diff --git a/create_bib_file.py b/create_bib_file.py
index 607cc3dddf7c30faa2e26fa43a7e17ebda5353ee..f70eb15c8c0b3e4be5c91d9f17dde3bed904044c 100644
--- a/create_bib_file.py
+++ b/create_bib_file.py
@@ -9,7 +9,7 @@ import requests
import yaml
-def replace_key(key, bib_entry):
+def edit_raw_bibtex_entry(key, bib_entry):
bib_type, *_ = bib_entry.split("{")
_, *rest = bib_entry.split(",")
rest = ",".join(rest)
@@ -40,8 +40,6 @@ def replace_key(key, bib_entry):
), # fix for PhysRevLett.96.026804
]
- # I got these by using JabRef and converting to abbr journals
- # and parsing the git diff.
journals = [
("Advanced Materials", "Adv. Mater."),
("Annals of Physics", "Ann. Phys."),
@@ -73,10 +71,6 @@ def replace_key(key, bib_entry):
("Science Advances", "Sci. Adv."),
("Scientific Reports", "Sci. Rep."),
("Semiconductor Science and Technology", "Semicond. Sci. Technol."),
- ]
-
- # Manually added
- journals += [
(
"Annual Review of Condensed Matter Physics",
"Annu. Rev. Condens. Matter Phys.",
@@ -108,12 +102,12 @@ def doi2bib(doi):
return r.text
-fname = 'paper.yaml'
+fname = "paper.yaml"
print("Reading: ", fname)
with open(fname) as f:
- mapping = yaml.safe_load(f)
-dois = dict(sorted(mapping.items()))
+ dois = yaml.safe_load(f)
+dois = dict(sorted(dois.items()))
with ThreadPoolExecutor() as ex:
@@ -121,19 +115,19 @@ with ThreadPoolExecutor() as ex:
bibs = list(futs)
-entries = [replace_key(key, bib) for key, bib in zip(dois.keys(), bibs)]
+entries = [edit_raw_bibtex_entry(key, bib) for key, bib in zip(dois.keys(), bibs)]
-with open("paper.bib", "w") as outfile:
+with open("paper.bib", "w") as out_file:
fname = "not_on_crossref.bib"
- outfile.write("@preamble{ {\\providecommand{\\BIBYu}{Yu} } }\n\n")
- outfile.write(f"\n% Below is from `{fname}`.\n\n")
- with open(fname) as infile:
- outfile.write(infile.read())
- outfile.write("\n% Below is from `paper.yaml` files.\n\n")
+ out_file.write("@preamble{ {\\providecommand{\\BIBYu}{Yu} } }\n\n")
+ out_file.write(f"\n% Below is from `{fname}`.\n\n")
+ with open(fname) as in_file:
+ out_file.write(in_file.read())
+ out_file.write("\n% Below is from `paper.yaml`.\n\n")
for e in entries:
- for line in e.split('\n'):
+ for line in e.split("\n"):
# Remove the url line
if "url = {" not in line:
- outfile.write(f"{line}\n")
- outfile.write('\n')
+ out_file.write(f"{line}\n")
+ out_file.write("\n")
diff --git a/ipynb_filter.py b/ipynb_filter.py
index 7165a27c72d2e207412be7e6b83585bc2fc69cf7..4d8c9fbd7f299add031cb57cfde3f059cf8dd4b0 100644
--- a/ipynb_filter.py
+++ b/ipynb_filter.py
@@ -16,14 +16,16 @@ from nbconvert.preprocessors import Preprocessor
class RemoveMetadata(Preprocessor):
def preprocess(self, nb, resources):
- nb.metadata = {"language_info": {"name":"python",
- "pygments_lexer": "ipython3"}}
