def main():
from permafrost import draw_regions
dm = SweDataManager(var_name="SWE")
b, lons2d, lats2d = draw_regions.get_basemap_and_coords()
x, y = b(dm.lons2d, dm.lats2d)
fig = plt.figure()
start_year = 1981
end_year = 1997
levels = [
10,
] + list(range(20, 120, 20)) + [150, 200, 300, 500, 1000]
cmap = mpl.cm.get_cmap(name="jet_r", lut=len(levels))
norm = colors.BoundaryNorm(levels, cmap.N)
gs = gridspec.GridSpec(1, 2)
ax = fig.add_subplot(gs[0, 0])
data = dm.get_mean(start_year, end_year, months=[3])
img = b.contourf(x,
y,
data.copy(),
ax=ax,
cmap=cmap,
norm=norm,
levels=levels)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("SWE (not interp.), \n DJF period: {0} - {1}".format(
start_year, end_year))
ax = fig.add_subplot(gs[0, 1])
data_projected = dm.interpolate_data_to(data,
lons2d,
lats2d,
nneighbours=1)
x, y = b(lons2d, lats2d)
img = b.contourf(x, y, data_projected, ax=ax, levels=img.levels)
# add pretty colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("SWE ( interp.), \n DJF period: {0} - {1}".format(
start_year, end_year))
plt.savefig("swe_rb_djf.png")
pass
def main():
from permafrost import draw_regions
dm = SweDataManager(var_name="SWE")
b, lons2d, lats2d = draw_regions.get_basemap_and_coords()
x, y = b(dm.lons2d, dm.lats2d)
fig = plt.figure()
start_year = 1981
end_year = 1997
levels = [10, ] + list(range(20, 120, 20)) + [150, 200, 300, 500, 1000]
cmap = mpl.cm.get_cmap(name="jet_r", lut=len(levels))
norm = colors.BoundaryNorm(levels, cmap.N)
gs = gridspec.GridSpec(1, 2)
ax = fig.add_subplot(gs[0, 0])
data = dm.get_mean(start_year, end_year, months=[3])
img = b.contourf(x, y, data.copy(), ax=ax, cmap=cmap, norm=norm, levels=levels)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("SWE (not interp.), \n DJF period: {0} - {1}".format(start_year, end_year))
ax = fig.add_subplot(gs[0, 1])
data_projected = dm.interpolate_data_to(data, lons2d, lats2d, nneighbours=1)
x, y = b(lons2d, lats2d)
img = b.contourf(x, y, data_projected, ax=ax, levels=img.levels)
# add pretty colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("SWE ( interp.), \n DJF period: {0} - {1}".format(start_year, end_year))
plt.savefig("swe_rb_djf.png")
pass
def main():
from permafrost import draw_regions
dm = CRUDataManager()
b, lons2d, lats2d = draw_regions.get_basemap_and_coords()
x, y = b(dm.lons2d, dm.lats2d)
fig = plt.figure()
gs = gridspec.GridSpec(1, 2)
ax = fig.add_subplot(gs[0, 0])
data = dm.get_mean(1981, 2009, months=[6, 7, 8])
img = b.contourf(x, y, data.copy(), ax=ax)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("CRU (not interp.), \n JJA period: {0} - {1}".format(
1981, 2009))
ax = fig.add_subplot(gs[0, 1])
data_projected = dm.interpolate_data_to(data, lons2d, lats2d)
x, y = b(lons2d, lats2d)
img = b.contourf(x, y, data_projected, ax=ax, levels=img.levels)
# add pretty colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("CRU ( interp.), \n JJA period: {0} - {1}".format(1981, 2009))
plt.show()
plt.savefig("t_cru_jja.png")
pass
def main():
from permafrost import draw_regions
dm = CRUDataManager()
b, lons2d, lats2d = draw_regions.get_basemap_and_coords()
x, y = b(dm.lons2d, dm.lats2d)
fig = plt.figure()
gs = gridspec.GridSpec(1, 2)
ax = fig.add_subplot(gs[0, 0])
data = dm.get_mean(1981, 2009, months=[6, 7, 8])
img = b.contourf(x, y, data.copy(), ax=ax)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("CRU (not interp.), \n JJA period: {0} - {1}".format(1981, 2009))
ax = fig.add_subplot(gs[0, 1])
