[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

abacusnbody/data/asdf.py

@@ -65,7 +65,7 @@ def compress(self, data, **kwargs):
             shuffle = blosc.SHUFFLE
         elif shuffle == 'bitshuffle':
             shuffle = blosc.BITSHUFFLE
-        elif shuffle == None:
+        elif shuffle is None:
             shuffle = blosc.NOSHUFFLE
         else:
             raise ValueError(shuffle)

abacusnbody/data/compaso_halo_catalog.py

@@ -17,7 +17,7 @@
 from glob import glob
 from os.path import abspath, basename, dirname, isdir, isfile
 from os.path import join as pjoin
-from os.path import normpath, samefile
+from os.path import samefile
 from pathlib import PurePath
 
 # Stop astropy from trying to download time data; nodes on some clusters are not allowed to access the internet directly
@@ -167,7 +167,7 @@ def __init__(self, path, cleaned=True, subsamples=False, convert_units=True, unp
         # said `cleaned=True` or because this is a halo light cone catalog, which is already cleaned
         self.cleaned = cleaned
 
-        if halo_lc == None:
+        if halo_lc is None:
             halo_lc = self._is_path_halo_lc(path)
             if verbose and halo_lc:
                 print('Detected halo light cone catalog.')
@@ -388,14 +388,14 @@ def _setup_load_subsamples(self, load_subsamples):
         Will be returned as lists of strings in `load_AB` and `load_pidrv`.
         `unpack_subsamples` is for pipelining, to keep things in rvint.
         '''
-        if load_subsamples == False:
+        if load_subsamples is False:
             # stub
             load_AB = []
             load_pidrv = []
             unpack_subsamples = True
         else:
             # If user has not specified which subsamples, then assume user wants to load everything
-            if load_subsamples == True:
+            if load_subsamples is True:
                 load_subsamples = dict(A=True, B=True, rv=True, pid=True)
 
             if type(load_subsamples) == dict:
@@ -1221,7 +1221,7 @@ def _unpack_euler16(bin_this):
     bin_this = bin_this - cap*(EULER_TBIN*EULER_TBIN)
 
     it = (np.floor(np.sqrt(bin_this))).astype(int)
-    its = np.sum(np.isnan(it))
+    np.sum(np.isnan(it))
 
 
     ir = bin_this - it*it
@@ -1238,23 +1238,23 @@ def _unpack_euler16(bin_this):
     # and zz=1
     norm = 1.0/np.sqrt(1.0+xx*xx+yy*yy)
     zz = norm
-    yy *= norm; xx *= norm;  # These are now a unit vector
+    yy *= norm; xx *= norm  # These are now a unit vector
 
     # TODO: legacy code, rewrite
-    major[cap==0,0] = zz[cap==0]; major[cap==0,1] = yy[cap==0]; major[cap==0,2] = xx[cap==0];
-    major[cap==1,0] = zz[cap==1]; major[cap==1,1] =-yy[cap==1]; major[cap==1,2] = xx[cap==1];
-    major[cap==2,0] = zz[cap==2]; major[cap==2,1] = xx[cap==2]; major[cap==2,2] = yy[cap==2];
-    major[cap==3,0] = zz[cap==3]; major[cap==3,1] = xx[cap==3]; major[cap==3,2] =-yy[cap==3];
+    major[cap==0,0] = zz[cap==0]; major[cap==0,1] = yy[cap==0]; major[cap==0,2] = xx[cap==0]
+    major[cap==1,0] = zz[cap==1]; major[cap==1,1] =-yy[cap==1]; major[cap==1,2] = xx[cap==1]
+    major[cap==2,0] = zz[cap==2]; major[cap==2,1] = xx[cap==2]; major[cap==2,2] = yy[cap==2]
+    major[cap==3,0] = zz[cap==3]; major[cap==3,1] = xx[cap==3]; major[cap==3,2] =-yy[cap==3]
 
-    major[cap==4,1] = zz[cap==4]; major[cap==4,2] = yy[cap==4]; major[cap==4,0] = xx[cap==4];
-    major[cap==5,1] = zz[cap==5]; major[cap==5,2] =-yy[cap==5]; major[cap==5,0] = xx[cap==5];
-    major[cap==6,1] = zz[cap==6]; major[cap==6,2] = xx[cap==6]; major[cap==6,0] = yy[cap==6];
-    major[cap==7,1] = zz[cap==7]; major[cap==7,2] = xx[cap==7]; major[cap==7,0] =-yy[cap==7];
+    major[cap==4,1] = zz[cap==4]; major[cap==4,2] = yy[cap==4]; major[cap==4,0] = xx[cap==4]
+    major[cap==5,1] = zz[cap==5]; major[cap==5,2] =-yy[cap==5]; major[cap==5,0] = xx[cap==5]
+    major[cap==6,1] = zz[cap==6]; major[cap==6,2] = xx[cap==6]; major[cap==6,0] = yy[cap==6]
+    major[cap==7,1] = zz[cap==7]; major[cap==7,2] = xx[cap==7]; major[cap==7,0] =-yy[cap==7]
 
