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This topic describes how to download, build and use the Device Explorer tool. You can use this tool to manage devices connecting to your IoT hub. For example, you can register a device with your IoT hub, monitor messages from your devices, and send messages to your devices.

Device Explorer runs on your local machine and connects to your IoT hub in Azure. It communicates with the following IoT Hub endpoints:

A lot of those types of tools are included with the Mac OS, and some are available separately from Apple. There's no Mac equivalent of the registry - everything goes in the file system somewhere - and you can use fs_usage to watch that. SAS JMP was added by tarekali in Apr 2016 and the latest update was made in Apr 2016. The list of alternatives was updated Nov 2018 There is a history of all activites on SAS JMP in our Activity Log.

  • Device identity management to provision and manage devices registtered with your IoT hub.
  • Receive device-to-cloud to enable you to monitor messages sent from your device to your IoT hub.
  • Send cloud-to-device to enable you to send messages to your devices from your IoT hub.

It includes Base SAS, SAS/STAT, SAS/IML and SAS/ACCESS to PC Files. In addition, this software package will be downloadable to Mac or PC. Click Here to read the Press Release and learn more information about SAS University Edition. Statistical Analysis Software Free Trial Download. For Mac and Windows. With JMP ®, you can solve problems, reveal opportunities and inform decisions with a rich set of comprehensive capabilities for statistical analysis.Statistical Analysis capabilities in JMP ® include. Univariate and multivariate linear and nonlinear analysis.

The last version of SAS produced for Mac was the PowerPC version of SAS 6.2 so this is not an option for most people. JMP does have a universal distribution for Mac that is very nice but expensive. For regular SAS users the only option is to do some sort of Windows installation using Boot Camp or a virtualization tool. Native SAS for Mac. The last version of SAS produced for Mac was the PowerPC version of SAS 6.2 so this is not an option for most people. JMP does have a universal distribution for Mac that is very nice but expensive. For regular SAS users the only option is to do some sort of Windows installation using Boot Camp or a virtualization tool. SAS/STAT includes exact techniques for small data sets, high-performance statistical modeling tools for large data tasks and modern methods for analyzing data with missing values. And because the software is updated regularly, you'll benefit from using the newest methods in the rapidly expanding field of statistics.

Note: The Device Explorer utility only runs on Windows. On other platforms use the iothub explorer tool.

To learn more about IoT Hub, see What is Azure IoT Hub?.

Table of Contents

  • Manage devices

Getting Device Explorer

You can either download a pre-built version of Device Explorer or build it yourself.

Download a pre-built version of the Device Explorer application

A pre-built version of the Device Explorer application for Windows can be downloaded from https://aka.ms/aziotdevexp as well as the Releases page. (Scroll down for SetupDeviceExplorer.msi). The default installation directory for this application is 'C:Program Files (x86)MicrosoftDeviceExplorer'. You might want to pin the DeviceExplorer.exe application to the taskbar for easier access.

Build the Device Explorer application

To build Device Explorer yourself and generate the MSI, the following component needs to be added to Visual Studio:Microsoft Visual Studio 2017 Installer ProjectsYou will need to add the highlighted key: ComputerHKEY_CURRENT_USERSoftwareMicrosoftVisualStudio15.0_cbfbe12c_ConfigMSBuild1. Add the node 15.0_[User ID]_Config like as shown below 15.0_cbfbe12c_Config.2. Add MSBuild folder (key) in it.3. In MSBuild, create new DWORD Value “EnableOutOfProcBuild” and set its value as 0.

Once installed, open the toolsDeviceExplorerDeviceExplorerWithInstaller.sln file in your local copy of this repository in Visual Studio 2017. Then build and run the solution.

Alternatively run the following in the root of the repository: jenkinswindows_csharp_deviceexplorer.cmd

Configure an IoT Hub connection

  • In the Configuration tab, add the connection string for your IoT Hub. For information about how to find this connection string, see the document Setup your IoT Hub. Then click Update.

Configure a Protocol Gateway

You only need to complete this step if you are using a Protocol Gateway (such as the Microsoft Azure IoT Protocol Gateway).

Add the address of your protocol gateway to the Protocol Gateway HostName field with the address of your protocol gateway before you try to get a device connection string on the Management tab. If you are running the protocol gateway locally on the same machine as Device Explorer, you can use localhost as the address, otherwise you will need the network address of the machine where you deployed the protocol gateway.

Manage devices

  • Click the Management tab to manage the devices connected to the IoT hub.
  • Click List to query the list of registered devices with the connected IoT Hub.

Create device

Creating a device adds device details to the device identity registry. IoT Hub uses this information to generate a device-specific connection string that enables the device to connect to the IoT hub.

