Merge branch 'use-more-than-1000-sltac-files' into 'master'

Use all available SLTAC data and stick to ipynb

See merge request !4

example_01_SLTAC_adt_variance_local_and_opendap.md

-
-# Using git.geomar.de/data to calculate SSH variance
-
-This example uses version v1.0.0 of <https://git.geomar.de/data/SLTAC_GLO_PHY_L4_REP/> and calculates sea-surface-height variance for the year 1993.
-
-## Preamble
-
-Import a few modules:
-
-- `pyplot` is loaded by `xarray`, but we need to explicitly modify a dictionary to increase figure size
-- `pathlib.Path` is good for handling paths
-- `seaborn` makes plots nice
-- `xarray` will do the calculations and call the plotting
-
-
-```python
-# suppress warnings
-import warnings
-warnings.filterwarnings('ignore')
-```
-
-
-```python
-%matplotlib inline
-import matplotlib.pyplot as plt
-from pathlib import Path
-import seaborn as sns
-import xarray as xr
-```
-
-
-```python
-plt.rcParams['figure.figsize'] = [15, 9]
-sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})
-```
-
-## Using a local checkout
-
-In `taurus:/data/c2/TMdata/git_geomar_de_data/`, there's a checkout of `SLTAC_GLO_PHY_L4_REP`, `v1.0.0`.  Generate a list of all files for the year 1993 and open all files as one data set and calculate temporal variance of absolute dynamic topography, `adt`.
-
-
-```python
-data_path = Path("/data/c2/TMdata/git_geomar_de_data/")
-data_set = "SLTAC_GLO_PHY_L4_REP"
-ref = "v1.0.0"
-full_path = data_path / data_set / ref / "data"
-file_list = [str(fp) for fp in full_path.glob("1993/*.nc")]
-```
-
-
-```python
-xr.open_mfdataset(file_list).adt.var(dim="time").plot()
-```
-
-
-
-
-    <matplotlib.collections.QuadMesh at 0x7feb95eacf28>
-
-
-
-
-![png](example_01_SLTAC_adt_variance_local_and_opendap_files/example_01_SLTAC_adt_variance_local_and_opendap_6_1.png)
-
-
-## OPeNDAP
-
-There is <https://data.geomar.de/thredds/tmdata/git_geomar_de_data/catalog.html>.  Clicking will bring a list of data-file urls (which, lacking a decent crawler, we'll have to improvise here).  Open all urls in one dataset and again create the variance plot with `xarray`.
-
-
-```python
-# wave hands and pull a list of urls from somewhere (the local file listing for now...)
-base_url = "https://data.geomar.de/thredds/dodsC/tmdata/git_geomar_de_data/SLTAC_GLO_PHY_L4_REP/v1.0.0/data/1993/{file_name}"
-url_list = [base_url.format(file_name=fn) for fn in [p.name for p in full_path.glob("1993/*.nc")]]
-```
-
-
-```python
-xr.open_mfdataset(url_list).adt.var(dim="time").plot()
-```
-
-
-
-
-    <matplotlib.collections.QuadMesh at 0x7feb8e6582b0>
-
-
-
-
-![png](example_01_SLTAC_adt_variance_local_and_opendap_files/example_01_SLTAC_adt_variance_local_and_opendap_9_1.png)
-