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_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_diff(var,
file_path_1,
file_path_2,
precomputed=False,
grid=None,
lon0=None,
lat0=None,
fig_name=None,
monthly=True):
# Set parameters (only care about title and units)
title, units = set_parameters(var)[2:4]
# Edit the title to show it's a difference plot
title = 'Change in ' + title[0].lower() + title[1:]
if precomputed:
# Read the time arrays
time_1 = netcdf_time(file_path_1, monthly=False)
time_2 = netcdf_time(file_path_2, monthly=False)
# 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_1, var_name)
data_2 = read_netcdf(file_path_2, var_name)
time, data_diff = trim_and_diff(time_1, time_2, data_1, data_2)
return time, data_diff
if var.endswith('mass_balance'):
if precomputed:
shelf = var[:var.index('_mass_balance')]
time, melt_diff = read_and_trim(shelf + '_total_melt')
freeze_diff = read_and_trim(shelf + '_total_freeze')[1]
else:
# Calculate the difference timeseries
time, melt_diff, freeze_diff = calc_special_timeseries_diff(
var, file_path_1, file_path_2, grid=grid, monthly=monthly)
timeseries_multi_plot(
time, [melt_diff, freeze_diff, melt_diff + freeze_diff], [
'Change in melting\n(>0)', 'Change in freezing\n(<0)',
'Change in net'
], ['red', 'blue', 'black'],
title=title,
units=units,
monthly=monthly,
fig_name=fig_name)
else:
if precomputed:
time, data_diff = read_and_trim(var)
else:
time, data_diff = calc_special_timeseries_diff(var,
file_path_1,
file_path_2,
grid=grid,
lon0=lon0,
lat0=lat0,
monthly=monthly)
make_timeseries_plot(time,
data_diff,
title=title,
units=units,
monthly=monthly,
fig_name=fig_name)
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)