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PixelMath: Processing view: integration_with_reference_0003
Executing PixelMath expression: combined RGB/K channels: n += $T <= 1.19e-07: done
* Global variables:
n(+) = { 0, 0, 0 }
35.835 ms
PixelMath: Processing view: integration_with_reference_0001
Executing PixelMath expression: combined RGB/K channels: n += $T <= 1.19e-07: done
* Global variables:
n(+) = { 1, 1, 1 }
42.657 ms
The version downloaded for the trial worked fine. I uninstalled from Windows control panel, installed the new version, and the screen came up OK, but everything inside the workspace was not "calibrated" for the mouse. If I loaded process icons to the desktop, I had to click underneath them (not on them) to select it. The main menu was not present, but if I clicked below where it was supposed to be, it activated.
As a last ditch effort, I decided to open PixInsight by double clicking on an XISF file on my hard drive. Everything opens up fine. If I load my process icons, they behave properly, and the main menu is present. If I close PixInsight and open by double clicking the shortcut on my desktop, the white screen syndrome returns.
This behavior seems to indicate it is not a computer configuration problem, but maybe an initialization problem when the software starts up. On my system, this is very repeatable behavior.
/**
* Estimation of the standard deviation of the noise, assuming a Gaussian
* noise distribution.
*
* - Use MRS noise evaluation when the algorithm converges for 4 >= J >= 2
*
* - Use k-sigma noise evaluation when either MRS doesn't converge or the
* length of the noise pixels set is below a 1% of the image area.
*
* - Automatically iterate to find the highest layer where noise can be
* successfully evaluated, in the [1,3] range.
*
* Returned noise estimates are scaled by the Sn robust scale estimator of
* Rousseeuw and Croux.
*/
function ScaledNoiseEvaluation( image )
{
let scale = image.Sn();
if ( 1 + scale == 1 )
throw Error( "Zero or insignificant data." );
let a, n = 4, m = 0.01*image.selectedRect.area;
for ( ;; )
{
a = image.noiseMRS( n );
if ( a[1] >= m )
break;
if ( --n == 1 )
{
console.writeln( "<end><cbr>** Warning: No convergence in MRS noise evaluation routine - using k-sigma noise estimate." );
a = image.noiseKSigma();
break;
}
}
this.sigma = a[0]/scale; // estimated scaled stddev of Gaussian noise
this.count = a[1]; // number of pixels in the noisy pixels set
this.layers = n; // number of layers used for noise evaluation
}
function main()
{
let window = ImageWindow.activeWindow;
if ( window.isNull )
throw new Error( "No active image" );
console.show();
console.writeln( "<end><cbr><br><b>" + window.currentView.fullId + "</b>" );
console.writeln( "Calculating scaled noise standard deviation..." );
console.flush();
console.abortEnabled = true;
let image = window.currentView.image;
console.writeln( "<end><cbr><br>Ch | noise | count(%) | layers |" );
console.writeln( "---+-----------+-----------+--------+" );
for ( let c = 0; c < image.numberOfChannels; ++c )
{
console.flush();
image.selectedChannel = c;
let E = new ScaledNoiseEvaluation( image );
console.writeln( format( "%2d | <b>%.3e</b> | %6.2f | %d |", c, E.sigma, 100*E.count/image.selectedRect.area, E.layers ) );
console.flush();
}
console.writeln( "---+-----------+-----------+--------+" );
}
main();