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@rmjarvis
rmjarvis committed Jan 4, 2024
1 parent 7e04a30 commit 37f39a9
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17 changes: 9 additions & 8 deletions treecorr/corr3base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,19 +68,20 @@ class Corr3(object):
-0.6 < v < -0.2 in addition to those with 0.2 < v < 0.6.
2. The triangle can be defined by two of the sides and the angle between them (i.e. SAS
congruence). The two sides are called d2 and d3, and the angle between them is called
phi.
congruence). The vertex point between the two sides is considered point "1" (P1), so
the two sides (opposite points 2 and 3) are called d2 and d3. The angle between them is
called phi, and it is measured in radians.
The orientation is defined such that 0 <= phi <= pi is the angle sweeping from d2 to d3
counter-clockwise.
Unlike the SSS definition where every triangle is uniquely placed in a single bin, this
definition forms a triangle with each object at the central vertex, so for auto-correlations,
each triangle is placed in bins three times. For cross-correlations, the order of the
points is such that objects in the first catalog are at the central vertex, objects in
the second catalog are at the end of line segment d3, and objects in the third catalog
are at the end of d2 (using the normal notation that side number 2 is opposite vertex 2
in the triangle, likewise for side 3 and vertex 3).
definition forms a triangle with each object at the central vertex, P1, so for
auto-correlations, each triangle is placed in bins three times. For cross-correlations,
the order of the points is such that objects in the first catalog are at the central
vertex, P1, objects in the second catalog are at P2, which is opposite d2 (i.e.
at the end of line segment d3 from P1), and objects in the third catalog are at P3,
opposite d3 (i.e. at the end of d2 from P1).
The constructor for all derived classes take a config dict as the first argument,
since this is often how we keep track of parameters, but if you don't want to
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113 changes: 74 additions & 39 deletions treecorr/gggcorrelation.py
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Expand Up @@ -56,32 +56,32 @@ class GGGCorrelation(Corr3):
Ojects of this class holds the following attributes:
Attributes:
nbins: The number of bins in logr where r = d2
bin_size: The size of the bins in logr
min_sep: The minimum separation being considered
max_sep: The maximum separation being considered
nbins: The number of bins in logr where r = d2.
bin_size: The size of the bins in logr.
min_sep: The minimum separation being considered.
max_sep: The maximum separation being considered.
logr1d: The nominal centers of the nbins bins in log(r).
If the bin_type is LogSAS, then it will have these attributes:
Attributes:
nphi_bins: The number of bins in v where v = +-(d1-d2)/d3
phi_bin_size: The size of the bins in v
min_phi: The minimum v being considered
max_phi: The maximum v being considered
phi1d: The nominal centers of the nvbins bins in v.
nphi_bins: The number of bins in phi.
phi_bin_size: The size of the bins in phi.
min_phi: The minimum phi being considered.
max_phi: The maximum phi being considered.
phi1d: The nominal centers of the nphi_bins bins in phi.
If the bin_type is LogRUV, then it will have these attributes:
Attributes:
nubins: The number of bins in u where u = d3/d2
ubin_size: The size of the bins in u
min_u: The minimum u being considered
max_u: The maximum u being considered
nvbins: The number of bins in v where v = +-(d1-d2)/d3
vbin_size: The size of the bins in v
min_v: The minimum v being considered
max_v: The maximum v being considered
nubins: The number of bins in u where u = d3/d2.
ubin_size: The size of the bins in u.
min_u: The minimum u being considered.
max_u: The maximum u being considered.
nvbins: The number of bins in v where v = +-(d1-d2)/d3.
vbin_size: The size of the bins in v.
min_v: The minimum v being considered.
max_v: The maximum v being considered.
u1d: The nominal centers of the nubins bins in u.
v1d: The nominal centers of the nvbins bins in v.
Expand Down Expand Up @@ -446,7 +446,7 @@ def finalize(self, varg1, varg2, varg3):
The `process_auto`, `process_cross12` and `process_cross` commands accumulate values in
each bin, so they can be called multiple times if appropriate. Afterwards, this command
finishes the calculation of meanlogr, meanu, meanv by dividing by the total weight.
finishes the calculation by dividing by the total weight.
Parameters:
varg1 (float): The variance per component of the first shear field.
Expand Down Expand Up @@ -567,10 +567,10 @@ def process(self, cat1, cat2=None, cat3=None, *, metric=None, ordered=False, num
- If 3 arguments are given, then compute a three-way cross-correlation function.
For cross correlations, the default behavior is to allow the three triangle vertices
(v1, v2, v3) to come from any of the three (or two) catalogs. However, if you want to
(P1, P2, P3) to come from any of the three (or two) catalogs. However, if you want to
keep track of the order of the catalogs, you can set ``ordered=True``, which will fix
v1 to come from ``cat1``, v2 from ``cat2`` and v3 from ``cat3``. The sides d1, d2, d3
are taken to be opposite v1, v2, v3 respectively.
P1 to come from ``cat1``, P2 from ``cat2`` and P3 from ``cat3``. The sides d1, d2, d3
are taken to be opposite P1, P2, P3 respectively.
