def load_jobst(variable, dts_to_take, nc_file_in, mask_dem, origin):
nc_file = nc.Dataset(nc_file_in + '01-Jan-{}to31-dec-{}.nc'.format(
dts_to_take[0].year, dts_to_take[0].year))
# nc_datetimes = nc.num2date(nc_file.variables['time'][:], nc_file.variables['time'].units)
data = nc_file.variables[variable][:]
# the data is in a funny grid - need to swap last two axes, then flip to align with vcsn grid
# plt.imshow(np.flipud(np.transpose(hi_res_max_temp,(0,2,1))[0][mask]),origin=0)
if mask_dem == True:
hi_res_precip_trimmed = []
for precip in data:
if origin == 'topleft':
_, _, trimmed_precip, _, _ = trim_lat_lon_bounds(
mask, lat_array, lon_array, np.transpose(precip),
y_centres, x_centres)
elif origin == 'bottomleft':
_, _, trimmed_precip, _, _ = trim_lat_lon_bounds(
mask, lat_array, lon_array,
np.flipud(np.transpose(precip)), y_centres, x_centres)
hi_res_precip_trimmed.append(trimmed_precip)
data = np.asarray(hi_res_precip_trimmed)
if variable in ['tmin', 'tmax']:
data = data + 273.15
return data
def load_mask_modis(catchment, mask_folder, modis_dem):
'''
load mask and trimmed mask of catchment for modis clutha domain
'''
if modis_dem == 'clutha_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.2e6,
extent_e=1.4e6,
extent_n=5.13e6,
extent_s=4.82e6,
resolution=250,
origin='bottomleft')
if modis_dem == 'si_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250,
origin='bottomleft')
if modis_dem == 'modis_si_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250,
origin='bottomleft')
if modis_dem == 'modis_nz_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=2.10e6,
extent_n=6.20e6,
extent_s=4.70e6,
resolution=250,
origin='bottomleft')
# # Get the masks for the individual regions of interest
mask = np.load(mask_folder + '/{}_{}.npy'.format(catchment, modis_dem))
_, _, trimmed_mask, _, _ = trim_lat_lon_bounds(mask, lat_array, lon_array,
mask.copy(), y_centres,
x_centres)
return mask, trimmed_mask
def load_mask_modis(catchment, output_dem, mask_folder, dem_folder, modis_dem):
'''
load mask and trimmed mask of catchment for modis clutha domain
'''
if modis_dem == 'clutha_dem_250m':
# dem_file = dem_folder + modis_dem + '.tif'
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(None)
if modis_dem == 'si_dem_250m':
# dem_file = dem_folder + modis_dem + '.tif'
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
if modis_dem == 'modis_si_dem_250m':
# dem_file = dem_folder + modis_dem + '.tif'
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
# # Get the masks for the individual regions of interest
mask = np.load(mask_folder + '/{}_{}.npy'.format(catchment, modis_dem))
_, _, trimmed_mask, _, _ = trim_lat_lon_bounds(mask, lat_array, lon_array,
mask.copy(), y_centres,
x_centres)
return mask, trimmed_mask
if mask_created == True: # load precalculated mask
mask = np.load(mask_folder +
'/{}_{}.npy'.format(catchment, output_dem))
if origin == 'topleft':
mask = np.flipud(mask)
else: # create mask and save to npy file
# masks = get_masks() #TODO set up for multiple masks
mask = create_mask_from_shpfile(lat_array, lon_array, mask_shpfile)
if origin == 'topleft': # flip to save as bottom left
mask = np.flipud(mask)
np.save(mask_folder + '/{}_{}.npy'.format(catchment, output_dem),
mask)
if origin == 'topleft': # flip back to topleft
mask = np.flipud(mask)
# Trim down the number of latitudes requested so it all stays in memory
lats, lons, elev, northings, eastings = trim_lat_lon_bounds(
mask, lat_array, lon_array, nztm_dem, y_centres, x_centres)
_, _, trimmed_mask, _, _ = trim_lat_lon_bounds(mask,
lat_array, lon_array,
mask.copy(), y_centres,
x_centres)
else:
mask = None
lats = lat_array
lons = lon_array
elev = nztm_dem
northings = y_centres
eastings = x_centres
