def run_all_plots():
#***********************
# MODIS - centre
cubelist = iris.load(
os.path.join(data_loc,
"MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR)))
for c, cube in enumerate(cubelist):
if cube.name() == "SOS":
sos_cube = cubelist[c]
# deal with NANS
sos_cube.data = np.ma.masked_where(sos_cube.data != sos_cube.data,
sos_cube.data)
# read in sites
us_locations = read_us_phenocam(os.path.join(data_loc,
"phenocam_locs.txt"))
fig = plt.figure(figsize=(8, 12))
plt.clf()
# set up plot settings
BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]
LABELS = "(c) Start of Season (SOS)" #, "(b) End of Season (EOS)"]
# boundary circle
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# axes for polar plot
ax = plt.axes(
[0.05, 0.01, 0.9, 0.65],
projection=cartopy.crs.NorthPolarStereo(central_longitude=300.0))
plot_cube = sos_cube
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print(
"Regridding cube for {} output to {} degree resolution".format(
settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
cmap = settings.COLOURMAP_DICT["phenological_r"]
norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N)
mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax)
# plot scatter
COL = "yellow"
ax.scatter(us_locations[1],
us_locations[0],
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
ax.scatter(-2.9376,
54.3739,
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
# uk box
region = [-10.0, 49.0, 3.0, 60.0]
ax.plot([region[0], region[0]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[2], region[2]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[0], region[2]], [region[1], region[1]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
ax.plot([region[0], region[2]], [region[3], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
COL = "k"
ax.plot([region[0], region[0]], [region[1], region[3]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.PlateCarree())
ax.plot([region[2], region[2]], [region[1], region[3]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.PlateCarree())
ax.plot([region[0], region[2]], [region[1], region[1]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.Geodetic())
ax.plot([region[0], region[2]], [region[3], region[3]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.Geodetic())
# label axes
ax.text(-0.1,
1.0,
LABELS,
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
cb = plt.colorbar(mesh,
orientation='horizontal',
ticks=BOUNDS[1:-1],
label="Anomaly (days)",
drawedges=True,
fraction=0.1,
pad=0.05,
aspect=15,
shrink=0.8)
# prettify
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree())
for lat in range(30, 100, 10):
ax.text(180,
lat,
'{}$^\circ$N'.format(lat),
transform=cartopy.crs.Geodetic())
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
del sos_cube
del cubelist
#***********************
# MODIS timeseries - 2018
sos_na, sos_ea, sprt_na_orig, sprt_ea_orig = read_modis_ts(
os.path.join(
data_loc,
"MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(settings.YEAR)))
dummy, sos_na = utils.calculate_climatology_and_anomalies_1d(
sos_na, 2000, 2010)
dummy, sos_ea = utils.calculate_climatology_and_anomalies_1d(
sos_ea, 2000, 2010)
dummy, sprt_na = utils.calculate_climatology_and_anomalies_1d(
sprt_na_orig, 2000, 2010)
dummy, sprt_ea = utils.calculate_climatology_and_anomalies_1d(
sprt_ea_orig, 2000, 2010)
ax = plt.axes([0.1, 0.7, 0.8, 0.15])
label = "(b) North America"
anomalies = [
"2018 SOS Anomaly = 1.86 days",
"2018 Spring T anomaly = -0.75 " + r'$^{\circ}$' + "C"
]
plot_modis_ts(ax, sos_na, sprt_na, sprt_na_orig, label, anomalies,
LEGEND_LOC)
na_trend = -0.64 / 10
ax.plot([sos_na.times[0], sos_na.times[-1]],
utils.trendline(na_trend, sos_na.times),
ls="--",
zorder=10,
c="g",
lw=2)
print(utils.trendline(na_trend, sos_na.times))
ax1 = plt.axes([0.1, 0.85, 0.8, 0.15], sharex=ax)
label = "(a) Eurasia"
anomalies = [
"2018 SOS Anomaly = 2.01 days",
"2018 Spring T anomaly = 0.13 " + r'$^{\circ}$' + "C"
]
plot_modis_ts(ax1, sos_ea, sprt_ea, sprt_ea_orig, label, anomalies, "")
ea_trend = -1.59 / 10
ax1.plot([sos_ea.times[0], sos_ea.times[-1]],
utils.trendline(ea_trend, sos_ea.times),
ls="--",
zorder=10,
c="g",
lw=2)
print(utils.trendline(ea_trend, sos_ea.times))
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible=True)
fig.text(0.05, 0.92, "SOS Anomaly (days)", rotation="vertical")
fig.text(0.95,
0.92,
"Temperature Anomaly (" + r'$^{\circ}$' + "C)",
rotation="vertical")
plt.savefig(image_loc +
"PHEN_modis_{}{}".format(settings.YEAR, settings.OUTFMT))
#***********************
# US timeseries - 2018
fig = plt.figure(figsize=(8, 10))
plt.clf()
barrow, ozarks, turkey = read_us_phenocam_csv(
os.path.join(data_loc, "Richardson PhenoCam plots for Sidebar.csv"))
ax = plt.axes([0.1, 0.03, 0.35, 0.17])
plot_us_phenocam(ax, barrow, [160, 229], "Barrow")
ax.text(0.05, 0.85, "(e) Barrow", transform=ax.transAxes)
ax = plt.axes([0.1, 0.23, 0.35, 0.17])
plot_us_phenocam(ax,
ozarks, [90, 159],
"Ozarks",
ylabel="Vegetation Greenness Index",
legend=True)
ax.text(0.05, 0.85, "(d) Ozarks", transform=ax.transAxes)
ax = plt.axes([0.1, 0.43, 0.35, 0.17])
plot_us_phenocam(ax, turkey, [60, 129], "Turkey Point")
ax.text(0.05, 0.85, "(c) Turkey Point", transform=ax.transAxes)
ax = plt.axes([0.48, 0.02, 0.25, 0.2])
plot_images(ax, "barrow_2017_06_27_100002.jpg")
ax = plt.axes([0.48, 0.22, 0.25, 0.2])
plot_images(ax, "missouriozarks_2017_04_30_163113.jpg")
ax = plt.axes([0.48, 0.42, 0.25, 0.2])
plot_images(ax, "turkeypointenf39_2017_04_18_113106.jpg")
ax = plt.axes([0.73, 0.02, 0.25, 0.2])
plot_images(ax, "barrow_2018_06_27_103003.jpg")
ax = plt.axes([0.73, 0.22, 0.25, 0.2])
plot_images(ax, "missouriozarks_2018_04_30_163113.jpg")
ax = plt.axes([0.73, 0.42, 0.25, 0.2])
plot_images(ax, "turkeypointenf39_2018_04_18_113302.jpg")
plt.figtext(0.6, 0.02, "2017")
plt.figtext(0.85, 0.02, "2018")
#***********************
# UK timeseries - 2018
ax = plt.axes([0.1, 0.65, 0.85, 0.15])
oak = read_uk_oak_csv(
os.path.join(data_loc, "UK Oak budburst 2000-2018.csv"))
utils.plot_ts_panel(ax, [oak], "-", "phenological", loc="lower left")
ax.set_ylim([94, 124])
ax.text(0.03, 0.8, "(b) Oak Budburst", transform=ax.transAxes)
# fix the legend label to be italic
handles, labels = ax.get_legend_handles_labels()
labels[
0] = "$\mathregular{\mathit{Quercus}}$" + " " + "$\mathregular{\mathit{robur}}$"
ax.legend(handles, labels, frameon=False, loc="lower left")
# need legend once have other panel's data
north, south = read_windermere_csv(
os.path.join(data_loc, "Windermere_2000-2018.csv"))
ax2 = plt.axes([0.1, 0.8, 0.85, 0.15], sharex=ax)
utils.plot_ts_panel(ax2, [north, south],
"-",
"phenological",
loc="lower right")
ax2.set_ylim([79, 159])
plt.setp(ax2.get_xticklabels(), visible=False)
ax2.text(0.03, 0.8, "(a) Windermere", transform=ax2.transAxes)
ax2.set_xlim([1998, 2020])
ax.set_xlim([1998, 2020])
fig.text(0.03, 0.835, "Day of year", rotation="vertical")
plt.savefig(image_loc +