-    major[cap==8,2] = zz[cap==8]; major[cap==8,0] = yy[cap==8]; major[cap==8,1] = xx[cap==8];
-    major[cap==9,2] = zz[cap==9]; major[cap==9,0] =-yy[cap==9]; major[cap==9,1] = xx[cap==9];
-    major[cap==10,2] = zz[cap==10]; major[cap==10,0] = xx[cap==10]; major[cap==10,1] = yy[cap==10];
-    major[cap==11,2] = zz[cap==11]; major[cap==11,0] = xx[cap==11]; major[cap==11,1] =-yy[cap==11];
+    major[cap==8,2] = zz[cap==8]; major[cap==8,0] = yy[cap==8]; major[cap==8,1] = xx[cap==8]
+    major[cap==9,2] = zz[cap==9]; major[cap==9,0] =-yy[cap==9]; major[cap==9,1] = xx[cap==9]
+    major[cap==10,2] = zz[cap==10]; major[cap==10,0] = xx[cap==10]; major[cap==10,1] = yy[cap==10]
+    major[cap==11,2] = zz[cap==11]; major[cap==11,0] = xx[cap==11]; major[cap==11,1] =-yy[cap==11]
 
     # Next, we can get the minor axis
     az = (iaz+0.5)*(1.0/EULER_ABIN)*np.pi
@@ -1265,13 +1265,13 @@ def _unpack_euler16(bin_this):
     # are perpendicular.
 
     eq2 = (cap//4) == 2
-    minor[eq2,0] = xx[eq2]; minor[eq2,1] = yy[eq2];
+    minor[eq2,0] = xx[eq2]; minor[eq2,1] = yy[eq2]
     minor[eq2,2] = (minor[eq2,0]*major[eq2,0]+minor[eq2,1]*major[eq2,1])/(-major[eq2,2])
     eq4 = (cap//4) == 0
-    minor[eq4,1] = xx[eq4]; minor[eq4,2] = yy[eq4];
+    minor[eq4,1] = xx[eq4]; minor[eq4,2] = yy[eq4]
     minor[eq4,0] = (minor[eq4,1]*major[eq4,1]+minor[eq4,2]*major[eq4,2])/(-major[eq4,0])
     eq1 = (cap//4) == 1
-    minor[eq1,2] = xx[eq1]; minor[eq1,0] = yy[eq1];
+    minor[eq1,2] = xx[eq1]; minor[eq1,0] = yy[eq1]
     minor[eq1,1] = (minor[eq1,2]*major[eq1,2]+minor[eq1,0]*major[eq1,0])/(-major[eq1,1])
     minor *= (1./np.linalg.norm(minor,axis=1).reshape(N,1))
 

abacusnbody/data/pipe_asdf.py

@@ -83,7 +83,6 @@
 import gc
 import sys
 from os.path import isfile
-from os.path import join as pjoin
 from timeit import default_timer as timer
 
 import asdf

abacusnbody/hod/GRAND_HOD.py

@@ -6,7 +6,7 @@
 import numpy as np
 from astropy.io import ascii
 from astropy.table import Table
-from numba import jit, njit, types
+from numba import njit, types
 from numba.typed import Dict
 
 # import yaml
@@ -171,7 +171,7 @@ def gen_cent(pos, vel, mass, ids, multis, randoms, vdev, deltac, fenv,
                 ELG_marker += N_cen_ELG_v1(mass[i], pmax_E, Q_E, logM_cut_E_temp, sigma_E, gamma_E) * ic_E * multis[i]
             QSO_marker = ELG_marker
             if want_QSO:
-                logM_cut_Q_temp = logM_cut_Q + Ac_Q * deltac[i] + Bc_Q * fenv[i]
+                logM_cut_Q + Ac_Q * deltac[i] + Bc_Q * fenv[i]
                 QSO_marker += N_cen_QSO(mass[i], logM_cut_Q, sigma_Q) * ic_Q * multis[i]
 
