def plot_two_variables(lat1,
lon1,
var1,
lat2,
lon2,
var2,
figsize=None,
region_limit=None,
cl_res=None,
xtick=np.arange(-180, 180.1, 5),
ytick=np.arange(-90, 90.1, 5),
xtick1=None,
ytick1=None,
xtick2=None,
ytick2=None,
title1=None,
title2=None,
vmin1=None,
vmax1=None,
vmin2=None,
vmax2=None,
cl_color2='black',
lw1=1,
lw2=1,
unit1='',
unit2='',
y_offset=-0.08,
cmap1=None,
cmap2=None):
""" Plot two variables
Parameters
----------
"""
out_data = {}
out_data['pout'] = []
# figure and ax_list
if figsize is None:
figsize = (10, 5)
fig = plt.figure(figsize=figsize)
ax_list = []
# ticks
if xtick1 is None:
xtick1 = xtick
if ytick1 is None:
ytick1 = ytick
if xtick2 is None:
xtick2 = xtick
if ytick2 is None:
ytick2 = []
# colorbar paramters
h = 0.03
# plot var1
ax1 = add_geoaxes(fig,
121,
title=title1,
xtick=xtick1,
ytick=ytick1,
cl_res=cl_res,
lw=lw1)
pout1 = cartopy_plot(lon1,
lat1,
var1,
ax=ax1,
vmin=vmin1,
vmax=vmax1,
cbar=False,
cmap=cmap1)
ax1.reset_position()
ax_list.append(ax1)
out_data['pout'].append(pout1)
pos1 = pout1['ax'].get_position()
cax1 = fig.add_axes([pos1.x0, pos1.y0 + y_offset, pos1.width, h])
cb1 = plt.colorbar(pout1['mesh'],
cax=cax1,
orientation='horizontal',
extend='max')
cb1.set_label(unit1)
# plot var2
ax2 = add_geoaxes(fig,
122,
title=title2,
xtick=xtick2,
ytick=ytick2,
cl_res=cl_res,
lw=lw2,
cl_color=cl_color2)
ax_list.append(ax2)
pout2 = cartopy_plot(lon2,
lat2,
var2,
ax=ax2,
vmin=vmin2,
vmax=vmax2,
cbar=False,
cmap=cmap2)
out_data['pout'].append(pout2)
pos2 = pout2['ax'].get_position()
cax2 = fig.add_axes([pos2.x0, pos2.y0 + y_offset, pos2.width, h])
cb2 = plt.colorbar(pout2['mesh'],
cax=cax2,
orientation='horizontal',
extend='max')
cb2.set_label(unit2)
# set region limit
if region_limit is not None:
for i in range(len(ax_list)):
ax = ax_list[i]
ax.set_xlim(region_limit[1], region_limit[3])
ax.set_ylim(region_limit[0], region_limit[2])
return out_data
import matplotlib.pyplot as plt
import numpy as np
from mylib.cartopy_plot import add_geoaxes, pcolormesh
from mylib.pro_modis.read_modis import get_modis_1km_rgb
#filename = './MYD021KM.A2015344.0725.006.2015344193947.hdf'
filename = 'MYD021KM.A2018301.1825.061.2018302153109.hdf'
# get data
lat, lon, rgb = get_modis_1km_rgb(filename)
# plot
fig = plt.figure()
xtick = np.arange(-180, 180.1, 5)
ytick = np.arange(-90, 90.1, 5)
ax = add_geoaxes(fig, 111, xtick=xtick, ytick=ytick)
title = '.'.join(filename.split('/')[-1].split('.')[0:4])
pcolormesh(ax, lon, lat, rgb, title=title)
figname = './' + title + '.png'
plt.savefig(figname, format='png', dpi=300)
# get latitude and longitude
lon_c, lat_c = np.meshgrid(mo_lon, mo_lat)
lat_e, lon_e = calculate_pixel_edge2(lat_c, lon_c)
#print(np.min(lat_c), np.max(lat_c))
#print(np.min(lon_c), np.max(lon_c))
#
#print(np.min(lat_e), np.max(lat_e))
#print(np.min(lon_e), np.max(lon_e))
# fig
fig = plt.figure(figsize=(6,7))
# average of AOD
ave_AOD = np.mean(AOD, axis=0)
ax1 = add_geoaxes(fig, 211)
cb1_prop = dict()
cb1_prop['orientation'] = 'horizontal'
cb1_prop['shrink'] = 0.7
cb1_prop['aspect'] = 25
cb1_prop['pad'] = 0.12
cb1_prop['extend'] = 'max'
res1 = pcolormesh(ax1, lon_e, lat_e, ave_AOD, vmin=0.0, vmax=1.0, cbar=True, cbar_prop=cb1_prop)
