diff --git a/dapper/mods/__init__.py b/dapper/mods/__init__.py
index f5cc6d66..c0afec5e 100644
--- a/dapper/mods/__init__.py
+++ b/dapper/mods/__init__.py
@@ -49,36 +49,37 @@ class HiddenMarkovModel(struct_tools.NicePrint):
         imports all configurations, which might then unintentionally interact.
         To avoid this, you should use the `copy` method of the `HMM`
         before making any changes to it.
-
-
-    Parameters
-    ----------
-    Dyn: `Operator` or dict
-        Operator for the dynamics.
-    Obs: `Operator` or dict
-        Operator for the observations
-        Can also be time-dependent, ref `TimeDependentOperator`.
-    tseq: [`tools.chronos.Chronology`][]
-        Time sequence of the HMM process.
-    X0: [`tools.randvars.RV`][]
-        Random distribution of initial condition
-    liveplotters: `list`, optional
-        A list of tuples. See example use in function `LPs` of [`mods.Lorenz63`][].
-        - The first element of the tuple determines if the liveplotter
-        is shown by default. If `False`, the liveplotter is only shown when
-        included among the `liveplots` argument of `assimilate`
-        - The second element in the tuple gives the corresponding liveplotter
-        function/class.
-    sectors: `dict`, optional
-        Labelled indices referring to parts of the state vector.
-        When defined, field-mean statistics are computed for each sector.
-        Example use can be found in  `examples/param_estim.py`
-        and `dapper/mods/Lorenz96/miyoshi2011.py`
-    name: str, optional
-        Label for the `HMM`.
     """
 
     def __init__(self, *args, **kwargs):
+        """Initialize.
+
+        Parameters
+        ----------
+        Dyn : Operator or dict
+            Operator for the dynamics.
+        Obs : Operator or dict
+            Operator for the observations
+            Can also be time-dependent, ref `TimeDependentOperator`.
+        tseq : tools.chronos.Chronology
+            Time sequence of the HMM process.
+        X0 : tools.randvars.RV
+            Random distribution of initial condition
+        liveplotters : list, optional
+            A list of tuples. See example use in function `LPs` of [`mods.Lorenz63`][].
+            - The first element of the tuple determines if the liveplotter
+            is shown by default. If `False`, the liveplotter is only shown when
+            included among the `liveplots` argument of `assimilate`
+            - The second element in the tuple gives the corresponding liveplotter
+            function/class.
+        sectors : dict, optional
+            Labelled indices referring to parts of the state vector.
+            When defined, field-mean statistics are computed for each sector.
+            Example use can be found in  `examples/param_estim.py`
+            and `dapper/mods/Lorenz96/miyoshi2011.py`
+        name : str, optional
+            Label for the `HMM`.
+        """
         # Expected args/kwargs, along with type and default.
         attrs = dict(
             Dyn=(Operator, None),
@@ -216,11 +217,11 @@ class Operator(struct_tools.NicePrint):
 
     Parameters
     ----------
-    M: int
+    M : int
         Length of output vectors.
-    model: function
+    model : function
         The actual operator.
-    noise: RV, optional
+    noise : RV, optional
         The associated additive noise. The noise can also be a scalar or an
         array, producing `GaussRV(C=noise)`.
 
diff --git a/dapper/mods/utils.py b/dapper/mods/utils.py
index 78dbeeb7..1aeb223c 100644
--- a/dapper/mods/utils.py
+++ b/dapper/mods/utils.py
@@ -116,14 +116,14 @@ def direct_obs_matrix(Nx, obs_inds):
 
     Parameters
     ----------
-    Nx: int
+    Nx : int
         Length of state vector
-    obs_inds: ndarray
+    obs_inds : ndarray
         Indices of elements of the state vector that are (directly) observed.
 
     Returns
     -------
-    H: ndarray
+    H : ndarray
         The observation matrix for direct partial observations.
     """
     Ny = len(obs_inds)
@@ -143,14 +143,14 @@ def partial_Id_Obs(Nx, obs_inds):
 
     Parameters
     ----------
-    Nx: int
+    Nx : int
         Length of state vector
-    obs_inds: ndarray
+    obs_inds : ndarray
         The observed indices.
 
