Quick Start (oda api v1.2.15)#
[1]:
from oda_api.api import DispatcherAPI
from oda_api.plot_tools import OdaImage,OdaLightCurve
from oda_api.data_products import BinaryData
import os
#%matplotlib notebook
Connection to the dispatcher#
build the dispatcher object#
[2]:
disp=DispatcherAPI(url='https://www.astro.unige.ch/mmoda/dispatch-data',instrument='mock')
[3]:
instr_list=disp.get_instruments_list()
for i in instr_list:
print (i)
magic
isgri
jemx
polar
antares
spi_acs
get the description of the instrument#
[4]:
disp.get_instrument_description('isgri')
[4]:
[[{'instrumet': 'isgri'},
{'prod_dict': {'isgri_image': 'isgri_image_query',
'isgri_lc': 'isgri_lc_query',
'isgri_spectrum': 'isgri_spectrum_query',
'spectral_fit': 'spectral_fit_query'}},
'[{"query_name": "src_query"}, {"name": "src_name", "units": "str", "value": "test"}, {"name": "RA", "units": "deg", "value": 0.0}, {"name": "DEC", "units": "deg", "value": 0.0}, {"name": "T1", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "T2", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "token", "units": "str", "value": None}]',
'[{"query_name": "isgri_parameters"}, {"name": "user_catalog", "units": "str", "value": None}, {"name": "scw_list", "units": "names_list", "value": []}, {"name": "selected_catalog", "units": "str", "value": None}, {"name": "radius", "units": "deg", "value": 5.0}, {"name": "max_pointings", "units": None, "value": 50}, {"name": "osa_version", "units": "str", "value": None}, {"name": "integral_data_rights", "units": "str", "value": "public"}, {"name": "E1_keV", "units": "keV", "value": 15.0}, {"name": "E2_keV", "units": "keV", "value": 40.0}]',
'[{"query_name": "isgri_image_query"}, {"product_name": "isgri_image"}, {"name": "detection_threshold", "units": "sigma", "value": 0.0}, {"name": "image_scale_min", "units": None, "value": None}, {"name": "image_scale_max", "units": None, "value": None}]',
'[{"query_name": "isgri_spectrum_query"}, {"product_name": "isgri_spectrum"}]',
'[{"query_name": "isgri_lc_query"}, {"product_name": "isgri_lc"}, {"name": "time_bin", "units": "sec", "value": 1000.0}]',
'[{"query_name": "spectral_fit_query"}, {"product_name": "spectral_fit"}, {"name": "xspec_model", "units": "str", "value": "powerlaw"}, {"name": "ph_file_name", "units": "str", "value": ""}, {"name": "arf_file_name", "units": "str", "value": ""}, {"name": "rmf_file_name", "units": "str", "value": ""}]']]
get the description of the product#
[5]:
disp.get_product_description(instrument='isgri',product_name='isgri_image')
[5]:
[[{'instrumet': 'isgri'},
{'prod_dict': {'isgri_image': 'isgri_image_query',
'isgri_lc': 'isgri_lc_query',
'isgri_spectrum': 'isgri_spectrum_query',
'spectral_fit': 'spectral_fit_query'}},
'[{"query_name": "src_query"}, {"name": "src_name", "units": "str", "value": "test"}, {"name": "RA", "units": "deg", "value": 0.0}, {"name": "DEC", "units": "deg", "value": 0.0}, {"name": "T1", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "T2", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "token", "units": "str", "value": None}]',
'[{"query_name": "isgri_parameters"}, {"name": "user_catalog", "units": "str", "value": None}, {"name": "scw_list", "units": "names_list", "value": []}, {"name": "selected_catalog", "units": "str", "value": None}, {"name": "radius", "units": "deg", "value": 5.0}, {"name": "max_pointings", "units": None, "value": 50}, {"name": "osa_version", "units": "str", "value": None}, {"name": "integral_data_rights", "units": "str", "value": "public"}, {"name": "E1_keV", "units": "keV", "value": 15.0}, {"name": "E2_keV", "units": "keV", "value": 40.0}]',
'[{"query_name": "isgri_image_query"}, {"product_name": "isgri_image"}, {"name": "detection_threshold", "units": "sigma", "value": 0.0}, {"name": "image_scale_min", "units": None, "value": None}, {"name": "image_scale_max", "units": None, "value": None}]']]
check query before submission#
in case of error in the parameters, an error is issued
[6]:
data_collection=disp.get_product(instrument='isgri',
product='isgri_image',
T1='2003-03-15T23:27:40.0',
T2='2003-03-16T00:03:12.0',
osa_version='OSA10.2',
RA='a',
DEC=-37.844167,
detection_threshold=5.0,
radius=15.,
product_type='Real')
---------------------------------------------------------------------------
RequestNotUnderstood Traceback (most recent call last)
/tmp/ipykernel_11979/2086683828.py in <module>
----> 1 data_collection=disp.get_product(instrument='isgri',
2 product='isgri_image',
3 T1='2003-03-15T23:27:40.0',
