def plot_alt(alt, tmin, tmax, levelheights, annual_means, coordfile="",
lon1=-97.0, lat1=47.50,
lon2=-7, lat2=0
):
fig = plt.figure()
ax = fig.add_subplot(111)
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(resolution="c",
# llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74,
file_path=coordfile,
lon1=lon1, lat1=lat1,
lon2=lon2, lat2=lat2
)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
x, y = basemap(lons2d, lats2d)
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
# mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3) | (alt < 0)
alt = np.ma.masked_where(alt < 0, alt)
bounds = [0, 0.1, 0.5, 1, 2, 3, 5, 8, 9, 10, 11]
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=10) #cm.get_cmap("jet",10)
norm = BoundaryNorm(boundaries=bounds, ncolors=cmap.N, clip=True)
qm = basemap.pcolormesh(x, y, alt, cmap=cmap, norm=norm, ax=ax)
#qm = basemap.contourf(x, y, alt, levels = bounds, ax = ax)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(qm, cax=cax, extend="max", ticks=bounds)
basemap.drawcoastlines(ax=ax, linewidth=0.5)
#basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
# ax=ax, linewidth=1.5)
basemap.readshapefile("data/permafrost_lat-lon1/permafrost_latlon", name="zone",
ax=ax, linewidth=1.5)
#crossPlotter = SoundingPlotter(ax, basemap, tmin, tmax, lons2d, lats2d, levelheights=levelheights)
sectPlotter = SoundingAndCrossSectionPlotter(ax, basemap, tmin, tmax, lons2d, lats2d,
levelheights=levelheights, temporal_data=annual_means)
i_inds = [55, 47, 59, 43, 43, 50, 70, 67, 80, 77, 97, 117, 121, 143, 132, 148, 143, 154, 144, 144, 120, 118, 94, 74,
66, 82, 99, 126, 106]
j_inds = [79, 89, 94, 104, 114, 122, 123, 139, 134, 149, 145, 152, 141, 135, 133, 106, 57, 55, 40, 35, 39, 49, 53,
63, 54, 46, 44, 69, 59]
sectPlotter.plot_cross_sections_for(i_inds, j_inds)
plt.show()
def main():
path = "/home/huziy/skynet1_rech3/cordex/NorthAmerica_0.44deg_ERA40-Int_195801_static_data.rpn"
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1" #for coordinates
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=40.0, llcrnrlon=-143, urcrnrlon=-20, urcrnrlat=74
)
#read depth to bedrock field
rObj = RPN(path)
dpth_to_bdrck = rObj.get_first_record_for_name("8L")
rObj.close()
#dpth_to_bdrck[:,:] = 4
bounds = [0,0.5,1,1.5,2,2.5,3,3.5,4,4.6]
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=len(bounds) - 1)
norm = BoundaryNorm(boundaries=bounds, ncolors=cmap.N)
x,y = basemap(lons2d, lats2d)
fig = plt.figure()
CS = basemap.pcolormesh(x, y, dpth_to_bdrck, norm = norm, cmap = cmap)
print(( dpth_to_bdrck.min(), dpth_to_bdrck.max()))
basemap.drawcoastlines()
basemap.drawparallels(np.arange(-80.,81.,20.))
basemap.drawmeridians(np.arange(-180.,181.,20.))
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "5%", pad="3%")
cax.set_title("m")
fig.colorbar(CS, cax = cax, ticks=bounds)
fig.tight_layout()
fig.savefig("dpth_to_bdrck.png")
plt.show()
pass
def main():
path = "/home/huziy/skynet1_rech3/cordex/NorthAmerica_0.44deg_ERA40-Int_195801_static_data.rpn"
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1" #for coordinates
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=40.0,
llcrnrlon=-143,
urcrnrlon=-20,
urcrnrlat=74)
#read depth to bedrock field
rObj = RPN(path)
dpth_to_bdrck = rObj.get_first_record_for_name("8L")
rObj.close()
#dpth_to_bdrck[:,:] = 4
bounds = [0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.6]
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=len(bounds) - 1)
norm = BoundaryNorm(boundaries=bounds, ncolors=cmap.N)
x, y = basemap(lons2d, lats2d)
fig = plt.figure()
CS = basemap.pcolormesh(x, y, dpth_to_bdrck, norm=norm, cmap=cmap)
print((dpth_to_bdrck.min(), dpth_to_bdrck.max()))
basemap.drawcoastlines()
basemap.drawparallels(np.arange(-80., 81., 20.))
basemap.drawmeridians(np.arange(-180., 181., 20.))
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "5%", pad="3%")
cax.set_title("m")
fig.colorbar(CS, cax=cax, ticks=bounds)
fig.tight_layout()
fig.savefig("dpth_to_bdrck.png")
plt.show()
pass
def plot_alt_from_recent_jpp_and_andrey():
driver = "CanESM"
path = "/home/huziy/skynet1_rech3/jpp_progs_recent/pf/Arctic_Andrey_ALT_canesm1"
rObj = RPN(path)
altt = rObj.get_all_time_records_for_name(varname="FALT")
alt = np.mean( np.array(list(altt.values())), axis = 0)
rObj.close()
coord_file = path
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(resolution="c",
file_path = coord_file, lon1 = 60, lat1 = 89.999, lon2 = -30, lat2 = 0.00001,
projection = "omerc", round = True
)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
x, y = basemap(lons2d, lats2d)
#permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
#mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3)
alt = np.ma.masked_where(alt < 0, alt)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
bounds = list(range(6))
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=len(bounds) - 1) #cm.get_cmap("jet",10)
#bounds = [0,0.1,0.5,1,2,3,5,8,9,10,11]
norm = BoundaryNorm(boundaries=bounds,ncolors=len(bounds)-1, clip = False)
#norm = None
cmap.set_over(cmap(1.0))
clevels = np.arange(0,h_max+1,1)
gs = gridspec.GridSpec(1,1)
#ax = fig.add_axes([0,0,1,1], polar = True)
ax = fig.add_subplot(gs[0,0])
assert isinstance(ax, Axes)
#hc = np.ma.masked_where(mask_cond | (np.min(altt.values(), axis = 0) < 0), alt)
#hc = np.ma.masked_where( (hc < 0), hc)
img = basemap.pcolormesh(x, y, alt, cmap = cmap, vmax = h_max, norm=norm)
