def plot_all(folder_path="", limit_levels=1):
if folder_path == "":
folder_path = sys.argv[1]
for f in os.listdir(folder_path):
# skip files other than monthly
if not "monthly_fields.rpn" in f.lower():
continue
r = RPN(os.path.join(folder_path, f))
#Exclude coordinate variables
vlist = [v for v in r.get_list_of_varnames() if v not in ["^^", ">>"]]
#remove coordinates from the list
for varname in vlist:
data = r.get_4d_field(name=varname)
img_folder = os.path.join(folder_path, "img")
params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**params)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
plot_variable(varname, data, img_folder=img_folder,
lons=lons, lats=lats,
bmap=rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats),
limit_levels=limit_levels)
def main():
# path = "/skynet3_exec2/aganji/2GW_new/guziy-water_route_offline-d4627cd00b84/infocell.nc"
path = "/skynet3_rech1/huziy/temp/directions_north_america_0.44deg_arman.v5.nc"
# projection definition
rll = RotatedLatLon(lon1=-97, lat1=47.5, lon2=-7.0, lat2=0.0)
ds = Dataset(path)
print(list(ds.variables.keys()))
# read data from the infocell file
fdv = ds.variables["flow_direction_value"][:]
lons, lats = ds.variables["lon"][:], ds.variables["lat"][:]
# get basemap object
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="c")
x, y = bmp(lons, lats)
colors = get_basin_map(fdv)
save_matrix_to_netcdf(colors)
im = bmp.pcolormesh(x, y, colors)
bmp.colorbar(im)
bmp.drawcoastlines()
plt.show()
def get_target_lons_lats_basemap(run_config: RunConfig=None):
base_dir = Path(run_config.data_path)
for month_dir in base_dir.iterdir():
if month_dir.is_dir():
for f in month_dir.iterdir():
if f.name.startswith("."):
continue
with RPN(str(f)) as r:
assert isinstance(r, RPN)
vlist = r.get_list_of_varnames()
vname = [v for v in vlist if v not in [">>", "^^", "HY"]][0]
r.get_first_record_for_name(vname)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)
return lons, lats, basemap
def get_basemap(self, varname_internal, **bmap_kwargs):
if self.data_source_type == data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT:
for month_dir in self.base_folder.iterdir():
if not month_dir.is_dir():
continue
for data_file in month_dir.iterdir():
try:
# skip files that do not contain the variable
if varname_internal in self.varname_to_file_prefix:
if not data_file.name.startswith(self.varname_to_file_prefix[varname_internal]):
continue
# print(self.varname_mapping)
# print(data_file)
with RPN(str(data_file)) as r:
r.get_first_record_for_name(self.varname_mapping[varname_internal])
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
return rll.get_basemap_object_for_lons_lats(lons, lats, **bmap_kwargs)
except Exception as exc:
# Try to look into several files before giving up
print(exc)
else:
raise NotImplementedError("Not impelmented for the data_source_type = {}".format(self.data_source_type))
def plot_all(folder_path="", limit_levels=1):
if folder_path == "":
folder_path = sys.argv[1]
for f in os.listdir(folder_path):
# skip files other than monthly
if not "monthly_fields.rpn" in f.lower():
continue
r = RPN(os.path.join(folder_path, f))
#Exclude coordinate variables
vlist = [v for v in r.get_list_of_varnames() if v not in ["^^", ">>"]]
#remove coordinates from the list
for varname in vlist:
data = r.get_4d_field(name=varname)
img_folder = os.path.join(folder_path, "img")
params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**params)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
plot_variable(varname,
data,
img_folder=img_folder,
lons=lons,
lats=lats,
bmap=rll.get_basemap_object_for_lons_lats(
lons2d=lons, lats2d=lats),
limit_levels=limit_levels)
def get_basemap(self, varname_internal, **bmap_kwargs):
if self.data_source_type == data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT:
for month_dir in self.base_folder.iterdir():
if not month_dir.is_dir():
continue
for data_file in month_dir.iterdir():
try:
# skip files that do not contain the variable
if varname_internal in self.varname_to_file_prefix:
if not data_file.name.startswith(self.varname_to_file_prefix[varname_internal]):
continue
# print(self.varname_mapping)
# print(data_file)
with RPN(str(data_file)) as r:
r.get_first_record_for_name(self.varname_mapping[varname_internal])
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
return rll.get_basemap_object_for_lons_lats(lons, lats, **bmap_kwargs)
except Exception as exc:
# Try to look into several files before giving up
print(exc)
else:
raise NotImplementedError("Not impelmented for the data_source_type = {}".format(self.data_source_type))
def get_basemap(self, **kwargs):
from rpn.domains.rotated_lat_lon import RotatedLatLon
rll = RotatedLatLon(**self.projection_params)
return rll.get_basemap_object_for_lons_lats(lons2d=self.lons,
lats2d=self.lats,
**kwargs)
def main():
#data_folder = "/home/huziy/b2_fs2/sim_links_frm_Katja/Arctic_0.5deg_Peat_SC_26L_CanHR85_spn_Vspng_Diagnostics/1971-2000"
data_folder = "/home/huziy/b2_fs2/sim_links_frm_Katja/Arctic_0.5deg_Peat_SC_26L_CanHR85_spn_Vspng_Diagnostics/2071-2100"
var_name = "I0"
# layer_widths = [0.1, 0.2, 0.3, 0.5, 0.9, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
# 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
# 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5]
layer_widths = [0.1, 0.2, 0.3, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
1.0, 3.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, ]
print(len(layer_widths))
crcm_data_manager = CRCMDataManager(layer_widths=layer_widths, data_folder=data_folder)
#get the mask
r = RPN("/b1_fs2/winger/Arctic/land_sea_glacier_lake_mask_free")
msk = r.get_first_record_for_name("FMSK")
start_year = 2071
end_year = 2100
# alt - using globally max temperature profile
#alt = crcm_data_manager.get_alt_using_files_in(data_folder, vname=var_name)
#calculate alt for each year and then take mean
alt_list = []
for y in range(start_year, end_year + 1):
tmax = crcm_data_manager.get_Tmax_profiles_for_year_using_monthly_means(y, var_name=var_name)
alt1 = crcm_data_manager.get_alt(tmax)
alt1[alt1 < 0] = np.nan
alt_list.append(alt1)
alt = np.mean(alt_list, axis=0)
alt[np.isnan(alt)] = -1
alt = np.ma.masked_where(alt < 0, alt)
alt = np.ma.masked_where(msk < 0.1, alt)
#get the coordinates
fcoord = os.listdir(data_folder)[0]
fcoord = os.path.join(data_folder, fcoord)
r = RPN(fcoord)
i0 = r.get_first_record_for_name(var_name)
lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d)
plot_values(basemap, lons2d, lats2d, alt, "{}-{}(Arctic-peat-26L)".format(start_year, end_year))
def main():
bathymetry_path = ""
topo_path = "/RECH2/huziy/coupling/coupled-GL-NEMO1h_30min/geophys_452x260_directions_new_452x260_GL+NENA_0.1deg_SAND_CLAY_LDPT_DPTH.fst"
plot_utils.apply_plot_params()
with RPN(topo_path) as r:
assert isinstance(r, RPN)
topo = r.get_first_record_for_name("ME")
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
print(lons.shape)
prj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**prj_params)
bmap = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="i")
xx, yy = bmap(lons, lats)
plt.figure()
ax = plt.gca()
lons1 = np.where(lons <= 180, lons, lons - 360)
topo = maskoceans(lons1, lats, topo)
topo_clevs = [0, 100, 200, 300, 400, 500, 600, 800, 1000, 1200]
# bn = BoundaryNorm(topo_clevs, len(topo_clevs) - 1)
cmap = cm.get_cmap("terrain")
ocean_color = cmap(0.18)
cmap, norm = colors.from_levels_and_colors(topo_clevs, cmap(np.linspace(0.3, 1, len(topo_clevs) - 1)))
add_rectangle(ax, xx, yy, margin=20, edge_style="solid")
add_rectangle(ax, xx, yy, margin=10, edge_style="dashed")
im = bmap.pcolormesh(xx, yy, topo, cmap=cmap, norm=norm)
bmap.colorbar(im, ticks=topo_clevs)
bmap.drawcoastlines(linewidth=0.3)
bmap.drawmapboundary(fill_color=ocean_color)
bmap.drawparallels(np.arange(-90, 90, 10), labels=[1, 0, 0, 1], color="0.3")
bmap.drawmeridians(np.arange(-180, 190, 10), labels=[1, 0, 0, 1], color="0.3")
plt.savefig("GL_452x260_0.1deg_domain.png", dpi=300, bbox_inches="tight")
def get_basemap_obj_and_coords_from_rpn_file(path=""):
assert len(path) > 0, "The path should not be empty."
r = RPN(path)
vname = [v for v in r.get_list_of_varnames() if v.strip() not in [">>", "^^"]][0]
r.get_first_record_for_name(vname)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
projparams = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**projparams)
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="l")
return bmp, lons, lats
def get_basemap_obj_and_coords_from_rpn_file(path=""):
assert len(path) > 0, "The path should not be empty."
