autolens/data/ccd.py
def background_noise_from_edges(self, no_edges):
        """Estimate the background signal_to_noise_ratio by binning data_to_image located at the edge(s) of an image
        into a histogram and fitting a Gaussian profiles to this histogram. The standard deviation (sigma) of this
        Gaussian gives a signal_to_noise_ratio estimate.

        Parameters
        ----------
        no_edges : int
            Number of edges used to estimate the background signal_to_noise_ratio.

        """

        edges = []

        for edge_no in range(no_edges):
            top_edge = self.image[edge_no, edge_no:self.image.shape[1] - edge_no]
            bottom_edge = self.image[self.image.shape[0] - 1 - edge_no, edge_no:self.image.shape[1] - edge_no]
            left_edge = self.image[edge_no + 1:self.image.shape[0] - 1 - edge_no, edge_no]
            right_edge = self.image[edge_no + 1:self.image.shape[0] - 1 - edge_no, self.image.shape[1] - 1 - edge_no]

            edges = np.concatenate((edges, top_edge, bottom_edge, right_edge, left_edge))

        return norm.fit(edges)[1]
def background_noise_from_edges(self, no_edges):
        """Estimate the background signal_to_noise_ratio by binning data_to_image located at the edge(s) of an image
        into a histogram and fitting a Gaussian profiles to this histogram. The standard deviation (sigma) of this
        Gaussian gives a signal_to_noise_ratio estimate.

        Parameters
        ----------
        no_edges : int
            Number of edges used to estimate the background signal_to_noise_ratio.

        """

        edges = []

        for edge_no in range(no_edges):
            top_edge = self.image[edge_no, edge_no:self.image.shape[1] - edge_no]
            bottom_edge = self.image[self.image.shape[0] - 1 - edge_no, edge_no:self.image.shape[1] - edge_no]
            left_edge = self.image[edge_no + 1:self.image.shape[0] - 1 - edge_no, edge_no]
            right_edge = self.image[edge_no + 1:self.image.shape[0] - 1 - edge_no, self.image.shape[1] - 1 - edge_no]

            edges = np.concatenate((edges, top_edge, bottom_edge, right_edge, left_edge))

        return norm.fit(edges)[1]
def background_noise_from_edges ( self , no_edges ) : edges = [ ] for edge_no in range ( no_edges ) : top_edge = self . image [ edge_no , edge_no : self . image . shape [ 1 ] - edge_no ] bottom_edge = self . image [ self . image . shape [ 0 ] - 1 - edge_no , edge_no : self . image . shape [ 1 ] - edge_no ] left_edge = self . image [ edge_no + 1 : self . image . shape [ 0 ] - 1 - edge_no , edge_no ] right_edge = self . image [ edge_no + 1 : self . image . shape [ 0 ] - 1 - edge_no , self . image . shape [ 1 ] - 1 - edge_no ] edges = np . concatenate ( ( edges , top_edge , bottom_edge , right_edge , left_edge ) ) return norm . fit ( edges ) [ 1 ]
Estimate the background signal_to_noise_ratio by binning data_to_image located at the edge(s) of an image
        into a histogram and fitting a Gaussian profiles to this histogram. The standard deviation (sigma) of this
        Gaussian gives a signal_to_noise_ratio estimate.

        Parameters
        ----------
        no_edges : int
            Number of edges used to estimate the background signal_to_noise_ratio.
Estimate the background signal_to_noise_ratio by binning data_to_image located at the edge ( s ) of an image into a histogram and fitting a Gaussian profiles to this histogram . The standard deviation ( sigma ) of this Gaussian gives a signal_to_noise_ratio estimate .