+ nb.metadata = {
+ "language_info": {"name": "python", "pygments_lexer": "ipython3"}
+ }
return nb, resources
-if __name__ == '__main__':
+if __name__ == "__main__":
# The filter is getting activated
import os
+
git_cmd = 'git config filter.ipynb_filter.clean "jupyter nbconvert --to notebook --config ipynb_filter.py --stdin --stdout"'
os.system(git_cmd)
else:
@@ -31,4 +33,8 @@ else:
c.Exporter.preprocessors = [RemoveMetadata]
c.ClearOutputPreprocessor.enabled = True
c.ClearOutputPreprocessor.remove_metadata_fields = [
- "deletable", "editable", "collapsed", "scrolled"]
+ "deletable",
+ "editable",
+ "collapsed",
+ "scrolled",
+ ]
diff --git a/pfaffian.py b/pfaffian.py
index 88d3f849a5d5d196285cbe7619eff1bb0e1e84cb..651dc4e2b366f7a98fcafd77e45122992bd4ee7f 100644
--- a/pfaffian.py
+++ b/pfaffian.py
@@ -1,11 +1,12 @@
"""A package for computing Pfaffians"""
+import cmath
+import math
+
import numpy as np
import scipy.linalg as la
import scipy.sparse as sp
-import math
-import cmath
def householder_real(x):
@@ -24,7 +25,7 @@ def householder_real(x):
if sigma == 0:
return (np.zeros(x.shape[0]), 0, x[0])
else:
- norm_x = math.sqrt(x[0]**2+sigma)
+ norm_x = math.sqrt(x[0] ** 2 + sigma)
v = x.copy()
@@ -57,17 +58,17 @@ def householder_complex(x):
if sigma == 0:
return (np.zeros(x.shape[0]), 0, x[0])
else:
- norm_x = cmath.sqrt(x[0].conjugate()*x[0]+sigma)
+ norm_x = cmath.sqrt(x[0].conjugate() * x[0] + sigma)
v = x.copy()
- phase = cmath.exp(1j*math.atan2(x[0].imag, x[0].real))
+ phase = cmath.exp(1j * math.atan2(x[0].imag, x[0].real))
- v[0] += phase*norm_x
+ v[0] += phase * norm_x
v /= np.linalg.norm(v)
- return (v, 2, -phase*norm_x)
+ return (v, 2, -phase * norm_x)
def skew_tridiagonalize(A, overwrite_a=False, calc_q=True):
@@ -90,7 +91,7 @@ def skew_tridiagonalize(A, overwrite_a=False, calc_q=True):
# Check if matrix is square
assert A.shape[0] == A.shape[1] > 0
# Check if it's skew-symmetric
- assert abs((A+A.T).max()) < 1e-14
+ assert abs((A + A.T).max()) < 1e-14
n = A.shape[0]
A = np.asarray(A) # the slice views work only properly for arrays
@@ -109,24 +110,24 @@ def skew_tridiagonalize(A, overwrite_a=False, calc_q=True):
if calc_q:
Q = np.eye(A.shape[0], dtype=A.dtype)
- for i in range(A.shape[0]-2):
+ for i in range(A.shape[0] - 2):
# Find a Householder vector to eliminate the i-th column
- v, tau, alpha = householder(A[i+1:, i])
- A[i+1, i] = alpha
- A[i, i+1] = -alpha
- A[i+2:, i] = 0
- A[i, i+2:] = 0
+ v, tau, alpha = householder(A[i + 1 :, i])
+ A[i + 1, i] = alpha
+ A[i, i + 1] = -alpha
+ A[i + 2 :, i] = 0
+ A[i, i + 2 :] = 0
# Update the matrix block A(i+1:N,i+1:N)
- w = tau * A[i+1:, i+1:] @ v.conj()
- A[i+1:, i+1:] += np.outer(v, w)-np.outer(w, v)
+ w = tau * A[i + 1 :, i + 1 :] @ v.conj()
+ A[i + 1 :, i + 1 :] += np.outer(v, w) - np.outer(w, v)