             if randoms[i] <= LRG_marker:
@@ -291,7 +291,7 @@ def gen_cent(pos, vel, mass, ids, multis, randoms, vdev, deltac, fenv,
                 qso_z[j3] = pos[i,2]
                 qso_vz[j3] = vel[i,2] + alpha_c_Q * vdev[i] # velocity bias
                 # rsd only applies to the z direction
-                if rsd and origin != None:
+                if rsd and origin is not None:
                     nx = qso_x[j3] - origin[0]
                     ny = qso_y[j3] - origin[1]
                     nz = qso_z[j3] - origin[2]
@@ -554,7 +554,7 @@ def gen_sats(ppos, pvel, hvel, hmass, hid, weights, randoms, hdeltac, hfenv,
                 qso_vy[j3] = hvel[i, 1] + alpha_s_Q * (pvel[i, 1] - hvel[i, 1]) # velocity bias
                 qso_z[j3] = ppos[i, 2]
                 qso_vz[j3] = hvel[i, 2] + alpha_s_Q * (pvel[i, 2] - hvel[i, 2]) # velocity bias
-                if rsd and origin != None:
+                if rsd and origin is not None:
                     nx = qso_x[j3] - origin[0]
                     ny = qso_y[j3] - origin[1]
                     nz = qso_z[j3] - origin[2]
@@ -907,7 +907,7 @@ def gen_gal_cat(halo_data, particle_data, tracers, params, Nthread = 16,
 
     """
 
-    if not type(rsd) is bool:
+    if type(rsd) is not bool:
         raise ValueError("Error: rsd has to be a boolean")
 
     # find the halos, populate them with galaxies and write them to files
@@ -938,7 +938,7 @@ def gen_gal_cat(halo_data, particle_data, tracers, params, Nthread = 16,
             os.makedirs(outdir, exist_ok = True)
 
             # save to file
-            outdict = HOD_dict[tracer].pop('Ncent', None)
+            HOD_dict[tracer].pop('Ncent', None)
             table = Table(HOD_dict[tracer], meta = {'Ncent': Ncent, 'Gal_type': tracer, **tracers[tracer]})
             if params['chunk'] == -1:
                 ascii.write(table, outdir / (f"{tracer}s.dat"), overwrite = True, format = 'ecsv')

abacusnbody/hod/prepare_sim.py

@@ -18,10 +18,7 @@
 import numba
 import numpy as np
 import yaml
-from astropy.table import Table
-from numba import jit, njit, types
-from scipy.interpolate import NearestNDInterpolator
-from scipy.ndimage import gaussian_filter
+from numba import njit
 from scipy.spatial import cKDTree
 
 from abacusnbody.data.compaso_halo_catalog import CompaSOHaloCatalog
@@ -474,8 +471,8 @@ def prepare_slab(i, savedir, simdir, simname, z_mock, tracer_flags, MT, want_ran
     ranksr_parts = np.full(len_old, -1.0)
     ranksp_parts = np.full(len_old, -1.0)
     ranksc_parts = np.full(len_old, -1.0)
-    pos_parts = np.full((len_old, 3), -1.0)
-    vel_parts = np.full((len_old, 3), -1.0)
+    np.full((len_old, 3), -1.0)
+    np.full((len_old, 3), -1.0)
     hvel_parts = np.full((len_old, 3), -1.0)
     Mh_parts = np.full(len_old, -1.0)
     Np_parts = np.full(len_old, -1.0)
@@ -606,7 +603,7 @@ def prepare_slab(i, savedir, simdir, simname, z_mock, tracer_flags, MT, want_ran
     if os.path.exists(outfilename_halos):
         os.remove(outfilename_halos)
     newfile = h5py.File(outfilename_halos, 'w')
-    dataset = newfile.create_dataset('halos', data = halos[mask_halos])
+    newfile.create_dataset('halos', data = halos[mask_halos])
     newfile.close()
 
     # output the new particle file
@@ -636,7 +633,7 @@ def prepare_slab(i, savedir, simdir, simname, z_mock, tracer_flags, MT, want_ran
     if os.path.exists(outfilename_particles):
         os.remove(outfilename_particles)
     newfile = h5py.File(outfilename_particles, 'w')
-    dataset = newfile.create_dataset('particles', data = parts)
+    newfile.create_dataset('particles', data = parts)
     newfile.close()
 
     print("pre process particle number ", len_old, " post process particle number ", len(parts))

abacusnbody/metadata/abacussummit.py

@@ -50,7 +50,7 @@ def get_meta(simname, redshift=None):
     if 'CLASS_power_spectrum' in metadata[simname]:
         res['CLASS_power_spectrum'] = metadata[simname]['CLASS_power_spectrum']
 
