def plot_ave(
ave,
lat_e,
lon_e,
xticks=np.arange(-180.0, 180.1, 60.0),
yticks=np.arange(-90.0, 90.1, 30.0),
region_limit=[-90.0, -180.0, 90.0, 180.0],
vmin=None,
vmax=None,
units='',
):
""" plot average
(ywang, 04/12/20)
Parameters
----------
ave : 2D array
PCA modes
lat_e : shape (n_lat+1, n_lon+1)
Latitude edges
lon_e : shape (n_lat+1, n_lon+1)
Longitude edges
"""
# begin plot
nrow = 2
ncol = 1
figsize = (8, 6)
projPos = [0]
layout_dict = multiFigure(nrow, ncol, figsize=figsize, projPos=projPos)
fig = layout_dict['fig']
axes = layout_dict['axes']
# plot average
pout = cartopy_plot(lon_e,
lat_e,
ave,
ax=axes[0],
vmin=vmin,
vmax=vmax,
cmap=deepcopy(WhGrYlRd_map),
cbar=True)
pout['cb'].set_label(units)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
panel_tick_label(axes[0:1], ncol, xticks=xticks, yticks=yticks)
axes[0].add_feature(cfeature.BORDERS)
axes[0].add_feature(states_provinces, edgecolor='k', linewidth=0.5)
axes[0].add_feature(cfeature.COASTLINE, zorder=200)
axes[0].set_xlim((region_limit[1], region_limit[3]))
axes[0].set_ylim((region_limit[0], region_limit[2]))
def plot_comparison(
var1,
var2,
lat_e,
lon_e,
figsize=(6, 7),
vmin=None,
vmax=None,
diff_vmin=None,
diff_vmax=None,
cb_diff_ticks=None,
left=0.25,
right=0.75,
top=0.95,
bottom=0.1,
hspace=None,
wspace=None,
cl_color='k',
lw=None,
region_limit=None,
xticks=np.arange(-180, 180.0, 60),
yticks=np.arange(-90, 90.1, 30),
title_dict={},
cmap=deepcopy(WhGrYlRd_map),
diff_cmap=deepcopy(gbcwpry_map),
units='',
cb_ratio=0.03,
cb_y_off=-0.05,
mask_ocean=False,
):
""" Plot var1, var2 and var2 - var1.
(Yi Wang, 09/02/2020)
Parameters
----------
var1 : 2-D numpy array
The first variable.
var2 : 2-D numpy array
The second variable.
lat_e : 2-D numpy array
Latitude edges.
lon_e : 2-D numpy array
Longitude edge.
figsize : 2-element tuple
Figure size
vmin : float or None
Minimum to be plotted for var1 and var2.
If vmin is None, it will be set as the valid minimum of var1
or var2, which is smaller.
vmax : float or None
Similar to vmin, but of maximum
diff_vmin : float or None
Minimum to be plotted for var2 - var1.
diff_vmax : float or None
Maximum to be plotted for var2 = var1.
