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Commit 6630ad0b authored by Nils Brüggemann's avatar Nils Brüggemann
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examp_easyms2022: Allow section plotting with accessor syntax.

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%% Cell type:markdown id:f0bb0189-edc5-4e70-97bf-471aa88b9d48 tags:
 
# Basic plotting of ICON data
 
%% Cell type:markdown id:5f679850-f814-4a20-9974-0d9d362a3f15 tags:
 
In this notebook, we explain how to make some basic plots of ICON data using pyicon, a python based post-processing toolbox for ICON.
 
We assume that you have some basic knowledge about xarray, how to load data with it and how to select time and depth / height slices.
 
%% Cell type:markdown id:b4df0cef-8622-42ce-adb1-c6f3b9f9f27c tags:
 
## Some stuff for debugging
 
%% Cell type:code id:de28e5fc-21e3-4a73-8795-f54b55169e7d tags:
 
``` python
%load_ext autoreload
%autoreload 2
```
 
%% Cell type:code id:f0fb70b2-d4f2-45c6-87ae-018dfb9ddd50 tags:
 
``` python
# temporarily necessary to ignore some warnings
import shapely
import warnings
from shapely.errors import ShapelyDeprecationWarning
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
```
 
%% Cell type:code id:e861f91b-74ce-42ca-aeac-aca4136161b8 tags:
 
``` python
import sys
# sys.path.append('~/pyicon/')
sys.path = ['~/pyicon']+sys.path
sys.path
```
 
%% Output
 
['~/pyicon',
'/home/m/m300602/pyicon/notebooks',
'/home/m/m300602/pyicon',
'/home/m/m300602/miniconda3/envs/pyicon_py39_cartopy19/lib/python39.zip',
'/home/m/m300602/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9',
'/home/m/m300602/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/lib-dynload',
'',
'/home/m/m300602/.local/lib/python3.9/site-packages',
'/home/m/m300602/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages']
 
%% Cell type:markdown id:0b6086ed-73ab-4778-8c42-1a3e9e60f67c tags:
 
## Loading necessary modules
 
%% Cell type:code id:01f5aca0-ec02-4aab-9743-3b373b9dc989 tags:
 
``` python
import pyicon as pyic
import xarray as xr
import numpy as np
```
 
%% Output
 
-----calc
sys glob os
numpy
netcdf
Done modules calc.
-----calc_xr
sys glob os
numpy
netcdf
xarray
Done modules calc.
-----tb
sys
json
numpy
scipy
netcdf datetime
matplotlib
mybreak
pnadas
xarray
done xarray
-----IconData
-----plotting
-----accessor
-----view
-----calc
-----calc_xr
-----tb
-----IconData
-----plotting
-----accessor
-----view
-----quickplots
-----quickplots
 
%% Cell type:markdown id:1a7dc458-3f4d-4b5a-989e-60e9a1269060 tags:
 
## Loading the data
 
%% Cell type:code id:0cf700c9-dcb6-4225-a5d5-551d875dceb3 tags:
 
``` python
run = 'sfx0080'
ds_3d = xr.open_dataset(f'/home/m/m300602/pyicon/example_data/{run}/{run}_oce_P1M_3d_20100101.nc')
ds_2d = xr.open_dataset(f'/home/m/m300602/pyicon/example_data/{run}/{run}_oce_P1M_2d_20100101.nc')
```
 
%% Cell type:markdown id:3a2ff0e5-519f-4e4a-bb27-21c7e2f58349 tags:
 
## Basic horizontal plotting
 
%% Cell type:markdown id:5c2feaf7-8b8e-41f4-a597-c4a6d1000f99 tags:
 
### Just plotting
 
%% Cell type:code id:c1c08f80-89df-4677-9fc6-a16d85723374 tags:
 
``` python
ds_3d.to.isel(time=-1,depth=0).pyic.plot()
```
 
%% Output
 
 
%% Cell type:markdown id:1f760137-61c0-4fe4-b80e-b39ad00a6cc8 tags:
 
### Select a region
 
%% Cell type:code id:4ea8660e-b0cf-423d-9df5-6e899223c135 tags:
 
``` python
ds_3d.to.isel(time=-1,depth=0).pyic.plot(lon_reg=[-50,10], lat_reg=[0,30])
```
 
%% Output
 
 
%% Cell type:markdown id:436a6385-0ec0-4c8c-b1b5-090ee37b59c0 tags:
 
### Change how things look
 
%% Cell type:code id:31e94131-cec2-4bae-936c-638cd35a80db tags:
 
``` python
ds_3d.to.isel(time=-1,depth=0).pyic.plot(
lon_reg=[-50,10], lat_reg=[0,30],
clim=[24,30], cmap='plasma', #conts=np.linspace(24,40),
)
```
 
%% Output
 
 
%% Cell type:markdown id:800e0750-c613-451a-a7a1-23bf0ea853f9 tags:
 
### Change projection
 
%% Cell type:code id:710a0509-2526-4dd8-a4da-162d3a5c01a5 tags:
 
``` python
ds_2d.hi.isel(time=-1,lev=0).pyic.plot(projection='np')
```
 