-    if redshift != None:
+    if redshift is not None:
         if type(redshift) != str:
             redshift = f'z{redshift:.3f}'
         if not redshift.startswith('z'):

docs/tutorials/light_cones/run_lc_hod.py

@@ -9,7 +9,6 @@
 
 import argparse
 
-import numpy as np
 import yaml
 
 from abacusnbody.hod.abacus_hod import AbacusHOD
@@ -31,7 +30,7 @@ def main(path2config):
 
     # run the HODs (note: the first time you call the function run_hod, the script takes a bit to compile)
     newBall = AbacusHOD(sim_params, HOD_params, clustering_params)
-    mock_dict = newBall.run_hod(tracers=newBall.tracers, want_rsd=want_rsd, write_to_disk=write_to_disk, Nthread=16)
+    newBall.run_hod(tracers=newBall.tracers, want_rsd=want_rsd, write_to_disk=write_to_disk, Nthread=16)
 
 class ArgParseFormatter(argparse.RawDescriptionHelpFormatter, argparse.ArgumentDefaultsHelpFormatter):
     pass

docs/tutorials/light_cones/util.py

@@ -1,4 +1,3 @@
-import numpy as np
 from numba import jit
 
 

scripts/emulator/generate_cfs/generate_cf.py

@@ -24,7 +24,8 @@
 DEFAULT_NTHREAD = len(os.sched_getaffinity(0))  # guess based on affinity mask
 DEFAULT_OUTDIR = '.'
 
-log = lambda *args,**kwargs: print(*args,**kwargs,flush=True)
+def log(*args, **kwargs):
+    return print(*args, **kwargs, flush=True)
 
 def prepare_cat(halo_cat_path, ndens):
     '''Load and downsample the cat

scripts/hod/plot_chains.py

@@ -1,7 +1,6 @@
 import argparse
 import os
 
-import getdist
 import matplotlib.pyplot as plt
 import numpy as np
 import yaml
@@ -25,11 +24,10 @@ def main(path2config):
     ch_params = config['ch_config_params']
 
     # parameters
-    n_iter = ch_params['sampleIterations']
+    ch_params['sampleIterations']
     w_rat = ch_params['walkersRatio']
     b_iter = ch_params['burninIterations']
     par_names = fit_params.keys()
-    lab_names = par_names
     n_par = len(par_names)
 
     # what are we plotting

scripts/hod/run_emcee.py

@@ -46,7 +46,7 @@ def persistValues(self, posFile, probFile, pos, prob):
 
         probFile.write("\n".join([str(p) for p in prob]))
         probFile.write("\n")
-        probFile.flush();
+        probFile.flush()
 
     def close(self):
         self.samplesFile.close()

scripts/hod/run_hod.py

@@ -41,7 +41,7 @@ def main(path2config):
     mock_dict = newBall.run_hod(newBall.tracers, want_rsd, write_to_disk = False, Nthread = 16)
     # mock_dict = newBall.gal_reader()
     start = time.time()
-    xirppi = newBall.compute_xirppi(mock_dict, rpbins, pimax, pi_bin_size, Nthread = 32)
+    newBall.compute_xirppi(mock_dict, rpbins, pimax, pi_bin_size, Nthread = 32)
     print("Done xi, total time ", time.time() - start)
     # print(xirppi)
     # wp = newBall.compute_wp(mock_dict, rpbins, pimax, pi_bin_size)
@@ -61,7 +61,7 @@ def main(path2config):
         start = time.time()
         # ngal_dict = newBall.compute_ngal()
         # print("Done ngal, took time ", time.time() - start, ngal_dict)
-        xirppi = newBall.compute_xirppi(mock_dict, rpbins, pimax, pi_bin_size, Nthread = 32)
+        newBall.compute_xirppi(mock_dict, rpbins, pimax, pi_bin_size, Nthread = 32)
         deltat = time.time() - start
         print("Done xi, total time ", deltat)
         meantime += deltat

scripts/hod/run_lc_hod.py

@@ -33,13 +33,13 @@ def main(path2config):
     want_rsd = HOD_params['want_rsd']
     write_to_disk = HOD_params['write_to_disk']
     bin_params = clustering_params['bin_params']
-    rpbins = np.logspace(bin_params['logmin'], bin_params['logmax'], bin_params['nbins'] + 1)
-    pimax = clustering_params['pimax']
-    pi_bin_size = clustering_params['pi_bin_size']
+    np.logspace(bin_params['logmin'], bin_params['logmax'], bin_params['nbins'] + 1)
+    clustering_params['pimax']
+    clustering_params['pi_bin_size']
 
