def main(self):
input_file = self.input_response.out_response_3d_reconstructed.get_path(
)
area_factor = 450.
if self.input_response.nchan == 62:
emin, emax = (lambda x: (x['E_MIN'], x['E_MAX']))(
pyfits.open("/unsaved_data/savchenk/rmf_62bands.fits")
['ISGR-EBDS-MOD'].data) # we should put this in IC or smth
elif self.input_response.nchan == 256:
emin, emax = (lambda x: (x['E_MIN'], x['E_MAX']))(pyfits.open(
os.environ['CURRENT_IC'] +
"/ic/ibis/mod/isgr_ebds_mod_0001.fits")['ISGR-EBDS-MOD'].data)
print(("nchan", emin.shape))
de = emax - emin
#de=ones_like(de)*0.4787
r3d = pyfits.open(input_file)[0].data #.transpose()[::-1,::-1]
m_emin = arange(2466) * 0.4787 + 13
m_emax = (arange(2466) + 1) * 0.4787 + 13
# m_emin=arange(2200)*0.5+13
# m_emax=(arange(2200)+1)*0.5+13
m_de = m_emax - m_emin
def write_response(rt1, rt2):
rmap = r3d[:, :, rt1:rt2].sum(2) / outer(
m_de, ones_like(de)) / area_factor
#rmap=r3d[:,:,rt1:rt2].sum(2)/outer(de,ones_like(de))
print((rmap.shape))
rmap[:1, :] = 0
rmap[-2:, :] = 0
rmap[:, :1] = 0
rmap[:, -2:] = 0
key = "rt%.5lg_%.5lg" % (rt1, rt2)
rmffn = "rmf_%s.fits" % key
ogip.spec.RMF.from_arrays(m_emin, m_emax, rmap, emin,
emax).to_fits(rmffn)
#rmap_normalized=rmap/outer(ones_like(de),rmap.sum(1))
#ogip.spec.RMF(emin,emax,emin,emax,rmap_normalized).write("rmf_normalized.fits")
ogip.spec.PHAI.from_arrays(1,
rate=rmap[50, :] * 100,
stat_err=rmap[50, :] * 5).to_fits(
"pha_%s.fits" % key)
return rmffn
self.rmf_16_116 = da.DataFile(write_response(16, 116))
self.rmf_16_50 = da.DataFile(write_response(16, 50))
self.rmf_16_30 = da.DataFile(write_response(16, 30))
def main(self):
print("test")
fn="file.txt"
with open(fn,"w") as f:
f.write("test")
fn1="file1.txt"
with open(fn1,"w") as f:
f.write("test")
self.data = [da.DataFile(fn)]
self.data1 = da.DataFile(fn1)
def main(self):
new_e = fits.open(self.input_ebins.ebds_mod_fn)[1]
new_bins = zip(new_e.data['E_MIN'], new_e.data['E_MAX'])
print("new bins:", new_bins)
orig_e = fits.open(self.input_ic_ebds.member_location)[1]
orig_bins = zip(orig_e.data['E_MIN'], orig_e.data['E_MAX'])
print("original bins", orig_bins)
print("original rmf", self.input_rsp.rmf_path)
rmf_e = fits.open(self.input_rsp.rmf_path)[1]
orig_rsp_fn = "original_rsp_assembled.fits"
fits.HDUList([fits.PrimaryHDU(), orig_e,
rmf_e]).writeto(orig_rsp_fn, overwrite=True)
ebins_compress_fn = "ebins.txt"
with open(ebins_compress_fn, "w") as f:
for oi, (e1, e2) in enumerate(new_bins):
i1 = list(orig_e.data['E_MIN']).index(e1)
i2 = list(orig_e.data['E_MAX']).index(e2)
print(oi, i1, i2, e1, e2)
f.write("%i %i %i\n" % (i1, i2, i2 - i1 + 1))
new_rsp_fn = "rsp_rebinned.fits"
ht = pilton.heatool("rbnrmf")
ht['infile'] = orig_rsp_fn
ht['binfile'] = ebins_compress_fn
ht['outfile'] = new_rsp_fn
ht['clobber'] = "yes"
ht.run()
self.rmf = da.DataFile(new_rsp_fn)
def main(self):
r1 = self.r1
r2 = self.r2
depth_limit = self.depth_limit
cmd = "bash " + os.environ['IBISMM_PROCESS_ROOT'] + "/run_gen.sh " + os.environ[
'IBISMM_PROCESS_DATA'] + "/response_3d_diff.fits " + self.input_lut2.lut2_1d.get_path(
) + " 0.1 %.5lg %.5lg %.5lg %i" % (r1, r2, depth_limit, self.nchan)
print(cmd)
os.system(cmd)
self.out_response_3d_reconstructed = da.DataFile(
"out_response_3d_reconstructed.fits")
self.out_response_3d_nores = da.DataFile("out_response_3d_nores.fits")
self.out_response_3d_depth = da.DataFile("out_response_3d_depth.fits")
self.out_response_3d = da.DataFile("out_response_3d.fits")
def main(self):
self.events = pd.DataFrame()
self.events['channel'] = np.arange(self.input_dataunit.ndata)
fn = "event_file.txt"
self.events.to_csv(fn)
self.event_file = da.DataFile(fn)
def main(self):
self.data = {'test': 'data'}
wd = os.getcwd()
with open(wd + "/data-file.txt", "w") as f:
f.write("data")
self.data_file = da.DataFile("data-file.txt")
def main(self):
self.data = "datax"
open("file.txt", "w").write("filedata")
self.datafile = da.DataFile("file.txt")