  • On the Management tab, click Create to register a new device with that IoT Hub. The Create Device dialog appears. In the Device ID field, type a unique name for your device (or select Auto Generate ID to generate a unique ID instead). Then click Create.
  • A Device Created window appears, indicating that your device has been successfully registered with this IoT Hub.

Update device

  • Click Update to change the primary or secondary key for the selected device. You will need to update your device to use any new keys you generate.

Delete device

  • Click Delete to delete the selected devices from the connected IoT hub.

SAS Token

  • Click SAS Token... to generate a device specific connection string with SAS token.

Get device connection string or configuration data

  • Use the mouse right click for context menu for the selected device. The menu has the following options

Monitor device-to-cloud events

The following steps describe how to use Device Explorer to monitor device-to-cloud events sent from a sample application on a client to an IoT hub and use the Device Explorer utility to observe as the IoT hub receives the event data.

Obtain the device connection string and setup monitoring

You can use one of the sample applications included in this repository (azure-iot-sdks) to send device-to-cloud events to your IoT Hub. Follow the step by step tutorial to set up the sample application. Use Device Explorer to create a device and generate a connection string.

  • You can obtain the IoT Hub connection string from Device Explorer. In the Management tab, right-click on the selected device and select 'Copy connection string for selected device'. The device connection string is silently copied to the clipboard.

Note: Device Explorer requires an IoT hub connection string obtained from the Azure portal to enable it to connect to the hub. You can then use Device Explorer to generate device-specific connection strings that enable devices to communicate with the hub.

  • Update the device connection string placeholder in the application you are using with the connection string from Device Explorer.

  • In the Device Explorer Data tab, select the device name you created from the drop-down list of device IDs and leave the other fields with their default values for now.

  • Click Monitor.

  • Device Explorer is now monitoring data sent from that device to the IoT hub.

Run the sample application

Run your sample application. Device Explorer should now show that the IoT hub has successfully received device-to-cloud data from this device.

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Send cloud-to-device messages

  • To verify that you can send cloud-to-device messages from the IoT hub to your device, go to the Messages To Device tab in Device Explorer.

  • Select the device you created.

  • Add some text to the Message field, then click Send.

  • You should be able to see the command received in the console window for the client sample application you are using.

Note: You must be sure to use the correct format when you send a message to your device. For example, the simplesample_amqp sample for the C serializer library accepts the following JSON commands:

Call Method on device

  • To call Method on device, you can use this sample application from IoT client sdk.

  • Open this sample application in Visual Studio; Update device connection string in sample application. Then hit F5 to run it.

  • Go to **Call Method on Device' tab in Device Explorer.

  • Input WriteToConsole in Method name field and { 'MethodPayload': 'hello world!' } in Method payload field; then click Call Method.

  • You should be able to see the Method payload message printed in the console window for the client sample application you are using.

ReadStat: Read (and write) data sets from SAS, Stata, and SPSS

Originally developed for Wizard, ReadStat is acommand-line tool and MIT-licensed C library for reading files from popularstats packages. Supported data formats include:

  • SAS: SAS7BDAT (binary file) and XPORT (transport file)
  • Stata: DTA (binary file) versions 104-119
  • SPSS: POR (portable file), SAV (binary file), and ZSAV (compressed binary)

Supported metadata formats include:

  • SAS: SAS7BCAT (catalog file) and .sas (command file)
  • Stata: .dct (dictionary file)
  • SPSS: .sps (command file)

There is also write support for all the data formats, but not the metadataformats. The produced SAS7BDAT files still cannot be read by SAS, but feelfree to contribute your binary-format expertise here.

For reading in R data files, please see the relatedlibrdata project.

Grab the latest release andthen proceed as usual:

If you're cloning the repository, first make sure you have autotools installed,and then run ./autogen.sh to generate the configure file.

If you're on Mac and see errors about AM_ICONV when you run ./autogen.sh,you'll need to install gettext.

If you're on Windows see Windows specific notes.

  • Julia: ReadStat.jl
  • Perl 6: ReadStat.pm6
  • Python: pyreadstat
  • R: haven

A dockerized version is available here

Standard usage:

Where:

  • <input file> ends with .dta, .por, .sav, .sas7bdat, or .xptand
  • <output file> ends with .dta, .por, .sav, .sas7bdat, .xpt or .csv

If libxlsxwriter is found at compile-time, anXLSX file (ending in .xlsx) can be written instead.

If zlib is found at compile-time, compressed SPSS files (.zsav) can be readand written as well.

Use the -f option to overwrite an existing output file.

