Getting Started

General XINA information and concepts.

Introduction and Ecosystem

XINA is an integrated data management platform, developed at NASA GSFC. XINA is provided as a managed service hosted on Amazon Web Services.

Overview

The XINA platform provides five primary functions:

XINA supports Launchpad and NAMS integration for user management and authentication.

Limitations

XINA is not a single standalone application, and cannot currently be installed locally. We provide the software as a service through a NASA AWS account integration. Specific AWS requirements and costs will vary depending on project parameters.

XINA is not recommended as a gold copy for data storage. Although AWS cloud services are highly reliable and XINA is often used as a valuable backup tool, a full gold copy should always be kept onsite.

XINA System Components

The XINA platform is composed of several interconnected components and applications:

XINA Server

The XINA server is the core application of the XINA platform. It manages all incoming and outgoing XINA data and provides API access. The server is built on a MySQL database backend and uses the AWS S3 service for large file storage.

XINA Web

XINA Web (formerly XINA Online) is the primary XINA front-end application, written in TypeScript with Google's Angular web application platform. Authentication is integrated with NASA Launchpad and managed through NAMS.

XINA Tunnel

The XINA Tunnel utility is a Java application intended to facilitate communication with the XINA API. The tunnel connects directly to the XINA server and manages connection security and authentication. It then opens a local webserver to which client applications can connect and communicate with the core XINA server. Full reference is available here.

XINA Import

The XINA Import utility is a Java application to simplify importing data to the XINA server by importing XINA API actions from JSON files. Full reference is available here.

XINA Run

XINA Run is a Java application for managing and executing asynchronous tasks though the XINA platform. Full reference is available here.

XINA Lambda

XINA Lambda is an integrated service for executing asynchronous tasks from the AWS Lambda platform. Full reference is available here.

Terms and Concepts

Database

Databases are the core data storage structures in XINA. A database essentially defines a MySQL table, with additional features managed by the XINA server system.

Each database is defined by a set of fields, which specify the columns of the table. Fields are primarily defined by:

A single unit of data in a database is a record, corresponding to a row of the table. Each record contains a value for each field of the database.

Structural database changes (adding / changing / removing fields) are very slow (hours to days for very large databases) so initial time investment to optimize database requirements is worthwhile.

Group

Databases in XINA are organized into a heirarchical structure of groups, which can each contain any number of groups and databases. For example:

A dot notation is used to reference groups and databases. For example, moma.data.science refers to the science database in the above configuration.

Importing Data

There are several approaches for importing data into XINA, but for most projects we recommend the XINA Import utility.

XINA Import reads XINA API calls from JSON files and passes them to the XINA server. Each JSON file corresponds to a single API action, but may be paired with additional files of other types depending on the content.

For example, to upload some housekeeping data from a CSV requires two files. First, the JSON file:

{
  "action": "load",
  "database": "demo.model.data.hk.full",
  "columns": true,
  "delimit": ",",
  "line": "\n",
  "$object_id": "{local}/hk.csv"
}

The CSV file then looks like:

t,name,value
1602086313288000,SCAN_INDEX(Step),-1
1602086313288000,MO1_LD1_CURR(mA),0
1602086313288000,MO1_LD2_CURR(mA),0
1602086313288000,MO1_CASE_TEC(C),21.739
...

A couple notes on these:

While this CSV approach is recommended for large data sets, data can alternatively be embedded directly in JSON files. For example, a file to insert a new instant might look like:

{
  "action": "insert",
  "database": "demo.model.data.ins",
  "records": [
    {
      "u_id": "58ea870a-52c3-33c7-b858-c20795ec3301",
      "p_id": 0,
      "s_id": 0,
      "type": 20,
      "level": 0,
      "t": 1606333792000000,
      "label": "SPECTRA_Startf-0_Stopf-1k",
      "content": "some additional text here...",
      "meta": {
        "Resolution Bandwidth": 2.07014,
        "Stop Frequency": 1000,
        "Average Factor": 30,
        "Start Frequency": 0
      }
    }
  ]
}

The full API reference can be found here.

