Merge pull request #1331 from pymc-devs/pep8

STY Ran autopep8 on full code-base.

Author: springcoil

pymc3/backends/base.py

@@ -24,6 +24,7 @@ class BaseTrace(object):
         Sampling values will be stored for these variables. If None,
         `model.unobserved_RVs` is used.
     """
+
     def __init__(self, name, model=None, vars=None):
         self.name = name
 
@@ -35,17 +36,16 @@ def __init__(self, name, model=None, vars=None):
         self.varnames = [var.name for var in vars]
         self.fn = model.fastfn(vars)
 
-
-        ## Get variable shapes. Most backends will need this
-        ## information.
+        # Get variable shapes. Most backends will need this
+        # information.
         var_values = list(zip(self.varnames, self.fn(model.test_point)))
         self.var_shapes = {var: value.shape
                            for var, value in var_values}
         self.var_dtypes = {var: value.dtype
                            for var, value in var_values}
         self.chain = None
 
-    ## Sampling methods
+    # Sampling methods
 
     def setup(self, draws, chain):
         """Perform chain-specific setup.
@@ -76,7 +76,7 @@ def close(self):
         """
         pass
 
-    ## Selection methods
+    # Selection methods
 
     def __getitem__(self, idx):
         if isinstance(idx, slice):
@@ -149,6 +149,7 @@ class MultiTrace(object):
     of the MultiTrace instance, which returns the number of draws), the
     trace with the highest chain number is always used.
     """
+
     def __init__(self, straces):
         self._straces = {}
         for strace in straces:

pymc3/backends/ndarray.py

@@ -19,13 +19,14 @@ class NDArray(base.BaseTrace):
         Sampling values will be stored for these variables. If None,
         `model.unobserved_RVs` is used.
     """
+
     def __init__(self, name=None, model=None, vars=None):
         super(NDArray, self).__init__(name, model, vars)
         self.draw_idx = 0
         self.draws = None
         self.samples = {}
 
-    ## Sampling methods
+    # Sampling methods
 
     def setup(self, draws, chain):
         """Perform chain-specific setup.
@@ -70,12 +71,12 @@ def record(self, point):
     def close(self):
         if self.draw_idx == self.draws:
             return
-        ## Remove trailing zeros if interrupted before completed all
-        ## draws.
+        # Remove trailing zeros if interrupted before completed all
+        # draws.
         self.samples = {var: vtrace[:self.draw_idx]
                         for var, vtrace in self.samples.items()}
 
-    ## Selection methods
+    # Selection methods
 
     def __len__(self):
         if not self.samples:  # `setup` has not been called.

pymc3/backends/sqlite.py

@@ -34,8 +34,8 @@
                          'WHERE chain = ?'),
     'draw_count':       ('SELECT COUNT(*) FROM [{table}] '
                          'WHERE chain = ?'),
-    ## Named placeholders are used in the selection templates because
-    ## some values occur more than once in the same template.
+    # Named placeholders are used in the selection templates because
+    # some values occur more than once in the same template.
     'select':           ('SELECT * FROM [{table}] '
                          'WHERE (chain = :chain)'),
     'select_burn':      ('SELECT * FROM [{table}] '
@@ -71,6 +71,7 @@ class SQLite(base.BaseTrace):
         Sampling values will be stored for these variables. If None,
         `model.unobserved_RVs` is used.
     """
+
     def __init__(self, name, model=None, vars=None):
         super(SQLite, self).__init__(name, model, vars)
         self._var_cols = {}
@@ -80,13 +81,13 @@ def __init__(self, name, model=None, vars=None):
         self._len = None
 
         self.db = _SQLiteDB(name)
-        ## Inserting sampling information is queued to avoid locks
-        ## caused by hitting the database with transactions each
-        ## iteration.
+        # Inserting sampling information is queued to avoid locks
+        # caused by hitting the database with transactions each
+        # iteration.
         self._queue = {varname: [] for varname in self.varnames}
         self._queue_limit = 5000
 
-    ## Sampling methods
+    # Sampling methods
 
     def setup(self, draws, chain):
         """Perform chain-specific setup.
@@ -127,7 +128,7 @@ def _create_table(self):
     def _create_insert_queries(self, chain):
         template = TEMPLATES['insert']
         for varname, var_cols in self._var_cols.items():
-            ## Create insert statement for each variable.
+            # Create insert statement for each variable.
             var_str = ', '.join(var_cols)
             placeholders = ', '.join(['?'] * len(var_cols))
             statement = template.format(table=varname,
@@ -164,7 +165,7 @@ def close(self):
         self._execute_queue()
         self.db.close()
 
