In the next paragraphs I will show you how did I build my first TV led strip. I will show you the parts that I’ve used, how to install the software, and also the way I’ve assembled everything in a small box on the back of my TV.

The first thing that I did was to buy an ESP8266. I bought this one from Mauser.

I’ve connected the ESP8266 to my PC with the USB cable. And uploaded to the device. How did I do that?

The WLED binary that I’ve used was WLED_0.10.2_ESP8266.bin that I’ve downloaded from the releases page.

I use this great tool esphome-flasher. Downloaded the latest release here. Just run the tool and choose the previous downloaded binary. Don’t forget to have the device connected to the USB port of your computer.

In my computer the device shows up in COM4 port.

After that your newly flashed WLED device should give you an access point named WLED-AP. Connect to it with wled1234 password. I connected with my phone and I was immediately redirected to the first time configuration web page. If this doesn’t happen to you, open the browser and go to 4.3.2.1 you should see this page.

Insert your home wifi SSID and password, click Save & Connect.

Your device will reboot and connect to your wifi network.

Find the IP address of your device using one of the thousands of network tools available on the internet and open your browser with that IP address.

And that’s it. You have your WLED device installed and ready to use.

For the LED strip I bought from AliExpress. It’s a 5 meter WS2810 LED strip (individually addressable leds). The next configuration is the LED count. For that, I needed to measure my TV, cut the strip and count the number of LEDs that I was going to control. For my TV I have 18 leds on each side and 32 leds, top and bottom. That gives me a total led count of 100.

Now for the wire connection, this is how you should do it. Im using a 5V led strip so I followed the first option in the schematics picture below.

I took this picture from projectwiki – https://github.com/Aircoookie/WLED/wiki

If the distance between the device and the led strip is long you should use a level shifter. That was not my case so I didnt have the need to include one in my solution. I used the 470 ohm resistor and and the 1000 uF capacitor.

Because I used the nodemcu, it is an ESP8266 device, I connected the D4 pin to the data input on the led strip. The led strip has3 conductors, 5V, GND and DATA.

I connected the 5V and GND not only in the beginning of the led strip but also in the end. The data input, that is connected to D4 pin, it is only connected in the beginning of the strip, and there is an arrow on the strip pointing to the data signal flow direction.

And that’s it! I can know control my TV led strip thru the web interface and I can also use the WLED mobile that I’ve installed on my smartphone.

Enjoy!

The post WLED – Check how I built my first TV led strip super easy appeared first on Blog IT.

And I wanted to have a sensor that measures the total power instead of having 2 sensors separately.

So let’s see how I’ve configured a template sensor in home assistant that adds two power sensors. In the next yaml code you can see the sensors.

  - platform: mqtt
    unique_id: sensor_potencia_sala_jantar_tecto
    name: "Potência da luz do tecto da sala de jantar"
    state_topic: "shellies/sala_tecto/relay/0/power"
    unit_of_measurement: 'W'
  - platform: mqtt
    unique_id: sensor_potencia_sala_estar_tecto
    name: "Potência da luz do tecto da sala de estar"
    state_topic: "shellies/sala_tecto/relay/1/power"
    unit_of_measurement: 'W'

And for the custom sensor that has the sum of the two previous sensors, this is how I’ve configured the yaml

  - platform: template
    sensors:
      sensor_potencia_sala:
        unit_of_measurement: "W"
        value_template: "{{ (states('sensor.sensor_potencia_sala_jantar_tecto') | float) +
 (states('sensor.sensor_potencia_sala_estar_tecto') | float)}}"

Check out my previous post about configuring the Shelly 2.5 switch in Home Assistant.

The post Home Assistant template sensor, adding two sensor explained appeared first on Blog IT.

I use the Home Assistant add-on named mosquitto as MQTT for everything so I can keep the communication system the same for all my devices and keep the management system simple.

First of all I configured my Shelly 2.5 thru the web interface to use MQTT. This is done in the “Internet & Security” configuration settings, “Advance Developer – Settings”.

I configured the username and password that was set in Mosquitto MQTT server in Home Assistant. Don’t forget the IP address of the Home Assistant server (port 1883).

