fix some string syntax issues in docs, and apply isort

ultranest/__init__.py

@@ -10,7 +10,6 @@
 from .integrator import NestedSampler, ReactiveNestedSampler, read_file
 from .utils import vectorize
 
-
 __author__ = """Johannes Buchner"""
 __email__ = '[email protected]'
 __version__ = '4.3.1'

ultranest/calibrator.py

@@ -4,9 +4,11 @@
 ---------------------------
 """
 
+import os
+
 import numpy as np
+
 from ultranest.integrator import ReactiveNestedSampler
-import os
 
 
 def _substitute_log_dir(init_args, nsteps):

ultranest/dychmc.py

@@ -3,9 +3,11 @@
 Uses gradient to reflect at nested sampling boundaries.
 """
 
-from __future__ import print_function, division
-import numpy as np
+from __future__ import division, print_function
+
 import matplotlib.pyplot as plt
+import numpy as np
+
 
 def stop_criterion(thetaminus, thetaplus, rminus, rplus):
     """Compute the stop condition in the main loop

ultranest/dyhmc.py

@@ -4,8 +4,8 @@
 A helper surface is created using the live points.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 import scipy.special
 import scipy.stats
 

ultranest/flatnuts.py

@@ -51,9 +51,10 @@
 """
 
 
+import matplotlib.pyplot as plt
 import numpy as np
 from numpy.linalg import norm
-import matplotlib.pyplot as plt
+
 from .samplingpath import angle, extrapolate_ahead
 
 

ultranest/hotstart.py

@@ -12,7 +12,8 @@
 
 import numpy as np
 import scipy.stats
-from .utils import vectorize, resample_equal
+
+from .utils import resample_equal, vectorize
 
 
 def get_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_factor, df=1):

ultranest/integrator.py

@@ -11,29 +11,36 @@
 # Some parts are from the Nestle library by Kyle Barbary (https://github.com/kbarbary/nestle)
 # Some parts are from the nnest library by Adam Moss (https://github.com/adammoss/nnest)
 
-from __future__ import print_function, division
+from __future__ import division, print_function
 
-import os
-import sys
 import csv
 import json
 import operator
+import os
+import sys
 import time
 import warnings
 
-from numpy import log, exp, logaddexp
 import numpy as np
+from numpy import exp, log, logaddexp
 
-from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
-from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
-from ultranest.mlfriends import MLFriends, AffineLayer, LocalAffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
-from .store import HDF5PointStore, TextPointStore, NullPointStore
-from .viz import get_default_viz_callback
-from .ordertest import UniformOrderAccumulator
-from .netiter import PointPile, SingleCounter, MultiCounter, BreadthFirstIterator, TreeNode, count_tree_between, find_nodes_before, logz_sequence
-from .netiter import dump_tree, combine_results
-from .hotstart import get_auxiliary_contbox_parameterization
+from ultranest.mlfriends import (AffineLayer, LocalAffineLayer, MLFriends,
+                                 RobustEllipsoidRegion, ScalingLayer,
+                                 WrappingEllipsoid, find_nearby)
 
+from .hotstart import get_auxiliary_contbox_parameterization
+from .netiter import (BreadthFirstIterator, MultiCounter, PointPile,
+                      SingleCounter, TreeNode, combine_results,
+                      count_tree_between, dump_tree, find_nodes_before,
+                      logz_sequence)
+from .ordertest import UniformOrderAccumulator
+from .store import HDF5PointStore, NullPointStore, TextPointStore
+from .utils import (create_logger, distributed_work_chunk_size,
+                    is_affine_transform)
+from .utils import listify as _listify
+from .utils import (make_run_dir, normalised_kendall_tau_distance,
+                    resample_equal, vectorize, vol_prefactor)
+from .viz import get_default_viz_callback
 
