In this section we show how to control the local cache
| National Science Data Fabric | WIRED Global Center | NASA Open Climate Workbench (OCW) |
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created by Valerio Pascucci
import numpy as np
import matplotlib.pyplot as plt
import os,sys
# # sys.path.append(r"C:\projects\OpenVisus\build\RelWithDebInfo")
import OpenVisus as ov
os.environ["VISUS_CACHE"]=os.environ.get("VISUS_CACHE","./visus_cache_can_be_erased")
print("VISUS_CACHE is ",os.environ["VISUS_CACHE"])VISUS_CACHE is ./visus_cache_can_be_erasedfrom tqdm.notebook import tqdm
variables = {
"hurs": "Near-Surface Relative Humidity",
"huss": "Near-Surface Specific Humidity",
"pr": "Precipitation",
"rlds" : "Surface Downwelling Longwave Radiation",
"rsds": "Surface Downwelling Shortwave Radiation",
"sfcWind": "Daily-Mean Near-Surface Wind Speed",
"tas": "Daily Near-Surface Air Temperature",
"tasmax": "Daily Maximum Near-Surface Air Temperature",
"tasmin": "Daily Minimum Near-Surface Air Temperature"
}
cmpas = ["YlGnBu","YlGnBu","YlGnBu","rainbow","rainbow","turbo","Reds","Reds","Reds"]
model = "ACCESS-CM2"
year = 2015 # This is the year whne the data switches from historical to simulated
scenario = "historical" if year<2015 else "ssp585"
quality = 0
# Open (connect to) dataset
variable="tas"
field = f"{variable}_day_{model}_{scenario}_r1i1p1f1_gn"
print(field)
db = ov.LoadDataset(f"http://atlantis.sci.utah.edu/mod_visus?dataset=nex-gddp-cmip6&cached=arco")
data=db.read(field=field)
y_max,x_max = data.shape
print(y_max,x_max)
xlabels = [str(x) for x in range(0,360,50)]
ylabels = [str(x) for x in range(-60,90,20)]
folder = "./images/"
from pathlib import Path
Path(folder).mkdir(parents=True, exist_ok=True)
for variable, my_cmap in tqdm(zip(variables,cmpas)):
field= f"{variable}_day_{model}_{scenario}_r1i1p1f1_gn"
my_fig, my_plt = plt.subplots(figsize=(18, 6))
data=db.read(time=year*365+364,quality=quality,field=field)
y,x = data.shape
xticks = [i*x/x_max for i in range(0,x_max, 200)]
yticks = [i*y/y_max for i in range(y_max,0,-80)]
plot = my_plt.imshow(np.flip(data, 0), cmap=my_cmap)
my_plt.set_xticks(xticks, labels=xlabels)
my_plt.set_yticks(yticks, labels=ylabels)
my_plt.set(xlabel='longitude [degrees east]', ylabel='latitude [degrees north]',
title=f"{variable} ({1.0/(2**(-1*quality)):.03f} of the data)")
# add a colorbar to our figure, based on the plot we just made above
my_fig.colorbar(plot,location='right', label='Temperature [K]')
plt.savefig(folder + "NEX-GDDP-CMIP6_ACCESS-CM2_"+variable+"_ssp585_2020_day202.png")
plt.show()tas_day_ACCESS-CM2_ssp585_r1i1p1f1_gn
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