Python code to help read ASEG GDF2 packages
Project description
aseg_gdf2
Python code to help read ASEG GDF2 packages. See the ASEG technical standards page for more information about the file format.
Still very much a work in progress.
Usage
In [1]: import aseg_gdf2
In [2]: gdf = aseg_gdf2.read(r'tests/example_datasets/3bcfc711/GA1286_Waveforms')
In [3]: gdf.field_names()
Out[3]: ['FLTNUM', 'Rx_Voltage', 'Flight', 'Time', 'Tx_Current']
In [4]]: for row in gdf.iterrows():
...: print(row)
...:
OrderedDict([('Index', 0), ('FLTNUM', 1.0), ('Rx_Voltage', -0.0), ('Flight', 1), ('Time', 0.0052), ('Tx_Current', 0.00176)])
OrderedDict([('Index', 1), ('FLTNUM', 1.0), ('Rx_Voltage', -0.0), ('Flight', 1), ('Time', 0.0104), ('Tx_Current', 0.00176)])
OrderedDict([('Index', 2), ('FLTNUM', 1.0), ('Rx_Voltage', -0.0), ('Flight', 1), ('Time', 0.0156), ('Tx_Current', 0.00176)])
...
For .dat files that will fit in memory, you can read them into a pandas.DataFrame:
In [5]: gdf.df()
Out[5]:
FLTNUM Rx_Voltage Flight Time Tx_Current
0 1.0 -0.0 1 0.0052 0.00176
1 1.0 -0.0 1 0.0104 0.00176
2 1.0 -0.0 1 0.0156 0.00176
3 1.0 -0.0 1 0.0208 0.00176
4 1.0 -0.0 1 0.0260 0.00176
5 1.0 -0.0 1 0.0312 0.00176
... ... ... ... ... ...
23034 2.0 0.0 2 59.9687 -0.00170
23035 2.0 -0.0 2 59.9740 -0.00170
23036 2.0 -0.0 2 59.9792 -0.00170
23037 2.0 -0.0 2 59.9844 -0.00170
23038 2.0 -0.0 2 59.9896 -0.00170
23039 2.0 -0.0 2 59.9948 -0.00170
[23040 rows x 5 columns]
For .dat files that are too big for memory, you can use the chunksize= keyword argument to specify the number of rows. Normally you could get away with a few hundred thousand, but for the example we'll use something less:
In [6]: for chunk in gdf.df_chunked(chunksize=10000):
...: print('{} length = {}'.format(type(chunk), len(chunk)))
...:
<class 'pandas.core.frame.DataFrame'> length = 10000
<class 'pandas.core.frame.DataFrame'> length = 10000
<class 'pandas.core.frame.DataFrame'> length = 3040
The metadata from the .dfn file is there too:
In [7]: gdf.record_types
Out[7]:
{'': {'fields': [{'cols': 1,
'comment': '',
'format': 'F10.1',
'long_name': 'FlightNumber',
'name': 'FLTNUM',
'null': None,
'unit': '',
'width': 10},
{'cols': 1,
'comment': '',
'format': 'F10.5',
'long_name': 'Rx_Voltage',
'name': 'Rx_Voltage',
'null': '-99.99999',
'unit': 'Volt',
'width': 10},
{'cols': 1,
'comment': '',
'format': 'I6',
'long_name': 'Flight',
'name': 'Flight',
'null': '-9999',
'unit': '',
'width': 6},
{'cols': 1,
'comment': '',
'format': 'F10.4',
'long_name': 'Time',
'name': 'Time',
'null': '-999.9999',
'unit': 'msec',
'width': 10},
{'cols': 1,
'comment': '',
'format': 'F13.5',
'long_name': 'Tx_Current',
'name': 'Tx_Current',
'null': '-99999.99999',
'unit': 'Amp',
'width': 13}],
'format': None}}
Get the data just for one field/column:
In [8]: gdf.get_field('Time')
Out[8]:
array([ 5.20000000e-03, 1.04000000e-02, 1.56000000e-02, ...,
5.99844000e+01, 5.99896000e+01, 5.99948000e+01])
What about fields which are 2D arrays? Some GDF2 data files have fields with more than one value per row/record. e.g. in this one the last four fields each take up 30 columns:
In [9]: gdf = aseg_gdf2.read(r'tests/example_datasets/9a13704a/Mugrave_WB_MGA52.dfn')
In [10]: print(gdf.dfn_contents)
DEFN ST=RECD,RT=COMM;RT:A4;COMMENTS:A76
