def read_gc(fname,varname,cat='IJ-AVG-$',
gc_dir = '/short/m19/jaf574/GC.v11-01/runs.v11-02e/geosfp_025x03125_tropchem_au.base/',
**kwargs):
# Some species involve multiple GEOS-Chem species...
varname_gc = gcname_to_names(varname)
# Expand wildcard if necessary and link to directory
if '*' in fname:
fname = glob(gc_dir+fname)
else:
fname = [gc_dir + f for f in fname]
# Put files in order!
fname.sort()
# Read using xbpch
# one file
if isinstance(fname,str):
ds = open_bpchdataset(fname,categories=[cat,],fields=varname_gc,
diaginfo_file=gc_dir+'diaginfo.dat',
tracerinfo_file=gc_dir+'tracerinfo.dat',**kwargs)
# multiple files
else:
ds = open_mfbpchdataset(fname,dask=True,categories=[cat,],fields=varname_gc,
diaginfo_file=gc_dir+'diaginfo.dat',
tracerinfo_file=gc_dir+'tracerinfo.dat',**kwargs)
# load dataset
ds.load()
# extract variables
cat=cat.replace('$','S').replace('-','_')
dfg = ds[[cat+'_'+v for v in varname_gc]]
# If needed, sum GEOS-Chem variables
if len(varname_gc) > 1:
dfg = sum_gc_vars(dfg, [cat+'_'+v for v in varname_gc],
varname=cat+'_'+varname)
return dfg
# Print month
print('Processing month: {}'.format(mstr))
# File names (EDIT THESE ACCORDINGLY)
bpch_file = join(bpchdir, 'trac_avg.GC_12.4.0.2016{}01'.format(mstr))
tinfo_file = join(bpchdir, 'tracerinfo.dat')
dinfo_file = join(bpchdir, 'diaginfo.dat')
ncfile = join(ncdir, 'GEOSChem.TagCOInputs.2016{}01.nc'.format(mstr))
# Open the bpch file and save it into an xarray Dataset object
# NOTE: For best results, also specify the corresponding
# tracerinfo.dat diaginfo.dat metadata files.
try:
ds = xb.open_bpchdataset(filename=bpch_file,
tracerinfo_file=tinfo_file,
diaginfo_file=dinfo_file)
except FileNotFoundError:
print('Could not find file {}'.format(bpch_file))
raise
# ------------------------------------------------------------------
# Further manipulate the Dataset
# ------------------------------------------------------------------
# Only extract the bpch fields we need for the tagged CO
varlist = ['PORL_L_S_PCO', 'PORL_L_S_LCO',
'PORL_L_S_PCO_CH4', 'PORL_L_S_PCO_NMVO']
ds = ds[varlist]
# Transpose the order of the xarray Dataset object read by
For a given field timeseries, compute seasonal averages over all data and
plot each average on a four-panel figure.
"""
# Author: Daniel Rothenberg
# Version: June 2, 2017
import matplotlib.pyplot as plt
plt.style.use(['seaborn-talk', 'seaborn-ticks'])
import xbpch
# First we read in a sample dataset containing GEOS-Chem output
ds = xbpch.open_bpchdataset(
"/Users/daniel/workspace/bpch/test_data/ref_e2006_m2008.bpch",
diaginfo_file="/Users/daniel/Desktop/sample_nd49/diaginfo.dat",
tracerinfo_file="/Users/daniel/Desktop/sample_nd49/tracerinfo.dat",
dask=True,
memmap=True)
# Compute seasonal averages by doing splitting along the "seasons" corresponding
# to each timestep, and taking the average over time in that group
seasonal_o3 = (
ds['IJ_AVG_S_O3'].isel(lev=0) # select just surface values
.groupby('time.season').mean('time'))
print(seasonal_o3)
# Note that we now have a new dimension, "season", corresponding to the groups
# we split the dataset by. We can use this dimension as a 'facet' to assemble
# a collection of plots.
# TODO: Cleanup axis proportions
import cartopy.crs as ccrs
print("ERROR: Couldn't find the following input files:")
for fn in bad_files:
print(" " + fn)
sys.exit(1)
# Else, we should be good to read in and concatenate
open_kws = {
"tracerinfo_file": args.tracerinfo,
"diaginfo_file": args.diaginfo,
"memmap": True,
"dask": True
}
print("\nReading in file(s)...")
if len(args.bpch_files) == 1:
ds = open_bpchdataset(args.bpch_files[0], **open_kws)
else:
ds = open_mfbpchdataset(args.bpch_files, **open_kws)
# This block of code is hack to fix the encoding of attributes
# on the DataArrays in this Dataset. They are being
# set at a very low level when we read in the data, and manually
# specifying the encoding doesn't work.
# However, deleting them from the attributes dict
# doesn't end up removing them from the final output file - they get
# written just fine.
print("\nDecoding variables...")
