feat: spaghetti_plot method for monitoring exceptional configurations added to Corr class, tests added.

pyerrors/correlators.py

@@ -794,7 +794,34 @@ class Corr:
             else:
                 raise Exception("'save' has to be a string.")
 
-        return
+    def spaghetti_plot(self, logscale=True):
+        """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
+
+        Parameters
+        ----------
+        logscale : bool
+            Determines whether the scale of the y-axis is logarithmic or standard.
+        """
+        if self.N != 1:
+            raise Exception("Correlator needs to be projected first.")
+
+        mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist]))
+        x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
+
+        for name in mc_names:
+            data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T
+
+            fig = plt.figure()
+            ax = fig.add_subplot(111)
+            for dat in data:
+                ax.plot(x0_vals, dat, ls='-', marker='')
+
+            if logscale is True:
+                ax.set_yscale('log')
+
+            ax.set_xlabel(r'$x_0 / a$')
+            plt.title(name)
+            plt.draw()
 
     def dump(self, filename, datatype="json.gz", **kwargs):
         """Dumps the Corr into a file of chosen type

tests/correlators_test.py

@@ -325,10 +325,20 @@ def test_corr_vector_operations():
     assert np.all([o == 0 for o in ((my_corr * my_vec) / my_vec) - my_corr])
     assert np.all([o == 0 for o in ((my_corr / my_vec) * my_vec) - my_corr])
 
-def _gen_corr(val):
+
+def test_spaghetti_plot():
+    corr = _gen_corr(12, 50)
+    corr += pe.pseudo_Obs(0.0, 0.1, 'another_ensemble')
+    corr += pe.cov_Obs(0.0, 0.01 ** 2, 'covobs')
+
+    corr.spaghetti_plot(True)
+    corr.spaghetti_plot(False)
+
+
+def _gen_corr(val, samples=2000):
     corr_content = []
     for t in range(16):
-        corr_content.append(pe.pseudo_Obs(val, 0.1, 't', 2000))
+        corr_content.append(pe.pseudo_Obs(val, 0.1, 't', samples))
 
     return pe.correlators.Corr(corr_content)