if calc_q:
# Accumulate the individual Householder reflections
# Accumulate them in the form P_1*P_2*..., which is
# (..*P_2*P_1)^dagger
- y = tau * Q[:, i+1:] @ v
- Q[:, i+1:] -= np.outer(y, v.conj())
+ y = tau * Q[:, i + 1 :] @ v
+ Q[:, i + 1 :] -= np.outer(y, v.conj())
if calc_q:
return (np.asmatrix(A), np.asmatrix(Q))
@@ -150,7 +151,7 @@ def skew_LTL(A, overwrite_a=False, calc_L=True, calc_P=True):
# Check if matrix is square
assert A.shape[0] == A.shape[1] > 0
# Check if it's skew-symmetric
- assert abs((A+A.T).max()) < 1e-14
+ assert abs((A + A.T).max()) < 1e-14
n = A.shape[0]
A = np.asarray(A) # the slice views work only properly for arrays
@@ -164,50 +165,50 @@ def skew_LTL(A, overwrite_a=False, calc_L=True, calc_P=True):
if calc_P:
Pv = np.arange(n)
- for k in range(n-2):
+ for k in range(n - 2):
# First, find the largest entry in A[k+1:,k] and
# permute it to A[k+1,k]
- kp = k+1+np.abs(A[k+1:, k]).argmax()
+ kp = k + 1 + np.abs(A[k + 1 :, k]).argmax()
# Check if we need to pivot
- if kp != k+1:
+ if kp != k + 1:
# interchange rows k+1 and kp
- temp = A[k+1, k:].copy()
- A[k+1, k:] = A[kp, k:]
+ temp = A[k + 1, k:].copy()
+ A[k + 1, k:] = A[kp, k:]
A[kp, k:] = temp
# Then interchange columns k+1 and kp
- temp = A[k:, k+1].copy()
- A[k:, k+1] = A[k:, kp]
+ temp = A[k:, k + 1].copy()
+ A[k:, k + 1] = A[k:, kp]
A[k:, kp] = temp
if calc_L:
# permute L accordingly
- temp = L[k+1, 1:k+1].copy()
- L[k+1, 1:k+1] = L[kp, 1:k+1]
- L[kp, 1:k+1] = temp
+ temp = L[k + 1, 1 : k + 1].copy()
+ L[k + 1, 1 : k + 1] = L[kp, 1 : k + 1]
+ L[kp, 1 : k + 1] = temp
if calc_P:
# accumulate the permutation matrix
- temp = Pv[k+1]
- Pv[k+1] = Pv[kp]
+ temp = Pv[k + 1]
+ Pv[k + 1] = Pv[kp]
Pv[kp] = temp
# Now form the Gauss vector
- if A[k+1, k] != 0.0:
- tau = A[k+2:, k].copy()
- tau /= A[k+1, k]
+ if A[k + 1, k] != 0.0:
+ tau = A[k + 2 :, k].copy()
+ tau /= A[k + 1, k]
# clear eliminated row and column
- A[k+2:, k] = 0.0
- A[k, k+2:] = 0.0
+ A[k + 2 :, k] = 0.0
+ A[k, k + 2 :] = 0.0
# Update the matrix block A(k+2:,k+2)
- A[k+2:, k+2:] += np.outer(tau, A[k+2:, k+1])
- A[k+2:, k+2:] -= np.outer(A[k+2:, k+1], tau)
+ A[k + 2 :, k + 2 :] += np.outer(tau, A[k + 2 :, k + 1])
+ A[k + 2 :, k + 2 :] -= np.outer(A[k + 2 :, k + 1], tau)
if calc_L:
- L[k+2:, k+1] = tau
+ L[k + 2 :, k + 1] = tau
if calc_P:
# form the permutation matrix as a sparse matrix
@@ -225,7 +226,7 @@ def skew_LTL(A, overwrite_a=False, calc_L=True, calc_P=True):
return np.asmatrix(A)
-def pfaffian(A, overwrite_a=False, method='P', sign_only=False):
+def pfaffian(A, overwrite_a=False, method="P", sign_only=False):
""" pfaffian(A, overwrite_a=False, method='P')
Compute the Pfaffian of a real or complex skew-symmetric
@@ -237,12 +238,12 @@ def pfaffian(A, overwrite_a=False, method='P', sign_only=False):
