For more information, see the user guide or the github repository.
Each tool has a specific structure for processing multidimensional data with the following consequences:
- interfaces dedicated to each tool,
- partially processed data,
- no unified representation of data structures
The proposed format is based on the following principles:
- neutral format available for tabular or multidimensional tools (e.g. Numpy, pandas, xarray, scipp, astropy),
- taking into account a wide variety of data types as defined in NTV format,
- high interoperability: reversible (lossless round-trip) interface with tabular or multidimensional tools,
- reversible and compact JSON format (including categorical and sparse format),
- Ease of sharing and exchanging multidimensional and tabular data,
The NTV-Numpy converter uses this format to:
- provide lossless and reversible interfaces with multidimensional and tabular data processing tools,
- offer data exchange and sharing solutions with neutral or standardized formats (e.g. JSON, Numpy).
NTV-NumPy was developped originally in the json-NTV project
In the example below, a dataset available in JSON is shared with scipp or Xarray.
---
title: Example of interoperability
---
flowchart LR
A[Xarray] <--lossless--> B[Neutral\nXdataset]
D[Scipp] <--lossless--> B
C[NDData] <--lossless--> B
B <--lossless--> E[JSON]
B <--lossless--> F[DataFrame]
In [1]: example = {
'example:xdataset': {
'var1': [['float[kg]', [2, 2], [10.1, 0.4, 3.4, 8.2]], ['x', 'y']],
'var1.variance': [[[2, 2], [0.1, 0.2, 0.3, 0.4]]],
'var1.mask1': [[[True, False]], ['x']],
'var1.mask2': [[[2, 2], [True, False, False, True]]],
'var2': [['var2.ntv'], ['x', 'y']],
'x': [['string', ['23F0AE', '578B98']], {'test': 21}],
'y': [['date', ['2021-01-01', '2022-02-02']]],
'ranking': [['month', [2, 2], [1, 2, 3, 4]], ['var1']],
'z': [['float', [10, 20]], ['x']],
'z.uncertainty': [[[0.1, 0.2]]],
'z_bis': [[['z1_bis', 'z2_bis']]],
'info': {'path': 'https://github.com/loco-philippe/ntv-numpy/tree/main/example/',
'location': [['string', ['paris']]]}
}
}
In [2]: from ntv_numpy import Xdataset
x_example = Xdataset.read_json(example)
x_example.info['structure']
Out[2]: {'name': 'example',
'xtype': 'group',
'data_vars': ['var1', 'var2'],
'data_arrays': ['z_bis'],
'dimensions': ['x', 'y'],
'coordinates': ['ranking', 'z'],
'additionals': ['var1.mask1', 'var1.mask2', 'var1.variance', 'z.uncertainty'],
'metadata': ['info'],
'uniques': ['location'],
'validity': 'undefined',
'length': 4,
'width': 13}The JSON representation is equivalent to the Xdataset entity (Json conversion reversible)
In [3]: x_json = x_example.to_json()
x_example_json = Xdataset.read_json(x_json)
x_example_json == x_example
Out[3]: TrueIn [4]: x_xarray = x_example.to_xarray()
print(x_xarray)
Out[4]: <xarray.Dataset> Size: 202B
Dimensions: (x: 2, y: 2)
Coordinates:
* x (x) <U6 48B '23F0AE' '578B98'
* y (y) datetime64[ns] 16B 2021-01-01 2022-02-02
ranking (x, y) int32 16B 1 2 3 4
z (x) float64 16B 10.0 20.0
location <U5 20B 'paris'
var1.mask1 (x) bool 2B True False
var1.mask2 (x, y) bool 4B True False False True
var1.variance (x, y) float64 32B 0.1 0.2 0.3 0.4
z.uncertainty (x) float64 16B 0.1 0.2
Data variables:
var1 (x, y) float64 32B 10.1 0.4 3.4 8.2
Attributes:
info: {'path': 'https://github.com/loco-philippe/ntv-numpy/tree/main/...
name: example
var2: [['var2.ntv'], ['x', 'y']]
z_bis: [['string', ['z1_bis', 'z2_bis']]]Reversibility:
In [5]: x_example_xr = Xdataset.from_xarray(x_xarray)
x_example_xr == x_example_json == x_example
Out[5]: TrueIn [6]: x_dataframe = x_example.to_dataframe()
print(x_example.to_dataframe(json_name=False))
print(x_xarray)
Out[6]:
ranking z z.uncertainty var1 var1.mask1 var1.mask2 \
x y
23F0AE 2021-01-01 1 10.0 0.1 10.1 True True
2022-02-02 2 10.0 0.1 0.4 True False
578B98 2021-01-01 3 20.0 0.2 3.4 False False
2022-02-02 4 20.0 0.2 8.2 False True
var1.variance location
x y
23F0AE 2021-01-01 0.1 paris
2022-02-02 0.2 paris
578B98 2021-01-01 0.3 paris
2022-02-02 0.4 parisReversibility:
In [7]: x_example_pd = Xdataset.from_dataframe(x_dataframe)
x_example_pd == x_example_xr == x_example_json == x_example
Out[7]: TrueIn [8]: x_scipp = x_example.to_scipp()
print(x_scipp['example'])
Out[8]: <scipp.Dataset>
Dimensions: Sizes[x:string:2, y:date:2, ]
Coordinates:
* ranking:month int32 [dimensionless] (x:string, y:date) [1, 2, 3, 4]
* x:string string [dimensionless] (x:string) ["23F0AE", "578B98"]
* y:date datetime64 [ns] (y:date) [2021-01-01T00:00:00.000000000, 2022-02-02T00:00:00.000000000]
* z:float float64 [dimensionless] (x:string) [10, 20]
Data:
var1:float float64 [kg] (x:string, y:date) [10.1, 0.4, 3.4, 8.2] [0.1, 0.2, 0.3, 0.4]
Masks:
mask1:boolean bool [dimensionless] (x:string) [True, False]
mask2:boolean bool [dimensionless] (x:string, y:date) [True, False, False, True]Reversibility:
In [9]: x_example_sc = Xdataset.from_scipp(x_scipp)
x_example_sc == x_example_pd == x_example_xr == x_example_json == x_example
Out[9]: TrueIn [1]: example = {
'example:xdataset': {
'data': [['float[erg/s]', [1,2,3,4]]],
'data.mask': [[[False, False, True, True]]],
'data.uncertainty': [['float64[std]', [1.0, 1.414, 1.732, 2.0]]],
'meta': {'object': 'fictional data.'},
'wcs': {'WCSAXES': 2, 'CRPIX1': 2048.0, 'CRPIX2': 1024.0, 'PC1_1': 1.2905625619716e-05,
'PC1_2': 5.9530912331034e-06, 'PC2_1': 5.0220581265601e-06, 'PC2_2': -1.2644774105568e-05,
'CDELT1': 1.0, 'CDELT2': 1.0, 'CUNIT1': 'deg', 'CUNIT2': 'deg', 'CTYPE1': 'RA---TAN',
'CTYPE2': 'DEC--TAN', 'CRVAL1': 5.63056810618, 'CRVAL2': -72.05457184279, 'LONPOLE': 180.0,
'LATPOLE': -72.05457184279, 'WCSNAME': 'IDC_qbu1641sj', 'MJDREF': 0.0, 'RADESYS': 'ICRS'},
'psf': [['float[erg/s]', [1,2,3,4]]]
}
}
n_example = Xdataset.read_json(example)
n_example.info
Out[1]: {'name': 'example',
'xtype': 'group',
'data_arrays': ['data', 'psf'],
'additionals': ['data.mask', 'data.uncertainty'],
'metadata': ['meta', 'wcs'],
'validity': 'valid',
'width': 6}In [2]: n_nddata = n_example.to_nddata()
n_nddata
Out[2]: NDData([1., 2., ——, ——], unit='erg / s')Reversibility:
In [3]: n_example_ndd = Xdataset.from_nddata(n_nddata)
n_example_ndd == n_example
Out[3]: TrueIn the example, only structural data is exchanged with json format.
In [1]: example = {
'example:xdataset': {
'var1': [['float[kg]', [2, 2], 'var1.ntv'], ['x', 'y']],
'var1.variance': [[[2, 2], 'var1_variance.ntv']],
'var1.mask1': [['var1_mask1.ntv'], ['x']],
'var1.mask2': [[[2, 2], 'var1_mask2.ntv']],
'var2': [['var2.ntv'], ['x', 'y']],
'x': [['x.ntv'], {'test': 21}],
'y': [['date', 'y.ntv']],
'ranking': [['month', [2, 2], 'ranking.ntv'], ['var1']],
'z': [['float', 'z.ntv'], ['x']],
'z.uncertainty': [['z_uncertainty.ntv']],
'z_bis': [['z_bis.ntv']],
'info': {'path': 'https://github.com/loco-philippe/ntv-numpy/tree/main/example/'}
}
}The complete example can be rebuild with loading data (path + file name).
In [2]: # simulation of reading files at the indicated "path"
var1 = np.array([10.1, 0.4, 3.4, 8.2])
var1_variance = Ndarray([0.1, 0.2, 0.3, 0.4], ntv_type='float')
var1_mask1 = np.array([True, False])
var1_mask2 = np.array([True, False, False, True])
var2 = Ndarray('var2.ntv')
x = np.array(['23F0AE', '578B98'])
y = np.array(['2021-01-01', '2022-02-02'], dtype='datetime64[D]')
ranking = np.array([1, 2, 3, 4])
z = np.array([10.0, 20.0])
z_uncertainty = np.array([0.1, 0.2])
z_bis = np.array(['z1_bis', 'z2_bis'])
array_data = [var1, var1_variance, var1_mask1, var1_mask2, var2, x, y, ranking, z, z_uncertainty, z_bis]
x_example_mixte_numpy = copy(x_example_mixte)
for data, xnda in zip(array_data, x_example_mixte_numpy.xnd):
xnda.set_ndarray(Ndarray(data))
x_example_mixte_numpy == x_example_mixte_json == x_example_sc == x_example_xr == x_example_json == x_example
Out[2]: True