3Y0vgePlzzfSzYfAdWVD2KhVv+gPdWXYOK5k8YNO4nUq0BFNvbVQEbXRBH2jyi1u3AJ9U5ne+FfgJPKFOVx6VwuZMQ8JsQChgR9Bvh8dhRVVkIt2uoKr+3rigtrXdgYW2BanBvW+bGynq7xYNJ2epvoj0ipzOH3Y7kpER97pPzbGwyW8z6gkTsETt+f0A/eufff7sfd/zqp3jljbfx1cJvcPZppyA3JwsVVdUoKS7EN0uW4dQTj1Pto1ZkZqTrQG/smPEpKfMyyfIYKds3qPPuh59+jg8+/gyfzPsSr7/zvr7Rh+fePWOyJqJwzElwJOeo/ALkZiQgM9WGvMxEpCZa1Pcags1qQUFOsn6mZWqiEfmZciFKbgAKIS05sm12mkPvm+Qw6fO1pF7I2wpP41qYbMnImnIlUotnqp1UeR/06MkQ8umLbDrlZajp6HKZwn5VsVPnjpCvHUmpOcgqmgyDkY2lA0PVix1pMA+bDeu0y2DMKFU/lO0Ef81WGNMGwzr1EphHngBjQobafTcv7+m8G9fooj2n8qSnuRot9ZW6TIy0fSJT4aA83PHrW/Gtcy6ExWJBfn4BiooGq/I5F21tjdi4YT3cqpyXi9JSLleqMntzRTVMZhMmjs5FbpYFm8qb0N4eqbPpS1rq81ISfWhsalDbV+q6dGFBMY47urjHgDXtPps9QQ8RvWH9ekwamg+vOtdu73JipE279XlTyDtZ1NDq1M8+7ul6pASDv15boXskjyjMlqRVZXwYHvV5EvzQB1bHOG/WJKxdt161fzfC5uBNPPtC7FpEfLp0TyN5F1vWub1+FxG/XmyzLjLbKba/XLMyqfOwzMtrbF56Sur6Oe21zvSKT5+4eSHvYss6t9fvIuLXi23WRWY7xfZn+u5fUrYmOuyqDpwMm82m20ZBewbsmYOR4TBAVY2RZJeeweo7V1+4ThP1j5xnJU64orodvmAYX21pxQ+fWonFm9okcVTd3Igmpx8LtzRjdU0HvIFI41qOIUFj9TFIUMeUQHSSVZ6FrN6kl6p0V+22BFV/T03GyNJiVXb37XBr3/7rt0MuUthSVeobQvqH0J0elloJeFVDStpT3XNqdL28JGWrjMy2Ua8gFzZa21WG3s1nrK1cvQ4rVq3BCXOOQXJ0OB86OEIWAxrGm1Az04xqNQVUAR5W+U+ec9o60oiKQ40IqELXr+q+NYeYUTvNhMbxZjSNM6NOzTdMNKljqH3U1DLaioYxRvjVMWoPs6B5pEkyvw4yBq1A1SEGVB1pQvMIAzoKDKg+yoKa6ZGAs5xY2gsNqJgtx7V0BZFpj/ltYaw7xoXKyW6VRpGeEX57GBuP8aChJNKwbS70Y82xTqw9ol1P69X2HZmRofZqRqnthvrRlqMaZMe44UlWhbM6hgSKq8a4sfZ4N9qzgyif5kb1GG8kwK/Wu9KD2HC4Cw2l0YCHWpZr8uC27MU4ylGr39Oe87RXob5yCSo3fA5X7SYYPU6YpAWrMlEQCXjro3X488Nvo90lF7IDaPK78fGqCjhdIVgCLrVZCHoUajmhyp0dSltrO37yk1/iggsuw5o163QD2qAqzSuWr8LZZ52P8vJKHWj884N/xznnXISrvnsDvn3pVbj00suxdOlS1NXW4+KLrsLJJ52Dc866CGerba684jo888zz+oI67Xty0SsWUNjRJGLB4phd2ZeBhv1DehNvanwblS2fINGaj8K0Y5CbMhXZyRNVA2oYvil/EGtqn1NpwBtr+rphGT4kmj16PhasjWc0h7GotgC1Hd2afuF0VNeUwO+PnCxVyykydbafwmhoSIPPW6DvlI73WlmRrnN1/zzZNbZsZPrWPVVp1zU2NWPhN0twzRWX4oKzT++c5PnDP7rhe2hsbsZnXyyA3W7TN9X+8Nbb8Oqb7+hHBrz9wccYXFyIKy7+Fh78+7/0M41POm4WOlwuHSSW58TX1jfg5huuxoXnnKGPK72EjzniUP3Zw4YMxnlnzN3qc1976z2kpCTpyS4XZUwmFBcWYPTI4SrNY78X2lUyDHRa3mgkJOUi4PPgmGmD8cvvHIobvzUVV585Hj+7/BCce+wo5GYkwmoxIic9ATecNxm3XDJdryvMScGVp47DrVccgmvPmYjrzpmk5mfi1CNLVT3NjcalT8DfVqGycBBNy5+Bp3EdGpf8Bw3fPKan+kWPoHn1KzCFvfA1b0Tdwr93rpPtY/ONS5+EzRCAPTEz+pfTgWJMyYV10rmwjD45EjSONWpUfjOkl8Ay8kS1/lswphZElu+JUBBhH4eQ3xf8qo00Yfw4eDpaVbbzIiM9BXk5GcjJTsfEsSN0j9Ps7GxkZGShtaoK/s2bkZKVjaTEFJQOHQ6zOscGVHpsqajAy2++q+rTAUwZl4vbf3IkTjy6FGNG5CA5MXLRInKN0wCH3Y9QwKMD0DU1Vao+Lb2XrWpVv7zMe1AYLVZs3LABM0YV6TbL9thtZl1Pau2I1MW6CwRDer3c+NOdBIQb2pz4YkUZjplU2tk26nB5VdmfpIPUMoT1zNFFeOSfDyMxRW72skT3pr2RlGDXk9RppC5jVXkpLTlBvyao+pVDTVarRb/abVa1rUMHqJKTEvRrQnRetpV8KeuTEhP0Iz8kl6YkJ6r8aUaK2kb2lx6H6WqZwWxEgSOMkoRI4LAkIYQcuwEWNT80MQidjSN/Iu0Fpm//JWkwY+JYjBo1Gpnp6ap8NcFny0WKSu8E6QGsJonjSq9iSQPpGSxFuMcfQn2HD83uAD5a1Yg/vrsFI4tSsa7dhxe+rsGLC6qwpqJNqlqoc/qwrLoDHb6QatmqtFSfa1Vpa9O9i6XXslEdH3CkZCJsdujfVUnxYAwpGoQhhXmRP7SP6pe1CIP6ETjSVNtIdytWk/wyYtRbudNuh6Kby2a2ZKkg7GR7OiCyszJ1ITDvq4XRu6Z3jdzxLs/kKiku0s/vooNJZS6VP+ELwpkXRuMoVQRJrxS1KNbRxagyXluhAf40E7IXB1H0nl9PmcuD6BhmRYf0JpZdVKEfI71RdW9TyapqcfV0I1yFqtQOqYq5PFOgKgRbY0j3Rq461ITmYQbUzLQgkGBA6toALBw5b++o71x697oT/agb6kfVGK9OC0kTT6qakoO6R7AEeW0tBgxenKAnk9+A2vF+BK0huDJD8KVL7+QwvDkGBCXgrI7RONiL2jE+uNUxgmaVjq1G1Iz3oWKsC56kIDYd4oIrIwR7e3S0AbVPojGAYpsTRWZX5DdBe059rSZjEMYkValqWYPGsq8wdoRZVagb1HIfDP5kvP7GOrz2zmJ4VbG8or4WX2+ohN+jfhS+VhhCEnYwRDu7RfOsIayH9Fq2dDV+ePNP0VDfrIeDkaG9mhplKOIwysrL8PLLr+OGG67DM88+hn899lf84P++h+LiQpX/DWhu7sC06dPxj3/+BX/4wx1Iz0jFbbfdiVdffSfyGXRQFOTnYeK4MXo+PT0NI4aVQp6nSQeH29+EhrYVqnGUiqHZpyI/7VCMzLsApZlnYGz+VThy2F0oSj9G90bxeDydU3zwPjIkfGT49xiZjy2P37b78th+Xq+389iBQOQGIbG949Duy03wqHI0UjeWxu02VN3rg6qtG63Sk6WhPrczUCx3wHfqnJVBqENoaU5T6bX1EC6Lauyo86gGVw9ifZqKEji8/J6SAPB/n39J9xKWZxMXFRboSZ4Hv2L1Gj3kf1NzMz78ZJ4OAre1tevA8Fdff4OZ06agcFA+/vbof/DF/IV4850P9DOGH/z7o/jrI4/rcvnOP/4Zq9euR4HaTo47RLWTZOSmRUuWY1NZOQoLBnV+ZkJCAt776FP868lnkZaa2rm8ID9XD13N/Lv7zNYEpOWO0vOBYBiDshPQ3ObBbx/9Cr/713wsW9+IoyYVqClfDzn7nzdX6eXvzS/TvYiHDEpDVnoCVm5s1Mtlvw2VrThS7ZOQmID0UWeoIxuQkD8VGWPPVZkyCL+zFsmlc5A16RLYs0bBXb8C3tYq+Nur1OoAsseegdShx8LfUQNH3kRkTrxYtbesCLRuQsagsfpvpQPMaIEpdwws485Q02kwZY2AZcypsE04E6b8CfJDim64B0IBBLbMQ6h2ZXQB7Q17YiI+/fRT/YzbE2cdgR9edxWuu/IiXH3p+Rg5tESVla1YsXIFmluakblwIYruvx+hDz6Ey+tDRnq67oVkMVsQ8Pt1XXrCqCGqvC/Aex878c3KpshpOXZ+169hmNW53e9r14HIxIRE2GxpaNW9j/WYAbQPyHDPkm7jijP0ENEy3HR3cgq0W1ReVWkoPYi731wnMlIccHv9Pa7T+9usePqDb3DE+CFISZTnIwNOtX1xTrqqOwdxzIQhcDXX4tXXXtPPUjaq8zXtHQnSD8nPxOiSQRhVos61VguGFeep+QIMLchGcb6a8jJRkJuFoUX5KMzJ0MtLBmVjzBBVbyrIQVFuJkYOzseY0kJkZ6SiVK0frY41XB3H4bBjhHodlJ2BYYW5av9MlBblYdywIgzNScYPi9binqLFODoniF8Vr8JvCtfiqJwA7i76Bj8uqmAe3ktM3/5Nrh9IoN9qs+mbYMMGE4KOTJjN6lxqDOlhp6WYNKnfgVm9yuT2h+D0BdHk8qHFHcDji+uwtM6Fuz8uwyNfVePpZY14+osK3PO/5bjrmaVoaffB5QtgeXXk5mcpviWcYTVLT2NpSyMyDHZiCgK2TPWbsMHucOiezlbpgtyHbXum6gcMpjAcqeo1VpmKJ2fd+BU6oNwziTGbbSGY+nYa9wtyF5Bc9JC77Ndv2BRdumvkYqTcmS930J9y/BykJCdH19CBJsMIh+UZOwED0tYBLSOMaC3eOqP6HAa0j1SFfn0A6evCMAVU+qs6eUp5CDmfeCRBYQwbkLLMg4xVW980IB0XG0cb4S61IXVVAFYJWKmMbHECeV8GYeswwJNtQN10E0LWMNKW+5G5KqyPT/uASlt7vQSAA6gv9UZ6HEkZK8G9Aj98qlwuWGpHQrMJjhYTChark2hTGEFVxqaVmZFWEdY3C8S0ZftRNskFhzqmBBwN6jh5q23qd2FC/bggVs5phy8NKPnCjtRqC4rNTpyQWIWp9kbYDUGUWtoxO6EG463N0SPS7gqpSo48t67okCkYctpsFEwdjF/8ZC4eeeASHD0jDampRrR7THj4X5/iowVlePnDpahrakXQ5ULI5el5GDR9GlZlekERNmwow1//+ihcrsiFjcgUWS+R6tWrNuvnL8pzEo899njk5ubr9eGwD5lZKZg8ZQIOP3w67r33DkycMBmP//sZvZ72XiQNdo3caTtu9Eg8/MA9+L9rv6uH0Dth9tF49M/34mR13t0du/O5tGN1rUtVQyUF6YnDUN06Hw31bpx9+U046rSrMePkMzHtuAvwuz8+hjnfOgcTj5+NaScfjyknHouTLj4fy1evwjHnnIEJxx6jp9v+eC/aOzpw6mUXY+ysIzH+2KMxbvZROPmSC7FMbXvkmafq97LtCRd+C+9+8jEmnzAHdz74AEoPm46pJx+npuPVfsfg1XfexlU/ugljZx+ptz9c7fvB559F/2raE7mO6OgaPZBeviGfAVVOe3RJRCDogD9gjwaJ5aae+Gbh1vkwELTA6916yEPV3ka1J0U1lqIL4sjwtvJsgmybK7qE9oSc/z75/Cs88LdHcPf9D+lJgr7Pv/y6bhNJoPap/72M9vYO3RNJhqWW5xA/rZa9//FneP3t9/RxpCeT3+fX09IVq3QQubm5RR/nngf+qo97+x/u1/ts3LwFf3jgb52fJ9O/nnhaH1tGaor/W+78419QW1unP4N2T0JqASz6ggVUOW1CdqoD5bXtaGrzwhc0oKbJqS9KZaSoPKrOi6u2yHMv/Uh0mPVzME3GsNrPiMr6Dnj9YTS0euBTmdLjCyIQMsKnh81T9eqUITCYE+B3NcFoTYItYwSMtnTYM0fov0OGuva2bIY5KRehhAIEA6qtpT7PmpQHkyMLIb9HbZ+s/146SFR6GKwJMOWNh2XSeZEgsWUvnksb9CJYvxberx5GYOOnCAe2f/6gXRcMG2GyJ2HS5EmYqiZnRzu8Hr9u2iQnp6C6ulqV2e1Yt3ED6keNRLbDgQmvvAxrRYV+3uLyVavw8ptvY/Gq1fB43Jh15GEYP/YwJCYPxoaydCxaUqvOwyF9c0kw0BUMDqj0lN5u7a52dfwWfL2kunMd7T1HYjKWLV+p03PSsEGqfO35wlG67q1ogtPj05ec4zlsqi2dk46yulbJzj2SnuWryuqwZEM1Lp4zWZX3PnjVOTsjJQEpiXYcPm4w/vKXh6RUR0o6R3rYF/TAtCo9JSjvsFkxpCAXVnXerW5sRUqSPPLBAJvFjASVfjIEbYLdqs6xkVEuJX3Ka5t0erpUmsvIWukpqlxW51TZRtI8NzNdPyYrVR0rFArCrpY51Hm7oq5ZVaK9yDT4kGhsR5Y1jOSwD2kGFzKtATgMXmQb1bl3O78V2jVM3/5N2j1yc+uq1at1m0g1dmF2pCDRblbpKr2JI88olqCukK/bperJsREi1tU4URYwojkxEbUmqzRb9ainYbMJHnVOXrCxBf/38AI0tHjVbyiILU1+/ZuS34Rcr4o8w9iIRKuU8aqNbXHA3d6KTZs3Y7U6n5dX1+vP6avirwr0GzJstMWxgyqSrFLn+KBfuqFvJ4fKjySkTsXmMEw2VrcONsmM+bk5eihqudN+d1XX1uHdDz/BiOFDVaV7lC446MDT37pq4AQsQOaqEJLLgfpDLOiI6+ziT5Ln3KoTaoXaLsmAqjk2VB5nQ9VRVrSPMKO11ISAKpA9eWq/gq4izBg0oL3YgJZxFiSvUifn1SFd2GvqxdYaRuKWaHBZNbAMHUGkb5BAMfP3viInz+wtNmSsN6Fymg8dWYHOHuPu9CAszWFY3UaUTfdi49FuVE30wpsahjs1iIAqsz0JMvRlZHtPSghlh3mR3GxF3lp753PKjQFVFqyxw+AMIphoQMpmA9KrInfVn5u0GbdkrsCVaeuRZArg0MQG/CRzOW5JXxH98dHusockWGxC2XvzVSaywX7VuXha5WFPTgZ+/bML8aufzcIpc4vgDwZx959fw7sfr0XQGYDZ6VK1MbfuqRKG5Lu4hnW0/J0950j88OYb8dyz/8PLL72hn1McqZYYMKS0CJdddgHeeecdnHvuhbj2ezfgif88g7ZWp05L9ScpQX23oEXVAtPSUzB69BisWLlKVtBeknNuSnISTjx2Fs445cSdTqeefDyOPeZIfYdtU3OLHq50yfKVOt9OnTS+x316muRZmjKsKe0bra4tSDDnIdk+GBOLrsHmTW7VkLXjzSeewpv/eRq/vvlHePHuoUyFAAD/9ElEQVTN11WDOQn/uf/PeOepZ/HiI4+pxk01/vvi/+D2eHDKnGNx2vEnYlBuLjZs2oiNW7bgub//U+//v4f/hcSEBCxY/A0qa2tw0uw5OPOkUzDnyKNUozkaqFC/pfNPOwNvPfE03lXHv+SsczB/8SJ8vWwpDps6XW9/0jGzIcM2Rfah/cETssMViJxHYwKqMhaOPh5g+8Kq9I60l4KqntVdo3frADTtO1IOy7B5MsyalKXyGj/1tEy2j18u8zLFlsfmY+ti2+1sim0b2zd+Yu+mPWNNSIXJYtfBWv2c4qxErCtvRnKSHYXZDhwzpRC1TS4dmGhp96KtvUMHh/PVdhIUlrSwq/pPU5tP90icOjIHE4ZlYWNVGzx+A0Jtm2Aw2+FITNPVrkBHNaypRXrY8nDAA1fNNzCY5OJWMgKeVvX3ZOlhTf1tlWq5FSZHBnzOBgQ9LbAmD4LR6oj+5dRnhQIINW6Af+Xr8K94CWG5gSCtGIYEBp72ldyiEhyq6kAy2oPUadweN8rKK/DWex/iyf+9gkeeeBavvzcPn2woQ+W4cajPz0daQZF+zmEoFMbaDeVYtnIjFi5djfLKGn2jjxzHqBo+f3t8CV5+vxwLltWqc7p0arHA5rAiLakek8aaMLzYh+defh1rt3BY8X1JznGNLa1Yv349Tpk5KnItuRspY9tcHn0jT3KCKmPjNvL5g5hQmge7xYSVm2uiS7clAQwp11//chVOPXQ0xpbkIqiWtTk9GDs4F3ZfM1568UVk5RfBxCGo9wmdTirt7GaTzmtBaYcYjGhoadd1MElPq1onQwtLL34ZilhuBpC8Kr0JZThjyZ8Wk4wKYFb53qPrSx5fAL5AGA67VV+CkoCk9DKU51+3u33ITk1CjduA51pH4On2mXityoxXnKPx7/ZxeKPahlddk/H3hqG6X9u2NW/aVUzf/q+qugZOp1Oli1l9mao9Yo7UVS1Gg0pDVX7LF6wmfeOAnpXnFav0VWnqVm3gkEp7EVb16ECCA/LI2qBKV5GWYEFOWiK+Wd+IvBQ7atqlTt5DRxjFYgzDaE/VvytpWVdWVUVW9GE7u0LQZ0mQVzKf7iPejRQa8j8dLN7edanobvJ7k5M/HVzSCJaL0G0dkTvnhTxza/yYyPBdOyNDFLzzwcd66FO5S9OujkUHQVgVz5IxVRqa/GFkLw3A1BpE7UwLXGlquTxboEWdqGvDaClV28pJuNyPBDVZmoNwqm0SalWDSe3rSzfAlxrNnMEwXOlA7RT1O6nyI2tZSEa61ZOmXtsGG9A6yqyOqfZXxw05DHpIanmGMe0jKh0MQaB4kR2ORvX9TvQiYFSFrDp5JteY4M8xoT07gMyNFmSttap0BDxpIdg6jPrZxTKstBS+wXAQVWO9gErnIV8kwOxVZXXkE+BzBLFlsgtIMCKx1oC2EQbUDos8G+jR9uG4pvoQ/KZ+IpoDVrzSVoirq2filsYp+jdAu89gUg1eVTm2qYqy64sFKLIn4pX6dvxm5QY8WlGOvPGD8cPrTsZvfnGOqqQZ0NLohbHNC5OqTEnv4rBUyvWX35UAkmvlXUidgM857zTMmTMLv/n1nVixYr1aYVOTKgccZlzxnQvw0iuP49e//pmq4IfUNr/H008/r48gpwEDTGoyqnO0agD4A2hra9XPZaS9J0OKHjpjGm667ircfP3VO51+eP01+jmaDrsdm7aU6Z5qcs6VBpWcc3vap6fpRzd+D+eeMRc2myofWPnaa1ZTom4cp9qH6vcSlEh0JKCksAiDCwsxKDdP14+MquGck5Wl3+dm5+iLVf5AAKnJyfp9bnYWEhMjx5Ln4pUUFev9iwsKkKyWDysZgod+fxcGFxQiKzMD5ZUVePz5Z/Xw1pLhk5OS9bFlkseJSMX6r3fchcnjxuvtpSH+t//8G82trfrvpH3PbvCo38PWjR6Tei/Pi9f0a1c53UVKWXVuV+tN8riPbjJtPT+bj/aetHsOmzkdRx0+s1dPE8aOVr8Plte7y+dqRdAfCS5kpqcgOcGCU48cijuuPQw/vHiaKlut+NcrK+CwmVHX7NK9ijNT7CjMSZbqtg4qywXKs2apfa47DDeePxltLj9e+XQTDEYz/O2VsCTm6oBxOOhDwN2kKl5BtGz6BM2rX4KrZimSC6epciGog8eW1EL9mACfsx4mezoM1lSEnNX6vGFOyFTH2Ho0J+pbwt52+Fa9Af/ylxCsXQmDJQHWsafDMv4sGDPleci0LwRV5lywZC3+/czL+PezL+Kxp17As6++ja++WYGa2gbdQ00CEeVVtdhy/Amovu46BDPSda/S8WNG44gZE/QQmtJb7ZtFC7ClfLOe18+0VeXAo08vxJ0PzMMv7vxYfcYyfPhZOdZtqMLbH3yFBx75GB9/sVb9Ddu7wEl7Qm6isdgcOlA7oXQQkhyRQEI8aZdWN7ap9PVj1pRhutNR7LQo58erTpmBZZtqUKG20RXj7ZBa1oI1FVi8sRY/OOdIFGalYrna76Zzj8BrL7+Ilg4XMnMHRTamvRet1kqgf/WWajS2tOu8lpWWrNLNqM67Xh1g9Pt9cKu8K8EDp1omaesNyI1ckUeeyYhacrVKhhnXgUarWQ9P6/FKL/MwPH51/lTbmUxGuLxyI30Iw3MScHrqOpyf/CXmDgrgtMRVuCxlOU7J9+LUhG9wTdYGHeiK/om0J5i+/Z6Ur2b1vXdvh6hFnWSNfNc+lUzyfGEZhtphtaA41QxV041spEiPYp34SkildZrdiOvnDker04v8ZCu8eghrSd0dM5hM6jejjtXH9dtg8Y7IyTz2Gpun3k0KYQkSJzoid4okqNcrL7kAl55/jmos79owTHX1DXjz3Q8xddIEDBtSEl1KB5K+TUOXm5FC2NIBDJqnilt1MvanqkI+oLYxhJGyTpXkqRY0jjYhZUsIqRvVNmo/m8uI1M1dmdbsjsxLhdyTo04UfiOyloZgjo2mpT5Grn960gyom2pByAKkrQlj8PvqJN9hhCfLgPrJJrU88vfQXoqeXE0+ow7yGr0GhMxS0KrkrLXAUR5E9RgP/PZIMF+CwxmbrCotVOJGklKTvC49kod84YDF3ZU2YVMYVeO8cBUABQusGPFpMhI3BVE53YfGwT40B61Y509BbdCOoEr8dpXgmwJJqA5sPXQm7bqgRW7JM8NrC2HuoRNxz9RJOFylwwaVLA+3ePD9BSvxz8pqVKg0rTW0qm09MLS3I9zRhrC7A0FV6Za01RU4HYxQb1X6RlI1jNTUJNxyy40YNCgfv/n1b1UFXsp6oLXZifKyCqSnp+Lkk0/GL3/xCzgcyfjyi/lqP+nnpsqMgB8upwtNja349NPP8dFHH+O0007SR6a9J5VcCdpu2LwZr7z5Dl5/573tTq+9/S6+XPiNvqAlz7k845QT9LlW8vKqtet73Cd+kuN/Pn8h7DabPr9L8JL2Xn7GNJWnVN5sX6gavS3Iz0tDbUMtzrvmO/jWNVfhtj/+AVPHT4TL48YNv/iZXnb5/31fD6V0wtGz9Egu0mt4/jff4PX33kVldQ3SUyN3zQp5SU5KwrpNm/Do00/hswXz8fmCBdhYtkUHmjPS0lReNeCVd97Chdddo47/XTz98osoLR6MP/z9b/jkqy/19t+sWIbkxKR+0bA6WOo9tujctqTENdrCGJS4dWDXZJI75AO6RI2cgyVd407Gej7yXp6HbLZsPVSpNLzz7E5Vh4suiBOSk7g6fci5mPaMy+XGx599oYeG7s3ToiXLon8x7Q5XayX8nja4vWGU5Drg9ATw9pdb8NqnG/D46ytx470f6GcQf7CgXC3fDLvViHZ3EM+9vx4Pv7gUSQlmtDl9eP6DdXj9s434y3OL8fOHPkFTixOhgEdNXphTh6hsbULAqxpcqj4W8LbDVbcaIb8TqSNOQnLpcfC6nap8SIUlaZBqh6n8rLazpw/RPdckwGxJHoSE1Hx4O2qjfzn1KT4nguWqDvDl3xGqXqraV0aYCibDOu3bMOaNg8GaqE4G7KW4r8gZU27Cc3Z0oK6uHo0tKo97fJGzq6o0yXqL9EDzeNEuN+tZZISPrvOuBIxTk5MwOC2Am483Y86MIAL+Daiu3oRxo3IxYVS27tW2YEkl/vHkIvzq3o9w6z0f4m9PLMDKddWqHh7c6ni09wxGIxKSUvDSK6/AptrA40rytvmOpV7c4vTg3+98jW8fPxVFOanR6lMYwwrkmal5ePK9RbuUNhK8+vurn2PGqCIdnB46KBNJYReefu5/yCkYHBnNg2m8z5hU+dfmdMHl9qKtw6UDQnkZyWhp60BDc5u+oUuGfneqPCu9CmUbqS8nJdhRnJuh6sYWOL0+uFW+zE5P1m1fCUTJoyBqG1v081TbnG51Ko6M0lKQnYZQMIT6VvWZYbVvMBkNfnV+N1jRHE5Eg88Md9iGupBdXxOhvcP07d+kTDWG/bAafUi2dMDgj4ys4Q1s/eVKp5YmZ0C/bml0w6rK8vGDkjFrSDpM0esaRp8fBt3RJWLsiDwMzUpCgmroyk0CMuS0P9hDozcmrFq96jdgMQT0NRTdk70PM6gvd5d/ojNmnxKd691kCOpRJwbgSA/K1Qr1y4i7UKHeS5qZzGasfsuI3HEBpBWobeRrkKseMfKt6G0NWPWmFc76vnHhcv4Hr0fndl9vTl/pYXTyCXNw9OGH4ue33YnhQ4fgjl//DCZVefvNXfdhwaIlu1T5GjK4GL/52Q+xdv0G/Pkf/0JrW/su7ddb9PX0DZsNqJ1qhCc5jMEfhDuHgHbmG1A3RVV8VaFe/KEqZFXWrZ1mQHuBCUYZZ0nYTchcFkTaesnA6jgzzQipBlHOkhCqjjTr5xVnfx1AQkNk84Adeujq5C0BVfBDB4XT1gSjzygOw5sqAWQzgrYwCj4OwuI6+L+Dvpy+AfU9lk9xI2ud+s4bVXqoZe3ZfjSMUWm2wYiMcitcqkyW3sYxppAReYstcLSZUDfMC3PQAEu7SpfxqlzeKPvYdHzRnRxEzSQ/8pZY0FQSgE1tk7XRKvU4BKxhVI5zIbnBgvSyyFAw2QYPvpO+Hp85c/CpJyfyYb1AX0zftatU/lFlpNnowVtvPowx40qxss2D29ZtwmpnZNhwK3xIcnrRsGwtWtdsRrCmUaVtGJnpIfzj1utw5OgRCOuH/8tTlgyor2vCd77zPRQVFePPf7lLLTfg00++xFXfvQ5OVZl/++1XUVFZgd/dfgdycrMxtHQYKivq8NHHn+K23/4cRx9zKE6dey5sVgemT5+G1tZWrFixElOnTsZPVfl+8fdu0H/7gdbfzr9zTzwWP/r+9/SQ0n948G962fbOl3KRZMrEcfj9L3+qh6+WxpKQXkpyrv3v8y/pbXa0v4wU8s8H/6A/T3ome71e3SDrLfpi+s579zlUNn+CoGpEhUI+1cjJQF2NGevLFiHZXgxPsBZDBk3EL+78My4561zkZmfrtCguLERR/iCsXr8Ojc3Nnek2onQoKqqrMHXCRJ3GckF07YYN+qaCTeXlehshQf+hg0uwReXjr5csxsKlS3DxWefoC1wybPW4ESPx+dcLo1urc4Gqy40YOlT3PD5Y31Vfz7+nDW/Cz8d9ibAqlPXzguNI4NZoA/65ahL+sSzy3PeYUDAfVZWFak72UQW67Kt3j7SJVCmvg8mpqW1ISduofgtdjeShGSE8PusDmAL+Hj/TYA7jq4ZSfP/jXRsFaH/qb+Xz9kj+FZJnu893XyZi72Niy/uavpi+0qsls2gK0ooOxc0XTkZqkg2/+9d8uL2RPCZDTEv6yBCmqojUIz4Ir3ovQyLecuk0eNS2t6t95Dl5MpKL9EKOZOBYuqp6l8kCZ/0atK99GekTr4A1ITK6g3x+KCjP5otsG/uNdOk6TjDgQ9nSl+Bqq9bvD7S+mn/NRTNgHj5HFaQ7vhEq1FIGY6Jqr1j24chn8lzi2lUIVC1FuLVC/Q1mGHNGwjxoMozpRWqD6G9Epbl/w4cIbv488v4g6F/lcxglaSHMGmOH2WJFZYMLCzb7Ue+UvKnOmyWFGFKUjw2bynD2qSchQdWJ9MXJaP6T0VtefP1NtNWux8/OzkegaDRGjsrDmvWNGFmahXc/rcb/3liG1RvqdQr2hRK7P6Sv3+vB2qUL8Ne/PIhwzhj86YV5qh5slOzTKVbq/vrbxyI/IxkPv7EAi9ZV4qwjx+H4qSNw6V3PqHp45BrJjshxfIEgvn3CVJx5xDhVXzfj57fchKee+x9yC0tUeRzQk9lq1b2MpSw/mPpy+sp3nZTo0M+G9vkj516b1aLOpVa4PF69LDE6IqWMriTr2p1uPVyxpL/04tedYaKJKj0cZVauXbu9PtUODiJZHV+GJ5beqLK93BgrIyo5XR4UJIRgVX9EmcuIwWreFTKi0QOUJgZR6zejxStHP7iYvkzf7ekN5XNqog2HlBhwSKkfK5uLUJF2PAZPmoWhmWYMSjMjO8mgewgnqanR6dfDU7eq14/XNuLIURmoagniP0vr8MWWVlR0+OF3eWF2e1FsN+HnZ41CdqIJazbV4/ITxmBNnRPF6QnqWCZ4/GG0uUNodIVR3hzA+no/qj97DBMtn6n6lxNvLzdhQ6M6d6jfyMG0N+nbL7tuhFR6+N3Sl2EHVK6U7YRZnnLeXUg1qNUPKRQwqLr2Do9EB4BcMK6orEZuTjYG5eeiZHCRHu6ytr4Bh8+coS9M7orKqmq899EnOPLQGfj1T2/GnKOP2OWeybT3DMEwshcHUfhZaKtnBSfUhFH4QQBFnwRh8qmCSeXNnEVhDHkniOKPQ3oa/LZf9zCW65H6OAv9yF2sTsjqhDvo0wAKPu4KFAu9/BMfUjfIs5HVMd7x6+ckxz5XnmE86DPZL9jZQ5n2nAwVPXiBA0mNqjEsaaSWJddbMPgzG9IrI3esJzSbMGSeo3Mq/twGR1uk/M3apLbbYlX7W1Dyqb0zUCzHkm0i25qQv1w1jDZFh39S6+RzixYnIL3M0rl9Q8iO+5tH4QtvdmQ72mMmg0lViAM46cQjMW7scJjVlzwuyYEfDC1BocqsprAB7RYHGlMS4Zg6AUPmHoeU6RPhlDsuPapSLvd66Bux1DlVHxGwO2yYM/tozJw5Rb2TDUI49LAZuPePd+Kcc09DaloKpk+fit/9/jZMmDAOVdXV+jx9/wN345xzTkdCQiJOP+N0HKLKcQlSyTb33HMn7rzrtxhcUqw/g3adXCCWRk/3KUYaS6Kn7WLkAteylWvwmzvvw+/ufQC33f1HPcn79z5WleboNqL7MeQxE0KGeNpK9+3URLvHYnIgN3UavP5m2MxpcPpqkZrVhENnjEPW4NXIH1KP7Iw8jBw6DEfOnIljDjscRx96GIYUFev0GDdqtH4vy2UalJeHGZOn6ECxkNfRI0agdHAJ5hxxZOd0+PQZyMvJwSFq27zsHP1eH0Mda/rESXA4HFttr4+dm6ePSXtmU0sSAqHIuTF2UWMrqlE7M6sWKd2qyyZTLbKyWtScymNSHnfbVwLFyUl+JKVsUsftChSLUwdXqt+YX23R0wdGLG9Oi87R/iZlZGFBPuaecKx+X1JchMMOmYbSksG4+Lyz1LnTgTPnnoSJ48bom2flsQ0zpkzS28symR9WOkTvS/ufDO/cUrMKAVcN3vyqCo+9sUaWIsEuFzIlUKxPg/o5xfFD7Nll+EOzES99Uoan39+MlAQrHFaT3ieSgWPnyki+NBhNCKnPMCdkwWpP0p8bDgXVFIgeU7aTi6GxfBx5H5uT3jLN1cvg7qjXy2jf0kOEr30P3sVPI9S0SZZEVuyFUGs5fN88jcCadxBuKYMhZRAsk86HdeRJMKZLPTn2G6F9Sb7VBEsYZ07w4ughbTh1WhquP7UUd12Yj6Jkr77BatywIqxZvwXTJo2H1WrVN3noHKfyXywPWiw2jM0NICPJiraWDh2EGDUiB+9/Xoavv65Ae0OD/qy9/6XQrrJYbUjJyMZf//Z3zBwzWJXT1s4yuZN66/L6MH91OYYX5eB3V56IZ35xEa6ee4hu68w9ZHRks51lP7Vetlm0pgKJ6nMqyzbizbffgdflgqetEaNLizDnyJnqd1CN8g1y3qA9JXlInkPrjwYShQQWWztcncFFp9ujJ1kugUTJpxJobFfbyHIZOcCt3suQxHKsDrWNrJNAomwr+8jIW7JOAozSc1XmRbXLgC1qCqo/ZLN6rfOEpbqO9U4jWn0HP5DY1zF9+79gKKzSxwW7yYfCxCYEPC1weoOQ+y6lx3hANW3l+5fX7CQLGpxepKvXo0Zm4YF3yrCivANnjkjGxaNSkezxwKLSM81kwHdnDUFRdgJq2vwYNThX17NNRoPuXSzk2H712dKDWeLBbrcL6YYaFKQ4UdmeDL+MnHqQA8V7q4coad8XDhrgbo7LfHFBqZ5s74QtuwU8Rqi2FPUCW8or0NLSimOPOVL3fnGrzPzU8y+htKQYQ4cMjm61Yz5VkL/46pv426P/0T1irrrsYvz21ltw+Mzp+iIK7WcqT5m8kUBuPAnyWdxquZpiTH61rCMMqxN6sqhJgsiaHEcdQ44l5HjdjylkmVHlX5msHepzJC4Vx6RO0tKjWAcZaa8ZVdnbWZyq71S+V1NALZPup1HyPn5SW+nlkrayvd5HjiNpIpMW3Va9N6pjdU8vvazrk/VurpAZARkGk/aK2WBGbk4KbrrpO/p5WvLeYAljYnoiLizNQ6LVC4c3BHPQDLfNipbsJCTNnoaxF5+E4dPGIDE1Wfc+Nkovs2h6JCcn4vs/uBYXX3KBfi/MZgNOPvk43PfHuzGoIAdp6ck4/IhDcOsvfoJ///sfePRff8GZZ52EpOQE5OZm43e/+ykeeuhO3P/g7fjpz2/ACScdjty8DNh0rxraVXLxwmI26+f4d5+sFouqH8lzYMz65iy73bbVepvNptfHyHA7MpS09AyOTW9/8LF+BEQ8CULGH0eOK8ePZ7Vaevy8WGCZdpUBDksWRudfDH+wAy3OtahqnYeN9a8jI3E0ppf8CIW5Q3DvL3+z34K1Z50yF1decBHTbj+rd4ZR5U9WmTq6II70+pWRkorSmjE+q22rdo9aA0fCJlWu1sGkyvbIPfORwLE8p1j3KE5fqealB2KXguQAjsnehLC+K37rc60eglpeA0YsbUjS83RgSPvoiENn6PlBebmYNH4scrIzcdJxszBy2FBMmzxBLyspLkSKBIunTtKPCrjuu5dh1lGHo7GpiTfmHEABnxNly17F8pUrUVkjN22oPCmP74iS2JH0GovvOSYXJ+Xi2MoNVahvbIEMqSfLJN/KJMkn5+bYfvJc5KSiI5A58dsIGySgHNlO9pGpa1u1WOd/od6oWZM6/7fWrkX9pq90cJn2AwneBzy6969/8TMIrH4TIVeTWr6bFxnlBgBPK/xr34Nv0X8RaqlQlalEmIcfC9uUi2GSILF522et0r4l98c6zEH43B2oLVsBr8eFkkEZ+MUZ6fjFmZnI6vgE5dV1WLpybeeNd0LKXcmLUpfOCFfjrEOzVf25CYbacmwua0FtjTp3V27EDUcFMDxHlRE6v9IBo9ImK3cQNm/ahC2rF2P2pFK0Oz3RcjNCAgOzJg7FD844BFdf8W0cOXM6Hrj7t2htrENGsh03nn04rjt9JgIStdiBQDCEkYXZeOwn38LmmiYkZRVh8fJVKKuux4LFy/HYf5/Fw//8J37205+io6URwcDW9TPaPfpcGJ2PiZxTtxVbHr/PVsti89H3ej66bfx6IXPyqJagqjPrefUam5drWDsJYdAuin3/8eLTIV5sefw+Wy2LzUff6/notvHrhcwxffc/l9uHpg5gTX0StrSmwuppgtMdgNNvgMsXhlcVj35VpkpwV5JnSGYCylvcyEwy47dnDldH8OGfX9Xiv8sbkZ+ViG/NKMSDl4zHlOHyKC3VSlbHOWx0Ntz+sB7Vx242qONFjiWDgkh93KXWBZ2NqG8zYFVjNnITWiB9Zvq6fhosVgnWLGfu2LR9kvjbiOZcOfk7G1Wm9u34GHRgtLa24X+vvqGHoj7myMPQ2NSMxUuXY8myFTjjlBP10Ia7or3DiZdefxu/+v29uO2u+9T+K3HB2WfgiovOR+GgfF1RJ6I9IEXnzio+sW162i62bHvrxfb22972tFfkGR6HzpyCwcU5qmyUipEBZvW/JEMQZ2Rn4szMVCSEXer7DyFskD5oQSSomsXM4QX48aXnYmxhvvRVU0uVzqJVEkuWqkkeYi5jyHeujC7Xe0QnfXeALJNal3qv01vtEz/p6kxsO9pVOVmZuObKS/HT/7t+m+nk4+foYS8nTRiLW35w7Tbrb77hah10iA8sRC46bz3FyHxeTjauvvzibY4lx//2Befq7aZMHI+br796m21kSGzpJUe7TwIBQ3NOx8yhv8QRw+7AMSPvw7CcM2A27sMhL+mgavdbsK45WRWXkZNh3PUKPS8N3uSwF+cMLUOKdesWbFiVrXZHOVJT2lUJKnlWQshGWC1epKWXq9/P1hci5TlPp5dWICvJo7ZU+VyXvRGxz5LDbPGkY1MLgxMHUiAQ0I9ziJHAg8lowhcLFumAcYtqS8kztPQFLfWf9KSorauDW3pPuN1wutxxqUn7nTovSqCwcuXbaG3coMtqg35+rOTDyDk0/uJj3Cm184au2Oquc25sI1khU+QYkSB0ZJ1sFgsmi8h6vUZP0ptYRhKo37IA1WvfV1U8BiMOBAnIByoWwb/oSQQ2fYawO3IDwc6Eve0IlH+lg8TBsi9hsCTAVHIorJPOg7n4EFWZ39GNlHLTdNdvjPacfIuegAFr6i2w2qwIBQNoaShDe4cbBQV5SEi0Y3P0/snyqlpUVFaqc69DB43luZjybNzK6mpsaTVh3uZEvPJ5Od6ZX4mNXy5AcP0K1bZKVNtaMaYoARZj99u0aH9zJKVAhop/6aWXcPGcichLT9bDzko7SIadnT6yEL+4ZA5u+tGP8cprr+nhaOWRSzXtAVxx93P40d9ex9SRxUh0WGHUo271TJ47PffQMVi5pQ7f+cPzuOSOp/B/f3kNv3zsXTz4wudYV1GPZUuX4amnnkZiSrp+vjwR0UCjS1H1T1WbGV9stKHWlQibpwa+9lp0eEORXr+q6iuTBHjl1WoyojQrAZWtftR0+HDalDw8cPYIPH7haNxz2lBcfNggpCZZkGxT53HVfjp1eq4OEDeqbXOT7VCHUe2oyDORnT41eWVSn1W/EZawD4srk/DiIhtavX2/A0ukhdDPSNXJ1WRSFW4T9PPupOa2nTpwrO0T1o2rqOi28my19lqDqgRE3tPBJWn5uaos/+eZ51XlugpPPvsCGptb8PLrbyOgKuPHzz5ql3uuSKO4w+nE8lVr8MQz/8OD/3gUWVkZuOXGazF6xPBoY5uIaGBLSjLjuNmHISUpQZWLqmakG7cGWGBGmmqcXlNShHMzktSiMOyq9jQl4MctpXn46bgROLJoEOyqkmVSVY2t28TyJhrgjVyxjC6LTdF1nVNsudyBL/vIW/WPXOSMTVttT7vq5z/8Pi485wycMOeYbaYJY0frXsDFhQU4+bjZ26w/9cTjcNVlFyE1NbUzYBy54Lz1JGS99GA+94y5uPDcM7c5lhz/0BlT9bbyqIkTj521zTbyDOVf/fgmvQ3tOaPuWUb9jStgxMK6THjNVt0Oir+MHM2eakkIM7Kq8N1xlZEFcSSPBoJdDdvIs4pVW0pu6OlmUo4L5xStg1k1kOSTutOfrRYvb0xDi48XMQ+0USOG4sZrrkRebo4uw+WCdXVNrQ5IyPDT6zZsxEmqzP32heeirKISF513Fj6e96VOydEjhnWW23QAyHlSvXhdjahe/R6q1n0Iv6cNxmhwL/LcfjmXSoBf1cGUWFC4+7QtWRZZHtkmUj+KbNsVQI6kt5rkVU0mVYZ4nPUoW/kmajd+joCfNxAcCAazDebhs2HMHql7CAc2zYNv0ZMIVi7SQeQeqfQL1izX2wU2fIKw+h0Zc0bDMvFcWIYeDUNitiR4dOPt6aqr0d4Lho14Y5UVH66zIcmh8rG/A3Xly+F2diAtJQWpjkh6yDCnL77xHr7+ZjGqa+uwau1avP3Bh1i+ei0OnXk4PtkQwvsbE/DeWuChV6vw8kdrsWZDBTaXlWPpZif8caN20YGTlV+MZ597HnWb1+Cuq0/WvYDbXR6kJNrxo28dg3/+7UEMLsjDF198iTfeeANzTj8fdz//OTbVNGFQVooeVlp6p+2I0WRQ52sjinLS9DGvmjsT58+ehB+cfThuOPMwNG1chksuvQQbtpShoHREdC8iogEmWp+VnsMetwuhgBfGcADGqq/Q5jWiwwcdNHbr3sXhyBQKI8lqxsgcB7ISrWjzBFHR6oHTH4TNbESq3Yyx+SkYnZeIycVyzjbD6ZP6twFpDhN8gbCepKexM3r8DnV+99at0X1OfT6XvinX4w9Ea+B9l0FVDne5dtgbHmC9q+Ra2LCjfcgoVY0glZiaOvFKCkp3caPRglVvGJE9IoD8cerHo34k+mq2bCpfifrhBbwmrHzVDE9b30nmvv6A8l2l7+CL++nKc7dOP+UEfDH/a2zYuHmrdTsjjWYJMmdlZuCsuSdh8sRxepjqxctW6LsEe5OBkr4DFdO3f+uL6TtqyERc9Z1zkZ+XqutjYZi2qvhIFaLSF8C/NpYjyWjC3MH5KFQVMIdstYO7pven86+4Njp3YPXF9P3q/ddQ39Cony/c0dHVG21nvD4vbvvZj2C32XDdD3+mh5re0XlXztkylLT0EJbA709/c4cOUlh24274a664BDOnTzlo31VfTN+nH30oOtd3MP/uueIUP/541FcoNLVFl+jmjBbLnhLINViBh1dPwlOrc1XjOHLR0mh0oKZyGHx+eahxZBhqozmM7JzNMJmb9DbSo/iowjb8ctIi2ND13JDYZwj5HH3TrtmK382fgvfL06NrDq6BUr+S5/jn58pIIEbdi1hupvV6vLBEe6HKEP9NzS0oKhyE5MQkrFq7DkUFg3R5LCM0SWC5ta09erS+oz+lr9nqQHbJTKTljlLzCbp3osq1Kl/F2qSS4SRDby9IvH1dQ1zHjhHJ7RKyNhrN8Llb0Fi1FA1lCyOZuZfoq+lrKj4ElmGzVQG7/Zu0pGe5b/6j6tUL64RzYEwrQrBhHYIbP0FYnhMdCsKQUQLzsFkwJuWoY5nVsgBCHQ0IbPoUIbWt/D4MyTkwlx4JY9ZuBo/CQfjXfah7JB8s/bV8HpPtxrEjfBihks1hDSMlNRu17Wbc/EwbvKFI/Te+V7d0XpH3yfYw2r2xh0LEcmpkW7NRpVfYvNXy3q4/pa88Y7qmfCOC7nbM++ILLK/swHtfr8e1px+K9159Di6XE9fd8AM889FSvPHlKmyoakSiw4ZjpwzDj849DJVNLnzn3v/B5fUjFB3Nsju5/viXG89EltWPLVs2q99NOhrrqrBk6TI88eR/sWHDRqTn5GPQ4GGqfnbwe68NlPrVQMX07d/6evrKudBqMiAz0YzUtDRkZWTq2I5h0qUoHVKKojQj8lLUOjv0zVryzGGb2aBDgzLCg+wv17Ck17FF3suCKFkuxXSzK6gDxX61jdsXQqsnhDZPGHVtIWxpCaNi1Vdwr3pDXx+rr69HQ0s72uQxTWr/g32e3pv07bfBYpGUG8TokwLqVxCOVMQk5eVVxooPmbD8FTPyx/sjwWK3DHWpdtKrVWPabsTmL42o/Obgn4B3x0AuzFOSk/Xzi2vrG/Qd9DsjF68zMtL1M7xKhwxGdmYGUlNSMHzoEKxYtQYP/uNfaGvvXRdMeLLu35i+/RvTt3/ri+n72VsvorauAdf/6OeoqauPLt01/3zwD0hOTMQNP/6FqhjvXrD4ou9ej/UbN0fX7pw801iGqpYeyAfru2L+7d/6S/p+d3wVvjtiMcKq+SNBW3lecUwsi8pyv9GMd6uG4P5vStDqkyFnU1FXXYpgQIeJdbBYOiJmZFXAZq+FwxTCOSNrcHnpCiSaIsMy9XR8WWYwhbGoZRBumTdBX/DuDQZS/tW9SNWrpIw08+V9rLkfu9k2FmSMvwzQ/UbcvqS/pG8s3YxGE+xJ2XCkDkJiagES0gbBZLbr4aFFOCg9TWNpFUnLyPvYfExsWVy6qoxtiAYvwwE//L52OJsr4Wqr0q9+T6v6XYTUdmq/XvJ76KvpayqeCcvw2fo7356egsV6ubcdwdpVCFYvQbijDgaV/tJr2KB+E/I+WLMC8LlgTM6GKW8CjLljYLCn6H13i0pr//oPENzCYPH+IMNF5ycHUJgSwKDUIMJGK15dboIvNHBGeelv6ev3eVG9eT2yM1Jx2eVXYOiQwVi8ZAn++fDDKCouxq9+9SvkDxmJ+nY/bBYTUuxGVG9cgw8/eA8XX/k9XPfXt+H2BXoMFssSs9GAp269EP/8873445/uh8VqhdlshdVmh9WRgKSUVCSnZUZ26AUGUv1qIGL69m99PX2lPSM3uo4ozNEjISYmJuq6bSBlMNImn4vinGTkJRuRlWREmsOARJsBjriAsdSQe3osQKw95NUDu0jQ16ADxdLLuM0D1LUHUd8RxrqyGjTNfwIJcMHrD8Dt8WBjZR0aWzria94Hzd6kb+9owe8nHXUm1KwwwSS/glhK6dcwAnJbgOL3xH4Y0W3UN2JSP5zmcqBmed8KFA90Ethdu36jHupSLnjsjFVVvM4/63T88sc/wBknn4DkpCSsWbcej/33GTz9wstwubt6TRAREfVnEjTYnUmGSNtT8T2Kezp2/KSDGuo/0y4+ZoJoIHtydR7WdeSpDCPZRvJQdIUSqxrLcgsCKE5pV/kqsizgt6mGcVce00+gD6nJL+NEGBFSOxc72pFk9qu95QgSkOw6uHyOLNPzar8n1o7sNYHigUbKTbnIIa+x9zGxix+x8jVeXw0U9yexFAiFgnC116Kx4htUrn4HG79+Sr82VS6Ft6NBrffDaLLAZLGryaaHrTaZbToHbj0Z1HJrdDu7KgNMCPk9cLdVo37Tlyhf8Ro2f/M8qtd9iJbqFfC7m9XvItr7mL+HvRf0SmJG3+wegy0ZpuLpsE44F+bBh+obBIJV3yCw+m0Ey+arQturls+EZcJ5MA0+ZM8CxZpKZ/V7o/1Dhosub7Xg83IHXlyRiJeWWQZUoLg/slhtKCgdiTZPAL/97W34zne+iz/+6QE40nOxbuMWfPvSS3HBGSfjjluuwe23XIurLvkWvv3tS/HKK6/q4UmD8tDL7ZB6VYLNArOqnG3evBl5RUMwbNxUDBk9AUXDRiO/uLRXBYqJiA62UDCEnJwc5Ofnw2azIRhWLdf2CrSsn4fGDj8anSE0u0JocUM/Y9jlDcETCOtJwoLBUHibSarA0ttYKtN+VUWSfVzRQHGTOl6jM4y65g50LH8FZn+73kc6IpaUlCAhIVHtJ7Xwvq1/t+JVAlctMaF5i6qQyUVGlYC65SSrZF6oCpzMqZ9D9NswwNtuwKZ5FgSjzzOmvkMCvDKU2q5c8PD5/digKmGS5stWrsLDjz+Jp//3Mt754BNs2LQFgcB2ng9EREREe617sKJHvGBNtMtcfiMeWDpSNYDNqmUTafR0z0Ky3Bcy4+V1BWjyRG6MDQRMOsjbJTI4bTAkw1Ib4Q0Y8OKmElQHU9SaSLg4Ro6vg8cG1ZoyGfB8xRjMq3BE1xLRHtEZK6yfweZzt6GlZhWqVr+LdfMfx7qv/o0NXz+N8hVvoH7LArQ3bkRb4yZ0tFbC2V6jX2NTS90a1G74AmXLXsH6hU9i7ZePYeOC/6J24zy0NWyE39OOUNCvPkpfDaF9KFS/FsHGdQj7PdElO9a9TiTBfoMjDeZhs2GdejGM6UUw2BLU6xBYp10K8/A5en3nBa7dFJZ0bylHSP1+aP+JpWooLKM39P0LyCSdi8zILSzB8IkzUDJ2KkZOnon07DyUjpmE3CGj9HMwl6xYja8XL0NlfQsyCobo69E2i2WnN9s6osHi1avWwKy2l+C02WKF0cSbDIiI4km9SW7CqW1s0efa2tpatLS2SAQZqFqIys2rUdseRF17CC2uIJpc0ENEOz3yXOFIwNgTALxqc190knm3LJfnEqvt2tXU4g6h2Q21f0hPte0e1Kz+GOHWch03qqmrg9PpQlu7Ex2uXavz9Xb9/pbvoM+ALfONaK0wwWCRyx6qgqb+i11A6aR+WUZ1Uva7jNj0uQmeVlbk+jt5HshnX8zXAeLxY0fj/669CkccOkM/g5GIiIiIqK9ZUufA05tH6qGmI2FdNal2jp7UvAwTvbI9C/NqI71TZNDpcNCq76mVIajjhUKmzjbTmhYHXt88FIbowEudx5Vjqn/Dat9FTfl4ZFlBZAMi2msqi3WRngpyYczrhLulEi1Vy1G99kOULX2lc9qy5EWUx70vX/Yaajd+itaa1fC21yEU8Ohjxq50bHV82qfCPicCy16Cf+Wr+jnE4b3owWtMLYBl0kWwTv02rFPOhzFlUHTNHgiHEGqtQGDtu/AteR5hV2N0Be1PkteY3/oXg0HVoLoFce0JSRhUMkwHjmWS+cTkNFV0hxAIBnQgeHukTpWZkgCbxYyy8i36+EREtGMr12/G518vR1lFFZqbm9HhciHk9yK86mVUrvsa1W1+1LZLkDeAJlcYjWrSzx52SzA4qKcOb2SSeadXehHHgsRhPdW2BdUUQnl9B6qWfQDP5q9VOzmsg9MtTY1YumI1PvjiazS1yONc+v7ZfkCcfTwtRqz/yITqJSYYzXJBI9LWipF5k9WItmoDVr9t0YHl2AhM1L+1dzjx3+dfxB8fehipKcm45cZrcekF5yI5KTG6BRERERFR3+AJGvHvVYX4tLYYBqsEhcKRGFM0PCSPGf7z8rFockcucEqQNxBySMhYv48X9FtVmyjSXJQ7t59Ynbv1MNfqX3kVDW47/rJsFFp8fIwP0X4Rvfgk/8YmIUHIUNAXmQI+BGPzaooNK73NPtFX2r/CYZU29WvhX/EyAiteQtjdEl2z++RZ03q4acNe9DAMevUziv1L/4dg5SL9zGQi2v9kZEt5HrGph+djxsvLSEFHa6PupSY9i4mIaOdqG5v1qA7y3OCO9jYMTWtAhkPVhde/i/qlb2FLgxu17UBVS6DzmcMSNNbBYPXaol4lkCzzDU61Tk0N0SGsq1uDqGkLYmNFDTZ9+jQKm99EgaMWTa2taGlphtOvtu1Q9St//xmddsDcqiQ9jCsWmrHufRM6muQe+q7/6942oHyhCWvetsLdbGCgeIDxen2Y9+UC/Py3d+G/z7+EaZMn4OzTTkFCAofQIyIiIqK+xeU34bYF4/BBZSkCJrMe+lIHdc0G/K9iLJbXWaNbytCYRgQDPQd4XR4zLEZ79F1kmOt/ry2FL2xRR1PHlKGnDUbUexJw08IjsLLRFotnEdEBwizX26kU8nsQrF0F3/yHEdzyJcK+jsjyOIb9+Yw7/fkr4PviH5HP97ZHVxDR/ia9j6WuFA74dM9i6Y3WE1k6KDMJZVs2w2g06iGoiYho13gDIXR4w2hrrUcCGjAqeQNOGbwUofJ5qPnicazfvBllDW5sagyiqiWIiuaADgLLUNXVrSHdc1iGrK5pVe/bAqhsCWFTgw/l9e1Yv3yBOsYTKAovhR3tGJNZA5O3Dm0uD5y+MILBUL+qjw+ocS1CIaBpoxlr3rJi83wjUouCCAaAVW+ZUfmNmc8oHsBkmID6hkY8/fxL+O09f9LPPj5k6mQY92ejjYiIqJeQi5S7Ou0rco7t6fjxk/onujUR7Q6P34/fLRiG5zYNh8doVnkJqHSm4L+rtx4m2mQwI+DftvdKrCeyN5CkX2PmVaTgo+pinTdDRiO+bsnHLQsPxdpG9igmItqRsD++Z+8SwOfUJe3+qunIc4lDdavhX/kK/CteRcjTGl1DRAeSxIdbW1uQ7LDpx+F1J80daRdlpiRi+dJlMJktsNkTomuJiGhnJFjrCoTR5DbjzSVh1La48cH6dLj9ZlidFQh88x9Uf/UsNq1ahHVbqrG53o2NjUFsagyhojUylUuAuCmIDVXtWLtxCzYsmYfNnzwO96o3YPG1oKLFhM82JuKpryyoagvDHTTD388CxWJABYtjAl4DOuqMyBsTRDhkgLNhQH4N1INQOIzyiiq8+ua7KKuo5EVqIiIaEOSmqV2dhM+353fYeX1efRw553Y/dvdJLqjI0G2BwJ4/649ooOrwm/C3pYPxu8Uz0BFMxqtbilDZsfUQpqFwInyqER3qFq6INXr9vq0vVjr9Rry5ZRDaDTY8un4ibv1iLFY1sfcLEdEukWcGt5TDv+Zt+BY/hZA7FsDdt9cdZMjrwMpX4V/5GoL1a1Vh33+GRyTqa8xWOxrq65GVmoCgatd0v8woN8hKm6cwKxULFn4NR1IyDEZepyYi2h1SlroDYVS3G/HqIgOWb2pHS2szOpxuhKS3aMtm+Fe/hqYvH8OWTx/H6s9fw8ov3sDyhZ9i6RfvYOm817Hmsxew5ZN/oXXBf+BZ/6FqUNeq9rAPLe0dqKpvR0NjA6pU1a3JGUYg2D+HJh64Zx91cjaY5J/oe6I4Ms79hk1berzrj4iIqD8Ih4GkpATMPvoInHP6Kbs1Zaanw2A07Ho1Sn1WbNi1k46djTNOObHH4/Y0nXTcbAwu2ro3JBHtGnmG8bubUnH6O7PwwsYiyJDU8YIBh74xI0JeY1OE15uA7o/YW1CXhkvfn42Hl+aj2WvWZQkREe2GkB9hV4sqboPqvwDC7iY1v3fXHsJSdnvbEdz8BXxf/gPB2pV8LjFRb2C2YMXKlZg7czR8/oBqP8VXrMLwB4IYWZyD/CTg83mfIbdgcHQdERHtqlib1hMIwwWHHia6uakRzc3NaG5phtVu0x0WQn4PjB3VMFZ9CUP5ZwitexPY/BFQ9hmMNQthcDUg6PepotsMm9qnubUVjQ0NaFTHafPIcNchfeNPf20CG9QXucv/32bMPiU61/dZk8MYe6ofNctNqF669R32fdn8D16Pzu2+/pS+/RXTt39j+vZvTN/+rS+m79WXX4yLzzsbNlvX80t3VSAQwJvvfYg//uVhOF2uuGDTtuQOT5PJhDPnnojrr7ocDnvXM1B3x/KVq3HF9TdH3x1YzL/920BOX2fbGNV43v5QhxZLGMXFK+H1u6NL+h7m3/6N6du/DZT0NSRkwDRoMkz542CwJUeX7ga/B4HalQhVL0GorXqvA88HCvNv/8b0jfC6nUizG/HUf5/A395ejtc+V/WqQFA/wzglwYbDxpbggqPH4slH/oKnn38R+SUjonv2bkzf/o3p278NhPSVURysxjByMlIxccwomC0WlFdUwO10wuZwqDLYpK9VySSkc0MoFEQwFILH5UJGVjZysrPg8Xjx9dIVaHF54Q/0//oVg8UMFndiYd77MX37N6Zv/8b07d/6YvrabTZMGDdaV4C7mzB2NE4+fg6WrViN1995L7q0i64wL16Klta2HQaK48nnTZ00AenpqdEl8nwuo6qMBzG4qBCXXXieDgj/79U3OivsMe3tHVi5Zh3qGxqjSw4s5t/+bSCnrxG5Kj8nw+e1w+vrenaxxRKEyRiA3dGBpMQauH2+6Jq+h/m3f2P69m8DKn0NJhgTM2EacgRM2SNVAb3z61TSryXcuBmBjR8h1F6neyz3Jcy//RvTNyIcCqG6bANmHTETf/37w1i5pQ41TR3ITE1AQWayavcYcc9vb8XLr72BrIIhsNj27MbaA43p278xffu3gZS+VosFRx8yCekpSVizZg0qKiphs1lgMZu3ue4UDEaCxTLiw7ChpcjPH4SqukYsXLZGr+srvYn3Jn0H7jDUIcDvNCDQd697EBEREe0xn8+H+V8vxmtvvdc5vfvhJ/jg43lYvnKN7j28uaxMv3//o8+22u69jz5Fc0vrLgeKhcfrxbyvFmx1nDfe/QAffvI5Pvr0C73N5rIKfPbFfP15b7z9fud2n3z+FRobm/Q2RLTvhFALq309klKXIzP7m84pJW0pElNWwmQp69OBYiKiPiMcRKijDv7lL+oprOa3+6xhGb7a3YzA6rfgW/IMQq2VfS5QTDRQyPOHs/OL8Nobb2HmjGn47M3nYGvdhBXz3sLvfvFjTBk3Qvco7kuBYiKivsLn9+Pj+Yvx2gefY1VZPbxGB1o9ITR1uFHf4uycmtpdaHb60OwJwxW24uvVZXjz46+wcPkaBPpQoHhvDdhgsd9lwJp3zGja0H96FRMRERHtKnleS2zYHaNcxMjMwPeuuBQ/+b/rMPfEY/UdmNOnTMItP7gWt9x4LYYMLoZRto3uszdin3vlJefr48uQ2GLKxPG4+fqr8cPvX4OJ48d2bifk7yUiIiLq11R9J1i3Gr5FTyKw4WOEXd1GVfG0IrDpc70+WPH19gPKRNRrSBC4dMwktHtDuPu+B3DJlVfj17+/G+9+8gXySkbodQwUExHtHz6fH26vD36/Hx1uD5y+ENp9BjiDxs6pTb3v8ANuXxBOt1cHmT1qv0AgGD3KwDBgg8VyvdHvNiDImy+JiIhogJKewbp3sJoyM9Jx3OyjMfuowzFenuliNqO4sAAnqGUnHz8bpSXF+m5KCdruTo/i7ZEg8JyjjtDHnzl9il42KD8Xs4+OLJPgdOzv2xefR0RERNRXhH1OBMq+gm/Rf9Xrlwh72hCs/BoeCSJv+gxhd0t0SyLqC0xmC3ILS1BYOgrFw8eioGQEcgYVIzG56xE9RES0f+jrStH5XTUQr0MN3GGoiYiIiEiTAPDqdRtwyrmX4IgTz8Shx52mn0Ujk8wfMmcu3v/4s31WWZbjhEIhnHf5NZ3Hj33eESecof+GF159I7o1ERER0QAUDiEsPYnXvgfvZw/Av+pNwNWklg+sXi5E/YnRZNKTDE+N6AhKREREvQGDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREAxCDxUREREREREREREREREREA5AhrETniYiIiIiIiIiIiIiIiIhogGDPYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAYjBYiIiIiIiIiIiIiIiIiKiAcgQVqLzOzVj9inROeqt5n/wenRu9zF9ez+mb//G9O3fmL79G9O3f2P69m9M3/6N6du/MX37N6Zv/8b07d+Yvv0b07d/Y/r2b0zf/m1v0pc9i4mIiIiIiIiIiIiIiIiIBiAGi4mIiIiIiIiIiIiIiIiIBiAGi4mIiIiIiIiIiIiIiIiIBiAGi4mIiIiIiIiIiIiIiIiIBiAGi4mIiIiIiIiIiIiIiIiIBiAGi4mIiIiIiIiIiIiIiIiIBiAGi4mIiIiIiIiIiIiIiIiIBiAGi4mIiIiIiIiIiIiIiIiIBqABGiw+Cw899ntcFH1HRL3MLX/Ds7dE52PO+T2eve+s6JuYG/HsK8zL/d1F9z0b+T2o38X72/wGqLe69bG/4dbofExnWvakxzxOvdEO07Gn8pv6od07/+7wN0O9HNtN/QbPswNKT/Uw6m96OherMvsVpn3ftvvXOFjP6nuYZkREvZeU0fM/eBYP3fJ7vB89Jw+UunW/Chbf+tjrKiFjk0rQc6IrqG+75W9x6fr6fgwWqUr5YzdG52nfk4ZrfB7dF4Xs/TjvtJ/hSZmND1Do30z88VXaqs+MrY8U+tG/g8HmA+ccdZLtTH817W5+u/sazLnphegbOmB2J912Eih88qbzcN7d0Te7olv5v3Ue38W/ifbcdtJ+d9Px1se66mS6/FXl7r3qdavzuaQp03HPxKdT7Jyml8WdB9X7WJBI15fjv+v4c2a3vLWv6ly7nfdpn4hvG/GCZP8RX4/dZ+2iuHKE9eXejfm6n+npHL7L4trCtA91v24hU6SeFF8uxvJfb7gOueN6Fm/62ie6tz/VtDfnYNaNe5+t6j3RaX+eZy+672+MW/Qq2y/7qY/Yh+X0kAxg4UPn4dq7f4Y5A6yu1c96FpfjldmnYIZMbzVh5GHqB6F+KJ0/DDXfvaDvrNh1VswjQSW9LHYhTfZ7LO5CGh1wm9+Kpuvsd9A4bJpKq/i7LWNBXqkEq7SKFu6xdJc7P56NpXPnxdHu6SwnheNRUnw8L1bvT01LcH80j96/OAMTdH7sIc+JMbFCPi7fZRwfbUzHGmHRho80sk8sQsmJXZW5zYubMCt6vFsfOxwoc+p5aZBfMWx9598x47MMXLGvLrTRTjUufiSalx/BwozR0bTt6TcQW/YsTlcnaa0z2BFXiWN+PSB6TLe4ipjOdz3kwwnRsneb87CkpSqbI/m5+7k1kr6xfdrjPrvhiK4LMPHLu35LtK9tL+0jabyd8luLrIv9FjS13xV4GjNUZfvmm+ZFz+cRt47JwMLX7o++o91x0WEZWPOQpJGaHgdOj+adxjJ0nge3Vo6FTYd3peER6pzZoVdo29a5unReRHlsdHSJ0q0skG06013Wyd/Q+Zvpofzutr/Ytn5Ou+9GTMA70bQ8BUtVvUryb9eFsK7yNL6u/OwtXWnUmY7IwOnd6tc9HYcOhLNweEZXPfZRHB/5/jvzUXy6RtKsxzZs93z3/M8w56Elqv78SOTiNevLvVTP+bqncrRTZ52LebU36vkcHlcOS9rpZep83eP1j+iy2G/gleORJe9pL7yAa0+TNFF1347YNcZrcHu3cnHpmFie6uE6ZJxtzpfbaQf1VMfatWtZikp/nfd7OPatj12JacUTcaP+/fRQD6Ndc/c10TQu72yHzrkJXd9nLH/2mL5xaaYmnad3kGaC9awDTwL4kq73L3Z2tofOuzsuz/SUxrKss/yNro9L71i9eZs6mdrnxklFmHYt07f32E7ZH0tfNcXqWJ1lM8vR3mV75bSkl8rHkh97Klu3WabS/LTiRJU/I+m+TTrr30RXed3fDPBnFsc1tlQD+HSV6S+6LxdLdYFwCl5B18XnEqyKFBLR99RLZWSg4fFI4b5m2PHR9CsCVsbS9HCd8SV42BBtlN3fNFotUyeFx5egvUz9Hi7jxeoDacd5Ti1/C5gVrWBlZjThUVn20HqMnBtXWD//MzwardB13p1Zo5bJxXB1UpjV9DReboosvuiwYWj8LO6uoLtXbXMxnA6sbfOjLDse0BX0p9GQkRjdMuqcachaHznxx7anA001gI5oil4seQQNY1TDaJt8GCt7tw04iVh+7rpxRBSpxtj5gCrHt+1F/gLmrQeySiPvkiddGa3QXYmsz3h+Phi2V35HGsij9brOMrn093h/zKq4dL0fS5uG4XCdf1V9LGM95j2vV9Buy4g2ZFSDReXD82LfcdM7kfNgZ/7qUnWTKluPkEauKn8ffwcN0eU7dE7cRdKVcp4W25YFuGkeMCZyjr4oD1vdBHDRfeer/Br5zdyv/7YeypIe6ue0ByRvZRyvy0lpGN9+mZSTN6qa8NOR71bVpbI6L2h3ldc4MVIGz3hoCTKj6bht/Xp7x6EDImMiboxeuHjypmtw7fPx+Wgesi5V+Ujl10HR9o/UpbvOs6KnfLc11pd7qR7z9Vl4aExceqqyvfvFq0id6zz1W4kuoF5k23O4nCtHRts6S6V83imVp09E5ML2403ITIoupn2qe7l4+2VPQ9V4dkzqThnzImWxlM/RaxjbtIN6rGOJba9lXXTf6Li287b1vO7Hvv2yd7C5Ywnul55RbEfvU/H12vg66zbpe8vo6LUNlY5l5fiwWzu3x99DD78bOghuOX6rNI7dONeZZuuH4UbVxtVpG23bxl/f+jDjfDz02x7qZHdfo34LzkjPRZ6be7Ht1bGiZTPjB31DtC0756aibduwPZW3nflTlqlzaPQwmgSKdZ7vv9cg+1mwuAin6QvHajoxA2s+39lwpferyneksTVfVa6X3g08WZPReYzTiqObKZtXsgA4mKSnmk6nD45H5vqF2+/+3xS72PwCqpoyMEhXfst12opNTbFAQ9cdInI3Vyz4QPtZ7OKWfO+qMfTyruS5jSrRMiKN5MZY2j9fi8aM3J1esHpSLoZDVeK2CTrRwZIZF+Abuf6d6Mm1p/wYy7cSIIz2Co95fiEahkWOc+OwJgaYDoAe0y0plp/lbvUMDNFbxoulYTkaojdrxIvl5ydrulZmTjoe05KaULULadrVszj+zn7a13rOsxHbK79LTjweJR1N2BR9r/P4EcCa6EWumNtXxkaBGQ3EByVot8Tugp/xUBNmqbSI3cEunpTA7Yk93fUqN8mpvNf09DYXKHZU5+o8D9+9qqvRtE1ZIPVruXlAekB2L6Ob0LAxMtc5/F6P+29dP6c98LwMTxopIz/MkHwsv4NyIHr+nH/tRGRGN92qvC6L1qWlriWLxDb16+0dh/a/WK8HudB/uEqD6PmvqTaaV6PD0qr0y4rl5RN7CDbt9BxOvVKP+VrVnRFL//g2cJfOspt6nZ7P4U2d17JuX6nK211RtipSR1PtpDVxo4XQ/vSCKmvldSfXIYujdRpVryqJXsPoqR3UYx2r8/zcdS1LhsaMtZVk/8y8rnqf6OnYndiO3uc6683qXBtLi23S4O530HBEZLvT1Dl3e2X0VmnWw++GDjy58TXWdunp2qSkWeza5aamWPp1Xc+4/bLzcO0vdlIno15se3WsrrKZ+oDOtux22rC7XN6q873Kw+1Nu1g366P67zDUsTtn4wrzi/JiY5nG3IhBTdGLzdG7e/QwiNE7gF4pi25GB13XkIix3mblaED0wsY5uT1cpDoLgzK2DThIxTpyoo/dIXIK7n8oOtwa7X+xYahlSIho5Wqnea5UJVr3gljSvPOi2I68gGsv2zoA8eTn65F5RFwPiltGo2SXjkX7QmxIW7lLq6sh3VN+LIrccdmTc6YBsZ5p6zOiPRNpf+ox3eQOdbVM7rC8/6F9c1ed/hy5WNbjcD5n4fBhcY01OiB6zrMR2yu/9Tl7qyFLVR5//GeqPJ6HrGvjApd3v4M1GdPw0BgGBPdG5zOho8PIxuq9EffjPBmhY273OrAS3ws5zrZ1ri6ZPfUs7KEsuH2lqlffNw1ZTZFzfZeMzhv0Op/Ttc3+29bPaQ+c83u8Hy1Lb79M8mgGBv12GkY2RXttq99KZzB4l0Xr19jb49Ceu7FzKFoJMt2/OHojbOeFDbVe0l3VbzNjN1Wpevc2dnIO76m+vMMbdunA6ClfSznamf49t4Gp9+r5HJ6xzVDGO73+URw/wgvtD7pcjI24ociwspFevT1ch4wno9fFyltVH95eOdpjHStO17WsrmCjXONsrNm2LrddbEfvc5315oce2X4nhbjvvcffSE928XdD+1tX26XHa5M96sqjMlzxrTurk1HvxjpW/6HK4h7bsLtc3kbO93rEgH58/uz/w1DLnZXRoZquGBZdJjIm4or/pKEK50fv7olcqJReLrEebrMyMjqHv6XeRnobRns0XRqXsJ09V+N7QHXd6Xka5umK2e36gnVk2Y3XxmXy4uO36pFD+8nd10Tuhn/sxu3mua47NNE5TE9nD7drh2FND8+2LDlxF3oXqob4ozJUjBxHJhmGj0OHHHCxPCgN7J7yowyXhehv4MZJ3YahxkJgTHQd74g+oLrS7X6c91lGZ3l7owx5Gd1ml/Lhjqg8+qH0KoxeDN16uOmuxnXX8q6ynfaf+Dwbs8M6kwSCh13Z7ZwaCVye1vl8F3UubxqGaYj2hqE9cBY2vdZVhs6/NgMfdj+nyTlX1YX2uvfnVufP46NDJG6nLLh7FTBpGBq63WCgR/yI9qy4UXod97T/OeXb1M9pTyzEh7rnaeT7lkdyXPuLrnaR1J9lNIddCi50r1/Hta926zi091T+eLkzz0g+mofz7o7PR4ejYWU5LpJho2PnySMyOgO/mZOkntVTvvs93r92IkrUPrqc76G+zJF6eoMe8rX0No5Lz65Hc6g2cLQuRb1Vz+dwOVdKHUov6+yFtp3rH5r6Dej6VeQ3MI3DUO8fUi7G5b8JK+Met7I9W+2jytvtDSfcYx1L7ORalj4HRDfdERlNQpcHbEfvS/H12vnXXrn9R6eUZmBa7PqWmroPHb6NXf3d0H63VRrvYl0oPo/OalqFTRt7rpNFRtiLu5GaeqHt1bGoT+qpDbsH5e3tr63HyH78vHFDWInO75RE2al3kx/3nur76XsWHnpsGuZ160kqd3JBP8+p7xvY6buPyUWxubWY04sCxUzf/o3p278xffs3pm//xvTt35i+/RvTt38b8Ol7y9/wft4724z00V8w/+5YX7+WxfSNuOi+32PITT+LpqOMAAKc1w86LDB9+zemb//G9O3f9iZ9+3/PYiKinsiz+IrZk5yIiIiIiIiIiPaHYZ09xKXnOKLPuCUiIuptGCymfmTb59OK2/tJr2La1+7HebO3fR4jERERERER0UF39zVsrw5gvJbVPzx503m6J15s2qWhw4mIiA4CBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgBouJiIiIiIiIiIiIiIiIiAYgQ1iJzu/UjNmnROcOrPkfvB6d6zv64nd1sP5m2nVM3/6N6du/MX0jDNHXmF2uhPRyTN/+rS+m71fvvxadi5Aqv8Fg6HztvsxoNOpXmURP28ReRWy9iK2L131Z/H7xx4q9Go0G9V3N1dscaMy//VtfTN+9+ZsPlr74XTF9dx3Tl/YHpm//xvTt35i+/RvTt39j+vZve5O+7FlMREREB0xiQgIG5eWiqHAQUlKTYeoWYCKifUeCsDKFQqHOVyGv8ZMIBoN6Pn6f2LrYctlGxG8jy2Lz8cu7v8b2674s9l79RweZQf5nteqJiIiIiIiIiAYOBouJiIhov4n1LMzKSMf3Lr8Y9/3+V/jLvb/Hn+/5HR6667e4+fqrMHr4UL0NEe1bEoSVYG4sH8Z6D4vYsvgevjHx28eCuqJ772N5jS2L7RMfBJZ1MbF94j9Xton/XDqwDBmZ8I2fiLbjTkTDOeej6rIrUXbF1ai6+DJUX3w5Gs84B+6ZRyA0uAQGiyW6FxERERERUf9kNBiRak1Dli0LJdYhndNg22C9LNmSHN2S+gK53rCn1xxkr4F2vYLBYiIiItpvJEA0tKQYd//2Vlx28fmYOH4scnOykZ2VidKhpTj7jFPxyIN/wElzjobFbI7uRUT7islk6gzmxgdnY4FeER8Ujg/qxgK+3ffvviz2GbFlwqzys6yLP55MsW3kNfa5sX1oPzOZEUhNg3fKVNRdcjnW3HAT1l16JcqPPxm1hxyGpjHj0FFQgKYhpWiaOBk1hx+FjWefhzXX3ohNP/gRmk89E4HCIoQddknQ6EGJiIiIiIj6JpPBhCRjEgptRZjuOAxzbCfhcMMsHGo4GuONUzqnCYZpatkxmGk6CjMdR2CUfQxSTWmwGKwwbvPANeo15DpFKAhDOARjwKdeA0DAi6DPrSaXnkJeNalXWW4IBdQ2QbVfECG1j+w/kFKXwWIiIiLab8aMGIbbfvYjjBk5QgeFZJIgQyw4JO+NZjN+eOO1OOrQGTAyAEG0T8UHZ2PvRSwPyvv4gG0sgOz3+7Fs2TK88sor+Mc//oE777yzM0is83FULOC7YcMG3Hfffbj//vuxefPmrbYRsl/3v0FeY8ek/SuYm4emY09A+VXfw4Yzz0XDmPHwyw06LicM7sgEt1s1iNXGMsny6LqQxwNXWjqqDjsSmy77LmrOuQCusePZ25iIiIiIiPoko/pfpjkb42wTMcN2BMZhEnJCubCEraotG4Qv5Ic/6Ntq8gW9MAVNyAxlozQ8EoeYj8BU2wyUWIbBYUqIHpl6A7m6YQj6YQh41ORFKOBDUDV0w6GQbu4ajSY1mfVkMKlJvcq1Skn7cMCvGtB+GCV4HHCr9nFQrxsIGCymPsmosvWOpgGSf4n6JTkxZZgMyFX5ONHE01RfJYGn1ORk/OB730HpkMGdwSAJDMlkiL2q7WSSZxefedpJSE3hkD5E+1J8MFbymszLa3yAWMTm5XXp0qW4+OKLcfrpp+GKK67AzTffjF//+td6fXyAN7aPzEuw+J577sFdd92lg8WyLv5zY6/x+9H+JwFd1yEzsfGq61Bz5DHwpKarBrJqJPt9creO/EAQNkQmmVeJE5n0ckmvyHHCgQCg9vFZrWgaMQrl512EygsvQSA3P7IBERERERFRH+CwODDRPhXTLTMxKFwMa9Cq2kYGBEMBHSwUBt0OknZr1yT/E7HtjCEj0kIZGGkcg0NMh6HAWqjX00Gm2rEBn1u1e1U6mawIm+2qXWyHUU0w2/S8waJezVYY1SSv8j62DrJe7SOvIaNFtYMlaKzazwMAr8JTn2MyhpFoC+14sqgCO1KqUy+WaAhghL0NxyTWYJqjEUck1uGwhHokm/zRLbrI6TjD5NXrZTvZXvYbZmuH1bB17yXqexIQRiGCOC3sxm3uWtzTuAn3NW7AHS3luM7fignqt5IcDoGDFPch4TAmTxiL0qFD9FsdHDaZ9PNT3W65M0+V0aoCJ0Gj5uZmfLB0OUqLizFyWKnenoj2TiwoGx8UlmUS6BUydHRAAoBRsq6qqgo/+clPMGPGDPzvf/9DbW0dbDYbCgsLMXr0aH0s2V9eJS8/99xzWLVqVefxZbkcRyaZX7dune6Z7PP5Ov+W2P5Clsm2IvZK+04oJRUNc0/HljPOg096EYeCnd+9Fv0tbJekq2wTm2JUWgXUObl5+CiUXfYdeMeM1UNcExERERER9VYS7M0yZ2Ga8VAUoAjhoGqPSvuosykaadfuXGw7taP6L6TaR/ZwAiaapmGUfSzsRlt0PR0MhoAfFkMYZluCaqaaYVbpIymmk1narXYHkJgCpGYAaZlASgYMSamAIxGQwLEcQ+0jnVrMFiuMVgdCAa9uB/d3O7lCQNT7lKR78csTyvCrk7Y/ff+YKqTag7qXMfVOJpU2Z6SV49Giz3Fn/jf4c+F8/GHQ17g7/2tckb4eNmPkTq6YJJMfP8pegT/kL9Tbyfay398LvtTB4109nVPvISegPIQwx9eB671N+HlbFS4KuTDc3Q6Lz6MrW+muNhzV0YAftFTgJlcdzve2YFQ4oH4fTPHeTFLHYrFg4rgxSHSoSpiIVs4aGhpQVVmlh7jVVOUrNTUVJx8yHRmZGThhzqzIciLaKz0FYuODxrHArZDlEvT9zne+o4eSluWzZh2DO+64QweEP/vsMyxcuLDzmPL69ddf6x7Hl19+Oaqrq/Vx4jU2NuLSSy/F97//fcyfP79zP3mNfW7s74qto33HX1CI6m9dhNpph6iGbaS87awVy/e/J9+37COTpJu8qt+QOzkFW9TntB15NAxWXhQhItotsXJ1f0xsIRMREW1lsGUIJpqnITGcBL9+Nm10xR6LnGtjxwkFQhgSGoaJ1qlIM6VFFtIBJcF/qzGIgtxsFGSloCAnDRPrKpFhNMEwZjIMR50E48nnw3jW5TCc810Yzo1OZ1+J0NyLEZx9BozjpmFYRxuGh93qOGnIy0hCZloKwvJc436OwWLqM2JNnVR7ADML2nDE4NbtTtMHtcNmYm/T3sykzqSFFifMkIvGalLvZTLr5S5YsXX62Q1BFFpdahtVcEW3FQ5TQC13qt/HXp/h6QCaiACu8Tbhl20VuNRVjwkdzUh2d8A0dDiy7/szko6aDUtBETKuvg6O086CPBVxpNeJ452N+HFHFX7eXI7jgh7wsnTvZbNaUFjQNTyp9CyWXJqVlYUhQ0p0MDlGehzHgkaTJ45DciKf9UK0r0kwVgLE8UHZ2Hx9fT0uuugivP/+ezpv/va3v8V///sUrr/+ehx22GHIzMzUPZFjQV2ZHn/8cd0T2eGwIyMjQx9HyCFlO7kJxOv1oqKiAo8++shWnxsjy2J/U6wMoL0ngeKK8y9BS/FghIPBSHBXSBr0kA67LRZsluMGA/CrhnfV0bPQfOTR7GFMRLRLImWxLy0LjWOPQcOkk/bZJMfzZBRFP2IflPlERET9wHDbKIw2T4AlZFXtGHWG3Ivmp8+97YiYMeFwCBnBbEy2zGDA+ACSawqmUAgjGqpRkpYMq9WsmqZG2AI+DKvYjKTRk2E7Zi4MIyfCn1OIkPQkttoQNlsRkCGpk5IxffRwHHf0oSicczLSM3JRvH49DCYj7OpYKRlpGGsJI6e1WX9Wf2UI78aVmRmzT4nOHVjzP3g9Otd39MXv6mD9zbtKsqH8WCcXdOBPp26ATWVQWRgwRzOoJwyzKTJb47Xhmv8ORV2HBaF+1EDqT+krQ0f/X/ZKnJ1WhlDY0Bn8DYUM+NSZg9tqJ6A91BVMyjZ7cN+ghRhubdfBZSHbqv/wt8YReKKptM+ndX/Nv5IqqiqGJFVhGhX2Yba7FeP9LnUCCsEVDOlhPaQXscFkgjk/HyVPPA9TerreN+TxoP7eO9Hy0gsI+7z6hCyT3OlkUwduVCf1V23pWGK1owEmqKPq/Xqj/lw+9yQ3Jwt3/eqnGDliuH4vweJYhSoWcNLD/UTTNCQBDUV6HH73hh9i6crV+n1fMdDSd6Dpi+n71fuv6UCs5Lf4HsQyfHQsL8prU1MTzjrrLN17uKioCI8++iiOPvpo3ft/vWocPf74v/H662+grq5OB5Vjxxw5ciQ2bdqEF198EXPnzsV7772nn3Msx3z66afUMY7Bs88+q4PQJSUlWLt2rV4nU6z5IceKBaHlbzxkzly9/EDrT/k3lJaO6nMv0IFi9cVGFspr9Dewz0XLcaNRzs1GFD/7BBxLl0RX9g59MX335m8+WPrid8X03XVM331JnYstNjSNm43GcbNUoyYxUpbuQ6pYRtLGRcj++hVY2xrVkuj5oJfpi+n7xbuv6Jvh5BEbCQkJuh7jdDp1XSZRvYe/DR2tzTCbzXA4HPqRHy6XS23r0ENZetxuXcdKTExU7SOD2teFJo8V9sQUlUoGdHQ4YbVaYLfb1XYBeFR7WOblZj6ZD/vdSEtNhEmd110udyStk5I6P8dqVcdS23u9PrXMj7S0NBxx4pnRv/7A6p/5l2KYvv1bf0pfGXpaehRLoDgckOtOe37dMNZuzSpKRd2WHQcNZZ3T2IavffPhDDmjS3uH/pp/U90uzC5bi5WHHQa/Og+HVVoN3bQFE31eZD70MMaMGaWvP39Y1YLXN9UiEK0eDU5x4NrxgzE0xa5rTO3+IN7515No/vVP8dm0qfCqc3ZIHS+7vBzJ6zfh06GjEZDrGJHde529SV/2LKY+rdlpxl8/zMcD7+XjP4ty4ZXI4Z6X+XSQsFdw/5VpCGNSwINLfS241VWL7zVXYLSnHd5gUAeKjaryJKlvVife1DPORs7NP4W/sgKhjna9vwQYk487Eamnnwlzapo+qQdDIQTVqyukGuR+Hy521uIXrZW43tOI2b4OPbQ1T24HjwT/c0qKkX3IdH0BRUg6xsSCRFpcxTq+kj1j6qToHBHtrfghn7v3LJbA8VNPPaWHlE5OTsbPf/5zHHnkkejo6MCf/vQnnHzyybj33vuwevVqtLS06P2FHEN6FcuxJRAshgwZgptuugk/+tGPMHhwid5m2LBhel1ra+tWf0fsVZbJq2y7VdlAe6zp2BPQWjJEvuDIAnmNK4P3ifhjy+9JvYbUOV2egF1+9rfgGT4ysp6IiLqRc3BYB4obJsxGoLkGwY3fILh5yR5N/vLl8FWs3GqSZf7qDWgpGIGqOZcikJC4VZ2b9o4EiqXeIgFZqUfJe5vNpidvex38n/wS1k9vgumj78P/9jUIv3cdbJ/dhJB69b91NYxqufXT/0Pwne8h8Pb34H//R7j+tkdx0a9fxiV3foSrH1yIK+79Ehff8TEuu2cerrp/gXr9DBfd8ZFa/jnu+OtzePw/T+K1197Ex598pqZ5ePOtd/Huex/ik08/xwcffoK33n4PH3zwEdZv2KgDyEREA1mmORNDTSMQVu2VyHl4z0h7NT0vBWMOHwJXqwd5QzJhtkZ7rfVAehgnhlIw0jIWFlNXZyjafwI2KzZPmICAOTLaVYrfjzmHH4Y5j/wTs6dPxKAku54uHpmPu44YjYtG5OPK0QW4dUoJkr0d+sYrSedkiwknXng2pv/whyiuqdfXmI1qXUtuLpqKCvWx+6t9fOWA6MBq95rw4opMPL80G2+uToc3pH7SbAf1ObG2q/Qwpv4hS52XLwi042etFfiBswZHttQgq6leB3uRkqqHmLbm5QPqxB1yORFQjdjWV19C9c9/hLKrvo2Gv/8FYRm+9IZrUPXLn6DtrdcRVNvJHdQySbC43BPEGmdA5fswEoMBTHC24Nuuej20tQxxPdkY5LONDyC5aCKVipxRIxE452y0jB2rK1mxdTLtaF7NRF6VGVMmITHB0bWOiHZbLAgbC/DGxPKlBGqlt/DTTz+te6rMmTMHF154oe4hc8899+D2229HZWUFiouLcdddd+mexzFyzFzVUJLXNWvW6GMOHToUP/7xj3XAWALIsk56Hou8vDz9Gsvzsi6Wv+U19jfR3nFOPwT1U2dERm0Q8r3u60BxTE9pFh2Suvr0sxFK7xqanIh6p3DAD+/GtXAvX4Sgqqf3mK9p31KnPk9GMZomnYBgYzXC7naE5Ly4G1PAakPQYUPIZESytxGpHRuR3rAGGXWr9ZTSvgkJqv0VrtsCT/oQtIw4LPrhtC/EgsUSHJZgsfQSll7E0vPXX7cKoYp5MDZ8A0PdYqDma6B2EYz1S2Go/Ua/N9Qu1u9Rt0i9X4Qly1bhw4Wb8NXKKny1ph4L1jVhwdpGNd+gXuuxcH0j5q9V82rd4rXV2LDyG2xW9avqmhrU1zfoqba2Tk8NDY2qblePmppa1Kt5q/qtSL2OiGigclgcGG3qGnp6TzmS7UhKT8C0k0ejubYdp1x3BDIL01TzZ0cjg0g7N4R8QwGGWiIj7tH+FVTn49a0tEhSGw0YpepKY849G96cfGzYvEmfs/U1CbW6INGK0tQElKQmonLjOrz9zvvqHFqrOzXJ+oTEBNiOOw7jMtKQ1Nau29UhdU5tKC5GyNJ/H73EYDH1aQZDGBZjGAb1S2ZMqO+KBYljQ1F3J0NWU+8nA2dNDPtwi7cRD9ZtwLnt9cjxe/RJNvHo2cj93V0Y9v5nKH3rQ5S+/i5K33gPQ978AAUP/BWJ0w7RgWODxwM4nWh/9220vvYyOj5+H+HaWr0sFAig1R9CmSeANS559eOL1lY8WdOMb9q9aJVeTYEgkv0+zHI146dNZbilvQZHhvhs4wNBgj1ZJarSdMpJcKZnIKAqWC6fL7o2sj4WwOhpXlfY1CRBpOLiQgwtiRtClYh2W6wnr1wklIuZQvKYLJdXWbZs2TJ89dVX+gLn/fffr4dLfOyxx3DnnXfovHnzzT/UvYpvvvlmTJs2TR9DyEXR0047Tc/fffddaG1t0fOyjxxbtLW14fe//71eJvvqvK4myeOyv5BtY8tj+9GeCebmoerEuQj5vJHhTNV3uk2gWJYJWb83U/TYARn+Uu7Qj32O/L5UXcCTlISWSZP1sF9E1MuoPBxoqIV33QpsuWIuyq4+HTW//QHq/nw7tqj5lpf+g2BzA+tg+5GzYDjCJgvCXqd6Ne9kMulXeR582GZSLwHkNKzH2IqFGLnpC5xgd2N42IRDJw3BmGml8EwerJK4EZnVy1UZbIHX3YCOgjEISY8mnmf3CRk+WuotMvR0bKhpCSC73S6YW1bB6OsARn4LpuP/DHN0Mh3/YOe8WeaPexCGQ25RaWTFc6tSYLcnYMboQbjziun48J4T8OmfTsL935uGUYUpMMrFLgk4qH8zbR5MHjMEs2fNwoknHocTTjgWc+YcjSmTJyEjPX2rupRF/W3JyUl6WGoi2k9UWRD0+1FXvgGujjZdNmzv/CnXPNxqm5ot6zofwUX7lzwiZ5RpHFIN0eDhHpJHBhSNycGxl01HWk4Szv3Zsdi8rBpLP1yn0nVnR5Z2dwDFGII0M59fvL+FY+dB9Zrg8SLX7YWjpBRuGeFSnael45FsIakmoyI6LCYkqiktMxNTpk1GSmrkkYiynUgZPQ6FJ0xGtjy7OJpv5dr0drJ5v8AWPPVp6Y4ArjqkBtcfVoWLJtfBZpQLWNGV1CdIARsLEm+vsPWFWVT1djLc9E2eBtzcWokpzmYEVGI6VWU4pCrOyTMPR8Gd9yLt3AtgzsqGMTY0sckMa/FgJB01G4PuvR+OqdN1BVpO7oGaatT94Q4YbHbdG7nNH8QmdxBlnhDaZZzLOD61z8K2drxe34rVzoA6qQPuoKqIqxP4GG8HbmqrwsiQP7o17S+Z6WkwnToXTXIBRS5Eer14ZsMmbKisim6hxF3A6Gk+VgTIRZjpkyfC3I/v1iPa32JBWAnOxg8BHZvkguLzzz+v18+aNQuDBg1CbW0t/vKXv6jtTTj33HP1sNSy77x583DnnXfqYwjZX9bn5+dj0aJv8N3vXoUvv/xSP79Phjv85JNPcPXVV2PJkiX6OcjXXnttdE/J7pGbQkT830J7QZ1PWydNhV+dM/V3KVP3QK36nmOk8bu3U1h9VsratbBt2hw5qHymWi7XS0Lqs1unTEcoMyuyjoh6B1X2uhZ9jtq7fwLvhtXwV23R+TZh2pEI1FXDt3EtGv56J+rV5K+tjO5E+5pHRl4IqzaPKitDZtMOp7DJirCqDyd01GN4/Xqc6XDjaK8F3z/1NFx47Pk4/szTMfaUE1FVOBqbM0oQyihCy7TD0ZqRCLurTrXJVNsoIUV9Fs+z+4rchCd1F6nLyLxMMh/0tMHQsEwubsAy+WpYhp8Kc3SKn5fJNPwUtZ0VDc4w1rlzcfVZM/DPHx+Pa08fi+kjczBpaBauOGk0nvjZLEwsjVy4lhQsTPZj7MhSTJgwDiNHjMCwoaUYNXIkDj10Bk5Rv4OCgkF6WyFB7PS0NNaxiPYnlb/M5iAGpbtQvnYxOpqqIplVlRHxpIxorK3CmiULMKbUDL/c3NltG9r30s2ZyDXkwR8KYDt9k3YqpBo3GXkpOOzMCRg+oxiFo3LxxQtL8bmazJZdHbnBAGPIiGLTEKizRnQZ7WuS9eQZxF7p7a3qWCVfL4RX1aOWu0PIzyvA4OIS2Cxdw4HXu/0oa2zFe59/odq3QElRMRIdkWcWy7HafUGVV/2oG34Uklpakdjapm/gc3kDkKsZ/TUHMwJDfVqSLYSzJzTggsn1OHVsE2xGlVXVf4m2IIZkelTmlZ5q0Y2pV5L0ifUsZlr1XdnBAIY5W2FSlWCfqkzp4aaFOpH6K8r0c4i3x2Czqm0q4K+WinXkrmkZ9gMuV2eAI8FsRJLJoPK43Dyw9SlZ7gYbkeDAnIxkTEyySPtc7y/LncEQVjkDcPDOzf3KYjIie/o0NOXkqncqDb1epLd34NOkVPxx2Sq0NDdv1RiSixaxCxfx8zrdFbPJhBPmHI3TTpjduY56r/g0Ynr1TrGAbKxMFbJMArzixBNP1OslKLxx40ZkZGTg4osvRkpKin6e8eWXX47bbrutc3/ZV3oLy5DT8mzyV155Beecc45eNmPGDFxwwQV44YUX9BCN3/ve9zB58mR9kSQm9juR48T/TbRnAklJaJ0wQUrfnhutsfI3+r3PSk/B3Ky0PZ4mJieoc6w6nN8Px6JFMAbUOVY+Q44f/SxXZjack6bqeSLqHaRHcf1DdyDkdiEso/kolkHFsI+dCO/6Vfq9wZGgb9asvefnCHW062W077kdZoRsarKaYDCrstNiRNhq1O+7lplgNfkxtvJzHBlowpk543HLeafhju+dgZIRg7G+dBBuLfPh044wDrPYkV7mgVG1eVI2rUJymxOGcGSEH28CL0zvSx0dHbruIje3xm6Sk/qOAx4YGlcCmaNUWiYi6KxBwN2ob6JSOQuhoBsBZ61eHlSvgXUvY21LAo46dArOO34iBmUn6wvczR3e6KnUgNJBqZg1KU/NSfsoiKK0EPJys5CcrLZV7S15jIjUpURKSjImTRzfWa8qLCpAZmaG/huJaP+R4vpbp49BYY4Fm9et0UHhBjXVlm/SU31VGRprKlCxcQ2OPaoYF501LhIspv3KZDCh0FwEQ0idUyPF5B6Rx+ANmVCAwtG5SEixY9kHG7DordW7HCjufNqi+huykQu7wR5dQPuaJLOcc2tbnMheshRprU58eeRcVHrDWNKsztFWuw7yBtRGW1rdWNDkxE8uPhu3XHo+LjjnTKx1+lHt8qHe6cHyJhcW1rWhQh1rWVoR6sZNx5hPP0F7SzvaPP7ONm9/xKsz1Peo/Ch3xupKt0XfIN05xa6QJRsCuGl2JUbnqoawWhYrm6l3ivUs5jOL+zCV0VY4g9jgCsCr0tGgh8uS68YGuDesx5ZvX4Ca39yKtjdfg+urL/Tk/PxTNP37EVT+3w0ou/As+CvK9T5CeiWFTeoYDgdMqamwqOPk20wYnmDCCIcJaWYgw2LGlOQUnJuTgSPTE5FtNasTf+S35AqGVYUghBWuIFp3+AwR2luSxhlZWaiaPEneIDkUxGWmEM4dXKBqYQEsdSTipSXL9YUM2Vb3Ho9WrLrPy/6xIJIMpXb2aXNx+UXn6mHUqHeS9JIgf1pSApIce9bwkWPYrRY4rFbdGKN9I5a3dBpFv1cZejqWx2RZY2Ojfj9lyhQdzF23bp2+6CkXIceMGaOX/eAHP9DPHpZ5mWL7y4XR66+/Hvfee6/uPSzDL65duxbr169T824UFhbqoa1lCGvZp6ehp+VviB0z9vfS7gsMGwZ3SmpkSDv5HqNp1Eneq8mupn+PLMIb44fguTGD93i6uTAbCdGLXJaaWiR+Ng9hu0O/15+l/gZ5qZ82Q23Qdfc2ER1EKl+GvG6E3E7YR02Ev6YCBosVmZfdiLZXn9HPLzalpCPtjEt0r2P3kq/Q+J8/RxvZtK/JwFm6t6/6L6jaPCE1r4dOVP/Jna8B1c5J9DVgyNIPcd742fjtDVfi29OK4FJl6q2eRHx3fRj17X78viABV/j8+HhVHZxDbQhWrkPWhjJsGXkc2rIHq3OvTX8W7T9Se1G5C4GGlTB0VMJYtwz+xw+D77HDEPjkV5EghZr8i/4J378P0cv9/z4chvrl8DoKUFpagjTV7nljQRUmfOc5jL/yebzw6UZ9bGkOJznM+pyaYAihNNOErOxs/Vzix/79BB791+N48613dEBY6lF2VReXOpX8jAYXD9bHoN2zoKZ926l262lhtXqtU1NP2+7S1KGO06qO0RyZl2V1bV3ra2U+7v1OJtqPpF4dN+n2ikxx/IEACkvS8c/7z8S3zyzG/106CM/8aTouOS0bh4wGnn1gJu65ZTR+fMNMPHrfXDjsJg5DfQA4DA5kGnJUcu15PUbSW9qqBaOzYbGZUbW2Hp8+/w383m7DHe5AfKDaDgcKbIX/z955AFhVXH38/3rZ/rZSFpZeVIpiBQv2bjRqTDRqNH4aU4glxl6xJyomMaZoTDEao8beARtIk87S2YVtbO+vl++cufe+fbvsUhfYcn4w3Hvnzsx93HNn5sycKfqV0F1k51hht3HNp8ksEolh1aSp+Ojhv2LVkLEoa/Wj0hfAF2X1+HRbLT7eVoMl1U0YGajAKcNyMJnax98/ZTzcTSX4urQGc8oasKG+BY2BEMop7td1Piz40a347JpbUd1K9a02CkzBqlwm1c0Oh/b8vkCfVBs7djYZ13zseM9gd8IIBxdDKttb7PjXkmz8Y0kuXpqfi398q7m/0/W/l+egJcAtLyA/NYAHz96KI/Nb4nGFnomR5XhGjNB74dnEVaEoCltDKPKFUUPn3OlhtlgAbysa/vNvVNw6AyXX/whlN1yD0huvU0tNB9avhW34SLinHIXkc85D1o+uQ959MzHw8aeR//yLyPj+D8GqNH8dXD67LSbk2c0Y67bg0GQrXWv+vNxII/2zhZ69pjWM0kAEISnP9z/0js25pIQ7HBjU0oRfZKTgh0cfiZMmHIYp4SAsdjs+JSWrsqpaNYpMutGKZdbxXNW/3MFBLjk5CYFgEBeeezZOPuHYuLFL6DkYBr6M1GQMHZCD4YNykeR07tEALU7DbrVgaF4WRg7OQ1qyZnDqO6r2wYPfLaPylX5kP27wGtdsCH7ggQcwYsQIde/YY4/FPffcg1tvvRVZWdoSwrw3MYdhZ8jcgA3A1113HT799FM8//yf8Oijj+Chh2bS+fP46KOP8OMf/1gtz9jRKGz8Fj7yvcQ0hT2njvf9t9pV+alcR/jdx6I41O3ACWlJaqsIrrP3xnUmKWttHaJ+H39Ubc8PheD3ZCIw7hDtWhCEg0qoogTBrVtgyxsM59hDESzfCvfkYxBtaYR/81rY84ch49Jr4Fu+EIENq1Wc1q8/VfGE7oX3Ho46rYjy8sU2OiY4tT+x2YaU4o24xBTAPd/5Hs7ISEORxYmHUwfg9o1BjGhoxr/GO3BtURmWfLQOXyWbMWhwErwl22AK+NDsyUfMnaJmM0Vsoj93N8nJyUqn4RnGrAfxCis+rx+RTR+Q/hqhelKrc0njgnnkWUqpjYVbEds6FxYeG0th2AUiNjRaBiE7Oxe+iBW/f3sdtjeE0BKMoaRa68MKBCMoq/FS/QvkuLwYlJMCT4YHlZVVdJfrWxPq6uoRDofpzITWFq8aGJickkx6nEf9RpsM2to7eIs7Q+tRqg2f6zo0X+u39g6KzPFjFkqbElMrJNJRiVQ3bHWmzwkHlAjlq+bGejTUVaOpvhZV5SXYTmVzc0OdHkJD9VOEo5gyNhtPP/QdHDlpIFp8Ifzke4fh2u9NwMTRWfjumaPx4M3TkJudpOJ0rlEL3Um6LQP2KJV/e/mqo5EoDj9jLIZNHAjPwDTUlDbg9cfnwN8SUHsY7zk86S2KARgMm0XK5e6C+xR+8tNUTJ7Cg6W0az5pTslEzbAxlI8j+HBbDT7YWo23iyrx8oYK/Ivc+0UVsDStw313XYHX/3gPfnTZucj1rsTi4mK8smk73qKw726twmubK1HvDyOSkorqMRPVM419kfkwcLAdt9+VhSF0VM/uA/R6zZH7JFTHBZ+o6x2PRmcUH7sSXEd/Iz6TeC4cfLY32/G3JXn4y6L27q+L8/D8NwPw0Gf6CErSsfJTArjrjG04ekgz7KSAdeWEgwvlTnXsmD3thqIs9Bq4LOWmcW0oimJfBIWtYWz1RxE0WWBxOGCy21U43u+Qxc37FjtGjELu7fdg0FO/Q97dDyDzZzOQdv6FSDntTFizc9Dw9pvUVmub0WaUyUaZzp3elcGo2q94kzeM+hDdJ3+e7ch/+LxFDI37BX7/3DhqycpCRmMD7hg2BKcdMk51SqS4XTgxOxNOUogbTWZU8ZJtPHBAj8euq3N2LOVBA/LQ0tSEyy/5LjIz0lQYYf+i8qUuj85Q8tHPDawWs+osc6iZwZrsDIz0OqaZ6GeE5wUJOoZjVFj9XNgz+N11NMaqDg2dX/3qV7jjjjuQl5enwpx44om48847lZHXmAnM1xzm9ttvb1f+GudcNg8fPlztYXzTTTerNC+99FKMGTOm3XPj8iY/4zckpiPsHSZPJpoHDlazAull8kvV7+xIMr13K+UmDsFbNTRFoqgL75mrDUXU6h0GkbQ0NJ94vLYqAD+7w29oGjlSPxP2liXbvHj8k0rc+XY5fje3GstKfGr5tP3F+ko/Xl7UviPUT7rVh2sa1e94gtyna5sRYH1L6DV4ly9EuLIU9iHDYR8+Vg3wSJ52GppnvwtrVh4yLr0WrfPnwLdmqQpvIp3NdcRUqjR2b5lFYfeJ2S2IOXk/YqqfWW9KdA4rhtdtwmEpHhziHIOBk4fhvqx83LLOi6M8VvzR2oCTF6xD1boKvFuQia+Ge3B0qgPhNZ/DNe9z5G5ai6bBI6mM8CFG9XiEZ9q4tfaX0D0Yg+4MXUatjhRqhrl8vrpmuHSMOdNh8YxVdWKkbiPQUopYwhQzX8yBJttgZGZnoaQ2gBVbtHI3yWFBricJDS1+bKtuxorNdRQvihHpXmRT2JTUFBRv3arCMjzA1ul00nOAsvJy5Td0aL6qlxN1PmHPsJhtsCa5YUtyqT4LU8wEq8NJfi5Y3U59tuA+1INsHI6SfKitnEL53pPsoDY061G6Ew4epMv6fa0oK9qAUNBPcrGrfozy4o0o37oZm9YsQ/W2NfHZwaROo77ZDwvJcWsp72lqRlFpIyobgzj+2ALVl1W4oQavvb8OrXTu9YUpb0rdur8ZYKZykFo+JFDNYw+JkGCrt9YjhcrjNV9sQfnGatWGMvOSD3uFZrtKQRpSLam6n7AvGH0Jq1eGMe14rgu5PUo3VHsUsEZCauuklmAIK+tasYLchgYvtjT6sLbBj9uKs1A34CQ4R5wC2/DT8ED1CMxrNmN7awAbG3worPeihOpiKxX4pkhYrcijoPS5uct9X9OmulBXF0bxNmqL9xF6hebQVSfStsYYbp0dw0WvA39bSY3YsPahMF0dma7SM/wTwzJ8vbM4xr3Ec2H/wa84EiWFO8Hx8sV85C3TvtqSil+/P0ybYUzkuYP47QVb8PrVhfjvle3d2z9ag5tP1PZSbS914UDScRlqHhfLBscwXVMTWjlqTsPW0XhMwTmGYe83wu7KCd1PjdWOSqo4rdQg5XJQGWrJ+SMxbA+EsaolhI3eELzhqDYel8RgzFJqnvMJtl37Q1TcdRu8SxZTY5oaxOGw2ve25vdPI1yyVcXhNI0ylpe5DvA+E1TwF7ZGUEwKd4Bu8TONMptH7YVtNsweMARrLNJJsl+g98yyOMxqxdPHTsGEkcNhoXNlFKZ7h2akI8XnQ8TlQrWf99/S5MdH1bHS1TnJkNNITUtDi9eHEcMLcMbJJ6owQveTqPfwLND4lfJvu2dglKLqLv1TXd+E8soaFJdXorHVq90k+D6PuuQw8cEbCu2ovgf9m9CWrid/PXE+cKj4bzOOwm6j8hM5Y+lpPuejMfDGOGf4yLI34jB85E5Gjm9cG0cjHp8npsMY4Tgep8kYz+dwnCYfDb/Eo7DnBAYNRtidxEKkjLb7TbsGUprPXV2E4YsKMWzRWhTsgfvZphK02BwkWDNaDjsUoUwPC15LmOWoyxQ+L7wD82GiOkDYc9ge/NrSevzxi2oMy7LjvAlpGJhuwz8X1mJD5f7b625bXRBvLm/Qr0Btqgge+3g7FhR5cXi+C4cNcuGzdU3KcMz3hO6By0zefzQYDGn5Zy9ppXqY0+mIfeAQRL1euCYfA4s7GdaMLJh4+Xh6Vu6vHoZ3+QL4N6yC1ZOtjMg5Nz1EOnkN/GuX6ykI3U2M9zu0Jji7FSn1FfC0RHHFJcei4YQC/GxjFDZbDK+ktuKCWAvqHXb8MWLBs9tb0WA24bHDMrHik0JsXF+KjWddj42TL0arK12tX6y29KEwysV1MGFfaW1tVboMzzAOhUJ03QJr9QpYQm3lJr9tc9YhMDs9dE76TxXlI3/i/RjKgxmwpA5CJrWXPlhUhlBEk1GTN4Lrn/4aQ3/wKqb85B0s2VgHByIYmx1TxuLGpiY0N7eosFxWFAwtUDoUlyHFRcWqGs7LzVU/wviNwp4z8qhJOP/Bm3DeXT9D1vDBsKe5MPW67+GC+2/GaTdeCVuam96xVlZr7ZxE2srwHe8RHI89TVHO9vjNHTfh/RefxZETx5MnlQUqOunMFKZj3M78hG6E8hTnpYaq7cgbMhyenIFISk1HcloGRh02BdmDC5CRlYtIjHJlJKzyYDBswr/+sx7frqjAqCFpGDvCA4fVDF8oDF8kimUbavDp/G04/ohBsIRjWL6mEnaH7Fu7PzFTGyU9lqHnxL3LMTbKnPWVzcgpyMDqLzchf1wuxhxbgFBw95egbg+V/Cr/mpBmonpa2GcMffmLL1rBi6INGGQFLxGdZIvgoqwi5AS2w24KI0LfA38FWtHL/Q58YUJxwIptARuqg2Y0RO0opKo1BAqr5ETh9KKa/3FGvDgiuRpTcltUnct/srItmHC4He/8txVhXuqyj9ArjMVGJ1IiPsqbTy4EBqUAd0wF5pUAH2xm44AWviOJ8Y377NfRv7O4TKJ/x3gGO4sv7F8McbCBkbIz5hen4KkvB6PeT5oX3aPiArmpQeRltHee9DCykve2oBe6i2hUyzdsNDaRm+Sqx+XpRbjCsyXurvJsxmCbV0nYgMNbyJ2WUoErM9rC7sx9L70YQ+2tegpCd1FDYlk8aCjsqW6kWLUyW80epjKRDchcNteGYljr1Wb/VtM5DwrgxpPV5VazF1rmzkbFHbei5Mbr1F7GtX/7Cxr++ypMVptKhyXPebw+ArXU9DpKpzIQRZDzuN5Bzs/kAiGNfsNQpxWjk6w4LOJDFtfyQrfDbzU52YXvn3wCBmZmKr/EejAjPQ0DwkH9qgOJ9WUn54YxyeWwqw6RIfmDSfrC/oDzK79bs5nyzYBsZKRoy2NplWtb3rFbrcj1pKn9iVOTXBiSlwNPSrKKxwM/GM7TDP/LS4t7KK3B2ZnIz8lEbmYanCTPjBS3SifFndBI1uPHoXQ4bHZ6CnIzUpHicqjvQdh9jPfFx0TjLHdw8tEw3DJ85M7ERYsW4Te/+Q0efPBBtez0vffeq87vv/9+dbzvvvuUv3GfHfsZYfj6ySefxLfffht/hvFc9Z11+A2MEUbYO/x5A9RxT19hfTiCCn9Qz+FcBuyu47qWYoRD8FK5HBo/rnP5sR/JOOjJRDhdOkT2hpqWMD5a04TvH+nBJYdn4NhhSbhocjruPmsARmQ79FD7F9bf/rWoTpXxN5+Sg9PGpeKsQ1Jxy6m5qCc97PWlDSxmYR/gvUdf/8//8Mxv/4Cnn/w9nnryWbzwl79j7dr18XJyT3jnf+9j3tcL9Ks2XJOOhnvKcXDT0eR0Iu2cS7nwR9YNv0bLlx8jVF6iZhennfc9mOwOVP/xUfhWLuZRZHoKQrfCZSQP0LNqDtTeSW6owpiGjRh08mS8WN6K96v9+EGaH3cPs2PpwmI89E05/uI1IXziUJxzWB5uRoTq7RJqX21EzfhJiHobuFFE7SpynCYbi530HC6ie0XPX+/Arq+UxfsEsz5j5TxS/JHyM4iZyC9nAuBIQSzkQ7RqFVWebW0irk3X+4ep/YetzmR8vKRC+WqQ/hQf5q7Vr/nuZuRmpSGD2ltbt26jz0cTqIN05REjh6l6uKysHE3UZkpLo3AZGeq+8RuFPcfitMOeQfoLtXmCPj+s9K5TBubBSu0ZS3oyScYEu8WG06ZOwc9/+F18/6zpyKJ376Ly9ZIzp+Pq75yJ8046Bj+/8rv4zinTqF1jQ/4Aqs/PPAnXXXo+rv/+Bbj+kvNw9MRxsJGMKsoq0FDfhAtPPwE/uvAMnHvisfjZDy/CxWecALfLTmFMOPHwSfjp5Rfhh985AwOofcW9I0I3Q3kp4GuFw+2GjerCxHo4KTUNQ0aMRcHYw5A3VFs5p6m2Cpm2MqS6Q+TsCJOSvHZNFZatqkQoGKHzSvz3gw0YOyxDXTfUtGLOl5tV2qoeEPYLKbZU2GJ2amfu/aBGXoY6Z0gGgv4QfQ82VZbyktQOp22v9DMFxYvEokiH1ncm7Dtc/zU1RrFufQhnnKENUOb9iy9LW4lnt9+OJ2ofxc8a/44zW+diXHgzUmPaPu8hthSZrfjt8mLcNG8Dfv7VetRRNR01aUuEp8WaMD60EWd75+DWhj/jqbqH8Jj39zgmz6fuc9vohBOTUFcLFJXwIN7ObYW9kV6jNbDwF5YDj82PKffr2dSAJkX5ukkmHD3QhBuPMOHVQuDJBTE8sziGL7ZRBqTW7etvvYd/vvo63nr/IzQ2NqFw3QZU1dSoNOsbGrF85WrU1zcqf0OoPIpo0ZJl+Mvf/oXP5n4ZH5m7vbIK8xYsUr+FWV24Hv+itF/+zxuY/flXqrNLOPiwFHmW8acb0jF7fTo1dnX/iAmxcHunekCEgwrPJjbrU4P5nLOXxxLA1ZmbcINnA67P3Ij/82zERWnbYGNTYSel1ihnE67P2oAbMnftfpq9XhmMnSaZCdGdDDPFcHNTOSZQlTvKbVX7CWfb2EgcQ5jLRhIsL3nJDd4mevU8I3hNSwhFft6vieVqVktUh5ubENqwDtW/exo1f30eFjYkq3hAeTCK9d4wNpNrCMcQ4m+H7nGJzEZiLsNTLGaMSrJiJClz2aQgZEdCONfXgKGhLgyWwj4zLD8fhxwyVp0nKkd8HggEMZRka6Z6NM3O+8Vo91mmRl3a1bmSLbnUtFR4fT7U19Wr70DoPnhkel5SBE+e3IBxWWHSY2Ior6lDY6umALNBNye9bYkkm9WirvPIf0huFjLTU1QeZ//czHTlb+GOSYLz+pCcTGUoZuNyRloKBmZ5UEDxONyAzAy1VLkmU/pXE7s6sLNZLMhXYdMpfhKCvHSIsEck5kejo7CjX6LuumTJEvzgBz9QexY/9NBDmDlzJh555BE8/PBM5WbOfEhds79x/fDDDys/Ds/nfOT4V1xxBebPnx/Pz/xc4zewH18n/hbDT9hzvDm5+tneEaC8tSeO8yLL0hQKIZqbg6jTwQLWnC5vhS5PXgY1nJSizoU9o8EXRos/Ck9S24oPfORrh7XtXXuDMawu96OoNqjavwbljUFlcDbwh6Oo97aVpRy0tCGIopqAclvIBROWGGfqW8OYv7kVZx2ahjRXm9EwJ8WqZjp/sbGl3TOEPWNt4XrcdvPd8JO+dMZZp+Kyyy/BRRefj/T0dPzm8Wcxd85Xe1w2VlRsR21t+2XEDZxjJsCSnglLSjqch0xWhmNentp16BRk/uiXCJYVo+HNf6J57geItjbDTHo4z0gW9gPJVI6yIsZ1I5WdDmq3pG5dho3TTsOnfgsyXCb8ZKAVGxcU48211fhkRA6Ks5IwLMWM24el4qpsJ1a7rJg1eymqYmE05hRQgUvpsuGS0+RymnUyvk6SvRG7E17ymfUnH7VPLKQD26NNMNesojttedVkS4IpZwLJ2IZYoAExNhbH78fgi9pRhqHIzsrG8qImlNW3rczDJNSmRAwjU73I4e196NncL2n4T5xwKFL0PZRXrORnkP6em01liLZ9D/9GY1sRYc/hbip/aytCvgBsLicc1Hbh5ahJi1XtKDbqvvjwXbjqwrPw8M0/wQM3XYP8vDTcfePVeObuGfjjA7fhkRk/xhO/+glOPmoi/nDfr/HUbT/Hjyn84xT+jv+7CtOOmIjjjpiA4QUFcJgsuPu6K/DsXb/EH++/neLegCdv/RmOPHQ0vnPGNPz9N3fjukvOx303/AiP3XIDkuj3CN2Pz9sKq82htUsTdFtVH9N1NBJGZWkRKUnLcMs5pXjsqmYcM6pZDaS0u2xIS3ciK9NNbdkUZVjk9vIho7MwaGQmFhZWoTWSrvq/hP2HzWSFxWTe6xXdg74QRh6RjxO+PxlLP16PcCiKuvJGDJ88CEeffyh9A23l/Z7AvZfRaBhJsSTZt7ib4HzJs3oXzPNiylHJGDHcjmDMhtagCWmxZowObsD53k9xU/ML+E3tQ5hVcz/uqv8dLvR+hlHhEpQ1e7G12Y+tTV4MC5WQ/6fq/u9q7sWTdTMxo+lFnOL/GsOCxXDHWrVB0/TMgQPsOO2sFMz+tBk+P/vpP6gP0KtKp/xU4MQhJuQkm/BNWQw3Hw2k6IOqx2Vp91uCwNEDTMhP0TqZDxk7CoeNH6M6Mmc992esWrMWdXXa8i98XLZyDWrr6rC6cJ0SNrvNW4rx1fwFmHbsUfTBRdDq9Snl65tFS/DPV15HOTXAOO3VhYUIhkIYP3Y0Vqxag7ffbz+aUDh4RKkADkfN8IaokbSXlYNwYODZwcby08Zy1CwzLpzYWShP8sxwNjywodgIa2Bc87+szO/KWSm9JHNYKfdC9+EgxTjq9yMciaj3nGQ1Y7jbikOSbMi0W0hZI08uYymskhUJOUJn1UFq2LZGUNgSRhPXuixkUpzV7EQqf0MUhpexXkH3S3xheHmVzcRMTWlycy3bbsYYet74ZCtSeVOKGBuozWikNNeRosdyF/YPwwqGwK0vMaoaUHykOpP3yeIOiuJwGFkWM0boM48ZVd/qRqouz3WXnJKi6uCKqmrlL3QvnIN4QMfwnCzk5War2cM8U5iXbjLyahzKx5zHbRTGHwiguHw7mlp9Kgzv2cTGZi2HAwMy0+BJT6UyOoJmrx9lVTWorG0AL0vMjmXa1XgtNjgX5GXBZbep522trEFgH5fl7I8YBlg+8vvmY6KBmI/GMtEc7mc/+xm2bt2qBk1yWM1p9zTa0tO8tGvDj+FvJ0x5fuPGjfj5z38ev288V4urhTfO+V5iGsLuw4OsYg5qDPl9Ki/vCcOcdrx12HC8fdiIPXI/zc9GOhsgWIYktzgkx3bQff4eYvSN8VYEwp4zON2ulp/+/dxqLCxuRW1rGMGEJc749RdW+PHMnEpsrvbj07VNWFGqDfZpDkTx5CdVeGp2VTzO1toQ3l7Ztgwq73v8NN1ng++CYq9aZrrR135gTkUT6V6kqw3J2LFDa1imXRmfSxpkQN7e4PX68PgjT+GqH12OK678Hg49bDyGDR+K0WNG4eJLv4P7HrgdJdtK4Pd345LjRj6lo9qD05WE9AuvhMlmQ+2LTyOwaR0snmy4JkxB6qnfQeZVP4d9yAgtjtB9sByoHNWWiaZ2D2WvYZsXoQB+ZK9ZhrG2FkS3efGPpTVYOjoLn4XCVBa48etRGfj5kFQ4lpfh6ZcW43crvoQrpRQN449VycZs1Gq2WtqcSp/qhg7Fs7Bv8EosrLPYKN/wcpehCt4Tr1a/q+P0wJI9gU5ID2oshqlhk+avMGFjSy7cGXnI8Hjw/sIyKs87FxLX7EmWIIZmmpCTk6OuM9LTkZ8/CCecMBWTJ09SOtSWLUWorKxS7S9Phkf5sVO/MbGuFnYbQ6sKNbUiGgzDlZYKm9NJ/vw+YygYmIufX30ZamprcctDT+L9uV/i1GOPxpRx45Cdlobt1O75BfnPW7YaGckpOPKwcZg0fhSWrCnEvU//Ea2tfixeux6Llq1FqtuN+qYmOF12ZHlSUdPcglsefRKffbOQ2sJJGFuQj9t+fKXqf7798Wfx0n/fxPFHTsLwQdrqMsIBgPIRG/kaayuQFliEI/PLEAk247BRZjWbmPchnvWvpfhmWTmykh1Uxwfx5mebMH9pGZKT7Eh2WPHVV8WY/cUWhGNUPuvtMWH/kBzTBqq2a6vsBlxeWqxmZRCeeOoo/PfR2QgFwnC6bZj3xkp178wbjsO1T12AQWNyuCjYC7gNRe0kqZy7DRbzhvVhrFzhxUWXJCMWCcMX4i0QTdpKHVRnh7mejUWRF63GKf6v8OuGZ/HXml/hybpH4Ik14dH6x/FCza3k/zt1PztapxJW8VSNoNUK3GVhs5pw+VXJKFztJRfay++g59KrvkwWiDcMrKsBrpsMTMnTFKCWYAylzVRZp1GDucaELHcMHpc20is3NxeDBw7E8KFDwZuTG0sQaAqTLk2WN50aHVdut0uNvlu/cTPyqfJNTUlGQ2MjWlpbccmF5+GdDz5WnZcMh3M4HKSQpcNKDS5BEPYMzoqawt0eNhzzjOO40w3JxtEg0b9d+E4cL3Et7D+2+KLYSIV0VYhn/WqGiGSqREe6LBjrtmKI04JUZZfgAQJUftMZzzZmmql83tjKS1SHUOoPK8f7EK+j663+qOrQNJaa1uLG4KbLwU6rMhIPo7TTbZrBgfcy5iWvOa313gia+McI+w1e8szr9arMzHWoMgiRrPit5+cPxnWjR+C7TjvSqZ5U/pzpic7OVT2sy1mlpRsmOf1tpWXKX+g+2Fhb5TXjjs89+GSzDw5SnpOdDqQluZGW7EKLL4CqhmZdLSa4UUNyYeNeNfnXNrYgojd0VVYmx+W5w2aj+G6EgkFlTN5WVYOq+maU1dRhayUb/enBLOt4wgR70R9eyi8/O1ONlA+FoyitrlMzrrQvRdgTOE8ZhljjaBhsGcOAy/AqOsuX77gvpZZF2airpWfEZfiaae+nXbMrLCyMh0n8DYzx3EQ/4yjsGWFexm4P4TfNK3Ic4nbgjIzkPXKPFgzAzwZlqcF8alYck/ANxBF57jNuuxn/Ny0LY3Kd+MeCOjz4wXb8ZV4NlpZ4lV7U5I/gq00t+NGxmbhgYjqum5qFw4e4VdwNlX6cPCYFOSk2bKzWjI0spY6S4rx64aR0fH9KBn5wZAayk9u3Z9l4zLONDR0sEbfdouqRZtLThD2nnPSnJKpvTzr5eN2nPUOG5itDssvVfl/D2tp6zPnsC7zz9gdY+M3i+N6lXcED97ZsLlblbkN9IxYuaItjdjgR83thSkpG2nnfR8Z3r0bK9HNgzx+GWCQE9xHHwST7KnY/lC9hsfPoOMScTkzZugyPHjoMF3k8OOyrj+FZ/A0WOH2oy7Dj8HQnTrVZcCzlzW++2Yy73/gS921djRfKlqKy1YKtUy9GwOpAjDc+VYbhBGelhhfpZEL3wnmK9ZekpCTSdf0IbV8GhNq2uVK1X/ahMCXnUr6LIbz5Y1J82lZg4M7rdf7ByMnORhAOLN5Yu0PZbEA1LbKdfuRlpiAzMxMDBgzA2WedgfPPOweHHnKI6o8sr6jA4iVL1YpODmpzeTwZWtuMcLlcailqYc/R5iTE4GtsRiQYgTPToy3vTu0l0moxICcHntRUZeC9/oqLUTB4IFauXY/8QTlwu2z4cuG3mLtwuZoN7gsFsLRwA4q3luDQUSNx45XfU8tSf7XkW6RlUBpUzpZt346sjAzVBlq4Yi0+mbeM6lm32sceMTNy0tPV0qpXXXwuDp90GJYXrle/T3NCd+J0JyES1tqfqn1CrqmhDjnm9bhw8lYcMSqKmdcn44ozklBcGiK5cL9WFBsKN+LDuVuQnGTDT68+Ak6bGcluO370g4mwOa2Yv2grPvy8QvVx8H7Hoiv3PAaOzML5M05QOtN7v/sajTUt8AxIVQOsi1eVY/knnO+AYRMG4qrHzsXUSya2awvvLmbwhBqpn7ub99/xIn+IAxlZDjQGrbDz1hzkz463sGTH536qe5tNSVQHWzE8XILsSC3ywxVqaWr25/uJcRLhQbjZWSZkpDnx5n+bEAxRndDH8nKvMhYXN8SwuCKGs0YAPzzUhNXVwH1fARe+YcIvPjbhi60m1fHxzCITvirhDusYnnzmD3jp5f9g7lfzcfn3vguH3YFQKKwyc5QqeaOPQ30FOgMH5OGKyy7GkPxB+GTul9iwaQvWrN2Ades3oayiEmvXbUBZOe8pAhQVb1UzinmE16nTT1B+Qs/GZKFMnCBv4eDCRfVe1K17xYF6Tn+Fjbi8PDQvMb22JYSSQFRbYppevIMK20xq3PDy0KPdVjUTmFFLVBPKaEyOl6gup3jsaiktf1QzTmlltjZAKJ3SGcZG4iQrcujcbdHCtFD4skBE7WVcTL+BfwvHEfYfLJc1Gzbhy3nfxDMY/8sNIMPoe9iI4RidkaZmKzIch11n50ziLGOGO2MaqJFcQg1ooXvhpZ55iWeeRRwOBtHo86G2SdvDJUi6koXyba4nFem8LzHLh0VEjmXpC4RUuB2g+7yai81KjR+K09DiVfqYQavXjxZvQMnVaCDTV6CMTpFoBJ7UZLhdDrWyS0VNHZr1JbGFvYPzkZGX+GgYZ+P5Tb/Hs2QSw+q3iTbZKZl1QEunLYwRX4MaV/SNMcZz2SXmeeM60V/oZtrJRMNK71oN/NgLx6sfj3DawZLdqcQorLDv8N7Ev5iejXvOzlMG3a11QTz1WRXWVwbQ7OclwYFM3cBrvHIucotqgjhkoBOT811qiemu4LZwI6XDM4T9pDftiObHbeyOqOeRa5/vhd2F9xxlI8CelH0fffgZfv6TW7Bs6QrUVNfi7bc+wN13PIjt2yv1EDtSTeHefft9vPHfd3Ddj36K/73xLlraGZhjiFSWonXBHNT+/Xeo+8fv1B7GycefAWuOzFrbb3BbyG6FyVeHa3OTMbxgKMYecwKWrFqOYfUVSG0qh23pXMz/ZDb+9s5i3P36W3jp2w/w+dpFmOtIR820k1E59BBqRFH+44KZZxHb2Dic4Aw/an8J3QcbYNmQwAZZc8QLW8MGmGJtxmDWay0jz6TymeQbCyG8pf0KhE0hJxqtg5Gdk4tN232oaeS4nZejJlMUA5KCGJCbgfSMDGUcNsoM7tdctnwlPqFvpK6uXvmxYdLj8cDtdivdS5ah3geUSGLwUdsoEg4hIy9bMyDz+6fyu5rbKS0tKCmtxJN/fhl3PfMn/IfKaJvNCofVgTUbi9TEoxxqB3Pbp9UXUnpxXX09Pl/8LS6/9T787Y0PkJvlgcNuw5aScrXKk5PKhbWbtpAsHcjLyYQ34MOKjRvpO2lCZU0jZr30Ku6Z9Tz+SeV/yXbecmD36xBhNyCdxuF0IUB5xzDohprLke9ahwn5Dbj2XCd+cFoSfP4ozjrOhUNH2NS4nAtPdOO3v0jF9m2bUeOjeKSMXXHuWFx61mg46TxIZf7G0jCSs0cgM28QrDa79i0JPYa0nGSc+IPDMf/NVVj26QYEWkJULw+DtzkAb4sfntwUfPmf5fDRNeNw2TD9h1Nw0uVHqGWr9wSe/BKi+kHoPrg9smVLEPf+ah3WrdqMv6wdhLJgEqyI7FDDcvuHHedAK0LIC1fBFfNTOG7vavcS4fg8KaLOF8OnFVkoXL4JD9+zDlu3Udp9sB20Y69PD2ZSjgnXTAA8TuD+r2O4ZTaQQefPnBrDmxcDr10EvHWxCb8/I4bzRmnlLlfGN1x7FW687mqMHjVCGYCXLF1GFXoZVq1ei8GDB1HKJjW6trSsHOVsDF6/AavXrEVOdhZysrIQCAYxb8FinDr9eBw6fgymnzgNK1dpsyWmHXs0/u/qK1BVVYOauto++ZH0Rox8befGUwd47+IdSgrhoMEzgjsuLb0nGHH5GI0muAR/Yf8TpQJXVaB05DcepIvt+hLTRaQsNxtLTMeiSLGYMMxlxeRkCwroaKOYxp7DHNfokDYkxwZfTj3LZsbYJAtGU5wsUrZJJ6fnAoEo1D7Ga1t5VnJE38tYH0HGv8tpQ5VusBC6F5ZZS6sXTz/3Aj746FPVYFbfQYJRiTsqnA6H6hDd5dLTLGs9rtERwvEjlG7Q143LMAoKh8OGrPQ0eseasS4YiqhBdfWkE7mddrT4gmjxB5CVkarkQFLRHTdvtPyaCC/zFKP8x6LTSgQKSY1j8uYzPSbUbOSOcePEb3C5QN8F52HdR9gzVHlMjmXHnZp8zkfWjVW+4/zGwiI4jBGevfieJi3t22B/zU+7lxjXCNMRHiRgLGltPN9Ih59npGHcE/acGLVRrFQ+0gvUfTpgyIXfM8np66ZWPFFShd+UVO+Re6a0Gita9nDghpItPZ/l3Mn3Iew+LtJ58lJtOGZYEu45awCGZznw2rf18Id4awDA2qFHg5eNDkWiakbLoHTSgZq1pdg6IxSO4eGPtuPOt8vx7spG3beNrGSrSocN0x3hGce880eKU3SsvaFg2BBldPrfG++ovgguLzsrSxn2/2Lu13jl5dfw2JMP4JbbfoFrfvxDPPTI3Tj5lBNx310Po0kf7NURLmPnzP4Sb//vXfxm1qMU/0EMGJin36X7ra2wDx+HrGtvwZC/vI3Bz/4Hg578G5KnnUr5uFd1F/UuONvYrLCGA2gur0CgtQVPPHwXxowdj4HjJiFtyQI0OqyoSnXBlV2PUIoDJVNOwppTL0Vd3hAEuC63kny4jdOVYwuG6vXUHil0D7y0M8Mr6MBfD3P9WnVtELOnwZI/XZ2HKhbC0tp+wGt5MAP2tDx4MrOwpqQVTcrI0Hk9bjdFkZ8eU32TFosVCxctwRdffo2PPv4UL//7VXzzzULwkvYGAwbkwcqDNvXlp3kwoDFwT9gDSBysw8RI9/HX1yNGcnCnpSBK7dEwbw3g92NTSSmefulVDB82FK88PROvPvsozjv1BCQ7nGgNBFC4cSMyeEseqj6LKI97UlwYkZ+PQQNzceiYkchITaH2lgMZKUnU5vJhzcZipKW4qe0VwuoNG5CemqxMHKUVVdiwtQy/ffFVpKWl4aUn7sPrzz6GS887E+GwzBrvdkh/ZUOu3emCr7kJMe9WXHF8Gf7662SceqQL67aGUVodhsthVvukRkgX4r4r1nfN5C47IYQXXpiLIOlOTicbks2weVz4zTOLsagwApvDRX5tgz6E/QPnWe246/dstElPvfoozH9jFbZvroEr2YFx0wpQW9aAurJGRIIxOJIcaknqopXlekyqD6iePumKI9Sy1ZEudO3OiKjcLe2j7obL7bo6Cw4bnIpbr78IK92Hk+/O37M1FlFLTtvQNuirM8zUmq6xZePXv/ghzjpuKLWNHCrf90V6ldq4thZ4pRB4b1MME7OBv59nwi+PBMZnaYMlje7ExEKXDbx2uy3ud9ih4zFyxDB8s+hbpKQk47ijpqglpNOpEl+4eCmWLF2ulgDhCmLB4m/VyK4hgwfhmCMPx0nHT8XhEw/DKScej8GDBmLcmNFqBGhmpgeXXHQ+Kitr1DOEg4sh/dHZPhwzjCr3TkfJCz0FLluNvLuncFzD2OyPmlEddsQdXxv3jfK7j5bjPYJyiw3L0zIRIsWXXzOXucYS07wv8QavtsQ0L1HtpQqcDcA2sxm5djPGJ9uU8TjDZlLfAhuODeMxeWGAw4JxSTYMdZqRSso2p+8jpbyGlDFe9nptaxjVrJjR84xlEjk+G8BSSEH/JnMgingzMGG/0djcjCd/9yf86a9/h58au4l78LAcG3gPJoddGYL5munsnL8bw09d6/K0cGeX/j0J3QePct9ctl3roKZrXu7ZabWq/OkNhJDidqj9hreUamFIKCoeSYb+3VEe3BhjMaltPygsy9DJOphKXYP3UnM5bKrz2oAbSmxUZgNiXXMrvP4ghbNgYJYHSU5Z/nJfYBkkGmMNI21HEv20U86LWvi7774bd911lzoa4Tg9w//OO+9UHZF8z3gOw+eJzzNknhimYxxhz7G3NAPUdtlBl9LfO8N1IufJEIV5prQK9xSV75krrsA7tU0qnd1GD2uOhGFp3fkyucLu47abMDzbjgC1b6xsqSUSV29gtjeFUFQbxDdbWrGsxIeS+iCafJ13YHEaT1w4CH/8fj4uOTxd922D901OdVqwupz3xW4PL3Wd7rJgiMeu+wh7Apebv77zl1hbuB6znnoOf3j2z3jphX/hlZf/i88+nYuiIm0PeaaxsQmfz/0Kt995s1qe2oDTOPf8MzH5iIn48/N/0313hMPd+8AdGJoQ18CaOwiRxjp4VyxEpKYSzlHj4RgxjisAPYSwX+BGDrVVcreuw3+8Efgsdjz8+G9w5YwHsbHJjFDMgk0TT0L1mCOwadJ0bBo3DV7PAIC35uDBdIZuzLOH2bFR2Dg3rtmxQVnoVni2LusuPIs3VrUC8PIWK22YCk6CyZ6kzqMbP1RHg2jMjKpwDlI9eTDbXFhZ1IhIhzI8EbclhIJsK7Kys1FfX4/ly1di9epCbN5c1M5ITJUu6QEmjBo1UulevL0IwwNSwuGdd4ALnUAiqVi7CQv//RZKl6/lkVXY8uUSLPnHm8qtfHc2gtSOeuGN93DpjDvxq9/8Hr98+CncP+uveO2TL3D9vU9iSeFmlFZW4zYq33/z4ivIzsiktpcX8xYvxZjBQ/CnB27HL6/5Ad7/YgH+795H8cWSFfjwy8W44d7H8fXS1dhe24g7fvs87n32r2hs8eI/H3yCS3/6a9z6xO9x82PP4q7f/lEN6hX2A6TDJqWmo6p0E86eUIHzp9rR3BrF4GwLtpRrA2F5n2Iugvmc9TBt8DWQ6zHj8MHl+MsfPsDnKyooj9fh9gfn4825LUhO86jwKqKwX2mlPzzwvMMKwp0SDkXUjOKStZXYvLwUOQUeDBqdjS3Ly1C9rR4pHjfSctyoLK5D5sBUNNe2bTtgcNT5hyAtK5m+gZ0/kPs9zGYLwuawWlVN6H5ycz0YMTAHCxauhIu+g13JhLHw6iC7Ec4fteK5l95GftZgZGak6r59j16hORqCZaPwlYcBNx1lwvcPMWFAcluHU8fOJuP6xGnHxUfVMQ67Hccfdwwu/s65atlo3gOIjcUXnn8OLrrgHJx39ulqZNhJxx+Hi8hv+gnTkJXpwQlTj1HxGTYyT5pwCCYcegiGFQxRzxkzaoTaNyLxNwgHHuPt5ySHcOdp21CQEVAziXnpabODPvgOzsRLNgkHFdWs2UsxcDw2FNeF7bhr+2RcX3ZM3P2E3Le+TBWOn8FI9tx/tFARm+q2Y4TbpgzADHcq8ytXIy3pjJeYLvFHsF7tTRxBS1jruOQlqrNsJgx3WTFO39t4oMOsjuOTrBhE507S8tjIxDOWeQ/j9a0hFPsoDUqTUzEM07ystZkkzktej3dZMNpuwpW+GkzcxSgxYd/xB4NwJbnVPv4MG4y5s2LDps1IT0uDWR/VzvWkUVd2PDfq7vhMY/3IS6nJEmrdDxuX+J27nHYuiZUxiXfh4n2Dvf4AnKQzuWxWNRN416qzJkcO6A+F4AsEVd7PTEtBitup5MozHQZkZihdTKHLXqGfhkNhlFTV0vP9sNOz83M8cPFGUMJewfI1DLZGXjOMtnzke+xvOI02abPM7r//ftx333245557lJ8Rjo3FfO+BBx5QhggtDe2ZiY4x4vAx8bckngt7h72xQT9LgN+74aw2JPNg2LJy0nvtSlZ76kLRGNW/bd/FbmGEpzxta2rSzoU9ooF0pfLG9kvUNZEOtK7CjyOHupHiNCtdq5mXWElgU3UAEwa5MH10snJszN1Wt5PZRzsRbRLpYBdMSMOn65pR2dT2W2pawnh3VSPOOSQVmaSrCXvHoMGDcNMtP8MPr/o+jj72SOQNyFXbOKwr3IBHHnwSf//bv9V+o3W19UoPGlowRI/ZBufRH151mdq/uLaWlyTdkYmTDqNnDdSv2mOy2ZB09ElIO+d7cIw+lBR3MS4eEKjuc4db4fbVY/P46Xj182V46O9vYeZ//o3m8eORkzcQGZXFgDMZSHYiqbkcY5Z9Bk9NBXdqwURy5/1TeWClcnxtnOvX9HHQc0Se3Q3P1mWsNgvM2+awBq2umZiJ6s0RZymfaMiL2PZv2xWx/ogVteYByMzJpdapHSu3aHnWZjXhkhOG4DfXT8FDV0/CIUPTlG6e5/ZhUHYqMjI8ar/bncETYPJyc9W5sU+xnXRuY9CJsGc0Vldjy+KVaNxerWYplq/dhC2LlqNo4XKUrliHSChC+pEJSws34+3Z3+DjrxajqLQSS9duxBuffYn6phbU1NXhvS8XYOmaQpx8/BQkpbqwflsJ1pMsOWumJSVh9bpiij8P22tr1NLVb83+GpW1DWqw9UdfL8SXS1arZbB5obaVm7fhnbkL8AE9a0spz25sX/8L3QDpVdw+ibRux7O/sCoj8YZtITQ0R9HQEqXrMLVvzSrPpibxCmo8OJa3U9LicTNr2EALzju8HnPe+RiPPvYuFq/2IzUjU90XDgyBaABBBCjv6h5dwAPWk9JcyB+XgyUfrEV6ToqaVbxxSQm8TX4kpbvJuRAKRuBw21XzJhaJUp5sX65mD8lAwaQB6v7O4J9joczvU8ZsKZu7G+5XqKM2yvvLSnHfky9hfOtKKiYDMIW98ZWuuJ5mx7DhPhHjOjEMYyZZmaMhJNVuxNy5C/HWohI0efvuMuK9SnN0Ujs0w0lKteozbJ8Djc6njhgdUIn3+cidY0YHGaOWPaVrJu6nwrTFN9Jgx+fGPcbwEw4MbAzqzHHHNxuKn71wE8bmeNVn4qeC+B+LcvHz/4zY0b06An9bmKPS3PHrEQ4E+7IMtRHvi9ZcLPZmoTzkjru1/jS83zQIvigV8LpwZUnq/cdRphAuba3BQFNYLS09IcmCPLsZPM9YzTCkMPz2uayN0FkdNa7WkKK93htBA50bzRy31YyBDgvyqcDPo6MWn0dwAWX+CFa1hLA9EEaI/FS5THE4bS6DOWyew4pDkqxqb2Qn72Uci8ISCMAZEmPxgaCmpg5etb9PRI2637KliIWDgmEFLDAVhg3ALK/Ozo16VHVyJdSpyS73DstsCt0AvWOHjfLMiGFIdmtGfpYd7znMcnPYLGqfLQMTD83V5UUn+pHh2pfRjM08unp7XQOdmZTheWheNsbk52E0uay0ZGWkVOH1JCinqnPjW+DfUFxRrQzObKzMz81ShmvRs/YMQzfl983GBL7mc+M9suFByYL8NcclMctA03c5nHGPzw1nwGkyiX4s+87CchqJenZYn+WS6MdhhD0nacsmarzy8rV0QfKMw++fGrz2bduQtGwZWgYMQArl0CnJLhy5h+6o1CQcl+qOD8zabegbcVdth7lpx+WNhV2zuSaAe98tx2OfVOJ/Kxrw5vIGPPrRdmSQnnP6+FR43FaMzXXi7ZUNykC8ptynZvuurfDj8CFueCgcLyM9cbALi7a2qk+iojGMeVta8U1RK52HYLeasKzEi/nkx66M/Li+ddnaugnOm5CmjM9PflKlfsP7q5vw7NwqjM9z4qLD00m30wMKe4XL7cLQgnwcfcwUnHXO6bj4e9/Bz2ZcjyefehjrCtdj0YIlan9iN4XjmYydkZSUhIED8+L7lnaEDUZGeSv0ECjjWAM+NFEezM5yYPzYEag95DgUjB6J8rAf1x19Ho5evwzDN83DyKVzcdrSbzAp5sTAirUkUGr1WMzkqG7fiVPGYn0FAqH7SE5OVtpSc1UZUD6PzhL0l+SBsGSNh4V0qWjtOpj9tXEtmfGSDFvsQ5GVlY3CslZsr9dmhnqS7bjurLG4/uxxuHDqMARDpJ+R3j06oxXZ2dkq75eUlKqwXTFk6BC0trYq3Y7LBNatmpub48tmC3sI68xsy1Hb2LHTvPloYsstX3NHRfwG5zX2p7KWl8A18zl9CZRGc7Nf7Wv8xgdf4LCxY9UEpCf+9C/c88xzlDw/hOJyJc1JmfXZkPG+K75gnZvD6NcqAJ8aYYRug+QQaK3F946rQl6mCRUNdlz5cABn3VSD035ehSdepvL5KQt+OiuMx/4dwkeLIiipjiES5YHRnNu1thM3p68714rrzonC6V0Nv1cGTh5ImkPNaDE3waoMfnp+6QS+wwbiuoomBLwhZOWnY1vhdljtFuQMzUBalhu1ZY1oqfXDMyBFGZBVvA5JmqmuTctMUf2dO8NYFrsBteoodC+q74LK5eSMPLz86itIOuVJhA69EZHBJyKSWoCYM4OKU549SHqUmfcNNyNqcZJzUXHNMwpJt2J/us/hOHwsaSDCeUfDN/pyZJ/7JN5/5y3kDshVz+mr9IoWQ2Jnk0GiX2f3O2J0dO2KrsIY/nxkpz5APU3jaIQR9i8pzgiOH9HUqTttdAOeOH8LCjL9JBcTwqRN/emLPPx1UR4WlaZgUcmOrrBKWyJIODhQ9iEp7V0hy/HY2NwVLVEbwqys6+wsrLBvNIVjarZwZYBH2EZhN5vUzOBxybx8tBWppKOxvNQSluR4uWhWpJrCvER1BBtawygPROGlhhcbl3kmo58q30ZqOxX7w1jrDaOU0ub6mONyectpsXSTKe0ClwVj9KWqnfTsIMel37TFF8ZGSl84MMz5ah5e+uerKC0tQwk5d1ISRo0cod/V62K9s7Kzcz6yS/Rj2Khl4uHXQrfC7zgQCmP15mK0+gIqT3rSU+Gy21WpvL2uCRV1bbMW2Q4VprzFy24lwulwnuUBtqwJsfNSesUVVfAFg3SP93fj2cRmSrNRPYv3d+I4TDQWVfmZR2TzkVMPRSIoraqFP6jttZadnrzLxpfQHkMvZQMBdxwyibqq4dcG67R63lPhSB4kvMcffxyPPPIIHnvsMS2YzsMPP4xHH31UHaP6MloddWEjPxv+fM0u0Whh+HWMK+welvIyuGqqYepk9YVYaytSZs+BWV+q8ugUN/43vgDvHjpsj9xHhw3HWZ5Ulbd3C0OeDieSi4oQC/Xdkdf7kyOGuPHw+QNxJB15b2EuSy85IgM/PykbKQ7WhYCTxyTjiHy3Wia6oimMVNKHThiVjNyUtu9hdI4TRxUkITfVisPzXWr/YXac73jWsJ/SbqJrdvycsXlOXH98lh5b4/+mZuHKYzwsWrSSPnbR5AxcfWymlMv7kfSMNJx25ilY8M1iJCW71bKyxkCbjoSpAm5qboFTX91F6AVQ3rGaoqp9hJAJlaRz+dZ+hM2e8aj5ehn+G9iGS86/FFfZXLg6fxjc5D4ePgKrjzuHBE5xuMxnawQbg/kYd1S/Jl6L/rxfUANeS6l+DScuBW2COXcSTNy5jCii25cjFmjbS5wHR24NZMOZkQePJwMt1MblFcMzqL18+SnDMHFkptKBP11aiqLtzUgyBzEqh3TgrGyUl1eowZRdwQbh/PxBdNamd3EZb5wLewG/Qzb48utjo6zRl0SnqouJ/dWr1cOoc/UPeZGfMuTylBaAJbdozTrMmPk0LvnFnfjBLffg4b/+C7WNbHji+Bxez6sUTzMokePIalg93eNz7gyJH/UwQrfS0tSAaQVFSCF96tYXMrCuaQIGjZoMR2ouWv1RaptSuyVpGBqsk7Go4jA8P2cY7vxXDu76ezKe/Z8ZX64Iob6Zt2LiwbExpCWb8fD1yXD61iAWbCK5KaEKB4CGWJ2WhXaRTxxuG/wtQdgcFrWNJe9LzIZiV4pTLT3NRkFXig3NtV61JHVEXyExEd42LS0vSS1pvVNI/iH6Ux2RbUz3JwOyUzBu2DD4so5G4LAbEZz6FIKn/An+6ez+iMAJsxCY9lv42R3/GwQGngD/1MfVNfvzfQ6nwp/8ZwSnPY3Q4b9C68AzkJ49AHnZ7dtJfY0+pTkmKkEdz7u6t7ckptkd6Qm7xnjLIzx+3HvGNtx7zo7ujrNKMSab98ukipmUp4c/ycdrq7IRjLARQtOrOnPCwSNGkt3bLCRZr2fRSEpTUSCCdd4IqkJRNfuXDbfZNhNGurUlpgeTAsYzk3i5aGUwoHMuQ1tJ3yqnuGtbQyhsjWCjL4p1arnqMOopIf5O1IwmPS5n6GybGaPdFoyidHPsFrgtqrsF24NtcWvD3JaSXH6gCARDaGhoRHZODkaOGIGcnOy4wbdjvdnVebsGlMhuv8P50OcPqCPnleq6RjT7/EoWwXAYwYRZ+Wy43Vy6HVvKtiMU1vY5ZryBIDaVkH95ZdyfXbPXh63bq7GlohKbyyqwsbQCVfWNKK2uw/ptZahp1DrQwhEqO8qrKI1ytScXw18EzywuqiB/iseG6477cgq7xshbRochG4jZUGvku846ErWVc9hfm5V87733qqWmeSlqLZhm3DX8+MiGCnVHTy8R4zmJzzDOE3+DsHewITZ1yybEeNksfq/G+6Rz94YNsNTvuEy1i2S8J463i+CBHGE97SJ/CFFKf8euEh0Kx3I1B/xIW7tK9xT2htxUG04Zm4JLDs/AxZPT1fLTbn27D4b1qMMGuXDBxHScSuHyKDwbmS0kM4Nkh1nF472HeUbymeROH5eKgkyHMiLzteEKMu0YkGbD8SOT9dgavLcxz1C+aFK6+i2T6NwmMxb3CS5fdxy00x5enSNAdWFmpgc+qlObmtoMT4msXL5SfQu5edoStEIvgMpIX3o23FRGV8W8WObKxzBbMpK++gfMI0aiYt4qPPba3/Hq/C/xytMP44OcPDQMHqFZndSexLrj2cPGuXIdriWbdjteL69gF4a94kt6vQk6jMUGc+5kyrhJQMin7Wcc0WYOcyiuF7+pGYzs7FykpFC5fsIIvPXgaXjnodPx68smw2m3YsXmajz6ygo1OHOouxl52enI8HiwbRezitNSU5HkdiMpKZnEblYzjBmeYWwsmy3sHsPSnJpLdZPTz9Nc2rnh4mH4mHCvXVzyT3NoTt13YQgd85PYuTBU+fE9jq+nka7H5TRVXLpO42vj3HDs13YtdAOUP4Oktw5ybMT3TrHh7597YE0tgN3ugNPlxtDRh2DwiNFq0EZV2Vb4vc2qHZOSlgFH2mDUxkZgccVY/GvBWNz612Q8+YqX2sZh5HmoTKYC4Inr7TgyvxiRkDEzNaHsEPYLFaFyhMHl387fNc8ojka01bei4RhsVBZn5KaibH2VWn46NTsJnkGpCLSE4Uiyq+WoOyMaobi8JG6X8KQZC1rMzfCGd9z3WOg+7KEGuL65Ba5FD8K28VVY67X6OObKQTRtBKLZkxHNOwqh3CPpehx8tgzEPIdo1+Sv7lM4Ds9rY3J865a34Fz8MJI+uwZJzWv6dB4m7VEQehe8DKaVGtZJsYhy9nAUbj6nDOzkmS3U5g6FTfjTV3n4dIMHvPSw1MM9l+5Yhrqzgsxu0vauVcumCgcMzms+UpKKvGGsJmWq1B9BgDxZUrws9GCnBYcmWzHMbVXXHMGQEHdycchWUtR4xnGAGsmssCljAt1nQ5aN5MlpjEuyYQSlwQZiM8XxUZwqUuRW0TOLfWE1K9mAz8KUhrB/4TdsIeU305MBJ++lRh5x+VGZbShTOztn2hmX9XOVuLBfMXJMY6tXGf1ZLh0VYM6DPOOXXSIcTvl3MuOJZ6b6A0F4/UFleOY0wnpYo5Oc47NfMMyrB+jfg/pXi88GaA7fVloIuwu/W37PnJ+YxKWn+WgYjvmanXHOmc7wY9cWR7uXGIc7TYz0jaOBMYOY/cP695EYluMmzjIW9o7UxQthCWqdTyQU7Ugy8x8+Gf5xY/hlKy8v+ZVS/q4IhvfavVfbjBcqahDhZcvV59Be5vxcLrNNDgfSthUDpTvv4BZ2D37NHV91Iju51a3s6ncIu8/6dRvxwL2PYMumovj+ogZcttbXN2DZshU4+tgp8GRmIIP0q48//Ax+v2Z8YjhcZWUV/viHF3Dljy6H3W7jElr5Cz0fX0o6IkdMQ/r7r2KObxNWTT0XVfmT0br+c7SOysGQAUfhvNMuwi9ufASXfjkbmTXbtNnCNsqEcUd1aLvrBGclZ+m8U1vYe+itItJUAlP9ekQT+xrsqTDlTKD2iwUxfx2ibCzWdVfOl2WtTry/gepGTyZcLhcyUpw4cUIeJo3IhM1ixpL1lbj+mfmoagiqfoyRGa3IzeYt02JobNj5dg6ZWZlquXlh38ly2Q6ws+uus3u754R9R6m0/go8+CMr3p5HbU9rtnZDV3wsFivy8kdg2NjD0NLYgE2rl6O8eCMi3AdNdS73ZfFKWjFbGsxpY7C87kjc9EI27nwBaPZZ1D7HN5wTRYF7FSJBn9qHWurq/Ys36kWTqUGTYRfwHV5aOtnjVhdR+sPGYW8zr+YSUfsX5w3PVMtUO1KssDttyCnwqD3rEwlTG6l6qzYLuWtMVGcA2yKb9Wthf5E5ZBysU29GzOmBpfwrWBc+Cufs6+D85Aokzb4G9tn/B8cXv4B7zvXwf3ErNpdvhO+L29Q1+/N9Dsfh+Zzj27d+CDhSETjsOnjGHr/T76q3I70zQq/GHzJjdU0SllcmY221Wy3PxtRFbPh8S7qqfKX+7dmwfEx6I2pPUYZmqm3HOBuRb2uFjSp2wzlNERztroGDl/cSg/F+p9lig58UaCtVmLxsEhv5eAYSLx3Ns3x5OWheFpoNwHaqU3NsZoxPsmKYixRnKxt8SZYqv2qKtuHUstV0z0W1Vb7TgrFuKwY5LEimOCFKq57S5LTXefkZEZU+x2PUrBqrDYVpHhSrJXCF/Q2vdOd0OeNG3ngDSJeJYhfnRhw+RiOaUVIZqvaynBAOLvsqtfg3JOw1iQbcxPfJ/sY1n7eFa++fyB133IHbbrsNt99+O93T/NrCdC4rToudYZhO9ONr4yjsPebqaqQWF2nvkR29Uz7GqAxtOfxwRJLcKtzCJi8uWVOMs1ZujrtzV21pdzyHjmev1M47c1et20p1e4iknfAsA/25XAeYQ2F4vv5SvyEIQkdGjhqO40+cipf+9jL+8Oyf8b8338WXX8zDZ5/MxT9e+jdmPfUHpKam4tjjjlJGoO9fcQmKtmzFs8/8EV/M/Rorlq/G++9+hD899yLOOPtUHH/CsSrdocOHYNGCbzF3zpcoXLNO+Qk9EJ58RGV08aHHofrkC5FaWwsULUQsJwXRwaPg3rQNreXz8VJLBZ5tLkLloWMwatl8akcFqZwlPZudWlFiJ+dqGWrtcUL34aY61VxbCFOgMb4yMRNzpiNmdyPcXIJwxWJqIJfpd5gYPtzsxqoKEz5aXofSGh9a/dQ+9gaxYksNZv1vNW6c9Q3WlTQpkSVbfMhJpjZwUjLq6hsQDLUfUJIIDwQcOGCA2teY9yjmdhPvq8zwtRiRBWHXhAOtOHxoreqTWrIBsFN+atc+Yf2WnCdnIMZMOgpjDz8GAwtGUfuGy1ldJ04Ib7XZkZE7FBubxuLuV/Lw8Kt2fLUqgpu+a8GFk9Zh+oiNcIRKVH4V9g+RWAhVkUolt67mJ5nMJrW8NPchjjl6KCLhCNwpTtgc2gzh1KwkVOlLUfO5O80JeycDNHwtQZSsqyJ5dt4eVtBvaDLVozJUqXsI+wuHMxnhzCPgn/BT+I9/Gr5T/oLASb9D8Mh74T/kekRGXoTw0LMQGXEhfMMvhNfugbfg/DZ/OnI4Du/T43tP/B0CE36OYO40JCWn61m+iw+rl0MapCD0XmpbbXjoo3zc+V4BfjtnEHxRrUHE+xWHItwBqQcUeizc2bi35SvPLGZD8BhHE54evATPDl4cdy/kz8e5aaWwKWOxHkHYb1SQHP+TOwRb0zxquUon50N68fzqw5QP60K8p3FYGY55mWg2JLNh2WMzq/2GxyfZMNBhhpXi8jLT7FhRT7GYMZL3I3ZbkWszwUXXbFioC0WxntLbTI7PKUkFP9NGafCS1eWpGfgoLx+fpuegtivtUOhWzCaz6qjo+LZZiTIUqZ2dK/SCO35N8HJvxjK3giDsGcYMYgMuQ5nEPKZ1VLRXmvi+ZsjlK82g++CDD2LmzJlq+WkKwTfi6RnXiSQ+oy097WhgXCf6CXuG2e9D6orl2mApfo8Jx0huDrxHH6XCcd1bFgyiONDmtnQ4FvnJBQLtwij/QIjC8C5bulzZMcaR0Z/Jh9RtRbAXb9FvCILQEd5j9NTTpuOmW36qDMJ1tXX4+qtvsGploVp2+vIrvocrrvyeWkaWycvLxS9vuRHHTT0aC75ZhH/+/RWUlVXgsh9cjAu+c068nD/r7NMx+fAJ+HbxMjQ2NiMtLZXiHKUMSkJPgspOXkI6GkHzkNGoPul8bD/pYtRMPQW1J5yFzaecBovTgYbxR6Bw0rF4d+JJ2HrkVDh5WWM7yZLj8swmNdO4k3O+z+WzVWYddjfhoB+mmjUwhbWtUwxMTSUIf3A9Qm99H5F5M2GKGss/x6hta8UHW9LhjSXj+Q+34TsPzMHpv/4QZ93+ES59aC4ee3UV1pc1U/uXPgmK4QjVoLpim1pdYMWK1QglbAvTEYfdrvZAZl2O8znX0cb+xnwt+pUg7BpeyWrBpnT88s+pKG/OphI6Qb/tAM8g5uWp+ah04q6gezaHE670AdjUOBp/+3o47vybGQGfF5muRgxyF6OhRgyH+wueblARLYMv1tpuYM8O0M3P/7Uch58+BsGWMNLzUpE1OB12h03NILbaLTBbzEjJTEJyukuP1J7Cr7dg++Ya0sW6+h6oDUxplIZLEIy2rRAjdD9c51UXr4Bp4RNwbH4Dpvp1Kj+H3YMRzZ6E6MDjERl6JsIFZyEy7Bw0eQ5HdcSBKtd4RArOIf9z6P5ZKhyHR8pQamNbqI7fDFvxu3Av/y3KVn+lBgb01fpVjMVCr4YNheGoGc1+K1qDFqVY76ROF3og+7IMNcdl3cxMBXSuxYcj3LU43KW5IfZWUNNIhdvb9IXdh/PedlKW/5eRgzcGD8e6VA8iNpvquOLXz0ZchpeH5mWilzVrs42bIzxf1AQHyTLfacXEJG2J6aEuqzIQj0+2Is1qBq9YHaFw2wNhLG+JYH1rCC0UNy5ZOuGZTDFqLG9Jy8D/Ckbj1YxcbLTaIarYgYM7KRL3xTIaT3uzDHUizS1eRPT7giDsGcbS00yi4ZjznmGo1fzajLZG3uWj1hAyzrU9Ni3cCU0khu2M8C6WnjbSFPYRkkPKquVI2lqkGQgMWD4kA98h4+Gw8KYNyovqZF7Pg+RNzpCvOpI/70Ws7ic6FZPktTN5cyJ0j0OzSSptwXyYeV9HQRB2Ci8vfcxxR+Ha667CnXffiptu/SnOPf8sjBozUg3AS4RnC047/lj8+s6b8ZunH8b1P7kGoykcG54NXC4nrrjyMtz66xk49rgjkZGRjhOnH09hxFjcU3DxEv0sWjuV12zgpbYOL00ZpLKWB/XwoNmmzMHYeuLpWpls4Xo8hooBI9CUlq3KWo6jjlS2U6W64zl3WHN9kOqGo6oSlgC1iLicFvYZb1MNos0lJMN0tcRlzJFB5x7AxnsVt5Igm1UNC2eGds+RibJoLmrsI5CZm4ekJDd8gSjKa32oqPMjGIoixWVDZooDmakO5KTZMCILyMv2qBWbeO9yp9MBh8OhygTO43ytOadapp6XoGf9igeXcD2duGdxx2XuBUHYEXdyGpKyRsKUMhKevKEwJ+rTHeEy1nC7iZnKcQuVE373ZLy9ZhJeWToBhY2TkZqRqYcQ9ge+iBcbsQ4mVWfqnh3gMrO6tBYrP9+EKeeMR3NNC5xJdhRMGIBwKILsIR5VB9uozk7JdCMtWxvEx/Bex1tXluPjPy5UBuWuMFF7qiFSh/JoCdcOwn6E3+/aiiDue3U5Glb9E+5Pr4bz4+/B9cmVcM29Ac4F98D+7eOwLX4UtoV34j+btiBALdjXtlUAS38L17eP0P3HVDjHVzfB9emP4PjoUtg++RH8Cx7BrP/MxZy1Leq76at0/SULQi/AYo5huMeHiQOaMSbHpxkHpeTtVai+xX0VWkIZbXR+Kt1NT5eNyoy0jw8MRWYb3s/IwTsDh2KRJweNKWngvZvUstAEGwN5lF9VMIoNrWFs8oZQE4qpfYe5wk23mTHQYVFHnl3MBuWt/gjWUbit/ihC5MdpcGpq5B6dNySnYmFmDt4aMBTvZuSiWKq3gwLLq6WlNW78YUOSIlGR2sm5EV4ZrIw0yBVt2wavz5+Y1QVB2A24TI3nQ8K45mPiPZXn9HP2TyQx7P3334d77rkHd911VzydjhjpMGwUNsKwP5cNRlqGn3G/s7SE3ScWCmHA++/A4ffTi7fwC9XvEAkyifvzkR0bFgzXFYlhjetEOH39GVGrDZnLlyJ5vSx/KwiC0BnpSxfBvbEESE2m8pQ82HH5yoZjNlDwMRJEac4IhC0OFUcZgmMRHtnTFmdnjoKbkp0UzY+cj94hD6G7cCRlwDTxOoRPfAo4eRZMpzyLyElPI0wOJz+j/PheZPrT6h77pZ3xOJ68bwb+du938ZdbjsefZhyFv958rHJ/pvM/zzgaL/D1L4/Bn39xNGb+8hL8+MfX4Nxzzsb06SfiZHKnnHwiTjrxeJx4wjScMv0k5cfXU444XBmNWecKBAJKt2LDMsPXxgA/QRAOIgm6s9OdpJaYdyWlqOWqhf1LWbAURbGNVDWyDBLaRAnw0tKr5m5C6frtOGz6SNVmHTQ6Wy1HzfsRDx6Tg4LDBmDssQVwpWgD+cLBCFbO3oT/PPIZAsGup6hwGzdkCWFtZCVCkbaJFcL+ZVldCmak3YOZmbfgNesJmO9PJr8WLC8rwtclW/FmhR/3Nk7GIl8q7KYYVgZTcUftaPxrewxflWxT4VZUVWO+z4W3zMfg6cyfY0bkZ/hwa6bKz4l9Hn0NE/3ndvt/d9TJ5+hnB5ZFc97Xz3oPvfFdHazfvLsYxfrkQS145rzNcNh4RirQHNNGSXP3VYoprAJuDzhww79HoKqFd69tq5R7O31JvnZTFDdlF+LCtJK4MfdAwHscf9wyEI9XHQovL1veg+iL+ZfzpTsWxUBSig5vbkBOYz147kNQr3r4vnYWg91sQpbNrGYSG1QEomiORBGhQJyTeXQ9d0jzMtN8WpuegQXJGSg3W9GqZj/1XPpy+WzAivBZp5yEX9/0U7U/ljLqs6B2E8NwFDcU07U/EMBjTz2Lj+d8pfx6Kv1Bvv2Z3ijfhbPfazdwg/OWyl96+ctH7ljkY0tLC9LTee8dzq98vy3fGnH1K3JaGm1+7Um8xzOLjetEQ7HxXMa4x52ZR59yrvI70PSl/Os/dAK2XvZDNShLyZ/fM8tDl0m3w+mzTFmOVhvSS7dhwN/+DLPPpwc4+PRG+e7Lbz5Y9MZ3JfLdfUS+3UvrsJHYcutt8KfkAHVN1MjRV+KIUL1psWpHHvizN3B5zzNM02wY+vyfkf3Fx5q/Xu/2JHqjfK+6/BJESL+JhKNqVj/rNMGgZgAwZvDztdligo3qRV4dKRwOqZVeeH/TMMk2GomouLxfZige10Y1d0xdc5rGEtK8ahOfWzgupcMziG3czqI4xopOfM11Pl+zPsXhOZwKS+n+45XXVbgDTV/Nv4KGyLdv05fk6zQ7MNF+BDyRbFXOdkWUyvWMASk49KSRGHpIHhwuG5XXMaRkJSE9N0UZj33NfpRvqMY3/1uFohXlCAXCqizvHGqPmaMoxEqUBkp0v55BX8+/LJHYJdfBlDeEZBgC1ca6L69aqfVEawMI2mN8HfratSqs9s2YYZr7DmJrl2l6Vg/UqRLZF/mKsXg/0RvfVU/P7Cqjkzt8UAtmnb+ZFOKuP92KRjt+8vpIMRYn0NPkaxiLv5u+jRo23IG9/wta3suaEWPxwSOfcuTolkaMbm6EOxxEKKJ1ZBv5m2emMqrTWT9nw7A6o0DcwI5S47fIlYTV6VnYajKq8J5Pf5Avc8yUSXjorl8jOTlJLd/Ehl+1RDjLk2Wtnxv+7GeoIvwdsNy5g8Sgsqoat9z1ADYXbdW+gx5Kf5Fvf6U3ynfBZ++qI+cvw0jLcP4yDLd85GvDWGwQz5PcEIrDfl0bitvS5zDqNL4MtfEcI67x/MTfwBxz6nnqeKDpU/nXYkXD9JNRcfxJLAw1sFIJRH/H3Qqny46/Byrvk6urMOhvf4G1sUEP0DPojfLdl998sOiN70rku/uIfLsRLjOp7PTnDEbZJT9A64jRgKsby+gItbWrq5H7/ltqFrMppg8c6oH0SfkKcUS+fRuRb9+mr8k33ZKOybaj4Iy4qFXLvYg7tmcN/K1BuFOcGDAyU+1V7HDbEApE0FTTgsbtrajb3kjVakzNSO4SSj5K9e8W00ZsCW1U5z2Jvpx/uY+BDbzmMRNhOvUi0otCO0ibt0rUeqLbo3x1nYn/5RDcnja1NiPyv5fo2KRk39PZF/nuh14DQdi/8P6zdQErKlttXbpa/04KbKFHocpYKn25kN7fTv01xRCib0h1ngoHnBKqdmYnZ+C/eUMwO3MAKtPSEbNYNOMCVdZsPGRDMaPOyfEVe3ldyViemYM3c/PVUtNFvchQ3J9oaGxCMNRmIIobigl1Tn6c8ePn7E8C5r21qqtr0FDf0Bae/BctWYqtJaWUhwVB2BNUI6lDQ8YwzjJ8zzDg8tEw2DIcjl0iRlId/RlOw4jT4ZHxe4m/xUgj8Z7hJ+wjkTDS532JvG++1upVqmPp5WoCTJDBPsHp6N+RSttqU4bivHf+B1tjo+YvCIIgdI5eFjuryzDiuScx7v5bMea+O7rPPUhu5l3I+HZBjzYUC4IgCMKBoiHSgGWhRWi1NFPzhdu9XdeNvGcxt5m3rtmOVV9swpIP12LFnA0oWlmOxtpm2JzWnRqKVbuWHsGG4m3hoh5nKO7rqL4F+hPdugnRsmJE1exgEyLktKN23RnG/Sh9I1E9jLIhrF4Ck69Fpd3XEWOx0GswsuPaKpeaNfyT17p2d71fgBpv35pV3NcIxcz4rHkAlvk82ORPOWCOn/dh0yAEYrJ3z8GkhireFe4UvJORi/8NHoYtmTkwWa1wJ2RZM1XCDrpuSXLjq8EFeC17IL5OSkepRQaD9FRYfDwTePv27coYrDqnWFFjgwU5Ng4byhWfs3GqpbkFhWvXo6GhAUkk6wxPhnafwoVDIbz/yWyEwrxkjCAIe4NhjGWMGb0dcblcOOSQ8ep8xwaQdt1ZvK7gNCZPntzOMB0vB/Tzzo5CN+HzI2P2pyh4721YwyHNoGvAMtmXd63LlApwqBXXbNrS0zyj2LllM30tIkdBEITdgsticrbmRjiqq7rVmaKkO+vpC4IgCIKgGYy/DS5EpakCFouVWi1R6AtQdorVZoHNbm3nzJadmdK0di3vUbw6tkzNKA5Gg/o94YDj98I27yO4Ay1o5SYs6UQ8cYz7HaJ0ws5A9UkYA+zIqb5L8m+JmjAtshWujUuoHdw/+iXFWCz0OgJhM8qbHDt1lc02hClDCz0XLnS/9WXihtJjcEXJtAPmbiw9Giv8HunK7AGwDHwwodRsw9vJHvx70HAs8eSiwe5Ei8OJ8uRULBqYj1dzh2Kp1YUm3qdJiyr0UFim9Y3N+GbR4vhQHfZTyliEFCvdYMFL09bV1ePtd97HL359D576/fNqTy+nyxU3MrOSPeeLr7F8daGKIwjCntLeGMvwAA3DYGzcY9jvD394DsOHD1f7jWszjbUwPPKaj+xnxGvveAViS/ya98njdGbNmqXiMMZzjd9i/AY+clzNXwUVuotQCO6FCzD0tVfgbmpqa/Txi2a507tXx93FiEPRjX25eO2P3CWL1B7FvPS0GIoFQRD2Ai5f94cTBEEQBKEdrdFWrAovw2asR8xCbdB4+2Uf601uI1ktaEA9lgTnqz2KZUbxwSSG9CQHbjtjGP49rgrHJrXCabfCZNH6OYy+jjaVic55xjm3lemv2WKDK+TDXelF+M3EEK4+9RBY9T6Pvo4YiwVB6FfwbPPwzoaOCQcNnm08OyUD/80fjtcHDcMb2YPwhS0JLdLX0ev4ZM6XqKyqiitSarlp3UBRWlqOf736Om666348+fs/oXDDJqxevwl3zXwca9auiy9NzUtS//cd3mdD8qsg7B1thmI2yjLGNR/ZGbAx9+ijj8Z//vMfPP7447jnnntw33334/772xz7PfDAA+R/n7o2jux37733xu898cQTePnll3HUUUe1e4ZRHvBv6WhE1pC83u3EonCsWYWhL/0FuQvnw9XcrJaM5tnALA/1xvn9G3LiY0fH98kZ0jHZHTCTHpWxrRiDX/s3st99C2afT78rCIIgCIIgCILQcwlFQljnL8Si8DyUm0oRNgdhMVvj7VVqBKk/ncP+dFcPajZblAGy2dKIwvBKLIl8o2YwCwcPrb8DmDgqF2dOHQO3JYijt82G7eNXEV02HyjfilgjySgYoHA8oJ3auvRNoLUZproqmDasRHTu24i8/les/+Jr+EIRTB4/GKlue7/osTDFEntxdsHB2sB6XzZlPlj0xnfV0zcoF0S+fR2Rb9+mP8mXzUDfveBs3PyzGzQPorWlFR9/NgdvffAJtpaWIxTSlkZlNcRQykePKMAVl1yE1JRkvP3+R/hq4bcIRyIqTE9H8m/fpjfKd8Fn76qjYZw18pHWeGozIhv5zzgmhjfCGueM4cdHI37ivQjl2cRrpmOaifES0z7m1PPU8UDTL/Kv1YqwxwPvxCNQPeUo+D2Zmr/Pq4zCPLqal+JiUdBle3jfY4cT5oAPadu2wvP1l7AXb4HZS3F7Ab1Rvvvymw8WvfFdiXx3H5GvsD8Q+fZtRL59G5Fv36a/yNdCf5wmJwY5BmMQhiIJyeAhtZFYlNpGOy47bDaZYTFr2xqG6E+LuRnbIptRGapEMBro0sTc0+gP8s3NSMblZxyKb9aUYMm6CoTDUcTM1rbB03Q0UxuZiUXDiIUiiLHROBgEwiHVLuZ+isNGZCMas2DNlu2qvdwb2Bf5ysxiQRAEQRC6FZ4n+OFnn+PNt99DaWkZvvxqHu5/9Dd4+vm/YWPRVmUoZhWLjUSMcVy/qQj3ULgZd96POfMW9hpDsSD0RAxDrDF7N9E4y7BRl5eAZgx/w6hrhGe/xHh8NMIbRmHDj++zH8dn2J/TYX8jTYaPxnP5nnE00hH2E+EwrFVVSP30Q4z47aMY+e+XkLl0MVyNDbCzTGLaEuK8fxcfeZktO5XUdiqv03kW8ScfYtzzv8PAPz8HZ+HqXmMoFgRBEARBEARB6IwI/WmNtWKDfz2+Ds3FN9EvsCa2HNtRhlZTM7xogc/khU+dNaPOVIttsSKsjC3Bl+HZ+No7F9sC2xDoRYbi/kJlfQue/s9CfLOqTDMUk4BM0QjMQT9MrU2INdQiUlOpXLSuFmhphNnnhSkcUvE5PPdTrNhYhdWbe4+heF8RY7EgCIIgCN1Oa6sXz734T7Xc9EO/fRbzFi9FOKLtOt2ZimUYjUh9012inyAIe4thiOWjkaf4mGgUNvyMcHzPCGsYeQ0/duxnGIWNcIxhIGbY30g7MbzxXMa4bzxXOEAEg3AsW4q8V/6JEX95DsP/+kcM+eeLyH/1X3E3hNzQl/6KkX98FoP/9hekffohUFqqJyAIgiAIgiAIgtB34OWpa4O12BLYjKWBRVgQ+RoLol9jUXQ+Hb+i4zwsDs7HisBSbA1shTfcqscUejJaX4N2rvoyyHXW9RC/p18zHE7F70dDAcRYLAiCIAhCt8OqVKvXh7LySrS2+pTiJQjCgYPtsLy0MBtnNaedc2OHZ/byUbvWwrOfEZbh+1rYNr+2+O3T1O7xuZY2H434fN+YSdwxHeOcMa6FA0usqQmWslI4N66He9WKuHOtXgl70RaYamsQ420DBEEQBEEQBEEQ+glsPA5GgvCFvQhEAuo8EttxaWqh58L9kPvaF6ni96PuzF6xZ7Gw+8ieEX0bkW/fRuTbtxH59m1Evn0bkW/fRuTbtxH59m1Evn0bkW/fRuTbtxH59m1Evn0bkW/fRuTbt9kX+WpD+QVBEARBEARBEARBEARBEARBEARBEIR+hRiLBUEQBEEQBEEQBEEQBEEQBEEQBEEQ+iFiLBYEQRAEQRAEQRAEQRAEQRAEQRAEQeiHiLFYEARBEARBEARBEARBEARBEARBEAShHyLGYkEQBEEQBEEQBEEQBEEQBEEQBEEQhH6IGIsFQRAEQRAEQRAEQRAEQRAEQRAEQRD6IWIsFgRBEARBEARBEARBEARBEARBEARB6IeIsVgQBEEQBEEQBEEQBEEQBEEQBEEQBKEfIsZiQRAEQRAEQRAEQRAEQRAEQRAEQRCEfogYiwVBEARBEARBEARBEARBEARBEARBEPohYiwWBEEQBEEQBEEQBEEQBEEQBEEQBEHoh4ixWBAEQRAEQRAEQRAEQRAEQRAEQRAEoR8ixmJBEARBEARBEARBEARBEARBEARBEIR+iBiLBUEQBEEQBEEQBEEQBEEQBEEQBEEQ+iFiLBYEQRAEQRAEQRAEQRAEQRAEQRAEQeiHiLFYEARBEARBEARBEARBEARBEARBEAShHyLGYkEQBEEQBEEQBEEQBEEQBEEQBEEQhH6IGIsFQRAEQRAEQRAEQRAEQRAEQRAEQRD6IWIsFgRBEARBEARBEARBEARBEARBEARB6IeIsVgQBEEQBEEQBEEQBEEQBEEQBEEQBKEfIsZiQRAEQRAEQRAEQRAEQRAEQRAEQRCEfogYiwVBEARBEARBEARBEARBEARBEARBEPohYiwWBEEQBEEQBEEQBEEQBEEQBEEQBEHoh4ixWBAEQRAEQRAEQRAEQRAEQRAEQRAEoR8ixmJBEARBEARBEARBEARBEARBEARBEIR+iBiLBUEQBEEQBEEQBEEQBEEQBEEQBEEQ+iFiLBYEQRAEQRAEQRAEQRAEQRAEQRAEQeiHiLFYEARBEARBEARBEARBEARBEARBEAShHyLGYkEQBEEQBEEQBEEQBEEQBEEQBEEQhH6IKUbo54IgCIIgCIIgCIIgCIIgCIIgCIIgCEI/QWYWC4IgCIIgCIIgCIIgCIIgCIIgCIIg9EPEWCwIgiAIgiAIgiAIgiAIgiAIgiAIgtAPEWOxIAiCIAiCIAiCIAiCIAiCIAiCIAhCP0SMxYIgCIIgCIIgCIIgCIIgCIIgCIIgCP0QMRYLgiAIgiAIgiAIgiAIgiAIgiAIgiD0Q8RYLAiCIAiCIAiCIAiCIAiCIAiCIAiC0A8RY7EgCIIgCIIgCIIgCIIgCIIgCIIgCEI/RIzFgiAIgiAIgiAIgiAIgiAIgiAIgiAI/RAxFguCIAiCIAiCIAiCIAiCIAiCIAiCIPRDxFgsCIIgCIIgCIIgCIIgCIIgCIIgCILQDxFjsSAIgiAIgiAIgiAIgiAIgiAIgiAIQj9EjMWCIAiCIAiCIAiCIAiCIAiCIAiCIAj9EDEWC4IgCIIgCIIgCIIgCIIgCIIgCIIg9EPEWCwIgiAIgiAIgiAIgiAIgiAIgiAIgtAPMcUI/XyXHHXyOfqZ0FNZNOd9/WzPEfn2fES+fRuRb99G5Nu3Efn2bUS+fRuRb99G5Nu3Efn2bUS+fRuRb99G5Nu3Efn2bUS+fRuRb99mX+QrM4sFQRAEQRAEQRAEQRAEQRAEQRAEQRD6IWIsFgRBEARBEARBEARBEARBEARBEARB6IeIsVgQBEEQBEEQBEEQBEEQBEEQBEEQBKEfIsZiQRAEQRAEQRAEQRAEQRAEQRAEQRCEfogYiwVBEARBEARBEARBEARBEARBEARBEPohYiwWBEEQBEEQBEEQBEEQBEEQBEEQBEHoh4ixWBAEQRAEQRAEQRAEQRAEQRAEQRAEoR8ixmJBEARBEARBEARBEARBEARBEARBEIR+SP82Ft/2PF67TT8X+jyXP/VaF/K+CM+99Agu168EQdhLLn4Erz11kX5xkJHyvfsR+QqJiAwEoYcxA6+9I/ps/4PaMe88j7v1qz3D+Gb2JQ1hz9nNtmdP0ruEvUf0JaHbkTK79yCyEgRB6H3spq7eGX1Af++VxuK7X0qsbNs6Ru5+6X0smmO41/Dcxfr9uF/7Svru8cDKJ7qIR8KdHfd7H7MNQXcQetcGSKE74Pcbf/cMNbYWvTRDv9gzXr75UlxK8hYOJpwfE/MhK89GXu0OOL2E/ErwN7Qo3nmaWB68L3l3J7TLe1weJnRA3/1Sd8pMOBiIfPs3rPe0lZNaubin5aGmQ2llbjyu0p060aOMepvrcMOPXLv6Xeh+EmWg3J7l7cSyoH1dKghC3+JN3Hj+DZipX7VnHzpLBEHYAzSdKl5nS53bB9lVeSrlbc+B2kd72e8o9D12p+8k0a7Q1q7u3B4RDyvlfLeh2qo7vP/dZK8GdXUuW+EA065/qZM8ltj/kRBW+qF2Th+bWVyCd04+B0ex+6gOY44j4d82DvhI9zt5LSbEK/wZmIC1eqO4k3jMtk/0eOdgrufavSg8hH3l5ZvnoXbklLaKeLwHS96blVARGBmfFWsq4HVDIQ8oeE0vHOKFgFEBJHSetsnUgwv0xpnIeX8yC28v92CC8Y4vnoIxdfNw4+sJjWNDYWJ5kRy1Ar9Nvoky2vE7IOpWYL3nMu2aZH2Npw7F2h36Lk5PKA9eQM00qdS74uX5myhb5Kvzy4/zoLbOg6nqHVPZ6dmEeZ3JjPGcruevBJkoEsInlMNxBSvBmKTJ/WV8kDAKlyt7JfeECt74DuL5vYsGXWffSaIyr35/p+VC30Xk27+ZeTXrNVo5efdLU1Hz3DlqMNXO6tb2787QodjAYJSlFPZKD+aefKlWpl85EuspXVXeFo6L18XF8TL4k3b1e5z4N6LLTnkmNsZEud9tXr8Tp+h67FHPrUDzNq5vyb+TfNapnwHduwav4qjz78TLiXk9Lp9O/CjPsRzblxc7C9exTBF2l3i+fYnaPDqd6cHxcjEutx1pS4vzPYdjmRllNZ0bndg7LaufToijpSnl7n7AkME7pyNL92K/HcrPuKza8lhX34ySWYfwd790LaYMmYgZO/luhL1HyYLfLb33HdqsTCd6V1x+XZWbnZW/HcraHdJguesyVvde+nN8QD4jAwl3E5adkQcTO4/Hd8iXndaRTEK/1NcezNB13za9NqE83lm9q39T7e53ppe3+x0Jv1foHgwZ6DLuWJ52lGv7+wkyTmhHaTJNKPeFXdNODol6jVbecXnbXkfiMKejYMjp+rvfWZtW84uXqXsgq93S1booK3b8dox7/Fv1/x/Fnc2/J/G3xssDoWvoHcbfE53TO9x13wm3jdvsByupzNfkktj/uBYT9HS5Lf7ONirvVdtK2CPoe47rS/yN8zul4zUjN2FW/P3r+SGe9ztpr5CfSofTODMfBWcaYXbM721tHD1/M13YmnbU0bj9RPGpPJ/9+ss7/vbO6vN4nm0rqwSt3NPen/bOWF6X5wFLjD6nk/WBr/Rupxv58blNGHMuy4bkOq1O/0ZewPqRp3d4t21yj8uoMztRJ99Um19bGa2h/c629HoPfX4Zas5omZTpNaHNwqVXz9JucMYu1M93g5mFJcjM630C7v3Mwsq6kTtUxFO501LP+FmGcd/jQc0/zsEpN79JF1SRF3IhsGNnNFfuWgf2C6gZrxfGelzuTM0cn1ABCN0OK1rGO778uJGopXx4+VOXIetrlgk5ahhfoBe6BVhLfnqB31FGrBB45mlxTp6HLFUBaMy7mq6vpAqWDRdXr9V9+ftZgbfjs8vfxLxNCYZroT2vL8F65CrlZaqnDm+/V6fltYtzgU1LgC5klklhX9TzplYp61w8BVmbXlDhZ9WNU5WoYaRK9GM0uV+Os7+GLp+LMBAsu/YVfDz/GvndKN/bMQMXJH4nV3KcBGX+oxIUf30n0Fm50JcR+fZ7ZnI5eeP7mF73qmZA7KpM7ax+bKdDvYkb/1GH6XOupW/GUNCNgUAqAPDEDXhxvn6+G2jfCDWijfo/oTHGDeu5qp4Xdh/KW1cCL6o8RI2W8Qn5zDD07+CnM5wasuPX6roVN4B3LBu6qsM7lhc7D8eDDLRrYQ/gfGt0jhRy3jHoqAcnlIsJ774dXabVkV2V1TfhxoTyfSrp7m26l9A9kAzOhGZUovI3M1n3JjqWn7wKhGZ80sv1Xci5Y/iZV3+C4pYVmCUdmt3OsKdeSxiI0zk76F07af8k0pm+1lbWdqI7UT2tyoan9PSv/j+8Te2keBscCXW6sFOMPHjUR8B0vYNwB72G6ExG7XhiLYo9pKuTzAeq8lxLk8vWXdW7szaNxAyquxOfeflT4xL08qnxTs54nMQB3UK3sNPytBO5tr/fsW3Vdbkv7Jz2csjvREfpqCNx24YHWZIf6c47b9NSHsPpWllOOtESzzjSoXdXVrunq+1QVnT27RRSW344Baa2fM22Ogzk3zjco/ramM7KIGEP2EXfycv8Tj2nK4MRG4ZmXs1tYpJnu/7HWbj0H0v0c2GveeITrPfoffz8jau+npHqaOhSM69+lbSWXbRXjHz3+p14cXmrGtDOg+c7z+879kdxWjvYmrrS0QxbxcVv7Pjbu6jPtTzb1ao//RNtwsPpeE5/Z8bKsVnnth+sY9gZFK9XopZ1Ke0qAY9WTuokyt2YVLFjP1hn31Sin65TqxTz8dw7lwEs917Yd9XHjMX5OF+38C8604P189/Ey9Tw0WZVkNBuTPh48urUEtQaO8YTeg6s+MRniasKoAQYea0mrxsnIlMPhzo2JOvnFEaTbwlq6pRHnJfnU3r8Lcy5FmPqqGJnTyMuFyR8Lew/WNFSSnT7DkQeyaXlwfz4wIzixAEdncloiKaQLZpzOgraVQBUWVMjAIbhQtgL3kQ5uDFBihHnE3rv8OSrwRY1ehnZmcxqWVnmk46VMsm9Rs+3M0bW6Xk1CVNUXiS/SflaA4eIy/2JtYAaGECNZSOvJk/EDCVzHnntwTAV0MjvncAKfB2VGQoqD0gpGMYz3OumtvvtnZYLfRqRb7+H3l1mS6uSe5zOytROyt72OhRBjR1QWsZ30hkvv97hu6JnZKrviRRsdd02OtP4Rorq9AqcGoU107Qw59N3kajYC7uC3+84rIwbIygvolI/p3Kgjt9nZ358Tnl4GrAeU+MdY0xnZcPulBeH0+lOwwl7Rfz9sWFB+TAd9eBZWAk9f5+pbcPTGZ2n1Qm7Kqs7K9+F7mWbvkIW69UtykfRsfycWQi9nXs6jEE+O5NzZ+GF/YBnIs6flITa7Tvvd+hYjqp6eYe6esd6tLN4cb9OdSeC69qRI7H+PU3urDupzvA9HGTf34nruVsoD+o61g56DdGpbDuD7mcl1J0GO6tPX95et8Mzh5GqVq70c+1+Z3GE7mWn5WkXco3TWduqi3Jf2Dk7yGEHHWVXOtLO27RFVJ5qfcisQyuvTmUVn3EYn7W6e7raDmVFZ98OlzeUpy8/jvL5e1Blt7ZlkHa7szJI2BN20Xfy+ixcer5haOJ823FGqF5P33gZpaF7CXvJm5inz+xO/Mbb8ybJiA47ba8Y+a4jneX3HfujurI17bQ/pYvf3ll9HtclhHbMfK8OY0YmDEQm3aWmUJtM+E6HPov2zMLbPJDO+B6S6+I6kUbb9cyr9UHsndkgOvum6ox+FC4HtD6XzEmnd/KM3kMvNRbnt4165M7OuGASlu3RZygYy4oooZ38iTY6UxmpqKJUcZgd43Xk7vH58cZcZsJMVVa6a7boF8L+QR859JxRmJJSN6ZOH3H33Io9Nu6yAjVXyfqFtlE9wgHkTW0051PtOxDjS5M+9wJe3N2RN/Gl4l/ArH+0jSRTUOXdfo9qnqU+MWE2DZUDIyX/7gw1QvXccchSZR83XsbhgoSOhp3KrF3ZTFC+hT5iblZ8pkJrfMmQWZTGjnuKa898LsGACR5tbcj8ud0bDJAZN4axQaSOyn6SPYwRf9pIr/5YLoh8+zMXaUtGn38p5uL0tiV0dlamxumoQ2mjKV88/1WsH6mPwuSOETUoSIeX5tEbUG3LUBujLFk/0653/EZ0Er6vrvQ0oTN46aNxWNlxVHK8U/oiDPTojZjO/DgP03dwo5qF3tbp0VnZsDvlxVI63Wk4Ya9IbJd0zQwMrNNmKBmzUDpjx7QSBw9QGUt5X7HLsrqT8l3oXoYklLE74e48Y7T7J1rnOLGzb6az8MJ+oI7zUMJKDgmGxcvzjIyWQKLetUNdvZN6tKO+prOj7sT9JjxTI2EmDBurEtvgwp7BA+niRvmd0IWM2EhfwP50zFxulN9t6e15fdo22I6/sV0NVBD2nZ2Wp13INU5nbavdLPeF9uwoh446yq50pF21aTuhE1m9fPOl2jO6XE1iF7/DKCs6+3ZUeX26assX8TmmYKBetgt7SsIgKn7nym8XfScX60t+czi1xDSXtxQm3v+o1dPvbFNBhH1EG8z2CIytRRNXr2R4uWLVv7EXfUu7m987tzURu+hP6fjbGWkf7y56H1bCDOxh4z0of8J4Z0nKuN/ue0jQsYwyeNbyVjQv/6TD99CmI8W35umMzr6peD/KDLVsPVPLZfRzdZhuDCLoZfRKY/HM91bo0/3J3dg2+rVT5s+Lz0ZZNGcqajizkuK1W6PchxgjQniZRr2QeP1OzKUCXxtJ8D7OlyWZDgA8+mYkqTsJo/P0JT4WXTlSjdjYI6V5PileSn4yw+ygwaM5J42MdyC+fPOrbfn0xmsTDLo7gZcLMWYPkixndLZ8Vwd4aScYZQfFyfpajA47ZUsdMoe0dRAV1XnUciicD7uSWeYkY9Z/x7J5CTXOtPDG6GhjGVzld6NuZOoAjwSeEp+pSooYKQbGSK4Z8SU+OqfgTKrk230neh3AHWNUbmh+5LgC74/lgsi3n8IGxLYlo41y8bXhu1mmttOheJS08d55ybZNGHMjL8VkLE2ty4CXQux0GfHdZLgHU+Jld8L+MMLOuY1HtCasnmMsk5WQz7TvoDO/ROj+Rzwr43kM66Rs2N3yYq/qemHncBkYHyV9OpXhXXBxCcpxmfbuu5pZ3EVa3DmmjbCfSo1t9tm9srp9+S50L0ae1GQwpcslLi9C0XbgGl2makbVTr+ZTsIzPIq+l3Z29Gy0AbRctt6d0L69ZqR+m9hB72qn93RdV3etrxGd6E546jXM8MzDpU/oq7MoeXdogwu7RdsMIex024zOZZRQZ/Oyhqw7Ubu51gg7zYPMaY8Ae1GfttPLlaz1G8J+YhflaSdyVXVpvLzt2Lba3XJfaE/ncmino3SlIw05XV9SeNdt2vbspay6+B07lBWdfjs88zWf9DRuo9G5Z6K0efcabbs6Jb8rEyrknfSdcH6dG69X27Z5at//yCtkqcDKmHn+ECrv47PMhT2CB0SMnNhWrrbTa97HhEIePLd77RWDgjO1/WZ3O793ZmvaHR2tw2+X9vHuktCHpa8699ptVL6/t1bv52P73CdtdjtjlYZ2OpY2w5+3Dei4NHSi3KfXrUVRp3Lv7JtK9KPvoLCk7TszfkcvbEOZYoR+vkvYAt8X4M3/B77XN41E/GHvLX1Fvn0ZkW/30FPLAJHvAeLiR/Dc8Dtxo67o88gxqH1l9i8i3wOEyHe/cjDKz8ufegTDbr5TlyGP2IS2L9ABRPLvHkL58LXjluDSXjI6WuTbEcpn7+Ti7S5nv+wGtz2P2Xmf9Ih9mkS+fRuRbwe6sfzlmTMXbL/0oBoWe5V8qdx7DR1Xt+qEXlZH7k8k//Zteqx8e5COslN6eFkh+bdvI/Lt24h8+zb7It8+tmfx7hFff1wQhH5JUR0wJWFJTaH/MSZxhKfMmOhziHz3HwdHhxrZNtMmcXaGIAh7CY/ObisnlevOUc8XP4LZZ3r0PfwEQeiV3PY8Zkyq63wlAkEQhN6K6CiCIAiC0CX9cmZxX4Y7e/YWkW/PR+TbtxH59m1Evn0bkW/fRuTbtxH59m1Evn0bkW/fRuTbtxH59m1Evn0bkW/fRuTbtxH59m32Rb79cmaxIAiCsBN4tC3vn6Zfdh8X4bmXesG+LPvt/99Hue15LNrn/XZ4ydOOafDMN5KDyEMQBEEQBEEQBEEQBEE4APB2ZtwHxVtyvCZ7CQv9CDEWC4IgCO24+1wP5p5s7PGqLVU5+6mL1NVBo6NBkvcE0xU2Vt541BS7+O9UBkbNT4XraHCk69c4bId0736J4pxbiVOeq8OEg/1/7hXQ9zG+DrN4X8uEd77nxuNZuLSrvTFfv1PkIQiCIAiCIAiCIAiCIOwWqq9wHyc2vHzzpbhUtuQQ+hFiLBYEQRASmIGBdZ+07fF68RRkbfoE6z1T2htqX3peN9AaBtgZeM0wFMb3PWzzazM2e3CBvk/ijsbe1/DcxZpfZxTXeXBNR4PhxY/gmpGbMOvkc9RSKC/iMi1dZWBcgeLlL8QVu9ptwPSOezI+cQOO+lpPl/5f0+tewFFXz1LxVyLh/yx0Dn0fKNSMvJcf58H65zQ5HPUP4AIlq7Z9Mfmb0b6DxFnEdK5kkjDrnA34/D28czqy+JoReQiCIAiCIAiCIAiCIAi75CJM9WzCO5s8mGr0M/KkEe6Xatf/yH1Rz+M1vd9qh4ky8YkqnfR5tkvP6Btt6wOLhzP6uMgZ/aCC0FMRY7EgCILQxsW5bQY64vLjgJU3z8KNhR5ckKDUFGCtZhT8CJhOytTlT+VipW6wfQfjlJJ090tTUaMbD+d6LtMUMY8HNf8gv+dWIHM8KU5s7PXM09I6eR6yzu1gzE2k8Ia2dHQuP24kar9um5HKo/7e3tJBuTOo+wQv1k3dUTl7gtO9Foum1eHFm9/UPQn6rcP0U6ELhnsSvhcPptyoK8mv34lL6V1e/tRlGLPpBSXflcjXw+0MUsDPBN7h7+EfdchM1r0ZkYcgCIIgCIIgCIIgCIKwM/SJDTNvXtuunzHTU4cXub/puU0YY/gb/ZQnv4D1I09vW5Ewgc76PBkjvVnLPZhwG4e7DFlfa+Fmqf7HGXhtWp0+weUF1Izft5nOgrC/EWOxIAiC0MZwDzL1UzUSb+REnM8j4M7MRwEbd3WKC2dpJ1vqSLHKx8vbPVo4cucP0W4BdSh/XTubefWluJHP6zZhHh9fr0StukMMOV0fZXc6Cjy5O1WcZr5HCt2VnStXxgzlGed2bZR8+eZ5wJkJy1ErLsJATyua0dEY6cHAncx0Fuid53n0M81Qr4z+z9VhOslBG5FZh/XzNQP8zMISddwl29ZqM9tfX4L1LcpHR+QhCIIgCIIgCIIgCIIgdA1PLJlypt7POKTNuFu7aYk22YT7JI3+R6OfEm+ivK7zfqfO+zzb0nt5e53mgTrUbNHO4ktYJ0/EDBX3WkwZIpMghJ6NGIsFQRCENp5Yi/X6KW47PT4rtOPouTjDPaQLleDu8R4s0WcRv7NNv5dg3Lv7pY4G2gS2faI/4wXM+kcX+9YavH4nXtw0EheM1y5fnr8JmdM047EyVj63As3arS6YhUt5NvS5bUZObeTfpTjla+D8xGWq4wqj0BX8/mv087tf0pfx0ZcA50EE/A2MOa7jTO8SiqMryBfnJgxO0ElQ5Nsh8hAEQRAEQRAEQRAEQRC6ZAYuSNiujldE5Fm/7eC+qLrKDv2PPJGkbdJLIp33eXaGB1nDtbPLn3pe6yNrWRGfWTzruRvatv0ThB6IGIsFQRCEBGah3MPLrlyE56Z54rNCmZmF2pLTTIEaoUfuTGDuzW/SvTp9CeL3Md3jUeFmXj0PWYZf3VoUdTYrlI2/dVO1tOZcixm8DAzv5xHf03ZHXr75VdQoQyShG4+1UXrkbpyIFPa/+BHMpvOCSdd2vuw08pWRkmcjz/DM00b7kf87OF3bV4TiT4A+4lDomteXAGoZnYtQ9F6bvBfd6MHcq2cpWa0fea3md6Yx4/tNzNukj8q8cqTuZ6AZ87URm9diirEMtchDEARBEARBEARBEARB2AmXPzUVmcYMYuaJtYA+ySRzkt4/deNIrH9PXzHR0zbzd8ymT3Rjbn67ySSd9Xl2huqvnKaFm+Gpwzzu4/raE09/RhcrJQpCT8EUI/TzXcKjJ4SeDRdGe4vIt+cj8u3b9Bj5KkOrB3NP7mLE223P4zXcoBlY9wN3v8TG5RdwSuL+wQeSXf3/95I+m3/ZuM97sJy/i1nhFG523id7Ltf9JI/uRsrnvo3It28j8u3biHz7NiLfvo3It28j8u3biHz7NiLfvk2flO/Fj+C145bg0nZ9UhfhuZemYN7Vu+jP6mNI/u3b7It8ZWaxIAiC0B5eRvggGuZmXn3OwTMUMwf5/9/reOIGHLUrQ/G+IPIQBEEQBEEQBEEQBEEQBEHYb4ixWBAEQdgznth/s4qFPgx9Nwd1EIAgCPtMNBZDDDZ4ckZg4NAJ8OSOgjMpAyaTRQ8hCIIgCIIgCIIgCD2M1+/sMKuYeRM39rNZxYKwM8RYLAiCIAiCIAhC18RiiESiaG32o6rOh9LqRjQ0NqG11Y9Q0Aqbk3eBF4OxIAiCIAiCIAiCIAhCb6RX7Fm8cPZ7+pkG/2STyRQ/dvQzm83qyI7pLIxxZIz7jHEvkY5+ifES0zKOZrOJ3tW5KsyBRtac79v0Rvl+9Po/9bPew5kX/1A/O7BI/u3biHz7NiLfvk1/ly+rvpFIBEFfCIW1aThy4hicOqUAsUgIFaVbsKywCGZLGFZzlBRkPVIvoq/K12KxYOTwAvzk2isxZdIE3bf7KKvYjhf++QrmfDkf4XBY9+15iHz3DpGv0BPojfJdmjQOJrMV0WiE6kYrYLNThrXBZLWTXxQmO51bHKpuhdkEizMZkagZZocT4WgQFgrHcaLBGEwOG2A3w22mI1Wwx9VvxqjqZnwyehi2xTwIprhgioboeXZYQiFEqA6OmC0wW+m5gRBs0RiCfJ+qZ4vVglDIBzOFs9mc8HtbYeEbFGnyt39Sv/1AI/m3byPy7duIfPs2It++jci3b7Mv8u01M4vZCMuOlWvjyPAx0THcmcXniXGMe4Y/h2ESw7CfcZ7o3/FoxOvoZ1zTX0EQBEEQBEHoE5gQg8VihskUxvXHWfHw2TYM889DeOvnqiO8PuiH2US6dS80FPdVeBDrwAG5uPe2m3D0EZNRWVXT7S4nOwu3zbgRU4+eEn+mcGAQ+QpCz0XllFj7CQcmswlR7oNiP+5DivE51at0j43KiEQRCQXIy4xYNEpHB9WvQdjCMbgsNoxsqMCvPn4Bv3zltzjn/Sfwo8/+C7sjgtRoDFaKzh170XAQkRjFDUVgIQdzjK7pSH5sLeb7NrMFJrqOkb+Fjdjcd8X3BUEQBEEQhH5Pr5lZzIZYdjyCmn8yzx42jLSshPO5MaOYr43/lnHO99gYbCjshn9iWolHhs+NtK1Wazy8gZGGEcZ4Ph+PPkVmFgvdT2+Ur8ws3n0k/6rB9Wiqr0FLYwPC4RDS0jNgsTuRlJrRrvztjYh8Rb5dIfVvz6e/y1fTnoHsJDOuO2kIJowchCSnDRu3bMWG4iK8VRhFRYseSte1exN9Tb5GG+WMU07Cg3feiv++/R5e/k/37xk/dMhgPHrfHfh2+SrceveD8ef2NES+e4fIV+gJ9Eb5Lk0aS/+aYbLaNKOx1Q6Tw4lQWDfQUjUZNVthtTkRhn4/ZkGEzs1ut2b8tZpgtnOey8CZjbX4/ps3w+U3I2C3qXARtwu3XnEPqlMLEI1FYbZYEG5tIr3aRWmaKG0rTJEYwqEAuHuLa+ZIMASbxUZ+Pvp1EXoGhfX6YIqGMXnpC+q3H2gk//ZtRL59G5Fv30bk27cR+Ur/ZFf0qj2LDUOxYZzlI8N+hoHXMPgyhmCN8IYxNzF+Rz/jGYYfw4ZivpeYHjsjDB+N5xpxBEEQhD2AytHqsq1YNm8O1i5bhJItG1CxrQjrVi7FmiXzsWHFIoQCPj2w0OsQ+QpCj4bV151qsPp9i80GRAKoa2xBSyCC7LyBGD96PMwWu5o1xU7oOSS53aptsnzlGlTX1uH8s0/HX559EuFIBC2trXjgzluVa25pRWVVNSoqq9q57eTHe1NzW6e2rl6F4XiBYBBVNbXYWlKGpuYWpKYkq+cZbSXhwCDyFYQejMmMGM8kJqKIqVnFVqtd+ZmtvCS1DbFwSBl5ueakGhRWytPRYACwumA2ORALOnDW9gX48Su/pHgpaLI50RoJo5VnBrdsx7FN2xGxOEApIUL51up0IUqJWejZVnIRerLN5lAzjXm2so2eGQoFVd8Vrz4dCvqpDKFzuhYE4cCjtGbKw+aUfMTMTpUfBUEQhP2M9E/ulF5VE3ED1TDOGteMYaDl60SDLSvBTCgUwqpVq/DOO+/gz3/+Mx577LG4kZjDGxgG382bN+Opp57CrFmzUFxc3C4Mw/E6/gY+GmkKgtA1Rg7RGsW6gnyAUM/THyh5tWdRW1GC4g1rEAz4dZ/2NNRWY/3yRfqV0NsQ+QpCz4WrxVSXHdlJvB9iWz2ZiKoyyX97YwBLilsxriAPySnpGJSdji2VDShrCqsKXqrWnoXRhnE6HbDbbRg8cIBaWnhAbg4y0tORR8dcuk7RjYGd8d3zz8asxx/ExMMOQZQE/PPrr8E9t/0SqSkpMNPHYrVY9JDCgUbkKyTCg94zPR51FA4yXBdSfjLzACquU3kwFR15GWgz59uwtuKd1WpGNBKFJWaBGWGVBy2xEOyRAJzhKC5Z9yku+/AvaLQlwxf1IhAOIEBpB2Mm+EIWDG5o4aUCkUSPcEcisPu8SIpF4aIwdorvitIzAiE4TBbYTFZlqLbSb7HG6LmhCMw28jNzOSKVtyAcaKw2O8LOAWiyjUEthqHBPh4BRz6sdpceQhAEQdgfSP/kzulVxuJEY6xhNOYjN5QNAzFjnPNx5cqVuOKKK3DBBefjmmuuwS233IL7779f3U808Bpx+JyNxU8++SQef/xxZSzme4nPNY6J8QRB2DWcZyyUZ7hJ2hAMkQsirPvtTzj9EJUT9YEgGqnBzM+3yOCOHgPvz7V18zr9qmt83lY0VpXpV0JvQeQrCD2fkbnJOHqQW53vqkbeUBdEbQs1rGzJaGzxYUt1k35H6Gl0HPBqsCfGpNLyCmwrLVczT5n1G7egeGsJgqTDCQcXka9gMH7MaMy8+zY1s5yPfC0cRKiJqWYTc58RXZrUP+QXCSBqiSJstWJyxWbcaK5EGunJqVYvrPTHQvkuKeqG1Qzc8u3fcN4Xr8EbtcAf8SMYi8BPqYY4XUqPj0kV2xCJtKIBTvjsSfBb3fCabPDZTGi1ReF3WsnPDB+F5WavPeRHKqWRHAkhnY4ufxQ2Sp9+mPrZwv7D7XIhMyO925z0Q/ZeeCa/KzUPteYxqIvlIylvDMZOOgrZwyei2TIY1eZD4MwYpozJgiAIQvci/ZO7plcYiw2jbKJRmP3Y0MtwgzgcDqtzhu+Vl5fj9ttvx1FHHYU33ngDlZVVcDgcGDx4MMaNG6fSMmYS817G//3vf7F27dp4+uzP6bDj840bN6qZydxwNn6LEZ9hPw7LGEfhwMHvv/0focdB+SJC7quqaty+fCV+uGQJLid387LleK+sHF7Khxymu/FR2fBGaRluWbYC36fn/fDbJbhv5Sosrq1VjXfh4NNYU4lIQhm+MzYWrtwv34mw/xD5CkLPhlVfNi3ZzEbe21GLYh/unM4YaEX6JAtipHvbgtVoqquBz90A22DSjzmU5N8eBW+lw3Bbx+8P4JvFS/Hp3C+xqagYVdU1WLRkGT6ftwD19Q0qXEe4TfP519/gV/c8hC3FW5XfG++8j1nPvwCfz6faQhG9zSQceES+/Ru324URwwvwy5/9DHfe+TC+rZ6Iq38PfLL5UPzy1gdx2003YeyoEWq5cuEAQ9WhmZeTjUTBVSvP6I1GwrA4nUiKWeEIRpG99gPUv/o3TC5bDYQdFCWC5GgAYVMYVy9+ByOWfIUqlwPhkB/+WAQBNhbHXQhBmxV5a7/EtYVz8euiL3FP4Wzcu2Y27l/5Oe5ZOQ93rJqPO1Z8gxlb1uK7VZtxSHUxsv0+uBubYIvZ6Hk8cNoLiyWEDK7ghf0C91OlpSTj7NNOxCmnTMP06VP3yZ00/ThccN7pOHryBDX4Xeg98BLTvNR0NGkYSkIFgCsXow6fhpFjD4HD4UT+kKEYM/FIOFJzUOLLQ6t9FGLWZKqLtbpeEARB2Hekf3LXmKjxt9v/64O1gfXC2e/FjbIGhlG3489n/8LCQjWDeM6cOcqQPHXqcTj33PNw5JFHYtSoUfB4PKrxa7Bw4UJceumlGDhwIN588021ZPUPf/hDFebf//43DjvsMIp/LrZv346XX36Z0puqx9Sel/gb+JyfefQp5+o+B5b+ukG5yWKD1ZVC8rBQpg8iFvJSoyys5+k2+XT13fQWeqN8P3r9n+rIM4j/XlSMv5WVoZHyc5bKO0ADycdG55fm5OKnI0fAbe2epdNYwvWBAB7buAkf1tbCSde8CF+MntVMDWKXxYyrB+fjyqH5sHdoaJ158Q/1swNLf82/xetWoLpi90dsTZ56Mqx2lmjvQuS7e4h8hZ5IX5Uv60W8SuYRQ9Ix0BrCW5taVOcjG4gMWHXiPu8Bw10Yc3Q6yhqq8bORh2NsahYKS7fh1bJCeF02VK8LY/sG3hcxRnWwpm/1FvqafI32yVFHTMJvZ96HFasL8dHsufrd7oENlGNGjcR3zzsbH8/5HDOfnBV/bk9D5LvniHx7Lna7HccedQSmHXsMQu5DsHizA/PWBFDfEoHVYkI4EkOq24zjD3Xi6NExOLwrMW/BAnKL4ae2UW+jN8p3qXss/WuBlfcl5jWtzDbYbRYETTFYHClwBlowccE/0NBgxuQjRuPwaWfimQoTkmNRDK7eiHPnvAGzxYFoqBURqx3RaIja0lFw12aU6lfOiyBZ51r8mBQbCHO4CWb41QxmbklzOK6FrbAhbHMhYjerPqog3Q3azGhxeVBP6RanD8aG1AEoycpDzbZPKMaBpy/nX6PMPOHoI0gGFsxbvkKT3T7A/aKpycn4zkknYs7ceSivrEro7ep59LfyuSusdje85my0wgM40pEzMB85eQNVGZHY1218M/V11dhesg2BllokmxrgjFQiFmrmjkw9ZM9A5Nu3Efn2bfqrfKV/ctf0SmMxV6B8zQqv4W9UqjU1NTj99NOxZs1qakg58MADD+DKK69UBmIjvDEj2DAY33jjjWov4+OPn4YPP/wIX3zxhTIWWyxm/OtfL5P/8WqGMhuRr7rqSrz44t/iDWXjuXzkRjWnyefHnHqeun+g6W+ZXanaFhsGH3oCTjvxWJJzBvzeZixZtRGFK75FuLGUNOowNYxDJPO25ZV4Dy6WW09WrDujN8qXjcUsp2+qqnD16kIMoDzCC9ttpfzCDKJrF8mjhc7vGjEcFw/JVzOQ9wUjfz6xpQhvl5TCQee15FdHzkpuoNkCJ/2oGnruX8aPw9HZWSq8gRiLDyybV3+LEQVDcNNNN+G2225Djs/YjQAAetJJREFUaSnlW4JXfOCy9+OPP1bltLGCxNjJRyMlPVOd9yZEviLfrujN8u0v9EX5shbMuu6UUdkYkeVEa2Ul1nid2FjeoDqcVU1KdaXNYULucCeGHpYMkyWCpuZmJJmtOGvASHxTV461Vdtht5rgdqWgdFUQNds0g7GupfUK+qR8ScfJyEjH4w/chcPGj+12IxHrWnabDYFgED+79S6sWbdBv9PzEPnuOSLfnslJ047FpReej9rwIPxzbgTrSkM8eTXetuU9cqNU/nJ/BB+5GB490IrLp9uRay/FW++91+uMxr1RvstcY9US1BabXcnFZrYh6LLBHQzgNISAxiZsXfs1qr0tOMZlwi/yRqHWWwNLtR8lgXIEYKc6WqtDWYwRcmGqldnx8tZ8x0T1dzodJ9mTYLZEYSFfS4znC7etsmYM3OIa2YwI1e3U/iYvc4xC8YcT5fqavgUKcM2Uo1XYA01fzr/89nMyM3DGGdPxxqez4e2mfMfl8vjhw1CQlYsv5i9COEJypO+sJ9KfyueucKQPwXZ/DoJRCwYOH4u8AQNg4YEkCX3cHTH6mSurq1CyfjXl7SDykloRrt/10qkHEpFv30bk27fpr/KV/sld0346XQ+HK0uuNBk2/LJxNpH6+npccsklyqg7aNBgvPvuu0r4qampWLduHX7969swYcIE5OTkqPicHhuNP/30UxX/5ptvUUtVGwZf1re4E81GDeU77rhD+X355VcqDmNU4Az/Fk6TMX6jsP/hBhC/b2dyGvKHDMZh4/Ix/YTJuPOGC3HzL36CyadcjN889ije+vcL+Psfn8GMn/wY0445kr6BbFhJrtywFnntZyiP+Ch/vLStDOmUXfzU2h1gt+OtCYfhw0mTMCE5Ba3hKFzk/3zRVpR7vfEG7t7AOZLlWtzqxdztlbDR81uplT3d48GcIybjn4eMRxY9P0h+adRIfr2qhhrhvPdTz2xg9QcikTB+97vf4YgjjlBlLXPVVVfhlFNOUXvIX3fddarSNoh2KPuFno3IVxB6JrzSht1mxrlThuCCY4fj/FMn455rpmHauFxVl5KChKQMC0Yc6ULuGKC6sRxVNVWIRYPY3tqI+XVrUdSwna5DaGhsQktrE1IGhGFzRbR6XNSrgwrrNQWkG48YNlQtSfzhp3O61X302VysXrseLqcTJ584TT1TdOoDh8i3f8BtmvS0VBxz9FF48J77cd6lN+NPXw7EjD95sao4RCE0w6BhIOa+CY6jXUMN5NlQHsavXmjB05/m4exLbsYjMx9Xs5I5XQ4rdD/8WnnJ2Wg4DAvJxG2O4uebF+CFee/j/z58EWfPewMVpnrYTVFUNATwzaolsGyrRFm4GUU2N7xRwEfOT3L0U/wAogiSC5GLUA2tDMfUjm2m68ZYmNrSYbSQ/twajdCRrsnx0RsJkQtSW5xdRKXpIzXbS3F99OX4LDZ47Snw2djsLHQ3ZosFp5xwHD5buAjNXq/uu++kuFzYXFoGs8OCTE96vE9S6DnwvsS8hHQg6RCU+geppaUPmXIshhQMg9ls2amhmDFkOnDAQEw65nik5w5BuTcDza4jYHFlaYWMIAiCsMdI/+Su6VXGYmMmsGHkTWywsrH2lVdewbfffouUlBTcddddakZwS0sLnnnmGZx99tn47W+fUkbjhoaGdgZf3t+Y0y4oKFB+w4YNw80334xf/epXGDq0QIUZOXKkutfY2NjudxhH9uOj0UgTuhOWc+eOP4Fklx2jMyJYuWoD/vvBEsxfWoQtlV6MGz0IP75oOuYu34riWi9GjxqJy757AR69/048/ch9+Mm1V2LkCFbWtMEBwv6BO6NbgiEsbvUhJWZShttrhg7Fsbk5mJjlwYzhBSinBqudsk25L4jfby5GgK73NhexJLko/3fRVlR4/bBzo5h8fzJ0CEanpeH4vDycQc8O0O+w0L0Gnw8hLk9UbOFgwMsvcR4cM2aM2i6A+b//+z88/vjjagsALq/zSG4GRhks9A5EvoLQM2F91UJ50xzyo7W5BZ60JDiDPtx43mScMnEgho1zYfSxSbBlBLC9qhJlGxvR3NyK+qZGBHxeNIeVJgab2wRvtQUVRa2IOX1Izut9y1D3NQy9Nn/QILVf6d9f+S+eeOY5fPTZ5yjaWoJnnvsrfvu7P2HlmnXK8fnjdL8z99jTf1Cus+uHnngGdfUNamarcOAQ+fYPMtLTcd7ZZ+FXN9+G4868FW+uHIZf/qke36z1I9ltgc3KK5tp/Q8WiwkuuwmXnpCMFCqT+RNh/2CIynmzidrLFqwuDuBnv6/Gi1+kY9L0X2LGz3+lZikPGtCmgwndQyxGjUxyJnYmMw4tL8bowsUo81Vhs82GLQ4LBjbZkRQMwMGzgpPdqKRG6/ZoCAiH1SpbAYrLhuIQHfmcDcZh0plD0Yhqu/K5j1wduRZqOHvpyM5PYX0qLjttr2Ntv2PNXzmqpX2Unk/do7RNvO6X0J1wKT18yCA0k4yLy8rjE0u6gmcwJTqjv7IzghFtJnFRxXYcMWG8epZRLwgHGZKDyZ6KoLMAddZxCLnySZ8+DGMOmwy3OxnhEA/y2X14X00772c8fBSSUtMozzrhsw+F2WLXQwiCIAh7gvRP7ppe8T82jLAdFSbDKMuCq6qqwquvvgq/369GA/zgBz9QCtmTTz6JmTNnoqysFEOGDFHC//rrr1U8htPMzc1Vx/Xr16s0R4wYgV//+tfKYMwGZL5XVFSkwhsfDP8e4zcZihkfjd8k7DvcAcn/kqRhdiTBmpoDZ/ZwuHNHwJlVALPbg8zsXNxz2y/xqxt+iOsvPAbHj8vF2sKNeOW9+Xjniw2o95lw0elT8MU3a1Bb36w+eN6Lb2j+YHz/uxfgtzPvxUXnnqnLTRTs/YXKFdEYtZljiEao8UsNHJYDd1IHOV+TX5ju8T7G/6uowur6+r2WBmfHJZXV+Ef5dqTGKE9yW53Sj7BxmG7y3ozBcARW/j0kd21vRf03CgcFi8WqBu0wa9asweeffw6Xy6XKZF7ZgeXEWwwYsIyF3oPIVxB6JjyjrNkXwltLSigvWlFZXYfGynJkOKK49ZJJOGpKGpoCNSgrqUDlGsBXlsYrVsJijyFEtWZOigUmK51H/fA1x5CUZkFyShJMEb1DVCrWg47RdvL6/Gr1pPPOOg2/uOFaNVgyJzsL1/7wMlxzxffUNi5c9Cq9SHcM+40fMxq3zbgRWZke5c/L4H7vovNht2v77BlhmcRzYf8j8u27sBz+8NSTyDvk+3j60wF48vV6LN4YUP0NPFuYZw6zONhIzHoTX2ckm3HFKcnIprKY77E/3+dzlh3HY8Pxsi1BPP2/Rjzz2QAkj7gETz32CM478zT9yUJ3wItFm00xmENBqieBvIr1qIpZUEuVaH0whJQQcHosgNOtdpxjdSCFGqxN1D5tjkUQjoURMIURRARBEzmSbYDkx8cQHTUXpXtsMI6gJhKkeCa00jP9CJOLJDhtRjIbmtv5U7o+3fljIQSi2lKLQvfA+dTtcuLQiYegzB3D6FOmoOCEiV26Y844HqecdxouvfhCXHnhBbiU3FlnnoxRJx2u7o84aTKGnTipXZxhJ0xCbHgW0kcMxeRDxnAm158uHExs6aNQZxqOumgeBoyehLETj4AnM0fd29M6lPu5eTXEyqoqFC5djNbmZjicvW/fTEEQhJ6E9E/uml5hLDas+IlLT7MCxv58ZD9eenrhwoVKsLNmzVKCfumll/DYY48qQd9yy61qVvEtt9yCKVOmqDQYq9WK888/X50/8cTjaGxsUOcch9Nmmpqa8Mgjjyg/jstHdtyA5vgMhzX8jXjC3qPeIL1HR1oORh1xCr5/1fWYefft+Mtv7lPuj7+diXvvvB0nnfM9rCkPoqy6EWluJ46fNBK3fe9E/GBSPurWrML7sxdgfVkLDjl0BP7x3kI0VNQhEghRQ9miFOqsrEzc+ouf4I6bf4aUZLf2XKFb4XeaYrfhcMqTDeEokui9/2nDFryyuRjvbCvDo4UbMYBChagBzEttpVJD+c/FpWqpLM5PewQFr/MH8eiGImRRozlC115KMzkSxePrN+OT8kq8vGUr/le2ncWvnpnrdMGmlzHCwYGl/Nxzz+Gtt97C1VdfjRNPPBGTJ0/GG2+8gddee03tGbFixQotsNDrEPkKQs/E2H5hNelJ/5q9FvMWr8OLXxTjzhc+x2P/+BQLvt2AksIm1BemINSQgqTsGJJSHLDEHHCTvl3V2Kr2vQy0RJCcHUVGjgvhJhtaqnjHRNaHVfLCQcRop/A+piZyvAetjf1I9tyu4nYTX3c2Ylq1a+h47pmn4uQTpmLCIeOUPxsS2bCUnpamviHeK1E4OIh8+yYjhw/DT667Hnf+24YnXm/F9roIlackQxIYiUSR2N9gLEHd6o+hviWGBnL8TRhhGSO8OpI/b7VV2xTFU2+04Gd/AS7/wZUyg7wb4bcdi1Jb1u6Aq7ka6Q2lqI+EEAgBrSTLBjYFm32wRpvhi/lQT2GbSDAhym6hcAyBaFQZhHkGccAUQYSOQbr2U7pBSp0dh2UDsp/OWxBFEwmcXSs5XvBYOXXNx46OZxbz6lt0zk7q626Fy9cRwwoQzU1BNMWOtBzPDi43LwfTxhyGH590DmacfTF+/t0rcPn5F+G7F16My8lde8kPccd55HfkdIwePASZOZlIzcmIx+fzpKx0rAnVYNIRk5CemqI/XTjQmMwWmJyZaE2ehHJ/NpJzhmHS0dPUEtKqLKDvYU8wyuvmlmasXroQRauXqTo+Z9BguFKS1T1BEARh7+ASWfond06vsJCoxiw5Ns4ajV3Djx1Xpq+//rq6P336dAwcOBCVlZX4wx/+QOEtah9jXpaa486bNw+PPfaYSoPh+Hx/wIABWLp0Ga677v+wYMECBINBeL1efPnll7j++uvVh5Kfn682uTbg5xojuhN/i7Cv8AhpM7KHHYqLfnAN7r3pR/jB+VMx8ZAC5GanIDcrGQV5STjpyFH46SUn4shxQ/HO3GX4z/sLsOLzZZj/37nwbSrDuBQX/DXV+GLuAmoQ29EcieGD/87Bsg8WYsvSDQj6w0p5446Qs08/BT+//hq43W6R4X7AZbXgqiGDUB6OwkZZpsYfwl2r1uPW5YXY0uxVs3wjdC/Kju5/W1WHb7ZXw8r7ueymbs3B2L1dVoHi5haY6SIaofTI8azmVXUNmLFsNR4s3Ihmaqk7yLs1ZsLZOVn6jGOR+8GC3/wLL7yACy64QPPQ4TKbtxMYOnSoqsANekXFJcQR+QpCz4XrTS/pQ0UVzSgpb8L8Ih+Wl4cwe6MXWzbYEajIQjTAsxi0epINT+xY53VZSTePRRCjTJvrcSLXnIaGbUA4QKlS2N2tv4X9h9FOCYV4WcsIqmvrUF1Ti/9v7zwA4yiuPv6/fifp1Lvk3ruNG733kpBAEsIHSSiBQIAEAoRejCH00A2EFkoqvRdDaDbYuODeZbmol5Ou9/vem72VzkIGF9lWeT8z2t2Z2dlj387Mm3lTfP4A/IGAOme/YDCk4nXGsy/9G/c8NAvzF32rdOS7H5yFvz72JFyuFhaz6E/7EJFv72Tk8CH4cKkJC9cF4UxrX7qWDcAd4WWmo9TGTbcbUJZvRl6GAaV5JtitbExMqPCOcDpsYGZ4OevVW8J4ZW4Co0YME3l3ISy5KL3m4V437JEA5ScrXUcQMkXBy0u3Urs3kDCjhepRXygEL8mEV9zipiv3UuiziIOURphclM75GKbwCDs65+rWl4jDR7W5P8UFKG6Awti4rC05zUZlcoakI3/+DRxPPwpdA5ejmRnpmDR5HJrjYZSZ05FuTVPlNS8dzvrTQGc+Th93EE7a72CM6D8ERnuaGpgTiUTaXDweQ0ZmJiaOHIufTz4cxw6egDxKiwcOcFpGgxHFlgyYEwY0OYBRVG7wym2Sg/ceRhPpxGn58FsGodk4FKasARgxfj8MGTFGLXPKS0jvLLzPtd/vxZaN67Fu2SIEfF7kFBQgKz+fwqSVLAiCsLtwPSn9k99Pj/t/1g2yutGYYT828DLHH3+8CmejcEVFBXJzc3HWWWchkxQt3s/4nHPOwYwZM9ru53t5tjAvOc2GwjfffBOnn3668uMNrX/5y1/i1VdfVUt7XXTRRWq0gd4wZ/hZDKeT+puE3SGB7AHjcNSJP8eBU0nRSrPBFwM81NpqpVaRi1pJTaE43OEY/CQKtTfx6YfDSw2uz5dWwGY04dsWN5abHfBZ0rC1qRVvfvw1SnPSsdUfRrDVh42L1mHBW1/CXd/S1vl5wtFH4P9+/pO2b0zoOuiVYv+SQpxZkEcy5CWggUxyTnJsKOalqXmpaM1RA4jiPLpqA1qo4dxJv0incLSGYBCvb6qGjdIJ0ffQnmZCGYeddMymo4Pc5nAUp+TnYkpBrpaAsM8IBrSBOew4/+l88803aikQ3ktC3wqAaWluXxJE6P6IfAWh+2OzmJFh1/Y/MxgSMCfMMEZsMOh5lirZoC+BAOlRHq8PbrcXq2ta1HUgFMHJuf1xeFoZbC4e/EX3tGd1YR+it1mKCwtU5/Pf//EfXPCHq7Fpy1Y0N7tw3Yw7cf1tvLJSqxo82REus9ng+PFnX8Lj8arrjZs2Y+G3yxCm9LIzs1QbKZrsEBX9ee8i8u198OzwQQMHY2FFHHarkWScbJeSbHQ7AV+zlzcQQ3mBGVf+LBfPXlGA+y/IQXGOCfeen4vnryzAlafnqHAPxWPZ6vLjdFJF6XQYsXRTHCUlZTDR84UuIBGHiQ16pggGuBupbRtFJBFWM4JNMSNqQz4MvOAcHHzXzZh4xR8QivgQMZoQQYz+sXHYgEDSsYGYDbwRupf3Mo4iTue8vLRmAPbFo/DHk3sP03WA4mpLUmvXbAxmx4ZlNjSzC9F3FSDHq3qpcDoKXQPntcMOmIIGRwKNYTbjA7+ecAhOGzYVU7L6YXruQJw28WD0LypT/Ye8SmJCxdJgg68Ol/FsHE5Lz8DEIaOU0Xh67gAcVz4aZ449GBOKB6jvYVO4Ff1GDYIzPT0lJWFPYjSaEbUPQG20P/zmUvQbPh7DR49HhjNLfQM7i4lXATGZUFu9FWuWLkbd1k1Id2Yir7gEaXRkdiFZQRAEoQPSP/nDGOjF7HCVM+3Ik5Jne5evZ7+1TQOHUaPpSJHS/cvKytS+xbzW+AEHHIC7774bt9xyi9qn+LPPPlP7Eh900EFYsGCBih8KaXv+6PdzA1tbtvpOuFzN8Pn85A84HGnIzs7GddddpwzNHJeX/OLn6/ez49+i/yZm/6NPUce9zfxP3kme7Tz7Sr6pUNMXRrsTB53wcxx96AEIhaOwWUg+0SjcngDc3jA5r1KCrRY7nGkOlJY4MaQ0E1lWE554aTYMHi8ipDgfMnkYcrLsJNsoNlRswv++XoZckumRJQWws9yN1FgzmzDxuKnIKspRjS9Wzv90wwx8+dV8JdfuRk+U7/svv6COnDNWuNz4/dxF8IWpifs9r9dMeauG5HbD2KE4d/QQujelR6MTOCmeHTxr+Vo8tLYSGSTb7YqPkuJZxx6TEe8dNg0Ds5zf6UA7/vSzk2d7l56ef3cFLjOXzP0EBx14gNomIJ0audwxyfBgH95LYu3atWhoaFArRDAGumfqocciwUvy9SBEviLf7dEt6l8qQzPS0+B0Zqi6OBU2GjQ1u9p0Hobjs8vLyYbVqhkZU2HZuz0etLo9SZ+eTW/OvyztiUV2DDQF8Vo1yY48tqmj6ZpqVeQPi6N8tAPhcAh+XwCONBvVvUa0xEI41d4Px4+ZiDnLN+Lxjzei2cfLpmp6ck+gt8q3vLQEs+7/C3Ion66vaG/06oRJH+O9aXcFvrdfWakqMx5+4hn8+9U3u63MRb47j8h33+DMSMfFF1+OWZ8PQotXGwwQDCXwi8MycOqBabjqqSbUtcSQbjPi/4504tfHpMFCbRq3j/smSLdyGtHs4dmLQFa6EeFYHH//yI/nZ3sQCMfVfsa3/zoHX60O45E3WyiONnM5O8OIq46vw/0P3AVXS6vy6y70RPkutg2i+hGwGaI4Y84HMAabqSI1qwrWZ4hg+G9/i8IJY/DV199gsNUMy/8+w+YNG+GjVi+3U83k9PZpwsA7ICchPz0HGhMG5Z9hNiPTbATndDupziaKwWEmTovOuf5muK5Xm0Rwna4nkjznsOcmT9P89jK9Kf+y7tu/rBhHHXM45sYaYDOZEQqFcfqIqZg8eoKSV1wZhzWjssqonHH16w5wmavLXD9noyKfx0JB/Pvrj7HO06gEWGpOQ2J9Az749Mu2e7oDvav+pRdNMjU6CuGKlyCcsCO/tBQDBg1XMtEHcO0Mer3KxotNFWvgbm5GRmYW0jIzk1tNGCjMR+dWJftQ0A+4t8LuW454dPurhuwteqt+xXLRZbMjek9qfP069V4uG1K/DxVfO2nz7xiHrymBTgf77S16q3wFjb4oX85X0j/5w/SI/1MuSBm9oOUj++kFKV//8Y9/xK233oohQ4aoMDYY33jjjbjyyiuRn5+v4vHexByHnV5463BF/FtS2j/66CM8/vgT+Mtf7sBtt82k88fx/vvv4/zzz1d7P+kFuH6//lv4yGGpaQo7j3p7sTCqNm3A+x9+hrff/QAvPv8innr6GTz/7JNo2rQUh4wswAHD8zFxQAYSNZVYMPsrvPj6PHyzvhZZRXlozSrAlNEDkZFmIbkakZ1px6EHjsWfL/gxhowdjA9qGuHl0b70qGgkimUfL4LP5eUnq5GbvzvnbBTm5ym5Cl1HjIQ7PNuJE0uL4I9SHiIPfZnoji4cjaGUCuIH127GmqYf7rDghu+KRheepvj2uDarqbN02YGe7afjn4cNVIZiHiQg7Dv8rU2qcfTTn/4URx99tBrcw9sC8OAf3iuCl//gsvm8885L3kHlBOXTGiojhO6PyLfnwDXe8KFDcN/Mm/HEA3e1uScfugf33X4zBg/srzVqCV6Rw0w60cH7T8W9M2/aJr7urvvTpSgpKkzeIXR3NJ1Hqw871oocYs0ACvulwW61w5mRCaczkxpWdG1zKL+KRIB0YGBISS4KnVqDS3kI+wyWaX1DIx5/9gWsWrMOaXbHd1x2ZuY21znZ2Rg5bCgG9Cvfxr8zx/c2u1rw0r9fxTsffKyeKe2gvYfIt3ficDgQNeaj1cezgXmAfILqW2De6iC2NERx+2/ycPyUNNxwZg7OPz4D1Y1xPPmuBxc83IRH3tIGZz35Hl0/1IRH33KjisJ/S/Fm/CoHJ05Nw53n5qHRHcdnSwPK4Mzp83Pc9DxfIg92O287IOwuvCoH55ZhfjeinlqEKb/yMtI8C9RqsSIrLxfvfTQbb73yMt54+lnUrK9AyGhGJBFDmOKwixg0F6b79CWp1TnpyRGSW4jOeTYxzyD207U/FkMLL0dPrj4eQVMsiiA1fZuo3dtEYUFqe7fQsT4aUW1hDq+Paq6Z7hF2D9aVLJRZh1MZW2HwqcF0XGaarWYsqK1AU0tzcnlp7quIwutuRWuLC25yQb9fMwwlYTMSX7NhkOOw8/s86pvimcgxkt+azRWo9DazUq6eUxcNoGhYf/QrKeIKQktI6FLM6cXw24ahJlwOR7625PSAwSPU+0818u0IXIfzTGIvyb5y/WqsWDQfQZ8feUXFyMzNBS9jrdW5CbQ2NiEcCtL1zj1D2HWyMp04YOpkTBo/Vl3/UN/w4QcfgInjxmjb9Vgs6vzEY4/EKccfo7Y85OtUOH+XlhTjiEMOxI9OPBY/OuFYjBg2RIXxsyaMHa3CCgo0W4YgCF2D9E/uGD1iZvG8j99WFSUXmqmVMF/rP18/1+Pohlvdj49s7O1YiaemwcdUg2/HNPmo+zOpz0lNh89lZvGuo94vNZZ432JWjhJxarzQuy3Mz8UDd96qOjh4BrCrqglLZi9UinmAFO7ZLT5k5GZTAmYcf8R+yj/NYVFLeDksRthMBtgMCXwxfw3++8E8/GJoOZxGk2q4ZRflYPwxk+lezWB83yNP4vV33m+TaXehJ8pXn1nM8Nvc3OrFjz+eh4xwFH56v529YZYdfwZ8PG5gKW6dPk6NbOlsHy2+3x+J4ldzFqGizqVmJcc7TVWLa6PwAVkZePTQKchLLrnZEZlZvHeIRSNYNu8zRMJhrFixAqNHj1Z7xVdWVqpBPgUFBVp5kIQH/Lz11lvq3MxK+P6HU3bvOcvliXxFvtujO8iXZcGN2x+dcAyuuuyibes/Cvtm0be49KobkpcGnPajE3HhOWerWVAd+d8XczHr6eexpap6m3RUGU6uoy6X+h1wfc8jqFm/0s8ZvpfP9lW93JvzL7/9STyz2OjDqzUmFhG/eg0WGV1kFgODpzpgd9gQjoVQtbVKGRUynBmIG0hHrrZgcCIP9a1BLN7kRkSpbvtGVrtCb5Uv5y1u/2SSnDh//xADScf+619uwXuz/4cnn3sp6bt9OC/zCgI8c6o7I/LVEPl+l+4m3zGjRuGIn9yI+171wGxKGYjOdWA8gfOOz8J5x6XDaqZ6eV0EVz7ZgFAE8AYTOPPwDNx4ZhYefMODx99xw+ng/glgxq9ycex+doSjCTz9gY+cW5XzekGv6mGqYS8+OQNLP30Ac+fNV/7dhZ4o36WWAUiYzZhSsxyDl3xNDVCbGijNLz6GCAqn7Yes/GIs+eQzOKMxWBIx+GNR0nk0/YgHQifnnZEfu+R3kEKC0opSamn0naQbzLDTvV6K10r5NpvKhQjd3s9gQx3V2bxX7hCTBY3USq5JBDHWmIbKeJjqbzOslE4oEcWqA9r359ub9Kb8O2hAOSYefQA2xNyaHpuENCucMfYQDCgtRzwaRUN9LYoKi5Cf7UQwHMWWmmq173xhUUmbDtxQVwsn6Vk8iYH3GecZ/3XNLhQUFdHnZMcnC+bgs9p1bc/h8tpBMh7mNuGVt95H4Hv2qt+b9Ab5mq0ZcBvLETTkwOzIQP8hw5GZnavK0F3Rdbmdy/Vq1ab1aKytUeVxRnY26dVpyoAcpTZ0OBhAWoa2/HTt5k1wUnh6ZpaaZSwzi/c8hx44HTf9+XJqy9bgnIsvT/p+F26fZmdn4eXnn1R59NKrb1CTj6ZPmaTaSVy/8jcSDAbx5nsf4dG/