# aseg_gdf2
[](https://github.com/kinverarity1/aseg_gdf2/blob/master/LICENSE)
Python code to help read ASEG GDF2 data packages.
GDF2 files are a plain text format for storing geophysical data. The file format is [defined](https://www.aseg.org.au/technical/aseg-technical-standards) by the Australian Society of Exploration Geophysicists (ASEG). This module provides Python functions for reading the definition file (.dfn) and extracting data from the data table file (.dat). It's designed to work on machines with low-ish memory, and to do so it has a dependency on [pandas](https://pandas.pydata.org/) and [dask](https://docs.dask.org/en/latest/dataframe.html).
It is still in very early stages of development. Help would be very welcome!
## Examples
Take a look at the [example notebooks](notebooks)!
### Quick start example
```python
>>> import aseg_gdf2
>>> gdf = aseg_gdf2.read(r'tests/example_datasets/3bcfc711/GA1286_Waveforms')
>>> gdf.nrecords
23039
>>> gdf.field_names()
['FLTNUM', 'Rx_Voltage', 'Flight', 'Time', 'Tx_Current']
>>> for row in gdf.iterrows():
... print(row)
... break
{'Index': 0, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0052, 'Tx_Current': 0.00176}
{'Index': 1, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0104, 'Tx_Current': 0.00176}
{'Index': 2, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0156, 'Tx_Current': 0.00176}
{'Index': 3, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0208, 'Tx_Current': 0.00176}
{'Index': 4, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.026, 'Tx_Current': 0.00176}
{'Index': 5, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0312, 'Tx_Current': 0.00176}
```
You can also get the data table as a pandas.DataFrame:
```python
>>> print(gdf.df())
```
```
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
```
Get the data just for one field/column:
```python
>>> gdf.get_field_data('Time')
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:
```python
>>> gdf = aseg_gdf2.read(r'tests/example_datasets/9a13704a/Mugrave_WB_MGA52.dfn')
>>> print(gdf.record_types.df()[["name", "unit", "format", "cols"]])
```
```
name unit format cols
0 RT A4 1
1 COMMENTS A76 1
0 GA_Project I10 1
1 Job_No I10 1
2 Fiducial F15.2 1
3 DATETIME days F18.10 1
4 LINE I10 1
5 Easting m F12.2 1
6 NORTH m F15.2 1
7 DTM_AHD F10.2 1
8 RESI1 F10.3 1
9 HEIGHT m F10.2 1
10 INVHEI m F10.2 1
11 DOI m F10.2 1
12 Elev m 30F12.2 30
13 Con mS/m 30F15.5 30
14 Con_doi mS/m 30F15.5 30
15 RUnc 30F12.3 30
```
You can see the field names in the normal manner:
```python
>>> gdf.field_names()
['GA_Project',
'Job_No',
'Fiducial',
'DATETIME',
'LINE',
'Easting',
'NORTH',
'DTM_AHD',
'RESI1',
'HEIGHT',
'INVHEI',
'DOI',
'Elev',
'Con',
'Con_doi',
'RUnc']
```
Or you can see the column names:
```python
>>> gdf.column_names()
['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]']
```
We can get the data in exactly the same way as a normal "column" field.
```python
>>> gdf.get_field_data("Elev")
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]])
```
We can also get a combination of ordinary column fields and 2D fields:
```python
>>> gdf.get_fields_data(["Easting", "NORTH", "Elev"])
(array([948001.6, 948001.9, 948001.5, 948000.6, 947999.1, 947997.2,
947995.1, 947993.4, 947992.5, 947992.5, 947993.3, 947994.7,
947996. , 947997.1, 947997.8, 947997.9, 800001.6, 800002.4,
800003. , 800003.5, 800003.5, 800003.3, 800002.9, 800002.8,
800002.8, 800003.1, 800003.7, 800004.1, 800004.3, 800004.5,
800004.4, 800004.2, 800004.1, 800004.1, 800003.9, 800003.7,
800003.3, 800002.6]),
array([7035223.1, 7035196.8, 7035169.5, 7035141.6, 7035113.6, 7035085.9,
7035058.5, 7035031.3, 7035004.2, 7034976.6, 7034948.3, 7034919.2,
7034889.4, 7034859. , 7034828.4, 7034797.9, 7029884.1, 7029855.3,
7029826.9, 7029798.6, 7029770.1, 7029741.5, 7029712.8, 7029684.3,
7029656.1, 7029628.1, 7029600.1, 7029572. , 7029543.8, 7029515.5,
7029487.4, 7029459.7, 7029432.1, 7029404.5, 7029376.8, 7029348.7,
7029320.2, 7029291.4]),
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]]))
```
Under the hood this works using pandas' [``usecols`` keyword argument](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.read_fwf.html).
## Installation
```python
pip install -U aseg_gdf2
```
## List of changes
### Version 0.3
- Fix #19 (`GDF2(..., method='fixed-widths')` was broken)
### Version 0.2
- Add transparent support for using either pandas or dask to read the data table file
- Simplify field data API: `gdf.get_field_data()` and `gdf.get_fields_data()`
### Version 0.1.2
- Fix #16 - expanded column names not working in gdf2.df_chunked()
### Version 0.1
- Initial development
## License
MIT.
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"description": "# aseg_gdf2\n\n[](https://github.com/kinverarity1/aseg_gdf2/blob/master/LICENSE)\n\nPython code to help read ASEG GDF2 data packages.\n\nGDF2 files are a plain text format for storing geophysical data. The file format is [defined](https://www.aseg.org.au/technical/aseg-technical-standards) by the Australian Society of Exploration Geophysicists (ASEG). This module provides Python functions for reading the definition file (.dfn) and extracting data from the data table file (.dat). It's designed to work on machines with low-ish memory, and to do so it has a dependency on [pandas](https://pandas.pydata.org/) and [dask](https://docs.dask.org/en/latest/dataframe.html).\n\nIt is still in very early stages of development. Help would be very welcome!\n\n## Examples\n\nTake a look at the [example notebooks](notebooks)!\n\n### Quick start example\n\n```python\n>>> import aseg_gdf2\n>>> gdf = aseg_gdf2.read(r'tests/example_datasets/3bcfc711/GA1286_Waveforms')\n>>> gdf.nrecords\n23039\n>>> gdf.field_names()\n['FLTNUM', 'Rx_Voltage', 'Flight', 'Time', 'Tx_Current']\n>>> for row in gdf.iterrows():\n... print(row)\n... break\n{'Index': 0, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0052, 'Tx_Current': 0.00176}\n{'Index': 1, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0104, 'Tx_Current': 0.00176}\n{'Index': 2, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0156, 'Tx_Current': 0.00176}\n{'Index': 3, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0208, 'Tx_Current': 0.00176}\n{'Index': 4, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.026, 'Tx_Current': 0.00176}\n{'Index': 5, 'FLTNUM': 1.0, 'Rx_Voltage': -0.0, 'Flight': 1, 'Time': 0.0312, 'Tx_Current': 0.00176}\n```\n\nYou can also get the data table as a pandas.DataFrame:\n\n```python\n>>> print(gdf.df())\n```\n\n```\n FLTNUM Rx_Voltage Flight Time Tx_Current\n0 1.0 -0.0 1 0.0052 0.00176\n1 1.0 -0.0 1 0.0104 0.00176\n2 1.0 -0.0 1 0.0156 0.00176\n3 1.0 -0.0 1 0.0208 0.00176\n4 1.0 -0.0 1 0.0260 0.00176\n```\n\nGet the data just for one field/column:\n\n```python\n>>> gdf.get_field_data('Time')\narray([ 