cb1 = res1['cb']
cb1.set_label('Average AOD [unitless]')
# AOD trend
trend = np.zeros(ave_AOD.shape)
trend_sigma = np.zeros(ave_AOD.shape)
for i in range(ave_AOD.shape[0]):
def compare_two_varilables(filename, varname1, varname2,
vmin=0.0, vmax=0.8,
diff_min=None, diff_max=None,
seperate_cbar=False,
region_limit=None,
sc_xlabel='',
sc_ylabel='',
sc_max=1.0,
verbose=True):
"""
"""
# read data
coord_varns = ['Latitude_e', 'Longitude_e']
some_varns = [varname1, varname2]
varnames = coord_varns + some_varns
data_dict = read_nc(filename, varnames, verbose=verbose)
# region_limit
if region_limit is not None:
lat_e_1D = data_dict['Latitude_e'][:,0]
lon_e_1D = data_dict['Longitude_e'][0,:]
# index
i1 = get_center_index(lat_e_1D, region_limit[0])
i2 = get_center_index(lat_e_1D, region_limit[2])
j1 = get_center_index(lon_e_1D, region_limit[1])
j2 = get_center_index(lon_e_1D, region_limit[3])
for varn in varnames:
if varn in coord_varns:
data_dict[varn] = data_dict[varn][i1:i2+2,j1:j2+2]
if varn in some_varns:
data_dict[varn] = data_dict[varn][i1:i2+1,j1:j2+1]
# plot
fig = plt.figure(figsize=(8,7))
plt.subplots_adjust(top=0.98)
ax_list = []
xtick = [100, 110, 120]
ytick = [30, 40, 50]
xtick_list = [xtick, xtick, xtick]
ytick_list = [ytick, [], ytick]
for i in range(len(xtick_list)):
ax = add_geoaxes(fig, int('22'+str(i+1)),
xtick=xtick_list[i], ytick=ytick_list[i])
ax_list.append(ax)
lat_e = data_dict['Latitude_e']
lon_e = data_dict['Longitude_e']
# colorbar height
h = 0.02
# variables
for i in range(len(some_varns)):
varn = some_varns[i]
var = data_dict[varn]
ax = ax_list[i]
var_out = cartopy_plot(lon_e, lat_e, var, ax=ax,
vmin=vmin, vmax=vmax,
cbar=seperate_cbar, cmap=deepcopy(WhGrYlRd_map))
add_China_province(ax)
# colorbar for variables
ax1 = ax_list[0]
ax2 = ax_list[1]
cax_v = h_2_ax(fig, ax1, ax2, y_off=-0.06, height=h)
plt.colorbar(var_out['mesh'], cax=cax_v, orientation='horizontal')
right_center_label(cax_v, '[DU]')
# difference
ax = ax_list[2]
var_diff = data_dict[varname2] - data_dict[varname1]
diff_out = cartopy_plot(lon_e, lat_e, var_diff, ax=ax,
vmin=diff_min, vmax=diff_max,
cbar=seperate_cbar, cmap=plt.get_cmap('seismic'))
add_China_province(ax)
# colorbar of difference
ax = ax_list[2]
cax_diff = h_1_ax(fig, ax, y_off=-0.06, height=h)
plt.colorbar(diff_out['mesh'], cax=cax_diff, orientation='horizontal')
right_center_label(cax_diff, '[DU]')
# scatter plot
ax_sc = fig.add_subplot(224)
ax_sc.set_aspect('equal')
ax_sc.set_xlabel(sc_xlabel)
ax_sc.set_ylabel(sc_ylabel)
ax_sc.set_xlim([0,1.0])
ax_sc.set_ylim([0,1.0])
label_ul = ['R', 'linear_eq', 'rmse', 'nmb', 'mb', 'N']
scatter(ax_sc, data_dict[varname1], data_dict[varname2], s=3,
label_ul=label_ul)
# region
if region_limit is not None:
for ax in ax_list:
ax.set_xlim(region_limit[1], region_limit[3])
ax.set_ylim(region_limit[0], region_limit[2])
def plot_NO2_combine(dir_mean,
dir_month,
start_year,
end_year,
year,
month,
verbose=False,
region_limit=[-90.0, -180.0, 90.0, 180.0],
dir_fig=None,
vmin_mean=0.0,
vmax_mean=0.5,
vmin_ano=-0.1,
vmax_ano=0.1,
vmin_ano_per=-50.0,
vmax_ano_per=50.0):
""" Plot mutli-year mean of monthly NO2, monthly NO2
anomaly and monthly NO2 relative anomaly.