Returns
-------
"""
# define figure
nrow = 3
ncol = 1
projPos = list(range(nrow * ncol))
layout_dict = multiFigure(nrow,
ncol,
left=left,
right=right,
top=top,
bottom=bottom,
wspace=wspace,
hspace=hspace,
figsize=figsize,
projPos=projPos,
countries=True,
states=True,
coastlines=True,
mask_ocean=mask_ocean)
fig = layout_dict['fig']
axes = layout_dict['axes']
# get min and max for var1 and var2
if vmin is None:
vmin = min(np.min(var1), np.min(var2))
if vmax is None:
vmax = max(np.max(var1), np.max(var2))
# plot var1 and var2
var_list = [var1, var2]
varname_list = ['var1', 'var2']
for i in range(len(var_list)):
ax = axes[i]
var = var_list[i]
title = title_dict.get(varname_list[i], None)
pout = cartopy_plot(lon_e,
lat_e,
var,
ax=ax,
title=title,
cbar=False,
vmin=vmin,
vmax=vmax,
cmap=deepcopy(cmap))
# plot var2 - var1
diff = var2 - var1
ax = axes[2]
title = title_dict.get('diff', None)
diff_pout = cartopy_plot(lon_e,
lat_e,
diff,
ax=ax,
title=title,
cbar=False,
vmin=diff_vmin,
vmax=diff_vmax,
cmap=deepcopy(diff_cmap))
# ticks
panel_tick_label(axes, ncol, xticks=xticks, yticks=yticks)
# set limit
for ax in axes:
if region_limit is not None:
ax.set_xlim((region_limit[1], region_limit[3]))
ax.set_ylim((region_limit[0], region_limit[2]))
# colorbar for var1 and var2
cax1 = h_1_ax(fig, axes[1], ratio=cb_ratio, y_off=cb_y_off)
cb1 = plt.colorbar(pout['mesh'], cax=cax1, orientation='horizontal')
right_center_label(cax1, units)
# colorbar for diff
cax2 = h_1_ax(fig, axes[2], ratio=cb_ratio, y_off=cb_y_off)
cb2 = plt.colorbar(diff_pout['mesh'], cax=cax2, orientation='horizontal')
if cb_diff_ticks is not None:
cb2.set_ticks(cb_diff_ticks)
right_center_label(cax2, units)
def plot_scatter_comparison_2(
var1,
var2,
lat_e,
lon_e,
figsize=(8, 6),
vmin=None,
vmax=None,
diff_vmin=None,
diff_vmax=None,
rel_pct_vmin=None,
rel_pct_vmax=None,
cb_diff_ticks=None,
cb_rel_pct_ticks=None,
extend='neither',
diff_extend='neither',
rel_pct_extend='neither',
left=0.08,
right=0.80,
top=0.95,
bottom=0.1,
hspace=None,
wspace=None,
cl_color='k',
lw=None,
region_limit=None,
xticks=np.arange(-180, 180.0, 60),
yticks=np.arange(-90, 90.1, 30),
title_dict={},
cmap=deepcopy(WhGrYlRd_map),
diff_cmap=deepcopy(gbcwpry_map),
rel_pct_cmap=deepcopy(gbcwpry_map),
units='',
diff_units='',
cb_ratio=0.06,
cb_y_off_t=-0.04,
cb_y_off_b=-0.07,
mask_ocean=False,
):
""" Plot var1, var2 and var2 - var1.
(Yi Wang, 10/21/2020)
Parameters
----------
var1 : 2-D numpy array
The first variable.
var2 : 2-D numpy array
The second variable.
lat_e : 2-D numpy array
Latitude edges.
lon_e : 2-D numpy array
Longitude edge.
figsize : 2-element tuple
Figure size
vmin : float or None
Minimum to be plotted for var1 and var2.
If vmin is None, it will be set as the valid minimum of var1
or var2, which is smaller.
vmax : float or None
Similar to vmin, but of maximum
diff_vmin : float or None
Minimum to be plotted for var2 - var1.
diff_vmax : float or None
Maximum to be plotted for var2 = var1.