-    ## Selection methods
+    # Selection methods
 
     def __len__(self):
         if not self._is_setup:
@@ -252,6 +253,7 @@ def point(self, idx):
 
 
 class _SQLiteDB(object):
+
     def __init__(self, name):
         self.name = name
         self.con = None
@@ -306,8 +308,8 @@ def load(name, model=None):
 
 def _get_table_list(cursor):
     """Return a list of table names in the current database."""
-    ## Modified from Django. Skips the sqlite_sequence system table used
-    ## for autoincrement key generation.
+    # Modified from Django. Skips the sqlite_sequence system table used
+    # for autoincrement key generation.
     cursor.execute("SELECT name FROM sqlite_master "
                    "WHERE type='table' AND NOT name='sqlite_sequence' "
                    "ORDER BY name")

pymc3/backends/text.py

@@ -37,6 +37,7 @@ class Text(base.BaseTrace):
         Sampling values will be stored for these variables. If None,
         `model.unobserved_RVs` is used.
     """
+
     def __init__(self, name, model=None, vars=None):
         if not os.path.exists(name):
             os.mkdir(name)
@@ -49,7 +50,7 @@ def __init__(self, name, model=None, vars=None):
         self._fh = None
         self.df = None
 
-    ## Sampling methods
+    # Sampling methods
 
     def setup(self, draws, chain):
         """Perform chain-specific setup.
@@ -96,7 +97,7 @@ def close(self):
         self._fh.close()
         self._fh = None  # Avoid serialization issue.
 
-    ## Selection methods
+    # Selection methods
 
     def _load_df(self):
         if self.df is None:
@@ -194,5 +195,6 @@ def dump(name, trace, chains=None):
 
     for chain in chains:
         filename = os.path.join(name, 'chain-{}.csv'.format(chain))
-        df = ttab.trace_to_dataframe(trace, chains=chain, flat_names=flat_names)
+        df = ttab.trace_to_dataframe(
+            trace, chains=chain, flat_names=flat_names)
         df.to_csv(filename, index=False)

pymc3/blocking.py

@@ -17,6 +17,7 @@ class ArrayOrdering(object):
     """
     An ordering for an array space
     """
+
     def __init__(self, vars):
         self.vmap = []
         dim = 0
@@ -33,6 +34,7 @@ class DictToArrayBijection(object):
     """
     A mapping between a dict space and an array space
     """
+
     def __init__(self, ordering, dpoint):
         self.ordering = ordering
         self.dpt = dpoint
@@ -85,6 +87,7 @@ class DictToVarBijection(object):
     """
     A mapping between a dict space and the array space for one element within the dict space
     """
+
     def __init__(self, var, idx, dpoint):
         self.var = str(var)
         self.idx = idx
@@ -111,6 +114,7 @@ class Compose(object):
     """
     Compose two functions in a pickleable way
     """
+
     def __init__(self, fa, fb):
         self.fa = fa
         self.fb = fb

pymc3/data.py

@@ -3,9 +3,10 @@
 
 __all__ = ['get_data_file']
 
+
 def get_data_file(pkg, path):
     """Returns a file object for a package data file.
-    
+
     Parameters
     ----------
     pkg : str
@@ -18,4 +19,3 @@ def get_data_file(pkg, path):
     """
 
     return io.BytesIO(pkgutil.get_data(pkg, path))
-

pymc3/diagnostics.py

@@ -75,7 +75,8 @@ def geweke(x, first=.1, last=.5, intervals=20):
     last_start_idx = (1 - last) * end
 
     # Calculate starting indices
-    start_indices = np.arange(0, int(last_start_idx), step=int((last_start_idx) / (intervals - 1)))
+    start_indices = np.arange(0, int(last_start_idx), step=int(
+        (last_start_idx) / (intervals - 1)))
 
     # Loop over start indices
     for start in start_indices:
@@ -151,9 +152,9 @@ def calc_rhat(x):
             W = np.mean(np.var(x, axis=1, ddof=1))
 
             # Estimate of marginal posterior variance
-            Vhat = W*(n - 1)/n + B/n
+            Vhat = W * (n - 1) / n + B / n
 
