def compare_swe_diff_for_era40_driven():
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-10)
lons2d[lons2d > 180] -= 360
x, y = b(lons2d, lats2d)
#period
start_year = 1981
end_year = 1997
the_months = [12,1,2]
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)
swe_obs_manager = SweDataManager(var_name="SWE")
swe_obs = swe_obs_manager.get_mean(start_year, end_year, months=the_months)
print("Calculated obs swe")
swe_obs_interp = swe_obs_manager.interpolate_data_to(swe_obs, lons2d, lats2d)
axes_list = []
levels_diff = np.arange(-100, 110, 10)
#plot model res. (ERA40 driven 1)
paths = ["data/CORDEX/na/era40_1", "data/CORDEX/na/era40_2"]
prefixes = ["pmNorthAmerica_0.44deg_ERA40-Int_{0}_1958-1961".format("DJF"),
"pmNorthAmerica_0.44deg_ERA40-Int2_{0}_1958-1961".format("DJF")
]
pf_kinds = draw_regions.get_permafrost_mask(lons2d, lats2d)
for i, the_path in enumerate(paths):
base = os.path.basename(the_path)
fig = plt.figure()
ax = plt.gca()
axes_list.append(ax)
swe_model_era = CRCMDataManager.get_mean_2d_from_climatologies(path=the_path,
var_name="I5", file_prefixes=prefixes)
swe_model_era = maskoceans(lons2d, lats2d, swe_model_era)
#plot model(ERA40 driven) - obs
axes_list.append(ax)
img = b.contourf(x, y, swe_model_era - swe_obs_interp, levels = levels_diff)
draw_colorbar(fig, img, ax = ax, boundaries=levels_diff)
ax.set_title("Model ({0} 1958-1961) - Obs.".format(base))
b.drawcoastlines(ax = ax, linewidth = 0.2)
b.contour(x, y, pf_kinds, ax = ax, colors = "k")
fig.savefig("swe_{0}.png".format(base))
def compare_vars(vname_model="TT", vname_obs="tmp", r_config=None,
season_to_months=None,
obs_path=None, nx_agg=5, ny_agg=5, bmp_info_agg=None,
diff_axes_list=None, obs_axes_list=None,
model_axes_list=None, bmp_info_model=None,
mask_shape_file=None):
"""
if obs_axes_list is not None, plot observation data in those
:param mask_shape_file:
:param bmp_info_model: basemap info native to the model
:param model_axes_list: Axes to plot model outputs
:param vname_model:
:param vname_obs:
:param r_config:
:param season_to_months:
:param obs_path:
:param nx_agg:
:param ny_agg:
:param bmp_info_agg:
:param diff_axes_list: if it is None the plots for each variable is done in separate figures
"""
if vname_obs is None:
vname_model_to_vname_obs = {"TT": "tmp", "PR": "pre"}
vname_obs = vname_model_to_vname_obs[vname_model]
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=r_config,
varname=vname_model, level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(field, nagg_x=nx_agg, nagg_y=ny_agg)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if vname_obs in ["SWE", ]:
obs_manager = SweDataManager(path=obs_path, var_name=vname_obs)
elif obs_path is None:
obs_manager = CRUDataManager(var_name=vname_obs)
else:
obs_manager = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = obs_manager.get_seasonal_means(season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
# Derive the mask from a shapefile if provided
if mask_shape_file is not None:
the_mask = get_mask(bmp_info_agg.lons, bmp_info_agg.lats, shp_path=mask_shape_file)
else:
the_mask = np.zeros_like(bmp_info_agg.lons)
for season, obs_field in seasonal_clim_fields_obs.items():
obs_field = obs_manager.interpolate_data_to(obs_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=1)
obs_field = np.ma.masked_where(the_mask > 0.5, obs_field)
seasonal_clim_fields_obs_interp[season] = obs_field
# assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
print("-------------var: {} (PE with CRU)---------------------".format(vname_model))
for season in seasonal_clim_fields_obs_interp:
seasonal_clim_fields_obs_interp[season] = np.ma.masked_where(np.isnan(seasonal_clim_fields_obs_interp[season]),