rel_pct_vmin : float or None
Minimum to be plotted for (var2 - var1) / var1 * 100
rel_pct_vmax : float or None
Maximum to be plotted for (var2 - var1) / var1 * 100
Returns
-------
"""
# define figure
nrow = 2
ncol = 2
projPos = list(range(nrow * ncol))
layout_dict = multiFigure(nrow,
ncol,
left=left,
right=right,
top=top,
bottom=bottom,
wspace=wspace,
hspace=hspace,
figsize=figsize,
projPos=projPos,
countries=True,
states=True,
coastlines=True,
mask_ocean=mask_ocean)
fig = layout_dict['fig']
axes = layout_dict['axes']
# get min and max for var1 and var2
if vmin is None:
vmin = min(np.nanmin(var1), np.nanmin(var2))
if vmax is None:
vmax = max(np.nanmax(var1), np.nanmax(var2))
# plot var1 and var2
var_list = [var1, var2]
varname_list = ['var1', 'var2']
for i in range(len(var_list)):
ax = axes[i]
var = var_list[i]
title = title_dict.get(varname_list[i], None)
pout = cartopy_plot_scatter(lon_e,
lat_e,
c=var,
ax=ax,
title=title,
cbar=False,
vmin=vmin,
vmax=vmax,
cmap=deepcopy(cmap))
# plot (var2 - var1) / var1 * 100
rel_pct = (var2 - var1) / var1 * 100
if (rel_pct_vmin is None) and (rel_pct_vmax is None):
rel_pct_vmax = np.nanmax(np.absolute(rel_pct))
rel_pct_vmin = -rel_pct_vmax
ax = axes[2]
title = title_dict.get('relative_diff', None)
rel_pct_pout = cartopy_plot_scatter(lon_e,
lat_e,
c=rel_pct,
ax=ax,
title=title,
cbar=False,
vmin=rel_pct_vmin,
vmax=rel_pct_vmax,
cmap=deepcopy(rel_pct_cmap))
# plot var2 - var1
diff = var2 - var1
if (diff_vmin is None) and (diff_vmax is None):
diff_vmax = np.nanmax(np.absolute(diff))
diff_vmin = -diff_vmax
ax = axes[3]
title = title_dict.get('diff', None)
diff_pout = cartopy_plot_scatter(lon_e,
lat_e,
c=diff,
ax=ax,
title=title,
cbar=False,
vmin=diff_vmin,
vmax=diff_vmax,
cmap=deepcopy(diff_cmap))
# ticks
panel_tick_label(axes, ncol, xticks=xticks, yticks=yticks)
# set limit
for ax in axes:
if region_limit is not None:
ax.set_xlim((region_limit[1], region_limit[3]))
ax.set_ylim((region_limit[0], region_limit[2]))
# colorbar for var1 and var2
cax1 = h_2_ax(fig, axes[0], axes[1], ratio=cb_ratio, y_off=cb_y_off_t)
cb1 = plt.colorbar(pout['paths'],
cax=cax1,
orientation='horizontal',
extend=extend)
right_center_label(cax1, units)
# colorbar for (var2 - var1) / var1 * 100
cax2 = h_1_ax(fig, axes[2], ratio=cb_ratio, y_off=cb_y_off_b)
cb2 = plt.colorbar(rel_pct_pout['paths'],
cax=cax2,
orientation='horizontal',
extend=rel_pct_extend)
if cb_rel_pct_ticks is not None:
cb2.set_ticks(cb_rel_pct_ticks)
right_center_label(cax2, '[%]')
# colorbar for diff
cax3 = h_1_ax(fig, axes[3], ratio=cb_ratio, y_off=cb_y_off_b)
cb3 = plt.colorbar(diff_pout['paths'],
cax=cax3,
orientation='horizontal',
extend=diff_extend)
if cb_diff_ticks is not None:
cb3.set_ticks(cb_diff_ticks)
right_center_label(cax3, diff_units)
def plot_2D_components_and_coeffs(
components,
lat_e,
lon_e,
coeffs,
explained_variance_ratio,
fig_dir,
name='',
n_components=5,
xticks=np.arange(-180.0, 180.1, 60.0),
yticks=np.arange(-90.0, 90.1, 30.0),
region_limit=[-90.0, -180.0, 90.0, 180.0],
time_ticks=None,
rotation=30.0,
reverse=[],
):
""" plot 2D PCA spatial pattern
(ywang, 04/12/20)
Parameters
----------
components : shape (n_lat, n_lon, n_components)
PCA modes
lat_e : shape (n_lat+1, n_lon+1)
Latitude edges
lon_e : shape (n_lat+1, n_lon+1)
Longitude edges
coeffs : shape (n_samples, n_components)
PCA coefficients
explained_variance_ratio : shape (n_components, )
Explained variance ratio
fig_dir : str
Directory to save figures
n_components : int
The first *n_component* modes and coeffs will be plotted.
