apply 'black' coding style (stricter subset of PEP8)

- supply consistent flake8 settings in setup.cfg

examples/MCMC_examples/fully_coherent_search_using_MCMC.py

@@ -13,38 +13,45 @@ F1 = -1e-10
 F2 = 0
 Alpha = np.radians(83.6292)
 Delta = np.radians(22.0144)
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 10
 h0 = sqrtSX / depth
-label = 'fully_coherent_search_using_MCMC'
-outdir = 'data'
+label = "fully_coherent_search_using_MCMC"
+outdir = "data"
 
 data = pyfstat.Writer(
-    label=label, outdir=outdir, tref=tref,
-    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
-    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label=label,
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 # The predicted twoF, given by lalapps_predictFstat can be accessed by
 twoF = data.predict_fstat()
-print('Predicted twoF value: {}\n'.format(twoF))
+print("Predicted twoF value: {}\n".format(twoF))
 
 DeltaF0 = 1e-7
 DeltaF1 = 1e-13
 VF0 = (np.pi * duration * DeltaF0) ** 2 / 3.0
-VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
+VF1 = (np.pi * duration ** 2 * DeltaF1) ** 2 * 4 / 45.0
+print("\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n".format(VF0 * VF1, VF0, VF1))
 
-theta_prior = {'F0': {'type': 'unif',
-                      'lower': F0-DeltaF0/2.,
-                      'upper': F0+DeltaF0/2.},
-               'F1': {'type': 'unif',
-                      'lower': F1-DeltaF1/2.,
-                      'upper': F1+DeltaF1/2.},
-               'F2': F2,
-               'Alpha': Alpha,
-               'Delta': Delta
+theta_prior = {
+    "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2.0},
+    "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2.0},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
 }
 
 ntemps = 2
@@ -53,13 +60,22 @@ nwalkers = 100
 nsteps = [300, 300]
 
 mcmc = pyfstat.MCMCSearch(
-    label=label, outdir=outdir,
-    sftfilepattern='{}/*{}*sft'.format(outdir, label), theta_prior=theta_prior,
-    tref=tref, minStartTime=tstart, maxStartTime=tend, nsteps=nsteps,
-    nwalkers=nwalkers, ntemps=ntemps, log10beta_min=log10beta_min)
+    label=label,
+    outdir=outdir,
+    sftfilepattern="{}/*{}*sft".format(outdir, label),
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tend,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
+    log10beta_min=log10beta_min,
+)
 mcmc.transform_dictionary = dict(
-    F0=dict(subtractor=F0, symbol='$f-f^\mathrm{s}$'),
-    F1=dict(subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$'))
+    F0=dict(subtractor=F0, symbol="$f-f^\mathrm{s}$"),
+    F1=dict(subtractor=F1, symbol="$\dot{f}-\dot{f}^\mathrm{s}$"),
+)
 mcmc.run()
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/MCMC_examples/semi_coherent_search_using_MCMC.py

@@ -13,38 +13,45 @@ F1 = -1e-10
 F2 = 0
 Alpha = np.radians(83.6292)
 Delta = np.radians(22.0144)
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 10
 h0 = sqrtSX / depth
-label = 'semicoherent_search_using_MCMC'
-outdir = 'data'
+label = "semicoherent_search_using_MCMC"
+outdir = "data"
 
 data = pyfstat.Writer(
-    label=label, outdir=outdir, tref=tref,
-    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
-    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label=label,
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 # The predicted twoF, given by lalapps_predictFstat can be accessed by
 twoF = data.predict_fstat()
-print('Predicted twoF value: {}\n'.format(twoF))
+print("Predicted twoF value: {}\n".format(twoF))
 
 DeltaF0 = 1e-7
 DeltaF1 = 1e-13
 VF0 = (np.pi * duration * DeltaF0) ** 2 / 3.0
-VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
+VF1 = (np.pi * duration ** 2 * DeltaF1) ** 2 * 4 / 45.0
+print("\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n".format(VF0 * VF1, VF0, VF1))
 
-theta_prior = {'F0': {'type': 'unif',
-                      'lower': F0-DeltaF0/2.,
-                      'upper': F0+DeltaF0/2.},
-               'F1': {'type': 'unif',
-                      'lower': F1-DeltaF1/2.,
-                      'upper': F1+DeltaF1/2.},
-               'F2': F2,
-               'Alpha': Alpha,
-               'Delta': Delta
+theta_prior = {
+    "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2.0},
+    "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2.0},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
 }
 
 ntemps = 1
@@ -53,14 +60,23 @@ nwalkers = 100
 nsteps = [300, 300]
 
 mcmc = pyfstat.MCMCSemiCoherentSearch(
-    label=label, outdir=outdir, nsegs=10,
-    sftfilepattern='{}/*{}*sft'.format(outdir, label),
-    theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
-    nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
-    log10beta_min=log10beta_min)
+    label=label,
+    outdir=outdir,
+    nsegs=10,
+    sftfilepattern="{}/*{}*sft".format(outdir, label),
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tend,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
+    log10beta_min=log10beta_min,
+)
 mcmc.transform_dictionary = dict(
-    F0=dict(subtractor=F0, symbol='$f-f^\mathrm{s}$'),
-    F1=dict(subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$'))
+    F0=dict(subtractor=F0, symbol="$f-f^\mathrm{s}$"),
+    F1=dict(subtractor=F1, symbol="$\dot{f}-\dot{f}^\mathrm{s}$"),
+)
 mcmc.run()
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/followup_examples/semi_coherent_directed_follow_up.py

@@ -13,36 +13,44 @@ sqrtSX = 1e-23
 tstart = 1000000000
 duration = 100 * 86400
 tend = tstart + duration
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 40
-label = 'semicoherent_directed_follow_up'
-outdir = 'data'
+label = "semicoherent_directed_follow_up"
+outdir = "data"
 
 h0 = sqrtSX / depth
 
 data = pyfstat.Writer(
-    label=label, outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
-    F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label=label,
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 # The predicted twoF, given by lalapps_predictFstat can be accessed by
 twoF = data.predict_fstat()
-print('Predicted twoF value: {}\n'.format(twoF))
+print("Predicted twoF value: {}\n".format(twoF))
 