Parameters
----------
dir_mean : str
Directory of mutli-year mean of monthly NO2
dir_month : str
Directory of monthly NO2
start_year : str
Used to generate filename and longname
end_year : str
Used to generate filename and longname
year : str
Used to generate filename and longname
month : str
Used to generate filename and longname
verbose : logical
output more information
region_limit : list
The region for plot. [min_lat, min_lon, max_lat, max_lon]
dir_fig : str or None
If it is str, save figure to the directory
"""
# used to convert unit
toDU = 2.69e16
# colorbar parameters
pad = 0.01
aspect = 15
# read monthly data
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
# 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 anoanmly
NO2_ano = NO2_month - NO2_mean
# NO2 realtive anoamly
NO2_ano_percent = NO2_ano / NO2_mean * 100.0
# 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])
NO2_mean = NO2_mean[i1:i2 + 1, j1:j2 + 1]
NO2_ano = NO2_ano[i1:i2 + 1, j1:j2 + 1]
NO2_ano_percent = NO2_ano_percent[i1:i2 + 1, j1:j2 + 1]
lat_e = lat_e[i1:i2 + 2]
lon_e = lon_e[j1:j2 + 2]
# begin plot
#fig = plt.figure(figsize=(4, 8))
fig = plt.figure()
xtick = np.arange(-180, 180.1, 20)
ytick = np.arange(-90, 90.1, 10)
ax_list = []
# mutli-year mean of monthly NO2
title1 = start_year + '-' + end_year + ', ' + month \
+ r', NO$_2$'
ax1 = add_geoaxes(fig, 311, xtick=[], ytick=ytick, title=title1)
ax_list.append(ax1)
pout1 = pcolormesh(ax1,
lon_e,
lat_e,
NO2_mean,
vmin=vmin_mean,
vmax=vmax_mean)
cbar1 = fig.colorbar(pout1['mesh'],
ax=ax1,
extend='max',
pad=pad,
aspect=aspect)
cbar1.set_label(r'[DU]')
# anomaly
title2 = year + ', ' + month + ', anomaly'
ax2 = add_geoaxes(fig, 312, xtick=[], ytick=ytick, title=title2)
ax_list.append(ax2)
pout2 = pcolormesh(ax2,
lon_e,
lat_e,
NO2_ano,
cmap=plt.get_cmap('seismic'),
vmin=vmin_ano,
vmax=vmax_ano)
cbar2 = fig.colorbar(pout2['mesh'],
ax=ax2,
extend='both',
pad=pad,
aspect=aspect)
cbar2.set_label(r'[DU]')
# relative anomaly
title3 = year + ', ' + month + ', relative anomaly'
ax3 = add_geoaxes(fig, 313, xtick=xtick, ytick=ytick, title=title3)
ax_list.append(ax3)
pout3 = pcolormesh(ax3,
lon_e,
lat_e,
NO2_ano_percent,
cmap=plt.get_cmap('seismic'),
vmin=vmin_ano_per,
vmax=vmax_ano_per)
cbar3 = fig.colorbar(pout3['mesh'],
ax=ax3,
extend='both',
pad=pad,
aspect=aspect)
cbar3.set_label(r'[%]')
# set axes
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
for ax in ax_list:
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)
ax.set_xlim((region_limit[1], region_limit[3]))
ax.set_ylim((region_limit[0], region_limit[2]))
plt.subplots_adjust(hspace=0.3)
# save figure
if dir_fig is not None:
figname = dir_fig + 'NO2_anomaly_' + year + '-' + month \
+ '_from_' + start_year + '-' + end_year + '.png'
plt.savefig(figname, format='png', dpi=300)
return fig