Impact of WPF (Silverlight) layout (Render)Transform on app performance
Date : March 29 2020, 07:55 AM
seems to work fine When designing UI part of WPF or Silverlight application we can apply some display transformations (LayoutTransform or RenderTransform) to visual elements. Some of these transformation are: , Here's a prototype you can use to experiment with various options: <Grid>
<Grid.Resources>
<local:Range x:Key="sampleData" Minimum="1" Maximum="900"/>
</Grid.Resources>
<ItemsControl ItemsSource="{StaticResource sampleData}">
<ItemsControl.ItemsPanel>
<ItemsPanelTemplate>
<UniformGrid Rows="30" Columns="30"/>
</ItemsPanelTemplate>
</ItemsControl.ItemsPanel>
<ItemsControl.ItemTemplate>
<DataTemplate>
<TextBlock Text="{Binding}" FontSize="8">
<TextBlock.LayoutTransform>
<RotateTransform Angle="30"/>
</TextBlock.LayoutTransform>
</TextBlock>
</DataTemplate>
</ItemsControl.ItemTemplate>
</ItemsControl>
</Grid>
class Range : List<int>, ISupportInitialize
{
public int Minimum { get; set; }
public int Maximum { get; set; }
public void BeginInit() { }
public void EndInit()
{
for (int i = Minimum; i <= Maximum; i++) Add(i);
}
}

ZF2: Zend Framework 2  how to render output without layout
Date : March 29 2020, 07:55 AM
may help you . I know that I can use this , In your example you could write like this: public function providerAction()
{
$result = new ViewModel();
$result>setTerminal(true);
$result>setVariables(array('items' => 'items'));
return $result;
}

Performing an EC public key calculation for given secret key gives wrong results
Tag : perl , By : Mario Tristan
Date : March 29 2020, 07:55 AM
Any of those help I'm not used to Perl but i cannot see you initializing or using a cyclic group. Elliptic curve operations and asymmetric crypto in general are (mostly) always performed in a cyclic group

Output array after performing fast fast fourier transform of a data set
Date : March 29 2020, 07:55 AM
like below fixes the issue I'm trying to perform a fourier transform of a data set that I have and subsequently writing its real and imaginary parts separately. , As others have indicated, include a modified version of >>> np.set_printoptions(edgeitems=5,linewidth=80,precision=2,suppress=True,threshold=10)
>>> a = np.arange(0,100.)
>>>
>>> a
array([ 0., 1., 2., 3., 4., ..., 95., 96., 97., 98., 99.])
>>> np.set_printoptions(edgeitems=5,linewidth=80,precision=2,suppress=True,threshold=100)
>>> a
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.,
12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.,
24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35.,
36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47.,
48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,
72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83.,
84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95.,
96., 97., 98., 99.])
np.set_printoptions(edgeitems=3,linewidth=80,precision=2,suppress=True,threshold=5)
>>> list(a)

Performing convolution in frequency domain manually but getting wrong output image in CPP/Opencv
Date : March 29 2020, 07:55 AM
should help you out I followed the following steps: 1. Calculated dft of image 2. Calculated dft of kernel (but 1st padded it to size of image) 3. Multiplied real and imaginary parts of both dft individually 4. Calculated inverse dft I tried to display the images in each intermediate step but the final image comes out to be almost black except in corners. Image fourier transform output after multiplication and its inverse dft output , There are two immediately apparent issues: Mat image = imread(file, CV_LOAD_IMAGE_GRAYSCALE);
// Expand input image to optimal size, on the border add zero values
Mat padded;
int m = getOptimalDFTSize(image.rows);
int n = getOptimalDFTSize(image.cols);
copyMakeBorder(image, padded, 0, m  image.rows, 0, n image.cols, BORDER_CONSTANT, Scalar::all(0));
// Computing DFT
Mat DFTimage;
dft(padded, DFTimage);
// Forming the Gaussian filter
Mat kernel = createGausFilterMask(padded.size(), r);
shift(kernel);
Mat DFTkernel;
dft(kernel, DFTkernel);
// Convolution
mulSpectrums(DFTimage, DFTkernel, DFTimage, DFT_ROWS);
// Display Fourierdomain result
Mat magI = updateMag(DFTimage);
imshow("spectrum magnitude", magI);
// IDFT
Mat work;
idft(complex, work); // < NOTE! Don't inverse transform logtransformed magnitude image!

