Convert citations

dapper/da_methods/baseline.py

@@ -1,6 +1,6 @@
 """Unsophisticated" but robust (widely applicable) DA methods.
 
-Many are based on `bib.raanes2016thesis`.
+Many are based on [raanes2016thesis][].
 """
 
 from typing import Callable, Optional

dapper/da_methods/ensemble.py

@@ -28,7 +28,7 @@ class ens_method:
 class EnKF:
     """The ensemble Kalman filter.
 
-    Refs: `bib.evensen2009ensemble`.
+    Refs: [evensen2009ensemble][].
     """
 
     upd_a: str
@@ -67,8 +67,8 @@ def EnKF_analysis(E, Eo, hnoise, y, upd_a, stats=None, ko=None):
     This implementation includes several flavours and forms,
     specified by `upd_a`.
 
-    Main references: `bib.sakov2008deterministic`,
-    `bib.sakov2008implications`, `bib.hoteit2015mitigating`
+    Main references: [sakov2008deterministic][],
+    [sakov2008implications][], [hoteit2015mitigating][]
     """
     R = hnoise.C  # Obs noise cov
     N, Nx = E.shape  # Dimensionality
@@ -261,7 +261,7 @@ def post_process(E, infl, rot):
 def add_noise(E, dt, noise, method):
     """Treatment of additive noise for ensembles.
 
-    Refs: `bib.raanes2014ext`
+    Refs: [raanes2014ext][]
     """
     if noise.C == 0:
         return E
@@ -354,7 +354,7 @@ def sqrt_core():
 class EnKS:
     """The ensemble Kalman smoother.
 
-    Refs: `bib.evensen2009ensemble`
+    Refs: [evensen2009ensemble][]
 
     The only difference to the EnKF
     is the management of the lag and the reshapings.
@@ -414,7 +414,7 @@ def assimilate(self, HMM, xx, yy):
 class EnRTS:
     """EnRTS (Rauch-Tung-Striebel) smoother.
 
-    Refs: `bib.raanes2016thesis`
+    Refs: [raanes2016thesis][]
     """
 
     upd_a: str
@@ -484,7 +484,7 @@ def serial_inds(upd_a, y, cvR, A):
 class SL_EAKF:
     """Serial, covariance-localized EAKF.
 
-    Refs: `bib.karspeck2007experimental`.
+    Refs: [karspeck2007experimental][].
 
     In contrast with LETKF, this iterates over the observations rather
     than over the state (batches).
@@ -614,7 +614,7 @@ def local_analysis(ii):
 class LETKF:
     """Same as EnKF (Sqrt), but with localization.
 
-    Refs: `bib.hunt2007efficient`.
+    Refs: [hunt2007efficient][].
 
     NB: Multiproc. yields slow-down for [`mods.Lorenz96`][],
     even with `batch_size=(1,)`. But for [`mods.QG`][]
@@ -778,7 +778,7 @@ def hyperprior_coeffs(s, N, xN=1, g=0):
 
     - Reason 1: mode correction.
       These parameters bridge the Jeffreys (`xN=1`) and Dirac (`xN=Inf`) hyperpriors
-      for the prior covariance, B, as discussed in `bib.bocquet2015expanding`.
+      for the prior covariance, B, as discussed in [bocquet2015expanding][].
       Indeed, mode correction becomes necessary when $$ R \rightarrow \infty $$
       because then there should be no ensemble update (and also no inflation!).
       More specifically, the mode of `l1`'s should be adjusted towards 1
@@ -790,7 +790,7 @@ def hyperprior_coeffs(s, N, xN=1, g=0):
     - Reason 2: Boosting the inflation prior's certainty from N to xN*N.
       The aim is to take advantage of the fact that the ensemble may not
       have quite as much sampling error as a fully stochastic sample,
-      as illustrated in section 2.1 of `bib.raanes2019adaptive`.
+      as illustrated in section 2.1 of [raanes2019adaptive][].
 
     - Its damping effect is similar to work done by J. Anderson.
 
@@ -809,7 +809,7 @@ def hyperprior_coeffs(s, N, xN=1, g=0):
     eN = (N + 1) / N
     cL = (N + g) / N1
 
-    # Mode correction (almost) as in eqn 36 of `bib.bocquet2015expanding`
+    # Mode correction (almost) as in eqn 36 of [bocquet2015expanding][]
     prior_mode = eN / cL  # Mode of l1 (before correction)
     diagonal = pad0(s**2, N) + N1  # diag of [email protected]@Y + N1*I
     #                                           (Hessian of J)
@@ -847,7 +847,7 @@ def zeta_a(eN, cL, w):
 class EnKF_N:
     """Finite-size EnKF (EnKF-N).
 