# Check if matrix is square
assert A.shape[0] == A.shape[1] > 0
# Check if it's skew-symmetric
- assert abs((A+A.T).max()) < 1e-14, abs((A+A.T).max())
+ assert abs((A + A.T).max()) < 1e-14, abs((A + A.T).max())
# Check that the method variable is appropriately set
- assert method == 'P' or method == 'H'
- if method == 'H' and sign_only:
+ assert method == "P" or method == "H"
+ if method == "H" and sign_only:
raise Exception("Use `method='P'` when using `sign_only=True`")
- if method == 'P':
+ if method == "P":
return pfaffian_LTL(A, overwrite_a, sign_only)
else:
return pfaffian_householder(A, overwrite_a)
@@ -259,7 +260,7 @@ def pfaffian_LTL(A, overwrite_a=False, sign_only=False):
# Check if matrix is square
assert A.shape[0] == A.shape[1] > 0
# Check if it's skew-symmetric
- assert abs((A+A.T).max()) < 1e-14
+ assert abs((A + A.T).max()) < 1e-14
n = A.shape[0]
A = np.asarray(A) # the slice views work only properly for arrays
@@ -273,40 +274,40 @@ def pfaffian_LTL(A, overwrite_a=False, sign_only=False):
pfaffian_val = 1.0
- for k in range(0, n-1, 2):
+ for k in range(0, n - 1, 2):
# First, find the largest entry in A[k+1:,k] and
# permute it to A[k+1,k]
- kp = k+1+np.abs(A[k+1:, k]).argmax()
+ kp = k + 1 + np.abs(A[k + 1 :, k]).argmax()
# Check if we need to pivot
- if kp != k+1:
+ if kp != k + 1:
# interchange rows k+1 and kp
- temp = A[k+1, k:].copy()
- A[k+1, k:] = A[kp, k:]
+ temp = A[k + 1, k:].copy()
+ A[k + 1, k:] = A[kp, k:]
A[kp, k:] = temp
# Then interchange columns k+1 and kp
- temp = A[k:, k+1].copy()
- A[k:, k+1] = A[k:, kp]
+ temp = A[k:, k + 1].copy()
+ A[k:, k + 1] = A[k:, kp]
A[k:, kp] = temp
# every interchange corresponds to a "-" in det(P)
pfaffian_val *= -1
# Now form the Gauss vector
- if A[k+1, k] != 0.0:
- tau = A[k, k+2:].copy()
- tau /= A[k, k+1]
+ if A[k + 1, k] != 0.0:
+ tau = A[k, k + 2 :].copy()
+ tau /= A[k, k + 1]
if sign_only:
- pfaffian_val *= np.sign(A[k, k+1])
+ pfaffian_val *= np.sign(A[k, k + 1])
else:
- pfaffian_val *= A[k, k+1]
+ pfaffian_val *= A[k, k + 1]
- if k+2 < n:
+ if k + 2 < n:
# Update the matrix block A(k+2:,k+2)
- A[k+2:, k+2:] += np.outer(tau, A[k+2:, k+1])
- A[k+2:, k+2:] -= np.outer(A[k+2:, k+1], tau)
+ A[k + 2 :, k + 2 :] += np.outer(tau, A[k + 2 :, k + 1])
+ A[k + 2 :, k + 2 :] -= np.outer(A[k + 2 :, k + 1], tau)
else:
# if we encounter a zero on the super/subdiagonal, the
# Pfaffian is 0
@@ -331,7 +332,7 @@ def pfaffian_householder(A, overwrite_a=False):
# Check if matrix is square
assert A.shape[0] == A.shape[1] > 0
# Check if it's skew-symmetric
- assert abs((A+A.T).max()) < 1e-14
+ assert abs((A + A.T).max()) < 1e-14
n = A.shape[0]
@@ -352,26 +353,26 @@ def pfaffian_householder(A, overwrite_a=False):
if not overwrite_a:
A = A.copy()
- pfaffian_val = 1.
