fix typo (#2883)

`lenght` --> `length`

Author: Junpeng Lao

pymc3/backends/base.py

@@ -370,7 +370,7 @@ def add_values(self, vals):
         vals : dict (str: array-like)
              The keys should be the names of the new variables. The values are
              expected to be array-like object.
-             For traces with more than one chain the lenght of each value
+             For traces with more than one chain the length of each value
              should match the number of total samples already in the trace
              (chains * iterations), otherwise a warning is raised.
         """
@@ -384,7 +384,7 @@ def add_values(self, vals):
             l_samples = len(self) * len(self.chains)
             l_v = len(v)
             if l_v != l_samples:
-                warnings.warn("The lenght of the values you are trying to "
+                warnings.warn("The length of the values you are trying to "
                               "add ({}) does not match the number ({}) of "
                               "total samples in the trace "
                               "(chains * iterations)".format(l_v, l_samples))

pymc3/plots/densityplot.py

@@ -70,23 +70,23 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
     if not isinstance(trace, (list, tuple)):
         trace = [trace]
 
-    lenght_trace = len(trace)
+    length_trace = len(trace)
 
     if models is None:
-        if lenght_trace > 1:
-            models = ['m_{}'.format(i) for i in range(lenght_trace)]
+        if length_trace > 1:
+            models = ['m_{}'.format(i) for i in range(length_trace)]
         else:
             models = ['']
-    elif len(models) != lenght_trace:
+    elif len(models) != length_trace:
         raise ValueError(
             "The number of names for the models does not match the number of models")
 
-    lenght_models = len(models)
+    length_models = len(models)
 
     if colors == 'cycle':
-        colors = ['C{}'.format(i % 10) for i in range(lenght_models)]
+        colors = ['C{}'.format(i % 10) for i in range(length_models)]
     elif isinstance(colors, str):
-        colors = [colors for i in range(lenght_models)]
+        colors = [colors for i in range(length_models)]
 
     if varnames is None:
         varnames = []
@@ -117,7 +117,7 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
                     _d_helper(vec, vname, colors[t_idx], bw, alpha, point_estimate,
                               hpd_markers, outline, shade, dplot[v_idx])
 
-    if lenght_trace > 1:
+    if length_trace > 1:
         for m_idx, m in enumerate(models):
             dplot[0].plot([], label=m, c=colors[m_idx])
         dplot[0].legend(fontsize=textsize)

pymc3/sampling.py

@@ -1126,8 +1126,8 @@ def sample_ppc_w(traces, samples=None, models=None, weights=None,
     if len(models) != len(weights):
         raise ValueError('The number of models and weights should be the same')
 
-    lenght_morv = len(models[0].observed_RVs)
-    if not all(len(i.observed_RVs) == lenght_morv for i in models):
+    length_morv = len(models[0].observed_RVs)
+    if not all(len(i.observed_RVs) == length_morv for i in models):
         raise ValueError(
             'The number of observed RVs should be the same for all models')
 
@@ -1161,17 +1161,16 @@ def sample_ppc_w(traces, samples=None, models=None, weights=None,
     obs = [x for m in models for x in m.observed_RVs]
     variables = np.repeat(obs, n)
 
-    lengths = [np.atleast_1d(observed).shape for observed in obs]
-    lenghts = list(set(lengths))
+    lengths = list(set([np.atleast_1d(observed).shape for observed in obs]))
 
-    if len(lenghts) == 1:
+    if len(lengths) == 1:
         size = [None for i in variables]
-    elif len(lenghts) > 2:
+    elif len(lengths) > 2:
         raise ValueError('Observed variables could not be broadcast together')
     else:
         size = []
-        x = np.zeros(shape=lenghts[0])
-        y = np.zeros(shape=lenghts[1])
+        x = np.zeros(shape=lengths[0])
+        y = np.zeros(shape=lengths[1])
         b = np.broadcast(x, y)
         for var in variables:
             shape = np.shape(np.atleast_1d(var.distribution.default()))