 __all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file', 'warmstart_from_similar_file']
 
@@ -914,8 +921,8 @@ def print_results(self):
     def plot(self):
         """Make corner plot."""
         if self.log_to_disk:
-            import matplotlib.pyplot as plt
             import corner
+            import matplotlib.pyplot as plt
             data = np.array(self.results['weighted_samples']['points'])
             weights = np.array(self.results['weighted_samples']['weights'])
             cumsumweights = np.cumsum(weights)
@@ -2989,8 +2996,9 @@ def plot_corner(self):
             cornerplot(results)
 
         """
-        from .plot import cornerplot
         import matplotlib.pyplot as plt
+
+        from .plot import cornerplot
         if self.log:
             self.logger.debug('Making corner plot ...')
         cornerplot(self.results, logger=self.logger if self.log else None)
@@ -3011,8 +3019,9 @@ def plot_trace(self):
             traceplot(results=results, labels=paramnames + derivedparamnames)
 
         """
-        from .plot import traceplot
         import matplotlib.pyplot as plt
+
+        from .plot import traceplot
         if self.log:
             self.logger.debug('Making trace plot ... ')
         paramnames = self.paramnames + self.derivedparamnames
@@ -3035,8 +3044,9 @@ def plot_run(self):
             runplot(results=results)
 
         """
-        from .plot import runplot
         import matplotlib.pyplot as plt
+
+        from .plot import runplot
         if self.log:
             self.logger.debug('Making run plot ... ')
         # get dynesty-compatible sequences

ultranest/netiter.py

@@ -21,12 +21,14 @@
 can ignore the rootids it does not know about.
 """
 
-import numpy as np
-from numpy import log, log1p, exp, logaddexp
 import math
 import sys
-from .utils import resample_equal
+
+import numpy as np
+from numpy import exp, log, log1p, logaddexp
+
 from .ordertest import UniformOrderAccumulator
+from .utils import resample_equal
 
 
 class TreeNode(object):

ultranest/ordertest.py

@@ -22,7 +22,7 @@
 
 """
 
-from __future__ import print_function, division
+from __future__ import division, print_function
 
 __all__ = ['infinite_U_zscore', 'UniformOrderAccumulator']
 

ultranest/pathsampler.py

@@ -3,16 +3,17 @@
 These features are experimental.
 """
 
-import numpy as np
-
 import matplotlib.pyplot as plt
+import numpy as np
 
-from ultranest.samplingpath import SamplingPath, ContourSamplingPath, extrapolate_ahead
-from ultranest.stepsampler import StepSampler
-from ultranest.stepsampler import generate_region_oriented_direction, generate_region_random_direction, generate_random_direction
-
-from ultranest.flatnuts import ClockedStepSampler, ClockedBisectSampler, ClockedNUTSSampler
-from ultranest.flatnuts import SingleJumper, DirectJumper, IntervalJumper
+from ultranest.flatnuts import (ClockedBisectSampler, ClockedNUTSSampler,
+                                ClockedStepSampler, DirectJumper,
+                                IntervalJumper, SingleJumper)
+from ultranest.samplingpath import (ContourSamplingPath, SamplingPath,
+                                    extrapolate_ahead)
+from ultranest.stepsampler import (StepSampler, generate_random_direction,
+                                   generate_region_oriented_direction,
+                                   generate_region_random_direction)
 
 
 class SamplingPathSliceSampler(StepSampler):

ultranest/plot.py

@@ -5,25 +5,23 @@
 
 """
 
-from __future__ import (print_function, division)
-from six.moves import range
+from __future__ import division, print_function
 
 import logging
 import types
 import warnings
 
-import numpy as np
 import matplotlib.pyplot as pl
-from matplotlib.ticker import MaxNLocator, NullLocator
-# from matplotlib.colors import LinearSegmentedColormap, colorConverter
-from matplotlib.ticker import ScalarFormatter
-
-import scipy.stats
 import matplotlib.pyplot as plt
 import numpy
+import numpy as np
+import scipy.stats
+# from matplotlib.colors import LinearSegmentedColormap, colorConverter
+from matplotlib.ticker import MaxNLocator, NullLocator, ScalarFormatter
+from six.moves import range
 