for year_to_take in years_to_take:
# load data
# create timestamp to get - this is in NZST
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=2.10e6,
extent_n=6.20e6,
extent_s=4.70e6,
resolution=250,
origin='bottomleft')
nztm_dem = np.load(dem_folder + '/{}.npy'.format(modis_dem))
modis_output_dem = 'modis_nz_dem_250m'
mask = np.load(
mask_folder + '/{}_{}.npy'.format(catchment, modis_dem)
) # just load the mask the chooses land points from the dem. snow data has modis hy2018_2020 landpoints mask applied in NZ_evaluation_otf
# mask = np.load("C:/Users/conwayjp/OneDrive - NIWA/projects/CARH2101/snow reanalysis/modis_mask_hy2018_2020_landpoints.npy")
lat_array, lon_array, nztm_dem, y_centres, x_centres = trim_lat_lon_bounds(
mask, lat_array, lon_array, nztm_dem, y_centres, x_centres)
# # modis options
modis_sc_threshold = 50 # value of fsca (in percent) that is counted as being snow covered
modis_output_folder = 'C:/Users/conwayjp/OneDrive - NIWA/projects/CARH2101/snow reanalysis'
# modis_output_folder = '/nesi/nobackup/niwa00004/jonoconway/snow_sims_nz'
[ann_ts_av_sca_m, ann_ts_av_sca_thres_m, ann_dt_m, ann_scd_m] = pickle.load(
open(
modis_output_folder + '/summary_MODIS_{}_{}_{}_{}_thres{}.pkl'.format(
hydro_years_to_take[0], hydro_years_to_take[-1], catchment,
modis_output_dem, modis_sc_threshold), 'rb'))
# model options
run_id = 'cl09_default_ros' ## 'cl09_tmelt275'#'cl09_default' #'cl09_tmelt275_ros' ##TODO
which_model = 'clark2009' #TODO
#subcatchment = 'qldc_ta_area'
catchment = 'Clutha'
mask_folder = r'C:\Users\conwayjp\OneDrive - NIWA\Temp\Masks'
# dem_file = dem_folder + dem + '.tif'
# elev, easting, northing, lat_array, lon_array = setup_nztm_dem(dem_file)
# create new outlons and out_lats using trim for the qldc mask here
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
dem_file=None)
mask = np.load(mask_folder + '/{}_{}.npy'.format(catchment, dem))
# qldc_mask = np.load(mask_folder + '/{}_{}.npy'.format(subcatchment, dem))
out_lats, out_lons, trimmed_mask, _, _ = trim_lat_lon_bounds(
mask, lat_array, lon_array, mask.copy(), y_centres, x_centres)
# returns: lats, lons, elev, northings, eastings
mask = trimmed_mask
#out_lons = lon_array
out_lats = np.flipud(out_lats) #(lat_array)
# northing = np.flipud(northing)
# out_elev = elev
out_elev = trimmed_mask * 0.0
# # Clip to the same extent as the met data
# northing_clip = (4861875, 5127625)
# easting_clip = (1214375, 1370375)
# northing_mask = (northing >= northing_clip[0]) & (northing <= northing_clip[1])
# easting_mask = (easting >= easting_clip[0]) & (easting <= easting_clip[1])
# out_lats = out_lats[northing_mask][:, easting_mask]
extent_e=2.10e6,
extent_n=6.275e6,
extent_s=4.70e6,
resolution=250,
origin='bottomleft')
# mask for nz modis domain on nz 250m dem domain
modis_nz_ew_extent = np.logical_and(x_centres > 1.085e6, x_centres < 2.10e6)
modis_nz_ns_extent = np.logical_and(y_centres < 6.20e6, y_centres > 4.70e6)
modis_nz_mask = modis_nz_ns_extent[:, np.newaxis] * modis_nz_ew_extent[
np.newaxis, :]
nztm_dem2 = nztm_dem[modis_nz_mask].reshape(lat_array2.shape)
NZ_mask_nz = nztm_dem > 0
NZ_mask_modis_nz = nztm_dem2 > 0
#to get trimmed coordinates for modis domain trimmed to elevation > 0
trim_lat_lon_bounds(NZ_mask_modis_nz, lat_array2, lon_array2, nztm_dem2,
y_centres2, x_centres2)
print(NZ_mask_nz.shape)
print(NZ_mask_modis_nz.shape)
#trim 116 points off the top of the NZ masked domain to make the same as the modis masked domain
assert np.all(
trim_lat_lon_bounds(NZ_mask_modis_nz, lat_array2, lon_array2, nztm_dem2,
y_centres2, x_centres2)[0] == trim_lat_lon_bounds(
NZ_mask_nz, lat_array, lon_array, nztm_dem,
y_centres, x_centres)[0][:-116])
print(trim_data_to_mask(NZ_mask_modis_nz, NZ_mask_modis_nz).shape)
