fixed columns identified allowing for some floating error

getdist/chains.py

@@ -1001,9 +1001,11 @@ def deleteFixedParams(self):
         fixed = []
         values = []
         for i in range(self.samples.shape[1]):
-            if np.all(self.samples[:, i] == self.samples[0, i]):
-                fixed.append(i)
-                values.append(self.samples[0, i])
+            if np.isclose(self.samples[0, i], self.samples[-1, i]):
+                mean = np.average(self.samples[:, i])
+                if np.allclose(self.samples[:, i], mean, rtol=1e-12, atol=0):
+                    fixed.append(i)
+                    values.append(mean)
         self.changeSamples(np.delete(self.samples, fixed, 1))
         return fixed, values
 
@@ -1072,7 +1074,7 @@ def __init__(self, root=None, jobItem=None, paramNamesFile=None, names=None, lab
         """
 
         self.chains = None
-        WeightedSamples.__init__(self, **kwargs)
+        super().__init__(**kwargs)
         self.jobItem = jobItem
         self.ignore_lines = float(kwargs.get('ignore_rows', 0))
         self.root = root
@@ -1236,11 +1238,11 @@ def getParamSampleDict(self, ix, want_derived=True):
         :return: ordered dictionary of parameter values
         """
         res = dict()
+        res['weight'] = self.weights[ix]
+        res['loglike'] = self.loglikes[ix]
         for i, name in enumerate(self.paramNames.names):
             if want_derived or not name.isDerived:
                 res[name.name] = self.samples[ix, i]
-        res['weight'] = self.weights[ix]
-        res['loglike'] = self.loglikes[ix]
         return res
 
     def _makeParamvec(self, par):
@@ -1250,7 +1252,7 @@ def _makeParamvec(self, par):
             par = par.name
         if isinstance(par, str):
             return self.samples[:, self.index[par]]
-        return WeightedSamples._makeParamvec(self, par)
+        return super()._makeParamvec(par)
 
     def updateChainBaseStatistics(self):
         # old name, use updateBaseStatistics
@@ -1450,17 +1452,11 @@ def deleteFixedParams(self):
         Delete parameters that are fixed (the same value in all samples)
         """
         if self.samples is not None:
-            fixed, values = WeightedSamples.deleteFixedParams(self)
+            fixed, values = super().deleteFixedParams()
             self.chains = None
         else:
-            fixed = []
-            values = []
-            chain = self.chains[0]
-            for i in range(chain.n):
-                if np.all(chain.samples[:, i] == chain.samples[0, i]):
-                    fixed.append(i)
-                    values.append(chain.samples[0, i])
-            for chain in self.chains:
+            fixed, values = self.chains[0].deleteFixedParams()
+            for chain in self.chains[1:]:
                 chain.changeSamples(np.delete(chain.samples, fixed, 1))
         if hasattr(self, 'ranges'):
             for ix, value in zip(fixed, values):

getdist/cobaya_interface.py

@@ -87,17 +87,20 @@ def MCSamplesFromCobaya(info, collections, name_tag=None,
     if ignore_rows != 0 and skip != 0:
         logging.warning("You are asking for rows to be ignored (%r), but some (%r) were "
                         "already ignored in the original chain.", ignore_rows, skip)
-    var_params = [k for k, v in info_params.items() if is_sampled_param(v) or is_derived_param(v)]
+    var_params = [k for k, v in info_params.items() if
+                  is_sampled_param(v) or is_derived_param(v)]
     assert set(columns[2:]) == set(var_params), (
             "Info and collection(s) are not compatible, because their parameters differ: "
-            "the collection(s) have %r and the info has %r. " % (columns[2:], var_params) +
+            "the collection(s) have %r and the info has %r. " % (
+                columns[2:], var_params) +
             "Are you sure that you are using an *updated* info dictionary "
             "(i.e. the output of `cobaya.run`)?")
     # We need to use *collection* sorting, not info sorting!
     names = [p + ("*" if is_derived_param(info_params[p]) else "")
              for p in columns[2:]]
     labels = [(info_params[p] or {}).get(_p_label, p) for p in columns[2:]]
-    ranges = {p: get_range(info_params[p]) for p in info_params}  # include fixed parameters not in columns
+    ranges = {p: get_range(info_params[p]) for p in
+              info_params}  # include fixed parameters not in columns
     renames = {p: info_params.get(p, {}).get(_p_renames, []) for p in columns[2:]}
     samples = [c[c.data.columns[2:]].values for c in collections]
     weights = [c[_weight].values for c in collections]
@@ -111,6 +114,10 @@ def MCSamplesFromCobaya(info, collections, name_tag=None,
                      settings=settings)
 
 
+def str_to_list(x):
+    return [x] if isinstance(x, str) else x
+
+
 def get_info_params(info):
     """
     Extracts parameter info from the new yaml format.
@@ -128,9 +135,9 @@ def get_info_params(info):
     remove = info.get(_post, {}).get("remove", {})
     for param in remove.get(_params, []) or []:
         info_params_full.pop(param, None)
-    for like in remove.get(_likelihood, []) or []:
+    for like in str_to_list(remove.get(_likelihood) or []):
         likes.remove(like)
-    for prior in remove.get(_prior, []) or []:
+    for prior in str_to_list(remove.get(_prior)) or []:
         priors.remove(prior)
     add = info.get(_post, {}).get("add", {})
     # Adding derived params and updating 1d priors
@@ -181,7 +188,8 @@ def get_range(param_info):
             value = float(value)
         except ValueError:
             # e.g. lambda function values
-            lims = (lambda i: [i.get("min", -np.inf), i.get("max", np.inf)])(param_info or {})
+            lims = (lambda i: [i.get("min", -np.inf), i.get("max", np.inf)])(
+                param_info or {})
         else:
             lims = (value, value)
     return lims[0] if lims[0] != -np.inf else None, lims[1] if lims[1] != np.inf else None
@@ -242,7 +250,8 @@ def expand_info_param(info_param):
 
 
 def get_sampler_type(filename_or_info, default_sampler_for_chain_type="mcmc"):
-    sampler = list(yaml_file_or_dict(filename_or_info).get(_sampler, [default_sampler_for_chain_type]))[0]
+    sampler = list(yaml_file_or_dict(filename_or_info).get(_sampler, [
+        default_sampler_for_chain_type]))[0]
     return {"mcmc": "mcmc", "polychord": "nested", "minimize": "minimize"}[sampler]
 
 

getdist/inifile.py

@@ -60,7 +60,7 @@ def expand_placeholders(self, s):
                     index = s.index(')')
                     var = s[:index]
                     if var in os.environ:
-                        res = res + os.environ[var]
+                        res += os.environ[var]
             else:
                 res = res + c
             index += 1