yt.analysis_modules.two_point_functions.two_point_functions module
Two Point Functions Framework.

class
yt.analysis_modules.two_point_functions.two_point_functions.
FcnSet
(tpf, function, min_edge, out_labels, sqrt, corr_norm)[source]
Bases: yt.analysis_modules.two_point_functions.two_point_functions.TwoPointFunctions

add_function
(function, out_labels, sqrt, corr_norm=None)
Add a function to the list that will be evaluated at the
generated pairs of points.
Parameters: 
 function (Function) – The two point function of the form fcn(a, b, r1, r2, vec).
 out_labels (List of strings) – A list of strings labeling the outputs of the function.
 sqrt (List of booleans) – A list of booleans which when True will squareroot the corresponding
element of the output in the text output (write_out_means()).
 corr_norm (Float) – Used when calculating two point correlations. If set, the output
of the function is divided by this number. Default = None.

Examples
>>> f1 = tpf.add_function(function=rms_vel, out_labels=['RMSvdiff'],
... sqrt=[True])

comm
= None

get_dependencies
(fields)

partition_index_2d
(axis)

partition_index_3d
(ds, padding=0.0, rank_ratio=1)

partition_index_3d_bisection_list
()
Returns an array that is used to drive _partition_index_3d_bisection,
below.

partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)
Given a region, it subdivides it into smaller regions for parallel
analysis.

run_generator
()
After all the functions have been added, run the generator.
Examples

set_pdf_params
(bin_type='lin', bin_number=1000, bin_range=None)[source]
Set the parameters used to build the Probability Distribution Function
for each ruler length for this function. The values output by the
function are slotted into the bins described here.
Parameters: 
 bin_type (String) – Controls the edges of the bins spaced evenly in
logarithmic or linear space, set by “log” or “lin”, respectively.
A single string, or list of strings for Ndim binning.
Default = “lin”.
 bin_number (Integer) – Sets how many bins to create, evenly spaced by the above
parameter. A single integer, or a list of integers for Ndim
binning. Default = 1000.
 bin_range (Float) – A pair of values giving the range for the bins.
A pair of floats (a list), or a list of pairs for Ndim binning.
Default = None.

Examples
>>> f1.set_pdf_params(bin_type='log', bin_range=[5e4, 5.5e13],
... bin_number=1000)

write_out_arrays
(fn='%s.h5')
Writes out the raw probability bins and the bin edges to an HDF5 file
for each of the functions. The files are named
‘function_name.txt’ and saved in the current working directory.
Examples
>>> tpf.write_out_arrays()

write_out_correlation
()
A special output function for doing two point correlation functions.
Outputs the correlation function xi(r) in a text file
‘function_name_corr.txt’ in the current working directory.
Examples
>>> tpf.write_out_correlation()

write_out_means
(fn='%s.txt')
Writes out the weightedaverage value for each function for
each dimension for each ruler length to a text file. The data is written
to files of the name ‘function_name.txt’ in the current working
directory.
Examples
>>> tpf.write_out_means()

class
yt.analysis_modules.two_point_functions.two_point_functions.
TwoPointFunctions
(ds, fields, left_edge=None, right_edge=None, total_values=1000000, comm_size=10000, length_type='lin', length_number=10, length_range=None, vol_ratio=1, salt=0, theta=None, phi=None)[source]
Bases: yt.utilities.parallel_tools.parallel_analysis_interface.ParallelAnalysisInterface
Initialize a two point functions object.
Parameters: 
 total_values (Integer) – How many total (global) pair calculations to run for each of the
functions specified. Default: 1000000.
 comm_size (Integer) – How entries are sent during communication. Default: 10000.
 length_type (String) – Controls the even spacing of the rulers lengths in
logarithmic or linear space, set by “log” or “lin”, respectively.
Default: “lin”.
 length_number (Integer) – Sets how many lengths to create, evenly spaced by the above
parameter. Default: 10.
 length_range (Float) – A min/max pair for the range of values to search the over
the simulated volume. Default: [sqrt(3)dx, 1/2*shortest box edge],
where dx is the smallest grid cell size.
 vol_ratio (Integer) – How to multiplyassign subvolumes to the parallel
tasks. This number must be an integer factor of the total number of tasks or
very bad things will happen. The default value of 1 will assign one task
to each subvolume, and there will be an equal number of subvolumes as tasks.
A value of 2 will assign two tasks to each subvolume and there will be
onehalf as many subvolumes as tasks.
A value equal to the number of parallel tasks will result in each task
owning a complete copy of all the fields data, meaning each task will be
operating on the identical full volume.
Setting it to 1 will automatically adjust it such that each task
owns the entire volume. Default = 1.
 salt (Integer) – A number that will be added to the random number generator
seed. Use this if a different random series of numbers is desired when
keeping everything else constant from this set: (MPI task count,
number of ruler lengths, ruler min/max, number of functions,
number of point pairs per ruler length). Default = 0.
 theta (Float) – For random pairs of points, the second point is found by traversing
a distance along a ray set by the angle (phi, theta) from the first
point. To keep this angle constant, set
theta to a value in the
range [0, pi]. Default = None, which will randomize theta for
every pair of points.
 phi (Float) – Similar to theta above, but the range of values is [0, 2*pi).
Default = None, which will randomize phi for every pair of points.

Examples
>>> tpf = TwoPointFunctions(ds, ["velocity_x", "velocity_y", "velocity_z"],
... total_values=1e5, comm_size=10000,
... length_number=10, length_range=[1./128, .5],
... length_type="log")

add_function
(function, out_labels, sqrt, corr_norm=None)[source]
Add a function to the list that will be evaluated at the
generated pairs of points.
Parameters: 
 function (Function) – The two point function of the form fcn(a, b, r1, r2, vec).
 out_labels (List of strings) – A list of strings labeling the outputs of the function.
 sqrt (List of booleans) – A list of booleans which when True will squareroot the corresponding
element of the output in the text output (write_out_means()).
 corr_norm (Float) – Used when calculating two point correlations. If set, the output
of the function is divided by this number. Default = None.

Examples
>>> f1 = tpf.add_function(function=rms_vel, out_labels=['RMSvdiff'],
... sqrt=[True])

comm
= None

get_dependencies
(fields)

partition_index_2d
(axis)

partition_index_3d
(ds, padding=0.0, rank_ratio=1)

partition_index_3d_bisection_list
()
Returns an array that is used to drive _partition_index_3d_bisection,
below.

partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)
Given a region, it subdivides it into smaller regions for parallel
analysis.

run_generator
()[source]
After all the functions have been added, run the generator.
Examples

write_out_arrays
(fn='%s.h5')[source]
Writes out the raw probability bins and the bin edges to an HDF5 file
for each of the functions. The files are named
‘function_name.txt’ and saved in the current working directory.
Examples
>>> tpf.write_out_arrays()

write_out_correlation
()[source]
A special output function for doing two point correlation functions.
Outputs the correlation function xi(r) in a text file
‘function_name_corr.txt’ in the current working directory.
Examples
>>> tpf.write_out_correlation()

write_out_means
(fn='%s.txt')[source]
Writes out the weightedaverage value for each function for
each dimension for each ruler length to a text file. The data is written
to files of the name ‘function_name.txt’ in the current working
directory.
Examples
>>> tpf.write_out_means()