do a spatial-mean reynolds average of a 3d field array3d
needs dimensions arranged as (z,y,x)
returns avg(z),perturb(z,y,x)
"""
avg = array3d.mean(axis=2).mean(axis=1)
perturb = array3d.T - avg
perturb = perturb.T
return avg, perturb
# %% [markdown]
# 2\. Identify the file structure using ncdump
# %%
with Dataset(a500.data_dir / 'tropical_subset.nc') as ncin:
ncdump(ncin)
# %% [markdown]
# 3\. Read a variable using the netCDF4 module: http://unidata.github.io/netcdf4-python/
# %%
with Dataset(a500.data_dir / 'tropical_subset.nc', 'r') as nc_in:
print(list(nc_in.variables.keys()))
the_temp = nc_in.variables['TABS'][0, ...]
#
# remove the time dimension since we only have one timestep
#
print('temp shape', the_temp.shape)
the_height = nc_in.variables['z'][...]
xvals = nc_in.variables['x'][...]
yvals = nc_in.variables['y'][...]
except RuntimeError:
#
# only create ncout z vector once
#
pass
ncout.history = 'written by read_cabauw.ipynb'
for var in ['lat', 'lon']:
setattr(ncout, var, float(var_attrs[var]))
ncout.lat_units = 'degrees north'
ncout.lon_units = 'degrees east'
filelist = []
for key, value in data_dict.items():
filelist.append('{};'.format(value['name']))
filelista = np.array(filelist)
setattr(ncout, 'filelist', filelist)
# %%
with Dataset('cabauw_ubc.nc') as input:
ncdump.ncdump(input)
# %%
from netCDF4 import Dataset
with Dataset('cabauw_ubc.nc', 'r') as nc:
the_groups = nc.groups
my_groups = list(the_groups.keys())
my_H = nc['m201407'].variables['H'][1, :3, 3]
#print(getattr(my_H,'accuracy'))
print(my_H)
# %%
file_dict = {}
for the_file in listFD(url, ext):
print(the_file)
out = download(the_file, root=url, dest_folder=a500.data_dir)
file_dict[the_file] = a500.data_dir / the_file
# %%
import time
time.sleep(5)
# %%
file_dict
# %%
from a500.utils import ncdump
fieldfile = file_dict['fielddump.000.000.001.nc']
with Dataset(fieldfile, 'r') as nc_in:
ncdump.ncdump(nc_in)
# %%
with Dataset(fieldfile, 'r') as nc_in:
var_dict = {}
var_list = ['qt', 'w', 'zt', 'yt', 'xt', 'time']
for a_var in var_list:
var_dict[a_var] = nc_in.variables[a_var][...]
# %%
var_dict
# %%
# %% [markdown]
# # Read ncfile names into a dictionary
# %%
the_files = list(context.data_dir.glob('*.nc'))
file_dict = {item.name: item for item in the_files}
print(list(file_dict.keys()))
# %% [markdown]
# ## Read all attributes and get thetav
# %%
filename = file_dict['profiles.001.nc']
with Dataset(filename, 'r', format="NETCDF4") as prof_nc:
print(f"opening {filename.name}")
all_attributes = ncdump(prof_nc, verbose=False)
print(list(prof_nc.variables))
print(list(prof_nc.dimensions))
thetav = prof_nc.variables['thv'][0, :]
height = prof_nc.variables['zm'][...]
# %% [markdown]
# # make a plot
# %%
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
ax.set(xlabel='thetav (K)', ylabel='height (m)')
ax.grid(True)
ax.plot(thetav, height)
# %%