time_ticks : tuple
The first element is tick positions, and the second
element is tick labels.
reverse : list
Mode and time series to be reversed. 1-based
"""
# directory
if fig_dir[-1] != '/':
fig_dir = fig_dir + '/'
# n_components
n_components = min([n_components, components.shape[2], coeffs.shape[1]])
for ic in range(n_components):
# begin plot
nrow = 2
ncol = 1
figsize = (8, 6)
projPos = [0]
layout_dict = multiFigure(nrow, ncol, figsize=figsize, projPos=projPos)
fig = layout_dict['fig']
axes = layout_dict['axes']
# reverse
if (ic + 1) in reverse:
sign = -1
else:
sign = 1
# plot mode
mode = components[:, :, ic] * sign
vmax = np.max(np.absolute(mode))
vmin = -vmax
pout = cartopy_plot(lon_e,
lat_e,
mode,
ax=axes[0],
vmin=vmin,
vmax=vmax,
cmap=deepcopy(gbcwpry_map),
cbar=True)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
axes[0].add_feature(cfeature.BORDERS)
axes[0].add_feature(states_provinces, edgecolor='k', linewidth=0.5)
axes[0].add_feature(cfeature.COASTLINE, zorder=200)
axes[0].set_xlim((region_limit[1], region_limit[3]))
axes[0].set_ylim((region_limit[0], region_limit[2]))
# add title
y_p = 1.03
left_title = 'Mode {}'.format(ic + 1)
axes[0].text(0,
y_p,
left_title,
ha='left',
va='bottom',
transform=axes[0].transAxes)
right_title = '{:.1f}%'.format(explained_variance_ratio[ic] * 100)
axes[0].text(1,
y_p,
right_title,
ha='right',
va='bottom',
transform=axes[0].transAxes)
# ticks
panel_tick_label(axes[0:1], ncol, xticks=xticks, yticks=yticks)
# plot coeff
coeff = coeffs[:, ic] * sign
x = range(len(coeff))
axes[1].plot(x, coeff, '-o', markersize=3)
axes[1].set_xlabel('Month')
if time_ticks is not None:
axes[1].set_xticks(time_ticks[0])
axes[1].set_xticklabels(time_ticks[1], rotation=rotation)
# save figure
if (name != ''):
figname = fig_dir + name + '_mode{}'.format(ic + 1) + '.png'
else:
figname = fig_dir + 'mode{}'.format(ic + 1) + '.png'
plt.savefig(figname, format='png', dpi=300)
def plot_monthly_anomaly_for_6yrs(dir_mean,
dir_month,
start_year,
end_year,
year_list,
month,
verbose=False,
region_limit=[-90.0, -180.0, 90.0, 180.0],
left=0.05,
right=0.98,
top=0.9,
bottom=0.1,
wspace=0.05,
hspace=0.05,
y_off=-0.06,
dir_fig=None,
vmin_ano=-0.1,
vmax_ano=0.1,
xticks=np.arange(-180.0, 180.1, 20.0),
yticks=np.arange(-90.0, 90.1, 10.0),
name='',
vmin_ano_per=-50.0,
vmax_ano_per=50.0):
""" Plot monhtly anomaly for 6 years
"""
# used to convert unit
toDU = 2.69e16
# read monthly data
NO2_month_dict = {}
for year in year_list:
file_month = dir_month + 'OMI_NO2_' + year + '-' + month \
+ '_monthly.nc'
NO2_month = io.read_month_OMI_NO2_L3(file_month, verbose=verbose)
NO2_month = NO2_month / toDU
NO2_month_dict[year] = NO2_month
# read mutli-year mean of monthly NO2
NO2_mean = io.read_multi_year_month_OMI_NO2_L3(dir_mean,