+ pfaffian_val = 1.0
- for i in range(A.shape[0]-2):
+ for i in range(A.shape[0] - 2):
# Find a Householder vector to eliminate the i-th column
- v, tau, alpha = householder(A[i+1:, i])
- A[i+1, i] = alpha
- A[i, i+1] = -alpha
- A[i+2:, i] = 0
- A[i, i+2:] = 0
+ v, tau, alpha = householder(A[i + 1 :, i])
+ A[i + 1, i] = alpha
+ A[i, i + 1] = -alpha
+ A[i + 2 :, i] = 0
+ A[i, i + 2 :] = 0
# Update the matrix block A(i+1:N,i+1:N)
- w = tau * A[i+1:, i+1:] @ v.conj()
- A[i+1:, i+1:] += np.outer(v, w) - np.outer(w, v)
+ w = tau * A[i + 1 :, i + 1 :] @ v.conj()
+ A[i + 1 :, i + 1 :] += np.outer(v, w) - np.outer(w, v)
if tau != 0:
- pfaffian_val *= 1-tau
+ pfaffian_val *= 1 - tau
if i % 2 == 0:
pfaffian_val *= -alpha
- pfaffian_val *= A[n-2, n-1]
+ pfaffian_val *= A[n - 2, n - 1]
return pfaffian_val
@@ -388,7 +389,8 @@ def pfaffian_schur(A, overwrite_a=False):
"""
assert np.issubdtype(A.dtype, np.number) and not np.issubdtype(
- A.dtype, np.complexfloating)
+ A.dtype, np.complexfloating
+ )
assert A.shape[0] == A.shape[1] > 0
@@ -398,7 +400,7 @@ def pfaffian_schur(A, overwrite_a=False):
if A.shape[0] % 2 == 1:
return 0
- (t, z) = la.schur(A, output='real', overwrite_a=overwrite_a)
+ (t, z) = la.schur(A, output="real", overwrite_a=overwrite_a)
l = np.diag(t, 1)
return np.prod(l[::2]) * la.det(z)
@@ -415,7 +417,7 @@ def pfaffian_sign(A, overwrite_a=False):
# Check if matrix is square
assert A.shape[0] == A.shape[1] > 0
# Check if it's skew-symmetric
- assert abs((A+A.T).max()) < 1e-14, abs((A+A.T).max())
+ assert abs((A + A.T).max()) < 1e-14, abs((A + A.T).max())
return pfaffian_LTL_sign(A, overwrite_a)
@@ -431,7 +433,7 @@ def pfaffian_LTL_sign(A, overwrite_a=False):
# Check if matrix is square
assert A.shape[0] == A.shape[1] > 0
# Check if it's skew-symmetric
- assert abs((A+A.T).max()) < 1e-14
+ assert abs((A + A.T).max()) < 1e-14
n = A.shape[0]
A = np.asarray(A) # the slice views work only properly for arrays
@@ -445,37 +447,37 @@ def pfaffian_LTL_sign(A, overwrite_a=False):
pfaffian_val = 1.0
- for k in range(0, n-1, 2):
+ for k in range(0, n - 1, 2):
# First, find the largest entry in A[k+1:,k] and
# permute it to A[k+1,k]
- kp = k+1+np.abs(A[k+1:, k]).argmax()
+ kp = k + 1 + np.abs(A[k + 1 :, k]).argmax()
# Check if we need to pivot
- if kp != k+1:
+ if kp != k + 1:
# interchange rows k+1 and kp
- temp = A[k+1, k:].copy()
- A[k+1, k:] = A[kp, k:]
+ temp = A[k + 1, k:].copy()
+ A[k + 1, k:] = A[kp, k:]
A[kp, k:] = temp
# Then interchange columns k+1 and kp
- temp = A[k:, k+1].copy()
- A[k:, k+1] = A[k:, kp]
+ temp = A[k:, k + 1].copy()
+ A[k:, k + 1] = A[k:, kp]
A[k:, kp] = temp
# every interchange corresponds to a "-" in det(P)
pfaffian_val *= -1
# Now form the Gauss vector
- if A[k+1, k] != 0.0:
- tau = A[k, k+2:].copy()
- tau /= A[k, k+1]
+ if A[k + 1, k] != 0.0:
+ tau = A[k, k + 2 :].copy()
+ tau /= A[k, k + 1]
- pfaffian_val *= A[k, k+1]
+ pfaffian_val *= A[k, k + 1]
- if k+2 < n:
+ if k + 2 < n:
# Update the matrix block A(k+2:,k+2)
- A[k+2:, k+2:] += np.outer(tau, A[k+2:, k+1])
- A[k+2:, k+2:] -= np.outer(A[k+2:, k+1], tau)
+ A[k + 2 :, k + 2 :] += np.outer(tau, A[k + 2 :, k + 1])
+ A[k + 2 :, k + 2 :] -= np.outer(A[k + 2 :, k + 1], tau)
else:
# if we encounter a zero on the super/subdiagonal, the
# Pfaffian is 0