5.20000000e-03, 1.04000000e-02, 1.56000000e-02, ...,\n 5.99844000e+01, 5.99896000e+01, 5.99948000e+01])\n```\n\nWhat 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:\n\n```python\n>>> gdf = aseg_gdf2.read(r'tests/example_datasets/9a13704a/Mugrave_WB_MGA52.dfn')\n>>> print(gdf.record_types.df()[[\"name\", \"unit\", \"format\", \"cols\"]])\n```\n\n```\n name unit format cols\n0 RT A4 1\n1 COMMENTS A76 1\n0 GA_Project I10 1\n1 Job_No I10 1\n2 Fiducial F15.2 1\n3 DATETIME days F18.10 1\n4 LINE I10 1\n5 Easting m F12.2 1\n6 NORTH m F15.2 1\n7 DTM_AHD F10.2 1\n8 RESI1 F10.3 1\n9 HEIGHT m F10.2 1\n10 INVHEI m F10.2 1\n11 DOI m F10.2 1\n12 Elev m 30F12.2 30\n13 Con mS/m 30F15.5 30\n14 Con_doi mS/m 30F15.5 30\n15 RUnc 30F12.3 30\n```\n\nYou can see the field names in the normal manner:\n\n```python\n>>> gdf.field_names()\n['GA_Project',\n 'Job_No',\n 'Fiducial',\n 'DATETIME',\n 'LINE',\n 'Easting',\n 'NORTH',\n 'DTM_AHD',\n 'RESI1',\n 'HEIGHT',\n 'INVHEI',\n 'DOI',\n 'Elev',\n 'Con',\n 'Con_doi',\n 'RUnc']\n```\n\n Or you can see the column names:\n\n```python\n>>> gdf.column_names()\n['GA_Project', 'Job_No', 'Fiducial', 'DATETIME', 'LINE', 'Easting', 'NORTH', 'DTM_AHD', 'RESI1',\n 'HEIGHT', 'INVHEI', 'DOI', 'Elev[0]', 'Elev[1]', 'Elev[2]', 'Elev[3]', 'Elev[4]', 'Elev[5]',\n 'Elev[6]', 'Elev[7]', 'Elev[8]', 'Elev[9]', 'Elev[10]', 'Elev[11]', 'Elev[12]', 'Elev[13]',\n 'Elev[14]', 'Elev[15]', 'Elev[16]', 'Elev[17]', 'Elev[18]', 'Elev[19]', 'Elev[20]', 'Elev[21]',\n 'Elev[22]', 'Elev[23]', 'Elev[24]', 'Elev[25]', 'Elev[26]', 'Elev[27]', 'Elev[28]', 'Elev[29]',\n 'Con[0]', 'Con[1]', 'Con[2]', 'Con[3]', 'Con[4]', 'Con[5]', 'Con[6]', 'Con[7]', 'Con[8]', 'Con[9]',\n 'Con[10]', 'Con[11]', 'Con[12]', 'Con[13]', 'Con[14]', 'Con[15]', 'Con[16]', 'Con[17]', 'Con[18]',\n 'Con[19]', 'Con[20]', 'Con[21]', 'Con[22]', 'Con[23]', 'Con[24]', 'Con[25]', 'Con[26]', 'Con[27]',\n 'Con[28]', 'Con[29]', 'Con_doi[0]', 'Con_doi[1]', 'Con_doi[2]', 'Con_doi[3]', 'Con_doi[4]',\n 'Con_doi[5]', 'Con_doi[6]', 'Con_doi[7]', 'Con_doi[8]', 'Con_doi[9]', 'Con_doi[10]', 'Con_doi[11]',\n 'Con_doi[12]', 'Con_doi[13]', 'Con_doi[14]', 'Con_doi[15]', 'Con_doi[16]', 'Con_doi[17]',\n 'Con_doi[18]', 'Con_doi[19]', 'Con_doi[20]', 'Con_doi[21]', 'Con_doi[22]', 'Con_doi[23]',\n 'Con_doi[24]', 'Con_doi[25]', 'Con_doi[26]', 'Con_doi[27]', 'Con_doi[28]', 'Con_doi[29]', 'RUnc[0]',\n 'RUnc[1]', 'RUnc[2]', 'RUnc[3]', 'RUnc[4]', 'RUnc[5]', 'RUnc[6]', 'RUnc[7]', 'RUnc[8]', 'RUnc[9]',\n 'RUnc[10]', 'RUnc[11]', 'RUnc[12]', 'RUnc[13]', 'RUnc[14]', 'RUnc[15]', 'RUnc[16]', 'RUnc[17]',\n 'RUnc[18]', 'RUnc[19]', 'RUnc[20]', 'RUnc[21]', 'RUnc[22]', 'RUnc[23]', 'RUnc[24]', 'RUnc[25]',\n 'RUnc[26]', 'RUnc[27]', 'RUnc[28]', 'RUnc[29]']\n```\n\nWe can get the data in exactly the same way as a normal \"column\" field.