def __get_lons_lats_basemap_from_rpn(path=DEFAULT_PATH_FOR_GEO_DATA,
vname="STBM",
region_of_interest_shp=None,
**bmp_kwargs):
"""
:param path:
:param vname:
:return: get basemap object for the variable in the given file
"""
with RPN(str(path)) as r:
_ = r.variables[vname][:]
proj_params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
if region_of_interest_shp is not None:
mask = get_mask(lons, lats, region_of_interest_shp)
delta_points = 10
i_arr, j_arr = np.where(mask >= 0.5)
i_min, i_max = i_arr.min() - delta_points, i_arr.max() + delta_points
j_min, j_max = j_arr.min() - delta_points, j_arr.max() + delta_points
slices = (slice(i_min, i_max + 1), slice(j_min, j_max + 1))
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[slices],
lats2d=lats[slices],
**bmp_kwargs)
else:
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
**bmp_kwargs)
return lons, lats, bmp
def __get_lons_lats_basemap_from_rpn(path=DEFAULT_PATH_FOR_GEO_DATA,
vname="STBM", region_of_interest_shp=None, **bmp_kwargs):
"""
:param path:
:param vname:
:return: get basemap object for the variable in the given file
"""
with RPN(str(path)) as r:
_ = r.variables[vname][:]
proj_params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
if region_of_interest_shp is not None:
mask = get_mask(lons, lats, region_of_interest_shp)
delta_points = 10
i_arr, j_arr = np.where(mask >= 0.5)
i_min, i_max = i_arr.min() - delta_points, i_arr.max() + delta_points
j_min, j_max = j_arr.min() - delta_points, j_arr.max() + delta_points
slices = (slice(i_min,i_max + 1), slice(j_min,j_max + 1))
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[slices], lats2d=lats[slices], **bmp_kwargs)
else:
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, **bmp_kwargs)
return lons, lats, bmp
def get_lons_lats_basemap(rpnfile_path="", varname=None, index_subset=None):
"""
Get longitudes, latitudes and the basemap object corresponding to the rpn file
:param rpnfile_path:
:param varname:
:return:
"""
with RPN(rpnfile_path) as r:
assert isinstance(r, RPN)
if varname is None:
varname = next(v for v in r.get_list_of_varnames()
if v not in [">>", "^^", "HY"])
r.get_first_record_for_name(varname)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
nx, ny = lons.shape
if index_subset is None:
index_subset = IndexSubspace(i_start=0,
i_end=nx - 1,
j_start=0,
j_end=ny - 1)
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
bmp = rll.get_basemap_object_for_lons_lats(
lons2d=lons[index_subset.get_islice(),
index_subset.get_jslice()],
lats2d=lats[index_subset.get_islice(),
index_subset.get_jslice()])
return lons, lats, bmp
def main(
path="/skynet3_rech1/huziy/geof_lake_infl_exp/geophys_Quebec_0.1deg_260x260_with_dd_v6_with_ITFS"
):
r = RPN(path)
varnames = ["ITFS"]
ncols = 3
nrows = len(varnames) // 3
fig = plt.figure()
varname_to_field = {}
for vname in varnames:
data = r.get_first_record_for_name(vname)
varname_to_field[vname] = data
data = np.ma.masked_where(data < 0, data)
lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
params = r.get_proj_parameters_for_the_last_read_rec()
print(params)
rll = RotatedLatLon(**params)
b = rll.get_basemap_object_for_lons_lats(lons2d, lats2d)
x, y = b(lons2d, lats2d)
b.drawcoastlines()
img = b.pcolormesh(x, y, data)
b.colorbar()
fig = plt.figure()
itfs = varname_to_field["ITFS"]
plt.hist(itfs[itfs >= 0], bins=100)
plt.show()
r.close()
pass
def get_lons_lats_basemap(rpnfile_path="", varname=None, index_subset=None):
"""
Get longitudes, latitudes and the basemap object corresponding to the rpn file
:param rpnfile_path:
:param varname:
:return:
"""
with RPN(rpnfile_path) as r:
assert isinstance(r, RPN)
if varname is None:
varname = next(v for v in r.get_list_of_varnames() if v not in [">>", "^^", "HY"])
r.get_first_record_for_name(varname)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
nx, ny = lons.shape
if index_subset is None:
index_subset = IndexSubspace(i_start=0, i_end=nx - 1, j_start=0, j_end=ny - 1)
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[index_subset.get_islice(), index_subset.get_jslice()],
lats2d=lats[index_subset.get_islice(), index_subset.get_jslice()])
return lons, lats, bmp
def get_basemap_and_coords_improved(
file_path="data/CORDEX/NorthAmerica_0.44deg_CanHistoE1/Samples/NorthAmerica_0.44deg_CanHistoE1_198101/pm1950010100_00816912p",
field_name="PR"):
rpnobj = RPN(file_path)
the_mask = rpnobj.get_first_record_for_name(field_name)
# plt.figure()
# plt.pcolormesh(the_mask.transpose())
# plt.show()
proj_params = rpnobj.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
lons2d, lats2d = rpnobj.get_longitudes_and_latitudes_for_the_last_read_rec()
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d)
rpnobj.close()