 # Search
 VF0 = VF1 = 1e5
 DeltaF0 = np.sqrt(VF0) * np.sqrt(3) / (np.pi * duration)
 DeltaF1 = np.sqrt(VF1) * np.sqrt(180) / (np.pi * duration ** 2)
-theta_prior = {'F0': {'type': 'unif',
-                      'lower': F0-DeltaF0/2.,
-                      'upper': F0+DeltaF0/2},
-               'F1': {'type': 'unif',
-                      'lower': F1-DeltaF1/2.,
-                      'upper': F1+DeltaF1/2},
-               'F2': F2,
-               'Alpha': Alpha,
-               'Delta': Delta
+theta_prior = {
+    "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2},
+    "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
 }
 
 ntemps = 3
@@ -51,23 +59,35 @@ nwalkers = 100
 nsteps = [100, 100]
 
 mcmc = pyfstat.MCMCFollowUpSearch(
-    label=label, outdir=outdir,
-    sftfilepattern='{}/*{}*sft'.format(outdir, label),
-    theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
-    nwalkers=nwalkers, nsteps=nsteps, ntemps=ntemps,
-    log10beta_min=log10beta_min)
+    label=label,
+    outdir=outdir,
+    sftfilepattern="{}/*{}*sft".format(outdir, label),
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tend,
+    nwalkers=nwalkers,
+    nsteps=nsteps,
+    ntemps=ntemps,
+    log10beta_min=log10beta_min,
+)
 
 NstarMax = 1000
 Nsegs0 = 100
 fig, axes = plt.subplots(nrows=2, figsize=(3.4, 3.5))
 fig, axes = mcmc.run(
-    NstarMax=NstarMax, Nsegs0=Nsegs0, labelpad=0.01,
-    plot_det_stat=False, return_fig=True, fig=fig,
-    axes=axes)
+    NstarMax=NstarMax,
+    Nsegs0=Nsegs0,
+    labelpad=0.01,
+    plot_det_stat=False,
+    return_fig=True,
+    fig=fig,
+    axes=axes,
+)
 for ax in axes:
     ax.grid()
     ax.set_xticks(np.arange(0, 600, 100))
     ax.set_xticklabels([str(s) for s in np.arange(0, 700, 100)])
-axes[-1].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.1)
+axes[-1].set_xlabel(r"$\textrm{Number of steps}$", labelpad=0.1)
 fig.tight_layout()
-fig.savefig('{}/{}_walkers.png'.format(mcmc.outdir, mcmc.label), dpi=400)
+fig.savefig("{}/{}_walkers.png".format(mcmc.outdir, mcmc.label), dpi=400)

examples/glitch_examples/make_simulated_data.py

 from pyfstat import Writer, GlitchWriter
 import numpy as np
 
-outdir = 'data'
+outdir = "data"
 # First, we generate data with a reasonably strong smooth signal
 
 # Define parameters of the Crab pulsar as an example
@@ -22,8 +22,19 @@ tend = tstart+duration
 tref = tstart + 0.5 * duration
 
 data = Writer(
-    label='0_glitch', outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
-    F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label="0_glitch",
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 # Next, taking the same signal parameters, we include a glitch half way through
@@ -32,9 +43,22 @@ delta_F0 = 5e-6
 delta_F1 = 0
 
 glitch_data = GlitchWriter(
-    label='1_glitch', outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
-    F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX,
-    dtglitch=dtglitch, delta_F0=delta_F0, delta_F1=delta_F1)
+    label="1_glitch",
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+    dtglitch=dtglitch,
+    delta_F0=delta_F0,
+    delta_F1=delta_F1,
+)
 glitch_data.make_data()
 
 # Making data with two glitches
@@ -46,8 +70,22 @@ delta_F1_2 = [0, 0]
 delta_F2_2 = [0, 0]
 
 two_glitch_data = GlitchWriter(
-    label='2_glitch', outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
-    F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX,
-    dtglitch=dtglitch_2, delta_phi=delta_phi_2, delta_F0=delta_F0_2,
-    delta_F1=delta_F1_2, delta_F2=delta_F2_2)
+    label="2_glitch",
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+    dtglitch=dtglitch_2,
+    delta_phi=delta_phi_2,
+    delta_F0=delta_F0_2,
+    delta_F1=delta_F1_2,
+    delta_F2=delta_F2_2,
+)
 two_glitch_data.make_data()

examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py

@@ -3,33 +3,41 @@ import matplotlib.pyplot as plt
 import pyfstat
 import gridcorner
 import time
-from make_simulated_data import tstart, duration, tref, F0, F1, F2, Alpha, Delta, delta_F0, dtglitch, outdir
+from make_simulated_data import (
+    tstart,
+    duration,
+    tref,
+    F0,
+    F1,
+    F2,
+    Alpha,
+    Delta,
+    delta_F0,
+    dtglitch,
+    outdir,
+)
 
-plt.style.use('./paper.mplstyle')
+plt.style.use("./paper.mplstyle")
 
-label = 'semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch'
+label = "semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch"
 
 Nstar = 1000
 F0_width = np.sqrt(Nstar) * np.sqrt(12) / (np.pi * duration)
 F1_width = np.sqrt(Nstar) * np.sqrt(180) / (np.pi * duration ** 2)
 
 theta_prior = {
-    'F0': {'type': 'unif',
-           'lower': F0-F0_width/2.,
-           'upper': F0+F0_width/2.},
-    'F1': {'type': 'unif',
-           'lower': F1-F1_width/2.,
-           'upper': F1+F1_width/2.},
-    'F2': F2,
-    'delta_F0': {'type': 'unif',
-                 'lower': 0,
-                 'upper': 1e-5},
-    'delta_F1': 0,
-    'tglitch': {'type': 'unif',
-                'lower': tstart+0.1*duration,
-                'upper': tstart+0.9*duration},
-    'Alpha': Alpha,
-    'Delta': Delta,
+    "F0": {"type": "unif", "lower": F0 - F0_width / 2.0, "upper": F0 + F0_width / 2.0},
+    "F1": {"type": "unif", "lower": F1 - F1_width / 2.0, "upper": F1 + F1_width / 2.0},
+    "F2": F2,
+    "delta_F0": {"type": "unif", "lower": 0, "upper": 1e-5},
+    "delta_F1": 0,
+    "tglitch": {
+        "type": "unif",
+        "lower": tstart + 0.1 * duration,
+        "upper": tstart + 0.9 * duration,
+    },
+    "Alpha": Alpha,
+    "Delta": Delta,
 }
 