4 T2='2003-03-16T00:03:12.0',
5 osa_version='OSA10.2',
~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py in get_product(self, product, instrument, verbose, dry_run, product_type, **kwargs)
886
887 # >
--> 888 self.request(kwargs)
889
890 if self.is_failed:
~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py in func_wrapper(*args, **kwargs)
121 while True:
122 try:
--> 123 return func(*args, **kwargs)
124 except UserError as e:
125 logger.exception("user error: %s", e)
~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py in request(self, parameters_dict, handle, url, wait, quiet)
661
662 while True:
--> 663 self.poll()
664
665 if not self.wait:
~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py in func_wrapper(*args, **kwargs)
121 while True:
122 try:
--> 123 return func(*args, **kwargs)
124 except UserError as e:
125 logger.exception("user error: %s", e)
~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py in poll(self, verbose, silent)
558
559 # >
--> 560 self.response_json = self.request_to_json()
561 # <
562
~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py in request_to_json(self, verbose)
392
393 if response.status_code == 400:
--> 394 raise RequestNotUnderstood(
395 response.json())
396
RequestNotUnderstood: [ RequestNotUnderstood: RequestNotUnderstood():[ InstrumentQueryBackEnd : isgri ] constructor failed: Invalid character at col 0 in angle 'a' ]
Get ODA products#
now we actually get the products with the right parameters
[9]:
data_collection=disp.get_product(instrument='isgri',
product='isgri_image',
T1='2003-03-15T23:27:40.0',
T2='2003-03-16T00:03:15.0',
E1_keV=20.0,
E2_keV=40.0,
osa_version='OSA10.2',
RA=257.815417,
DEC=-41.593417,
detection_threshold=5.0,
radius=15.,
product_type='Real')
the ODA data structure#
[10]:
data_collection.show()
ID=0 prod_name=mosaic_image_0_mosaic meta_data: {'product': 'mosaic', 'instrument': 'isgri', 'src_name': '', 'query_parameters': '[{"query_name": "isgri_image_query"}, {"product_name": "isgri_image_query"}, {"name": "detection_threshold", "units": "sigma", "value": "5.0"}, {"name": "image_scale_min", "units": None, "value": None}, {"name": "image_scale_max", "units": None, "value": None}]'}
ID=1 prod_name=dispatcher_catalog_1 meta_data:
you can acess memeber by name:
[11]:
data_collection.mosaic_image_0_mosaic
[11]:
<oda_api.data_products.NumpyDataProduct at 0x7f54f77a95e0>
or by position in the data list
[12]:
data_collection._p_list[0]
[12]:
<oda_api.data_products.NumpyDataProduct at 0x7f54f77a95e0>
the ODA catalog#
[13]:
data_collection.dispatcher_catalog_1.table
[13]:
Table length=4
| meta_ID | src_names | significance | ra | dec | NEW_SOURCE | ISGRI_FLAG | FLAG | ERR_RAD |
|---|---|---|---|---|---|---|---|---|
| deg | deg | |||||||
| int64 | str12 | float64 | float64 | float64 | int64 | int64 | int64 | float64 |
| 68 | OAO 1657-415 | 17.827661514282227 | 255.19740295410156 | -41.653175354003906 | -32768 | 2 | 0 | 0.00014000000373926014 |
| 74 | 4U 1700-377 | 21.19563102722168 | 255.9771270751953 | -37.83724594116211 | -32768 | 2 | 0 | 0.0002800000074785203 |
| 77 | GX 349+2 | 13.917550086975098 | 256.42901611328125 | -36.41566467285156 | -32768 | 2 | 0 | 0.0002800000074785203 |
| 130 | GX 354-0 | 8.97690486907959 | 262.9885559082031 | -33.82877731323242 | -32768 | 2 | 0 | 0.0002800000074785203 |
you can use astropy.table commands to modify the table of the catatlog http://docs.astropy.org/en/stable/table/modify_table.html
to generate a catalog to pass to the dispatcher api
[14]:
api_cat=data_collection.dispatcher_catalog_1.get_api_dictionary()
[15]:
api_cat
[15]:
'{"cat_frame": "fk5", "cat_coord_units": "deg", "cat_column_list": [[68, 74, 77, 130], ["OAO 1657-415", "4U 1700-377", "GX 349+2", "GX 354-0"], [17.827661514282227, 21.19563102722168, 13.917550086975098, 8.97690486907959], [255.19740295410156, 255.9771270751953, 256.42901611328125, 262.9885559082031], [-41.653175354003906, -37.83724594116211, -36.41566467285156, -33.82877731323242], [-32768, -32768, -32768, -32768], [2, 2, 2, 2], [0, 0, 0, 0], [0.00014000000373926014, 0.0002800000074785203, 0.0002800000074785203, 0.0002800000074785203]], "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", "<U12"], ["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"}'
[16]:
data_collection=disp.get_product(instrument='isgri',
product='isgri_image',
T1='2003-03-15T23:27:40.0',
T2='2003-03-16T00:03:15.0',
E1_keV=20.0,
E2_keV=40.0,
osa_version='OSA10.2',
RA=255.986542,
DEC=-37.844167,
