Rutgers Center for Ocean Observing Leadership High Frequency Radar (CODAR) Processing toolbox
Download and follow installation instructions for the appropriate Miniconda installer from http://conda.pydata.org/miniconda.html.
Make sure to add the channel, conda-forge, to your .condarc. You can find out more about conda-forge from their website: https://conda-forge.org/
You can do this with the following command:
conda config --add channels conda-forge
Either use git to clone:
git clone https://github.com/rucool/codar_processing.git
or download the zip from file from, https://github.com/rucool/codar_processing/archive/master.zip and extract to the directory of your choice
Change your current working directory to the location that you downloaded codar_processing to.
cd /Users/mikesmith/Documents/git/codar_processing/
Create conda environment from the included environment.yml file:
conda create env -f environment.yml
Once the environment is done building, you can activate the environment by typing:
source activate codar_processing # OSX/Unix
Now we need to install the toolbox to the conda environment. We can do this as follows from the root directory of the codar_processing toolbox:
pip install .
The toolbox should now be installed to your conda environment.
(codar_processing)
mikesmith@nbp-56-180: ~
$ python
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:44:09)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>
The toolbox is based on the LLUV file format developed by CODAR Ocean Sensors. There is a generic, inheritable class called 'LLUV' that is written to dynamically read in all CODAR LLUV format files. There are currently two subclasses, Radial and Waves, that inherit from this LLUV class. Other LLUV files are currently being implemented. In order to load one of these files, you will need to import one (or all) of the the classes into your python environment before you can utilize the toolbox.
- For loading radial files:
from codar_processing.radials import Radial - For loading wave files:
from codar_processing.waves import Waves
In [1]: radial_file = '/Users/mikesmith/Documents/git/rucool/codar_processing/data/radials/SEAB/2018_03/RDLi_SEAB_2018_03_01_0000.ruv'
In [2]: r = Radial(radial_file)
This command lists methods (functions) of the built-in class. Don't bother with any methods starting with a _
In [3]: r.__dir__()
['file_path', 'file_name', 'header', 'tables', 'footer', 'data', 'diags_radial', 'diags_hardware', '__module__', '__doc__', '__init__', 'file_type', 'validate_key', 'validate_header', 'create_ruv', 'export', 'qc_qartod_location', 'qc_qartod_radial_count', 'qc_qartod_speed', 'reset', '__metaclass__', 'is_valid', '_parse_header_line', '__dict__', '__weakref__', '__repr__', '__hash__', '__str__', '__getattribute__', '__setattr__', '__delattr__', '__lt__', '__le__', '__eq__', '__ne__', '__gt__', '__ge__', '__new__', '__reduce_ex__', '__reduce__', '__subclasshook__', '__init_subclass__', '__format__', '__sizeof__', '__dir__', '__class__']