r = RPN(path)
vname = [v for v in r.get_list_of_varnames() if v.strip() not in [">>", "^^"]][0]
r.get_first_record_for_name(vname)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
projparams = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**projparams)
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="l")
return bmp, lons, lats
def main():
bathymetry_path = ""
topo_path = "/RECH2/huziy/coupling/coupled-GL-NEMO1h_30min/geophys_452x260_directions_new_452x260_GL+NENA_0.1deg_SAND_CLAY_LDPT_DPTH.fst"
plot_utils.apply_plot_params()
with RPN(topo_path) as r:
assert isinstance(r, RPN)
topo = r.get_first_record_for_name("ME")
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
print(lons.shape)
prj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**prj_params)
bmap = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
resolution="i")
xx, yy = bmap(lons, lats)
plt.figure()
ax = plt.gca()
lons1 = np.where(lons <= 180, lons, lons - 360)
topo = maskoceans(lons1, lats, topo)
topo_clevs = [0, 100, 200, 300, 400, 500, 600, 800, 1000, 1200]
# bn = BoundaryNorm(topo_clevs, len(topo_clevs) - 1)
cmap = cm.get_cmap("terrain")
ocean_color = cmap(0.18)
cmap, norm = colors.from_levels_and_colors(
topo_clevs, cmap(np.linspace(0.3, 1,
len(topo_clevs) - 1)))
add_rectangle(ax, xx, yy, margin=20, edge_style="solid")
add_rectangle(ax, xx, yy, margin=10, edge_style="dashed")
im = bmap.pcolormesh(xx, yy, topo, cmap=cmap, norm=norm)
bmap.colorbar(im, ticks=topo_clevs)
bmap.drawcoastlines(linewidth=0.3)
bmap.drawmapboundary(fill_color=ocean_color)
bmap.drawparallels(np.arange(-90, 90, 10),
labels=[1, 0, 0, 1],
color="0.3")
bmap.drawmeridians(np.arange(-180, 190, 10),
labels=[1, 0, 0, 1],
color="0.3")
plt.savefig("GL_452x260_0.1deg_domain.png", dpi=300, bbox_inches="tight")
def read_and_plot_ts_cross(path="", exp_name=""):
var_interest = "ADD"
path_to_dpth_to_bedrock = "/skynet1_rech3/huziy/CLASS_offline_VG/GEOPHYSICAL_FIELDS/test_analysis.rpn"
# read depth to bedrock
r = RPN(path_to_dpth_to_bedrock)
_ = r.get_first_record_for_name("DPTH")
lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
b = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d, resolution="c")
r.close()
layer_widths = [0.1, 0.2, 0.3, 0.5, 0.9, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5]
nlayers = 7
nt = 200*12
layer_widths = layer_widths[:nlayers]
print(len(layer_widths))
#calculate depths of soil layer centers
soil_lev_tops = np.cumsum([0, ] + layer_widths[:-1])
soil_lev_bottoms = np.cumsum(layer_widths)
soil_levs = 0.5 * (soil_lev_tops + soil_lev_bottoms)
i_interest_list, j_interest_list = [120, 120, 160, 170], [50, 60, 60, 60]
r = RPN(path)
data = r.get_4d_field_fc_hour_as_time(name=var_interest)
lev_sorted = list(sorted(list(data.items())[0][1].keys()))[:nlayers]
fc_sorted = list(sorted(data.keys()))[:nt]
for i_interest, j_interest in zip(i_interest_list, j_interest_list):
data1 = np.asarray(
[[data[fc][lev][i_interest, j_interest] for lev in lev_sorted] for fc in fc_sorted])
plot_time_series(data=data1, soil_levels=soil_levs, basemap=b, i_interest=i_interest,
j_interest=j_interest,
longitude=lons2d[i_interest, j_interest],
latitude=lats2d[i_interest, j_interest], exp_name=exp_name)
def __update_bmp_info_from_rpnfile_obj(self, r):
# save projection paarams for a possible re-use in the future
proj_params = r.get_proj_parameters_for_the_last_read_rec()
self.lons, self.lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rlons, rlats = r.get_tictacs_for_the_last_read_record()
rll = RotatedLatLon(**proj_params)
bmp = rll.get_basemap_object_for_lons_lats(self.lons, self.lats)
assert isinstance(bmp, Basemap)
self.basemap_info_of_the_last_imported_field = {
"rlon": rlons,
"rlat": rlats,
}
self.basemap_info_of_the_last_imported_field.update(bmp.projparams)
def __update_bmp_info_from_rpnfile_obj(self, r):
# save projection paarams for a possible re-use in the future
proj_params = r.get_proj_parameters_for_the_last_read_rec()
self.lons, self.lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rlons, rlats = r.get_tictacs_for_the_last_read_record()
rll = RotatedLatLon(**proj_params)
bmp = rll.get_basemap_object_for_lons_lats(self.lons, self.lats)
assert isinstance(bmp, Basemap)
self.basemap_info_of_the_last_imported_field = {
"rlon": rlons,
"rlat": rlats,
}
self.basemap_info_of_the_last_imported_field.update(bmp.projparams)
def get_data_and_coords():
"""
:rtype: (dict, np.ndarray, np.ndarray, Basemap)
:return: dict with {level: field} data
"""
path = "/skynet3_rech1/huziy/geofields_interflow_exp/pm1979010100_00000000p"
vname = "D9"
r = RPN(path)
data = list(r.get_4d_field(name=vname).items())[0][1]
params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**params)
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)
r.close()
return data, lons, lats, bmp
def get_data_and_coords():
"""
:rtype: (dict, np.ndarray, np.ndarray, Basemap)
:return: dict with {level: field} data
"""
path = "/skynet3_rech1/huziy/geofields_interflow_exp/pm1979010100_00000000p"
vname = "D9"
r = RPN(path)
data = list(r.get_4d_field(name=vname).items())[0][1]
params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**params)
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)
r.close()
return data, lons, lats, bmp
def plot_basin_outlets(shape_file=BASIN_BOUNDARIES_FILE,
bmp_info=None,
directions=None,
accumulation_areas=None,
lake_fraction_field=None):
assert isinstance(bmp_info, BasemapInfo)
driver = ogr.GetDriverByName("ESRI Shapefile")
print(driver)
ds = driver.Open(shape_file, 0)
assert isinstance(ds, ogr.DataSource)
layer = ds.GetLayer()
assert isinstance(layer, ogr.Layer)
print(layer.GetFeatureCount())
latlong_proj = osr.SpatialReference()
latlong_proj.ImportFromEPSG(4326)
utm_proj = layer.GetSpatialRef()
# create Coordinate Transformation
coord_transform = osr.CoordinateTransformation(latlong_proj, utm_proj)
utm_coords = coord_transform.TransformPoints(
list(zip(bmp_info.lons.flatten(), bmp_info.lats.flatten())))
utm_coords = np.asarray(utm_coords)
x_utm = utm_coords[:, 0].reshape(bmp_info.lons.shape)
y_utm = utm_coords[:, 1].reshape(bmp_info.lons.shape)
basin_mask = np.zeros_like(bmp_info.lons)
cell_manager = CellManager(directions,
accumulation_area_km2=accumulation_areas,
lons2d=bmp_info.lons,
lats2d=bmp_info.lats)
index = 1
basins = []
basin_names = []
basin_name_to_mask = {}
for feature in layer:
assert isinstance(feature, ogr.Feature)
# print feature["FID"]
geom = feature.GetGeometryRef()
assert isinstance(geom, ogr.Geometry)
basins.append(ogr.CreateGeometryFromWkb(geom.ExportToWkb()))
basin_names.append(feature["abr"])
accumulation_areas_temp = accumulation_areas.copy()
lons_out, lats_out = [], []
basin_names_out = []
name_to_ij_out = OrderedDict()
min_basin_area = min(b.GetArea() * 1.0e-6 for b in basins)
while len(basins):
fm = np.max(accumulation_areas_temp)
i, j = np.where(fm == accumulation_areas_temp)
i, j = i[0], j[0]
p = ogr.CreateGeometryFromWkt("POINT ({} {})".format(
x_utm[i, j], y_utm[i, j]))
b_selected = None
name_selected = None
for name, b in zip(basin_names, basins):
assert isinstance(b, ogr.Geometry)
assert isinstance(p, ogr.Geometry)
if b.Contains(p.Buffer(2000 * 2**0.5)):
# Check if there is an upstream cell from the same basin
the_mask = cell_manager.get_mask_of_upstream_cells_connected_with_by_indices(
i, j)
# Save the mask of the basin for future use
basin_name_to_mask[name] = the_mask
# if is_part_of_points_in(b, x_utm[the_mask == 1], y_utm[the_mask == 1]):
# continue
b_selected = b
name_selected = name
# basin_names_out.append(name)
lons_out.append(bmp_info.lons[i, j])
lats_out.append(bmp_info.lats[i, j])
name_to_ij_out[name] = (i, j)
basin_mask[the_mask == 1] = index
index += 1
break
if b_selected is not None:
basins.remove(b_selected)
basin_names.remove(name_selected)
outlet_index_in_basin = 1
current_basin_name = name_selected
while current_basin_name in basin_names_out:
current_basin_name = name_selected + str(outlet_index_in_basin)
outlet_index_in_basin += 1
basin_names_out.append(current_basin_name)
print(len(basins), basin_names_out)
accumulation_areas_temp[i, j] = -1
plot_utils.apply_plot_params(font_size=12,
width_pt=None,
width_cm=20,
height_cm=20)
gs = GridSpec(2, 2, width_ratios=[1.0, 0.5], wspace=0.01)
fig = plt.figure()
ax = fig.add_subplot(gs[1, 0])
xx, yy = bmp_info.get_proj_xy()
bmp_info.basemap.drawcoastlines(linewidth=0.5, ax=ax)
bmp_info.basemap.drawrivers(zorder=5, color="0.5", ax=ax)
upstream_edges = cell_manager.get_upstream_polygons_for_points(
model_point_list=[
ModelPoint(ix=i, jy=j) for (i, j) in name_to_ij_out.values()
],
xx=xx,
yy=yy)
upstream_edges_latlon = cell_manager.get_upstream_polygons_for_points(
model_point_list=[
ModelPoint(ix=i, jy=j) for (i, j) in name_to_ij_out.values()
],
xx=bmp_info.lons,
yy=bmp_info.lats)
plot_utils.draw_upstream_area_bounds(ax,
upstream_edges=upstream_edges,
color="r",
linewidth=0.6)
plot_utils.save_to_shape_file(upstream_edges_latlon, in_proj=None)
xs, ys = bmp_info.basemap(lons_out, lats_out)
bmp_info.basemap.scatter(xs, ys, c="0.75", s=30, zorder=10)
bmp_info.basemap.drawparallels(np.arange(-90, 90, 5),
labels=[True, False, False, False],
linewidth=0.5)
bmp_info.basemap.drawmeridians(np.arange(-180, 180, 5),
labels=[False, False, False, True],
linewidth=0.5)
cmap = cm.get_cmap("rainbow", index - 1)
bn = BoundaryNorm(list(range(index + 1)), index - 1)
# basin_mask = np.ma.masked_where(basin_mask < 0.5, basin_mask)
# bmp_info.basemap.pcolormesh(xx, yy, basin_mask, norm=bn, cmap=cmap, ax=ax)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
print(xmin, xmax, ymin, ymax)
dx = xmax - xmin
dy = ymax - ymin
step_y = 0.1
step_x = 0.12
y0_frac = 0.75
y0_frac_bottom = 0.02
x0_frac = 0.35
bname_to_text_coords = {
"RDO": (xmin + x0_frac * dx, ymin + y0_frac_bottom * dy),
"STM": (xmin + (x0_frac + step_x) * dx, ymin + y0_frac_bottom * dy),
"SAG":
(xmin + (x0_frac + 2 * step_x) * dx, ymin + y0_frac_bottom * dy),
"BOM":
(xmin + (x0_frac + 3 * step_x) * dx, ymin + y0_frac_bottom * dy),
"MAN":
(xmin + (x0_frac + 4 * step_x) * dx, ymin + y0_frac_bottom * dy),
"MOI":
(xmin + (x0_frac + 5 * step_x) * dx, ymin + y0_frac_bottom * dy),
"ROM": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac_bottom + step_y) * dy),
"NAT": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac_bottom + 2 * step_y) * dy),
######
"CHU": (xmin + (x0_frac + 5 * step_x) * dx, ymin + y0_frac * dy),
"GEO": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac + step_y) * dy),
"BAL": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"PYR": (xmin + (x0_frac + 4 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"MEL": (xmin + (x0_frac + 3 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"FEU": (xmin + (x0_frac + 2 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"ARN": (xmin + (x0_frac + 1 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
######
"CAN": (xmin + 0.1 * dx, ymin + 0.80 * dy),
"GRB": (xmin + 0.1 * dx, ymin + (0.80 - step_y) * dy),
"LGR": (xmin + 0.1 * dx, ymin + (0.80 - 2 * step_y) * dy),
"RUP": (xmin + 0.1 * dx, ymin + (0.80 - 3 * step_y) * dy),
"WAS": (xmin + 0.1 * dx, ymin + (0.80 - 4 * step_y) * dy),
"BEL": (xmin + 0.1 * dx, ymin + (0.80 - 5 * step_y) * dy),
}
# bmp_info.basemap.readshapefile(".".join(BASIN_BOUNDARIES_FILE.split(".")[:-1]).replace("utm18", "latlon"), "basin",
# linewidth=1.2, ax=ax, zorder=9)
for name, xa, ya, lona, lata in zip(basin_names_out, xs, ys, lons_out,
lats_out):
ax.annotate(name,
xy=(xa, ya),
xytext=bname_to_text_coords[name],
textcoords='data',
ha='right',
va='bottom',
bbox=dict(boxstyle='round,pad=0.4', fc='white'),
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3,rad=0',
linewidth=0.25),
font_properties=FontProperties(size=8),
zorder=20)
print(r"{} & {:.0f} \\".format(
name, accumulation_areas[name_to_ij_out[name]]))
# Plot zonally averaged lake fraction
ax = fig.add_subplot(gs[1, 1])
ydata = range(lake_fraction_field.shape[1])
ax.plot(lake_fraction_field.mean(axis=0) * 100, ydata, lw=2)
ax.fill_betweenx(ydata, lake_fraction_field.mean(axis=0) * 100, alpha=0.5)
ax.set_xlabel("Lake fraction (%)")
ax.set_ylim(min(ydata), max(ydata))
ax.xaxis.set_tick_params(direction='out', width=1)
ax.yaxis.set_tick_params(direction='out', width=1)
ax.xaxis.set_ticks_position("bottom")
ax.yaxis.set_ticks_position("none")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for tl in ax.yaxis.get_ticklabels():
tl.set_visible(False)
# plot elevation, buffer zone, big lakes, grid cells
ax = fig.add_subplot(gs[0, :])
geophy_file = "/RESCUE/skynet3_rech1/huziy/from_guillimin/geophys_Quebec_0.1deg_260x260_with_dd_v6"
r = RPN(geophy_file)
elev = r.get_first_record_for_name("ME")
lkfr = r.get_first_record_for_name("LKFR")
fldr = r.get_first_record_for_name("FLDR")
params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**params)
bsmp = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
resolution="l")
xx, yy = bsmp(lons, lats)
dx = (xx[0, 0] - xx[-1, 0]) / xx.shape[0]
dy = (yy[0, 0] - yy[0, -1]) / yy.shape[1]
xx_ll_crnrs = xx - dx / 2
yy_ll_crnrs = yy - dy / 2
xx_ur_crnrs = xx + dx / 2
yy_ur_crnrs = yy + dy / 2
ll_lon, ll_lat = bsmp(xx_ll_crnrs[0, 0], yy_ll_crnrs[0, 0], inverse=True)
ur_lon, ur_lat = bsmp(xx_ur_crnrs[-1, -1],
yy_ur_crnrs[-1, -1],
inverse=True)
crnr_lons = np.array([[ll_lon, ll_lon], [ur_lon, ur_lon]])
crnr_lats = np.array([[ll_lat, ll_lat], [ur_lat, ur_lat]])
bsmp = rll.get_basemap_object_for_lons_lats(lons2d=crnr_lons,
lats2d=crnr_lats)
# plot elevation
levs = [0, 100, 200, 300, 500, 700, 1000, 1500, 2000, 2800]
norm = BoundaryNorm(levs, len(levs) - 1)
the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(
path="colormap_files/OceanLakeLandSnow.rgb", ncolors=len(levs) - 1)
lons[lons > 180] -= 360
me_to_plot = maskoceans(lons, lats, elev, resolution="l")
im = bsmp.contourf(xx,
yy,
me_to_plot,
cmap=the_cmap,
levels=levs,
norm=norm,
ax=ax)
bsmp.colorbar(im)
bsmp.drawcoastlines(linewidth=0.5, ax=ax)
# show large lake points
gl_lakes = np.ma.masked_where((lkfr < 0.6) | (fldr <= 0) | (fldr > 128),
lkfr)
gl_lakes[~gl_lakes.mask] = 1.0
bsmp.pcolormesh(xx,
yy,
gl_lakes,
cmap=cm.get_cmap("Blues"),
ax=ax,
vmin=0,
vmax=1,
zorder=3)
# show free zone border
margin = 20
x1 = xx_ll_crnrs[margin, margin]
x2 = xx_ur_crnrs[-margin, margin]
y1 = yy_ll_crnrs[margin, margin]
y2 = yy_ur_crnrs[margin, -margin]
pol_corners = ((x1, y1), (x2, y1), (x2, y2), (x1, y2))
ax.add_patch(Polygon(xy=pol_corners, fc="none", ls="solid", lw=3,
zorder=5))
# show blending zone border (with halo zone)
margin = 10
x1 = xx_ll_crnrs[margin, margin]
x2 = xx_ur_crnrs[-margin, margin]
y1 = yy_ll_crnrs[margin, margin]
y2 = yy_ur_crnrs[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, zorder=5))
# show the grid
step = 20
xx_ll_crnrs_ext = np.zeros([n + 1 for n in xx_ll_crnrs.shape])
yy_ll_crnrs_ext = np.zeros([n + 1 for n in yy_ll_crnrs.shape])
xx_ll_crnrs_ext[:-1, :-1] = xx_ll_crnrs
yy_ll_crnrs_ext[:-1, :-1] = yy_ll_crnrs
xx_ll_crnrs_ext[:-1, -1] = xx_ll_crnrs[:, -1]
yy_ll_crnrs_ext[-1, :-1] = yy_ll_crnrs[-1, :]
xx_ll_crnrs_ext[-1, :] = xx_ur_crnrs[-1, -1]
yy_ll_crnrs_ext[:, -1] = yy_ur_crnrs[-1, -1]
bsmp.pcolormesh(xx_ll_crnrs_ext[::step, ::step],
yy_ll_crnrs_ext[::step, ::step],
np.ma.masked_all_like(xx_ll_crnrs_ext)[::step, ::step],
edgecolors="0.6",
ax=ax,
linewidth=0.05,
zorder=4,
alpha=0.5)
ax.set_title("Elevation (m)")
# plt.show()
fig.savefig("qc_basin_outlets_points.png", bbox_inches="tight")
# plt.show()
plt.close(fig)
return name_to_ij_out, basin_name_to_mask