-    Refs: `bib.bocquet2011ensemble`, `bib.bocquet2015expanding`
+    Refs: [bocquet2011ensemble][], [bocquet2015expanding][]
 
     This implementation is pedagogical, prioritizing the "dual" form.
     In consequence, the efficiency of the "primal" form suffers a bit.
@@ -985,7 +985,7 @@ def nvrs(w):
                 # l1 = 1.0
 
                 # Explicitly inflate prior
-                # => formulae look different from `bib.bocquet2015expanding`.
+                # => formulae look different from [bocquet2015expanding][].
                 A *= l1
                 Y *= l1
 
@@ -1000,7 +1000,7 @@ def nvrs(w):
                 else:
                     # Also include angular-radial co-dependence.
                     # Note: denominator not squared coz
-                    # unlike `bib.bocquet2015expanding` we have inflated Y.
+                    # unlike [bocquet2015expanding][] we have inflated Y.
                     Hw = (
                         Y @ R.inv @ Y.T / N1
                         + eye(N)

dapper/da_methods/other.py

@@ -15,7 +15,7 @@
 class RHF:
     """Rank histogram filter.
 
-    Refs: `bib.anderson2010non`.
+    Refs: [anderson2010non][].
 
     Quick & dirty implementation without attention to (de)tails.
     """
@@ -86,9 +86,9 @@ def assimilate(self, HMM, xx, yy):
 class LNETF:
     """The Nonlinear-Ensemble-Transform-Filter (localized).
 
-    Refs: `bib.wiljes2016second`, `bib.todter2015second`.
+    Refs: [wiljes2016second][], [todter2015second][].
 
-    It is (supposedly) a deterministic upgrade of the NLEAF of `bib.lei2011moment`.
+    It is (supposedly) a deterministic upgrade of the NLEAF of [lei2011moment][].
     """
 
     N: int

dapper/da_methods/particle.py

@@ -23,7 +23,7 @@ class particle_method:
 class PartFilt:
     r"""Particle filter ≡ Sequential importance (re)sampling SIS (SIR).
 
-    Refs: `bib.wikle2007bayesian`, `bib.van2009particle`, `bib.chen2003bayesian`
+    Refs: [wikle2007bayesian][], [van2009particle][], [chen2003bayesian][]
 
     This is the bootstrap version: the proposal density is just
 
@@ -37,7 +37,7 @@ class PartFilt:
      - qroot: "Inflate" (anneal) the proposal noise kernels
        by this root to increase diversity.
        The weights are updated to maintain un-biased-ness.
-       See `bib.chen2003bayesian`, section VI-M.2
+       See [chen2003bayesian][], section VI-M.2
     """
 
     N: int
@@ -92,7 +92,7 @@ def assimilate(self, HMM, xx, yy):
 class OptPF:
     """'Optimal proposal' particle filter, also known as 'Implicit particle filter'.
 
-    Ref: `bib.bocquet2010beyond`.
+    Ref: [bocquet2010beyond][].
 
     !!! note Regularization (`Qs`) is here added BEFORE Bayes' rule.
         If `Qs==0`: OptPF should be equal to the bootstrap filter `PartFilt`.
@@ -520,7 +520,7 @@ def mask_unique_of_sorted(idx):
 def auto_bandw(N, M):
     """Optimal bandwidth (not bandwidth^2), as per Scott's rule-of-thumb.
 
-    Refs: `bib.doucet2001sequential` section 12.2.2, [Wik17]_ section "Rule_of_thumb"
+    Refs: [doucet2001sequential][] section 12.2.2, [Wik17]_ section "Rule_of_thumb"
     """
     return N ** (-1 / (M + 4))
 
@@ -534,7 +534,7 @@ def regularize(C12, E, idx, no_uniq_jitter):
     (so-named because Dirac-deltas are approximated  Gaussian kernels),
     which controls the strength of the jitter.
     This causes a bias. But, as N-->∞, the reg. bandwidth-->0, i.e. bias-->0.
-    Ref: `bib.doucet2001sequential`, section 12.2.2.
+    Ref: [doucet2001sequential][], section 12.2.2.
     """
     # Select
     E = E[idx]
@@ -554,7 +554,7 @@ def regularize(C12, E, idx, no_uniq_jitter):
 def resample(w, kind="Systematic", N=None, wroot=1.0):
     """Multinomial resampling.
 
-    Refs: `bib.doucet2009tutorial`, `bib.van2009particle`, `bib.liu2001theoretical`.
+    Refs: [doucet2009tutorial][], [van2009particle][], [liu2001theoretical][].
 
