def test_ar07w(datafiles, out):
ar07w = pd.read_csv(FIXTURE_DIR + 'AR07W_stations.txt',
skipinitialspace=True)
ds = xr.open_dataset(FIXTURE_DIR + 'woa_labrador.nc', decode_times=False)
proj = lib_easy_coloc.projection(ar07w['lon'].values,
ar07w['lat'].values,
grid=ds)
fld = proj.run(ds['t_an'][:], outtype=out)
if out == 'ndarray':
assert isinstance(fld, np.ndarray)
assert fld.shape == (1, 102, 30)
elif out == 'dataframe':
assert isinstance(fld, pd.DataFrame)
return None
# shift dates to middle of the month
ds['time'] = pd.date_range(start=f'{ds.time.dt.year[0].values}-{ds.time.dt.month[0].values:02}',
end=f'{ds.time.dt.year[-1].values}-{ds.time.dt.month[-1].values:02}',
freq='MS')
# ==========================================
# Here we start making the ovar dataset
# ==========================================
# Trim the dates to sample_dates
ovar = ds[ovar_name].sel(time=sample_dates)
ovar['lat'] = ds.latitude
ovar['lon'] = ds.longitude
# create source grid and target section objects
# this requires lon,lat from stations and the source grid dataset containing lon,lat
proj = lib_easy_coloc.projection(df['longitude'].values,df['latitude'].values,grid=ovar,
from_global=True)
# 4-D max for easy_coloc. Not entirely sure what we are squeezing out?
ovar = ovar.squeeze()
# run the projection on the WOA analyzed temperature (t_an)
fld = np.zeros((len(sample_dates),len(ovar.lev),len(df)))
#
for ind in range(5, 130, 5):
dates = sample_dates[ind-5:ind]
fld_tem = proj.run(ovar.sel(time=dates)[:])
fld[ind-5:ind,:,:] = fld_tem
# create datarray with sampling information
sampled_var = xr.DataArray(fld,
def model_to_glodap(ovar_name=None,
model=None,
catalog_path='../catalogs/pangeo-cmip6.json',
qc_path='../qc'):
'''
generate_model_section(ovar_name, model)
Input
==========
ovar_name : variable name (eg 'dissic')
model : model name (eg CanESM5)
Output
===========
ds : dataset of section output
'''
institue = {
'CanESM5': 'CCCma',
'CNRM-ESM2-1': 'CNRM-CERFACS',
'IPSL-CM6A-LR': 'IPSL',
'MIROC-ES2L': 'MIROC',
'UKESM1-0-LL': 'MOHC',
'GISS-E2-1-G-CC': 'NASA-GISS',
'GISS-E2-1-G': 'NASA-GISS'
}
# Get CMIP6 output from intake_esm
col = intake.open_esm_datastore(catalog_path)
cat = col.search(experiment_id='historical',
table_id='Omon',
variable_id=ovar_name,
grid_label='gn')
# dictionary of subset data
dset_dict = cat.to_dataset_dict(zarr_kwargs={'consolidated': True},
cdf_kwargs={'chunks': {}})
# Put data into dataset
ds = dset_dict[f'CMIP.{institue[model]}.{model}.historical.Omon.gn']
# Rename olevel to lev
coord_dict = {
'olevel': 'lev'
} # a dictionary for converting coordinate names
if 'olevel' in ds.dims:
ds = ds.rename(coord_dict)
# load GLODAP station information from csv file
# drop nans, reset index, and drop uneeded variable
df = pd.read_csv(f'{qc_path}/GLODAPv2.2019_COORDS.csv')
df = df.dropna()
df = df.reset_index().drop('Unnamed: 0', axis=1)
# Genearte times list and put into dataframe
times = [
f'{int(year)}-{int(month):02d}'
for year, month in zip(df.year, df.month)
]
df['dates'] = times
# Find unique dates, these are the sample dates
sample_dates = df['dates'].sort_values().unique()
# Parse the historical period
sample_dates = sample_dates[0:125]
sample_dates = [
dateutil.parser.parse(date) - pd.Timedelta('16 day')
for date in sample_dates
]
# shift dates to middle of the month
ds['time'] = pd.date_range(
start=f'{ds.time.dt.year[0].values}-{ds.time.dt.month[0].values:02}',
end=f'{ds.time.dt.year[-1].values}-{ds.time.dt.month[-1].values:02}',
freq='MS')
# ==========================================
# Here we start making the ovar dataset
# ==========================================
# Trim the dates to sample_dates
ovar = ds[ovar_name].sel(time=sample_dates)
ovar['lat'] = ds.latitude
ovar['lon'] = ds.longitude
# create source grid and target section objects
# this requires lon,lat from stations and the source grid dataset containing lon,lat
proj = lib_easy_coloc.projection(df['longitude'].values,
df['latitude'].values,
grid=ovar,
from_global=True)