"PHEN_timeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries plot
if True:
monthly = read_data(DATALOC + "monthlist", "AOD monthly")
annual = read_data(DATALOC + "yearlist", "AOD annual")
minor_tick_interval = 1
minorLocator = MultipleLocator(minor_tick_interval)
COLOURS = settings.COLOURS["composition"]
fig = plt.figure(figsize=(8, 5))
ax = plt.axes([0.13, 0.07, 0.85, 0.86])
plt.plot(monthly.times,
monthly.data,
COLOURS[monthly.name],
ls='-',
label=monthly.name,
lw=LW)
plt.plot(annual.times,
annual.data,
COLOURS[annual.name],
ls='-',
label=annual.name,
lw=LW)
ax.legend(loc=LEGEND_LOC,
ncol=1,
frameon=False,
prop={'size': settings.LEGEND_FONTSIZE},
labelspacing=0.1,
columnspacing=0.5)
# prettify
ax.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax)
fig.text(0.03,
0.5,
"AOD",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
plt.xlim([2002.5, int(settings.YEAR) + 1.5])
plt.ylim([0.09, 0.21])
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "ASL_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Global maps
# total
if True:
total = iris.load(DATALOC + "diffAOD.nc") #.format(settings.YEAR))
bounds = np.array([
-10, -0.15, -0.10, -0.05, -0.025, -0.01, 0.01, 0.025, 0.05, 0.10,
0.15, 10
])
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_total_anomalies_{}".format(settings.YEAR),
total[0][0], settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 2003-{} (AOD)".format(settings.YEAR[2:]))
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_total_anomalies_{}".format(settings.YEAR),
total[0][0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1),
figtext="(x) Total Aerosol")
# extreme AOD
if True:
extreme = iris.load(DATALOC + "NB999.nc") #.format(settings.YEAR))
bounds = np.array([0, 2.5, 5, 10, 20, 30, 40, 90])
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_extreme_days_{}".format(settings.YEAR),
extreme[0][0], plt.cm.YlOrBr, bounds,
"Number of days with AOD above the 99.9th percentile (days)")
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_extreme_days_{}".format(settings.YEAR),
extreme[0][0],
plt.cm.YlOrBr,
bounds,
"Number of days with AOD above the 99.9th percentile (days)",
figtext="(y) Extreme Aerosol Days")
# Biomass Burning
if False:
BB = iris.load(DATALOC + "diffBB.nc") #.format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_BB_anomalies_{}".format(settings.YEAR),
BB[0], settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1))
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_BB_anomalies_{}".format(settings.YEAR),
BB[0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1),
figtext="(y) Black Carbon & Organic Matter Aerosol")
# Dust
if False:
dust = iris.load(DATALOC + "diffDU.nc") #.format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_dust_anomalies_{}".format(settings.YEAR),
dust[0], settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1))
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_dust_anomalies_{}".format(settings.YEAR),
dust[0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1),
figtext="(z) Dust Aerosol")
#************************************************************************
# Total, Trend and Event map
if False:
# as one colorbar per map, don't use the utils Iris routine
fig = plt.figure(figsize=(8, 15))
#total = iris.load(DATALOC + "Aerosol_Fig2a_Total_Averages.nc")
#trend = iris.load(DATALOC + "Aerosol_Fig2b_Total_Trends.nc")
#event = iris.load(DATALOC + "Aerosol_Fig2c_ExtremeDayCounts_{}.nc".format(settings.YEAR))
total = iris.load(DATALOC + "meanAOD.nc")
trend = iris.load(DATALOC + "trend.nc")
number = iris.load(DATALOC + "numbermonth.nc")
bounds_a = np.array(
[0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 1.0])
bounds_b = np.array([
-10, -0.010, -0.005, -0.003, -0.002, -0.001, 0.001, 0.002, 0.003,
0.005, 0.010, 10
])
bounds_c = np.array([0, 1, 2, 3, 4, 5, 6, 7, 10])
all_cubes = [total, trend, number]
all_bounds = [bounds_a, bounds_b, bounds_c]
# do colourmaps by hand
cmap = [
plt.cm.YlOrBr, settings.COLOURMAP_DICT["composition"],
plt.cm.YlOrBr
]
PLOTLABELS = ["(a)", "(b)", "(c)"]
cb_label = ["AOD", "AOD yr" + r'$^{-1}$', "Months"]
# spin through axes
for a in range(3):
norm = mpl.cm.colors.BoundaryNorm(all_bounds[a], cmap[a].N)
ax = plt.subplot(3, 1, a + 1, projection=cartopy.crs.Robinson())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ext = ax.get_extent() # save the original extent
mesh = iris.plot.pcolormesh(all_cubes[a][0],
cmap=cmap[a],
norm=norm,
axes=ax)
ax.set_extent(
ext, ax.projection) # fix the extent change from colormesh
ax.text(0.0,
1.0,
PLOTLABELS[a],
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
# sort the colourbar
cb = fig.colorbar(mesh, ax=ax, orientation='horizontal', \
ticks=all_bounds[a][1:-1], label=cb_label[a], drawedges=True, pad=0.05, fraction=0.07, aspect=30)
if a == 1:
cb.set_ticklabels(
["{:6.3f}".format(b) for b in all_bounds[a][1:-1]])
else:
cb.set_ticklabels(
["{:g}".format(b) for b in all_bounds[a][1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
plt.savefig(settings.IMAGELOC + "ASL_Trends{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Total, and two trends maps
if True:
# as one colorbar per map, don't use the utils Iris routine
fig = plt.figure(figsize=(8, 15))
total = iris.load(DATALOC + "AOD2003-2009.nc")[0]
trend1 = iris.load(DATALOC + "trend2003-2019.nc")[0]
trend1.data = np.ma.masked_where(trend1.data == -99, trend1.data)
trend2 = iris.load(DATALOC + "medianaod12-19sig.nc")[0]
trend2.data = np.ma.masked_where(trend2.data == -99, trend2.data)
bounds_a = np.array(
[0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 1.0])
bounds_b = np.array([
-10, -0.020, -0.010, -0.006, -0.004, -0.002, 0.002, 0.004, 0.006,
0.010, 0.020, 10
])
bounds_c = np.array([
-10, -0.020, -0.010, -0.006, -0.004, -0.002, 0.002, 0.004, 0.006,
0.010, 0.020, 10
])
all_cubes = [total, trend1, trend2]
all_bounds = [bounds_a, bounds_b, bounds_c]
# do colourmaps by hand
cmap = [
plt.cm.YlOrBr, settings.COLOURMAP_DICT["composition"],
settings.COLOURMAP_DICT["composition"]
]
PLOTLABELS = [
"(a) Mean 2003-19", "(b) Trend 2003-19", "(c) Trend 2012-19"
]
cb_label = ["AOD", "AOD yr" + r'$^{-1}$', "AOD yr" + r'$^{-1}$']
# spin through axes
for a in range(3):
norm = mpl.cm.colors.BoundaryNorm(all_bounds[a], cmap[a].N)
ax = plt.subplot(3, 1, a + 1, projection=cartopy.crs.Robinson())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ext = ax.get_extent() # save the original extent
cube = all_cubes[a][0]
if settings.OUTFMT in [".eps", ".pdf"]:
if cube.coord("latitude").points.shape[0] > 180 or cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print(
"Regridding cube for {} output to {} degree resolution"
.format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(cube.data.shape))
plot_cube = utils.regrid_cube(cube, regrid_size,
regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
else:
plot_cube = copy.deepcopy(cube)
else:
plot_cube = copy.deepcopy(cube)
mesh = iris.plot.pcolormesh(plot_cube,