ax.set_title("ALT, JPP-Andrey, ({0} - CRCM5) \n".format(driver))
basemap.drawcoastlines(ax = ax, linewidth=0.5)
#basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
# ax=ax, linewidth=1.5)
basemap.readshapefile("data/permafrost_lat-lon1/permafrost_latlon", name="zone",
ax=ax, linewidth=1.5)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax = cax, extend = "max", ticks = bounds)
cax.set_title("m \n")
fig.tight_layout(w_pad=0.0)
fig.savefig("alt_jpp_current_{0}.png".format(driver))
def plot_current_alts():
from . import plot_dpth_to_bdrck
bdrck_field = plot_dpth_to_bdrck.get_depth_to_bedrock()
start_year = 1980
end_year = 1996
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
coord_file = os.path.join(sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
#coord_file = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NA_1.0deg_soil_spinup2/pmNA_1.0deg_soil_spinup2_228006_moyenne"
sim_names = ["ERA40", "MPI","CanESM"]
simname_to_path = {
#"ERA40": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1",
"ERA40": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_ERA40-Int_old_snow_cond",
#"ERA40" : "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NA_1.0deg_soil_spinup2",
"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"
}
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)
#basemap.transform_scalar()
#basemap = Basemap()
lons2d[lons2d > 180] -= 360
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=20, height_cm=40, font_size=16)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
bounds = [0,0.1,0.5,1,2,3,4,5]
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=len(bounds) - 1) #cm.get_cmap("jet",10)
#cmap = my_colormaps.get_cmap_wo_red(ncolors=len(bounds) - 1)
norm = BoundaryNorm(boundaries=bounds,ncolors=len(bounds), clip=True)
cmap.set_over(cmap(1.0))
clevels = np.arange(0,h_max+1,1)
gs = gridspec.GridSpec(3,2, width_ratios=[1,0.06], hspace=0, wspace=0.0,
left=0.05, bottom = 0.02, top=0.95)
all_axes = []
all_img = []
i = 0
hc_list = []
for name in sim_names:
path = simname_to_path[name]
dm = CRCMDataManager(data_folder=path)
hc0, t3d_min, t3d_max = dm.get_alt_using_monthly_mean_climatology(list(range(start_year,end_year+1)))
hc_list.append(hc0)
ax = fig.add_subplot(gs[i,0])
#cp = SoundingPlotter(ax, basemap, t3d_min, t3d_max, lons2d, lats2d, levelheights=dm.level_heights)
assert isinstance(ax, Axes)
hc = np.ma.masked_where(mask_cond | (hc0 < 0), hc0)
#hc = np.ma.masked_where( (hc0 < 0), hc0)
hc5 = np.ma.masked_where((hc0 <= 15) | hc.mask, hc)
img = basemap.pcolormesh(x, y, hc, cmap = cmap, vmax = h_max, norm=norm)
if not i:
ax.set_title("ALT ({0} - {1}) \n".format(start_year, end_year))
i += 1
ax.set_ylabel("CRCM ({0})".format(name))
all_axes.append(ax)
all_img.append(img)
print(np.ma.min(hc), np.ma.max(hc))
#hc5 = np.ma.masked_where((hc0 <= 6) | hc.mask, hc)
#print "Number of cells with alt > 5 is {0}, and the range is {1} ... {2}".format(hc5.count(), hc5.min(), hc5.max())
#bdrck_field5 = np.ma.masked_where(hc5.mask, bdrck_field)
#print "Bedrock ranges for those points: {0} ... {1}".format(bdrck_field5.min(), bdrck_field5.max())
ind = np.where(~hc5.mask)
xs = ind[0]
ys = ind[1]
all_months, all_temps = dm.get_monthly_mean_soiltemps(year_range=range(start_year,end_year+1))
all_months_ord = date2num(all_months)
# mpl.rcParams['contour.negative_linestyle'] = 'solid'
#
# for the_i, the_j in zip(xs,ys):
# #plot profile
# plt.figure()
# plt.plot(t3d_max[the_i, the_j, :] - dm.T0, dm.level_heights, color = "r")
# plt.plot(t3d_min[the_i, the_j, :] - dm.T0, dm.level_heights, color = "b")
# plt.plot([0 , 0], [dm.level_heights[0], dm.level_heights[-1]], color = "k")
#
# x1, x2 = plt.xlim()
# #plt.plot( [x1, x2], [bdrck_field[the_i, the_j], bdrck_field[the_i, the_j]], color = "k", lw = 3 )
# #plt.title(str(i) + ", dpth_to_bedrock = {0} m".format(bdrck_field[the_i, the_j]))
# plt.title(str(i))
# plt.gca().invert_yaxis()
# plt.savefig("prof{0}.png".format(i))
# ax.annotate(str(i), (x[the_i, the_j], y[the_i, the_j]), font_properties =
# FontProperties(size=10))
#
#
# #plot vertical temp cross-section
# plt.figure()
# plt.title(str(i) + ", ({0} - {1})".format(start_year, end_year))
#
# levs2d, times2d = np.meshgrid(dm.level_heights, all_months_ord)
# clevs = [-25,-20,-10,-5,-1,0,1,5,10,20,25]
# norm = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
# cmap = cm.get_cmap("jet", len(clevs) - 1)
#
# img = plt.contourf(times2d, levs2d, all_temps[:,the_i, the_j, :] - dm.T0, levels = clevs, cmap = cmap, norm = norm)
# #plt.contour(times2d, levs2d, all_temps[:,the_i, the_j, :] - dm.T0, levels = clevs, colors = "k", linewidth = 1)
# the_ax = plt.gca()
# assert isinstance(the_ax, Axes)
# the_ax.invert_yaxis()
# the_ax.xaxis.set_major_formatter(FuncFormatter(
# lambda x, pos: num2date(float(x)).strftime("%Y")
# ))
#
# print "i = {0}; lon, lat = {1}, {2}".format(i, lons2d[the_i, the_j], lats2d[the_i, the_j])
#
# plt.colorbar(img, ticks = clevs)
#
# plt.savefig("temp_section_{0}.png".format(i))
#
# i += 1
print("lons = [{0}]".format(",".join([str(x) for x in lons2d[np.array(xs), np.array(ys)]])))
print("lats = [{0}]".format(",".join([str(x) for x in lats2d[np.array(xs), np.array(ys)]])))