 ntemps = 3
@@ -38,33 +46,49 @@ nwalkers = 100
 nsteps = [250, 250]
 
 mcmc = pyfstat.MCMCGlitchSearch(
-    label=label, sftfilepattern='data/*1_glitch*sft', theta_prior=theta_prior,
-    tref=tref, minStartTime=tstart, maxStartTime=tstart+duration,
-    nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
-    log10beta_min=log10beta_min, nglitch=1)
-mcmc.transform_dictionary['F0'] = dict(
-    subtractor=F0, multiplier=1e6, symbol='$f-f_\mathrm{s}$')
-mcmc.unit_dictionary['F0'] = '$\mu$Hz'
-mcmc.transform_dictionary['F1'] = dict(
-    subtractor=F1, multiplier=1e12, symbol='$\dot{f}-\dot{f}_\mathrm{s}$')
-mcmc.unit_dictionary['F1'] = '$p$Hz/s'
-mcmc.transform_dictionary['delta_F0'] = dict(
-    multiplier=1e6, subtractor=delta_F0,
-    symbol='$\delta f-\delta f_\mathrm{s}$')
-mcmc.unit_dictionary['delta_F0'] = '$\mu$Hz/s'
-mcmc.transform_dictionary['tglitch']['subtractor'] = tstart + dtglitch
-mcmc.transform_dictionary['tglitch']['label'] ='$t^\mathrm{g}-t^\mathrm{g}_\mathrm{s}$\n[d]'
+    label=label,
+    sftfilepattern="data/*1_glitch*sft",
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tstart + duration,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
+    log10beta_min=log10beta_min,
+    nglitch=1,
+)
+mcmc.transform_dictionary["F0"] = dict(
+    subtractor=F0, multiplier=1e6, symbol="$f-f_\mathrm{s}$"
+)
+mcmc.unit_dictionary["F0"] = "$\mu$Hz"
+mcmc.transform_dictionary["F1"] = dict(
+    subtractor=F1, multiplier=1e12, symbol="$\dot{f}-\dot{f}_\mathrm{s}$"
+)
+mcmc.unit_dictionary["F1"] = "$p$Hz/s"
+mcmc.transform_dictionary["delta_F0"] = dict(
+    multiplier=1e6, subtractor=delta_F0, symbol="$\delta f-\delta f_\mathrm{s}$"
+)
+mcmc.unit_dictionary["delta_F0"] = "$\mu$Hz/s"
+mcmc.transform_dictionary["tglitch"]["subtractor"] = tstart + dtglitch
+mcmc.transform_dictionary["tglitch"][
+    "label"
+] = "$t^\mathrm{g}-t^\mathrm{g}_\mathrm{s}$\n[d]"
 
 t1 = time.time()
 mcmc.run()
 dT = time.time() - t1
 fig_and_axes = gridcorner._get_fig_and_axes(4, 2, 0.05)
-mcmc.plot_corner(label_offset=0.25, truths=[0, 0, 0, 0],
-                 fig_and_axes=fig_and_axes, quantiles=(0.16, 0.84),
+mcmc.plot_corner(
+    label_offset=0.25,
+    truths=[0, 0, 0, 0],
+    fig_and_axes=fig_and_axes,
+    quantiles=(0.16, 0.84),
     hist_kwargs=dict(lw=1.5, zorder=-1),
-                 truth_color='C3')
+    truth_color="C3",
+)
 
 mcmc.print_summary()
 
-print(('Prior widths =', F0_width, F1_width))
+print(("Prior widths =", F0_width, F1_width))
 print(("Actual run time = {}".format(dT)))

examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py

 import pyfstat
 import numpy as np
 import matplotlib.pyplot as plt
-from make_simulated_data import tstart, duration, tref, F0, F1, F2, Alpha, Delta, delta_F0, outdir, dtglitch
+from make_simulated_data import (
+    tstart,
+    duration,
+    tref,
+    F0,
+    F1,
+    F2,
+    Alpha,
+    Delta,
+    delta_F0,
+    outdir,
+    dtglitch,
+)
 import time
 
 try:
@@ -9,18 +21,19 @@ try:
 except ImportError:
     raise ImportError(
         "Python module 'gridcorner' not found, please install from "
-        "https://gitlab.aei.uni-hannover.de/GregAshton/gridcorner")
+        "https://gitlab.aei.uni-hannover.de/GregAshton/gridcorner"
+    )
 
-label = 'semicoherent_glitch_robust_directed_grid_search_on_1_glitch'
+label = "semicoherent_glitch_robust_directed_grid_search_on_1_glitch"
 
-plt.style.use('./paper.mplstyle')
+plt.style.use("./paper.mplstyle")
 
 Nstar = 1000
 F0_width = np.sqrt(Nstar) * np.sqrt(12) / (np.pi * duration)
 F1_width = np.sqrt(Nstar) * np.sqrt(180) / (np.pi * duration ** 2)
 N = 20
-F0s = [F0-F0_width/2., F0+F0_width/2., F0_width/N]
-F1s = [F1-F1_width/2., F1+F1_width/2., F1_width/N]
+F0s = [F0 - F0_width / 2.0, F0 + F0_width / 2.0, F0_width / N]
+F1s = [F1 - F1_width / 2.0, F1 + F1_width / 2.0, F1_width / N]
 F2s = [F2]
 Alphas = [Alpha]
 Deltas = [Delta]
@@ -33,10 +46,21 @@ delta_F1s = [0]
 
 t1 = time.time()
 search = pyfstat.GridGlitchSearch(
-    label, outdir, 'data/*1_glitch*sft', F0s=F0s, F1s=F1s, F2s=F2s,
-    Alphas=Alphas, Deltas=Deltas, tref=tref, minStartTime=tstart,
-    maxStartTime=tstart+duration, tglitchs=tglitchs, delta_F0s=delta_F0s,
-    delta_F1s=delta_F1s)
+    label,
+    outdir,
+    "data/*1_glitch*sft",
+    F0s=F0s,
+    F1s=F1s,
+    F2s=F2s,
+    Alphas=Alphas,
+    Deltas=Deltas,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tstart + duration,
+    tglitchs=tglitchs,
+    delta_F0s=delta_F0s,
+    delta_F1s=delta_F1s,
+)
 search.run()
 dT = time.time() - t1
 
@@ -44,24 +68,32 @@ F0_vals = np.unique(search.data[:, 0]) - F0
 F1_vals = np.unique(search.data[:, 1]) - F1
 delta_F0s_vals = np.unique(search.data[:, 5]) - delta_F0
 tglitch_vals = np.unique(search.data[:, 7])
-tglitch_vals_days = (tglitch_vals-tstart) / 86400. - dtglitch/86400.
+tglitch_vals_days = (tglitch_vals - tstart) / 86400.0 - dtglitch / 86400.0
 
-twoF = search.data[:, -1].reshape((len(F0_vals), len(F1_vals),
-                                   len(delta_F0s_vals), len(tglitch_vals)))
+twoF = search.data[:, -1].reshape(
+    (len(F0_vals), len(F1_vals), len(delta_F0s_vals), len(tglitch_vals))
+)
 xyz = [F0_vals * 1e6, F1_vals * 1e12, delta_F0s_vals * 1e6, tglitch_vals_days]
-labels = ['$f - f_\mathrm{s}$\n[$\mu$Hz]',
-          '$\dot{f} - \dot{f}_\mathrm{s}$\n[$p$Hz/s]',
-          '$\delta f-\delta f_\mathrm{s}$\n[$\mu$Hz]',
-          '$t^\mathrm{g} - t^\mathrm{g}_\mathrm{s}$\n[d]',
-          '$\widehat{2\mathcal{F}}$']
+labels = [
+    "$f - f_\mathrm{s}$\n[$\mu$Hz]",
+    "$\dot{f} - \dot{f}_\mathrm{s}$\n[$p$Hz/s]",
+    "$\delta f-\delta f_\mathrm{s}$\n[$\mu$Hz]",
+    "$t^\mathrm{g} - t^\mathrm{g}_\mathrm{s}$\n[d]",
+    "$\widehat{2\mathcal{F}}$",
+]
 fig, axes = gridcorner(
-    twoF, xyz, projection='log_mean', labels=labels,
-    showDvals=False, lines=[0, 0, 0, 0], label_offset=0.25,
-    max_n_ticks=4)
-fig.savefig('{}/{}_projection_matrix.png'.format(outdir, label),
-            bbox_inches='tight')
+    twoF,
+    xyz,
+    projection="log_mean",
+    labels=labels,
+    showDvals=False,
+    lines=[0, 0, 0, 0],
+    label_offset=0.25,
+    max_n_ticks=4,
+)
+fig.savefig("{}/{}_projection_matrix.png".format(outdir, label), bbox_inches="tight")
 
 
-print(('Prior widths =', F0_width, F1_width))
+print(("Prior widths =", F0_width, F1_width))
 print(("Actual run time = {}".format(dT)))
 print(("Actual number of grid points = {}".format(search.data.shape[0])))

examples/glitch_examples/standard_directed_MCMC_search_on_1_glitch.py

@@ -3,24 +3,20 @@ import matplotlib.pyplot as plt
 import pyfstat
 from make_simulated_data import tstart, duration, tref, F0, F1, F2, Alpha, Delta, outdir
 
-plt.style.use('paper')
+plt.style.use("paper")
 
-label = 'standard_directed_MCMC_search_on_1_glitch'
+label = "standard_directed_MCMC_search_on_1_glitch"
 
 Nstar = 10000
 F0_width = np.sqrt(Nstar) * np.sqrt(12) / (np.pi * duration)
 F1_width = np.sqrt(Nstar) * np.sqrt(180) / (np.pi * duration ** 2)
 
 theta_prior = {
-    'F0': {'type': 'unif',
-           'lower': F0-F0_width/2.,
-           'upper': F0+F0_width/2.},
-    'F1': {'type': 'unif',
-           'lower': F1-F1_width/2.,
-           'upper': F1+F1_width/2.},
-    'F2': F2,
-    'Alpha': Alpha,
-    'Delta': Delta,
+    "F0": {"type": "unif", "lower": F0 - F0_width / 2.0, "upper": F0 + F0_width / 2.0},
+    "F1": {"type": "unif", "lower": F1 - F1_width / 2.0, "upper": F1 + F1_width / 2.0},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
 }
 
 ntemps = 2
@@ -29,15 +25,22 @@ nwalkers = 100
 nsteps = [500, 2000]
 
 mcmc = pyfstat.MCMCSearch(
-    label=label, sftfilepattern='data/*1_glitch*sft', theta_prior=theta_prior,
-    tref=tref, minStartTime=tstart, maxStartTime=tstart+duration,
-    nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
-    log10beta_min=log10beta_min)
-
-mcmc.transform_dictionary['F0'] = dict(
-    subtractor=F0, symbol='$f-f^\mathrm{s}$')
-mcmc.transform_dictionary['F1'] = dict(
-    subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$')
+    label=label,
+    sftfilepattern="data/*1_glitch*sft",
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tstart + duration,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
+    log10beta_min=log10beta_min,
+)
+
+mcmc.transform_dictionary["F0"] = dict(subtractor=F0, symbol="$f-f^\mathrm{s}$")
+mcmc.transform_dictionary["F1"] = dict(
+    subtractor=F1, symbol="$\dot{f}-\dot{f}^\mathrm{s}$"
+)
 
 mcmc.run()
 mcmc.plot_corner()

examples/grid_examples/grid_F0F1F2.py

@@ -7,7 +7,8 @@ try:
 except ImportError:
     raise ImportError(
         "Python module 'gridcorner' not found, please install from "
-        "https://gitlab.aei.uni-hannover.de/GregAshton/gridcorner")
+        "https://gitlab.aei.uni-hannover.de/GregAshton/gridcorner"
+    )
 
 F0 = 30.0
 F1 = 1e-10
@@ -20,18 +21,28 @@ sqrtSX = 1e-23
 tstart = 1000000000
 duration = 10 * 86400
 tend = tstart + duration
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 20
-label = 'grid_F0F1F2'
-outdir = 'data'
+label = "grid_F0F1F2"
+outdir = "data"
 
 h0 = sqrtSX / depth
 
 data = pyfstat.Writer(
-    label=label, outdir=outdir, tref=tref,
-    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
-    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label=label,
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 m = 0.01
@@ -42,14 +53,24 @@ N = 100
 DeltaF0 = N * dF0
 DeltaF1 = N * dF1
 DeltaF2 = N * dF2
-F0s = [F0-DeltaF0/2., F0+DeltaF0/2., dF0]
-F1s = [F1-DeltaF1/2., F1+DeltaF1/2., dF1]
-F2s = [F2-DeltaF2/2., F2+DeltaF2/2., dF2]
+F0s = [F0 - DeltaF0 / 2.0, F0 + DeltaF0 / 2.0, dF0]
+F1s = [F1 - DeltaF1 / 2.0, F1 + DeltaF1 / 2.0, dF1]
+F2s = [F2 - DeltaF2 / 2.0, F2 + DeltaF2 / 2.0, dF2]
 Alphas = [Alpha]
 Deltas = [Delta]
 search = pyfstat.GridSearch(
-    'grid_F0F1F2', 'data', data.sftfilepath, F0s, F1s,
-    F2s, Alphas, Deltas, tref, tstart, tend)
+    "grid_F0F1F2",
+    "data",
+    data.sftfilepath,
+    F0s,
+    F1s,
+    F2s,
+    Alphas,
+    Deltas,
+    tref,
+    tstart,
+    tend,
+)
 search.run()
 
 F0_vals = np.unique(search.data[:, 2]) - F0
@@ -57,8 +78,13 @@ F1_vals = np.unique(search.data[:, 3]) - F1
 F2_vals = np.unique(search.data[:, 4]) - F2
 twoF = search.data[:, -1].reshape((len(F0_vals), len(F1_vals), len(F2_vals)))
 xyz = [F0_vals, F1_vals, F2_vals]
-labels = ['$f - f_0$', '$\dot{f} - \dot{f}_0$', '$\ddot{f} - \ddot{f}_0$',
-          '$\widetilde{2\mathcal{F}}$']
+labels = [
+    "$f - f_0$",
+    "$\dot{f} - \dot{f}_0$",
+    "$\ddot{f} - \ddot{f}_0$",
+    "$\widetilde{2\mathcal{F}}$",
+]
 fig, axes = gridcorner(
-    twoF, xyz, projection='log_mean', labels=labels, whspace=0.1, factor=1.8)
-fig.savefig('{}/{}_projection_matrix.png'.format(outdir, label))
+    twoF, xyz, projection="log_mean", labels=labels, whspace=0.1, factor=1.8
+)
+fig.savefig("{}/{}_projection_matrix.png".format(outdir, label))

examples/grid_examples/grided_frequency_search.py

@@ -13,50 +13,70 @@ sqrtSX = 1e-23
 tstart = 1000000000
 duration = 100 * 86400
 tend = tstart + duration
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 70
-data_label = 'grided_frequency_depth_{:1.0f}'.format(depth)
+data_label = "grided_frequency_depth_{:1.0f}".format(depth)
 
 h0 = sqrtSX / depth
 
 data = pyfstat.Writer(
-    label=data_label, outdir='data', tref=tref,
-    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
-    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label=data_label,
+    outdir="data",
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 m = 0.001
 dF0 = np.sqrt(12 * m) / (np.pi * duration)
 DeltaF0 = 800 * dF0
-F0s = [F0-DeltaF0/2., F0+DeltaF0/2., dF0]
+F0s = [F0 - DeltaF0 / 2.0, F0 + DeltaF0 / 2.0, dF0]
 F1s = [F1]
 F2s = [F2]
 Alphas = [Alpha]
 Deltas = [Delta]
 search = pyfstat.GridSearch(
-    'grided_frequency_search', 'data', 'data/*'+data_label+'*sft', F0s, F1s,
-    F2s, Alphas, Deltas, tref, tstart, tend)
+    "grided_frequency_search",
+    "data",
+    "data/*" + data_label + "*sft",
+    F0s,
+    F1s,
+    F2s,
+    Alphas,
+    Deltas,
+    tref,
+    tstart,
+    tend,
+)
 search.run()
 
 fig, ax = plt.subplots()
-xidx = search.keys.index('F0')
+xidx = search.keys.index("F0")
 frequencies = np.unique(search.data[:, xidx])
 twoF = search.data[:, -1]
 
 # mismatch = np.sign(x-F0)*(duration * np.pi * (x - F0))**2 / 12.0
-ax.plot(frequencies, twoF, 'k', lw=1)
+ax.plot(frequencies, twoF, "k", lw=1)
 DeltaF = frequencies - F0
 sinc = np.sin(np.pi * DeltaF * duration) / (np.pi * DeltaF * duration)
 A = np.abs((np.max(twoF) - 4) * sinc ** 2 + 4)
-ax.plot(frequencies, A, '-r', lw=1)
-ax.set_ylabel('$\widetilde{2\mathcal{F}}$')
-ax.set_xlabel('Frequency')
+ax.plot(frequencies, A, "-r", lw=1)
+ax.set_ylabel("$\widetilde{2\mathcal{F}}$")
+ax.set_xlabel("Frequency")
 ax.set_xlim(F0s[0], F0s[1])
 dF0 = np.sqrt(12 * 1) / (np.pi * duration)
 xticks = [F0 - 10 * dF0, F0, F0 + 10 * dF0]
 ax.set_xticks(xticks)
-xticklabels = ['$f_0 {-} 10\Delta f$', '$f_0$', '$f_0 {+} 10\Delta f$']
+xticklabels = ["$f_0 {-} 10\Delta f$", "$f_0$", "$f_0 {+} 10\Delta f$"]
 ax.set_xticklabels(xticklabels)
 plt.tight_layout()
-fig.savefig('{}/{}_1D.png'.format(search.outdir, search.label), dpi=300)
+fig.savefig("{}/{}_1D.png".format(search.outdir, search.label), dpi=300)

examples/other_examples/sliding_window.py

@@ -13,23 +13,43 @@ F1 = -1e-10
 F2 = 0
 Alpha = np.radians(83.6292)
 Delta = np.radians(22.0144)
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 60
 h0 = sqrtSX / depth
-data_label = 'sliding_window'
+data_label = "sliding_window"
 
 data = pyfstat.Writer(
-    label=data_label, outdir='data', tref=tref,
-    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
-    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label=data_label,
+    outdir="data",
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 DeltaF0 = 1e-5
 search = pyfstat.FrequencySlidingWindow(
-        label='sliding_window', outdir='data', sftfilepattern='data/*sliding_window*sft',
-        F0s=[F0-DeltaF0, F0+DeltaF0, DeltaF0/100.], F1=F1, F2=0,
-        Alpha=Alpha, Delta=Delta, tref=tref, minStartTime=tstart,
-        maxStartTime=tend, window_size=25*86400, window_delta=1*86400)
+    label="sliding_window",
+    outdir="data",
+    sftfilepattern="data/*sliding_window*sft",
+    F0s=[F0 - DeltaF0, F0 + DeltaF0, DeltaF0 / 100.0],
+    F1=F1,
+    F2=0,
+    Alpha=Alpha,
+    Delta=Delta,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tend,
+    window_size=25 * 86400,
+    window_delta=1 * 86400,
+)
 search.run()
 search.plot_sliding_window()

examples/other_examples/twoF_cumulative.py

@@ -13,37 +13,45 @@ F1 = -1e-10
 F2 = 0
 Alpha = np.radians(83.6292)
 Delta = np.radians(22.0144)
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 100
 h0 = sqrtSX / depth
-data_label = 'twoF_cumulative'
+data_label = "twoF_cumulative"
 
 data = pyfstat.Writer(
-    label=data_label, outdir='data', tref=tref,
-    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
-    Delta=Delta, h0=h0, sqrtSX=sqrtSX, detectors='H1,L1')
+    label=data_label,
+    outdir="data",
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+    detectors="H1,L1",
+)
 data.make_data()
 
 # The predicted twoF, given by lalapps_predictFstat can be accessed by
 twoF = data.predict_fstat()
-print('Predicted twoF value: {}\n'.format(twoF))
+print("Predicted twoF value: {}\n".format(twoF))
 
 DeltaF0 = 1e-7
 DeltaF1 = 1e-13
 VF0 = (np.pi * duration * DeltaF0) ** 2 / 3.0
-VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
+VF1 = (np.pi * duration ** 2 * DeltaF1) ** 2 * 4 / 45.0
+print("\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n".format(VF0 * VF1, VF0, VF1))
 
-theta_prior = {'F0': {'type': 'unif',
-                      'lower': F0-DeltaF0/2.,
-                      'upper': F0+DeltaF0/2.},
-               'F1': {'type': 'unif',
-                      'lower': F1-DeltaF1/2.,
-                      'upper': F1+DeltaF1/2.},
-               'F2': F2,
-               'Alpha': Alpha,
-               'Delta': Delta
+theta_prior = {
+    "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2.0},
+    "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2.0},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
 }
 
 ntemps = 1
@@ -52,10 +60,18 @@ nwalkers = 100
 nsteps = [50, 50]
 
 mcmc = pyfstat.MCMCSearch(
-    label='twoF_cumulative', outdir='data',
-    sftfilepattern='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
-    minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
-    ntemps=ntemps, log10beta_min=log10beta_min)
+    label="twoF_cumulative",
+    outdir="data",
+    sftfilepattern="data/*" + data_label + "*sft",
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tend,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
+    log10beta_min=log10beta_min,
+)
 mcmc.run()
 mcmc.plot_corner(add_prior=True)
 mcmc.print_summary()

examples/other_examples/using_initialisation.py

@@ -13,38 +13,45 @@ F1 = -1e-10
 F2 = 0
 Alpha = np.radians(83.6292)
 Delta = np.radians(22.0144)
-tref = .5*(tstart+tend)
+tref = 0.5 * (tstart + tend)
 
 depth = 10
 h0 = sqrtSX / depth
-label = 'using_initialisation'
-outdir = 'data'
+label = "using_initialisation"
+outdir = "data"
 
 data = pyfstat.Writer(
-    label=label, outdir=outdir, tref=tref,
-    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
-    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+    label=label,
+    outdir=outdir,
+    tref=tref,
+    tstart=tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+)
 data.make_data()
 
 # The predicted twoF, given by lalapps_predictFstat can be accessed by
 twoF = data.predict_fstat()
-print('Predicted twoF value: {}\n'.format(twoF))
+print("Predicted twoF value: {}\n".format(twoF))
 
 DeltaF0 = 1e-7
 DeltaF1 = 1e-13
 VF0 = (np.pi * duration * DeltaF0) ** 2 / 3.0
-VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
+VF1 = (np.pi * duration ** 2 * DeltaF1) ** 2 * 4 / 45.0
+print("\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n".format(VF0 * VF1, VF0, VF1))
 
-theta_prior = {'F0': {'type': 'unif',
-                      'lower': F0-DeltaF0/2.,
-                      'upper': F0+DeltaF0/2.},
-               'F1': {'type': 'unif',
-                      'lower': F1-DeltaF1/2.,
-                      'upper': F1+DeltaF1/2.},
-               'F2': F2,
-               'Alpha': Alpha,
-               'Delta': Delta
+theta_prior = {
+    "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2.0},
+    "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2.0},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
 }
 
 ntemps = 1
@@ -53,11 +60,18 @@ nwalkers = 100
 nsteps = [100, 100]
 
 mcmc = pyfstat.MCMCSearch(
-    label=label, outdir=outdir,
-    sftfilepattern='{}/*{}*sft'.format(outdir, label),
-    theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
-    nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
-    log10beta_min=log10beta_min)
+    label=label,
+    outdir=outdir,
+    sftfilepattern="{}/*{}*sft".format(outdir, label),
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=tstart,
+    maxStartTime=tend,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
+    log10beta_min=log10beta_min,
+)
 mcmc.setup_initialisation(100, scatter_val=1e-10)
 mcmc.run()
 mcmc.plot_corner(add_prior=True)

examples/transient_examples/long_transient_search_MCMC.py

@@ -16,19 +16,18 @@ tref = minStartTime
 DeltaF0 = 6e-7
 DeltaF1 = 1e-13
 
-theta_prior = {'F0': {'type': 'unif',
-                      'lower': F0-DeltaF0/2.,
-                      'upper': F0+DeltaF0/2.},
-               'F1': {'type': 'unif',
-                      'lower': F1-DeltaF1/2.,
-                      'upper': F1+DeltaF1/2.},
-               'F2': F2,
-               'Alpha': Alpha,
-               'Delta': Delta,
-               'transient_tstart': minStartTime,
-               'transient_duration': {'type': 'halfnorm',
-                                      'loc': 0.001*Tspan,
-                                      'scale': 0.5*Tspan}
+theta_prior = {
+    "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2.0},
+    "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2.0},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
+    "transient_tstart": minStartTime,
+    "transient_duration": {
+        "type": "halfnorm",
+        "loc": 0.001 * Tspan,
+        "scale": 0.5 * Tspan,
+    },
 }
 
 ntemps = 2
@@ -37,12 +36,19 @@ nwalkers = 100
 nsteps = [100, 100]
 
 mcmc = pyfstat.MCMCTransientSearch(
-    label='transient_search', outdir='data_l',
-    sftfilepattern='data_l/*simulated_transient_signal*sft',
-    theta_prior=theta_prior, tref=tref, minStartTime=minStartTime,
-    maxStartTime=maxStartTime, nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+    label="transient_search",
+    outdir="data_l",
+    sftfilepattern="data_l/*simulated_transient_signal*sft",
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=minStartTime,
+    maxStartTime=maxStartTime,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
     log10beta_min=log10beta_min,
-    transientWindowType='rect')
+    transientWindowType="rect",
+)
 mcmc.run()
 mcmc.plot_corner(label_offset=0.7)
 mcmc.print_summary()

examples/transient_examples/long_transient_search_make_simulated_data.py

@@ -19,8 +19,20 @@ h0 = 1e-23
 sqrtSX = 1e-22
 
 transient = pyfstat.Writer(
-    label='simulated_transient_signal', outdir='data_l', tref=tref,
-    tstart=transient_tstart, F0=F0, F1=F1, F2=F2, duration=transient_duration,
-    Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX, minStartTime=minStartTime,
-    maxStartTime=maxStartTime, transientWindowType='rect')
+    label="simulated_transient_signal",
+    outdir="data_l",
+    tref=tref,
+    tstart=transient_tstart,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    duration=transient_duration,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    sqrtSX=sqrtSX,
+    minStartTime=minStartTime,
+    maxStartTime=maxStartTime,
+    transientWindowType="rect",
+)
 transient.make_data()

examples/transient_examples/short_transient_search_MCMC.py

@@ -18,21 +18,22 @@ Tsft = 1800
 DeltaF0 = 1e-2
 DeltaF1 = 1e-9
 
-theta_prior = {'F0': {'type': 'unif',
-                      'lower': F0-DeltaF0/2.,
-                      'upper': F0+DeltaF0/2.},
-               'F1': {'type': 'unif',
-                      'lower': F1-DeltaF1/2.,
-                      'upper': F1+DeltaF1/2.},
-               'F2': F2,
-               'Alpha': Alpha,
-               'Delta': Delta,
-               'transient_tstart': {'type': 'unif',
-                                    'lower': minStartTime,
-                                    'upper': maxStartTime-2*Tsft},
-               'transient_duration': {'type': 'unif',
-                                      'lower': 2*Tsft,
-                                      'upper': Tspan-2*Tsft}
+theta_prior = {
+    "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2.0},
+    "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2.0},
+    "F2": F2,
+    "Alpha": Alpha,
+    "Delta": Delta,
+    "transient_tstart": {
+        "type": "unif",
+        "lower": minStartTime,
+        "upper": maxStartTime - 2 * Tsft,
+    },
+    "transient_duration": {
+        "type": "unif",
+        "lower": 2 * Tsft,
+        "upper": Tspan - 2 * Tsft,
+    },
 }
 
 ntemps = 2
@@ -41,12 +42,19 @@ nwalkers = 100
 nsteps = [100, 100]
 
 mcmc = pyfstat.MCMCTransientSearch(
-    label='transient_search', outdir='data_s',
-    sftfilepattern='data_s/*simulated_transient_signal*sft',
-    theta_prior=theta_prior, tref=tref, minStartTime=minStartTime,
-    maxStartTime=maxStartTime, nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+    label="transient_search",
+    outdir="data_s",
+    sftfilepattern="data_s/*simulated_transient_signal*sft",
+    theta_prior=theta_prior,
+    tref=tref,
+    minStartTime=minStartTime,
+    maxStartTime=maxStartTime,
+    nsteps=nsteps,
+    nwalkers=nwalkers,
+    ntemps=ntemps,
     log10beta_min=log10beta_min,
-    transientWindowType='rect')
+    transientWindowType="rect",
+)
 mcmc.run()
 mcmc.plot_corner(label_offset=0.7)
 mcmc.print_summary()

examples/transient_examples/short_transient_search_gridded.py

@@ -5,7 +5,7 @@ import os
 import numpy as np
 import matplotlib.pyplot as plt
 
-datadir = 'data_s'
+datadir = "data_s"
 
