VISAR Scripts

For a discussion of the theory, please visit my blog post

Analysis

Run the analysis script in an iPython terminal. Click-and-drag the image (png format) in the terminal.

>>> %run visar_analysis.py
>>> time, velocity, analysis_params = analyze()

Load png Streak image
Drag and drop location into command prompt
path > /<Path>/smith.png

Select working data and reference

This will open up the PNG image in a matplotlib figure. The figure has event handeling enabled, and you will be propted to Select Working data from raw image by drawing a rectangle (red) on the figure by clicking and dragging. You must drag from the bottom-left to top-right.

Press enter in the terminal to select the region of data. You will then be propted to select a region of reference fringes, drag away from the start of the rectangle (green). Press enter in the terminal to confirm selection.

Select the fundamental frequency

A new window will open for you to select the fringe-spacing frequency of interest. Click on a location of the plot to select the minimum frequency (green), press enter (at the window focus, not the terminal), and select the maximum frequency (red). Press enter in the terminal to end selections.

Pick Etalon

A menu will prompt you to choose the VPF. The VPFs listed in this menu are determind my the JSON config file etalon_conf.json.

Pick an etalon thickness:

	[0]____________1e+03 mm________46.29 km s-1
	[1]____________2e+03 mm________23.14 km s-1
	[2]____________5e+03 mm________9.258 km s-1
	[3]________1.144e+04 mm________4.047 km s-1
	[4]__________1.5e+04 mm________3.086 km s-1
	[5]__________2.5e+04 mm________1.852 km s-1
	[6]__________4.5e+04 mm________1.029 km s-1
	[7]_________9.97e+03 mm________5.175 km s-1
	[8]_________2.53e+04 mm________2.039 km s-1
	[9]______________custom
> 9
Enter custom vpf: 5.4603

Subtract Background (optional)

Enter "yes" in the terminal to subtract background fringes. You will be prompted to drag-and-drop a reference image. This will then be processed with the same selections you made to the data. (These parameters are stored in the ANALYSIS_PARAMS dictionary.

Take a line-out of the data

Using the click-and-drag technique, select a rectangle of interest (green) from the veloctiy map.

analyse() will now return time and velocity arrays from your selection, along with a dictionary of the parameters you selected during the analysis

EXTRA

You will then be propted to add fringe shifts, or invert the velocity trace

Recalling an old Analysis

Saving analysis parameters

At the end of the analysis, a analysis_param dictionary will be returned. Save this dictionary using the method call

save_parameters(analysis_params, name="name/of/file")

This will generat a pickle file which can be recalled later

Loading alaysis parameters

To recall an old analysis session, run the automation method with the pickle analysis_params passed as an argument

automated_analysis(params="name/of/file.npy")

Simulate Line-VISAR data

Building simulated data from a velocity profile.

Simple Example

target = Target(velocity_equation="step")
    
ray = Ray(pulse_length=10)
ray = target.reflect_off_target(ray)
    
etalon = Etalon(1, 1.5195)
etalon.set_VPF(2., lambda0=.532)
interferometer = Interferometer(etalon=etalon)
    
sweep = interferometer.output(ray, target)

plt.figure()
plt.imshow(sweep, aspect='auto', cmap="gray", extent=(0, 10, -2, 2))
plt.xlabel("Time [ns]")

Target class

The target class defines the velocity profile that will be recorded by the visar system (i.e. the shock front moving / interface velocity of a target of interest).

This velocity profile is loaded during the initialization of the target instance. Two built-in profiles are available for testing.

• step : a discontinuous velocity jump
• sigmoid : a sigmoid-shaped velocity jump
• stationary : 0 velocity change, used in generating reference images

To help visualize the velocity profile, a helper plotting function is available, and can be called via the following command:

target.plot_velocity()

This generates two plots: a 3D plot of the velocity profile, and a 2D color plot. The 2D should be identical to what the "Visar analysis" script would return for the velocity map, if this simulated data were to be analyzed.

Loading a generic velocity profile

User-defined velocity profiles can be loaded. An example of how this is done is demonstrated in the functions

• sin_step
• spatial_var_step

In this case, a callable function is defined, which must except a time value, and a spatial location. If the user defined function requires more arguments, this can be simplified with use of a lambda function. e.g.

velocity_equation = lambda t, y : sin_step(20, .5, t, y, max_velocity=1)

the function is then loaded into the target during the initialization of the target instance.

target = Target(velocity_equation=velocity_equation)

Ray class

The ray instance defines the duration, as well as the spatial location over which the velocity profile will be recorded.

The values must be less than target._t and target._y, currently these are hard-coded into the Target.__init__, feel free to change them.

Etalon class

The etalon instance sets the VPF of the generated visar data, determined by twice the thickness (two passes through the etalon for a Mach-Zehnder interferometer) and the index of refraction:

etalon = Etalon(thickness=1, n=1.5195)

If you would like to explicitly set the VPF instead, this can be done with the helper function set_VPF, this then chanced the etalon thickness:

etalon.set_VPF(2, labda0=.532)

Similarly, this can be done with the etalon tau (temporal delay, in ns), set_tau

Interferometer class

The interferometer instance will carry out the mechanics of generating the fringe-comb pattern.

Initilization of the interferometer instance requires 2 arguments.

Interferometer(
    etalon,
    tau,
)

• etalon An Etalon instance, used in determining the VPF of the generated data
• tau the slit opening on the streak camera, this sets the temporal resolution.

Generation of the desired data is then accomplished by calling the output class method.

sweep = interferometer.output(ray, target, noise=False)

output excepts a Ray argument and a Target argument, and optional boolean argument to add noise to the generated data is also available; however, this method is still in development.

Reference Shot

A helper function is included to quickly generate a reference shot. This just generates the output from a stationary target.

reference_shot(
    save=False,
    noise=False
)

Cite this work

@misc{https://doi.org/10.5281/zenodo.2578671,
  doi = {10.5281/zenodo.2578671},
  url = {https://zenodo.org/record/2578671},
  author = {Hammel,  Ben},
  title = {bdhammel/line-visar-analysis: Initial release},
  publisher = {Zenodo},
  year = {2019}
}

B. Hammel. bdhammel/line-visar-analysis: Initial release, 2019.