data_projected = dm.interpolate_data_to(data, lons2d, lats2d)
x, y = b(lons2d, lats2d)
img = b.contourf(x, y, data_projected, ax=ax, levels=img.levels)
# add pretty colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax)
b.drawcoastlines(ax=ax)
ax.set_title("CRU ( interp.), \n JJA period: {0} - {1}".format(1981, 2009))
plt.show()
plt.savefig("t_cru_jja.png")
pass
def plot_mean(data_path="", file_prefix="pm"):
"""
"""
var_name = "CLDP"
means = []
means_dict = {}
for file in os.listdir(data_path):
if not file.startswith(file_prefix): continue
file_path = os.path.join(data_path, file)
r = RPN(file_path)
levels = r.get_all_time_records_for_name(varname=var_name)
means_dict.update(levels)
means.append(np.mean(list(levels.values()), axis=0))
mean_level = np.array(means).mean(axis=0)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(
file_path="data/from_guillimin/vary_lake_level1/pm1985010100_00000000p",
lon1=-68,
lat1=52,
lon2=16.65,
lat2=0)
std_level = np.std(np.array(means), axis=0)
#plot mean
plt.figure()
_plot_depth(mean_level,
lons2d,
lats2d,
basemap=b,
clevels=range(0, 310, 10))
plt.savefig("mean_lake_levels.png")
#plot std
plt.figure()
_plot_depth(std_level,
lons2d,
lats2d,
basemap=b,
clevels=np.arange(0, 2.2, 0.2),
lowest_value=1e-3)
plt.savefig("std_lake_levels.png")
#plot initial lake depth
plt.figure()
plot_initial_lake_depth(lons2d=lons2d, lats2d=lats2d, basemap=b)
plt.savefig("initial_lake_depth.png")
#plot lake fraction
plt.figure()
plot_lake_fraction(lons2d=lons2d, lats2d=lats2d, basemap=b)
plt.savefig("lake_fraction.png")
fig = plt.figure()
assert isinstance(fig, Figure)
gs = GridSpec(2, 2)
seasons = [
"(a) Winter (DJF)", "(b) Spring (MAM)", "(c) Summer (JJA)",
"(d) Fall (SON)"
]
months_list = [[12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]
i = 0
for season, months in zip(seasons, months_list):
ax = fig.add_subplot(gs[i // 2, i % 2])
assert isinstance(ax, Axes)
data = _get_seasonal_mean_anomaly(means_dict,
mean_level,
months=months)
the_max = np.round(np.max(np.abs(data)) * 10) / 10.0
data = np.ma.masked_where(mean_level < 0.1, data)
_plot_depth(data,
lons2d,
lats2d,
ax=ax,
basemap=b,
clevels=np.arange(-0.5, 0.6, 0.1),
lowest_value=0.001)
ax.set_title(season)
i += 1
fig.tight_layout()
fig.savefig("seasonal_anomalies.png")
def main():
path = "/home/huziy/skynet3_exec1/from_guillimin/cell_area.rpn"
r = RPN(path)
tile = r.get_first_record_for_name("TILE")
lkid = r.get_first_record_for_name("LKID")
larea = r.get_first_record_for_name("AREA")
r.close()
#path = "/home/huziy/skynet3_exec1/from_guillimin/infocell.rpn"
path = "infocell_260x260.rpn"
rObj = RPN(path)
dirs = rObj.get_first_record_for_name("FLDR")
facc = rObj.get_first_record_for_name("FACC")
lkfr = rObj.get_first_record_for_name("LKFR")
lkou = rObj.get_first_record_for_name("LKOU")
lons2d, lats2d = rObj.get_longitudes_and_latitudes()
rObj.close()
di_list = np.array([1,1,0,-1,-1,-1,0,1])
dj_list = np.array([0,-1,-1,-1,0,1,1,1])
delta_indices = np.log2(dirs[dirs > 0])
delta_indices = delta_indices.astype(int)
di = di_list[delta_indices].astype(float)
dj = dj_list[delta_indices].astype(float)
du = di / np.sqrt(di ** 2 + dj ** 2)
dv = dj / np.sqrt(di ** 2 + dj ** 2)
for i in range(100):
print(du[i], dv[i], np.log2(dirs[dirs > 0][i]))
du2d = np.ma.masked_all(dirs.shape)
dv2d = np.ma.masked_all(dirs.shape)
du2d[dirs>0] = du
dv2d[dirs>0] = dv
print(du2d[161,145],dv2d[161,145],dirs[161,145])
iv = range(dirs.shape[0])
jv = range(dirs.shape[1])
