def diagnose(station_ids=None, model_data_path=None):
manager = Crcm5ModelDataManager(samples_folder_path=model_data_path,
file_name_prefix="pm",
all_files_in_samples_folder=True,
need_cell_manager=True)
nx, ny = manager.lons2D.shape
rot_lat_lon = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)
x00, y00 = rot_lat_lon.toProjectionXY(manager.lons2D[0, 0],
manager.lats2D[0, 0])
x10, y10 = rot_lat_lon.toProjectionXY(manager.lons2D[1, 0],
manager.lats2D[1, 0])
x01, y01 = rot_lat_lon.toProjectionXY(manager.lons2D[0, 1],
manager.lats2D[0, 1])
dx = x10 - x00
dy = y01 - y00
print("dx, dy = {0}, {1}".format(dx, dy))
areas = rot_lat_lon.get_areas_of_gridcells(
dx, dy, nx, ny, y00, 1) #1 -since the index is starting from 1
print(areas[0, 0])
start_date = datetime(1986, 1, 1)
end_date = datetime(1986, 12, 31)
stations = cehq_station.read_station_data(selected_ids=station_ids,
start_date=start_date,
end_date=end_date)
stations.sort(key=lambda x: x.latitude, reverse=True)
for i, s in enumerate(stations):
fig = plt.figure()
#3 columns
gs = GridSpec(5,
3,
hspace=0.2,
wspace=0.2,
right=0.98,
left=0.1,
top=0.98)
model_ts = manager.get_streamflow_timeseries_for_station(
s, start_date=start_date, end_date=end_date, nneighbours=9)
print(model_ts.time[0], model_ts.time[-1])
i_model0, j_model0 = model_ts.metadata["ix"], model_ts.metadata["jy"]
mask = manager.get_mask_for_cells_upstream(i_model0, j_model0)
#hydrographs
ax = fig.add_subplot(gs[0, 0])
plot_streamflows(ax, s, model_ts)
#relative error
ax = fig.add_subplot(gs[1, 0])
plot_streamflow_re(ax, s, model_ts)
#directions
plot_directions_and_positions(fig.add_subplot(gs[:2, 1]),
s,
model_ts,
manager,
rot_lat_lon,
mask=mask)
#runoff
ax = fig.add_subplot(gs[2, 0])
plot_runoff(ax, manager, areas, model_ts, mask=mask)
#runoff from gldas
ax = fig.add_subplot(gs[2, 1])
#plot_gldas_runoff(ax, manager, areas, model_ts, mask = mask)
#temperature
ax_temp = fig.add_subplot(gs[3, 0])
ax_prec = fig.add_subplot(gs[4, 0])
plot_total_precip_and_temp_re_1d(ax_prec,
ax_temp,
manager,
rot_lat_lon,
areas,
model_ts,
mask=mask)
#swe timeseries
ax = fig.add_subplot(gs[3, 1])
plot_swe_timeseries(ax, manager, areas, model_ts, mask=mask)
#print np.where(mask == 1)
print("(i, j) = ({0}, {1})".format(model_ts.metadata["ix"],
model_ts.metadata["jy"]))
fig.savefig("diagnose_{0}_{1:.2f}deg.pdf".format(s.id, dx))
