flip.gridding

flip.gridding#

Attributes#

log

_GRID_KIND

Functions#

_compute_grid_window(grid_size, k, order, n)

The _compute_grid_window function computes the window function for a given grid size.

compute_grid_window(grid_size, kh[, kind, n])

The compute_grid_window function computes the window function for a given grid size and kind.

construct_grid_regular_sphere(grid_size, rcom_max)

The construct_grid_regular_sphere function constructs a regular spherical grid.

construct_grid_regular_rectangular(grid_size, rcom_max)

The construct_grid_regular_rectangular function constructs a regular rectangular grid.

ngp_weight(ds)

Nearest Grid Point.

ngp_errw_weight(ds)

Nearest Grid Point with Weighted error.

cic_weight(ds)

Cloud In Cell.

tsc_weight(ds)

Triangular Shaped Cloud.

pcs_weight(ds)

Triangular Shaped Cloud.

attribute_weight_density(grid_size, xobj, yobj, zobj, ...)

The attribute_weight_density function takes in the grid size, xobj, yobj, zobj (the coordinates of the objects),

define_randoms(random_method, xobj, yobj, zobj, raobj, ...)

grid_data_density(grid, grid_size, ra, dec, rcom[, ...])

The grid_data_density function takes in the data and grids it using a given grid size.

cut_grid(grid[, remove_nan_density, ...])

The cut_grid function is used to remove grid cells from the catalog.

grid_data_density_pypower(raobj, decobj, rcomobj, ...)

The grid_data_density_pypower function takes in the ra, dec, and rcom values of a galaxy catalog

grid_data_velocity_pypower(raobj, decobj, rcomobj, ...)

The grid_data_velocity_pypower function takes in the ra, dec, rcom, variance, and velocity values of a velocity catalog

Module Contents#

flip.gridding.log#
flip.gridding._GRID_KIND = ['ngp', 'ngp_errw', 'cic', 'tsc', 'pcs']#
flip.gridding._compute_grid_window(grid_size, k, order, n)[source]#

The _compute_grid_window function computes the window function for a given grid size.

Parameters:

  • grid_size – Determine the size of the grid

  • k – Compute the window function

  • order – Determine the order of the sinc function

  • n – Determine the number of points in the grid

Returns:

The window function for a given grid size, k, order and n

flip.gridding.compute_grid_window(grid_size, kh, kind='ngp', n=1000)[source]#

The compute_grid_window function computes the window function for a given grid size and kind.

Parameters:

  • grid_size – Define the size of the grid

  • kh – Compute the window function

  • kind – Specify the type of grid

  • n – Define the number of points used to compute the window function

Returns:

A window function

flip.gridding.construct_grid_regular_sphere(grid_size, rcom_max)[source]#

The construct_grid_regular_sphere function constructs a regular spherical grid.

Parameters:

  • grid_size – Determine the number of grid voxels per axis

  • rcom_max – Cut the grid with rcom_max

Return type:

A dictionary with the following keys

flip.gridding.construct_grid_regular_rectangular(grid_size, rcom_max)[source]#

The construct_grid_regular_rectangular function constructs a regular rectangular grid.

Parameters:

  • grid_size – Determine the number of grid voxels per axis

  • rcom_max – Determine the size of the grid

Returns:

A dictionary with the grid coordinates

flip.gridding.ngp_weight(ds)[source]#

Nearest Grid Point.

flip.gridding.ngp_errw_weight(ds)[source]#

Nearest Grid Point with Weighted error.

flip.gridding.cic_weight(ds)[source]#

Cloud In Cell.

flip.gridding.tsc_weight(ds)[source]#

Triangular Shaped Cloud.

flip.gridding.pcs_weight(ds)[source]#

Triangular Shaped Cloud.

flip.gridding.attribute_weight_density(grid_size, xobj, yobj, zobj, xgrid, ygrid, zgrid, weight_fun)[source]#

The attribute_weight_density function takes in the grid size, xobj, yobj, zobj (the coordinates of the objects), xgrid, ygrid and zgrid (the coordinates of the grid) and a weight function. It then calculates how many objects are in each cell by using a for loop to iterate through all of them. The dX is calculated by taking the difference between each object’s x coordinate and each cell’s x coordinate divided by grid_size. This is done for both dY and dZ as well. The w variable is calculated using this formula: w = weight_fun(dX)

Parameters:

  • grid_size – Normalize the distances between objects and grid points

  • xobj – Store the x coordinates of all objects in a galaxy

  • yobj – Create a grid of y values

  • zobj – Calculate the z-component of the distance between a grid point and an object