# draw barplot with numbers of alt in given ranges
# plt.figure()
# alt_ranges = xrange(0,18)
# numbers = []
# for the_alt in alt_ranges:
# hci = np.ma.masked_where( (hc0 < the_alt) | (hc0 > the_alt + 1) | hc.mask ,hc)
# numbers.append(hci.count())
# plt.bar(alt_ranges, numbers, width=1)
# plt.xlabel("ALT (m)")
# plt.ylabel("Number of cells")
# plt.savefig("numbers_in_range.png")
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("right", "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"] not in ["C"]: continue
poly = mpl.patches.Polygon(seg,edgecolor = "k", facecolor="none", zorder = 10, lw = 1.5)
the_ax.add_patch(poly)
# if i != 1:
# axs_to_hide.append(cax)
i += 1
cax = fig.add_subplot(gs[:,1])
cax.set_anchor("W")
cax.set_aspect(35)
formatter = FuncFormatter(
lambda x, pos: "{0: <6}".format(x)
)
cb = fig.colorbar(all_img[0], ax = cax, cax = cax, extend = "max", ticks = bounds, format = formatter)
cax.set_title("m")
#fig.tight_layout(h_pad=0)
# for the_ax in axs_to_hide:
# the_ax.set_visible(False)
fig.savefig("alt_from_climatology_current.png")
#print ALT for selected points
site_names = ["S","K","T"]
sel_lons = [-75.646, -65.92, -69.95]
sel_lats = [62.197, 58.709, 58.67]
xo,yo,zo = lat_lon.lon_lat_to_cartesian(sel_lons, sel_lats)
xi, yi, zi = lat_lon.lon_lat_to_cartesian(lons2d.flatten(), lats2d.flatten())
ktree = KDTree(list(zip(xi,yi,zi)))
dists, indexes = ktree.query(list(zip(xo,yo,zo)))
for name, data in zip(sim_names, hc_list):
print(name)
flat_data = data.flatten()
for p_name, ind in zip(site_names, indexes):
print(p_name, "{0} m".format(flat_data[ind]))
print("--" * 10)
pass
def plot_current_alts_nyear_rule(nyear = 2):
start_year = 1981
end_year = 2008
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
sim_names = ["ERA40", "MPI","CanESM"]
all_data_f = "/home/huziy/skynet1_rech3/cordex/for_Samira"
simname_to_path = {
"ERA40": os.path.join(all_data_f, "alt_era_b1_yearly.nc"),
"MPI": os.path.join(all_data_f, "alt_mpi_b1_yearly.nc"),
"CanESM": os.path.join(all_data_f, "alt_canesm_b1_yearly.nc")
}
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=40.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74
)
assert isinstance(basemap, Basemap)
#basemap.transform_scalar()
#basemap = Basemap()
lons2d[lons2d > 180] -= 360
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 >= 3)
# plot_utils.apply_plot_params(width_pt=None, width_cm=20, height_cm=40, font_size=25)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=10) #cm.get_cmap("jet",10)
bounds = [0,0.1,0.5,1,2,3,5,8,9,10,11]
norm = BoundaryNorm(boundaries=bounds,ncolors=len(bounds), clip=True)
cmap.set_over(cmap(1.0))
clevels = np.arange(0,h_max+1,1)
gs = gridspec.GridSpec(3,1)
all_axes = []
all_img = []
i = 0
hc_list = []
hct_list = []
for name in sim_names:
path = simname_to_path[name]
#select data and needed alt
ds = Dataset(path)
years = ds.variables["year"][:]
hct = ds.variables["alt"][(years >= start_year) & (years <= end_year),:,:]
hct_list.append(hct)
print("hct.shape = ", hct.shape)
#hc = get_alt_using_nyear_rule(hct, nyears = nyear)
hc = np.mean(hct, axis = 0)
hc_list.append(hc)
ax = fig.add_subplot(gs[i,0])
assert isinstance(ax, Axes)
hc = np.ma.masked_where(mask_cond | (np.min(hct, axis = 0) < 0), hc)
#hc = np.ma.masked_where( (hc < 0), hc)
img = basemap.pcolormesh(x, y, hc, cmap = cmap, vmax = h_max, norm=norm)
if not i:
ax.set_title("ALT, mean ({0} - {1}) \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):
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)
divider = make_axes_locatable(the_ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(the_img, cax = cax, extend = "max", ticks = bounds)
cax.set_title("m \n")
if i != 2:
axs_to_hide.append(cax)
i += 1
fig.tight_layout(w_pad=0.0)
for the_ax in axs_to_hide:
the_ax.set_visible(False)
fig.savefig("alt_mean_current.png")
#print ALT for selected points
site_names = ["S","K","T"]
sel_lons = [-75.646, -65.92, -69.95]
sel_lats = [62.197, 58.709, 58.67]
xo,yo,zo = lat_lon.lon_lat_to_cartesian(sel_lons, sel_lats)
xi, yi, zi = lat_lon.lon_lat_to_cartesian(lons2d.flatten(), lats2d.flatten())
ktree = KDTree(list(zip(xi,yi,zi)))
dists, indexes = ktree.query(list(zip(xo,yo,zo)))
for name, data, the_hct in zip(sim_names, hc_list, hct_list):
print(name)
flat_data = data.flatten()
for p_name, ind in zip(site_names, indexes):
in_data = []
for t in range(the_hct.shape[0]):
in_data.append(the_hct[t,:,:].flatten()[ind])
print(",".join(["{0:.1f}".format(float(x)) for x in in_data]))
print(p_name, "{0:.1f} m".format(float(flat_data[ind])))
print("--" * 10)
def main():
start_year = 1981
end_year = 2008
#mean alt
path_to_yearly = "alt_era_b1_yearly.nc"
ds = Dataset(path_to_yearly)
hm = ds.variables["alt"][:]
years = ds.variables["year"][:]
years_sel = np.where(( start_year <= years ) & (years <= end_year))[0]
print(years_sel)
hm = hm[np.array(years_sel),:,:]
print(hm.shape)
good_points = ~np.any(hm < 0, axis = 0)
hm2d = np.ma.masked_all(good_points.shape)
hm2d[good_points] = np.mean( hm[ : , good_points],
axis = 0)
#alt from climatology
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
# dm = CRCMDataManager(data_folder=sim_data_folder)
# hc = dm.get_alt_using_monthly_mean_climatology(xrange(start_year,end_year+1))
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=40.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74
)
assert isinstance(basemap, Basemap)
#basemap.transform_scalar()
#basemap = Basemap()
lons2d[lons2d > 180] -= 360
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 >= 3)