def main(plot_vals=False):
varname = "RFAC"
multiplier = 24 * 3600
data_folder = Path(
"/home/huziy/skynet3_rech1/CNRCWP/Calgary_flood/atm_data_for_Arman_simulations"
)
day_range = range(19, 22)
dates_of_interest = [datetime(2013, 6, d) for d in day_range]
img_folder = Path("calgary_flood/2D")
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
lons, lats, bmp = None, None, None
the_mask = get_bow_river_basin_mask()
i_list, j_list = np.where(the_mask > 0.5)
imin, imax = i_list.min() - 2, i_list.max() + 5
jmin, jmax = j_list.min() - 2, j_list.max() + 5
# Calculate daily means
sim_label_to_date_to_mean = OrderedDict()
for sim_dir in data_folder.iterdir():
mr = MultiRPN(str(sim_dir.joinpath("pm*")))
print(str(sim_dir))
print(mr.get_number_of_records())
label = sim_dir.name.split("_")[-2].replace("NoDrain", "").replace(
"frozen", "Frozen").replace("Bow", "")
sim_label_to_date_to_mean[label] = OrderedDict()
data = mr.get_4d_field(varname)
data = {
d: list(v.items())[0][1]
for d, v in data.items() if d.day in day_range
}
for d in dates_of_interest:
sim_label_to_date_to_mean[label][d] = np.array([
field for d1, field in data.items() if d1.day == d.day
]).mean(axis=0) * multiplier
if lons is None:
lons, lats = mr.get_longitudes_and_latitudes_of_the_last_read_rec()
for f in sim_dir.iterdir():
if f.name.startswith("pm"):
r = RPN(str(f))
r.get_first_record_for_name(varname=varname)
rll = RotatedLatLon(
**r.get_proj_parameters_for_the_last_read_rec())
bmp = rll.get_basemap_object_for_lons_lats(
lons2d=lons[imin:imax, jmin:jmax],
lats2d=lats[imin:imax, jmin:jmax],
resolution="i")
r.close()
break
mr.close()
# reorder simulations
sim_label_to_date_to_mean = OrderedDict([
(k, sim_label_to_date_to_mean[k]) for k in sorted(
sim_label_to_date_to_mean, key=lambda z: len(z), reverse=True)
])
key_list = [k for k in sim_label_to_date_to_mean]
key_list[-2], key_list[-1] = key_list[-1], key_list[-2]
sim_label_to_date_to_mean = OrderedDict([(k, sim_label_to_date_to_mean[k])
for k in key_list])
# do the plots (subplots: vertically - simulations, horizontally - days)
plot_utils.apply_plot_params(width_cm=24, height_cm=28, font_size=10)
fig = plt.figure()
nrows = len(sim_label_to_date_to_mean)
ncols = len(day_range)
gs = GridSpec(nrows, ncols, wspace=0, hspace=0.1)
clevs_vals = [0, 0.1, 20, 50, 100, 150, 200, 300]
base_label = None
clevs_diff = [1, 5, 10, 20, 50, 100]
clevs_diff = [-c for c in reversed(clevs_diff)] + [0] + clevs_diff
cmap_diff = cm.get_cmap("bwr", len(clevs_diff) - 1)
cmap_vals = get_cmap_from_ncl_spec_file(
path="colormap_files/precip3_16lev.rgb", ncolors=len(clevs_vals) - 1)
xx, yy = bmp(lons, lats)
title = "Total runoff ({}, mm/day)".format(varname)
fig.suptitle(title)
for row, (sim_label,
date_to_field) in enumerate(sim_label_to_date_to_mean.items()):
if row == 0 or plot_vals:
base_sim = OrderedDict([(k, 0) for k, v in date_to_field.items()])
base_label = sim_label
plot_label = sim_label
clevs = clevs_vals
cmap = cmap_vals
extend = "max"
else:
base_sim = list(sim_label_to_date_to_mean.items())[0][1]
plot_label = "{}\n-\n{}".format(sim_label, base_label)
clevs = clevs_diff
cmap = cmap_diff
extend = "both"
bn = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
for col, (the_date, field) in enumerate(date_to_field.items()):
ax = fig.add_subplot(gs[row, col])
to_plot = np.ma.masked_where(the_mask < 0.5,
field - base_sim[the_date])
# cs = bmp.contourf(xx[~to_plot.mask], yy[~to_plot.mask], to_plot[~to_plot.mask], levels=clevs, extend="max", tri=True)
cs = bmp.pcolormesh(xx,
yy,
to_plot[:-1, :-1],
norm=bn,
vmin=clevs[0],
vmax=clevs[-1],
cmap=cmap)
bmp.drawcoastlines(ax=ax, linewidth=0.3)
assert isinstance(bmp, Basemap)
bmp.readshapefile(BOW_RIVER_SHP[:-4], "basin", zorder=5)
cb = bmp.colorbar(cs,
ax=ax,
ticks=clevs,
extend=extend,
pad="4%",
size="10%")
if plot_vals:
cb.ax.set_visible(col == ncols - 1 and row == 0)
else:
cb.ax.set_visible(col == ncols - 1 and row in (0, 1))
if col == 0:
ax.set_ylabel(plot_label)
if row == 0:
ax.set_title(the_date.strftime("%b %d"))
if plot_vals:
img_file = img_folder.joinpath("{}.png".format(varname))
else:
img_file = img_folder.joinpath("{}_diff.png".format(varname))
fig.savefig(str(img_file))
plt.close(fig)
def main(save_to_nc=True):
"""
Read geotiff file with the field capacity field in mm,
interpolate it to model grid, divide by the depth to bedrock,
and save to netcdf file
"""
# Read model data
obs_fields_path = "/skynet3_rech1/huziy/geofields_interflow_exp/pm1979010100_00000000p"
robj = RPN(obs_fields_path)
thfc = robj.get_first_record_for_name_and_level(varname="D9", level=1)
bdrck_depth = _get_depth_to_bedrock(obs_fields_path)
proj_params = robj.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
t_lons, t_lats = robj.get_longitudes_and_latitudes_for_the_last_read_rec()
t_lons[t_lons > 180] -= 360.0
bmp = rll.get_basemap_object_for_lons_lats(lons2d=t_lons, lats2d=t_lats)
robj.close()
intp_img = read_observed_bfc().resample(
SwathDefinition(t_lons.flatten(), t_lats.flatten())
)
fig = plt.figure(figsize=(10, 6))
assert isinstance(fig, Figure)
gs = GridSpec(1, 3, width_ratios=[0.95, 1, 1])
# Observed bulk field capacity
ax = fig.add_subplot(gs[0, 0])
x, y = bmp(t_lons, t_lats)
to_plot = intp_img.image_data.reshape(t_lons.shape)
to_plot = np.ma.masked_where(to_plot < 0, to_plot) / (1.0e3 * bdrck_depth)
im = bmp.pcolormesh(x, y, to_plot, vmin=0, vmax=0.5)
bmp.drawcoastlines()
ax.set_title("ORNL DAAC")
# Currently used in the model
ax = fig.add_subplot(gs[0, 1])
thfc_to_plot = np.ma.masked_where(to_plot.mask, thfc)
im = bmp.pcolormesh(x, y, thfc_to_plot, vmin=0, vmax=0.5)
bmp.colorbar(im, ax=ax)
bmp.drawcoastlines(ax=ax)
ax.set_title("Current")
bmp.colorbar(im, ax=ax)
# Current - (ORNL DAAC)
ax = fig.add_subplot(gs[0, 2])
x, y = bmp(t_lons, t_lats)
cmap = cm.get_cmap("RdBu_r", 21)
im = bmp.pcolormesh(x, y, thfc_to_plot - to_plot, vmin=-0.5, vmax=0.5, cmap=cmap)
bmp.colorbar(im, ax=ax)
bmp.drawcoastlines(ax=ax)
ax.set_title("Current - (ORNL-DAAC)")
bmp.colorbar(im, ax=ax)
plt.show()
pass
def get_basemap(self, **kwargs):
from rpn.domains.rotated_lat_lon import RotatedLatLon
rll = RotatedLatLon(**self.projection_params)
return rll.get_basemap_object_for_lons_lats(lons2d=self.lons, lats2d=self.lats, **kwargs)
def main():
plot_permafrost = True
plot_glaciers = True
if not os.path.isdir(img_folder):
print(os.mkdir(img_folder))
geophy_file = "misc_plots/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat_with_directions"
# Save the permafrost mask to file
pfmask_file = os.path.join(img_folder, "pf_mask_na_0.11deg.nc")
if not os.path.isfile(pfmask_file):
save_pf_mask_to_netcdf(rpn_field_name_with_target_grid="VF",
path_to_rpn_with_target_grid=geophy_file,
path=pfmask_file)
with Dataset(pfmask_file) as ds:
pf_mask = ds.variables["pf_type"][:]
r = RPN(geophy_file)
lkfr = r.get_record_for_name_and_level(varname="VF", level=3)
glac = r.get_record_for_name_and_level(varname="VF", level=2)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
ill, jll = 0, 250
right_margin = 50
lkfr = lkfr[ill:-right_margin, jll:]
lons = lons[ill:-right_margin, jll:]
lats = lats[ill:-right_margin, jll:]
glac = glac[ill:-right_margin, jll:]
pf_mask = pf_mask[ill:-right_margin, jll:]
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
resolution="l")
xx, yy = bmp(lons, lats)
plot_utils.apply_plot_params(font_size=8, width_cm=10, height_cm=10)
fig = plt.figure()
# Plot lakes
clevs = np.arange(0, 1.1, 0.1)
lkfr = np.ma.masked_where(lkfr <= 0, lkfr)
cs = bmp.contourf(xx, yy, lkfr, levels=clevs, cmap="Blues")
# bmp.colorbar(cs)
bmp.drawcoastlines(linewidth=0.1)
bmp.drawcountries(linewidth=0.1)
# plot glaciers
glval = 0.65
glac = (glac > 0.001) * glval
glac = np.ma.masked_where(glac < 0.5, glac)