\n\n```python\n>>> gdf.get_field_data(\"Elev\")\narray([[ 354.1, 352.1, 349.8, ..., -105.8, -171.2, -245.7],\n [ 353.8, 351.8, 349.5, ..., -106.1, -171.5, -246. ],\n [ 353.7, 351.7, 349.4, ..., -106.2, -171.6, -246.1],\n ...,\n [ 510.5, 508.5, 506.2, ..., 50.6, -14.8, -89.3],\n [ 510.5, 508.5, 506.2, ..., 50.6, -14.8, -89.3],\n [ 510.6, 508.6, 506.3, ..., 50.7, -14.7, -89.2]])\n```\n\nWe can also get a combination of ordinary column fields and 2D fields:\n\n```python\n>>> gdf.get_fields_data([\"Easting\", \"NORTH\", \"Elev\"])\n(array([948001.6, 948001.9, 948001.5, 948000.6, 947999.1, 947997.2,\n 947995.1, 947993.4, 947992.5, 947992.5, 947993.3, 947994.7,\n 947996. , 947997.1, 947997.8, 947997.9, 800001.6, 800002.4,\n 800003. , 800003.5, 800003.5, 800003.3, 800002.9, 800002.8,\n 800002.8, 800003.1, 800003.7, 800004.1, 800004.3, 800004.5,\n 800004.4, 800004.2, 800004.1, 800004.1, 800003.9, 800003.7,\n 800003.3, 800002.6]),\n array([7035223.1, 7035196.8, 7035169.5, 7035141.6, 7035113.6, 7035085.9,\n 7035058.5, 7035031.3, 7035004.2, 7034976.6, 7034948.3, 7034919.2,\n 7034889.4, 7034859. , 7034828.4, 7034797.9, 7029884.1, 7029855.3,\n 7029826.9, 7029798.6, 7029770.1, 7029741.5, 7029712.8, 7029684.3,\n 7029656.1, 7029628.1, 7029600.1, 7029572. , 7029543.8, 7029515.5,\n 7029487.4, 7029459.7, 7029432.1, 7029404.5, 7029376.8, 7029348.7,\n 7029320.2, 7029291.4]),\n array([[ 354.1, 352.1, 349.8, ..., -105.8, -171.2, -245.7],\n [ 353.8, 351.8, 349.5, ..., -106.1, -171.5, -246. ],\n [ 353.7, 351.7, 349.4, ..., -106.2, -171.6, -246.1],\n ...,\n [ 510.5, 508.5, 506.2, ..., 50.6, -14.8, -89.3],\n [ 510.5, 508.5, 506.2, ..., 50.6, -14.8, -89.3],\n [ 510.6, 508.6, 506.3, ..., 50.7, -14.7, -89.2]]))\n```\n\nUnder the hood this works using pandas' [``usecols`` keyword argument](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.read_fwf.html).\n\n## Installation\n\n```python\npip install -U aseg_gdf2\n```\n\n## List of changes\n\n### Version 0.3\n- Fix #19 (`GDF2(..., method='fixed-widths')` was broken)\n\n### Version 0.2\n- Add transparent support for using either pandas or dask to read the data table file\n- Simplify field data API: `gdf.get_field_data()` and `gdf.get_fields_data()`\n\n### Version 0.1.2\n- Fix #16 - expanded column names not working in gdf2.df_chunked()\n\n### Version 0.1\n- Initial development\n\n## License\n\nMIT.\n",
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