def file_length(input_file, input_subsetter, HDF5=False, QFIT=False):
#-- subset the data to indices if specified
if input_subsetter:
file_lines = len(input_subsetter)
elif HDF5:
#-- read the size of an input variable within a HDF5 file
with h5py.File(input_file, 'r') as fileID:
file_lines, = fileID[HDF5].shape
elif QFIT:
#-- read the size of a QFIT binary file
file_lines = read_ATM1b_QFIT_binary(input_file)
else:
#-- read the input file, split at lines and remove all commented lines
with open(input_file, 'r') as f:
i = [i for i in f.read().splitlines() if re.match(r'^(?!#)', i)]
file_lines = len(i)
#-- return the number of lines
return file_lines
def read_ATM_qfit_file(input_file, input_subsetter):
#-- regular expression pattern for extracting parameters
mission_flag = '(BLATM1B|ILATM1B|ILNSA1B)'
regex_pattern = r'{0}_(\d+)_(\d+)(.*?).(qi|TXT|h5)'.format(mission_flag)
#-- extract mission and other parameters from filename
MISSION, YYMMDD, HHMMSS, AUX, SFX = re.findall(regex_pattern,
input_file).pop()
#-- early date strings omitted century and millenia (e.g. 93 for 1993)
if (len(YYMMDD) == 6):
ypre, month, day = np.array([YYMMDD[:2], YYMMDD[2:4], YYMMDD[4:]],
dtype='i')
year = (ypre + 1900.0) if (ypre >= 90) else (ypre + 2000.0)
elif (len(YYMMDD) == 8):
year, month, day = np.array([YYMMDD[:4], YYMMDD[4:6], YYMMDD[6:]],
dtype='i')
#-- output python dictionary with variables
ATM_L1b_input = {}
#-- Version 1 of ATM QFIT files (ascii)
#-- output text file from qi2txt with proper filename format
#-- do not use the shortened output format from qi2txt
if (SFX == 'TXT'):
#-- compile regular expression operator for reading lines
regex_pattern = r'[-+]?(?:(?:\d*\.\d+)|(?:\d+\.?))(?:[Ee][+-]?\d+)?'
rx = re.compile(regex_pattern, re.VERBOSE)
#-- read the input file, split at lines and remove all commented lines
with open(input_file, 'r') as f:
file_contents = [
i for i in f.read().splitlines() if re.match(r'^(?!#)', i)
]
#-- number of lines of data within file
file_lines = file_length(input_file, input_subsetter)
#-- create output variables with length equal to the number of lines
ATM_L1b_input['lat'] = np.zeros_like(file_contents, dtype=np.float)
ATM_L1b_input['lon'] = np.zeros_like(file_contents, dtype=np.float)
ATM_L1b_input['data'] = np.zeros_like(file_contents, dtype=np.float)
hour = np.zeros_like(file_contents, dtype=np.float)
minute = np.zeros_like(file_contents, dtype=np.float)
second = np.zeros_like(file_contents, dtype=np.float)
#-- for each line within the file
for i, line in enumerate(file_contents):
#-- find numerical instances within the line
line_contents = rx.findall(line)
ATM_L1b_input['lat'][i] = np.float(line_contents[1])
ATM_L1b_input['lon'][i] = np.float(line_contents[2])
ATM_L1b_input['data'][i] = np.float(line_contents[3])
hour[i] = np.float(line_contents[-1][:2])
minute[i] = np.float(line_contents[-1][2:4])
second[i] = np.float(line_contents[-1][4:])
#-- Version 1 of ATM QFIT files (binary)
elif (SFX == 'qi'):
#-- read input QFIT data file and subset if specified
fid, h = read_ATM1b_QFIT_binary(input_file)
#-- number of lines of data within file
file_lines = file_length(input_file, input_subsetter, QFIT=True)
ATM_L1b_input['lat'] = fid['latitude'][:]
ATM_L1b_input['lon'] = fid['longitude'][:]
ATM_L1b_input['data'] = fid['elevation'][:]
time_hhmmss = fid['time_hhmmss'][:]
#-- extract hour, minute and second from time_hhmmss
hour = np.zeros_like(time_hhmmss, dtype=np.float)
minute = np.zeros_like(time_hhmmss, dtype=np.float)
second = np.zeros_like(time_hhmmss, dtype=np.float)
#-- for each line within the file
for i, packed_time in enumerate(time_hhmmss):
#-- convert to zero-padded string with 3 decimal points
line_contents = '{0:010.3f}'.format(packed_time)
hour[i] = np.float(line_contents[:2])
minute[i] = np.float(line_contents[2:4])
second[i] = np.float(line_contents[4:])
#-- Version 2 of ATM QFIT files (HDF5)
elif (SFX == 'h5'):
#-- Open the HDF5 file for reading
fileID = h5py.File(os.path.expanduser(input_file), 'r')
#-- number of lines of data within file
file_lines = file_length(input_file, input_subsetter, HDF5='elevation')
#-- create output variables with length equal to input elevation
ATM_L1b_input['lat'] = fileID['latitude'][:]
ATM_L1b_input['lon'] = fileID['longitude'][:]
ATM_L1b_input['data'] = fileID['elevation'][:]
time_hhmmss = fileID['instrument_parameters']['time_hhmmss'][:]
#-- extract hour, minute and second from time_hhmmss
hour = np.zeros_like(time_hhmmss, dtype=np.float)
minute = np.zeros_like(time_hhmmss, dtype=np.float)
second = np.zeros_like(time_hhmmss, dtype=np.float)
#-- for each line within the file
for i, packed_time in enumerate(time_hhmmss):
#-- convert to zero-padded string with 3 decimal points
line_contents = '{0:010.3f}'.format(packed_time)
hour[i] = np.float(line_contents[:2])
minute[i] = np.float(line_contents[2:4])
second[i] = np.float(line_contents[4:])
#-- close the input HDF5 file
fileID.close()
#-- calculate the number of leap seconds between GPS time (seconds
#-- since Jan 6, 1980 00:00:00) and UTC
gps_seconds = pyTMD.time.convert_calendar_dates(year,
month,
day,
hour=hour,
minute=minute,
second=second,
epoch=(1980, 1, 6, 0, 0,
0),
scale=86400.0)
leap_seconds = pyTMD.time.count_leap_seconds(gps_seconds)
#-- calculation of Julian day taking into account leap seconds
#-- converting to J2000 seconds
ATM_L1b_input['time'] = pyTMD.time.convert_calendar_dates(
year,
month,
day,
hour=hour,
minute=minute,
second=second - leap_seconds,
epoch=(2000, 1, 1, 12, 0, 0, 0),
scale=86400.0)
#-- subset the data to indices if specified
if input_subsetter:
for key, val in ATM_L1b_input.items():
ATM_L1b_input[key] = val[input_subsetter]
#-- hemispheric shot count
count = {}
count['N'] = np.count_nonzero(ATM_L1b_input['lat'] >= 0.0)
count['S'] = np.count_nonzero(ATM_L1b_input['lat'] < 0.0)
#-- determine hemisphere with containing shots in file
HEM, = [key for key, val in count.items() if val]
#-- return the output variables
return ATM_L1b_input, file_lines, HEM
