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Project Description
# pyaxiom [![Build Status](https://travis-ci.org/axiom-data-science/pyaxiom.svg)](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 object

A subclass of the `netCDF4.Dataset` object that adds some additional features

###### Safe closing
Vanilla `netCDF4.Dataset` objects raise a RuntimeError when trying to close
an already closed file. This won't raise.


```python
from netCDF4 import Dataset

nc = 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 ID

from pyaxiom.netcdf.dataset import EnhancedDataset as Dataset
nc = 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
```python
from pyaxiom.netcdf.dataset import EnhancedDataset as Dataset
nc = 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 latitude
unlimited 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 attribute
variables = 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

```python
from pyaxiom.urn import IoosUrn
u = 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'
```

```python
from pyaxiom.urn import IoosUrn
u = 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

```python
from pyaxiom.utils import urnify, dictify_urn

# NetCDF variable attributes from a "sensor" urn
print 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 attributes
attributes = {'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 object
nc = 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 many
files into a single file. Useful for cleanup and optimization.

If you have a script that is opening and reading hundreds of files, those open operations
are slow, and you should combine them into a single file. This doesn't handle files that
overlap 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
```bash
binner \
--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
```bash
binner \
--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
```bash
binner \
--output ./output/monthly_bins \
-n pyaxiom/tests/resources/coamps_10km_wind.ncml \
-d month \
-f 1
```


### Creating CF1.6 TimeSeries files

###### TimeSeries
```python
from pyaxiom.netcdf.sensors import TimeSeries
filename = 'test_timeseries.nc'
times = [0, 1000, 2000, 3000, 4000, 5000]
verticals = None
ts = 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
```python
from pyaxiom.netcdf.sensors import TimeSeries

times = [0, 1000, 2000, 3000, 4000, 5000] # Seconds since Epoch
verticals = [0, 1, 2] # Meters down
ts = 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 Integration

Pandas integration assumes that there is a Series column `time` and a Series
column `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.

```python
from pyaxiom.netcdf.sensors import TimeSeries
df = 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')
```

```python
df = 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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Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.11

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.10

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.10-dev

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.9

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.8

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.7

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.6

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.5

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.4

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.4-dev

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pyaxiom-1.2.5.tar.gz (40.9 kB) Copy SHA256 Checksum SHA256 Source Sep 14, 2016

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