def read_plot_timeseries(var,
file_path,
precomputed=False,
grid=None,
lon0=None,
lat0=None,
fig_name=None,
monthly=True,
legend_in_centre=False,
dpi=None):
# Set parameters (only care about title and units)
title, units = set_parameters(var)[2:4]
if precomputed:
# Read the time array; don't need to back up one month
time = netcdf_time(file_path, monthly=False)
if var.endswith('mass_balance'):
if precomputed:
# Read the fields from the timeseries file
shelf = var[:var.index('_mass_balance')]
melt = read_netcdf(file_path, shelf + '_total_melt')
freeze = read_netcdf(file_path, shelf + '_total_freeze')
else:
# Calculate the timeseries from the MITgcm file(s)
time, melt, freeze = calc_special_timeseries(var,
file_path,
grid=grid,
monthly=monthly)
timeseries_multi_plot(time, [melt, freeze, melt + freeze],
['Melting', 'Freezing', 'Net'],
['red', 'blue', 'black'],
title=title,
units=units,
monthly=monthly,
fig_name=fig_name,
dpi=dpi,
legend_in_centre=legend_in_centre)
else:
if precomputed:
data = read_netcdf(file_path, var)
else:
time, data = calc_special_timeseries(var,
file_path,
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)
make_timeseries_plot(time,
data,
title=title,
units=units,
monthly=monthly,
fig_name=fig_name,
dpi=dpi)
def precompute_timeseries (mit_file, timeseries_file, timeseries_types=None, monthly=True, lon0=None, lat0=None, key='PAS'):
# Timeseries to compute
if timeseries_types is None:
if key == 'WSS':
timeseries_types = ['fris_mass_balance', 'eta_avg', 'seaice_area', 'fris_temp', 'fris_salt', 'fris_age']
elif key == 'WSK':
timeseries_types = ['fris_mass_balance', 'hice_corner', 'mld_ewed', 'eta_avg', 'seaice_area', 'fris_temp', 'fris_salt']
elif key == 'PAS':
timeseries_types = ['all_massloss', 'eta_avg', 'seaice_area']
# Build the grid
grid = Grid(mit_file)
# Set up or update the file and time axis
id = set_update_file(timeseries_file, grid, 't')
num_time = set_update_time(id, mit_file, monthly=monthly)
# Now process all the timeseries
for ts_name in timeseries_types:
print 'Processing ' + ts_name
# Get information about the variable; only care about title and units
title, units = set_parameters(ts_name)[2:4]
if ts_name == 'fris_mass_balance':
melt, freeze = calc_special_timeseries(ts_name, mit_file, grid=grid, monthly=monthly)[1:]
# We need two titles now
title_melt = 'Total melting beneath FRIS'
title_freeze = 'Total refreezing beneath FRIS'
# Update two variables
set_update_var(id, num_time, melt, 't', 'fris_total_melt', title_melt, units)
set_update_var(id, num_time, freeze, 't', 'fris_total_freeze', title_freeze, units)
else:
data = calc_special_timeseries(ts_name, mit_file, grid=grid, lon0=lon0, lat0=lat0, monthly=monthly)[1]
set_update_var(id, num_time, data, 't', ts_name, title, units)
# Finished
if isinstance(id, nc.Dataset):
id.close()
elif isinstance(id, NCfile):
id.close()
def precompute_timeseries (mit_file, timeseries_file, timeseries_types=None, monthly=True, lon0=None, lat0=None):
# Timeseries to compute
if timeseries_types is None:
timeseries_types = ['fris_mass_balance', 'eta_avg', 'seaice_area', 'fris_temp', 'fris_salt', 'fris_age'] #['fris_mass_balance', 'hice_corner', 'mld_ewed', 'eta_avg', 'seaice_area', 'fris_temp', 'fris_salt']
# Build the grid
grid = Grid(mit_file)
# Check if the timeseries file already exists
file_exists = os.path.isfile(timeseries_file)
if file_exists:
# Open it
id = nc.Dataset(timeseries_file, 'a')
else:
# Create it
ncfile = NCfile(timeseries_file, grid, 't')
# Define/update time
# Read the time array from the MITgcm file, and its units
time, time_units = netcdf_time(mit_file, return_units=True)
if file_exists:
# Update the units to match the old time array
time_units = id.variables['time'].units
# Also figure out how many time indices are in the file so far
num_time = id.variables['time'].size
# Convert to numeric values
time = nc.date2num(time, time_units)
# Append to file
id.variables['time'][num_time:] = time
else:
# Add the time variable to the file
ncfile.add_time(time, units=time_units)
# Inner function to define/update non-time variables
def write_var (data, var_name, title, units):
if file_exists:
# Append to file
id.variables[var_name][num_time:] = data
else:
# Add the variable to the file
ncfile.add_variable(var_name, data, 't', long_name=title, units=units)
# Now process all the timeseries
for ts_name in timeseries_types:
print 'Processing ' + ts_name
# Get information about the variable; only care about title and units
title, units = set_parameters(ts_name)[2:4]
if ts_name == 'fris_mass_balance':
melt, freeze = calc_special_timeseries(ts_name, mit_file, grid=grid, monthly=monthly)[1:]
# We need two titles now
title_melt = 'Total melting beneath FRIS'
title_freeze = 'Total refreezing beneath FRIS'
# Update two variables
write_var(melt, 'fris_total_melt', title_melt, units)
write_var(freeze, 'fris_total_freeze', title_freeze, units)
else:
data = calc_special_timeseries(ts_name, mit_file, grid=grid, lon0=lon0, lat0=lat0, monthly=monthly)[1]
write_var(data, ts_name, title, units)
# Finished
if file_exists:
id.close()
else:
ncfile.close()
def read_plot_timeseries_multi(var_names,
file_path,
diff=False,
precomputed=False,
grid=None,
lon0=None,
lat0=None,
fig_name=None,
monthly=True,
legend_in_centre=False,
dpi=None,
colours=None):
if diff:
if not isinstance(file_path, list) or len(file_path) != 2:
print 'Error (read_plot_timeseries_multi): must pass a list of 2 file paths when diff=True.'
sys.exit()
file_path_1 = file_path[0]
file_path_2 = file_path[1]
time_1 = netcdf_time(file_path_1,
monthly=(monthly and not precomputed))
time_2 = netcdf_time(file_path_2,
monthly=(monthly and not precomputed))
time = trim_and_diff(time_1, time_2, time_1, time_2)[0]
else:
time = netcdf_time(file_path, monthly=(monthly and not precomputed))
data = []
labels = []
units = None
if colours is None:
colours = [
'blue', 'red', 'black', 'green', 'cyan', 'magenta', 'yellow'
]
if len(var_names) > len(colours):
print 'Error (read_plot_timeseries_multi): need to specify colours if there are more than 7 variables.'
sys.exit()
colours = colours[:len(var_names)]
for var in var_names:
if var.endswith('mass_balance'):
print 'Error (read_plot_timeseries_multi): ' + var + ' is already a multi-plot by itself.'
sys.exit()
title, units_tmp = set_parameters(var)[2:4]
labels.append(title)
if units is None:
units = units_tmp
elif units != units_tmp:
print 'Error (read_plot_timeseries_multi): units do not match for all timeseries variables'
sys.exit()
if precomputed:
if diff:
data_1 = read_netcdf(file_path_1, var)
data_2 = read_netcdf(file_path_2, var)
data.append(trim_and_diff(time_1, time_2, data_1, data_2)[1])
else:
data.append(read_netcdf(file_path, var))
else:
if diff:
data.append(
calc_special_timeseries_diff(var,
file_path_1,
file_path_2,
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)[1])
else:
data.append(
calc_special_timeseries(var,
file_path,
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)[1])
title, labels = trim_titles(labels)
if diff:
title = 'Change in ' + title
timeseries_multi_plot(time,
data,
labels,
colours,
title=title,
units=units,
monthly=monthly,
fig_name=fig_name,
dpi=dpi,
legend_in_centre=legend_in_centre)
def read_plot_timeseries_ensemble(var_name,
file_paths,
sim_names=None,
precomputed=False,
grid=None,
lon0=None,
lat0=None,
plot_mean=False,
first_in_mean=True,
annual_average=False,
time_use=0,
colours=None,
linestyles=None,
fig_name=None,
monthly=True,
legend_in_centre=False,
dpi=None,
smooth=0,
title=None,
units=None,
print_mean=False,
operator='add',
vline=None,
alpha=False,
plot_anomaly=False,
base_year_start=None,
base_year_end=None,
trim_before=False,
base_year_start_first=None,
percent=False,
year_ticks=None):
if isinstance(var_name, str):
var_name = [var_name]
if (plot_anomaly or percent) and (base_year_start is None
or base_year_end is None):
print 'Error (read_plot_timeseries_ensemble): must set base_year_start and base_year_end'
sys.exit()
# Read data
all_times = []
all_datas = []
for f in file_paths:
data = None
for var in var_name:
if var.endswith('mass_balance'):
print 'Error (read_plot_timeseries_ensemble): This function does not work for mass balance terms.'
sys.exit()
if precomputed:
time = netcdf_time(f, monthly=False)
data_tmp = read_netcdf(f, var)
else:
time, data_tmp = calc_special_timeseries(var,
f,
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)
if data is None:
data = data_tmp
else:
if operator == 'add':
data += data_tmp
elif operator == 'subtract':
data -= data_tmp
else:
print 'Error (read_plot_timeseries_ensemble): invalid operator ' + operator
sys.exit()
if plot_anomaly or percent or trim_before:
# Find the time indices that define the baseline period
if time[0].year > base_year_start:
if (not plot_anomaly) and (not percent):
# This is ok
t_start = 0
if base_year_start_first is not None and f == file_paths[0]:
# A tighter constraint on start year
t_start = index_year_start(time, base_year_start_first)
else:
print 'Error (read_plot_timeseries_ensemble): this simulation does not cover the baseline period'
sys.exit()
else:
t_start, t_end = index_period(time, base_year_start,
base_year_end)
# Calculate the mean over that period
data_mean = np.mean(data[t_start:t_end])
if plot_anomaly:
# Subtract the mean
data -= data_mean
if percent:
# Express as percentage of mean
data = data / data_mean * 100
if trim_before:
# Trim everything before the baseline period
data = data[t_start:]
time = time[t_start:]
all_times.append(time)
all_datas.append(data)
if time_use is None:
time = all_times
else:
# Make sure all simulations are the same length, and then choose one time axis to use
if any([t.size != all_times[0].size for t in all_times]):
print 'Error (read_plot_timeseries_ensemble): not all the simulations are the same length.'
sys.exit()
time = all_times[time_use]
if annual_average:
# Make sure it's an integer number of 30-day months
calendar = netcdf_time(file_paths[0], return_units=True)[2]
if calendar != '360_day' or not monthly or time.size % 12 != 0:
print 'Error (read_plot_timeseries_ensemble): can only do true annual averages if there are an integer number of 30-day months.'
sys.exit()
time, all_datas = calc_annual_averages(time, all_datas)
if smooth != 0:
for n in range(len(all_datas)):
if time_use is None:
data_tmp, time_tmp = moving_average(all_datas[n],
smooth,
time=time[n])
time[n] = time_tmp
else:
data_tmp, time_tmp = moving_average(all_datas[n],
smooth,
time=time)
all_datas[n] = data_tmp
if time_use is not None:
time = time_tmp
# Set other things for plot
if len(var_name) == 1:
title, units = set_parameters(var_name[0])[2:4]
elif title is None or units is None:
print 'Error (read_plot_timeseries_ensemble): must set title and units'
sys.exit()
if percent:
units = '% of ' + str(base_year_start) + '-' + str(
base_year_end) + ' mean'
if plot_anomaly:
title += ' \n(anomaly from ' + str(base_year_start) + '-' + str(
base_year_end) + ' mean)'
if colours is None:
colours = default_colours(len(file_paths))
if alpha:
alphas = [1] + [0.5 for n in range(len(file_paths) - 1)]
else:
alphas = None
if plot_mean:
if first_in_mean:
n0 = 0
else:
n0 = 1
if time_use is None and any(
[t.size != all_times[n0].size for t in all_times[n0:]]):
print 'Error (read_plot_timeseries_ensemble): can only calculate mean if simulations are same length.'
sys.exit()
# Calculate the mean
all_datas.append(np.mean(all_datas[n0:], axis=0))
all_times.append(all_times[n0])
if len(colours) != len(all_datas):
# Choose a colour
# First replace any black in the colours array
if 'black' in colours:
colours[colours.index('black')] = (0.4, 0.4, 0.4)
colours.append('black')
if alphas is not None:
alphas.append(1)
if sim_names is not None:
sim_names.append('Mean')
if print_mean:
print 'Mean values for ' + title + ':'
for data, sim in zip(all_datas, sim_names):
print sim + ': ' + str(np.mean(data)) + ' ' + units
timeseries_multi_plot(time,
all_datas,
sim_names,
colours,
title=title,
units=units,
monthly=monthly,
fig_name=fig_name,
dpi=dpi,
legend_in_centre=legend_in_centre,
thick_last=plot_mean,
thick_first=(plot_mean and not first_in_mean),
linestyles=linestyles,
alphas=alphas,
first_on_top=(plot_mean and not first_in_mean),
vline=vline,
year_ticks=year_ticks)
def read_plot_timeseries_multi(var_names,
file_path,
diff=False,
precomputed=False,
grid=None,
lon0=None,
lat0=None,
fig_name=None,
monthly=True,
legend_in_centre=False,
dpi=None,
colours=None,
smooth=0,
annual_average=False):
if diff and (not isinstance(file_path, list) or len(file_path) != 2):
print 'Error (read_plot_timeseries_multi): must pass a list of 2 file paths when diff=True.'
sys.exit()
if precomputed:
# Read time arrays
if diff:
time_1 = netcdf_time(file_path[0], monthly=False)
time_2 = netcdf_time(file_path[1], monthly=False)
time = trim_and_diff(time_1, time_2, time_1, time_2)[0]
else:
time = netcdf_time(file_path, monthly=False)
# Set up the colours
if colours is None:
colours = default_colours(len(var_names))
data = []
labels = []
units = None
if annual_average:
time_orig = np.copy(time)
# Loop over variables
for var in var_names:
if var.endswith('mass_balance'):
print 'Error (read_plot_timeseries_multi): ' + var + ' is already a multi-plot by itself.'
sys.exit()
title, units_tmp = set_parameters(var)[2:4]
labels.append(title)
if units is None:
units = units_tmp
elif units != units_tmp:
print 'Error (read_plot_timeseries_multi): units do not match for all timeseries variables'
sys.exit()
if precomputed:
if diff:
data_1 = read_netcdf(file_path[0], var)
data_2 = read_netcdf(file_path[1], var)
data_tmp = trim_and_diff(time_1, time_2, data_1, data_2)[1]
else:
data_tmp = read_netcdf(file_path, var)
else:
if diff:
time, data_tmp = calc_special_timeseries_diff(var,
file_path[0],
file_path[1],
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)
else:
time, data_tmp = calc_special_timeseries(var,
file_path,
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)
if annual_average:
time, data_tmp = calc_annual_averages(time_orig, data_tmp)
data.append(data_tmp)
for n in range(len(data)):
data_tmp, time_tmp = moving_average(data[n], smooth, time=time)
data[n] = data_tmp
time = time_tmp
title, labels = trim_titles(labels)
if diff:
title = 'Change in ' + title[0].lower() + title[1:]
timeseries_multi_plot(time,
data,
labels,
colours,
title=title,
units=units,
monthly=monthly,
fig_name=fig_name,
dpi=dpi,
legend_in_centre=legend_in_centre)
def read_plot_timeseries(var,
file_path,
diff=False,
precomputed=False,
grid=None,
lon0=None,
lat0=None,
fig_name=None,
monthly=True,
legend_in_centre=False,
dpi=None,
annual_average=False,
smooth=0):
if diff and (not isinstance(file_path, list) or len(file_path) != 2):
print 'Error (read_plot_timeseries): must pass a list of 2 file paths when diff=True.'
sys.exit()
if precomputed:
# Read time arrays
if diff:
time_1 = netcdf_time(file_path[0],
monthly=(monthly and not precomputed))
time_2 = netcdf_time(file_path[1],
monthly=(monthly and not precomputed))
calendar = netcdf_time(file_path[0], return_units=True)[2]
time = trim_and_diff(time_1, time_2, time_1, time_2)[0]
else:
time = netcdf_time(file_path,
monthly=(monthly and not precomputed))
calendar = netcdf_time(file_path, return_units=True)[2]
# Set parameters (only care about title and units)
title, units = set_parameters(var)[2:4]
if diff:
title = 'Change in ' + title[0].lower() + title[1:]
# Inner function to read a timeseries from both files and calculate the differences, trimming if needed. Only useful if precomputed=True.
def read_and_trim(var_name):
data_1 = read_netcdf(file_path[0], var_name)
data_2 = read_netcdf(file_path[1], var_name)
data_diff = trim_and_diff(time_1, time_2, data_1, data_2)[1]
return data_diff
if var.endswith('mass_balance'):
if precomputed:
# Read the fields from the timeseries file
shelf = var[:var.index('_mass_balance')]
if diff:
melt = read_and_trim(shelf + '_total_melt')
freeze = read_and_trim(shelf + '_total_freeze')
else:
melt = read_netcdf(file_path, shelf + '_total_melt')
freeze = read_netcdf(file_path, shelf + '_total_freeze')
else:
# Calculate the timeseries from the MITgcm file(s)
if diff:
time, melt, freeze = calc_special_timeseries_diff(
var,
file_path[0],
file_path[1],
grid=grid,
monthly=monthly)
else:
time, melt, freeze = calc_special_timeseries(var,
file_path,
grid=grid,
monthly=monthly)
if annual_average:
time, [melt, freeze] = calc_annual_averages(time, [melt, freeze])
melt = moving_average(melt, smooth, time=time)[0]
freeze, time = moving_average(freeze, smooth, time=time)
timeseries_multi_plot(time, [melt, freeze, melt + freeze],
['Melting', 'Freezing', 'Net'],
['red', 'blue', 'black'],
title=title,
units=units,
monthly=monthly,
fig_name=fig_name,
dpi=dpi,
legend_in_centre=legend_in_centre)
else:
if precomputed:
if diff:
data = read_and_trim(var)
else:
data = read_netcdf(file_path, var)
else:
if diff:
time, data = calc_special_timeseries_diff(var,
file_path[0],
file_path[1],
grid=grid,
monthly=monthly)
else:
time, data = calc_special_timeseries(var,
file_path,
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)
if annual_average:
time, data = calc_annual_averages(time, data)
data, time = moving_average(data, smooth, time=time)
make_timeseries_plot(time,
data,
title=title,
units=units,
monthly=monthly,
fig_name=fig_name,
dpi=dpi)