     # run the HODs
     newBall = AbacusHOD(sim_params, HOD_params, clustering_params)
-    mock_dict = newBall.run_hod(tracers=newBall.tracers, want_rsd=want_rsd, write_to_disk=write_to_disk, Nthread=16)
+    newBall.run_hod(tracers=newBall.tracers, want_rsd=want_rsd, write_to_disk=write_to_disk, Nthread=16)
 
     # can change some parameter and run again to time
     zs = [0.1]
@@ -48,7 +48,7 @@ def main(path2config):
         sim_params['z_mock'] = zs[i]
         newBall = AbacusHOD(sim_params, HOD_params, clustering_params)
         start = time.time()
-        mock_dict = newBall.run_hod(tracers=newBall.tracers, want_rsd=want_rsd, write_to_disk=False, Nthread=16)
+        newBall.run_hod(tracers=newBall.tracers, want_rsd=want_rsd, write_to_disk=False, Nthread=16)
         print("Done hod, took time ", time.time() - start)
 
 

scripts/hod/run_nested.py

@@ -7,7 +7,6 @@
 import numpy as np
 import yaml
 from dynesty import NestedSampler
-from likelihood import PowerData
 from scipy import stats
 
 from abacusnbody.hod.abacus_hod import AbacusHOD
@@ -27,7 +26,7 @@ def lnprob(p, param_mapping, param_tracer, Data, Ball):
     # pass them to the mock dictionary
     mock_dict = Ball.run_hod(Ball.tracers, Ball.want_rsd, Nthread = 64)
 
-    clustering = Ball.compute_wp(mock_dict, Ball.rpbins, Ball.pimax, Ball.pi_bin_size, Nthread = 16)
+    Ball.compute_wp(mock_dict, Ball.rpbins, Ball.pimax, Ball.pi_bin_size, Nthread = 16)
 
     lnP = Data.compute_likelihood(theory_density)
 
@@ -77,7 +76,7 @@ def main(path2config):
     nlive = dynesty_config_params['nlive']
     maxcall = dynesty_config_params['maxcall']
     method = dynesty_config_params['method']
-    bound = dynesty_config_params['bound']
+    dynesty_config_params['bound']
 
     # where to record
     prefix_chain = os.path.join(os.path.expanduser(dynesty_config_params['path2output']),

tests/test_data.py

@@ -237,7 +237,7 @@ def test_halo_lc():
     cat = CompaSOHaloCatalog(curdir / 'halo_light_cones/AbacusSummit_base_c000_ph001-abridged/z2.250/',
                              fields='all',
                              subsamples=True)
-    assert(cat.halo_lc == True)
+    assert(cat.halo_lc is True)
 
     HALO_LC_CAT = refdir / 'halo_lc_cat.asdf'
     HALO_LC_SUBSAMPLES = refdir / 'halo_lc_subsample.asdf'

tests/test_hod.py

@@ -17,7 +17,6 @@
 import h5py
 import numba
 import numpy as np
-import pytest
 import yaml
 from astropy.io import ascii
 from common import check_close
@@ -94,11 +93,11 @@ def test_hod(tmp_path, reference_mode = False):
 
         # additional parameter choices
         want_rsd = HOD_params['want_rsd']
-        write_to_disk = HOD_params['write_to_disk']
+        HOD_params['write_to_disk']
         bin_params = clustering_params['bin_params']
-        rpbins = np.logspace(bin_params['logmin'], bin_params['logmax'], bin_params['nbins'])
-        pimax = clustering_params['pimax']
-        pi_bin_size = clustering_params['pi_bin_size']
+        np.logspace(bin_params['logmin'], bin_params['logmax'], bin_params['nbins'])
+        clustering_params['pimax']
+        clustering_params['pi_bin_size']
 
         # create a new abacushod object
         newBall = AbacusHOD(sim_params, HOD_params, clustering_params)
@@ -127,7 +126,7 @@ def test_hod(tmp_path, reference_mode = False):
         config['zcv_params']['tracer_dir'] = pjoin(tmp_path, 'zcv_tracer_data')
         mock_dict = newBall.run_hod(newBall.tracers, want_rsd = config['HOD_params']['want_rsd'], write_to_disk = False, Nthread = 2)
         del mock_dict['ELG']  # drop ELG since zcv works with a single tracer currently
-        zcv_dict = newBall.apply_zcv(mock_dict, config)
+        newBall.apply_zcv(mock_dict, config)
 
 if __name__ == '__main__':
     with tempfile.TemporaryDirectory() as tmpdir: