!test -f aircraft_small.bufr \
    || wget https://sites.ecmwf.int/repository/pdbufr/test-data/aircraft_small.bufr \
             --output-document=aircraft_small.bufr

Flat reader: aircraft data

import pdbufr

The notebook demonstrates the usage of the flat reader with aircraft data in block extraction mode.

Block extraction is activated by passing one of the special values “all”, “header” or “data” to the columns parameter. The default (columns=[]) is equivalent to columns=”all”.

Using columns

By default all the header and data keys are extracted.

df = pdbufr.read_bufr("aircraft_small.bufr", reader="flat")
df.T

	0	1	2	3	4	5	6	7	8	9
edition	3	3	3	3	3	3	3	3	3	3
masterTableNumber	0	0	0	0	0	0	0	0	0	0
bufrHeaderSubCentre	0	0	0	0	0	0	0	0	0	0
bufrHeaderCentre	98	98	98	98	98	98	98	98	98	98
updateSequenceNumber	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...
#1#dewpointTemperature	None	None	None	None	None	None	None	None	None	None
#1#relativeHumidity	None	None	None	None	None	None	None	None	None	None
#1#airframeIcing	None	None	None	None	None	None	None	None	None	None
#1#centre	98	98	98	98	98	98	98	98	98	98
#1#generatingApplication	1	1	1	1	1	1	1	1	1	1

81 rows × 10 columns

However, we can extract only the header keys:

df = pdbufr.read_bufr("aircraft_small.bufr", columns="header", reader="flat")
df.T[:6]

	0	1	2	3	4	5	6	7	8	9
edition	3	3	3	3	3	3	3	3	3	3
masterTableNumber	0	0	0	0	0	0	0	0	0	0
bufrHeaderSubCentre	0	0	0	0	0	0	0	0	0	0
bufrHeaderCentre	98	98	98	98	98	98	98	98	98	98
updateSequenceNumber	0	0	0	0	0	0	0	0	0	0
dataCategory	4	4	4	4	4	4	4	4	4	4

or only the data keys:

df = pdbufr.read_bufr("aircraft_small.bufr", columns="data", reader="flat")
df.T[:18]

	0	1	2	3	4	5	6	7	8	9
subsetNumber	1	1	1	1	1	1	1	1	1	1
#1#aircraftFlightNumber	QGOBTRRA	QGOBTRRA	UOZDOZ2S	UOZDOZ2S	UOZDOZ2S	UOZDOZ2S	VUVTEWZQ	4IPASOZA	WSSASKBA	WSSASKBA
#1#aircraftRegistrationNumberOrOtherIdentification	HGSKJFBA	HGSKJFBA	O2RYR4JA	O2RYR4JA	O2RYR4JA	O2RYR4JA	4NK13QZA	0IKWU1JA	P4MAWDZA	P4MAWDZA
#1#aircraftNavigationalSystem	None	None	None	None	None	None	None	None	None	None
#1#aircraftDataRelaySystemType	3	3	3	3	3	3	3	3	3	3
#1#instrumentationForWindMeasurement	4	4	4	4	4	4	4	4	4	4
#1#temperatureObservationPrecision	0.1	0.1	0.25	0.25	0.25	0.25	0.25	0.25	0.25	0.25
#1#originalSpecificationOfLatitudeOrLongitude	1	1	10	10	10	10	10	1	10	10
#1#aircraftRollAngle	None	None	None	None	None	None	None	None	None	None
#1#stationType	0	0	0	0	0	0	0	0	0	0
#1#year	2009	2009	2009	2009	2009	2009	2009	2009	2009	2009
#1#month	1	1	1	1	1	1	1	1	1	1
#1#day	23	23	23	23	23	23	23	23	23	23
#1#hour	13	13	12	12	13	13	13	13	13	13
#1#minute	0	1	56	58	0	2	2	2	0	0
#1#latitude	35.1	35.07	41.45	41.52	41.59	41.67	38.26	19.62	42.31	42.29
#1#longitude	-89.97	-89.97	-75.43	-75.63	-75.87	-76.16	-78.57	73.75	-70.7	-70.67
#1#phaseOfAircraftFlight	6.0	6.0	NaN	NaN	NaN	NaN	3.0	NaN	5.0	5.0

Filtering

Filtering works similarly to the hierarchical (i.e. non-flat) mode. In the following example only the messages/subsets matching a given flight number are extracted.

df = pdbufr.read_bufr(
    "aircraft_small.bufr", columns="data", filters={"aircraftFlightNumber": "UOZDOZ2S"}, reader="flat"
)
df.T.iloc[:18]

	0	1	2	3
subsetNumber	1	1	1	1
#1#aircraftFlightNumber	UOZDOZ2S	UOZDOZ2S	UOZDOZ2S	UOZDOZ2S
#1#aircraftRegistrationNumberOrOtherIdentification	O2RYR4JA	O2RYR4JA	O2RYR4JA	O2RYR4JA
#1#aircraftNavigationalSystem	None	None	None	None
#1#aircraftDataRelaySystemType	3	3	3	3
#1#instrumentationForWindMeasurement	4	4	4	4
#1#temperatureObservationPrecision	0.25	0.25	0.25	0.25
#1#originalSpecificationOfLatitudeOrLongitude	10	10	10	10
#1#aircraftRollAngle	None	None	None	None
#1#stationType	0	0	0	0
#1#year	2009	2009	2009	2009
#1#month	1	1	1	1
#1#day	23	23	23	23
#1#hour	12	12	13	13
#1#minute	56	58	0	2
#1#latitude	41.45	41.52	41.59	41.67
#1#longitude	-75.43	-75.63	-75.87	-76.16
#1#phaseOfAircraftFlight	None	None	None	None