def _get_comparison_data_swe( start_date = datetime(1980,1,1),
end_date = datetime(1997, 12, 31),
crcm4_data_folder = "/home/huziy/skynet1_rech3/crcm4_data/aex_p1sno",
swe_obs_data_path = "data/swe_ross_brown/swe.nc",
swe_var_name = "swe", season_to_months = None, crcm4_id = "aex"):
cache_file = "seasonal_bias_{0}.bin".format(swe_var_name)
if not os.path.isfile(cache_file):
#get crcm4 data
crcm4_data_store = {}
for season, month_list in season_to_months.iteritems():
data = ccc_analysis.get_seasonal_mean(crcm4_data_folder, start_date=start_date,
end_date=end_date, months=month_list
)
crcm4_data_store[season] = data
#get cru data
cru_data_store = {}
cru = CruReader(path=swe_obs_data_path, var_name=swe_var_name, transpose_xy_dimensions=False)
lons2d_amno, lats2d_amno = polar_stereographic.lons, polar_stereographic.lats
for season, month_list in season_to_months.iteritems():
data = cru.get_seasonal_mean_field(months=month_list, start_date=start_date, end_date=end_date)
nneighbors = 1 if swe_var_name in ["pre", "swe"] else 4
data = cru.interpolate_data_to(data, lons2d_amno, lats2d_amno, nneighbors=nneighbors)
cru_data_store[season] = data
data_list = [cru_data_store, crcm4_data_store]
pickle.dump(data_list, open(cache_file, "w"))
else:
data_list = pickle.load(open(cache_file))
cru_data_store, crcm4_data_store = data_list
return cru_data_store, crcm4_data_store
def _get_comparison_data( start_date = datetime(1970,1,1),
end_date = datetime(1999, 12, 31),
crcm4_data_folder = "data/ccc_data/aex/aex_p1st",
cru_data_path = "data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc",
cru_var_name = "tmp", season_to_months = None, crcm4_id = "aex"):
cache_file = "seasonal_bias_{0}.bin".format(cru_var_name)
if not os.path.isfile(cache_file):
#get crcm4 data
crcm4_data_store = {}
day_seconds = 24.0 * 60.0 * 60.0
dt = members.id_to_step[crcm4_id]
crcm4_coef = day_seconds
for season, month_list in season_to_months.iteritems():
data = ccc_analysis.get_seasonal_mean(crcm4_data_folder, start_date=start_date,
end_date=end_date, months=month_list
)
if cru_var_name == "pre":
data *= crcm4_coef
crcm4_data_store[season] = data
#get cru data
cru_data_store = {}
cru = CruReader(path=cru_data_path, var_name=cru_var_name)
lons2d_amno, lats2d_amno = polar_stereographic.lons, polar_stereographic.lats
dt = cru.get_time_step()
cru_coef = day_seconds / (dt.seconds + dt.days * day_seconds)
for season, month_list in season_to_months.iteritems():
data = cru.get_seasonal_mean_field(months=month_list, start_date=start_date, end_date=end_date)
nneighbors = 1 if cru_var_name == "pre" else 4
data = cru.interpolate_data_to(data, lons2d_amno, lats2d_amno, nneighbors=nneighbors)
if cru_var_name == "pre":
data *= cru_coef
cru_data_store[season] = data
data_list = [cru_data_store, crcm4_data_store]
pickle.dump(data_list, open(cache_file, "w"))
else:
data_list = pickle.load(open(cache_file))
cru_data_store, crcm4_data_store = data_list
return cru_data_store, crcm4_data_store
def main():
start_date = datetime(1970, 1, 1)
end_date = datetime(1999, 12, 31)
#compare 2m temperatures
cache_file = "compare_basinwise.bin"
if not os.path.isfile(cache_file):
cru = CruReader(path="data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc",
var_name="tmp", create_tree_for_interpolation=False)
cru_t2m = cru.get_monthly_normals(start_date = start_date, end_date = end_date)
data_store = {"cru_t2m": cru_t2m, "cru_lons": cru.lons_2d, "cru_lats": cru.lats_2d}
crcm4_t2m = ccc_analysis.get_monthly_normals("data/ccc_data/aex/aex_p1st",
start_date = start_date, end_date = end_date
)
data_store["crcm4_t2m"] = crcm4_t2m
#create basin masks
#read basins polygons
basins_map = read_shape_file.get_basins_as_shapely_polygons()
data_store["basin_polygons_map"] = basins_map
print( "created basin polygons from the shape file." )
#obs mask
obs_mask = {}
cru_lons1d = cru.lons_2d.flatten()
cru_lats1d = cru.lats_2d.flatten()
p = Point()
for basin_name, basin_poly in basins_map.iteritems():
assert isinstance(basin_poly, Polygon)
the_mask = map( lambda x: int( basin_poly.intersects(get_point(x[0], x[1], p))), zip(cru_lons1d, cru_lats1d))
the_mask = np.reshape( np.array(the_mask), cru.lons_2d.shape )
obs_mask[basin_name] = the_mask
print( "created mask for {0}".format( basin_name ))
data_store["obs_mask"] = obs_mask
#model mask
data_store["crcm4_mask"] = get_basin_masks_from_file()
pickle.dump(data_store, open(cache_file, "wb"))
else:
data_store = pickle.load(open(cache_file))
#plot data
compare_basin_wise(data_store["cru_t2m"], data_store["obs_mask"],
data_store["crcm4_t2m"], data_store["crcm4_mask"], data_store["basin_polygons_map"]
)
pass
def compare_precip(
path_to_ccc="data/ccc_data/aex/aex_p1pcp",
mask=None,
label="",
subplot_count=-1,
start_date=None,
end_date=None,
subplot_total=10,
):
"""
compare total precip from gpcc and from CRCM4
"""
global figure, cru_reader
import compare_swe
ccc_data = compare_swe.getMonthlyNormalsAveragedOverMask(
path_to_ccc=path_to_ccc, mask=mask, startDate=start_date, endDate=end_date
)
lons = polar_stereographic.lons[mask == 1]
lats = polar_stereographic.lats[mask == 1]
if cru_reader is None:
cru_reader = CruReader()
cru_data = cru_reader.get_monthly_normals_integrated_over(lons, lats, start_date=start_date, end_date=end_date)
#
if subplot_count == 1:
plot_utils.apply_plot_params(font_size=25, width_pt=1200, aspect_ratio=2)
figure = plt.figure()
figure.subplots_adjust(hspace=0.9, wspace=0.4, top=0.9)
if figure is None:
print "figure object was not created"
return
ax = figure.add_subplot(5, 2, subplot_count)
ax.set_title("Upstream of {0}".format(label))
# tmp: data stub
# ccc_data = np.ones((12,)) * 5000
# cru_data = np.ones((12,)) * 7000
line1 = ax.plot(ccc_data, label="CRCM4", lw=3, color="b")
line2 = ax.plot(np.array(cru_data), label="CRU", lw=3, color="r")
months = ("", "Feb", "", "Apr", "", "Jun", "", "Aug", "", "Oct", "", "Dec")
ax.set_xticks(xrange(12))
ax.set_xticklabels(months)
ax.yaxis.set_major_formatter(ticker.FuncFormatter(format_yaxis))
if subplot_count == subplot_total:
lines = (line1, line2)
labels = ("CRCM4", "CRU")
figure.legend(lines, labels, "upper center")
# figure.text(0.05, 0.5, 'TOTAL PRECIPITATION (${\\rm mm / month}$)',
# rotation=90,
# ha = 'center', va = 'center'
# )
figure.savefig("tp.pdf")
cru_reader.close_data_connection() # close the file