Data Types

XINA has a fixed set of data types which apply to attributes and fields. They are intended to provide consistent behavior across MySQL, Java, and JavaScript data types.

Numeric Types

Numeric data types form the backbone of most XINA data sets. Particularly in high data volume environments it is worth considering the smallest byte size type needed, as storage savings and query performance impacts can be considerable.

int(n)

Signed integer values:

Type Java MySQL JavaScript Notes
int(1) byte tinyint number 1 byte signed integer, -27 to 27-1
int(2) short smallint number 2 byte signed integer, -215 to 215-1
int(4) int int number 4 byte signed integer, -231 to 231-1
int(8) long bigint number 8 byte signed integer, -263 to 263-1 ⚠️

⚠️ In JavaScript number primitives are stored as an 8 byte float, so only -253 to 253-1 is available with exact precision when working in a JavaScript client (including the XINA web application).

An error is returned if a value provided is outside the range a required type, or contains a fractional value.

float(n)

Standard floating point values:

Type Java MySQL JavaScript Notes
float(4) float float number IEEE 754 4 byte floating point
float(8) double double number IEEE 754 8 byte floating point

An error will be returned if a floating point value is provided outside the representable magnitude of the required type. (Some languages denote this as Infinity/-Infinity, but this is not supported in XINA). XINA also does not recognize a NaN (not a number) floating point literal.

Boolean Type

Simple true / false type.

Type Java MySQL JavaScript
boolean boolean tinyint boolean

Note that MySQL treats 0 as false, non-zero as true.

Character Types

Character data types are used to store text information.

Type Java MySQL JavaScript Notes
utf8string(n) string char(n) string n up to 128, uses n*4 bytes, normalized
utf8vstring(n) string varchar(n) string n up to 128, uses up to n*4 bytes, normalized
utf8string string mediumtext string up to 224 bytes, normalized
utf8text string mediumtext string up to 224 bytes, not normalized
utf8filename string varchar(255) string uses up to 255 bytes, file-safe characters only
asciistring(n) string char(n) string n up to 256, uses n bytes, normalized
asciivstring(n) string varchar(n) string n up to 256, uses up to n bytes, normalized
asciistring string mediumtext string up to 224 bytes, normalized
asciitext string mediumtext string up to 224 bytes, not normalized
asciifilename string varchar(255) string uses up to 255 bytes, file-safe characters only

Note, all string operations are case-insensitive by default. This can be overridden with the COLLATE expression by specifying a binary collation.

Character Encoding

XINA offers two types of character encoding:

UTF-8

UTF-8 is the recommended default encoding. It has a variable length of 1 to 4 bytes per character.

ASCII

SQL Types

MySQL provides several types for character data storage with different considerations.

char(n)

varchar(n)

text

Categories

string

Text is normalized before insertion:

text

Text is kept exactly as input, preserving all whitespace.

filename

Text is normalized before insertion. Content is invalid if it contains common unsafe file characters or patterns. Specifically, it is checked against the following rules:

These rules are more restrictive than certain platforms, but are intended to provide a safe common baseline.

Temporal Types

Temporal data types store time data.

Instant

Instant types identify specific moment in time, independent of time zone. The value is stored in the database in Unix time, in one of three resolutions:

Type Java MySQL JavaScript
instant(s) DateTime bigint date
instant(ms) DateTime bigint date
instant(us) DateTime bigint date

Additionally, XINA supports standalone date and time components of instants:

Type Java MySQL JavaScript
date(s) XDate bigint date
date(ms) XDate bigint date
date(us) XDate bigint date
time(s) LocalTime int number
time(ms) LocalTime int number
time(us) LocalTime bigint number

The date types are stored as the instant at start of date in UTC, as Unix time. The time types are stored as their respective unit limited to less than 24 hours.