for v in ds.data_vars:
da = ds[v]
da = _maybe_del_attr(da, 'scale_factor')
da = _maybe_del_attr(da, 'units')
def bpch_2_netcdf(
bpchfile, # Name of Bpch file
tinfo_file, # tracerinfo.dat and diaginfo,dat fiels
dinfo_file,
ncfile, # Name of netCDF file
interval=86400.0 * 31.0
): # Number of seconds in the diagnostic interval (assume 1-month), does not matter for CO2
# ----------------------------------------------------------------------
# Open the bpch file and save it into an xarray Dataset object
# NOTE: For best results, also specify the corresponding
# tracerinfo.dat diaginfo.dat metadata files.
# ----------------------------------------------------------------------
try:
ds = xb.open_bpchdataset(filename=bpchfile,
tracerinfo_file=tinfo_file,
diaginfo_file=dinfo_file)
except FileNotFoundError:
print('Could not find file {}'.format(bpchfile))
raise
# ----------------------------------------------------------------------
# Further manipulate the Dataset
# ----------------------------------------------------------------------
# Transpose the order of the xarray Dataset object read by
# xbpch so that its dimensions will be in the same order as
# Dataset objects read from netCDF files.
ds = ds.transpose()
# Convert the bpch variable names to the same naming
# convention as the netCDF ("History") diagnostics.
ds = core.convert_bpch_names_to_netcdf_names(ds)
# xbpch does not include a time dimension, so we'll add one here
coords = ds.coords
coords['time'] = 0.0
# ------------------------------------------------------------------
# Further edit variable attributes
# ------------------------------------------------------------------
for v in ds.data_vars.keys():
# Append time to the data array
ds[v] = xr.concat([ds[v]], 'time')
# Add long_name attribute for COARDS netCDF compliance
ds[v].attrs['long_name'] = ds[v].attrs['full_name']
# Remove some extraneous attributes that xbpch sets
del ds[v].attrs['name']
del ds[v].attrs['full_name']
del ds[v].attrs['scale_factor']
del ds[v].attrs['hydrocarbon']
del ds[v].attrs['tracer']
del ds[v].attrs['category']
del ds[v].attrs['chemical']
del ds[v].attrs['original_shape']
del ds[v].attrs['origin']
del ds[v].attrs['number']
del ds[v].attrs['molwt']
del ds[v].attrs['C']
# Make the units attribute consistent with the units
# attribute from the GEOS-Chem History diagnostics
# NOTE: There probably is a more Pythonic way to code
# this, but this will work for sure.
if 'ug/m3' in ds[v].units:
ds[v].attrs['units'] = 'ug m-3'
if 'ug Celsius/m3' in ds[v].units:
ds[v].attrs['units'] = 'ug C m-3'
if 'count/cm3' in ds[v].units:
ds[v].attrs['units'] = 'molec m-3'
if 'cm/s' in ds[v].units:
ds[v].attrs['units'] = 'cm s-1'
if 'count/cm2/s' in ds[v].units:
ds[v].attrs['units'] = 'molec cm-2 s-1'
if 'kg/m2s' in ds[v].units:
ds[v].attrs['units'] = 'kg m-2 s-1'
if 'kg/m2/s' in ds[v].units:
ds[v].attrs['units'] = 'kg m-2 s-1'
if 'kg/s' in ds[v].units:
ds[v].attrs['units'] = 'kg s-1'
if 'W/m2' in ds[v].units:
ds[v].attrs['units'] = 'W m-2'
if 'm/s' in ds[v].units:
ds[v].attrs['units'] = 'm s-1'
if 'Pa/s' in ds[v].units:
ds[v].attrs['units'] = 'Pa s-1'
if 'g/kg' in ds[v].units:
ds[v].attrs['units'] = 'g kg-1'
if v.strip() == 'TotalOC':
ds[v].attrs['units'] = 'ug m-3'
if v.strip() in ['HO2concAfterChem']:
ds[v].attrs['units'] = 'ppb'
if v.strip() in [
'O1DconcAfterChem', 'O3PconcAfterChem', 'OHconcAfterChem'
]:
ds[v].attrs['units'] = 'molec cm-3'
if v.strip() in [
'Loss_CO', 'Prod_CO', 'Loss_Ox', 'Prod_Ox', 'Prod_SO4'
]:
ds[v].attrs['units'] = 'molec/cm3/s'
if v.strip() in 'Met_CLDTOPS':
ds[v].attrs['units'] = 'level'
if v.strip() in 'Met_PHIS':
ds[v].attrs['units'] = 'm2 s-1'
if v.strip() in ['Met_PRECCON', 'Met_PRECTOT']:
ds[v].attrs['units'] = 'kg m-2 s-1'
if v.strip() in 'Met_AVGW':
ds[v].attrs['units'] = 'vol vol-1'
if v.strip() in 'Met_AIRNUMDEN':
ds[v].attrs['units'] = 'molec cm-3'
if v.strip() in ['ProdCOfromCH4', 'ProdCOfromNMVOC']:
ds[v].attrs['units'] = 'molec cm-3 s-1'
# Convert these prodloss diagnostics from kg (bpch) to kg/s
# to be consistent with the GEOS-Chem History diagnostics
# NOTE: Assume a 1-month interval (
if v.strip() in [
