Display of products and saving in fits format using api.plot_tools#

This notebooks documents the functinality of oda_api.plot_tools to visualize images, spectra, and light curves.

Each of these products can also be saved in fits format. The user can add systematic uncertainty to the light curves and spectra; the latter can also be grouped for a quicker fit at the moment of saving files.

Utilities to extract a manipulate the image catalog are also given in order to optimize spectral and light curve extraction.

author: Carlo Ferrigno (carlo.ferrigno@unige.ch)

[1]:

source_name="GX 1+4"
tstart='2019-01-01T00:00:00'
tstop='2019-03-31T23:59:59'
n_scw_ima = 10
n_scw_spec_lc = 20
radius_isgri = 8
radius_j1 = 3
run_spec=True
make_image=True
make_j1_image=True
make_lc=True
run_lc_jemx1 = True
e_min = 28.
e_max = 50.
e_min_j1 = 3.
e_max_j1 = 20.
use_token = False

[2]:

from importlib import reload

[3]:

from importlib import reload

import oda_api
import oda_api.token
import logging

#logging.getLogger().setLevel(logging.WARNING)
logging.getLogger().setLevel(logging.INFO) #for more verbose logging

logging.getLogger('').addHandler(logging.StreamHandler())

Get the token (if available)#

[4]:

import oda_api.token

if use_token:
    token = oda_api.token.discover_token()
    oda_api.token.decode_oda_token(token)
else:
    token = None


[5]:

from astroquery.simbad import Simbad
from astropy import units as u
from astropy.coordinates import SkyCoord


simbad = Simbad.query_object(source_name)
coord = SkyCoord(simbad['RA'], simbad['DEC'], unit=[u.hour, u.deg])

ra = coord.ra.deg
dec = coord.dec.deg

print("Coordinates for %s are RA=%.4f, Dec=%.4f"%(source_name,ra,dec ) )

WARNING: AstropyDeprecationWarning: "pedantic" was deprecated in version 5.0 and will be removed in a future version. Use argument "verify" instead. [astropy.io.votable.table]
AstropyDeprecationWarning: "pedantic" was deprecated in version 5.0 and will be removed in a future version. Use argument "verify" instead.

Coordinates for GX 1+4 are RA=263.0090, Dec=-24.7456

We make an image#

[6]:

par_dict = {
"DEC": "%f" % dec,
"E1_keV": e_min,
"E2_keV": e_max,
"RA": "%f" % ra,
"T1": tstart,
"T2": tstop,
"radius": radius_isgri,
"src_name": source_name,
"token": token,
"max_pointings": n_scw_ima,
"detection_threshold": "7.0",
"instrument": "isgri",
"integral_data_rights": "public",
"oda_api_version": "1.1.22",
"off_line": "False",
"osa_version": "OSA11.2",
"product": "isgri_image",
"product_type": "Real",

}

[7]:

import oda_api.api
host="https://www.astro.unige.ch/mmoda/dispatch-data"

disp = oda_api.api.DispatcherAPI(url=host)

if make_image:
    isgri_image=disp.get_product(**par_dict)

please beware that by default, in a typical setup, oda_api will not output much. To learn how to increase the verbosity, please refer to the documentation: https://oda-api.readthedocs.io/en/latest/user_guide/ScienceWindowList.html?highlight=logging#Let's-get-some-logging .
To disable this message you can pass `.get_product(..., silent=True)`
/home/ferrigno/Soft/oda_api/oda_api/api.py:922: UserWarning:
----------------------------------------------------------------------------
the parameter: oda_api_version   is not among valid ones:
['src_name', 'RA', 'DEC', 'T1', 'T2', 'token', 'scw_list', 'selected_catalog', 'radius', 'max_pointings', 'osa_version', 'integral_data_rights', 'E1_keV', 'E2_keV', 'detection_threshold', 'image_scale_min', 'image_scale_max']
this will throw an error in a future version
and might break the current request!
 ----------------------------------------------------------------------------

  warnings.warn(msg)
- waiting for remote response (since 2021-12-20 14:37:33), please wait for https://www.astro.unige.ch/mmoda/dispatch-data/run_analysis
session: YLB634XMX5B1O5ZG job: 67b5ec7d9240c4db

... query status prepared => done
... assigned job id: 67b5ec7d9240c4db
query COMPLETED SUCCESSFULLY (state done)
query complete: terminating

[8]:

%matplotlib notebook

We can look at the image and use sliders to adjust the contrast#

[9]:

isgri_image.mosaic_image_0_mosaic

[9]:

<oda_api.data_products.NumpyDataProduct at 0x7fd4ddc5efa0>

[10]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
image = oda_api.plot_tools.OdaImage(isgri_image)
_=image.show()

WARNING: FITSFixedWarning: RADECSYS= 'FK5 '
the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]
FITSFixedWarning: RADECSYS= 'FK5 '
the RADECSYS keyword is deprecated, use RADESYSa.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2000-01-01' from MJDREF.
Set MJD-OBS to 58526.447905 from DATE-OBS.
Set MJD-END to 58557.991262 from DATE-END'. [astropy.wcs.wcs]
FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2000-01-01' from MJDREF.
Set MJD-OBS to 58526.447905 from DATE-OBS.
Set MJD-END to 58557.991262 from DATE-END'.

You can avoid displaying the sliders#

[11]:

_=image.show(sliders=False)

We get the catalog from the image to be used in the light curves and spectra#

  • note that we can add an object of interest in the case it is not detected in the mosaic and to assign a FLAG=1

  • Coordinates are derived from Simbad, or dirctly given as objects_of_interest=[(source_name, coord)], see above for the coord definition

[11]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
image = oda_api.plot_tools.OdaImage(isgri_image)
catalog_string = image.extract_catalog_string_from_image(objects_of_interest=[source_name])

Elaborating object of interest: GX 1+4 263.008977 -24.745590
RA=263.009 Dec=-24.7456
Found GX 1+4 in catalog

[12]:

catalog_string

[12]:

'{"cat_frame": "fk5", "cat_coord_units": "deg", "cat_column_list": [[3, 20, 46, 47, 54, 128, 150, 245, 262, 277], ["1E 1740.7-2942", "4U 1700-377", "GRS 1758-258", "GX 1+4", "GX 354-0", "IGR J17252-3616", "IGR J17391-3021", "OAO 1657-415", "SLX 1735-269", "Swift J1728.9-3613"], [37.77094268798828, 35.79611587524414, 23.00543785095215, 38.75000762939453, 33.56985092163086, 7.799460411071777, 8.421113014221191, 8.176356315612793, 8.559125900268555, 9.243657112121582], [265.9716796875, 255.99024963378906, 270.3056640625, 263.0048828125, 262.99346923828125, 261.2395935058594, 264.7843322753906, 255.21841430664062, 264.5645751953125, 262.23870849609375], [-29.741472244262695, -37.84401321411133, -25.738916397094727, -24.74757957458496, -33.83165740966797, -36.302249908447266, -30.321863174438477, -41.690799713134766, -26.958356857299805, -36.21576690673828], [-32768, -32768, -32768, -32768, -32768, -32768, -32768, -32768, -32768, -32768], [2, 2, 2, 2, 2, 1, 2, 2, 2, 2], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [2.9999999242136255e-05, 0.0002800000074785203, 0.0002800000074785203, 0.0002800000074785203, 0.0002800000074785203, 0.0011099999537691474, 0.0002800000074785203, 0.00014000000373926014, 0.00016999999934341758, 0.0004644808650482446]], "cat_column_names": ["meta_ID", "src_names", "significance", "ra", "dec", "NEW_SOURCE", "ISGRI_FLAG", "FLAG", "ERR_RAD"], "cat_column_descr": [["meta_ID", "<i8"], ["src_names", "<U18"], ["significance", "<f8"], ["ra", "<f8"], ["dec", "<f8"], ["NEW_SOURCE", "<i8"], ["ISGRI_FLAG", "<i8"], ["FLAG", "<i8"], ["ERR_RAD", "<f8"]], "cat_lat_name": "dec", "cat_lon_name": "ra"}'

We make an image for JEM-X1 and visualize it#

[13]:

par_dict = {
"DEC": "%f" % dec,
"E1_keV": e_min_j1,
"E2_keV": e_max_j1,
"RA": "%f" % ra,
"T1": tstart,
"T2": tstop,
"radius": radius_j1,
"src_name": source_name,
"token": token,
"max_pointings": n_scw_ima,
"jemx_num": "1",
"osa_version": "OSA11.2",
"detection_threshold": "7.0",
"integral_data_rights": "public",
"instrument": "jemx",
"product": "jemx_image",
"product_type": "Real",
"off_line": "False",
}