jv, iv = meshgrid(jv, iv)
iv = iv.astype(float)
jv = jv.astype(float)
iv -= 0.5
jv -= 0.5
print(iv.min(), iv.max())
plt.figure()
#field = np.ma.masked_where((field > 1e10) | (field < 1e7) , field)
#tile1 = np.ma.masked_where((tile != 25) & (tile != 26), tile)
plt.pcolormesh(iv,jv,tile,cmap = cm.get_cmap("jet", 36))
plt.colorbar()
plt.xlim(75, 100)
plt.ylim(130,142)
plt.figure()
lkid = lkid.astype(int)
print(lkid[161,146], lkid[161,145],lkid[160,145:148])
for i in range(1,121):
x = np.ma.masked_where(lkid != i, lkid)
if x.count() == 1 or x.count() == 0:
print(i, x.count())
#lkid = np.ma.masked_where(~((lkid == 27)|(lkid == 28)) , lkid)
lkid = np.ma.masked_where(lkid <= 0, lkid)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(
file_path=path,
lon1=-68, lat1=52, lon2=16.65, lat2=0
)
x,y = b(lons2d, lats2d)
img = b.pcolormesh(x,y,lkid,cmap = cm.get_cmap("jet", 100))
plt.colorbar(img, ticks = MultipleLocator(base = 10))
b.contour(x, y,tile, levels = range(48), colors = "k", linewidth = 0.5)
b.drawcoastlines(linewidth=0.5)
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 30, width = 0.005, color="k", pivot="middle")
#plt.xlim(30, 70)
#plt.ylim(10,35)
plt.figure()
plt.pcolormesh(iv,jv,lkfr,cmap = cm.get_cmap("jet", 11))
plt.colorbar()
plt.xlim(155, 165)
plt.ylim(140,150)
plt.figure()
d = np.ma.masked_all(dirs.shape)
d[dirs > 0] = np.log2(dirs[dirs > 0])
plt.pcolormesh(iv, jv, d,cmap = cm.get_cmap("jet", 8))
plt.xlim(155, 165)
plt.ylim(140,150)
plt.colorbar(ticks = MultipleLocator(base = 1))
plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 4.5, width = 0.035, color="k", pivot="middle", units="inches")
plt.figure()
plt.title("Lake area")
plt.pcolormesh(iv, jv, np.ma.masked_where( larea < 1.0e8, np.log(larea) ),cmap = cm.get_cmap("jet", 8))
#plt.xlim(155, 165)
#plt.ylim(140,150)
print(larea.min(), larea.max())
plt.colorbar(ticks = LinearLocator(numticks = 10))
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 4.5, width = 0.035, color="k", pivot="middle", units="inches")
plt.show()
pass
def main():
path = "/home/huziy/skynet3_exec1/from_guillimin/cell_area.rpn"
r = RPN(path)
tile = r.get_first_record_for_name("TILE")
lkid = r.get_first_record_for_name("LKID")
larea = r.get_first_record_for_name("AREA")
r.close()
#path = "/home/huziy/skynet3_exec1/from_guillimin/infocell.rpn"
path = "infocell_260x260.rpn"
rObj = RPN(path)
dirs = rObj.get_first_record_for_name("FLDR")
facc = rObj.get_first_record_for_name("FACC")
lkfr = rObj.get_first_record_for_name("LKFR")
lkou = rObj.get_first_record_for_name("LKOU")
lons2d, lats2d = rObj.get_longitudes_and_latitudes()
rObj.close()
di_list = np.array([1, 1, 0, -1, -1, -1, 0, 1])
dj_list = np.array([0, -1, -1, -1, 0, 1, 1, 1])
delta_indices = np.log2(dirs[dirs > 0])
delta_indices = delta_indices.astype(int)
di = di_list[delta_indices].astype(float)
dj = dj_list[delta_indices].astype(float)
du = di / np.sqrt(di**2 + dj**2)
dv = dj / np.sqrt(di**2 + dj**2)
for i in range(100):
print(du[i], dv[i], np.log2(dirs[dirs > 0][i]))
du2d = np.ma.masked_all(dirs.shape)
dv2d = np.ma.masked_all(dirs.shape)
du2d[dirs > 0] = du
dv2d[dirs > 0] = dv
print(du2d[161, 145], dv2d[161, 145], dirs[161, 145])
iv = range(dirs.shape[0])
jv = range(dirs.shape[1])
jv, iv = meshgrid(jv, iv)
iv = iv.astype(float)
jv = jv.astype(float)
iv -= 0.5
jv -= 0.5
print(iv.min(), iv.max())
plt.figure()
#field = np.ma.masked_where((field > 1e10) | (field < 1e7) , field)
#tile1 = np.ma.masked_where((tile != 25) & (tile != 26), tile)