detection_threshold=5.0,
radius=15.,
product_type='Real',
selected_catalog=api_cat)
you can explore the image with the following command
[17]:
data_collection.mosaic_image_0_mosaic.show()
------------------------------
name: mosaic_image
meta_data dict_keys(['product', 'instrument', 'src_name', 'query_parameters'])
number of data units 6
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: GROUPING
data uniti 2 ,name: ISGR-MOSA-IMA
data uniti 3 ,name: ISGR-MOSA-IMA
data uniti 4 ,name: ISGR-MOSA-IMA
data uniti 5 ,name: ISGR-MOSA-IMA
[18]:
data_collection.mosaic_image_0_mosaic.show_meta()
------------------------------
product : mosaic
instrument : isgri
src_name :
query_parameters : [{"query_name": "isgri_image_query"}, {"product_name": "isgri_image_query"}, {"name": "detection_threshold", "units": "sigma", "value": "5.0"}, {"name": "image_scale_min", "units": None, "value": None}, {"name": "image_scale_max", "units": None, "value": None}]
------------------------------
[19]:
data_collection.mosaic_image_0_mosaic.data_unit[1].data
[19]:
FITS_rec([('IMAGE', 'ISGR-MOSA-IMA', 1, 3, '', '', 'INTENSITY', 20, 40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375),
('IMAGE', 'ISGR-MOSA-IMA', 2, 4, '', '', 'VARIANCE', 20, 40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375),
('IMAGE', 'ISGR-MOSA-IMA', 3, 5, '', '', 'SIGNIFICANCE', 20, 40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375),
('IMAGE', 'ISGR-MOSA-IMA', 4, 6, '', '', 'EXPOSURE', 20, 40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375)],
dtype=(numpy.record, [('MEMBER_XTENSION', 'S8'), ('MEMBER_NAME', 'S32'), ('MEMBER_VERSION', '>i4'), ('MEMBER_POSITION', '>i4'), ('MEMBER_LOCATION', 'S256'), ('MEMBER_URI_TYPE', 'S3'), ('IMATYPE', 'S32'), ('CHANMIN', '>i2'), ('CHANMAX', '>i2'), ('E_MIN', '>f4'), ('E_MEAN', '>f4'), ('E_MAX', '>f4'), ('TFIRST', '>f8'), ('TLAST', '>f8'), ('TELAPSE', '>f8'), ('ONTIME', '>f8')]))
[20]:
hdu=data_collection.mosaic_image_0_mosaic.to_fits_hdu_list()
[21]:
data_collection.mosaic_image_0_mosaic.data_unit[1].data.shape
[21]:
(4,)
[22]:
data_collection.mosaic_image_0_mosaic.write_fits_file('test.fits',overwrite=True)
the ODA Image plotting tool#
[23]:
#interactive
#%matplotlib notebook
%matplotlib inline
im=OdaImage(data_collection)
[24]:
im.show(unit_ID=4)
[25]:
data_collection.mosaic_image_0_mosaic.data_unit[1].header
[25]:
{'BASETYPE': 'DAL_GROUP',
'BITPIX': 8,
'CHECKSUM': '59K3A7H157H1A7H1',
'COMMENT': ' on the next keyword which has the name CONTINUE.',
'CONFIGUR': 'osa_2019-07-29T14:07:33',
'CREATOR': 'ii_skyimage 5.4.4',
'DATASUM': '910507346',
'DATE': '2021-09-01T13:14:08',
'DATE-END': '2003-03-15T23:57:39',
'DATE-OBS': '2003-03-15T23:27:53',
'DETNAM': 'ISGRI',
'EXTNAME': 'GROUPING',
'EXTREL': '7.2',
'EXTVER': 1,
'GCOUNT': 1,
'GRPID1': -1,
'GRPLC1': 'ogg.fits',
'GRPNAME': 'ISGR-MOSA-IMA-IDX',
'IDXMEMBR': 'ISGR-MOSA-IMA',
'INSTRUME': 'IBIS',
'ISDCLEVL': 'IMA',
'LONGSTRN': 'OGIP 1.0',
'MJDREF': 51544.0,
'NAXIS': 2,
'NAXIS1': 387,
'NAXIS2': 4,
'ORIGIN': 'ISDC',
'PCOUNT': 0,
'STAMP': '2021-09-01T13:14:08 ii_skyimage 5.4.4',
'TELESCOP': 'INTEGRAL',
'TFIELDS': 16,
'TFORM1': '8A',
'TFORM10': '1E',
'TFORM11': '1E',
'TFORM12': '1E',
'TFORM13': '1D',
'TFORM14': '1D',
'TFORM15': '1D',
'TFORM16': '1D',
'TFORM2': '32A',
'TFORM3': '1J',
'TFORM4': '1J',
'TFORM5': '256A',
'TFORM6': '3A',
'TFORM7': '32A',
'TFORM8': '1I',
'TFORM9': '1I',
'TIMEREF': 'LOCAL',
'TIMESYS': 'TT',
'TIMEUNIT': 'd',
'TNULL3': 0,
'TNULL4': 0,
'TSCAL8': 1,
'TSCAL9': 1,
'TSTART': 1169.97844975867,
'TSTOP': 1169.99912106495,
'TTYPE1': 'MEMBER_XTENSION',
'TTYPE10': 'E_MIN',
'TTYPE11': 'E_MEAN',
'TTYPE12': 'E_MAX',
'TTYPE13': 'TFIRST',
'TTYPE14': 'TLAST',
'TTYPE15': 'TELAPSE',
'TTYPE16': 'ONTIME',
'TTYPE2': 'MEMBER_NAME',
'TTYPE3': 'MEMBER_VERSION',
'TTYPE4': 'MEMBER_POSITION',
'TTYPE5': 'MEMBER_LOCATION',
'TTYPE6': 'MEMBER_URI_TYPE',
'TTYPE7': 'IMATYPE',
'TTYPE8': 'CHANMIN',
'TTYPE9': 'CHANMAX',
'TUNIT10': 'keV',
'TUNIT11': 'keV',
'TUNIT12': 'keV',
'TUNIT13': 'd',
'TUNIT14': 'd',
'TUNIT15': 's',
'TUNIT16': 's',
'TZERO8': 32768,
'TZERO9': 32768,
'XTENSION': 'BINTABLE'}
the ODA LC plotting tool#
[26]:
data_collection=disp.get_product(instrument='isgri',
product='isgri_lc',
T1='2003-03-15T23:27:40.0',
T2='2003-03-16T00:03:12.0',
time_bin=70,
osa_version='OSA10.2',
RA=255.986542,
DEC=-37.844167,
radius=15.,
product_type='Real')
explore LC#
[27]:
data_collection.show()
ID=0 prod_name=isgri_lc_0_OAO1657m415 meta_data: {'src_name': 'OAO 1657-415', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
