def __init__(self, samples_dir="", start_year=-np.Inf, end_year=np.Inf):
self.samples_dir = Path(samples_dir)
self.year_month_date_to_file_list = get_monthyeardate_to_paths_map(file_prefix="pm", start_year=start_year,
end_year=end_year,
samples_dir_path=self.samples_dir)
self.varname = "LC"
self.level = -1
self.level_kind = level_kinds.ARBITRARY
self.cached_data = {}
def __init__(self, samples_dir="", start_year=-np.Inf, end_year=np.Inf):
self.samples_dir = Path(samples_dir)
self.year_month_date_to_file_list = get_monthyeardate_to_paths_map(
file_prefix="pm", start_year=start_year, end_year=end_year, samples_dir_path=self.samples_dir
)
self.varname = "LC"
self.level = -1
self.level_kind = level_kinds.ARBITRARY
self.cached_data = {}
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
file_prefix = "pm"
PR_level = -1
PR_level_type = level_kinds.ARBITRARY
tprecip_vname = "PR"
sprecip_vname = "SN"
TT_level = 1
TT_level_type = level_kinds.HYBRID
sim_label_to_path = OrderedDict([
(HL_LABEL,
"/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"
),
(NEMO_LABEL,
"/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples"
)
])
# get a coord file ...
coord_file = ""
found_coord_file = False
for mdir in os.listdir(sim_label_to_path[HL_LABEL]):
mdir_path = os.path.join(sim_label_to_path[HL_LABEL], mdir)
if not os.path.isdir(mdir_path):
continue
for fn in os.listdir(mdir_path):
print(fn)
if fn[:2] not in ["pm", "dm", "pp", "dp"]:
continue
coord_file = os.path.join(mdir_path, fn)
found_coord_file = True
if found_coord_file:
break
bmp, lons, lats = nemo_hl_util.get_basemap_obj_and_coords_from_rpn_file(
path=coord_file)
xx, yy = bmp(lons, lats)
# read necessary input and calculate snowfall and save to the file
for sim_label, samples_dir in sim_label_to_path.items():
samples_dir_path = Path(samples_dir)
TT_monthdate_to_paths = get_monthyeardate_to_paths_map(
file_prefix="dm",
start_year=start_year,
end_year=end_year,
samples_dir_path=samples_dir_path)
PR_monthdate_to_paths = get_monthyeardate_to_paths_map(
file_prefix="pm",
start_year=start_year,
end_year=end_year,
samples_dir_path=samples_dir_path)
with Dataset(
str(samples_dir_path.parent / "{}_snow_fall_{}-{}.nc".format(
sim_label, start_year, end_year)), "w") as ds:
assert isinstance(ds, Dataset)
ds.createDimension("time", None)
ds.createDimension("lon", lons.shape[0])
ds.createDimension("lat", lons.shape[1])
# create the schema of the output file
snow_fall = ds.createVariable(sprecip_vname,
"f4",
dimensions=("time", "lon", "lat"))
lons_var = ds.createVariable("lon",
"f4",
dimensions=("lon", "lat"))
lats_var = ds.createVariable("lat",
"f4",
dimensions=("lon", "lat"))
time_var = ds.createVariable("time", "i8", dimensions=("time", ))
time_var.units = "hours since {:%Y-%m-%d %H:%M:%S}".format(
datetime(start_year, 1, 1))
lons_var[:] = lons
lats_var[:] = lats
# use sorted dates
record_count = 0
for month_date in sorted(TT_monthdate_to_paths):
tt = MultiRPN(path=TT_monthdate_to_paths[month_date]
).get_all_time_records_for_name_and_level(
varname="TT",
level=TT_level,
level_kind=TT_level_type)
pr = MultiRPN(path=PR_monthdate_to_paths[month_date]
).get_all_time_records_for_name_and_level(
varname="PR",
level=PR_level,
level_kind=PR_level_type)
print("Processing {}".format(month_date))
for d in sorted(tt):
t_field = tt[d]
pr_field = pr[d]
sn = get_snow_fall_m_per_s(precip_m_per_s=pr_field,
tair_deg_c=t_field)
snow_fall[record_count, :, :] = sn
time_var[record_count] = date2num(d, time_var.units)
record_count += 1
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
file_prefix = "pm"
PR_level = -1
PR_level_type = level_kinds.ARBITRARY
tprecip_vname = "PR"
sprecip_vname = "SN"
TT_level = 1
TT_level_type = level_kinds.HYBRID
sim_label_to_path = OrderedDict(
[(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
(NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
)
