Adding min_estimate to read_histogram

TurTLE/DNS_statistics.py

@@ -301,6 +301,7 @@ def read_histogram(
         base_group = 'statistics',
         pp_file = None,
         iteration_range = None,
+        min_estimate = None,
         max_estimate = None,
         niter_stat = None,
         nbins = None,
@@ -326,6 +327,17 @@ def read_histogram(
     if (hh.shape[-1] == 4):
         hh = hh[..., :3]
         max_estimate /= 3**0.5
+    if type(min_estimate) == type(None):
+        if quantity[:5] == 'log2_':
+            if 'min_' + quantity[5:] + '_estimate' in self.parameters:
+                min_estimate = np.log2(self.parameters['min_' + quantity[5:] + '_estimate'])
+            else:
+                min_estimate = -max_estimate # for compatibility with old data sets
+        else:
+            if 'min_' + quantity + '_estimate' in self.parameters:
+                min_estimate = self.parameters['min_' + quantity + '_estimate']
+            else:
+                min_estimate = -max_estimate # for compatibility with old data sets
     ngrid = self.parameters['nx']*self.parameters['ny']*self.parameters['nz']
     if return_full_history:
         npoints_expected = ngrid
@@ -334,7 +346,7 @@ def read_histogram(
         npoints_expected = ngrid*hh.shape[0]
         hh = np.sum(hh, axis = 0)
         npoints = np.sum(hh, axis = 0, keepdims=True)
-    bb0 = np.linspace(-max_estimate, max_estimate, nbins+1)
+    bb0 = np.linspace(min_estimate, max_estimate, nbins+1)
     bb = (bb0[1:] + bb0[:-1])*0.5
     if (undersample_warning_on):
         if not (npoints == npoints_expected).all():