Increase anesthetic version + typos (#97)

* change trivial things which have literally being hanging around for years

* bump version number

* update anesthetic syntax

* add anesthetic test

* remember how syntax works

* install correct anesthetic version

* check python version for anesthetic

* add spaces

* exclude 3.6 and 3.7

* put whole expression in brackets

* correct python version

* change to not contains

* put brackets in the right place

* change to single string

* correct silly version mistake

.github/workflows/CI.yml

@@ -44,3 +44,9 @@ jobs:
 
  - name: Test pypolychord (MPI)
  run: mpirun -np 2 python run_pypolychord.py
+
+ - name: Test pypolychord (anesthetic)
+ if: ${{ ! contains( '3.6, 3.7', matrix.python-version ) }}
+ run: |
+ pip install -r requirements.txt
+ python run_pypolychord.py

Python_Functions/run_pypolychord_gaussian_shells.py

@@ -41,7 +41,7 @@ def likelihood(theta):
  r2 = 0
 
 
- def logincexp(loga,logb,logx=None):
+ def logincexp(loga,logb,logc=None):
  if (loga>logb):
  loga = loga + log(exp(logb-loga) + 1)
  else:

README.rst

@@ -5,7 +5,7 @@
  :target: https://arxiv.org/abs/1506.00171
  :alt: Open-access paper
 
-PolyChord v 1.20.1
+PolyChord v 1.20.2
 
 Will Handley, Mike Hobson & Anthony Lasenby
 

pypolychord/__init__.py

@@ -1,3 +1,3 @@
-__version__ = "1.20.1"
+__version__ = "1.20.2"
 from pypolychord.settings import PolyChordSettings
 from pypolychord.polychord import run_polychord

requirements.txt

@@ -1,3 +1,3 @@
 numpy
 scipy
-anesthetic>=2.0.0b11
+anesthetic>=2.0.0b19

run_pypolychord.py

@@ -1,4 +1,4 @@
-from numpy import pi, log, sqrt
+from numpy import pi, log
 import pypolychord
 from pypolychord.settings import PolyChordSettings
 from pypolychord.priors import UniformPrior
@@ -58,10 +58,11 @@ def dumper(live, dead, logweights, logZ, logZerr):
 
 #| Make an anesthetic plot (could also use getdist)
 try:
- from anesthetic import NestedSamples
- samples = NestedSamples(root= settings.base_dir + '/' + settings.file_root)
- fig, axes = samples.plot_2d(['p0','p1','p2','p3','r'])
- fig.savefig('posterior.pdf')
+ from matplotlib import pyplot as plt
+ from anesthetic import read_chains
+ samples = read_chains(settings.base_dir + '/' + settings.file_root)
+ samples.plot_2d(['p0','p1','p2','p3','r'])
+ plt.savefig('posterior.pdf')
 
 except ImportError:
  try:

src/polychord/clustering.f90

@@ -5,12 +5,12 @@ module KNN_clustering
  contains
 
  !> This function returns a clustering from a similarity matrix based on
- !! 'nearest neighbor' clustering.
+ !! 'nearest neighbour' clustering.
  !!
  !! Points belong to the same cluster if they are in either of each others k
  !! nearest neighbor sets. 
  !!
- !! The algorithm computes the k nearest neihbor sets from the similarity
+ !! The algorithm computes the k nearest neighbour sets from the similarity
  !! matrix, and then tests
  recursive function NN_clustering(similarity_matrix,num_clusters) result(cluster_list)
  use utils_module, only: relabel
@@ -113,8 +113,8 @@ function do_clustering_k(knn) result(c)
 
  ! If they're not in the same cluster already...
  if(c(i)/=c(j)) then
- ! ... check to see if they are within each others k nearest neihbors...
- if( neighbors( knn(:,i),knn(:,j) ) ) then
+ ! ... check to see if they are within each others k nearest neighbours...
+ if( neighbours( knn(:,i),knn(:,j) ) ) then
 
  ! If they are then relabel cluster_i and cluster_j to the smaller of the two
  where(c==c(i).or.c==c(j)) 
@@ -137,10 +137,10 @@ function compute_knn(similarity_matrix,k) result(knn)
  !> The data to compute on
  real(dp), intent(in),dimension(:,:) :: similarity_matrix
 
- !> The number of nearest neighbors to compute
+ !> The number of nearest neighbours to compute
  integer, intent(in) :: k
 
- ! The indices of the k nearest neighbors to output
+ ! The indices of the k nearest neighbours to output
  integer, dimension(k,size(similarity_matrix,1)) :: knn
 
  integer :: nPoints,i,j
@@ -154,7 +154,7 @@ function compute_knn(similarity_matrix,k) result(knn)
  knn=0
 
  do i=1,nPoints
- ! Find the k nearest neighbors for each point
+ ! Find the k nearest neighbours for each point
  distance2s = huge(1d0)
  do j=1,nPoints
  ! If the distance between i and j is too large to be considered,
@@ -174,22 +174,22 @@ function compute_knn(similarity_matrix,k) result(knn)
  end function compute_knn
 
 
- ! Return whether they're each others n nearest neighbor list
- function neighbors(knn1,knn2) result(same_list)
+ ! Return whether they're each others n nearest neighbour list
+ function neighbours(knn1,knn2) result(same_list)
  implicit none
  integer,intent(in), dimension(:) :: knn1
  integer,intent(in), dimension(:) :: knn2 
 
  logical :: same_list
 
- ! Check to see if they're in each others neighbors lists
+ ! Check to see if they're in each others neighbours lists
  same_list= any(knn1==knn2(1)) .or. any(knn2==knn1(1))
 
- end function neighbors
+ end function neighbours
 
 
 
- ! Return the number of matches in the n nearest neighbor list
+ ! Return the number of matches in the n nearest neighbour list
  function matches(knn1,knn2)
  implicit none
  integer,intent(in), dimension(:) :: knn1

src/polychord/feedback.f90

@@ -28,7 +28,7 @@ subroutine write_opening_statement(settings)
  write(stdout_unit,'("")')
  write(stdout_unit,'("PolyChord: Next Generation Nested Sampling")')
  write(stdout_unit,'("copyright: Will Handley, Mike Hobson & Anthony Lasenby")')
- write(stdout_unit,'(" version: 1.20.1")')
+ write(stdout_unit,'(" version: 1.20.2")')
  write(stdout_unit,'(" release: 1st June 2021")')
  write(stdout_unit,'(" email: wh260@mrao.cam.ac.uk")')
  write(stdout_unit,'("")')