return basemap, lons2d, lats2d
def main(path = "/skynet3_rech1/huziy/geof_lake_infl_exp/geophys_Quebec_0.1deg_260x260_with_dd_v6_with_ITFS"):
r = RPN(path)
varnames = ["ITFS"]
ncols = 3
nrows = len(varnames) // 3
fig = plt.figure()
varname_to_field = {}
for vname in varnames:
data = r.get_first_record_for_name(vname)
varname_to_field[vname] = data
data = np.ma.masked_where(data < 0, data)
lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
params = r.get_proj_parameters_for_the_last_read_rec()
print(params)
rll = RotatedLatLon(**params)
b = rll.get_basemap_object_for_lons_lats(lons2d, lats2d)
x, y = b(lons2d, lats2d)
b.drawcoastlines()
img = b.pcolormesh(x, y, data)
b.colorbar()
fig = plt.figure()
itfs = varname_to_field["ITFS"]
plt.hist(itfs[itfs >= 0], bins = 100)
plt.show()
r.close()
pass
def get_coords_and_basemap(self):
"""
:return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
"""
# Read longitudes and latitudes and create the basemap only if they are not initialized
if self.lons is None:
with Dataset(os.path.join(self.data_folder, self.bathymetry_file)) as ds:
self.lons, self.lats = ds.variables["nav_lon"][:].transpose(), ds.variables["nav_lat"][:].transpose()
import re
lon1, lat1 = None, None
lon2, lat2 = None, None
with open(os.path.join(self.data_folder, self.proj_file)) as f:
for line in f:
if "Grd_xlat1" in line and "Grd_xlon1" in line:
groups = re.findall(r"-?\b\d+.?\d*\b", line)
lat1, lon1 = [float(s) for s in groups]
if "Grd_xlat2" in line and "Grd_xlon2" in line:
groups = re.findall(r"-?\b\d+.?\d*\b", line)
lat2, lon2 = [float(s) for s in groups]
rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)
self.basemap = rll.get_basemap_object_for_lons_lats(lons2d=self.lons, lats2d=self.lats)
print(lon1, lat1, lon2, lat2)
# self.basemap.drawcoastlines()
# xx, yy = self.basemap(self.lons, self.lats)
# self.basemap.pcolormesh(xx, yy, ds.variables["Bathymetry"][:].transpose())
# plt.show()
self.lons[self.lons > 180] -= 360
return self.lons, self.lats, self.basemap
def plot_lake_fraction_field():
folder = "/home/huziy/skynet3_rech1/geof_lake_infl_exp"
fName = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
path = os.path.join(folder, fName)
rObj = RPN(path)
lkf = rObj.get_first_record_for_name_and_level(
varname="VF", level=3, level_kind=level_kinds.ARBITRARY)
proj_params = rObj.get_proj_parameters_for_the_last_read_rec()
lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()
lons2d[lons2d >= 180] -= 360
rObj.close()
rll = RotatedLatLon(**proj_params)
margin = 20
lons2d = lons2d[margin:-margin, margin:-margin]
lats2d = lats2d[margin:-margin, margin:-margin]
lkf = lkf[margin:-margin, margin:-margin]
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
lats2d=lats2d,
resolution="l")
x, y = basemap(lons2d, lats2d)
fig = plt.figure()
gs = GridSpec(1, 2, width_ratios=[1, 1])
ax = fig.add_subplot(gs[0, 0])
df = 0.1
levels = np.arange(0, 1.1, df)
cMap = get_cmap("gist_ncar_r", len(levels) - 1)
bn = BoundaryNorm(levels, cMap.N)
basemap.drawmapboundary(fill_color="0.75")
lkf_plot = maskoceans(lons2d, lats2d, lkf, inlands=False)
print("Percentage of lakes in the sim domain: {}".format(lkf_plot.mean() *
100))
img = basemap.pcolormesh(x, y, lkf_plot, norm=bn, cmap=cMap)
basemap.drawcoastlines()
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax, ticks=levels, orientation="horizontal")
ax = fig.add_subplot(gs[0, 1])
df1 = df
levels1 = np.arange(0, 1.1, df1)
cell_numms = np.zeros((len(levels1) - 1, ))
left = levels[0]
right = levels[1]
lefts = []
rights = []
lkf_land = lkf[lkf > 0.01]
for i in range(len(cell_numms)):
cell_numms[i] = ((lkf_land > left) &
(lkf_land <= right)).astype(int).sum()
lefts.append(left)
rights.append(right)
left += df1
right += df1
assert isinstance(ax, Axes)
ax.bar(lefts, cell_numms, width=df1)
# ax.semilogy(rights, cell_numms)
ax.xaxis.set_ticks(levels)
ax.yaxis.set_ticks(np.arange(1000, 10000, 1000))
sf = ScalarFormatter(useMathText=True)
sf.set_powerlimits([-2, 1])
ax.yaxis.set_major_formatter(sf)
ax.grid("on")
ax.set_xlabel("fraction")
ax.set_ylabel("# gridcells")
plt.show()
fig.tight_layout()
fig.savefig("lake_fractions_220x220_0.1deg.jpeg")
plt.show()
pass
def get_coords_and_basemap(self,
subregion=None,
reload=True,
**basemap_kwargs):
"""
:return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
if reload is True, do not use cached arrays even if they are available
"""
# Read longitudes and latitudes and create the basemap only if they are not initialized
if self.basemap is None or reload:
with Dataset(os.path.join(self.data_folder,
self.bathymetry_file)) as ds:
if "nav_lon" in ds.variables:
self.lons, self.lats = ds.variables[
"nav_lon"][:].transpose(
), ds.variables["nav_lat"][:].transpose()