[14]:

reload(oda_api)
disp = oda_api.api.DispatcherAPI(url=host)

if make_j1_image:
    j1_image=disp.get_product(**par_dict)

please beware that by default, in a typical setup, oda_api will not output much. To learn how to increase the verbosity, please refer to the documentation: https://oda-api.readthedocs.io/en/latest/user_guide/ScienceWindowList.html?highlight=logging#Let's-get-some-logging .
To disable this message you can pass `.get_product(..., silent=True)`
- waiting for remote response (since 2021-12-08 17:10:57), please wait for https://www.astro.unige.ch/mmoda/dispatch-data/run_analysis
session: NR8H0O1IZ3Q2WYNO job: d6715d9b2bd2bc2f

... query status prepared => done
... assigned job id: d6715d9b2bd2bc2f
query COMPLETED SUCCESSFULLY (state done)
query complete: terminating

[15]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
image = oda_api.plot_tools.OdaImage(j1_image)
_=image.show()

WARNING: FITSFixedWarning: RADECSYS= 'FK5 '
the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]
FITSFixedWarning: RADECSYS= 'FK5 '
the RADECSYS keyword is deprecated, use RADESYSa.
WARNING: FITSFixedWarning: TELAPSE = 'Real '
a floating-point value was expected. [astropy.wcs.wcs]
FITSFixedWarning: TELAPSE = 'Real '
a floating-point value was expected.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2000-01-01' from MJDREF.
Invalid DATE-OBS format 'UTC_format'.
Invalid DATE-END format 'UTC_format''. [astropy.wcs.wcs]
FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2000-01-01' from MJDREF.
Invalid DATE-OBS format 'UTC_format'.
Invalid DATE-END format 'UTC_format''.

We can write the image in a fits file#

[16]:

reload(oda_api.plot_tools)
image = oda_api.plot_tools.OdaImage(j1_image)
image.write_fits('J1_')

We can make a light curve and simply visualize it#

(note that we use the catalog extracted from the image)

[17]:

par_dict = {
"DEC": "%f" % dec,
"E1_keV": e_min,
"E2_keV": e_max,
"RA": "%f" % ra,
"T1": tstart,
"T2": tstop,
"radius": radius_isgri,
"src_name": source_name,
"token": token,
"max_pointings": n_scw_spec_lc,
"instrument": "isgri",
"integral_data_rights": "public",
"osa_version": "OSA11.2",
"product": "isgri_lc",
"product_type": "Real",
"time_bin": "1000",
"token": token,
"selected_catalog": catalog_string
}

[18]:

reload(oda_api)
disp = oda_api.api.DispatcherAPI(url=host)

if make_lc:
    combined_data=disp.get_product(**par_dict)

please beware that by default, in a typical setup, oda_api will not output much. To learn how to increase the verbosity, please refer to the documentation: https://oda-api.readthedocs.io/en/latest/user_guide/ScienceWindowList.html?highlight=logging#Let's-get-some-logging .
To disable this message you can pass `.get_product(..., silent=True)`
- waiting for remote response (since 2021-12-08 17:11:17), please wait for https://www.astro.unige.ch/mmoda/dispatch-data/run_analysis
session: DBOBBVQP7M5VX2MX job: 615de97eb762dbed

... query status prepared => done
... assigned job id: 615de97eb762dbed
query COMPLETED SUCCESSFULLY (state done)
query complete: terminating

[19]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
lc = oda_api.plot_tools.OdaLightCurve(combined_data)
_=lc.show(source_name)

If no source is given all light curves are shown#

[20]:

_=lc.show()

We can search for thermonuclear bursts in the JEM-X light curve#

[21]:

par_dict = {
"DEC": "%f" % dec,
"E1_keV": e_min_j1,
"E2_keV": e_max_j1,
"RA": "%f" % ra,
"T1": tstart,
"T2": tstop,
"radius": radius_j1,
"src_name": source_name,
"token": token,
"max_pointings": n_scw_spec_lc,
"instrument": "jemx",
"integral_data_rights": "public",
"jemx_num": "1",
"oda_api_version": "1.1.22",
"off_line": "False",
"osa_version": "OSA11.2",
"product": "jemx_lc",
"product_type": "Real",
"selected_catalog": catalog_string,
"src_name": "1E 1740.7-2942",
"time_bin": "4",
"token": token}