# 4-D max for easy_coloc. Not entirely sure what we are squeezing out?
ovar = ovar.squeeze()
# run the projection on the WOA analyzed temperature (t_an)
fld = np.zeros((len(sample_dates), len(ovar.lev), len(df)))
#
for ind in range(5, 130, 5):
dates = sample_dates[ind - 5:ind]
fld_tem = proj.run(ovar.sel(time=dates)[:])
fld[ind - 5:ind, :, :] = fld_tem
# create datarray with sampling information
sampled_var = xr.DataArray(fld,
dims=['time', 'lev', 'all_stations'],
coords={
'time': ovar['time'],
'lev': ovar['lev'],
'all_stations': df.index.values,
'dx': ('all_stations', df.dx.values),
'bearing':
('all_stations', df.bearing.values),
'lat': ('all_stations', df.latitude.values),
'lon':
('all_stations', df.longitude.values),
},
attrs={
'units': ovar.units,
'long_name': ovar.long_name
})
# Glodap expo codes
expc = pd.read_csv(f'{qc_path}/FILTERED_GLODAP_EXPOCODE.csv')
# convert datarray to dataset
# This grabs everything
ds = sampled_var.to_dataset(name=ovar.name)
return ds
def model_to_glodap(ovar_name=None,
model=None,
catalog_path='../catalogs/pangeo-cmip6.json',
qc_path='../qc',
output_path='../../sections/'):
"""Interpolates model to GLODAP points.
This function samples the model as GLODAP and writes the
resampled data to disk. Runtime of about <5 minutes per
model.
Temporal sampling is done as though every cruise was conduct-
ed at the same time. Temporal sampling is adjusted to match
cruises with model_to_section, among other things.
This function must be run before model_to_section, but only
needs to be run once
Args:
ovar_name: ocean variable name
model: name of CMIP6 model
catalog_path: path to catalog used by intake-esm
qc_path: location of qc'd model sections
output_path: where the output is written
Returns:
xarray Dataset
"""
# Get CMIP6 output from intake-esm
col = intake.open_esm_datastore(catalog_path)
cat = col.search(experiment_id='historical',
table_id='Omon',
source_id=model,
variable_id=ovar_name,
grid_label='gn')
# dictionary of xarray datasets
dset_dict = cat.to_dataset_dict(zarr_kwargs={'consolidated': True},
cdf_kwargs={'chunks': {}})
# we need to know the intitute that ran the model to get the correct xarray dataset
model_institute_df = cat.df.drop_duplicates(subset='source_id')[['source_id','institution_id']]
institute = model_institute_df.institution_id[model_institute_df.source_id==model].iloc[0]
# get the xarray dataset for the corresponding model
ds = dset_dict[f'CMIP.{institute}.{model}.historical.Omon.gn']
# CMIP6 files were submitted with inconsistent coordinate names
# make coordinate names consistent by renaming
coord_rename_map = make_rename_map(ds,model)
ds = ds.rename(coord_rename_map[model])
# load GLODAP station information from csv file
# drop nans, reset index, and drop uneeded variable
df = pd.read_csv(f'{qc_path}/GLODAPv2.2019_COORDS.csv')
df = df.dropna()
df = df.reset_index().drop('Unnamed: 0', axis=1)
# Generate list of dates from the separate year and month columns and put into dataframe
dates = [f'{int(year)}-{int(month):02d}-01' for year,month in zip(df.year,df.month)]
df['dates'] = dates
# Find unique dates, these are the sample dates
sample_dates = df['dates'].sort_values().unique()
# Look only at the historical period
# convert to datetime
sample_dates = sample_dates[0:125]
sample_dates = [dateutil.parser.parse(date) for date in sample_dates]
# homogenize model dates to first of the month
ds['time'] = pd.date_range(start=f'{ds.time.dt.year[0].values}-{ds.time.dt.month[0].values:02}',
end=f'{ds.time.dt.year[-1].values}-{ds.time.dt.month[-1].values:02}',
freq='MS')
# ==========================================
# Here we start making the ovar dataset
# ==========================================
# Trim model dates to sample_dates
ovar = ds[ovar_name].sel(time=sample_dates)
ovar['latitude'] = ds.latitude
ovar['longitude'] = ds.longitude
# create source grid and target section objects
# this requires lon,lat from stations and the source grid dataset containing 'longitude','latitude'
proj = lib_easy_coloc.projection(df['longitude'].values,df['latitude'].values,grid=ovar,coord_names=['longitude', 'latitude'],
from_global=True)
# get the realization (ex: r10i1p1f1)
realizations = cat.df[cat.df['source_id']==model].member_id.values
