def test_that_can_convert_tai_to_datetime_obj():
import numpy as np
a = np.arange(6).reshape(2, 3)
b = convert_sec_since_to_std_time_array(a, dt.datetime(1993, 1, 1))
eq_(a.shape, b.shape)
assert_almost_equal(b[0][0], days_since_standard_epoch)
assert_almost_equal(b[0][1], days_since_standard_epoch+1*sec)
assert_almost_equal(b[0][2], days_since_standard_epoch+2*sec)
assert_almost_equal(b[1][0], days_since_standard_epoch+3*sec)
assert_almost_equal(b[1][1], days_since_standard_epoch+4*sec)
assert_almost_equal(b[1][2], days_since_standard_epoch+5*sec)
def _generate_time_array(self, vdata):
import cis.data_io.hdf_vd as hdf_vd
import datetime as dt
from cis.time_util import convert_sec_since_to_std_time_array
Cloudsat_start_time = dt.datetime(1993, 1, 1, 0, 0, 0)
arrays = []
for i, j in zip(vdata['Profile_time'], vdata['TAI_start']):
time = hdf_vd.get_data(i)
start = hdf_vd.get_data(j)
time += start
# Do the conversion to standard time here before we expand the time array...
time = convert_sec_since_to_std_time_array(time, Cloudsat_start_time)
arrays.append(time)
return utils.concatenate(arrays)
def _create_coord_list(self, filenames, index_offset=0):
from cis.data_io.hdf_vd import get_data
from cis.data_io.hdf_vd import VDS
from pyhdf.error import HDF4Error
from cis.data_io import hdf_sd
import datetime as dt
from cis.time_util import convert_sec_since_to_std_time_array, cis_standard_time_unit
variables = ['Latitude', 'Longitude', "Profile_Time", "Pressure"]
logging.info("Listing coordinates: " + str(variables))
# reading data from files
sdata = {}
for filename in filenames:
try:
sds_dict = hdf_sd.read(filename, variables)
except HDF4Error as e:
raise IOError(str(e))
for var in sds_dict.keys():
utils.add_element_to_list_in_dict(sdata, var, sds_dict[var])
alt_name = "altitude"
logging.info("Additional coordinates: '" + alt_name + "'")
# work out size of data arrays
# the coordinate variables will be reshaped to match that.
# NOTE: This assumes that all Caliop_L1 files have the same altitudes.
# If this is not the case, then the following line will need to be changed
# to concatenate the data from all the files and not just arbitrarily pick
# the altitudes from the first file.
alt_data = get_data(VDS(filenames[0], "Lidar_Data_Altitudes"), True)
alt_data *= 1000.0 # Convert to m
len_x = alt_data.shape[0]
lat_data = hdf.read_data(sdata['Latitude'], "SD")
len_y = lat_data.shape[0]
new_shape = (len_x, len_y)
# altitude
alt_data = utils.expand_1d_to_2d_array(alt_data, len_y, axis=0)
alt_metadata = Metadata(name=alt_name, standard_name=alt_name, shape=new_shape)
alt_coord = Coord(alt_data, alt_metadata)
# pressure
pres_data = hdf.read_data(sdata['Pressure'], "SD")
pres_metadata = hdf.read_metadata(sdata['Pressure'], "SD")
# Fix badly formatted units which aren't CF compliant and will break if they are aggregated
if pres_metadata.units == "hPA":
pres_metadata.units = "hPa"
pres_metadata.shape = new_shape
pres_coord = Coord(pres_data, pres_metadata, 'P')
# latitude
lat_data = utils.expand_1d_to_2d_array(lat_data[:, index_offset], len_x, axis=1)
lat_metadata = hdf.read_metadata(sdata['Latitude'], "SD")
lat_metadata.shape = new_shape
lat_coord = Coord(lat_data, lat_metadata, 'Y')
# longitude
lon = sdata['Longitude']
lon_data = hdf.read_data(lon, "SD")
lon_data = utils.expand_1d_to_2d_array(lon_data[:, index_offset], len_x, axis=1)
lon_metadata = hdf.read_metadata(lon, "SD")
lon_metadata.shape = new_shape
lon_coord = Coord(lon_data, lon_metadata, 'X')
# profile time, x
time = sdata['Profile_Time']
time_data = hdf.read_data(time, "SD")
time_data = convert_sec_since_to_std_time_array(time_data, dt.datetime(1993, 1, 1, 0, 0, 0))
time_data = utils.expand_1d_to_2d_array(time_data[:, index_offset], len_x, axis=1)
time_coord = Coord(time_data, Metadata(name='Profile_Time', standard_name='time', shape=time_data.shape,
units=str(cis_standard_time_unit),
calendar=cis_standard_time_unit.calendar), "T")
# create the object containing all coordinates
coords = CoordList()
coords.append(lat_coord)
coords.append(lon_coord)
coords.append(time_coord)
coords.append(alt_coord)
if pres_data.shape == alt_data.shape:
# For MODIS L1 this may is not be true, so skips the air pressure reading. If required for MODIS L1 then
# some kind of interpolation of the air pressure would be required, as it is on a different (smaller) grid
# than for the Lidar_Data_Altitudes.
coords.append(pres_coord)
return coords
def convert_TAI_time_to_std_time(self, ref):
from cis.time_util import convert_sec_since_to_std_time_array, cis_standard_time_unit
self._data = convert_sec_since_to_std_time_array(self.data, ref)
self.units = str(cis_standard_time_unit)
self.metadata.calendar = cis_standard_time_unit.calendar
