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pyaxiom 1.2.5
# pyaxiom [](https://travis-ci.org/axiom-data-science/pyaxiom)An ocean data toolkit developed and used by [Axiom Data Science](http://axiomdatascience.com)## Installation##### Stable pip install pyaxiom##### Development pip install git+https://github.com/axiom-data-science/pyaxiom.git### Enhanced `netcdf4-python` Dataset objectA subclass of the `netCDF4.Dataset` object that adds some additional features###### Safe closingVanilla `netCDF4.Dataset` objects raise a RuntimeError when trying to closean already closed file. This won't raise.```pythonfrom netCDF4 import Datasetnc = Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')nc.close()nc.close()---------------------------------------------------------------------------RuntimeError Traceback (most recent call last)<ipython-input-18-db44c06d8538> in <module>()----> 1 nc.close()/home/kwilcox/.virtualenvs/overlord/lib/python2.7/site-packages/netCDF4.so in netCDF4.Dataset.close (netCDF4.c:23432)()RuntimeError: NetCDF: Not a valid IDfrom pyaxiom.netcdf.dataset import EnhancedDataset as Datasetnc = Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')nc.close()nc.close()```###### Retrieving variables by attributes and values/callables```pythonfrom pyaxiom.netcdf.dataset import EnhancedDataset as Datasetnc = Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')# Return variables with a standard_name attribute equal to 'latitude'print nc.get_variables_by_attributes(standard_name='latitude')[<type 'netCDF4.Variable'>float64 latitude() units: degrees_north standard_name: latitude long_name: sensor latitudeunlimited dimensions:current shape = ()filling off]# Return all variables with a 'standard_name attribute'variables = nc.get_variables_by_attributes(standard_name=lambda v: v is not None)print [s.name for s in variables][u'latitude', u'longitude', u'depth', u'T_28', u'CS_300', u'CD_310', u'u_1205', u'v_1206', u'O_60', u'DO', u'time']# Get creative... return all variablse with the attribute units equal to m/s and a grid_mapping attributevariables = nc.get_variables_by_attributes(grid_mapping=lambda v: v is not None, units='m/s')print [s.name for s in variables][u'CS_300', u'u_1205', u'v_1206']```### IOOS URNs[More Information](https://geo-ide.noaa.gov/wiki/index.php?title=IOOS_Conventions_for_Observing_Asset_Identifiers)###### URN Normalization```pythonfrom pyaxiom.urn import IoosUrnu = IoosUrn(asset_type='station', authority='axiom', label='station1')print u.__dict__{'asset_type': 'station', 'authority': 'axiom', 'component': None, 'label': 'station1', 'version': None}print u.urn'urn:ioos:station:axiom:station1'``````pythonfrom pyaxiom.urn import IoosUrnu = IoosUrn.from_string('urn:ioos:station:axiom:station1')print u.__dict__{'asset_type': 'station', 'authority': 'axiom', 'component': None, 'label': 'station1', 'version': None}print u.urn'urn:ioos:station:axiom:station1'```###### NetCDF Integration```pythonfrom pyaxiom.utils import urnify, dictify_urn# NetCDF variable attributes from a "sensor" urnprint dictify_urn('urn:ioos:sensor:axiom:station1'){'standard_name': 'wind_speed'}print dictify_urn('urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h'){'standard_name': 'lwe_thickness_of_precipitation_amount', 'cell_methods': 'time: mean time: variance (interval: PT1H)'}# URN from `dict` of variable attributesattributes = {'standard_name': 'wind_speed', 'cell_methods': 'time: mean (interval: PT24H)'}print urnify('authority', 'label', attributes)'urn:ioos:sensor:authority:label:wind_speed#cell_methods=time:mean;interval=pt24h'# URN from a `netCDF4` Variable objectnc = netCDF4.Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')print urnify('authority', 'label', nc.variables['T_28'])'urn:ioos:sensor:authority:label:sea_water_temperature'```### Gridded NetCDF Collections#### Binning files`pyaxiom` installs an executable called `binner` that will combine manyfiles into a single file. Useful for cleanup and optimization.If you have a script that is opening and reading hundreds of files, those open operationsare slow, and you should combine them into a single file. This doesn't handle files thatoverlap in time or files that have data on both sides of a bin boundary.