cmap = cm.get_cmap("Greys")
if plot_glaciers:
cs = bmp.pcolormesh(xx,
yy,
glac,
cmap="Greys",
alpha=0.7,
vmin=0,
vmax=1)
#plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(glval)), ], ["Glaciers", ], loc=3)
# Plot permafrost boundaries
with Dataset("permafrost_types_arctic_using_contains.nc") as ds:
# pf_mask = ds.variables["pf_type"][:]
# lons_s, lats_s = ds.variables["longitude"][:], ds.variables["latitude"][:]
#
# x, y, z = lat_lon.lon_lat_to_cartesian(lons_s.flatten(), lats_s.flatten())
# ktree = KDTree(list(zip(x, y, z)))
#
# lons_copy = lons.copy()
# lons_copy[lons > 180] -= 360
# tmp = np.zeros_like(lons)
# tmp = maskoceans(lons_copy, lats, tmp, inlands=False)
#
# xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons[~tmp.mask], lats[~tmp.mask])
# dists, inds = ktree.query(list(zip(xt, yt, zt)))
#
# pf_mask1[~tmp.mask] = pf_mask.flatten()[inds]
pf_mask1 = np.ma.masked_where((pf_mask > 2) | (pf_mask < 1), pf_mask)
pf_mask1[~pf_mask1.mask] = 1
# bmp.contourf(xx, yy, pf_mask, levels=[2.5, 3.5, 4.5], cmap=ListedColormap(["yellow", "orange", "red"]), alpha=0.7)
#bmp.contour(xx, yy, pf_mask, levels=[2.5, 3.5, 4.5], colors="k", linewidths=1)
# bmp.drawrivers(color=cm.get_cmap("Blues")(0.8))
ncolors = 2
norm = BoundaryNorm([0.5, 1.5, 2.5], ncolors=ncolors)
cmap = ListedColormap(["c", "violet"])
alpha = 0.7
if plot_permafrost:
bmp.pcolormesh(xx, yy, pf_mask1, norm=norm, cmap=cmap, alpha=alpha)
# plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(0), alpha=alpha), ], ["Permafrost",], loc=3)
# tree line
bmp.readshapefile(os.path.join(img_folder, "Bernardo/treeline_current"),
"treeline",
linewidth=0.5,
color="orange")
bmp.readshapefile(os.path.join(img_folder, "Bernardo/treeline_future"),
"treeline",
linewidth=0.5,
color="red")
print(os.path.join(img_folder, "lakes_glaciers_pf_veg.png"))
fig.savefig(os.path.join(img_folder, "lakes_glaciers_pf_veg.png"),
dpi=400,
bbox_inches="tight",
transparent=True)
def main():
path = "/snow3/huziy/geophysics_files/geophys_452x260_0.1deg_GL_NENA_and_directions_452x260_GL+NENA_0.1deg_SAND_CLAY_LDPT_DPTH.fst"
# get rotated latlon projection and grid parameters
with RPN(path) as r:
assert isinstance(r, RPN)
lkfr = r.variables["LKFR"][:].squeeze()
print(r.variables)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll_obj = RotatedLatLon(
**r.get_proj_parameters_for_the_last_read_rec())
rlon, rlat = r.get_tictacs_for_the_last_read_record()
plot_utils.apply_plot_params()
lons[lons > 180] -= 360
crs_map = ccrs.Orthographic(central_latitude=50, central_longitude=-80)
ax = plt.axes(projection=crs_map)
# b = Basemap(projection="ortho", lat_0=50, lon_0=-80)
#
# b.drawcoastlines(linewidth=0.5)
# b.shadedrelief()
# b.warpimage()
# b.drawmapboundary(fill_color="#94e3e8")
# aximg = b.etopo()
# assert isinstance(aximg, AxesImage)
# aximg.set_cmap("bwr")
topo, lonst, latst = get_etopo()
lonst[lonst >= 180] -= 360
oc_mask = maskoceans(lonst, latst, topo)
clevs = [
-500, 0, 100, 200, 300, 400, 500, 600, 700, 800, 1000, 1200, 1400,
1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000, 3500, 4000, 4500, 5000
]
cmap = get_cmap(clevs)
topo[topo < 0] = -600
topo[oc_mask.mask] = -600
cs = ax.contourf(lonst,
latst,
topo,
cmap=cmap,
levels=clevs,
transform=ccrs.Geodetic())
ax.colorbar(cs)
# show_coast_lines(b, resolution="h")
ax = plt.gca()
for gc in get_gridcell_polygons(rll_obj, rlon, rlat, step=20):
ax.add_patch(gc)
plt.savefig("carrtopy_GL_NENA_ortho0.1deg.png",
bbox_inches="tight",
dpi=400)
def plot_domain_and_interest_region(ax: Axes,
topo_nc_file_path: Path,
focus_region_lonlat_nc_file: Path = None):
"""
:param focus_region_lonlat_nc_file: Path to the file containing focus region lons and lats
:param region_mask_lats: latitudes corresponding to the region mask
:param region_mask_lons:
:param ax:
:param topo_nc_file_path:
:param region_mask:
Note: below is the expected structure of the input netcdf file
$ ncdump -h geophys_452x260_me.nc
netcdf geophys_452x260_me {
dimensions:
x = 452 ;
y = 260 ;
variables:
float ME(x, y) ;
float lon(x, y) ;
float lat(x, y) ;
int proj_params ;
proj_params:grid_type = "E" ;
proj_params:lat1 = 0. ;
proj_params:lon1 = 180. ;
proj_params:lat2 = 1. ;
proj_params:lon2 = 276. ;
}
"""
# read the model topography from the file
with xarray.open_dataset(topo_nc_file_path) as topo_ds:
topo_lons, topo_lats, topo = [
topo_ds[k].values for k in ["lon", "lat", "ME"]
]
prj_params = topo_ds["proj_params"]
rll = RotatedLatLon(lon1=prj_params.lon1,
lat1=prj_params.lat1,
lon2=prj_params.lon2,
lat2=prj_params.lat2)
rot_pole_cpy = rll.get_cartopy_projection_obj()
ax.set_visible(False)
ax = ax.figure.add_axes(ax.get_position().bounds, projection=rot_pole_cpy)
# ax.coastlines()
gl_mask = get_gl_mask(topo_nc_file_path)
region_mask = get_mask_of_points_near_lakes(gl_mask, npoints_radius=20)
topo_lons[topo_lons > 180] -= 360
xll, yll = rot_pole_cpy.transform_point(topo_lons[0, 0], topo_lats[0, 0],
cartopy.crs.PlateCarree())
xur, yur = rot_pole_cpy.transform_point(topo_lons[-1, -1], topo_lats[-1,
-1],
cartopy.crs.PlateCarree())
map_extent = [xll, xur, yll, yur]
print("Map extent: ", map_extent)
topo_clevs = [0, 100, 200, 300, 400, 500, 600, 800, 1000, 1200]
# bn = BoundaryNorm(topo_clevs, len(topo_clevs) - 1)
cmap = cm.get_cmap("terrain")
ocean_color = cmap(0.18)
cmap, norm = colors.from_levels_and_colors(
topo_clevs, cmap(np.linspace(0.3, 1,
len(topo_clevs) - 1)))
xyz_coords = rot_pole_cpy.transform_points(cartopy.crs.PlateCarree(),
topo_lons, topo_lats)
xx = xyz_coords[:, :, 0]
yy = xyz_coords[:, :, 1]
add_rectangle(ax,
xx,
yy,
margin=20,
edge_style="solid",
zorder=10,
linewidth=0.5)
add_rectangle(ax,
xx,
yy,
margin=10,
edge_style="dashed",
zorder=10,
linewidth=0.5)
# plot a rectangle for the focus region
if focus_region_lonlat_nc_file is not None:
with xarray.open_dataset(focus_region_lonlat_nc_file) as fr:
focus_lons, focus_lats = fr["lon"].data, fr["lat"].data
xyz_coords = rot_pole_cpy.transform_points(
cartopy.crs.PlateCarree(), focus_lons, focus_lats)
xxf, yyf = xyz_coords[..., 0], xyz_coords[..., 1]
add_rectangle(ax,
xxf,
yyf,
edge_style="solid",
margin=0,
edgecolor="magenta",
zorder=10,
linewidth=1)
cs = ax.pcolormesh(topo_lons[:, :],
topo_lats[:, :],
topo[:, :],
transform=cartopy.crs.PlateCarree(),
cmap=cmap,
norm=norm)
to_plot = np.ma.masked_where(region_mask < 0.5, region_mask)
ax.scatter(topo_lons,
topo_lats,
to_plot * 0.01,
c="cyan",
transform=cartopy.crs.PlateCarree(),
alpha=0.5)
# Add geographic features
line_color = "k"
ax.add_feature(common_params.LAKES_50m,
facecolor=cartopy.feature.COLORS["water"],
edgecolor=line_color,
linewidth=0.5)
ax.add_feature(common_params.OCEAN_50m,
facecolor=cartopy.feature.COLORS["water"],
edgecolor=line_color,
linewidth=0.5)
ax.add_feature(common_params.COASTLINE_50m,
facecolor="none",
edgecolor=line_color,
linewidth=0.5)
ax.add_feature(common_params.RIVERS_50m,
facecolor="none",
edgecolor=line_color,
linewidth=0.5)
ax.set_extent(map_extent, crs=rot_pole_cpy)
# improve colorbar
divider = make_axes_locatable(ax)
ax_cb = divider.new_vertical(size="5%",
pad=0.1,
axes_class=plt.Axes,
pack_start=True)
ax.figure.add_axes(ax_cb)
cb = plt.colorbar(cs, cax=ax_cb, orientation="horizontal")
cb.ax.set_xticklabels(topo_clevs, rotation=45)
return ax
def to_gridconfig(self):
return GridConfig(rll=self.rll,
ni=self.nx,
nj=self.ny,
iref=self.iref,
jref=self.jref,
dx=self.dx,
dy=self.dy,
xref=self.lonref,
yref=self.latref)
known_projections = {
"GLK_210x130_0.1deg": RotatedLatLon(lon1=180.,
lat1=0.,
lon2=-84.,
lat2=1.0)
}
known_domains = {
"GLK_210x130_0.1deg":
Grid(rll=RotatedLatLon(lon1=180., lat1=0., lon2=-84., lat2=1.0),
nx=210,
ny=130,
iref=105,
jref=100,
lonref=-84,
latref=48,
dx=0.1,
dy=0.1),
"GLK_440x260_0.1deg":