# len(realizations) gives the number of ensemble members
# if block for models with only one ensemble member in the database
if len(realizations) < 2:
fld = np.zeros((len(sample_dates),len(ovar.lev),len(df)))
ovar = ovar.squeeze()
for ind in range(5, 130, 5):
dates = sample_dates[ind-5:ind]
fld_tem = proj.run(ovar.sel(time=dates)[:])
fld[ind-5:ind,:,:] = fld_tem
# create datarray with sampling information
sampled_var = xr.DataArray(fld,
dims=['time','lev','all_stations'],
coords={'time':ovar['time'],
'lev':ovar['lev'],
'all_stations':df.index.values,
'dx':('all_stations',df.dx.values),
'bearing':('all_stations',df.bearing.values),
'lat':('all_stations',df.latitude.values),
'lon':('all_stations',df.longitude.values),
},
attrs={'units':ovar.units,
'long_name':ovar.long_name
}
)
ds = sampled_var.to_dataset(name=ovar.name)
ds.to_netcdf(f'{output_path}/{ovar.name}_{model}_{realizations[0]}.nc')
# right now, if there are multiple ensemble members, we only sample one
if len(realizations) > 2:
fld = np.zeros((len(sample_dates),len(ovar.lev),len(df)))
ovar = ovar[0,].squeeze()
for ind in range(5, 130, 5):
dates = sample_dates[ind-5:ind]
fld_tem = proj.run(ovar.sel(time=dates)[:])
fld[ind-5:ind,:,:] = fld_tem
# create datarray with sampling information
sampled_var = xr.DataArray(fld,
dims=['time','lev','all_stations'],
coords={'time':ovar['time'],
'lev':ovar['lev'],
'all_stations':df.index.values,
'dx':('all_stations',df.dx.values),
'bearing':('all_stations',df.bearing.values),
'lat':('all_stations',df.latitude.values),
'lon':('all_stations',df.longitude.values),
},
attrs={'units':ovar.units,
'long_name':ovar.long_name
}
)
ds = sampled_var.to_dataset(name=ovar.name)
ds.to_netcdf(f'{output_path}/{ovar.name}_{model}_{realizations[0]}.nc')
from easy_coloc import lib_easy_coloc
import xarray as xr
import pandas as pd
import cartopy as cart
import matplotlib.pylab as plt
from matplotlib import cm
# load stations information from csv file
ar07w = pd.read_csv('../easy_coloc/test/test_files/AR07W_stations.txt',skipinitialspace=True)
# load gridded dataset
ds = xr.open_dataset('../easy_coloc/test/test_files/woa_labrador.nc',decode_times=False)
# create source grid and target section objects
# this requires lon,lat from stations and the source grid dataset containing lon,lat
proj = lib_easy_coloc.projection(ar07w['lon'].values,ar07w['lat'].values,grid=ds,
from_global=False)
# run the projection on the WOA analyzed temperature (t_an)
fld = proj.run(ds['t_an'][:])
plt.figure(figsize=[6,6])
m = plt.axes(projection=cart.crs.PlateCarree())
m.scatter(ar07w['lon'].values,ar07w['lat'].values,c=fld[0,0,:])
m.coastlines()
m.add_feature(cart.feature.LAND, facecolor='0.75')
m.set_extent([-75, -35, 35, 65], crs=cart.crs.PlateCarree())
gl = m.gridlines(draw_labels=True)
plt.figure(figsize=[6,6])
plt.contourf(ar07w['lat'].values,-ds['depth'],fld[0,:,:],30,cmap=cm.gist_ncar)
import xmitgcm
import numpy as np
# load stations information from csv file
ar07w = pd.read_csv('../data/AR07W_stations.txt',skipinitialspace=True)
# load gridded dataset
ds = xmitgcm.open_mdsdataset('../data/global_oce_llc90/',prefix=['T'],geometry='llc')
# quick look at the input data, the face we need for AR07W is #10
#ds['T'].sel(face=10,k=0,time=8).plot(cmap=cm.gist_ncar); plt.show()
# create source grid and target section objects
# this requires lon,lat from stations and the source grid dataset containing lon,lat
# here subsetting face 10 of ds and passing the names of lon/lat coords in ds
proj = lib_easy_coloc.projection(ar07w['lon'].values,ar07w['lat'].values,grid=ds.sel(face=10),
coord_names=['XC','YC'],from_global=False)
# run the projection on the WOA analyzed temperature (t_an)
fld = proj.run(ds['T'].sel(face=10),mask_value=0)
plt.figure(figsize=[6,6])
m = plt.axes(projection=cart.crs.PlateCarree())
m.scatter(ar07w['lon'].values,ar07w['lat'].values,c=fld[0,0,:])
m.coastlines()
m.add_feature(cart.feature.LAND, facecolor='0.75')
m.set_extent([-75, -35, 35, 65], crs=cart.crs.PlateCarree())
gl = m.gridlines(draw_labels=True)
plt.figure(figsize=[6,6])
plt.contourf(ar07w['lat'].values,ds['Z'],np.ma.masked_values(fld[0,:,:],0),30,cmap=cm.gist_ncar)
plt.colorbar()