else:
for vname, v in ds.variables.items():
if "lon" in vname.lower():
self.lons = v[:].T
continue
if "lat" in vname.lower():
self.lats = v[:].T
continue
if self.lons is not None and self.lats is not None:
break
import re
lon1, lat1 = None, None
lon2, lat2 = None, None
with open(os.path.join(self.data_folder, self.proj_file)) as f:
for line in f:
if "Grd_xlat1" in line and "Grd_xlon1" in line:
groups = re.findall(r"-?\b\d+.?\d*\b", line)
lat1, lon1 = [float(s) for s in groups]
if "Grd_xlat2" in line and "Grd_xlon2" in line:
groups = re.findall(r"-?\b\d+.?\d*\b", line)
lat2, lon2 = [float(s) for s in groups]
rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)
nx, ny = self.lons.shape
if subregion is not None:
ill, iur, jll, jur = int(nx * subregion[0]), int(
nx * subregion[1]), int(ny * subregion[2]), int(
ny * subregion[3])
else:
ill, iur, jll, jur = 0, self.lons.shape[
0], 0, self.lons.shape[1]
self.basemap = rll.get_basemap_object_for_lons_lats(
lons2d=self.lons[ill:iur, jll:jur],
lats2d=self.lats[ill:iur, jll:jur],
**basemap_kwargs)
print(lon1, lat1, lon2, lat2)
# self.basemap.drawcoastlines()
# xx, yy = self.basemap(self.lons, self.lats)
# self.basemap.pcolormesh(xx, yy, ds.variables["Bathymetry"][:].transpose())
# plt.show()
self.lons[self.lons > 180] -= 360
return self.lons, self.lats, self.basemap
def main():
#path = "/RECH/data/Simulations/CRCM5/North_America/NorthAmerica_0.44deg_ERA40-Int_B1/Diagnostics/NorthAmerica_0.44deg_ERA40-Int_B1_2007{:02d}"
path = "/RESCUE/skynet3_rech1/huziy/from_guillimin/new_outputs/current_climate_30_yr_sims/quebec_0.1_crcm5-hcd-rl-intfl_ITFS/Samples/quebec_crcm5-hcd-rl-intfl_1988{:02d}"
months = [6, 7, 8]
pm_list = []
dm_list = []
for m in months:
print(path.format(m))
month_folder = path.format(m)
for fn in os.listdir(month_folder):
# if not fn.endswith("moyenne"):
# continue
if fn.startswith("pm"):
pm_list.append(os.path.join(month_folder, fn))
elif fn.startswith("dm"):
dm_list.append(os.path.join(month_folder, fn))
pm = MultiRPN(pm_list)
dm = MultiRPN(dm_list)
tsurf_mean = np.mean([
field for field in pm.get_all_time_records_for_name_and_level(
varname="J8").values()
],
axis=0)
tair_mean = np.mean([
field for field in dm.get_all_time_records_for_name_and_level(
varname="TT", level=1, level_kind=level_kinds.HYBRID).values()
],
axis=0)
lons, lats = pm.get_longitudes_and_latitudes_of_the_last_read_rec()
projparams = pm.linked_robj_list[
0].get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**projparams)
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)
xx, yy = bmp(lons, lats)
plt.figure()
cs = bmp.contourf(xx, yy, tsurf_mean - 273.15, 40)
bmp.drawcoastlines()
plt.title("Tsurf")
plt.colorbar()
plt.figure()
bmp.contourf(xx,
yy,
tair_mean,
levels=cs.levels,
norm=cs.norm,
cmap=cs.cmap)
bmp.drawcoastlines()
plt.title("Tair")
plt.colorbar()
plt.figure()
bmp.contourf(xx,
yy,
tsurf_mean - 273.15 - tair_mean,
levels=np.arange(-2, 2.2, 0.2),
cmap=cs.cmap)
bmp.drawcoastlines()
plt.title("Tsurf - Tair")
plt.colorbar()
pm.close()
dm.close()
plt.show()
def main():
"""
"""
# s_lons = [-3.38, 3.40, -12.45, -11.04,-2.92, 0.1,32.55,30.48,23.6,31.27,15.31,23.91,17.51,21.08]
# s_lats = [16.26,11.88,14.9,13.91,10.57,6.2,15.61,19.18,-14.02,-21.13,-4.26,4.97,-28.71,-28.69]
# assert len(s_lons) == len(s_lats)
#path = "data/directions_Africa_Bessam_0.44/infocell_Africa.nc"
#path = "/home/huziy/skynet3_exec1/for_offline_routing/directions_africa_dx0.44deg_2.nc"
path = "/skynet3_rech1/huziy/for_Arman_routing_data/infocell_na_0.44deg_arman.nc"
ds = Dataset(path)
dirs = ds.variables["flow_direction_value"][:]
acc_area = ds.variables["accumulation_area"][:]
lons2d = ds.variables["lon"][:]
lats2d = ds.variables["lat"][:]
lon_1, lat_1 = -97, 47.5
lon_2, lat_2 = -7, 0
lons2d[lons2d >= 180] -= 360
#min_lon = lons2d[0,0]
#max_lon = lons2d[-1,-1]
#min_lat = lats2d[0, 0]
#max_lat = lats2d[-1,-1]
#plot_utils.apply_plot_params(width_pt=None, width_cm=80)
#print max_lon
fig = plt.figure(dpi=800)
#TODO: change projection to rotpole (it will require params)
#b = Basemap(projection="rotpole", llcrnrlon=min_lon,
# llcrnrlat=min_lat,
# urcrnrlon=max_lon, urcrnrlat=max_lat, resolution="i")
myproj = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
print(lons2d.shape)
b = myproj.get_basemap_object_for_lons_lats(lons2d[100:150, 70:132], lats2d[100:150, 70:132])
x, y = b(lons2d, lats2d)
b.drawcoastlines(linewidth=0.2, color="0.5")
# b.pcolormesh(x, y, np.ma.masked_where(dirs <= 0, dirs ))
# plt.colorbar()
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]
dj = dj_list[delta_indices]
acc_area = np.ma.masked_where(acc_area < 0, acc_area)
#img = b.pcolormesh(x, y, np.ma.log(acc_area))
ax = plt.gca()