XINA contains the following redundant legacy types, which will deprecated in a future release:

Type Java MySQL JavaScript Notes
datetime DateTime bigint date replaced by instant(ms)
date XDate bigint date replaced by date(ms)
time LocalTime int number replaced by time(ms)

Instant Parsing

Instant types support parsing from either Unix time or ISO style formatted timestamps. The general goal is to allow as many formats as possible while avoiding ambiguity.

Unix Parsing

A value provided as a JSON number will be interpretted with automatic precision detection based on the following rules:

This approach allows time values in the range of roughly 1973 to 5138.

A string value will be treated as Unix time if it matches the following format:

Examples:

ISO Parsing

String values not matching the numeric format described above will be parsed as permissive ISO style formatted timestamps with an explicit offset. The ISO formats are detailed below in the local types section. The offset can be provided as:

Local

Local temporal types store values as ISO 8601 formatted strings without a time zone. This has the advantage of being plain-text searchable.

Type Java MySQL JavaScript Format
localdate LocalDate char(10) string yyyy-MM-dd
localtime(s) LocalTime char(8) string HH:mm:ss
localtime(ms) LocalTime char(12) string HH:mm:ss.SSS
localtime(us) LocalTime char(15) string HH:mm:ss.SSSSSS
localdatetime(s) LocalDateTime char(19) string yyyy-MM-ddTHH:mm:ss
localdatetime(ms) LocalDateTime char(23) string yyyy-MM-ddTHH:mm:ss.SSS
localdatetime(us) LocalDateTime char(26) string yyyy-MM-ddTHH:mm:ss.SSSSSS

localdate and localdatetime(*) are directly comparable in the database. A localdate and localtime(*) can be merged into a localdatetime(*) with CONCAT([localdate], 'T', [localtime]).

XINA contains the following redundant legacy types, which will deprecated in a future release:

Type Java MySQL JavaScript Notes
localtime LocalTime char(12) string replaced by localtime(ms)
localdatetime LocalDateTime char(23) string replaced by localdatetime(ms)

Local Parsing

Local date and time parsers use a strict resolution style with ISO chronology to ensure date validity (e.g., February 30th would be rejected). Additionally, all parsers support multiple separator characters for maximum flexibility:

Note that local types are stored in the database in the standard ISO format regardless of the import format.

Date Parsing

Dates can be parsed from the standard ISO format:

Or an ordinal (day-of-year) ISO format:

Time Parsing
Datetime Parsing

Combines local date and local time parsers.

Again, this supports standard ISO format:

Or an ordinal (day-of-year) ISO format:

Duration

Duration types contain a postive or negative integer duration of a specified unit.

Type Java MySQL JavaScript
duration(s) Duration bigint number
duration(ms) Duration bigint number
duration(us) Duration bigint number

JSON Types

JSON data types store JSON data directly in the database.

Type Java MySQL JavaScript
jsonarray JsonArray json array
jsonobject JsonObject json object

Enum Types

Enum types map a series of discrete numeric integer values to text names. Though additional values may be added in the future, existing values will not change names or IDs.

notification_level

ID Name Notes
0 none default level, no associated formatting
1 success green
2 info cyan
3 notice yellow
4 warning red
5 primary blue, elevated over none
6 secondary grey, below none

notification_type

ID Name Notes
0 post
1 task
2 request request received
3 response response to request received

post_level

ID Name Notes
0 none default level, no associated formatting
1 success green
2 info cyan
3 notice yellow
4 warning red
5 primary blue, elevated over none
6 secondary grey, below none

System Types

ID

Wall ID

Path

Structured Data Types

UUID

HTML

XML

Collection Types

List

Set

Null Value

Although not a discrete XINA data type, null values may appear in any type depending on the context.