If you have a plain-text file described by a Stata dictionary file, a SAScommand file, or an SPSS command file, a second invocation style is supported:

Where:

  • <input file> can be anything
  • <dictionary file> ends with .dct, .sas, or .sps
  • <output file> ends with .dta, .por, .sav, .xpt, or .csv

If you have a SAS catalog file containing the data set's value labels, youcan use the same invocation:

Except where:

  • <input file> ends with .sas7bdat
  • <catalog file> ends with .sas7bcat
  • <output file> ends with .dta, .por, .sav, .xpt, or .csv

If the file conversion succeeds, ReadStat will report the number of rows andvariables converted, e.g.

At the moment value labels are supported, but the finer nuances of convertingformat strings (e.g. %8.2g) are not.

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A prerequisite for CSV input is that the libcsvlibrary is found at compile time.

CSV input is supported together with a metadata file describing the data:

The <output file> should end with .dta, .sav, or .csv.

The <input file.csv> is a regular CSV file.

The <input metadata.json> is a JSON file describing column types, valuelabels and missing values. The easiest way to create such a metadata file is touse the provided extract_metadata program on an existing file:

The schema of this JSON file is fully described invariablemetadata_schema.json using JSONSchema.

The following is an example of a valid metadata file:

Here the column citizenship is a numeric column with four possible values 1, 2, 98, and 99.1 has the label Afghanistan, 2 has Albania, 98 has No answer and 99 has Not applicable.98 and 99 are defined as missing values.

Other column types are STRING and DATE.All values in DATE columns are expected to conform to ISO 8601 date.Here is an example of DATE metadata:

Value labels are supported for DATE.

The last column type is STRING:

Value labels are not supported for STRING.

The ReadStat API is callback-based. It uses very little memory, and is suitablefor programs with progress bars. ReadStat usesiconv to automatically transcodetext data into UTF-8, so you don't have to worry about character encodings.

See src/readstat.h for the complete API. In general you'll provide a filenameand a set of optional callback functions for handling various information anddata found in the file. It's up to the user to store this information in anappropriate data structure. If a context pointer is passed to the parse_* functions,it will be made available to the various callback functions.

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Callback functions should return READSTAT_HANDLER_OK (zero) on success.Returning READSTAT_HANDLER_ABORT will abort the parsing process.

Example: Return the number of records in a DTA file.

Example: Convert a DTA to a tab-separated file.

ReadStat can write data sets to a number of file formats, and uses largely thesame API for each of them. Files are written incrementally, with the headerwritten first, followed by individual rows of data, and ending with some kindof trailer. (So the full data file never resides in memory.) Unlike like thecallback-based API for reading files, the writer API consists of function thatthe developer must call in a particular order. The complete API can be found insrc/readstat.h.

Basic usage:

You need to install and configure an msys2 environment to compile ReadStat.

First, download and install msys2 from here. Makesure you update your initial msys2 installation as described on that page.

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Second, install a number of additional packages at the msys2 command line:

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Finally, start a MINGW command line (not the msys2 prompt!) and follow the general install instructions for this package.

To assist in fuzz testing, ReadStat ships with target files designed to workwith libFuzzer. Clang 6 or later isrequired.

  1. ./configure --enable-fuzz-testing turns on useful sanitizer and sanitizer-coverage flags
  2. make will create a new binary called generate_corpus. Running thisprogram will use the ReadStat test suite to create a corpus of test files incorpus/. There is a subdirectory for each sub-format (dta104, dta105,etc.). Currently a total of 468 files are created.
  3. If fuzz-testing has been enabled, make will also create fourteen fuzzertargets, one for each of seven file formats, five for internally usedgrammars, and two fuzzers for testing the compression routines.
    • fuzz_format_dta
    • fuzz_format_por
    • fuzz_format_sas7bcat
    • fuzz_format_sas7bdat
    • fuzz_format_sav
    • fuzz_format_xport
    • fuzz_format_stata_dictionary
    • fuzz_grammar_dta_timestamp
    • fuzz_grammar_por_double
    • fuzz_grammar_sav_date
    • fuzz_grammar_sav_time
    • fuzz_grammar_spss_format
    • fuzz_compression_sas_rle
    • fuzz_compression_sav

For best results, each sub-directory of the corpus should be passed to the relevant fuzzer, e.g.:

  • ./fuzz_format_dta corpus/dta104
  • ./fuzz_format_dta corpus/dta110
  • ...
  • ./fuzz_format_sav corpus/sav
  • ./fuzz_format_sav corpus/zsav
  • ./fuzz_format_xport corpus/xpt5
  • ./fuzz_format_xport corpus/xpt8

Finally, the compression fuzzers can be invoked without a corpus:

  • ./fuzz_compression_sas_rle
  • ./fuzz_compression_sav