  • xgrid – Define the x-coordinate of each grid cell

  • ygrid – Calculate the distance between the object and grid

  • zgrid – Determine the z-coordinate of the grid cell

  • weight_fun – Determine the weight of each object

Returns:

A tuple of three arrays

flip.gridding.define_randoms(random_method, xobj, yobj, zobj, raobj, decobj, rcomobj, Nrandom=None, coord_randoms=None, max_coordinates=None)[source]#
flip.gridding.grid_data_density(grid, grid_size, ra, dec, rcom, kind='ngp', n_cut=None, weight_min=None, verbose=False, compute_density=True, Nrandom=10, random_method='cartesian', coord_randoms=None)[source]#

The grid_data_density function takes in the data and grids it using a given grid size. It also computes the density of each voxel, as well as its error.

Parameters:

  • grid – Store the grid properties

  • grid_size – Define the size of each voxel in mpc/h

  • ra – Define the right ascension of the object

  • dec – Compute the cartesian coordinates of the objects

  • rcom – Compute the comoving distance of each object

  • kind – Select the method used to compute voxcell values

  • n_cut – Cut the grid in cells with a minimum number of objects

  • weight_min – Cut the grid

  • verbose – Print the number of cells in the grid

  • compute_density – Compute the density field

  • Nrandom – Create random points in the grid_data_density function

  • random_method – Choose the method to create random points

:param : Define the grid size

Return type:

A dictionary with the following keys

flip.gridding.cut_grid(grid, remove_nan_density=True, remove_empty_cells=False, n_cut=None, weight_min=None, rcom_max=None, xmax=None, ymax=None, zmax=None, remove_origin=False)[source]#

The cut_grid function is used to remove grid cells from the catalog.

Parameters:

  • grid – Pass the grid data to the function

  • remove_nan_density – Remove any cells that have a density of nan

  • n_cut – Remove grid cells with less than n_cut stars

  • weight_min – Remove cells with too few stars

  • rcom_max – Cut the grid by a maximum comoving distance

  • xmax – Remove the cells that are too far away from the center of mass

  • ymax – Cut the grid in y direction

  • zmax – Cut the grid in z direction

  • remove_origin – Remove the origin of the grid

Returns:

A dictionary with the same keys as grid, but where

flip.gridding.grid_data_density_pypower(raobj, decobj, rcomobj, rcom_max, grid_size, grid_type, kind, Nrandom=10, random_method='cartesian', interlacing=2, compensate=False, coord_randoms=None, min_count_random=0, overhead=20)[source]#

The grid_data_density_pypower function takes in the ra, dec, and rcom values of a galaxy catalog and returns a grid of density contrast values. The function uses pypower to create the grid. The function also has options for creating random points using different methods: choice, healpix, or cartesian.

Parameters:

  • raobj – Pass the ra values of the data

  • decobj – Calculate the z coordinate of the object in cartesian coordinates

  • rcomobj – Calculate the comoving distance of each galaxy

  • rcom_max – Cut the grid in a sphere of radius rcom_max

  • grid_size – Determine the size of each cell in the grid

  • grid_type – Determine whether to use a rectangular or spherical grid

  • kind – Set the resampler in the catalogmesh function

  • Nrandom – Determine the number of random points to be generated

  • random_method – Choose the method used to generate random points

  • interlacing – Reduce the variance of the density field

  • compensate – Correct for the fact that we are using a finite number of random points

:param : Remove the nan values from the grid

Returns:

A dictionary with the grid coordinates and density contrast values

flip.gridding.grid_data_velocity_pypower(raobj, decobj, rcomobj, rcom_max, variance, velocity, grid_size, grid_type, kind, interlacing=0, compensate=False, overhead=20)[source]#

The grid_data_velocity_pypower function takes in the ra, dec, rcom, variance, and velocity values of a velocity catalog and returns a grid of variance and velocities. The function uses pypower to create the grid.

Parameters:

  • raobj – Pass the ra values of the data

  • decobj – Calculate the z coordinate of the object in cartesian coordinates

  • rcomobj – Calculate the comoving distance of each galaxy

  • rcom_max – Cut the grid in a sphere of radius rcom_max

  • variance – variance of the data points

  • velocity – veloctity of the data

  • grid_size – Determine the size of each cell in the grid

  • grid_type – Determine whether to use a rectangular or spherical grid

  • kind – Set the resampler in the catalogmesh function

  • Nrandom – Determine the number of random points to be generated

  • random_method – Choose the method used to generate random points

  • interlacing – Reduce the variance of the density field

  • compensate – Correct for the fact that we are using a finite number of random points

:param : Remove the nan values from the grid

Returns:

A dictionary with the grid coordinates and density contrast values

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