 F0 = 30.0
 F1 = -1e-10
@@ -23,37 +23,53 @@ Tsft = 1800
 m = 0.001
 dF0 = np.sqrt(12 * m) / (np.pi * Tspan)
 DeltaF0 = 100 * dF0
-F0s = [F0-DeltaF0/2., F0+DeltaF0/2., dF0]
+F0s = [F0 - DeltaF0 / 2.0, F0 + DeltaF0 / 2.0, dF0]
 F1s = [F1]
 F2s = [F2]
 Alphas = [Alpha]
 Deltas = [Delta]
 
-print('Standard CW search:')
+print("Standard CW search:")
 search1 = pyfstat.GridSearch(
-    label='CW', outdir=datadir,
-    sftfilepattern=os.path.join(datadir,'*simulated_transient_signal*sft'),
-    F0s=F0s, F1s=F1s, F2s=F2s, Alphas=Alphas, Deltas=Deltas, tref=tref,
-    minStartTime=minStartTime, maxStartTime=maxStartTime,
-    BSGL=False)
+    label="CW",
+    outdir=datadir,
+    sftfilepattern=os.path.join(datadir, "*simulated_transient_signal*sft"),
+    F0s=F0s,
+    F1s=F1s,
+    F2s=F2s,
+    Alphas=Alphas,
+    Deltas=Deltas,
+    tref=tref,
+    minStartTime=minStartTime,
+    maxStartTime=maxStartTime,
+    BSGL=False,
+)
 search1.run()
 search1.print_max_twoF()
 
-search1.plot_1D(xkey='F0',
-               xlabel='freq [Hz]', ylabel='$2\mathcal{F}$')
+search1.plot_1D(xkey="F0", xlabel="freq [Hz]", ylabel="$2\mathcal{F}$")
 
-print('with t0,tau bands:')
+print("with t0,tau bands:")
 search2 = pyfstat.TransientGridSearch(
-    label='tCW', outdir=datadir,
-    sftfilepattern=os.path.join(datadir,'*simulated_transient_signal*sft'),
-    F0s=F0s, F1s=F1s, F2s=F2s, Alphas=Alphas, Deltas=Deltas, tref=tref,
-    minStartTime=minStartTime, maxStartTime=maxStartTime,
-    transientWindowType='rect', t0Band=Tspan-2*Tsft, tauBand=Tspan,
+    label="tCW",
+    outdir=datadir,
+    sftfilepattern=os.path.join(datadir, "*simulated_transient_signal*sft"),
+    F0s=F0s,
+    F1s=F1s,
+    F2s=F2s,
+    Alphas=Alphas,
+    Deltas=Deltas,
+    tref=tref,
+    minStartTime=minStartTime,
+    maxStartTime=maxStartTime,
+    transientWindowType="rect",
+    t0Band=Tspan - 2 * Tsft,
+    tauBand=Tspan,
     BSGL=False,
     outputTransientFstatMap=True,
-    tCWFstatMapVersion='lal')
+    tCWFstatMapVersion="lal",
+)
 search2.run()
 search2.print_max_twoF()
 
-search2.plot_1D(xkey='F0',
-               xlabel='freq [Hz]', ylabel='$2\mathcal{F}$')
+search2.plot_1D(xkey="F0", xlabel="freq [Hz]", ylabel="$2\mathcal{F}$")

examples/transient_examples/short_transient_search_make_simulated_data.py

@@ -17,15 +17,27 @@ tref = minStartTime
 
 h0 = 1e-23
 sqrtSX = 1e-22
-detectors = 'H1,L1'
+detectors = "H1,L1"
 
 Tsft = 1800
 
 transient = pyfstat.Writer(
-    label='simulated_transient_signal', outdir='data_s',
-    tref=tref, tstart=transient_tstart, duration=transient_duration,
-    F0=F0, F1=F1, F2=F2, Alpha=Alpha, Delta=Delta, h0=h0,
-    detectors=detectors,sqrtSX=sqrtSX,
-    minStartTime=minStartTime, maxStartTime=maxStartTime,
-    transientWindowType='rect', Tsft=Tsft)
+    label="simulated_transient_signal",
+    outdir="data_s",
+    tref=tref,
+    tstart=transient_tstart,
+    duration=transient_duration,
+    F0=F0,
+    F1=F1,
+    F2=F2,
+    Alpha=Alpha,
+    Delta=Delta,
+    h0=h0,
+    detectors=detectors,
+    sqrtSX=sqrtSX,
+    minStartTime=minStartTime,
+    maxStartTime=maxStartTime,
+    transientWindowType="rect",
+    Tsft=Tsft,
+)
 transient.make_data()

pyfstat/__init__.py

-
-
-from .core import BaseSearchClass, ComputeFstat, SemiCoherentSearch, SemiCoherentGlitchSearch
-from .make_sfts import Writer, GlitchWriter, FrequencyModulatedArtifactWriter, FrequencyAmplitudeModulatedArtifactWriter
-from .mcmc_based_searches import MCMCSearch, MCMCGlitchSearch, MCMCSemiCoherentSearch, MCMCFollowUpSearch, MCMCTransientSearch
-from .grid_based_searches import GridSearch, GridUniformPriorSearch, GridGlitchSearch, FrequencySlidingWindow, DMoff_NO_SPIN, SliceGridSearch, TransientGridSearch
+from .core import (
+    BaseSearchClass,
+    ComputeFstat,
+    SemiCoherentSearch,
+    SemiCoherentGlitchSearch,
+)
+from .make_sfts import (
+    Writer,
+    GlitchWriter,
+    FrequencyModulatedArtifactWriter,
+    FrequencyAmplitudeModulatedArtifactWriter,
+)
+from .mcmc_based_searches import (
+    MCMCSearch,
+    MCMCGlitchSearch,
+    MCMCSemiCoherentSearch,
+    MCMCFollowUpSearch,
+    MCMCTransientSearch,
+)
+from .grid_based_searches import (
+    GridSearch,
+    GridUniformPriorSearch,
+    GridGlitchSearch,
+    FrequencySlidingWindow,
+    DMoff_NO_SPIN,
+    SliceGridSearch,
+    TransientGridSearch,
+)