[22]:

if run_lc_jemx1 :

    disp = oda_api.api.DispatcherAPI(url=host)

    combined_data=disp.get_product(**par_dict)

please beware that by default, in a typical setup, oda_api will not output much. To learn how to increase the verbosity, please refer to the documentation: https://oda-api.readthedocs.io/en/latest/user_guide/ScienceWindowList.html?highlight=logging#Let's-get-some-logging .
To disable this message you can pass `.get_product(..., silent=True)`
/home/savchenk/.pyenv/versions/3.9.6/lib/python3.9/site-packages/oda_api/api.py:922: UserWarning:
----------------------------------------------------------------------------
the parameter: oda_api_version   is not among valid ones:
['src_name', 'RA', 'DEC', 'T1', 'T2', 'token', 'scw_list', 'selected_catalog', 'radius', 'max_pointings', 'osa_version', 'integral_data_rights', 'jemx_num', 'E1_keV', 'E2_keV', 'time_bin']
this will throw an error in a future version
and might break the current request!
 ----------------------------------------------------------------------------

  warnings.warn(msg)
- waiting for remote response (since 2021-12-08 17:11:38), please wait for https://www.astro.unige.ch/mmoda/dispatch-data/run_analysis
session: E98AW76ZNB1Z0YGI job: 8784da5439049838

... query status prepared => done
... assigned job id: 8784da5439049838
query COMPLETED SUCCESSFULLY (state done)
query complete: terminating

Advanced display of light curves#

  • with the keyword ng_sig_limit you can select the limit for variability in standard deviations

  • with the keyword find_excesses you can find points above this limit, which can be useful to find thermonuclear bursts

[23]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
lc = oda_api.plot_tools.OdaLightCurve(combined_data)
_=lc.show(source_name, ng_sig_limit=3.0, find_excesses=True)

This writes the light curve as a fits file#

[24]:

lc.write_fits(source_name)

[24]:

('./JMX1_lc_GX_1+4.fits', 58526.424768534154, 58558.94444446016, -1)

We extact spectral products for JEMX1#

[25]:

par_dict = {
    "DEC": "%f" % dec,
"E1_keV": e_min_j1,
"E2_keV": e_max_j1,
"RA": "%f" % ra,
"T1": tstart,
"T2": tstop,
"radius": radius_j1,
"src_name": source_name,
"token": token,
"max_pointings": n_scw_spec_lc,
"api": "True",
"instrument": "jemx",
"integral_data_rights": "public",
"jemx_num": "1",
"off_line": "False",
"osa_version": "OSA11.2",
"product": "jemx_spectrum",
"product_type": "Real",
"selected_catalog": catalog_string,
"token": token,
}

[26]:

j1_spec = disp.get_product(**par_dict)

please beware that by default, in a typical setup, oda_api will not output much. To learn how to increase the verbosity, please refer to the documentation: https://oda-api.readthedocs.io/en/latest/user_guide/ScienceWindowList.html?highlight=logging#Let's-get-some-logging .
To disable this message you can pass `.get_product(..., silent=True)`
/home/savchenk/.pyenv/versions/3.9.6/lib/python3.9/site-packages/oda_api/api.py:922: UserWarning:
----------------------------------------------------------------------------
the parameter: api   is not among valid ones:
['src_name', 'RA', 'DEC', 'T1', 'T2', 'token', 'scw_list', 'selected_catalog', 'radius', 'max_pointings', 'osa_version', 'integral_data_rights', 'jemx_num', 'E1_keV', 'E2_keV']
this will throw an error in a future version
and might break the current request!
 ----------------------------------------------------------------------------

  warnings.warn(msg)
- waiting for remote response (since 2021-12-08 17:12:01), please wait for https://www.astro.unige.ch/mmoda/dispatch-data/run_analysis
session: E98AW76ZNB1Z0YGI job: 83c4a034536df28c

... query status prepared => done
... assigned job id: 83c4a034536df28c
query COMPLETED SUCCESSFULLY (state done)
query complete: terminating

If no source is given, one can see the spectra avalable#

[27]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
spec = oda_api.plot_tools.OdaSpectrum(j1_spec)
spec.show()

