Axis#

These objects manipulate axes as they can be found in NetCDF files:

float lat(lat) ;
    lat:long_name = "latitude" ;
    lat:units = "degrees_north" ;
    lat:standard_name = "latitude" ;
float lon(lon) ;
    lon:long_name = "longitude" ;
    lon:units = "degrees_east" ;
    lon:standard_name = "longitude" ;

Regular axis#

For example, let’s construct an axis representing a regular axis.

import numpy

import pyinterp

axis = pyinterp.Axis(numpy.arange(-90, 90, 0.25))
axis
<pyinterp.core.Axis>
  min_value: -90
  max_value: 89.75
  step     : 0.25
  is_circle: false

This object can be queried to obtain its properties.

print(f'is ascending ? {axis.is_ascending()}')
print(f'is regular ? {axis.is_regular()}')
print(f'is circle ? {axis.is_circle}')
is ascending ? True
is regular ? True
is circle ? False

The most useful interfaces allow you to search for the index of the closest value.

axis.find_index([1e-3])
array([360])

It is also possible to find the indices around a value.

axis.find_indexes([1e-3])
array([[360, 361]])

The list of available methods is described in the online help .

Irregular axis#

When the axis is regular, the pitch is constant between each element of the axis, the search is performed using a simple calculation and therefore very fast. When the pitch is not constant between two successive elements of the axis, the search is performed by a binary search. Even these two operating modes are managed by the same object. So let’s build an irregular axis:

MERCATOR_LATITUDES = numpy.array([
    -89.000000, -88.908818, -88.809323, -88.700757, -88.582294, -88.453032,
    -88.311987, -88.158087, -87.990161, -87.806932, -87.607008, -87.388869,
    -87.150861, -86.891178, -86.607851, -86.298736, -85.961495, -85.593582,
    -85.192224, -84.754402, -84.276831, -83.755939, -83.187844, -82.568330,
    -81.892820, -81.156357, -80.353575, -79.478674, -78.525397, -77.487013,
    -76.356296, -75.125518, -73.786444, -72.330344, -70.748017, -69.029837,
    -67.165823, -65.145744, -62.959262, -60.596124, -58.046413, -55.300856,
    -52.351206, -49.190700, -45.814573, -42.220632, -38.409866, -34.387043,
    -30.161252, -25.746331, -21.161107, -16.429384, -11.579629, -6.644331,
    -1.659041, 3.338836, 8.311423, 13.221792, 18.035297, 22.720709, 27.251074,
    31.604243, 35.763079, 39.715378, 43.453560, 46.974192, 50.277423,
    53.366377, 56.246554, 58.925270, 61.411164, 63.713764, 65.843134,
    67.809578, 69.623418, 71.294813, 72.833637, 74.249378, 75.551083,
    76.747318, 77.846146, 78.855128, 79.781321, 80.631294, 81.411149,
    82.126535, 82.782681, 83.384411, 83.936179, 84.442084, 84.905904,
    85.331111, 85.720897, 86.078198, 86.405707, 86.705898, 86.981044,
    87.233227, 87.464359, 87.676195, 87.870342, 88.048275, 88.211348,
    88.360799, 88.497766, 88.623291, 88.738328, 88.843755, 88.940374
])

axis = pyinterp.Axis(MERCATOR_LATITUDES)
axis
<pyinterp.core.Axis>
  values   : [-89.       -88.908818 -88.809323 -88.700757 -88.582294 -88.453032
              -88.311987 -88.158087 -87.990161 -87.806932 -87.607008 -87.388869
              -87.150861 -86.891178 -86.607851 -86.298736 -85.961495 -85.593582
              -85.192224 -84.754402 -84.276831 -83.755939 -83.187844 -82.56833
              -81.89282  -81.156357 -80.353575 -79.478674 -78.525397 -77.487013
              -76.356296 -75.125518 -73.786444 -72.330344 -70.748017 -69.029837
              -67.165823 -65.145744 -62.959262 -60.596124 -58.046413 -55.300856
              -52.351206 -49.1907   -45.814573 -42.220632 -38.409866 -34.387043
              -30.161252 -25.746331 -21.161107 -16.429384 -11.579629  -6.644331
               -1.659041   3.338836   8.311423  13.221792  18.035297  22.720709
               27.251074  31.604243  35.763079  39.715378  43.45356   46.974192
               50.277423  53.366377  56.246554  58.92527   61.411164  63.713764
               65.843134  67.809578  69.623418  71.294813  72.833637  74.249378
               75.551083  76.747318  77.846146  78.855128  79.781321  80.631294
               81.411149  82.126535  82.782681  83.384411  83.936179  84.442084
               84.905904  85.331111  85.720897  86.078198  86.405707  86.705898
               86.981044  87.233227  87.464359  87.676195  87.870342  88.048275
               88.211348  88.360799  88.497766  88.623291  88.738328  88.843755
               88.940374]
  is_circle: false

Let’s display its properties.

print(f'is ascending ? {axis.is_ascending()}')
print(f'is regular ? {axis.is_regular()}')
print(f'is circle ? {axis.is_circle}')
is ascending ? True
is regular ? False
is circle ? False

It is possible to query this axis as before.

axis.find_index([1e-3])
array([54])

Longitude#

It is also possible to represent longitudes going around the earth, i.e. making a circle.

axis = pyinterp.Axis(numpy.arange(0, 360, 1), is_circle=True)
axis
<pyinterp.core.Axis>
  min_value: 0
  max_value: 359
  step     : 1
  is_circle: true

In this case, you don’t have to worry about the bounds of the axis.

axis.find_index([-180]), axis.find_index([180])
(array([180]), array([180]))

TemporalAxis#

Time axes allow for manipulating axes representing dates or time differences. These objects are specialized to handle the 64-bit integers used by numpy to describe dates without losing information during calculations. In a netCDF file these axes are described as follows:

double time(time) ;
    time:long_name = "time" ;
    time:units = "days since 1990-1-1 0:0:0" ;

Note

These axes can be regular or irregular as before.

dates = numpy.datetime64('2020-01-01') + numpy.arange(
    10**6, step=500).astype('timedelta64[ms]')
axis = pyinterp.TemporalAxis(dates)
axis
<pyinterp.core.TemporalAxis>
  min_value: 2020-01-01T00:00:00.000
  max_value: 2020-01-01T00:16:39.500
  step     : 500 milliseconds

It is possible to search for a date in this axis.

axis.find_index(numpy.array([numpy.datetime64('2020-01-01T00:10:34.000')]))
array([1268])

You can pass any date unit to the axis.

axis.find_index(numpy.array([numpy.datetime64('2020-01-01')]))
array([0])

This object also makes it possible to manipulate timedeltas.

axis = pyinterp.TemporalAxis(dates - numpy.datetime64('2020-01-01'))
axis
<pyinterp.core.TemporalAxis>
  min_value: 0 milliseconds
  max_value: 999500 milliseconds
  step     : 500 milliseconds

Total running time of the script: ( 0 minutes 0.007 seconds)

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