#plot_utils.apply_plot_params(width_pt=None, width_cm=25,height_cm=35, font_size=12)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=10) #cm.get_cmap("jet",10)
bounds = [0,0.1,0.5,1,2,3,5,8,9,10,11]
norm = BoundaryNorm(boundaries=bounds,ncolors=len(bounds), clip=True)
cmap.set_over(cmap(1.0))
clevels = np.arange(0,h_max+1,1)
gs = gridspec.GridSpec(1,1)
all_axes = []
all_img = []
print(basemap(-96.999, 68.42))
print(basemap(-2.4e7,6.3e6, inverse = True))
ax = fig.add_subplot(gs[0,0])
hm2d = np.ma.masked_where(mask_cond, hm2d)
img = basemap.pcolormesh(x, y, hm2d, cmap = cmap, vmax = h_max, norm=norm)
#img = basemap.contourf(x, y, hm2d, levels = clevels, cmap = cmap)
ax.set_title("Mean ALT")
all_axes.append(ax)
all_img.append(img)
print(("hm2d(min,max) = ",hm2d.min(), hm2d.max()))
# ax = fig.add_subplot(gs[1,0])
# hc = np.ma.masked_where(hc < 0, hc)
# hc = np.ma.masked_where(mask_cond | (hc > h_max) | hm2d.mask, hc)
# img = basemap.contourf(x, y, hc, levels = clevels)
# all_img.append(img)
# all_axes.append(ax)
# ax.set_title("ALT from climatology")
# print("hc(min,max) = ",hc.min(), hc.max())
# ax = fig.add_subplot(gs[2,0])
# delta = hm2d - hc
# delta = np.ma.masked_where(hc.mask | hm2d.mask, delta)
# img = basemap.contourf(x, y, delta, levels = np.arange(-1,1.2,0.2),ax = ax,
# cmap = my_colormaps.get_red_blue_colormap())
# all_img.append(img)
# all_axes.append(ax)
# ax.set_title("Mean - Derived from climatology")
#print(np.where((hm2d < hc) & ~(hc.mask | hm2d.mask)))
#zones = get_zone_polygons(path = "data/permafrost/permaice.shp", basemap=basemap)
permafrost_mask = np.ma.masked_where((permafrost_mask < 0)|(permafrost_mask >= 4), permafrost_mask)
for the_ax, the_img in zip( all_axes, all_img ):
assert isinstance(the_ax, Axes)
basemap.drawcoastlines(ax = the_ax, linewidth=0.5)
divider = make_axes_locatable(the_ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(the_img, cax = cax, extend = "max", ticks = bounds)
#CS = basemap.contour(x,y, permafrost_mask, levels = [1,2],
# ax = the_ax, colors = "k", linewidth= 5)
#the_ax.clabel(CS,colors = 'k', fmt="%d" , fontsize=8)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
ax=the_ax, linewidth=1.5)
#for p in zones:
# the_ax.add_patch(p)
fig.tight_layout()
#cax_to_hide.set_visible(False)
fig.savefig("alt_b1.png")
plt.show()
do_stats = False
if not do_stats:
return
def plot_mean_alt_from_jpp_results():
start_year = 1981
end_year = 2008
path = "data/alts_by_jpp/MonTS_NA_ERA40_ALT_{0}_{1}".format(start_year, end_year)
rObj = RPN(path)
altt = rObj.get_all_time_records_for_name(varname="FALT")
alt = np.mean( np.array(list(altt.values())), axis = 0)
rObj.close()
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_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=40.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74
)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
x, y = basemap(lons2d, lats2d)
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3)
alt = np.ma.masked_where(mask_cond, alt)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=10) #cm.get_cmap("jet",10)
bounds = [0,0.1,0.5,1,2,3,5,8,9,10,11]
norm = BoundaryNorm(boundaries=bounds,ncolors=len(bounds), clip=True)
#norm = None
cmap.set_over(cmap(1.0))
clevels = np.arange(0,h_max+1,1)
gs = gridspec.GridSpec(3,1)
ax = fig.add_subplot(gs[0,0])
assert isinstance(ax, Axes)
hc = np.ma.masked_where(mask_cond | (np.min(list(altt.values()), axis = 0) < 0), alt)
#hc = np.ma.masked_where( (hc < 0), hc)
img = basemap.pcolormesh(x, y, hc, cmap = cmap, vmax = h_max, norm=norm)
ax.set_title("ALT, JPP ({0} - {1}) \n".format(start_year, end_year))
basemap.drawcoastlines(ax = ax, linewidth=0.5)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
ax=ax, linewidth=1.5)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax = cax, extend = "max", ticks = bounds)
cax.set_title("m \n")
fig.tight_layout(w_pad=0.0)
fig.savefig("alt_jpp_current.png")
def compare_means_2d(manager_list,
start_date=None,
end_date=None,
months=list(range(1, 13)),
var_name="STFL",
level=-1,
level_kind=level_kinds.ARBITRARY,
out_img=None,
impose_min=None,
bounds=None):
"""
Plots a panel of plots for each run
"""
ncols = 2
nrows = len(manager_list) // ncols + 1
# gs = gridspec.GridSpec(nrows, ncols, top=0.985, right=0.985, hspace=0.2, left = 0.01)
fig = plt.figure()
vmin = np.Infinity
vmax = -np.Infinity
quadMesh = None
run_id_to_field = {}
run_id_to_x = {}
run_id_to_y = {}
run_id_to_basemap = {}
run_id_to_manager = {}
for i, theManager in enumerate(manager_list):
assert isinstance(theManager, Crcm5ModelDataManager)
data_field = theManager.get_mean_field(start_date.year,
end_date.year,
months=months,
var_name=var_name,
level=level,
level_kind=level_kind)
theId = theManager.run_id
run_id_to_field[theId] = data_field
run_id_to_manager[theId] = theManager
run_id_to_basemap[theId] = theManager.get_omerc_basemap()
b = run_id_to_basemap[theId]
x, y = b(theManager.lons2D, theManager.lats2D)
run_id_to_x[theId] = x
run_id_to_y[theId] = y
vmax = max(np.max(data_field), vmax)
vmin = min(np.min(data_field), vmin)
if impose_min is not None:
vmin = impose_min
print(vmin, vmax)
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=15)
dcol = (vmax - vmin) / float(cmap.N)
if bounds is None:
bounds = [vmin + i * dcol for i in range(cmap.N + 1)]
if vmin * vmax < 0:
bounds.append(0)
bounds = list(sorted(bounds))
norm = BoundaryNorm(bounds, len(bounds))
imgGrid = ImageGrid(fig,
111,
nrows_ncols=(nrows, ncols),