seasonal_clim_fields_obs_interp[season])
season_to_err[season] = season_to_clim_fields_model_agg[season] - seasonal_clim_fields_obs_interp[season]
if vname_model in ["I5"]:
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err[season] = maskoceans(lons, bmp_info_agg.lats, season_to_err[season])
good_vals = season_to_err[season]
good_vals = good_vals[~good_vals.mask]
print("{}: min={}; max={}; avg={}".format(season,
good_vals.min(),
good_vals.max(),
good_vals.mean()))
print("---------percetages --- CRU ---")
print("{}: {} \%".format(season, good_vals.mean() / seasonal_clim_fields_obs_interp[season][~season_to_err[season].mask].mean() * 100))
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=diff_axes_list)
if obs_axes_list is not None and vname_model in ["I5"]:
clevs = [0, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 500]
cs_obs = None
xx, yy = bmp_info_agg.get_proj_xy()
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
lons_model = None
xx_model, yy_model = None, None
cs_mod = None
norm = BoundaryNorm(clevs, 256)
for col, (season, obs_field) in enumerate(seasonal_clim_fields_obs_interp.items()):
# Obsrved fields
ax = obs_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
to_plot = maskoceans(lons, bmp_info_agg.lats, obs_field)
cs_obs = bmp_info_agg.basemap.contourf(xx, yy, to_plot, levels=clevs, ax=ax, norm=norm, extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
ax.set_title(season)
# Model outputs
if model_axes_list is not None:
ax = model_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
if lons_model is None:
lons_model = bmp_info_model.lons.copy()
lons_model[lons_model > 180] -= 360
xx_model, yy_model = bmp_info_model.basemap(lons_model, bmp_info_model.lats)
model_field = seasonal_clim_fields_model[season]
to_plot = maskoceans(lons_model, bmp_info_model.lats, model_field)
cs_mod = bmp_info_agg.basemap.contourf(xx_model, yy_model, to_plot, levels=cs_obs.levels, ax=ax,
norm=cs_obs.norm, cmap=cs_obs.cmap, extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
plt.colorbar(cs_obs, cax=obs_axes_list[-1])
return cs
def main():
swe_obs_manager = SweDataManager(var_name="SWE")
data_path = "/home/huziy/skynet3_exec1/from_guillimin/quebec_86x86_0.5deg_without_lakes_v3"
coord_file = os.path.join(data_path, "pm1985050100_00000000p")
managerLowRes = Crcm5ModelDataManager(samples_folder_path=data_path,
file_name_prefix="pm",
all_files_in_samples_folder=True,
need_cell_manager=True)
data_path = "/home/huziy/skynet3_exec1/from_guillimin/quebec_highres_spinup_12_month_without_lakes_v3"
coord_file = os.path.join(data_path, "pm1985050100_00000000p")
managerHighRes = Crcm5ModelDataManager(samples_folder_path=data_path,
file_name_prefix="pm",
all_files_in_samples_folder=True,
need_cell_manager=True)
start_year = 1987
end_year = 1987
months = [1, 2, 12]
rot_lat_lon = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)
basemap = Basemap(projection="omerc",
llcrnrlon=managerHighRes.lons2D[0, 0],
llcrnrlat=managerHighRes.lats2D[0, 0],
urcrnrlon=managerHighRes.lons2D[-1, -1],
urcrnrlat=managerHighRes.lats2D[-1, -1],
lat_1=rot_lat_lon.lat1,
lat_2=rot_lat_lon.lat2,
lon_1=rot_lat_lon.lon1,
lon_2=rot_lat_lon.lon2,
no_rot=True)
swe_obs = swe_obs_manager.get_mean(start_year, end_year, months=months)
obs_ihr = swe_obs_manager.interpolate_data_to(swe_obs,
managerHighRes.lons2D,
managerHighRes.lats2D,
nneighbours=1)
obs_ilr = swe_obs_manager.interpolate_data_to(swe_obs,
managerLowRes.lons2D,
managerLowRes.lats2D,
nneighbours=1)
lowResSwe = managerLowRes.get_mean_field(start_year,
end_year,
months=months,
var_name="I5")
lowResErr = (lowResSwe - obs_ilr)