def main():
plot_permafrost = False
plot_glaciers = False
if not os.path.isdir(img_folder):
print(os.mkdir(img_folder))
geophy_file = "/BIG1/skynet1_exec1/winger/Floods/Geophys/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"
# Save the permafrost mask to file
pfmask_file = os.path.join(img_folder, "pf_mask_na_0.11deg.nc")
if not os.path.isfile(pfmask_file):
save_pf_mask_to_netcdf(rpn_field_name_with_target_grid="VF", path_to_rpn_with_target_grid=geophy_file, path=pfmask_file)
with Dataset(pfmask_file) as ds:
pf_mask = ds.variables["pf_type"][:]
r = RPN(geophy_file)
lkfr = r.get_record_for_name_and_level(varname="VF", level=3)
glac = r.get_record_for_name_and_level(varname="VF", level=2)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
ill, jll = 0, 250
right_margin = 50
lkfr = lkfr[ill:-right_margin, jll:]
lons = lons[ill:-right_margin, jll:]
lats = lats[ill:-right_margin, jll:]
glac = glac[ill:-right_margin, jll:]
pf_mask = pf_mask[ill:-right_margin, jll:]
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="l")
xx, yy = bmp(lons, lats)
plot_utils.apply_plot_params(font_size=8, width_cm=10, height_cm=10)
fig = plt.figure()
# Plot lakes
clevs = np.arange(0, 1.1, 0.1)
lkfr = np.ma.masked_where(lkfr <= 0, lkfr)
cs = bmp.contourf(xx, yy, lkfr, levels=clevs, cmap="Blues")
# bmp.colorbar(cs)
bmp.drawcoastlines(linewidth=0.1)
bmp.drawcountries(linewidth=0.1)
# plot glaciers
glval = 0.65
glac = (glac > 0.001) * glval
glac = np.ma.masked_where(glac < 0.5, glac)
cmap = cm.get_cmap("Greys")
if plot_glaciers:
cs = bmp.pcolormesh(xx, yy, glac, cmap="Greys", alpha=0.7, vmin=0, vmax=1)
#bmp.readshapefile(os.path.join(img_folder, "sasha_glaciers/sasha_glaciers"), "glacier_poly", color=cmap(glval))
plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(glval)), ], ["Glaciers", ], loc=3)
# Plot permafrost boundaries
with Dataset("permafrost_types_arctic_using_contains.nc") as ds:
# pf_mask = ds.variables["pf_type"][:]
# lons_s, lats_s = ds.variables["longitude"][:], ds.variables["latitude"][:]
#
# x, y, z = lat_lon.lon_lat_to_cartesian(lons_s.flatten(), lats_s.flatten())
# ktree = KDTree(list(zip(x, y, z)))
#
# lons_copy = lons.copy()
# lons_copy[lons > 180] -= 360
# tmp = np.zeros_like(lons)
# tmp = maskoceans(lons_copy, lats, tmp, inlands=False)
#
# xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons[~tmp.mask], lats[~tmp.mask])
# dists, inds = ktree.query(list(zip(xt, yt, zt)))
#
# pf_mask1[~tmp.mask] = pf_mask.flatten()[inds]
pf_mask1 = np.ma.masked_where((pf_mask > 2) | (pf_mask < 1), pf_mask)
pf_mask1[~pf_mask1.mask] = 1
# bmp.contourf(xx, yy, pf_mask, levels=[2.5, 3.5, 4.5], cmap=ListedColormap(["yellow", "orange", "red"]), alpha=0.7)
#bmp.contour(xx, yy, pf_mask, levels=[2.5, 3.5, 4.5], colors="k", linewidths=1)
# bmp.drawrivers(color=cm.get_cmap("Blues")(0.8))
ncolors = 2
norm = BoundaryNorm([0.5, 1.5, 2.5], ncolors=ncolors)
cmap = ListedColormap(["c", "violet"])
alpha = 0.7
if plot_permafrost:
bmp.pcolormesh(xx, yy, pf_mask1, norm=norm, cmap=cmap, alpha=alpha)
plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(0), alpha=alpha), ], ["Permafrost",], loc=3)
# tree line
# bmp.readshapefile(os.path.join(img_folder, "Bernardo/treeline_current"), "treeline", linewidth=0.5, color="orange")
# bmp.readshapefile(os.path.join(img_folder, "Bernardo/treeline_future"), "treeline", linewidth=0.5, color="orange")
print(os.path.join(img_folder, "lakes_glaciers_pf_veg.png"))
fig.savefig(os.path.join(img_folder, "lakes_glaciers_pf_veg.png"), dpi=400, bbox_inches="tight", transparent=True)
from rpn.domains.rotated_lat_lon import RotatedLatLon
from domains.grid_config import GridConfig
# The projection used in the last 2 papers of Huziy's thesis
qc_rll_projection_huziy_thesis = RotatedLatLon(lon1=360 - 68,
lat1=52,
lon2=16.65,
lat2=0)
qc_rll_domain_huziy_thesis = GridConfig(rll=qc_rll_projection_huziy_thesis,
iref=142,
jref=122,
dx=0.1,
dy=0.1,
xref=180,
yref=0,
ni=260,
nj=260)
qc_rll_domain_huziy_thesis_04 = qc_rll_domain_huziy_thesis.decrease_resolution_keep_free_domain_same(
4)
if __name__ == "__main__":
print(qc_rll_domain_huziy_thesis_04)
def main():
soiltemp_var_name = "TBAR"
path1 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_var/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_var_1980-2009/TBAR.rpn"
label1 = "Variable (real) depth to bedrock"
path2 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_3.6m/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_constant_1980-2009/TBAR.rpn"
label2 = "Fixed depth to bedrock (3.6 m)"
path_to_dpth_to_bedrock = "/skynet1_rech3/huziy/CLASS_offline_VG/GEOPHYSICAL_FIELDS/test_analysis.rpn"
# read depth to bedrock
r = RPN(path_to_dpth_to_bedrock)
dpth = r.get_first_record_for_name("DPTH")
r.close()
layer_widths = [0.1, 0.2, 0.3, 0.5, 0.9, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5]
print(len(layer_widths))
crcm_data_manager = CRCMDataManager(layer_widths=layer_widths)
# Read in the temperature profiles
r1 = RPN(path1)
soiltemp1 = r1.get_4d_field(name=soiltemp_var_name)
lons2d, lats2d = r1.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r1.get_proj_parameters_for_the_last_read_rec())
b = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d, resolution="c")
r1.close()
r2 = RPN(path2)
soiltemp2 = r2.get_4d_field(name=soiltemp_var_name)
r2.close()
dates_sorted = list(sorted(soiltemp1.keys()))
levels_sorted = list(sorted(list(soiltemp1.items())[0][1].keys()))
# group dates for each year
alt1_list = []
alt2_list = []
for year, dates_group in itertools.groupby(dates_sorted, lambda par: par.year):
t1 = []
t2 = []
for d in dates_group:
t1.append([soiltemp1[d][lev] for lev in levels_sorted])
t2.append([soiltemp2[d][lev] for lev in levels_sorted])
# Get maximum temperature profiles
t1max = np.max(t1, axis=0).transpose(1, 2, 0)
t2max = np.max(t2, axis=0).transpose(1, 2, 0)
print(t1max.shape, t2max.shape)
#calculate and save alts
h1 = crcm_data_manager.get_alt(t1max)
h2 = crcm_data_manager.get_alt(t2max)
h1[h1 < 0] = np.nan
h2[h2 < 0] = np.nan
alt1_list.append(h1)
alt2_list.append(h2)
#take into account permafrost
alt1_list_pf = []
alt2_list_pf = []
n_years_for_pf = 3
for i in range(len(alt1_list) - n_years_for_pf):
alt1_list_pf.append(np.max(alt1_list[i:i+n_years_for_pf], axis=0))
alt2_list_pf.append(np.max(alt2_list[i:i+n_years_for_pf], axis=0))
# calculate climatological mean
alt1 = np.mean(alt1_list_pf, axis=0)
alt2 = np.mean(alt2_list_pf, axis=0)
print(np.isnan(alt1).any(), np.isnan(alt2).any())
#mask nans
alt1 = np.ma.masked_where(np.isnan(alt1), alt1)
alt2 = np.ma.masked_where(np.isnan(alt2), alt2)
#calculate change due to fixed depth to bedrock
delta = alt2 - alt1
#
for i in range(len(alt1_list)):
alt1_list[i] = np.ma.masked_where(np.isnan(alt1_list[i]), alt1_list[i])
alt2_list[i] = np.ma.masked_where(np.isnan(alt2_list[i]), alt2_list[i])
#tval, pval = ttest_ind(alt1_list, alt2_list)
#mask oceans
lons2d[lons2d > 180] -= 360
dpth = maskoceans(lons2d, lats2d, dpth)
delta = np.ma.masked_where(dpth.mask, delta)
#calculate differences in SWE
path_swe_1 = path1.replace("TBAR.rpn", "SNO.rpn")
r1 = RPN(path_swe_1)
swe1 = r1.get_all_time_records_for_name("SNO")
r1.close()
swe1_winter_clim = np.mean(
[field for key, field in swe1.items() if key.month in [1, 2, 12]], axis=0)
swe1_winter_clim = np.ma.masked_where((swe1_winter_clim >= 999) | dpth.mask, swe1_winter_clim)
path_swe_2 = path2.replace("TBAR.rpn", "SNO.rpn")
r2 = RPN(path_swe_2)
swe2 = r2.get_all_time_records_for_name("SNO")
r2.close()
swe2_winter_clim = np.mean(
[field for key, field in swe2.items() if key.month in [1, 2, 12]], axis=0)
swe2_winter_clim = np.ma.masked_where((swe2_winter_clim >= 999) | dpth.mask, swe2_winter_clim)
#plotting
print("Start plotting ...")
