def mom_preproc_files(data_path, output_dir, analysis_period, payu_style):
# Get Mom data
if payu_style:
all_mom_files = glob.glob(data_path + '/output*' + '/ocean/ocean.nc')
all_mom_scalar_files = glob.glob(data_path + '/output*' + '/ocean/ocean_scalar.nc')
else:
all_mom_files = glob.glob(data_path + '/ocn/ocean_month.nc-*')
all_mom_scalar_files = glob.glob(data_path + '/ocn/ocean_scalar.nc-*')
# Weed out any files which we don't have permission to read.
mom_data_files = [f for f in all_mom_files if os.access(f, os.R_OK)]
mom_scalar_data_files = [f for f in all_mom_scalar_files if os.access(f, os.R_OK)]
assert(mom_data_files != [])
assert(mom_scalar_data_files != [])
# Get outputfiles for the last x years.
x_years_files, date_str, _ = get_last_x_years(mom_data_files, analysis_period)
total_mean_file = os.path.join(output_dir,
'ocean_mean_{}.nc'.format(date_str))
monthly_mean_file = os.path.join(output_dir,
'ocean_monthly_mean_{}.nc'.format(date_str))
return (mom_data_files, mom_scalar_data_files, x_years_files,
total_mean_file, monthly_mean_file)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input_files', nargs='+',
help="The input data file in NetCDF format.")
parser.add_argument('--preproc_dir', default='./',
help='Directory where preprocessed files are placed.')
parser.add_argument('--output_file', default='amoc.png',
help="""The name of the AMOC output file.""")
parser.add_argument('--global_output_file', default=None,
help="""
The name of the output file for the global moc.
The global MOC is only calculated if this is set.
""")
parser.add_argument('--output_atlantic_mask', action='store_true',
default=False,
help="""
Output the mask file used to define the Atlantic basin.
""")
args = parser.parse_args()
with nc.Dataset(args.input_files[0]) as f:
lons = f.variables['geolon_t'][:]
lats = f.variables['geolat_t'][:]
times = f.variables['time'][:]
if len(args.input_files) == 1 and len(times) == 1:
mean_file = args.input_files[0]
else:
# Calculate a mean if one doesn't already exist.
# Figure out output filename based on input files
files, date_str, runtime = get_last_x_years(args.input_files, -1)
mean_file = os.path.join(args.preproc_dir,
'ocean_mean_{}.nc'.format(date_str))
if not os.path.exists(mean_file):
vars = ['ty_trans', 'dzt', 'geolon_t', 'geolat_t']
create_output_file(args.input_files[0], vars, mean_file + '.tmp')
calc_overall_mean(args.input_files, ['ty_trans', 'dzt'], mean_file + '.tmp')
shutil.move(mean_file + '.tmp', mean_file)
with nc.Dataset(mean_file) as f:
ty_trans = f.variables['ty_trans'][0, :, :, :]
dzt = f.variables['dzt'][0, :, :, :]
plot_atlantic_moc(ty_trans, dzt, lons, lats, args.output_file,
args.output_atlantic_mask, date_str)
if args.global_output_file is not None:
plot_global_moc(ty_trans, dzt, lons, lats,
args.global_output_file, date_str)
return 0
def cice_preproc_files(data_path, output_dir, analysis_period, payu_style):
# Get CICE data
if payu_style:
data_files = glob.glob(data_path + '/output*' + '/ice/HISTORY/iceh.*.nc')
else:
data_files = glob.glob(data_path + '/ice/iceh.*.nc')
# Filter out any files which we don't have permission to read.
data_files = [f for f in data_files if os.access(f, os.R_OK)]
assert(data_files != [])
x_years_files, date_str, runtime = get_last_x_years(data_files,
analysis_period, model='ice')
monthly_mean_file = os.path.join(output_dir,
'ice_monthly_mean_{}.nc'.format(date_str))
total_mean_file = os.path.join(output_dir,
'ice_mean_{}.nc'.format(date_str))
return (data_files, x_years_files, monthly_mean_file, total_mean_file, runtime)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input_files", nargs="+", help="The MOM ocean.nc input data files.")
