Ruff unused variables

compas_python_utils/compas_runner.py

@@ -4,7 +4,8 @@
 import subprocess
 import sys
 from pathlib import Path
-from typing import Iterable, Optional, Sequence
+from typing import Optional
+from collections.abc import Iterable, Sequence
 
 
 PACKAGE_ROOT = Path(__file__).resolve().parent

compas_python_utils/cosmic_integration/ClassCOMPAS.py

@@ -16,7 +16,7 @@
     ) from exc
 
 
-class COMPASData(object):
+class COMPASData:
     def __init__(
         self,
         path=None,
@@ -31,7 +31,7 @@ def __init__(
         if self.path is None:
             print("Template COMPASData object created with no data path")
         elif not os.path.isfile(path):
-            raise ValueError( "h5 file not found. Wrong path given? {}".format(path))
+            raise ValueError( f"h5 file not found. Wrong path given? {path}")
 
         # Crucial values to be able to calculate MSSFR
         self.metallicityGrid = None

compas_python_utils/cosmic_integration/ClassCosmicIntegrator.py

@@ -8,7 +8,7 @@
 from . import ClassMSSFR
 from .cosmology import get_cosmology
 import selection_effects
-class CosmicIntegrator(object):
+class CosmicIntegrator:
     """
 
     The cosmological integrator calculates the rate 

compas_python_utils/cosmic_integration/ClassMSSFR.py

@@ -8,7 +8,7 @@
 
 
 
-class MSSFR(object):
+class MSSFR:
     """
     This class is to calculate the metallicity specific star formation
     rate in a specific metallicity bin.
@@ -416,7 +416,7 @@ def Ma2015ZM(self, ZZSun, z):
 
     def Langer2005ZM(self, ZZsun, z):
         Mstar  =  7.64*10**10
-        logM   =  np.log10((((ZZsun*(10**(0.3*z)))**2)*Mstar)) 
+        logM   =  np.log10(((ZZsun*(10**(0.3*z)))**2)*Mstar) 
         return logM
 
     def Langer2005OffsetZM(self, ZZsun, z):
@@ -500,7 +500,7 @@ def SFR_Strolger(self, tGyrs):
 
         SFR = 10**9 *a * (tGyrs**b * np.exp(-tGyrs/c) + d*np.exp(d*(tGyrs-t0)/c))
         if np.isnan(SFR.any()):
-            raise ValueError("Nan in SFR calculation for %s" %(self.SFRprescription))
+            raise ValueError(f"Nan in SFR calculation for {self.SFRprescription}")
         return SFR #Msun yr-1 Gpc-3 in comoving volume
 
     def SFR_Neijssel(self, z):

compas_python_utils/cosmic_integration/FastCosmicIntegration.py

@@ -250,7 +250,7 @@ def compute_snr_and_detection_grids(dco_type, sensitivity="O1", snr_threshold=8.
     """
     # If DCO type includes a WD, return empty arrays since we currently only support LVK sensitivity
     if dco_type in ["WDWD", "NSWD", "WDBH"]:
-        warnings.warn("!! Detected rate is not computed since DCO type {} doesn't work with LVK sensitivity {}".format(dco_type, sensitivity))
+        warnings.warn(f"!! Detected rate is not computed since DCO type {dco_type} doesn't work with LVK sensitivity {sensitivity}")
 
     # get interpolator given sensitivity
     interpolator = selection_effects.SNRinterpolator(sensitivity)
@@ -419,7 +419,7 @@ def find_detection_rate(path, dco_type="BHBH", merger_output_filename=None, weig
 
 
     for arg, arg_str in nonnegative_args:
-        assert arg >= 0.0, "{} must be nonnegative".format(arg_str)
+        assert arg >= 0.0, f"{arg_str} must be nonnegative"
 
     # warn if input is not advisable
     if redshift_step > max_redshift_detection:
@@ -570,7 +570,7 @@ def append_rates(path, detection_rate, formation_rate, merger_rate, redshifts, C
 
         #################################################
         # Create a new group where we will store data
-        new_rate_group = 'Rates_mu0{}_muz{}_alpha{}_sigma0{}_sigmaz{}'.format(mu0, muz, alpha, sigma0, sigmaz)
+        new_rate_group = f'Rates_mu0{mu0}_muz{muz}_alpha{alpha}_sigma0{sigma0}_sigmaz{sigmaz}'
         if append_binned_by_z:
             new_rate_group  = new_rate_group + '_zBinned'
 
@@ -656,11 +656,11 @@ def append_rates(path, detection_rate, formation_rate, merger_rate, redshifts, C
             if rate_list_names[i] in h_new[new_rate_group].keys():
                 del h_new[new_rate_group][rate_list_names[i]]
             # write rates as a new data set
-            dataNew     = h_new[new_rate_group].create_dataset(rate_list_names[i], data=data)
+            h_new[new_rate_group].create_dataset(rate_list_names[i], data=data)
 
     #Always close your files again ;)
     h_new.close()
-    print(('Done with append_rates :) your new files are here: {}'.format(path)))
+    print(f'Done with append_rates :) your new files are here: {path}')
 
 
 
@@ -684,12 +684,8 @@ def delete_rates(path, mu0=0.035, muz=-0.23, sigma0=0.39, sigmaz=0., alpha=0., a
     #Open hdf5 file that we will write on
     print('pathToData', path)
     with h5.File(path, 'r+') as h_new:
-        # The rate info is shaped as BSE_Double_Compact_Objects[COMPAS.DCOmask] , len(redshifts)
-        DCO             = h_new['BSE_Double_Compact_Objects']#
-
-        #################################################
         # Name of the group that has the data stored
-        new_rate_group = 'Rates_mu0{}_muz{}_alpha{}_sigma0{}_sigmaz{}'.format(mu0, muz, alpha, sigma0, sigmaz)
+        new_rate_group = f'Rates_mu0{mu0}_muz{muz}_alpha{alpha}_sigma0{sigma0}_sigmaz{sigmaz}'
         if append_binned_by_z:
             new_rate_group  = new_rate_group + '_zBinned'
 
@@ -699,7 +695,7 @@ def delete_rates(path, mu0=0.035, muz=-0.23, sigma0=0.39, sigmaz=0., alpha=0., a
             h_new.close()
             return
         else:
-            print('You want to remove this group, %s, from the hdf5 file, removing now..'%(new_rate_group))
+            print(f'You want to remove this group, {new_rate_group}, from the hdf5 file, removing now..')
             del h_new[new_rate_group]
             #Always close your files again ;)
             h_new.close()
@@ -777,13 +773,13 @@ def plot_rates(save_dir, formation_rate, merger_rate, detection_rate, redshifts,
     #Plotvalues
 
     # Add text upper left corner
-    axes[0,0].text(0.05, 0.8, "mu0=%s \nmuz=%s \nsigma0=%s \nsigmaz=%s \nalpha=%s"%(mu0,muz,sigma0,sigmaz,alpha), transform=axes[0,0].transAxes, size = fs) 
+    axes[0,0].text(0.05, 0.8, f"mu0={mu0} \nmuz={muz} \nsigma0={sigma0} \nsigmaz={sigmaz} \nalpha={alpha}", transform=axes[0,0].transAxes, size = fs) 
 
     for ax in axes.flatten():
         ax.tick_params(labelsize=0.9*fs)
 
     # Save and show :)
-    plt.savefig(save_dir +'Rate_Info'+"mu0%s_muz%s_alpha%s_sigma0%s_sigmaz%s"%(mu0,muz,alpha,sigma0, sigmaz)+'.png', bbox_inches='tight') 
+    plt.savefig(save_dir +'Rate_Info'+f"mu0{mu0}_muz{muz}_alpha{alpha}_sigma0{sigma0}_sigmaz{sigmaz}"+'.png', bbox_inches='tight') 
     if show_plot:
         plt.show()
     else:
@@ -863,7 +859,7 @@ def parse_cli_args():
 def set_cosmology(cosmology_name="Planck18"):
     # Set cosmology using astropy, print a warning if TNG fit is used with Planck18 cosmology (since TNG uses Planck15)
     if cosmology_name == "Planck18": print("USING PLANCK18 AS COSMOLOGY! If working with TNG fit, you may want to use Planck15 instead for self-consistency.")
-    else: print("Using %s as cosmology!"%cosmology_name)
+    else: print(f"Using {cosmology_name} as cosmology!")
 
     return getattr(importlib.import_module('astropy.cosmology'), cosmology_name)
 

compas_python_utils/cosmic_integration/binned_cosmic_integrator/binary_population.py

@@ -1,7 +1,7 @@
 import numpy as np
 import h5py
 from .gpu_utils import xp
-from typing import List, Optional
+from typing import Optional
 
 from ..totalMassEvolvedPerZ import (
     analytical_star_forming_mass_per_binary_using_kroupa_imf,
@@ -57,7 +57,7 @@ def __init__(
             t_delay: np.ndarray,
             z_zams: np.ndarray,
             n_systems: int,
-            dcos_included: List[str],
+            dcos_included: list[str],
             m1_min: float = None,
             m1_max: float = None,
             m2_min: float = None,
@@ -94,7 +94,7 @@ def __init__(
     def from_compas_h5(
             cls,
             path: str,
-            dcos_included: List[str] = ["BBH"],
+            dcos_included: list[str] = ["BBH"],
             m1_min: float = None,
             m1_max: float = None,
             m2_min: float = None,
@@ -148,7 +148,7 @@ def from_compas_h5(
     @staticmethod
     def _generate_mask(
             path: str,
-            dcos_included: List[str],
+            dcos_included: list[str],
     ) -> xp.ndarray:
         type_mask = _generate_dco_mask(path, dcos_included)
 
@@ -258,7 +258,7 @@ def __str__(self):
 
 def _generate_dco_mask(
         compas_path: str,
-        dcos_included: List[str]
+        dcos_included: list[str]
 ) -> xp.ndarray:
     # Load fundamental DCO variables
     t1, t2 = _load_data(
@@ -283,7 +283,7 @@ def _generate_dco_mask(
     return type_mask
 
 
-def _load_data(path: str, group: str, var_names: List[str], mask: Optional[xp.ndarray] = None):
+def _load_data(path: str, group: str, var_names: list[str], mask: Optional[xp.ndarray] = None):
     with h5py.File(path, "r") as f:
         data = [f[group][v][...].squeeze().flatten() for v in var_names]
     if mask is not None:

compas_python_utils/cosmic_integration/binned_cosmic_integrator/detection_matrix.py

@@ -1,6 +1,5 @@
 import numpy as np
 import os
-from typing import Dict, List
 import h5py as h5
 from tqdm.auto import trange
 
@@ -18,15 +17,15 @@ class DetectionMatrix:
     def __init__(
             self,
             compas_path: str,
-            cosmological_parameters: Dict,
+            cosmological_parameters: dict,
             rate_matrix: np.ndarray,
             chirp_mass_bins: np.array,
             redshift_bins: np.array,
             n_systems: int,
             n_dcos: int,
             outdir: str = None,
             sens: str = 'O1',
-            dcos_included: List[str] = ["BBH"],
+            dcos_included: list[str] = ["BBH"],
             bootstrapped_rate_matrices: np.ndarray = None
     ):
         self.compas_path = compas_path
@@ -57,15 +56,15 @@ def outdir(self, outdir):
     def from_compas_output(
             cls,
             compas_path: str,
-            cosmological_parameters: Dict = dict(aSF=0.01, dSF=4.70, mu_z=-.23, sigma_z=0),
+            cosmological_parameters: dict = dict(aSF=0.01, dSF=4.70, mu_z=-.23, sigma_z=0),
             max_detectable_redshift: float = 1.0,
             chirp_mass_bins: int = None,
             redshift_bins: int = None,
             outdir: str = None,
             save_plots: bool = False,
             n_bootstrapped_matrices: int = 0,
             sens: str = 'O1',
-            dcos_included: List[str] = ["BBH"],
+            dcos_included: list[str] = ["BBH"],
     ) -> "DetectionMatrix":
 
         dco_population = BinaryPopulation.from_compas_h5(compas_path, dcos_included=dcos_included)
@@ -126,7 +125,7 @@ def save(self):
         with h5.File(f"{self.outdir}/{self.label}.h5", "w") as f:
             recursively_save_dict_contents_to_group(f, '/', self.to_dict())
 
-    def to_dict(self) -> Dict:
+    def to_dict(self) -> dict:
         return dict(
             compas_path=self.compas_path,
             cosmological_parameters=self.cosmological_parameters,

compas_python_utils/cosmic_integration/binned_cosmic_integrator/io.py

@@ -1,5 +1,4 @@
 import h5py
-from typing import Dict
 import numpy as np
 
 
@@ -15,7 +14,7 @@ def recursively_load_dict_contents_from_group(h5file: h5py.File, group: str):
     return output
 
 
-def recursively_save_dict_contents_to_group(h5file: h5py.File, group: str, dic: Dict):
+def recursively_save_dict_contents_to_group(h5file: h5py.File, group: str, dic: dict):
     for key, item in dic.items():
         item = encode_for_hdf5(key, item)
         if isinstance(item, dict):

compas_python_utils/cosmic_integration/binned_cosmic_integrator/plotting.py

@@ -1,7 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from corner import corner
-from typing import List
 import warnings
 from .conversions import m1_m2_to_eta_chirp_mass
 
@@ -127,7 +126,7 @@ def plot_sfr_and_metallicity(
         p_draw_metallicity: np.array,
         metallicity_label: str,
         sf_label: str,
-        redshift_range: List, logZ_range: List,
+        redshift_range: list, logZ_range: list,
 ) -> plt.Figure:
     fig, axes = plt.subplots(3, 1, figsize=(5, 8))
     ax = axes[0]
@@ -220,7 +219,7 @@ def plot_snr_grid(
 
 
 def plot_binary_population(
-        data: np.ndarray, params: List[str]
+        data: np.ndarray, params: list[str]
 ) -> plt.Figure:
     n_sys = len(data)
     # mask out the outliers

compas_python_utils/cosmic_integration/generate_frame_file.py

@@ -8,7 +8,7 @@ def multiple_injections (path="/Users/ilyam/Work/COMPASresults/popsynth/Arash/",
                          filename="mergers.txt", dz=0.001, Tobs=1./365.25/24/60, T0=1234567):
     random.seed()
     #path="./"
-    input=open(path+filename, 'r')
+    input=open(path+filename)
     input.readline()
     input.readline()
     count=0
@@ -59,8 +59,8 @@ def one_injection (m1, m2, z, distance, T0, Tobs):
                               waveform_generator=waveform_generator)
 
 
-    for interferometer in interferometers:
-        signal = interferometer.get_detector_response(waveform_generator.frequency_domain_strain(), injection_parameters)
+    # for interferometer in interferometers:
+        # signal = interferometer.get_detector_response(waveform_generator.frequency_domain_strain(), injection_parameters)
         #interferometer.plot_data(signal=signal, outdir=path, label='DCO)
 
 

compas_python_utils/cosmic_integration/selection_effects.py

@@ -1,13 +1,11 @@
 #! /usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created for Python 3
 
 @author: Sebastian M. Gaebel
 @email: sgaebel@star.sr.bham.ac.uk
 """
 
-from __future__ import division, print_function
 import h5py
 import numpy as np
 import os
@@ -155,11 +153,11 @@ def __init__(self, first_arg, second_arg, mode='scipy'):
         """
         if isinstance(first_arg, str) and isinstance(second_arg, str):
             if not os.path.isfile(first_arg):
-                raise FileNotFoundError('HDF5 file expected: %r' % first_arg)
+                raise FileNotFoundError(f'HDF5 file expected: {first_arg!r}')
             with h5py.File(first_arg, 'r') as hdf:
                 mass_axis = hdf['mass_axis'][...]
                 if second_arg not in hdf['snr_values']:
-                    raise ValueError('Group %r not found.' % second_arg)
+                    raise ValueError(f'Group {second_arg!r} not found.')
                 snr_grid = hdf['snr_values'][second_arg][...]
         else:
             mass_axis = first_arg
@@ -174,7 +172,7 @@ def __init__(self, first_arg, second_arg, mode='scipy'):
             self.mass_axis = np.log(mass_axis)
             self.snr_grid = snr_grid
         else:
-            raise ValueError('Invalid mode: %r' % mode)
+            raise ValueError(f'Invalid mode: {mode!r}')
 
     def __call__(self, m1, m2):
         """

compas_python_utils/cosmic_integration/totalMassEvolvedPerZ.py

@@ -4,7 +4,7 @@
 import h5py as h5
 import functools
 
-@functools.lru_cache()
+@functools.lru_cache
 def __get_imf_normalisation_values(m1=0.01, m2=0.08, m3=0.5, m4=200.0, a12=0.3, a23=1.3, a34=2.3):
     b1 = 1 / (
             (m2 ** (1 - a12) - m1 ** (1 - a12)) / (1 - a12)

compas_python_utils/debugging_utils.py

@@ -517,7 +517,7 @@ def _remap_stype(int_stype):
             # event type: CEE, RLOF 2->1, RLOF 1->2
             char_m = '&' if is_mrg else '=' if is_cee else '<' if is_rl2 else '>'
         # event string for this star, _ is event separator
-        event_str += "{}{}{}_".format(char_l, char_m, char_r)
+        event_str += f"{char_l}{char_m}{char_r}_"
     # return event string for this star (pop the last underscore first)
     event_str = np.array(event_str[:-1], dtype=np.str_)
     return event_str