In [4]: r.file_name
'RDLi_SEAB_2018_03_01_0000.ruv'
In [5]: r.file_path
'/Users/mikesmith/Documents/git/rucool/codar_processing/data/radials/SEAB/2018_03/RDLi_SEAB_2018_03_01_0000.ruv'
In [6]: r.file_type()
'radial'
In [6]: import pprint
In [7]: pprint.pprint(r.header)
OrderedDict([('CTF', '1.00'),
('FileType', 'LLUV rdls "RadialMap"'),
('LLUVSpec', '1.26 2016 10 07'),
('UUID', '0D8D79F8-6BD0-4A82-8DD5-B7AD5F924C78'),
('Manufacturer', 'CODAR Ocean Sensors. SeaSonde'),
('Site', 'SEAB ""'),
('TimeStamp', '2018 03 01 00 00 00'),
('TimeZone', '"UTC" +0.000 0 "Atlantic/Reykjavik"'),
('TimeCoverage', '75.000 Minutes'),
('Origin', '40.3668167 -73.9735333'),
('GreatCircle', '"WGS84" 6378137.000 298.257223562997'),
('GeodVersion', '"CGEO" 1.70 2014 09 09'),
('LLUVTrustData', 'all all lluv xyuv rbvd'),
('RangeStart', '2'),
('RangeEnd', '21'),
('RangeResolutionKMeters', '3.020300'),
('RangeCells', '31'),
('DopplerCells', '512'),
('DopplerInterpolation', '2'),
('AntennaBearing', '152.0 True'),
('ReferenceBearing', '0 True'),
('AngularResolution', '5 Deg'),
('SpatialResolution', '5 Deg'),
('PatternType', 'Ideal'),
('PatternDate', '2110 05 10 14 59 41'),
('PatternResolution', '1.0 deg'),
('TransmitCenterFreqMHz', '13.450000'),
('TransmitBandwidthKHz', '-49.629688'),
('TransmitSweepRateHz', '2.000000'),
('DopplerResolutionHzPerBin', '0.001953125'),
('FirstOrderMethod', '0'),
('BraggSmoothingPoints', '4'),
('CurrentVelocityLimit', '100.0'),
('BraggHasSecondOrder', '1'),
('RadialBraggPeakDropOff', '100.000'),
('RadialBraggPeakNull', '6.310'),
('RadialBraggNoiseThreshold', '5.000'),
('PatternAmplitudeCorrections', '1.0000 1.0000'),
('PatternPhaseCorrections', '50.00 62.00'),
('PatternAmplitudeCalculations', '0.2313 0.4446'),
('PatternPhaseCalculations', '50.90 61.40'),
('RadialMusicParameters', '40.000 20.000 2.000'),
('RadialMinimumMergePoints', '2'),
('FirstOrderCalc', '1'),
('MergeMethod', '1 MedianVectors'),
('PatternMethod', '1 PatternVectors'),
('MergedCount', '7')])
In [8]: pprint.pprint(r.footer)
OrderedDict([('ProcessedTimeStamp', '2018 03 01 00 38 56'),
('ProcessingTool',
['"RadialMerger" 11.5.0',
'"SpectraToRadial" 11.5.1',
'"RadialSlider" 12.0.0',
'"RadialArchiver" 12.0.0',
'"AnalyzeSpectra" 10.9.6']),
('End', '')])
In [9]: r.data
LOND LATD VELU VELV VFLG ESPC ETMP MAXV MINV ERSC ERTC XDST YDST RNGE BEAR VELO HEAD SPRC
0 -73.971049 40.421183 0.413 11.818 128 1.089 2.614 -9.647 -11.825 2 5 0.2108 6.0369 6.0406 2.0 -11.825 182.0 2
1 -73.964859 40.420810 1.974 16.060 128 999.000 999.000 -16.181 -16.181 1 2 0.7362 5.9956 6.0406 7.0 -16.181 187.0 2
2 -73.946871 40.417252 15.865 39.234 128 999.000 13.349 -42.320 -42.320 1 3 2.2628 5.6007 6.0406 22.0 -42.320 202.0 2