parser.add_argument("--preproc_dir", default="./", help="Directory where preprocessed files are placed.")
parser.add_argument("--output_file", default="nino34.png", help="Name of plot to output.")
args = parser.parse_args()
# Figure out output filename based on input files
files, date_str, runtime = get_last_x_years(args.input_files, 30)
monthly_mean_file = os.path.join(args.preproc_dir, "ocean_monthly_mean_{}.nc".format(date_str))
# Calculate the monthly means over the last 30 years of the time series
mom_monthly_mean(files, monthly_mean_file, ["temp"])
lat_start_idx, lat_end_idx, lon_start_idx, lon_end_idx = get_indices_for_nino34_region(monthly_mean_file)
# Grab surface temp spatial mean in the nino34 region
with nc.Dataset(monthly_mean_file) as f:
temp_var = f.variables["temp"]
mean_ssts = []
for t in range(temp_var.shape[0]):
sst = np.mean(temp_var[t, 0, lat_start_idx:lat_end_idx, lon_start_idx:lon_end_idx])
mean_ssts.append(sst)
assert len(mean_ssts) == 12
# Split up the job and make the timeseries.
args_len = len(args.input_files)
results = futures.map(
make_nino34_timeseries,
args.input_files,
[mean_ssts] * args_len,
[lat_start_idx] * args_len,
[lat_end_idx] * args_len,
[lon_start_idx] * args_len,
[lon_end_idx] * args_len,
)
ts = pd.Series()
for t in results:
ts = ts.append(t)
# Plot timeseries' with a 5 month running mean
ts = ts.sort_index()
ts_rm = pd.rolling_mean(ts, window=5)
# Convert periods to datetime
dates = [datetime.date(pi.year, pi.month, pi.day) for pi in ts.index]
fig, axarr = plt.subplots(2, sharex=True)
# FIXME: this assumes the data is monthly, an assumption should be checked
# above but is not.
smooth_years = 10.0
smooth_fraction = smooth_years * 12 / len(ts.values)
lowess = sm.nonparametric.lowess(ts.values, range(len(ts.index)), frac=smooth_fraction)
x_lowess = lowess[:, 0]
y_lowess = lowess[:, 1]
axarr[0].plot(dates, ts.values, "red", lw=1.0)
axarr[0].plot(dates, y_lowess, "red", lw=3.0, label="Lowess 10 yr")
axarr[0].set_ylabel("Temperature Anomaly (C)")
axarr[0].set_title("Decadal Temperature Anomaly Trend in Nino3.4 Region")
axarr[0].legend()
# Include information about monotonically decreasing segments.
text = "Mostly decreasing segments:\n"
mdss = monotonically_decreasing_segments(x_lowess, y_lowess, tolerance=24)
for i in range(5):
temp_change = max(y_lowess[mdss[i]]) - min(y_lowess[mdss[i]])
text += "{0:.2f} deg C around {1} to {2}\n".format(temp_change, dates[mdss[i][0]], dates[mdss[i][-1]])
print(text)
props = dict(boxstyle="round", facecolor="wheat", alpha=0.5)
axarr[0].text(0.05, 0.95, text, transform=axarr[0].transAxes, verticalalignment="top", bbox=props)
axarr[1].fill_between(dates, 0, ts_rm.values, where=ts_rm.values >= 0, facecolor="blue", interpolate=True)
axarr[1].fill_between(dates, 0, ts_rm.values, where=ts_rm.values <= 0, facecolor="red", interpolate=True)
axarr[1].set_ylabel("Temperature Anomaly (C)")
axarr[1].set_xlabel("Time (years)")
axarr[1].set_title("Temperature Anomalies in Nino3.4 Region")
fig.set_size_inches(16, 10)
plt.tight_layout()
plt.savefig(args.output_file)
plt.close()