lowResErr[obs_ilr > 0] /= obs_ilr[obs_ilr > 0]
lowResErr = np.ma.masked_where(obs_ilr <= 0, lowResErr)
highResSwe = managerHighRes.get_mean_field(start_year,
end_year,
months=months,
var_name="I5")
highResErr = (highResSwe - obs_ihr)
highResErr[obs_ihr > 0] /= obs_ihr[obs_ihr > 0]
highResErr = np.ma.masked_where(obs_ihr <= 0, highResErr)
upscaledHighResSwe = upscale(managerHighRes, managerLowRes, highResSwe)
upscaledHighResErr = upscaledHighResSwe - obs_ilr
good_points = obs_ilr > 0
upscaledHighResErr[good_points] /= obs_ilr[good_points]
upscaledHighResErr = np.ma.masked_where(~good_points, upscaledHighResErr)
plot_and_compare_2fields(lowResSwe,
"low res",
upscaledHighResSwe,
"high res (upscaled)",
basemap=basemap,
manager1=managerLowRes,
manager2=managerLowRes)
plot_and_compare_2fields(lowResErr,
"low res err",
upscaledHighResErr,
"high res (upscaled) err",
basemap=basemap,
manager1=managerLowRes,
manager2=managerLowRes,
clevs=np.arange(-1, 1.2, 0.2))
plot_and_compare_2fields(lowResSwe,
"low res",
highResSwe,
"high res",
basemap=basemap,
manager1=managerLowRes,
manager2=managerHighRes)
plot_and_compare_2fields(lowResErr,
"low res err",
highResErr,
"high res err",
basemap=basemap,
manager1=managerLowRes,
manager2=managerHighRes,
clevs=np.arange(-1, 1.2, 0.2))
plt.show()
def main():
swe_obs_manager = SweDataManager(var_name="SWE")
data_path = "/home/huziy/skynet3_exec1/from_guillimin/quebec_86x86_0.5deg_without_lakes_v3"
coord_file = os.path.join(data_path, "pm1985050100_00000000p")
managerLowRes = Crcm5ModelDataManager(samples_folder_path=data_path,
file_name_prefix="pm", all_files_in_samples_folder=True, need_cell_manager=True
)
data_path = "/home/huziy/skynet3_exec1/from_guillimin/quebec_highres_spinup_12_month_without_lakes_v3"
coord_file = os.path.join(data_path, "pm1985050100_00000000p")
managerHighRes = Crcm5ModelDataManager(samples_folder_path=data_path,
file_name_prefix="pm", all_files_in_samples_folder=True, need_cell_manager=True
)
start_year = 1987
end_year = 1987
months = [1,2,12]
rot_lat_lon = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)
basemap = Basemap(
projection="omerc",
llcrnrlon=managerHighRes.lons2D[0,0],
llcrnrlat=managerHighRes.lats2D[0, 0],
urcrnrlon=managerHighRes.lons2D[-1,-1],
urcrnrlat=managerHighRes.lats2D[-1,-1],
lat_1=rot_lat_lon.lat1,
lat_2=rot_lat_lon.lat2,
lon_1=rot_lat_lon.lon1,
lon_2=rot_lat_lon.lon2,
no_rot=True
)
swe_obs = swe_obs_manager.get_mean(start_year, end_year, months=months)
obs_ihr = swe_obs_manager.interpolate_data_to(swe_obs, managerHighRes.lons2D,
managerHighRes.lats2D, nneighbours=1)
obs_ilr = swe_obs_manager.interpolate_data_to(swe_obs,managerLowRes.lons2D,
managerLowRes.lats2D, nneighbours=1)
lowResSwe = managerLowRes.get_mean_field(start_year, end_year, months=months, var_name="I5")
lowResErr = (lowResSwe - obs_ilr)
lowResErr[obs_ilr > 0] /= obs_ilr[obs_ilr > 0]
lowResErr = np.ma.masked_where(obs_ilr <= 0, lowResErr)
highResSwe = managerHighRes.get_mean_field(start_year, end_year, months= months, var_name="I5")
highResErr = (highResSwe - obs_ihr)
highResErr[obs_ihr > 0 ] /= obs_ihr[obs_ihr > 0]
highResErr = np.ma.masked_where(obs_ihr <= 0, highResErr)
upscaledHighResSwe = upscale(managerHighRes, managerLowRes, highResSwe)
upscaledHighResErr = upscaledHighResSwe - obs_ilr
good_points = obs_ilr > 0
upscaledHighResErr[good_points] /= obs_ilr[good_points]
upscaledHighResErr = np.ma.masked_where(~good_points, upscaledHighResErr)
plot_and_compare_2fields(lowResSwe, "low res", upscaledHighResSwe, "high res (upscaled)", basemap=basemap,
manager1 = managerLowRes, manager2 = managerLowRes)
plot_and_compare_2fields(lowResErr, "low res err", upscaledHighResErr, "high res (upscaled) err", basemap=basemap,