-            return np.sqrt(Vhat/W)
+            return np.sqrt(Vhat / W)
 
         except ValueError:
 
@@ -223,7 +224,7 @@ def calc_vhat(x):
             W = np.mean(np.var(x, axis=1, ddof=1))
 
             # Estimate of marginal posterior variance
-            Vhat = W*(n - 1)/n + B/n
+            Vhat = W * (n - 1) / n + B / n
 
             return Vhat
 
@@ -243,21 +244,22 @@ def calc_n_eff(x):
 
         Vhat = calc_vhat(x)
 
-        variogram = lambda t: (sum(sum((x[j][i] - x[j][i-t])**2
-                            for i in range(t,n)) for j in range(m)) / (m*(n - t)))
+        variogram = lambda t: (sum(sum((x[j][i] - x[j][i - t])**2
+                                       for i in range(t, n)) for j in range(m)) / (m * (n - t)))
 
         rho = np.ones(n)
-        # Iterate until the sum of consecutive estimates of autocorrelation is negative
+        # Iterate until the sum of consecutive estimates of autocorrelation is
+        # negative
         while not negative_autocorr and (t < n):
 
-            rho[t] = 1. - variogram(t)/(2.*Vhat)
+            rho[t] = 1. - variogram(t) / (2. * Vhat)
 
             if not t % 2:
-                negative_autocorr = sum(rho[t-1:t+1]) < 0
+                negative_autocorr = sum(rho[t - 1:t + 1]) < 0
 
             t += 1
 
-        return int(m*n / (1. + 2*rho[1:t].sum()))
+        return int(m * n / (1. + 2 * rho[1:t].sum()))
 
     n_eff = {}
     for var in mtrace.varnames:

pymc3/distributions/__init__.py

@@ -62,53 +62,51 @@
 from .transforms import sum_to_1
 
 __all__ = ['Uniform',
-    'Flat',
-    'Normal',
-    'Beta',
-    'Exponential',
-    'Laplace',
-    'StudentT',
-    'Cauchy',
-    'HalfCauchy',
-    'Gamma',
-    'Weibull',
-    'Bound',
-    'StudentTpos',
-    'Lognormal',
-    'ChiSquared',
-    'HalfNormal',
-    'Wald',
-    'Pareto',
-    'InverseGamma',
-    'ExGaussian',
-    'VonMises',
-    'Binomial',
-    'BetaBinomial',
-    'Bernoulli',
-    'Poisson',
-    'NegativeBinomial',
-    'ConstantDist',
-    'ZeroInflatedPoisson',
-    'ZeroInflatedNegativeBinomial',
-    'DiscreteUniform',
-    'Geometric',
-    'Categorical',
-    'DensityDist',
-    'Distribution',
-    'Continuous',
-    'Discrete',
-    'NoDistribution',
-    'TensorType',
-    'MvNormal',
-    'MvStudentT',
-    'Dirichlet',
-    'Multinomial',
-    'Wishart',
-    'WishartBartlett',
-    'LKJCorr',
-    'AR1',
-    'GaussianRandomWalk',
-    'GARCH11'
-]
-           
-           
+           'Flat',
+           'Normal',
+           'Beta',
+           'Exponential',
+           'Laplace',
+           'StudentT',
+           'Cauchy',
+           'HalfCauchy',
+           'Gamma',
+           'Weibull',
+           'Bound',
+           'StudentTpos',
+           'Lognormal',
+           'ChiSquared',
+           'HalfNormal',
+           'Wald',
+           'Pareto',
+           'InverseGamma',
+           'ExGaussian',
+           'VonMises',
+           'Binomial',
+           'BetaBinomial',
+           'Bernoulli',
+           'Poisson',
+           'NegativeBinomial',
+           'ConstantDist',
+           'ZeroInflatedPoisson',
+           'ZeroInflatedNegativeBinomial',
+           'DiscreteUniform',
+           'Geometric',
+           'Categorical',
+           'DensityDist',
+           'Distribution',
+           'Continuous',
+           'Discrete',
+           'NoDistribution',
+           'TensorType',
+           'MvNormal',
+           'MvStudentT',
+           'Dirichlet',
+           'Multinomial',
+           'Wishart',
+           'WishartBartlett',
+           'LKJCorr',
+           'AR1',
+           'GaussianRandomWalk',
+           'GARCH11'
+           ]