Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

some modify to utils convert function #335

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

some modify to utils convert function #335

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
55 changes: 40 additions & 15 deletions Corrfunc/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,21 +29,26 @@ def convert_3d_counts_to_cf(ND1, ND2, NR1, NR2,
estimator='LS'):
"""
Converts raw pair counts to a correlation function.
I rewrite the code so that it take weighting into account.

Parameters
----------

ND1 : integer
Number of points in the first dataset
ND1 : integer | float
Number of points in the first dataset
Or sum of weights in the first dataset

ND2 : integer
ND2 : integer | float
Number of points in the second dataset
Or sum of weights in the second dataset

NR1 : integer
NR1 : integer | float
Number of points in the randoms for first dataset
Or sum of weights in the randoms for first dataset

NR2 : integer
NR2 : integer | float
Number of points in the randoms for second dataset
Or sum of weights in the randoms for second dataset

D1D2 : array-like, integer
Pair-counts for the cross-correlation between D1 and D2
Expand Down Expand Up @@ -124,6 +129,7 @@ def convert_3d_counts_to_cf(ND1, ND2, NR1, NR2,

import numpy as np
pair_counts = dict()
weights = dict()
fields = ['D1D2', 'D1R2', 'D2R1', 'R1R2']
arrays = [D1D2, D1R2, D2R1, R1R2]
for (field, array) in zip(fields, arrays):
Expand All @@ -133,6 +139,21 @@ def convert_3d_counts_to_cf(ND1, ND2, NR1, NR2,
except IndexError:
pair_counts[field] = array

try:
weight = array['weightavg']
if np.isclose(weight, 0.0).all():
# this happens if you call DD- function without providing the weighting.
weights[field] = np.ones_like(pair_counts[field])
else:
weights[field] = weight
except IndexError:
weights[field] = np.ones_like(pair_counts[field])

pair_weight_product = {
k: pair_counts[k]*weights[k]
for k in fields
}

nbins = len(pair_counts['D1D2'])
if (nbins != len(pair_counts['D1R2'])) or \
(nbins != len(pair_counts['D2R1'])) or \
Expand All @@ -146,10 +167,10 @@ def convert_3d_counts_to_cf(ND1, ND2, NR1, NR2,
fN2 = np.float64(NR2) / np.float64(ND2)
cf = np.zeros(nbins)
cf[:] = np.nan
cf[nonzero] = (fN1 * fN2 * pair_counts['D1D2'][nonzero] -
fN1 * pair_counts['D1R2'][nonzero] -
fN2 * pair_counts['D2R1'][nonzero] +
pair_counts['R1R2'][nonzero]) / pair_counts['R1R2'][nonzero]
cf[nonzero] = (fN1 * fN2 * pair_weight_product['D1D2'][nonzero] -
fN1 * pair_weight_product['D1R2'][nonzero] -
fN2 * pair_weight_product['D2R1'][nonzero] +
pair_weight_product['R1R2'][nonzero]) / pair_weight_product['R1R2'][nonzero]
if len(cf) != nbins:
msg = 'Bug in code. Calculated correlation function does not '\
'have the same number of bins as input arrays. Input bins '\
Expand All @@ -174,17 +195,21 @@ def convert_rp_pi_counts_to_wp(ND1, ND2, NR1, NR2,
Parameters
----------

ND1 : integer
Number of points in the first dataset
ND1 : integer | float
Number of points in the first dataset
Or sum of weights in the first dataset

ND2 : integer
ND2 : integer | float
Number of points in the second dataset
Or sum of weights in the second dataset

NR1 : integer
NR1 : integer | float
Number of points in the randoms for first dataset
Or sum of weights in the randoms for first dataset

NR2 : integer
NR2 : integer | float
Number of points in the randoms for second dataset
Or sum of weights in the randoms for second dataset

D1D2 : array-like, integer
Pair-counts for the cross-correlation between D1 and D2
Expand Down Expand Up @@ -1046,7 +1071,7 @@ def sys_pipes():
see also https://github.com/manodeep/Corrfunc/issues/157,
https://github.com/manodeep/Corrfunc/issues/269.
'''

kwargs = {}
if sys.stdout.isatty() or (sys.stdout is sys.__stdout__):
kwargs['stdout'] = None
Expand Down
51 changes: 51 additions & 0 deletions README_fork.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,51 @@
# Corrfunc forked:
I rewrite the utils.py, so that the `convert_rp_pi_counts_to_wp` and `convert_3d_counts_to_cf` works more properly. The formal call is still valid, and I added that if you provide weight in the pair-counting, it will account for the weighting.

```python
# load some catalogue...

# Code 1
wei_norm = galaxy['w'] / (galaxy['w'].mean())
wei_norm_r = random['w'] / (random['w'].mean())

dd = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=wei_norm, is_comoving_dist=True, weight_type='pair_product')
dr = DDrppi_mocks(
autocorr=False, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=wei_norm,
RA2=random['ra'], DEC2=random['dec'], CZ2=random['distance'], weights2=wei_norm_r,
is_comoving_dist=True, weight_type='pair_product')
rr = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
RA1=random['ra'], DEC1=random['dec'], CZ1=random['distance'], weights1=wei_norm_r, is_comoving_dist=True, weight_type='pair_product')

Nd = len(galaxy)
Nr = len(random)

wp_1 = convert_rp_pi_counts_to_wp(Nd, Nd, Nr, Nr, dd, dr, dr, rr, pimax=pimax, nrpbins=Nbins)


# Code 2
dd = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=galaxy['w'], is_comoving_dist=True, weight_type='pair_product')
dr = DDrppi_mocks(
autocorr=False, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=galaxy['w'],
RA2=random['ra'], DEC2=random['dec'], CZ2=random['distance'], weights2=random['w'],
is_comoving_dist=True, weight_type='pair_product')
rr = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
RA1=random['ra'], DEC1=random['dec'], CZ1=random['distance'], weights1=random['w'], is_comoving_dist=True, weight_type='pair_product')

Nd = galaxy['w'].sum()
Nr = random['w'].sum()

wp_2 = convert_rp_pi_counts_to_wp(Nd, Nd, Nr, Nr, dd, dr, dr, rr, pimax=pimax, nrpbins=Nbins)
assert np.isclose(wp_1, wp_2).all()
```
This code will work.

Note that, for simplicity, I didn't add new parameters to the function.
Instead you can
- normalize the weight first, e.g. `weight_normal = weight / weight.mean()`, and pass the parameter in the old way.
- pass the sum of weight of dataset1 to `ND1`, sum of weight of dataset2 to `ND2`, etc. This is reasonable, because if you assume no weighting(that is weight of every point is 1), then the sum of weights equals to number of points.

Both way will work.