Full Disk Simulations - Revision history

Dgary: /* Comparing Data and Model */

2019-09-11T20:10:00Z

Comparing Data and Model

← Older revision		Revision as of 20:10, 11 September 2019
Line 138:		Line 138:
	customsymbol=True, symbolcolor='0000ff' , xsharedaxis=True)		customsymbol=True, symbolcolor='0000ff' , xsharedaxis=True)
	plotms(vis=vis, xaxis='uvwave', yaxis='amp', antenna='0~3', correlation='XX',		plotms(vis=vis, xaxis='uvwave', yaxis='amp', antenna='0~3', correlation='XX',
			timerange='14:00:00~23:00:00',
	gridrows=4, gridcols=1, ydatacolumn='model', rowindex=i, plotindex=2*i+1,		gridrows=4, gridcols=1, ydatacolumn='model', rowindex=i, plotindex=2*i+1,
	plotrange=[10,1000,0,400000], clearplots=False, spw=spws[i],		plotrange=[10,1000,0,400000], clearplots=False, spw=spws[i],

Sjyu: /* Comparing Data and Model */

2019-09-11T18:24:52Z

Comparing Data and Model

← Older revision		Revision as of 18:24, 11 September 2019
Line 143:		Line 143:

	Note that some amplitude scaling issues are apparent, so we should try amplitude selfcal to correct the data.		Note that some amplitude scaling issues are apparent, so we should try amplitude selfcal to correct the data.

			The source code of the following procedures can be found in the [https://github.com/suncasa/suncasa/blob/master/eovsa/eovsa_diskmodel.py suncasa] package.

Sjyu: /* Creating Disk Models in CASA */

2019-09-11T18:24:30Z

Creating Disk Models in CASA

← Older revision		Revision as of 18:24, 11 September 2019
Line 7:		Line 7:

	= Creating Disk Models in CASA =		= Creating Disk Models in CASA =
	~~The source code of the following procedures can be found in the [https://github.com/suncasa/suncasa/blob/master/eovsa/eovsa_diskmodel.py suncasa] package.~~
	The first step in simulating a source is to create a "sky model," which is an image and associated descriptive header information to represent a source in the sky. CASA's [https://casaguides.nrao.edu/index.php/Simulation_Guide_Component_Lists_(CASA_5.4) componentlist] routines provide a recipe for creating different image components, including gaussian sources, point sources, disks, and so on. I created a python script diskmodel.py that eases the work of creating a disk model, which allows one to create a disk with unequal axes (generally larger in the equatorial axis and smaller in the polar axis). However, before it can be used one must have a good idea of the disk size and flux density of the disk.		The first step in simulating a source is to create a "sky model," which is an image and associated descriptive header information to represent a source in the sky. CASA's [https://casaguides.nrao.edu/index.php/Simulation_Guide_Component_Lists_(CASA_5.4) componentlist] routines provide a recipe for creating different image components, including gaussian sources, point sources, disks, and so on. I created a python script diskmodel.py that eases the work of creating a disk model, which allows one to create a disk with unequal axes (generally larger in the equatorial axis and smaller in the polar axis). However, before it can be used one must have a good idea of the disk size and flux density of the disk.

Sjyu: /* Creating Disk Models in CASA */

2019-09-11T18:24:07Z

Creating Disk Models in CASA

← Older revision		Revision as of 18:24, 11 September 2019
Line 7:		Line 7:

	= Creating Disk Models in CASA =		= Creating Disk Models in CASA =
	The source code of the following procedures can be found in the [~~suncasa~~ https://github.com/suncasa/suncasa/blob/master/eovsa/eovsa_diskmodel.py] package.		The source code of the following procedures can be found in the [https://github.com/suncasa/suncasa/blob/master/eovsa/eovsa_diskmodel.py suncasa] package.
	The first step in simulating a source is to create a "sky model," which is an image and associated descriptive header information to represent a source in the sky. CASA's [https://casaguides.nrao.edu/index.php/Simulation_Guide_Component_Lists_(CASA_5.4) componentlist] routines provide a recipe for creating different image components, including gaussian sources, point sources, disks, and so on. I created a python script diskmodel.py that eases the work of creating a disk model, which allows one to create a disk with unequal axes (generally larger in the equatorial axis and smaller in the polar axis). However, before it can be used one must have a good idea of the disk size and flux density of the disk.		The first step in simulating a source is to create a "sky model," which is an image and associated descriptive header information to represent a source in the sky. CASA's [https://casaguides.nrao.edu/index.php/Simulation_Guide_Component_Lists_(CASA_5.4) componentlist] routines provide a recipe for creating different image components, including gaussian sources, point sources, disks, and so on. I created a python script diskmodel.py that eases the work of creating a disk model, which allows one to create a disk with unequal axes (generally larger in the equatorial axis and smaller in the polar axis). However, before it can be used one must have a good idea of the disk size and flux density of the disk.

Sjyu: /* Creating Disk Models in CASA */

2019-09-11T18:23:37Z

Creating Disk Models in CASA

← Older revision		Revision as of 18:23, 11 September 2019
Line 7:		Line 7:

	= Creating Disk Models in CASA =		= Creating Disk Models in CASA =
			The source code of the following procedures can be found in the [suncasa https://github.com/suncasa/suncasa/blob/master/eovsa/eovsa_diskmodel.py] package.
	The first step in simulating a source is to create a "sky model," which is an image and associated descriptive header information to represent a source in the sky. CASA's [https://casaguides.nrao.edu/index.php/Simulation_Guide_Component_Lists_(CASA_5.4) componentlist] routines provide a recipe for creating different image components, including gaussian sources, point sources, disks, and so on. I created a python script diskmodel.py that eases the work of creating a disk model, which allows one to create a disk with unequal axes (generally larger in the equatorial axis and smaller in the polar axis). However, before it can be used one must have a good idea of the disk size and flux density of the disk.		The first step in simulating a source is to create a "sky model," which is an image and associated descriptive header information to represent a source in the sky. CASA's [https://casaguides.nrao.edu/index.php/Simulation_Guide_Component_Lists_(CASA_5.4) componentlist] routines provide a recipe for creating different image components, including gaussian sources, point sources, disks, and so on. I created a python script diskmodel.py that eases the work of creating a disk model, which allows one to create a disk with unequal axes (generally larger in the equatorial axis and smaller in the polar axis). However, before it can be used one must have a good idea of the disk size and flux density of the disk.

Dgary: /* Inserting the Model into the Observation Measurement Set */

2019-09-10T20:34:38Z

Inserting the Model into the Observation Measurement Set

← Older revision		Revision as of 20:34, 10 September 2019
Line 124:		Line 124:
	spw = int(files[i].split('disk')[1].split('_')[0])		spw = int(files[i].split('disk')[1].split('_')[0])
	ft(vis=vis, spw=str(spw), field='', model=str(files[i]), nterms=1,		ft(vis=vis, spw=str(spw), field='', model=str(files[i]), nterms=1,
	reffreq=\'\', complist=\'\', incremental=False, usescratch=~~False~~)		reffreq="''", complist="''", incremental=False, usescratch=True)

	This will result in filling the MODEL column of the dataset for each spw with the Fourier transform of the corresponding disk image.		This will result in filling the MODEL column of the dataset for each spw with the Fourier transform of the corresponding disk image.

Dgary: /* Inserting the Model into the Observation Measurement Set */

2019-09-10T20:31:55Z

Inserting the Model into the Observation Measurement Set

← Older revision		Revision as of 20:31, 10 September 2019
Line 124:		Line 124:
	spw = int(files[i].split('disk')[1].split('_')[0])		spw = int(files[i].split('disk')[1].split('_')[0])
	ft(vis=vis, spw=str(spw), field='', model=str(files[i]), nterms=1,		ft(vis=vis, spw=str(spw), field='', model=str(files[i]), nterms=1,
	reffreq='', complist='', incremental=False, usescratch=False)		reffreq=\'\', complist=\'\', incremental=False, usescratch=False)

	This will result in filling the MODEL column of the dataset for each spw with the Fourier transform of the corresponding disk image.		This will result in filling the MODEL column of the dataset for each spw with the Fourier transform of the corresponding disk image.