axes_pad=0.5,
cbar_size="5%",
cbar_pad="5%",
share_all=True)
i = 0
for theId, data_field in run_id_to_field.items():
ax = imgGrid[i] # fig.add_subplot(gs[i // ncols, i % ncols])
ax.set_title(theId)
b = run_id_to_basemap[theId]
x = run_id_to_x[theId]
y = run_id_to_y[theId]
theManager = run_id_to_manager[theId]
assert isinstance(theManager, Crcm5ModelDataManager)
if hasattr(theManager, "slope"):
data_field = np.ma.masked_where(theManager.slope < 0, data_field)
quadMesh = b.pcolormesh(x,
y,
data_field,
vmin=vmin,
vmax=vmax,
cmap=cmap,
ax=ax,
norm=norm)
b.drawcoastlines(ax=ax)
i += 1
assert isinstance(fig, Figure)
# ax = fig.add_subplot(gs[i // ncols, i % ncols])
# assert isinstance(ax, Axes)
#ax.set_aspect(20)
#ax.set_anchor("W")
cax = imgGrid.cbar_axes[len(run_id_to_field) - 1]
cax.colorbar(quadMesh, ticks=bounds, format="%.3g")
# fig.colorbar(quadMesh, cax = ax, ticks = bounds, format = "%.2g")
if out_img is None:
fig.savefig("_".join(map(str, months)) + "_2d_means.png")
else:
fig.savefig(out_img)
pass
def plot_alt_from_recent_jpp_and_andrey():
driver = "CanESM"
path = "/home/huziy/skynet1_rech3/jpp_progs_recent/pf/Arctic_Andrey_ALT_canesm1"
rObj = RPN(path)
altt = rObj.get_all_time_records_for_name(varname="FALT")
alt = np.mean(np.array(list(altt.values())), axis=0)
rObj.close()
coord_file = path
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(
resolution="c",
file_path=coord_file,
lon1=60,
lat1=89.999,
lon2=-30,
lat2=0.00001,
projection="omerc",
round=True)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
x, y = basemap(lons2d, lats2d)
#permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
#mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3)
alt = np.ma.masked_where(alt < 0, alt)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
bounds = list(range(6))
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=len(bounds) -
1) #cm.get_cmap("jet",10)
#bounds = [0,0.1,0.5,1,2,3,5,8,9,10,11]
norm = BoundaryNorm(boundaries=bounds, ncolors=len(bounds) - 1, clip=False)
#norm = None
cmap.set_over(cmap(1.0))
clevels = np.arange(0, h_max + 1, 1)
gs = gridspec.GridSpec(1, 1)
#ax = fig.add_axes([0,0,1,1], polar = True)
ax = fig.add_subplot(gs[0, 0])
assert isinstance(ax, Axes)
#hc = np.ma.masked_where(mask_cond | (np.min(altt.values(), axis = 0) < 0), alt)
#hc = np.ma.masked_where( (hc < 0), hc)
img = basemap.pcolormesh(x, y, alt, cmap=cmap, vmax=h_max, norm=norm)
ax.set_title("ALT, JPP-Andrey, ({0} - CRCM5) \n".format(driver))
basemap.drawcoastlines(ax=ax, linewidth=0.5)
#basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
# ax=ax, linewidth=1.5)
basemap.readshapefile("data/permafrost_lat-lon1/permafrost_latlon",
name="zone",
ax=ax,
linewidth=1.5)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax, extend="max", ticks=bounds)
cax.set_title("m \n")
fig.tight_layout(w_pad=0.0)
fig.savefig("alt_jpp_current_{0}.png".format(driver))
def plot_alt(alt,
tmin,
tmax,
levelheights,
annual_means,
coordfile="",
lon1=-97.0,
lat1=47.50,
lon2=-7,
lat2=0):
fig = plt.figure()
ax = fig.add_subplot(111)
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(
resolution="c",
# llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74,
file_path=coordfile,
lon1=lon1,
lat1=lat1,
lon2=lon2,
lat2=lat2)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
x, y = basemap(lons2d, lats2d)
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
# mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3) | (alt < 0)
alt = np.ma.masked_where(alt < 0, alt)
bounds = [0, 0.1, 0.5, 1, 2, 3, 5, 8, 9, 10, 11]
cmap = my_colormaps.get_lighter_jet_cmap(
ncolors=10) #cm.get_cmap("jet",10)
norm = BoundaryNorm(boundaries=bounds, ncolors=cmap.N, clip=True)
qm = basemap.pcolormesh(x, y, alt, cmap=cmap, norm=norm, ax=ax)
#qm = basemap.contourf(x, y, alt, levels = bounds, ax = ax)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(qm, cax=cax, extend="max", ticks=bounds)
basemap.drawcoastlines(ax=ax, linewidth=0.5)
#basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
# ax=ax, linewidth=1.5)
basemap.readshapefile("data/permafrost_lat-lon1/permafrost_latlon",
name="zone",
ax=ax,
linewidth=1.5)
#crossPlotter = SoundingPlotter(ax, basemap, tmin, tmax, lons2d, lats2d, levelheights=levelheights)
sectPlotter = SoundingAndCrossSectionPlotter(ax,
basemap,
tmin,
tmax,
lons2d,
lats2d,
levelheights=levelheights,
temporal_data=annual_means)
i_inds = [
55, 47, 59, 43, 43, 50, 70, 67, 80, 77, 97, 117, 121, 143, 132, 148,
143, 154, 144, 144, 120, 118, 94, 74, 66, 82, 99, 126, 106
]
j_inds = [
79, 89, 94, 104, 114, 122, 123, 139, 134, 149, 145, 152, 141, 135, 133,
106, 57, 55, 40, 35, 39, 49, 53, 63, 54, 46, 44, 69, 59
]
sectPlotter.plot_cross_sections_for(i_inds, j_inds)
plt.show()
def plot_current_alts():
from . import plot_dpth_to_bdrck
bdrck_field = plot_dpth_to_bdrck.get_depth_to_bedrock()
start_year = 1980
end_year = 1996
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
coord_file = os.path.join(
sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
#coord_file = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NA_1.0deg_soil_spinup2/pmNA_1.0deg_soil_spinup2_228006_moyenne"
sim_names = ["ERA40", "MPI", "CanESM"]
simname_to_path = {
#"ERA40": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1",
"ERA40":
"/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_ERA40-Int_old_snow_cond",
#"ERA40" : "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NA_1.0deg_soil_spinup2",