# get a coord file ...
coord_file = ""
found_coord_file = False
for mdir in os.listdir(sim_label_to_path[HL_LABEL]):
mdir_path = os.path.join(sim_label_to_path[HL_LABEL], mdir)
if not os.path.isdir(mdir_path):
continue
for fn in os.listdir(mdir_path):
print(fn)
if fn[:2] not in ["pm", "dm", "pp", "dp"]:
continue
coord_file = os.path.join(mdir_path, fn)
found_coord_file = True
if found_coord_file:
break
bmp, lons, lats = nemo_hl_util.get_basemap_obj_and_coords_from_rpn_file(path=coord_file)
xx, yy = bmp(lons, lats)
# read necessary input and calculate snowfall and save to the file
for sim_label, samples_dir in sim_label_to_path.items():
samples_dir_path = Path(samples_dir)
TT_monthdate_to_paths = get_monthyeardate_to_paths_map(file_prefix="dm", start_year=start_year, end_year=end_year, samples_dir_path=samples_dir_path)
PR_monthdate_to_paths = get_monthyeardate_to_paths_map(file_prefix="pm", start_year=start_year, end_year=end_year, samples_dir_path=samples_dir_path)
with Dataset(str(samples_dir_path.parent / "{}_snow_fall_{}-{}.nc".format(sim_label, start_year, end_year)), "w") as ds:
assert isinstance(ds, Dataset)
ds.createDimension("time", None)
ds.createDimension("lon", lons.shape[0])
ds.createDimension("lat", lons.shape[1])
# create the schema of the output file
snow_fall = ds.createVariable(sprecip_vname, "f4", dimensions=("time", "lon", "lat"))
lons_var = ds.createVariable("lon", "f4", dimensions=("lon", "lat"))
lats_var = ds.createVariable("lat", "f4", dimensions=("lon", "lat"))
time_var = ds.createVariable("time", "i8", dimensions=("time", ))
time_var.units = "hours since {:%Y-%m-%d %H:%M:%S}".format(datetime(start_year, 1, 1))
lons_var[:] = lons
lats_var[:] = lats
# use sorted dates
record_count = 0
for month_date in sorted(TT_monthdate_to_paths):
tt = MultiRPN(path=TT_monthdate_to_paths[month_date]).get_all_time_records_for_name_and_level(varname="TT", level=TT_level, level_kind=TT_level_type)
pr = MultiRPN(path=PR_monthdate_to_paths[month_date]).get_all_time_records_for_name_and_level(varname="PR", level=PR_level, level_kind=PR_level_type)
print("Processing {}".format(month_date))
for d in sorted(tt):
t_field = tt[d]
pr_field = pr[d]
sn = get_snow_fall_m_per_s(precip_m_per_s=pr_field, tair_deg_c=t_field)
snow_fall[record_count, :, :] = sn
time_var[record_count] = date2num(d, time_var.units)
record_count += 1
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
file_prefix = "dm"
level = 1
level_type = level_kinds.HYBRID
wind_comp_names = ["UU", "VV"]
sim_label_to_path = OrderedDict([
(HL_LABEL,
"/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"
),
(NEMO_LABEL,
"/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples"
)
])
# get a coord file ...
coord_file = ""
found_coord_file = False
for mdir in os.listdir(sim_label_to_path[HL_LABEL]):
mdir_path = os.path.join(sim_label_to_path[HL_LABEL], mdir)
if not os.path.isdir(mdir_path):
continue
for fn in os.listdir(mdir_path):
print(fn)
if fn[:2] not in ["pm", "dm", "pp", "dp"]:
continue
coord_file = os.path.join(mdir_path, fn)
found_coord_file = True
if found_coord_file:
break
bmp, lons, lats = nemo_hl_util.get_basemap_obj_and_coords_from_rpn_file(
path=coord_file)
xx, yy = bmp(lons, lats)
lons[lons > 180] -= 360
# loop through all files rotate vaectors and save to netcdf
for sim_label, samples_dir in sim_label_to_path.items():
samples = Path(samples_dir)
po = samples.parent
monthdate_to_path_list = nemo_hl_util.get_monthyeardate_to_paths_map(
file_prefix=file_prefix,
start_year=start_year,
end_year=end_year,
samples_dir_path=samples)
# Netcdf output file to put rotated winds
po /= "rotated_wind_{}.nc".format(sim_label)
with Dataset(str(po), "w") as ds:
ds.createDimension("time", None)
ds.createDimension("lon", lons.shape[0])
ds.createDimension("lat", lons.shape[1])
# create the schema of the output file
vname_to_ncvar = {}
for vname in wind_comp_names:
vname_to_ncvar[vname] = ds.createVariable(vname,
