index.qmd

# Preface {.unnumbered}

This book documents a set of simple python tools to easily **access** and, most importantly, **use** the NEX-GDDP-CMIP6 data in a simple way that does not require bulk dowloads of many large netcdf files. 

In short a user can directly "open" a remote dataset in a Jupyter notebook and immediately process it or visualizate it with
standard python libraries like `Numpy` and `matplotlib` as shown in Figure @fig-easy-cmip6.


```{python}
#| label: fig-easy-cmip6
#| fig-cap: "Simple plot"

from PIL import Image

img = Image.open("Notebooks/NEX-GDDP-CMIP6_ACCESS-CM2_tas_ssp585_2020_day202.png")

box = (185, 150, 1630, 770)
img2 = img.crop(box)
img2.save('Notebooks/NEX-GDDP-CMIP6_ACCESS-CM2_tas_ssp585_2020_day202_cropped.png')

```


![Immediate viasualization of a timestep of the NEX-GDDP-CMIP6 data wihout any bulk data download.](Notebooks/NEX-GDDP-CMIP6_ACCESS-CM2_tas_ssp585_2020_day202_cropped.png){#fig-easy-cmip6 width=80%}

In particular, loading the dataset diaplayed in @fig-easy-cmip6 requires only the following two lines of Python code:

```{python}
#| echo: false

import OpenVisus as ov

# Set climate variables
model     = "ACCESS-CM2"
variable  = "huss" 
year      = 2020 # 2015 is the year whne the data switches from historical to simulated
scenario  = "ssp585"
dataset_name = f"huss_day_ACCESS-CM2_ssp585_r1i1p1f1_gn"

# Set timestep to day corresponds to July 21. See https://nsidc.org/data/user-resources/help-center/day-year-doy-calendar
day_of_the_year = 202 
timestep =year*365 + day_of_the_year
```

```{python}
#| echo: true

# Open remote dataset to variable db
db = ov.LoadDataset(f"http://atlantis.sci.utah.edu/mod_visus?dataset={dataset_name}")
# Load anta in a numpy array data
data=db.read(time=timestep) 
```

The full Jupyter notebook for this example is provided in @sec-hellocmip6code.