def get_area_def(hdf_file):
generic_m3 = str(hdf_file)
metadata = parseMeta(generic_m3)
# Read the lats and lons from the MYD03 file
print(f'reading {generic_m3}')
m3_file = SD(str(generic_m3), SDC.READ)
lats = m3_file.select('Latitude').get()
lons = m3_file.select('Longitude').get()
m3_file.end()
proj_params = get_proj_params(generic_m3)
swath_def = SwathDefinition(lons, lats)
area_def = swath_def.compute_optimal_bb_area(proj_dict=proj_params)
return (area_def, lons, lats, metadata, swath_def)
def main():
"""
run the test
"""
generic_m3 = a301.data_dir / Path("m3_file_2018_10_1.hdf")
if not generic_m3.exists():
raise ValueError(f"couldn't find {generic_m3}")
modis_meta = parseMeta(generic_m3)
print(f"working on {generic_m3}, originally was { modis_meta['filename']}")
m3_file = SD(str(generic_m3), SDC.READ)
lats = m3_file.select('Latitude').get()
lons = m3_file.select('Longitude').get()
stars = '*' * 40
print(
f"{stars}\nlats.shape, lons.shape: {lats.shape},{lons.shape}\n{stars}")
m3_file.end()
generic_rad = a301.data_dir / Path("rad_file_2018_10_1.hdf")
if not generic_rad.exists():
raise ValueError(f"couldn't find {generic_rad}")
rad_file = SD(str(generic_rad), SDC.READ)
ch30 = rad_file.select('ch30').get()
print(f"working on {generic_rad}, originally was {rad_file.filename}")
print(f"{stars}\narray shape is: {ch30.shape}\n{stars}")
rad_file.end()
print(f'reading {generic_m3}')
from pyresample import SwathDefinition
proj_params = get_proj_params(generic_m3)
swath_def = SwathDefinition(lons, lats)
area_def = swath_def.compute_optimal_bb_area(proj_dict=proj_params)
fill_value = -9999.
area_name = 'modis swath 5min granule'
image_30 = kd_tree.resample_nearest(swath_def,
ch30.ravel(),
area_def,
radius_of_influence=5000,
nprocs=2,
fill_value=fill_value)
print(f'\ndump area definition:\n{area_def}\n')
print((f'\nx and y pixel dimensions in meters:'
f'\n{area_def.pixel_size_x}\n{area_def.pixel_size_y}\n'))
def runit():
from pyresample import SwathDefinition, kd_tree, geometry
proj_params = get_proj_params(m5_file)
swath_def = SwathDefinition(lons_5km, lats_5km)
area_def_lr = swath_def.compute_optimal_bb_area(proj_dict=proj_params)
area_def_lr.name = "ir wv retrieval modis 5 km resolution (lr=low resolution)"
area_def_lr.area_id = 'modis_ir_wv'
area_def_lr.job_id = area_def_lr.area_id
fill_value = -9999.
image_wv_ir = kd_tree.resample_nearest(swath_def,
wv_ir_scaled.ravel(),
area_def_lr,
radius_of_influence=5000,
nprocs=2,
fill_value=fill_value)
image_wv_ir[image_wv_ir < -9000] = np.nan
print(f'\ndump area definition:\n{area_def_lr}\n')
print((f'\nx and y pixel dimensions in meters:'
f'\n{area_def_lr.pixel_size_x}\n{area_def_lr.pixel_size_y}\n'))
pdb.set_trace()
def main():
"""
run the test
"""
stars = '*' * 50
print(f'\n{stars}\nrunning test: {__file__}\n{stars}\n')
generic_m3 = a301.data_dir / Path("m3_file_2018_10_1.hdf")
if not generic_m3.exists():
raise ValueError(f"couldn't find {generic_m3}")
modis_meta = parseMeta(generic_m3)
print(f"working on {generic_m3}, originally was { modis_meta['filename']}")
m3_file = SD(str(generic_m3), SDC.READ)
lats_1km = m3_file.select('Latitude').get()
lons_1km = m3_file.select('Longitude').get()
stars = '*' * 40
print(
f"{stars}\nlats_1km.shape, lons_1km.shape: {lats_1km.shape},{lons_1km.shape}\n{stars}"
)
m3_file.end()
generic_m5 = a301.data_dir / Path("myd05_l2_10_7.hdf")
if not generic_m5.exists():
raise ValueError(f"couldn't find {generic_m5}")
m5_file = SD(str(generic_m5), SDC.READ)
vapor_ir = m5_file.select('Water_Vapor_Infrared').get()
vapor_near_ir = m5_file.select('Water_Vapor_Near_Infrared').get()
lats_5km = m5_file.select('Latitude').get()
lons_5km = m5_file.select('Longitude').get()
m5_file.end()
print('through')
m5_meta = parseMeta(generic_m5)
print(f"working on {generic_m5}, originally was {m5_meta['filename']}")
print(
f"{stars}\nnearir vapor array shape is: {vapor_near_ir.shape}\n{stars}"
)
print(f"{stars}\nir vapor array shape is: {vapor_ir.shape}\n{stars}")
print(f"{stars}\nlats_5km arrayshape is: {lats_5km.shape}\n{stars}")
print(f"{stars}\nlons_5km arrayshape is: {lons_5km.shape}\n{stars}")
# print(f'reading {generic_m3}')
from pyresample import SwathDefinition
proj_params = get_proj_params(generic_m3)
swath_def = SwathDefinition(lons_1km, lats_1km)
area_def = swath_def.compute_optimal_bb_area(proj_dict=proj_params)
fill_value = -9999.
area_name = 'modis swath 5min granule'
image_nearir = kd_tree.resample_nearest(swath_def,
vapor_near_ir.ravel(),
area_def,
radius_of_influence=5000,
nprocs=2,
fill_value=fill_value)
print(
f'was able to regrid the nearir image, xy shape is {image_nearir.shape}'
)
proj_params = get_proj_params(generic_m5)
swath_def = SwathDefinition(lons_5km, lats_5km)
area_def = swath_def.compute_optimal_bb_area(proj_dict=proj_params)
fill_value = -9999.
area_name = 'modis swath 5min granule'
image_ir = kd_tree.resample_nearest(swath_def,
vapor_ir.ravel(),
area_def,
radius_of_influence=5000,
nprocs=2,
fill_value=fill_value)
print(f'was able to regrid the ir image, xy shape is {image_ir.shape}')
print('data looks good, ready to go')
# Read the lats and lons from the MYD03 file
print(f'reading {generic_m3}')
m3_file = SD(str(generic_m3), SDC.READ)
lats = m3_file.select('Latitude').get()
lons = m3_file.select('Longitude').get()
m3_file.end()
dir(metadata)
# %% {"scrolled": true}
from pyresample import load_area, save_quicklook, SwathDefinition
proj_params = get_proj_params(generic_m3)
swath_def = SwathDefinition(lons, lats)
area_def = swath_def.compute_optimal_bb_area(proj_dict=proj_params)
area_def
# %%
print(swath_def.shape)
print(area_def.shape)
# %% {"scrolled": false}
#p_utm = Proj(crs)
p_lonlat = Proj(proj='latlong', datum='WGS84')
stn_lon = -105.237
stn_lat = 40.125
stn_x, stn_y = proj_transform(p_lonlat, Proj(area_def.proj_dict), stn_lon,
stn_lat)
min_x, min_y = proj_transform(p_lonlat, Proj(area_def.proj_dict),
metadata['min_lon'], metadata['min_lat'])
# the low resolution (lr) image. The cell below let's pyresample create the
# area_def object, which we will reuse for the 1 km watervapor retrieval to
# get both onto the same grid.
#
# The cell below produces:
#
# * `image_wv_ir` -- resampled 5 km infrared water vapor
# * `area_def_lr` -- area_def used for the resample
# %%
from pyresample import SwathDefinition, kd_tree, geometry
proj_params_5km = get_proj_params(m5_file_str)
proj_params_1km = get_proj_params(m3_file_str)
swath_def = SwathDefinition(lons_5km, lats_5km)
area_def_lr = swath_def.compute_optimal_bb_area(proj_dict=proj_params_5km)
#area_def_lr.name = "ir wv retrieval modis 5 km resolution (lr=low resolution)"
#area_def_lr.area_id = "modis_ir_wv"
#area_def_lr.job_id = area_def_lr.area_id
fill_value = -9999.0
image_wv_ir = kd_tree.resample_nearest(
swath_def,
wv_ir_scaled.ravel(),
area_def_lr,
radius_of_influence=5000,
nprocs=2,
fill_value=fill_value,
)
image_wv_ir[image_wv_ir < -9000] = np.nan
print(f"\ndump area definition:\n{area_def_lr}\n")
print(