```usage: binner [-h] -o OUTPUT -d {day,month,week,year} [-f [FACTOR]] [-n [NCML_FILE]] [-g [GLOB_STRING]] [-a] [-s HARD_START] [-e HARD_END]optional arguments: -h, --help show this help message and exit -o OUTPUT, --output OUTPUT Directory to output the binned files to -d {day,month,week,year}, --delta {day,month,week,year} Timedelta to bin by -f [FACTOR], --factor [FACTOR] Factor to apply to the delta. Passing a '2' would be (2) days or (2) months. Defauts to 1. -n [NCML_FILE], --ncml_file [NCML_FILE] NcML containing an aggregation scan to use for the individual files. One of 'ncml_file' or 'glob_string' is required. If both are passed in, the 'glob_string' is used to identify files for the collection and the 'ncml_file' is applied against each member. -g [GLOB_STRING], --glob_string [GLOB_STRING] A Python glob.glob string to use for file identification. One of 'ncml_file' or 'glob_string' is required. If both are passed in, the 'glob_string' is used to identify files for the collection and the 'ncml_file' is applied against each member. -a, --apply_to_members Flag to apply the NcML to each member of the aggregation before extracting metadata. Ignored if using a 'glob_string'. Defaults to False. -s HARD_START, --hard_start HARD_START A datetime string to start the aggregation from. Only members starting on or after this datetime will be processed. -e HARD_END, --hard_end HARD_END A datetime string to end the aggregation on. Only members ending before this datetime will be processed.```##### Examples###### Directory globbing```bashbinner \ --output ./output/monthly_bins \ --glob_string "pyaxiom/tests/resources/coamps/cencoos_4km/wnd_tru/10m/*.nc" \ -d month \ -f 1```###### Directory globbing and applying NcML file to each member```bashbinner \ --output ./output/monthly_bins \ --glob_string "pyaxiom/tests/resources/coamps/cencoos_4km/wnd_tru/10m/*.nc" \ -n pyaxiom/tests/resources/coamps_10km_wind.ncml \ -d month \ -f 1```###### NcML aggregation reading the `<scan>` element```bashbinner \ --output ./output/monthly_bins \ -n pyaxiom/tests/resources/coamps_10km_wind.ncml \ -d month \ -f 1```### Creating CF1.6 TimeSeries files###### TimeSeries```pythonfrom pyaxiom.netcdf.sensors import TimeSeriesfilename = 'test_timeseries.nc'times = [0, 1000, 2000, 3000, 4000, 5000]verticals = Nonets = TimeSeries(output_directory='./output', latitude=32, # WGS84 longitude=-74, # WGS84 station_name='timeseries_station', global_attributes=dict(id='myid'), output_filename='timeseries.nc', times=times, verticals=verticals)values = [20, 21, 22, 23, 24, 25]attrs = dict(standard_name='sea_water_temperature')ts.add_variable('temperature', values=values, attributes=attrs)ts.close()```###### TimeSeriesProfile```pythonfrom pyaxiom.netcdf.sensors import TimeSeriestimes = [0, 1000, 2000, 3000, 4000, 5000] # Seconds since Epochverticals = [0, 1, 2] # Meters downts = TimeSeries(output_directory='./output', latitude=32, # WGS84 longitude=-74, # WGS84 station_name='timeseriesprofile_station', global_attributes=dict(id='myid'), output_filename='timeseriesprofile.nc', times=times, verticals=verticals)values = np.repeat([20, 21, 22, 23, 24, 25], len(verticals))attrs = dict(standard_name='sea_water_temperature')ts.add_variable('temperature', values=values, attributes=attrs)ts.close()```###### Pandas IntegrationPandas integration assumes that there is a Series column `time` and a Seriescolumn `depth` in your DataFrame. Data values are pulled from a column named'value', but you may also pass in the `data_column` attribute for more control.```pythonfrom pyaxiom.netcdf.sensors import TimeSeriesdf = pd.DataFrame({ 'time': [0, 1, 2, 3, 4, 5, 6], 'value': [10, 20, 30, 40, 50, 60], 'depth': [0, 0, 0, 0, 0, 0] })TimeSeries.from_dataframe(df, output_directory='./output', latitude=30, # WGS84 longitude=-74, # WGS84 station_name='dataframe_station', global_attributes=dict(id='myid'), variable_name='values', variable_attributes=dict(), output_filename='from_dataframe.nc')``````pythondf = pd.DataFrame({ 'time': [0, 1, 2, 3, 4, 5, 6], 'temperature': [10, 20, 30, 40, 50, 60], 'depth': [0, 0, 0, 0, 0, 0] })TimeSeries.from_dataframe(df, output_directory='./output', latitude=30, # WGS84 longitude=-74, # WGS84 station_name='dataframe_station', global_attributes=dict(id='myid'), output_filename='from_dataframe.nc', variable_name='temperature', variable_attributes=dict(standard_name='air_temperature'), data_column='temperature')```
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