Several minor updates

1) Add ability to in F0s, F1s etc in grid search directly (use tuple as (is)
2) Update cumulative legend
3) Add option to produce pfs_input directly to calculate_pfs
4) Fix spelling mistake
5) Update transient example in the paper

Paper/Examples/transient_search_using_MCMC.py

@@ -16,7 +16,7 @@ data_tstart = tstart - duration
 data_tend = data_tstart + 3*duration
 tref = .5*(data_tstart+data_tend)
 
-h0 = 3e-24
+h0 = 4e-24
 sqrtSX = 1e-22
 
 transient = pyfstat.Writer(
@@ -56,8 +56,11 @@ mcmc = pyfstat.MCMCSearch(
     log10temperature_min=log10temperature_min)
 mcmc.run()
 fig, ax = plt.subplots()
+mcmc.write_par()
+mcmc.generate_loudest()
 mcmc.plot_cumulative_max(ax=ax)
 ax.set_xlabel('Days from $t_\mathrm{start}$')
+ax.legend_.remove()
 fig.savefig('data/transient_search_initial_stage_twoFcumulative')
 mcmc.print_summary()
 

pyfstat/core.py

@@ -512,7 +512,6 @@ class ComputeFstat(object):
                                   minfraction=0.01, maxfraction=1):
         """ Calculate the cumulative twoF along the obseration span """
         duration = tend - tstart
-        tstart = tstart + minfraction*duration
         taus = np.linspace(minfraction*duration, maxfraction*duration, npoints)
         twoFs = []
         if self.transient is False:
@@ -526,12 +525,15 @@ class ComputeFstat(object):
 
         return taus, np.array(twoFs)
 
-    def calculatate_pfs(self, label, outdir, N=15, IFO=None):
-        if os.path.isfile('{}/{}.loudest'.format(outdir, label)) is False:
-            raise ValueError('Need a loudest file to add the predicted Fstat')
-        loudest = read_par(label, outdir, suffix='loudest')
-        pfs_input = {key: loudest[key] for key in
-                     ['h0', 'cosi', 'psi', 'Alpha', 'Delta', 'Freq']}
+    def calculate_pfs(self, label, outdir, N=15, IFO=None, pfs_input=None):
+
+        if pfs_input is None:
+            if os.path.isfile('{}/{}.loudest'.format(outdir, label)) is False:
+                raise ValueError(
+                    'Need a loudest file to add the predicted Fstat')
+            loudest = read_par(label, outdir, suffix='loudest')
+            pfs_input = {key: loudest[key] for key in
+                         ['h0', 'cosi', 'psi', 'Alpha', 'Delta', 'Freq']}
         times = np.linspace(self.minStartTime, self.maxStartTime, N+1)[1:]
         times = np.insert(times, 0, self.minStartTime + 86400/2.)
         out = [predict_fstat(minStartTime=self.minStartTime, maxStartTime=t,
@@ -560,14 +562,15 @@ class ComputeFstat(object):
             self.detector_names = detector_names
 
         if add_pfs:
-            times, pfs, pfs_sigma = self.calculatate_pfs(label, outdir, N=N)
+            times, pfs, pfs_sigma = self.calculate_pfs(label, outdir, N=N)
             ax.fill_between(
                 (times-self.minStartTime)/86400., pfs-pfs_sigma, pfs+pfs_sigma,
-                color=c, label='Predicted $2\mathcal{F}$ 1-$\sigma$ band',
+                color=c,
+                label=r'Predicted $\langle 2\mathcal{F} \rangle\pm $ 1-$\sigma$ band',
                 zorder=-10, alpha=0.2)
             if len(self.detector_names) > 1:
                 for d in self.detector_names:
-                    times, pfs, pfs_sigma = self.calculatate_pfs(
+                    times, pfs, pfs_sigma = self.calculate_pfs(
                         label, outdir, IFO=d.upper(), N=N)
                     ax.fill_between(
                         (times-self.minStartTime)/86400., pfs-pfs_sigma,
@@ -585,7 +588,7 @@ class ComputeFstat(object):
         else:
             ax.set_ylabel(r'$\widetilde{2\mathcal{F}}_{\rm cumulative}$')
         ax.set_xlim(0, taus[-1]/86400)
-        ax.legend(frameon=False)
+        ax.legend(frameon=False, loc=2, fontsize=6)
         if title:
             ax.set_title(title)
         if savefig:

pyfstat/grid_based_searches.py

@@ -75,8 +75,11 @@ class GridSearch(BaseSearchClass):
     def get_array_from_tuple(self, x):
         if len(x) == 1:
             return np.array(x)
+        elif len(x) == 3:
+            return np.arange(x[0], x[1], x[2])
         else:
-            return np.arange(x[0], x[1]*(1+1e-15), x[2])
+            logging.info('Using tuple as is')
+            return np.array(x)
 
     def get_input_data_array(self):
         arrays = []