Suppress matplotlib output

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# Low-Pass filtered ADT variance

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``` 

# suppress warnings

import warnings

warnings.filterwarnings('ignore')

```

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``` 

%matplotlib inline

import matplotlib.pyplot as plt

from pathlib import Path

import seaborn as sns

import xarray as xr

import scipy.signal as signal

```

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``` 

plt.rcParams['figure.figsize'] = [15, 9]

sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})

sns.set_style('darkgrid')

```

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## Using a local checkout

In `taurus:/data/c2/TMdata/git_geomar_de_data/`, there's a checkout of `SLTAC_GLO_PHY_L4_REP`, `v1.x.x`.  Generate a list of all files for the years 1993 and 1994.

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``` 

data_path = Path("/data/c2/TMdata/git_geomar_de_data/")

data_set = "SLTAC_GLO_PHY_L4_REP"

ref = "v1.x.x"

full_path = data_path / data_set / ref / "data"

file_list = [str(fp) for fp in full_path.glob("199[34]/*.nc")]

```

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## Open and select region

Select 0°…30°N and 30°W…15°W.

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``` 

adt = xr.open_mfdataset(

    file_list, autoclose=True

).sel(

    latitude=slice(0, 30),

    longitude=slice(360-30, 360-15)

).adt

```

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``` 

print(adt)

```

%% Output

    <xarray.DataArray 'adt' (time: 365, latitude: 0, longitude: 0)>

    dask.array<shape=(365, 0, 0), dtype=float64, chunksize=(1, 0, 0)>

    Coordinates:

      * latitude   (latitude) float32

      * longitude  (longitude) float32

      * time       (time) datetime64[ns] 1993-01-01T00:00:21.864513536 ...

    Attributes:

        long_name:      Absolute dynamic topography

        standard_name:  sea_surface_height_above_geoid

        units:          m

        comment:        The absolute dynamic topography is the sea surface height...

        grid_mapping:   crs

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## Build a filter

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``` 

def butterworth_lowpass_filter(data, order=2, cutoff_freq=1.0/10.0, axis=0):

    """Filter input data.

    For unfiltered data, use `cutoff_freq=1`.

    Currently, this returns a numpy array.

    """

    B, A = signal.butter(order, cutoff_freq, output="ba")

    return signal.filtfilt(B, A, data, axis=0)

```

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## Calculate filtered fields

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``` 

# intial filters

adt_unfiltered = butterworth_lowpass_filter(adt, cutoff_freq=1)

adt_05day_lowpass = butterworth_lowpass_filter(adt, cutoff_freq=1 / 5.0)

adt_90day_lowpass = butterworth_lowpass_filter(adt, cutoff_freq=1 / 90.0)

# combined filters

adt_90day_highpass = adt_unfiltered - adt_90day_lowpass

adt_05day_90day_bandpass = adt_05day_lowpass - adt_90day_lowpass

```

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# Plot time series

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``` 

fig, ax = plt.subplots(2, 1, sharex=True)

ax[0].plot(adt.coords["time"], adt_unfiltered[:, 0, 0]);

ax[0].plot(adt.coords["time"], adt_05day_lowpass[:, 0, 0]);

ax[0].plot(adt.coords["time"], adt_90day_lowpass[:, 0, 0]);

ax[0].set_ylabel("SSH [m]");

ax[0].legend(["unfiltered", "5day low pass", "90day low pass"], loc=0, ncol=3);

ax[1].plot(adt.coords["time"], adt_90day_highpass[:, 0, 0]);

ax[1].plot(adt.coords["time"], adt_05day_90day_bandpass[:, 0, 0]);

ax[1].set_ylabel("SSH [m]");

ax[1].legend(["90day high pass", "5day-90day band pass"], loc=0, ncol=2);

```

%% Output

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# Plot fields

_ (This still needs work.) _

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``` 

plt.contourf(adt_05day_90day_bandpass[0, :, :], cmap="inferno")

plt.colorbar()

plt.contour(adt_unfiltered[0, :, :], colors=('white',))

plt.contourf(adt_05day_90day_bandpass[0, :, :], cmap="inferno");

plt.colorbar();

plt.contour(adt_unfiltered[0, :, :], colors=('white',));

```

%% Output

    <matplotlib.contour.QuadContourSet at 0x7fa648074c18>