ID=1 prod_name=isgri_lc_1_4U1700m377 meta_data: {'src_name': '4U 1700-377', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
ID=2 prod_name=isgri_lc_2_GX349p2 meta_data: {'src_name': 'GX 349+2', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
ID=3 prod_name=isgri_lc_3_H1705m440 meta_data: {'src_name': 'H 1705-440', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
ID=4 prod_name=isgri_lc_4_GX354m0 meta_data: {'src_name': 'GX 354-0', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
ID=5 prod_name=isgri_lc_5_GX1p4 meta_data: {'src_name': 'GX 1+4', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
[28]:
data_collection.isgri_lc_2_GX349p2.show()
------------------------------
name: isgri_lc
meta_data dict_keys(['src_name', 'time_bin', 'time', 'rate', 'rate_err'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: ISGR-SRC.-LCR
[29]:
for ID,s in enumerate(data_collection._p_list):
print (ID,s.meta_data['src_name'])
0 OAO 1657-415
1 4U 1700-377
2 GX 349+2
3 H 1705-440
4 GX 354-0
5 GX 1+4
[30]:
lc=data_collection._p_list[0]
lc.data_unit[1].data
[30]:
array([(1169.97924981, 189.06061, 208.61382 , 3.0928352, 36.32327 , 8.367494 , 0.9999995, 0.00040507, 0.00081014),
(1169.98006 , 184.00623, 128.52094 , 3.0739527, 44.749542, 8.25954 , 0.9999995, 0.00040507, 0.00081014),
(1169.98087017, 156.61322, 107.2609 , 2.7437818, 19.096039, 7.5178704, 0.9999995, 0.00040507, 0.00081014),
(1169.98168037, 160.68495, 113.83268 , 2.8039339, 19.251225, 7.650763 , 0.9999995, 0.00040507, 0.00081014),
(1169.98249057, 169.17253, 113.41755 , 2.8749352, 25.947037, 7.839797 , 0.9999995, 0.00040507, 0.00081014),
(1169.98330074, 161.08427, 107.967094, 2.8938215, 23.221224, 7.6593533, 0.9999995, 0.00040507, 0.00081014),
(1169.98411092, 168.77126, 117.82072 , 2.8845947, 31.26426 , 7.8425374, 0.9999995, 0.00040507, 0.00081014),
(1169.98492112, 160.38077, 115.27191 , 2.942873 , 25.611507, 7.637102 , 0.9999995, 0.00040507, 0.00081014),
(1169.9857313 , 159.43456, 114.650406, 2.8541234, 25.057646, 7.6175227, 0.9999995, 0.00040507, 0.00081014),
(1169.98654149, 154.47977, 101.08613 , 2.791371 , 32.808315, 7.4923277, 0.9999995, 0.00040507, 0.00081014),
(1169.98735168, 150.6925 , 102.68129 , 2.76442 , 22.8526 , 7.3896174, 0.9999995, 0.00040507, 0.00081014),
(1169.98816182, 146.80734, 100.66747 , 2.6543057, 46.189266, 7.3128138, 0.9980645, 0.00040507, 0.00081014),
(1169.98897204, 148.31848, 105.12519 , 2.8063016, 30.23531 , 7.36563 , 0.9999995, 0.00040507, 0.00081014),
(1169.98978224, 151.88261, 106.98407 , 2.8313267, 20.044998, 7.464697 , 0.9999995, 0.00040507, 0.00081014),
(1169.9905924 , 193.02788, 135.57555 , 3.1867957, 18.478785, 8.478564 , 0.9987744, 0.00040507, 0.00081014),
(1169.9914026 , 176.79617, 122.84473 , 3.1522508, 30.608587, 8.218244 , 0.9656138, 0.00040507, 0.00081014),
(1169.99221276, 165.54268, 118.42887 , 2.8857293, 35.064762, 7.792433 , 0.9999995, 0.00040507, 0.00081014),
(1169.99302296, 155.6777 , 107.725555, 2.7923737, 30.44729 , 7.5125117, 0.9999995, 0.00040507, 0.00081014),
(1169.99383313, 153.74835, 109.006096, 2.8350844, 41.316975, 7.4837537, 0.9999995, 0.00040507, 0.00081014),
(1169.99464334, 152.44218, 100.14908 , 2.7715 , 36.908443, 7.4560814, 0.9983044, 0.00040507, 0.00081014),
(1169.99545352, 152.91246, 107.17616 , 2.7963147, 35.36147 , 7.4534373, 0.9999995, 0.00040507, 0.00081014),
(1169.99626372, 154.3732 , 103.014656, 2.7716458, 27.397203, 7.483038 , 0.9999995, 0.00040507, 0.00081014),
(1169.99695709, 157.10977, 109.46534 , 3.3767562, 40.66689 , 8.954308 , 0.9999995, 0.0002883 , 0.00057659)],
dtype=[('TIME', '>f8'), ('TOT_COUNTS', '>f4'), ('BACKV', '>f4'), ('BACKE', '>f4'), ('RATE', '>f4'), ('ERROR', '>f4'), ('FRACEXP', '>f4'), ('XAX_E', '>f4'), ('TIMEDEL', '<f8')])
[31]:
lc.show()
------------------------------
name: isgri_lc
meta_data dict_keys(['src_name', 'time_bin', 'time', 'rate', 'rate_err'])
number of data units 2
------------------------------
data uniti 0 ,name: PRIMARY
data uniti 1 ,name: ISGR-SRC.-LCR
[32]:
lc.meta_data
[32]:
{'src_name': 'OAO 1657-415',
'time_bin': 0.000810143479094966,
'time': 'TIME',
'rate': 'RATE',
'rate_err': 'ERROR'}
[33]:
%matplotlib inline
OdaLightCurve(lc).show(unit_ID=1)
[34]:
lc.data_unit[0].header
[34]:
{'BITPIX': 8, 'EXTEND': True, 'NAXIS': 0, 'SIMPLE': True}
Polar LC#
[35]:
data_collection=disp.get_product(instrument='polar',product='polar_lc',T1='2016-12-18T08:32:21.000',T2='2016-12-18T08:34:01.000',time_bin=0.5,verbose=True)
[36]:
data_collection.show()
