{
    "info": {
        "author": "Kyle Wilcox",
        "author_email": "kyle@axiomdatascience.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering"
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        "description": "# pyaxiom  [![Build Status](https://travis-ci.org/axiom-data-science/pyaxiom.svg)](https://travis-ci.org/axiom-data-science/pyaxiom)\n\nAn ocean data toolkit developed and used by [Axiom Data Science](http://axiomdatascience.com)\n\n\n## Installation\n\n##### Stable\n\n    pip install pyaxiom\n\n##### Development\n\n    pip install git+https://github.com/axiom-data-science/pyaxiom.git\n\n\n### Enhanced `netcdf4-python` Dataset object\n\nA subclass of the `netCDF4.Dataset` object that adds some additional features\n\n###### Safe closing\nVanilla `netCDF4.Dataset` objects raise a RuntimeError when trying to close\nan already closed file.  This won't raise.\n\n\n```python\nfrom netCDF4 import Dataset\n\nnc = Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')\nnc.close()\nnc.close()\n---------------------------------------------------------------------------\nRuntimeError                              Traceback (most recent call last)\n<ipython-input-18-db44c06d8538> in <module>()\n----> 1 nc.close()\n/home/kwilcox/.virtualenvs/overlord/lib/python2.7/site-packages/netCDF4.so in netCDF4.Dataset.close (netCDF4.c:23432)()\nRuntimeError: NetCDF: Not a valid ID\n\nfrom pyaxiom.netcdf.dataset import EnhancedDataset as Dataset\nnc = Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')\nnc.close()\nnc.close()\n```\n\n###### Retrieving variables by attributes and values/callables\n```python\nfrom pyaxiom.netcdf.dataset import EnhancedDataset as Dataset\nnc = Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')\n\n# Return variables with a standard_name attribute equal to 'latitude'\nprint nc.get_variables_by_attributes(standard_name='latitude')\n[<type 'netCDF4.Variable'>\nfloat64 latitude()\n    units: degrees_north\n    standard_name: latitude\n    long_name: sensor latitude\nunlimited dimensions:\ncurrent shape = ()\nfilling off\n]\n\n# Return all variables with a 'standard_name attribute'\nvariables = nc.get_variables_by_attributes(standard_name=lambda v: v is not None)\nprint [s.name for s in variables]\n[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']\n\n# Get creative... return all variablse with the attribute units equal to m/s and a grid_mapping attribute\nvariables = nc.get_variables_by_attributes(grid_mapping=lambda v: v is not None, units='m/s')\nprint [s.name for s in variables]\n[u'CS_300', u'u_1205', u'v_1206']\n```\n\n\n\n\n\n### IOOS URNs\n[More Information](https://geo-ide.noaa.gov/wiki/index.php?title=IOOS_Conventions_for_Observing_Asset_Identifiers)\n\n###### URN Normalization\n\n```python\nfrom pyaxiom.urn import IoosUrn\nu = IoosUrn(asset_type='station', authority='axiom', label='station1')\nprint u.__dict__\n{'asset_type': 'station',\n 'authority': 'axiom',\n 'component': None,\n 'label': 'station1',\n 'version': None}\nprint u.urn\n'urn:ioos:station:axiom:station1'\n```\n\n```python\nfrom pyaxiom.urn import IoosUrn\nu = IoosUrn.from_string('urn:ioos:station:axiom:station1')\nprint u.__dict__\n{'asset_type': 'station',\n 'authority': 'axiom',\n 'component': None,\n 'label': 'station1',\n 'version': None}\nprint u.urn\n'urn:ioos:station:axiom:station1'\n```\n\n###### NetCDF Integration\n\n```python\nfrom pyaxiom.utils import urnify, dictify_urn\n\n# NetCDF variable attributes from a \"sensor\" urn\nprint dictify_urn('urn:ioos:sensor:axiom:station1')\n{'standard_name': 'wind_speed'}\n\nprint dictify_urn('urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h')\n{'standard_name': 'lwe_thickness_of_precipitation_amount',\n 'cell_methods': 'time: mean time: variance (interval: PT1H)'}\n\n# URN from `dict` of variable attributes\nattributes = {'standard_name': 'wind_speed',\n              'cell_methods': 'time: mean (interval: PT24H)'}\nprint urnify('authority', 'label', attributes)\n'urn:ioos:sensor:authority:label:wind_speed#cell_methods=time:mean;interval=pt24h'\n\n# URN from a `netCDF4` Variable object\nnc = netCDF4.Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')\nprint urnify('authority', 'label', nc.variables['T_28'])\n'urn:ioos:sensor:authority:label:sea_water_temperature'\n```\n\n\n### Gridded NetCDF Collections\n\n#### Binning files\n\n`pyaxiom` installs an executable called `binner` that will combine many\nfiles into a single file.  Useful for cleanup and optimization.\n\nIf you have a script that is opening and reading hundreds of files, those open operations\nare slow, and you should combine them into a single file.  This doesn't handle files that\noverlap in time or files that have data on both sides of a bin boundary.\n\n```\nusage: binner [-h] -o OUTPUT -d {day,month,week,year} [-f [FACTOR]]\n              [-n [NCML_FILE]] [-g [GLOB_STRING]] [-a] [-s HARD_START]\n              [-e HARD_END]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -o OUTPUT, --output OUTPUT\n                        Directory to output the binned files to\n  -d {day,month,week,year}, --delta {day,month,week,year}\n                        Timedelta to bin by\n  -f [FACTOR], --factor [FACTOR]\n                        Factor to apply to the delta. Passing a '2' would be\n                        (2) days or (2) months. Defauts to 1.