"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"
}
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)
#basemap.transform_scalar()
#basemap = Basemap()
lons2d[lons2d > 180] -= 360
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=20, height_cm=40, font_size=16)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
bounds = [0, 0.1, 0.5, 1, 2, 3, 4, 5]
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=len(bounds) -
1) #cm.get_cmap("jet",10)
#cmap = my_colormaps.get_cmap_wo_red(ncolors=len(bounds) - 1)
norm = BoundaryNorm(boundaries=bounds, ncolors=len(bounds), clip=True)
cmap.set_over(cmap(1.0))
clevels = np.arange(0, h_max + 1, 1)
gs = gridspec.GridSpec(3,
2,
width_ratios=[1, 0.06],
hspace=0,
wspace=0.0,
left=0.05,
bottom=0.02,
top=0.95)
all_axes = []
all_img = []
i = 0
hc_list = []
for name in sim_names:
path = simname_to_path[name]
dm = CRCMDataManager(data_folder=path)
hc0, t3d_min, t3d_max = dm.get_alt_using_monthly_mean_climatology(
list(range(start_year, end_year + 1)))
hc_list.append(hc0)
ax = fig.add_subplot(gs[i, 0])
#cp = SoundingPlotter(ax, basemap, t3d_min, t3d_max, lons2d, lats2d, levelheights=dm.level_heights)
assert isinstance(ax, Axes)
hc = np.ma.masked_where(mask_cond | (hc0 < 0), hc0)
#hc = np.ma.masked_where( (hc0 < 0), hc0)
hc5 = np.ma.masked_where((hc0 <= 15) | hc.mask, hc)
img = basemap.pcolormesh(x, y, hc, cmap=cmap, vmax=h_max, norm=norm)
if not i:
ax.set_title("ALT ({0} - {1}) \n".format(start_year, end_year))
i += 1
ax.set_ylabel("CRCM ({0})".format(name))
all_axes.append(ax)
all_img.append(img)
print(np.ma.min(hc), np.ma.max(hc))
#hc5 = np.ma.masked_where((hc0 <= 6) | hc.mask, hc)
#print "Number of cells with alt > 5 is {0}, and the range is {1} ... {2}".format(hc5.count(), hc5.min(), hc5.max())
#bdrck_field5 = np.ma.masked_where(hc5.mask, bdrck_field)
#print "Bedrock ranges for those points: {0} ... {1}".format(bdrck_field5.min(), bdrck_field5.max())
ind = np.where(~hc5.mask)
xs = ind[0]
ys = ind[1]
all_months, all_temps = dm.get_monthly_mean_soiltemps(
year_range=range(start_year, end_year + 1))
all_months_ord = date2num(all_months)
# mpl.rcParams['contour.negative_linestyle'] = 'solid'
#
# for the_i, the_j in zip(xs,ys):
# #plot profile
# plt.figure()
# plt.plot(t3d_max[the_i, the_j, :] - dm.T0, dm.level_heights, color = "r")
# plt.plot(t3d_min[the_i, the_j, :] - dm.T0, dm.level_heights, color = "b")
# plt.plot([0 , 0], [dm.level_heights[0], dm.level_heights[-1]], color = "k")
#
# x1, x2 = plt.xlim()
# #plt.plot( [x1, x2], [bdrck_field[the_i, the_j], bdrck_field[the_i, the_j]], color = "k", lw = 3 )
# #plt.title(str(i) + ", dpth_to_bedrock = {0} m".format(bdrck_field[the_i, the_j]))
# plt.title(str(i))
# plt.gca().invert_yaxis()
# plt.savefig("prof{0}.png".format(i))
# ax.annotate(str(i), (x[the_i, the_j], y[the_i, the_j]), font_properties =
# FontProperties(size=10))
#
#
# #plot vertical temp cross-section
# plt.figure()
# plt.title(str(i) + ", ({0} - {1})".format(start_year, end_year))
#
# levs2d, times2d = np.meshgrid(dm.level_heights, all_months_ord)
# clevs = [-25,-20,-10,-5,-1,0,1,5,10,20,25]
# norm = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
# cmap = cm.get_cmap("jet", len(clevs) - 1)
#
# img = plt.contourf(times2d, levs2d, all_temps[:,the_i, the_j, :] - dm.T0, levels = clevs, cmap = cmap, norm = norm)
# #plt.contour(times2d, levs2d, all_temps[:,the_i, the_j, :] - dm.T0, levels = clevs, colors = "k", linewidth = 1)
# the_ax = plt.gca()
# assert isinstance(the_ax, Axes)
# the_ax.invert_yaxis()
# the_ax.xaxis.set_major_formatter(FuncFormatter(
# lambda x, pos: num2date(float(x)).strftime("%Y")
# ))
#
# print "i = {0}; lon, lat = {1}, {2}".format(i, lons2d[the_i, the_j], lats2d[the_i, the_j])
#
# plt.colorbar(img, ticks = clevs)
#
# plt.savefig("temp_section_{0}.png".format(i))
#
# i += 1
print("lons = [{0}]".format(",".join(
[str(x) for x in lons2d[np.array(xs), np.array(ys)]])))
print("lats = [{0}]".format(",".join(
[str(x) for x in lats2d[np.array(xs), np.array(ys)]])))
# draw barplot with numbers of alt in given ranges
# plt.figure()
# alt_ranges = xrange(0,18)
# numbers = []
# for the_alt in alt_ranges:
# hci = np.ma.masked_where( (hc0 < the_alt) | (hc0 > the_alt + 1) | hc.mask ,hc)
# numbers.append(hci.count())
# plt.bar(alt_ranges, numbers, width=1)
# plt.xlabel("ALT (m)")
# plt.ylabel("Number of cells")
# plt.savefig("numbers_in_range.png")
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("right", "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"] not in ["C"]: continue
poly = mpl.patches.Polygon(seg,
edgecolor="k",
facecolor="none",
zorder=10,
lw=1.5)
the_ax.add_patch(poly)
# if i != 1:
# axs_to_hide.append(cax)
i += 1
cax = fig.add_subplot(gs[:, 1])
cax.set_anchor("W")
cax.set_aspect(35)
formatter = FuncFormatter(lambda x, pos: "{0: <6}".format(x))
cb = fig.colorbar(all_img[0],
ax=cax,
cax=cax,
extend="max",
ticks=bounds,
format=formatter)
cax.set_title("m")
#fig.tight_layout(h_pad=0)
# for the_ax in axs_to_hide:
# the_ax.set_visible(False)
fig.savefig("alt_from_climatology_current.png")
#print ALT for selected points
site_names = ["S", "K", "T"]
sel_lons = [-75.646, -65.92, -69.95]
sel_lats = [62.197, 58.709, 58.67]
xo, yo, zo = lat_lon.lon_lat_to_cartesian(sel_lons, sel_lats)
xi, yi, zi = lat_lon.lon_lat_to_cartesian(lons2d.flatten(),
lats2d.flatten())
ktree = KDTree(list(zip(xi, yi, zi)))