ID=0 prod_name=polar_lc_0_lc meta_data: {'src_name': 'lc', 'time_bin': 0.5, 'time': 'time', 'rate': 'rate', 'rate_err': 'rate_err'}
ID=1 prod_name=polar_lc_1 meta_data:
[37]:
data_collection._p_list[0].meta_data
[37]:
{'src_name': 'lc',
'time_bin': 0.5,
'time': 'time',
'rate': 'rate',
'rate_err': 'rate_err'}
[38]:
lc=data_collection._p_list[0]
root=data_collection._p_list[1]
open('lc.root', "wb").write(root)
[38]:
5022
[39]:
open('lc.root', "wb").write(root)
[39]:
5022
[40]:
%matplotlib inline
OdaLightCurve(lc).show(unit_ID=1)
SPIACS LC#
[41]:
disp.get_instrument_description('spi_acs')
[41]:
[[{'instrumet': 'spi_acs'},
{'prod_dict': {'spi_acs_lc': 'spi_acs_lc_query'}},
'[{"query_name": "src_query"}, {"name": "src_name", "units": "str", "value": "test"}, {"name": "RA", "units": "deg", "value": 0.0}, {"name": "DEC", "units": "deg", "value": 0.0}, {"name": "T1", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "T2", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "token", "units": "str", "value": None}]',
'[{"query_name": "spiacs_parameters"}, {"name": "user_catalog", "units": "str", "value": None}, {"name": None, "units": "names_list", "value": []}, {"name": "selected_catalog", "units": "str", "value": None}]',
'[{"query_name": "spi_acs_lc_query"}, {"product_name": "spi_acs_lc"}, {"name": "time_bin", "units": "sec", "value": 1000.0}]']]
[42]:
data_collection=disp.get_product(instrument='spi_acs',
product='spi_acs_lc',
T1='2003-03-15T23:27:40.0',
T2='2003-03-15T23:57:12.0',
time_bin=2,
RA=255.986542,
DEC=-37.844167,
product_type='Real')
[43]:
data_collection.show()
ID=0 prod_name=spi_acs_lc_0_query meta_data: {'src_name': 'query', 'time_bin': 2.0, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
[44]:
lc=data_collection._p_list[0]
[45]:
lc.meta_data
[45]:
{'src_name': 'query',
'time_bin': 2.0,
'time': 'TIME',
'rate': 'RATE',
'rate_err': 'ERROR'}
[46]:
lc.show()
------------------------------
name:
meta_data dict_keys(['src_name', 'time_bin', 'time', 'rate', 'rate_err'])
number of data units 2
------------------------------
data uniti 0 ,name: Primary
data uniti 1 ,name: RATE
[47]:
lc.data_unit[1].header
[47]:
{'BITPIX': 8,
'DATE-END': '2003-03-15T23:28:44.216',
'DATE-OBS': '2003-03-15T23:28:44.216',
'EXTNAME': 'RATE',
'GCOUNT': 1,
'INSTRUME': 'SPI-ACS',
'MJDREF': 51544.0,
'NAXIS': 2,
'NAXIS1': 24,
'NAXIS2': 886,
'ONTIME': 1772.0008558046072,
'PCOUNT': 0,
'TASSIGN': 'SATELLITE',
'TELESCOP': 'INTEGRAL',
'TFIELDS': 3,
'TFORM1': 'D',
'TFORM2': 'D',
'TFORM3': 'D',
'TIMEDEL': 2.0,
'TIMEREF': 'LOCAL',
'TIMESYS': 'TT',
'TIMEUNIT': 's',
'TIMEZERO': 101087010.19105513,
'TSTART': 101086124.21562724,
'TSTOP': 101087896.21648304,
'TTYPE1': 'TIME',
'TTYPE2': 'RATE',
'TTYPE3': 'ERROR',
'XTENSION': 'BINTABLE'}
[48]:
lc.data_unit[1].data[0:10]
[48]:
array([(-885.00042667, 1515580., 870.51134398),
(-883.00042573, 1509680., 868.81528532),
(-881.00042474, 1523800., 872.86883322),
(-879.00042396, 1513890., 870.02586168),
(-877.00042318, 1506440., 867.88248052),
(-875.00042222, 1520350., 871.88015231),
(-873.0004213 , 1510580., 869.07422008),
(-871.00042036, 1516300., 870.71809445),
(-869.00041942, 1526480., 873.63607984),
(-867.00041848, 1508970., 868.6109601 )],
dtype=[('TIME', '<f8'), ('RATE', '<f8'), ('ERROR', '<f8')])
[49]:
OdaLightCurve(lc).show(unit_ID=1)
the ODA and spectra#
[50]:
data_collection=disp.get_product(instrument='isgri',
product='isgri_spectrum',
T1='2003-03-15T23:27:40.0',
T2='2003-03-16T00:03:12.0',
osa_version='OSA10.2',
RA=255.986542,
DEC=-37.844167,
radius=15.,
product_type='Real')
explore spectra#
[51]:
data_collection.show()
ID=0 prod_name=isgri_spectrum_0_OAO1657m415_isgri_spectrum meta_data: {'src_name': 'OAO 1657-415', 'product': 'isgri_spectrum'}
ID=1 prod_name=isgri_spectrum_1_OAO1657m415_isgri_arf meta_data: {'src_name': 'OAO 1657-415', 'product': 'isgri_arf'}
ID=2 prod_name=isgri_spectrum_2_OAO1657m415_isgri_rmf meta_data: {'src_name': 'OAO 1657-415', 'product': 'isgri_rmf'}
ID=3 prod_name=isgri_spectrum_3_4U1700m377_isgri_spectrum meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_spectrum'}
ID=4 prod_name=isgri_spectrum_4_4U1700m377_isgri_arf meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_arf'}
ID=5 prod_name=isgri_spectrum_5_4U1700m377_isgri_rmf meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_rmf'}
ID=6 prod_name=isgri_spectrum_6_GX349p2_isgri_spectrum meta_data: {'src_name': 'GX 349+2', 'product': 'isgri_spectrum'}
ID=7 prod_name=isgri_spectrum_7_GX349p2_isgri_arf meta_data: {'src_name': 'GX 349+2', 'product': 'isgri_arf'}