cmap=cmap[a],
norm=norm,
axes=ax)
ax.set_extent(
ext, ax.projection) # fix the extent change from colormesh
ax.text(0.0,
1.0,
PLOTLABELS[a],
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
# sort the colourbar
cb = fig.colorbar(mesh, ax=ax, orientation='horizontal', \
ticks=all_bounds[a][1:-1], label=cb_label[a], drawedges=True, pad=0.05, fraction=0.07, aspect=30)
if a == 1:
cb.set_ticklabels(
["{:6.3f}".format(b) for b in all_bounds[a][1:-1]])
else:
cb.set_ticklabels(
["{:g}".format(b) for b in all_bounds[a][1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
plt.savefig(settings.IMAGELOC + "ASL_Trends{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Forcing map
if True:
bounds = np.array([
-10, -1.4, -1.0, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 1.0, 1.4, 10
])
RFari = iris.load(
DATALOC + "aerorf_camsra_rfari_2003-{}.nc".format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_RFari_anomalies_{}".format(settings.YEAR),
RFari[0][0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-{}".format(settings.YEAR),
figtext="(a) CAMSRA: RFari (SW). Mean -0.56" + r'$\pm$' +
"0.16 Wm" + r'$^{-1}$')
RFaci = iris.load(
DATALOC + "aerorf_camsra_rfaci_2003-{}.nc".format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_RFaci_anomalies_{}".format(settings.YEAR),
RFaci[0][0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-{}".format(settings.YEAR),
figtext="(c) CAMSRA: RFaci (SW). Mean -0.80" + r'$\pm$' +
"0.53 Wm" + r'$^{-1}$')
# and timeseries
cubelist = iris.load(
DATALOC +
"aerorf_camsra_timeseries_2003-{}.nc".format(settings.YEAR))
LABELS = {"RFari": "(b)", "RFaci": "(d)"}
ERRORS = {"RFari": 0.286, "RFaci": 0.667}
for cube in cubelist:
name = str(cube.var_name)
name = name[:2].upper() + name[2:]
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.13, 0.10, 0.8, 0.86])
iris.plot.plot(cube, 'k', label=cube.var_name, lw=LW)
timeUnits = cube.coord("time").units
dt_time = timeUnits.num2date(cube.coord("time").points)
upper = cube.data * (1 + ERRORS[name])
lower = cube.data * (1 - ERRORS[name])
ax.fill_between(dt_time, upper, lower, color="0.7", label=None)
# prettify
ax.set_ylabel("{} (W m".format(name) + r'$^{-2}$' + ")",
fontsize=settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.yaxis.set_ticks_position('left')
utils.thicken_panel_border(ax)
minorLocator = MultipleLocator(365.242199) # in days since
ax.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax)
# set labels
if cube.var_name == "rfari":
ax.text(0.02, 0.9, "(b)")
ax.set_ylim([-0.8, -0.3])
else:
ax.text(0.02, 0.9, "(d")
ax.set_ylim([-1.6, -0.2])
ax.text(0.02,
0.9,
LABELS[name],
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
plt.savefig(settings.IMAGELOC +
"ASL_ts_{}{}".format(cube.var_name, settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
if True:
#***************
# Figure 1
euro, africa, tibet, canada = read_ts(DATALOC +
"BAMS2020Fig1_data_LSWT.csv")
# euro_fit, africa_fit, tibet_fit, canada_fit = read_ts(DATALOC + "Fig1_lines_LSWT.csv")
euro_fit = utils.Timeseries("Lake", [1994, 2020],
[-0.5136, (2020 - 1994) * 0.0386 - 0.5136])
africa_fit = utils.Timeseries(
"Lake", [1994, 2020], [0.0204, (2020 - 1994) * 0.0036 + 0.0204])
tibet_fit = utils.Timeseries("Lake", [1994, 2020],
[0.0878, (2020 - 1994) * 0.0017 + 0.0878])
canada_fit = utils.Timeseries(
"Lake", [1994, 2020], [-0.2018, (2020 - 1994) * 0.0223 - 0.2018])
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,
figsize=(8, 10),
sharex=True)
#***************
# the timeseries
LEGEND_LOC = ""
utils.plot_ts_panel(ax1, [euro], "-", "temperature", loc=LEGEND_LOC)
ax1.plot(euro_fit.times,
euro_fit.data,
c=settings.COLOURS["temperature"][euro_fit.name],
lw=2,
ls="--")
ax1.text(1995, 0.8, "Europe, 127 lakes", fontsize=settings.FONTSIZE)
utils.plot_ts_panel(ax2, [africa], "-", "temperature", loc=LEGEND_LOC)
ax2.plot(africa_fit.times,
africa_fit.data,
c=settings.COLOURS["temperature"][africa_fit.name],
lw=2,
ls="--")
ax2.text(1995, 0.8, "Africa, 68 lakes", fontsize=settings.FONTSIZE)
utils.plot_ts_panel(ax3, [tibet], "-", "temperature", loc=LEGEND_LOC)
ax3.plot(tibet_fit.times,
tibet_fit.data,
c=settings.COLOURS["temperature"][tibet_fit.name],
lw=2,
ls="--")
ax3.text(1995,
0.8,
"Tibetan Plateau, 106 lakes",
fontsize=settings.FONTSIZE)
utils.plot_ts_panel(ax4, [canada], "-", "temperature", loc=LEGEND_LOC)
ax4.plot(canada_fit.times,
canada_fit.data,
c=settings.COLOURS["temperature"][canada_fit.name],
lw=2,
ls="--")
ax4.text(1995, 0.8, "Canada, 244 lakes", fontsize=settings.FONTSIZE)
# prettify
for ax in [ax1, ax2, ax3, ax4]:
ax.axhline(0, c='0.5', ls='--')
utils.thicken_panel_border(ax)
ax.set_ylim([-1, 1.2])
ax.set_xlim([euro.times[0] - 1, int(settings.YEAR) + 1])
ax.yaxis.set_ticks_position('left')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax4.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
fig.text(0.01,
0.35,
"Anomaly from 1996-2016 (" + r'$^\circ$' + "C)",
fontsize=settings.FONTSIZE,
rotation="vertical")
fig.subplots_adjust(bottom=0.03, right=0.96, top=0.99, hspace=0.001)
plt.savefig(settings.IMAGELOC + "LKT_ts{}".format(settings.OUTFMT))
plt.close()
#***************
# Anomaly Scatter map
if True:
anomalies = read_lakes(DATALOC + "PlateX_data_LSWT.csv")
bounds = [-8, -2, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2, 8]
# bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
lons = np.arange(-90, 120, 30)
lats = np.arange(-180, 210, 30)
dummy = np.ma.zeros((len(lats), len(lons)))
dummy.mask = np.ones(dummy.shape)
cube = utils.make_iris_cube_2d(dummy, lats, lons, "blank", "m")
utils.plot_smooth_map_iris(settings.IMAGELOC + "LKT_anomaly", cube, settings.COLOURMAP_DICT["temperature"], \
bounds, "Anomalies from 1996-2016 ("+r"$^{\circ}$"+"C)", \
scatter=[anomalies[1], anomalies[0], anomalies[2]], figtext="", title="")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LKT_anomaly", cube, settings.COLOURMAP_DICT["temperature"], \
bounds, "Anomalies from 1996-2016 ("+r"$^{\circ}$"+"C)", \
scatter=[anomalies[1], anomalies[0], anomalies[2]], \
figtext="(b) Lake Temperatures", title="")
#***************
# Insets Scatter map
if True:
fig = plt.figure(figsize=(8, 7))
plt.clf()
anomalies = read_lakes(DATALOC + "Fig2_data_LSWT.csv")
bounds = [-8, -2, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2, 8]
# bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
cmap = settings.COLOURMAP_DICT["temperature"]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
this_cmap = copy.copy(cmap)
# first_cube = iris.load(DATALOC + "amaps_1st_quarter_2018_250km.nc")[0]
# third_cube = iris.load(DATALOC + "amaps_3rd_quarter_2018_250km.nc")[0]
# annual_cube = iris.load(DATALOC + "amaps_annual_2018_250km.nc")[0]
cube = iris.load(DATALOC + "lswt_anom_1979_2019.nc")[0]
if settings.OUTFMT in [".eps", ".pdf"]:
if cube.coord("latitude").points.shape[0] > 180 or cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print("Regridding cube for {} output to {} degree resolution".
