MAS_CP_MASTER

MAS_CP_MASTER shows the current MAS configuration.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

Obsolete; Replaced by MSATS

Source

MAS_CP_MASTER updates daily with each MAS export.

Note

In_Use Value	Meaning
Y	ACTIVE
N	CLOSED
X	EXTINCT

Column Schema

NMI

National Metering Identifier

SQL Type: VARCHAR2(10) Polars Type: str

This column is mandatory, and a primary key.

VALID_TO_DATE

Record valid to date

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

AGGREGATE_DATA

Aggregate data flag (YIN)

SQL Type: VARCHAR2(1) Polars Type: pl.Boolean

This column is not mandatory.

AVERAGE_DAILY_LOAD_KWH

Average daily load in KWh

SQL Type: NUMBER(8,0) Polars Type: pl.Int32

This column is not mandatory.

CITY_SUBURB

Connection point city/suburb

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

CP_DETAIL_1

Connection point detail 1

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

CP_DETAIL_2

Connection point detail 2

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

CP_NAME

Connection point name

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

CP_SECURITY_CODE

Security Code

SQL Type: VARCHAR2(4) Polars Type: str

This column is not mandatory.

DISTRIBUTION_LOSS

Distribution loss factor

SQL Type: NUMBER(5,4) Polars Type: pl.Float64

This column is not mandatory.

FINANCIAL_PARTICIPANT

FRMP

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

FRMP_TEXT

FRMP text field

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

IN_USE

Active/Inactive Status flag (NEW/N/Y/X)

SQL Type: VARCHAR2(1) Polars Type: pl.Boolean

This column is not mandatory.

JURISDICTION

SQL Type: VARCHAR2(3) Polars Type: str

This column is not mandatory.

LASTCHANGED

Last changed date

For more information, see the dedicated documentation page.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2026/01/20 00:05:00

LOCAL_NETWORK_PROVIDER

LNSP

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

LOCAL_RETAILER

Local Retailer

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

LR_TEXT

LR text field

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

LSNP_TEXT

LNSP text field (name has misspelt LNSP)

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

MDA_TEXT

MDA text field

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

METERING_DATA_AGENT

MDA

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

METERING_TYPE

SQL Type: VARCHAR2(9) Polars Type: pl.Categorical

This column is not mandatory.

NMI_CLASS

SQL Type: VARCHAR2(9) Polars Type: str

This column is not mandatory.

POST_CODE

Connection point postcode

SQL Type: VARCHAR2(4) Polars Type: str

This column is not mandatory.

RESPONSIBLE_PERSON

Responsible Person

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

RETAILER_OF_LAST_RESORT

ROLR

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

ROLR_TEXT

ROLR text field

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

RP_TEXT

RP text field

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

STATE

State of Australia

SQL Type: VARCHAR2(3) Polars Type: str

This column is not mandatory.

TX_NODE

Connection point TNI

SQL Type: VARCHAR2(4) Polars Type: str

This column is not mandatory.

VALID_FROM_DATE

Record valid from date

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory (but not a primary key).

Example: 2026/01/20 00:05:00

New Data Timing

Daily

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .with_columns(
        pl.col("VALID_TO_DATE", "LASTCHANGED", "VALID_FROM_DATE").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    # Polars can't cast string straight to bool. Need to go via something else.
    .cast({"AGGREGATE_DATA": pl.UInt8, "IN_USE": pl.UInt8})
    .cast(
        {
            "NMI": str,
            "AGGREGATE_DATA": pl.Boolean,
            "AVERAGE_DAILY_LOAD_KWH": int,  # or pl.Int32
            "CITY_SUBURB": str,
            "CP_DETAIL_1": str,
            "CP_DETAIL_2": str,
            "CP_NAME": str,
            "CP_SECURITY_CODE": str,
            "DISTRIBUTION_LOSS": pl.Float64,
            "FINANCIAL_PARTICIPANT": str,
            "FRMP_TEXT": str,
            "IN_USE": pl.Boolean,
            "JURISDICTION": str,
            "LOCAL_NETWORK_PROVIDER": str,
            "LOCAL_RETAILER": str,
            "LR_TEXT": str,
            "LSNP_TEXT": str,
            "MDA_TEXT": str,
            "METERING_DATA_AGENT": str,
            "METERING_TYPE": pl.Categorical,
            "NMI_CLASS": str,
            "POST_CODE": str,
            "RESPONSIBLE_PERSON": str,
            "RETAILER_OF_LAST_RESORT": str,
            "ROLR_TEXT": str,
            "RP_TEXT": str,
            "STATE": str,
            "TX_NODE": str,
        }
    )
    # Deduplicate
    # We want one row per ['NMI', 'VALID_TO_DATE']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["NMI", "VALID_TO_DATE"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Deduplication and PDR Loader Configuration

The primary keys are:

• NMI

• VALID_TO_DATE

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

You should probably take the row with the maximum LASTCHANGED.

Indexes

• LASTCHANGED

Links

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: HISTORICAL TABLES

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