#divider = make_axes_locatable(ax)
#cax = divider.append_axes("right", "5%", pad="3%")
#plt.colorbar(img, cax = cax)
i_indices_1d = list(range(dirs.shape[0]))
j_indices_1d = list(range(dirs.shape[1]))
j_indices_2d, i_indices_2d = np.meshgrid(j_indices_1d, i_indices_1d)
i_indices_2d_next = np.zeros_like(i_indices_2d)
j_indices_2d_next = np.zeros_like(i_indices_2d)
i_indices_2d_next[dirs > 0] = i_indices_2d[dirs > 0] + di
j_indices_2d_next[dirs > 0] = j_indices_2d[dirs > 0] + dj
for i, j, i_next, j_next in zip(i_indices_2d[dirs > 0], j_indices_2d[dirs > 0],
i_indices_2d_next[dirs > 0], j_indices_2d_next[dirs > 0]):
# ax.add_line(Line2D([x[i,j], x[i_next, j_next]], [y[i,j], y[i_next, j_next]], linewidth=0.5))
if i == i_next and j == j_next:
continue
p1 = [x[i, j], y[i, j]]
p2 = [x[i_next, j_next], y[i_next, j_next]]
dr = [p2[0] - p1[0], p2[1] - p1[1]]
ax.add_patch(FancyArrow(p1[0], p1[1], dr[0], dr[1], linewidth=0.5,
fc="k", head_width=0.1,
length_includes_head=True))
# x1, y1 = b(s_lons, s_lats)
# b.scatter(x1, y1, c="r", linewidth=0, zorder = 7, ax = ax)
# b.drawrivers(linewidth=0.5, color="#0cf5f8", zorder=8, ax=ax)
# b.drawmeridians(np.arange(-10, 90,30))
# b.drawparallels(np.arange(-50, 40, 5), labels=[1,1,1,1], linewidth=0.1)
plt.tight_layout()
# plt.show()
#b.readshapefile("/home/huziy/skynet3_exec1/other_shape/af_major_basins/af_basins", "basin",
# linewidth=3, zorder=9, ax=ax
#)
plt.savefig("with_station_riv_af_dirs_basin_1.0.eps")
pass
def plot_acc_area_with_glaciers():
gmask_vname = "VF"
gmask_level = 2
# gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85"
gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"
# stab reading of the glacier mask
# r = RPN(gmask_path)
# gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
# level=gmask_level)
r = RPN("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p")
r.get_first_record_for_name("PR") # Because I almost sure that PR is there
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="i")
# gmask = np.ma.masked_where(gmask < 0.01, gmask)
gmask = np.ma.masked_all(lons.shape)
mask_value = 0.25
gmask[~gmask.mask] = mask_value
path = "/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Java/DDM/directions_WestCaUs_dx0.11deg.nc"
# path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/infocell_West_NA_0.25deg_104x75_GLNM_PRSF_CanHR85_104x75.nc"
ds = Dataset(path)
margin = 20
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][:][margin:-margin, margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
x, y = basemap(lons, lats)
fig = plt.figure(figsize=(15, 15))
dx = (x[-1, -1] - x[0, 0]) / float(x.shape[0])
dy = (y[-1, -1] - y[0, 0]) / float(y.shape[1])
x1 = x - dx / 2.0
y1 = y - dy / 2.0
# im = basemap.pcolormesh(x1, y1, data, norm=LogNorm(vmin=1e3, vmax=1e7), cmap=cm.get_cmap("jet", 12))
# cb = basemap.colorbar(im)
# cb.ax.tick_params(labelsize=25)
cmap = cm.get_cmap("gray_r", 10)
basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]
basemap.quiver(x, y, u, v,
pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1)
basemap.drawcoastlines(linewidth=0.5)
basemap.drawrivers(color="b")
# plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(mask_value)), ], ["Glaciers", ], loc=3)
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
# "upsas",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
# zorder=2, linewidth=0.5, color="b")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
# zorder=2, linewidth=0.5, color="b")
basemap.readshapefile("/skynet3_exec2/aganji/NE_can/bow_river/bow_projected", "basin", color="m", linewidth=2)
# plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions.png", bbox_inches="tight")
plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png", bbox_inches="tight")
plt.show()
def get_coords_and_basemap(self, subregion=None, reload=True, **basemap_kwargs):
"""
:return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
if reload is True, do not use cached arrays even if they are available
"""
# Read longitudes and latitudes and create the basemap only if they are not initialized
if self.basemap is None or reload:
with Dataset(os.path.join(self.data_folder, self.bathymetry_file)) as ds:
if "nav_lon" in ds.variables:
self.lons, self.lats = ds.variables["nav_lon"][:].transpose(), ds.variables["nav_lat"][:].transpose()
else:
for vname, v in ds.variables.items():
if "lon" in vname.lower():
self.lons = v[:].T
continue
if "lat" in vname.lower():
self.lats = v[:].T
continue
if self.lons is not None and self.lats is not None:
break
import re
lon1, lat1 = None, None
lon2, lat2 = None, None
with open(os.path.join(self.data_folder, self.proj_file)) as f:
for line in f:
if "Grd_xlat1" in line and "Grd_xlon1" in line:
groups = re.findall(r"-?\b\d+.?\d*\b", line)
lat1, lon1 = [float(s) for s in groups]
if "Grd_xlat2" in line and "Grd_xlon2" in line:
groups = re.findall(r"-?\b\d+.?\d*\b", line)
lat2, lon2 = [float(s) for s in groups]
rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)
nx, ny = self.lons.shape
if subregion is not None:
ill, iur, jll, jur = int(nx * subregion[0]), int(nx * subregion[1]), int(ny * subregion[2]), int(ny * subregion[3])
else:
ill, iur, jll, jur = 0, self.lons.shape[0], 0, self.lons.shape[1]
self.basemap = rll.get_basemap_object_for_lons_lats(lons2d=self.lons[ill:iur, jll:jur], lats2d=self.lats[ill:iur, jll:jur],
**basemap_kwargs)
print(lon1, lat1, lon2, lat2)
# self.basemap.drawcoastlines()
# xx, yy = self.basemap(self.lons, self.lats)
# self.basemap.pcolormesh(xx, yy, ds.variables["Bathymetry"][:].transpose())
# plt.show()
self.lons[self.lons > 180] -= 360
return self.lons, self.lats, self.basemap
def main():
# plot_utils.apply_plot_params(20)
folder = "/home/huziy/skynet3_rech1/from_guillimin"
fname = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
path = os.path.join(folder, fname)
rObj = RPN(path)
mg = rObj.get_first_record_for_name_and_level("MG", level=0, level_kind=level_kinds.PRESSURE)
# j2 = rObj.get_first_record_for_name("J2")
levs = [0, 100, 200, 300, 500, 700, 1000, 1500, 2000, 2800]
norm = BoundaryNorm(levs, len(levs) - 1)
me = rObj.get_first_record_for_name_and_level("ME", level=0, level_kind=level_kinds.ARBITRARY)
proj_params = rObj.get_proj_parameters_for_the_last_read_rec()
print(me.shape)
lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()
lons2d[lons2d > 180] -= 360
# me_to_plot = np.ma.masked_where(mg < 0.4, me)
me_to_plot = me
# print me_to_plot.min(), me_to_plot.max()
rll = RotatedLatLon(**proj_params)
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d)
rObj.close()
x, y = basemap(lons2d, lats2d)
plt.figure()
ax = plt.gca()
# the_cmap = cm.get_cmap(name = "gist_earth", lut=len(levs) -1)
# the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(path="colormap_files/topo_15lev.rgb")
the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(
path="colormap_files/OceanLakeLandSnow.rgb", ncolors=len(levs) - 1
)
# new_cm = matplotlib.colors.LinearSegmentedColormap('colormap',new_dict,len(levs) - 1)
me_to_plot = maskoceans(lons2d, lats2d, me_to_plot, resolution="l")
basemap.contourf(x, y, me_to_plot, cmap=the_cmap, levels=levs, norm=norm)
# basemap.fillcontinents(color = "none", lake_color="aqua")
basemap.drawmapboundary(fill_color="#479BF9")
basemap.drawcoastlines(linewidth=0.5)
basemap.drawmeridians(np.arange(-180, 180, 20), labels=[1, 0, 0, 1])
basemap.drawparallels(np.arange(45, 75, 15), labels=[1, 0, 0, 1])
basemap.readshapefile("data/shape/contour_bv_MRCC/Bassins_MRCC_latlon", name="basin", linewidth=1)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
plt.colorbar(ticks=levs, cax=cax)
# basemap.scatter(x, y, color="k", s=1, linewidths=0, ax=ax, zorder=2)
margin = 20
x1 = x[margin, margin]
x2 = x[-margin, margin]
y1 = y[margin, margin]
y2 = y[margin, -margin]
pol_corners = ((x1, y1), (x2, y1), (x2, y2), (x1, y2))
ax.add_patch(Polygon(xy=pol_corners, fc="none", ls="dashed", lw=3))
# plt.tight_layout()
# plt.show()
plt.savefig("free_domain_260x260.png", dpi=cpp.FIG_SAVE_DPI)
pass
def plot_acc_area_with_glaciers(
gmask_vname: str = "VF",
gmask_level=2,
gmask_path="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85",
route_data_path="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions_north_america_0.25deg_glaciers.nc",
lons_target=None,
lats_target=None,
basin_shape_files=None):
plot_scatter = False
# stab reading of the glacier mask
with RPN(gmask_path) as r:
gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
level=gmask_level)
# r = RPN("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p")
r.get_first_record_for_name(
"VF") # Because I almost sure that PR is there
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
lons_gmask, lats_gmask = r.get_longitudes_and_latitudes_for_the_last_read_rec(
)
gl_fraction_limit = 0.01
gmask = np.ma.masked_where(gmask < gl_fraction_limit, gmask)
mask_value = 0.25
gmask[~gmask.mask] = mask_value
if str(route_data_path).endswith(".nc"):
print("route_data_path ends with .nc => assuming netcdf format: {}".
format(route_data_path))
with Dataset(route_data_path) as ds:
var_name = "accumulation_area"
data = ds.variables[var_name][:]
# flow directions
fldr = ds.variables["flow_direction_value"][:]
coord_names = ["lon", "lat"] if "lon" in ds.variables else [
"longitudes", "latitudes"
]
lons_route, lats_route = [ds.variables[k] for k in coord_names]
else:
print(
"route_data_path does not end with .nc => assuming rpn format: {}".