plt.pcolormesh(iv, jv, tile, cmap=cm.get_cmap("jet", 36))
plt.colorbar()
plt.xlim(75, 100)
plt.ylim(130, 142)
plt.figure()
lkid = lkid.astype(int)
print(lkid[161, 146], lkid[161, 145], lkid[160, 145:148])
for i in range(1, 121):
x = np.ma.masked_where(lkid != i, lkid)
if x.count() == 1 or x.count() == 0:
print(i, x.count())
#lkid = np.ma.masked_where(~((lkid == 27)|(lkid == 28)) , lkid)
lkid = np.ma.masked_where(lkid <= 0, lkid)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(file_path=path,
lon1=-68,
lat1=52,
lon2=16.65,
lat2=0)
x, y = b(lons2d, lats2d)
img = b.pcolormesh(x, y, lkid, cmap=cm.get_cmap("jet", 100))
plt.colorbar(img, ticks=MultipleLocator(base=10))
b.contour(x, y, tile, levels=range(48), colors="k", linewidth=0.5)
b.drawcoastlines(linewidth=0.5)
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 30, width = 0.005, color="k", pivot="middle")
#plt.xlim(30, 70)
#plt.ylim(10,35)
plt.figure()
plt.pcolormesh(iv, jv, lkfr, cmap=cm.get_cmap("jet", 11))
plt.colorbar()
plt.xlim(155, 165)
plt.ylim(140, 150)
plt.figure()
d = np.ma.masked_all(dirs.shape)
d[dirs > 0] = np.log2(dirs[dirs > 0])
plt.pcolormesh(iv, jv, d, cmap=cm.get_cmap("jet", 8))
plt.xlim(155, 165)
plt.ylim(140, 150)
plt.colorbar(ticks=MultipleLocator(base=1))
plt.quiver(iv + 0.5,
jv + 0.5,
du2d,
dv2d,
scale=4.5,
width=0.035,
color="k",
pivot="middle",
units="inches")
plt.figure()
plt.title("Lake area")
plt.pcolormesh(iv,
jv,
np.ma.masked_where(larea < 1.0e8, np.log(larea)),
cmap=cm.get_cmap("jet", 8))
#plt.xlim(155, 165)
#plt.ylim(140,150)
print(larea.min(), larea.max())
plt.colorbar(ticks=LinearLocator(numticks=10))
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 4.5, width = 0.035, color="k", pivot="middle", units="inches")
plt.show()
pass
def main():
stations = cehq_station.read_station_data(folder="data/cehq_levels")
lons_s = []
lats_s = []
values = []
for s in stations:
lon, lat = s.longitude, s.latitude
lons_s.append(lon)
lats_s.append(lat)
values.append(s.get_mean_value())
lons_s = np.array(lons_s)
lats_s = np.array(lats_s)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(
file_path="data/from_guillimin/vary_lake_level1/pm1985010100_00000000p",
lon1=-68, lat1=52, lon2=16.65, lat2=0, resolution="h"
)
x, y = b(lons_s, lats_s)
fig = plt.figure()
ax = plt.gca()
b.warpimage()
b.scatter(x, y, c = "r", zorder = 2)
b.drawcoastlines(linewidth = 0.2)
#draw inset axes
axins = zoomed_inset_axes(plt.gca(), 2, loc=1)
b.scatter(x, y, c = "r", zorder = 2, ax = axins)
b.warpimage(ax = axins)
ins_ll_lon = -76
ins_ll_lat = 45
ins_ur_lon = -68
ins_ur_lat = 48
x_ll, y_ll = b(ins_ll_lon, ins_ll_lat)
x_ur, y_ur = b(ins_ur_lon, ins_ur_lat)
axins.set_xlim(x_ll, x_ur)
axins.set_ylim(y_ll, y_ur)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
fig.savefig("cehq_level_stations.png")
fig = plt.figure()
ax = plt.gca()
b.warpimage()
b.scatter(x, y, c = values, zorder = 2)
plt.colorbar()
b.drawcoastlines(linewidth = 0.2)
#draw inset axes
axins = zoomed_inset_axes(plt.gca(), 4, loc=1)
b.scatter(x, y, c = values, zorder = 2, ax = axins)
b.warpimage(ax = axins)
b.drawcoastlines(linewidth = 0.2, ax = axins)
ins_ll_lon = -73
ins_ll_lat = 45
ins_ur_lon = -70
ins_ur_lat = 47
x_ll, y_ll = b(ins_ll_lon, ins_ll_lat)
x_ur, y_ur = b(ins_ur_lon, ins_ur_lat)
axins.set_xlim(x_ll, x_ur)
axins.set_ylim(y_ll, y_ur)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
fig.savefig("cehq_levels.png")
def main():
stations = cehq_station.read_station_data(folder="data/cehq_levels")