Type Java MySQL JavaScript JSON
null null NULL null null, "" (empty string)

Broadly, the XINA interpretation of a null value is simply "no value". For this reason XINA treats empty strings in JSON as equivalent to null (because they contain no actual information). The implication of this concept in SQL is that operations on null values return null:

Record Formatting

UNDER CONSTRUCTION, details may change

A record can be formatted to a string by wrapping each field name in brackets. For example, the record:

{ "foo": "bar" }

with the format:

"Foo: {foo}"

would resolve to "Foo: bar".

Additionally, values can be passed through pipes, allowing custom formatting to be used, or modifying the value entirely. The pipe format is:

| @<pipe name> <pipe args>

Sandbox Quick Start Guide

In order to streamline onboarding for new XINA projects we have created a XINA "Sandbox" environment to test data pipelines and tools. Everything in the sandbox is fully configured as a typical XINA production environment.

Step 1: Request NAMS Access

To get started, first request access through the NAMS service. The application name is "GSFC XINA Sandbox".

Step 2: First Login

Once you receive confirmation that the account is approved, perform your first login to the XINA Sandbox by going to sandbox.xina.io. This initial login creates your user account. You will initially have access to a series of default Sandbox data. If you require access for a specific project contact our team and we will help finish setting up required permissions.

Step 3: Create an API Key

To access the XINA API for importing data you will require a XINA API key. In the XINA web application, click your user name in the top right, then "User Profile" in the drop down.

user_profile.png

Switch to the "Access Keys" tab and click "Create New Key".

user_profile_key.png

Copy the entire displayed text and save it to a local file in your preferred location. You will need to reference this file when opening the XINA Tunnel to use the XINA API. A common naming convention is <mission>_xina_key.json. The key will only be displayed once upon creation, if lost you will need to make a new key.

user_profile_key2.png

Step 4: Download XINA Tunnel and XINA Import

The XINA Tunnel and XINA Import utilities are the recommended starting point for importing data. Details for each are available on their respective wiki pages.

Step 5: Import Sample Data

UNDER CONSTRUCTION

Spectra Quickstart

XINA Structs includes dedicated support for spectra data files (or basically any numeric XY data plotting). Spectra data is stored in files attached to event records.

Getting Started

Once the Sandbox Quick Start Guide is complete, you can create a new model in the Sandbox project, and then a new spectra database in that model.

Create Model

{
  "action": "struct_create",
  "create": "model",
  "parent": "sandbox",
  "name": "model_name",
  "label": "Model Name",
  "desc": "example model",
  "group_teams": ["sandbox", "sandbox_dev"],
  "database_teams": ["sandbox", "sandbox_dev"]
}

Note that for this example we use "model_name" as the name of the model, so if using a different name substitute anywhere "model_name" is used in the following examples.

Create Spectra Database

Finally, create a new event file database to hold the spectra. This is an example using many of the spectra-specific features:

{
  "action": "struct_create",
  "create": "event",
  "type": "file",
  "group": "sandbox.model_name",
  "name": "spectra",
  "label": "Spectra",
  "desc": "spectra",
  "singular": "spectrum",
  "plural": "spectra",
  "teams": [ "sandbox", "sandbox_dev" ],
  "fields": [
    {
      "name": "test_stage",
      "label": "Test Stage",
      "type": "asciivstring(64)",
      "nul": true
    },
    {
      "name": "group_id",
      "label": "Group ID",
      "type": "asciivstring(128)",
      "nul": true
    },
    {
      "name": "active_mo",
      "label": "Active MO",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "active_pa",
      "label": "Active PA",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "optical_axis",
      "label": "Optical Axis",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "osa_confs",
      "label": "OSA Configurations",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "pa_current_sp",
      "label": "PA Current Setpoint",
      "type": "float(8)",
      "unit": "mA",
      "nul": true
    },
    {
      "name": "lom_temp",
      "label": "LOM Temperature",
      "type": "float(8)",
      "unit": "C",
      "nul": true
    },
    {
      "name": "peak_wavelength",
      "label": "Peak Wavelength",
      "type": "float(8)",
      "unit": "nm",
      "nul": true
    },
    {
      "name": "peak_amplitude",
      "label": "Peak Amplitude",
      "type": "float(8)",
      "unit": "dBm",
      "nul": true
    },
    {
      "name": "sidemode_lo_wavelength",
      "label": "Sidemode Lo Wavelength",
      "type": "float(8)",
      "unit": "nm",
      "nul": true
    },
    {
      "name": "sidemode_lo_amplitude",
      "label": "Sidemode Lo Amplitude",
      "type": "float(8)",
      "unit": "dBm",
      "nul": true
    },
    {
      "name": "sidemode_hi_wavelength",
      "label": "Sidemode Hi Wavelength",
      "type": "float(8)",
      "unit": "nm",
      "nul": true
    },
    {
      "name": "sidemode_hi_amplitude",
      "label": "Sidemode Hi Amplitude",
      "type": "float(8)",
      "unit": "dBm",
      "nul": true
    }
  ],
  "conf": {
    "spectrum": {
      "charts": {
        "summary": {
          "x": [
            "t_start",
            "t_end",
            "$groupRelativeTime",
            "$groupIndex",
            "lom_temp",
            "pa_current_sp",
            "$id"
          ],
          "y": [
            "peak_wavelength",
            "peak_amplitude",
            "sidemode_lo_wavelength",
            "sidemode_lo_amplitude",
            "sidemode_hi_wavelength",
            "sidemode_hi_amplitude"
          ]
        },
        "spectrum": {
          "x": [
            {
              "field": "Wavelength (nm)",
              "label": "Wavelength (nm)",
              "source": "file"
            },
            {
              "field": "Spectral Width About Peak (GHz)",
              "label": "Spectral Width About Peak (GHz)",
              "source": "file"
            }
          ],
          "y": [
            {
              "field": "Amplitude (dBm)",
              "label": "Amplitude (dBm)",
              "source": "file"
            },
            {
              "field": "Out of Band (%)",
              "label": "Out of Band (%)",
              "source": "file"
            }
          ]
        }
      },
      "filters": [
        {
          "name": "fast",
          "label": "Fast",
          "desc": "Fast",
          "color": "green",
          "checks": [
            {
              "field": "optical_axis",
              "value": "fast"
            }
          ]
        },
        {
          "name": "slow",
          "label": "Slow",
          "desc": "Slow",
          "color": "red",
          "checks": [
            {
              "field": "optical_axis",
              "value": "slow"
            }
          ]
        },
        {
          "name": "narrow",
          "label": "Narrow",
          "desc": "Narrow",
          "color": "purple",
          "checks": [
            {
              "field": "osa_confs",
              "value": "narrow"
            }
          ]
        },
        {
          "name": "wide",
          "label": "Wide",
          "desc": "Wide",
          "color": "orange",
          "checks": [
            {
              "field": "osa_confs",
              "value": "wide"
            }
          ]
        }
      ],
      "grouping": [
        "t_start",
        "group_id",
        "test_stage",
        "active_mo",
        "active_pa",
        "optical_axis",
        "osa_confs",
        "pa_current_sp"
      ]
    }
  }
}

There's a lot happening here, so we can unpack in sections.

Basic Database Parameters

{
  "action": "struct_create",
  "create": "event",
  "type": "file",
  "group": "sandbox.model_name",
  "name": "spectra",
  "label": "Spectra",
  "desc": "spectra",
  "singular": "spectrum",
  "plural": "spectra",
  "teams": [ "sandbox", "sandbox_dev" ]
}

This is the basic database configuration. The "type": "file" indicates each record will have an associated file (the spectrum data). The "teams" determines which users have read/write access to the database, and may need to be different depending on the XINA environment.