Pafy7NRJE3DlZb9Dfl6u0uMY3ubnpf+8in+9/DoeuHOGMh4//OQzePv92Sp8X9Bb5Sto9DX5Sv/kjtOuwXRjuHBlx6Po9MKWj6wQqcZN8pzhIxe2+j0MH7lA5vv1a/2o38fnqekwejy+Ty/A9edzPE6Tj7pf6lHYddR7j0WQiIbJ0ZHfMTVgx4waQUp1gYrDM0QrFmnKMS8JwEc/tZQtjhxMHDOclK/20bEsDnbUplbLdJ14yDgcc8gkvLqxRo3O5Qq+tb4FTVsblOw4rQOoAZeeTsqayLLL6Z+dgT8P74+KYARGav3yfsUdXYxcNKq5jyprsbSuGRaSC38aHWEJfVlVh1U1LjjoPErfRupexbrjdC2UvZspzTMGlytDsZ7HhX1BAlsr1qiKetSoUaqiZnjveD4vLCz8TlmaOrorGolg4ZezEQr4kz5C90Lk29Pg8pDf+ytvvot/vvKGGjil14nMtP0m4rLfnacatX+gIxuU1ZLVFEfJklyAGsJvUUP4jvsfhj8QQGlxkRrgNbB/uTry6Og0B5fU7WRnZSIvN6fNZWY6Vd3LRugSun/QgP7qXg7r+M0IXQjXh53UiWqpSqMBtkyqS3mmUzSGYDgAvzeolqmNRWJIhBIIpofw7soqzN/QqgwSUr92D1gO3I5paWlFQ0Pj9zqepRqiMpvh7Xjq6hvUfradxdVdU1Oz+g6EfYPIt/cxbsxINLRq7VMdNhRx+RyjdlNFTQQOK9XN5JXnNGK/oXbNkEj/8dZNjJ3CGfYfP9iKoaUWFZ/va/ZEtf4OCuN09Xo1Sl6bGhIYNXKkuhZ2D37HMXqpRncQCUMUYboOGcgluI/CjJq5X2PdW68jM+yGKRaCL6zN/OU9jdnx/sL6XsPhBNW9SRdlR8Jmx/0YKh59F2q2sQonuSYMcMTN5B9HbSSACKXL6VRHQ3Bzf1rMCD+FBemXmBIm5VxUvwu7By/5P33aZNSb6R1T3tL7FbmMLs7IQb+iEpWv62urMWrIYAwoKUQ66cR5WU5MHDkCmc5MKstdauWeFlcTivNyMHzIQNKTnWpA0IB+ZRgxaCBczU1qhvL44WOQZba36Vuclz0kY09BJkZS+tKP1TUYTRb4rQPhRR7Sc/Ixbsr+ylDMfZQ7q+vq5a2rsR4rF3+Nui2bYbXZkF9SirR03hpN+2aCPq9ajjoWi6pr7u/kJaiFvQPLaf3GSgQCQYwYOlht+aHDbdobrvwDcnOy29rIPPs4Iz0dm7dW4bijDsfxRx+h+iSfePZFXD/jLrz21ntqyyaePTxl0gSUlRTjrluvV33bH/3vcxXnVWp/2+lbOO/sX+LQg/ZX34LDYafaQPLxzmK3WWCl/JL65rhcddhtpB+RPkTls9VihoPeN1/zRDQbHdMo3EZy4nfO4an7x5tMRpWGDpevvJIEfyt8r8NmVc/leCqcnsHx+RuxU5qcFv8efp6d4nL6Kp6B0uXlY4S9gPRP7gw9wlisC4yPqcZZznh8ZD+9oOYjK0/z58/HvffeixkzZqhlp2+66SZ1zvsY8/Hmm29W/no4O/bT4/D1Pffcg4ULF7Y9Q38uu46/gdHjCLsPv1HuluR/bChmY/3okcNVBUovGa21LgTcPqU0cYH8VYMLabnlGNqvkArqbRUp0s+psk2e0zFCHj87dj/sP3E45je3wkAFNc9ibtxcT7LU4o0YOgRZmZn0ZKEr4dzBHc+nDB+I6fnZCHEnszLkdr5kNC/T5A+G8eDitfBFoiz679AcDOG51ZupURynOHyfZmzu6Hg0t5eed0BuJo4oL1aylfy672iu2Yr6qs3q/IILLlDHH+KII45Inmlw2bt5zTJRsLohIt+eCXcs83t/5oV/4av5Sf2HHDeIuLz86Skn4J7bbsTpPz5JK0PJ8ZHdFmog3//Ik5g7fwF+f/5vcN/Mm9QS1k8/ci+eelhzD991G66/8jIcddjBapkuTpdHbT/wl1vb3GUXnofjjzkSt11/NZ588G48N+uv6t7777gFJx93ND1J2BPwHoYJqnc7ovY2NMSRMAfhcjejqYENTkGKS7pWzIRQKIimlkZE4IMlXTqbuyN6Pv4hx+jGRJ0duZfjCPsOkW/vYvTo8VhXQ41XKnu5HaT1OVAAHfOzTDj/BCfc/jjeme9HSY4Rd52Xgz+cmo3CbBMGFGuzHYaVWtT1RSdl4aHf5aE014h3vvGr+359dDrK8ykepactQU1ypiP3b26oiWDc2EkqDWH3SUvE4PS4qao0IkrvWDf4+smFzA4EbZloiVnQZDDBS3FCJJMwVbpRkneEXJiqVHa8/DTvcssrZ8WoIc3n1KxNGpT1Y0LFDVF7OEBxqo1R5Ye4EV6K76XvyUV1eQuFBek5fvJjI3NdNIjGWAC2GGt0wq7Cb2/6pHGI5TrgDvnoun2wJWtG0/IHwmK1or6uFiXFxW2r8rS6vQhFNIPgwNIitc81z0CMhUMoSU6S4CWn/QFeghhwOtOR7XQq3SvTkY4BOdt2bpsNRlQFmzBk9BA18UGk2gXwaocmm5pV5mlpwYZVy+H1+dQM4B3tS+JvgQ2+XioP1q1cgnUrltC9RuQWFSGnoJDCTfB7POD9rDtFldOiY+8tWKo8qK5y8xbwdpXTp+zX1hY+9aTjlUF47KgRSiYs10MOmKb0pS1bqzF8yGBEo1G8/vZ7+O9rb2HO1/Px2FN/x7sffqIG6bER8Rc//RHsDjs++exLPDDrqbY4/3ntTTQ0NqrniO6164wbMggjB/dTfUj8Lq2Ud0cN7o/JI4dgIrkh5SWYMHwIpowehkkjh2Kwuh6M/UYNxaQRg5Gfk6nK48LcbHU/fw9lBfkY0q9UM/qSX26WUw34YSPxIIrbr6gAg8tK1DkbiZ1paRhUVoj+xQUY1r8EmVQel6s4xSgvzMPw/qXIzXQiM8OBYl4dVdjjSP/kztEjjMWpBaWudHX0S608FyxYgDPPPFPtWXzbbbdh5syZuOOOO3D77TOVmznzNnXN/vr17bffrvw4Pp/zke8/66yzMHfuXFUgMPxc/TfohXjqb9H9hK6D3ynv38T7JTJcQLOhWFfQXKRU1UYNGFheRBXBtpk2HImTomwlJZ0aX9QgCpCy7Y3G0RqO4qSjJsFvt6KOlG+b3QJvoxvRYERVBoWknA/sV6Zkqcte6Br4nWbSe79q/DCYeedoau12nAWsuzjJyho34OPN9fhg9aZkChqcy3h5ttkbqrCslkde8oziDsbmpGN/NhbzrOIrJ45AtsMmjad9SDwSwqb1q9vy1jHHHKOOP0RmZiaeeOIJVQbr9/DI642rlqpzoXsg8u35cCfIrKf/rmadpZaVDrsdo4YPVcudcqOZy2EO30SN6ZvvuA9DBg3ADVf9EaeefDxGUjwuo5958d/47+tvq46x/lSv8v5LV//hIjVqOj8/D9MmT1L36e6oww/G1ZddhP2n7oe8vFw1wpfvHUYNvz9cdL76HcIegGczbEd/NZgMsGca1RLUZqsRsXgE8TDJP2ZSnR05ObmwUmM5PT+KhJE1LqEnwjMdeI80JicnG8OHDlb5T+gdiHy7P7ysf0FBOSprtPaojm7UHT/IhgEFJqzcEsWtL7kw81+tarbwmYen45/XFODkqdp+wweOtuKffy7Ar47OQLrdgAfecOOm512YuyqM/CwzRvcnuVNxz3qarqslEgZsbYghu6Cf+h3C7sHv1ZiIweCvQdBgo+Yu1Z30jtkUFKUjb9UQCvGy00Zq7/LMbjYkQ80ijiZ4cWlqw1J9yo6iIE71cIxc3GhAlEQWpC+EDb4Rvqb0AvSNeKhd7Sd/nnHM30w63RhVs5n5+wHS6HfwxOY4ta19FM5xwvQkO6WRwQYxYZdgWRcW5GHkyOFYHWpWuhTPJmbHkxQKLA4MKCtX/ZVs5C0rKlT3NTQ2473ZH2HxipVqFqLZZEYG6VQ+jwcFpFfpfY5fLVyI9z79FP5gQOnezowMhAJBNeiyNDtfpcvwkcsJNRM9245x9Ht0PV3YfTJJJvn9BqGlqQFrvv1G7TPMctPL0O3BcmQ5rFvxLdYsXYSg30flbAGy8/JJp9bq4Hg8Bk9Ls1p6ujNYrjyTWeg6OC/x7OACamuqCUlJ0tPSVPuTDfhr11eofHXAtMlq8lIGtUdPPv5o2KjN85NTTlDxM51OjB45gvJ2COs2bFSGYu6n5rTZ0MyyZ7/Hn30Bf7phBlav3YDxpItx/IXfLkMwGFJxwpEIXvjXK/jDNTfjy6/mw0TPF3YNL5WP2aTHONPTVHnMqzew4bfF41MrfpRSee1ye1BV36gmnhXlZaPR1Yq6pma1QkRxfi48Pj+K6B7+TowUJy+L9SlqBysDNMmd0vb4ggiGw6ioqkNFdS02bK1Rcst2pqs8W5iTo47rNlfTN2NGVkYaNlTVorK6HptqGjCotJB/Hlq8vuQvF/YU0j+58/QIrZAFypmMj1xY85GFlaoY6ctEc7xLLrkEmzZtUgoax9WcFqbRnp7mpV3rfgyP+OFCfd26dbj00kvbwvXnavdq8fVzpQikpCF0HXYqmEsKNcWaXjIsdispV9ro6SpvAKb0bGSl8zI8VNFSBcB7vtRQof307U/iwZufRVNtE4LUMIpR+CdzVuDrJVthIOVs/+nj8GlNozZil1ptEaq0uXJnBvQvV0eha+H8wYr11LICHF1WCDPPBqbWcTzcuQtSeBE1YG9ZuB5VrT6lTDGURbGlxYtHF69Ty2vFeNlLFvB2nJ/k+6dh5RhXnCfK9j6E3/2aJd9gyJDBKC0txUEHHaSWAdlReBTYwIED2/aYYBpqq9Sy9cK+R+TbO2BdZmPlZtx2zwNqtRYdLr9Z19H1nDA1kD785DOcf+mVGDl8iGo4p6c5VD3N3PnXR9Wo6Tfe+QCbt1RxAupeXrljvwnjlAFYLemUTJeposbWVTfNVGlWVdcoPw5jp9IW9gi8F2Y03smMBhKlyR5GRqYNGXanagTzHsXxKG/5QqKjBnSc6t+gL0x1cQgGI9XFogbvc3amLcIdITxD4m8P3YPLL/6t6tw67sjD8Mwj9+HEY49Kxtoxdua5wq4j8u2dlBUXw+TIQ4Nb68MwmbQ+DHWk8IFFZlgtBsxbHUaI2k6vz/Xg4kea4PbHUJpnAq9qWN0UU2UzX3sDMVz1tAsvzOZ95BOYszIEhxUYNYBX9tDqaa7v9ec0uaPwBg0YR9+LsHskeABWVFvyWy0XjTi5mDqGEzGE+UjXbNhTflTZUmzySc4cpnNeUlo59qd4ylF6PDs5QPVwmFyMwmN8Tmly+hE2KJMs+bkRus9viPEiIMrY3JoIq+fyEtauRAgR+gYyDBaYqOqPkA4g7Bpcxg4dPACrc+0IUtsnWNbuQuScOYVIt1qVnpWK0WanQnXnu2N5aWLOtzyTscCZA0NZOXzFRW3PDJQUY0OuE8MmjUEG6c0i2a4hTnmMl58eMHoSsvML1BLSvJR0q6uZQr/7lrkMZ996ascu/PITtDQ1Ip3aPzmFxbDaHJRee3+U6ks2bX+ZaVmGuuvhmfe8otW/n3tcGYP19u2Jxx6J1156GtdecQnWVVQiGAph6KCBahumE44+Qg3W4PL1oOlTlW41aEA/atdmqJnIvHT1bGr3cvv4tB+dhBnXX4WD95+G3Nwc+Lw+NDW71NLSmTzgIxRGXUOD+i26DYG3c+ItQ9iOIew6bASmV4rS/FxYSDEqzMtW/RmbaurVKh9Rer98XtPkIv+o2uKjls4319K7p3qbl5duavWoQe8ZaXbkZToRIf9WkiEbfHmgTprdStdetdw17z3PM4iVcdloUEtNU2sYAfoOahqb1RZOPNOYDdRcX3A4/wZeVYJnPXv9vCmEsKeQ/sldo0cYi1XlSYV36lE32DLsx9cML9vy7bffqvNUuLCgalYd9cJYh6+Zbf20a3YrV65si5P6Gxj9ual++lHoGvhtcqHLI7gYHgXLS7TY0+iaRNYaisCZnqdiRiLaiEpedjgnPx9F/QciHApSw8iIYJgaZNR6WvXVCiz63zxEjSYM6FeMPFKql9Q1qRFDEV97QV1UoC39I3Q9XEny/tFnjh6gRkbbo9TYpUq0UxeJKYNx2BPAyysqlfxZxkHyv3f+alS3BmEjeUcpTkcXSx6N0QQGkEJ4+rgheuZO/hJhb+N1NaolmEaOHInq6mq88cYbqvzcGX7zm9+oewcPHqxGe7FSt2jOJyLWboDIt3fAr5oH4dmsNnip8dtRr+EOKm48//vVN5VBmGXMM4V5lhqfK52I4l3y23Pw6L13KFdaUqTSVSmRMHkk77jR7Xsjcpo8qvrlN97Bwm+XYktVDRqbuANGg39DHTWghT1DlOphf3DbvUn1LJeZZ1FGfdaxQ9Eg1atRxIM8sI71LvIjF+WpUWppasrvHb4XYe/CbRfe35D3TOPl8n7InXLisTj68ENUB1azqwVvvz8bS5avVGXA5InjOr2nM3fK8cegf3lZ8lcIewqRb+9l2uSJWFcdR4AHzCYNS/qRS+TiHCPMVMSu3hIGr1yb6zSh1hVHZZ3Wsfzylz78390NePZDbZZKXUsCNc0xFOVYqH4FKqojqg1WkMlGh3alSm9b8ZMWrY+qPRWF3YReppH7LOiEjb9suI3QkY3CPEidZxKzY+Otvjw1h3N8/Z548p8yCNO12qs4GTeojMXJtOlZAYoXIn8LtYnZcdqhRFTziwNWNaM4DpvRBDu5BIyw0JGN0q0Uz5ccPCDsPIUF+Sjdbwzqsm0w2O3buLjdBqeVzkmf5r4mu82Bqrp6dV+eMw0nHnccJo0ZrYwS0VhUzYhLdzrR4GpW8mIOnDIZJxx+ONI4PfoWeElqR3qaCneYzMqQ2PG5/nQrakqycOShBypjhNA1cDuFB2xkF5Yhjeph7pPkGcNrly9Re0mzLHjiCecmF89AJv/N69bAxrNVi0qQkaUta8uDSfjepppqkuMOGAZJ7vr3IHQdPIuU5cGD6PS2C7d32I9ZvW49PB6vMiyPGjEMxx55mKor2ajLefFk0ot44DPPRq5vbFQDoz/78is8/MQzaknqQw+cjrtnXI97Z96In/3kFGRlOpFBafHsVDYI857IDO93fOpJx+GXp/0YZ/z0R5g6eZJ6jrBr+Ek+bJjNykhHQU62MtTWu1rVLGBNtFQDJjsl1HtW7zpBWpGB5Krp2ezl8nqRSbLhJaeb3V64fT7kUL5nQ3E4QrUnRRrWv5zKWDNaKNwXDHHinAD/gc8fVOUFw7oXD3rnWc65WRnIy85UxmdfoPPVBISuQ/ond42de0P7ENWAUZlYO1cdkXTUC3I9jEeMpMZtF552zXT2YWjptMfR79fQ9sxl9Oey05+tFSbadaq/0HVwwa3LjUVDbSA4stKR3a8AUYcNVqpwlT+V7uEwNZ6i1HQij5zyLGxcvpn8KSwQQsATQMnAUpSX5iMcipBSnsDYof3UZvRKtpTpdZTSIOxRxpfk4bcj+qHBE4I5Sg3epIG3owuFSZ7ROP65rBIbmtwwUe2+aEs93llThUzKbyGKo2Ynd3AxcmbSvz30TZw+rBz9M9OVAiDsG6iEROW6lSgoKMCGDRtw8MEHIy+PB3rsHH/84x/VqLC3334bQ4cOVX7cgPK1utS5sG8Q+fYOuC7kfbnOoAbrDVddppbRStVruGHEV6wX8RJaPHrabrejtLhYhXPdy3EWL1mOp174B/7x8uvqeNvdD+Cm2+/BzUn3zgcfY+K4MSptbkBxJR6gBtPGTdpeMryiSHFyqT5Ok4vulavXqmuh6+GlEt2h5AjZTupJbzyMeh83tAPwt4RIp7IgYYijzudRLhIJw+pIwJLON2uz1IR9A+eXA6ZNwRW/vwB/uuTCH3RXXvI7tQ85LzPP+e+eh2apFQM4j/Oy8Z3d05m76g8X4WennqwGd6o8LewRRL69l/GjR+LTJVrHIc/2ZSOuXv+S2NU5Xw4qtuCCk7LwyCV5ePzSXIwbpA2orm8FNtaGUePSjA/DSs148He5ePCiXFz+0xwMKtHatso4TEfeck1Pnwdm8+mcFUGMGzdhm2U5hZ2H36fFkDTwGrQBdLzkdIQdvecohUeNdKT6kruXQ9yHQfENJGhznPQiOhpI92XHwmc9mFf/YIMyD+sK07kyONM1fzFho4mOfB4BhdDfKIIUzvsWxw1RSiMKruGD8Qi5sPIPUr3Ps5EDxgT8hm0Hiwk7Tph0J3sswZtrfS9sHMovKkJNbS28yWVHM9PssPGSAERldZ3qp2Cd2mS1oaZem3XIenmaw67yqsfjQ4vHS+WwtuQ8Y+KOrk5w0neUiETVDDaha1H9kpSH7M5slAwdjVDQjw0rlyrDcau7FetXLcV6uo5HQ8ghmWfl5sFKcg0Hg4iEgyRLozIW8zexI8Zi1Q7iskDocjhfsT7EbdeO1NbWY2t1rRpsd9JxR2Fg/3K4Wlrx1nsfIRgI4qDpU3DkoQepdvOyFatVO5bTe+XNd3H1zTPx1PP/UDOFR48YjksvOAd33HStMix3fBYbkc87+5e4hOJccuG5OPzg/ak0/+7vEXYM7uNvaHap/vwBxQWUf2Koqm9SYSov8atN6j4anb/rhmY38rKdsFktaGxxw+MLqME35YUFcPv8ykhss5pV2mxMbm71IMwj+drky/W31o/lD4aV7lXV0IwtdU2opnvYgB0KR9Q3I+wZpH9y1+kRxmLOPOy4UmaB8DkfuVDXMnt7Q4rj6PHZi8O0zN9uyNX8tLDUe/U4HeFMrS9prT9fT4efp6ehhwldD1eovCw4w6OATPSuLTYLLOkOBOkzDoSjaPEG1cyYaJQbVIA/EMHYyRNx3u0XwmSxwf78HKQ99hEOnTQU0447EPXNAbXPwNbKKozJyaIPgr6plCUuec9GYc9iNhlw9pThyHSmI6oMwpqRtzPHBuHGZj+e+WoVPBT3uaUbYafK2B1mQzE5kv02exXTNe9VbIrE0J++kx+N7J/Mu8mHC3udptoqUqz9uPXWW7F27Vr87ne/S4bsHE6nUy0HwkybNk0dmdotG6UM3oeIfHs2+rvlGWuX/e5cXEqNVV4uWod1HHZsJOC4JtJ/zvzZTzBuzChVP3esM7dWVWPuvAX49Mu5asmu4cOGtLkRw4fC5w+o2Ricpq57eb1+bKH72I/30yyixliqzMVY3HVw44mx+OphdG1ByO9WI+W93OiJaMYKjsE6l3urEQObszHakAl/lRmtK/MQ9abDuzYdkyJlONhaCENtBurW2BH10fdBd3aiTgt7EZ61wEa9DZWVePO9D/HOh7O3697+4CN8vWCxGnBbXlaqZjdMnjhe6d6r1q7v9J5Ux+nPnb9AGZfSHA76ZnpE87JHI/LtfXB9WFgyAN9WBGE1c59He79Fms2I8gITSnM1o9LVpztx6SkZGFVuVUZlt5/rZ+C3x6Xj6SuKcc3PMtV1i1czLgwttuD849Iw81fZ6ro036zSy7Bzvwn7aIZpC6W1fFMIdnsasrPa639h5+GZgyw77r3QZgqzYZiveUZwHCHleKZxTJ27zdq1WnaajvxPhw3I2gl/DwYE6BgkR81mNetYpa+epR01qWtwOjyTmWc2pxoo6BFq5rORfpiBnK6HCTtPi9uNyuVrUB6CWlK8I7x8OL1gdc76bWFxKVZuqFDLoPLqPfVNLfh29Rq4PW5kZeco4252Tp5aFnXthkq0tHoozItNW6qwZmMlcvLy274OllvYtu3MYQ7LiBuQ2dCKz79ZqJ4pdC26AZ41XovVhvJhY1FQWgZPawtWfjMXHpcLuUXFyMorUIZEhmXl93rga23dpm2zI+jLUEs+3bvwKlrfLluulh0+cNoUtT8x72P8j/++hsbmZtVOnTBWW5b20y+/Ukcd3s7pqef/iV+ccxFmPf28MgqOHztKrdLC8PLImZlOdd7Q2IQrb7wN7374iZrZLGLeXRJweXxwe32U/8xwuX3UxuVhWe19wazzKNiDB3TRpV7vau8/gQDJ30r5mw29bAQOkgx59Yf83Bw1K9gfCCn/fkX5ap/issJcNQuZ4fYz/SXH6RvQ4GpRS1oP7VeiZhcPKivCiAFlSXmLwPcU0j+56/SY1h5nIFZ0dEHoRtqOpPppp6xUa/FvuOEGXH/99eqox+P0dP/rrruuzQCdKnA+T32ernClxul4j9B18Fvlaf5ev9Z5ye+Z98hjjKR855gNCAdbqAAOwxeMUkUcRTgcQyQapwo+goyMTDpGsfbYsVh35BjU2zKo4gijjhT0xV8vwxhqd9uSM4vNVk0J42dU19SqZwh7Dq6kizLT8JeDR8MUo3zGxuLtuQg3oA3496pq3P7uQizc1Ai/CmPDcIe47GIJNVu5NhjDn6ePQGGG7HW5r6mr2oysrCw1yo9HTf/yl79Mhuw8J5xwgjqm7jfRVF+rOtiEfYPIt+eiG4B576Vbr70Sp//4ZNWpyPUiH7lerKjcpJyuC3FYWlqa2tOJO7lffetd1djlUK63TzjmCNx2/dW469br8YeLzsfZvzhNLa81ffIkNVK7pLhQGZHpweoeTm9rdQ1aW92cPMaMHK7S4TB2PIt58bIVHCR0AaTpqKMpEUNewgMEvKjzhpBeMR+xRm12N8Pi5jrVUu9DSW0ckc0ZiIfsSiie5ijGOMtx1uj94KswwByzqk5tlpcmVWFfs2LVWtz78OO464HH1JLxnbm7H5yFf778mhrAUZCXi8t/f4GaKcGzXj7+9AsV5/vu5/Rf+s+ryScKexORb++hiOrRmhYrGltj4GauNtM3gYtPycKsS/Nxxzl5KMg2IRoD6luimPWOB7f/s1XtWXzD31vg8sWRbjfgqAlWdWzxx3HLS624bFYzrn3OhYffcmNTQ0yttuV0GPHnn+fiuT8V0DGHnqW1rxkP3VfvcWDggH7qWtg1WFfivug4tV3ZUMtGYmXMJccGYd6rmF2QDcZ0NFE9yzew4Vdzmu7DLk6iUef0h5rDaiYxb+PEcVR6KY5NWJoztDu6UYun7U3MTu2jTM/m5/NsZf4uhF2DlxldsmwVCre6kEbyZvmlUhMOIhLi2aTcp2iC2WpVBmO1mk5NHRpcLqSnZ6KktFxbxpgcxy0sLuF1crGloRGb6hoQJrmVlPdT7SwO52+sNRRQgwFS4eeX+WOY9+lXaHV72/K20HV0XF2DZ/ln5BahsKyfWoY6Oz8fFrVPNenN1H7h+pZh+ZJA1PnOwAPEuA9a7YUu7BF0mXJ+1tu6zLdL29ue7P/q2++pbZE+nztPXbNrpPbv6rXrVZuZUxkxbAgcPLiO5B0Kh/Hiv1/Bm+9+oIzOxUUFaGlpVX0l/cpK1f086HrNug1texgLuw8bcddvqcbmmnq1agNP/uNBHny+YUuN2oOYZVNZXYsKisfGfHYbttZgE/lFqVJkt7ZyM7bWa9tgsWxrG11YTrJiwzHLe+3mKgRCYdK77Mr2sKZyqzJU+4Mh1DY1Uxwtz3L8lRWb1f7EDrsVgWBY3RuKyKoeexLpn9x1esz/FReiXNjyUVd4dKMtH3WlSnca7YU8V7C33HILbr75Ztx4443KT4/HxmIO49EGbCzW0tCemeoY/R4+pv6W1HOh6+FRXbW1deqcuyGt6Q61ny3VAjh+zECMTIugqmoNNlY1oqYxiObWACngXCHwiKCIMh4bzHb4s7JR2+DFh3NWYcWydZhopwo7KwP2rHSk5WTA4rCp74mXE6qgikHYs3CeYeX6yJHlmFaaB3uYKmW1pPR3HS9HzUeDN4SXn/8CzS4/ItRijlHjus3xddIlyEXoG/i/4aU4ZtQA7Xnqr7AvsFst8HvdKC4uxqpVq/CTn/xkt8rMESNGqGP//v3VUae5UdsHSti7iHx7NqzhTJ8yCTNv+LPaM5Elx41mrg/5fOmKVbjx9nuU0cHn97fpXxxnQL9yXHXZRfhm0RJc/Kfr1D5Nn3w+B8tWrkF+Xo7Sq+YvWIzX33kf1824E1feMAMvv/kO6qjh9Qod//bcS8o9/8+X8eJ/XlWNN2bTlq147qX/qLCnn/8Hnn7hn8pP6GIsDiSMZjW7JRoKgpfETISDpFxzFzNrXECB0YVQ5Vw0rPsK9pib8jZ5UuM3GvLhow/n4v0P/oeErx4FZg8mpNVgmK2BGhjatyPsWbic5XzY0enw3uBMZ/F0uA3D+fXWO+/H7fc9hBl3/1U5vp792ZdtcZiOaXD7i+FG+DZ0jEdO2HlEvn2H8rISbGk2KaMdy4NFwn2MC9aG8MibLbjwwXpc/VQzGt3U/qX3/cWKEN5b4MP6qjDsFgNs5HzBBN6cF0SrL440qwE5GQas3BzGnBUBfPJtUBmhmz1x3Ph3F/44qwE3v+DC/74NqOexCNlAzSzfApSVlKhzYdfg+pOXnDbQv0g8Suex5H7D7J/QZhCrc8DHfQ/06pWRl65j8aSRl4QSJRcxUFw28lIEH4X7Vd2s5VmuafkelR59L5G4CcGEGT6DSTk/r8AGE7zkfHSu+2thvHQ1+5vhp3uEXYf3NV0yfxEmt7JE2stDE53XW3h1tGZVnra4GtDUUAevx4NQJIRYjA0FBoRDIdTXVat9bt0tLWil+HzOfVIkfsSiPLMtAr/Pq/z5yOVuXcCvwlNx0ndiWbMFGzdtUXWDXr4LXYdeN6ai6mFTez7i5aX53XtaXGqrlt0hp6AIjrR0rZ9S9jjtUnhmp8NuUyUq51xeQYVXz9JZvHQ5auvqVXt2zfoKfDV/oYr3xjsfKOMjs2T5StU+Zl3piksvpPb01Ziy3wQlL/4GOF5DU7OKW9/YRG3mb5XhePKEcWoPY342x4vwEsZCl+HxB5RxmA23+jvmpaLrmltUOBt765tb1TWfM/V03kRxOC67Fo8+K5nuJ8ezjRtcbhXGBKmMrml0oaqhSS1VzctT+wJBRKIxtHqptk6mw7ABu6axGVtqG1FNRzYuC3sO6Z/cPb5by3VDOHOxULmw1Wf+qg7MpKC5UNYLYs1xJyZnyHaDrh7G57rT4TSZVD996aeOcTkNXTlgfx4FxKT6cRyh6+D3yYVw5UZWeLUBA7asdBjMJjVyz0IN2xMPGIXzJhUjY+UcVMx9D8sWLMKCJZuxcPFWrK6ox7erqVJe3oTP5m/E7GdeROi+q1H24UtIS/iRP7AI2UW5KBnRDxabtudTVXWNGjEm8twbGJBhs+Cc6cOo0ZRAJrV4o+E4Yp05NhZvbYKxzkVCciFOreYE+XfmTBQ33W7Fr6ePVN+IsG+p2VKpymlm0qRJ2H///dX5rsIzGu+//36Ul5ejJKVDKxETJXtfIPLtmbDuwnsn/eqM0/HgnTNImS5s02e40cQ6zpdfz8dNd9yrOp141PP7sz9V4Vw3smO5jx4xTC1JzfUmz0Bjo/BFV1yDM8+/BOdcfDmuuP5W/OX+R9QSXdxI5nvWV2zEE8++iGde/Jdyjz71HBYsXtJmdOA9jR9/9gUVxst4vfCvV1RnnNC1xMx2GM1WhANeJIJugBpWpliI6lduWFP9bAzi6JzNGFVkRXmODQNNtSR3upHklGONwdC6FcGatTiqvxtHZlVif2cVDnVuRnbCpWniokPtMTivcmeT3WH/juM9tViHNZvM4L1qeQ/w1HCbzabCdXjvcV5q+O33Z7e5Dz75TO21looamZ2SDqfL6adipW+os+fpZYuwY4h8+xbcSTxhEMnTxn0PWl8En3yxPIDPloYQJvVnS0ME734TRGGWCb86KgMWkxEGauMcMtaGDLsBs78N4conG/Hq3ADsVgP2H0nfAaXN2/78/NB0lOYa8dXqMNbXhKmOBxauD+GzZX6KYUCMLY5UXrPReWSpCR6vR/0uYdfgGYVxA7VdSQAqJ9IL53fOepNmJGbDcVwZkKPQDMk829dj4P2Dk8ZfcmpZ6Tj3RxgRoZRCnB4lZCJ56XqYpotpaQbBs5VjCCU0x9fepAvw0tf0rO05YddhTWf56vXwrq5EYYDncWuw7AMOC1a7G5WczCYrosY4FvkXYlNjFSykg7/qqsSj1ctgsdjxeVM9rlg6D1WuBrQE/Xh0zjv4ZO1ildKt992Ha2beBn8opMrbUCSMjQEXYilFb5iePLw+gDVLVykDBz9T6Hp0I2Eqen90R1Lr4h2F5avXqdFoRH1IPo8bzbU1iPmbYYnUUBkjhqbdweP1YcPGTaTTWNWKWrziyuGHHIiTjz9a2QdYatwm5UHMvJUHy5cNxLylB88Q37y1Cv/7fK46n/P1N0rOPIPc7fairKQYl11wLn584nFqRa2fnHwCzvr5T9WAj1Vr1uGl/76GZlcLDjlwulp9a8qkCZg4bgxGDtf2Qt2FT0YQhA5I/+TuYaAKbYc1iGlHnpQ827vM+/htJWQugH/o53q9XuTk5LTF0ypnNvCa1MxhPZ2bbrpJhTM8q5grY75nxoxbqbGkfVCpcCOb79PS0xT9VFJ/G6c1/aiT1fneZv4n7yTPdp59Jd8dgSvqyZPG4/Ybr1Gjr0gSqF2zFf6WVtQsW4wNH72DprVrEGpqUqM0g2k5COYNgMlmR7y0DLbcchYM4mu+QvGGFRgUNSBIDeyWNAvKTj0dU399LnL7FcKeQfFJjG+/9xHue/QJJXd9lFF3oCfK9/2XX0iefT+87/TV/56LdxZuQtRmogZzMiAFSyAI++otoEwK2G0IjR2AuH3bfXoYK30vVpLbbw4YhiuPmwiTQd+Ta8c4/vSzk2d7l96afxlX7RasX7UMmZmZaGlpwdy5c3HQQQclQ3eeiy66CI8//jjy8/Px4IMP4t5778XixYsxasIUZOQWJmN1L0S+O47Id+9QXlqCc886A8ceeahqLKfWdy5qxP7j5dfVjGAfNag5jOvisrIS3HXL9Rg8SFuxQYcNuQ89/jTeev8jFS81Ld0AzD6p+pPun0pn9+nsy/q4N+ZfpdfGoyhJtMDftAXN9Tzq3QCT1YxY/hAYckqRY/Di5MxlCJLe5ArEsdFrxYa0KXRfDHmRTRhtrUVJppnu4qVTDQhR9ewOxDDPPxCttjIWOD9Ie2A3pifKt7iwAD//6Y/U0sIdKSkuUoM4qmvrsHzVmm1mSTC89BoPyFiybEVbvtLbOKmktqd4mdyfnXqyWnY+FV6yLycrCwdMm4zqmjosXbHyO2mxIeyTL+bgy6/mJ332LiJfke/26C7lc1amE5df/FtsjU3DrLe1FRxYPDzbV5cTH6wWAx75fT7GDbDg31/48docH+67IE8Zi8+9vwHVTVFkZ5jw9OUFSLMBlzzahIPG2HHecU5sbojiD483oaE1Bn3VPJalNkhe2TNxPsUbnb1MLU3uIn2uO9AT5bs4UQALvdAJiz6AMehChAdDkz8vFc0GPTY1xflIeTdMecxEAjDDSD5xcE9HOoWbklNGTXESVsIAH8nIbaC7OD7F4bHQ6siR6ONIcPxk3uQ5zXy38qJ7FXShpdg5aYcfmzzbu/SG/KvKRJJBeUkxppx6DFYVp6vlwxk2HReGDfhV/zEoyMpBIB7G/Lr5yDMWY2LZcDxRsQR1vlZcN/oQvL95A55ZtxK3TJmOvDjw9JwPMb50EE6ZsD/e+Xg2glS2n3LMMbDa7NiwtRIvNW+En3Q2fhR/T+UxE7LnrcH7//tSbd/yffLeW/R0+RrNNgTTxwKZ5cguLIe3pRlpmTmor1wDi8OJnGLSdTkefQNhfyu2bliHvKJimCyUow0muBrqkE51qM2eBrermfJvDNn5hQgF/Giqq0VheTl9P0Y019XBkZ6GDPpGeFZylGRtslgQ8vvVnpuGsBfpRhfs4RoK6z6DZ3uqfDnPDhsyCDddfTmG0pHzr7Y3+Fa1PDTPIOZVWLj+Pe6ow3HBr/8Pf7z2ZjWpiPMV+7Mh+Lo/XYo/kH9lcia/jdrTl1x4Dg45YLrSqzge61FNTc348H+f4x//fR2tra2YNGEsLjznbAwfMlgNwmNdLUF1hj8QUFs7DR00UM1Ovuehx/Huhx9rP3of0BvKZ2H79Gb5Sv/k7sm3xxiL9Z+pG3v5yKOfO/r7fD5kZ2cr//bGlaapcTg7zU8ficmNMK21xGG8lr0eh486bDTUDcp85Ocxelz20+8VY/Huo79XHb62kML10F23qVFX6v1TZfqfcy/Eps8+RnHUiByTBVZSyDLsGTDaHahx1SAUi5OCZ0ZaegayzWkwhoIIhvwkI0rPbEV6WhY+qVuLI+5/AJPPOI2SjKsOljvueQizP/9ym9/QHeiJ8t1RYzFnt421rZh69zuwk4LMYyXbhm2QGDg3pm+th6mpVfMj+UfL8hAcWELhFLM9uyKf8yEp169dfBQGFWanBu0QYizueppqNqNi9XJ1/uMf/xj/+te/wPtG7AqvvfYazj77bFXeMzz687jjjsPWrVsRMTmQkZ2n/LsbIt8dQ+S79xg6eBAG9i//jqGBqamrx4rVa1U9qOs8DHeGsKF4yMBtjcUML2U8Z943yaveRW/Mv3rdmGuOIM/gh4+XyiNZO2w2ROyZqIunw+CpxqB4hVoaNRiNwxUywF0wnXQyCyzujciJVCPNBFjJsW7lCcbRGkqg2ZANa9lE1fmiKvhuTk+U78N334ap+01U7Y6dhfP1oiXLcN2Mu1Sn1fcNxOA8z22uC885S60goK/ItLO0uj045tQzkld7F5GvyHd7dKfy2ZmRjttuuhGvLynDhwsDJIv2Pgm9Tcp/8zNN+OuFeRhRbkGTO4aCLBPufbkVD7/ZqmYm+6kM/sVhGbjv/FzUt8SQlWFEdVMcV/6tGRvrwqrs5+T0vhJOn7+RIyc6cM4hblxxzbVoaXWr53UHeqJ8l8byEE0YMGntQjjrV8JvMCsjcYj8ggZutxqRoPceIBmYqG5lM7HBaFIziS0JI6wUx5yIwUhyslEoy8dnjKnB1KpGpftN+jlfpp7TCQ+sV+e6B0NpULTtkiHG4t2GV4M48vCD4DtgFLZaErAm5cAzwY+y5ODQoWMQi0XR6neRWKzIzcrG46sWY3PUg5mjDsaHWzfi2fWrcMvkaSh2pOPNrz/F4PwyHDI6qU8lCYdDeHL5fGxNp7T5WyI/M4Uf3BLHOy+9hurkHpvdgZ4u31RjcW5RP1RXrEZILUubQGZeIbIKtBlm3JYK+dhYvHanjcVmswV1W7YgPdOJNGcmfRtGRCMheFwtaonyDJMHzuhGxCMBur97zWLr6fLlgXmDqU3rcNiVoZhn/PJS1Gy05XPG6cxQ7d4Vq9aoWeV6fWyzWZVRd11FpbIX6LB/fm4uhlB7mdtLnNaWrdVqda1oRNvrlnUv3ku1rKQI6ek8PAhqgEdTs0vpU1lZTrX6V119A1wtyf7PfUBvKp+F79Kb5Sv9k7sn3x41s1hvzDCpBlv21xvSHo9HGYu1ePy/1t640u9lUtPS6cxPh5di1GGjMD8vNS7/FvbTf4sYi7seft9TJo3HfTNvJoXKpDLo+n+/hy233gK7nypnqojNjjRUhdxU0QZQbEnDgEMOg/OcM2AdPQqfb2hCkasJ9udmYfFXn8NGIi1Jy8JAyuRFM6+BxZmmlhHhPScuv/YWBKhS/77OlX1BT5TvjhqLOe+wXJ96ZzHueHkxfDaj2sdJhZGzBYNI21zNUxw0T4YCAqMGIJLu4MKMszvS6Dvx+SJ44Iyp+OVhYzjhZOQdR4zFXQsvzVGUacMjDz+EDRs24Pzzz1d7Rlx99dVqD4mdoaKiAh988AGeeuopLFq0KOkLZGRkYOTIkTCk5VDt/d3Z5t0Bke8PI/Ldu6TqUjsD18e6btVX6M36ldmQQLoxCns8ApORdFyDGd6EGa0xM+J+FxpqqlRHtINUbavNDEvxUPoGTIj5mpAIuNTgLSN9DzzgzhPmZTOBrKxsWHK12RY9gZ4oX24fNTQ2qZkPXi8vJbtjhMIhzLjuKthtNvz+yuvUUsQ/ZEzkpYavvfwSNbvi2lv/opbfs5h3vCz+3blnY/+p++2zdyXyFfluj+5WPvOs8ZtvnIlb/2NSM4F5xQZ9eyy9r4LlWZJrxl3n5mLcQAvC0Tg21saxYF0Yrf4YstJMmDLMikHFRljNRqzZGsWNL7Rg3VZevrY9Hb0e57/FOSbccFocsx67E2vXb1D+3YWeKN8l0VxEE0aMrViEnOoVCBrN4Hme4YQBEWMcMdJlw1SPRuhoIV2M61BSqFWz1RgJwcwDoemCDYC8CyrLPGSI05F1MK0fig3JnfdcaSgDMsWjP5oHpdGmu3VyY9bhxyXP9i69Kf/y+y7Mz8Wxp5+MOYVsKtb+sZk+KxTHWaXD0L+wVOU5PQ9We1vRHAthdGYBPkgai2/ebxom5ZWo8plnnm/TFxmP4ZO1y/ER3LBo88rV9zEkaoRn9nx8PX+REm9S0vucni7fVGNxVmE/JTNPYw1aGhtgtduQXVhGxzTVpgr73aiq2PmZxWws1mXM+dvX2oKAzwsrAsg11SHmrVJh3ZHelH+7Cs7bnAc76l6dtbtVXHK70h7fG4h8eze9Vb7SP6mxO/Ld+aHK+xAuRHXaFF1CV7T08/Z42/qncu2116oP5ZprrqEwza89TueqFafFjgt5Pa7ux9f6Udgz0OvFN4uW4N+vvqGMumy0H3zqkRj5p8vQagVWmsP4oiATvl+cgUqbES0+N9zeFiQGDYInbsSi2hA2Z+bDGEsgcsJPsYyUPcOk0ci7+mJlKObqubGpGQ889jc1+uv7OlaErofzDi8Bf9IBwzC6LAfOEDWY2YVjMJCzNzTxBjHJ2DoJWLY2AIEI4hGSIO9X7IvigGEFOHnqUM2ALOxzMik/vv3Wmxg4cCCOOuooLF++HLNmzcKYMWNQVbVzjZ8bbrgBP/vZz3DppZcmfYDCwkJUVlbim2++wXGHHZD0FfYWIt+eCzdMd0Vv0e8Qnafnw/WkOR5GkPSeJq8fNa4gqlt8cIeo/qT/zI5MTBxeiknDCzBsUAHyS4pIBzMr2dvS0pGXm4MBJbkoK8tHv34lmDC0CBOGFCA9+7tL5wpdC++bFgqF1WyFVWvX7bCrqNyMYCik9Ohd0ZLYkLi+orLTtDtzlZu3oLmbLGfbkxD59k3q6urx3/++gGtOtyHdZkA4qkmR+xn0IxuQNtVF8ddX3QiEE1hSEVV7FJ883YHzjstQx+wMo/Ln8FnvurG0IthmKNbTYdj4aDUb8OfT7fh09ivYsLFS+Qu7B79fdhGzSeVD1pZ0x4tIt+aUwz35JLRMPx11h/wStYeciZoDf4GGA86Ae/SR8Of3Q8xoQtBgQojuChq0gViJNg2Mz9sd92Pw/sjseDlidjG+h4+JuOZ0P92/gxN2H5Z5fUMTNi1ajuHNUTQ1BeBqDsDTHEKlN4wn1q3E2urNSvfipWm5v6nMmY3xOVrndZ7dgQOKipFjtiJK4f5wuM2IyPleGYor1uLNpgb4m8NoobQ5/VBTEGkVNVixco36QkSaewaWAcMGYpvdhhjV03WV6+BvbVR6cTJ4p+Hvhu+PUN3tqq9FPNCMHEMNcmNrurWhuLfQ1e1ZlmdnfcmdGYT5ybvaHhcEoXOkf3L36VHGYi5E2VCrw4Uwk1qwagXwtgUzh2sVMF9pFfGMGTMwc+ZM3HzzzRyDA9rS069TSX1Ge3rtDS5Gv071E7oSrXH8z1fewPwF2ohJmM0oOut0DH3kYUQmTET07MuxZfBkNJxwFryZGaifNx/1V1yLxatr8IuDB+Lw1gq4VqyEY/9DcczfnsR+D98HR1mxqswj4QjueuBRrFyzTj1N2PtwzinITMOvjxyBhC+OrDjlp0gcaa2tMHr8nMnUN2C3GOlIkemP2RuAqdmjjMWWaBx2sxFXnTAR2ek2MfjvSxJxNFVtxBHTxuPxxx5OemrwUjs//elP0dzcjGHDhuH1119Phnw/XLZ+/fXXyM3NxVlnnYXPPvsMX375JVavXo28PG3pj/POPQcBt0udC3sQkW+vYVd0Fr1sFX2n52OMRwDenoNqYF5CLxoKIpYwIG40qyUy4wYzmuNZSFiyYU13ImRyqrqYwyKwIjO3EAXFZWiOZaA5kYmmeCYdcxA12JJPEPYGnBd3xplMu94ETJ1x2lnaqU61n+i/zpa6F3aczt7t9zmRb8+F69fPv5yLhXNfw32/zVOGXPWSk1CwwmoGzj46DZ8tC+HiRxpwzv0NuODBBlz4UKM6nn1PAy56uBGvzAnirCPSlTFZv1fJLXnk9tT9v83FppUf4b3Zn6hBBsLuQ28bBmMMCZtT7SvMPryHMVWvCGdkwTPqAATSchEhQfKqHDHKQjGrA2GqZ1sHjIJr0rEIDR1BYTG1rzHfbzRw+1eTHec/ZRBOOj5vy5scm/6wU0JPcbo//acMzKlO6Br43X69eCmy6ppRRDLjmcEhcrzEeA0dn6hYjW83rFb9mlx2stGYl7ZlphaW4PJxU1CantXmxxLnrQJ46enXly/Cm3VVCFFanG6YvikfHSfQd7F52Vr4fH71fGHPwLJiVJ80ycDuzEbJkFF0zEmWq1r+3BHMFgtyCwupvjYjxAM2a2vgcTXBmahHZmgVLMFKxMLakqfCnoXLzX2FtKkFoYuQ/skupUe17FhJ0kfjpBqOWYBcOfNR82s32qY2hngJJ/JKnseV0/dlSo3bGe3LgrSnpy9HrV/rv03Yc3Bl2tLSigcff0btDWEk+SXouxh67ME457kn8eMjx+PXZxyOY/7vF2jMGwK70YK6OXMw5KkHUPLVl6i47jYUHnYwTv3xQTh08hA48nNVmuFIBM//4z/46ptFqhHG34Ow91G5i/4cM3UQBg/MBdwR2IJhWFrcnMlUOC/T5XXFYKD2k2oEU5PI5nLDFIzCGYzhpJFlmDa0GJGojNDbl6RbDPh2wTzcdedfMGzo0KRvOzxgx+l0quXeef8HHpX1Q/AeEUOGDFHlLtcHhx56KA466CDk5OQkYwDl5eWwGaUs3tOIfAWhd8CzykImB4y2NKTZ7UhkFcGQns09YSqcq9mo1YxQtgMepx3IisKUHqLwGGyZCQQccVTFAzBmUVo2ujZYEUhYkDD0qCaGsAvskK4s+nSPReS774hEo3j+ny+jdsNnuOD4NPWeeVYwt2vUPsYUZ/JwByYNtuG1uT6EIgl4/HGs3RrBgjVhdXR5YqqN+/LnHgwpseKICZQOwemkLmt95hHpCLuW4tG/PbfNnovC7qHyT8KIiMmsDLFaK5a8yEUySK+12KjSjFK7lkLI08AyoXsMCYodjyNqSUPtiKOobh1BcXbSgL8j2XJH4gi7TDgcwfx5C1FOeTOWlD3DZy5694/WbcVri+ehpqWJ8mkcJu7n4H4LCkvENIM/w/1S4WgEyyrW4pHFX+GtVpcyFOuwATqL9K3cuhasqdio8rywZ2D56H1LbUf6Z7HZYTJbVF/wD9eb7WUvncBstcHVUA9PcwPsCQ9KDath8a4ixduLhPQtC4Ig7DDSP9m19JienLZKNYl+zcfUMD7q5+yfSmrcW265GTfeeCOuv/76tnQ6oqfD8Mehx2F/Vgb0tHQ/PbyztISug5XgjZs246Y77sVnc75WI/z4/VtsFgzvl4sCqwGTRhTjiCvOQ8nlv0P5FZfAuf8UxEIhjHnyrxjywB2wZ2W2jZyub2zCg7Oewov/fU0NChAle9/CuScz3Y4ZZ0+FLwA4QkEYSS4qv1Fj6LCDxuIvN52CQ6eOALyA2WiAKRxCXjBELSYzzjx6JBXkRtUZIuw7hg8eqCrj7cGV7i233KLOvV4vbr/9dnX+ffCSjP369UtedQ5X4i/8/VkEvO6kj7AnEPkKQu8gYbYiYbHDarEinfQos9VKSq82kFLHaE4gao4jbjXBlm6FLSsGkz0Koy2BoDkGr4F0YjvV31bSx7jDG+0dnYIgCMKu8ehTz2JgRgUOn+BAMNzeEcUtHH8whjfn+fHNmiDMSQMyd0FYLdoxxiOBiIraCF74xAuPn9u9mh+HhyJxTBthx4SiLXhg1uPKX+g6uG+CuxQiPLCdrlWrlF48SyrmyEGMZwmzHxGPRLmDQxsEHY0pw3GCvAxxG9yjhiNmzaAAlcIO0TEmJ5vqGC2O1NV7Cn6/VdV1iK+rRIHRsM0y3xzGS0y/4XbhgRWL8PTSb/DpxjVYVbMFlQ21ym1sqMHirRvx6poVmLV4HmZt2YBl4RCsnHmTcIq8p/kB0QQ+nzMPAe4LEfYYMc6nsYgy4He2AgP3VaWI5ztwWFqGExlZ2YjHotSW9aKpthamUBPysAU50TUI+5qSsQVBEISdQfonu5YeYyzWFG7NMbpBlq91w20q2vW26q9+Lx/vvPNO3HPPPbj77ruV3w+hDFUp9+vXqc9NjSPsWfg9b96yFTPveQCP/e05Uo6D2oxgCuNRufkZdoz/6fEYcumFGPrHi1B20W+QderxsE0aS7nZpGTFbs68Bbjyhhl4490P1exikV/3gEe8Tx/bDxf9ZBSMzWElVwf9nTaoDHf9+RSccfJUzLj6JGTYM2ClUsxOzr3Rg98cMxTjhxRpiQj7lB3Zv+78889XlbbD4cAhhxyS9N0+1dXV2H///ZNX22fUqFE4+ehDKatva/AQug6RryD0HpQmazTAkjQSa9pUO2G/AeEgD8wjfTthhAlmGM2a7m2ipoTFYISB7o2pRXj47m11ckEQBGHnaXV78I9/vYDfHx/HmAE2ZeDVVkpLYNXmCO5/hTumtPJWny2c6hg2JP/9IzcWrddmDXM8NiQXZZtx1akGvPbav1BbW6/ChC4kEaPaMQqv1Ub1IxtleTlpA8xGM+JpuUjEI9oMUt3oRDKN87YQdA8SdOTwRADBzDKEc8pJnlw/U71Lf3k2MjuuqvlbUP0X5LgPhI9cg8cpmJ22jzE9nR2F8HLVfNR+kebPmLSD0EXw6+QVAjau2YBpBgtLdRtIa4KZ8mhDLI75Hjde2FqJhytW4751yzW3dgWe2LQe79ZXYVkogAAlqC1nvi2DLVb41m3Glpo6JXuh60nEY7AnmuF3N2Pr+lXwe1rURKKOaGXud2XUPuOY8i21XUMBP5rr6xHyNKPQXANneDUQ2IpIOKDdIAiCIOw00j/ZtfQIYzFXrnqDR6+Y2U8/56NuME5tHKkOK1aYk4pTu79WWaemm4p+T8eZiRyXn9PxuUxqmP48Yc/CM4C9Xp/aw/jM83+Pl19/GzXVtQiGQpoMKU6MlDvVKOLvg2QTIYW81e3G4qXLceud9+OaW+/Aho2bWOgit26EypYkj9+cMh5l/TJhpUtqZyPdboXNalGyt9ksMOVY1CymeDiOrKGZOPeUSbDwYACVirAv4SU7fojMzEy1/McVV1yByy67LOm7fSoqKnDaaaclr76f3/z6V2hpbkheCV2NyFcQehFc6bLTWwWqEm6H+5KjAd4r0QSr1Ur1rB1mqwFGM0clvZv0ZUPcBKfJAafdrAbvCYIgCLsHt01Xrl6LJ556HDPPcqA4V+uE4iYrd0eYqQymK1UOsx8bglOd1s+hHfmU4/B5ntOEJy7Nwj/++TwWLP5WPUfoWrRXmkCY5MR9EUb6S61Wtd+/muPNMuJI5JSRl/uUyK/NcWuWjmEepGUxgyXPLVwTpWMy6I78KD12LGA2DGsG4qQhmBw/m1NTLhmuG5G5F4vrdzM5C10LXQvnq8qtVQiuWo+JlFlZHh3h1669egMiJO9gLK4c73GsPgMVosfZFjOlN6DFg2/mL1LP+m7qQleQiEcRd29EMdbAkWhGw6b1qNu8juRDb5wEo5WzlJ9IZqllqdlsVfpxTmEh7I40xKIReFwueFuakU7pFGIdEu71lBlDqhwQBEEQdh3pn+xa9G6hbo3WANq2AmU/3VDLYbqRlo+6MZfRGkfbqld6Uh39GU5Dv6fDI9vCUn+LnkZqmO4n7HlYSeP3XVvXgPsf+xsu/fONmHnPg5j19PN47e338cEnn2nu40/xr1ffwP2PPoGrb7odf/jzzco/GiW1ndJQyp7QreDs3a8oC784ehzgpgaQ2YhvVmzB8/+dg1XrqvDMS3PgrW6BwcRjc024/dcHoSgnva1cEPYdPKLKblU9WD8I7/dwxBFHYObMmUmfzvn0009x0003IS8vL+nz/Vx/w43IyStMXgldichXEHobrEuRTpuiP/OZ8qMjdzBHQgbwpAddz7VYrTBbLErnjhsNKLDZMGVAIUaUZMDBS34k7xf2DiyTHXVdBQ8K6Cz9VEd/krGF3aGzd7s911WIfLsPn375FV5/7UVcdoqDdDAuh7WymA3CjFYua+JIdezPcDy9vcvNpPOOdeDdt/+Nt97/SIULXQ+vxMHWvgAvEW7kNTgSsLLcKEwZiymc9yRVcolp/RFqz2KWaVKuxhibeuMwUBuYtW5ORTMaJ5RjI6+ZKmh2JG4SOt1Kgo8bjIjRkR2f8zZOPLogwfU1x6FL/g18zvnYQuc2vl/ocvj9fvrVAgxxBZDLskj67y5seB5BX0HDsrXw+P2a/IU9Ci8RnR5YjQJTFWKtVQgHQ4iFw4iTcmyivKXVi8nIRJwnrXAep/zsdjWjpaEORn8NchOVyAivQcQvS04LgiB0BdI/2fW09wr1ALgCbm/0aDOJO8LTyceMGa3O9bjtaNed3bc9OI1Jkyap5+nXmiLQbhju7CjsPfh9s2MZ1dTW4dMv5uLF/7yKvz72JO5+4DHc8+AsdXzkiWfxxjsfYPnK1QiFk7OPRVbdFspKKj+d9qPxsJfmqr1hYqYI7nx0Nn5x+hP429NfwJgFWKkhPX1Mfxx98EhEKQ7fI+xbeN+GUSNHJq9+mKOOOgoLFy7E2LFjcdFFF+GZZ57Bu+++ixdffBFXX301pk2bhmOOOQaDBw9O3tE5tbW1uPj3l2DaoUdjU4NbLf8ldD0iX0HoXXCtGY8lqA7l2U90zaqRqktJT1IHOpIaHPJGEQoGEQoFEYmEYEqEtfMQnccTqHV7sKrGDV8wqhmZRcXaa+g67Y44Jhzm5U53DV2HZsNTx7Q7Or2jlAdnCrtOZ+92e44R+fY+3nz3Q/hr52HGr/KQmW6A1bzzLjPNiCtPz4EzugSvvvV2MmVhT2DgZaTJxYxWJBy57EPVqlnNBmajrSkSoyMvOR2mIGq/IkZ5jtq66kj5KhZSftyuNRptsFMYD45ms7C+DDXX122GY7qyUpgvbkRIGaI1w7OBK2/dMM0ONsq3QMScgbjBhDj/FlMCFjXPWOhquJz0eH1YtuBbDDNqs4uphN1tl2YyYYQ3iFXrKlQfiahbeweeZcxLRvPS0UXWOkT8vDT1arTUVyX7oNhpGAxG+N1uuBoaEPG2IA8blZHYGKxFNKJtCyAIgiDsPtI/2fUYSIHZYd1i2pEnJc/2LvM+fosULU3Z0o2xfOQGqj6LWP/fYL958+bhN7/5DbZs2aKUJwpV9+voaXwXTlebocyYSAnjzaz//ve/48ADD9zmuXxMPecwjq/9JgOmH3WKSmNvM/+Td5JnO8++ku+eouMyiCzVzuXec+iJ8n3/5ReSZ7sG56eV66rwizOeRcgRRIbVBDP5RSmv+akRXGpJx6OP/QojhpQk79h9jj/97OTZ3qU35d9+BZm4Y8YtKCoqgtPpVOXj9/HYY4/h97//vTrnMlWLb6AyPNqWb3m5kPvuu0+dp8LhX3zxBa654RbEzLy7dfdE5Cvy3R69rf7tjfRW+XJ+TLcZUei0wWGMozXM+iyv2AG0JALwhyNqL+JExASDJQF7ZpzCSf+lfJnlsMAbiqqBWgF/Aja7GRajGf7mBKLhbXWw7k5PlO8X770Gf8CP5//1CkKhUNJ3x/jlaacqg8SlV92A+oZGJc/twfq03W7Hn//4e5xwzBF4/p8vY2t1DTXMv7/c1+FZjScfdxTGjh65z96VyFfkuz26c/nckeysLPzxot9iv4ljkz47z/qNVbjnwYfUAOueQk+U7yKvGaC6NB43YsrGhbA1boXZZIGL2rDrRx2BYEaWMtRy3xMrtqwXx/jcwKZfHrhloFM+B0o3LkH+hvmIGMwwJCJt5ijKem0mXp5R7Kc83zjuSCAvH4hGYeA9kTkFbQQYHXg7CQvCNhuskRBsC99BRiSALMRgobiGw0/UEtvL9Pb8y3pWmt2GE447Eva8HLU92u6SlZaG+Z9+icUr1qhvpDvTm+VrtqbDax4IT9QBW0YOMrKy0VC1Bc6cHAR8PsTDPuTYQ8iIbUXQ98P7afZE+kr921cR+fZuepN8pX/yu+yOfHuIsfjtNqOsfvw+OM7SpUuV8FpbW9sMyjoczh+Cnha/Aj5ystzgVUvqURivZ86bWe+3335tH5oen+Ejx9Xv1/3Zb/pRJ6vzvY0U5r2bnijf3TUWM5y/1m+qx7/+Ow/Lvt0CtyeI7CwHhkwZiN+eNg39S3nEdtchxuLdh8vBSMCDluYmmA0JTBo/FpdeeikOOuigZIxtuebaa/HaW+8hK7cAVkc6whFtVozVagHvz+WnBtf4kUPw9N+eVP6pfPDBB7jx9nuoxda9F1ET+Yp8t4fUv92f3ipfs8mIEaWZyHda6coAI+nCLYEoapv98MTDSJhJZ7by3nkJxCJxGC0JdU8oHkOuwwYf+cViEYT9PKrXCIvViEALL1ktxuI9zYXnnIWzfn4abDaW3c4RjUbx3uz/4a+P/g0+v7+tDdMZrINxO+gnJx+PS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\ No newline at end of file diff --git a/docs/diagrams/registry.puml b/docs/diagrams/registry.puml new file mode 100644 index 00000000..51a337fa --- /dev/null +++ b/docs/diagrams/registry.puml @@ -0,0 +1,40 @@ +@startuml +!include config.puml + +title netdata registry operation +actor "web browser" as user +participant "netdata 1" as n1 +participant "registry 1" as r1 +autonumber "<b>0." + +== standard dashboard communication == + +user ->n1 : \ + hi, give me the dashboard + +n1 --> user : \ + welcome, here it is... + +... a few seconds later ... + +== registry related communication == + +user -> n1 : \ + now give me registry information + +n1 --> user: \ + here it is, talk to <b>registry 1</b> + +note left of r1 #eee: \ + only your web browser \n\ + talks to the registry + +user -> r1 : \ + Hey <b>registry 1</b>, \ +I am accessing <b>netdata 1</b>... + +r1 --> user : \ + nice!, here are other netdata servers \ +you have accessed in the past + +@enduml diff --git a/docs/diagrams/simple-parent-child-no-cloud.xml b/docs/diagrams/simple-parent-child-no-cloud.xml new file mode 100644 index 00000000..69ad3112 --- /dev/null +++ b/docs/diagrams/simple-parent-child-no-cloud.xml @@ -0,0 +1,125 @@ +<mxfile host="app.diagrams.net" modified="2023-03-30T15:35:30.235Z" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36" etag="69YYMeBeNNOuD-JN0AOX" version="21.1.2" type="device"> + <diagram name="Page-1" id="0"> + <mxGraphModel dx="2924" dy="1116" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0"> + <root> + <mxCell id="0" style=";html=1;" /> + <mxCell id="1" style=";html=1;" parent="0" /> + <mxCell id="630" value="" 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Restart your Agent with `sudo systemctl restart netdata`, or -the [appropriate method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system, to begin exporting to your OpenTSDB +the [appropriate method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, to begin exporting to your OpenTSDB database. The Netdata Agent exports metrics _beginning from the time the process starts_, and because it exports as metrics are collected, you should start seeing data in your external database after only a few seconds. Any further configuration is optional, based on your needs and the configuration of your OpenTSDB database. See the -[OpenTSDB connector doc](https://github.com/netdata/netdata/blob/master/exporting/opentsdb/README.md) -and [exporting engine reference](https://github.com/netdata/netdata/blob/master/exporting/README.md#configuration) for +[OpenTSDB connector doc](https://github.com/netdata/netdata/blob/master/src/exporting/opentsdb/README.md) +and [exporting engine reference](https://github.com/netdata/netdata/blob/master/src/exporting/README.md#configuration) for details. ## Example: Enable the Graphite connector @@ -78,28 +78,28 @@ Replace `my_graphite_instance` with an instance name of your choice, and change address or hostname of your Graphite-supported database. Restart your Agent with `sudo systemctl restart netdata`, or -the [appropriate method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system, to begin exporting to your +the [appropriate method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, to begin exporting to your Graphite-supported database. Because the Agent exports metrics as they're collected, you should start seeing data in your external database after only a few seconds. Any further configuration is optional, based on your needs and the configuration of your Graphite-supported database. -See [exporting engine reference](https://github.com/netdata/netdata/blob/master/exporting/README.md#configuration) for +See [exporting engine reference](https://github.com/netdata/netdata/blob/master/src/exporting/README.md#configuration) for details. ## What's next? If you want to further configure your exporting connectors, see -the [exporting engine reference](https://github.com/netdata/netdata/blob/master/exporting/README.md#configuration). +the [exporting engine reference](https://github.com/netdata/netdata/blob/master/src/exporting/README.md#configuration). For a comprehensive example of using the Graphite connector, read our documentation on -[exporting metrics to Graphite providers](https://github.com/netdata/netdata/blob/master/exporting/graphite/README.md). Or, start +[exporting metrics to Graphite providers](https://github.com/netdata/netdata/blob/master/src/exporting/graphite/README.md). Or, start [using host labels](https://github.com/netdata/netdata/blob/master/docs/guides/using-host-labels.md) on exported metrics. ### Related reference documentation -- [Exporting engine reference](https://github.com/netdata/netdata/blob/master/exporting/README.md) -- [OpenTSDB connector](https://github.com/netdata/netdata/blob/master/exporting/opentsdb/README.md) -- [Graphite connector](https://github.com/netdata/netdata/blob/master/exporting/graphite/README.md) +- [Exporting engine reference](https://github.com/netdata/netdata/blob/master/src/exporting/README.md) +- [OpenTSDB connector](https://github.com/netdata/netdata/blob/master/src/exporting/opentsdb/README.md) +- [Graphite connector](https://github.com/netdata/netdata/blob/master/src/exporting/graphite/README.md) diff --git a/docs/export/external-databases.md b/docs/export/external-databases.md index 715e8660..1679947c 100644 --- a/docs/export/external-databases.md +++ b/docs/export/external-databases.md @@ -11,7 +11,7 @@ learn_rel_path: "Concepts" # Export metrics to external time-series databases Netdata allows you to export metrics to external time-series databases with the [exporting -engine](https://github.com/netdata/netdata/blob/master/exporting/README.md). This system uses a number of **connectors** to initiate connections to [more than +engine](https://github.com/netdata/netdata/blob/master/src/exporting/README.md). This system uses a number of **connectors** to initiate connections to [more than thirty](#supported-databases) supported databases, including InfluxDB, Prometheus, Graphite, ElasticSearch, and much more. @@ -29,48 +29,48 @@ analysis, or correlation with other tools, such as application tracing. ## Supported databases Netdata supports exporting metrics to the following databases through several -[connectors](https://github.com/netdata/netdata/blob/master/exporting/README.md#features). Once you find the connector that works for your database, open its +[connectors](https://github.com/netdata/netdata/blob/master/src/exporting/README.md#features). Once you find the connector that works for your database, open its documentation and the [enabling a connector](https://github.com/netdata/netdata/blob/master/docs/export/enable-connector.md) doc for details on enabling it. -- **AppOptics**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **AWS Kinesis**: [AWS Kinesis Data Streams](https://github.com/netdata/netdata/blob/master/exporting/aws_kinesis/README.md) -- **Azure Data Explorer**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Azure Event Hubs**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Blueflood**: [Graphite](https://github.com/netdata/netdata/blob/master/exporting/graphite/README.md) -- **Chronix**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Cortex**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **CrateDB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **ElasticSearch**: [Graphite](https://github.com/netdata/netdata/blob/master/exporting/graphite/README.md), [Prometheus remote - write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Gnocchi**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Google BigQuery**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Google Cloud Pub/Sub**: [Google Cloud Pub/Sub Service](https://github.com/netdata/netdata/blob/master/exporting/pubsub/README.md) -- **Graphite**: [Graphite](https://github.com/netdata/netdata/blob/master/exporting/graphite/README.md), [Prometheus remote - write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **InfluxDB**: [Graphite](https://github.com/netdata/netdata/blob/master/exporting/graphite/README.md), [Prometheus remote - write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **IRONdb**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **JSON**: [JSON document databases](https://github.com/netdata/netdata/blob/master/exporting/json/README.md) -- **Kafka**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **KairosDB**: [Graphite](https://github.com/netdata/netdata/blob/master/exporting/graphite/README.md), [OpenTSDB](https://github.com/netdata/netdata/blob/master/exporting/opentsdb/README.md) -- **M3DB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **MetricFire**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **MongoDB**: [MongoDB](https://github.com/netdata/netdata/blob/master/exporting/mongodb/README.md) -- **New Relic**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **OpenTSDB**: [OpenTSDB](https://github.com/netdata/netdata/blob/master/exporting/opentsdb/README.md), [Prometheus remote - write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **PostgreSQL**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) +- **AppOptics**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **AWS Kinesis**: [AWS Kinesis Data Streams](https://github.com/netdata/netdata/blob/master/src/exporting/aws_kinesis/README.md) +- **Azure Data Explorer**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Azure Event Hubs**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Blueflood**: [Graphite](https://github.com/netdata/netdata/blob/master/src/exporting/graphite/README.md) +- **Chronix**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Cortex**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **CrateDB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **ElasticSearch**: [Graphite](https://github.com/netdata/netdata/blob/master/src/exporting/graphite/README.md), [Prometheus remote + write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Gnocchi**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Google BigQuery**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Google Cloud Pub/Sub**: [Google Cloud Pub/Sub Service](https://github.com/netdata/netdata/blob/master/src/exporting/pubsub/README.md) +- **Graphite**: [Graphite](https://github.com/netdata/netdata/blob/master/src/exporting/graphite/README.md), [Prometheus remote + write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **InfluxDB**: [Graphite](https://github.com/netdata/netdata/blob/master/src/exporting/graphite/README.md), [Prometheus remote + write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **IRONdb**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **JSON**: [JSON document databases](https://github.com/netdata/netdata/blob/master/src/exporting/json/README.md) +- **Kafka**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **KairosDB**: [Graphite](https://github.com/netdata/netdata/blob/master/src/exporting/graphite/README.md), [OpenTSDB](https://github.com/netdata/netdata/blob/master/src/exporting/opentsdb/README.md) +- **M3DB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **MetricFire**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **MongoDB**: [MongoDB](https://github.com/netdata/netdata/blob/master/src/exporting/mongodb/README.md) +- **New Relic**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **OpenTSDB**: [OpenTSDB](https://github.com/netdata/netdata/blob/master/src/exporting/opentsdb/README.md), [Prometheus remote + write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **PostgreSQL**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) via [PostgreSQL Prometheus Adapter](https://github.com/CrunchyData/postgresql-prometheus-adapter) -- **Prometheus**: [Prometheus scraper](https://github.com/netdata/netdata/blob/master/exporting/prometheus/README.md) -- **TimescaleDB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md), - [netdata-timescale-relay](https://github.com/netdata/netdata/blob/master/exporting/TIMESCALE.md) -- **QuasarDB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **SignalFx**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Splunk**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **TiKV**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Thanos**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **VictoriaMetrics**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) -- **Wavefront**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/exporting/prometheus/remote_write/README.md) +- **Prometheus**: [Prometheus scraper](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/README.md) +- **TimescaleDB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md), + [netdata-timescale-relay](https://github.com/netdata/netdata/blob/master/src/exporting/TIMESCALE.md) +- **QuasarDB**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **SignalFx**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Splunk**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **TiKV**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Thanos**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **VictoriaMetrics**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) +- **Wavefront**: [Prometheus remote write](https://github.com/netdata/netdata/blob/master/src/exporting/prometheus/remote_write/README.md) Can't find your preferred external time-series database? Ask our [community](https://community.netdata.cloud/) for solutions, or file an [issue on diff --git a/docs/getting-started/introduction.md b/docs/getting-started/introduction.md index 5586bff2..65fd059d 100644 --- a/docs/getting-started/introduction.md +++ b/docs/getting-started/introduction.md @@ -63,7 +63,7 @@ Netdata works with tons of applications, notifications platforms, and other time - **300+ system, container, and application endpoints**: Collectors autodetect metrics from default endpoints and immediately visualize them into meaningful charts designed for troubleshooting. See [everything we - support](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md). + support](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md). - **20+ notification platforms**: Netdata's health watchdog sends warning and critical alerts to your [favorite platform](https://github.com/netdata/netdata/blob/master/docs/monitor/enable-notifications.md) to inform you of anomalies just seconds after they affect your node. @@ -120,7 +120,7 @@ Here are a few ways to enrich your existing monitoring and troubleshooting stack ### Collect metrics from Prometheus endpoints Netdata automatically detects 600 popular endpoints and collects per-second metrics from them via the [generic -Prometheus collector](https://github.com/netdata/go.d.plugin/blob/master/modules/prometheus/README.md). This even +Prometheus collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/prometheus/README.md). This even includes support for Windows 10 via [`windows_exporter`](https://github.com/prometheus-community/windows_exporter). This collector is installed and enabled on all Agent installations by default, so you don't need to waste time diff --git a/docs/glossary.md b/docs/glossary.md index 26817d42..e25a6e6b 100644 --- a/docs/glossary.md +++ b/docs/glossary.md @@ -15,13 +15,13 @@ Use the alphabatized list below to find the answer to your single-term questions - [**Agent** or **Netdata Agent**](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md): Netdata's distributed monitoring Agent collects thousands of metrics from systems, hardware, and applications with zero configuration. It runs permanently on all your physical/virtual servers, containers, cloud deployments, and edge/IoT devices. -- [**Agent-cloud link** or **ACLK**](https://github.com/netdata/netdata/blob/master/aclk/README.md): The Agent-Cloud link (ACLK) is the mechanism responsible for securely connecting a Netdata Agent to your web browser through Netdata Cloud. +- [**Agent-cloud link** or **ACLK**](https://github.com/netdata/netdata/blob/master/src/aclk/README.md): The Agent-Cloud link (ACLK) is the mechanism responsible for securely connecting a Netdata Agent to your web browser through Netdata Cloud. - [**Aggregate Function**](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md#aggregate-functions-over-time): A function applied When the granularity of the data collected is higher than the plotted points on the chart. - [**Alerts** (formerly **Alarms**)](https://github.com/netdata/netdata/blob/master/docs/cloud/alerts-notifications/notifications.md): With the information that appears on Netdata Cloud and the local dashboard about active alerts, you can configure alerts to match your infrastructure's needs or your team's goals. -- [**Alarm Entity Type**](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#health-entity-reference): Entity types that are attached to specific charts and use the `alarm` label. +- [**Alarm Entity Type**](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#health-entity-reference): Entity types that are attached to specific charts and use the `alarm` label. - [**Anomaly Advisor**](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/anomaly-advisor.md): A Netdata feature that lets you quickly surface potentially anomalous metrics and charts related to a particular highlight window of interest. @@ -33,9 +33,9 @@ Use the alphabatized list below to find the answer to your single-term questions - [**Child**](https://github.com/netdata/netdata/blob/master/docs/metrics-storage-management/enable-streaming.md#streaming-basics): A node, running Netdata, that streams metric data to one or more parent. -- [**Cloud** or **Netdata Cloud**](https://github.com/netdata/netdata/blob/master/docs/quickstart/infrastructure.md): Netdata Cloud is a web application that gives you real-time visibility for your entire infrastructure. With Netdata Cloud, you can view key metrics, insightful charts, and active alerts from all your nodes in a single web interface. +- [**Cloud** or **Netdata Cloud**](https://github.com/netdata/netdata/blob/master/docs/netdata-cloud/README.md): Netdata Cloud is a web application that gives you real-time visibility for your entire infrastructure. With Netdata Cloud, you can view key metrics, insightful charts, and active alerts from all your nodes in a single web interface. -- [**Collector**](https://github.com/netdata/netdata/blob/master/collectors/README.md#collector-architecture-and-terminology): A catch-all term for any Netdata process that gathers metrics from an endpoint. +- [**Collector**](https://github.com/netdata/netdata/blob/master/src/collectors/README.md#collector-architecture-and-terminology): A catch-all term for any Netdata process that gathers metrics from an endpoint. - [**Community**](https://community.netdata.cloud/): As a company with a passion and genesis in open-source, we are not just very proud of our community, but we consider our users, fans, and chatters to be an imperative part of the Netdata experience and culture. @@ -43,7 +43,7 @@ Use the alphabatized list below to find the answer to your single-term questions - [**Context**](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md#contexts): A way of grouping charts by the types of metrics collected and dimensions displayed. It's kind of like a machine-readable naming and organization scheme. -- [**Custom dashboards**](https://github.com/netdata/netdata/blob/master/web/gui/custom/README.md) A dashboard that you can create using simple HTML (no javascript is required for basic dashboards). +- [**Custom dashboards**](https://github.com/netdata/netdata/blob/master/src/web/gui/custom/README.md) A dashboard that you can create using simple HTML (no javascript is required for basic dashboards). ## D @@ -53,11 +53,9 @@ Use the alphabatized list below to find the answer to your single-term questions - [**Dimension**](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md#dimensions): A dimension is a value that gets shown on a chart. -- [**Distributed Architecture**](https://github.com/netdata/netdata/blob/master/docs/store/distributed-data-architecture.md): The data architecture mindset with which Netdata was built, where all data are collected and stored on the edge, whenever it's possible, creating countless benefits. - ## E -- [**External Plugins**](https://github.com/netdata/netdata/blob/master/collectors/plugins.d/README.md): These gather metrics from external processes, such as a webserver or database, and run as independent processes that communicate with the Netdata daemon via pipes. +- [**External Plugins**](https://github.com/netdata/netdata/blob/master/src/collectors/plugins.d/README.md): These gather metrics from external processes, such as a webserver or database, and run as independent processes that communicate with the Netdata daemon via pipes. ## F @@ -77,15 +75,15 @@ Use the alphabatized list below to find the answer to your single-term questions - [**Headless Collector Streaming**](https://github.com/netdata/netdata/blob/master/docs/metrics-storage-management/enable-streaming.md#supported-streaming-configurations): Streaming configuration where child `A`, _without_ a database or web dashboard, streams metrics to parent `B`. -- [**Health Configuration Files**](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#edit-health-configuration-files): Files that you can edit to configure your Agent's health watchdog service. +- [**Health Configuration Files**](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#edit-health-configuration-files): Files that you can edit to configure your Agent's health watchdog service. -- [**Health Entity Reference**](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#health-entity-reference): +- [**Health Entity Reference**](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#health-entity-reference): - [**Home** tab](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/overview.md#home): Tab in Netdata Cloud that provides a predefined dashboard of relevant information about entities in the War Room. ## I -- [**Internal plugins**](https://github.com/netdata/netdata/blob/master/collectors/README.md#collector-architecture-and-terminology): These gather metrics from `/proc`, `/sys`, and other Linux kernel sources. They are written in `C` and run as threads within the Netdata daemon. +- [**Internal plugins**](https://github.com/netdata/netdata/blob/master/src/collectors/README.md#collector-architecture-and-terminology): These gather metrics from `/proc`, `/sys`, and other Linux kernel sources. They are written in `C` and run as threads within the Netdata daemon. ## K @@ -95,7 +93,7 @@ Use the alphabatized list below to find the answer to your single-term questions ## M -- [**Metrics Collection**](https://github.com/netdata/netdata/blob/master/collectors/README.md): With zero configuration, Netdata auto-detects thousands of data sources upon starting and immediately collects per-second metrics. Netdata can immediately collect metrics from these endpoints thanks to 300+ collectors, which all come pre-installed when you install Netdata. +- [**Metrics Collection**](https://github.com/netdata/netdata/blob/master/src/collectors/README.md): With zero configuration, Netdata auto-detects thousands of data sources upon starting and immediately collects per-second metrics. Netdata can immediately collect metrics from these endpoints thanks to 300+ collectors, which all come pre-installed when you install Netdata. - [**Metric Correlations**](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/metric-correlations.md): A Netdata feature that lets you quickly find metrics and charts related to a particular window of interest that you want to explore further. @@ -105,7 +103,7 @@ Use the alphabatized list below to find the answer to your single-term questions - [**Metrics Streaming Replication**](https://github.com/netdata/netdata/blob/master/docs/metrics-storage-management/enable-streaming.md): Each node running Netdata can stream the metrics it collects, in real time, to another node. Metric streaming allows you to replicate metrics data across multiple nodes, or centralize all your metrics data into a single time-series database (TSDB). -- [**Module**](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md#enable-and-disable-a-specific-collection-module): A type of collector. +- [**Module**](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md#enable-and-disable-a-specific-collection-module): A type of collector. ## N @@ -114,7 +112,7 @@ metrics, troubleshoot complex performance problems, and make data interoperable - [**Netdata Agent** or **Agent**](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md): Netdata's distributed monitoring Agent collects thousands of metrics from systems, hardware, and applications with zero configuration. It runs permanently on all your physical/virtual servers, containers, cloud deployments, and edge/IoT devices. -- [**Netdata Cloud** or **Cloud**](https://github.com/netdata/netdata/blob/master/docs/quickstart/infrastructure.md): Netdata Cloud is a web application that gives you real-time visibility for your entire infrastructure. With Netdata Cloud, you can view key metrics, insightful charts, and active alerts from all your nodes in a single web interface. +- [**Netdata Cloud** or **Cloud**](https://github.com/netdata/netdata/blob/master/docs/netdata-cloud/README.md): Netdata Cloud is a web application that gives you real-time visibility for your entire infrastructure. With Netdata Cloud, you can view key metrics, insightful charts, and active alerts from all your nodes in a single web interface. - [**Netdata Functions** or **Functions**](https://github.com/netdata/netdata/blob/master/docs/cloud/netdata-functions.md): Routines exposed by a collector on the Netdata Agent that can bring additional information to support troubleshooting or trigger some action to happen on the node itself. @@ -128,7 +126,7 @@ metrics, troubleshoot complex performance problems, and make data interoperable - [**Obsoletion**(of nodes)](https://github.com/netdata/netdata/blob/master/docs/cloud/manage/organize-your-infrastrucutre-invite-your-team.md#obsoleting-offline-nodes-from-a-space): Removing nodes from a space. -- [**Orchestrators**](https://github.com/netdata/netdata/blob/master/collectors/README.md#collector-architecture-and-terminology): External plugins that run and manage one or more modules. They run as independent processes. +- [**Orchestrators**](https://github.com/netdata/netdata/blob/master/src/collectors/README.md#collector-architecture-and-terminology): External plugins that run and manage one or more modules. They run as independent processes. - [**Overview** tab](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/overview.md#overview-and-single-node-view): Tab in Netdata Cloud that uses composite charts. These charts display real-time aggregated metrics from all the nodes (or a filtered selection) in a given War Room. @@ -142,9 +140,9 @@ metrics, troubleshoot complex performance problems, and make data interoperable ## R -- [**Registry**](https://github.com/netdata/netdata/blob/master/registry/README.md): Registry that allows Netdata to provide unified cross-server dashboards. +- [**Registry**](https://github.com/netdata/netdata/blob/master/src/registry/README.md): Registry that allows Netdata to provide unified cross-server dashboards. -- [**Replication Streaming**](https://github.com/netdata/netdata/blob/master/streaming/README.md): Streaming configuration where child `A`, _with_ a database and web dashboard, streams metrics to parent `B`. +- [**Replication Streaming**](https://github.com/netdata/netdata/blob/master/src/streaming/README.md): Streaming configuration where child `A`, _with_ a database and web dashboard, streams metrics to parent `B`. - [**Room** or **War Room**](https://github.com/netdata/netdata/blob/master/docs/cloud/manage/organize-your-infrastrucutre-invite-your-team.md#netdata-cloud-war-rooms): War Rooms organize your connected nodes and provide infrastructure-wide dashboards using real-time metrics and visualizations. @@ -158,9 +156,9 @@ metrics, troubleshoot complex performance problems, and make data interoperable ## T -- [**Template Entity Type**](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#entity-types): Entity type that defines rules that apply to all charts of a specific context, and use the template label. Templates help you apply one entity to all disks, all network interfaces, all MySQL databases, and so on. +- [**Template Entity Type**](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#entity-types): Entity type that defines rules that apply to all charts of a specific context, and use the template label. Templates help you apply one entity to all disks, all network interfaces, all MySQL databases, and so on. -- [**Tiers**](https://github.com/netdata/netdata/blob/master/database/engine/README.md#tiers): Tiering is a mechanism of providing multiple tiers of data with different granularity of metrics (the frequency they are collected and stored, i.e. their resolution). +- [**Tiers**](https://github.com/netdata/netdata/blob/master/src/database/engine/README.md#tiers): Tiering is a mechanism of providing multiple tiers of data with different granularity of metrics (the frequency they are collected and stored, i.e. their resolution). ## U diff --git a/docs/guides/collect-apache-nginx-web-logs.md b/docs/guides/collect-apache-nginx-web-logs.md index f5e37442..05521993 100644 --- a/docs/guides/collect-apache-nginx-web-logs.md +++ b/docs/guides/collect-apache-nginx-web-logs.md @@ -8,7 +8,7 @@ You can use the [LTSV log format](http://ltsv.org/), track TLS and cipher usage, ever. In one test on a system with SSD storage, the collector consistently parsed the logs for 200,000 requests in 200ms, using ~30% of a single core. -The [web_log](https://github.com/netdata/go.d.plugin/blob/master/modules/weblog/README.md) collector is currently compatible +The [web_log](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/weblog/README.md) collector is currently compatible with [Nginx](https://nginx.org/en/) and [Apache](https://httpd.apache.org/). This guide will walk you through using the new Go-based web log collector to turn the logs these web servers @@ -82,7 +82,7 @@ jobs: ``` Restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system. Netdata should pick up your web server's access log and +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system. Netdata should pick up your web server's access log and begin showing real-time charts! ### Custom log formats and fields @@ -91,7 +91,7 @@ The web log collector is capable of parsing custom Nginx and Apache log formats leave that topic for a separate guide. We do have [extensive -documentation](https://github.com/netdata/go.d.plugin/blob/master/modules/weblog/README.md#custom-log-format) on how +documentation](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/weblog/README.md#custom-log-format) on how to build custom parsing for Nginx and Apache logs. ## Tweak web log collector alerts @@ -100,7 +100,7 @@ Over time, we've created some default alerts for web log monitoring. These alert web server is receiving more than 120 requests per minute. Otherwise, there's simply not enough data to make conclusions about what is "too few" or "too many." -- [web log alerts](https://raw.githubusercontent.com/netdata/netdata/master/health/health.d/web_log.conf). +- [web log alerts](https://raw.githubusercontent.com/netdata/netdata/master/src/health/health.d/web_log.conf). You can also edit this file directly with `edit-config`: @@ -109,4 +109,4 @@ You can also edit this file directly with `edit-config`: ``` For more information about editing the defaults or writing new alert entities, see our -[health monitoring documentation](https://github.com/netdata/netdata/blob/master/health/README.md). +[health monitoring documentation](https://github.com/netdata/netdata/blob/master/src/health/README.md). diff --git a/docs/guides/collect-unbound-metrics.md b/docs/guides/collect-unbound-metrics.md index c5f4deb5..5467592a 100644 --- a/docs/guides/collect-unbound-metrics.md +++ b/docs/guides/collect-unbound-metrics.md @@ -59,7 +59,7 @@ You may not need to do any more configuration to have Netdata collect your Unbou If you followed the steps above to enable `remote-control` and make your Unbound files readable by Netdata, that should be enough. Restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system. You should see Unbound metrics in your Netdata +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system. You should see Unbound metrics in your Netdata dashboard!  @@ -104,13 +104,13 @@ Netdata will attempt to read `unbound.conf` to get the appropriate `address`, `c `tls_key` parameters. Restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system. +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system. ### Manual setup for a remote Unbound server Collecting metrics from remote Unbound servers requires manual configuration. There are too many possibilities to cover all remote connections here, but the [default `unbound.conf` -file](https://github.com/netdata/go.d.plugin/blob/master/config/go.d/unbound.conf) contains a few useful examples: +file](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/config/go.d/unbound.conf) contains a few useful examples: ```yaml jobs: @@ -132,7 +132,7 @@ jobs: ``` To see all the available options, see the default [unbound.conf -file](https://github.com/netdata/go.d.plugin/blob/master/config/go.d/unbound.conf). +file](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/config/go.d/unbound.conf). ## What's next? diff --git a/docs/guides/configure/performance.md b/docs/guides/configure/performance.md index 2e5e105f..e1b32778 100644 --- a/docs/guides/configure/performance.md +++ b/docs/guides/configure/performance.md @@ -1,103 +1,139 @@ # How to optimize the Netdata Agent's performance We designed the Netdata Agent to be incredibly lightweight, even when it's collecting a few thousand dimensions every -second and visualizing that data into hundreds of charts. However, the default settings of the Netdata Agent are not -optimized for performance, but for a simple, standalone setup. We want the first install to give you something you can -run without any configuration. Most of the settings and options are enabled, since we want you to experience the full thing. +second and visualizing that data into hundreds of charts. However, the default settings of the Netdata Agent are not +optimized for performance, but for a simple, standalone setup. We want the first install to give you something you can +run without any configuration. Most of the settings and options are enabled, since we want you to experience the full +thing. -By default, Netdata will automatically detect applications running on the node it is installed to start collecting metrics in -real-time, has health monitoring enabled to evaluate alerts and trains Machine Learning (ML) models for each metric, to detect anomalies. +By default, Netdata will automatically detect applications running on the node it is installed to start collecting +metrics in real-time, has health monitoring enabled to evaluate alerts and trains Machine Learning (ML) models for each +metric, to detect anomalies. -This document describes the resources required for the various default capabilities and the strategies to optimize Netdata for production use. +This document describes the resources required for the various default capabilities and the strategies to optimize +Netdata for production use. ## Summary of performance optimizations The following table summarizes the effect of each optimization on the CPU, RAM and Disk IO utilization in production. -Optimization | CPU | RAM | Disk IO --- | -- | -- |-- -[Use streaming and replication](#use-streaming-and-replication) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: -[Disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: -[Reduce data collection frequency](#reduce-collection-frequency) | :heavy_check_mark: | | :heavy_check_mark: -[Change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md) | | :heavy_check_mark: | :heavy_check_mark: -[Use a different metric storage database](https://github.com/netdata/netdata/blob/master/database/README.md) | | :heavy_check_mark: | :heavy_check_mark: -[Disable machine learning](#disable-machine-learning) | :heavy_check_mark: | | -[Use a reverse proxy](#run-netdata-behind-a-proxy) | :heavy_check_mark: | | -[Disable/lower gzip compression for the agent dashboard](#disablelower-gzip-compression-for-the-dashboard) | :heavy_check_mark: | | +| Optimization | CPU | RAM | Disk IO | +|-------------------------------------------------------------------------------------------------------------------------------|--------------------|--------------------|--------------------| +| [Use streaming and replication](#use-streaming-and-replication) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | +| [Disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | +| [Reduce data collection frequency](#reduce-collection-frequency) | :heavy_check_mark: | | :heavy_check_mark: | +| [Change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md) | | :heavy_check_mark: | :heavy_check_mark: | +| [Use a different metric storage database](https://github.com/netdata/netdata/blob/master/src/database/README.md) | | :heavy_check_mark: | :heavy_check_mark: | +| [Disable machine learning](#disable-machine-learning) | :heavy_check_mark: | | | +| [Use a reverse proxy](#run-netdata-behind-a-proxy) | :heavy_check_mark: | | | +| [Disable/lower gzip compression for the agent dashboard](#disablelower-gzip-compression-for-the-dashboard) | :heavy_check_mark: | | | ## Resources required by a default Netdata installation -Netdata's performance is primarily affected by **data collection/retention** and **clients accessing data**. +Netdata's performance is primarily affected by **data collection/retention** and **clients accessing data**. -You can configure almost all aspects of data collection/retention, and certain aspects of clients accessing data. +You can configure almost all aspects of data collection/retention, and certain aspects of clients accessing data. ### CPU consumption Expect about: - - 1-3% of a single core for the netdata core - - 1-3% of a single core for the various collectors (e.g. go.d.plugin, apps.plugin) - - 5-10% of a single core, when ML training runs -Your experience may vary depending on the number of metrics collected, the collectors enabled and the specific environment they -run on, i.e. the work they have to do to collect these metrics. +- 1-3% of a single core for the netdata core +- 1-3% of a single core for the various collectors (e.g. go.d.plugin, apps.plugin) +- 5-10% of a single core, when ML training runs -As a general rule, for modern hardware and VMs, the total CPU consumption of a standalone Netdata installation, including all its components, -should be below 5 - 15% of a single core. For example, on 8 core server it will use only 0.6% - 1.8% of a total CPU capacity, depending on -the CPU characteristics. +Your experience may vary depending on the number of metrics collected, the collectors enabled and the specific +environment they run on, i.e. the work they have to do to collect these metrics. -The Netdata Agent runs with the lowest possible [process scheduling policy](https://github.com/netdata/netdata/blob/master/daemon/README.md#netdata-process-scheduling-policy), which is `nice 19`, and uses the `idle` process scheduler. -Together, these settings ensure that the Agent only gets CPU resources when the node has CPU resources to space. If the -node reaches 100% CPU utilization, the Agent is stopped first to ensure your applications get any available resources. +As a general rule, for modern hardware and VMs, the total CPU consumption of a standalone Netdata installation, +including all its components, should be below 5 - 15% of a single core. For example, on 8 core server it will use only +0.6% - 1.8% of a total CPU capacity, depending on the CPU characteristics. -To reduce CPU usage you can [disable machine learning](#disable-machine-learning), -[use streaming and replication](#use-streaming-and-replication), -[reduce the data collection frequency](#reduce-collection-frequency), [disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors), [use a reverse proxy](#run-netdata-behind-a-proxy), and [disable/lower gzip compression for the agent dashboard](#disablelower-gzip-compression-for-the-dashboard). +The Netdata Agent runs with the lowest +possible [process scheduling policy](https://github.com/netdata/netdata/blob/master/src/daemon/README.md#netdata-process-scheduling-policy), +which is `nice 19`, and uses the `idle` process scheduler. Together, these settings ensure that the Agent only gets CPU +resources when the node has CPU resources to space. If the node reaches 100% CPU utilization, the Agent is stopped first +to ensure your applications get any available resources. + +To reduce CPU usage you can (either one or a combination of the following actions): + +1. [Disable machine learning](#disable-machine-learning), +2. [Use streaming and replication](#use-streaming-and-replication), +3. [Reduce the data collection frequency](#reduce-collection-frequency) +4. [Disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors) +5. [Use a reverse proxy](#run-netdata-behind-a-proxy), +6. [Disable/lower gzip compression for the agent dashboard](#disablelower-gzip-compression-for-the-dashboard). ### Memory consumption -The memory footprint of Netdata is mainly influenced by the number of metrics concurrently being collected. Expect about 150MB of RAM for a typical 64-bit server collecting about 2000 to 3000 metrics. +The memory footprint of Netdata is mainly influenced by the number of metrics concurrently being collected. Expect about +150MB of RAM for a typical 64-bit server collecting about 2000 to 3000 metrics. -To estimate and control memory consumption, you can [disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors), [change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md), or [use a different metric storage database](https://github.com/netdata/netdata/blob/master/database/README.md). +To estimate and control memory consumption, you can (either one or a combination of the following actions): +1. [Disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors) +2. [Change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md) +3. [Use a different metric storage database](https://github.com/netdata/netdata/blob/master/src/database/README.md). ### Disk footprint and I/O -By default, Netdata should not use more than 1GB of disk space, most of which is dedicated for storing metric data and metadata. For typical installations collecting 2000 - 3000 metrics, this storage should provide a few days of high-resolution retention (per second), about a month of mid-resolution retention (per minute) and more than a year of low-resolution retention (per hour). +By default, Netdata should not use more than 1GB of disk space, most of which is dedicated for storing metric data and +metadata. For typical installations collecting 2000 - 3000 metrics, this storage should provide a few days of +high-resolution retention (per second), about a month of mid-resolution retention (per minute) and more than a year of +low-resolution retention (per hour). + +Netdata spreads I/O operations across time. For typical standalone installations there should be a few write operations +every 5-10 seconds of a few kilobytes each, occasionally up to 1MB. In addition, under heavy load, collectors that +require disk I/O may stop and show gaps in charts. + +To optimize your disk footprint in any aspect described below you can: + + +To configure retention, you can: -Netdata spreads I/O operations across time. For typical standalone installations there should be a few write operations every 5-10 seconds of a few kilobytes each, occasionally up to 1MB. In addition, under heavy load, collectors that require disk I/O may stop and show gaps in charts. +1. [Change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md). -To configure retention, you can [change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md). -To control disk I/O [use a different metric storage database](https://github.com/netdata/netdata/blob/master/database/README.md), avoid querying the -production system [using streaming and replication](#use-streaming-and-replication), [reduce the data collection frequency](#reduce-collection-frequency), and [disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors). +To control disk I/O: + +1. [Use a different metric storage database](https://github.com/netdata/netdata/blob/master/src/database/README.md), + + +Minimize deployment impact on the production system by optimizing disk footprint: + +1. [Using streaming and replication](#use-streaming-and-replication) +2. [Reduce the data collection frequency](#reduce-collection-frequency) +3. [Disable unneeded plugins or collectors](#disable-unneeded-plugins-or-collectors). ## Use streaming and replication -For all production environments, parent Netdata nodes outside the production infrastructure should be receiving all -collected data from children Netdata nodes running on the production infrastructure, using [streaming and replication](https://github.com/netdata/netdata/blob/master/docs/metrics-storage-management/enable-streaming.md). +For all production environments, parent Netdata nodes outside the production infrastructure should be receiving all +collected data from children Netdata nodes running on the production infrastructure, +using [streaming and replication](https://github.com/netdata/netdata/blob/master/docs/metrics-storage-management/enable-streaming.md). ### Disable health checks on the child nodes -When you set up streaming, we recommend you run your health checks on the parent. This saves resources on the children +When you set up streaming, we recommend you run your health checks on the parent. This saves resources on the children and makes it easier to configure or disable alerts and agent notifications. -The parents by default run health checks for each child, as long as the child is connected (the details are in `stream.conf`). -On the child nodes you should add to `netdata.conf` the following: +The parents by default run health checks for each child, as long as the child is connected (the details are +in `stream.conf`). On the child nodes you should add to `netdata.conf` the following: ```conf [health] enabled = no ``` -### Use memory mode ram or save for the child nodes +### Use memory mode ram for the child nodes -See [using a different metric storage database](https://github.com/netdata/netdata/blob/master/database/README.md). +See [using a different metric storage database](https://github.com/netdata/netdata/blob/master/src/database/README.md). ## Disable unneeded plugins or collectors If you know that you don't need an [entire plugin or a specific -collector](https://github.com/netdata/netdata/blob/master/collectors/README.md#collector-architecture-and-terminology), you can disable any of them. -Keep in mind that if a plugin/collector has nothing to do, it simply shuts down and does not consume system resources. -You will only improve the Agent's performance by disabling plugins/collectors that are actively collecting metrics. +collector](https://github.com/netdata/netdata/blob/master/src/collectors/README.md#collector-architecture-and-terminology), +you can disable any of them. Keep in mind that if a plugin/collector has nothing to do, it simply shuts down and does +not consume system resources. You will only improve the Agent's performance by disabling plugins/collectors that are +actively collecting metrics. Open `netdata.conf` and scroll down to the `[plugins]` section. To disable any plugin, uncomment it and set the value to `no`. For example, to explicitly keep the `proc` and `go.d` plugins enabled while disabling `python.d` and `charts.d`. @@ -135,7 +171,8 @@ The fastest way to improve the Agent's resource utilization is to reduce how oft ### Global If you don't need per-second metrics, or if the Netdata Agent uses a lot of CPU even when no one is viewing that node's -dashboard, [configure the Agent](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md) to collect metrics less often. +dashboard, [configure the Agent](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md) to collect +metrics less often. Open `netdata.conf` and edit the `update every` setting. The default is `1`, meaning that the Agent collects metrics every second. @@ -153,21 +190,23 @@ seconds, respectively. Every collector and plugin has its own `update every` setting, which you can also change in the `go.d.conf`, `python.d.conf`, or `charts.d.conf` files, or in individual collector configuration files. If the `update -every` for an individual collector is less than the global, the Netdata Agent uses the global setting. See the [collectors configuration reference](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md) for details. +every` for an individual collector is less than the global, the Netdata Agent uses the global setting. See +the [collectors configuration reference](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md) for +details. -To reduce the frequency of an [internal -plugin/collector](https://github.com/netdata/netdata/blob/master/collectors/README.md#collector-architecture-and-terminology), open `netdata.conf` and -find the appropriate section. For example, to reduce the frequency of the `apps` plugin, which collects and visualizes -metrics on application resource utilization: +To reduce the frequency of +an [internal_plugin/collector](https://github.com/netdata/netdata/blob/master/src/collectors/README.md#collector-architecture-and-terminology), +open `netdata.conf` and find the appropriate section. For example, to reduce the frequency of the `apps` plugin, which +collects and visualizes metrics on application resource utilization: ```conf [plugin:apps] update every = 5 ``` -To [configure an individual collector](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md#configure-a-collector), open its specific configuration file with -`edit-config` and look for the `update_every` setting. For example, to reduce the frequency of the `nginx` collector, -run `sudo ./edit-config go.d/nginx.conf`: +To [configure an individual collector](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md#configure-a-collector), +open its specific configuration file with `edit-config` and look for the `update_every` setting. For example, to reduce +the frequency of the `nginx` collector, run `sudo ./edit-config go.d/nginx.conf`: ```conf # [ GLOBAL ] @@ -176,30 +215,33 @@ update_every: 10 ## Lower memory usage for metrics retention -See how to [change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md). +See how +to [change how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md). ## Use a different metric storage database -Consider [using a different metric storage database](https://github.com/netdata/netdata/blob/master/database/README.md) when running Netdata on IoT devices, -and for children in a parent-child set up based on [streaming and replication](https://github.com/netdata/netdata/blob/master/docs/metrics-storage-management/enable-streaming.md). +Consider [using a different metric storage database](https://github.com/netdata/netdata/blob/master/src/database/README.md) +when running Netdata on IoT devices, and for children in a parent-child set up based +on [streaming and replication](https://github.com/netdata/netdata/blob/master/docs/metrics-storage-management/enable-streaming.md). ## Disable machine learning -Automated anomaly detection may be a powerful tool, but we recommend it to only be enabled on Netdata parents -that sit outside your production infrastructure, or if you have cpu and memory to spare. You can disable ML -with the following: +Automated anomaly detection may be a powerful tool, but we recommend it to only be enabled on Netdata parents that sit +outside your production infrastructure, or if you have cpu and memory to spare. You can disable ML with the following: ```conf [ml] enabled = no ``` - + ## Run Netdata behind a proxy -A dedicated web server like nginx provides more robustness than the Agent's internal [web server](https://github.com/netdata/netdata/blob/master/web/README.md). +A dedicated web server like nginx provides more robustness than the Agent's +internal [web server](https://github.com/netdata/netdata/blob/master/src/web/README.md). Nginx can handle more concurrent connections, reuse idle connections, and use fast gzip compression to reduce payloads. -For details on installing another web server as a proxy for the local Agent dashboard, see [reverse proxies](https://github.com/netdata/netdata/blob/master/docs/category-overview-pages/reverse-proxies.md). +For details on installing another web server as a proxy for the local Agent dashboard, +see [reverse proxies](https://github.com/netdata/netdata/blob/master/docs/category-overview-pages/reverse-proxies.md). ## Disable/lower gzip compression for the dashboard diff --git a/docs/guides/monitor-cockroachdb.md b/docs/guides/monitor-cockroachdb.md index d0db69ab..9d4d3ea0 100644 --- a/docs/guides/monitor-cockroachdb.md +++ b/docs/guides/monitor-cockroachdb.md @@ -11,7 +11,7 @@ learn_rel_path: "Miscellaneous" [CockroachDB](https://github.com/cockroachdb/cockroach) is an open-source project that brings SQL databases into scalable, disaster-resilient cloud deployments. Thanks to -a [new CockroachDB collector](https://github.com/netdata/go.d.plugin/blob/master/modules/cockroachdb/README.md) +a [new CockroachDB collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/cockroachdb/README.md) released in [v1.20](https://blog.netdata.cloud/posts/release-1.20/), you can now monitor any number of CockroachDB databases with maximum granularity using Netdata. Collect more than 50 unique metrics and put them on interactive visualizations @@ -38,7 +38,7 @@ display them on the dashboard. If your CockroachDB instance is accessible through `http://localhost:8080/` or `http://127.0.0.1:8080`, your setup is complete. Restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system, and refresh your browser. You should see CockroachDB +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, and refresh your browser. You should see CockroachDB metrics in your Netdata dashboard! <figure> @@ -106,7 +106,7 @@ Netdata to see your new charts. This release also includes eight pre-configured alerts for live nodes, such as whether the node is live, storage capacity, issues with replication, and the number of SQL connections/statements. See [health.d/cockroachdb.conf on -GitHub](https://raw.githubusercontent.com/netdata/netdata/master/health/health.d/cockroachdb.conf) for details. +GitHub](https://raw.githubusercontent.com/netdata/netdata/master/src/health/health.d/cockroachdb.conf) for details. You can also edit these files directly with `edit-config`: @@ -115,4 +115,4 @@ cd /etc/netdata/ # Replace with your Netdata configuration directory, if not /et ./edit-config health.d/cockroachdb.conf # You may need to use `sudo` for write privileges ``` -For more information about editing the defaults or writing new alert entities, see our documentation on [configuring health alerts](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md). +For more information about editing the defaults or writing new alert entities, see our documentation on [configuring health alerts](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md). diff --git a/docs/guides/monitor-hadoop-cluster.md b/docs/guides/monitor-hadoop-cluster.md index 1ddac85e..b536e0fa 100644 --- a/docs/guides/monitor-hadoop-cluster.md +++ b/docs/guides/monitor-hadoop-cluster.md @@ -27,8 +27,8 @@ alternative, like the guide available from For more specifics on the collection modules used in this guide, read the respective pages in our documentation: -- [HDFS](https://github.com/netdata/go.d.plugin/blob/master/modules/hdfs/README.md) -- [Zookeeper](https://github.com/netdata/go.d.plugin/blob/master/modules/zookeeper/README.md) +- [HDFS](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/hdfs/README.md) +- [Zookeeper](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/zookeeper/README.md) ## Set up your HDFS and Zookeeper installations @@ -164,7 +164,7 @@ jobs: address : 203.0.113.10:2182 ``` -Finally, [restart Netdata](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md). +Finally, [restart Netdata](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation). ```sh sudo systemctl restart netdata @@ -178,7 +178,7 @@ showing real-time metrics for both in your Netdata dashboard. 🎉 The Netdata community helped us create sane defaults for alerts related to both HDFS and Zookeeper. You may want to investigate these to ensure they work well with your Hadoop implementation. -- [HDFS alerts](https://raw.githubusercontent.com/netdata/netdata/master/health/health.d/hdfs.conf) +- [HDFS alerts](https://raw.githubusercontent.com/netdata/netdata/master/src/health/health.d/hdfs.conf) You can also access/edit these files directly with `edit-config`: @@ -188,4 +188,4 @@ sudo /etc/netdata/edit-config health.d/zookeeper.conf ``` For more information about editing the defaults or writing new alert entities, see our -[health monitoring documentation](https://github.com/netdata/netdata/blob/master/health/README.md). +[health monitoring documentation](https://github.com/netdata/netdata/blob/master/src/health/README.md). diff --git a/docs/guides/monitor/anomaly-detection.md b/docs/guides/monitor/anomaly-detection.md index c0a00ef3..bc19a4f2 100644 --- a/docs/guides/monitor/anomaly-detection.md +++ b/docs/guides/monitor/anomaly-detection.md @@ -15,7 +15,7 @@ learn_rel_path: "Operations" As of [`v1.32.0`](https://github.com/netdata/netdata/releases/tag/v1.32.0), Netdata comes with some ML powered [anomaly detection](https://en.wikipedia.org/wiki/Anomaly_detection) capabilities built into it and available to use out of the box, with zero configuration required (ML was enabled by default in `v1.35.0-29-nightly` in [this PR](https://github.com/netdata/netdata/pull/13158), previously it required a one line config change). -This means that in addition to collecting raw value metrics, the Netdata agent will also produce an [`anomaly-bit`](https://github.com/netdata/netdata/blob/master/ml/README.md#anomaly-bit---100--anomalous-0--normal) every second which will be `100` when recent raw metric values are considered anomalous by Netdata and `0` when they look normal. Once we aggregate beyond one second intervals this aggregated `anomaly-bit` becomes an ["anomaly rate"](https://github.com/netdata/netdata/blob/master/ml/README.md#anomaly-rate---averageanomaly-bit). +This means that in addition to collecting raw value metrics, the Netdata agent will also produce an [`anomaly-bit`](https://github.com/netdata/netdata/blob/master/src/ml/README.md#anomaly-bit---100--anomalous-0--normal) every second which will be `100` when recent raw metric values are considered anomalous by Netdata and `0` when they look normal. Once we aggregate beyond one second intervals this aggregated `anomaly-bit` becomes an ["anomaly rate"](https://github.com/netdata/netdata/blob/master/src/ml/README.md#anomaly-rate---averageanomaly-bit). To be as concrete as possible, the below api call shows how to access the raw anomaly bit of the `system.cpu` chart from the [london.my-netdata.io](https://london.my-netdata.io) Netdata demo server. Passing `options=anomaly-bit` returns the anomaly bit instead of the raw metric value. @@ -23,19 +23,19 @@ To be as concrete as possible, the below api call shows how to access the raw an https://london.my-netdata.io/api/v1/data?chart=system.cpu&options=anomaly-bit ``` -If we aggregate the above to just 1 point by adding `points=1` we get an "[Anomaly Rate](https://github.com/netdata/netdata/blob/master/ml/README.md#anomaly-rate---averageanomaly-bit)": +If we aggregate the above to just 1 point by adding `points=1` we get an "[Anomaly Rate](https://github.com/netdata/netdata/blob/master/src/ml/README.md#anomaly-rate---averageanomaly-bit)": ``` https://london.my-netdata.io/api/v1/data?chart=system.cpu&options=anomaly-bit&points=1 ``` -The fundamentals of Netdata's anomaly detection approach and implementation are covered in lots more detail in the [agent ML documentation](https://github.com/netdata/netdata/blob/master/ml/README.md). +The fundamentals of Netdata's anomaly detection approach and implementation are covered in lots more detail in the [agent ML documentation](https://github.com/netdata/netdata/blob/master/src/ml/README.md). This guide will explain how to get started using these ML based anomaly detection capabilities within Netdata. ## Anomaly Advisor -The [Anomaly Advisor](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/anomaly-advisor.md) is the flagship anomaly detection feature within Netdata. In the "Anomalies" tab of Netdata you will see an overall "Anomaly Rate" chart that aggregates node level anomaly rate for all nodes in a space. The aim of this chart is to make it easy to quickly spot periods of time where the overall "[node anomaly rate](https://github.com/netdata/netdata/blob/master/ml/README.md#node-anomaly-rate)" is elevated in some unusual way and for what node or nodes this relates to. +The [Anomaly Advisor](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/anomaly-advisor.md) is the flagship anomaly detection feature within Netdata. In the "Anomalies" tab of Netdata you will see an overall "Anomaly Rate" chart that aggregates node level anomaly rate for all nodes in a space. The aim of this chart is to make it easy to quickly spot periods of time where the overall "[node anomaly rate](https://github.com/netdata/netdata/blob/master/src/ml/README.md#node-anomaly-rate)" is elevated in some unusual way and for what node or nodes this relates to.  @@ -53,20 +53,20 @@ Pressing the anomalies icon (next to the information icon in the chart header) w ## Anomaly Rate Based Alerts -It is possible to use the `anomaly-bit` when defining traditional Alerts within netdata. The `anomaly-bit` is just another `options` parameter that can be passed as part of an [alert line lookup](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md#alert-line-lookup). +It is possible to use the `anomaly-bit` when defining traditional Alerts within netdata. The `anomaly-bit` is just another `options` parameter that can be passed as part of an alert line lookup. You can see some example ML based alert configurations below: -- [Anomaly rate based CPU dimensions alert](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#example-8---anomaly-rate-based-cpu-dimensions-alert) -- [Anomaly rate based CPU chart alert](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#example-9---anomaly-rate-based-cpu-chart-alert) -- [Anomaly rate based node level alert](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#example-10---anomaly-rate-based-node-level-alert) -- More examples in the [`/health/health.d/ml.conf`](https://github.com/netdata/netdata/blob/master/health/health.d/ml.conf) file that ships with the agent. +- [Anomaly rate based CPU dimensions alert](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#example-8---anomaly-rate-based-cpu-dimensions-alert) +- [Anomaly rate based CPU chart alert](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#example-9---anomaly-rate-based-cpu-chart-alert) +- [Anomaly rate based node level alert](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#example-10---anomaly-rate-based-node-level-alert) +- More examples in the [`/health/health.d/ml.conf`](https://github.com/netdata/netdata/blob/master/src/health/health.d/ml.conf) file that ships with the agent. ## Learn More Check out the resources below to learn more about how Netdata is approaching ML: -- [Agent ML documentation](https://github.com/netdata/netdata/blob/master/ml/README.md). +- [Agent ML documentation](https://github.com/netdata/netdata/blob/master/src/ml/README.md). - [Anomaly Advisor documentation](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/anomaly-advisor.md). - [Metric Correlations documentation](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/metric-correlations.md). - Anomaly Advisor [launch blog post](https://www.netdata.cloud/blog/introducing-anomaly-advisor-unsupervised-anomaly-detection-in-netdata/). diff --git a/docs/guides/monitor/kubernetes-k8s-netdata.md b/docs/guides/monitor/kubernetes-k8s-netdata.md index 96d79935..982c35e7 100644 --- a/docs/guides/monitor/kubernetes-k8s-netdata.md +++ b/docs/guides/monitor/kubernetes-k8s-netdata.md @@ -146,7 +146,7 @@ Let's explore the most colorful box by hovering over it. container](https://user-images.githubusercontent.com/1153921/109049544-a8417980-7695-11eb-80a7-109b4a645a27.png) The **Context** tab shows `rabbitmq-5bb66bb6c9-6xr5b` as the container's image name, which means this container is -running a [RabbitMQ](https://github.com/netdata/go.d.plugin/blob/master/modules/rabbitmq/README.md) workload. +running a [RabbitMQ](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/rabbitmq/README.md) workload. Click the **Metrics** tab to see real-time metrics from that container. Unsurprisingly, it shows a spike in CPU utilization at regular intervals. @@ -175,13 +175,13 @@ for complete customization. For example, grouping the top chart by `k8s_containe Netdata has a [service discovery plugin](https://github.com/netdata/agent-service-discovery), which discovers and creates configuration files for [compatible services](https://github.com/netdata/helmchart#service-discovery-and-supported-services) and any endpoints covered by -our [generic Prometheus collector](https://github.com/netdata/go.d.plugin/blob/master/modules/prometheus/README.md). +our [generic Prometheus collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/prometheus/README.md). Netdata uses these files to collect metrics from any compatible application as they run _inside_ of a pod. Service discovery happens without manual intervention as pods are created, destroyed, or moved between nodes. Service metrics show up on the Overview as well, beneath the **Kubernetes** section, and are labeled according to the service in question. For example, the **RabbitMQ** section has numerous charts from the [`rabbitmq` -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/rabbitmq/README.md): +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/rabbitmq/README.md):  @@ -202,7 +202,7 @@ Netdata also automatically collects metrics from two essential Kubernetes proces The **k8s kubelet** section visualizes metrics from the Kubernetes agent responsible for managing every pod on a given node. This also happens without any configuration thanks to the [kubelet -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/k8s_kubelet/README.md). +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/k8s_kubelet/README.md). Monitoring each node's kubelet can be invaluable when diagnosing issues with your Kubernetes cluster. For example, you can see if the number of running containers/pods has dropped, which could signal a fault or crash in a particular @@ -218,7 +218,7 @@ configuration-related errors, and the actual vs. desired numbers of volumes, plu The **k8s kube-proxy** section displays metrics about the network proxy that runs on each node in your Kubernetes cluster. kube-proxy lets pods communicate with each other and accept sessions from outside your cluster. Its metrics are collected by the [kube-proxy -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/k8s_kubeproxy/README.md). +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/k8s_kubeproxy/README.md). With Netdata, you can monitor how often your k8s proxies are syncing proxy rules between nodes. Dramatic changes in these figures could indicate an anomaly in your cluster that's worthy of further investigation. @@ -238,9 +238,9 @@ clusters of all sizes. - [Netdata Helm chart](https://github.com/netdata/helmchart) - [Netdata service discovery](https://github.com/netdata/agent-service-discovery) - [Netdata Agent · `kubelet` - collector](https://github.com/netdata/go.d.plugin/blob/master/modules/k8s_kubelet/README.md) + collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/k8s_kubelet/README.md) - [Netdata Agent · `kube-proxy` - collector](https://github.com/netdata/go.d.plugin/blob/master/modules/k8s_kubeproxy/README.md) -- [Netdata Agent · `cgroups.plugin`](https://github.com/netdata/netdata/blob/master/collectors/cgroups.plugin/README.md) + collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/k8s_kubeproxy/README.md) +- [Netdata Agent · `cgroups.plugin`](https://github.com/netdata/netdata/blob/master/src/collectors/cgroups.plugin/README.md) diff --git a/docs/guides/monitor/lamp-stack.md b/docs/guides/monitor/lamp-stack.md index 2289c71c..cc649dba 100644 --- a/docs/guides/monitor/lamp-stack.md +++ b/docs/guides/monitor/lamp-stack.md @@ -64,12 +64,12 @@ replacing `NODE` with the hostname or IP address of your system. There's nothing you need to do to enable [system monitoring](https://github.com/netdata/netdata/blob/master/docs/collect/system-metrics.md) and Linux monitoring with the Netdata Agent, which autodetects metrics from CPUs, memory, disks, networking devices, and Linux processes like systemd without any configuration. If you're using containers, Netdata automatically collects resource utilization -metrics from each using the [cgroups data collector](https://github.com/netdata/netdata/blob/master/collectors/cgroups.plugin/README.md). +metrics from each using the [cgroups data collector](https://github.com/netdata/netdata/blob/master/src/collectors/cgroups.plugin/README.md). ## Enable Apache monitoring Let's begin by configuring Apache to work with Netdata's [Apache data -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/apache/README.md). +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/apache/README.md). Actually, there's nothing for you to do to enable Apache monitoring with Netdata. @@ -80,7 +80,7 @@ metrics](https://httpd.apache.org/docs/2.4/mod/mod_status.html), which is just _ ## Enable web log monitoring The Netdata Agent also comes with a [web log -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/weblog/README.md), which reads Apache's access +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/weblog/README.md), which reads Apache's access log file, processes each line, and converts them into per-second metrics. On Debian systems, it reads the file at `/var/log/apache2/access.log`. @@ -93,7 +93,7 @@ monitoring. Because your MySQL database is password-protected, you do need to tell MySQL to allow the `netdata` user to connect to without a password. Netdata's [MySQL data -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/mysql/README.md) collects metrics in _read-only_ +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/mysql/README.md) collects metrics in _read-only_ mode, without being able to alter or affect operations in any way. First, log into the MySQL shell. Then, run the following three commands, one at a time: @@ -105,15 +105,15 @@ FLUSH PRIVILEGES; ``` Run `sudo systemctl restart netdata`, or the [appropriate alternative for your -system](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md), to collect dozens of metrics every second for robust MySQL monitoring. +system](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation), to collect dozens of metrics every second for robust MySQL monitoring. ## Enable PHP monitoring Unlike Apache or MySQL, PHP isn't a service that you can monitor directly, unless you instrument a PHP-based application -with [StatsD](https://github.com/netdata/netdata/blob/master/collectors/statsd.plugin/README.md). +with [StatsD](https://github.com/netdata/netdata/blob/master/src/collectors/statsd.plugin/README.md). However, if you use [PHP-FPM](https://php-fpm.org/) in your LAMP stack, you can monitor that process with our [PHP-FPM -data collector](https://github.com/netdata/go.d.plugin/blob/master/modules/phpfpm/README.md). +data collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/phpfpm/README.md). Open your PHP-FPM configuration for editing, replacing `7.4` with your version of PHP: @@ -159,7 +159,7 @@ If the Netdata Agent isn't already open in your browser, open a new tab and navi > If you [signed up](https://app.netdata.cloud/sign-up?cloudRoute=/spaces) for Netdata Cloud earlier, you can also view > the exact same LAMP stack metrics there, plus additional features, like drag-and-drop custom dashboards. Be sure to -> [connecting your node](https://github.com/netdata/netdata/blob/master/claim/README.md) to start streaming metrics to your browser through Netdata Cloud. +> [connecting your node](https://github.com/netdata/netdata/blob/master/src/claim/README.md) to start streaming metrics to your browser through Netdata Cloud. Netdata automatically organizes all metrics and charts onto a single page for easy navigation. Peek at gauges to see overall system performance, then scroll down to see more. Click-and-drag with your mouse to pan _all_ charts back and @@ -203,7 +203,7 @@ shows any alerts currently triggered, while the **All** tab displays a list of _  -[Tweak alerts](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md) based on your infrastructure monitoring needs, and to see these alerts +[Tweak alerts](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md) based on your infrastructure monitoring needs, and to see these alerts in other places, like your inbox or a Slack channel, [enable a notification method](https://github.com/netdata/netdata/blob/master/docs/monitor/enable-notifications.md). @@ -215,7 +215,7 @@ services. The per-second metrics granularity means you have the most accurate in any LAMP-related issues. Another powerful way to monitor the availability of a LAMP stack is the [`httpcheck` -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/httpcheck/README.md), which pings a web server at +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/httpcheck/README.md), which pings a web server at a regular interval and tells you whether if and how quickly it's responding. The `response_match` option also lets you monitor when the web server's response isn't what you expect it to be, which might happen if PHP-FPM crashes, for example. @@ -231,8 +231,8 @@ source of issues faster with [Metric Correlations](https://github.com/netdata/ne ### Related reference documentation - [Netdata Agent · Get started](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md) -- [Netdata Agent · Apache data collector](https://github.com/netdata/go.d.plugin/blob/master/modules/apache/README.md) -- [Netdata Agent · Web log collector](https://github.com/netdata/go.d.plugin/blob/master/modules/weblog/README.md) -- [Netdata Agent · MySQL data collector](https://github.com/netdata/go.d.plugin/blob/master/modules/mysql/README.md) -- [Netdata Agent · PHP-FPM data collector](https://github.com/netdata/go.d.plugin/blob/master/modules/phpfpm/README.md) +- [Netdata Agent · Apache data collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/apache/README.md) +- [Netdata Agent · Web log collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/weblog/README.md) +- [Netdata Agent · MySQL data collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/mysql/README.md) +- [Netdata Agent · PHP-FPM data collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/phpfpm/README.md) diff --git a/docs/guides/monitor/pi-hole-raspberry-pi.md b/docs/guides/monitor/pi-hole-raspberry-pi.md index 4f0ff4cd..1e76cc09 100644 --- a/docs/guides/monitor/pi-hole-raspberry-pi.md +++ b/docs/guides/monitor/pi-hole-raspberry-pi.md @@ -81,7 +81,7 @@ service](https://discourse.pi-hole.net/t/how-do-i-configure-my-devices-to-use-pi finished setting up Pi-hole at this point. As far as configuring Netdata to monitor Pi-hole metrics, there's nothing you actually need to do. Netdata's [Pi-hole -collector](https://github.com/netdata/go.d.plugin/blob/master/modules/pihole/README.md) will autodetect the new service +collector](https://github.com/netdata/netdata/blob/master/src/go/collectors/go.d.plugin/modules/pihole/README.md) will autodetect the new service running on your Raspberry Pi and immediately start collecting metrics every second. Restart Netdata with `sudo systemctl restart netdata`, which will then recognize that Pi-hole is running and start a @@ -105,9 +105,9 @@ If you're completely new to Netdata, look at the [Introduction](https://github.c ### Enable temperature sensor monitoring You need to manually enable Netdata's built-in [temperature sensor -collector](https://github.com/netdata/netdata/blob/master/collectors/charts.d.plugin/sensors/README.md) to start collecting metrics. +collector](https://github.com/netdata/netdata/blob/master/src/collectors/charts.d.plugin/sensors/README.md) to start collecting metrics. -> Netdata uses a few plugins to manage its [collectors](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md), each using a different language: Go, +> Netdata uses a few plugins to manage its [collectors](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md), each using a different language: Go, > Python, Node.js, and Bash. While our Go collectors are undergoing the most active development, we still support the > other languages. In this case, you need to enable a temperature sensor collector that's written in Bash. @@ -125,7 +125,7 @@ Raspberry Pi temperature sensor monitoring. ### Storing historical metrics on your Raspberry Pi By default, Netdata allocates 256 MiB in disk space to store historical metrics inside the [database -engine](https://github.com/netdata/netdata/blob/master/database/engine/README.md). On the Raspberry Pi used for this guide, Netdata collects 1,500 metrics every +engine](https://github.com/netdata/netdata/blob/master/src/database/engine/README.md). On the Raspberry Pi used for this guide, Netdata collects 1,500 metrics every second, which equates to storing 3.5 days worth of historical metrics. You can increase this allocation by editing `netdata.conf` and increasing the `dbengine multihost disk space` setting to @@ -138,5 +138,5 @@ more than 256. Use our [database sizing calculator](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md#calculate-the-system-resources-ram-disk-space-needed-to-store-metrics) -and the [Database configuration documentation](https://github.com/netdata/netdata/blob/master/database/README.md) to help you determine the right +and the [Database configuration documentation](https://github.com/netdata/netdata/blob/master/src/database/README.md) to help you determine the right setting for your Raspberry Pi. diff --git a/docs/guides/monitor/process.md b/docs/guides/monitor/process.md index 9aa6911f..af36aefa 100644 --- a/docs/guides/monitor/process.md +++ b/docs/guides/monitor/process.md @@ -46,7 +46,7 @@ With Netdata's process monitoring, you can: ## How does Netdata do process monitoring? The Netdata Agent already knows to look for hundreds -of [standard applications that we support via collectors](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md), +of [standard applications that we support via collectors](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md), and groups them based on their purpose. Let's say you want to monitor a MySQL database using its process. The Netdata Agent already knows to look for processes with the string `mysqld` in their @@ -55,12 +55,12 @@ process-specific charts. The process and groups settings are used by two unique and powerful collectors. -[**`apps.plugin`**](https://github.com/netdata/netdata/blob/master/collectors/apps.plugin/README.md) looks at the Linux +[**`apps.plugin`**](https://github.com/netdata/netdata/blob/master/src/collectors/apps.plugin/README.md) looks at the Linux process tree every second, much like `top` or `ps fax`, and collects resource utilization information on every running process. It then automatically adds a layer of meaningful visualization on top of these metrics, and creates per-process/application charts. -[**`ebpf.plugin`**](https://github.com/netdata/netdata/blob/master/collectors/ebpf.plugin/README.md): Netdata's extended +[**`ebpf.plugin`**](https://github.com/netdata/netdata/blob/master/src/collectors/ebpf.plugin/README.md): Netdata's extended Berkeley Packet Filter (eBPF) collector monitors Linux kernel-level metrics for file descriptors, virtual filesystem IO, and process management, and then hands process-specific metrics over to `apps.plugin` for visualization. The eBPF collector also collects and visualizes @@ -146,7 +146,7 @@ others, and groups them into `sql`. That makes sense, since all these processes sql: mysqld* mariad* postgres* postmaster* oracle_* ora_* sqlservr ``` -These groups are then reflected as [dimensions](https://github.com/netdata/netdata/blob/master/web/README.md#dimensions) +These groups are then reflected as [dimensions](https://github.com/netdata/netdata/blob/master/src/web/README.md#dimensions) within Netdata's charts.  