ID=8 prod_name=isgri_spectrum_8_GX349p2_isgri_rmf meta_data: {'src_name': 'GX 349+2', 'product': 'isgri_rmf'}
ID=9 prod_name=isgri_spectrum_9_H1705m440_isgri_spectrum meta_data: {'src_name': 'H 1705-440', 'product': 'isgri_spectrum'}
ID=10 prod_name=isgri_spectrum_10_H1705m440_isgri_arf meta_data: {'src_name': 'H 1705-440', 'product': 'isgri_arf'}
ID=11 prod_name=isgri_spectrum_11_H1705m440_isgri_rmf meta_data: {'src_name': 'H 1705-440', 'product': 'isgri_rmf'}
ID=12 prod_name=isgri_spectrum_12_GX354m0_isgri_spectrum meta_data: {'src_name': 'GX 354-0', 'product': 'isgri_spectrum'}
ID=13 prod_name=isgri_spectrum_13_GX354m0_isgri_arf meta_data: {'src_name': 'GX 354-0', 'product': 'isgri_arf'}
ID=14 prod_name=isgri_spectrum_14_GX354m0_isgri_rmf meta_data: {'src_name': 'GX 354-0', 'product': 'isgri_rmf'}
ID=15 prod_name=isgri_spectrum_15_GX1p4_isgri_spectrum meta_data: {'src_name': 'GX 1+4', 'product': 'isgri_spectrum'}
ID=16 prod_name=isgri_spectrum_16_GX1p4_isgri_arf meta_data: {'src_name': 'GX 1+4', 'product': 'isgri_arf'}
ID=17 prod_name=isgri_spectrum_17_GX1p4_isgri_rmf meta_data: {'src_name': 'GX 1+4', 'product': 'isgri_rmf'}
ID=18 prod_name=isgri_spectrum_18_Background_isgri_spectrum meta_data: {'src_name': 'Background', 'product': 'isgri_spectrum'}
ID=19 prod_name=isgri_spectrum_19_Background_isgri_arf meta_data: {'src_name': 'Background', 'product': 'isgri_arf'}
ID=20 prod_name=isgri_spectrum_20_Background_isgri_rmf meta_data: {'src_name': 'Background', 'product': 'isgri_rmf'}
[52]:
d=data_collection._p_list[0]
[53]:
d.meta_data
[53]:
{'src_name': 'OAO 1657-415', 'product': 'isgri_spectrum'}
we can select all the products for the same soruce name and build a new data collection on-the-fly with a single instruction
[54]:
data_sel=data_collection.new_from_metadata('src_name','4U 1700-377')
[55]:
data_sel.show()
ID=0 prod_name=prod_0_4U1700m377_isgri_spectrum meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_spectrum'}
ID=1 prod_name=prod_1_4U1700m377_isgri_arf meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_arf'}
ID=2 prod_name=prod_2_4U1700m377_isgri_rmf meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_rmf'}
and we can save all these products
[56]:
data_sel.save_all_data()
[57]:
d=data_collection._p_list[3]
[58]:
d.data_unit[1].header
[58]:
{'ANCRFILE': 'NONE',
'AREASCAL': 1,
'BACKFILE': 'NONE',
'BACKSCAL': 1,
'BASETYPE': 'DAL_TABLE',
'BITPIX': 8,
'BKGPARAM': 'rebinned_back_spe.fits',
'CHANTYPE': 'PI',
'CHECKSUM': '9kKSIjKQ9jKQGjKQ',
'COMMENT': ' on the next keyword which has the name CONTINUE.',
'CONFIGUR': 'osa_2019-07-29T14:07:33',
'CORRFILE': 'NONE',
'CORRSCAL': 0,
'CREATOR': 'ISGRISpectraSum.v5.4.2.3.extractall',
'DATASUM': '2445425117',
'DATE': '2021-09-01T13:26:15.942451',
'DEADC': 0.775885283090927,
'DEC_OBJ': -37.8394165039062,
'DETCHANS': 62,
'DETNAM': 'ISGRI',
'EQUINOX': 2000.0,
'EXPOSURE': 1198.97207125461,
'EXP_SRC': 610.283081054688,
'EXTNAME': 'ISGR-EVTS-SPE',
'EXTREL': '10.4',
'EXTVER': 2,
'FILTER': 'none',
'FITTYPE': 6,
'GCOUNT': 1,
'GRPID1': 1,
'HDUCLAS1': 'SPECTRUM',
'HDUCLAS2': 'TOTAL',
'HDUCLAS3': 'RATE',
'HDUCLASS': 'OGIP',
'HDUVERS': '1.2.15',
'INSTRUME': 'IBIS',
'ISDCLEVL': 'SPE',
'LONGSTRN': 'OGIP 1.0',
'MJDREF': 51544.0,
'NAME': '4U 1700-377',
'NAXIS': 2,
'NAXIS1': 18,
'NAXIS2': 62,
'OFFCORR': 'rebinned_corr_spe.fits',
'ONTIME': 1587.05859375,
'ORIGIN': 'ISDC',
'PCOUNT': 0,
'RADECSYS': 'FK5',
'RA_OBJ': 255.984405517578,
'RESPFILE': 'NONE',
'REVOL': 51,
'SOURCEID': 'J170356.8-375039',
'STAMP': '',
'SW_TYPE': 'POINTING',
'TELAPSE': 1589.0,
'TELESCOP': 'INTEGRAL',
'TFIELDS': 6,
'TFORM1': '1I',
'TFORM2': '1E',
'TFORM3': '1E',
'TFORM4': '1E',
'TFORM5': '1I',
'TFORM6': '1I',
'TIMEREF': 'LOCAL',
'TIMESYS': 'TT',
'TIMEUNIT': 'd',
'TLMAX1': 61,
'TLMIN1': 0,
'TSTART': 1169.97844975867,
'TSTOP': 1169.99912106495,
'TTYPE1': 'CHANNEL',
'TTYPE2': 'RATE',
'TTYPE3': 'SYS_ERR',
'TTYPE4': 'STAT_ERR',
'TTYPE5': 'QUALITY',
'TTYPE6': 'GROUPING',
'TUNIT2': 'count/s',
'TUNIT3': '',
'TUNIT4': 'count/s',
'XTENSION': 'BINTABLE'}
JEM-X test#
[59]:
disp.get_instrument_description('jemx')
[59]:
[[{'instrumet': 'jemx'},
{'prod_dict': {'jemx_image': 'jemx_image_query',
'jemx_lc': 'jemx_lc_query',
'jemx_spectrum': 'jemx_spectrum_query',
'spectral_fit': 'spectral_fit_query'}},
'[{"query_name": "src_query"}, {"name": "src_name", "units": "str", "value": "test"}, {"name": "RA", "units": "deg", "value": 0.0}, {"name": "DEC", "units": "deg", "value": 0.0}, {"name": "T1", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "T2", "units": None, "value": "2001-12-11T00:00:00.000"}, {"name": "token", "units": "str", "value": None}]',
