def test_fromutc_a(self):
index = pystare.from_utc(datetime.astype(numpy.int64), 6)
expected = numpy.array([
34656606509596698, 35184372097220634, 35220842678627354,
35466003203795994
])
numpy.testing.assert_array_equal(index, expected)
def test_fromutc_b(self):
index = pystare.from_utc(datetime.astype(numpy.int64), 27)
expected = numpy.array([
34656606509596782, 35184372097220718, 35220842678627438,
35466003203796078
])
numpy.testing.assert_array_equal(index, expected)
def merra2_stare_time(ds, iTime=None, tType=2, centered=True):
if centered:
start_time_mn = ds['time'].begin_time / 100
start_time_sec = ds['time'].begin_time % 100
resolution = stare_temporal_resolutions[tType]['1/2hr']
else:
start_time_mn = 0
start_time_sec = 0
resolution = stare_temporal_resolutions[tType]['1hr']
yr, mo, dy = merra2_parse_begin_date(ds['time'].begin_date)
tm = []
if iTime is None:
i0 = 0
i1 = 24
else:
i0 = iTime
i1 = iTime + 1
for i in range(i0, i1):
# hr,mn = merra2_make_time(ds['time'].begin_date,ds['time'][i])
hr, mn = merra2_make_time(start_time_mn, ds['time'][i])
sc = start_time_sec
tm.append(format_time(yr, mo, dy, hr, mn, sc))
dt = np.array(tm, dtype='datetime64[ms]')
idx = ps.from_utc(dt.astype(np.int64), resolution)
return idx
def add_temporal_stare(self):
numeric_timestamp = numpy.datetime64(self.time_stamp).astype(
numpy.int64)
stare_value = pystare.from_utc([numeric_timestamp], 27)
stare_temporal = numpy.full(shape=self.lats.shape,
fill_value=stare_value,
dtype='int64')
self.data['stare_temporal'] = stare_temporal
self.data_types['stare_temporal'] = 'int64'
def temporal_id_centered_from_merra2_filename(m2name):
m2name_split = m2name.split(".")
yr = int(m2name_split[2][0:4])
mo = int(m2name_split[2][4:6])
dy = int(m2name_split[2][6:8])
hr = 12
mn = 0
sec = 0
m2dt_str = format_time(yr,mo,dy,hr,mn,sec)
m2dt_np = np.array([m2dt_str],dtype='datetime64[ms]')
m2tid_centered = ps.from_utc(m2dt_np.astype(np.int64),stare_temporal_resolutions[2]['1/2day'])
return m2tid_centered
def temporal_id_centered_from_goes_filename(gfname):
gfname_split = gfname.split(".")
yr = gfname_split[1]
ydy = gfname_split[2]
hr = int(gfname_split[3][0:2])
mn = int(gfname_split[3][2:4])
sec = int(gfname_split[3][4:6])
gdt = dt.datetime(int(yr),1,1)+dt.timedelta(int(ydy)-1)
gdt_str = format_time(int(yr),int(gdt.month),int(gdt.day),hr,mn,sec)
gdt_np = np.array([gdt_str],dtype='datetime64[ms]')
gtid_centered = ps.from_utc(gdt_np.astype(np.int64),stare_temporal_resolutions[2]['1/4hr'])
return gtid_centered
def temporal_id_centered_from_modis_filename(mfname):
mfname_split = mfname.split(".")
yr = mfname_split[1][1:5]
ydy = mfname_split[1][5:8]
hr = int(mfname_split[2][0:2])
mn = int(mfname_split[2][2:4])
# sec = int(mfname_split[2][4:6])
sec = 0
mdt = dt.datetime(int(yr),1,1)+dt.timedelta(int(ydy)-1)
mdt_str = format_time(int(yr),int(mdt.month),int(mdt.day),hr,mn,sec)
mdt_np = np.array([mdt_str],dtype='datetime64[ms]')
mtid_centered = ps.from_utc(mdt_np.astype(np.int64),stare_temporal_resolutions[2]['1/16hr'])
return mtid_centered
def load_modis():
dataPath = "/home/mrilee/data/MODIS/"
# MOD03.A2005349.2120.061.2017187042837.hdf MOD05_L2.A2005349.2120.061.2017294065852.hdf
# MOD03.A2005349.2125.061.2017187042720.hdf MOD05_L2.A2005349.2125.061.2017294065400.hdf
modis_base = "MOD05_L2."
# 1 modis_item = "A2005349.2120.061.2017294065852"
# 1 modis_time_start = "2005-12-15T21:20:00"
modis_item = "A2005349.2125.061.2017294065400"
modis_time_start = "2005-12-15T21:25:00"
modis_geofilename = "MOD03.A2005349.2125.061.2017187042720.hdf"
modis_suffix = ".hdf"
modis_filename = modis_base + modis_item + modis_suffix
fmt_suffix = ".h5"
workFileName = "sketchG." + modis_base + modis_item + fmt_suffix
key_across = 'Cell_Across_Swath_1km:mod05'
key_along = 'Cell_Along_Swath_1km:mod05'
print('loading: ', dataPath + modis_filename)
hdf = SD(dataPath + modis_filename, SDC.READ)
ds_wv_nir = hdf.select('Water_Vapor_Near_Infrared')
data = ds_wv_nir.get()
# MODIS_Swath_Type_GEO/Geolocation_Fields/
# Latitude
hdf_geo = SD(dataPath + modis_geofilename, SDC.READ)
print('hg info: ', hdf_geo.info())
# for idx,sds in enumerate(hdf_geo.datasets().keys()):
# print(idx,sds)
# hdf_geo_ds = hdf_geo.select['']
# hdf_geo_swath = hdf_geo.select('MODIS_Swath_Type_GEO')
# hdf_geo_swath_gf = hdf_geo_swath['Geolocation_Fields']
hdf_geo_lat = hdf_geo.select('Latitude').get()
hdf_geo_lon = hdf_geo.select('Longitude').get()
print('hgl type ', type(hdf_geo_lat))
print('hgl shape ', hdf_geo_lat.shape, hdf_geo_lon.shape)
print('hgl dtype ', hdf_geo_lat.dtype)
# exit()
add_offset = ds_wv_nir.attributes()['add_offset']
scale_factor = ds_wv_nir.attributes()['scale_factor']
print('scale_factor = %f, add_offset = %f.' % (scale_factor, add_offset))
data = (data - add_offset) * scale_factor
print('data mnmx: ', np.amin(data), np.amax(data))
nAlong = ds_wv_nir.dimensions()[key_along]
nAcross = ds_wv_nir.dimensions()[key_across]
print('ds_wv_nir nAlong,nAcross: ', nAlong, nAcross)
dt = np.array([modis_time_start], dtype='datetime64[ms]')
t_resolution = 26 # 5 minutes resolution? 2+6+10+6+6
tid = ps.from_utc(dt.astype(np.int64), t_resolution)
# print(np.arange(np.datetime64("2005-12-15T21:20:00"),np.datetime64("2005-12-15T21:25:00")))
# exit()
fill_value = ds_wv_nir.attributes()['_FillValue']
mod_lat = hdf_geo_lat.flatten()
mod_lon = hdf_geo_lon.flatten()
mod_dat = data.flatten()
return mod_lon, mod_lat, mod_dat # load_modis
print('hgl dtype ', hdf_geo_lat.dtype)
# exit()
add_offset = ds_wv_nir.attributes()['add_offset']
scale_factor = ds_wv_nir.attributes()['scale_factor']
print('scale_factor = %f, add_offset = %f.' % (scale_factor, add_offset))
data = (data - add_offset) * scale_factor
print('data mnmx: ', np.amin(data), np.amax(data))
nAlong = ds_wv_nir.dimensions()[key_along]
nAcross = ds_wv_nir.dimensions()[key_across]
print('ds_wv_nir nAlong,nAcross: ', nAlong, nAcross)
dt = np.array([modis_time_start], dtype='datetime64[ms]')
t_resolution = 26 # 5 minutes resolution? 2+6+10+6+6
tid = ps.from_utc(dt.astype(np.int64), t_resolution)
# print(np.arange(np.datetime64("2005-12-15T21:20:00"),np.datetime64("2005-12-15T21:25:00")))
# exit()
fill_value = ds_wv_nir.attributes()['_FillValue']
# print(ds_wv_nir.info())
# print(hdf.info())
# print(ds_wv_nir.attributes())
# print(hdf.attributes().keys())
# hdf_lat = hdf.select('Latitude')
# print('hdf.lat: ',type(hdf_lat),hdf_lat.info())
lat_5km = with_hdf_get(hdf, 'Latitude')
lon_5km = with_hdf_get(hdf, 'Longitude')
# This adds the silliness of how to interpret our intervals. If we keep the STARE scheme
# The start of the interval is the start, but then cmp-at-resolution is not quite right.
# Note cmp does the right thing.
print('')
for i in range(len(tim)):
print('%3d i,tim %04d '%(i,tim[i]),' %02d:%02d '%merra2_make_time(0,tim[i]))
formatted_times = []
idx = np.zeros([tim.size],dtype=np.int64)
for i in range(len(tim)):
yr,mo,dy = merra2_parse_begin_date(tim.begin_date)
hr,mn = merra2_make_time(0,tim[i])
sc = 0
tm = format_time(yr,mo,dy,hr,mn,sc)
dt = np.array([tm],dtype='datetime64[ms]')
idx[i] = ps.from_utc(dt.astype(np.int64),resolution_level_1hr)
# idx[i] = ps.from_utc(dt.astype(np.int64),resolution_level_1o4hr)
back = np.array(ps.to_utc_approximate(np.array([idx[i]],dtype=np.int64)),dtype='datetime64[ms]')
print(yr,mo,dy,hr,mn,tm,dt,hex(idx[i]),back)
formatted_times.append(tm)
### GOES BAND_05 # Check time information
goes_b5_dataPath = "/home/mrilee/data/"
goes_b5_dataFile = "goes10.2005.349.003015.BAND_05.nc"
goes_b5_fqFilename = goes_b5_dataPath+goes_b5_dataFile
goes_b5_ds = Dataset(goes_b5_fqFilename)
# print('keys: ',goes_b5_ds.variables.keys())
tim = goes_b5_ds['time']
def test_epoch(self):
datetime_x1 = datetime.astype(numpy.int64)
index = pystare.from_utc(datetime_x1, 27)
datetime_x2 = pystare.to_utc_approximate(index)
numpy.testing.assert_array_equal(datetime_x1, datetime_x2)
def test_utc_roundtrip(self):
index = pystare.from_utc(datetime.astype(numpy.int64), 27)
datetime_x = pystare.to_utc_approximate(index)
datetime_r = numpy.array(datetime_x, dtype='datetime64[ms]')
numpy.testing.assert_array_equal(datetime, datetime_r)
def goes10_img_stare_time(ds,tType=2,centered=True): resolution = stare_temporal_resolutions[2]['1/4hr'] dt = np.array(ds['time'][0]*1000,dtype='datetime64[ms]').reshape([1]) return ps.from_utc(dt.astype(np.int64),resolution)
