def test_get_pairwise_ksz():
testPath = '/'.join((catalogTools.__file__).split('/')[:-2]) + '/tests/'
testParamFileFullPath = os.path.join(testPath, 'data_toTestAPI/params.ini')
params = paramTools.params(testParamFileFullPath)
df = produceFakeCatalog()
rsep, p_uk = pairwiser.get_pairwise_ksz(df, params, multithreading=False)
tzav = pairwiser.get_tzav(df.dT.values, df.z.values, params.SIGMA_Z)
if not params.UNEVEN_BINS:
rsep0, p_uk0 = pairwiser.pairwise_ksz(df.Dc.values,
df.ra.values,
df.dec.values,
tzav,
df.dT.values,
params.BIN_SIZE_MPC,
params.N_BINS,
multithreading=False)
else:
rsep0, p_uk0 = pairwiser.pairwise_ksz_uneven_bins(df.Dc.values,
df.ra.values,
df.dec.values,
tzav,
df.dT.values,
params.BIN_EDGES,
multithreading=False)
rsep_diff_sq = np.sum((rsep - rsep0)**2)
p_uk_diff_sq = np.sum((p_uk - p_uk0)**2)
assert rsep_diff_sq < 1e-10
assert p_uk_diff_sq < 1e-10
def test_openConfigFile():
pars = paramTools.params(testConfigFileFullPath)
assert type(pars.CAT_FNAME) is str
assert type(pars.CAT_QUERY) is str
assert type(pars.FITS_FNAME) is str
assert type(pars.N_OBJ) is int
assert type(pars.NAME) is str
assert type(pars.PHOTODISKR) is float
assert type(pars.PHOTORINGR) is float
assert type(pars.REPROJECT) is bool
assert type(pars.REPIXELIZE) is bool
assert type(pars.JK_NGROUPS) is int
assert type(pars.N_BINS) is int
assert type(pars.BIN_SIZE_MPC) is float
assert type(pars.SIGMA_Z) is float
def test_varianceWeighted():
'''Tests variance_weighted_pairwise_ksz and
variance_weighted_pairwise_one_row'''
testPath = '/'.join((catalogTools.__file__).split('/')[:-2]) + '/tests/'
testParamFileFullPath = os.path.join(testPath, 'data_toTestAPI/params.ini')
params = paramTools.params(testParamFileFullPath)
df = produceFakeCatalog()
rsep, p_uk = pairwiser.get_pairwise_ksz(df, params, multithreading=False)
tzav = pairwiser.get_tzav(df.dT.values, df.z.values, params.SIGMA_Z)
div = np.ones(len(tzav))
rsep0, p_uk0 = pairwiser.variance_weighted_pairwise_ksz(
df.Dc.values,
df.ra.values, # noqa
df.dec.values,
tzav,
df.dT.values,
div,
params.BIN_SIZE_MPC,
params.N_BINS,
multithreading=False)
chisq = np.sum((p_uk - p_uk0)**2)
assert chisq < 1.0e10
#!/nfs/user/pag227/miniconda/bin/python
import glob
import pandas as pd
import numpy as np
from iskay import wiggly_tee_tools
from iskay import paramTools
import sys
sigma_z = 0.01
N_in_sigma = 20
gaussian_or_square = 'gaussian_conventional'
mean_or_median = 'mean'
assert len(sys.argv) == 2 # usage: correction paramfile.ini
p = paramTools.params(sys.argv[1])
filter_galaxies_by = p.CAT_QUERY # noqa
def wiggly_tee_correct(sigma_z, N_in_sigma,
gaussian_or_square,
mean_or_median,
filter_galaxies_by):
fnames = glob.glob('ApPhotoResults/*.csv')
fnames.sort()
df = pd.concat([pd.read_csv(fname) for fname in fnames])
df.query(filter_galaxies_by, inplace=True)
# remmeber to edit this later
df['ra_rad'] = np.deg2rad(df.ra.values)
df['dec_rad'] = np.deg2rad(df.dec.values)
submap = submapTools.getSubmap_originalPixelization(
theMap, ra, dec, 4 * photoringR_arcmin / 60.) # noqa
stamp = extractStamp(submap,
ra,
dec,
submapSemiWidthR_arcmin,
repixelize=params.REPIXELIZE,
reprojection=params.REPROJECT)
dset[j, :, :] = np.array(stamp)
if verbose:
bar.update(j + 1)
if verbose:
bar.finish()
f.close()
if howMany is not None:
print "Processing only %i objects, use howMany=None full cat" % howMany
params = paramTools.params('params.ini')
mapfnames = {'submaps': params.FITS_FNAME, 'divmaps': params.DIVMAP_FNAME}
theMap = mapTools.openMap_remote(fname=mapfnames[dsetName])
df = catalogTools.cat(fname=params.CAT_FNAME, howMany=howMany).df
writeSubapsToFile(theMap,
df,
params.PHOTORINGR,
params,
dsetName=dsetName,
verbose=True)
from iskay import paramTools
from iskay import catalogTools
import matplotlib.pyplot as plt
from iskay import pairwiser
import numpy as np
import scipy.stats
fnames = [
"params_disjoint_bin_lum_gt_04p3_and_06p1_jk.ini",
"params_disjoint_bin_lum_gt_06p1_and_07p9_jk.ini",
"params_lum_gt_07p9_jk.ini"
]
for j in range(len(fnames)):
fname = fnames[j]
p = paramTools.params(fname)
df = catalogTools.preProcessedCat(howMany=None, query=p.CAT_QUERY).df
dT = df.dT.values
z = df.z.values
tzav = pairwiser.get_tzav_fast(dT, z, p.SIGMA_Z)
dT_ksz = dT - tzav
mean, std = np.mean(dT_ksz), np.std(dT_ksz)
plt.figure(figsize=[8, 4.5])
plt.hist(dT_ksz,
normed=True,
histtype='step',
color='black',
lw=2,
def produceFakeParams():
testPath = '/'.join((catalogTools.__file__).split('/')[:-2]) + '/tests/'
testParamFileFullPath = os.path.join(testPath, 'data_toTestAPI/params.ini')
params = paramTools.params(testParamFileFullPath)
return params
#!/nfs/user/pag227/miniconda/bin/python
from iskay import paramTools
import glob
p = paramTools.params('./params.ini')
TITLE = p.NAME
s1 = []
s1.append('# %s\n\n' % p.NAME)
s1.append("**fits_fname** = %s \n\n" % p.FITS_FNAME)
s1.append("**divmap_fname** = %s \n\n" % p.DIVMAP_FNAME)
s1.append("**query** = %s\n\n" % p.CAT_QUERY)
s1.append("# Curves\n")
s1.append("\n")
s1.append("\n")
s1.append("# Covariance Matrices\n")
def get_files(token):
'''Find files under folder token'''
s = []
fnames = glob.glob("./%s/%s_lum*.png" % (token, p.NAME.replace('.', 'p')))
s.append("## Joint bins\n")
for fname in fnames:
fname = fname.split("/")[-1]
def howManyEntries():
'''Opens the catalog pointed to in params.ini and returns its length
are there.'''
params = paramTools.params('params.ini')
df = catalogTools.cat(fname=params.CAT_FNAME).df
return len(df)
#! /nfs/user/pag227/miniconda/bin/python
''' pairwsise kSZ analysis script example for iskay.
Usage: iskay_analysis.py param.ini
Written by: P. Gallardo.
'''
from iskay import paramTools
from iskay import catalogTools
from iskay import JK
import sys
param_fname = sys.argv[1]
params = paramTools.params(param_fname)
df = catalogTools.preProcessedCat(howMany=None,
query=params.CAT_QUERY).df
jk = JK.JK(df, params, distributed=True)
#!/nfs/user/pag227/miniconda/bin/python
'''Usage:
arguments: ngroups, N_chnk, r_max
N_chunk starts in 1 to make it compatible to sge.
N_groups: in how many chunks to split the computation
N_chunk: what chunk to compute'''
import sys
from iskay import pairwise_and_save_data as pw_save
from iskay import paramTools
import pandas as pd
assert len(sys.argv) == 4
N_groups, N_chunk = int(sys.argv[1]), int(sys.argv[2]) - 1
params = paramTools.params(sys.argv[3])
df = pd.read_csv('wtee_corrected_decrements_%s.csv' % params.NAME)
pw_save.compute_one_pairwise_chunk_saving_everything_to_lnx1032(df, N_groups,
N_chunk, params.BIN_EDGES, MAX_NPAIRS=100000) # noqa