\n  -n [NCML_FILE], --ncml_file [NCML_FILE]\n                        NcML containing an aggregation scan to use for the\n                        individual files. One of 'ncml_file' or 'glob_string'\n                        is required. If both are passed in, the 'glob_string'\n                        is used to identify files for the collection and the\n                        'ncml_file' is applied against each member.\n  -g [GLOB_STRING], --glob_string [GLOB_STRING]\n                        A Python glob.glob string to use for file\n                        identification. One of 'ncml_file' or 'glob_string' is\n                        required. If both are passed in, the 'glob_string' is\n                        used to identify files for the collection and the\n                        'ncml_file' is applied against each member.\n  -a, --apply_to_members\n                        Flag to apply the NcML to each member of the\n                        aggregation before extracting metadata. Ignored if\n                        using a 'glob_string'. Defaults to False.\n  -s HARD_START, --hard_start HARD_START\n                        A datetime string to start the aggregation from. Only\n                        members starting on or after this datetime will be\n                        processed.\n  -e HARD_END, --hard_end HARD_END\n                        A datetime string to end the aggregation on. Only\n                        members ending before this datetime will be processed.\n```\n\n##### Examples\n\n###### Directory globbing\n```bash\nbinner \\\n  --output ./output/monthly_bins \\\n  --glob_string \"pyaxiom/tests/resources/coamps/cencoos_4km/wnd_tru/10m/*.nc\" \\\n  -d month \\\n  -f 1\n```\n\n###### Directory globbing and applying NcML file to each member\n```bash\nbinner \\\n  --output ./output/monthly_bins \\\n  --glob_string \"pyaxiom/tests/resources/coamps/cencoos_4km/wnd_tru/10m/*.nc\" \\\n  -n pyaxiom/tests/resources/coamps_10km_wind.ncml \\\n  -d month \\\n  -f 1\n```\n\n###### NcML aggregation reading the `<scan>` element\n```bash\nbinner \\\n  --output ./output/monthly_bins \\\n  -n pyaxiom/tests/resources/coamps_10km_wind.ncml \\\n  -d month \\\n  -f 1\n```\n\n\n### Creating CF1.6 TimeSeries files\n\n###### TimeSeries\n```python\nfrom pyaxiom.netcdf.sensors import TimeSeries\nfilename = 'test_timeseries.nc'\ntimes = [0, 1000, 2000, 3000, 4000, 5000]\nverticals = None\nts = TimeSeries(output_directory='./output',\n                latitude=32,   # WGS84\n                longitude=-74, # WGS84\n                station_name='timeseries_station',\n                global_attributes=dict(id='myid'),\n                output_filename='timeseries.nc',\n                times=times,\n                verticals=verticals)\nvalues = [20, 21, 22, 23, 24, 25]\nattrs = dict(standard_name='sea_water_temperature')\nts.add_variable('temperature', values=values, attributes=attrs)\nts.close()\n```\n\n###### TimeSeriesProfile\n```python\nfrom pyaxiom.netcdf.sensors import TimeSeries\n\ntimes = [0, 1000, 2000, 3000, 4000, 5000]  # Seconds since Epoch\nverticals = [0, 1, 2]  # Meters down\nts = TimeSeries(output_directory='./output',\n                latitude=32,   # WGS84\n                longitude=-74, # WGS84\n                station_name='timeseriesprofile_station',\n                global_attributes=dict(id='myid'),\n                output_filename='timeseriesprofile.nc',\n                times=times,\n                verticals=verticals)\nvalues = np.repeat([20, 21, 22, 23, 24, 25], len(verticals))\nattrs = dict(standard_name='sea_water_temperature')\nts.add_variable('temperature', values=values, attributes=attrs)\nts.close()\n```\n\n###### Pandas Integration\n\nPandas integration assumes that there is a Series column `time` and a Series\ncolumn `depth` in your DataFrame.  Data values are pulled from a column named\n'value', but you may also pass in the `data_column` attribute for more control.\n\n```python\nfrom pyaxiom.netcdf.sensors import TimeSeries\ndf = pd.DataFrame({ 'time':   [0, 1, 2, 3, 4, 5, 6],\n                    'value':  [10, 20, 30, 40, 50, 60],\n                    'depth':  [0, 0, 0, 0, 0, 0] })\nTimeSeries.from_dataframe(df,\n                          output_directory='./output',\n                          latitude=30,   # WGS84\n                          longitude=-74, # WGS84\n                          station_name='dataframe_station',\n                          global_attributes=dict(id='myid'),\n                          variable_name='values',\n                          variable_attributes=dict(),\n                          output_filename='from_dataframe.nc')\n```\n\n```python\ndf = pd.DataFrame({ 'time':   [0, 1, 2, 3, 4, 5, 6],\n                    'temperature':  [10, 20, 30, 40, 50, 60],\n                    'depth':  [0, 0, 0, 0, 0, 0] })\nTimeSeries.from_dataframe(df,\n                          output_directory='./output',\n                          latitude=30,   # WGS84\n                          longitude=-74, # WGS84\n                          station_name='dataframe_station',\n                          global_attributes=dict(id='myid'),\n                          output_filename='from_dataframe.nc',\n                          variable_name='temperature',\n                          variable_attributes=dict(standard_name='air_temperature'),\n                          data_column='temperature')\n```\n",
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