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Binning

binning

LoadBinning

Get the different arrays used for a simulation instance from a pickle binning as created by xifu cluster sims. The count map is needed for returning the count_weighted barycentre of each bin.

Source code in src/binning.py 
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class LoadBinning : 
    """
    Get the different arrays used for a simulation instance from a pickle 
    binning as created by xifu cluster sims. The count map is needed for returning the
    count_weighted barycentre of each bin.
    """

    def __init__(self,
                 shape = (360,360),
                 binning_file = '/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_region_200/region_files/19p_region_dict.p',
                 count_map_file = '/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_count_image.fits'):
        """
        Initialize binning

        Parameters:
            shape (tuple): Shape of the final map to provide (should match the shape from SpatialGrid3D)
            binning_file (str): Path to pickle file containing binning dict
            count_map_file (str): Path to count map
        """
        self.shape = shape
        self.binning_dict, self.region_image = pickle.load(open(binning_file, 'rb'), encoding="bytes")
        self.countmap = jnp.array(fits.getdata(count_map_file), dtype = 'float32')

    def __call__(self):

        """
        Create the different quantities used from the binning

        Returns:
            X_pixels (jnp.array): Array of x coordinate of each pixel on the xifusim images
            Y_pixels (jnp.array): Array of y coordinate of each pixel on the xifusim images 
            bin_num_pix (jnp.array): Array of the bin number of each pixel
            nb_bins (int): Number of bins
            xBar_bins (jnp.array): Arrays of the count-wieghted barycenters, x coordinate
            yBar_bins (jnp.array): Arrays of the count-wieghted barycenters, y coordinate 
            bin_nb_map (jnp.array):  Map of the bin numbers (mainly used as a sanity check)
        """

        # Initialize empty lists
        binnum = []
        X = []
        Y = []
        xBar = []
        yBar = []

        # Iterate on the number of bins
        for k in range(len(self.binning_dict)-1):

            binnum.extend([k] * len(self.binning_dict[k][0]) )
            x = self.binning_dict[k][1][0]
            y = self.binning_dict[k][1][1]
            X.extend(x)
            Y.extend(y)
            #Count weighted average for the barycenter of the bin
            xBar.append(jnp.average(jnp.array(x), weights = self.countmap[x,y]))
            yBar.append(jnp.average(jnp.array(y), weights = self.countmap[x,y]))

        # Arrays of x and y coordinate of each pixel on the xifusim images
        self.X_pixels = jnp.array(X)
        self.Y_pixels = jnp.array(Y)

        # Array of the bin number of each pixel
        self.bin_num_pix = jnp.array(binnum)

        # Number of bins
        self.nb_bins = len(jnp.unique(jnp.array(binnum)))

        # Arrays of the count-wieghted barycenters
        self.xBar_bins = jnp.array(xBar)
        self.yBar_bins = jnp.array(yBar)

        # Map of the bin numbers (mainly used as a sanity check)
        self.bin_nb_map = jnp.zeros(self.shape)
        self.bin_nb_map = self.bin_nb_map.at[self.X_pixels, self.Y_pixels].set(self.bin_num_pix)

        return self.X_pixels, self.Y_pixels, self.bin_num_pix, self.nb_bins, self.xBar_bins, self.yBar_bins, self.bin_nb_map

__call__()

Create the different quantities used from the binning

Returns:

Name Type Description
X_pixels array

Array of x coordinate of each pixel on the xifusim images
Y_pixels array

Array of y coordinate of each pixel on the xifusim images

bin_num_pix array

Array of the bin number of each pixel
nb_bins int

Number of bins
xBar_bins array

Arrays of the count-wieghted barycenters, x coordinate
yBar_bins array

Arrays of the count-wieghted barycenters, y coordinate

bin_nb_map array

Map of the bin numbers (mainly used as a sanity check)
Source code in src/binning.py 
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def __call__(self):

    """
    Create the different quantities used from the binning

    Returns:
        X_pixels (jnp.array): Array of x coordinate of each pixel on the xifusim images
        Y_pixels (jnp.array): Array of y coordinate of each pixel on the xifusim images 
        bin_num_pix (jnp.array): Array of the bin number of each pixel
        nb_bins (int): Number of bins
        xBar_bins (jnp.array): Arrays of the count-wieghted barycenters, x coordinate
        yBar_bins (jnp.array): Arrays of the count-wieghted barycenters, y coordinate 
        bin_nb_map (jnp.array):  Map of the bin numbers (mainly used as a sanity check)
    """

    # Initialize empty lists
    binnum = []
    X = []
    Y = []
    xBar = []
    yBar = []

    # Iterate on the number of bins
    for k in range(len(self.binning_dict)-1):

        binnum.extend([k] * len(self.binning_dict[k][0]) )
        x = self.binning_dict[k][1][0]
        y = self.binning_dict[k][1][1]
        X.extend(x)
        Y.extend(y)
        #Count weighted average for the barycenter of the bin
        xBar.append(jnp.average(jnp.array(x), weights = self.countmap[x,y]))
        yBar.append(jnp.average(jnp.array(y), weights = self.countmap[x,y]))

    # Arrays of x and y coordinate of each pixel on the xifusim images
    self.X_pixels = jnp.array(X)
    self.Y_pixels = jnp.array(Y)

    # Array of the bin number of each pixel
    self.bin_num_pix = jnp.array(binnum)

    # Number of bins
    self.nb_bins = len(jnp.unique(jnp.array(binnum)))

    # Arrays of the count-wieghted barycenters
    self.xBar_bins = jnp.array(xBar)
    self.yBar_bins = jnp.array(yBar)

    # Map of the bin numbers (mainly used as a sanity check)
    self.bin_nb_map = jnp.zeros(self.shape)
    self.bin_nb_map = self.bin_nb_map.at[self.X_pixels, self.Y_pixels].set(self.bin_num_pix)

    return self.X_pixels, self.Y_pixels, self.bin_num_pix, self.nb_bins, self.xBar_bins, self.yBar_bins, self.bin_nb_map

__init__(shape=(360, 360), binning_file='/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_region_200/region_files/19p_region_dict.p', count_map_file='/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_count_image.fits')

Initialize binning

Parameters:

Name Type Description Default
shape tuple

Shape of the final map to provide (should match the shape from SpatialGrid3D)

 (360, 360)
binning_file str

Path to pickle file containing binning dict

 '/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_region_200/region_files/19p_region_dict.p'
count_map_file str

Path to count map

 '/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_count_image.fits'
Source code in src/binning.py 
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def __init__(self,
             shape = (360,360),
             binning_file = '/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_region_200/region_files/19p_region_dict.p',
             count_map_file = '/xifu/home/mola/Turbu_300kpc_mosaics/repeat10_125ks/19p_count_image.fits'):
    """
    Initialize binning

    Parameters:
        shape (tuple): Shape of the final map to provide (should match the shape from SpatialGrid3D)
        binning_file (str): Path to pickle file containing binning dict
        count_map_file (str): Path to count map
    """
    self.shape = shape
    self.binning_dict, self.region_image = pickle.load(open(binning_file, 'rb'), encoding="bytes")
    self.countmap = jnp.array(fits.getdata(count_map_file), dtype = 'float32')