for your system, to start collecting utilization metrics +the [appropriate method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, to start collecting utilization metrics from your application. Time to [visualize your process metrics](#visualize-process-metrics). ### Custom applications @@ -207,7 +207,7 @@ custom-app: custom-app ``` Restart Netdata with `sudo systemctl restart netdata`, or -the [appropriate method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system, to start collecting utilization metrics +the [appropriate method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, to start collecting utilization metrics from your application. ## Visualize process metrics diff --git a/docs/guides/monitor/raspberry-pi-anomaly-detection.md b/docs/guides/monitor/raspberry-pi-anomaly-detection.md index 935d0f6c..3c56ac79 100644 --- a/docs/guides/monitor/raspberry-pi-anomaly-detection.md +++ b/docs/guides/monitor/raspberry-pi-anomaly-detection.md @@ -6,7 +6,7 @@ We love IoT and edge at Netdata, we also love machine learning. Even better if w of monitoring increasingly complex systems. We recently explored what might be involved in enabling our Python-based [anomalies -collector](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/anomalies/README.md) on a Raspberry Pi. To our delight, it's actually quite +collector](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/anomalies/README.md) on a Raspberry Pi. To our delight, it's actually quite straightforward! Read on to learn all the steps and enable unsupervised anomaly detection on your on Raspberry Pi(s). @@ -24,7 +24,7 @@ Read on to learn all the steps and enable unsupervised anomaly detection on your First make sure Netdata is using Python 3 when it runs Python-based data collectors. Next, open `netdata.conf` using [`edit-config`](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#use-edit-config-to-edit-configuration-files) -from within the [Netdata config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#the-netdata-config-directory). Scroll down to the +from within the [Netdata config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory). Scroll down to the `[plugin:python.d]` section to pass in the `-ppython3` command option. ```conf @@ -53,7 +53,7 @@ LLVM_CONFIG=llvm-config-9 pip3 install --user llvmlite numpy==1.20.1 netdata-pan ## Enable the anomalies collector -Now you're ready to enable the collector and [restart Netdata](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md). +Now you're ready to enable the collector and [restart Netdata](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation). ```bash sudo ./edit-config python.d.conf @@ -75,7 +75,7 @@ centralized cloud somewhere) is the resource utilization impact of running a mon With the default configuration, the anomalies collector uses about 6.5% of CPU at each run. During the retraining step, CPU utilization jumps to between 20-30% for a few seconds, but you can [configure -retraining](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/anomalies/README.md#configuration) to happen less often if you wish. +retraining](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/anomalies/README.md#configuration) to happen less often if you wish.  diff --git a/docs/guides/python-collector.md b/docs/guides/python-collector.md index d89eb25e..4dd6d2c4 100644 --- a/docs/guides/python-collector.md +++ b/docs/guides/python-collector.md @@ -1,8 +1,8 @@ # Develop a custom data collector in Python -The Netdata Agent uses [data collectors](https://github.com/netdata/netdata/blob/master/collectors/README.md) to +The Netdata Agent uses [data collectors](https://github.com/netdata/netdata/blob/master/src/collectors/README.md) to fetch metrics from hundreds of system, container, and service endpoints. While the Netdata team and community has built -[powerful collectors](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md) for most system, container, +[powerful collectors](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md) for most system, container, and service/application endpoints, some custom applications can't be monitored by default. In this tutorial, you'll learn how to leverage the [Python programming language](https://www.python.org/) to build a @@ -27,7 +27,7 @@ want to make it available for other users, you should create the pull request in ### Quick start For a quick start, you can look at the -[example plugin](https://raw.githubusercontent.com/netdata/netdata/master/collectors/python.d.plugin/example/example.chart.py). +[example plugin](https://raw.githubusercontent.com/netdata/netdata/master/src/collectors/python.d.plugin/example/example.chart.py). **Note**: If you are working 'locally' on a new collector and would like to run it in an already installed and running Netdata (as opposed to having to install Netdata from source again with your new changes) you can copy over the relevant @@ -73,7 +73,7 @@ The basic elements of a Netdata collector are: - `get_data()`: The basic function of the plugin which will return to Netdata the correct values. **Note**: All names are better explained in the -[External Plugins Documentation](https://github.com/netdata/netdata/blob/master/collectors/plugins.d/README.md). +[External Plugins Documentation](https://github.com/netdata/netdata/blob/master/src/collectors/plugins.d/README.md). Parameters like `priority` and `update_every` mentioned in that documentation are handled by the `python.d.plugin`, not by each collection module. @@ -117,7 +117,7 @@ context, charttype]`, where: that is `A.B`, with `A` being the name of the collector, and `B` being the name of the specific metric. - `charttype`: Either `line`, `area`, or `stacked`. If null line is the default value. -You can read more about `family` and `context` in the [web dashboard](https://github.com/netdata/netdata/blob/master/web/README.md#families) doc. +You can read more about `family` and `context` in the [web dashboard](https://github.com/netdata/netdata/blob/master/src/web/README.md#families) doc. Once the chart has been defined, you should define the dimensions of the chart. Dimensions are basically the metrics to be represented in this chart and each chart can have more than one dimension. In order to define the dimensions, the @@ -410,7 +410,7 @@ ORDER = [ ] ``` -[Restart Netdata](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) with `sudo systemctl restart netdata` to see the new humidity +[Restart Netdata](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) with `sudo systemctl restart netdata` to see the new humidity chart:  @@ -467,7 +467,7 @@ ORDER = [ ] ``` -[Restart Netdata](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) with `sudo systemctl restart netdata` to see the new +[Restart Netdata](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) with `sudo systemctl restart netdata` to see the new min/max/average temperature chart with multiple dimensions:  @@ -487,7 +487,7 @@ configuration in [YAML](https://www.tutorialspoint.com/yaml/yaml_basics.htm) for more chances to work out-of-the-box. For example, if the data source supports both `HTTP` and `linux socket`, you can define 2 jobs named `local`, with each using a different method. - Check the `example` collector configuration file on - [GitHub](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/example/example.conf) to get a + [GitHub](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/example/example.conf) to get a sense of the structure. ```yaml @@ -518,10 +518,10 @@ find the configuration field. This allows you to define sane defaults for your c Moreover, when creating the configuration file, create a large comment section that describes the configuration variables and inform the user about the defaults. For example, take a look at the `example` collector on -[GitHub](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/example/example.conf). +[GitHub](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/example/example.conf). You can read more about the configuration file on the [`python.d.plugin` -documentation](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/README.md). +documentation](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/README.md). You can find the source code for the above examples on [GitHub](https://github.com/papajohn-uop/netdata). @@ -539,7 +539,7 @@ At minimum, to be buildable and testable, the PR needs to include: - A basic configuration for the plugin in the appropriate global config file: `collectors/python.d.plugin/python.d.conf`, which is also in YAML format. Either add a line that reads `# <module_name>: yes` if the module is to be enabled by default, or one that reads `<module_name>: no` if it is to be disabled by default. - A makefile for the plugin at `collectors/python.d.plugin/<module_dir>/Makefile.inc`. Check an existing plugin for what this should look like. - A line in `collectors/python.d.plugin/Makefile.am` including the above-mentioned makefile. Place it with the other plugin includes (please keep the includes sorted alphabetically). -- Optionally, chart information in `web/gui/dashboard_info.js`. This generally involves specifying a name and icon for the section, and may include descriptions for the section or individual charts. +- Optionally, chart information in `src/web/gui/dashboard_info.js`. This generally involves specifying a name and icon for the section, and may include descriptions for the section or individual charts. - Optionally, some default alert configurations for your collector in `health/health.d/<module_name>.conf` and a line adding `<module_name>.conf` in `health/Makefile.am`. ## Framework class reference diff --git a/docs/guides/troubleshoot/monitor-debug-applications-ebpf.md b/docs/guides/troubleshoot/monitor-debug-applications-ebpf.md index f393e8e0..728606c8 100644 --- a/docs/guides/troubleshoot/monitor-debug-applications-ebpf.md +++ b/docs/guides/troubleshoot/monitor-debug-applications-ebpf.md @@ -12,7 +12,7 @@ learn_rel_path: "Operations" When trying to troubleshoot or debug a finicky application, there's no such thing as too much information. At Netdata, we developed programs that connect to the [_extended Berkeley Packet Filter_ (eBPF) virtual -machine](https://github.com/netdata/netdata/blob/master/collectors/ebpf.plugin/README.md) to help you see exactly how specific applications are interacting with the +machine](https://github.com/netdata/netdata/blob/master/src/collectors/ebpf.plugin/README.md) to help you see exactly how specific applications are interacting with the Linux kernel. With these charts, you can root out bugs, discover optimizations, diagnose memory leaks, and much more. This means you can see exactly how often, and in what volume, the application creates processes, opens files, writes to @@ -29,7 +29,7 @@ To start troubleshooting an application with eBPF metrics, you need to ensure yo displays those metrics independent from any other process. You can use the `apps_groups.conf` file to configure which applications appear in charts generated by -[`apps.plugin`](https://github.com/netdata/netdata/blob/master/collectors/apps.plugin/README.md). Once you edit this file and create a new group for the application +[`apps.plugin`](https://github.com/netdata/netdata/blob/master/src/collectors/apps.plugin/README.md). Once you edit this file and create a new group for the application you want to monitor, you can see how it's interacting with the Linux kernel via real-time eBPF metrics. Let's assume you have an application that runs on the process `custom-app`. To monitor eBPF metrics for that application @@ -61,12 +61,12 @@ dev: custom-app ``` Restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system, to begin seeing metrics for this particular +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, to begin seeing metrics for this particular group+process. You can also add additional processes to the same group. You can set up `apps_groups.conf` to more show more precise eBPF metrics for any application or service running on your system, even if it's a standard package like Redis, Apache, or any other [application/service Netdata collects -from](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md). +from](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md). ```conf # ----------------------------------------------------------------------------- @@ -86,7 +86,7 @@ to show other charts that will help you debug and troubleshoot how it interacts ## Configure the eBPF collector to monitor errors -The eBPF collector has [two possible modes](https://github.com/netdata/netdata/blob/master/collectors/ebpf.plugin/README.md#ebpf-load-mode): `entry` and `return`. The default +The eBPF collector has [two possible modes](https://github.com/netdata/netdata/blob/master/src/collectors/ebpf.plugin/README.md#ebpf-load-mode): `entry` and `return`. The default is `entry`, and only monitors calls to kernel functions, but the `return` also monitors and charts _whether these calls return in error_. @@ -110,7 +110,7 @@ Replace `entry` with `return`: ``` Restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system. +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system. ## Get familiar with per-application eBPF metrics and charts @@ -122,7 +122,7 @@ Pay particular attention to the charts in the **ebpf file**, **ebpf syscall**, * sub-sections. These charts are populated by low-level Linux kernel metrics thanks to eBPF, and showcase the volume of calls to open/close files, call functions like `do_fork`, IO activity on the VFS, and much more. -See the [eBPF collector documentation](https://github.com/netdata/netdata/blob/master/collectors/ebpf.plugin/README.md#integration-with-appsplugin) for the full list +See the [eBPF collector documentation](https://github.com/netdata/netdata/blob/master/src/collectors/ebpf.plugin/README.md#integration-with-appsplugin) for the full list of per-application charts. Let's show some examples of how you can first identify normal eBPF patterns, then use that knowledge to identify @@ -147,7 +147,7 @@ followed by a similar spike from the Apache benchmark. > To see other charts, replace `apps.file_open` with the context of the chart you want to see data for. > > To see all the API options, visit our [Swagger -> documentation](https://editor.swagger.io/?url=https://raw.githubusercontent.com/netdata/netdata/master/web/api/netdata-swagger.yaml) +> documentation](https://editor.swagger.io/?url=https://raw.githubusercontent.com/netdata/netdata/master/src/web/api/netdata-swagger.yaml) > and look under the **/data** section. ## Troubleshoot and debug applications with eBPF diff --git a/docs/guides/troubleshoot/troubleshooting-agent-with-cloud-connection.md b/docs/guides/troubleshoot/troubleshooting-agent-with-cloud-connection.md index 9c69ee91..0c9962ba 100644 --- a/docs/guides/troubleshoot/troubleshooting-agent-with-cloud-connection.md +++ b/docs/guides/troubleshoot/troubleshooting-agent-with-cloud-connection.md @@ -31,7 +31,7 @@ and firewall settings for outbound connections. If your firewall is configured to completely prevent outbound connections, you need to whitelist `app.netdata.cloud` and `mqtt.netdata.cloud`. If you can't whitelist domains in your firewall, you can whitelist the IPs that the hostnames resolve to, but keep in mind that they can change without any notice. -If you use an outbound proxy, you need to [take some extra steps]( https://github.com/netdata/netdata/blob/master/claim/README.md#connect-through-a-proxy). +If you use an outbound proxy, you need to [take some extra steps]( https://github.com/netdata/netdata/blob/master/src/claim/README.md#connect-through-a-proxy). ### Troubleshoot claiming with kickstart.sh @@ -48,7 +48,7 @@ and can be executed. First verify that the NETDATA_CLAIM_TOKEN parameter is correctly configured and then check for any errors during initialization of the container. -The most common issue we have seen claiming nodes in Docker is [running on older hosts with seccomp enabled](https://github.com/netdata/netdata/blob/master/claim/README.md#known-issues-on-older-hosts-with-seccomp-enabled). +The most common issue we have seen claiming nodes in Docker is [running on older hosts with seccomp enabled](https://github.com/netdata/netdata/blob/master/src/claim/README.md#known-issues-on-older-hosts-with-seccomp-enabled). ## The Agent was claimed but can not connect @@ -91,7 +91,7 @@ the Netdata service cannot be enabled by default and you must do it manually, us > ### Note > > In some cases a simple restart of the Agent can fix the issue. -> Read more about [Starting, Stopping and Restarting the Agent](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md). +> Read more about [Starting, Stopping and Restarting the Agent](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation). #### Troubleshoot connection establishment with Docker diff --git a/docs/guides/using-host-labels.md b/docs/guides/using-host-labels.md index 5f3a467f..961f4b2d 100644 --- a/docs/guides/using-host-labels.md +++ b/docs/guides/using-host-labels.md @@ -139,9 +139,9 @@ child system. It's a vastly simplified way of accessing critical information abo > ⚠️ Because automatic labels for child nodes are accessible via API calls, and contain sensitive information like > kernel and operating system versions, you should secure streaming connections with SSL. See the [streaming -> documentation](https://github.com/netdata/netdata/blob/master/streaming/README.md#securing-streaming-communications) for details. You may also want to use -> [access lists](https://github.com/netdata/netdata/blob/master/web/server/README.md#access-lists) or [expose the API only to LAN/localhost -> connections](https://github.com/netdata/netdata/blob/master/docs/netdata-security.md#expose-netdata-only-in-a-private-lan). +> documentation](https://github.com/netdata/netdata/blob/master/src/streaming/README.md#securing-streaming-communications) for details. You may also want to use +> [access lists](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#access-lists) or [expose the API only to LAN/localhost +> connections](https://github.com/netdata/netdata/blob/master/docs/category-overview-pages/secure-nodes.md#expose-netdata-only-in-a-private-lan). You can also use `_is_parent`, `_is_child`, and any other host labels in both health entities and metrics exporting. Speaking of which... @@ -192,11 +192,11 @@ Or when ephemeral Docker nodes are involved: ``` Of course, there are many more possibilities for intuitively organizing your systems with host labels. See the [health -documentation](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#alert-line-host-labels) for more details, and then get creative! +documentation](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md#alert-line-host-labels) for more details, and then get creative! ### Host labels in metrics exporting -If you have enabled any metrics exporting via our experimental [exporters](https://github.com/netdata/netdata/blob/master/exporting/README.md), any new host +If you have enabled any metrics exporting via our experimental [exporters](https://github.com/netdata/netdata/blob/master/src/exporting/README.md), any new host labels you created manually are sent to the destination database alongside metrics. You can change this behavior by editing `exporting.conf`, and you can even send automatically-generated labels on with exported metrics. @@ -221,7 +221,7 @@ send automatic labels = yes ``` By applying labels to exported metrics, you can more easily parse historical metrics with the labels applied. To learn -more about exporting, read the [documentation](https://github.com/netdata/netdata/blob/master/exporting/README.md). +more about exporting, read the [documentation](https://github.com/netdata/netdata/blob/master/src/exporting/README.md). ## Metric labels diff --git a/docs/maintenance/backup-restore.md b/docs/maintenance/backup-restore.md new file mode 100644 index 00000000..7e603d21 --- /dev/null +++ b/docs/maintenance/backup-restore.md @@ -0,0 +1,66 @@ +# Backing up a Netdata Agent + +## Introduction + +When preparing to backup a Netdata Agent it is worth considering that there are different kinds of data that you may wish to backup independently or all together: + +| Data type | Description | Location | +|---------------------|------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------| +| Agent configuration | Files controlling configuration of the Netdata Agent | [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md) | +| Metrics | Database files | /var/cache/netdata | +| Identity | Claim token, API key and some other files | /var/lib/netdata | + + +## Scenarios + +### Backing up to restore data in case of a node failure + +In this standard scenario, you are backing up your Netdata Agent in case of a node failure or data corruption so that the metrics and the configuration can be recovered. The purpose is not to backup/restore the application itself. + +1. Verify that the directory paths in the table above contain the information you expect. + + > **Note** + > The specific paths may vary depending on installation method, Operating System, and whether it is a Docker/Kubernetes deployment. + +2. It is recommended that you [stop the Netdata Agent](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) when backing up the Metrics/database files. + Backing up the Agent configuration and Identity folders is straightforward as they should not be changing very frequently. + +3. Using a backup tool such as `tar` you will need to run the backup as _root_ or as the _netdata_ user to access all the files in the directories. + + ``` + sudo tar -cvpzf netdata_backup.tar.gz /etc/netdata/ /var/cache/netdata /var/lib/netdata + ``` + + Stopping the Netdata agent is typically necessary to back up the database files of the Netdata Agent. + +If you want to minimize the gap in metrics caused by stopping the Netdata Agent, consider implementing a backup job or script that follows this sequence: + +- Backup the Agent configuration Identity directories +- Stop the Netdata service +- Backup up the database files +- Restart the netdata agent. + +### Restoring Netdata + +1. Ensure that the Netdata agent is installed and is [stopped](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) + + If you plan to deploy the Agent and restore a backup on top of it, then you might find it helpful to use the [`--dont-start-it`](https://github.com/netdata/netdata/blob/master/packaging/installer/methods/kickstart.md#other-options) option upon installation. + + ``` + wget -O /tmp/netdata-kickstart.sh https://get.netdata.cloud/kickstart.sh && sh /tmp/netdata-kickstart.sh --dont-start-it + ``` + + > **Note** + > If you are going to restore the database files then you should first ensure that the Metrics directory is empty. + > + > ``` + > sudo rm -Rf /var/cache/netdata + > ``` + +2. Restore the backup from the archive + + ``` + sudo tar -xvpzf /path/to/netdata_backup.tar.gz -C / + ``` + +3. [Start the Netdata agent](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) diff --git a/docs/configure/start-stop-restart.md b/docs/maintenance/start-stop-restart.md index 45691bc9..2effe886 100644 --- a/docs/configure/start-stop-restart.md +++ b/docs/maintenance/start-stop-restart.md @@ -1,6 +1,6 @@ # Start, stop, or restart the Netdata Agent -When you install the Netdata Agent, the [daemon](https://github.com/netdata/netdata/blob/master/daemon/README.md) is +When you install the Netdata Agent, the [daemon](https://github.com/netdata/netdata/blob/master/src/daemon/README.md) is configured to start at boot and stop and restart/shutdown. You will most often need to _restart_ the Agent to load new or editing configuration files. @@ -34,7 +34,7 @@ If you start the daemon this way, close it with `sudo killall netdata`. ## Using `netdatacli` -The Netdata Agent also comes with a [CLI tool](https://github.com/netdata/netdata/blob/master/cli/README.md) capable of performing shutdowns. Start the Agent back up +The Netdata Agent also comes with a [CLI tool](https://github.com/netdata/netdata/blob/master/src/cli/README.md) capable of performing shutdowns. Start the Agent back up using your preferred method listed above. ```bash @@ -151,4 +151,3 @@ _There have been some recent work on the init script, see PR ### other systems You can start Netdata by running it from `/etc/rc.local` or equivalent. - diff --git a/docs/metrics-storage-management/enable-streaming.md b/docs/metrics-storage-management/enable-streaming.md index fcbb16c8..c8fb76b9 100644 --- a/docs/metrics-storage-management/enable-streaming.md +++ b/docs/metrics-storage-management/enable-streaming.md @@ -10,12 +10,10 @@ When one node streams metrics to another, the node receiving metrics can visuali [export](https://github.com/netdata/netdata/blob/master/docs/export/external-databases.md) all metrics to an external TSDB. When Netdata streams metrics to another Netdata, the receiving one is able to perform everything a Netdata instance is capable of. -Streaming lets you decide exactly how you want to store and maintain metrics data. While we believe Netdata's -[distributed architecture](https://github.com/netdata/netdata/blob/master/docs/store/distributed-data-architecture.md) is -ideal for speed and scale, streaming provides centralization options and high data availability. +Streaming lets you decide exactly how you want to store and maintain metrics data. While we believe Netdata's distributed architecture is ideal for speed and scale, streaming provides centralization options and high data availability. This document will get you started quickly with streaming. More advanced concepts and suggested production deployments -can be found in the [streaming and replication reference](https://github.com/netdata/netdata/blob/master/streaming/README.md). +can be found in the [streaming and replication reference](https://github.com/netdata/netdata/blob/master/src/streaming/README.md). ## Streaming basics @@ -80,7 +78,7 @@ protect them from the internet by making their UI accessible only via an nginx p for the parent and even each child, if necessary. Both children and the parent are connected to the cloud, to enable infrastructure observability, -[without transferring the collected data](https://github.com/netdata/netdata/blob/master/docs/netdata-security.md). +[without transferring the collected data](https://github.com/netdata/netdata/blob/master/docs/security-and-privacy-design/README.md). Requests for data are always serverd by a connected Netdata agent. When both a child and a parent are connected, the cloud will always select the parent to query the user requested data. @@ -114,7 +112,7 @@ itself while initiating a streaming connection. Copy that into a separate text f > Find out how to [install `uuidgen`](https://command-not-found.com/uuidgen) on your node if you don't already have it. Next, open `stream.conf` using [`edit-config`](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#use-edit-config-to-edit-configuration-files) -from within the [Netdata config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#the-netdata-config-directory). +from within the [Netdata config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory). ```bash cd /etc/netdata @@ -138,7 +136,7 @@ simplified version of the configuration, minus the commented lines, looks like t ``` Save the file and close it, then restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system. +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system. ### Enable streaming on the child node @@ -159,7 +157,7 @@ looks like the following: ``` Save the file and close it, then restart Netdata with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system. +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system. ### Enable TLS/SSL on streaming (optional) @@ -179,7 +177,7 @@ sudo chown netdata:netdata /etc/netdata/ssl/cert.pem /etc/netdata/ssl/key.pem Next, enforce TLS/SSL on the web server. Open `netdata.conf`, scroll down to the `[web]` section, and look for the `bind to` setting. Add `^SSL=force` to turn on TLS/SSL. See the [web server -reference](https://github.com/netdata/netdata/blob/master/web/server/README.md#enabling-tls-support) for other TLS/SSL options. +reference](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#enabling-tls-support) for other TLS/SSL options. ```conf [web] @@ -199,7 +197,7 @@ self-signed certificates. ``` Restart both the parent and child nodes with `sudo systemctl restart netdata`, or the [appropriate -method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) for your system, to stream encrypted metrics using TLS/SSL. +method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, to stream encrypted metrics using TLS/SSL. ### View streamed metrics in Netdata Cloud diff --git a/docs/monitor/enable-notifications.md b/docs/monitor/enable-notifications.md index 4bfebb4d..7fec9ed5 100644 --- a/docs/monitor/enable-notifications.md +++ b/docs/monitor/enable-notifications.md @@ -12,7 +12,7 @@ learn_rel_path: "Integrations/Notify" Netdata offers two ways to receive alert notifications on external platforms. These methods work independently _or_ in parallel, which means you can enable both at the same time to send alert notifications to any number of endpoints. -Both methods use a node's health alerts to generate the content of alert notifications. Read our documentation on [configuring alerts](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md) to change the preconfigured thresholds or to create tailored alerts for your +Both methods use a node's health alerts to generate the content of alert notifications. Read our documentation on [configuring alerts](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md) to change the preconfigured thresholds or to create tailored alerts for your infrastructure. Netdata Cloud offers [centralized alert notifications](#netdata-cloud) via email, which leverages the health status @@ -49,7 +49,7 @@ more. ## Netdata Agent -The Netdata Agent's [notification system](https://github.com/netdata/netdata/blob/master/health/notifications/README.md) runs on every node and dispatches +The Netdata Agent's [notification system](https://github.com/netdata/netdata/blob/master/src/health/notifications/README.md) runs on every node and dispatches notifications based on configured endpoints and roles. You can enable multiple endpoints on any one node _and_ use Agent notifications in parallel with centralized alert notifications in Netdata Cloud. @@ -61,30 +61,30 @@ notification platform. ### Supported notification endpoints -- [**alerta.io**](https://github.com/netdata/netdata/blob/master/health/notifications/alerta/README.md) -- [**Amazon SNS**](https://github.com/netdata/netdata/blob/master/health/notifications/awssns/README.md) -- [**Custom endpoint**](https://github.com/netdata/netdata/blob/master/health/notifications/custom/README.md) -- [**Discord**](https://github.com/netdata/netdata/blob/master/health/notifications/discord/README.md) -- [**Dynatrace**](https://github.com/netdata/netdata/blob/master/health/notifications/dynatrace/README.md) -- [**Email**](https://github.com/netdata/netdata/blob/master/health/notifications/email/README.md) -- [**Flock**](https://github.com/netdata/netdata/blob/master/health/notifications/flock/README.md) -- [**Gotify**](https://github.com/netdata/netdata/blob/master/health/notifications/gotify/README.md) -- [**IRC**](https://github.com/netdata/netdata/blob/master/health/notifications/irc/README.md) -- [**Kavenegar**](https://github.com/netdata/netdata/blob/master/health/notifications/kavenegar/README.md) -- [**Matrix**](https://github.com/netdata/netdata/blob/master/health/notifications/matrix/README.md) -- [**Messagebird**](https://github.com/netdata/netdata/blob/master/health/notifications/messagebird/README.md) -- [**Microsoft Teams**](https://github.com/netdata/netdata/blob/master/health/notifications/msteams/README.md) -- [**Netdata Agent dashboard**](https://github.com/netdata/netdata/blob/master/health/notifications/web/README.md) -- [**Opsgenie**](https://github.com/netdata/netdata/blob/master/health/notifications/opsgenie/README.md) -- [**PagerDuty**](https://github.com/netdata/netdata/blob/master/health/notifications/pagerduty/README.md) -- [**Prowl**](https://github.com/netdata/netdata/blob/master/health/notifications/prowl/README.md) -- [**PushBullet**](https://github.com/netdata/netdata/blob/master/health/notifications/pushbullet/README.md) -- [**PushOver**](https://github.com/netdata/netdata/blob/master/health/notifications/pushover/README.md) -- [**Rocket.Chat**](https://github.com/netdata/netdata/blob/master/health/notifications/rocketchat/README.md) -- [**Slack**](https://github.com/netdata/netdata/blob/master/health/notifications/slack/README.md) -- [**SMS Server Tools 3**](https://github.com/netdata/netdata/blob/master/health/notifications/smstools3/README.md) -- [**Syslog**](https://github.com/netdata/netdata/blob/master/health/notifications/syslog/README.md) -- [**Telegram**](https://github.com/netdata/netdata/blob/master/health/notifications/telegram/README.md) -- [**Twilio**](https://github.com/netdata/netdata/blob/master/health/notifications/twilio/README.md) +- [**alerta.io**](https://github.com/netdata/netdata/blob/master/src/health/notifications/alerta/README.md) +- [**Amazon SNS**](https://github.com/netdata/netdata/blob/master/src/health/notifications/awssns/README.md) +- [**Custom endpoint**](https://github.com/netdata/netdata/blob/master/src/health/notifications/custom/README.md) +- [**Discord**](https://github.com/netdata/netdata/blob/master/src/health/notifications/discord/README.md) +- [**Dynatrace**](https://github.com/netdata/netdata/blob/master/src/health/notifications/dynatrace/README.md) +- [**Email**](https://github.com/netdata/netdata/blob/master/src/health/notifications/email/README.md) +- [**Flock**](https://github.com/netdata/netdata/blob/master/src/health/notifications/flock/README.md) +- [**Gotify**](https://github.com/netdata/netdata/blob/master/src/health/notifications/gotify/README.md) +- [**IRC**](https://github.com/netdata/netdata/blob/master/src/health/notifications/irc/README.md) +- [**Kavenegar**](https://github.com/netdata/netdata/blob/master/src/health/notifications/kavenegar/README.md) +- [**Matrix**](https://github.com/netdata/netdata/blob/master/src/health/notifications/matrix/README.md) +- [**Messagebird**](https://github.com/netdata/netdata/blob/master/src/health/notifications/messagebird/README.md) +- [**Microsoft Teams**](https://github.com/netdata/netdata/blob/master/src/health/notifications/msteams/README.md) +- [**Netdata Agent dashboard**](https://github.com/netdata/netdata/blob/master/src/health/notifications/web/README.md) +- [**Opsgenie**](https://github.com/netdata/netdata/blob/master/src/health/notifications/opsgenie/README.md) +- [**PagerDuty**](https://github.com/netdata/netdata/blob/master/src/health/notifications/pagerduty/README.md) +- [**Prowl**](https://github.com/netdata/netdata/blob/master/src/health/notifications/prowl/README.md) +- [**PushBullet**](https://github.com/netdata/netdata/blob/master/src/health/notifications/pushbullet/README.md) +- [**PushOver**](https://github.com/netdata/netdata/blob/master/src/health/notifications/pushover/README.md) +- [**Rocket.Chat**](https://github.com/netdata/netdata/blob/master/src/health/notifications/rocketchat/README.md) +- [**Slack**](https://github.com/netdata/netdata/blob/master/src/health/notifications/slack/README.md) +- [**SMS Server Tools 3**](https://github.com/netdata/netdata/blob/master/src/health/notifications/smstools3/README.md) +- [**Syslog**](https://github.com/netdata/netdata/blob/master/src/health/notifications/syslog/README.md) +- [**Telegram**](https://github.com/netdata/netdata/blob/master/src/health/notifications/telegram/README.md) +- [**Twilio**](https://github.com/netdata/netdata/blob/master/src/health/notifications/twilio/README.md) diff --git a/docs/monitor/view-active-alerts.md b/docs/monitor/view-active-alerts.md index 14b1663d..02eed0a8 100644 --- a/docs/monitor/view-active-alerts.md +++ b/docs/monitor/view-active-alerts.md @@ -66,5 +66,5 @@ With the three icons beneath that and the **role** designation, you can: 3. Copy the code to embed the badge onto another web page using an `<embed>` element. The table on the right-hand side displays information about the health entity that triggered the alert, which you can -use as a reference to [configure alerts](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md). +use as a reference to [configure alerts](https://github.com/netdata/netdata/blob/master/src/health/REFERENCE.md). --> diff --git a/docs/netdata-agent/README.md b/docs/netdata-agent/README.md new file mode 100644 index 00000000..faf262fd --- /dev/null +++ b/docs/netdata-agent/README.md @@ -0,0 +1,84 @@ +# Netdata Agent + +The Netdata Agent is the main building block in a Netdata ecosystem. It is installed on all monitored systems to monitor system components, containers and applications. + +The Netdata Agent is an **observability pipeline in a box** that can either operate standalone, or blend into a bigger pipeline made by more Netdata Agents (Children and Parents). + +## Distributed Observability Pipeline + +The Netdata observability pipeline looks like in the following graph. + +The pipeline is extended by creating Metrics Observability Centralization Points that are linked all together (`from a remote Netdata`, `to a remote Netdata`), so that all Netdata installed become a vast integrated observability pipeline. + +```mermaid +stateDiagram-v2 + classDef userFeature fill:#f00,color:white,font-weight:bold,stroke-width:2px,stroke:yellow + classDef usedByNC fill:#090,color:white,font-weight:bold,stroke-width:2px,stroke:yellow + Local --> Discover + Local: Local Netdata + [*] --> Detect: from a remote Netdata + Others: 3rd party time-series DBs + Detect: Detect Anomalies + Dashboard:::userFeature + Dashboard: Netdata Dashboards + 3rdDashboard:::userFeature + 3rdDashboard: 3rd party Dashboards + Notifications:::userFeature + Notifications: Alert Notifications + Alerts: Alert Transitions + Discover --> Collect + Collect --> Detect + Store: Store + Store: Time-Series Database + Detect --> Store + Store --> Learn + Store --> Check + Store --> Query + Store --> Score + Store --> Stream + Store --> Export + Query --> Visualize + Score --> Visualize + Check --> Alerts + Learn --> Detect: trained ML models + Alerts --> Notifications + Stream --> [*]: to a remote Netdata + Export --> Others + Others --> 3rdDashboard + Visualize --> Dashboard + Score:::usedByNC + Query:::usedByNC + Alerts:::usedByNC +``` + +1. **Discover**: auto-detect metric sources on localhost, auto-discover metric sources on Kubernetes. +2. **Collect**: query data sources to collect metric samples, using the optimal protocol for each data source. 800+ integrations supported, including dozens of native application protocols, OpenMetrics and StatsD. +3. **Detect Anomalies**: use the trained machine learning models for each metric, to detect in real-time if each sample collected is an outlier (an anomaly), or not. +4. **Store**: keep collected samples and their anomaly status, in the time-series database (database mode `dbengine`) or a ring buffer (database modes `ram` and `alloc`). +5. **Learn**: train multiple machine learning models for each metric collected, learning behaviors and patterns for detecting anomalies. +6. **Check**: a health engine, triggering alerts and sending notifications. Netdata comes with hundreds of alert configurations that are automatically attached to metrics when they get collected, detecting errors, common configuration errors and performance issues. +7. **Query**: a query engine for querying time-series data. +8. **Score**: a scoring engine for comparing and correlating metrics. +9. **Stream**: a mechanism to connect Netdata agents and build Metrics Centralization Points (Netdata Parents). +10. **Visualize**: Netdata's fully automated dashboards for all metrics. +11. **Export**: export metric samples to 3rd party time-series databases, enabling the use of 3rd party tools for visualization, like Grafana. + +## Comparison to other observability solutions + +1. **One moving part**: Other monitoring solution require maintaining metrics exporters, time-series databases, visualization engines. Netdata has everything integrated into one package, even when [Metrics Centralization Points](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/metrics-centralization-points/README.md) are required, making deployment and maintenance a lot simpler. + +2. **Automation**: Netdata is designed to automate most of the process of setting up and running an observability solution. It is designed to instantly provide comprehensive dashboards and fully automated alerts, with zero configuration. + +3. **High Fidelity Monitoring**: Netdata was born from our need to kill the console for observability. So, it provides metrics and logs in the same granularity and fidelity console tools do, but also comes with tools that go beyond metrics and logs, to provide a holistic view of the monitored infrastructure (e.g. check [Top Monitoring](https://github.com/netdata/netdata/blob/master/docs/cloud/netdata-functions.md)). + +4. **Minimal impact on monitored systems and applications**: Netdata has been designed to have a minimal impact on the monitored systems and their applications. There are [independent studies](https://www.ivanomalavolta.com/files/papers/ICSOC_2023.pdf) reporting that Netdata excels in CPU usage, RAM utilization, Execution Time and the impact Netdata has on monitored applications and containers. + +5. **Energy efficiency**: [University of Amsterdam did a research to find the energy efficiency of monitoring tools](https://twitter.com/IMalavolta/status/1734208439096676680). They tested Netdata, Prometheus, ELK, among other tools. The study concluded that **Netdata is the most energy efficient monitoring tool**. + +## Dashboard Versions + +The Netdata agents (Standalone, Children and Parents) **share the dashboard** of Netdata Cloud. However, when the user is logged-in and the Netdata agent is connected to Netdata Cloud, the following are enabled (which are otherwise disabled): + +1. **Access to Sensitive Data**: Some data, like systemd-journal logs and several [Top Monitoring](https://github.com/netdata/netdata/blob/master/docs/cloud/netdata-functions.md) features expose sensitive data, like IPs, ports, process command lines and more. To access all these when the dashboard is served directly from a Netdata agent, Netdata Cloud is required to verify that the user accessing the dashboard has the required permissions. + +2. **Dynamic Configuration**: Netdata agents are configured via configuration files, manually or through some provisioning system. The latest Netdata includes a feature to allow users change some of the configuration (collectors, alerts) via the dashboard. This feature is only available to users of paid Netdata Cloud plan. diff --git a/docs/netdata-agent/configuration.md b/docs/netdata-agent/configuration.md new file mode 100644 index 00000000..85319984 --- /dev/null +++ b/docs/netdata-agent/configuration.md @@ -0,0 +1,43 @@ +# Netdata Agent Configuration + +The main Netdata agent configuration is `netdata.conf`. + +## The Netdata config directory + +On most Linux systems, by using our [recommended one-line installation](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#install-on-linux-with-one-line-installer), the **Netdata config +directory** will be `/etc/netdata/`. The config directory contains several configuration files with the `.conf` extension, a +few directories, and a shell script named `edit-config`. + +> Some operating systems will use `/opt/netdata/etc/netdata/` as the config directory. If you're not sure where yours +> is, navigate to `http://NODE:19999/netdata.conf` in your browser, replacing `NODE` with the IP address or hostname of +> your node, and find the `# config directory = ` setting. The value listed is the config directory for your system. + +All of Netdata's documentation assumes that your config directory is at `/etc/netdata`, and that you're running any scripts from inside that directory. + + +## edit `netdata.conf` + +To edit `netdata.conf`, run this on your terminal: + +```bash +cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata +sudo ./edit-config netdata.conf +``` + +Your editor will open. + +## downloading `netdata.conf` + +The running version of `netdata.conf` can be downloaded from a running Netdata agent, at this URL: + +``` +http://agent-ip:19999/netdata.conf +``` + +You can save and use this version, using these commands: + +```bash +cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata +curl -ksSLo /tmp/netdata.conf.new http://localhost:19999/netdata.conf && sudo mv -i /tmp/netdata.conf.new netdata.conf +``` + diff --git a/docs/netdata-agent/sizing-netdata-agents/README.md b/docs/netdata-agent/sizing-netdata-agents/README.md new file mode 100644 index 00000000..b945dc56 --- /dev/null +++ b/docs/netdata-agent/sizing-netdata-agents/README.md @@ -0,0 +1,89 @@ +# Sizing Netdata Agents + +Netdata automatically adjusts its resources utilization based on the workload offered to it. + +This is a map of how Netdata **features impact resources utilization**: + +| Feature | CPU | RAM | Disk I/O | Disk Space | Retention | Bandwidth | +|-----------------------------:|:---:|:---:|:--------:|:----------:|:---------:|:---------:| +| Metrics collected | X | X | X | X | X | - | +| Samples collection frequency | X | - | X | X | X | - | +| Database mode and tiers | - | X | X | X | X | - | +| Machine learning | X | X | - | - | - | - | +| Streaming | X | X | - | - | - | X | + +1. **Metrics collected**: The number of metrics collected affects almost every aspect of resources utilization. + + When you need to lower the resources used by Netdata, this is an obvious first step. + +2. **Samples collection frequency**: By default Netdata collects metrics with 1-second granularity, unless the metrics collected are not updated that frequently, in which case Netdata collects them at the frequency they are updated. This is controlled per data collection job. + + Lowering the data collection frequency from every-second to every-2-seconds, will make Netdata use half the CPU utilization. So, CPU utilization is proportional to the data collection frequency. + +3. **Database Mode and Tiers**: By default Netdata stores metrics in 3 database tiers: high-resolution, mid-resolution, low-resolution. All database tiers are updated in parallel during data collection, and depending on the query duration Netdata may consult one or more tiers to optimize the resources required to satisfy it. + + The number of database tiers affects the memory requirements of Netdata. Going from 3-tiers to 1-tier, will make Netdata use half the memory. Of course metrics retention will also be limited to 1 tier. + +4. **Machine Learning**: Byt default Netdata trains multiple machine learning models for every metric collected, to learn its behavior and detect anomalies. Machine Learning is a CPU intensive process and affects the overall CPU utilization of Netdata. + +5. **Streaming Compression**: When using Netdata in Parent-Child configurations to create Metrics Centralization Points, the compression algorithm used greatly affects CPU utilization and bandwidth consumption. + + Netdata supports multiple streaming compressions algorithms, allowing the optimization of either CPU utilization or Network Bandwidth. The default algorithm `zstd` provides the best balance among them. + +## Minimizing the resources used by Netdata Agents + +To minimize the resources used by Netdata Agents, we suggest to configure Netdata Parents for centralizing metric samples, and disabling most of the features on Netdata Children. This will provide minimal resources utilization at the edge, while all the features of Netdata are available at the Netdata Parents. + +The following guides provide instructions on how to do this. + +## Maximizing the scale of Netdata Parents + +Netdata Parents automatically size resource utilization based on the workload they receive. The only possible option for improving query performance is to dedicate more RAM to them, by increasing their caches efficiency. + +Check [RAM Requirements](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/sizing-netdata-agents/ram-requirements.md) for more information. + +## Innovations Netdata has for optimal performance and scalability + +The following are some of the innovations the open-source Netdata agent has, that contribute to its excellent performance, and scalability. + +1. **Minimal disk I/O** + + When Netdata saves data on-disk, it stores them at their final place, eliminating the need to reorganize this data. + + Netdata