3 -73.941222 40.415282 15.763 30.909 128 15.940 11.929 -9.647 -48.855 3 5 2.7424 5.3822 6.0406 27.0 -34.696 207.0 2
4 -73.935820 40.412944 22.441 35.880 128 999.000 15.095 -42.320 -42.320 1 5 3.2010 5.1227 6.0406 32.0 -42.320 212.0 2
[758 rows x 19 columns] # Cut down table for brevity
In [5]: r.diags_radial
%% TIME AMP1 AMP2 PH13 PH23 CPH1 CPH2 SNF1 SNF2 ... RABA RTYP STYP TYRS TMON TDAY THRS TMIN TSEC datetime
0 % -1800 0.239 0.498 49.1 61.6 50.0 62.0 -144.0 -144.0 ... 111.3 1 68 2018 2 28 23 30 0 2018-02-28 23:30:00
1 % -1200 0.241 0.473 49.3 60.3 50.0 62.0 -143.0 -144.0 ... 112.6 1 68 2018 2 28 23 40 0 2018-02-28 23:40:00
2 % -600 0.240 0.532 48.6 59.1 50.0 62.0 -144.0 -144.0 ... 114.9 1 68 2018 2 28 23 50 0 2018-02-28 23:50:00
3 % 0 0.231 0.493 51.0 61.3 50.0 62.0 -144.0 -145.0 ... 114.3 1 68 2018 3 1 0 0 0 2018-03-01 00:00:00
4 % 600 0.236 0.471 50.0 62.4 50.0 62.0 -144.0 -145.0 ... 115.7 1 68 2018 3 1 0 10 0 2018-03-01 00:10:00
5 % 1200 0.242 0.484 49.4 60.3 50.0 62.0 -144.0 -144.0 ... 121.0 1 68 2018 3 1 0 20 0 2018-03-01 00:20:00
6 % 1800 0.231 0.445 50.9 61.4 50.0 62.0 -143.0 -145.0 ... 117.8 1 68 2018 3 1 0 30 0 2018-03-01 00:30:00
r.diags_hardware
%% TIME RTMP MTMP XTRP RUNT SP24 SP05 SN05 SP12 ... EXTA EXTB CRUN TYRS TMON TDAY THRS TMIN TSEC datetime
% -35.0 29 41 0 5330369 0.0 5.08 -5.03 12.16 ... 0 0 1406.61 2018 2 28 23 25 0 2018-02-28 23:25:00
% -30.0 29 41 0 5330668 0.0 5.08 -5.05 12.16 ... 0 0 1411.78 2018 2 28 23 30 0 2018-02-28 23:30:00
% -25.0 29 41 0 5330967 0.0 5.08 -5.03 12.16 ... 0 0 1416.62 2018 2 28 23 35 0 2018-02-28 23:35:00
% -20.0 29 41 0 5331267 0.0 5.08 -5.05 12.16 ... 0 0 1421.64 2018 2 28 23 40 0 2018-02-28 23:40:00
% -15.0 29 41 0 5331567 0.0 5.08 -5.05 12.16 ... 0 0 1426.65 2018 2 28 23 45 0 2018-02-28 23:45:00
% -10.0 29 41 0 5331866 0.0 5.08 -5.05 12.16 ... 0 0 1431.66 2018 2 28 23 50 0 2018-02-28 23:50:00
% -5.0 29 41 0 5332166 0.0 5.08 -5.03 12.16 ... 0 0 1436.67 2018 2 28 23 55 0 2018-02-28 23:55:00
% 0.0 28 41 0 5332456 0.0 5.08 -5.05 12.16 ... 0 0 1.47 2018 3 1 0 0 0 2018-03-01 00:00:00
% 5.0 29 41 0 5332753 0.0 5.08 -5.05 12.16 ... 0 0 6.47 2018 3 1 0 5 0 2018-03-01 00:05:00
% 10.0 29 41 0 5333053 0.0 5.08 -5.05 12.16 ... 0 0 11.48 2018 3 1 0 10 0 2018-03-01 00:10:00
% 15.0 29 41 0 5333352 0.0 5.08 -5.05 12.16 ... 0 0 16.49 2018 3 1 0 15 0 2018-03-01 00:15:00
% 20.0 29 41 0 5333652 0.0 5.08 -5.03 12.16 ... 0 0 21.50 2018 3 1 0 20 0 2018-03-01 00:20:00
% 25.0 29 41 0 5333951 0.0 5.08 -5.03 12.16 ... 0 0 26.70 2018 3 1 0 25 0 2018-03-01 00:25:00
% 30.0 29 41 0 5334251 0.0 5.08 -5.05 12.16 ... 0 0 31.52 2018 3 1 0 30 0 2018-03-01 00:30:00
% 35.0 29 41 0 5334549 0.0 5.08 -5.05 12.16 ... 0 0 36.53 2018 3 1 0 35 0 2018-03-01 00:35:00
[15 rows x 35 columns]
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent