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plot_paper_fig_2.py
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"""
A first attempt at quickly plotting fields for the SWIFT paper, test 1
"""
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from netCDF4 import Dataset
import numpy as np
from tomplot import (set_tomplot_style, tomplot_contours, tomplot_cmap,
plot_contoured_field, add_colorbar_fig,
tomplot_field_title, extract_lfric_coords,
extract_lfric_field, plot_field_quivers,
regrid_horizontal_slice)
# ---------------------------------------------------------------------------- #
# Things that can be altered and parameters for the test case
# ---------------------------------------------------------------------------- #
# Directories for results and to plot to
branch = 'swift_revision'
results_dirname = f'/data/users/tbendall/results/{branch}'
plotdir = f'/data/users/tbendall/results/swift_revision'
mesh_dt = 'BiP128x128-1000x1000_2p0'
# To alter test-by-test
test_number = 2
fig_idx = 2
plot_stem = 'const_wind'
title = r'Test 1: Varying $\rho$, Constant $u$, $c_{max}=2.56$'
# Options relating to the different fields to be plotted
field_names = ['rho', 'tracer_con', 'tracer_adv']
cbar_labels = [r"$\rho$ (kg m$^{-3}$)",
r"$m$ (kg kg$^{-1}$)",
r"$m^L$ (kg kg$^{-1}$)"]
colour_schemes = ['YlGn', 'BuPu', 'BuPu']
remove_contours = [0.8, 0.0, 0.0]
# Options for different configurations
test_configs = ['cosmic', 'swift']
config_titles = ['COSMIC', 'SWIFT']
# Options shared for all plots and subplots
time_factor = 25.0 # For some reason LFRic outputs the wrong time
xlabel = r'$x \ /$ km'
ylabel = r'$y \ /$ km'
contour_method = 'tricontour'
time_idxs = [1]
level = 0
xlims = [-0.5, 0.5]
ylims = [-0.5, 0.5]
vector_magnitude_cutoff = 0.01
spatial_filter_step = 5
contour_dict = {'rho': np.linspace(0.5, 1.1, 13),
'tracer_adv': np.linspace(-0.2, 1.2, 15),
'tracer_con': np.linspace(-0.2, 1.2, 15)}
# This is declared BEFORE figure and ax are initialised
set_tomplot_style(16)
# ---------------------------------------------------------------------------- #
# Loop through the different time-points
# ---------------------------------------------------------------------------- #
for time_idx in time_idxs:
# Make figure
fig, axarray = plt.subplots(len(config_titles), len(field_names),
figsize=(12, 8), sharex='col', sharey='row')
all_cf = []
for i, (field_name, colour_scheme, remove_contour) in \
enumerate(zip(field_names, colour_schemes, remove_contours)):
# -------------------------------------------------------------------- #
# Loop through subplots
# -------------------------------------------------------------------- #
for j, (results_opt, config_title) in \
enumerate(zip(test_configs, config_titles)):
ax = axarray.flatten()[j*len(field_names)+i]
main_filename = f'{results_dirname}/{results_opt}_test_{test_number}_{mesh_dt}_diag.nc'
data_file = Dataset(main_filename, 'r')
# Extract coords
time = time_factor*data_file['time_instant'][time_idx]
coords_X, coords_Y = extract_lfric_coords(data_file, field_name)
field_data = extract_lfric_field(data_file, field_name, time_idx, level=level)
# Scale coordinates
coords_X *= 10.
coords_Y *= 10.
# ---------------------------------------------------------------- #
# Plot data
# ---------------------------------------------------------------- #
# Contours based on data for whole row
contours = contour_dict[field_name]
cmap, lines = tomplot_cmap(contours, colour_scheme,
remove_contour=remove_contour,
cmap_rescale_type='top', cmap_rescaling=0.75)
cf, _ = plot_contoured_field(ax, coords_X, coords_Y, field_data,
contour_method, contours, line_contours=lines,
cmap=cmap, plot_contour_lines=True)
# Save cf for later
all_cf.append(cf)
# ---------------------------------------------------------------- #
# Add wind vectors
# ---------------------------------------------------------------- #
if field_name == 'rho':
coords_X_raw, coords_Y_raw = extract_lfric_coords(data_file, 'u_in_w2h')
# Scale coordinates
coords_X_raw *= 10.
coords_Y_raw *= 10.
field_data_X_raw = extract_lfric_field(data_file, 'u_in_w2h', time_idx, level)
field_data_Y_raw = extract_lfric_field(data_file, 'v_in_w2h', time_idx, level)
# To be able to filter plotted vectors nicely, regrid to 2d-array
new_coords_X, new_coords_Y = \
np.meshgrid(np.linspace(xlims[0], xlims[1], 50),
np.linspace(ylims[0], ylims[1], 50), indexing='ij')
field_data_X = regrid_horizontal_slice(new_coords_X, new_coords_Y,
coords_X_raw, coords_Y_raw,
field_data_X_raw)
field_data_Y = regrid_horizontal_slice(new_coords_X, new_coords_Y,
coords_X_raw, coords_Y_raw,
field_data_Y_raw)
field_data_mag = np.sqrt(field_data_X**2 + field_data_Y**2)
# Quivers
_ = plot_field_quivers(ax, new_coords_X, new_coords_Y, field_data_X, field_data_Y,
scale=250, minlength=vector_magnitude_cutoff,
magnitude_filter=vector_magnitude_cutoff,
spatial_filter_step=spatial_filter_step)
data_file.close()
# ---------------------------------------------------------------- #
# Add labels
# ---------------------------------------------------------------- #
if i == 0:
ax.set_ylabel(ylabel, labelpad=-20)
ax.set_yticks(ylims)
ax.set_yticklabels(ylims)
ax.set_ylim(ylims)
if j == len(config_titles) - 1:
ax.set_xlabel(xlabel, labelpad=-12)
ax.set_xticks(xlims)
ax.set_xticklabels(xlims)
ax.set_xlim(xlims)
tomplot_field_title(ax, None, minmax=True, field_data=field_data,
minmax_format='.4f')
if i == len(field_names)-1:
ax2 = ax.twinx()
ax2.set_yticks([])
text = f'{config_title}, '+r'$t=$'+f' {time:0.0f} s'
ax2.set_ylabel(text, labelpad=10)
# -------------------------------------------------------------------- #
# Add colorbars and adjust figures
# -------------------------------------------------------------------- #
for i, cbar_label in enumerate(cbar_labels):
ij = (len(config_titles)-1)*len(field_names) + i
add_colorbar_fig(fig, all_cf[i*len(config_titles)+j], cbar_label,
ax_idxs=[ij], cbar_labelpad=-10,
location='bottom', cbar_thickness=0.025)
fig.subplots_adjust(hspace=0.2)
# -------------------------------------------------------------------- #
# Save figure
# -------------------------------------------------------------------- #
plt.suptitle(f'{title}')
plotname = f'{plotdir}/fig_{fig_idx}_{plot_stem}.jpg'
print(f'Saving figure to {plotname}')
fig.savefig(plotname, bbox_inches='tight', dpi=300)
plt.close()