'[{"query_name": "jemx_parameters"}, {"name": "user_catalog", "units": "str", "value": None}, {"name": "scw_list", "units": "names_list", "value": []}, {"name": "selected_catalog", "units": "str", "value": None}, {"name": "radius", "units": "deg", "value": 5.0}, {"name": "max_pointings", "units": None, "value": 50}, {"name": "osa_version", "units": "str", "value": None}, {"name": "integral_data_rights", "units": "str", "value": "public"}, {"name": "jemx_num", "units": None, "value": 2}, {"name": "E1_keV", "units": "keV", "value": 3.0}, {"name": "E2_keV", "units": "keV", "value": 35.0}]',
'[{"query_name": "jemx_image_query"}, {"product_name": "jemx_image"}, {"name": "detection_threshold", "units": "sigma", "value": 0.0}, {"name": "image_scale_min", "units": None, "value": None}, {"name": "image_scale_max", "units": None, "value": None}]',
'[{"query_name": "jemx_spectrum_query"}, {"product_name": "jemx_spectrum"}]',
'[{"query_name": "spectral_fit_query"}, {"product_name": "spectral_fit"}, {"name": "xspec_model", "units": "str", "value": "powerlaw"}, {"name": "ph_file_name", "units": "str", "value": ""}, {"name": "arf_file_name", "units": "str", "value": ""}, {"name": "rmf_file_name", "units": "str", "value": ""}]',
'[{"query_name": "jemx_lc_query"}, {"product_name": "jemx_lc"}, {"name": "time_bin", "units": "sec", "value": 1000.0}]']]
[60]:
data_collection=disp.get_product(instrument='jemx',
jemx_num='2',
product='jemx_lc',
scw_list=['010200230010.001'],
osa_version='OSA10.2',
radius=15.,
product_type='Real')
[61]:
data_collection.show()
ID=0 prod_name=jemx_lc_0_String meta_data: {'src_name': 'String', 'time_bin': 0.01157407, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}
[62]:
data_collection=disp.get_product(instrument='jemx',
jemx_num='2',
product='jemx_spectrum',
scw_list=['010200230010.001'],
osa_version='OSA10.2',
radius=15.,
product_type='Real')
[63]:
data_collection.show()
ID=0 prod_name=jemx_spectrum_0_Crab_jemx_spectrum meta_data: {'src_name': 'Crab', 'product': 'jemx_spectrum'}
ID=1 prod_name=jemx_spectrum_1_Crab_jemx_arf meta_data: {'src_name': 'Crab', 'product': 'jemx_arf'}
ID=2 prod_name=jemx_spectrum_2_Crab_jemx_rmf meta_data: {'src_name': 'Crab', 'product': 'jemx_rmf'}
This is an example of spectral analysis with threeML#
[64]:
from threeML.plugins.OGIPLike import OGIPLike
from threeML.io.package_data import get_path_of_data_file
from threeML import *
#warnings.filterwarnings('ignore')
%matplotlib inline
import matplotlib.pylab as plt
[WARNING ] The naima package is not available. Models that depend on it will not be available
[WARNING ] The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it will not be available.
[WARNING ] The ebltable package is not available. Models that depend on it will not be available
[INFO ] Starting 3ML!
[WARNING ] ROOT minimizer not available
[WARNING ] PyGMO is not available
[WARNING ] The cthreeML package is not installed. You will not be able to use plugins which require the C/C++ interface (currently HAWC)
[WARNING ] Could not import plugin FermiLATLike.py. Do you have the relative instrument software installed and configured?
[WARNING ] Could not import plugin HAWCLike.py. Do you have the relative instrument software installed and configured?
[65]:
ogip_data = OGIPLike('ogip',
observation='prod_0_4U1700m377_isgri_spectrum.fits',
arf_file= 'prod_1_4U1700m377_isgri_arf.fits' ,
response= 'prod_2_4U1700m377_isgri_rmf.fits')
[WARNING ] unable to find SPECTRUM extension: not OGIP PHA!
[WARNING ] File has no SPECTRUM extension, but found a spectrum in extension ISGR-EVTS-SPE
[WARNING ] Found TSTOP and TELAPSE. This file is invalid. Using TSTOP.
[WARNING ] POISSERR is not set. Assuming non-poisson errors as given in the STAT_ERR column
[WARNING ] The default choice for MATRIX extension failed:KeyError("Extension ('MATRIX', 1) not found.")available: 'PRIMARY' 'GROUPING' 'SPECRESP MATRIX' 'EBOUNDS'
[INFO ] Auto-probed noise models:
[INFO ] - observation: gaussian
[INFO ] - background: None
[66]:
ogip_data.set_active_measurements('20-60')
[INFO ] Range 20-60 translates to channels 7-24
[67]:
ogip_data.view_count_spectrum()
plt.ylim(1E-5,10)
[INFO ] bad channels shown in red hatching
WARNING RuntimeWarning: invalid value encountered in sqrt
WARNING UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.