'ProdSO4fromH2O2inCloud', 'ProdSO4fromO3inCloud',
'ProdSO4fromO2inCloudMetal', 'ProdSO4fromO3inSeaSalt',
'ProdSO4fromHOBrInCloud', 'ProdSO4fromSRO3',
'ProdSO4fromSRHObr', 'ProdSO4fromO3s'
]:
ds[v].attrs['units'] = 'kg S s-1'
ds[v] = ds[v] / interval
if v.strip() in ['LossHNO3onSeaSalt']:
ds[v].attrs['units'] = 'kg s-1'
ds[v] = ds[v] / interval
# ------------------------------------------------------------------
# Edit attributes for coordinate dimensions
# ------------------------------------------------------------------
# Time
ds['time'].attrs['long_name'] = 'time'
#ds['time'].attrs['units'] = \
# 'hours since {} 00:00:00.00 UTC'.format(datestr)
ds['time'].attrs['calendar'] = 'standard'
ds['time'].attrs['axis'] = 'T'
# "lon", "lat", "lev"
ds['lon'].attrs['axis'] = 'X'
ds['lat'].attrs['axis'] = 'Y'
ds['lev'].attrs['axis'] = 'Z'
ds['lev'].attrs['units'] = 'level'
# Global title
ds.attrs['title'] = 'Created by bpch2nc.py'
ds.attrs['conventions'] = 'COARDS'
ds.attrs['references'] = 'www.geos-chem.org; wiki.geos-chem.org'
# ------------------------------------------------------------------
# Create the netCDF file
# ------------------------------------------------------------------
ds.to_netcdf(ncfile)
def read_bpch(path,keys):
'''
Read generic bpch file into dictionary
keys = keys you want to read
'''
paths=path
if __VERBOSE__:
print('GC_fio.read_bpch called on paths:')
print(path)
multi=False
if isinstance(path,list):
path=path[0]
if len(path) > 1:
multi=True
if '*' in path:
multi=True
# make sure coordinates are in keys list
keys = list(set(keys + GC_coords)) # set removes any duplicates
# assume tracerinfo and diaginfo in same folder:
# otherwise use my generic one with coalesced info
splt=path.split('/')
splt[-1]='tracerinfo.dat'
tracinf='/'.join(splt)
if not os.path.isfile(tracinf):
tracinf='Data/GC_Output/tracerinfo.dat'
splt[-1]='diaginfo.dat'
diaginf='/'.join(splt)
if not os.path.isfile(diaginf):
diaginf='Data/GC_Output/diaginfo.dat'
# Improve read performance by only reading requested fields:
fields=set(); categories=set()
for key in keys:
if '_' in key:
# Split key on the underscores: Category_Field
c,_,f = key.rpartition('_')
categories.add(c)
fields.add(f)
else:
fields.add(key)
if __VERBOSE__:
print("categories: ",categories)
print("fields: ",fields)
# get bpch file:
data={}
attrs={}
bpchargs={'fields':list(fields), 'categories':list(categories),
'tracerinfo_file':tracinf,'diaginfo_file':diaginf,
'decode_cf':False,'dask':True}
mod_times=[]
if multi:
ds=open_mfbpchdataset(paths,**bpchargs)
for p in paths:
mod_times.append(time.ctime(os.path.getmtime(p)))
else:
ds=open_bpchdataset(path,**bpchargs)
mod_times=[time.ctime(os.path.getmtime(path))]
data,attrs=dataset_to_dicts(ds,keys)
data['modification_times']=np.array(mod_times)
attrs['modification_times']={'desc':'When was file last modified'}
return data,attrs
# lets check for a few dimensions:
for dims in [ [10,1,1], [100,100,100]]:
print("testing dummy trop column calculation for dims %s"%str(dims))
ppbv=np.zeros(dims)+100. # molec/1e9molecA
N_air=np.zeros(dims)+1e13 # molecA/m3
boxH=np.zeros(dims)+1. # m
tplev=np.zeros([dims[1],dims[2]]) + 4.4
# should give 100molec/cm2 per level : tropcol = 440molec/cm2
out= determine_trop_column(ppbv, N_air, boxH, tplev)
assert out.shape==(dims[1],dims[2]), 'trop column calc shape is wrong'
print("PASS: shape is ok from intputs to output")
assert np.isclose(out[0,0],440.0), 'trop column calc value is wrong, out=%f'%out[0,0]
print("PASS: Calculated trop column is OK")
assert np.isclose(np.min(out), np.max(out)), 'Trop column calc has some problem'
print("PASS: every box matches")
if __name__=='__main__':
#get_tropchem_data()
_test_trop_column_calc()
# How to check fields
sat_fold='Data/GC_Output/geos5_2x25_tropchem/satellite_output/'
sat_file=sat_fold+'ts_satellite_omi.20050101.bpch'
tracinf=sat_fold+'tracerinfo.dat'
diaginf=sat_fold+'diaginfo.dat'
ds=open_bpchdataset(sat_file,tracerinfo_file=tracinf,diaginfo_file=diaginf)
ds.keys()