plot_differences(b, lons2d, lats2d, dpth, delta, label="ALT(DPTH=3.6m) - ALT(DPTH~)", pvalue=None,
swe_diff=swe2_winter_clim - swe1_winter_clim)
def main(plot_vals=False):
varname = "RFAC"
multiplier = 24 * 3600
data_folder = Path("/home/huziy/skynet3_rech1/CNRCWP/Calgary_flood/atm_data_for_Arman_simulations")
day_range = range(19, 22)
dates_of_interest = [datetime(2013, 6, d) for d in day_range]
img_folder = Path("calgary_flood/2D")
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
lons, lats, bmp = None, None, None
the_mask = get_bow_river_basin_mask()
i_list, j_list = np.where(the_mask > 0.5)
imin, imax = i_list.min() - 2, i_list.max() + 5
jmin, jmax = j_list.min() - 2, j_list.max() + 5
# Calculate daily means
sim_label_to_date_to_mean = OrderedDict()
for sim_dir in data_folder.iterdir():
mr = MultiRPN(str(sim_dir.joinpath("pm*")))
print(str(sim_dir))
print(mr.get_number_of_records())
label = sim_dir.name.split("_")[-2].replace("NoDrain", "").replace("frozen", "Frozen").replace("Bow", "")
sim_label_to_date_to_mean[label] = OrderedDict()
data = mr.get_4d_field(varname)
data = {d: list(v.items())[0][1] for d, v in data.items() if d.day in day_range}
for d in dates_of_interest:
sim_label_to_date_to_mean[label][d] = (
np.array([field for d1, field in data.items() if d1.day == d.day]).mean(axis=0) * multiplier
)
if lons is None:
lons, lats = mr.get_longitudes_and_latitudes_of_the_last_read_rec()
for f in sim_dir.iterdir():
if f.name.startswith("pm"):
r = RPN(str(f))
r.get_first_record_for_name(varname=varname)
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
bmp = rll.get_basemap_object_for_lons_lats(
lons2d=lons[imin:imax, jmin:jmax], lats2d=lats[imin:imax, jmin:jmax], resolution="i"
)
r.close()
break
mr.close()
# reorder simulations
sim_label_to_date_to_mean = OrderedDict(
[
(k, sim_label_to_date_to_mean[k])
for k in sorted(sim_label_to_date_to_mean, key=lambda z: len(z), reverse=True)
]
)
key_list = [k for k in sim_label_to_date_to_mean]
key_list[-2], key_list[-1] = key_list[-1], key_list[-2]
sim_label_to_date_to_mean = OrderedDict([(k, sim_label_to_date_to_mean[k]) for k in key_list])
# do the plots (subplots: vertically - simulations, horizontally - days)
plot_utils.apply_plot_params(width_cm=24, height_cm=28, font_size=10)
fig = plt.figure()
nrows = len(sim_label_to_date_to_mean)
ncols = len(day_range)
gs = GridSpec(nrows, ncols, wspace=0, hspace=0.1)
clevs_vals = [0, 0.1, 20, 50, 100, 150, 200, 300]
base_label = None
clevs_diff = [1, 5, 10, 20, 50, 100]
clevs_diff = [-c for c in reversed(clevs_diff)] + [0] + clevs_diff
cmap_diff = cm.get_cmap("bwr", len(clevs_diff) - 1)
cmap_vals = get_cmap_from_ncl_spec_file(path="colormap_files/precip3_16lev.rgb", ncolors=len(clevs_vals) - 1)
xx, yy = bmp(lons, lats)
title = "Total runoff ({}, mm/day)".format(varname)
fig.suptitle(title)
for row, (sim_label, date_to_field) in enumerate(sim_label_to_date_to_mean.items()):
if row == 0 or plot_vals:
base_sim = OrderedDict([(k, 0) for k, v in date_to_field.items()])
base_label = sim_label
plot_label = sim_label
clevs = clevs_vals
cmap = cmap_vals
extend = "max"
else:
base_sim = list(sim_label_to_date_to_mean.items())[0][1]
plot_label = "{}\n-\n{}".format(sim_label, base_label)
clevs = clevs_diff
cmap = cmap_diff
extend = "both"
bn = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
for col, (the_date, field) in enumerate(date_to_field.items()):
ax = fig.add_subplot(gs[row, col])
to_plot = np.ma.masked_where(the_mask < 0.5, field - base_sim[the_date])
# cs = bmp.contourf(xx[~to_plot.mask], yy[~to_plot.mask], to_plot[~to_plot.mask], levels=clevs, extend="max", tri=True)
cs = bmp.pcolormesh(xx, yy, to_plot[:-1, :-1], norm=bn, vmin=clevs[0], vmax=clevs[-1], cmap=cmap)
bmp.drawcoastlines(ax=ax, linewidth=0.3)
assert isinstance(bmp, Basemap)
bmp.readshapefile(BOW_RIVER_SHP[:-4], "basin", zorder=5)
cb = bmp.colorbar(cs, ax=ax, ticks=clevs, extend=extend, pad="4%", size="10%")
if plot_vals:
cb.ax.set_visible(col == ncols - 1 and row == 0)
else:
cb.ax.set_visible(col == ncols - 1 and row in (0, 1))
if col == 0:
ax.set_ylabel(plot_label)
if row == 0:
ax.set_title(the_date.strftime("%b %d"))
if plot_vals:
img_file = img_folder.joinpath("{}.png".format(varname))
else:
img_file = img_folder.joinpath("{}_diff.png".format(varname))
fig.savefig(str(img_file))
plt.close(fig)
def main():
soiltemp_var_name = "TBAR"
path1 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_var/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_var_1980-2009/TBAR.rpn"
label1 = "Variable (real) depth to bedrock"
path2 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_3.6m/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_constant_1980-2009/TBAR.rpn"
label2 = "Fixed depth to bedrock (3.6 m)"
path_to_dpth_to_bedrock = "/skynet1_rech3/huziy/CLASS_offline_VG/GEOPHYSICAL_FIELDS/test_analysis.rpn"
# read depth to bedrock
r = RPN(path_to_dpth_to_bedrock)
dpth = r.get_first_record_for_name("DPTH")
r.close()
layer_widths = [
0.1, 0.2, 0.3, 0.5, 0.9, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5
]
print(len(layer_widths))
crcm_data_manager = CRCMDataManager(layer_widths=layer_widths)
# Read in the temperature profiles
r1 = RPN(path1)
soiltemp1 = r1.get_4d_field(name=soiltemp_var_name)
lons2d, lats2d = r1.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r1.get_proj_parameters_for_the_last_read_rec())
b = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
lats2d=lats2d,
resolution="c")
r1.close()
r2 = RPN(path2)
soiltemp2 = r2.get_4d_field(name=soiltemp_var_name)
r2.close()
dates_sorted = list(sorted(soiltemp1.keys()))
levels_sorted = list(sorted(list(soiltemp1.items())[0][1].keys()))
# group dates for each year
alt1_list = []
alt2_list = []
for year, dates_group in itertools.groupby(dates_sorted,
lambda par: par.year):
t1 = []
t2 = []
for d in dates_group:
t1.append([soiltemp1[d][lev] for lev in levels_sorted])
t2.append([soiltemp2[d][lev] for lev in levels_sorted])
# Get maximum temperature profiles
t1max = np.max(t1, axis=0).transpose(1, 2, 0)
t2max = np.max(t2, axis=0).transpose(1, 2, 0)
print(t1max.shape, t2max.shape)
#calculate and save alts
h1 = crcm_data_manager.get_alt(t1max)
h2 = crcm_data_manager.get_alt(t2max)
h1[h1 < 0] = np.nan
h2[h2 < 0] = np.nan
alt1_list.append(h1)
alt2_list.append(h2)
#take into account permafrost
alt1_list_pf = []
alt2_list_pf = []
n_years_for_pf = 3
for i in range(len(alt1_list) - n_years_for_pf):
alt1_list_pf.append(np.max(alt1_list[i:i + n_years_for_pf], axis=0))
alt2_list_pf.append(np.max(alt2_list[i:i + n_years_for_pf], axis=0))
# calculate climatological mean
alt1 = np.mean(alt1_list_pf, axis=0)
alt2 = np.mean(alt2_list_pf, axis=0)
print(np.isnan(alt1).any(), np.isnan(alt2).any())
#mask nans
alt1 = np.ma.masked_where(np.isnan(alt1), alt1)
alt2 = np.ma.masked_where(np.isnan(alt2), alt2)
#calculate change due to fixed depth to bedrock
delta = alt2 - alt1
#
for i in range(len(alt1_list)):
alt1_list[i] = np.ma.masked_where(np.isnan(alt1_list[i]), alt1_list[i])
alt2_list[i] = np.ma.masked_where(np.isnan(alt2_list[i]), alt2_list[i])
#tval, pval = ttest_ind(alt1_list, alt2_list)
#mask oceans
lons2d[lons2d > 180] -= 360
dpth = maskoceans(lons2d, lats2d, dpth)
delta = np.ma.masked_where(dpth.mask, delta)
#calculate differences in SWE
path_swe_1 = path1.replace("TBAR.rpn", "SNO.rpn")
r1 = RPN(path_swe_1)
swe1 = r1.get_all_time_records_for_name("SNO")
r1.close()
swe1_winter_clim = np.mean(
[field for key, field in swe1.items() if key.month in [1, 2, 12]],
axis=0)
swe1_winter_clim = np.ma.masked_where(
(swe1_winter_clim >= 999) | dpth.mask, swe1_winter_clim)
path_swe_2 = path2.replace("TBAR.rpn", "SNO.rpn")
r2 = RPN(path_swe_2)
swe2 = r2.get_all_time_records_for_name("SNO")
r2.close()
swe2_winter_clim = np.mean(
[field for key, field in swe2.items() if key.month in [1, 2, 12]],
axis=0)
swe2_winter_clim = np.ma.masked_where(
(swe2_winter_clim >= 999) | dpth.mask, swe2_winter_clim)
#plotting
print("Start plotting ...")