DEFN 1 ST=RECD,RT=;GA_Project:I10:Geoscience Australia airborne survey project number
DEFN 2 ST=RECD,RT=;Job_No:I10:SkyTEM Australia Job Number
DEFN 3 ST=RECD,RT=;Fiducial:F15.2:Fiducial
DEFN 4 ST=RECD,RT=;DATETIME:F18.10:UNIT=days,Decimal days since midnight December 31st 1899
DEFN 5 ST=RECD,RT=;LINE:I10:Line number
DEFN 6 ST=RECD,RT=;Easting:F12.2:NULL=-9999999.99,UNIT=m,Easting (GDA94 MGA Zone 52)
DEFN 7 ST=RECD,RT=;NORTH:F15.2:NULL=-9999999999.99,UNIT=m,Northing (GDA 94 MGA Zone 52)
DEFN 8 ST=RECD,RT=;DTM_AHD:F10.2:NULL=-99999.99,Digital terrain model (AUSGeoid09 datum)
DEFN 9 ST=RECD,RT=;RESI1:F10.3:NULL=-9999.999,Residual of data
DEFN 10 ST=RECD,RT=;HEIGHT:F10.2:NULL=-99999.99,UNIT=m,Laser altimeter measured height of Tx loop centre above ground
DEFN 11 ST=RECD,RT=;INVHEI:F10.2:NULL=-99999.99,UNIT=m,Calulated inversion height
DEFN 12 ST=RECD,RT=;DOI:F10.2:NULL=-99999.99,UNIT=m,Calculated depth of investigation
DEFN 13 ST=RECD,RT=;Elev:30F12.2:NULL=-9999999.99,UNIT=m,Elevation to the top of each layer
DEFN 14 ST=RECD,RT=;Con:30F15.5:NULL=-9999999.99999,UNIT=mS/m,Inverted Conductivity for each layer
DEFN 15 ST=RECD,RT=;Con_doi:30F15.5:NULL=-9999999.99999,UNIT=mS/m, Inverted conductivity for each layer, masked to the depth of investigation
DEFN 16 ST=RECD,RT=;RUnc:30F12.3:NULL=-999999.999,Relative uncertainty of conductivity layer;END DEFN
You can see the field names in the normal manner:
In [11]: gdf.field_names()
Out[11]:
['GA_Project',
'Job_No',
'Fiducial',
'DATETIME',
'LINE',
'Easting',
'NORTH',
'DTM_AHD',
'RESI1',
'HEIGHT',
'INVHEI',
'DOI',
'Elev',
'Con',
'Con_doi',
'RUnc']
Or you can see an "expanded" version of the fields, which is used for the column headings of the data table:
In [12]: gdf.column_names()
Out[12]:
['GA_Project', 'Job_No', 'Fiducial', 'DATETIME', 'LINE', 'Easting', 'NORTH', 'DTM_AHD', 'RESI1',
'HEIGHT', 'INVHEI', 'DOI', 'Elev[0]', 'Elev[1]', 'Elev[2]', 'Elev[3]', 'Elev[4]', 'Elev[5]',
'Elev[6]', 'Elev[7]', 'Elev[8]', 'Elev[9]', 'Elev[10]', 'Elev[11]', 'Elev[12]', 'Elev[13]',
'Elev[14]', 'Elev[15]', 'Elev[16]', 'Elev[17]', 'Elev[18]', 'Elev[19]', 'Elev[20]', 'Elev[21]',
'Elev[22]', 'Elev[23]', 'Elev[24]', 'Elev[25]', 'Elev[26]', 'Elev[27]', 'Elev[28]', 'Elev[29]',
'Con[0]', 'Con[1]', 'Con[2]', 'Con[3]', 'Con[4]', 'Con[5]', 'Con[6]', 'Con[7]', 'Con[8]', 'Con[9]',
'Con[10]', 'Con[11]', 'Con[12]', 'Con[13]', 'Con[14]', 'Con[15]', 'Con[16]', 'Con[17]', 'Con[18]',
'Con[19]', 'Con[20]', 'Con[21]', 'Con[22]', 'Con[23]', 'Con[24]', 'Con[25]', 'Con[26]', 'Con[27]',
'Con[28]', 'Con[29]', 'Con_doi[0]', 'Con_doi[1]', 'Con_doi[2]', 'Con_doi[3]', 'Con_doi[4]',
'Con_doi[5]', 'Con_doi[6]', 'Con_doi[7]', 'Con_doi[8]', 'Con_doi[9]', 'Con_doi[10]', 'Con_doi[11]',
'Con_doi[12]', 'Con_doi[13]', 'Con_doi[14]', 'Con_doi[15]', 'Con_doi[16]', 'Con_doi[17]',
'Con_doi[18]', 'Con_doi[19]', 'Con_doi[20]', 'Con_doi[21]', 'Con_doi[22]', 'Con_doi[23]',
'Con_doi[24]', 'Con_doi[25]', 'Con_doi[26]', 'Con_doi[27]', 'Con_doi[28]', 'Con_doi[29]', 'RUnc[0]',
'RUnc[1]', 'RUnc[2]', 'RUnc[3]', 'RUnc[4]', 'RUnc[5]', 'RUnc[6]', 'RUnc[7]', 'RUnc[8]', 'RUnc[9]',
'RUnc[10]', 'RUnc[11]', 'RUnc[12]', 'RUnc[13]', 'RUnc[14]', 'RUnc[15]', 'RUnc[16]', 'RUnc[17]',
'RUnc[18]', 'RUnc[19]', 'RUnc[20]', 'RUnc[21]', 'RUnc[22]', 'RUnc[23]', 'RUnc[24]', 'RUnc[25]',
'RUnc[26]', 'RUnc[27]', 'RUnc[28]', 'RUnc[29]']