All arguments may be lists, in which case all items in the list are used
for that element of the correlation.
Expand Down Expand Up @@ -669,30 +669,65 @@ def write(self, file_name, *, file_type=None, precision=None, write_patch_result
centroid as the projection point. There are 4 complex-valued natural components, so there
are 8 columns in the output file.
The output file will include the following columns:
For bin_type = LogRUV, the output file will include the following columns:
========== =============================================================
========== ================================================================
Column Description
========== =============================================================
========== ================================================================
r_nom The nominal center of the bin in r = d2 where d1 > d2 > d3
u_nom The nominal center of the bin in u = d3/d2
v_nom The nominal center of the bin in v = +-(d1-d2)/d3
meand1 The mean value :math:`\langle d1\rangle` of triangles that
meand1 The mean value :math:`\langle d1\rangle` of triangles that fell
into each bin
meanlogd1 The mean value :math:`\langle \log(d1)\rangle` of triangles that
fell into each bin
meand2 The mean value :math:`\langle d2\rangle` of triangles that fell
into each bin
meanlogd2 The mean value :math:`\langle \log(d2)\rangle` of triangles that
fell into each bin
meanlogd1 The mean value :math:`\langle \log(d1)\rangle` of triangles
that fell into each bin
meand2 The mean value :math:`\langle d2\rangle` of triangles that
meand3 The mean value :math:`\langle d3\rangle` of triangles that fell
into each bin
meanlogd3 The mean value :math:`\langle \log(d3)\rangle` of triangles that
fell into each bin
meanlogd2 The mean value :math:`\langle \log(d2)\rangle` of triangles
that fell into each bin
meand3 The mean value :math:`\langle d3\rangle` of triangles that
meanu The mean value :math:`\langle u\rangle` of triangles that fell
into each bin
meanv The mean value :math:`\langle v\rangle` of triangles that fell
into each bin
gam0r The real part of the estimator of :math:`\Gamma_0(r,u,v)`
gam0i The imag part of the estimator of :math:`\Gamma_0(r,u,v)`
gam1r The real part of the estimator of :math:`\Gamma_1(r,u,v)`
gam1i The imag part of the estimator of :math:`\Gamma_1(r,u,v)`
gam2r The real part of the estimator of :math:`\Gamma_2(r,u,v)`
gam2i The imag part of the estimator of :math:`\Gamma_2(r,u,v)`
gam3r The real part of the estimator of :math:`\Gamma_3(r,u,v)`
gam3i The imag part of the estimator of :math:`\Gamma_3(r,u,v)`
sigma_gam0 The sqrt of the variance estimate of :math:`\Gamma_0`
sigma_gam1 The sqrt of the variance estimate of :math:`\Gamma_1`
sigma_gam2 The sqrt of the variance estimate of :math:`\Gamma_2`
sigma_gam3 The sqrt of the variance estimate of :math:`\Gamma_3`
weight The total weight of triangles contributing to each bin
ntri The number of triangles contributing to each bin
========== ================================================================
For bin_type = LogSAS, the output file will include the following columns:
========== ================================================================
Column Description
========== ================================================================
d2_nom The nominal center of the bin in d2
d3_nom The nominal center of the bin in d3
phi_nom The nominal center of the bin in phi, the opening angle between
d2 and d3 in the counter-clockwise direction
meand2 The mean value :math:`\langle d2\rangle` of triangles that fell
into each bin
meanlogd2 The mean value :math:`\langle \log(d2)\rangle` of triangles that
fell into each bin
meanlogd3 The mean value :math:`\langle \log(d3)\rangle` of triangles
that fell into each bin
meanu The mean value :math:`\langle u\rangle` of triangles that
meand3 The mean value :math:`\langle d3\rangle` of triangles that fell
into each bin
meanlogd3 The mean value :math:`\langle \log(d3)\rangle` of triangles that
fell into each bin
meanv The mean value :math:`\langle v\rangle` of triangles that
fell into each bi.
meanphi The mean value :math:`\langle phi\rangle` of triangles that fell
into each bin
gam0r The real part of the estimator of :math:`\Gamma_0(r,u,v)`
gam0i The imag part of the estimator of :math:`\Gamma_0(r,u,v)`
gam1r The real part of the estimator of :math:`\Gamma_1(r,u,v)`
Expand All @@ -705,9 +740,9 @@ def write(self, file_name, *, file_type=None, precision=None, write_patch_result
sigma_gam1 The sqrt of the variance estimate of :math:`\Gamma_1`
sigma_gam2 The sqrt of the variance estimate of :math:`\Gamma_2`
sigma_gam3 The sqrt of the variance estimate of :math:`\Gamma_3`
weight The total weight of triangles contributing to each bin.
ntri The number of triangles contributing to each bin.
========== =============================================================
weight The total weight of triangles contributing to each bin
ntri The number of triangles contributing to each bin
========== ================================================================
If ``sep_units`` was given at construction, then the distances will all be in these units.
Otherwise, they will be in either the same units as x,y,z (for flat or 3d coordinates) or
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