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How to compare interpolated fields

In this notebook we will compare a set of data to it’s interpolated values.

In addition we will interpolate a field using the 3 available methods of interpolation.

from casys import (
    NadirData,
    AxeParams,
    CasysPlot,
    DateHandler,
    Field,
    PlotParams,
)
from casys.readers import CLSTableReader

NadirData.enable_loginfo()

Create your NadirData

start = DateHandler.from_orf(orf="C_J3_GDRD", cycle_nb=122, pass_nb=1, pos="first")
end = DateHandler.from_orf(orf="C_J3_GDRD", cycle_nb=122, pass_nb=1, pos="last")

table_name = "TABLE_C_J3_B_GDRD"
orf_name = "C_J3_GDRD"

reader = CLSTableReader(
    name=table_name,
    date_start=start,
    date_end=end,
    orf=orf_name,
    time="time",
    longitude="LONGITUDE",
    latitude="LATITUDE",
)

cd = NadirData(source=reader)

reader_interp = CLSTableReader(
    name=table_name,
    date_start=start,
    date_end=end,
    orf=orf_name,
    time="time",
    longitude="LONGITUDE",
    latitude="LATITUDE",
    reference_track="J3",
)

cd_interp = NadirData(source=reader_interp)

Define fields with their interpolation parameters

var_sla = Field(
    name="SLA",
    source="ORBIT.ALTI - RANGE.ALTI - MEAN_SEA_SURFACE.MODEL.CNESCLS15",
    unit="m",
)
var_sla_linear = Field(
    name="SLA_linear",
    source="ORBIT.ALTI - RANGE.ALTI - MEAN_SEA_SURFACE.MODEL.CNESCLS15",
    unit="m",
    interpolation="linear",
)
var_sla_nearest = Field(
    name="SLA_nearest",
    source="ORBIT.ALTI - RANGE.ALTI - MEAN_SEA_SURFACE.MODEL.CNESCLS15",
    unit="m",
    interpolation="nearest",
)
var_sla_spline = Field(
    name="SLA_spline",
    source="ORBIT.ALTI - RANGE.ALTI - MEAN_SEA_SURFACE.MODEL.CNESCLS15",
    unit="m",
    interpolation={"mode": "smoothing_spline", "noise_level": 0.1},
)

Add your diagnostics the same way you usually do

cd.add_raw_data(name="SLA", field=var_sla)

cd_interp.add_raw_data(name="SLA linear", field=var_sla_linear)
cd_interp.add_raw_data(name="SLA nearest", field=var_sla_nearest)
cd_interp.add_raw_data(name="SLA spline", field=var_sla_spline)

cd.compute()

2025-05-14 11:00:02 INFO    Reading ['LONGITUDE', 'LATITUDE', 'SLA', 'time']
2025-05-14 11:00:02 INFO    Computing done.

cd_interp.compute()

2025-05-14 11:00:02 INFO    Reading ['LONGITUDE', 'LATITUDE', 'SLA_spline', 'time', 'SLA_nearest', 'SLA_linear']
2025-05-14 11:00:02 INFO    Interpolating data along reference track.
2025-05-14 11:00:03 INFO    Computing done.

cd.data

<xarray.Dataset> Size: 110kB
Dimensions:    (time: 3428)
Coordinates:
  * time       (time) datetime64[ns] 27kB 2019-06-01T05:30:29.807497 ... 2019...
Data variables:
    LONGITUDE  (time) float64 27kB 17.01 17.14 17.27 ... -177.4 -177.3 -177.3
    LATITUDE   (time) float64 27kB -66.15 -66.15 -66.15 ... 66.15 66.15 66.15
    SLA        (time) float64 27kB nan nan nan nan nan ... nan nan nan nan nan

xarray.Dataset

• Dimensions:
  • time: 3428
• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2019-06-01T05:30:29.807497 ... 2...

    standard_name :

    time

    array(['2019-06-01T05:30:29.807497000', '2019-06-01T05:30:30.826208000',
           '2019-06-01T05:30:31.844918000', ..., '2019-06-01T06:26:40.368882000',
           '2019-06-01T06:26:41.074702000', '2019-06-01T06:26:41.577266000'],
          dtype='datetime64[ns]')

• Data variables: (3)
  • LONGITUDE
    (time)
    float64
    17.01 17.14 17.27 ... -177.3 -177.3

    array([  17.006113,   17.135617,   17.265119, ..., -177.438218,
           -177.348466, -177.284559])

  • LATITUDE
    (time)
    float64
    -66.15 -66.15 ... 66.15 66.15

    array([-66.149034, -66.148977, -66.148805, ...,  66.147723,  66.147863,
            66.14793 ])

  • SLA
    (time)
    float64
    nan nan nan nan ... nan nan nan nan

    array([nan, nan, nan, ..., nan, nan, nan])

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-06-01 05:30:29.807497', '2019-06-01 05:30:30.826208',
                   '2019-06-01 05:30:31.844918', '2019-06-01 05:30:32.863628',
                   '2019-06-01 05:30:33.882337', '2019-06-01 05:30:34.901048',
                   '2019-06-01 05:30:35.919757', '2019-06-01 05:30:36.938466',
                   '2019-06-01 05:30:37.957177', '2019-06-01 05:30:38.975886',
                   ...
                   '2019-06-01 06:26:33.237912', '2019-06-01 06:26:34.256623',
                   '2019-06-01 06:26:35.275331', '2019-06-01 06:26:36.294043',
                   '2019-06-01 06:26:37.312753', '2019-06-01 06:26:38.331462',
                   '2019-06-01 06:26:39.350173', '2019-06-01 06:26:40.368882',
                   '2019-06-01 06:26:41.074702', '2019-06-01 06:26:41.577266'],
                  dtype='datetime64[ns]', name='time', length=3428, freq=None))