------------------------------
src_name : 1E 1740.7-2942
product : jemx_spectrum
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: JMX1-PHA1-SPE
------------------------------
src_name : 1E 1740.7-2942
product : jemx_arf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: SPECRESP
------------------------------
src_name : 1E 1740.7-2942
product : jemx_rmf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 6
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: GROUPING
data uniti 2 ,name: SPECRESP MATRIX
data uniti 3 ,name: EBOUNDS
data uniti 4 ,name: JMX1-AXIS-ARF
data uniti 5 ,name: JMX1-IMAG-ARF
------------------------------
src_name : GRS 1758-258
product : jemx_spectrum
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: JMX1-PHA1-SPE
------------------------------
src_name : GRS 1758-258
product : jemx_arf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: SPECRESP
------------------------------
src_name : GRS 1758-258
product : jemx_rmf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 6
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: GROUPING
data uniti 2 ,name: SPECRESP MATRIX
data uniti 3 ,name: EBOUNDS
data uniti 4 ,name: JMX1-AXIS-ARF
data uniti 5 ,name: JMX1-IMAG-ARF
------------------------------
src_name : GX 1+4
product : jemx_spectrum
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: JMX1-PHA1-SPE
------------------------------
src_name : GX 1+4
product : jemx_arf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: SPECRESP
------------------------------
src_name : GX 1+4
product : jemx_rmf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 6
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: GROUPING
data uniti 2 ,name: SPECRESP MATRIX
data uniti 3 ,name: EBOUNDS
data uniti 4 ,name: JMX1-AXIS-ARF
data uniti 5 ,name: JMX1-IMAG-ARF
------------------------------
src_name : IGR J17391-3021
product : jemx_spectrum
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: JMX1-PHA1-SPE
------------------------------
src_name : IGR J17391-3021
product : jemx_arf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: SPECRESP
------------------------------
src_name : IGR J17391-3021
product : jemx_rmf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 6
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: GROUPING
data uniti 2 ,name: SPECRESP MATRIX
data uniti 3 ,name: EBOUNDS
data uniti 4 ,name: JMX1-AXIS-ARF
data uniti 5 ,name: JMX1-IMAG-ARF
------------------------------
src_name : SLX 1735-269
product : jemx_spectrum
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: JMX1-PHA1-SPE
------------------------------
src_name : SLX 1735-269
product : jemx_arf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: SPECRESP
------------------------------
src_name : SLX 1735-269
product : jemx_rmf
------------------------------
------------------------------
name:
meta_data dict_keys(['src_name', 'product'])
number of data units 6
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: GROUPING
data uniti 2 ,name: SPECRESP MATRIX
data uniti 3 ,name: EBOUNDS
data uniti 4 ,name: JMX1-AXIS-ARF
data uniti 5 ,name: JMX1-IMAG-ARF

[28]:

_=spec.show(source_name)

We extact spectral products for ISGRI#

[29]:

par_dict = {
"DEC": "%f" % dec,
"E1_keV": e_min,
"E2_keV": e_max,
"RA": "%f" % ra,
"T1": tstart,
"T2": tstop,
"radius": radius_isgri,
"src_name": source_name,
"token": token,
"max_pointings": n_scw_spec_lc,
"instrument": "isgri",
"integral_data_rights": "public",
"off_line": "False",
"osa_version": "OSA11.2",
"product": "isgri_spectrum",
"token": token,
    "selected_catalog": catalog_string
}

[30]:

isgri_spec = disp.get_product(**par_dict)

please beware that by default, in a typical setup, oda_api will not output much. To learn how to increase the verbosity, please refer to the documentation: https://oda-api.readthedocs.io/en/latest/user_guide/ScienceWindowList.html?highlight=logging#Let's-get-some-logging .
To disable this message you can pass `.get_product(..., silent=True)`
- waiting for remote response (since 2021-12-08 17:12:30), please wait for https://www.astro.unige.ch/mmoda/dispatch-data/run_analysis
session: E98AW76ZNB1Z0YGI job: af34b8da505f6abb

... query status prepared => done
... assigned job id: af34b8da505f6abb
query COMPLETED SUCCESSFULLY (state done)
query complete: terminating

This plots the spectra, it is possible to set limits for the energy range#

[31]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
spec = oda_api.plot_tools.OdaSpectrum(isgri_spec)
_ = spec.show(source_name, xlim=[20,100])

This saves spectrum arf and response in a way to be used with xspec#

  • systematic fraction adds a systematic fraction to the spectrum

  • grouping performs a grouping on the spectrum the argument is [minimum_energy, maximum_energy, bins]

    • bins is the number of bin spaced logarithmically if negative, linearly if positive

[32]:

import oda_api.plot_tools
reload(oda_api.plot_tools)
spec = oda_api.plot_tools.OdaSpectrum(isgri_spec)
spec.write_fits(source_name, systematic_fraction=0.01, grouping=[30,150,-20])

Saving spectrum ./IBIS_spectrum_GX_1p4.fits with rmf ./IBIS_rmf_GX_1p4.fits and arf ./IBIS_arf_GX_1p4.fits
Geometric grouping with step 1.084

[32]:

('./IBIS_spectrum_GX_1p4.fits',
 58526.44871680214,
 58566.35018981625,
 24718.52691800761)