format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(cube.data.shape))
plot_cube = utils.regrid_cube(cube, regrid_size, regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
else:
plot_cube = copy.deepcopy(cube)
else:
plot_cube = copy.deepcopy(cube)
# make axes by hand
axes = ([0.01, 0.55, 0.59,
0.41], [0.565, 0.45, 0.47,
0.50], [0.01, 0.13, 0.59,
0.41], [0.61, 0.07, 0.38,
0.41], [0.1, 0.1, 0.8, 0.03])
# Europe
ax = plt.axes(axes[0], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
#ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5)
ax.set_extent([-25, 40, 34, 72], cartopy.crs.PlateCarree())
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(a) Europe",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# Africa
ax = plt.axes(axes[1], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
#ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5)
ax.set_extent([-19, 43, -40, 33], cartopy.crs.PlateCarree())
# lat_constraint = utils.latConstraint([25, 90])
# nh_cube = third_cube.extract(lat_constraint)
# lat_constraint = utils.latConstraint([-90, -25])
# sh_cube = first_cube.extract(lat_constraint)
# lat_constraint = utils.latConstraint([-25, 25])
# trop_cube = annual_cube.extract(lat_constraint)
# mesh = iris.plot.pcolormesh(nh_cube, cmap=this_cmap, norm=norm, axes=ax)
# mesh = iris.plot.pcolormesh(trop_cube, cmap=this_cmap, norm=norm, axes=ax)
# mesh = iris.plot.pcolormesh(sh_cube, cmap=this_cmap, norm=norm, axes=ax)
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(b) Africa",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# Canada
ax = plt.axes(axes[2], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
#ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5)
ax.set_extent([-140, -55, 42, 82], cartopy.crs.PlateCarree())
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(c) Canada",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# Tibet
ax = plt.axes(axes[3], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
ax.add_feature(cartopy.feature.BORDERS.with_scale('50m'), linewidth=.5)
ax.set_extent([78, 102, 28, 39], cartopy.crs.PlateCarree())
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(d) Tibetan Plateau",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# colourbar
cb = plt.colorbar(mesh,
cax=plt.axes(axes[4]),
orientation='horizontal',
ticks=bounds[1:-1],
drawedges=True)
# prettify
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in',
size=0)
cb.set_label(label="Anomalies from 1996-2016 (" + r"$^{\circ}$" + "C)",
fontsize=settings.FONTSIZE)
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
plt.savefig(settings.IMAGELOC +
"LKT_Regions_scatter_map{}".format(settings.OUTFMT))
plt.close()
def run_all_plots():
#***********************
# MODIS - centre
if True:
cubelist = iris.load(
os.path.join(
DATALOC,
"MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR)))
names = np.array([c.name() for c in cubelist])
# set up plot settings
BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]
LABELS = {
"SOS": "(c) Start of Season (SOS)",
"EOS": "(d) End of Season (EOS)"
}
for season in ["SOS", "EOS"]:
c, = np.where(names == season)[0]
cube = cubelist[c]
# deal with NANS
cube.data = np.ma.masked_where(cube.data != cube.data, cube.data)
fig = plt.figure(figsize=(8, 11))
plt.clf()
# boundary circle
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# axes for polar plot
ax = plt.axes([0.01, 0.02, 0.98, 0.65],
projection=cartopy.crs.NorthPolarStereo(
central_longitude=300.0))
plot_cube = cube
# regrid depending on output format
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord(
"latitude").points.shape[0] > 90 or plot_cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print(
"Regridding cube for {} output to {} degree resolution"
.format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size,
regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
# prettify
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
if season == "SOS":
cmap = settings.COLOURMAP_DICT["phenological_r"]
elif season == "EOS":
cmap = settings.COLOURMAP_DICT["phenological"]
norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N)
mesh = iris.plot.pcolormesh(plot_cube,
cmap=cmap,
norm=norm,
axes=ax)
# # read in sites
if season == "EOS":
pass
elif season == "SOS":
lake_locations = read_us_phenocam(
os.path.join(DATALOC, "lake_coords.csv"))
# scatter
COL = "chartreuse"
ax.scatter(lake_locations[1],
lake_locations[0],
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
COL = "m"
# Harvard Forest - 2019
ax.scatter(-72.17,
42.54,
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
# uk box
COL = "y"
region = [-10.0, 49.0, 3.0, 60.0]
ax.plot([region[0], region[0]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[2], region[2]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[0], region[2]], [region[1], region[1]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
ax.plot([region[0], region[2]], [region[3], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
# COL = "k"
# ax.plot([region[0], region[0]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree())
# ax.plot([region[2], region[2]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree())
# ax.plot([region[0], region[2]], [region[1], region[1]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic())
# ax.plot([region[0], region[2]], [region[3], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic())
# label axes
ax.text(-0.1,
1.0,
LABELS[season],
fontsize=settings.FONTSIZE,
transform=ax.transAxes)
cb = plt.colorbar(mesh,
orientation='horizontal',
ticks=BOUNDS[1:-1],
drawedges=True,
fraction=0.1,
pad=0.01,
aspect=20,
shrink=0.8)
# prettify
cb.set_label(label="Anomaly (days)", fontsize=settings.FONTSIZE)
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in',
size=0)
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree())
for lat in range(30, 100, 10):
ax.text(180,
lat,
'{}$^\circ$N'.format(lat),
transform=cartopy.crs.Geodetic())
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
del cube
#***********************
# MODIS timeserise
sos_nh, eos_nh, sprt_nh_orig, falt_nh_orig = read_modis_ts(
os.path.join(
DATALOC, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(
settings.YEAR)))
dummy, sos_nh = utils.calculate_climatology_and_anomalies_1d(
sos_nh, 2000, 2010)
dummy, eos_nh = utils.calculate_climatology_and_anomalies_1d(
eos_nh, 2000, 2010)
dummy, sprt_nh = utils.calculate_climatology_and_anomalies_1d(
sprt_nh_orig, 2000, 2010)
dummy, falt_nh = utils.calculate_climatology_and_anomalies_1d(
falt_nh_orig, 2000, 2010)
ax = plt.axes([0.15, 0.73, 0.75, 0.23])
if season == "SOS":
label = "(a) Start of Season"
anomalies = [
"{} SOS Anomaly = -4.3 days".format(settings.YEAR),
"{} Spr. T anomaly = 0.19 ".format(settings.YEAR) +
r'$^{\circ}$' + "C"
]
plot_modis_ts(ax, sos_nh, sprt_nh, sprt_nh_orig, label,
anomalies, LEGEND_LOC)
elif season == "EOS":
label = "(b) End of Season"
anomalies = [
"{} EOS Anomaly = 2.4 days".format(settings.YEAR),
"{} Fall T anomaly = -0.53 ".format(settings.YEAR) +
r'$^{\circ}$' + "C"
]
plot_modis_ts(ax, eos_nh, falt_nh, falt_nh_orig, label,
anomalies, LEGEND_LOC)
plt.savefig(settings.IMAGELOC + "PHEN_modis_{}_{}{}".format(
settings.YEAR, season, settings.OUTFMT))
del cubelist
#***********************
# US timeseries - 2018
if True:
fig = plt.figure(figsize=(8, 9.5))
plt.clf()
modis_sos, modis_eos, pheno_sos, pheno_eos = read_us_phenocam_csv(
os.path.join(DATALOC, "Richardson Data for SOC 2019 Figures.csv"))
# images
ax = plt.axes([0.01, 0.66, 0.49, 0.3])
plot_images(ax, "HarvardForest_20190511.jpg")
ax = plt.axes([0.5, 0.66, 0.49, 0.3])
plot_images(ax, "HarvardForest_20191024.jpg")
# timeseries
ax = plt.axes([0.11, 0.35, 0.84, 0.3])
plot_us_phenocam(ax, modis_eos, pheno_eos, sos=False)
# ax.text(0.05, 0.85, "(a)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([275, 369])
plt.setp(ax.get_xticklabels(), visible=False)