format(route_data_path))
with RPN(route_data_path) as r:
data = r.get_first_record_for_name("FACC")
fldr = r.get_first_record_for_name("FLDR")
lons_route, lats_route = r.get_longitudes_and_latitudes_for_the_last_read_rec(
)
# do the spatial interpolation if required
xg, yg, zg = lat_lon.lon_lat_to_cartesian(lons_gmask.flatten(),
lats_gmask.flatten())
xr, yr, zr = lat_lon.lon_lat_to_cartesian(lons_route.flatten(),
lats_route.flatten())
if lons_target is None or lats_target is None:
lons_target, lats_target = lons_route, lats_route
xt, yt, zt = xr, yr, zr
else:
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons_target.flatten(),
lats_target.flatten())
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons_target,
lats2d=lats_target,
resolution="i")
ktree_route = KDTree(list(zip(xr, yr, zr)))
dists_route, inds_route = ktree_route.query(list(zip(xt, yt, zt)))
data = data.flatten()[inds_route].reshape(lons_target.shape)
fldr = fldr.flatten()[inds_route].reshape(lons_target.shape)
ktree_gmask = KDTree(list(zip(xg, yg, zg)))
dists_gmask, inds_gmask = ktree_gmask.query(list(zip(xt, yt, zt)))
gmask = gmask.flatten()[inds_gmask].reshape(lons_target.shape)
data = np.ma.masked_where(data <= 0, data)
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
xx, yy = basemap(lons_target, lats_target)
fig = plt.figure(figsize=(15, 15))
dx = (xx[-1, -1] - xx[0, 0]) / float(xx.shape[0])
dy = (yy[-1, -1] - yy[0, 0]) / float(yy.shape[1])
x1 = xx - dx / 2.0
y1 = yy - dy / 2.0
# Uncomment to plot the accumulation areas
im = basemap.pcolormesh(x1,
y1,
data,
norm=LogNorm(vmin=1e3, vmax=1e7),
cmap=cm.get_cmap("jet", 12))
cb = basemap.colorbar(im)
cmap = cm.get_cmap("gray_r", 10)
basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)
nx, ny = xx.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1),
inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1),
inds_j_next)
u = np.ma.masked_all_like(xx)
v = np.ma.masked_all_like(xx)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = xx[inds_i_next[good], inds_j_next[good]] - xx[inds_i[good],
inds_j[good]]
v[good] = yy[inds_i_next[good], inds_j_next[good]] - yy[inds_i[good],
inds_j[good]]
basemap.quiver(xx,
yy,
u,
v,
pivot="tail",
width=0.0005,
scale_units="xy",
headlength=20,
headwidth=15,
scale=1)
basemap.drawcoastlines(linewidth=0.5, zorder=5)
basemap.drawrivers(color="lightcoral", zorder=5, linewidth=3)
plt.legend([
Rectangle((0, 0), 5, 5, fc=cmap(mask_value)),
], [
r"Glacier ($\geq {}\%$)".format(gl_fraction_limit * 100),
],
loc=3)
watershed_bndry_width = 4
if basin_shape_files is not None:
for i, the_shp in enumerate(basin_shape_files):
basemap.readshapefile(the_shp[:-4],
"basin_{}".format(i),
zorder=2,
color="m",
linewidth=watershed_bndry_width)
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
# zorder=2, linewidth=watershed_bndry_width, color="m")
#
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
# zorder=2, linewidth=watershed_bndry_width, color="m")
#
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
# zorder=2, linewidth=watershed_bndry_width, color="m")
#
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
# zorder=2, linewidth=watershed_bndry_width, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
# "upsas",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
# zorder=2, linewidth=0.5, color="b")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
# zorder=2, linewidth=0.5, color="b")
if plot_scatter:
points_lat = [51.54, 49.2476]
points_lon = [-122.277, -122.784]
point_x, point_y = basemap(points_lon, points_lat)
basemap.scatter(point_x, point_y, c="g", s=20, zorder=3)
plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions_only.png",
bbox_inches="tight",
dpi=300)
# plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png", bbox_inches="tight")
plt.show()
def plot_acc_area_with_glaciers():
gmask_vname = "VF"
gmask_level = 2
# gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85"
gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"
# stab reading of the glacier mask
# r = RPN(gmask_path)
# gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
# level=gmask_level)
r = RPN(
"/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p"
)
r.get_first_record_for_name("PR") # Because I almost sure that PR is there
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
resolution="i")
# gmask = np.ma.masked_where(gmask < 0.01, gmask)
gmask = np.ma.masked_all(lons.shape)
mask_value = 0.25
gmask[~gmask.mask] = mask_value
path = "/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Java/DDM/directions_WestCaUs_dx0.11deg.nc"
# path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/infocell_West_NA_0.25deg_104x75_GLNM_PRSF_CanHR85_104x75.nc"
ds = Dataset(path)
margin = 20
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][:][margin:-margin,
margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
x, y = basemap(lons, lats)
fig = plt.figure(figsize=(15, 15))
dx = (x[-1, -1] - x[0, 0]) / float(x.shape[0])
dy = (y[-1, -1] - y[0, 0]) / float(y.shape[1])
x1 = x - dx / 2.0
y1 = y - dy / 2.0
# im = basemap.pcolormesh(x1, y1, data, norm=LogNorm(vmin=1e3, vmax=1e7), cmap=cm.get_cmap("jet", 12))
# cb = basemap.colorbar(im)
# cb.ax.tick_params(labelsize=25)
cmap = cm.get_cmap("gray_r", 10)
basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1),
inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1),
inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good],
inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good],
inds_j[good]]
basemap.quiver(x,
y,
u,
v,
pivot="tail",
width=0.0005,
scale_units="xy",
headlength=20,
headwidth=15,
scale=1)
basemap.drawcoastlines(linewidth=0.5)
basemap.drawrivers(color="b")
# plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(mask_value)), ], ["Glaciers", ], loc=3)
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
# "upsas",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
# zorder=2, linewidth=0.5, color="b")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
# zorder=2, linewidth=0.5, color="b")
basemap.readshapefile(
"/skynet3_exec2/aganji/NE_can/bow_river/bow_projected",
"basin",
color="m",
linewidth=2)
# plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions.png", bbox_inches="tight")
plt.savefig(
"/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png",
bbox_inches="tight")
plt.show()
def main():
#path = "/RECH/data/Simulations/CRCM5/North_America/NorthAmerica_0.44deg_ERA40-Int_B1/Diagnostics/NorthAmerica_0.44deg_ERA40-Int_B1_2007{:02d}"
path = "/RESCUE/skynet3_rech1/huziy/from_guillimin/new_outputs/current_climate_30_yr_sims/quebec_0.1_crcm5-hcd-rl-intfl_ITFS/Samples/quebec_crcm5-hcd-rl-intfl_1988{:02d}"
months = [6, 7, 8]
pm_list = []
dm_list = []
for m in months:
print(path.format(m))
month_folder = path.format(m)
for fn in os.listdir(month_folder):
# if not fn.endswith("moyenne"):
# continue
if fn.startswith("pm"):
pm_list.append(os.path.join(month_folder, fn))
elif fn.startswith("dm"):
dm_list.append(os.path.join(month_folder, fn))
pm = MultiRPN(pm_list)
dm = MultiRPN(dm_list)
tsurf_mean = np.mean([field for field in pm.get_all_time_records_for_name_and_level(varname="J8").values()], axis=0)
tair_mean = np.mean([field for field in dm.get_all_time_records_for_name_and_level(varname="TT", level=1, level_kind=level_kinds.HYBRID).values()], axis=0)
lons, lats = pm.get_longitudes_and_latitudes_of_the_last_read_rec()
projparams = pm.linked_robj_list[0].get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**projparams)
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)
xx, yy = bmp(lons, lats)
plt.figure()
cs = bmp.contourf(xx, yy, tsurf_mean - 273.15, 40)
bmp.drawcoastlines()
plt.title("Tsurf")
plt.colorbar()
plt.figure()
bmp.contourf(xx, yy, tair_mean, levels=cs.levels, norm=cs.norm, cmap=cs.cmap)
bmp.drawcoastlines()
plt.title("Tair")
plt.colorbar()
plt.figure()
bmp.contourf(xx, yy, tsurf_mean - 273.15 - tair_mean, levels=np.arange(-2, 2.2, 0.2), cmap=cs.cmap)
bmp.drawcoastlines()
plt.title("Tsurf - Tair")
plt.colorbar()
pm.close()
dm.close()
plt.show()
def plot_lake_fraction_field():
folder = "/home/huziy/skynet3_rech1/geof_lake_infl_exp"
fName = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
path = os.path.join(folder, fName)
rObj = RPN(path)
lkf = rObj.get_first_record_for_name_and_level(varname="VF", level=3, level_kind=level_kinds.ARBITRARY)
proj_params = rObj.get_proj_parameters_for_the_last_read_rec()
lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()
lons2d[lons2d >= 180] -= 360
rObj.close()
rll = RotatedLatLon(**proj_params)
margin = 20
lons2d = lons2d[margin:-margin, margin:-margin]
lats2d = lats2d[margin:-margin, margin:-margin]
lkf = lkf[margin:-margin, margin:-margin]
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d, resolution="l")
x, y = basemap(lons2d, lats2d)
fig = plt.figure()
gs = GridSpec(1, 2, width_ratios=[1, 1])
ax = fig.add_subplot(gs[0, 0])
df = 0.1
levels = np.arange(0, 1.1, df)
cMap = get_cmap("gist_ncar_r", len(levels) - 1)
bn = BoundaryNorm(levels, cMap.N)
basemap.drawmapboundary(fill_color="0.75")
lkf_plot = maskoceans(lons2d, lats2d, lkf, inlands=False)
print("Percentage of lakes in the sim domain: {}".format(lkf_plot.mean() * 100))
img = basemap.pcolormesh(x, y, lkf_plot, norm=bn, cmap=cMap)
basemap.drawcoastlines()
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", "5%", pad="3%")
cb = fig.colorbar(img, cax=cax, ticks=levels, orientation="horizontal")
ax = fig.add_subplot(gs[0, 1])
df1 = df
levels1 = np.arange(0, 1.1, df1)
cell_numms = np.zeros((len(levels1) - 1,))
left = levels[0]
right = levels[1]
lefts = []
rights = []
lkf_land = lkf[lkf > 0.01]
for i in range(len(cell_numms)):
cell_numms[i] = ((lkf_land > left) & (lkf_land <= right)).astype(int).sum()
lefts.append(left)
rights.append(right)
left += df1
right += df1
assert isinstance(ax, Axes)
ax.bar(lefts, cell_numms, width=df1)
# ax.semilogy(rights, cell_numms)
ax.xaxis.set_ticks(levels)
ax.yaxis.set_ticks(np.arange(1000, 10000, 1000))
sf = ScalarFormatter(useMathText=True)
sf.set_powerlimits([-2, 1])
ax.yaxis.set_major_formatter(sf)
ax.grid("on")
ax.set_xlabel("fraction")
ax.set_ylabel("# gridcells")
plt.show()
fig.tight_layout()
fig.savefig("lake_fractions_220x220_0.1deg.jpeg")
plt.show()
pass
def main():
"""
"""
# s_lons = [-3.38, 3.40, -12.45, -11.04,-2.92, 0.1,32.55,30.48,23.6,31.27,15.31,23.91,17.51,21.08]
# s_lats = [16.26,11.88,14.9,13.91,10.57,6.2,15.61,19.18,-14.02,-21.13,-4.26,4.97,-28.71,-28.69]
# assert len(s_lons) == len(s_lats)
#path = "data/directions_Africa_Bessam_0.44/infocell_Africa.nc"
