def test_save_longcomplex(self):
val = np.longcomplex(1.01 + 2.3j)
data = {'val': val}
save_data(data, 'bubu' + HEN_FILE_EXTENSION)
data_out = load_data('bubu' + HEN_FILE_EXTENSION)
assert np.allclose(data['val'], data_out['val'])
def test_treat_event_file_xmm(self):
treat_event_file(self.fits_file)
new_filename = 'monol_test_fake_xmm_epn_det01_ev' + HEN_FILE_EXTENSION
assert os.path.exists(os.path.join(self.datadir, new_filename))
data = load_data(os.path.join(self.datadir, new_filename))
assert 'instr' in data
assert 'gti' in data
assert 'mjdref' in data
def test_treat_event_file_nustar(self):
treat_event_file(self.fits_fileA)
new_filename = 'monol_testA_nustar_fpma_ev' + HEN_FILE_EXTENSION
assert os.path.exists(os.path.join(self.datadir, new_filename))
data = load_data(os.path.join(self.datadir, new_filename))
assert 'instr' in data
assert 'gti' in data
assert 'mjdref' in data
assert np.isclose(data['mjdref'], ref_mjd(self.fits_fileA))
def test_save_longcomplex(self):
val = np.complex256(1.01 + 2.3j)
data = {'val': val}
with pytest.warns(UserWarning) as record:
save_data(data, 'bubu' + HEN_FILE_EXTENSION)
assert "complex256 yet unsupported" in record[0].message.args[0]
data_out = load_data('bubu' + HEN_FILE_EXTENSION)
assert np.allclose(data['val'], data_out['val'])
def test_save_data(self):
struct = {'a': 0.1, 'b': np.longdouble('123.4567890123456789'),
'c': np.longdouble([[-0.5, 3.5]]),
'd': 1}
save_data(struct, self.dum)
struct2 = load_data(self.dum)
assert np.allclose(struct['a'], struct2['a'])
assert np.allclose(struct['b'], struct2['b'])
assert np.allclose(struct['c'], struct2['c'])
assert np.allclose(struct['d'], struct2['d'])
def get_lightcurve_fits_dataset_with_stingray(destination, hdulist, hduname='RATE',
column=CONFIG.TIME_COLUMN, gtistring=CONFIG.GTI_STRING, time_offset=0):
supported_rate_columns = set(['RATE', 'RATE1', 'COUNTS'])
found_rate_columns = set(hdulist[hduname].data.names)
intersection_columns = supported_rate_columns.intersection(found_rate_columns)
#Check if HDUCLAS1 = LIGHTCURVE column exists
logging.debug("Reading Lightcurve Fits columns")
if "HDUCLAS1" not in hdulist[hduname].header:
logging.warn("HDUCLAS1 not found in header: " + hduname)
return None
elif hdulist[hduname].header["HDUCLAS1"] != "LIGHTCURVE":
logging.warn("HDUCLAS1 is not LIGHTCURVE")
return None
elif len(intersection_columns) == 0:
logging.warn("RATE, RATE1 or COUNTS columns not found in " + str(hduname) + " HDU, found columns: " + str(hdulist[hduname].data.names))
return None
elif len(intersection_columns) > 1:
logging.warn("RATE, RATE1 or COUNTS ambiguous columns found in " + str(hduname) + " HDU, found columns: " + str(hdulist[hduname].data.names))
return None
ratecolumn = list(intersection_columns)[0]
if len(hdulist[hduname].data[ratecolumn].shape) != 1 \
or not (isinstance(hdulist[hduname].data[ratecolumn][0], int) \
or isinstance(hdulist[hduname].data[ratecolumn][0], np.integer) \
or isinstance(hdulist[hduname].data[ratecolumn][0], float) \
or isinstance(hdulist[hduname].data[ratecolumn][0], np.floating)):
logging.warn("Wrong data type found for column: " + str(ratecolumn) + " in " + str(hduname) + " HDU, expected Integer or Float.")
return None
header, header_comments = get_header(hdulist, hduname)
# Reads the lightcurve with HENDRICS
outfile = lcurve_from_fits(destination, gtistring=get_hdu_string_from_hdulist(gtistring, hdulist),
timecolumn=column, ratecolumn=ratecolumn, ratehdu=1,
fracexp_limit=CONFIG.FRACEXP_LIMIT)[0]
lcurve, events_start_time = substract_tstart_from_lcurve(load_data(outfile), time_offset)
dataset = DataSet.get_lightcurve_dataset_from_stingray_lcurve(lcurve, header, header_comments,
hduname, column)
# Stores the events_start_time in time column extra
dataset.tables[hduname].columns[column].set_extra("TSTART", events_start_time)
logging.debug("Read Lightcurve fits with stingray file successfully: " + str(destination) + ", tstart: " + str(events_start_time) + ", rate: " + str(len(lcurve["counts"])))
return dataset
def test_treat_event_file_xmm(self):
fits_file = os.path.join(self.datadir, 'monol_test_fake.evt')
hen.fake.main([
'--deadtime', '1e-4', '-m', 'XMM', '-i', 'epn', '--ctrate', '2000',
'-o', fits_file
])
treat_event_file(fits_file)
new_filename = 'monol_test_fake_xmm_epn_det01_ev' + HEN_FILE_EXTENSION
assert os.path.exists(os.path.join(self.datadir, new_filename))
data = load_data(os.path.join(self.datadir, new_filename))
assert 'instr' in data
assert 'gti' in data
assert 'mjdref' in data
treat_event_file(fits_file, gti_split=True)
new_filename = \
'monol_test_fake_xmm_epn_det01_gti0_ev' + HEN_FILE_EXTENSION
assert os.path.exists(os.path.join(self.datadir, new_filename))
data = load_data(os.path.join(self.datadir, new_filename))
assert 'instr' in data
assert 'gti' in data
assert 'mjdref' in data
def test_treat_event_file_xmm_lensplit(self):
treat_event_file(self.fits_file, length_split=10)
new_filename = \
'monol_test_fake_xmm_epn_det01_chunk000_ev' + HEN_FILE_EXTENSION
assert os.path.exists(os.path.join(self.datadir, new_filename))
data = load_data(os.path.join(self.datadir, new_filename))
assert 'instr' in data
assert 'gti' in data
assert 'mjdref' in data
gtis = data['gti']
lengths = np.array([g1 - g0 for (g0, g1) in gtis])
assert np.all(lengths <= 10)
def test_load_data_fails(self):
with pytest.raises(TypeError) as record:
load_data('afile.fits')
assert "The file type is not recognized" in str(record.value)
def get_lightcurve_fits_dataset_with_stingray(destination,
hdulist,
hduname='RATE',
column=CONFIG.TIME_COLUMN,
gtistring=CONFIG.GTI_STRING,
time_offset=0):
supported_rate_columns = set(['RATE', 'RATE1', 'COUNTS'])
found_rate_columns = set(hdulist[hduname].data.names)
intersection_columns = supported_rate_columns.intersection(
found_rate_columns)
#Check if HDUCLAS1 = LIGHTCURVE column exists
logging.debug("Reading Lightcurve Fits columns")
if "HDUCLAS1" not in hdulist[hduname].header:
logging.warn("HDUCLAS1 not found in header: " + hduname)
return None
elif hdulist[hduname].header["HDUCLAS1"] != "LIGHTCURVE":
logging.warn("HDUCLAS1 is not LIGHTCURVE")
return None
elif len(intersection_columns) == 0:
logging.warn("RATE, RATE1 or COUNTS columns not found in " +
str(hduname) + " HDU, found columns: " +
str(hdulist[hduname].data.names))
return None
elif len(intersection_columns) > 1:
logging.warn("RATE, RATE1 or COUNTS ambiguous columns found in " +
str(hduname) + " HDU, found columns: " +
str(hdulist[hduname].data.names))
return None
ratecolumn = list(intersection_columns)[0]
if len(hdulist[hduname].data[ratecolumn].shape) != 1 \
or not (isinstance(hdulist[hduname].data[ratecolumn][0], int) \
or isinstance(hdulist[hduname].data[ratecolumn][0], np.integer) \
or isinstance(hdulist[hduname].data[ratecolumn][0], float) \
or isinstance(hdulist[hduname].data[ratecolumn][0], np.floating)):
logging.warn("Wrong data type found for column: " + str(ratecolumn) +
" in " + str(hduname) +
" HDU, expected Integer or Float.")
return None
header, header_comments = get_header(hdulist, hduname)
# Reads the lightcurve with HENDRICS
outfile = lcurve_from_fits(destination,
gtistring=get_hdu_string_from_hdulist(
gtistring, hdulist),
timecolumn=column,
ratecolumn=ratecolumn,
ratehdu=1,
fracexp_limit=CONFIG.FRACEXP_LIMIT)[0]
lcurve, events_start_time = substract_tstart_from_lcurve(
load_data(outfile), time_offset)
dataset = DataSet.get_lightcurve_dataset_from_stingray_lcurve(
lcurve, header, header_comments, hduname, column)
# Stores the events_start_time in time column extra
dataset.tables[hduname].columns[column].set_extra("TSTART",
events_start_time)
logging.debug("Read Lightcurve fits with stingray file successfully: " +
str(destination) + ", tstart: " + str(events_start_time) +
", rate: " + str(len(lcurve["counts"])))
return dataset
def test_treat_event_file_nustar(self):
treat_event_file(self.fits_fileA)
new_filename = 'monol_testA_nustar_fpma_ev' + HEN_FILE_EXTENSION
assert os.path.exists(os.path.join(self.datadir, new_filename))
data = load_data(os.path.join(self.datadir, new_filename))
assert 'instr' in data