plot_differences(b,
lons2d,
lats2d,
dpth,
delta,
label="ALT(DPTH=3.6m) - ALT(DPTH~)",
pvalue=None,
swe_diff=swe2_winter_clim - swe1_winter_clim)
def main():
path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/Global_NA_v1/Samples/Global_NA_v1_201306/pm2013010100_00017280p"
varname = "PR"
plot_units = "mm/day"
mult_coeff = 1000 * 24 * 3600
add_offset = 0
img_folder = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/glob_sim/{}/monthly/{}".format(varname, os.path.basename(path))
img_folder = Path(img_folder)
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
r = RPN(path=path)
pr = r.get_all_time_records_for_name(varname=varname)
lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
lats2d=lats2d,
resolution="c", no_rot=True)
# bmp = Basemap(projection="robin", lon_0=180)
xx, yy = bmp(lons2d, lats2d)
dates = list(sorted(pr.keys()))
data = np.array([pr[d] for d in dates])
p = pd.Panel(data=data, items=dates, major_axis=range(data.shape[1]), minor_axis=range(data.shape[2]))
# p_daily = p.groupby(lambda d: d.day, axis="items").mean()
p_daily = p.apply(np.mean, axis="items")
print(p_daily.head())
lons2d[lons2d > 180] -= 360
bmap_params = bmp.projparams
bmap_params.update({
'llcrnrlon': lons2d[0, 0], 'urcrnrlon': lons2d[-1, -1], 'llcrnrlat': lats2d[0, 0], 'urcrnrlat': lats2d[-1, -1]
})
rpole_crs = ccrs.RotatedPole(pole_longitude=bmap_params["lon_0"] + 180,
pole_latitude=bmap_params["o_lat_p"])
clevs = [0, 0.01, 0.1, 1, 1.5, 2, 5, 10, 20, 40, 60, 80]
norm = BoundaryNorm(clevs, ncolors=len(clevs) - 1)
cmap = cm.get_cmap(cm_basemap.s3pcpn, len(clevs) - 1)
field = p_daily.values * mult_coeff + add_offset
fig = plt.figure()
plt.title("{}, {}/{}/{}, {}".format(varname, 1, dates[0].month, dates[0].year, plot_units))
ax = plt.axes(projection=rpole_crs)
ax.coastlines(resolution='110m')
ax.gridlines()
ax.gridlines()
# cs = bmp.contourf(xx, yy, field, clevs, norm=norm, extend="max", cmap=cmap)
cs = ax.pcolormesh(lons2d[:-1, :-1], lats2d[:-1, :-1], field[:-1, :-1], norm=norm, cmap=cmap, transform=rpole_crs)
plt.colorbar(cs, ticks=clevs, extend="max", ax=ax)
img_file = img_folder.joinpath("{:02d}-{:02d}-{}.png".format(1, dates[0].month, dates[0].year))
# bmp.drawcoastlines()
# bmp.drawstates()
# bmp.drawcounties()
# bmp.drawcountries()
plt.savefig(img_file.open("wb"))
plt.close(fig)
print(pr[dates[0]].mean())
def main():
path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/Global_NA_v1/Samples/Global_NA_v1_201306/pm2013010100_00017280p"
varname = "PR"
plot_units = "mm/day"
mult_coeff = 1000 * 24 * 3600
add_offset = 0
img_folder = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/glob_sim/{}/monthly/{}".format(
varname, os.path.basename(path))
img_folder = Path(img_folder)
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
r = RPN(path=path)
pr = r.get_all_time_records_for_name(varname=varname)
lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
lats2d=lats2d,
resolution="c",
no_rot=True)
# bmp = Basemap(projection="robin", lon_0=180)
xx, yy = bmp(lons2d, lats2d)
dates = list(sorted(pr.keys()))
data = np.array([pr[d] for d in dates])
p = pd.Panel(data=data,
items=dates,
major_axis=range(data.shape[1]),
minor_axis=range(data.shape[2]))
# p_daily = p.groupby(lambda d: d.day, axis="items").mean()
p_daily = p.apply(np.mean, axis="items")
print(p_daily.head())
lons2d[lons2d > 180] -= 360
bmap_params = bmp.projparams
bmap_params.update({
'llcrnrlon': lons2d[0, 0],
'urcrnrlon': lons2d[-1, -1],
'llcrnrlat': lats2d[0, 0],
'urcrnrlat': lats2d[-1, -1]
})
rpole_crs = ccrs.RotatedPole(pole_longitude=bmap_params["lon_0"] + 180,
pole_latitude=bmap_params["o_lat_p"])
clevs = [0, 0.01, 0.1, 1, 1.5, 2, 5, 10, 20, 40, 60, 80]
norm = BoundaryNorm(clevs, ncolors=len(clevs) - 1)
cmap = cm.get_cmap(cm_basemap.s3pcpn, len(clevs) - 1)
field = p_daily.values * mult_coeff + add_offset
fig = plt.figure()
plt.title("{}, {}/{}/{}, {}".format(varname, 1, dates[0].month,
dates[0].year, plot_units))
ax = plt.axes(projection=rpole_crs)
ax.coastlines(resolution='110m')
ax.gridlines()
ax.gridlines()
# cs = bmp.contourf(xx, yy, field, clevs, norm=norm, extend="max", cmap=cmap)
cs = ax.pcolormesh(lons2d[:-1, :-1],
lats2d[:-1, :-1],
field[:-1, :-1],
norm=norm,
cmap=cmap,
transform=rpole_crs)
plt.colorbar(cs, ticks=clevs, extend="max", ax=ax)
img_file = img_folder.joinpath("{:02d}-{:02d}-{}.png".format(
1, dates[0].month, dates[0].year))
# bmp.drawcoastlines()
# bmp.drawstates()
# bmp.drawcounties()
# bmp.drawcountries()
plt.savefig(img_file.open("wb"))
plt.close(fig)
print(pr[dates[0]].mean())
def main(save_to_nc=True):
"""
Read geotiff file with the field capacity field in mm,
interpolate it to model grid, divide by the depth to bedrock,
and save to netcdf file
"""
# Read model data
obs_fields_path = "/skynet3_rech1/huziy/geofields_interflow_exp/pm1979010100_00000000p"
robj = RPN(obs_fields_path)
thfc = robj.get_first_record_for_name_and_level(varname="D9", level=1)
bdrck_depth = _get_depth_to_bedrock(obs_fields_path)
proj_params = robj.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
t_lons, t_lats = robj.get_longitudes_and_latitudes_for_the_last_read_rec()
t_lons[t_lons > 180] -= 360.0
bmp = rll.get_basemap_object_for_lons_lats(lons2d=t_lons, lats2d=t_lats)
robj.close()
intp_img = read_observed_bfc().resample(
SwathDefinition(t_lons.flatten(), t_lats.flatten()))
fig = plt.figure(figsize=(10, 6))
assert isinstance(fig, Figure)
gs = GridSpec(1, 3, width_ratios=[0.95, 1, 1])
# Observed bulk field capacity
ax = fig.add_subplot(gs[0, 0])
x, y = bmp(t_lons, t_lats)
to_plot = intp_img.image_data.reshape(t_lons.shape)
to_plot = np.ma.masked_where(to_plot < 0, to_plot) / (1.0e3 * bdrck_depth)
im = bmp.pcolormesh(x, y, to_plot, vmin=0, vmax=0.5)
bmp.drawcoastlines()
ax.set_title("ORNL DAAC")
# Currently used in the model
ax = fig.add_subplot(gs[0, 1])
thfc_to_plot = np.ma.masked_where(to_plot.mask, thfc)
im = bmp.pcolormesh(x, y, thfc_to_plot, vmin=0, vmax=0.5)
bmp.colorbar(im, ax=ax)
bmp.drawcoastlines(ax=ax)
ax.set_title("Current")
bmp.colorbar(im, ax=ax)
# Current - (ORNL DAAC)
ax = fig.add_subplot(gs[0, 2])
x, y = bmp(t_lons, t_lats)
cmap = cm.get_cmap("RdBu_r", 21)
im = bmp.pcolormesh(x,
y,
thfc_to_plot - to_plot,
vmin=-0.5,
vmax=0.5,
cmap=cmap)
bmp.colorbar(im, ax=ax)
bmp.drawcoastlines(ax=ax)
ax.set_title("Current - (ORNL-DAAC)")
bmp.colorbar(im, ax=ax)
plt.show()
pass