ax = plt.axes([0.11, 0.05, 0.84, 0.3])
plot_us_phenocam(ax, modis_sos, pheno_sos)
# ax.text(0.05, 0.85, "(b)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([101, 144])
fig.text(0.02,
0.97,
"(a)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
fig.text(0.02,
0.3,
"Day of year",
rotation="vertical",
fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_UStimeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
#***********************
# US timeseries - 2018
if False:
fig = plt.figure(figsize=(8, 7))
plt.clf()
modis_sos, modis_eos, pheno_sos, pheno_eos = read_us_phenocam_csv(
os.path.join(DATALOC, "Richardson Data for SOC 2019 Figures.csv"))
# timeseries
ax = plt.axes([0.11, 0.5, 0.64, 0.45])
plot_us_phenocam(ax, modis_eos, pheno_eos, sos=False)
ax.text(0.05,
0.85,
"(c)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
ax.set_ylim([275, 369])
plt.setp(ax.get_xticklabels(), visible=False)
ax = plt.axes([0.75, 0.5, 0.25, 0.4])
plot_images(ax, "HarvardForest_20191024.jpg")
ax = plt.axes([0.11, 0.05, 0.64, 0.45])
plot_us_phenocam(ax, modis_sos, pheno_sos)
ax.text(0.05,
0.85,
"(d)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
ax.set_ylim([101, 144])
ax = plt.axes([0.75, 0.05, 0.25, 0.4])
plot_images(ax, "HarvardForest_20190511.jpg")
fig.text(0.02,
0.4,
"Day of year",
rotation="vertical",
fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_UStimeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
#***********************
# UK timeseries - 2018
if True:
from matplotlib.ticker import MultipleLocator
majorLocator = MultipleLocator(5)
fig = plt.figure(figsize=(8, 9.5))
plt.clf()
# images
ax = plt.axes([0.01, 0.66, 0.48, 0.3])
plot_images(ax, "Sarah Burgess first leaf.jpg")
ax = plt.axes([0.5, 0.66, 0.48, 0.3])
plot_images(ax, "Judith Garforth oak bare tree 2019.jpg")
sos_uk, eos_uk = read_modis_uk_ts(
os.path.join(
DATALOC, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.UK_DH.csv".format(
settings.YEAR)))
oak_sos, oak_eos = read_uk_oak_csv(
os.path.join(DATALOC, "UK_Oakleaf_data.csv"))
# timeseries
ax = plt.axes([0.11, 0.35, 0.84, 0.3])
utils.plot_ts_panel(ax, [oak_eos, eos_uk],
"-",
"phenological",
loc="center left")
# ax.text(0.05, 0.85, "(c)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([210, 354])
plt.setp(ax.get_xticklabels(), visible=False)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_major_locator(majorLocator)
# naughtily, manually tweak the upper oak plot
for line in ax.get_lines():
if line.get_color() == "g":
line.set_color("#d95f0e")
leg = ax.get_legend()
for line in leg.get_lines():
if line.get_color() == "g":
line.set_color("#d95f0e")
ax = plt.axes([0.11, 0.05, 0.84, 0.3])
utils.plot_ts_panel(ax, [oak_sos, sos_uk],
"-",
"phenological",
loc="center left")
# ax.text(0.05, 0.85, "(d)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([66, 132])
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_major_locator(majorLocator)
fig.text(0.02,
0.97,
"(b)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
fig.text(0.02,
0.3,
"Day of year",
rotation="vertical",
fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_UKtimeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
#***********************
# Lake Boxplot
if True:
import pandas as pd
df = pd.read_csv(DATALOC + "LakeData_forRobert.csv")
# rename columns
cols = []
for col in df.columns:
if len(col.split()) >= 2:
df.rename(columns={col: col.split()[0]}, inplace=True)
cols += [col.split()[0]]
fig = plt.figure(figsize=(8, 7))
plt.clf()
ax = plt.axes([0.1, 0.25, 0.89, 0.74])
df.boxplot(
column=cols,
ax=ax,
grid=False,
)
# messily pull out 2019
this_year = df.iloc[-1]
this_year = this_year.to_frame()
plt.plot(np.arange(11) + 1, this_year[19][1:], "ro")
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
tick.label.set_rotation("vertical")
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
utils.thicken_panel_border(ax)
plt.ylabel("Day of year", fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_lakes_boxplot_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
cubelist = iris.load(
os.path.join(DATALOC,
"era5_lic_anom_1979_2019_NH.nc".format(settings.YEAR)))
names = np.array([c.name() for c in cubelist])
LABELS = {
"Ice Start": "(a) Ice On",
"Ice End": "(b) Ice Off",
"Ice Duration": "(c) Ice Duration"
}
COLORS = {
"Ice Start": plt.cm.RdBu_r,
"Ice End": plt.cm.RdBu,
"Ice Duration": plt.cm.RdBu
}
BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]
for name in names:
if name == "Ice Depth":
continue
c, = np.where(names == name)[0]
cube = cubelist[c]
fig = plt.figure(figsize=(8, 9.5))
plt.clf()
# boundary circle
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# axes for polar plot
ax = plt.axes(
[0.01, 0.02, 0.98, 0.98],
projection=cartopy.crs.NorthPolarStereo(central_longitude=300.0))
plot_cube = cube
# regrid depending on output format
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord(
"latitude").points.shape[0] > 90 or plot_cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print("Regridding cube for {} output to {} degree resolution".
format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size,
regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
# prettify
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
cmap = COLORS[name]
norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N)
mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax)
# label axes
ax.text(0.01,
1.0,
"{}".format(LABELS[name]),
fontsize=settings.FONTSIZE,
transform=ax.transAxes)
cb = plt.colorbar(mesh,
orientation='horizontal',
ticks=BOUNDS[1:-1],
drawedges=True,
fraction=0.1,
pad=0.01,
aspect=20,
shrink=0.8)
# prettify
cb.set_label(label="Anomaly (days)", fontsize=settings.FONTSIZE)
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in',
size=0)
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree())
for lat in range(30, 100, 10):
ax.text(180,
lat,
'{}$^\circ$N'.format(lat),
transform=cartopy.crs.Geodetic())
plt.savefig(settings.IMAGELOC + "LIC_map_{}_{}{}".format(
settings.YEAR, "".join(name.split()), settings.OUTFMT))
#************************************************************************
# and temperatures
BOUNDS = [-100, -4, -3, -2, -1, 0, 1, 2, 3, 4, 100]
cubelist = iris.load(os.path.join(DATALOC, "amaps.nc"))
cube = cubelist[0]
fig = plt.figure(figsize=(8, 9.5))
plt.clf()
# boundary circle
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# axes for polar plot
ax = plt.axes(
[0.01, 0.02, 0.98, 0.98],
projection=cartopy.crs.NorthPolarStereo(central_longitude=300.0))
plot_cube = cube
# regrid depending on output format
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print(
"Regridding cube for {} output to {} degree resolution".format(
settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
# prettify
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
cmap = plt.cm.RdBu_r
norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N)
mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax)
# label axes
ax.text(0.01,
1.0,
"(d) Nov-Apr Air Temperature",
fontsize=settings.FONTSIZE,
transform=ax.transAxes)
cb = plt.colorbar(mesh,
orientation='horizontal',
ticks=BOUNDS[1:-1],
drawedges=True,
fraction=0.1,
pad=0.01,
aspect=20,
shrink=0.8)
# prettify
cb.set_label(label="Anomaly ($^\circ$C)", fontsize=settings.FONTSIZE)
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in',
size=0)
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree())
for lat in range(30, 100, 10):
ax.text(180,
lat,
'{}$^\circ$N'.format(lat),
transform=cartopy.crs.Geodetic())
plt.savefig(
settings.IMAGELOC +
"LIC_map_{}_{}{}".format(settings.YEAR, "AirT", settings.OUTFMT))
#************************************************************************
# Timeseries
era_start, era_end, era_length = read_column_csv(
os.path.join(DATALOC,
"era5_lic_anom_1979_{}_NH.csv".format(settings.YEAR)))
situ_start, situ_end, situ_length = read_column_csv(os.path.join(
DATALOC, "situ_lic_anom_1979_{}_NH.csv".format(settings.YEAR)),
era=False)
plt.clf()
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True)
# start
utils.plot_ts_panel(ax1, [era_start, situ_start],
"-",
"cryosphere",
loc="")
# end
utils.plot_ts_panel(ax2, [era_end, situ_end], "-", "cryosphere", loc="")
ax2.set_ylabel("Anomaly (day)", fontsize=settings.FONTSIZE)
# duration
utils.plot_ts_panel(ax3, [era_length, situ_length],
"-",
"cryosphere",
loc="lower left")
# sort formatting
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax3.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# sort labelling
ax1.text(0.02,
0.9,
"(a) Start of Ice Cover",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) End of Ice Cover",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02,
0.9,
"(c) Duration of Ice Cover",
transform=ax3.transAxes,
fontsize=settings.LABEL_FONTSIZE)
fig.subplots_adjust(bottom=0.05, right=0.95, top=0.95, hspace=0.001)
plt.savefig(settings.IMAGELOC +
"LIC_ts_{}{}".format(settings.YEAR, settings.OUTFMT))
#************************************************************************
# Timeseries
indata = read_continuous_csv(os.path.join(DATALOC, "great_lakes_anom.csv"))
fig = plt.figure(figsize=(8, 6))
plt.clf()
ax = plt.axes([0.1, 0.05, 0.88, 0.9])
utils.plot_ts_panel(ax, indata, "-", "cryosphere", loc="")
fig.text(0.02,
0.5,
"Maximum ice cover (anomaly, %)",
va='center',
rotation='vertical',
ha="center",
fontsize=settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC +
"LIC_GL_ts_{}{}".format(settings.YEAR, settings.OUTFMT))
return # run_all_plots
def run_all_plots():
# #***********************
# # Three panel timeseries - UK, DWD, Windermere
# chlorophyll = read_lake_csv(os.path.join(data_loc, "timeseries", "Windermere.csv"))
# betula_out = read_dwd_betula(os.path.join(data_loc, "timeseries", "dwd-data_per180112_Betula_pendula_leaf_out.csv"))
# quercus_out, quercus_fall = read_dwd_quercus(os.path.join(data_loc, "timeseries", "dwd-data_per-180112_Quercus robur leave unf.csv"))
# betula_fall, fagus_out, fagus_fall = read_dwd_fagus(os.path.join(data_loc, "timeseries", "dwd-data_per180112_Fagus sylvatica and Betula pendula leaf data.csv"))
# alder, chestnut, oak, beech = read_uk_csv(os.path.join(data_loc, "timeseries", "details_for_climate_report.csv"))
# long_oak = read_uk_oak_csv(os.path.join(data_loc, "timeseries", "NatureCalendar_pedunculate_oak_first_leaf_1753_1999.csv"))
# fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 7), sharex=True)
# # DWD data - budburst
# utils.plot_ts_panel(ax1, [betula_out, fagus_out, quercus_out], "-", "phenological", loc="")
# lines, labels = ax1.get_legend_handles_labels()
# newlabels = []
# for ll in labels:
# newlabels += [r'${}$'.format(ll)]
# ax1.legend(lines, newlabels, loc="lower left", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# ax4 = plt.axes([0.84, 0.84, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'DWD_oak_leaf.jpg'))
# ax4.imshow(img)
# ax4.set_xticks([])
# ax4.set_yticks([])
# # UK data - budburst
# utils.plot_ts_panel(ax2, [alder, chestnut, beech, long_oak, oak], "-", "phenological", loc="")
# ax2.plot(long_oak.times, long_oak.data, ls=None, marker="o", c=settings.COLOURS["phenological"][long_oak.name], markeredgecolor=settings.COLOURS["phenological"][long_oak.name])
# lines, labels = ax2.get_legend_handles_labels()
# newlabels = []
# for ll in labels:
# newlabels += [r'${}$'.format(ll)]
# ax2.legend(lines, newlabels, loc="lower left", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# ax5 = plt.axes([0.84, 0.54, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, '61d1c433-b65c-47bc-b831-7e44ab74a895.jpg'))
# ax5.imshow(img)
# ax5.set_xticks([])
# ax5.set_yticks([])
# # Windermere
# utils.plot_ts_panel(ax3, [chlorophyll], "-", "phenological", loc="lower left")
# ax6 = plt.axes([0.84, 0.23, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'Asterionella and aulacoseira 05 May 2017.jpg'))
# ax6.imshow(img)
# ax6.set_xticks([])
# ax6.set_yticks([])
# # prettify
# ax1.set_ylim([70, 160])
# ax2.set_ylim([70, 160])
# ax3.set_ylim([70, 160])
# ax2.set_ylabel("Day of year", fontsize=settings.FONTSIZE)
# ax1.text(0.02, 0.88, "(a) Germany - tree leaf unfurling", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# ax2.text(0.02, 0.88, "(b) UK - tree bud burst & leaf out", transform=ax2.transAxes, fontsize=settings.FONTSIZE)
# ax3.text(0.02, 0.88, "(c) UK - Windermere - plankton", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
# for tick in ax3.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax2, ax3]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# ax1.set_xlim([1950, 2020])
# plt.setp([a.get_xticklabels() for a in [ax1, ax2]], visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, bottom=0.05, hspace=0.001)
# plt.savefig(image_loc+"PHEN_tree_ts{}".format(settings.OUTFMT))
# plt.close()
# #***********************
# # Barlett start/end timeseries - image inset
# # Hand-copied data from "Bartlett Gcc Transition Dates 2008-2017.xlsx"
# # up, down, diff = read_bartlett_green(os.path.join(data_loc, "timeseries", "Bartlett_greenupdown.dat"))
# # fig = plt.figure(figsize = (8, 9))
# # plt.clf()
# # # make axes by hand
# # ax1 = plt.axes([0.15,0.3,0.8,0.65])
# # ax2 = plt.axes([0.15,0.1,0.8,0.2], sharex = ax1)
# # print "need inset axes for images"
# # # plot data
# # ax1.plot(up.times, up.data, c = "g", ls = "-", lw = 2, label = "Green up")
# # ax1.plot(down.times, down.data, c = "brown", ls = "-", lw = 2, label = "Green down")
# # ax1.fill_between(up.times, up.data, down.data, color = "0.75")
# # ax2.plot(diff.times, diff.data, c = "c", ls = "-", lw = 2)
# # ax1.legend(loc="upper right", ncol = 1, frameon = False, prop = {'size':settings.LEGEND_FONTSIZE}, labelspacing = 0.1, columnspacing = 0.5)
# # # sort axes
# # ax1.set_xlim([2008, 2018])
# # ax2.set_ylim([140,180])
# # ax1.text(0.02, 0.93, "(a) Bartlett seasons", transform = ax1.transAxes, fontsize = settings.FONTSIZE)
# # ax2.text(0.02, 0.85, "(b) Difference", transform = ax2.transAxes, fontsize = settings.FONTSIZE)
# # # prettify
# # ax1.set_ylabel("Day of Year", fontsize = settings.FONTSIZE)
# # ax2.set_ylabel("Days", fontsize = settings.FONTSIZE)
# # minorLocator = MultipleLocator(100)
# # for ax in [ax1, ax2]:
# # utils.thicken_panel_border(ax)
# # ax.set_yticks(ax.get_yticks()[1:-1])
# # ax.xaxis.set_minor_locator(minorLocator)
# # for tick in ax.yaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # for tick in ax.xaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # plt.setp(ax1.get_xticklabels(), visible=False)
# # plt.savefig(image_loc+"PHEN_bartlett_ts{}".format(settings.OUTFMT))
# # plt.close()
# #***********************
# # Barlett start/end timeseries - image inset
# # Hand-copied data from "Bartlett Gcc Transition Dates 2008-2017.xlsx"
# # https://matplotlib.org/examples/pylab_examples/broken_axis.html
# up, down, diff = read_bartlett_green(os.path.join(data_loc, "timeseries", "Bartlett_greenupdown.dat"))
# fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 9), sharex=True)
# print("need inset axes for images")
# # plot same on both
# ax1.plot(up.times, up.data, c="g", ls="-", lw=2, label="Green up", marker="o")
# ax1.plot(down.times, down.data, c="brown", ls="-", lw=2, label="Green down", marker="o")
# ax1.fill_between(up.times, up.data, down.data, color="lightgreen")
# ax2.plot(up.times, up.data, c="g", ls="-", lw=2, marker="o")
# ax2.plot(down.times, down.data, c="brown", ls="-", lw=2, marker="o")
# ax2.fill_between(up.times, up.data, down.data, color="lightgreen")
# ax1.invert_yaxis()
# ax2.invert_yaxis()
# # hide the spines between ax and ax2
# ax1.spines['bottom'].set_visible(False)
# ax2.spines['top'].set_visible(False)
# ax1.xaxis.tick_top()
# ax1.tick_params(labeltop='off') # don't put tick labels at the top
# ax2.xaxis.tick_bottom()
# # and the difference
# ax3.plot(diff.times, diff.data, c="c", ls="-", lw=2, marker="o")
# # plot the legend
# ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# # prettify
# ax1.set_xlim([2007, int(settings.YEAR)+1])
# ax2.set_ylabel("Day of Year", fontsize=settings.FONTSIZE)
# ax3.set_ylabel("Days", fontsize=settings.FONTSIZE)
# ax1.text(0.02, 0.88, "(a) Bartlett seasons", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# ax3.text(0.02, 0.85, "(b) Difference", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
# # zoom in
# ax1.set_ylim([150, 100])
# ax2.set_ylim([300, 250])
# ax3.set_ylim([136, 179])
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax2, ax3]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for tick in ax.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# plt.setp(ax1.get_xticklabels(), visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
# plt.savefig(image_loc+"PHEN_bartlett_ts_gap{}".format(settings.OUTFMT))
# plt.close()
# #***********************
# # Bartlett GPP & Duke separate
# fig = plt.figure(figsize=(8, 6.5))
# plt.clf()
# ax1 = plt.axes([0.12, 0.52, 0.78, 0.44])
# ax2 = ax1.twinx()
# ax3 = plt.axes([0.12, 0.08, 0.78, 0.44], sharex=ax1)
# gpp, gcc = read_bartlett_gpp(os.path.join(data_loc, "timeseries", "Bartlett GPP Flux and Canopy Greenness.csv"))
# bartlett, duke = read_bartlett_duke(os.path.join(data_loc, "timeseries", "Bartlett 2008-2017 and Duke 2017 Camera Greenness.csv"))
# utils.plot_ts_panel(ax1, [gcc], "-", "phenological", loc="")
# utils.plot_ts_panel(ax2, [gpp], "-", "phenological", loc="")
# utils.plot_ts_panel(ax3, [bartlett, duke], "-", "phenological", loc="upper right", ncol=1)
# ax3.axhline(0.36, c='0.5', ls='--')
# ax3.axvline(185, c='0.5', ls=":")
# # fix the legend
# lines1, labels1 = ax1.get_legend_handles_labels()
# lines2, labels2 = ax2.get_legend_handles_labels()
# ax2.legend(lines1 + lines2, labels1 + labels2, loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# # prettify
# ax2.yaxis.set_label_position("right")
# ax2.yaxis.set_ticks_position('right')
# ax1.set_ylim([0.34, 0.48])
# ax3.set_ylim([0.34, 0.48])
# ax2.set_ylim([-2, 12])
# ax1.set_ylabel("Canopy Greeness", fontsize=settings.FONTSIZE)
# ax2.set_ylabel("Gross Primary Productivity", fontsize=settings.FONTSIZE)
# ax3.set_ylabel("Canopy Greeness", fontsize=settings.FONTSIZE)
# ax1.text(0.02, 0.88, "(c)", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# ax3.text(0.02, 0.85, "(d)", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
# for tick in ax3.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax3]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# utils.thicken_panel_border(ax2)
# ax2.set_yticks(ax2.get_yticks()[1:-1])
# for tick in ax2.yaxis.get_major_ticks():
# tick.label2.set_fontsize(settings.FONTSIZE)
# plt.setp(ax1.get_xticklabels(), visible=False)
# plt.setp(ax2.get_xticklabels(), visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
# ax4 = plt.axes([0.14, 0.65, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_01_20_140005.jpg'))
# ax4.imshow(img)
# ax4.set_xticks([])
# ax4.set_yticks([])
# ax4.set_title("January")
# ax5 = plt.axes([0.72, 0.65, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_07_17_130005.jpg'))
# ax5.imshow(img)
# ax5.set_xticks([])
# ax5.set_yticks([])
# ax5.set_title("July")
# ax6 = plt.axes([0.14, 0.25, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_07_04_124505.jpg'))
# ax6.imshow(img)
# ax6.set_xticks([])
# ax6.set_yticks([])
# ax6.set_title("Bartlett")
# ax7 = plt.axes([0.72, 0.25, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'dukehw_2017_07_04_120110.jpg'))
# ax7.imshow(img)
# ax7.set_xticks([])
# ax7.set_yticks([])
# ax7.set_title("Duke")
# plt.savefig(image_loc+"PHEN_Bartlett_GPP_Duke_separate{}".format(settings.OUTFMT))
# plt.close()
# #***********************
# # Bartlett GPP & Duke combined
# # fig = plt.figure(figsize = (8, 5))
# # plt.clf()
# # ax1 = plt.axes([0.12, 0.1, 0.78, 0.8])
# # ax2 = ax1.twinx()
# # gpp, gcc = read_bartlett_gpp(os.path.join(data_loc, "timeseries", "Bartlett GPP Flux and Canopy Greenness.csv"))
# # bartlett, duke = read_bartlett_duke(os.path.join(data_loc, "timeseries", "Bartlett 2008-2017 and Duke 2017 Camera Greenness.csv"))
# # utils.plot_ts_panel(ax1, [gcc, duke], "-", "phenological", loc = "")
# # utils.plot_ts_panel(ax2, [gpp], "-", "phenological", loc = "")
# # # fix the legend
# # lines1, labels1 = ax1.get_legend_handles_labels()
# # lines2, labels2 = ax2.get_legend_handles_labels()
# # # have to remove duplicate label
# # ax2.legend(lines1 + lines2, labels1 + labels2, loc="upper right", ncol = 1, frameon = False, prop = {'size':settings.LEGEND_FONTSIZE}, labelspacing = 0.1, columnspacing = 0.5)
# # # prettify
# # ax2.yaxis.set_label_position("right")
# # ax1.set_ylim([0.34, 0.48])
# # ax2.set_ylim([-2, 12])
# # ax1.set_ylabel("Canopy Greeness", fontsize = settings.FONTSIZE)
# # ax2.set_ylabel("Gross Primary Productivity", fontsize = settings.FONTSIZE)
# # for tick in ax1.xaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # minorLocator = MultipleLocator(100)
# # for ax in [ax1, ax3]:
# # utils.thicken_panel_border(ax)
# # ax.set_yticks(ax.get_yticks()[1:])
# # ax.xaxis.set_minor_locator(minorLocator)
# # for tick in ax.yaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # utils.thicken_panel_border(ax2)
# # ax2.set_yticks(ax2.get_yticks()[1:-1])
# # for tick in ax2.yaxis.get_major_ticks():
# # tick.label2.set_fontsize(settings.FONTSIZE)
# # fig.subplots_adjust(right = 0.95, top = 0.95, hspace = 0.001)
# # plt.savefig(image_loc+"PHEN_Bartlett_GPP_Duke_combined{}".format(settings.OUTFMT))
# # plt.close()
# #***********************
# # UK Map
# import cartopy.feature as cfeature
# land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m',
# edgecolor='face',
# facecolor=cfeature.COLORS['land'])
# species, days, lats, lons = read_uk_map_csv(os.path.join(data_loc, "timeseries", "UK_Leafout_4Trees.csv"))
# cmap = plt.cm.YlGn
# bounds = np.arange(80, 160, 10)
# norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
# fig = plt.figure(figsize=(8, 10.5))
# plt.clf()
# ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection=cartopy.crs.LambertConformal(central_longitude=-7.5))
# ax.gridlines() #draw_labels=True)
# ax.add_feature(land_50m, zorder=0, facecolor="0.9", edgecolor="k")
# ax.set_extent([-10, 4, 48, 60], cartopy.crs.PlateCarree())
# scat = ax.scatter(lons, lats, c=days, transform=cartopy.crs.PlateCarree(), cmap=cmap, norm=norm, s=25, edgecolor='0.5', linewidth='0.5', zorder=10)
# utils.thicken_panel_border(ax)
# cb = plt.colorbar(scat, orientation='horizontal', ticks=bounds[1:-1], label="Day of year", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8)
# # prettify
# cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
# cb.outline.set_linewidth(2)
# cb.dividers.set_color('k')
# cb.dividers.set_linewidth(2)
# plt.savefig(image_loc + "PHEN_UK_map{}".format(settings.OUTFMT))
# plt.close()
#***********************
# MODIS
cubelist = iris.load(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR)))
for c, cube in enumerate(cubelist):
if cube.name() == "EOS":
eos_cube = cubelist[c]
elif cube.name() == "SOS":
sos_cube = cubelist[c]
elif cube.name() == "MAX":
max_cube = cubelist[c]
# deal with NANS
# eos_cube.data = np.ma.masked_where(eos_cube.data != eos_cube.data, eos_cube.data)
sos_cube.data = np.ma.masked_where(sos_cube.data != sos_cube.data, sos_cube.data)
# max_cube.data = np.ma.masked_where(max_cube.data != max_cube.data, max_cube.data)
# set up a 1 x 2 set of axes (2018 only needs one panel)
fig = plt.figure(figsize=(8, 8))
plt.clf()
# set up plot settings
BOUNDS = [[-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]]#, [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]]
CMAPS = [settings.COLOURMAP_DICT["phenological_r"]]#, settings.COLOURMAP_DICT["phenological"]]
CUBES = [sos_cube]# eos_cube]
LABELS = ["(a) Start of Season (SOS)"]#, "(b) End of Season (EOS)"]
# boundary circle
theta = np.linspace(0, 2*np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# spin through axes
for a in range(1):
ax = plt.subplot(1, 1, a+1, projection=cartopy.crs.NorthPolarStereo())
plot_cube = CUBES[a]
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord("longitude").points.shape[0] > 360:
regrid_size = 1.0
print("Regridding cube for {} output to {} degree resolution".format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
ext = ax.get_extent() # save the original extent
cmap = CMAPS[a]
norm = mpl.cm.colors.BoundaryNorm(BOUNDS[a], cmap.N)
mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax)
ax.set_extent(ext, ax.projection) # fix the extent change from colormesh
ax.text(-0.1, 1.0, LABELS[a], fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes)
cb = plt.colorbar(mesh, orientation='horizontal', ticks=BOUNDS[a][1:-1], label="Anomaly (days)", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8)
# prettify
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[a][1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
fig.subplots_adjust(bottom=0.05, top=0.95, left=0.04, right=0.95, wspace=0.02)
plt.title("")
plt.savefig(image_loc + "PHEN_modis_polar{}".format(settings.OUTFMT))
plt.close()
del eos_cube
del sos_cube
del cubelist
#***********************
# MODIS LAI
# max_cube.data = np.ma.masked_where(max_cube.data <= -9000, max_cube.data)
# if settings.OUTFMT in [".eps", ".pdf"]:
# if max_cube.coord("latitude").points.shape[0] > 180 or max_cube.coord("longitude").points.shape[0] > 360:
# regrid_size = 1.0
# print("Regridding cube for {} output to {} degree resolution".format(settings.OUTFMT, regrid_size))
# print("Old Shape {}".format(max_cube.data.shape))
# max_cube = utils.regrid_cube(max_cube, regrid_size, regrid_size)
# print("New Shape {}".format(max_cube.data.shape))
# fig = plt.figure(figsize=(8, 8))
# plt.clf()
# ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection=cartopy.crs.NorthPolarStereo())
# ax.gridlines() #draw_labels=True)
# ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k")
# ax.coastlines()
# ax.set_boundary(circle, transform=ax.transAxes)
# ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
# ext = ax.get_extent() # save the original extent
# cmap = settings.COLOURMAP_DICT["phenological_r"]
# bounds = [-100, -5, -3, -2, -1, 0, 1, 2, 3, 5, 100]
# norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
# mesh = iris.plot.pcolormesh(max_cube, cmap=cmap, norm=norm, axes=ax)
# ax.set_extent(ext, ax.projection) # fix the extent change from colormesh
# ax.text(-0.1, 1.0, "Max Leaf Area Index", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes)
# cb = plt.colorbar(mesh, orientation='horizontal', ticks=bounds[1:-1], label="Anomaly (m"+r'$^2'+"/m"+r'$^2'+")", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8)
# # prettify
# cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
# cb.outline.set_linewidth(2)
# cb.dividers.set_color('k')
# cb.dividers.set_linewidth(2)
# ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
# plt.savefig(image_loc + "PHEN_modis_lai{}".format(settings.OUTFMT))
# plt.close()
#***********************
# MODIS timeseries - 2017
# sos, eos, lai = read_modis_ts(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.MAX.AnomalyTS.csv".format(settings.YEAR)))
# plt.clf()
# fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 4), sharex=True)
# # plot same on both
# ax1.plot(sos.times, sos.data, c="g", ls="-", lw=2, label="Start of Season", marker="o")
# ax1.plot(eos.times, eos.data, c="brown", ls="-", lw=2, label="End of Season", marker="o")
# ax1.fill_between(eos.times, eos.data, sos.data, color="lightgreen")
# ax2.plot(sos.times, sos.data, c="g", ls="-", lw=2, marker="o")
# ax2.plot(eos.times, eos.data, c="brown", ls="-", lw=2, marker="o")
# ax2.fill_between(eos.times, eos.data, sos.data, color="lightgreen")
# # invert so green down is on the bottom
# ax1.invert_yaxis()
# ax2.invert_yaxis()
# # hide the spines between ax and ax2
# ax1.spines['bottom'].set_visible(False)
# ax2.spines['top'].set_visible(False)
# ax1.xaxis.tick_top()
# ax1.tick_params(labeltop='off') # don't put tick labels at the top
# ax2.xaxis.tick_bottom()
# # plot the legend
# ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# # prettify
# ax1.set_xlim([1999, 2018])
# ax2.set_ylabel("Day of Year", fontsize=settings.FONTSIZE)
# # zoom in
# ax1.set_ylim([160, 110])
# ax2.set_ylim([300, 250])
# # set label
# ax1.text(-0.1, 0.92, "(c)", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax2]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for tick in ax.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# plt.setp(ax1.get_xticklabels(), visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
# plt.savefig(image_loc+"PHEN_modis_ts{}".format(settings.OUTFMT))
# plt.close()
#***********************
# MODIS timeseries - 2018
sos_na, sos_ea, sprt_na, sprt_ea = read_modis_ts(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(settings.YEAR)))
dummy, sos_na = utils.calculate_climatology_and_anomalies_1d(sos_na, 2000, 2010)
dummy, sos_ea = utils.calculate_climatology_and_anomalies_1d(sos_ea, 2000, 2010)
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
# North America
# use un-anomalised spring T to get legend without the data
utils.plot_ts_panel(ax1, [sos_na, sprt_na], "-", "phenological", loc=LEGEND_LOC)
# Eurasia
utils.plot_ts_panel(ax2, [sos_ea, sprt_ea], "-", "phenological", loc=LEGEND_LOC)
# make twin axes for Spring T
dummy, sprt_na = utils.calculate_climatology_and_anomalies_1d(sprt_na, 2000, 2010)
dummy, sprt_ea = utils.calculate_climatology_and_anomalies_1d(sprt_ea, 2000, 2010)
ax3 = ax1.twinx()
utils.plot_ts_panel(ax3, [sprt_na], "-", "phenological", loc="")
ax4 = ax2.twinx()
utils.plot_ts_panel(ax4, [sprt_ea], "-", "phenological", loc="")
# prettify
ax1.set_ylim([-8, 8])
ax2.set_ylim([-8, 8])
ax3.set_ylim([2, -2])
ax4.set_ylim([2, -2])
# labels
ax1.text(0.02, 0.88, "(a) North America", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
ax2.text(0.02, 0.88, "(b) Eurasia", transform=ax2.transAxes, fontsize=settings.FONTSIZE)
ax1.text(0.47, 0.88, "2018 SOS Anomaly = 1.86 days", transform=ax1.transAxes, fontsize=settings.FONTSIZE*0.8)
ax1.text(0.47, 0.78, "2018 Spring T anomaly = -0.75 "+r'$^{\circ}$'+"C", transform=ax1.transAxes, fontsize=settings.FONTSIZE*0.8)
ax2.text(0.47, 0.88, "2018 SOS Anomaly = 2.01 days", transform=ax2.transAxes, fontsize=settings.FONTSIZE*0.8)
ax2.text(0.47, 0.78, "2018 Spring T anomaly = 0.13 "+r'$^{\circ}$'+"C", transform=ax2.transAxes, fontsize=settings.FONTSIZE*0.8)
fig.text(0.01, 0.5, "SOS Anomaly (days)", va='center', rotation='vertical', fontsize = settings.FONTSIZE)
fig.text(0.95, 0.5, "Temperature Anomaly ("+r'$^{\circ}$'+"C)", va='center', rotation='vertical', fontsize = settings.FONTSIZE)
# ticks and labels
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
minorLocator = MultipleLocator(1)
for ax in [ax1, ax2]:
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:-1])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax3, ax4]:
ax.yaxis.tick_right()
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:-1])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label2.set_fontsize(settings.FONTSIZE)
# final settings
ax1.set_xlim([1999, 2020])
plt.setp([a.get_xticklabels() for a in [ax1]], visible=False)
fig.subplots_adjust(left=0.1, right=0.85, top=0.95, bottom=0.05, hspace=0.001)
plt.savefig(image_loc+"PHEN_modis_ts{}".format(settings.OUTFMT))
plt.close()
return # run_all_plots