diff --git a/docs/fully_coherent_search.md b/docs/fully_coherent_search.md
index 6f274d60077c5f63fa3080a308e6d14ddec88eec..461649bda30272ad86df5c801922f03a26a6f6a8 100644
--- a/docs/fully_coherent_search.md
+++ b/docs/fully_coherent_search.md
@@ -66,11 +66,11 @@ simply called `nsteps = [500, 1000]`). Finally, the simulation run for `500`
steps of burn-in then `1000` steps of production to estimate the posterior.
Passing all this to the MCMC search, we also need to give it a label and
-directory to save the data and provide `sftlabel` and `sftdir` which defines
-which data to use in the search
+directory to save the data and provide `sftfilepath`, a string matching
+the data to use in the search
```
-mcmc = MCMCSearch('fully_coherent', 'data', sftlabel='basic', sftdir='data',
+mcmc = MCMCSearch('fully_coherent', 'data', sftfilepath='data/*basic*sft',
theta_prior=theta_prior, tref=tref, tstart=tstart, tend=tend,
nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
scatter_val=1e-10)
diff --git a/docs/fully_coherent_search_on_glitching_data.md b/docs/fully_coherent_search_on_glitching_data.md
index 8e5ca97f42c9437a5198fa18969455307137c09b..4332aeb943082322ff823b5cab092df97add6a25 100644
--- a/docs/fully_coherent_search_on_glitching_data.md
+++ b/docs/fully_coherent_search_on_glitching_data.md
@@ -47,11 +47,10 @@ nwalkers = 500
nsteps = [100, 100, 100]
mcmc = MCMCSearch('fully_coherent_on_glitching_data', 'data',
- sftlabel='glitch', sftdir='data',
+ sftfilepath='data/*_glitch*.sft',
theta_prior=theta_prior, tref=tref, tstart=tstart, tend=tend,
- nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps, betas=betas,
- scatter_val=1e-6)
-
+ nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+ log10temperature_min=log10temperature_min, scatter_val=1e-6)
mcmc.run()
mcmc.plot_corner(add_prior=True)
```
diff --git a/examples/fully_coherent_search.py b/examples/fully_coherent_search.py
index 8e6b4227d262f8ec67d3cb9a8089a2381662f345..9b11b66b576023736585d9aa453ca7fe4f2f2970 100644
--- a/examples/fully_coherent_search.py
+++ b/examples/fully_coherent_search.py
@@ -9,7 +9,7 @@ tref = 362750407.0
tstart = 1000000000
duration = 100*86400
-tend = tstart = duration
+tend = tstart + duration
theta_prior = {'F0': {'type': 'norm', 'loc': F0, 'scale': abs(1e-6*F0)},
'F1': {'type': 'norm', 'loc': F1, 'scale': abs(1e-6*F1)},
@@ -22,7 +22,7 @@ ntemps = 1
nwalkers = 100
nsteps = [100, 500, 1000]
-mcmc = MCMCSearch('fully_coherent', 'data', sftlabel='basic', sftdir='data',
+mcmc = MCMCSearch('fully_coherent', 'data', sftfilepath='data/*basic*sft',
theta_prior=theta_prior, tref=tref, tstart=tstart, tend=tend,
nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
scatter_val=1e-10)
diff --git a/examples/fully_coherent_search_on_glitching_data.py b/examples/fully_coherent_search_on_glitching_data.py
index 2ea029b0af205bd5ff8d7a5f0c35ade1eee9687a..fbb8b349d66f5decac87e82034b0352ed1397aea 100644
--- a/examples/fully_coherent_search_on_glitching_data.py
+++ b/examples/fully_coherent_search_on_glitching_data.py
@@ -10,7 +10,7 @@ tref = 362750407.0
tstart = 1000000000
duration = 100*86400
-tend = tstart = duration
+tend = tstart + duration
theta_prior = {'F0': {'type': 'unif', 'lower': F0-5e-5,
'upper': F0+5e-5},
@@ -21,15 +21,15 @@ theta_prior = {'F0': {'type': 'unif', 'lower': F0-5e-5,
}
ntemps = 10
-betas = np.logspace(0, -30, ntemps)
+log10temperature_min = -30
nwalkers = 500
nsteps = [100, 100, 100]
mcmc = MCMCSearch('fully_coherent_on_glitching_data', 'data',
- sftlabel='glitch', sftdir='data',
+ sftfilepath='data/*_glitch*.sft',
theta_prior=theta_prior, tref=tref, tstart=tstart, tend=tend,
- nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps, betas=betas,
- scatter_val=1e-6)
+ nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+ log10temperature_min=log10temperature_min, scatter_val=1e-6)
mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()
diff --git a/examples/semi_coherent_glitch_search.py b/examples/semi_coherent_glitch_search.py
index b4dc3b727de34433042c3e85bbc8cf0b9520c538..435ea6b4f7edfa77ba9f13981ba920784f8bf69d 100644
--- a/examples/semi_coherent_glitch_search.py
+++ b/examples/semi_coherent_glitch_search.py
@@ -28,7 +28,7 @@ nwalkers = 500
nsteps = [1000, 1000, 1000]
mcmc = pyfstat.MCMCGlitchSearch(
- 'semi_coherent_glitch_search', 'data', sftlabel='glitch', sftdir='data',
+ 'semi_coherent_glitch_search', 'data', sftfilepath='data/*_glitch*sft',
theta_prior=theta_prior, tref=tref, tstart=tstart, tend=tend,
nsteps=nsteps, nwalkers=nwalkers, scatter_val=1e-10, nglitch=1)
diff --git a/examples/semi_coherent_twoglitch_search.py b/examples/semi_coherent_twoglitch_search.py
index 0c44cb16b8f3a34f08aa65219c7f8ad4de51c6c2..6b0b77a3a3992df5069a5a61f95f5ca3e7596041 100644
--- a/examples/semi_coherent_twoglitch_search.py
+++ b/examples/semi_coherent_twoglitch_search.py
@@ -28,8 +28,8 @@ nwalkers = 100
nsteps = [500, 500, 500]
mcmc = pyfstat.MCMCGlitchSearch(
- 'semi_coherent_twoglitch_search', 'data', sftlabel='twoglitch',
- sftdir='data', theta_prior=theta_prior, tref=tref, tstart=tstart,
+ 'semi_coherent_twoglitch_search', 'data', sftfilepath='data/*twoglitch*sft',
+ theta_prior=theta_prior, tref=tref, tstart=tstart,
tend=tend, nsteps=nsteps, nwalkers=nwalkers, scatter_val=1e-10, nglitch=2)
mcmc.run()