lons_s = []
lats_s = []
values = []
for s in stations:
lon, lat = s.longitude, s.latitude
lons_s.append(lon)
lats_s.append(lat)
values.append(s.get_mean_value())
lons_s = np.array(lons_s)
lats_s = np.array(lats_s)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(
file_path="data/from_guillimin/vary_lake_level1/pm1985010100_00000000p",
lon1=-68,
lat1=52,
lon2=16.65,
lat2=0,
resolution="h")
x, y = b(lons_s, lats_s)
fig = plt.figure()
ax = plt.gca()
b.warpimage()
b.scatter(x, y, c="r", zorder=2)
b.drawcoastlines(linewidth=0.2)
#draw inset axes
axins = zoomed_inset_axes(plt.gca(), 2, loc=1)
b.scatter(x, y, c="r", zorder=2, ax=axins)
b.warpimage(ax=axins)
ins_ll_lon = -76
ins_ll_lat = 45
ins_ur_lon = -68
ins_ur_lat = 48
x_ll, y_ll = b(ins_ll_lon, ins_ll_lat)
x_ur, y_ur = b(ins_ur_lon, ins_ur_lat)
axins.set_xlim(x_ll, x_ur)
axins.set_ylim(y_ll, y_ur)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
fig.savefig("cehq_level_stations.png")
fig = plt.figure()
ax = plt.gca()
b.warpimage()
b.scatter(x, y, c=values, zorder=2)
plt.colorbar()
b.drawcoastlines(linewidth=0.2)
#draw inset axes
axins = zoomed_inset_axes(plt.gca(), 4, loc=1)
b.scatter(x, y, c=values, zorder=2, ax=axins)
b.warpimage(ax=axins)
b.drawcoastlines(linewidth=0.2, ax=axins)
ins_ll_lon = -73
ins_ll_lat = 45
ins_ur_lon = -70
ins_ur_lat = 47
x_ll, y_ll = b(ins_ll_lon, ins_ll_lat)
x_ur, y_ur = b(ins_ur_lon, ins_ur_lat)
axins.set_xlim(x_ll, x_ur)
axins.set_ylim(y_ll, y_ur)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
fig.savefig("cehq_levels.png")
def plot_mean(data_path = "", file_prefix = "pm"):
"""
"""
var_name = "CLDP"
means = []
means_dict = {}
for file in os.listdir(data_path):
if not file.startswith(file_prefix): continue
file_path = os.path.join(data_path, file)
r = RPN(file_path)
levels = r.get_all_time_records_for_name(varname=var_name)
means_dict.update(levels)
means.append(np.mean( list(levels.values()) , axis = 0))
mean_level = np.array(means).mean(axis = 0)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(
file_path="data/from_guillimin/vary_lake_level1/pm1985010100_00000000p",
lon1=-68, lat1=52, lon2=16.65, lat2=0
)
std_level = np.std( np.array(means), axis=0)
#plot mean
plt.figure()
_plot_depth(mean_level, lons2d, lats2d, basemap=b, clevels=range(0,310, 10))
plt.savefig("mean_lake_levels.png")
#plot std
plt.figure()
_plot_depth(std_level, lons2d, lats2d, basemap=b, clevels=np.arange(0,2.2, 0.2), lowest_value=1e-3)
plt.savefig("std_lake_levels.png")
#plot initial lake depth
plt.figure()
plot_initial_lake_depth(lons2d=lons2d, lats2d=lats2d, basemap=b)
plt.savefig("initial_lake_depth.png")
#plot lake fraction
plt.figure()
plot_lake_fraction(lons2d=lons2d, lats2d=lats2d, basemap=b)
plt.savefig("lake_fraction.png")
fig = plt.figure()
assert isinstance(fig, Figure)
gs = GridSpec(2,2)
seasons = ["(a) Winter (DJF)", "(b) Spring (MAM)", "(c) Summer (JJA)", "(d) Fall (SON)"]
months_list = [[12,1,2], [3,4,5], [6,7,8], [9,10,11]]
i = 0
for season, months in zip(seasons, months_list):
ax = fig.add_subplot(gs[i // 2, i % 2])
assert isinstance(ax, Axes)
data = _get_seasonal_mean_anomaly(means_dict, mean_level, months=months)
the_max = np.round(np.max(np.abs(data)) * 10 ) / 10.0
data = np.ma.masked_where(mean_level < 0.1, data)
_plot_depth(data, lons2d, lats2d, ax = ax, basemap=b,
clevels=np.arange(-0.5, 0.6, 0.1),
lowest_value=0.001
)
ax.set_title(season)
i += 1
fig.tight_layout()
fig.savefig("seasonal_anomalies.png")