Invalid limit will be ignored.
[67]:
(1e-05, 10)
[68]:
fit_function = Cutoff_powerlaw()
# define the point source
point_source = PointSource('ps', 0, 0, spectral_shape=fit_function)
#define the model
model = Model(point_source)
# create a data list
datalist = DataList(ogip_data)
# make the joint likelihood
jl = JointLikelihood(model, datalist)
#fit
jl.fit();
[INFO ] set the minimizer to minuit
[WARNING ] 4.22 percent of samples have been thrown away because they failed the constraints on the parameters. This results might not be suitable for error propagation. Enlarge the boundaries until you loose less than 1 percent of the samples.
Best fit values:
| result | unit | |
|---|---|---|
| parameter | ||
| ps.spectrum.main.Cutoff_powerlaw.K | 0.005 -0.005 +4 | 1 / (cm2 keV s) |
| ps.spectrum.main.Cutoff_powerlaw.index | 0.2 +/- 2.8 | |
| ps.spectrum.main.Cutoff_powerlaw.xc | (1.1 -0.7 +1.7) x 10 | keV |
Correlation matrix:
| 1.00 | -1.00 | 0.99 |
| -1.00 | 1.00 | -1.00 |
| 0.99 | -1.00 | 1.00 |
Values of -log(likelihood) at the minimum:
| -log(likelihood) | |
|---|---|
| ogip | 4.390866 |
| total | 4.390866 |
Values of statistical measures:
| statistical measures | |
|---|---|
| AIC | 16.496017 |
| BIC | 17.452847 |
[69]:
display_spectrum_model_counts(jl, step=True);
WARNING MatplotlibDeprecationWarning: The 'nonposy' parameter of __init__() has been renamed 'nonpositive' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
[70]:
plot_point_source_spectra(jl.results, ene_min=20, ene_max=60, num_ene=100,
flux_unit='erg / (cm2 s)')
[ERROR ] plot_point_source_spectra() has been replaced by plot_spectra().
[70]:
[71]:
# A uniform prior can be defined directly, like:
model.ps.spectrum.main.Cutoff_powerlaw.index.prior = Uniform_prior(lower_bound=-2.,
upper_bound=2.)
# or it can be set using the currently defined boundaries
model.ps.spectrum.main.Cutoff_powerlaw.xc.prior= Log_uniform_prior(lower_bound=1,
upper_bound=100)
# The same for the Log_uniform prior
model.ps.spectrum.main.Cutoff_powerlaw.K.prior = Log_uniform_prior(lower_bound=1e-3,
upper_bound=100)
model.display()
Model summary:
Free parameters (3):
Fixed parameters (4):
(abridged. Use complete=True to see all fixed parameters)
Linked parameters (0):
(none)
Independent variables:
(none)
| N | |
|---|---|
| Point sources | 1 |
| Extended sources | 0 |
| Particle sources | 0 |
Free parameters (3):
| value | min_value | max_value | unit | |
|---|---|---|---|---|
| ps.spectrum.main.Cutoff_powerlaw.K | 0.005445 | 0.0 | 1000.0 | keV-1 s-1 cm-2 |
| ps.spectrum.main.Cutoff_powerlaw.index | 0.188702 | -10.0 | 10.0 | |
| ps.spectrum.main.Cutoff_powerlaw.xc | 10.82301 | 1.0 | None | keV |
Fixed parameters (4):
(abridged. Use complete=True to see all fixed parameters)
Linked parameters (0):
(none)
Independent variables:
(none)
[72]:
bs = BayesianAnalysis(model, datalist)
[WARNING ] External parameter cons_ogip already exist in the model. Overwriting it...
[73]:
bs.set_sampler("multinest")
bs.sampler.setup(n_live_points=500)
bs.sample()
[INFO ] sampler set to multinest
*****************************************************
MultiNest v3.10
Copyright Farhan Feroz & Mike Hobson
Release Jul 2015
no. of live points = 500
dimensionality = 3
*****************************************************
analysing data from chains/fit-.txt ln(ev)= -11.862533639413844 +/- 0.11122341917410335
Total Likelihood Evaluations: 7493
Sampling finished. Exiting MultiNest
Maximum a posteriori probability (MAP) point:
| result | unit | |
|---|---|---|
| parameter | ||
| ps.spectrum.main.Cutoff_powerlaw.K | (1.7 -1.7 +2.2) x 10^-1 | 1 / (cm2 keV s) |
| ps.spectrum.main.Cutoff_powerlaw.index | (-6 +/- 10) x 10^-1 | |
| ps.spectrum.main.Cutoff_powerlaw.xc | (1.9 +/- 0.9) x 10 | keV |
Values of -log(posterior) at the minimum:
| -log(posterior) | |
|---|---|
| ogip | -2.457204 |
| total | -2.457204 |
Values of statistical measures:
| statistical measures | |
|---|---|
| AIC | 12.628695 |
| BIC | 13.585524 |
| DIC | -10870.282000 |
| PDIC | -10881.214804 |
| log(Z) | -5.151833 |
[74]:
bs.results.corner_plot()
WARNING MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap("viridis").copy()
[74]:
[75]:
plot_point_source_spectra(bs.results, ene_min=20, ene_max=60, num_ene=100,
flux_unit='erg / (cm2 s)')
[ERROR ] plot_point_source_spectra() has been replaced by plot_spectra().
[75]:
[76]:
fluxes_bs = bs.results.get_point_source_flux(100 * u.keV, 1 * u.MeV)
[ERROR ] get_point_source_flux() has been replaced by get_flux()
[77]:
fluxes_bs
[77]:
| flux | low bound | hi bound | |
|---|---|---|---|
| ps: total | 7.98682085812617e-12 erg / (cm2 s) | 2.2496983896203776e-12 erg / (cm2 s) | 3.2021187085022926e-11 erg / (cm2 s) |