     - kind: 'Systematic', 'Residual' or 'Stochastic'.
       'Stochastic' corresponds to `rng.choice` or `rng.multinomial`.
@@ -571,7 +571,7 @@ def resample(w, kind="Systematic", N=None, wroot=1.0):
     - wroot: Adjust weights before resampling by this root to
       promote particle diversity and mitigate thinning.
       The outcomes of the resampling are then weighted to maintain un-biased-ness.
-      Ref: `bib.liu2001theoretical`, section 3.1
+      Ref: [liu2001theoretical][], section 3.1
 
     Note: (a) resampling methods are beneficial because they discard
     low-weight ("doomed") particles and reduce the variance of the weights.

dapper/da_methods/variational.py

@@ -28,10 +28,10 @@ class var_method:
 class iEnKS:
     """Iterative EnKS.
 
-    Special cases: EnRML, ES-MDA, iEnKF, EnKF `bib.raanes2019revising`.
+    Special cases: EnRML, ES-MDA, iEnKF, EnKF [raanes2019revising][].
 
-    As in `bib.bocquet2014iterative`, optimization uses Gauss-Newton.
-    See `bib.bocquet2012combining` for Levenberg-Marquardt.
+    As in [bocquet2014iterative][], optimization uses Gauss-Newton.
+    See [bocquet2012combining][] for Levenberg-Marquardt.
     If MDA=True, then there's not really any optimization,
     but rather Gaussian annealing.
 
@@ -41,13 +41,13 @@ class iEnKS:
 
         - "Sqrt"   : as in ETKF  , using a deterministic matrix square root transform.
         - "PertObs": as in EnRML , using stochastic, perturbed-observations.
-        - "Order1" : as in DEnKF of `bib.sakov2008deterministic`.
+        - "Order1" : as in DEnKF of [sakov2008deterministic][].
 
       Lag:
         Length of the DA window (DAW, multiples of dko, i.e. cycles).
 
-        - Lag=1 (default) => iterative "filter" iEnKF `bib.sakov2012iterative`.
-        - Lag=0           => maximum-likelihood filter `bib.zupanski2005maximum`.
+        - Lag=1 (default) => iterative "filter" iEnKF [sakov2012iterative][].
+        - Lag=0           => maximum-likelihood filter [zupanski2005maximum][].
 
       Shift : How far (in cycles) to slide the DAW.
               Fixed at 1 for code simplicity.
@@ -60,7 +60,7 @@ class iEnKS:
               Recommended: 1e-5.
 
       MDA   : Use iterations of the "multiple data assimlation" type.
-              Ref `bib.emerick2012history`
+              Ref [emerick2012history][]
 
       bundle: Use finite-diff. linearization instead of of least-squares regression.
               Makes the iEnKS very much alike the iterative, extended KF (IEKS).
@@ -70,8 +70,8 @@ class iEnKS:
     Total number of model simulations (of duration dto): N * (nIter*Lag + 1).
     (due to boundary cases: only asymptotically valid)
 
-    Refs: `bib.bocquet2012combining`, `bib.bocquet2013joint`,
-    `bib.bocquet2014iterative`.
+    Refs: [bocquet2012combining][], [bocquet2013joint][],
+    [bocquet2014iterative][].
     """
 
     upd_a: str

dapper/mods/KS/bocquet2019.py

@@ -1,4 +1,4 @@
-"""Settings as in `bib.bocquet2019consistency`."""
+"""Settings as in [bocquet2019consistency][]."""
 
 import numpy as np
 

dapper/mods/LA/evensen2009.py

@@ -1,4 +1,4 @@
-"""A mix of `bib.evensen2009ensemble` and `bib.sakov2008implications`.
+"""A mix of [evensen2009ensemble][] and [sakov2008implications][].
 
 !!! note
     Since there is no noise, and the system is stable,

dapper/mods/LA/raanes2015.py

@@ -1,4 +1,4 @@
-"""Reproduce results from Fig. 2 of `bib.raanes2014ext`."""
+"""Reproduce results from Fig. 2 of [raanes2014ext][]."""
 
 import numpy as np
 

dapper/mods/Lorenz05/__init__.py

@@ -1,6 +1,6 @@
 """A multi-scale, smooth version of the classic Lorenz-96.
 
-This is an implementation of "Model III" of `bib.lorenz2005designing`.
+This is an implementation of "Model III" of [lorenz2005designing][].
 
 Similar to [`mods.LorenzUV`][] this model is designed
 to contain two different scales. However, in "Model III"
@@ -111,7 +111,7 @@ def step(self, E, t, dt):
 
 
 def summation_kernel(width):
-    """Prepare computation of the modified sum in `bib.lorenz2005designing`.
+    """Prepare computation of the modified sum in [lorenz2005designing][].
 