dists, indexes = ktree.query(list(zip(xo, yo, zo)))
for name, data in zip(sim_names, hc_list):
print(name)
flat_data = data.flatten()
for p_name, ind in zip(site_names, indexes):
print(p_name, "{0} m".format(flat_data[ind]))
print("--" * 10)
pass
def plot_current_alts_nyear_rule(nyear=2):
start_year = 1981
end_year = 2008
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
sim_names = ["ERA40", "MPI", "CanESM"]
all_data_f = "/home/huziy/skynet1_rech3/cordex/for_Samira"
simname_to_path = {
"ERA40": os.path.join(all_data_f, "alt_era_b1_yearly.nc"),
"MPI": os.path.join(all_data_f, "alt_mpi_b1_yearly.nc"),
"CanESM": os.path.join(all_data_f, "alt_canesm_b1_yearly.nc")
}
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=40.0,
llcrnrlon=-145,
urcrnrlon=-20,
urcrnrlat=74)
assert isinstance(basemap, Basemap)
#basemap.transform_scalar()
#basemap = Basemap()
lons2d[lons2d > 180] -= 360
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 >= 3)
# plot_utils.apply_plot_params(width_pt=None, width_cm=20, height_cm=40, font_size=25)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
cmap = my_colormaps.get_lighter_jet_cmap(
ncolors=10) #cm.get_cmap("jet",10)
bounds = [0, 0.1, 0.5, 1, 2, 3, 5, 8, 9, 10, 11]
norm = BoundaryNorm(boundaries=bounds, ncolors=len(bounds), clip=True)
cmap.set_over(cmap(1.0))
clevels = np.arange(0, h_max + 1, 1)
gs = gridspec.GridSpec(3, 1)
all_axes = []
all_img = []
i = 0
hc_list = []
hct_list = []
for name in sim_names:
path = simname_to_path[name]
#select data and needed alt
ds = Dataset(path)
years = ds.variables["year"][:]
hct = ds.variables["alt"][(years >= start_year) &
(years <= end_year), :, :]
hct_list.append(hct)
print("hct.shape = ", hct.shape)
#hc = get_alt_using_nyear_rule(hct, nyears = nyear)
hc = np.mean(hct, axis=0)
hc_list.append(hc)
ax = fig.add_subplot(gs[i, 0])
assert isinstance(ax, Axes)
hc = np.ma.masked_where(mask_cond | (np.min(hct, axis=0) < 0), hc)
#hc = np.ma.masked_where( (hc < 0), hc)
img = basemap.pcolormesh(x, y, hc, cmap=cmap, vmax=h_max, norm=norm)
if not i:
ax.set_title("ALT, mean ({0} - {1}) \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):
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)
divider = make_axes_locatable(the_ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(the_img, cax=cax, extend="max", ticks=bounds)
cax.set_title("m \n")
if i != 2:
axs_to_hide.append(cax)
i += 1
fig.tight_layout(w_pad=0.0)
for the_ax in axs_to_hide:
the_ax.set_visible(False)
fig.savefig("alt_mean_current.png")
#print ALT for selected points
site_names = ["S", "K", "T"]
sel_lons = [-75.646, -65.92, -69.95]
sel_lats = [62.197, 58.709, 58.67]
xo, yo, zo = lat_lon.lon_lat_to_cartesian(sel_lons, sel_lats)
xi, yi, zi = lat_lon.lon_lat_to_cartesian(lons2d.flatten(),
lats2d.flatten())
ktree = KDTree(list(zip(xi, yi, zi)))
dists, indexes = ktree.query(list(zip(xo, yo, zo)))
for name, data, the_hct in zip(sim_names, hc_list, hct_list):
print(name)
flat_data = data.flatten()
for p_name, ind in zip(site_names, indexes):
in_data = []
for t in range(the_hct.shape[0]):
in_data.append(the_hct[t, :, :].flatten()[ind])
print(",".join(["{0:.1f}".format(float(x)) for x in in_data]))
print(p_name, "{0:.1f} m".format(float(flat_data[ind])))
print("--" * 10)
def main():
start_year = 1981
end_year = 2008
#mean alt
path_to_yearly = "alt_era_b1_yearly.nc"
ds = Dataset(path_to_yearly)
hm = ds.variables["alt"][:]
years = ds.variables["year"][:]
years_sel = np.where((start_year <= years) & (years <= end_year))[0]
print(years_sel)
hm = hm[np.array(years_sel), :, :]
print(hm.shape)
good_points = ~np.any(hm < 0, axis=0)
hm2d = np.ma.masked_all(good_points.shape)
hm2d[good_points] = np.mean(hm[:, good_points], axis=0)
#alt from climatology
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
# dm = CRCMDataManager(data_folder=sim_data_folder)
# hc = dm.get_alt_using_monthly_mean_climatology(xrange(start_year,end_year+1))
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=40.0,
llcrnrlon=-145,
urcrnrlon=-20,
urcrnrlat=74)
assert isinstance(basemap, Basemap)
#basemap.transform_scalar()
#basemap = Basemap()
lons2d[lons2d > 180] -= 360
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 >= 3)
#plot_utils.apply_plot_params(width_pt=None, width_cm=25,height_cm=35, font_size=12)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
cmap = my_colormaps.get_lighter_jet_cmap(
ncolors=10) #cm.get_cmap("jet",10)
bounds = [0, 0.1, 0.5, 1, 2, 3, 5, 8, 9, 10, 11]
norm = BoundaryNorm(boundaries=bounds, ncolors=len(bounds), clip=True)
cmap.set_over(cmap(1.0))
clevels = np.arange(0, h_max + 1, 1)
gs = gridspec.GridSpec(1, 1)
all_axes = []
all_img = []
print(basemap(-96.999, 68.42))
print(basemap(-2.4e7, 6.3e6, inverse=True))
ax = fig.add_subplot(gs[0, 0])
hm2d = np.ma.masked_where(mask_cond, hm2d)
img = basemap.pcolormesh(x, y, hm2d, cmap=cmap, vmax=h_max, norm=norm)
#img = basemap.contourf(x, y, hm2d, levels = clevels, cmap = cmap)
ax.set_title("Mean ALT")
all_axes.append(ax)
all_img.append(img)
print(("hm2d(min,max) = ", hm2d.min(), hm2d.max()))
# ax = fig.add_subplot(gs[1,0])
# hc = np.ma.masked_where(hc < 0, hc)
# hc = np.ma.masked_where(mask_cond | (hc > h_max) | hm2d.mask, hc)
# img = basemap.contourf(x, y, hc, levels = clevels)
# all_img.append(img)
# all_axes.append(ax)
# ax.set_title("ALT from climatology")
# print("hc(min,max) = ",hc.min(), hc.max())
# ax = fig.add_subplot(gs[2,0])
# delta = hm2d - hc
# delta = np.ma.masked_where(hc.mask | hm2d.mask, delta)
# img = basemap.contourf(x, y, delta, levels = np.arange(-1,1.2,0.2),ax = ax,
# cmap = my_colormaps.get_red_blue_colormap())
# all_img.append(img)
# all_axes.append(ax)
# ax.set_title("Mean - Derived from climatology")
#print(np.where((hm2d < hc) & ~(hc.mask | hm2d.mask)))