%% Output
 
 
%% Cell type:markdown id:46b01f27-e5a4-4aea-bd2c-d24a5648e57e tags:
 
### Make continents transparent
 
%% Cell type:code id:1e647bde-282e-4d65-b240-eccbb552bfd6 tags:
 
``` python
ds_3d.to.isel(time=-1).sel(depth=20., method='nearest').pyic.plot(land_facecolor='none')
```
 
%% Output
 
 
%% Cell type:markdown id:0b98c794-e9e7-45b5-8107-e0f8030750f2 tags:
 
### Plot on the original grid
 
%% Cell type:code id:4165c490-c096-4a49-8fa8-42476322ba36 tags:
 
``` python
ds_3d.to.isel(time=-1,depth=0).pyic.plot(lon_reg=[-50,10], lat_reg=[0,30], plot_method='tgrid')
```
 
%% Output
 
Deriving triangulation object, this can take a while...
Done deriving triangulation object.
 
 
%% Cell type:markdown id:8ae82d57-a1ae-4d6c-8dd7-54540323f08c tags:
 
## Plotting meridional sections
 
%% Cell type:code id:bc2ba869-6b03-4fc7-a3c1-2d9c5b7c5237 tags:
``` python
ds_3d.to.isel(time=-1).pyic.plot_sec(section='gzave',
fpath_fx='/home/m/m300602/work/icon/grids/r2b4_oce_r0004/r2b4_oce_r0004_L40_fx.nc',
res=2.0,)
```
%% Output
%% Cell type:code id:d9b02f72-8439-4285-8da0-edec140bf57a tags:
 
``` python
pyic.plot_sec(ds_3d.to.isel(time=-1),
section='30W', # only two options yet, 30W and 170W
)
```
 
%% Output
 
 
%% Cell type:code id:d62fa20f-173a-4ae1-b767-eb5b82275baa tags:
 
``` python
pyic.plot_sec(ds_3d.to.isel(time=-1),
section='170W', # only two options yet, 30W and 170W
)
```
 
%% Output
 
 
%% Cell type:markdown id:b199c8c1-7131-4dd1-a2eb-991a04204128 tags:
 
## Plotting zonal averages
 
%% Cell type:markdown id:70a16bd1-16d9-46a6-b895-ecccaf97ba45 tags:
 
### Global zonal average
 
%% Cell type:code id:54357aa3-b360-45e0-80fe-84d5f83a5324 tags:
 
``` python
pyic.plot_sec(ds_3d.to.isel(time=-1),
section='gzave',
fpath_fx='/home/m/m300602/work/icon/grids/r2b4_oce_r0004/r2b4_oce_r0004_L40_fx.nc',
res=2.0, # choose something >= 1.0 to avoid
)
ds_3d.to.isel(time=-1).pyic.plot_sec(section='gzave',
fpath_fx='/home/m/m300602/work/icon/grids/r2b4_oce_r0004/r2b4_oce_r0004_L40_fx.nc',
res=2.0, # choose something >= 1.0 to avoid overprecision
)
```
 
%% Output
 
 
%% Cell type:markdown id:d382912f-020a-42e9-9755-368e30a1dfe1 tags:
 
### Atlantic zonal average
 
%% Cell type:code id:5cc04bab-6fc0-48f5-8d6a-09dda45d14fb tags:
 
``` python
pyic.plot_sec(ds_3d.to.isel(time=-1),
section='azave',
fpath_fx='/home/m/m300602/work/icon/grids/r2b4_oce_r0004/r2b4_oce_r0004_L40_fx.nc',
res=2.0, # choose something >= 1.0 to avoid
)
ds_3d.to.isel(time=-1).pyic.plot_sec(section='azave',
fpath_fx='/home/m/m300602/work/icon/grids/r2b4_oce_r0004/r2b4_oce_r0004_L40_fx.nc',
res=2.0, # choose something >= 1.0 to avoid overprecision
)
```
 
%% Output
 
 
%% Cell type:markdown id:47b5933c-331e-41f6-8f29-1c584a079785 tags:
 
### Indo-Pacific zonal average
 
%% Cell type:code id:01d4cbe3-c54c-4bf5-9696-19192382d459 tags:
 
``` python
pyic.plot_sec(ds_3d.to.isel(time=-1),
section='ipzave',
fpath_fx='/home/m/m300602/work/icon/grids/r2b4_oce_r0004/r2b4_oce_r0004_L40_fx.nc',
res=2.0, # choose something >= 1.0 to avoid
)
ds_3d.to.isel(time=-1).pyic.plot_sec(section='ipzave',
fpath_fx='/home/m/m300602/work/icon/grids/r2b4_oce_r0004/r2b4_oce_r0004_L40_fx.nc',
res=2.0, # choose something >= 1.0 to avoid overprecision
)
```
 
%% Output
 
 
%% Cell type:code id:17272b50-df87-4a12-8f80-9adea261e773 tags:
 
``` python
```
......
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