manager1 = managerLowRes, manager2 = managerLowRes, clevs=np.arange(-1, 1.2, 0.2))
plot_and_compare_2fields(lowResSwe, "low res", highResSwe, "high res", basemap=basemap,
manager1 = managerLowRes, manager2 = managerHighRes)
plot_and_compare_2fields(lowResErr, "low res err", highResErr, "high res err", basemap=basemap,
manager1 = managerLowRes, manager2 = managerHighRes, clevs = np.arange(-1, 1.2, 0.2))
plt.show()
def main():
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-10)
lons2d[lons2d > 180] -= 360
x, y = b(lons2d, lats2d)
#period
start_year = 1981
end_year = 1997
the_months = [12,1,2]
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)
swe_obs_manager = SweDataManager(var_name="SWE")
swe_obs = swe_obs_manager.get_mean(start_year, end_year, months=the_months)
print("Calculated obs swe")
swe_obs_interp = swe_obs_manager.interpolate_data_to(swe_obs, lons2d, lats2d, nneighbours=1)
gs = gridspec.GridSpec(2,3)
#plot_utils.apply_plot_params(width_pt=None, height_cm=20, width_cm=30, font_size=12)
fig = plt.figure()
coast_line_width = 0.25
axes_list = []
#plot obs on its own grid but in the model's projection
ax = fig.add_subplot(gs[0,0])
axes_list.append(ax)
x_obs, y_obs = b(swe_obs_manager.lons2d, swe_obs_manager.lats2d)
swe_obs = maskoceans(swe_obs_manager.lons2d, swe_obs_manager.lats2d, swe_obs)
img = b.contourf(x_obs, y_obs, swe_obs, levels = levels, norm = norm, cmap = cmap)
draw_colorbar(fig, img, ax = ax)
ax.set_title("Obs native grid")
#plot obs interpolated
ax = fig.add_subplot(gs[1,0])
axes_list.append(ax)
swe_obs_interp = maskoceans(lons2d, lats2d, swe_obs_interp)
img = b.contourf(x, y, swe_obs_interp, levels = levels, norm = norm, cmap = cmap)
draw_colorbar(fig, img, ax = ax)
ax.set_title("Obs interpolated \n to model grid")
#plot model res. (ERA40 driven)
ax = fig.add_subplot(gs[0,1])
axes_list.append(ax)
prefixes = ["pmNorthAmerica_0.44deg_ERA40-Int_{0}_1958-1977".format(m) for m in ["Dec", "Jan", "Feb"]]
swe_model_era = CRCMDataManager.get_mean_2d_from_climatologies(path="data/CORDEX/na/means_month/era40_driven",
var_name="I5", file_prefixes=prefixes)
swe_model_era = maskoceans(lons2d, lats2d, swe_model_era)
img = b.contourf(x, y, swe_model_era, levels = levels, norm = norm, cmap = cmap)
draw_colorbar(fig, img, ax = ax)
ax.set_title("Model (ERA40 driven 1958-1977)")
#plot model(ERA40 driven) - obs
ax = fig.add_subplot(gs[0,2])
axes_list.append(ax)
levels_diff = np.arange(-100, 110, 10)
img = b.contourf(x, y, swe_model_era - swe_obs_interp, levels = levels_diff)
draw_colorbar(fig, img, ax = ax, boundaries=levels_diff)
ax.set_title("Model (ERA40 driven 1958-1977) - Obs.")
#plot model res. (GCM driven, E2)
ax = fig.add_subplot(gs[1,1])
axes_list.append(ax)
path = "/skynet1_exec2/separovi/results/North_America/tests_E/all/means_season"
prefix = "pmNorthAmerica_0.44deg_CanHistoE2_A1979-1997"
suffixes = ["djf"]
swe_model_gcm = CRCMDataManager.get_mean_2d_from_climatologies(path=path, file_prefixes=[prefix],
file_suffixes=suffixes, var_name="I5")
swe_model_gcm = maskoceans(lons2d, lats2d, swe_model_gcm)
print("model: min = {0}; max = {1}".format(np.ma.min(swe_model_gcm), np.ma.max(swe_model_gcm)))
img = b.contourf(x, y, swe_model_gcm, levels = levels, norm = norm, cmap = cmap)
draw_colorbar(fig, img, ax = ax, boundaries=levels_diff)
ax.set_title("Model (GCM driven, E2, 1979-1997)")
#plot model(gcm driven) - obs
ax = fig.add_subplot(gs[1,2])
axes_list.append(ax)
levels_diff = np.arange(-100, 110, 10)
img = b.contourf(x, y, np.ma.array(swe_model_gcm) - swe_obs_interp, levels = levels_diff)
draw_colorbar(fig, img, ax = ax)
ax.set_title("Model (GCM driven) - Obs.")