In [13]: gdf.df().head()
Out[13]:
GA_Project Job_No Fiducial DATETIME LINE Easting NORTH \
0 1288 10013 3621109.0 42655.910984 112601 948001.6 7035223.1
1 1288 10013 3621110.0 42655.910995 112601 948001.9 7035196.8
2 1288 10013 3621111.0 42655.911007 112601 948001.5 7035169.5
3 1288 10013 3621112.0 42655.911019 112601 948000.6 7035141.6
4 1288 10013 3621113.0 42655.911030 112601 947999.1 7035113.6
DTM_AHD RESI1 HEIGHT ... RUnc[20] RUnc[21] RUnc[22] RUnc[23] \
0 354.1 1.091 40.98 ... 1.39 1.76 2.35 3.26
1 353.8 1.101 41.08 ... 1.43 1.84 2.47 3.41
2 353.7 0.813 41.03 ... 1.45 1.88 2.53 3.48
3 353.9 0.567 40.79 ... 1.45 1.87 2.53 3.49
4 354.2 0.522 40.37 ... 1.45 1.88 2.54 3.52
RUnc[24] RUnc[25] RUnc[26] RUnc[27] RUnc[28] RUnc[29]
0 4.45 5.74 6.94 8.00 8.99 98.0
1 4.62 5.90 7.09 8.15 9.15 98.0
2 4.70 5.97 7.16 8.22 9.21 98.0
3 4.71 5.98 7.16 8.21 9.20 98.0
4 4.74 6.01 7.18 8.23 9.22 98.0
[5 rows x 132 columns]
You can retrieve one of the original field arrays using get_field():
In [14]: gdf.get_field('Elev')
Out[14]:
array([[ 354.1, 352.1, 349.8, ..., -105.8, -171.2, -245.7],
[ 353.8, 351.8, 349.5, ..., -106.1, -171.5, -246. ],
[ 353.7, 351.7, 349.4, ..., -106.2, -171.6, -246.1],
...,
[ 510.5, 508.5, 506.2, ..., 50.6, -14.8, -89.3],
[ 510.5, 508.5, 506.2, ..., 50.6, -14.8, -89.3],
[ 510.6, 508.6, 506.3, ..., 50.7, -14.7, -89.2]])
Or one of the columns:
In [15]: gdf.get_field('Elev[0]')
Out[15]:
array([ 354.1, 353.8, 353.7, 353.9, 354.2, 354.5, 354.6, 354.7,
354.6, 354.5, 354.3, 354.1, 353.9, 353.8, 353.9, 354. ,
512.8, 512.6, 512.4, 512.3, 512.3, 512.5, 512.7, 512.9,
512.9, 512.8, 512.6, 512.4, 512. , 511.7, 511.4, 511.2,
511. , 510.6, 510.5, 510.5, 510.5, 510.6])
You can also retrieve a subset of fields and column names as a pandas.DataFrame using the usecols keyword argument -- you don't necessarily need to retrieve the whole file at once. Note that the multidimensional 'Con' field is expanded into the column names:
In [16]: gdf.df(usecols=['Easting', 'NORTH', 'Con']).head()
Out[16]:
Easting NORTH Con[0] Con[1] Con[2] Con[3] Con[4] \
0 948001.6 7035223.1 28.76870 31.88776 46.04052 83.68201 157.48031
1 948001.9 7035196.8 31.06555 35.47357 51.17707 92.08103 165.37126
2 948001.5 7035169.5 38.18251 42.48088 59.91612 103.59474 174.79462
3 948000.6 7035141.6 47.61905 51.84033 70.17544 114.31184 178.79492
4 947999.1 7035113.6 58.58231 61.12469 77.45933 118.04982 173.64126
Con[5] Con[6] Con[7] ... Con[20] Con[21] \
0 231.53508 242.01355 198.84669 ... 108.63661 145.39110
1 235.73786 237.41690 190.65777 ... 108.95620 144.84357
2 232.07241 225.88660 178.98693 ... 110.29006 146.09204
3 219.92523 212.44954 170.64846 ... 112.81588 148.58841
4 209.51184 204.49898 168.60563 ... 114.48197 150.03751
Con[22] Con[23] Con[24] Con[25] Con[26] Con[27] \
0 181.29079 191.60759 178.44397 162.31131 152.43902 148.38997
1 179.79144 190.36741 177.99929 162.33766 152.55530 148.47810
2 179.88847 189.35808 177.11654 161.89089 152.46227 148.54427
3 180.31013 187.68769 175.37706 161.00467 152.23017 148.65468
4 180.21265 186.35855 174.27675 160.56519 152.23017 148.83167
Con[28] Con[29]
0 147.49263 147.42739
1 147.53615 147.47087
2 147.66686 147.62327
3 147.88524 147.86337
4 148.10427 148.08233
[5 rows x 32 columns]
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