#zones = get_zone_polygons(path = "data/permafrost/permaice.shp", basemap=basemap)
permafrost_mask = np.ma.masked_where(
(permafrost_mask < 0) | (permafrost_mask >= 4), permafrost_mask)
for the_ax, the_img in zip(all_axes, all_img):
assert isinstance(the_ax, Axes)
basemap.drawcoastlines(ax=the_ax, linewidth=0.5)
divider = make_axes_locatable(the_ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(the_img, cax=cax, extend="max", ticks=bounds)
#CS = basemap.contour(x,y, permafrost_mask, levels = [1,2],
# ax = the_ax, colors = "k", linewidth= 5)
#the_ax.clabel(CS,colors = 'k', fmt="%d" , fontsize=8)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice",
name="zone",
ax=the_ax,
linewidth=1.5)
#for p in zones:
# the_ax.add_patch(p)
fig.tight_layout()
#cax_to_hide.set_visible(False)
fig.savefig("alt_b1.png")
plt.show()
do_stats = False
if not do_stats:
return
def plot_mean_alt_from_jpp_results():
start_year = 1981
end_year = 2008
path = "data/alts_by_jpp/MonTS_NA_ERA40_ALT_{0}_{1}".format(
start_year, end_year)
rObj = RPN(path)
altt = rObj.get_all_time_records_for_name(varname="FALT")
alt = np.mean(np.array(list(altt.values())), axis=0)
rObj.close()
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_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=40.0,
llcrnrlon=-145,
urcrnrlon=-20,
urcrnrlat=74)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
x, y = basemap(lons2d, lats2d)
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3)
alt = np.ma.masked_where(mask_cond, alt)
fig = plt.figure()
assert isinstance(fig, Figure)
h_max = 10
cmap = my_colormaps.get_lighter_jet_cmap(
ncolors=10) #cm.get_cmap("jet",10)
bounds = [0, 0.1, 0.5, 1, 2, 3, 5, 8, 9, 10, 11]
norm = BoundaryNorm(boundaries=bounds, ncolors=len(bounds), clip=True)
#norm = None
cmap.set_over(cmap(1.0))
clevels = np.arange(0, h_max + 1, 1)
gs = gridspec.GridSpec(3, 1)
ax = fig.add_subplot(gs[0, 0])
assert isinstance(ax, Axes)
hc = np.ma.masked_where(
mask_cond | (np.min(list(altt.values()), axis=0) < 0), alt)
#hc = np.ma.masked_where( (hc < 0), hc)
img = basemap.pcolormesh(x, y, hc, cmap=cmap, vmax=h_max, norm=norm)
ax.set_title("ALT, JPP ({0} - {1}) \n".format(start_year, end_year))
basemap.drawcoastlines(ax=ax, linewidth=0.5)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice",
name="zone",
ax=ax,
linewidth=1.5)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax, extend="max", ticks=bounds)
cax.set_title("m \n")
fig.tight_layout(w_pad=0.0)
fig.savefig("alt_jpp_current.png")
def compare_means_2d(manager_list, start_date=None, end_date=None, months=list(range(1, 13)),
var_name="STFL", level=-1, level_kind=level_kinds.ARBITRARY, out_img=None, impose_min=None,
bounds=None):
"""
Plots a panel of plots for each run
"""
ncols = 2
nrows = len(manager_list) // ncols + 1
# gs = gridspec.GridSpec(nrows, ncols, top=0.985, right=0.985, hspace=0.2, left = 0.01)
fig = plt.figure()
vmin = np.Infinity
vmax = -np.Infinity
quadMesh = None
run_id_to_field = {}
run_id_to_x = {}
run_id_to_y = {}
run_id_to_basemap = {}
run_id_to_manager = {}
for i, theManager in enumerate(manager_list):
assert isinstance(theManager, Crcm5ModelDataManager)
data_field = theManager.get_mean_field(start_date.year, end_date.year,
months=months, var_name=var_name, level=level, level_kind=level_kind)
theId = theManager.run_id
run_id_to_field[theId] = data_field
run_id_to_manager[theId] = theManager
run_id_to_basemap[theId] = theManager.get_omerc_basemap()
b = run_id_to_basemap[theId]
x, y = b(theManager.lons2D, theManager.lats2D)
run_id_to_x[theId] = x
run_id_to_y[theId] = y
vmax = max(np.max(data_field), vmax)
vmin = min(np.min(data_field), vmin)
if impose_min is not None:
vmin = impose_min
print(vmin, vmax)
cmap = my_colormaps.get_lighter_jet_cmap(ncolors=15)
dcol = (vmax - vmin) / float(cmap.N)
if bounds is None:
bounds = [vmin + i * dcol for i in range(cmap.N + 1)]
if vmin * vmax < 0:
bounds.append(0)
bounds = list(sorted(bounds))
norm = BoundaryNorm(bounds, len(bounds))
imgGrid = ImageGrid(fig, 111, nrows_ncols=(nrows, ncols), axes_pad=0.5, cbar_size="5%",
cbar_pad="5%", share_all=True)
i = 0
for theId, data_field in run_id_to_field.items():
ax = imgGrid[i] # fig.add_subplot(gs[i // ncols, i % ncols])
ax.set_title(theId)
b = run_id_to_basemap[theId]
x = run_id_to_x[theId]
y = run_id_to_y[theId]
theManager = run_id_to_manager[theId]
assert isinstance(theManager, Crcm5ModelDataManager)
if hasattr(theManager, "slope"):
data_field = np.ma.masked_where(theManager.slope < 0, data_field)
quadMesh = b.pcolormesh(x, y, data_field, vmin=vmin, vmax=vmax, cmap=cmap, ax=ax, norm=norm)
b.drawcoastlines(ax=ax)
i += 1
assert isinstance(fig, Figure)
# ax = fig.add_subplot(gs[i // ncols, i % ncols])
# assert isinstance(ax, Axes)
#ax.set_aspect(20)
#ax.set_anchor("W")
cax = imgGrid.cbar_axes[len(run_id_to_field) - 1]
cax.colorbar(quadMesh, ticks=bounds, format="%.3g")
# fig.colorbar(quadMesh, cax = ax, ticks = bounds, format = "%.2g")
if out_img is None:
fig.savefig("_".join(map(str, months)) + "_2d_means.png")
else:
fig.savefig(out_img)
pass