Dgary: /* Comparing Data and Model */

2019-09-10T12:40:44Z

Comparing Data and Model

← Older revision		Revision as of 12:40, 10 September 2019
Line 130:		Line 130:
	==Comparing Data and Model==		==Comparing Data and Model==

	To check how well the disk models fit the data, it is useful to compare the data and model using plotms. The following commands will display the result in Figure 5. [[file:'4band-comparison.png'~~] Caption~~]]		To check how well the disk models fit the data, it is useful to compare the data and model using plotms. The following commands will display the result in Figure 6. [[file:4band-comparison.png\|thumb\|right\|300px\|'''Fig. 6:''' Final comparison of MODEL data column (cyan) to the original DATA column (blue) for spectral windows 16, 26, 36, and 41.]]

	spws = ['16', '26', '36', '41']		spws = ['16', '26', '36', '41']

Dgary: /* fit_vs_freq() */

2019-09-10T12:39:20Z

fit_vs_freq()

← Older revision		Revision as of 12:39, 10 September 2019
Line 58:		Line 58:
	'polradius' The polar radius for each band, in arcsec		'polradius' The polar radius for each band, in arcsec
	'radius' The radius determined from the fit to all baselines		'radius' The radius determined from the fit to all baselines
	'disk_flux' The flux density one should use in a later routine described below (this is 2 x RSTN)		'disk_flux' The flux density one should use in a later routine
			described below (this is 2 x RSTN)
	One should perhaps not take the summary plot results at face value in the light of Figures 3 and 4, however, since clearly different answers can be obtained depending on the uv range used for the fitting. Nevertheless, the results as a whole are plausible as shown in Figure 5. This plot is the result of one such run, where the uv fit range (internal to the fit_vs_freq() routine) is scaled outward in an algorithmic way according to		One should perhaps not take the summary plot results at face value in the light of Figures 3 and 4, however, since clearly different answers can be obtained depending on the uv range used for the fitting. Nevertheless, the results as a whole are plausible as shown in Figure 5. This plot is the result of one such run, where the uv fit range (internal to the fit_vs_freq() routine) is scaled outward in an algorithmic way according to
	uvfitrange = np.array([10,150])+np.array([1,18])*i		uvfitrange = np.array([10,150])+np.array([1,18])*i

Dgary: /* Comparing Data and Model */

2019-09-10T12:38:33Z

Comparing Data and Model

← Older revision		Revision as of 12:38, 10 September 2019
Line 129:		Line 129:
	==Comparing Data and Model==		==Comparing Data and Model==

	To check how well the disk models fit the data, it is useful to compare the data and model using plotms.		To check how well the disk models fit the data, it is useful to compare the data and model using plotms. The following commands will display the result in Figure 5. [[file:'4band-comparison.png'] Caption]]

			spws = ['16', '26', '36', '41']
			for i in range(4):
			plotms(vis=vis, xaxis='uvwave', yaxis='amp', antenna='0~3', correlation='XX',
			gridrows=4, gridcols=1, ydatacolumn='data', rowindex=i, plotindex=2*i,
			plotrange=[10,1000,0,400000], clearplots=[True,False,False,False][i], spw=spws[i],
			customsymbol=True, symbolcolor='0000ff' , xsharedaxis=True)
			plotms(vis=vis, xaxis='uvwave', yaxis='amp', antenna='0~3', correlation='XX',
			gridrows=4, gridcols=1, ydatacolumn='model', rowindex=i, plotindex=2*i+1,
			plotrange=[10,1000,0,400000], clearplots=False, spw=spws[i],
			customsymbol=True, symbolcolor='00aaff' , xsharedaxis=True)

			Note that some amplitude scaling issues are apparent, so we should try amplitude selfcal to correct the data.