start_year,
end_year,
month,
verbose=verbose)
NO2_mean = NO2_mean / toDU
# NO2 anomaly and relative anoamly
NO2_ano_dict = {}
NO2_ano_percent_dict = {}
for year in year_list:
# anomaly
NO2_ano = NO2_month_dict[year] - NO2_mean
NO2_ano_dict[year] = NO2_ano
# relative anomaly
NO2_ano_percent = NO2_ano / NO2_mean * 100.0
NO2_ano_percent_dict[year] = NO2_ano_percent
# generate grid
nlat_c, nlon_c = 720, 1440
lat_e, lon_e, lat_c, lon_c = generate_grid(nlat_c, nlon_c)
# get region data
i1 = get_center_index(lat_e, region_limit[0])
i2 = get_center_index(lat_e, region_limit[2])
j1 = get_center_index(lon_e, region_limit[1])
j2 = get_center_index(lon_e, region_limit[3])
lat_e = lat_e[i1:i2 + 2]
lon_e = lon_e[j1:j2 + 2]
# begin plot
nrow = 3
ncol = 4
figsize = (12, 6)
projPos = list(range(nrow * ncol))
layout_dict = multiFigure(nrow,
ncol,
left=left,
right=right,
top=top,
bottom=bottom,
wspace=wspace,
hspace=hspace,
figsize=figsize,
projPos=projPos)
fig = layout_dict['fig']
axes = layout_dict['axes']
ano_ax_ind = [0, 1, 4, 5, 8, 9]
ano_per_ax_ind = [2, 3, 6, 7, 10, 11]
pout1_list = []
pout2_list = []
for iyr in range(len(year_list)):
year = year_list[iyr]
# anomaly
NO2_ano = NO2_ano_dict[year][i1:i2 + 1, j1:j2 + 1]
pout1 = cartopy_plot(lon_e, lat_e, NO2_ano, ax=axes[ano_ax_ind[iyr]], \
vmin=vmin_ano, vmax=vmax_ano, cbar=False, \
title=year+month, cmap=plt.get_cmap('seismic'))
pout1_list.append(pout1)
# relative anomaly
NO2_ano_percent = NO2_ano_percent_dict[year][i1:i2 + 1, j1:j2 + 1]
pout2 = cartopy_plot(lon_e, lat_e, NO2_ano_percent, \
ax=axes[ano_per_ax_ind[iyr]], cbar=False, \
vmin=vmin_ano_per, vmax=vmax_ano_per, \
title=year+month, cmap=plt.get_cmap('seismic'))
pout2_list.append(pout2)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
# ticks
panel_tick_label(axes, ncol, xticks=xticks, yticks=yticks)
# set limit
for ax in axes:
ax.set_xlim([lon_e[0], lon_e[-1]])
ax.set_ylim([lat_e[0], lat_e[-1]])
ax.add_feature(cfeature.BORDERS)
ax.add_feature(states_provinces, edgecolor='k', linewidth=0.5)
ax.add_feature(cfeature.COASTLINE, zorder=200)
ax.add_feature(cfeature.OCEAN, color='w', zorder=100)
# anomaly colorbar
cax1 = h_2_ax(fig, pout1_list[4]['ax'], pout1_list[5]['ax'], y_off=y_off)
cb1 = plt.colorbar(pout1_list[4]['mesh'], cax=cax1, \
orientation='horizontal', extend='both')
cb1.set_label('OMI NO2 anomaly [DU]')
# relative anomaly colorbar
cax2 = h_2_ax(fig, pout2_list[4]['ax'], pout2_list[5]['ax'], y_off=y_off)
cb2 = plt.colorbar(pout2_list[4]['mesh'], cax=cax2, \
orientation='horizontal', extend='both')
cb2.set_label('OMI NO2 relative anomaly [%]')
# save figure
if dir_fig is not None:
figname = dir_fig + name + 'NO2_anomaly_6yrs_' + month \
+ '_from_' + start_year + '-' + end_year + '.png'
plt.savefig(figname, format='png', dpi=300)