-from .utils import resample_equal
 from .utils import quantile as _quantile
+from .utils import resample_equal
 
 try:
     str_type = types.StringTypes
@@ -183,8 +181,8 @@ def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probabil
     >>> print('x = %.1f + %.2f - %.2f' % hdi)
     x = 0.0 + 1.02 - 0.96
     """
-    from getdist.mcsamples import MCSamples
     import getdist.chains
+    from getdist.mcsamples import MCSamples
     getdist.chains.print_load_details = False
     samples = MCSamples(
         samples=xsamples, names=['x'], ranges={'x':[xlo,xhi]},
@@ -431,6 +429,7 @@ def runplot(results, span=None, logplot=False, kde=True, nkde=1000,
         try:
             # from scipy.ndimage import gaussian_filter as norm_kde
             from scipy.stats import gaussian_kde
+
             # Derive kernel density estimate.
             wt_kde = gaussian_kde(resample_equal(-logvol, weights))  # KDE
             logvol_new = np.linspace(logvol[0], logvol[-1], nkde)  # resample
@@ -723,6 +722,7 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
         try:
             from scipy.ndimage import gaussian_filter as norm_kde
             from scipy.stats import gaussian_kde
+
             # Derive kernel density estimate.
             wt_kde = gaussian_kde(resample_equal(-logvol, weights))  # KDE
             logvol_grid = np.linspace(logvol[0], logvol[-1], nkde)  # resample

ultranest/popstepsampler.py

@@ -10,13 +10,18 @@
 """
 
 import numpy as np
-from ultranest.utils import submasks
-from ultranest.stepfuncs import evolve, step_back, update_vectorised_slice_sampler
-from ultranest.stepfuncs import generate_cube_oriented_direction, generate_cube_oriented_direction_scaled
-from ultranest.stepfuncs import generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
-from ultranest.stepfuncs import generate_differential_direction, generate_mixture_random_direction
 import scipy.stats
 
+from ultranest.stepfuncs import (evolve, generate_cube_oriented_direction,
+                                 generate_cube_oriented_direction_scaled,
+                                 generate_differential_direction,
+                                 generate_mixture_random_direction,
+                                 generate_random_direction,
+                                 generate_region_oriented_direction,
+                                 generate_region_random_direction, step_back,
+                                 update_vectorised_slice_sampler)
+from ultranest.utils import submasks
+
 int_t = int
 
 

ultranest/samplingpath.py

@@ -4,9 +4,9 @@
 """
 
 
+import matplotlib.pyplot as plt
 import numpy as np
 from numpy.linalg import norm
-import matplotlib.pyplot as plt
 
 
 def nearest_box_intersection_line(ray_origin, ray_direction, fwd=True):

ultranest/solvecompat.py

@@ -12,9 +12,10 @@
 """
 
 
-import numpy as np
 import string
 
+import numpy as np
+
 from .integrator import ReactiveNestedSampler
 from .stepsampler import SliceSampler, generate_mixture_random_direction
 

ultranest/stepsampler.py

@@ -9,11 +9,14 @@
 even if they do not terminate at the same number of iterations.
 """
 
-from __future__ import print_function, division
-import numpy as np
+from __future__ import division, print_function
+
+from warnings import warn
+
 import matplotlib.pyplot as plt
+import numpy as np
+
 from .utils import listify as _listify
-from warnings import warn
 
 
 def generate_random_direction(ui, region, scale=1):

ultranest/store.py

@@ -10,10 +10,12 @@
 
 """
 
-from __future__ import print_function, division
-import numpy as np
-import warnings
+from __future__ import division, print_function
+
 import os
+import warnings
+
+import numpy as np
 
 
 class NullPointStore(object):

ultranest/utils.py

@@ -4,13 +4,15 @@
 --------------------------------------------
 """
 
-from __future__ import print_function, division
+from __future__ import division, print_function
+
+import errno
 import logging
-import sys
 import os
+import sys
+
 import numpy as np
 from numpy import pi
-import errno
 
 
 def create_logger(module_name, log_dir=None, level=logging.INFO):