• Attributes: (0)

cd_interp.data

<xarray.Dataset> Size: 175kB
Dimensions:             (time: 3128)
Coordinates:
  * time                (time) datetime64[ns] 25kB 2019-06-01T05:30:30.445071...
    LONGITUDE           (time) float64 25kB 17.09 17.22 17.36 ... -177.5 -177.4
    LATITUDE            (time) float64 25kB -66.15 -66.15 -66.15 ... 66.15 66.15
Data variables:
    SLA_spline          (time) float64 25kB nan nan nan nan ... nan nan nan nan
    SLA_nearest         (time) float64 25kB nan nan nan nan ... nan nan nan nan
    SLA_linear          (time) float64 25kB nan nan nan nan ... nan nan nan nan
    interpolated_index  (time) int64 25kB 1000000000 1000000001 ... 1000003127

xarray.Dataset

• Dimensions:
  • time: 3128
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2019-06-01T05:30:30.445071690 .....

    array(['2019-06-01T05:30:30.445071690', '2019-06-01T05:30:31.522998174',
           '2019-06-01T05:30:32.600914646', ..., '2019-06-01T06:26:38.900461112',
           '2019-06-01T06:26:39.907585364', '2019-06-01T06:26:41.053324415'],
          dtype='datetime64[ns]')

  • LONGITUDE
    (time)
    float64
    17.09 17.22 17.36 ... -177.5 -177.4

    array([  17.087164,   17.224193,   17.36122 , ..., -177.624936,
           -177.487865, -177.350791])

  • LATITUDE
    (time)
    float64
    -66.15 -66.15 ... 66.15 66.15

    array([-66.147567, -66.14743 , -66.147164, ...,  66.14577 ,  66.146144,
            66.14639 ])

• Data variables: (4)
  • SLA_spline
    (time)
    float64
    nan nan nan nan ... nan nan nan nan

    array([nan, nan, nan, ..., nan, nan, nan])

  • SLA_nearest
    (time)
    float64
    nan nan nan nan ... nan nan nan nan

    array([nan, nan, nan, ..., nan, nan, nan])

  • SLA_linear
    (time)
    float64
    nan nan nan nan ... nan nan nan nan

    array([nan, nan, nan, ..., nan, nan, nan])

  • interpolated_index
    (time)
    int64
    1000000000 ... 1000003127

    array([1000000000, 1000000001, 1000000002, ..., 1000003125, 1000003126,
           1000003127])

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-06-01 05:30:30.445071690',
                   '2019-06-01 05:30:31.522998174',
                   '2019-06-01 05:30:32.600914646',
                   '2019-06-01 05:30:33.678838401',
                   '2019-06-01 05:30:34.756759324',
                   '2019-06-01 05:30:35.834674085',
                   '2019-06-01 05:30:36.912602579',
                   '2019-06-01 05:30:37.990516891',
                   '2019-06-01 05:30:39.068438758',
                   '2019-06-01 05:30:40.146365156',
                   ...
                   '2019-06-01 06:26:31.354674474',
                   '2019-06-01 06:26:32.432641019',
                   '2019-06-01 06:26:33.510619385',
                   '2019-06-01 06:26:34.588575592',
                   '2019-06-01 06:26:35.666549414',
                   '2019-06-01 06:26:36.744518741',
                   '2019-06-01 06:26:37.822496731',
                   '2019-06-01 06:26:38.900461112',
                   '2019-06-01 06:26:39.907585364',
                   '2019-06-01 06:26:41.053324415'],
                  dtype='datetime64[ns]', name='time', length=3128, freq=None))

• Attributes: (0)

Plot your comparison

common_params = {
    "grid": True,
    "color_limits": (-5, 0),
    "marker_size": 6,
    "fill_ocean": False,
    "x_limits": (98.5, 100.5),
    "y_limits": (-2.2, -1),
}

plot_par = PlotParams(color_bar=AxeParams(position="left"), **common_params)
plot_par2 = PlotParams(
    color_map="autumn",
    color_bar=AxeParams(position="right", label="SLA interp (m)"),
    **common_params,
)
plot_par3 = PlotParams(color_map="autumn", color_bar=False, **common_params)

sla_plot = CasysPlot(data=cd, data_name="SLA", plot="map", plot_params=plot_par)
sla_plot_linear = CasysPlot(
    data=cd_interp, data_name="SLA linear", plot="map", plot_params=plot_par2
)
sla_plot_nearest = CasysPlot(
    data=cd_interp, data_name="SLA nearest", plot="map", plot_params=plot_par3
)
sla_plot_spline = CasysPlot(
    data=cd_interp, data_name="SLA spline", plot="map", plot_params=plot_par3
)

sla_plot.add_stat_bar()
sla_plot_linear.add_stat_bar()
sla_plot_nearest.add_stat_bar()
sla_plot_spline.add_stat_bar()

# That's new!
sla_plot_nearest.adjust_coordinates(longitude=0.1, latitude=0)
sla_plot_spline.adjust_coordinates(longitude=0.2, latitude=0)

sla_plot.add_plot(sla_plot_linear)
sla_plot.add_plot(sla_plot_nearest)
sla_plot.add_plot(sla_plot_spline)

sla_plot.show()