Custom Fields

{
  "fields": [
    {
      "name": "test_stage",
      "label": "Test Stage",
      "type": "asciivstring(64)"
    },
    {
      "name": "group_id",
      "label": "Group ID",
      "type": "asciivstring(128)",
      "nul": true
    },
    {
      "name": "active_mo",
      "label": "Active MO",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "active_pa",
      "label": "Active PA",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "optical_axis",
      "label": "Optical Axis",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "osa_confs",
      "label": "OSA Configurations",
      "type": "asciivstring(8)",
      "nul": true
    },
    {
      "name": "pa_current_sp",
      "label": "PA Current Setpoint",
      "type": "float(8)",
      "unit": "mA",
      "nul": true
    },
    {
      "name": "lom_temp",
      "label": "LOM Temperature",
      "type": "float(8)",
      "unit": "C",
      "nul": true
    },
    {
      "name": "peak_wavelength",
      "label": "Peak Wavelength",
      "type": "float(8)",
      "unit": "nm",
      "nul": true
    },
    {
      "name": "peak_amplitude",
      "label": "Peak Amplitude",
      "type": "float(8)",
      "unit": "dBm",
      "nul": true
    },
    {
      "name": "sidemode_lo_wavelength",
      "label": "Sidemode Lo Wavelength",
      "type": "float(8)",
      "unit": "nm",
      "nul": true
    },
    {
      "name": "sidemode_lo_amplitude",
      "label": "Sidemode Lo Amplitude",
      "type": "float(8)",
      "unit": "dBm",
      "nul": true
    },
    {
      "name": "sidemode_hi_wavelength",
      "label": "Sidemode Hi Wavelength",
      "type": "float(8)",
      "unit": "nm",
      "nul": true
    },
    {
      "name": "sidemode_hi_amplitude",
      "label": "Sidemode Hi Amplitude",
      "type": "float(8)",
      "unit": "dBm",
      "nul": true
    }
  }
}

These are the custom fields to include in the database, which will be used in addition to the default event database fields. A value for each field must be provided, unless "nul" is set to true.

Spectra Configuration

{
  "conf": {
    "spectrum": {
      "charts": {
        "summary": {
          "x": [
            "t_start",
            "t_end",
            "$groupRelativeTime",
            "$groupIndex",
            "lom_temp",
            "pa_current_sp",
            "$id"
          ],
          "y": [
            "peak_wavelength",
            "peak_amplitude",
            "sidemode_lo_wavelength",
            "sidemode_lo_amplitude",
            "sidemode_hi_wavelength",
            "sidemode_hi_amplitude"
          ]
        },
        "spectrum": {
          "x": [
            {
              "field": "Wavelength (nm)",
              "label": "Wavelength (nm)",
              "source": "file"
            },
            {
              "field": "Spectral Width About Peak (GHz)",
              "label": "Spectral Width About Peak (GHz)",
              "source": "file"
            }
          ],
          "y": [
            {
              "field": "Amplitude (dBm)",
              "label": "Amplitude (dBm)",
              "source": "file"
            },
            {
              "field": "Out of Band (%)",
              "label": "Out of Band (%)",
              "source": "file"
            }
          ]
        }
      },
      "filters": [
        {
          "name": "fast",
          "label": "Fast",
          "desc": "Fast",
          "color": "green",
          "checks": [
            {
              "field": "optical_axis",
              "value": "fast"
            }
          ]
        },
        {
          "name": "slow",
          "label": "Slow",
          "desc": "Slow",
          "color": "red",
          "checks": [
            {
              "field": "optical_axis",
              "value": "slow"
            }
          ]
        },
        {
          "name": "narrow",
          "label": "Narrow",
          "desc": "Narrow",
          "color": "purple",
          "checks": [
            {
              "field": "osa_confs",
              "value": "narrow"
            }
          ]
        },
        {
          "name": "wide",
          "label": "Wide",
          "desc": "Wide",
          "color": "orange",
          "checks": [
            {
              "field": "osa_confs",
              "value": "wide"
            }
          ]
        }
      ],
      "grouping": [
        "t_start",
        "group_id",
        "test_stage",
        "active_mo",
        "active_pa",
        "optical_axis",
        "osa_confs",
        "pa_current_sp"
      ]
    }
  }
}

Finally, the "conf" object contains the information required for XINA to interpret the event database as a spectra database. This contains three sections.