is organizing its data structures in such a way that samples are committed to disk as evenly as possible across time, without affecting its memory requirements. + + Furthermore, Netdata Agents use direct-I/O for saving and loading metric samples. This prevents Netdata from polluting system caches with metric data. Netdata maintains its own caches for this data. + + All these features make Netdata an nice partner and a polite citizen for production applications running on the same systems Netdata runs. + +2. **4 bytes per sample uncompressed** + + To achieve optimal memory and disk footprint, Netdata uses a custom 32-bit floating point number. This floating point number is used to store the samples collected, together with their anomaly bit. The database of Netdata is fixed-step, so it has predefined slots for every sample, allowing Netdata to store timestamps once every several hundreds samples, minimizing both its memory requirements and the disk footprint. + + The final disk footprint of Netdata varies due to compression efficiency. It is usually about 0.6 bytes per sample for the high-resolution tier (per-second), 6 bytes per sample for the mid-resolution tier (per-minute) and 18 bytes per sample for the low-resolution tier (per-hour). + +3. **Query priorities** + + Alerting, Machine Learning, Streaming and Replication, rely on metric queries. When multiple queries are running in parallel, Netdata assigns priorities to all of them, favoring interactive queries over background tasks. This means that queries do not compete equally for resources. Machine learning or replication may slow down when interactive queries are running and the system starves for resources. + +4. **A pointer per label** + + Apart from metric samples, metric labels and their cardinality is the biggest memory consumer, especially in highly ephemeral environments, like kubernetes. Netdata uses a single pointer for any label key-value pair that is reused. Keys and values are also deduplicated, providing the best possible memory footprint for metric labels. + +5. **Streaming Protocol** + + The streaming protocol of Netdata allows minimizing the resources consumed on production systems by delegating features of to other Netdata agents (Parents), without compromising monitoring fidelity or responsiveness, enabling the creation of a highly distributed observability platform. + +## Netdata vs Prometheus + +Netdata outperforms Prometheus in every aspect. -35% CPU Utilization, -49% RAM usage, -12% network bandwidth, -98% disk I/O, -75% in disk footprint for high resolution data, while providing more than a year of retention. + +Read the [full comparison here](https://blog.netdata.cloud/netdata-vs-prometheus-performance-analysis/). + +## Energy Efficiency + +University of Amsterdam contacted a research on the impact monitoring systems have on docker based systems. + +The study found that Netdata excels in CPU utilization, RAM usage, Execution Time and concluded that **Netdata is the most energy efficient tool**. + +Read the [full study here](https://www.ivanomalavolta.com/files/papers/ICSOC_2023.pdf). diff --git a/docs/netdata-agent/sizing-netdata-agents/bandwidth-requirements.md b/docs/netdata-agent/sizing-netdata-agents/bandwidth-requirements.md new file mode 100644 index 00000000..092c8da1 --- /dev/null +++ b/docs/netdata-agent/sizing-netdata-agents/bandwidth-requirements.md @@ -0,0 +1,47 @@ +# Bandwidth Requirements + +## On Production Systems, Standalone Netdata + +Standalone Netdata may use network bandwidth under the following conditions: + +1. You configured data collection jobs that are fetching data from remote systems. There is no such jobs enabled by default. +2. You use the dashboard of the Netdata. +3. [Netdata Cloud communication](#netdata-cloud-communication) (see below). + +## On Metrics Centralization Points, between Netdata Children & Parents + +Netdata supports multiple compression algorithms for streaming communication. Netdata Children offer all their compression algorithms when connecting to a Netdata Parent, and the Netdata Parent decides which one to use based on algorithms availability and user configuration. + +| Algorithm | Best for | +|:---------:|:-----------------------------------------------------------------------------------------------------------------------------------:| +| `zstd` | The best balance between CPU utilization and compression efficiency. This is the default. | +| `lz4` | The fastest of the algorithms. Use this when CPU utilization is more important than bandwidth. | +| `gzip` | The best compression efficiency, at the expense of CPU utilization. Use this when bandwidth is more important than CPU utilization. | +| `brotli` | The most CPU intensive algorithm, providing the best compression. | + +The expected bandwidth consumption using `zstd` for 1 million samples per second is 84 Mbps, or 10.5 MiB/s. + +The order compression algorithms is selected is configured in `stream.conf`, per `[API KEY]`, like this: + +``` + compression algorithms order = zstd lz4 brotli gzip +``` + +The first available algorithm on both the Netdata Child and the Netdata Parent, from left to right, is chosen. + +Compression can also be disabled in `stream.conf` at either Netdata Children or Netdata Parents. + +## Netdata Cloud Communication + +When Netdata Agents connect to Netdata Cloud, they communicate metadata of the metrics being collected, but they do not stream the samples collected for each metric. + +The information transferred to Netdata Cloud is: + +1. Information and **metadata about the system itself**, like its hostname, architecture, virtualization technologies used and generally labels associated with the system. +2. Information about the **running data collection plugins, modules and jobs**. +3. Information about the **metrics available and their retention**. +4. Information about the **configured alerts and their transitions**. + +This is not a constant stream of information. Netdata Agents update Netdata Cloud only about status changes on all the above (e.g. an alert being triggered, or a metric stopped being collected). So, there is an initial handshake and exchange of information when Netdata starts, and then there only updates when required. + +Of course, when you view Netdata Cloud dashboards that need to query the database a Netdata agent maintains, this query is forwarded to an agent that can satisfy it. This means that Netdata Cloud receives metric samples only when a user is accessing a dashboard and the samples transferred are usually aggregations to allow rendering the dashboards. diff --git a/docs/netdata-agent/sizing-netdata-agents/cpu-requirements.md b/docs/netdata-agent/sizing-netdata-agents/cpu-requirements.md new file mode 100644 index 00000000..021a35fb --- /dev/null +++ b/docs/netdata-agent/sizing-netdata-agents/cpu-requirements.md @@ -0,0 +1,65 @@ +# CPU Requirements + +Netdata's CPU consumption is affected by the following factors: + +1. The number of metrics collected +2. The frequency metrics are collected +3. Machine Learning +4. Streaming compression (streaming of metrics to Netdata Parents) +5. Database Mode + +## On Production Systems, Netdata Children + +On production systems, where Netdata is running with default settings, monitoring the system it is installed at and its containers and applications, CPU utilization should usually be about 1% to 5% of a single CPU core. + +This includes 3 database tiers, machine learning, per-second data collection, alerts, and streaming to a Netdata Parent. + +## On Metrics Centralization Points, Netdata Parents + +On Metrics Centralization Points, Netdata Parents running on modern server hardware, we **estimate CPU utilization per million of samples collected per second**: + +| Feature | Depends On | Expected Utilization | Key Reasons | +|:-----------------:|:---------------------------------------------------:|:----------------------------------------------------------------:|:-------------------------------------------------------------------------:| +| Metrics Ingestion | Number of samples received per second | 2 CPU cores per million of samples per second | Decompress and decode received messages, update database. | +| Metrics re-streaming| Number of samples resent per second | 2 CPU cores per million of samples per second | Encode and compress messages towards Netdata Parent. | +| Machine Learning | Number of unique time-series concurrently collected | 2 CPU cores per million of unique metrics concurrently collected | Train machine learning models, query existing models to detect anomalies. | + +We recommend keeping the total CPU utilization below 60% when a Netdata Parent is steadily ingesting metrics, training machine learning models and running health checks. This will leave enough CPU resources available for queries. + +## I want to minimize CPU utilization. What should I do? + +You can control Netdata's CPU utilization with these parameters: + +1. **Data collection frequency**: Going from per-second metrics to every-2-seconds metrics will half the CPU utilization of Netdata. +2. **Number of metrics collected**: Netdata by default collects every metric available on the systems it runs. Review the metrics collected and disable data collection plugins and modules not needed. +3. **Machine Learning**: Disable machine learning to save CPU cycles. +4. **Number of database tiers**: Netdata updates database tiers in parallel, during data collection. This affects both CPU utilization and memory requirements. +5. **Database Mode**: The default database mode is `dbengine`, which compresses and commits data to disk. If you have a Netdata Parent where metrics are aggregated and saved to disk and there is a reliable connection between the Netdata you want to optimize and its Parent, switch to database mode `ram` or `alloc`. This disables saving to disk, so your Netdata will also not use any disk I/O. + +## I see increased CPU consumption when a busy Netdata Parent starts, why? + +When a Netdata Parent starts and Netdata children get connected to it, there are several operations that temporarily affect CPU utilization, network bandwidth and disk I/O. + +The general flow looks like this: + +1. **Back-filling of higher tiers**: Usually this means calculating the aggregates of the last hour of `tier2` and of the last minute of `tier1`, ensuring that higher tiers reflect all the information `tier0` has. If Netdata was stopped abnormally (e.g. due to a system failure or crash), higher tiers may have to be back-filled for longer durations. +2. **Metadata synchronization**: The metadata of all metrics each Netdata Child maintains are negotiated between the Child and the Parent and are synchronized. +3. **Replication**: If the Parent is missing samples the Child has, these samples are transferred to the Parent before transferring new samples. +4. Once all these finish, the normal **streaming of new metric samples** starts. +5. At the same time, **machine learning** initializes, loads saved trained models and prepares anomaly detection. +6. After a few moments the **health engine starts checking metrics** for triggering alerts. + +The above process is per metric. So, while one metric back-fills, another replicates and a third one streams. + +At the same time: + +- the compression algorithm learns the patterns of the data exchanged and optimizes its dictionaries for optimal compression and CPU utilization, +- the database engine adjusts the page size of each metric, so that samples are committed to disk as evenly as possible across time. + +So, when looking for the "steady CPU consumption during ingestion" of a busy Netdata Parent, we recommend to let it stabilize for a few hours before checking. + +Keep in mind that Netdata has been designed so that even if during the initialization phase and the connection of hundreds of Netdata Children the system lacks CPU resources, the Netdata Parent will complete all the operations and eventually enter a steady CPU consumption during ingestion, without affecting the quality of the metrics stored. So, it is ok if during initialization of a busy Netdata Parent, CPU consumption spikes to 100%. + +Important: the above initialization process is not such intense when new nodes get connected to a Netdata Parent for the first time (e.g. ephemeral nodes), since several of the steps involved are not required. + +Especially for the cases where children disconnect and reconnect to the Parent due to network related issues (i.e. both the Netdata Child and the Netdata Parent have not been restarted and less than 1 hour has passed since the last disconnection), the re-negotiation phase is minimal and metrics are instantly entering the normal streaming phase. diff --git a/docs/netdata-agent/sizing-netdata-agents/disk-requirements-and-retention.md b/docs/netdata-agent/sizing-netdata-agents/disk-requirements-and-retention.md new file mode 100644 index 00000000..d9e879cb --- /dev/null +++ b/docs/netdata-agent/sizing-netdata-agents/disk-requirements-and-retention.md @@ -0,0 +1,131 @@ +# Disk Requirements & Retention + +## Database Modes and Tiers + +Netdata comes with 3 database modes: + +1. `dbengine`: the default high-performance multi-tier database of Netdata. Metric samples are cached in memory and are saved to disk in multiple tiers, with compression. +2. `ram`: metric samples are stored in ring buffers in memory, with increments of 1024 samples. Metric samples are not committed to disk. Kernel-Same-Page (KSM) can be used to deduplicate Netdata's memory. +3. `alloc`: metric samples are stored in ring buffers in memory, with flexible increments. Metric samples are not committed to disk. + +## `ram` and `alloc` + +Modes `ram` and `alloc` can help when Netdata should not introduce any disk I/O at all. In both of these modes, metric samples exist only in memory, and only while they are collected. + +When Netdata is configured to stream its metrics to a Metrics Observability Centralization Point (a Netdata Parent), metric samples are forwarded in real-time to that Netdata Parent. The ring buffers available in these modes is used to cache the collected samples for some time, in case there are network issues, or the Netdata Parent is restarted for maintenance. + +The memory required per sample in these modes, is 4 bytes: + +- `ram` mode uses `mmap()` behind the scene, and can be incremented in steps of 1024 samples (4KiB). Mode `ram` allows the use of the Linux kernel memory dedupper (Kernel-Same-Page or KSM) to deduplicate Netdata ring buffers and save memory. +- `alloc` mode can be sized for any number of samples per metric. KSM cannot be used in this mode. + +To configure database mode `ram` or `alloc`, in `netdata.conf`, set the following: + +- `[db].mode` to either `ram` or `alloc`. +- `[db].retention` to the number of samples the ring buffers should maintain. For `ram` if the value set is not a multiple of 1024, the next multiple of 1024 will be used. + +## `dbengine` + +`dbengine` supports up to 5 tiers. By default, 3 tiers are used, like this: + +| Tier | Resolution | Uncompressed Sample Size | Usually On Disk | +|:--------:|:--------------------------------------------------------------------------------------------:|:------------------------:|:---------------:| +| `tier0` | native resolution (metrics collected per-second as stored per-second) | 4 bytes | 0.6 bytes | +| `tier1` | 60 iterations of `tier0`, so when metrics are collected per-second, this tier is per-minute. | 16 bytes | 6 bytes | +| `tier2` | 60 iterations of `tier1`, so when metrics are collected per second, this tier is per-hour. | 16 bytes | 18 bytes | + +Data are saved to disk compressed, so the actual size on disk varies depending on compression efficiency. + +`dbegnine` tiers are overlapping, so higher tiers include a down-sampled version of the samples in lower tiers: + +```mermaid +gantt + dateFormat YYYY-MM-DD + tickInterval 1week + axisFormat + todayMarker off + tier0, 14d :a1, 2023-12-24, 7d + tier1, 60d :a2, 2023-12-01, 30d + tier2, 365d :a3, 2023-11-02, 59d +``` + +## Disk Space and Metrics Retention + +You can find information about the current disk utilization of a Netdata Parent, at <http://agent-ip:19999/api/v2/info>. The output of this endpoint is like this: + +```json +{ + // more information about the agent + // then, near the end: + "db_size": [ + { + "tier": 0, + "metrics": 43070, + "samples": 88078162001, + "disk_used": 41156409552, + "disk_max": 41943040000, + "disk_percent": 98.1245269, + "from": 1705033983, + "to": 1708856640, + "retention": 3822657, + "expected_retention": 3895720, + "currently_collected_metrics": 27424 + }, + { + "tier": 1, + "metrics": 72987, + "samples": 5155155269, + "disk_used": 20585157180, + "disk_max": 20971520000, + "disk_percent": 98.1576785, + "from": 1698287340, + "to": 1708856640, + "retention": 10569300, + "expected_retention": 10767675, + "currently_collected_metrics": 27424 + }, + { + "tier": 2, + "metrics": 148234, + "samples": 314919121, + "disk_used": 5957346684, + "disk_max": 10485760000, + "disk_percent": 56.8136853, + "from": 1667808000, + "to": 1708856640, + "retention": 41048640, + "expected_retention": 72251324, + "currently_collected_metrics": 27424 + } + ] +} +``` + +In this example: + +- `tier` is the database tier. +- `metrics` is the number of unique time-series in the database. +- `samples` is the number of samples in the database. +- `disk_used` is the currently used disk space in bytes. +- `disk_max` is the configured max disk space in bytes. +- `disk_percent` is the current disk space utilization for this tier. +- `from` is the first (oldest) timestamp in the database for this tier. +- `to` is the latest (newest) timestamp in the database for this tier. +- `retention` is the current retention of the database for this tier, in seconds (divide by 3600 for hours, divide by 86400 for days). +- `expected_retention` is the expected retention in seconds when `disk_percent` will be 100 (divide by 3600 for hours, divide by 86400 for days). +- `currently_collected_metrics` is the number of unique time-series currently being collected for this tier. + +So, for our example above: + +| Tier | # Of Metrics | # Of Samples | Disk Used | Disk Free | Current Retention | Expected Retention | Sample Size | +|-----:|-------------:|--------------:|----------:|----------:|------------------:|-------------------:|------------:| +| 0 | 43.1K | 88.1 billion | 38.4Gi | 1.88% | 44.2 days | 45.0 days | 0.46 B | +| 1 | 73.0K | 5.2 billion | 19.2Gi | 1.84% | 122.3 days | 124.6 days | 3.99 B | +| 2 | 148.3K | 315.0 million | 5.6Gi | 43.19% | 475.1 days | 836.2 days | 18.91 B | + +To configure retention, in `netdata.conf`, set the following: + +- `[db].mode` to `dbengine`. +- `[db].dbengine multihost disk space MB`, this is the max disk size for `tier0`. The default is 256MiB. +- `[db].dbengine tier 1 multihost disk space MB`, this is the max disk space for `tier1`. The default is 50% of `tier0`. +- `[db].dbengine tier 2 multihost disk space MB`, this is the max disk space for `tier2`. The default is 50% of `tier1`. diff --git a/docs/netdata-agent/sizing-netdata-agents/ram-requirements.md b/docs/netdata-agent/sizing-netdata-agents/ram-requirements.md new file mode 100644 index 00000000..159c979a --- /dev/null +++ b/docs/netdata-agent/sizing-netdata-agents/ram-requirements.md @@ -0,0 +1,60 @@ +# RAM Requirements + +With default configuration about database tiers, Netdata should need about 16KiB per unique metric collected, independently of the data collection frequency. + +Netdata supports memory ballooning and automatically sizes and limits the memory used, based on the metrics concurrently being collected. + +## On Production Systems, Netdata Children + +With default settings, Netdata should run with 100MB to 200MB of RAM, depending on the number of metrics being collected. + +This number can be lowered by limiting the number of database tier or switching database modes. For more information check [Disk Requirements and Retention](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/sizing-netdata-agents/disk-requirements-and-retention.md). + +## On Metrics Centralization Points, Netdata Parents + +The general formula, with the default configuration of database tiers, is: + +``` +memory = UNIQUE_METRICS x 16KiB + CONFIGURED_CACHES +``` + +The default `CONFIGURED_CACHES` is 32MiB. + +For 1 million concurrently collected time-series (independently of their data collection frequency), the memory required is: + +``` +UNIQUE_METRICS = 1000000 +CONFIGURED_CACHES = 32MiB + +(UNIQUE_METRICS * 16KiB / 1024 in MiB) + CONFIGURED_CACHES = +( 1000000 * 16KiB / 1024 in MiB) + 32 MiB = +15657 MiB = +about 16 GiB +``` + +There are 2 cache sizes that can be configured in `netdata.conf`: + +1. `[db].dbengine page cache size MB`: this is the main cache that keeps metrics data into memory. When data are not found in it, the extent cache is consulted, and if not found in that either, they are loaded from disk. +2. `[db].dbengine extent cache size MB`: this is the compressed extent cache. It keeps in memory compressed data blocks, as they appear on disk, to avoid reading them again. Data found in the extend cache but not in the main cache have to be uncompressed to be queried. + +Both of them are dynamically adjusted to use some of the total memory computed above. The configuration in `netdata.conf` allows providing additional memory to them, increasing their caching efficiency. + +## I have a Netdata Parent that is also a systemd-journal logs centralization point, what should I know? + +Logs usually require significantly more disk space and I/O bandwidth than metrics. For optimal performance we recommend to store metrics and logs on separate, independent disks. + +Netdata uses direct-I/O for its database, so that it does not pollute the system caches with its own data. We want Netdata to be a nice citizen when it runs side-by-side with production applications, so this was required to guarantee that Netdata does not affect the operation of databases or other sensitive applications running on the same servers. + +To optimize disk I/O, Netdata maintains its own private caches. The default settings of these caches are automatically adjusted to the minimum required size for acceptable metrics query performance. + +`systemd-journal` on the other hand, relies on operating system caches for improving the query performance of logs. When the system lacks free memory, querying logs leads to increased disk I/O. + +If you are experiencing slow responses and increased disk reads when metrics queries run, we suggest to dedicate some more RAM to Netdata. + +We frequently see that the following strategy gives best results: + +1. Start the Netdata Parent, send all the load you expect it to have and let it stabilize for a few hours. Netdata will now use the minimum memory it believes is required for smooth operation. +2. Check the available system memory. +3. Set the page cache in `netdata.conf` to use 1/3 of the available memory. + +This will allow Netdata queries to have more caches, while leaving plenty of available memory of logs and the operating system. diff --git a/docs/netdata-agent/versions-and-platforms.md b/docs/netdata-agent/versions-and-platforms.md new file mode 100644 index 00000000..787874d6 --- /dev/null +++ b/docs/netdata-agent/versions-and-platforms.md @@ -0,0 +1,70 @@ +# Netdata Agent Versions & Platforms + +Netdata is evolving rapidly and new features are added at a constant pace. Therefore we have frequent release cadence to deliver all these features to use as soon as possible. + +Netdata Agents are available in 2 versions: + +| Release Channel | Release Frequency | Support Policy & Features | Support Duration | Backwards Compatibility | +|:---------------:|:---------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------:|:---------------------------------------------------------------------------------:| +| Stable | At most once per month, usually every 45 days | Receiving bug fixes and security updates between releases | Up to the 2nd stable release after them | Previous configuration semantics and data are supported by newer releases | +| Nightly | Every night at 00:00 UTC | Latest pre-released features | Up to the 2nd nightly release after them | Configuration and data of unreleased features may change between nightly releases | + +> "Support Duration" defines the time we consider the release as actively used by users in production systems, so that all features of Netdata should be working like the day they were released. However, after the latest release, previous releases stop receiving bug fixes and security updates. All users are advised to update to the latest release to get the latest bug fixes. + +## Binary Distribution Packages + +Binary distribution packages are provided by Netdata, via CI integration, for the following platforms and architectures: + +| Platform | Platform Versions | Released Packages Architecture | Format | +|:-----------------------:|:--------------------------------:|:------------------------------------------------:|:------------:| +| Docker under Linux | 19.03 and later | `x86_64`, `i386`, `ARMv7`, `AArch64`, `POWER8+` | docker image | +| Static Builds | - | `x86_64`, `ARMv6`, `ARMv7`, `AArch64`, `POWER8+` | .gz.run | +| Alma Linux | 8.x, 9.x | `x86_64`, `AArch64` | RPM | +| Amazon Linux | 2, 2023 | `x86_64`, `AArch64` | RPM | +| Centos | 7.x | `x86_64` | RPM | +| Debian | 10.x, 11.x, 12.x | `x86_64`, `i386`, `ARMv7`, `AArch64` | DEB | +| Fedora | 37, 38, 39 | `x86_64`, `AArch64` | RPM | +| OpenSUSE | Leap 15.4, Leap 15.5, Tumbleweed | `x86_64`, `AArch64` | RPM | +| Oracle Linux | 8.x, 9.x | `x86_64`, `AArch64` | RPM | +| Redhat Enterprise Linux | 7.x | `x86_64` | RPM | +| Redhat Enterprise Linux | 8.x, 9.x | `x86_64`, `AArch64` | RPM | +| Ubuntu | 20.04, 22.04, 23.10 | `x86_64`, `i386`, `ARMv7` | DEB | + +> IMPORTANT: Linux distributions frequently provide binary packages of Netdata. However, the packages you will find at the distributions' repositories may be outdated, incomplete, missing significant features or completely broken. We recommend to use the packages we provide. + +## Third party Supported Binary Packages + +The following distributions always provide the latest stable version of Netdata: + +| Platform | Platform Versions | Released Packages Architecture | +|:----------:|:-----------------:|:------------------------------------:| +| Arch Linux | Latest | All the Arch supported architectures | +| MacOS Brew | Latest | All the Brew supported architectures | + + +## Builds from Source + +We guarantee Netdata builds from source for the platforms we provide automated binary packages. These platforms are automatically checked via our CI, and fixes are always applied to allow merging new code into the nightly versions. + +The following builds from source should usually work, although we don't regularly monitor if there are issues: + +| Platform | Platform Versions | +|:-----------------------------------:|:--------------------------:| +| Linux Distributions | Latest unreleased versions | +| FreeBSD and derivatives | 13-STABLE | +| Gentoo and derivatives | Latest | +| Arch Linux and derivatives | latest from AUR | +| MacOS | 11, 12, 13 | +| Linux under Microsoft Windows (WSL) | Latest | + +## Static Builds and Unsupported Linux Versions + +The static builds of Netdata can be used on any Linux platform of the supported architectures. The only requirement these static builds have is a working Linux kernel, any version. Everything else required for Netdata to run, is inside the package itself. + +Static builds usually miss certain features that require operating-system support and cannot be provided in a generic way. These features include: + +- IPMI hardware sensors support +- systemd-journal features +- eBPF related features + +When platforms are removed from the [Binary Distribution Packages](https://github.com/netdata/netdata/blob/master/packaging/makeself/README.md) list, they default to install or update Netdata to a static build. This may mean that after platforms become EOL, Netdata on them may lose some of its features. We recommend to upgrade the operating system before it becomes EOL, to continue using all the features of Netdata. diff --git a/docs/netdata-cloud-onprem/getting-started-light-poc.md b/docs/netdata-cloud-onprem/getting-started-light-poc.md index dfe0a0c8..7e78638e 100644 --- a/docs/netdata-cloud-onprem/getting-started-light-poc.md +++ b/docs/netdata-cloud-onprem/getting-started-light-poc.md @@ -13,16 +13,25 @@ To install the whole environment, log in to the designated host and run: ```shell curl https://netdata-cloud-netdata-static-content.s3.amazonaws.com/provision.sh -o provision.sh chmod +x provision.sh -sudo ./provision.sh --install +sudo ./provision.sh install \ + -key-id "" \ + -access-key "" \ + -onprem-license-key "" \ + -onprem-license-subject "" \ + -onprem-url "" \ + -certificate-path "" \ + -private-key-path "" ``` What does the script do during installation? -1. Prompts user to provide: - - ID and KEY for accessing the AWS (to pull helm charts and container images) - - License Key - - URL under which Netdata Cloud Onprem PoC is going to function (without protocol like `https://`) - - Path for certificate file (PEM format) - - Path for private key file (PEM format) +1. Prompts for user to provide: + - `-key-id` - AWS ECR access key ID. + - `-access-key` - AWS ECR Access Key. + - `-onprem-license-key` - Netdata Cloud On-Prem license key. + - `-onprem-license-subject` - Netdata Cloud On-Prem license subject. + - `-onprem-url` - URL for the On-prem (without http(s) protocol). + - `-certificate-path` - path to your PEM encoded certificate. + - `-private-key-path` - path to your PEM encoded key. 2. After getting all of the information installation is starting. The script will install: - Helm - Kubectl @@ -40,11 +49,11 @@ Because this is a PoC with 0 configurations required, only log in by mail can wo 4. You are now logged into the netdata. Add your first nodes! ##### How to remove Netdata Cloud On-Prem PoC? -To uninstall the whole PoC, use the same script that installed it, with the `--uninstall` switch. +To uninstall the whole PoC, use the same script that installed it, with the `uninstall` switch. ```shell cd <script dir> -sudo ./provision.sh --uninstall +sudo ./provision.sh uninstall ``` #### WARNING diff --git a/docs/netdata-cloud/README.md b/docs/netdata-cloud/README.md new file mode 100644 index 00000000..acf8e42f --- /dev/null +++ b/docs/netdata-cloud/README.md @@ -0,0 +1,134 @@ +# Netdata Cloud + +Netdata Cloud is a service that complements Netdata installations. It is a key component in achieving optimal cost structure for large scale observability. + +Technically, Netdata Cloud is a thin control plane that allows the Netdata ecosystem to be a virtually unlimited scalable and flexible observability pipeline. With Netdata Cloud, this observability pipeline can span multiple teams, cloud providers, data centers and services, while remaining a uniform and highly integrated infrastructure, providing real-time and high-fidelity insights. + +```mermaid +flowchart TB + NC("<b>☁️ Netdata Cloud</b> + access from anywhere, + horizontal scalability, + role based access, + custom dashboards, + central notifications") + Users[["<b>✨ Unified Dashboards</b> + across the infrastructure, + multi-cloud, hybrid-cloud"]] + Notifications["<b>🔔 Alert Notifications</b> + Slack, e-mail, Mobile App, + PagerDuty, and more"] + Users <--> NC + NC -->|deduplicated| Notifications + subgraph On-Prem Infrastructure + direction TB + Agents("<b>🌎 Netdata Agents</b> + Standalone, + Children, Parents + (possibly overlapping)") + TimeSeries[("<b>Time-Series</b> + metric samples + database")] + PrivateAgents("<b>🔒 Private + Netdata Agents</b>") + Agents <--> TimeSeries + Agents ---|stream| PrivateAgents + end + NC <-->|secure connection| Agents +``` + +Netdata Cloud provides the following features, on top of what the Netdata agents already provide: + +1. **Horizontal scalability**: Netdata Cloud allows scaling the observability infrastructure horizontally, by adding more independent Netdata Parents and Children. It can aggregate such, otherwise independent, observability islands into one uniform and integrated infrastructure. + + Netdata Cloud is a fundamental component for achieving an optimal cost structure and flexibility, in structuring observability the way that is best suited for each case. + +2. **Role Based Access Control (RBAC)**: Netdata Cloud has all the mechanisms for user-management and access control. It allows assigning all users a role, segmenting the infrastructure into rooms, and associating rooms with roles and users. + +3. **Access from anywhere**: Netdata agents are installed on-prem and this is where all your data are always stored. Netdata Cloud allows querying all the Netdata agents (Standalone, Children and Parents) in real-time when dashboards are accessed via Netdata Cloud. + + This enables a much simpler access control, eliminating the complexities of setting up VPNs to access observability, and the bandwidth costs for centralizing all metrics to one place. + +4. **Central dispatch of alert notifications**: Netdata Cloud allows controlling the dispatch of alert notifications centrally. By default, all Netdata agents (Standalone, Children and Parents) send their own notifications. This becomes increasingly complex as the infrastructure grows. So, Netdata Cloud steps in to simplify this process and provide central control of all notifications. + + Netdata Cloud also enables the use of the **Netdata Mobile App** offering mobile push notifications for all users in commercial plans. + +5. **Custom Dashboards**: Netdata Cloud enables the creation, storage and sharing custom dashboards. + + Custom dashboards are created directly from the UI, without the need for learning a query language. Netdata Cloud provides all the APIs to the Netdata dashboards to store, browse and retrieve custom dashboards created by all users. + +6. **Advanced Customization**: Netdata Cloud provides all the APIs for the dashboard to have different default settings per space, per room and per user, allowing administrators and users to customize the Netdata dashboards and charts the way they see fit. + +## Data Exposed to Netdata Cloud + +Netdata is thin layer of top of Netdata agents. It does not receive the samples collected, or the logs Netdata agents maintain. + +This is a key design decision for Netdata. If we were centralizing metric samples and logs, Netdata would have the same constrains and cost structure other observability solutions have, and we would be forced to lower metrics resolution, filter out metrics and eventually increase significantly the cost of observability. + +Instead, Netdata Cloud receives and stores only metadata related to the metrics collected, such as the nodes collecting metrics and their labels, the metric names, their labels and their retention, the data collection plugins and modules running, the configured alerts and their transitions. + +This information is a small fraction of the total information maintained by Netdata agents, allowing Netdata Cloud to remain high-resolution, high-fidelity and real-time, while being able to: + +- dispatch alerts centrally for all alert transitions. +- know which Netdata agents to query when users view the dashboards. + +Metric samples and logs are transferred via Netdata Cloud to your Web Browser, only when you view them via Netdata Cloud. And even then, Netdata Cloud does not store this information. It only aggregates the responses of multiple Netdata agents to a single response for your web browser to visualize. + +## High-Availability + +You can subscribe to Netdata Cloud updates at the [Netdata Cloud Status](https://status.netdata.cloud/) page. + +Netdata Cloud is a highly available, auto-scalable solution, however being a monitoring solution, we need to ensure dashboards are accessible during crisis. + +Netdata agents provide the same dashboard Netdata Cloud provides, with the following limitations: + +1. Netdata agents (Children and Parents) dashboards are limited to their databases, while on Netdata Cloud the dashboard presents the entire infrastructure, from all Netdata agents connected to it. + +2. When you are not logged-in or the agent is not connected to Netdata Cloud, certain features of the Netdata agent dashboard will not be available. + + When you are logged-in and the agent is connected to Netdata Cloud, the agent dashboard has the same functionality as Netdata Cloud. + +To ensure dashboard high availability, Netdata agent dashboards are available by directly accessing them, even when the connectivity between Children and Parents or Netdata Cloud faces issues. This allows the use of the individual Netdata agents' dashboards during crisis, at different levels of aggregation. + +## Fidelity and Insights + +Netdata Cloud queries Netdata agents, so it provides exactly the same fidelity and insights Netdata agents provide. Dashboards have the same resolution, the same number of metrics, exactly the same data. + +## Performance + +The Netdata agent and Netdata Cloud have similar query performance, but there are additional network latencies involved when the dashboards are viewed via Netdata Cloud. + +Accessing Netdata agents on the same LAN has marginal network latency and their response time is only affected by the queries. However, accessing the same Netdata agents via Netdata Cloud has a bigger network round-trip time, that looks like this: + +1. Your web browser makes a request to Netdata Cloud. +2. Netdata Cloud sends the request to your Netdata agents. If multiple Netdata agents are involved, they are queried in parallel. +3. Netdata Cloud receives their responses and aggregates them into a single response. +4. Netdata Cloud replies to your web browser. + +If you are sitting on the same LAN as the Netdata agents, the latency will be 2 times the round-trip network latency between this LAN and Netdata Cloud. + +However, when there are multiple Netdata agents involved, the queries will be faster compared to a monitoring solution that has one centralization point. Netdata Cloud splits each query into multiple parts and each of the Netdata agents involved will only perform a small part of the original query. So, when querying a large infrastructure, you enjoy the performance of the combined power of all your Netdata agents, which is usually quite higher than any single-centralization-point monitoring solution. + +## Does Netdata Cloud require Observability Centralization Points? + +No. Any or all Netdata agents can be connected to Netdata Cloud. + +We recommend to create [observability centralization points](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/README.md), as required for operational efficiency (ephemeral nodes, teams or services isolation, central control of alerts, production systems performance), security policies (internet isolation), or cost optimization (use existing capacities before allocating new ones). + +We suggest to review the [Best Practices for Observability Centralization Points](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/best-practices.md). + +## When I have Netdata Parents, do I need to connect Netdata Children to Netdata Cloud too? + +No, it is not needed, but it provides high-availability. + +When Netdata Parents are connected to Netdata Cloud, all their Netdata Children are available, via these Parents. + +When multiple Netdata Parents maintain a database for the same Netdata Children (e.g. clustered Parents, or Parents and Grandparents), Netdata Cloud is able to detect the unique nodes in an infrastructure and query each node only once, using one of the available Parents. + +Netdata Cloud prefers: + +- The most distant (from the Child) Parent available, when doing metrics visualization queries (since usually these Parents have been added for this purpose). + +- The closest (to the Child) Parent available, for [Top Monitoring](https://github.com/netdata/netdata/blob/master/docs/cloud/netdata-functions.md) (since top-monitoring provides live data, like the processes running, the list of sockets open, etc). The streaming protocol of Netdata Parents and Children is able to forward such requests to the right child, via the Parents, to respond with live and accurate data. + +Netdata Children may be connected to Netdata Cloud for high-availability, in case the Netdata Parents are unreachable. diff --git a/docs/netdata-cloud/netdata-cloud-on-prem/README.md b/docs/netdata-cloud/netdata-cloud-on-prem/README.md new file mode 100644 index 00000000..29601686 --- /dev/null +++ b/docs/netdata-cloud/netdata-cloud-on-prem/README.md @@ -0,0 +1,77 @@ +# Netdata Cloud On-Prem + +Netdata Cloud is built as microservices and is orchestrated by a Kubernetes cluster, providing a highly available and auto-scaled observability platform. + +The overall architecture looks like this: + +```mermaid +flowchart TD + agents("🌍 <b>Netdata Agents</b><br/>Users' infrastructure<br/>Netdata Children & Parents") + users[["🔥 <b>Unified Dashboards</b><br/>Integrated Infrastructure<br/>Dashboards"]] + ingress("🛡️ <b>Ingress Gateway</b><br/>TLS termination") + traefik((("🔒 <b>Traefik</b><br/>Authentication &<br/>Authorization"))) + emqx(("📤 <b>EMQX</b><br/>Agents Communication<br/>Message Bus<br/>MQTT")) + pulsar(("⚡ <b>Pulsar</b><br/>Internal Microservices<br/>Message Bus")) + frontend("🌐 <b>Front-End</b><br/>Static Web Files") + auth("👨💼 <b>Users & Agents</b><br/>Authorization<br/>Microservices") + spaceroom("🏡 <b>Spaces, Rooms,<br/>Nodes, Settings</b><br/>Microservices for<br/>managing Spaces,<br/>Rooms, Nodes and<br/>related settings") + charts("📈 <b>Metrics & Queries</b><br/>Microservices for<br/>dispatching queries<br/>to Netdata agents") + alerts("🔔 <b>Alerts & Notifications</b><br/>Microservices for<br/>tracking alert<br/>transitions and<br/>deduplicating alerts") + sql[("✨ <b>PostgreSQL</b><br/>Users, Spaces, Rooms,<br/>Agents, Nodes, Metric<br/>Names, Metrics Retention,<br/>Custom Dashboards,<br/>Settings")] + redis[("🗒️ <b>Redis</b><br/>Caches needed<br/>by Microservices")] + elk[("🗞️ <b>Elasticsearch</b><br/>Feed Events Database")] + bridges("🤝 <b>Input & Output</b><br/>Microservices bridging<br/>agents to internal<br/>components") + notifications("📢 <b>Notifications Integrations</b><br/>Dispatch alert<br/>notifications to<br/>3rd party services") + feed("📝 <b>Feed & Events</b><br/>Microservices for<br/>managing the events feed") + users --> ingress + agents --> ingress + ingress --> traefik + traefik ==>|agents<br/>websockets| emqx + traefik -.- auth + traefik ==>|http| spaceroom + traefik ==>|http| frontend + traefik ==>|http| charts + traefik ==>|http| alerts + spaceroom o-...-o pulsar + spaceroom -.- redis + spaceroom x-..-x sql + spaceroom -.-> feed + charts o-.-o pulsar + charts -.- redis + charts x-.-x sql + charts -..-> feed + alerts o-.-o pulsar + alerts -.- redis + alerts x-.-x sql + alerts -..-> feed + auth o-.-o pulsar + auth -.- redis + auth x-.-x sql + auth -.-> feed + feed <--> elk + alerts ----> notifications + %% auth ~~~ spaceroom + emqx <.-> bridges o-..-o pulsar +``` + +## Requirements + +The following components are required to run Netdata Cloud On-Prem: + +- **Kubernetes cluster** version 1.23+ +- **Kubernetes metrics server** (for autoscaling) +- **TLS certificate** for secure connections. A single endpoint is required but there is an option to split the frontend, api, and MQTT endpoints. The certificate must be trusted by all entities connecting to it. +- Default **storage class configured and working** (persistent volumes based on SSDs are preferred) + +The following 3rd party components are used, which can be pulled with the `netdata-cloud-dependency` package we provide: + +- **Ingress controller** supporting HTTPS +- **PostgreSQL** version 13.7 (main database for all metadata Netdata Cloud maintains) +- **EMQX** version 5.11 (MQTT Broker that allows Agents to send messages to the On-Prem Cloud) +- **Apache Pulsar** version 2.10+ (message broken for inter-container communication) +- **Traefik** version 2.7.x (internal API Gateway) +- **Elasticsearch** version 8.8.x (stores the feed of events) +- **Redis** version 6.2 (caching) +- imagePullSecret (our ECR repos are secured) + +Keep in mind though that the pulled versions are not configured properly for production use. Customers of Netdata Cloud On-Prem are expected to configure these applications according to their needs and policies for production use. Netdata Cloud On-Prem can be configured to use all these applications as a shared resource from other existing production installations. diff --git a/docs/netdata-cloud-onprem/infrastructure.jpeg b/docs/netdata-cloud/netdata-cloud-on-prem/infrastructure.jpeg Binary files differindex a866e141..a866e141 100644 --- a/docs/netdata-cloud-onprem/infrastructure.jpeg +++ b/docs/netdata-cloud/netdata-cloud-on-prem/infrastructure.jpeg diff --git a/docs/netdata-cloud/netdata-cloud-on-prem/installation.md b/docs/netdata-cloud/netdata-cloud-on-prem/installation.md new file mode 100644 index 00000000..a02033c2 --- /dev/null +++ b/docs/netdata-cloud/netdata-cloud-on-prem/installation.md @@ -0,0 +1,212 @@ +# Netdata Cloud On-Prem Installation + +This installation guide assumes the prerequisites for installing Netdata Cloud On-Prem as satisfied. For more information please refer to the [requirements documentation](https://github.com/netdata/netdata/blob/master/docs/netdata-cloud/netdata-cloud-on-prem/README.md#requirements). + +## Installation Requirements + +The following components are required to install Netdata Cloud On-Prem: + +- **AWS** CLI +- **Helm** version 3.12+ with OCI Configuration (explained in the installation section) +- **Kubectl** + +## Preparations for Installation + +### Configure AWS CLI + +Install [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html). + +There are 2 options for configuring `aws cli` to work with the provided credentials. The first one is to set the environment variables: + +```bash +export AWS_ACCESS_KEY_ID=<your_secret_id> +export AWS_SECRET_ACCESS_KEY=<your_secret_key> +``` + +The second one is to use an interactive shell: + +```bash +aws configure +``` + +### Configure helm to use secured ECR repository + +Using `aws` command we will generate a token for helm to access the secured ECR repository: + +```bash +aws ecr get-login-password --region us-east-1 | helm registry login --username AWS --password-stdin 362923047827.dkr.ecr.us-east-1.amazonaws.com/netdata-cloud-onprem +``` + +After this step you should be able to add the repository to your helm or just pull the helm chart: + +```bash +helm pull oci://362923047827.dkr.ecr.us-east-1.amazonaws.com/netdata-cloud-dependency --untar #optional +helm pull oci://362923047827.dkr.ecr.us-east-1.amazonaws.com/netdata-cloud-onprem --untar +``` + +Local folders with the newest versions of helm charts should appear on your working dir. + +## Installation + +Netdata provides access to two helm charts: + +1. `netdata-cloud-dependency` - required applications for `netdata-cloud-onprem`. +2. `netdata-cloud-onprem` - the application itself + provisioning + +### netdata-cloud-dependency + +This helm chart is designed to install the necessary applications: + +- Redis +- Elasticsearch +- EMQX +- Apache Pulsar +- PostgreSQL +- Traefik +- Mailcatcher +- k8s-ecr-login-renew +- kubernetes-ingress + +Although we provide an easy way to install all these applications, we expect users of Netdata Cloud On-Prem to provide production quality versions for them. Therefore, every configuration option is available through `values.yaml` in the folder that contains your netdata-cloud-dependency helm chart. All configuration options are described in `README.md` which is a part of the helm chart. + +Each component can be enabled/disabled individually. It is done by true/false switches in `values.yaml`. This way, it is easier to migrate to production-grade components gradually. + +Unless you prefer otherwise, `k8s-ecr-login-renew` is responsible for calling out the `AWS API` for token regeneration. This token is then injected into the secret that every node is using for authentication with secured ECR when pulling the images. + +The default setting in `values.yaml` of `netdata-cloud-onprem` - `.global.imagePullSecrets` is configured to work out of the box with the dependency helm chart. + +For helm chart installation - save your changes in `values.yaml` and execute: + +```shell +cd [your helm chart location] +helm upgrade --wait --install netdata-cloud-dependency -n netdata-cloud --create-namespace -f values.yaml . +``` + +Keep in mind that `netdata-cloud-dependency` is provided only as a proof of concept. Users installing Netdata Cloud On-Prem should properly configure these components. + +### netdata-cloud-onprem + +Every configuration option is available in `values.yaml` in the folder that contains your `netdata-cloud-onprem` helm chart. All configuration options are described in the `README.md` which is a part of the helm chart. + +#### Installing Netdata Cloud On-Prem + +```shell +cd [your helm chart location] +helm upgrade --wait --install netdata-cloud-onprem -n netdata-cloud --create-namespace -f values.yaml . +``` + +##### Important notes + +1. Installation takes care of provisioning the resources with migration services. + +2. During the first installation, a secret called the `netdata-cloud-common` is created. It contains several randomly generated entries. Deleting helm chart is not going to delete this secret, nor reinstalling the whole On-Prem, unless manually deleted by kubernetes administrator. The content of this secret is extremely relevant - strings that are contained there are essential parts of encryption. Losing or changing the data that it contains will result in data loss. + +## Short description of Netdata Cloud microservices + +#### cloud-accounts-service + +Responsible for user registration & authentication. Manages user account information. + +#### cloud-agent-data-ctrl-service + +Forwards request from the cloud to the relevant agents. +The requests include: +- Fetching chart metadata from the agent +- Fetching chart data from the agent +- Fetching function data from the agent + +#### cloud-agent-mqtt-input-service + +Forwards MQTT messages emitted by the agent related to the agent entities to the internal Pulsar broker. These include agent connection state updates. + +#### cloud-agent-mqtt-output-service + +Forwards Pulsar messages emitted in the cloud related to the agent entities to the MQTT broker. From there, the messages reach the relevant agent. + +#### cloud-alarm-config-mqtt-input-service + +Forwards MQTT messages emitted by the agent related to the alarm-config entities to the internal Pulsar broker. These include the data for the alarm configuration as seen by the agent. + +#### cloud-alarm-log-mqtt-input-service + +Forwards MQTT messages emitted by the agent related to the alarm-log entities to the internal Pulsar broker. These contain data about the alarm transitions that occurred in an agent. + +#### cloud-alarm-mqtt-output-service + +Forwards Pulsar messages emitted in the cloud related to the alarm entities to the MQTT broker. From there, the messages reach the relevant agent. + +#### cloud-alarm-processor-service + +Persists latest alert statuses received from the agent in the cloud. +Aggregates alert statuses from relevant node instances. +Exposes API endpoints to fetch alert data for visualization on the cloud. +Determines if notifications need to be sent when alert statuses change and emits relevant messages to Pulsar. +Exposes API endpoints to store and return notification-silencing data. + +#### cloud-alarm-streaming-service + +Responsible for starting the alert stream between the agent and the cloud. +Ensures that messages are processed in the correct order, and starts a reconciliation process between the cloud and the agent if out-of-order processing occurs. + +#### cloud-charts-mqtt-input-service + +Forwards MQTT messages emitted by the agent related to the chart entities to the internal Pulsar broker. These include the chart metadata that is used to display relevant charts on the cloud. + +#### cloud-charts-mqtt-output-service + +Forwards Pulsar messages emitted in the cloud related to the charts entities to the MQTT broker. From there, the messages reach the relevant agent. + +#### cloud-charts-service + +Exposes API endpoints to fetch the chart metadata. +Forwards data requests via the `cloud-agent-data-ctrl-service` to the relevant agents to fetch chart data points. +Exposes API endpoints to call various other endpoints on the agent, for instance, functions. + +#### cloud-custom-dashboard-service + +Exposes API endpoints to fetch and store custom dashboard data. + +#### cloud-environment-service + +Serves as the first contact point between the agent and the cloud. +Returns authentication and MQTT endpoints to connecting agents. + +#### cloud-feed-service + +Processes incoming feed events and stores them in Elasticsearch. +Exposes API endpoints to fetch feed events from Elasticsearch. + +#### cloud-frontend + +Contains the on-prem cloud website. Serves static content. + +#### cloud-iam-user-service + +Acts as a middleware for authentication on most of the API endpoints. Validates incoming token headers, injects the relevant ones, and forwards the requests. + +#### cloud-metrics-exporter + +Exports various metrics from an On-Prem Cloud installation. Uses the Prometheus metric exposition format. + +#### cloud-netdata-assistant + +Exposes API endpoints to fetch a human-friendly explanation of various netdata configuration options, namely the alerts. + +#### cloud-node-mqtt-input-service + +Forwards MQTT messages emitted by the agent related to the node entities to the internal Pulsar broker. These include the node metadata as well as their connectivity state, either direct or via parents. + +#### cloud-node-mqtt-output-service + +Forwards Pulsar messages emitted in the cloud related to the charts entities to the MQTT broker. From there, the messages reach the relevant agent. + +#### cloud-notifications-dispatcher-service + +Exposes API endpoints to handle integrations. +Handles incoming notification messages and uses the relevant channels(email, slack...) to notify relevant users. + +#### cloud-spaceroom-service + +Exposes API endpoints to fetch and store relations between agents, nodes, spaces, users, and rooms. +Acts as a provider of authorization for other cloud endpoints. +Exposes API endpoints to authenticate agents connecting to the cloud. diff --git a/docs/netdata-cloud/netdata-cloud-on-prem/poc-without-k8s.md b/docs/netdata-cloud/netdata-cloud-on-prem/poc-without-k8s.md new file mode 100644 index 00000000..6be4066b --- /dev/null +++ b/docs/netdata-cloud/netdata-cloud-on-prem/poc-without-k8s.md @@ -0,0 +1,70 @@ +# Netdata Cloud On-Prem PoC without k8s + +These instructions are about installing a light version of Netdata Cloud, for clients who do not have a Kubernetes cluster installed. This setup is **only for demonstration purposes**, as it has no built-in resiliency on failures of any kind. + +## Requirements + +- Ubuntu 22.04 (clean installation will work best). +- 10 CPU Cores and 24 GiB of memory. +- Access to shell as a sudo. +- TLS certificate for Netdata Cloud On-Prem PoC. A single endpoint is required. The certificate must be trusted by all entities connecting to this installation. +- AWS ID and License Key - we should have provided this to you, if not contact us: <info@netdata.cloud>. + +To install the whole environment, log in to the designated host and run: + +```bash +curl https://netdata-cloud-netdata-static-content.s3.amazonaws.com/provision.sh -o provision.sh +chmod +x provision.sh +sudo ./provision.sh install \ + -key-id "" \ + -access-key "" \ + -onprem-license-key "" \ + -onprem-license-subject "" \ + -onprem-url "" \ + -certificate-path "" \ + -private-key-path "" +``` + +What does the script do during installation? + +1. Prompts for user to provide: + - `-key-id` - AWS ECR access key ID. + - `-access-key` - AWS ECR Access Key. + - `-onprem-license-key` - Netdata Cloud On-Prem license key. + - `-onprem-license-subject` - Netdata Cloud On-Prem license subject. + - `-onprem-url` - URL for the On-prem (without http(s) protocol). + - `-certificate-path` - path to your PEM encoded certificate. + - `-private-key-path` - path to your PEM encoded key. + +2. After all the above installation will begin. The script will install: + - Helm + - Kubectl + - AWS CLI + - K3s cluster (single node) + +3. When all the required software is installed script starts to provision the K3s cluster with gathered data. + +After cluster provisioning netdata is ready to be used. + +> WARNING: +> This script will automatically expose not only netdata but also a mailcatcher under `<URL from point 1.>/mailcatcher`. + +## How to log in? + +Only login by mail can work without further configuration. Every mail this Netdata Cloud On-Prem sends, will appear on the mailcatcher, which acts as the SMTP server with a simple GUI to read the mails. + +Steps: + +1. Open Netdata Cloud On-Prem PoC in the web browser on URL you specified +2. Provide email and use the button to confirm +3. Mailcatcher will catch all the emails so go to `<URL from point 1.>/mailcatcher`. Find yours and click the link. +4. You are now logged into Netdata Cloud. Add your first nodes! + +## How to remove Netdata Cloud On-Prem PoC? + +To uninstall the whole PoC, use the same script that installed it, with the `uninstall` switch. + +```shell +cd <script dir> +sudo ./provision.sh uninstall +``` diff --git a/docs/netdata-cloud/netdata-cloud-on-prem/troubleshooting.md b/docs/netdata-cloud/netdata-cloud-on-prem/troubleshooting.md new file mode 100644 index 00000000..c330984a --- /dev/null +++ b/docs/netdata-cloud/netdata-cloud-on-prem/troubleshooting.md @@ -0,0 +1,37 @@ +# Netdata Cloud On-Prem Troubleshooting + +Netdata Cloud is a sophisticated software piece relying on in multiple infrastructure components for its operation. + +We assume that your team already manages and monitors properly the components Netdata Cloud depends upon, like the PostgreSQL, Redis and Elasticsearch databases, the Pulsar and EMQX message brokers, the traffic controllers (Ingress and Traefik) and of course the health of the Kubernetes cluster itself. + +The following are questions that are usually asked by Netdata Cloud On-Prem operators. + +## Loading charts takes a long time or ends with an error + +The charts service is trying to collect data from the agents involved in the query. In most of the cases, this microservice queries many agents (depending on the room), and all of them have to reply for the query to be satisfied. + +One or more of the following may be the cause: + +1. **Slow Netdata Agent or Netdata Agents with unreliable connections** + + If any of the Netdata agents queried is slow or has an unreliable network connection, the query will stall and Netdata Cloud will have timeout before responding. + + When agents are overloaded or have unreliable connections, we suggest to install more Netdata Parents for providing reliable backends to Netdata Cloud. They will automatically be preferred for all queries, when available. + +2. **Poor Kubernetes cluster management** + + Another common issue is poor management of the Kubernetes cluster. When a node of a Kubernetes cluster is saturated, or the limits set to its containers are small, Netdata Cloud microservices get throttled by Kubernetes and does not get the resources required to process the responses of Netdata agents and aggregate the results for the dashboard. + + We recommend to review the throttling of the containers and increase the limits if required. + +3. **Saturated Database** + + Slow responses may also indicate performance issues at the PostgreSQL database. + + Please review the resources utilization of the database server (CPU, Memory, and Disk I/O) and take action to improve the situation. + +4. **Messages pilling up in Pulsar** + + Depending on the size of the infrastructure being monitored and the resources allocated to Pulsar and the microservices, messages may be pilling up. When this happens you may also experience that nodes status updates (online, offline, stale) are slow, or alerts transitions take time to appear on the dashboard. + + We recommend to review Pulsar configuration and the resources allocated of the microservices, to ensure that there is no saturation. diff --git a/docs/netdata-cloud/versions.md b/docs/netdata-cloud/versions.md new file mode 100644 index 00000000..1031aa76 --- /dev/null +++ b/docs/netdata-cloud/versions.md @@ -0,0 +1,19 @@ +# Netdata Cloud Versions + +Netdata Cloud is provided in two versions: + +- **SaaS**, we run and maintain Netdata Cloud and users use it to complement their observability with the additional features it provides. + +- **On Prem**, we provide a licensed copy of the Netdata Cloud software, that users can install and run at their premises. + +The pricing of both versions is similar, with the On-Prem version introducing a monthly fixed-fee for the extra support and packaging required when users are running Netdata Cloud by themselves. + +For more information check our [Pricing](https://www.netdata.cloud/pricing/) page. + +## SaaS Version + +[Sign-up to Netdata Cloud](https://app.netdata.cloud) and start connecting your Netdata agents. The commands provided once you have signed up, include all the information to install and automatically connect (claim) Netdata agents to your Netdata Cloud space. + +## On-Prem Version + +To deploy Netdata Cloud On-premises, take a look at the [related section](https://github.com/netdata/netdata/blob/master/docs/netdata-cloud/netdata-cloud-on-prem/README.md) on our Documentation. diff --git a/docs/observability-centralization-points/README.md b/docs/observability-centralization-points/README.md new file mode 100644 index 00000000..ede2037a --- /dev/null +++ b/docs/observability-centralization-points/README.md @@ -0,0 +1,19 @@ +# Observability Centralization Points + +Netdata supports the creation of multiple independent **Observability Centralization Points**, aggregating metric samples, logs and metadata within an infrastructure. + +Observability Centralization Points are crucial for ensuring comprehensive monitoring and observability across an infrastructure, particularly under the following conditions: + +1. **Ephemeral Systems**: For systems like Kubernetes nodes or ephemeral VMs that may not be persistently available, centralization points ensure that metrics and logs are not lost when these systems go offline. This is essential for maintaining historical data for analysis and troubleshooting. + +2. **Resource Constraints**: In scenarios where the monitored systems lack sufficient resources (disk space or I/O bandwidth, CPU, RAM) to handle observability tasks effectively, centralization points offload these responsibilities, ensuring that production systems can operate efficiently without compromise. + +3. **Multi-node Dashboards without Netdata Cloud**: For environments requiring aggregated views across multiple nodes but without the use of Netdata Cloud, Netdata Parents can aggregate this data to provide comprehensive dashboards, similar to what Netdata Cloud offers. + +4. **Netdata Cloud Access Restrictions**: In cases where monitored systems cannot connect to Netdata Cloud (due to a firewall policy), a Netdata Parent can serve as a bridge, aggregating data and interfacing with Netdata Cloud on behalf of these restricted systems. + +When multiple independent centralization points are available: + +- Netdata Cloud queries all of them in parallel, to provide a unified infrastructure view. + +- Without Netdata Cloud, the dashboards of each of the Netdata Parents provide unified views of the infrastructure aggregated to each of them (metrics and logs). diff --git a/docs/observability-centralization-points/best-practices.md b/docs/observability-centralization-points/best-practices.md new file mode 100644 index 00000000..49bd3d6c --- /dev/null +++ b/docs/observability-centralization-points/best-practices.md @@ -0,0 +1,39 @@ +# Best Practices for Observability Centralization Points + +When planning the deployment of Observability Centralization Points, the following factors need consideration: + +1. **Volume of Monitored Systems**: The number of systems being monitored dictates the scaling and number of centralization points required. Larger infrastructures may necessitate multiple centralization points to manage the volume of data effectively and maintain performance. + +2. **Cost of Data Transfer**: Particularly in multi-cloud or hybrid environments, the location of centralization points can significantly impact egress bandwidth costs. Strategically placing centralization points in each data center or cloud region can minimize these costs by reducing the need for cross-network data transfer. + +3. **Usability without Netdata Cloud**: When not using Netdata Cloud, observability with Netdata is simpler when there are fewer centralization points, making it easier to remember where observability is and how to access it. + +4. When Netdata Cloud is used, infrastructure level views are provided independently of the centralization points, so it is preferable to centralize as required for security (e.g. internet access), cost control (e.g. egress bandwidth, dedicated resources) and operational efficiency (regions, services or teams isolation). + +## Cost Optimization + +Netdata has been designed for observability cost optimization. For optimal cost we recommend using Netdata Cloud and multiple independent observability centralization points: + +- **Scale out**: add more, smaller centralization points to distribute the load. This strategy provides the least resource consumption per unit of workload, maintaining optimal performance and resource efficiency across your observability infrastructure. + +- **Use existing infrastructure resources**: use spare capacities before allocating dedicated resources for observability. This approach minimizes additional costs and promotes an economically sustainable observability framework. + +- **Unified or separate centralization for logs and metrics**: Netdata allows centralizing metrics and logs together or separately. Consider factors such as access frequency, data retention policies, and compliance requirements to enhance performance and reduce costs. + +- **Decentralized configuration management**: each Netdata centralization point can have its own unique configuration for retention and alerts. This enables 1) finer control on infrastructure costs and 2) localized control for separate services or teams. + +## Pros and Cons + +Compared to other observability solutions, the design of Netdata offers: + +- **Enhanced Scalability and Flexibility**: Netdata's support for multiple independent observability centralization points allows for a more scalable and flexible architecture. This feature is particularly advantageous in distributed and complex environments, enabling tailored observability strategies that can vary by region, service, or team requirements. + +- **Resilience and Fault Tolerance**: The ability to deploy multiple centralization points also contributes to greater system resilience and fault tolerance. Replication is a native feature of Netdata centralization points, so in the event of a failure at one centralization point, others can continue to function, ensuring continuous observability. + +- **Optimized Cost and Performance**: By distributing the load across multiple centralization points, Netdata can optimize both performance and cost. This distribution allows for the efficient use of resources and help mitigate the bottlenecks associated with a single centralization point. + +- **Simplicity**: Netdata agents (Children and Parents) require minimal configuration and maintenance, usually less than the configuration and maintenance required for the agents and exporters of other monitoring solutions. This provides an observability pipeline that has less moving parts and is easier to manage and maintain. + +- **Always On-Prem**: Netdata centralization points are always on-prem. Even when Netdata Cloud is used, Netdata agents and parents are queried to provide the data required for the dashboards. + +- **Bottom-Up Observability**: Netdata is designed to monitor systems, containers and applications bottom-up, aiming to provide the maximum resolution, visibility, depth and insights possible. Its ability to segment the infrastructure into multiple independent observability centralization points with customized retention, machine learning and alerts on each of them, while providing unified infrastructure level dashboards at Netdata Cloud, provides a flexible environment that can be tailored per service or team, while still being one unified infrastructure. diff --git a/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/README.md b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/README.md new file mode 100644 index 00000000..e40396a7 --- /dev/null +++ b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/README.md @@ -0,0 +1,55 @@ +# Logs Centralization Points with systemd-journald + +```mermaid +stateDiagram-v2 + classDef userFeature fill:#f00,color:white,font-weight:bold,stroke-width:2px,stroke:orange + classDef usedByNC fill:#090,color:white,font-weight:bold,stroke-width:2px,stroke:orange + classDef log2journal fill:#040,color:white,font-weight:bold,stroke-width:2px,stroke:orange + + logsQuery:::usedByNC + logsDashboard:::userFeature + journald:::thirdparty + journalRemote:::thirdparty + journalUpload:::thirdparty + journalFiles:::thirdparty + logSources:::thirdparty + textFiles:::thirdparty + log2journal:::log2journal + + journalRemote: systemd-journal-remote + journalUpload: systemd-journal-upload + journalFiles: Store + journalFiles: systemd-journal files + journald: systemd-journald + logSources: Local Logs Sources + log2journal:log2journal + log2journal:Convert text, json, logfmt files + log2journal:to structured journal entries. + logsDashboard: Netdata Dashboards + logsQuery: Query Journal Files + textFiles: Text Log Files + + logSources --> journald: journald API + logSources --> textFiles: write to log files + textFiles --> log2journal: tail log files + log2journal --> journald: journald API + journald --> journalFiles + + journalFiles --> Netdata + journalFiles --> journalUpload + + journalRemote -->journalFiles + journalUpload --> [*]: to a remote journald + [*] --> journalRemote: from a remote journald + + state Netdata { + [*]-->logsQuery + logsQuery --> logsDashboard + } +``` + +Logs centralization points can be built using the `systemd-journald` methodologies, by configuring `systemd-journal-remote` (on the centralization point) and `systemd-journal-upload` (on the production system). + +The logs centralization points and the metrics centralization points do not need to be the same. For clarity and simplicity however, when not otherwise required for operational or regulatory reasons, we recommend to have unified centralization points for both metrics and logs. + +A Netdata running at the logs centralization point, will automatically detect and present the logs of all servers aggregated to it in a unified way (i.e. logs from all servers multiplexed in the same view). This Netdata may or may not be a Netdata Parent for metrics. diff --git a/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/active-journal-source-without-encryption.md b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/active-journal-source-without-encryption.md new file mode 100644 index 00000000..cbed1e81 --- /dev/null +++ b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/active-journal-source-without-encryption.md @@ -0,0 +1,126 @@ +# Active journal source without encryption + +This page will guide you through creating an active journal source without the use of encryption. + +Once you enable an active journal source on a server, `systemd-journal-gatewayd` will expose an REST API on TCP port 19531. This API can be used for querying the logs, exporting the logs, or monitoring new log entries, remotely. + +> ⚠️ **IMPORTANT**<br/> +> These instructions will expose your logs to the network, without any encryption or authorization.<br/> +> DO NOT USE THIS ON NON-TRUSTED NETWORKS. + +## Configuring an active journal source + +On the server you want to expose their logs, install `systemd-journal-gateway`. + +```bash +# change this according to your distro +sudo apt-get install systemd-journal-gateway +``` + +Optionally, if you want to change the port (the default is `19531`), edit `systemd-journal-gatewayd.socket` + +```bash +# edit the socket file +sudo systemctl edit systemd-journal-gatewayd.socket +``` + +and add the following lines into the instructed place, and choose your desired port; save and exit. + +```bash +[Socket] +ListenStream=<DESIRED_PORT> +``` + +Finally, enable it, so that it will start automatically upon receiving a connection: + +```bash +# enable systemd-journal-remote +sudo systemctl daemon-reload +sudo systemctl enable --now systemd-journal-gatewayd.socket +``` + +## Using the active journal source + +### Simple Logs Explorer + +`systemd-journal-gateway` provides a simple HTML5 application to browse the logs. + +To use it, open your web browser and navigate to: + +``` +http://server.ip:19531/browse +``` + +A simple page like this will be presented: + + + +### Use it with `curl` + +`man systemd-journal-gatewayd` documents the supported API methods and provides examples to query the API using `curl` commands. + +### Copying the logs to a central journals server + +`systemd-journal-remote` has the ability to query instances of `systemd-journal-gatewayd` to fetch their logs, so that the central server fetches the logs, instead of waiting for the individual servers to push their logs to it. + +However, this kind of logs centralization has a key problem: **there is no guarantee that there will be no gaps in the logs replicated**. Theoretically, the REST API of `systemd-journal-gatewayd` supports querying past data, and `systemd-journal-remote` could keep track of the state of replication and automatically continue from the point it stopped last time. But it does not. So, currently the best logs centralization option is to use a **passive** centralization, where the clients push their logs to the server. + +Given these limitations, if you still want to configure an **active** journals centralization, this is what you need to do: + +On the centralization server install `systemd-journal-remote`: + +```bash +# change this according to your distro +sudo apt-get install systemd-journal-remote +``` + +Then, copy `systemd-journal-remote.service` to configure it for querying the active source: + +```bash +# replace "clientX" with the name of the active client node +sudo cp /lib/systemd/system/systemd-journal-remote.service /etc/systemd/system/systemd-journal-remote-clientX.service + +# edit it to make sure it the ExecStart line is like this: +# ExecStart=/usr/lib/systemd/systemd-journal-remote --url http://clientX:19531/entries?follow +sudo nano /etc/systemd/system/systemd-journal-remote-clientX.service + +# reload systemd +sudo systemctl daemon-reload +``` + +```bash +# enable systemd-journal-remote +sudo systemctl enable --now systemd-journal-remote-clientX.service +``` + +You can repeat this process to create as many `systemd-journal-remote` services, as the active source you have. + +## Verify it works + +To verify the central server is receiving logs, run this on the central server: + +```bash +sudo ls -l /var/log/journal/remote/ +``` + +You should see new files from the client's hostname or IP. + +Also, any of the new service files (`systemctl status systemd-journal-clientX`) should show something like this: + +```bash +● systemd-journal-clientX.service - Fetching systemd journal logs from 192.168.2.146 + Loaded: loaded (/etc/systemd/system/systemd-journal-clientX.service; enabled; preset: disabled) + Drop-In: /usr/lib/systemd/system/service.d + └─10-timeout-abort.conf + Active: active (running) since Wed 2023-10-18 07:35:52 EEST; 23min ago + Main PID: 77959 (systemd-journal) + Tasks: 2 (limit: 6928) + Memory: 7.7M + CPU: 518ms + CGroup: /system.slice/systemd-journal-clientX.service + ├─77959 /usr/lib/systemd/systemd-journal-remote --url "http://192.168.2.146:19531/entries?follow" + └─77962 curl "-HAccept: application/vnd.fdo.journal" --silent --show-error "http://192.168.2.146:19531/entries?follow" + +Oct 18 07:35:52 systemd-journal-server systemd[1]: Started systemd-journal-clientX.service - Fetching systemd journal logs from 192.168.2.146. +Oct 18 07:35:52 systemd-journal-server systemd-journal-remote[77959]: Spawning curl http://192.168.2.146:19531/entries?follow... +``` diff --git a/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/passive-journal-centralization-with-encryption-using-self-signed-certificates.md b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/passive-journal-centralization-with-encryption-using-self-signed-certificates.md new file mode 100644 index 00000000..7f0b7152 --- /dev/null +++ b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/passive-journal-centralization-with-encryption-using-self-signed-certificates.md @@ -0,0 +1,249 @@ +# Passive journal centralization with encryption using self-signed certificates + +This page will guide you through creating a **passive** journal centralization setup using **self-signed certificates** for encryption and authorization. + +Once you centralize your infrastructure logs to a server, Netdata will automatically detect all the logs from all servers and organize them in sources. With the setup described in this document, on recent systemd versions, Netdata will automatically name all remote sources using the names of the clients, as they are described at their certificates (on older versions, the names will be IPs or reverse DNS lookups of the IPs). + +A **passive** journal server waits for clients to push their metrics to it, so in this setup we will: + +1. configure a certificates authority and issue self-signed certificates for your servers. +2. configure `systemd-journal-remote` on the server, to listen for incoming connections. +3. configure `systemd-journal-upload` on the clients, to push their logs to the server. + +Keep in mind that the authorization involved works like this: + +1. The server (`systemd-journal-remote`) validates that the client (`systemd-journal-upload`) uses a trusted certificate (a certificate issued by the same certificate authority as its own). + So, **the server will accept logs from any client having a valid certificate**. +2. The client (`systemd-journal-upload`) validates that the receiver (`systemd-journal-remote`) uses a trusted certificate (like the server does) and it also checks that the hostname or IP of the URL specified to its configuration, matches one of the names or IPs of the server it gets connected to. So, **the client does a validation that it connected to the right server**, using the URL hostname against the names and IPs of the server on its certificate. + +This means, that if both certificates are issued by the same certificate authority, only the client can potentially reject the server. + +## Self-signed certificates + +To simplify the process of creating and managing self-signed certificates, we have created [this bash script](https://github.com/netdata/netdata/blob/master/src/collectors/systemd-journal.plugin/systemd-journal-self-signed-certs.sh). + +This helps to also automate the distribution of the certificates to your servers (it generates a new bash script for each of your servers, which includes everything required, including the certificates). + +We suggest to keep this script and all the involved certificates at the journals centralization server, in the directory `/etc/ssl/systemd-journal`, so that you can make future changes as required. If you prefer to keep the certificate authority and all the certificates at a more secure location, just use the script on that location. + +On the server that will issue the certificates (usually the centralizaton server), do the following: + +```bash +# install systemd-journal-remote to add the users and groups required and openssl for the certs +# change this according to your distro +sudo apt-get install systemd-journal-remote openssl + +# download the script and make it executable +curl >systemd-journal-self-signed-certs.sh "https://raw.githubusercontent.com/netdata/netdata/master/src/collectors/systemd-journal.plugin/systemd-journal-self-signed-certs.sh" +chmod 750 systemd-journal-self-signed-certs.sh +``` + +To create certificates for your servers, run this: + +```bash +sudo ./systemd-journal-self-signed-certs.sh "server1" "DNS:hostname1" "IP:10.0.0.1" +``` + +Where: + +- `server1` is the canonical name of the server. On newer systemd version, this name will be used by `systemd-journal-remote` and Netdata when you view the logs on the dashboard. +- `DNS:hostname1` is a DNS name that the server is reachable at. Add `"DNS:xyz"` multiple times to define multiple DNS names for the server. +- `IP:10.0.0.1` is an IP that the server is reachable at. Add `"IP:xyz"` multiple times to define multiple IPs for the server. + +Repeat this process to create the certificates for all your servers. You can add servers as required, at any time in the future. + +Existing certificates are never re-generated. Typically certificates need to be revoked and new ones to be issued. But `systemd-journal-remote` tools do not support handling revocations. So, the only option you have to re-issue a certificate is to delete its files in `/etc/ssl/systemd-journal` and run the script again to create a new one. + +Once you run the script of each of your servers, in `/etc/ssl/systemd-journal` you will find shell scripts named `runme-on-XXX.sh`, where `XXX` are the canonical names of your servers. + +These `runme-on-XXX.sh` include everything to install the certificates, fix their file permissions to be accessible by `systemd-journal-remote` and `systemd-journal-upload`, and update `/etc/systemd/journal-remote.conf` and `/etc/systemd/journal-upload.conf`. + +You can copy and paste (or `scp`) these scripts on your server and each of your clients: + +```bash +sudo scp /etc/ssl/systemd-journal/runme-on-XXX.sh XXX:/tmp/ +``` + +For the rest of this guide, we assume that you have copied the right `runme-on-XXX.sh` at the `/tmp` of all the servers for which you issued certificates. + +### note about certificates file permissions + +It is worth noting that `systemd-journal` certificates need to be owned by `systemd-journal-remote:systemd-journal`. + +Both the user `systemd-journal-remote` and the group `systemd-journal` are automatically added by the `systemd-journal-remote` package. However, `systemd-journal-upload` (and `systemd-journal-gatewayd` - that is not used in this guide) use dynamic users. Thankfully they are added to the `systemd-journal` remote group. + +So, by having the certificates owned by `systemd-journal-remote:systemd-journal`, satisfies both `systemd-journal-remote` which is not in the `systemd-journal` group, and `systemd-journal-upload` (and `systemd-journal-gatewayd`) which use dynamic users. + +You don't need to do anything about it (the scripts take care of everything), but it is worth noting how this works. + +## Server configuration + +On the centralization server install `systemd-journal-remote`: + +```bash +# change this according to your distro +sudo apt-get install systemd-journal-remote +``` + +Make sure the journal transfer protocol is `https`: + +```bash +sudo cp /lib/systemd/system/systemd-journal-remote.service /etc/systemd/system/ + +# edit it to make sure it says: +# --listen-https=-3 +# not: +# --listen-http=-3 +sudo nano /etc/systemd/system/systemd-journal-remote.service + +# reload systemd +sudo systemctl daemon-reload +``` + +Optionally, if you want to change the port (the default is `19532`), edit `systemd-journal-remote.socket` + +```bash +# edit the socket file +sudo systemctl edit systemd-journal-remote.socket +``` + +and add the following lines into the instructed place, and choose your desired port; save and exit. + +```bash +[Socket] +ListenStream=<DESIRED_PORT> +``` + +Next, run the `runme-on-XXX.sh` script on the server: + +```bash +# if you run the certificate authority on the server: +sudo /etc/ssl/systemd-journal/runme-on-XXX.sh + +# if you run the certificate authority elsewhere, +# assuming you have coped the runme-on-XXX.sh script (as described above): +sudo bash /tmp/runme-on-XXX.sh +``` + +This will install the certificates in `/etc/ssl/systemd-journal`, set the right file permissions, and update `/etc/systemd/journal-remote.conf` and `/etc/systemd/journal-upload.conf` to use the right certificate files. + +Finally, enable it, so that it will start automatically upon receiving a connection: + +```bash +# enable systemd-journal-remote +sudo systemctl enable --now systemd-journal-remote.socket +sudo systemctl enable systemd-journal-remote.service +``` + +`systemd-journal-remote` is now listening for incoming journals from remote hosts. + +> When done, remember to `rm /tmp/runme-on-*.sh` to make sure your certificates are secure. + +## Client configuration + +On the clients, install `systemd-journal-remote` (it includes `systemd-journal-upload`): + +```bash +# change this according to your distro +sudo apt-get install systemd-journal-remote +``` + +Edit `/etc/systemd/journal-upload.conf` and set the IP address and the port of the server, like so: + +```conf +[Upload] +URL=https://centralization.server.ip:19532 +``` + +Make sure that `centralization.server.ip` is one of the `DNS:` or `IP:` parameters you defined when you created the centralization server certificates. If it is not, the client may reject to connect. + +Next, edit `systemd-journal-upload.service`, and add `Restart=always` to make sure the client will keep trying to push logs, even if the server is temporarily not there, like this: + +```bash +sudo systemctl edit systemd-journal-upload.service +``` + +At the top, add: + +```conf +[Service] +Restart=always +``` + +Enable `systemd-journal-upload.service`, like this: + +```bash +sudo systemctl enable systemd-journal-upload.service +``` + +Assuming that you have in `/tmp` the relevant `runme-on-XXX.sh` script for this client, run: + +```bash +sudo bash /tmp/runme-on-XXX.sh +``` + +This will install the certificates in `/etc/ssl/systemd-journal`, set the right file permissions, and update `/etc/systemd/journal-remote.conf` and `/etc/systemd/journal-upload.conf` to use the right certificate files. + +Finally, restart `systemd-journal-upload.service`: + +```bash +sudo systemctl restart systemd-journal-upload.service +``` + +The client should now be pushing logs to the central server. + +> When done, remember to `rm /tmp/runme-on-*.sh` to make sure your certificates are secure. + +Here it is in action, in Netdata: + + + +## Verify it works + +To verify the central server is receiving logs, run this on the central server: + +```bash +sudo ls -l /var/log/journal/remote/ +``` + +Depending on the `systemd` version you use, you should see new files from the clients' canonical names (as defined at their certificates) or IPs. + +Also, `systemctl status systemd-journal-remote` should show something like this: + +```bash +systemd-journal-remote.service - Journal Remote Sink Service + Loaded: loaded (/etc/systemd/system/systemd-journal-remote.service; indirect; preset: disabled) + Active: active (running) since Sun 2023-10-15 14:29:46 EEST; 2h 24min ago +TriggeredBy: ● systemd-journal-remote.socket + Docs: man:systemd-journal-remote(8) + man:journal-remote.conf(5) + Main PID: 2118153 (systemd-journal) + Status: "Processing requests..." + Tasks: 1 (limit: 154152) + Memory: 2.2M + CPU: 71ms + CGroup: /system.slice/systemd-journal-remote.service + └─2118153 /usr/lib/systemd/systemd-journal-remote --listen-https=-3 --output=/var/log/journal/remote/ +``` + +Note the `status: "Processing requests..."` and the PID under `CGroup`. + +On the client `systemctl status systemd-journal-upload` should show something like this: + +```bash +● systemd-journal-upload.service - Journal Remote Upload Service + Loaded: loaded (/lib/systemd/system/systemd-journal-upload.service; enabled; vendor preset: disabled) + Drop-In: /etc/systemd/system/systemd-journal-upload.service.d + └─override.conf + Active: active (running) since Sun 2023-10-15 10:39:04 UTC; 3h 17min ago + Docs: man:systemd-journal-upload(8) + Main PID: 4169 (systemd-journal) + Status: "Processing input..." + Tasks: 1 (limit: 13868) + Memory: 3.5M + CPU: 1.081s + CGroup: /system.slice/systemd-journal-upload.service + └─4169 /lib/systemd/systemd-journal-upload --save-state +``` + +Note the `Status: "Processing input..."` and the PID under `CGroup`. diff --git a/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/passive-journal-centralization-without-encryption.md b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/passive-journal-centralization-without-encryption.md new file mode 100644 index 00000000..b70c2203 --- /dev/null +++ b/docs/observability-centralization-points/logs-centralization-points-with-systemd-journald/passive-journal-centralization-without-encryption.md @@ -0,0 +1,150 @@ +# Passive journal centralization without encryption + +This page will guide you through creating a passive journal centralization setup without the use of encryption. + +Once you centralize your infrastructure logs to a server, Netdata will automatically detects all the logs from all servers and organize them in sources. +With the setup described in this document, journal files are identified by the IPs of the clients sending the logs. Netdata will automatically do +reverse DNS lookups to find the names of the server and name the sources on the dashboard accordingly. + +A _passive_ journal server waits for clients to push their metrics to it, so in this setup we will: + +1. configure `systemd-journal-remote` on the server, to listen for incoming connections. +2. configure `systemd-journal-upload` on the clients, to push their logs to the server. + +> ⚠️ **IMPORTANT**<br/> +> These instructions will copy your logs to a central server, without any encryption or authorization.<br/> +> DO NOT USE THIS ON NON-TRUSTED NETWORKS. + +## Server configuration + +On the centralization server install `systemd-journal-remote`: + +```bash +# change this according to your distro +sudo apt-get install systemd-journal-remote +``` + +Make sure the journal transfer protocol is `http`: + +```bash +sudo cp /lib/systemd/system/systemd-journal-remote.service /etc/systemd/system/ + +# edit it to make sure it says: +# --listen-http=-3 +# not: +# --listen-https=-3 +sudo nano /etc/systemd/system/systemd-journal-remote.service + +# reload systemd +sudo systemctl daemon-reload +``` + +Optionally, if you want to change the port (the default is `19532`), edit `systemd-journal-remote.socket` + +```bash +# edit the socket file +sudo systemctl edit systemd-journal-remote.socket +``` + +and add the following lines into the instructed place, and choose your desired port; save and exit. + +```bash +[Socket] +ListenStream=<DESIRED_PORT> +``` + +Finally, enable it, so that it will start automatically upon receiving a connection: + +```bash +# enable systemd-journal-remote +sudo systemctl enable --now systemd-journal-remote.socket +sudo systemctl enable systemd-journal-remote.service +``` + +`systemd-journal-remote` is now listening for incoming journals from remote hosts. + +## Client configuration + +On the clients, install `systemd-journal-remote` (it includes `systemd-journal-upload`): + +```bash +# change this according to your distro +sudo apt-get install systemd-journal-remote +``` + +Edit `/etc/systemd/journal-upload.conf` and set the IP address and the port of the server, like so: + +```conf +[Upload] +URL=http://centralization.server.ip:19532 +``` + +Edit `systemd-journal-upload`, and add `Restart=always` to make sure the client will keep trying to push logs, even if the server is temporarily not there, like this: + +```bash +sudo systemctl edit systemd-journal-upload +``` + +At the top, add: + +```conf +[Service] +Restart=always +``` + +Enable and start `systemd-journal-upload`, like this: + +```bash +sudo systemctl enable systemd-journal-upload +sudo systemctl start systemd-journal-upload +``` + +## Verify it works + +To verify the central server is receiving logs, run this on the central server: + +```bash +sudo ls -l /var/log/journal/remote/ +``` + +You should see new files from the client's IP. + +Also, `systemctl status systemd-journal-remote` should show something like this: + +```bash +systemd-journal-remote.service - Journal Remote Sink Service + Loaded: loaded (/etc/systemd/system/systemd-journal-remote.service; indirect; preset: disabled) + Active: active (running) since Sun 2023-10-15 14:29:46 EEST; 2h 24min ago +TriggeredBy: ● systemd-journal-remote.socket + Docs: man:systemd-journal-remote(8) + man:journal-remote.conf(5) + Main PID: 2118153 (systemd-journal) + Status: "Processing requests..." + Tasks: 1 (limit: 154152) + Memory: 2.2M + CPU: 71ms + CGroup: /system.slice/systemd-journal-remote.service + └─2118153 /usr/lib/systemd/systemd-journal-remote --listen-http=-3 --output=/var/log/journal/remote/ +``` + +Note the `status: "Processing requests..."` and the PID under `CGroup`. + +On the client `systemctl status systemd-journal-upload` should show something like this: + +```bash +● systemd-journal-upload.service - Journal Remote Upload Service + Loaded: loaded (/lib/systemd/system/systemd-journal-upload.service; enabled; vendor preset: disabled) + Drop-In: /etc/systemd/system/systemd-journal-upload.service.d + └─override.conf + Active: active (running) since Sun 2023-10-15 10:39:04 UTC; 3h 17min ago + Docs: man:systemd-journal-upload(8) + Main PID: 4169 (systemd-journal) + Status: "Processing input..." + Tasks: 1 (limit: 13868) + Memory: 3.5M + CPU: 1.081s + CGroup: /system.slice/systemd-journal-upload.service + └─4169 /lib/systemd/systemd-journal-upload --save-state +``` + +Note the `Status: "Processing input..."` and the PID under `CGroup`. diff --git a/docs/observability-centralization-points/metrics-centralization-points/README.md b/docs/observability-centralization-points/metrics-centralization-points/README.md new file mode 100644 index 00000000..0efdacd4 --- /dev/null +++ b/docs/observability-centralization-points/metrics-centralization-points/README.md @@ -0,0 +1,48 @@ + +# Metrics Centralization Points (Netdata Parents) + +```mermaid +flowchart BT + C1["Netdata Child 1"] + C2["Netdata Child 2"] + C3["Netdata Child N"] + P1["Netdata Parent 1"] + C1 -->|stream| P1 + C2 -->|stream| P1 + C3 -->|stream| P1 +``` + +Netdata **Streaming and Replication** copies the recent past samples (replication) and in real-time all new samples collected (streaming) from production systems (Netdata Children) to metrics centralization points (Netdata Parents). The Netdata Parents then maintain the database for these metrics, according to their retention settings. + +Each production system (Netdata Child) can stream to **only one** Netdata Parent at a time. The configuration allows configuring multiple Netdata Parents for high availability, but only the first found working will be used. + +Netdata Parents receive metric samples **from multiple** production systems (Netdata Children) and have the option to re-stream them to another Netdata Parent. This allows building an infinite hierarchy of Netdata Parents. It also enables the configuration of Netdata Parents Clusters, for high availability. + +| Feature | Netdata Child (production system) | Netdata Parent (centralization point) | +|:---------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------:| +| Metrics Retention | Can be minimized, or switched to mode `ram` or `alloc` to save resources. Some retention is required in case network errors introduce disconnects. | Common retention settings for all systems aggregated to it. | +| Machine Learning | Can be disabled (enabled by default). | Runs Anomaly Detection for all systems aggregated to it. | +| Alerts & Notifications | Can be disabled (enabled by default). | Runs health checks and sends notifications for all systems aggregated to it. | +| API and Dashboard | Can be disabled (enabled by default). | Serves the dashboard for all systems aggregated to it, using its own retention. | +| Exporting Metrics | Not required (enabled by default). | Exports the samples of all metrics collected by the systems aggregated to it. | +| Netdata Functions | Netdata Child must be online. | Forwards Functions requests to the Children connected to it. | +| Connection to Netdata Cloud | Not required. | Each Netdata Parent registers to Netdata Cloud all systems aggregated to it. | + +## Supported Configurations + +For Netdata Children: + +1. **Full**: Full Netdata functionality is available at the Children. This means running machine learning, alerts, notifications, having the local dashboard available, and generally all Netdata features enabled. This is the default. +2. **Thin**: The Children are only collecting and forwarding metrics to a Parent. Some local retention may exist to avoid missing samples in case of network issues or Parent maintenance, but everything else is disabled. + +For Netdata Parents: + +1. **Standalone**: The Parent is standalone, either the only Parent available in the infrastructure, or the top-most of an hierarchy of Parents. +2. **Cluster**: The Parent is part of a cluster of Parents, all having the same data, from the same Children. A Cluster of Parents offers high-availability. +3. **Proxy**: The Parent receives metrics and stores them locally, but it also forwards them to a Grand Parent. + +A Cluster is configured as a number of circular **Proxies**, ie. each of the nodes in a cluster has all the others configured as its Parents. So, if multiple levels of metrics centralization points (Netdata Parents) are required, only the top-most level can be a cluster. + +## Best Practices + +Refer to [Best Practices for Observability Centralization Points](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/best-practices.md). diff --git a/docs/observability-centralization-points/metrics-centralization-points/clustering-and-high-availability-of-netdata-parents.md b/docs/observability-centralization-points/metrics-centralization-points/clustering-and-high-availability-of-netdata-parents.md new file mode 100644 index 00000000..d2202960 --- /dev/null +++ b/docs/observability-centralization-points/metrics-centralization-points/clustering-and-high-availability-of-netdata-parents.md @@ -0,0 +1,50 @@ +# Clustering and High Availability of Netdata Parents + +```mermaid +flowchart BT + C1["Netdata Child 1"] + C2["Netdata Child 2"] + C3["Netdata Child N"] + P1["Netdata Parent 1"] + P2["Netdata Parent 2"] + C1 & C2 & C3 -->|stream| P1 + P1 -->|stream| P2 + C1 & C2 & C3 .->|failover| P2 + P2 .->|failover| P1 +``` + +Netdata supports building Parent clusters of 2+ nodes. Clustering and high availability works like this: + +1. All Netdata Children are configured to stream to all Netdata Parents. The first one found working will be used by each Netdata Child and the others will be automatically used if and when this connection is interrupted. +2. The Netdata Parents are configured to stream to all other Netdata Parents. For each of them, the first found working will be used and the others will be automatically used if and when this connection is interrupted. + +All the Netdata Parents in such a cluster will receive all the metrics of all Netdata Children connected to any of them. They will also receive the metrics all the other Netdata Parents have. + +In case there is a failure on any of the Netdata Parents, the Netdata Children connected to it will automatically failover to another available Netdata Parent, which now will attempt to re-stream all the metrics it receives to the other available Netdata Parents. + +Netdata Cloud will receive registrations for all Netdata Children from all the Netdata Parents. As long as at least one of the Netdata Parents is connected to Netdata Cloud, all the Netdata Children will be available on Netdata Cloud. + +Netdata Children need to maintain a retention only for the time required to switch Netdata Parents. When Netdata Children connect to a Netdata Parent, they negotiate the available retention and any missing data on the Netdata Parent are replicated from the Netdata Children. + +## Restoring a Netdata Parent after maintenance + +Given the [replication limitations](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/metrics-centralization-points/replication-of-past-samples.md#replication-limitations), special care is needed when restoring a Netdata Parent after some long maintenance work on it. + +If the Netdata Children do not have enough retention to replicate the missing data on this Netdata Parent, it is preferable to block access to this Netdata Parent from the Netdata Children, until it replicates the missing data from the other Netdata Parents. + +To block access from Netdata Children, and still allow access from other Netdata Parent siblings: + +1. Use `iptables` to block access to port 19999 from Netdata Children to the restored Netdata Parent, or +2. Use separate streaming API keys (in `stream.conf`) for Netdata Children and Netdata Parents, and disable the API key used by Netdata Children, until the restored Netdata Parent has been synchronized. + +## Duplicating a Parent + +The easiest way is to `rsync` the directory `/var/cache/netdata` from the existing Netdata Parent to the new Netdata Parent. + +> Important: Starting the new Netdata Parent with default settings, may delete the new files in `/var/cache/netdata` to apply the default disk size constraints. Therefore it is important to set the right retention settings in the new Netdata Parent before starting it up with the copied files. + +To configure retention at the new Netdata Parent, set in `netdata.conf` the following to at least the values the old Netdata Parent has: + +- `[db].dbengine multihost disk space MB`, this is the max disk size for `tier0`. The default is 256MiB. +- `[db].dbengine tier 1 multihost disk space MB`, this is the max disk space for `tier1`. The default is 50% of `tier0`. +- `[db].dbengine tier 2 multihost disk space MB`, this is the max disk space for `tier2`. The default is 50% of `tier1`. diff --git a/docs/observability-centralization-points/metrics-centralization-points/configuration.md b/docs/observability-centralization-points/metrics-centralization-points/configuration.md new file mode 100644 index 00000000..e52f7309 --- /dev/null +++ b/docs/observability-centralization-points/metrics-centralization-points/configuration.md @@ -0,0 +1,105 @@ +# Configuring Metrics Centralization Points + +Metrics streaming configuration for both Netdata Children and Parents is done via `stream.conf`. + +`netdata.conf` and `stream.conf` have the same `ini` format, but `netdata.conf` is considered a non-sensitive file, while `stream.conf` contains API keys, IPs and other sensitive information that enable communication between Netdata agents. + +`stream.conf` has 2 main sections: + +- The `[stream]` section includes options for the **sending Netdata** (ie Netdata Children, or Netdata Parents that stream to Grand Parents, or to other sibling Netdata Parents in a cluster). +- The rest includes multiple sections that define API keys for the **receiving Netdata** (ie. Netdata Parents). + +## Edit `stream.conf` + +To edit `stream.conf`, run this on your terminal: + +```bash +cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata +sudo ./edit-config stream.conf +``` + +Your editor will open, with defaults and commented `stream.conf` options. + +## Configuring a Netdata Parent + +To enable the reception of metrics from Netdata Children, generate a random API key with this command: + +```bash +uuidgen +``` + +Then, copy the UUID generated, [edit `stream.conf`](#edit-stream.conf), find the section that reads like the following and replace `API_KEY` with the UUID you generated: + +```ini +[API_KEY] + # Accept metrics streaming from other Agents with the specified API key + enabled = yes +``` + +Save the file and restart Netdata. + +## Configuring Netdata Children + +To enable streaming metrics to a Netdata Parent, [edit `stream.conf`](#edit-stream.conf), and at the `[stream]` section at the top, set: + +```ini +[stream] + # Stream metrics to another Netdata + enabled = yes + # The IP and PORT of the parent + destination = PARENT_IP_ADDRESS:19999 + # The shared API key, generated by uuidgen + api key = API_KEY +``` + +Save the file and restart Netdata. + +## Enable TLS/SSL Communication + +While encrypting the connection between your parent and child nodes is recommended for security, it's not required to get started. + +This example uses self-signed certificates. + +> **Note** +> This section assumes you have read the documentation on [how to edit the Netdata configuration files](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md). +<!