#path = "/home/huziy/skynet3_exec1/for_offline_routing/directions_africa_dx0.44deg_2.nc"
path = "/skynet3_rech1/huziy/for_Arman_routing_data/infocell_na_0.44deg_arman.nc"
ds = Dataset(path)
dirs = ds.variables["flow_direction_value"][:]
acc_area = ds.variables["accumulation_area"][:]
lons2d = ds.variables["lon"][:]
lats2d = ds.variables["lat"][:]
lon_1, lat_1 = -97, 47.5
lon_2, lat_2 = -7, 0
lons2d[lons2d >= 180] -= 360
#min_lon = lons2d[0,0]
#max_lon = lons2d[-1,-1]
#min_lat = lats2d[0, 0]
#max_lat = lats2d[-1,-1]
#plot_utils.apply_plot_params(width_pt=None, width_cm=80)
#print max_lon
fig = plt.figure(dpi=800)
#TODO: change projection to rotpole (it will require params)
#b = Basemap(projection="rotpole", llcrnrlon=min_lon,
# llcrnrlat=min_lat,
# urcrnrlon=max_lon, urcrnrlat=max_lat, resolution="i")
myproj = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
print(lons2d.shape)
b = myproj.get_basemap_object_for_lons_lats(lons2d[100:150, 70:132],
lats2d[100:150, 70:132])
x, y = b(lons2d, lats2d)
b.drawcoastlines(linewidth=0.2, color="0.5")
# b.pcolormesh(x, y, np.ma.masked_where(dirs <= 0, dirs ))
# plt.colorbar()
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]
dj = dj_list[delta_indices]
acc_area = np.ma.masked_where(acc_area < 0, acc_area)
#img = b.pcolormesh(x, y, np.ma.log(acc_area))
ax = plt.gca()
#divider = make_axes_locatable(ax)
#cax = divider.append_axes("right", "5%", pad="3%")
#plt.colorbar(img, cax = cax)
i_indices_1d = list(range(dirs.shape[0]))
j_indices_1d = list(range(dirs.shape[1]))
j_indices_2d, i_indices_2d = np.meshgrid(j_indices_1d, i_indices_1d)
i_indices_2d_next = np.zeros_like(i_indices_2d)
j_indices_2d_next = np.zeros_like(i_indices_2d)
i_indices_2d_next[dirs > 0] = i_indices_2d[dirs > 0] + di
j_indices_2d_next[dirs > 0] = j_indices_2d[dirs > 0] + dj
for i, j, i_next, j_next in zip(i_indices_2d[dirs > 0],
j_indices_2d[dirs > 0],
i_indices_2d_next[dirs > 0],
j_indices_2d_next[dirs > 0]):
# ax.add_line(Line2D([x[i,j], x[i_next, j_next]], [y[i,j], y[i_next, j_next]], linewidth=0.5))
if i == i_next and j == j_next:
continue
p1 = [x[i, j], y[i, j]]
p2 = [x[i_next, j_next], y[i_next, j_next]]
dr = [p2[0] - p1[0], p2[1] - p1[1]]
ax.add_patch(
FancyArrow(p1[0],
p1[1],
dr[0],
dr[1],
linewidth=0.5,
fc="k",
head_width=0.1,
length_includes_head=True))
# x1, y1 = b(s_lons, s_lats)
# b.scatter(x1, y1, c="r", linewidth=0, zorder = 7, ax = ax)
# b.drawrivers(linewidth=0.5, color="#0cf5f8", zorder=8, ax=ax)
# b.drawmeridians(np.arange(-10, 90,30))
# b.drawparallels(np.arange(-50, 40, 5), labels=[1,1,1,1], linewidth=0.1)
plt.tight_layout()
# plt.show()
#b.readshapefile("/home/huziy/skynet3_exec1/other_shape/af_major_basins/af_basins", "basin",
# linewidth=3, zorder=9, ax=ax
#)
plt.savefig("with_station_riv_af_dirs_basin_1.0.eps")
pass
def main():
# plot_utils.apply_plot_params(20)
folder = "/home/huziy/skynet3_rech1/from_guillimin"
fname = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
path = os.path.join(folder, fname)
rObj = RPN(path)
mg = rObj.get_first_record_for_name_and_level(
"MG", level=0, level_kind=level_kinds.PRESSURE)
# j2 = rObj.get_first_record_for_name("J2")
levs = [0, 100, 200, 300, 500, 700, 1000, 1500, 2000, 2800]
norm = BoundaryNorm(levs, len(levs) - 1)
me = rObj.get_first_record_for_name_and_level(
"ME", level=0, level_kind=level_kinds.ARBITRARY)
proj_params = rObj.get_proj_parameters_for_the_last_read_rec()
print(me.shape)
lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()
lons2d[lons2d > 180] -= 360
# me_to_plot = np.ma.masked_where(mg < 0.4, me)
me_to_plot = me
# print me_to_plot.min(), me_to_plot.max()
rll = RotatedLatLon(**proj_params)
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
lats2d=lats2d)
rObj.close()
x, y = basemap(lons2d, lats2d)
plt.figure()
ax = plt.gca()
# the_cmap = cm.get_cmap(name = "gist_earth", lut=len(levs) -1)
# the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(path="colormap_files/topo_15lev.rgb")
the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(
path="colormap_files/OceanLakeLandSnow.rgb", ncolors=len(levs) - 1)
# new_cm = matplotlib.colors.LinearSegmentedColormap('colormap',new_dict,len(levs) - 1)
me_to_plot = maskoceans(lons2d, lats2d, me_to_plot, resolution="l")
basemap.contourf(x, y, me_to_plot, cmap=the_cmap, levels=levs, norm=norm)
# basemap.fillcontinents(color = "none", lake_color="aqua")
basemap.drawmapboundary(fill_color='#479BF9')
basemap.drawcoastlines(linewidth=0.5)
basemap.drawmeridians(np.arange(-180, 180, 20), labels=[1, 0, 0, 1])
basemap.drawparallels(np.arange(45, 75, 15), labels=[1, 0, 0, 1])
basemap.readshapefile("data/shape/contour_bv_MRCC/Bassins_MRCC_latlon",
name="basin",
linewidth=1)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
plt.colorbar(ticks=levs, cax=cax)
# basemap.scatter(x, y, color="k", s=1, linewidths=0, ax=ax, zorder=2)
margin = 20
x1 = x[margin, margin]
x2 = x[-margin, margin]
y1 = y[margin, margin]
y2 = y[margin, -margin]
pol_corners = ((x1, y1), (x2, y1), (x2, y2), (x1, y2))
ax.add_patch(Polygon(xy=pol_corners, fc="none", ls="dashed", lw=3))
# plt.tight_layout()
# plt.show()
plt.savefig("free_domain_260x260.png", dpi=cpp.FIG_SAVE_DPI)
pass