I’ve used a custom MQTT prefix as you can see in the yaml files configuration below. I use this prefix as a way to give a name to my switch

# lights
light:
  - platform: mqtt
    unique_id: luz_sala_jantar_tecto
    name: "Luz do tecto da sala de jantar"
    state_topic: "shellies/sala_tecto/relay/0"
    command_topic: "shellies/sala_tecto/relay/0/command"
    payload_on: 'on'
    payload_off: 'off'
    retain: false
    qos: 1
  - platform: mqtt
    unique_id: luz_sala_estar_tecto
    name: "Luz do tecto da sala de estar"
    state_topic: "shellies/sala_tecto/relay/1"
    command_topic: "shellies/sala_tecto/relay/1/command"
    payload_on: 'on'
    payload_off: 'off'
    retain: false
    qos: 1

The Shelly 2.5 has temperature sensor so I’ve configured it like this

sensor:
  - platform: mqtt
    unique_id: sensor_temperatura_sala_tecto
    name: "Temperatura da luz do tecto da sala"
    state_topic: "shellies/sala_tecto/temperature"
    unit_of_measurement: 'ºC'

There is a power sensor so check the configuration here

sensor:
  - platform: mqtt
    unique_id: sensor_potencia_sala_jantar_tecto
    name: "Potência da luz do tecto da sala de jantar"
    state_topic: "shellies/sala_tecto/relay/0/power"
    unit_of_measurement: 'W'

And for the energy sensor, this is the way I’ve set it up.

sensor
  - platform: mqtt
    unique_id: sensor_energia_sala_jantar_tecto
    name: "Energia da luz do tecto da sala de jantar"
    state_topic: "shellies/sala_tecto/relay/0/energy"
    unit_of_measurement: 'Wh'

And I’ve used a binary sensor for the the temperature alert.

binary_sensor:
  - platform: mqtt
    unique_id: alerta_temperatura_luz_sala_tecto
    name: "Alerta de temperatura da luz do tecto da sala"
    state_topic: "shellies/sala_tecto/overtemperature"
    device_class: "heat"
    payload_off: "0"
    payload_on: "1"

If you want you can check the Shelly 2.5 specification here at the official Selly website.

And I’m a Mauser.pt store fan so go check the site.

The post Home Assistant with Shelly 2.5 switch the easy way appeared first on Blog IT.

The solution was to copy Raven.Studio.zip to the root directory of the web api.

The post Raven.Studio.zip Index.html not available appeared first on Blog IT.

git-askpass.exe: No such file or directory.

To resolve this I had to:

1. Remove Git from Control Panel -> Uninstall
2. Remove Git Windows Credential Manager from Control Panel -> Uninstall
3. With Visual Studio 2017 Installer, remove Git for Windows
4. Install Git
5. Install Git Windows Credential Manager
6. With Visual Studio 2017 Installer, install Git for Windows

The post git-askpass.exe: No such file or directory. Visual Studio appeared first on Blog IT.

a) You need to create a new ASP.NET Core Web Application with Visual Studio 2017
b) Choose .NET Core; ASP.NET Core 2.1; API
c) Right click the new project -> Manage NuGet packages
d) You have to add a new package source. Settings. New package source.

Name: MyGet
Source: https://www.myget.org/F/ravendb/api/v3/index.json

e) Choose the new source. Add:

RavenDB.Embedded – 4.1.1-nightly-20180904-0430
RavenDB.Client – 4.1.1-nightly-20180904-0430

f) Create a DocumentStoreHolder class

internal class DocumentStoreHolder
{
    private static Lazy<IDocumentStore> store = new Lazy<IDocumentStore>(CreateStore);

    public static IDocumentStore Store => store.Value;

    private static IDocumentStore CreateStore()
    {
        var serverOptions = new ServerOptions()
        {
            ServerUrl = "http://127.0.0.1:60956/",
        };
        EmbeddedServer.Instance.StartServer(serverOptions);

        return EmbeddedServer.Instance.GetDocumentStore("MyRavenDBStore");
    }
}

g) And that’s it 🙂

The post RavenDB embedded with ASP NET Core web API appeared first on Blog IT.

So I had to use 2 different data sources. One for the documents and another to the metadata. So I chose Azure Blob Storage and Azure Table Storage. This is the full diagram of the final solution:

The indexers are responsible for updating the index with the contents of the 2 different data sources. There is a very important field that in my case it’s called the UniqueIdentifier field because this field is marked with the key property. This is the field that uniquely identifies each document on the Azure Search Index.

And it’s this field that is responsible for correlating the items that come from one data source (documents from blob storage) and items that come from the other data source (records from table storage).