-- here we need link to the section that will contain the restarting instructions --> + +1. **Parent node** + To generate an SSL key and certificate using `openssl`, take a look at the related section around [Securing Netdata Agents](https://github.com/netdata/netdata/blob/master/src/web/server/README.md#enable-httpstls-support) in our Documentation. + +2. **Child node** + Update `stream.conf` to enable SSL/TLS and allow self-signed certificates. Append ':SSL' to the destination and uncomment 'ssl skip certificate verification'. + + ```conf + [stream] + enabled = yes + destination = 203.0.113.0:SSL + ssl skip certificate verification = yes + api key = 11111111-2222-3333-4444-555555555555 + ``` + +3. Restart the Netdata Agent on both the parent and child nodes, to stream encrypted metrics using TLS/SSL. + + + +## Troubleshooting Streaming Connections + +You can find any issues related to streaming at Netdata logs. + +### From the UI + +Netdata logs to systemd-journald by default, and its logs are available at the `Logs` tab of the UI. At the `MESSAGE_ID` field look for `Netdata connection from child` and `Netdata connection to parent`. + +### From the terminal + +On the Parents: + +```bash +journalctl -r --namespace=netdata MESSAGE_ID=ed4cdb8f1beb4ad3b57cb3cae2d162fa +``` + +On the Children: + +```bash +journalctl -r --namespace=netdata MESSAGE_ID=6e2e3839067648968b646045dbf28d66 +``` diff --git a/docs/observability-centralization-points/metrics-centralization-points/faq.md b/docs/observability-centralization-points/metrics-centralization-points/faq.md new file mode 100644 index 00000000..3c019108 --- /dev/null +++ b/docs/observability-centralization-points/metrics-centralization-points/faq.md @@ -0,0 +1,70 @@ +# FAQ on Metrics Centralization Points + +## How much can a Netdata Parent node scale? + +Netdata Parents generally scale well. According [to our tests](https://blog.netdata.cloud/netdata-vs-prometheus-performance-analysis/) Netdata Parents scale better than Prometheus for the same workload: -35% CPU utilization, -49% Memory Consumption, -12% Network Bandwidth, -98% Disk I/O, -75% Disk footprint. + +For more information, Check [Sizing Netdata Parents](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/metrics-centralization-points/sizing-netdata-parents.md). + +## If I set up a parents cluster, will I be able to have more Child nodes stream to them? + +No. When you set up an active-active cluster, even if child nodes connect randomly to one or the other, all the parent nodes receive all the metrics of all the child nodes. So, all of them do all the work. + +## How much retention do the child nodes need? + +Child nodes need to have only the retention required in order to connect to another Parent if one fails or stops for maintenance. + +- If you have a cluster of parents, 5 to 10 minutes in `alloc` mode is usually enough. +- If you have only 1 parent, it would be better to run the child nodes with `dbengine` so that they will have enough retention to back-fill the parent node if it stops for maintenance. + +## Does streaming between child nodes and parents support encryption? + +Yes. You can configure your parent nodes to enable TLS at their web server and configure the child nodes to connect with TLS to it. The streaming connection is also compressed, on top of TLS. + +## Can I have an HTTP proxy between parent and child nodes? + +No. The streaming protocol works on the same port as the internal web server of Netdata Agents, but the protocol is not HTTP-friendly and cannot be understood by HTTP proxy servers. + +## Should I load balance multiple parents with a TCP load balancer? + +Although this can be done and for streaming between child and parent nodes it could work, we recommend not doing it. It can lead to several kinds of problems. + +It is better to configure all the parent nodes directly in the child nodes `stream.conf`. The child nodes will do everything in their power to find a parent node to connect and they will never give up. + +## When I have multiple parents for the same children, will I receive alert notifications from all of them? + +If all parents are configured to run health checks and trigger alerts, yes. + +We recommend using Netdata Cloud to avoid receiving duplicate alert notifications. Netdata Cloud deduplicates alert notifications so that you will receive them only once. + +## When I have only Parents connected to Netdata Cloud, will I be able to use the Functions feature on my child nodes? + +Yes. Function requests will be received by the Parents and forwarded to the Child via their streaming connection. Function requests are propagated between parents, so this will work even if multiple levels of Netdata Parents are involved. + +## If I have a cluster of parents and get one out for maintenance for a few hours, will it have missing data when it returns back online? + +Check [Restoring a Netdata Parent after maintenance](https://github.com/netdata/netdata/blob/master/docs/observability-centralization-points/metrics-centralization-points/clustering-and-high-availability-of-netdata-parents.md). + +## I have a cluster of parents. Which one is used by Netdata Cloud? + +When there are multiple data sources for the same node, Netdata Cloud follows this strategy: + +1. Netdata Cloud prefers Netdata agents having `live` data. +2. For time-series queries, when multiple Netdata agents have the retention required to answer the query, Netdata Cloud prefers the one that is further away from production systems. +3. For Functions, Netdata Cloud prefers Netdata agents that are closer to the production systems. + +## Is there a way to balance child nodes to the parent nodes of a cluster? + +Yes. When configuring the Parents at the Children `stream.conf`, configure them in different order. Children get connected to the first Parent they find available, so if the order given to them is different, they will spread the connections to the Parents available. + +## Is there a way to get notified when a child gets disconnected? + +It depends on the ephemerality setting of each Netdata Child. + +1. **Permanent nodes**: These are nodes that should be available permanently and if they disconnect an alert should be triggered to notify you. By default, all nodes are considered permanent (not ephemeral). + +2. **Ephemeral nodes**: These are nodes that are ephemeral by nature and they may shutdown at any point in time without any impact on the services you run. + +To set the ephemeral flag on a node, edit its netdata.conf and in the `[health]` section set `is ephemeral = yes`. This setting is propagated to parent nodes and Netdata Cloud. + +When using Netdata Cloud (via a parent or directly) and a permanent node gets disconnected, Netdata Cloud sends node disconnection notifications. diff --git a/docs/observability-centralization-points/metrics-centralization-points/replication-of-past-samples.md b/docs/observability-centralization-points/metrics-centralization-points/replication-of-past-samples.md new file mode 100644 index 00000000..5c776b86 --- /dev/null +++ b/docs/observability-centralization-points/metrics-centralization-points/replication-of-past-samples.md @@ -0,0 +1,60 @@ +# Replication of Past Samples + +Replication is triggered when a Netdata Child connects to a Netdata Parent. It replicates the latest samples of collected metrics a Netdata Parent may be missing. The goal of replication is to back-fill samples that were collected between disconnects and reconnects, so that the Netdata Parent does not have gaps on the charts for the time Netdata Children were disconnected. + +The same replication mechanism is used between Netdata Parents (the sending Netdata is treated as a Child and the receiving Netdata as a Parent). + +## Replication Limitations + +The current implementation is optimized to replicate small durations and have minimal impact during reconnects. As a result it has the following limitations: + +1. Replication can only append samples to metrics. Only missing samples at the end of each time-series are replicated. + +2. Only `tier0` samples are replicated. Samples of higher tiers in Netdata are derived from `tier0` samples, and therefore there is no mechanism for ingesting them directly. This means that the maximum retention that can be replicated across Netdata is limited by the samples available in `tier0` of the sending Netdata. + +3. Only samples of metrics that are currently being collected are replicated. Archived metrics (or even archived nodes) will be replicated when and if they are collected again. Netdata archives metrics 1 hour after they stop being collected, so Netdata Parents may miss data only if Netdata Children are disconnected for more than an hour from their Parents. + +When multiple Netdata Parents are available, the replication happens in sequence, like in the following diagram. + +```mermaid +sequenceDiagram + Child-->>Parent1: Connect + Parent1-->>Child: OK + Parent1-->>Parent2: Connect + Parent2-->>Parent1: OK + Child-->>Parent1: Metric M1 with retention up to Now + Parent1-->>Child: M1 stopped at -60sec, replicate up to Now + Child-->>Parent1: replicate M1 samples -60sec to Now + Child-->>Parent1: streaming M1 + Parent1-->>Parent2: Metric M1 with retention up to Now + Parent2-->>Parent1: M1 stopped at -63sec, replicate up to Now + Parent1-->>Parent2: replicate M1 samples -63sec to Now + Parent1-->>Parent2: streaming M1 +``` + +As shown in the diagram: + +1. All connections are established immediately after a Netdata child connects to any of the Netdata Parents. +2. Each pair of connections (Child->Parent1, Parent1->Parent2) complete replication on the receiving side and then initiate replication on the sending side. +3. Replication pushes data up to Now, and the sending side immediately enters streaming mode, without leaving any gaps on the samples of the receiving side. +4. On every pair of connections, replication negotiates the retention of the receiving party to back-fill as much data as necessary. + +## Configuration options for Replication + +The following `netdata.conf` configuration parameters affect replication. + +On the receiving side (Netdata Parent): + +- `[db].seconds to replicate` limits the maximum time to be replicated. The default is 1 day (86400 seconds). Keep in mind that replication is also limited by the `tier0` retention the sending side has. + +On the sending side (Netdata Children, or Netdata Parent when parents are clustered): + +- `[db].replication threads` controls how many concurrent threads will be replicating metrics. The default is 1. Usually the performance is about 2 million samples per second per thread, so increasing this number may allow replication to progress faster between Netdata Parents. + +- `[db].cleanup obsolete charts after secs` controls for how much time after metrics stop being collected will not be available for replication. The default is 1 hour (3600 seconds). If you plan to have scheduled maintenance on Netdata Parents of more than 1 hour, we recommend increasing this setting. Keep in mind however, that increasing this duration in highly ephemeral environments can have an impact on RAM utilization, since metrics will be considered as collected for longer durations. + +## Monitoring Replication Progress + +Inbound and outbound replication progress is reported at the dashboard using the Netdata Function `Streaming`, under the `Top` tab. + +The same information is exposed via the API endpoint `http://agent-ip:19999/api/v2/node_instances` of both Netdata Parents and Children. diff --git a/docs/observability-centralization-points/metrics-centralization-points/sizing-netdata-parents.md b/docs/observability-centralization-points/metrics-centralization-points/sizing-netdata-parents.md new file mode 100644 index 00000000..79b22334 --- /dev/null +++ b/docs/observability-centralization-points/metrics-centralization-points/sizing-netdata-parents.md @@ -0,0 +1,3 @@ +# Sizing Netdata Parents + +To estimate CPU, RAM, and disk requirements for your Netdata Parents, check [sizing Netdata agents](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/sizing-netdata-agents/README.md). diff --git a/docs/quickstart/infrastructure.md b/docs/quickstart/infrastructure.md index d2e7f2d8..26712283 100644 --- a/docs/quickstart/infrastructure.md +++ b/docs/quickstart/infrastructure.md @@ -10,7 +10,7 @@ nodes running the Netdata Agent. A node is any system in your infrastructure tha physical or virtual machine (VM), container, cloud deployment, or edge/IoT device. The Netdata Agent uses zero-configuration collectors to gather metrics from every application and container instantly, -and uses Netdata's [distributed data architecture](https://github.com/netdata/netdata/blob/master/docs/store/distributed-data-architecture.md) to store metrics +and uses Netdata's distributed data architecture to store metrics locally. Without a slow and troublesome centralized data lake for your infrastructure's metrics, you reduce the resources you need to invest in, and the complexity of, monitoring your infrastructure. @@ -143,17 +143,17 @@ sudo ./edit-config netdata.conf Our [configuration basics doc](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md) contains more information about `netdata.conf`, `edit-config`, along with simple examples to get you familiar with editing your node's configuration. -After you've learned the basics, you should [secure your infrastructure's nodes](https://github.com/netdata/netdata/blob/master/docs/netdata-security.md) using +After you've learned the basics, you should [secure your infrastructure's nodes](https://github.com/netdata/netdata/blob/master/docs/security-and-privacy-design/README.md) using one of our recommended methods. These security best practices ensure no untrusted parties gain access to the metrics collected on any of your nodes. ### Collect metrics from systems and applications -Netdata has [300+ pre-installed collectors](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md) that gather thousands of metrics with zero +Netdata has [300+ pre-installed collectors](https://github.com/netdata/netdata/blob/master/src/collectors/COLLECTORS.md) that gather thousands of metrics with zero configuration. Collectors search each of your nodes in default locations and ports to find running applications and gather as many metrics as they can without you having to configure them individually. -Most collectors work without configuration, should you want more info, you can read more on [how Netdata's metrics collectors work](https://github.com/netdata/netdata/blob/master/collectors/README.md) and the [Collectors configuration reference](https://github.com/netdata/netdata/blob/master/collectors/REFERENCE.md) documentation. +Most collectors work without configuration, should you want more info, you can read more on [how Netdata's metrics collectors work](https://github.com/netdata/netdata/blob/master/src/collectors/README.md) and the [Collectors configuration reference](https://github.com/netdata/netdata/blob/master/src/collectors/REFERENCE.md) documentation. In addition, find detailed information about which [system](https://github.com/netdata/netdata/blob/master/docs/collect/system-metrics.md), [container](https://github.com/netdata/netdata/blob/master/docs/collect/container-metrics.md), and [application](https://github.com/netdata/netdata/blob/master/docs/collect/application-metrics.md) metrics you can diff --git a/docs/running-through-cf-tunnels.md b/docs/running-through-cf-tunnels.md index 9dc263b5..324ce40e 100644 --- a/docs/running-through-cf-tunnels.md +++ b/docs/running-through-cf-tunnels.md @@ -86,7 +86,7 @@ RestartSec=5s WantedBy=multi-user.target ``` -You can edit the configuration file using the `edit-config` script from the Netdata [config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#the-netdata-config-directory). +You can edit the configuration file using the `edit-config` script from the Netdata [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration.md#the-netdata-config-directory). - Edit `netdata.conf` and input: @@ -102,7 +102,7 @@ You can edit the configuration file using the `edit-config` script from the Netd destination = tcp:127.0.0.1:19999 ``` -[Restart the Agents](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md), and you are done! +[Restart the Agents](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation), and you are done! You should now be able to have a Local Dashboard that gets its metrics from Child instances, running through Cloudflare tunnels. diff --git a/docs/netdata-security.md b/docs/security-and-privacy-design/README.md index 2716e08e..2fc6b126 100644 --- a/docs/netdata-security.md +++ b/docs/security-and-privacy-design/README.md @@ -1,4 +1,4 @@ -# Security and privacy design +# Security and Privacy Design This document serves as the relevant Annex to the [Terms of Service](https://www.netdata.cloud/service-terms/), the [Privacy Policy](https://www.netdata.cloud/privacy/) and @@ -19,7 +19,7 @@ maximum security. We aim to provide a secure environment from the ground up, rat ### Compliance with Open Source Security Foundation Best Practices Netdata is committed to adhering to the best practices laid out by the Open Source Security Foundation (OSSF). -Currently, the Netdata Agent follows the OSSF best practices at the passing level. Feel free to audit our approach to +Currently, the Netdata Agent follows the OSSF best practices at the passing level. Feel free to audit our approach to the [OSSF guidelines](https://bestpractices.coreinfrastructure.org/en/projects/2231) Netdata Cloud boasts of comprehensive end-to-end automated testing, encompassing the UI, back-end, and agents, where @@ -57,204 +57,6 @@ Foundation. This commitment reflects in every aspect of our operations, from the ensuring the delivery of a secure and reliable product to our users. For more information check [here](https://bestpractices.coreinfrastructure.org/en/projects/2231). -## Netdata Agent Security - -### Security by Design - -Netdata Agent is designed with a security-first approach. Its structure ensures data safety by only exposing chart -metadata and metric values, not the raw data collected. This design principle allows Netdata to be used in environments -requiring the highest level of data isolation, such as PCI Level 1. Even though Netdata plugins connect to a user's -database server or read application log files to collect raw data, only the processed metrics are stored in Netdata -databases, sent to upstream Netdata servers, or archived to external time-series databases. - -### User Data Protection - -The Netdata Agent is programmed to safeguard user data. When collecting data, the raw data does not leave the host. All -plugins, even those running with escalated capabilities or privileges, perform a hard-coded data collection job. They do -not accept commands from Netdata, and the original application data collected do not leave the process they are -collected in, are not saved, and are not transferred to the Netdata daemon. For the “Functions” feature, the data -collection plugins offer Functions, and the user interface merely calls them back as defined by the data collector. The -Netdata Agent main process does not require any escalated capabilities or privileges from the operating system, and -neither do most of the data collecting plugins. - -### Communication and Data Encryption - -Data collection plugins communicate with the main Netdata process via ephemeral, in-memory, pipes that are inaccessible -to any other process. - -Streaming of metrics between Netdata agents requires an API key and can also be encrypted with TLS if the user -configures it. - -The Netdata agent's web API can also use TLS if configured. - -When Netdata agents are claimed to Netdata Cloud, the communication happens via MQTT over Web Sockets over TLS, and -public/private keys are used for authorizing access. These keys are exchanged during the claiming process (usually -during the provisioning of each agent). - -### Authentication - -Direct user access to the agent is not authenticated, considering that users should either use Netdata Cloud, or they -are already on the same LAN, or they have configured proper firewall policies. However, Netdata agents can be hidden -behind an authenticating web proxy if required. - -For other Netdata agents streaming metrics to an agent, authentication via API keys is required and TLS can be used if -configured. - -For Netdata Cloud accessing Netdata agents, public/private key cryptography is used and TLS is mandatory. - -### Security Vulnerability Response - -If a security vulnerability is found in the Netdata Agent, the Netdata team acknowledges and analyzes each report within -three working days, kicking off a Security Release Process. Any vulnerability information shared with the Netdata team -stays within the Netdata project and is not disseminated to other projects unless necessary for fixing the issue. The -reporter is kept updated as the security issue moves from triage to identified fix, to release planning. More -information can be found [here](https://github.com/netdata/netdata/security/policy). - -### Protection Against Common Security Threats - -The Netdata agent is resilient against common security threats such as DDoS attacks and SQL injections. For DDoS, -Netdata agent uses a fixed number of threads for processing requests, providing a cap on the resources that can be -consumed. It also automatically manages its memory to prevent overutilization. SQL injections are prevented as nothing -from the UI is passed back to the data collection plugins accessing databases. - -Additionally, the Netdata agent is running as a normal, unprivileged, operating system user (a few data collections -require escalated privileges, but these privileges are isolated to just them), every netdata process runs by default -with a nice priority to protect production applications in case the system is starving for CPU resources, and Netdata -agents are configured by default to be the first processes to be killed by the operating system in case the operating -system starves for memory resources (OS-OOM - Operating System Out Of Memory events). - -### User Customizable Security Settings - -Netdata provides users with the flexibility to customize agent security settings. Users can configure TLS across the -system, and the agent provides extensive access control lists on all its interfaces to limit access to its endpoints -based on IP. Additionally, users can configure the CPU and Memory priority of Netdata agents. - -## Netdata Cloud Security - -Netdata Cloud is designed with a security-first approach to ensure the highest level of protection for user data. When -using Netdata Cloud in environments that require compliance with standards like PCI DSS, SOC 2, or HIPAA, users can be -confident that all collected data is stored within their infrastructure. Data viewed on dashboards and alert -notifications travel over Netdata Cloud, but are not stored—instead, they're transformed in transit, aggregated from -multiple agents and parents (centralization points), to appear as one data source in the user's browser. - -### User Identification and Authorization - -Netdata Cloud requires only an email address to create an account and use the service. User identification and -authorization are conducted either via third-party integrations (Google, GitHub accounts) or through short-lived access -tokens sent to the user’s email account. Email addresses are stored securely in our production database on AWS and are -also used for product and marketing communications. Netdata Cloud does not store user credentials. - -### Data Storage and Transfer - -Although Netdata Cloud does not store metric data, it does keep some metadata for each node connected to user spaces. -This metadata includes the hostname, information from the `/api/v1/info` endpoint, metric metadata -from `/api/v1/contexts`, and alerts configurations from `/api/v1/alarms`. This data is securely stored in our production -database on AWS and copied to Google BigQuery for analytics purposes. - -All data visible on Netdata Cloud is transferred through the Agent-Cloud link (ACLK) mechanism, which securely connects -a Netdata Agent to Netdata Cloud. The ACLK is encrypted and safe, and is only established if the user connects/claims -their node. Data in transit between a user and Netdata Cloud is encrypted using TLS. - -### Data Retention and Erasure - -Netdata Cloud maintains backups of customer content for approximately 90 days following a deletion. Users have the -ability to access, retrieve, correct, and delete personal data stored in Netdata Cloud. In case a user is unable to -delete personal data via self-services functionality, Netdata will delete personal data upon the customer's written -request, in accordance with applicable data protection law. - -### Infrastructure and Authentication - -Netdata Cloud operates on an Infrastructure as Code (IaC) model. Its microservices environment is completely isolated, -and all changes occur through Terraform. At the edge of Netdata Cloud, there is a TLS termination and an Identity and -Access Management (IAM) service that validates JWT tokens included in request cookies. - -Netdata Cloud does not store user credentials. - -### Security Features and Response - -Netdata Cloud offers a variety of security features, including infrastructure-level dashboards, centralized alerts -notifications, auditing logs, and role-based access to different segments of the infrastructure. The cloud service -employs several protection mechanisms against DDoS attacks, such as rate-limiting and automated blacklisting. It also -uses static code analysers to prevent other types of attacks. - -In the event of potential security vulnerabilities or incidents, Netdata Cloud follows the same process as the Netdata -agent. Every report is acknowledged and analyzed by the Netdata team within three working days, and the team keeps the -reporter updated throughout the process. - -### User Customization - -Netdata Cloud uses the highest level of security. There is no user customization available out of the box. Its security -settings are designed to provide maximum protection for all users. We are offering customization (like custom SSO -integrations, custom data retention policies, advanced user access controls, tailored audit logs, integration with other -security tools, etc.) on a per contract basis. - -### Deleting Personal Data - -Users who wish to remove all personal data (including email and activities) can delete their cloud account by logging -into Netdata Cloud and accessing their profile. - -## User Privacy and Data Protection - -Netdata Cloud is built with an unwavering commitment to user privacy and data protection. We understand that our users' -data is both sensitive and valuable, and we have implemented stringent measures to ensure its safety. - -### Data Collection - -Netdata Cloud collects minimal personal information from its users. The only personal data required to create an account -and use the service is an email address. This email address is used for product and marketing communications. -Additionally, the IP address used to access Netdata Cloud is stored in web proxy access logs. - -### Data Usage - -The collected email addresses are stored in our production database on Amazon Web Services (AWS) and copied to Google -BigQuery, our data lake, for analytics purposes. These analytics are crucial for our product development process. If a -user accepts the use of analytical cookies, their email address and IP are stored in the systems we use to track -application usage (Google Analytics, Posthog, and Gainsight PX). Subscriptions and Payments data are handled by Stripe. - -### Data Sharing - -Netdata Cloud does not share any personal data with third parties, ensuring the privacy of our users' data, but Netdata -Cloud does use third parties for its services, including, but not limited to, Google Cloud and Amazon Web Services for -its infrastructure, Stripe for payment processing, Google Analytics, Posthog and Gainsight PX for analytics. - -### Data Protection - -We use state-of-the-art security measures to protect user data from unauthorized access, use, or disclosure. All -infrastructure data visible on Netdata Cloud passes through the Agent-Cloud Link (ACLK) mechanism, which securely -connects a Netdata Agent to Netdata Cloud. The ACLK is encrypted, safe, and is only established if the user connects -their node. All data in transit between a user and Netdata Cloud is encrypted using TLS. - -### User Control over Data - -Netdata provides its users with the ability to access, retrieve, correct, and delete their personal data stored in -Netdata Cloud. This ability may occasionally be limited due to temporary service outages for maintenance or other -updates to Netdata Cloud, or when it is technically not feasible. If a customer is unable to delete personal data via -the self-services functionality, Netdata deletes the data upon the customer's written request, within the timeframe -specified in the Data Protection Agreement (DPA), and in accordance with applicable data protection laws. - -### Compliance with Data Protection Laws - -Netdata Cloud is fully compliant with data protection laws like the General Data Protection Regulation (GDPR) and the -California Consumer Privacy Act (CCPA). - -### Data Transfer - -Data transfer within Netdata Cloud is secure and respects the privacy of the user data. The Netdata Agent establishes an -outgoing secure WebSocket (WSS) connection to Netdata Cloud, ensuring that the data is encrypted when in transit. - -### Use of Tracking Technologies - -Netdata Cloud uses analytical cookies if a user consents to their use. These cookies are used to track the usage of the -application and are stored in systems like Google Analytics, Posthog and Gainsight PX. - -### Data Breach Notification Process - -In the event of a data breach, Netdata has a well-defined process in place for notifying users. The details of this -process align with the standard procedures and timelines defined in the Data Protection Agreement (DPA). - -We continually review and update our privacy and data protection practices to ensure the highest level of data safety -and privacy for our users. - ## Compliance with Regulations Netdata is committed to ensuring the security, privacy, and integrity of user data. It complies with both the General @@ -289,7 +91,7 @@ storage are carried out in full compliance with applicable data protection laws, Netdata ensures user privacy rights as mandated by the GDPR and CCPA. This includes the right to access, correct, and delete personal data. These functions are all available online via the Netdata Cloud User Interface (UI). In case a user wants to remove all personal information (email and activities), they can delete their cloud account by logging -into https://app.netdata.cloud and accessing their profile, at the bottom left of the screen. +into <https://app.netdata.cloud> and accessing their profile, at the bottom left of the screen. ### Regular Review and Updates @@ -426,4 +228,3 @@ respect for user autonomy. In summary, Netdata Cloud offers a highly secure, user-centric environment for system monitoring and troubleshooting. The company's emphasis on continuous security improvement and commitment to user privacy make it a trusted choice in the data monitoring landscape. - diff --git a/docs/security-and-privacy-design/netdata-agent-security.md b/docs/security-and-privacy-design/netdata-agent-security.md new file mode 100644 index 00000000..8cb6a9aa --- /dev/null +++ b/docs/security-and-privacy-design/netdata-agent-security.md @@ -0,0 +1,71 @@ +# Netdata Agent Security and Privacy Design + +## Security by Design + +Netdata Agent is designed with a security-first approach. Its structure ensures data safety by only exposing chart +metadata and metric values, not the raw data collected. This design principle allows Netdata to be used in environments +requiring the highest level of data isolation, such as PCI Level 1. Even though Netdata plugins connect to a user's +database server or read application log files to collect raw data, only the processed metrics are stored in Netdata +databases, sent to upstream Netdata servers, or archived to external time-series databases. + +## User Data Protection + +The Netdata Agent is programmed to safeguard user data. When collecting data, the raw data does not leave the host. All +plugins, even those running with escalated capabilities or privileges, perform a hard-coded data collection job. They do +not accept commands from Netdata, and the original application data collected do not leave the process they are +collected in, are not saved, and are not transferred to the Netdata daemon. For the “Functions” feature, the data +collection plugins offer Functions, and the user interface merely calls them back as defined by the data collector. The +Netdata Agent main process does not require any escalated capabilities or privileges from the operating system, and +neither do most of the data collecting plugins. + +## Communication and Data Encryption + +Data collection plugins communicate with the main Netdata process via ephemeral, in-memory, pipes that are inaccessible +to any other process. + +Streaming of metrics between Netdata agents requires an API key and can also be encrypted with TLS if the user +configures it. + +The Netdata agent's web API can also use TLS if configured. + +When Netdata agents are claimed to Netdata Cloud, the communication happens via MQTT over Web Sockets over TLS, and +public/private keys are used for authorizing access. These keys are exchanged during the claiming process (usually +during the provisioning of each agent). + +## Authentication + +Direct user access to the agent is not authenticated, considering that users should either use Netdata Cloud, or they +are already on the same LAN, or they have configured proper firewall policies. However, Netdata agents can be hidden +behind an authenticating web proxy if required. + +For other Netdata agents streaming metrics to an agent, authentication via API keys is required and TLS can be used if +configured. + +For Netdata Cloud accessing Netdata agents, public/private key cryptography is used and TLS is mandatory. + +## Security Vulnerability Response + +If a security vulnerability is found in the Netdata Agent, the Netdata team acknowledges and analyzes each report within +three working days, kicking off a Security Release Process. Any vulnerability information shared with the Netdata team +stays within the Netdata project and is not disseminated to other projects unless necessary for fixing the issue. The +reporter is kept updated as the security issue moves from triage to identified fix, to release planning. More +information can be found [here](https://github.com/netdata/netdata/security/policy). + +## Protection Against Common Security Threats + +The Netdata agent is resilient against common security threats such as DDoS attacks and SQL injections. For DDoS, +Netdata agent uses a fixed number of threads for processing requests, providing a cap on the resources that can be +consumed. It also automatically manages its memory to prevent overutilization. SQL injections are prevented as nothing +from the UI is passed back to the data collection plugins accessing databases. + +Additionally, the Netdata agent is running as a normal, unprivileged, operating system user (a few data collections +require escalated privileges, but these privileges are isolated to just them), every netdata process runs by default +with a nice priority to protect production applications in case the system is starving for CPU resources, and Netdata +agents are configured by default to be the first processes to be killed by the operating system in case the operating +system starves for memory resources (OS-OOM - Operating System Out Of Memory events). + +## User Customizable Security Settings + +Netdata provides users with the flexibility to customize agent security settings. Users can configure TLS across the +system, and the agent provides extensive access control lists on all its interfaces to limit access to its endpoints +based on IP. Additionally, users can configure the CPU and Memory priority of Netdata agents. diff --git a/docs/security-and-privacy-design/netdata-cloud-security.md b/docs/security-and-privacy-design/netdata-cloud-security.md new file mode 100644 index 00000000..1f1bb67d --- /dev/null +++ b/docs/security-and-privacy-design/netdata-cloud-security.md @@ -0,0 +1,125 @@ +# Netdata Cloud Security and Privacy Design + +Netdata Cloud is designed with a security-first approach to ensure the highest level of protection for user data. When +using Netdata Cloud in environments that require compliance with standards like PCI DSS, SOC 2, or HIPAA, users can be +confident that all collected data is stored within their infrastructure. Data viewed on dashboards and alert +notifications travel over Netdata Cloud, but are not stored—instead, they're transformed in transit, aggregated from +multiple agents and parents (centralization points), to appear as one data source in the user's browser. + +## User Identification and Authorization + +Netdata Cloud requires only an email address to create an account and use the service. User identification and +authorization are conducted either via third-party integrations (Google, GitHub accounts) or through short-lived access +tokens sent to the user’s email account. Email addresses are stored securely in our production database on AWS and are +also used for product and marketing communications. Netdata Cloud does not store user credentials. + +## Data Storage and Transfer + +Although Netdata Cloud does not store metric data, it does keep some metadata for each node connected to user spaces. +This metadata includes the hostname, information from the `/api/v1/info` endpoint, metric metadata +from `/api/v1/contexts`, and alerts configurations from `/api/v1/alarms`. This data is securely stored in our production +database on AWS and copied to Google BigQuery for analytics purposes. + +All data visible on Netdata Cloud is transferred through the Agent-Cloud link (ACLK) mechanism, which securely connects +a Netdata Agent to Netdata Cloud. The ACLK is encrypted and safe, and is only established if the user connects/claims +their node. Data in transit between a user and Netdata Cloud is encrypted using TLS. + +## Data Retention and Erasure + +Netdata Cloud maintains backups of customer content for approximately 90 days following a deletion. Users have the +ability to access, retrieve, correct, and delete personal data stored in Netdata Cloud. In case a user is unable to +delete personal data via self-services functionality, Netdata will delete personal data upon the customer's written +request, in accordance with applicable data protection law. + +## Infrastructure and Authentication + +Netdata Cloud operates on an Infrastructure as Code (IaC) model. Its microservices environment is completely isolated, +and all changes occur through Terraform. At the edge of Netdata Cloud, there is a TLS termination and an Identity and +Access Management (IAM) service that validates JWT tokens included in request cookies. + +Netdata Cloud does not store user credentials. + +## Security Features and Response + +Netdata Cloud offers a variety of security features, including infrastructure-level dashboards, centralized alerts +notifications, auditing logs, and role-based access to different segments of the infrastructure. The cloud service +employs several protection mechanisms against DDoS attacks, such as rate-limiting and automated blacklisting. It also +uses static code analysers to prevent other types of attacks. + +In the event of potential security vulnerabilities or incidents, Netdata Cloud follows the same process as the Netdata +agent. Every report is acknowledged and analyzed by the Netdata team within three working days, and the team keeps the +reporter updated throughout the process. + +## User Customization + +Netdata Cloud uses the highest level of security. There is no user customization available out of the box. Its security +settings are designed to provide maximum protection for all users. We are offering customization (like custom SSO +integrations, custom data retention policies, advanced user access controls, tailored audit logs, integration with other +security tools, etc.) on a per contract basis. + +## Deleting Personal Data + +Users who wish to remove all personal data (including email and activities) can delete their cloud account by logging +into Netdata Cloud and accessing their profile. + +## User Privacy and Data Protection + +Netdata Cloud is built with an unwavering commitment to user privacy and data protection. We understand that our users' +data is both sensitive and valuable, and we have implemented stringent measures to ensure its safety. + +### Data Collection + +Netdata Cloud collects minimal personal information from its users. The only personal data required to create an account +and use the service is an email address. This email address is used for product and marketing communications. +Additionally, the IP address used to access Netdata Cloud is stored in web proxy access logs. + +### Data Usage + +The collected email addresses are stored in our production database on Amazon Web Services (AWS) and copied to Google +BigQuery, our data lake, for analytics purposes. These analytics are crucial for our product development process. If a +user accepts the use of analytical cookies, their email address and IP are stored in the systems we use to track +application usage (Google Analytics, Posthog, and Gainsight PX). Subscriptions and Payments data are handled by Stripe. + +### Data Sharing + +Netdata Cloud does not share any personal data with third parties, ensuring the privacy of our users' data, but Netdata +Cloud does use third parties for its services, including, but not limited to, Google Cloud and Amazon Web Services for +its infrastructure, Stripe for payment processing, Google Analytics, Posthog and Gainsight PX for analytics. + +### Data Protection + +We use state-of-the-art security measures to protect user data from unauthorized access, use, or disclosure. All +infrastructure data visible on Netdata Cloud passes through the Agent-Cloud Link (ACLK) mechanism, which securely +connects a Netdata Agent to Netdata Cloud. The ACLK is encrypted, safe, and is only established if the user connects +their node. All data in transit between a user and Netdata Cloud is encrypted using TLS. + +### User Control over Data + +Netdata provides its users with the ability to access, retrieve, correct, and delete their personal data stored in +Netdata Cloud. This ability may occasionally be limited due to temporary service outages for maintenance or other +updates to Netdata Cloud, or when it is technically not feasible. If a customer is unable to delete personal data via +the self-services functionality, Netdata deletes the data upon the customer's written request, within the timeframe +specified in the Data Protection Agreement (DPA), and in accordance with applicable data protection laws. + +### Compliance with Data Protection Laws + +Netdata Cloud is fully compliant with data protection laws like the General Data Protection Regulation (GDPR) and the +California Consumer Privacy Act (CCPA). + +### Data Transfer + +Data transfer within Netdata Cloud is secure and respects the privacy of the user data. The Netdata Agent establishes an +outgoing secure WebSocket (WSS) connection to Netdata Cloud, ensuring that the data is encrypted when in transit. + +### Use of Tracking Technologies + +Netdata Cloud uses analytical cookies if a user consents to their use. These cookies are used to track the usage of the +application and are stored in systems like Google Analytics, Posthog and Gainsight PX. + +### Data Breach Notification Process + +In the event of a data breach, Netdata has a well-defined process in place for notifying users. The details of this +process align with the standard procedures and timelines defined in the Data Protection Agreement (DPA). + +We continually review and update our privacy and data protection practices to ensure the highest level of data safety +and privacy for our users. diff --git a/docs/store/change-metrics-storage.md b/docs/store/change-metrics-storage.md index 456ea6c2..133d6ca2 100644 --- a/docs/store/change-metrics-storage.md +++ b/docs/store/change-metrics-storage.md @@ -1,7 +1,7 @@ # Change how long Netdata stores metrics The Netdata Agent uses a custom made time-series database (TSDB), named the -[`dbengine`](https://github.com/netdata/netdata/blob/master/database/engine/README.md), to store metrics. +[`dbengine`](https://github.com/netdata/netdata/blob/master/src/database/engine/README.md), to store metrics. To see the number of metrics stored and the retention in days per tier, use the `/api/v1/dbengine_stats` endpoint. @@ -84,7 +84,7 @@ The DBENGINE memory is related to the number of metrics concurrently being colle on disk in relation with the queries running, and the number of metrics for which retention is maintained. The precise analysis of how much memory will be used by the DBENGINE itself is described in -[DBENGINE memory requirements](https://github.com/netdata/netdata/blob/master/database/engine/README.md#memory-requirements). +[DBENGINE memory requirements](https://github.com/netdata/netdata/blob/master/src/database/engine/README.md#memory-requirements). In addition to the DBENGINE, Netdata uses memory for contexts, metric labels (e.g. in a Kubernetes setup), other Netdata structures/processes (e.g. Health) and system overhead. @@ -160,7 +160,7 @@ Once you have decided how to size each tier, open `netdata.conf` with and make your changes in the `[db]` subsection. Save the file and restart the Agent with `sudo systemctl restart netdata`, or -the [appropriate method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) +the [appropriate method](https://github.com/netdata/netdata/blob/master/packaging/installer/README.md#maintaining-a-netdata-agent-installation) for your system, to change the database engine's size. ## Scaling dedicated parent nodes diff --git a/docs/store/distributed-data-architecture.md b/docs/store/distributed-data-architecture.md deleted file mode 100644 index b5e6f376..00000000 --- a/docs/store/distributed-data-architecture.md +++ /dev/null @@ -1,75 +0,0 @@ -# Distributed data architecture - -Learn how Netdata's distributed data architecture enables us to store metrics on the edge nodes for security, high performance and scalability. - -This way, it helps you collect and store per-second metrics from any number of nodes. -Every node in your infrastructure, whether it's one or a thousand, stores the metrics it collects. - -Netdata Cloud bridges the gap between many distributed databases by _centralizing the interface_ you use to query and -visualize your nodes' metrics. When you [look at charts in Netdata Cloud](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md) -, the metrics values are queried directly from that node's database and securely streamed to Netdata Cloud, which -proxies them to your browser. - -Netdata's distributed data architecture has a number of benefits: - -- **Performance**: Every query to a node's database takes only a few milliseconds to complete for responsiveness when - viewing dashboards or using features - like [Metric Correlations](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/metric-correlations.md). -- **Scalability**: As your infrastructure scales, install the Netdata Agent on every new node to immediately add it to - your monitoring solution without adding cost or complexity. -- **1-second granularity**: Without an expensive centralized data lake, you can store all of your nodes' per-second - metrics, for any period of time, while keeping costs down. -- **No filtering or selecting of metrics**: Because Netdata's distributed data architecture allows you to store all - metrics, you don't have to configure which metrics you retain. Keep everything for full visibility during - troubleshooting and root cause analysis. -- **Easy maintenance**: There is no centralized data lake to purchase, allocate, monitor, and update, removing - complexity from your monitoring infrastructure. - -## Ephemerality of metrics - -The ephemerality of metrics plays an important role in retention. In environments where metrics collection is dynamic and -new metrics are constantly being generated, we are interested about 2 parameters: - -1. The **expected concurrent number of metrics** as an average for the lifetime of the database. This affects mainly the - storage requirements. - -2. The **expected total number of unique metrics** for the lifetime of the database. This affects mainly the memory - requirements for having all these metrics indexed and available to be queried. - -## Granularity of metrics - -The granularity of metrics (the frequency they are collected and stored, i.e. their resolution) is significantly -affecting retention. - -Lowering the granularity from per second to every two seconds, will double their retention and half the CPU requirements -of the Netdata Agent, without affecting disk space or memory requirements. - -## Long-term metrics storage with Netdata - -Any node running the Netdata Agent can store long-term metrics for any retention period, given you allocate the -appropriate amount of RAM and disk space. - -Read our document on changing [how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md) on your nodes for -details. - -You can also stream between nodes using [streaming](https://github.com/netdata/netdata/blob/master/streaming/README.md), allowing to replicate databases and create -your own centralized data lake of metrics, if you choose to do so. - -While a distributed data architecture is the default when monitoring infrastructure with Netdata, you can also configure -its behavior based on your needs or the type of infrastructure you manage. - -To archive metrics to an external time-series database, such as InfluxDB, Graphite, OpenTSDB, Elasticsearch, -TimescaleDB, and many others, see details on [integrating Netdata via exporting](https://github.com/netdata/netdata/blob/master/docs/export/external-databases.md). - -When you use the database engine to store your metrics, you can always perform a quick backup of a node's -`/var/cache/netdata/dbengine/` folder using the tool of your choice. - -## Does Netdata Cloud store my metrics? - -Netdata Cloud does not store metric values. - -To enable certain features, such as [viewing active alerts](https://github.com/netdata/netdata/blob/master/docs/monitor/view-active-alerts.md) -or [filtering by hostname](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/node-filter.md), Netdata Cloud does -store configured alerts, their status, and a list of active collectors. - -Netdata does not and never will sell your personal data or data about your deployment. |