     Note: This gets repeatedly called, but actually the input is only ever
     `width = K` or `2*J`, so we should really cache or pre-compute.
@@ -131,7 +131,7 @@ def boxcar(x, n, method="direct"):
     For symmetry, if `n` is pair, the actual number of elements used is `n+1`,
     and the outer elements weighted by `0.5` to compensate for the `+1`.
 
-    This is the modified sum of `bib.lorenz2005designing`, used e.g. in eqn. 9.
+    This is the modified sum of [lorenz2005designing][], used e.g. in eqn. 9.
     For intuition: this type of summation (and the associated Sigma prime notation)
     may also be found for the "Trapezoidal rule" and in the inverse DFT used in
     spectral methods on a periodic domain.
@@ -216,7 +216,7 @@ def shift(x, k):
 
 
 def prodsum_self(x, k):
-    """Compute `prodsum(x, x, k)` efficiently: eqn 10 of `bib.lorenz2005designing`."""
+    """Compute `prodsum(x, x, k)` efficiently: eqn 10 of [lorenz2005designing][]."""
     W = boxcar(x, k)
     WW = shift(W, -2 * k) * shift(W, -k)
     WX = shift(W, -k) * shift(x, k)

dapper/mods/Lorenz63/anderson2010rhf.py

@@ -1,4 +1,4 @@
-"""Settings from `bib.anderson2010non`."""
+"""Settings from [anderson2010non][]."""
 
 import numpy as np
 

dapper/mods/Lorenz63/bocquet2012.py

@@ -1,4 +1,4 @@
-"""Reproduce results from Fig 11 of `bib.bocquet2012combining`."""
+"""Reproduce results from Fig 11 of [bocquet2012combining][]."""
 
 import numpy as np
 

dapper/mods/Lorenz63/mandel2016.py

@@ -1,4 +1,4 @@
-"""Settings from `bib.mandel2016hybrid`."""
+"""Settings from [mandel2016hybrid][]."""
 
 import dapper.mods as modelling
 from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM

dapper/mods/Lorenz63/sakov2012.py

@@ -1,4 +1,4 @@
-"""Reproduce results from Table 1 `bib.sakov2012iterative`."""
+"""Reproduce results from Table 1 [sakov2012iterative][]."""
 
 import numpy as np
 

dapper/mods/Lorenz63/wiljes2017.py

@@ -1,4 +1,4 @@
-"""Settings from `bib.wiljes2016second`."""
+"""Settings from [wiljes2016second][]."""
 
 import numpy as np
 

dapper/mods/Lorenz84/__init__.py

@@ -1,6 +1,6 @@
 """A chaotic system of size 3, like Lorenz-63, but with +complex geometry.
 
-Refs: `bib.lorenz1984irregularity`, `bib.lorenz2005look`
+Refs: [lorenz1984irregularity][], [lorenz2005look][]
 """
 
 import numpy as np

dapper/mods/Lorenz84/pajonk2012.py

@@ -1,4 +1,4 @@
-"""Settings from `bib.pajonk2012deterministic`.
+"""Settings from [pajonk2012deterministic][].
 
 There is nothing to reproduce from the paper as there are no
 statistically converged numbers.

dapper/mods/Lorenz96/__init__.py

@@ -1,6 +1,6 @@
 """A 1D emulator of chaotic atmospheric behaviour.
 
-`bib.lorenz1996predictability`
+[lorenz1996predictability][]
 
 For a short introduction, see
 

dapper/mods/Lorenz96/anderson2009.py

@@ -1,4 +1,4 @@
-"""A land-ocean setup from `bib.anderson2009spatially`."""
+"""A land-ocean setup from [anderson2009spatially][]."""
 
 import numpy as np
 

dapper/mods/Lorenz96/bocquet2010.py

@@ -1,4 +1,4 @@
-"""From Fig. 1 of `bib.bocquet2010beyond`."""
+"""From Fig. 1 of [bocquet2010beyond][]."""
 
 import numpy as np
 

dapper/mods/Lorenz96/bocquet2010_m40.py

@@ -1,4 +1,4 @@
-"""From `bib.bocquet2010beyond` (again), but `ndim=40` (i.e. Fig. 5 of paper)."""
+"""From [bocquet2010beyond][] (again), but `ndim=40` (i.e. Fig. 5 of paper)."""
 
 import numpy as np
 

dapper/mods/Lorenz96/bocquet2015loc.py

@@ -1,4 +1,4 @@
-"""Settings as in `bib.bocquet2016localization`."""
+"""Settings as in [bocquet2016localization][]."""
 
 import numpy as np