Every document inserted in blob storage has a custom metadata property named also UniqueIdentifier that will have a table storage record associated with the corresponding metadata.

// Retrieve storage account from connection string.
CloudStorageAccount storageAccount = CloudStorageAccount.Parse(
                CloudConfigurationManager.GetSetting("StorageConnectionString"));

// Create the blob client.
CloudBlobClient blobClient = storageAccount.CreateCloudBlobClient();

// Retrieve a reference to a container.
CloudBlobContainer container = blobClient.GetContainerReference("mycontainer");

// Create the container if it doesn't already exist.
container.CreateIfNotExists();

// Add some metadata to the container.
container.Metadata.Add("UniqueIdentifier", "bla bla bla");

The table storage records have these values stored on the RowKey and the indexer have a mapping instruction to map the origin (RowKey) to the destination (UniqueIdentifier). You can check this mapping instruction later in the indexer schema.

First of all I created the Index:

(I’ve highlighted the UniqueIdentifier field wiht the Key property set to TRUE)

{
  "@odata.context": "https://something.search.windows.net/$metadata#indexes/$entity",
  "@odata.etag": "\"0x8D477848BDEFA50\"",
  "name": "full-index",
  "fields": [
    {
      "name": "ClientCode",
      "type": "Edm.String",
      "searchable": false,
      "filterable": true,
      "retrievable": true,
      "sortable": true,
      "facetable": true,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "UniqueIdentifier",
      "type": "Edm.String",
      "searchable": false,
      "filterable": true,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": true,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "ETag",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": false,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "Timestamp",
      "type": "Edm.DateTimeOffset",
      "searchable": false,
      "filterable": false,
      "retrievable": false,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "Key",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": false,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "Opportunity",
      "type": "Edm.String",
      "searchable": true,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": "pt-Pt.lucene"
    },
    {
      "name": "OpportunityCode",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "DocumentExtension",
      "type": "Edm.String",
      "searchable": false,
      "filterable": true,
      "retrievable": true,
      "sortable": false,
      "facetable": true,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "DocumentType",
      "type": "Edm.String",
      "searchable": false,
      "filterable": true,
      "retrievable": true,
      "sortable": false,
      "facetable": true,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "ClientName",
      "type": "Edm.String",
      "searchable": true,
      "filterable": true,
      "retrievable": true,
      "sortable": true,
      "facetable": true,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": "pt-Pt.lucene"
    },
    {
      "name": "Year",
      "type": "Edm.Int32",
      "searchable": false,
      "filterable": true,
      "retrievable": true,
      "sortable": true,
      "facetable": true,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "Path",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "LocalPath",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "Name",
      "type": "Edm.String",
      "searchable": true,
      "filterable": false,
      "retrievable": true,
      "sortable": true,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": "pt-Pt.lucene"
    },
    {
      "name": "content",
      "type": "Edm.String",
      "searchable": true,
      "filterable": false,
      "retrievable": false,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": "pt-Pt.lucene"
    },
    {
      "name": "metadata_storage_content_type",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_storage_size",
      "type": "Edm.Int64",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_storage_last_modified",
      "type": "Edm.DateTimeOffset",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_storage_content_md5",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_storage_name",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_storage_path",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_author",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_content_type",
      "type": "Edm.String",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_creation_date",
      "type": "Edm.DateTimeOffset",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    },
    {
      "name": "metadata_last_modified",
      "type": "Edm.DateTimeOffset",
      "searchable": false,
      "filterable": false,
      "retrievable": true,
      "sortable": false,
      "facetable": false,
      "key": false,
      "indexAnalyzer": null,
      "searchAnalyzer": null,
      "analyzer": null
    }
  ],
  "scoringProfiles": [],
  "defaultScoringProfile": "",
  "corsOptions": null,
  "suggesters": [
    {
      "name": "temp-suggester",
      "searchMode": "analyzingInfixMatching",
      "sourceFields": [
        "ClientCode",
        "Opportunity",
        "ClientName",
        "Name"
      ]
    }
  ],
  "analyzers": [],
  "tokenizers": [],
  "tokenFilters": [],
  "charFilters": []
}

After the index was created I’ve created the container and uploaded my files. This is the corresponding data source:

{
  "@odata.context": "https://something.search.windows.net/$metadata#datasources/$entity",
  "@odata.etag": "\"0x8D526E01DDB35F4\"",
  "name": "opportunities-datasource",
  "description": "",
  "type": "azureblob",
  "subtype": null,
  "credentials": {
    "connectionString": null
  },
  "container": {
    "name": "opportunities",
    "query": null
  },
  "dataChangeDetectionPolicy": null,
  "dataDeletionDetectionPolicy": null
}

I’ve also create the Table Storage and inserted the metadata records. This is the data source:

{
  "@odata.context": "https://something.search.windows.net/$metadata#datasources/$entity",
  "@odata.etag": "\"0x8D526E01DB70AF2\"",
  "name": "ecmcreatemeta",
  "description": "",
  "type": "azuretable",
  "subtype": null,
  "credentials": {
    "connectionString": null
  },
  "container": {
    "name": "opportunities",
    "query": null
  },
  "dataChangeDetectionPolicy": null,
  "dataDeletionDetectionPolicy": null
}

And these are the indexers. The documents indexer:

{
  "@odata.context": "https://something.search.windows.net/$metadata#indexers/$entity",
  "@odata.etag": "\"0x8D477862D23289E\"",
  "name": "full-indexer-blob",
  "description": "",
  "dataSourceName": "opportunities-datasource",
  "targetIndexName": "full-index",
  "schedule": null,
  "parameters": {
    "batchSize": 10,
    "maxFailedItems": 10,
    "maxFailedItemsPerBatch": 10,
    "base64EncodeKeys": false,
    "configuration": {
      "dataToExtract": "contentAndMetadata",
      "failOnUnsupportedContentType": false,
      "indexedFileNameExtensions": ".pdf, .docx, .doc, .xlsx, .xls, .pptx, .ppt, .msg, .html, .htm, .xml, .zip, .eml, .txt, .json, .csv"
    }
  },
  "fieldMappings": [],
  "disabled": null
}

And the metadata indexer:

{
  "@odata.context": "https://something.search.windows.net/$metadata#indexers/$entity",
  "@odata.etag": "\"0x8D47784D3CE80DF\"",
  "name": "full-indexer-meta",
  "description": "",
  "dataSourceName": "ecmcreatemeta",
  "targetIndexName": "full-index",
  "schedule": null,
  "parameters": {
    "batchSize": null,
    "maxFailedItems": 0,
    "maxFailedItemsPerBatch": 0,
    "base64EncodeKeys": false,
    "configuration": {}
  },
  "fieldMappings": [
    {
      "sourceFieldName": "PartitionKey",
      "targetFieldName": "ClientCode",
      "mappingFunction": null
    },
    {
      "sourceFieldName": "RowKey",
      "targetFieldName": "UniqueIdentifier",
      "mappingFunction": null
    }
  ],
  "disabled": null
}

That is the RowKey UniqueIdentifier mapping function. That is how Azure Search will map the records from table storage to documents inside the index.

The post Azure Search Index with 2 datasources appeared first on Blog IT.

// Retrieve storage account from connection string.
CloudStorageAccount storageAccount = CloudStorageAccount.Parse(
CloudConfigurationManager.GetSetting("StorageConnectionString"));

// Create the blob client.
CloudBlobClient blobClient = storageAccount.CreateCloudBlobClient();

// Retrieve a reference to a container.
CloudBlobContainer container = blobClient.GetContainerReference("mycontainer");

// Create the container if it doesn't already exist.
container.CreateIfNotExists();

// Add some metadata to the container.
container.Metadata.Add("docType", "textDocuments");

According to Microsfot documentation:

“You will receive a 400 Bad Request if any name/value pairs contain non-ASCII characters. Metadata name/value pairs are valid HTTP headers, and so must adhere to all restrictions governing HTTP headers. It is therefore recommended that you use URL encoding or Base64 encoding for names and values containing non-ASCII characters.”

https://docs.microsoft.com/en-us/azure/storage/blobs/storage-properties-metadata

The post Azure Blob Storage Metadata 400 Bad Request appeared first on Blog IT.

The post Using KeywordQuery with CSOM SharePoint Online appeared first on Blog IT.

1 – In Service Studio, open your module and goto Module -> Export -> Save As…

2 – Navigate to Intellectual Property Protection (IPP) Rights Validation

Here you need to enter your email, the destination activation code for your target infrastructure, and upload your OML

3 – You will receive an email with the OML file without IPP protection. Download the file.

4 – In Service Studio goto Module -> Open File…

5 – Publish your module

The post Move modules – Remove IPP – OutSystems appeared first on Blog IT.