"f4",
dimensions=("time",
"lon",
"lat"))
vname_to_ncvar[vname].units = "knots"
lons_var = ds.createVariable("lon",
"f4",
dimensions=("lon", "lat"))
lats_var = ds.createVariable("lat",
"f4",
dimensions=("lon", "lat"))
time_var = ds.createVariable("time", "i8", dimensions=("time", ))
time_var.units = "hours since {:%Y-%m-%d %H:%M:%S}".format(
datetime(start_year, 1, 1))
lons_var[:] = lons
lats_var[:] = lats
# use sorted dates
record_count = 0
for month_date in sorted(monthdate_to_path_list):
# select only dm files
mr = MultiRPN(path=monthdate_to_path_list[month_date])
vname_to_fields = {}
for vname in wind_comp_names:
vname_to_fields[
vname] = mr.get_all_time_records_for_name_and_level(
varname=vname, level=level, level_kind=level_type)
for ti, t in enumerate(
sorted(vname_to_fields[wind_comp_names[0]])):
time_var[record_count] = date2num(t, time_var.units)
uu = vname_to_fields[wind_comp_names[0]][t]
vv = vname_to_fields[wind_comp_names[1]][t]
uu_rot, vv_rot = rotate_vecs_from_geo_to_rotpole(uu,
vv,
lons,
lats,
bmp=bmp)
# in knots not in m/s
vname_to_ncvar[wind_comp_names[0]][
record_count, :, :] = uu_rot
vname_to_ncvar[wind_comp_names[1]][
record_count, :, :] = vv_rot
record_count += 1
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
file_prefix = "dm"
level = 1
level_type = level_kinds.HYBRID
wind_comp_names = ["UU", "VV"]
sim_label_to_path = OrderedDict(
[(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
(NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
)
# get a coord file ...
coord_file = ""
found_coord_file = False
for mdir in os.listdir(sim_label_to_path[HL_LABEL]):
mdir_path = os.path.join(sim_label_to_path[HL_LABEL], mdir)
if not os.path.isdir(mdir_path):
continue
for fn in os.listdir(mdir_path):
print(fn)
if fn[:2] not in ["pm", "dm", "pp", "dp"]:
continue
coord_file = os.path.join(mdir_path, fn)
found_coord_file = True
if found_coord_file:
break
bmp, lons, lats = nemo_hl_util.get_basemap_obj_and_coords_from_rpn_file(path=coord_file)
xx, yy = bmp(lons, lats)
lons[lons > 180] -= 360
# loop through all files rotate vaectors and save to netcdf
for sim_label, samples_dir in sim_label_to_path.items():
samples = Path(samples_dir)
po = samples.parent
monthdate_to_path_list = nemo_hl_util.get_monthyeardate_to_paths_map(file_prefix=file_prefix,
start_year=start_year, end_year=end_year,
samples_dir_path=samples)
# Netcdf output file to put rotated winds
po /= "rotated_wind_{}.nc".format(sim_label)
with Dataset(str(po), "w") as ds:
ds.createDimension("time", None)
ds.createDimension("lon", lons.shape[0])
ds.createDimension("lat", lons.shape[1])
# create the schema of the output file
vname_to_ncvar = {}
for vname in wind_comp_names:
vname_to_ncvar[vname] = ds.createVariable(vname, "f4", dimensions=("time", "lon", "lat"))
vname_to_ncvar[vname].units = "knots"
lons_var = ds.createVariable("lon", "f4", dimensions=("lon", "lat"))
lats_var = ds.createVariable("lat", "f4", dimensions=("lon", "lat"))
time_var = ds.createVariable("time", "i8", dimensions=("time",))
time_var.units = "hours since {:%Y-%m-%d %H:%M:%S}".format(datetime(start_year, 1, 1))
lons_var[:] = lons
lats_var[:] = lats
# use sorted dates
record_count = 0
for month_date in sorted(monthdate_to_path_list):
# select only dm files
mr = MultiRPN(path=monthdate_to_path_list[month_date])
vname_to_fields = {}
for vname in wind_comp_names:
vname_to_fields[vname] = mr.get_all_time_records_for_name_and_level(varname=vname, level=level, level_kind=level_type)
for ti, t in enumerate(sorted(vname_to_fields[wind_comp_names[0]])):
time_var[record_count] = date2num(t, time_var.units)
uu = vname_to_fields[wind_comp_names[0]][t]
vv = vname_to_fields[wind_comp_names[1]][t]
uu_rot, vv_rot = rotate_vecs_from_geo_to_rotpole(uu, vv, lons, lats, bmp=bmp)
# in knots not in m/s
vname_to_ncvar[wind_comp_names[0]][record_count, :, :] = uu_rot
vname_to_ncvar[wind_comp_names[1]][record_count, :, :] = vv_rot
record_count += 1