Charts

The charts section contains two subsections, "summary", and "spectra". The summary chart is displayed on the top of the Spectra Tool and plots one data point per spectrum. The configuration specifies the fields which should be listed as selectable options for the X and Y axes. This can include any of the event database default fields and/or custom fields. It also may include macros, indicated by starting with the $ character. These add additional logic and are implemented in the spectra tool itself.

The spectra chart is displayed at the bottom of the tool, and plots the full set of data for each selected spectrum. The options for each axis must be defined here to be correctly located in the associated file.

Other Features

The "filters" section defines filter options that will appear on the spectra tool, and the "grouping" option defines which fields should be available as options to create spectra groupings in the summary chart. More info on other settings is available here.

Spectrum Data Files

The spectrum files may either use a JSON format or DSV format. More formats may be added in the future.

JSON Format

A spectrum JSON file must contain a single JSON object, where each member should have an array of the same length of numeric values. An example compatible with the above spectra database could look like:

{
 "Wavelength (nm)": [ 1, 2, 3 ],
 "Spectral Width About Peak (GHz)": [ 0, 1, 0 ],
 "Amplitude (dBm)": [ 100, 200, 300 ],
 "Out of Band (%)": [ 5, 6, 7 ],
 "Comment": "ignored"
}

Note that the keys for each array must exactly match the names in the spectra "conf" object, or they will not be recognized correctly. Keys may be included which are not listed there, but they will be ignored for data purposes.

DSV Format

The spectrum DSV file format is based on the standard XINA Structs DSV file format, but doesn't require a time field. For example, a file with the same data as the above JSON example could look like:

# ignored comment
Wavelength (nm), Spectral Width About Peak (GHz), Amplitude (dBm), Out of Band (%)
1, 0, 100, 5
2, 1, 200, 6
3, 0, 300, 7

Modifications

A number of actions are available for changing the structure of a spectra database.

Drop

The drop action can delete an entire database (or group/model). This is permanent and deletes all data within the database as well, but is useful during initial experimentation as it gives a clean slate.

Drop Group

{
  "action": "drop",
  "drop": "group",
  "group": "sandbox.model_name",
  "drop_children": true
}

Note that when dropping a group, the "drop_children" flag must be true if the group contains any child groups or databases. In doing so all child groups and databases (and all data within) are deleted permanently.

Drop Database

{
  "action": "drop",
  "drop": "database",
  "database": "sandbox.model_name.spectra"
}

Reset Action

The reset action deletes all data in a database permanently. This is the fastest way to clear a database.

{
  "action": "reset",
  "database": "sandbox.model_name.spectra"
}

Altering Fields

Fields can be added or removed using the alter actions.

Add Fields

{
  "action": "alter",
  "alter": "database",
  "op": "add_fields",
  "database": "sandbox.model_name.spectra",
  "fields": [
    {
      "name": "new_field",
      "label": "New Field",
      "type": "float(8)",
      "nul": true
    }
  ]
}

Note that when adding fields to a database containing data, "nul" should typically be set to true (since existing records won't have a value for the field).

Drop Fields

{
  "action": "alter",
  "alter": "database",
  "op": "drop_fields",
  "database": "sandbox.model_name.spectra",
  "fields": [
    "unused_field"
  ]
}

Note that any data in dropped fields is deleted permanently.

Alter Configuration

The struct_alter_database_conf action can be used to update the spectrum configuration object. Care should be used when making changes here to ensure updates align with existing/added fields, and do not reference dropped fields, or errors may occur when loading the tool. Additionally, this replaces the entire "spectrum" object, so any existing configuration must be included to be preserved.

{
  "action": "struct_alter",
  "alter": "database",
  "op": "conf",
  "database": "sandbox.model_name.spectra",
  "conf": {
    "spectrum": { ... }
  }
}