####Draw common elements
pf_kinds = draw_regions.get_permafrost_mask(lons2d, lats2d)
for the_ax in axes_list:
b.drawcoastlines(ax = the_ax, linewidth = coast_line_width)
b.contour(x, y, pf_kinds, ax = the_ax, colors = "k")
gs.tight_layout(fig, h_pad = 0.9, w_pad = 18)
fig.savefig("swe_validation.png")
def validate_using_monthly_diagnostics():
start_year = 1980
end_year = 1996
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
sim_names = ["ERA40","MPI","CanESM"]
simname_to_path = {
"ERA40": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1",
"MPI": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_MPI_B1",
"CanESM": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_CanESM_B1"
}
coord_file = os.path.join(sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(resolution="c",
file_path = coord_file, llcrnrlat=45.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74,
anchor="W"
)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
swe_obs_manager = SweDataManager(var_name="SWE")
swe_obs = swe_obs_manager.get_mean(start_year, end_year, months=[12,1,2])
swe_obs = swe_obs_manager.interpolate_data_to(swe_obs, lons2d, lats2d, nneighbours=1)
x, y = basemap(lons2d, lats2d)
#x = (x[1:,1:] + x[:-1, :-1]) /2.0
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 2)
#plot_utils.apply_plot_params(width_pt=None, width_cm=35,height_cm=55, font_size=35)
fig = plt.figure()
assert isinstance(fig, Figure)
cmap = my_colormaps.get_red_blue_colormap(ncolors=10)
gs = gridspec.GridSpec(3,2, width_ratios=[1,0.1], hspace=0, wspace=0,
left=0.05, bottom = 0.01, top=0.95)
all_axes = []
all_img = []
i = 0
for name in sim_names:
path = simname_to_path[name]
dm = CRCMDataManager(data_folder=path)
swe_mod = dm.get_mean_over_months_of_2d_var(start_year, end_year, months = [12,1,2], var_name="I5")
delta = swe_mod - swe_obs
ax = fig.add_subplot(gs[i,0])
assert isinstance(ax, Axes)
delta = np.ma.masked_where(mask_cond, delta)
img = basemap.pcolormesh(x, y, delta, cmap = cmap, vmin=-100, vmax = 100)
if not i:
ax.set_title("SWE, Mod - Obs, ({0} - {1}), DJF\n".format(start_year, end_year))
i += 1
#ax.set_ylabel(name)
all_axes.append(ax)
all_img.append(img)
i = 0
axs_to_hide = []
#zones and coastlines
for the_ax, the_img in zip(all_axes, all_img):
#divider = make_axes_locatable(the_ax)
#cax = divider.append_axes("bottom", "5%", pad="3%")
assert isinstance(the_ax, Axes)
basemap.drawcoastlines(ax = the_ax, linewidth=0.5)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
ax=the_ax, linewidth=1.5, drawbounds=False)
for nshape,seg in enumerate(basemap.zone):
if basemap.zone_info[nshape]["EXTENT"] != "C": continue
poly = mpl.patches.Polygon(seg,edgecolor = "k", facecolor="none", zorder = 10, lw = 1.5)
the_ax.add_patch(poly)
i += 1
cax = fig.add_subplot(gs[:,1])
assert isinstance(cax, Axes)
cax.set_anchor("W")
cax.set_aspect(30)
formatter = FuncFormatter(
lambda x, pos: "{0: <6}".format(str(x))
)
cb = fig.colorbar(all_img[0], ax = cax, cax = cax, extend = "both", format = formatter)
cax.set_title("mm")
print("aspect = ", cax.get_aspect())
#fig.tight_layout(h_pad=0)
# for the_ax in axs_to_hide:
# the_ax.set_visible(False)
fig.savefig("swe_validation_era_mpi_canesm_djf.png")
#swe_obs = swe_obs_manager.get_mean(start_year, end_year, months=the_months)
pass
def compare_vars(vname_model="TT",
vname_obs="tmp",
r_config=None,
season_to_months=None,
obs_path=None,
nx_agg_model=5,
ny_agg_model=5,
bmp_info_agg=None,
diff_axes_list=None,
obs_axes_list=None,
model_axes_list=None,
bmp_info_model=None,
mask_shape_file=None,
nx_agg_obs=1,
ny_agg_obs=1):
"""
if obs_axes_list is not None, plot observation data in those
:param mask_shape_file:
:param bmp_info_model: basemap info native to the model
:param model_axes_list: Axes to plot model outputs
:param vname_model:
:param vname_obs:
:param r_config:
:param season_to_months:
:param obs_path:
:param nx_agg_model:
:param ny_agg_model:
:param bmp_info_agg:
:param diff_axes_list: if it is None the plots for each variable is done in separate figures
"""
if vname_obs is None:
vname_model_to_vname_obs = {"TT": "tmp", "PR": "pre"}
vname_obs = vname_model_to_vname_obs[vname_model]
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(
run_config=r_config,
varname=vname_model,
level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(
field, nagg_x=nx_agg_model, nagg_y=ny_agg_model)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if vname_obs in [
"SWE",
]:
obs_manager = SweDataManager(path=obs_path, var_name=vname_obs)
elif obs_path is None:
obs_manager = CRUDataManager(var_name=vname_obs)
else:
obs_manager = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = obs_manager.get_seasonal_means(
season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
# Derive the mask from a shapefile if provided
if mask_shape_file is not None:
the_mask = get_mask(bmp_info_agg.lons,
bmp_info_agg.lats,
shp_path=mask_shape_file)
else:
the_mask = np.zeros_like(bmp_info_agg.lons)
for season, obs_field in seasonal_clim_fields_obs.items():
obs_field = obs_manager.interpolate_data_to(obs_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=nx_agg_obs *
ny_agg_obs)
obs_field = np.ma.masked_where(the_mask > 0.5, obs_field)
seasonal_clim_fields_obs_interp[season] = obs_field
# assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
print("-------------var: {} (PE with CRU)---------------------".format(
vname_model))
for season in seasonal_clim_fields_obs_interp:
seasonal_clim_fields_obs_interp[season] = np.ma.masked_where(
np.isnan(seasonal_clim_fields_obs_interp[season]),
seasonal_clim_fields_obs_interp[season])
season_to_err[season] = season_to_clim_fields_model_agg[
season] - seasonal_clim_fields_obs_interp[season]
if vname_model in ["I5"]:
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err[season] = maskoceans(lons, bmp_info_agg.lats,
season_to_err[season])
good_vals = season_to_err[season]
good_vals = good_vals[~good_vals.mask]
print("{}: min={}; max={}; avg={}".format(season, good_vals.min(),
good_vals.max(),
good_vals.mean()))
print("---------percetages --- CRU ---")
print("{}: {} \%".format(
season,
good_vals.mean() / seasonal_clim_fields_obs_interp[season]
[~season_to_err[season].mask].mean() * 100))
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=diff_axes_list)
if obs_axes_list is not None and vname_model in ["I5"]:
clevs = [0, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 500]
cs_obs = None
xx, yy = bmp_info_agg.get_proj_xy()
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
lons_model = None
xx_model, yy_model = None, None
cs_mod = None
norm = BoundaryNorm(clevs, 256)
for col, (season, obs_field) in enumerate(
seasonal_clim_fields_obs_interp.items()):
# Obsrved fields
ax = obs_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
to_plot = maskoceans(lons, bmp_info_agg.lats, obs_field)
cs_obs = bmp_info_agg.basemap.contourf(xx,
yy,
to_plot,
levels=clevs,
ax=ax,
norm=norm,
extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
ax.set_title(season)
# Model outputs
if model_axes_list is not None:
ax = model_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(
BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
if lons_model is None:
lons_model = bmp_info_model.lons.copy()
lons_model[lons_model > 180] -= 360
xx_model, yy_model = bmp_info_model.basemap(
lons_model, bmp_info_model.lats)
model_field = seasonal_clim_fields_model[season]
to_plot = maskoceans(lons_model, bmp_info_model.lats,
model_field)
cs_mod = bmp_info_agg.basemap.contourf(xx_model,
yy_model,
to_plot,
levels=cs_obs.levels,
ax=ax,
norm=cs_obs.norm,
cmap=cs_obs.cmap,
extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
plt.colorbar(cs_obs, cax=obs_axes_list[-1])
return cs