     Returns
     -------
-    Obs: dict
+    Obs : dict
         Observation operator including size of the observation space,
         observation operator/model and tangent linear observation operator
     """
@@ -181,12 +181,12 @@ def Id_Obs(Nx):
 
     Parameters
     ----------
-    Nx: int
+    Nx : int
         Length of state vector
 
     Returns
     -------
-    Obs: dict
+    Obs : dict
         Observation operator including size of the observation space,
         observation operator/model and tangent linear observation operator
     """
@@ -198,18 +198,18 @@ def linspace_int(Nx, Ny, periodic=True):
 
     Parameters
     ----------
-    Nx: int
+    Nx : int
         Range of integers
-    Ny: int
+    Ny : int
         Number of integers
-    periodic: bool, optional
+    periodic : bool, optional
         Whether the vector is periodic.
         Determines if `Nx == 0`.
         Default: True
 
     Returns
     -------
-    integers: ndarray
+    integers : ndarray
         The list of integers.
 
     Examples
diff --git a/dapper/stats.py b/dapper/stats.py
index ffe6aa4a..4a80aebd 100644
--- a/dapper/stats.py
+++ b/dapper/stats.py
@@ -631,10 +631,10 @@ def unpack_uqs(uq_list, decimals=None):
 
     Parameters
     ----------
-    uq_list: list
+    uq_list : list
         List of `uq`s.
 
-    decimals: int
+    decimals : int
         Desired number of decimals.
         Used for (only) the columns "val" and "prec".
         Default: `None`. In this case, the formatting is left to the `uq`s.
@@ -707,23 +707,23 @@ def center(E, axis=0, rescale=False):
 
     Parameters
     ----------
-    E: ndarray
+    E : ndarray
         Ensemble which going to be inflated
 
-    axis: int, optional
+    axis : int, optional
         The axis to be centered. Default: 0
 
-    rescale: bool, optional
+    rescale : bool, optional
         If True, inflate to compensate for reduction in the expected variance.
         The inflation factor is \(\sqrt{\frac{N}{N - 1}}\)
         where N is the ensemble size. Default: False
 
     Returns
     -------
-    X: ndarray
+    X : ndarray
         Ensemble anomaly
 
-    x: ndarray
+    x : ndarray
         Mean of the ensemble
     """
     x = np.mean(E, axis=axis, keepdims=True)
@@ -755,7 +755,7 @@ def inflate_ens(E, factor):
     E : ndarray
         Ensemble which going to be inflated
 
-    factor: `float`
+    factor : `float`
         Inflation factor
 
     Returns
@@ -777,10 +777,10 @@ def weight_degeneracy(w, prec=1e-10):
 
     Parameters
     ----------
-    w: ndarray
+    w : ndarray
         Importance weights. Must sum to 1.
 
-    prec: float, optional
+    prec : float, optional
         Tolerance of the distance between w and one. Default:1e-10
 
     Returns
@@ -796,21 +796,21 @@ def unbias_var(w=None, N_eff=None, avoid_pathological=False):
 
     Parameters
     ----------
-    w: ndarray, optional
+    w : ndarray, optional
         Importance weights. Must sum to 1.
         Only one of `w` and `N_eff` can be `None`. Default: `None`
 
-    N_eff: float, optional
+    N_eff : float, optional
         The "effective" size of the weighted ensemble.
         If not provided, it is computed from the weights.
         The unbiasing factor is $$ N_{eff} / (N_{eff} - 1) $$.
 
-    avoid_pathological: bool, optional
+    avoid_pathological : bool, optional
         Avoid weight collapse. Default: `False`
 
     Returns
     -------
-    ub: float
+    ub : float
         factor used to unbiasing variance
 
     Reference
diff --git a/dapper/tools/linalg.py b/dapper/tools/linalg.py
index c307847c..612951dc 100644
--- a/dapper/tools/linalg.py
+++ b/dapper/tools/linalg.py
@@ -39,11 +39,11 @@ def tsvd(A, threshold=0.99999, avoid_pathological=True):
 
     Parameters
     ----------
-    avoid_pathological: bool
+    avoid_pathological : bool
         Avoid truncating (e.g.) the identity matrix.
         NB: only applies for float threshold.
 
-    threshold: float or int
+    threshold : float or int
 
         - if `float`, `< 1.0` then "rank" = lowest number
           such that the "energy" retained >= threshold
diff --git a/dapper/tools/localization.py b/dapper/tools/localization.py
index 0cc16c46..f03f0076 100644
--- a/dapper/tools/localization.py
+++ b/dapper/tools/localization.py
@@ -20,13 +20,13 @@ def pairwise_distances(A, B=None, domain=None):
 
     Parameters
     ----------
-    A: array of shape `(nPoints, nDims)`.
+    A : array of shape `(nPoints, nDims)`.
         A collection of points.
 
-    B:
+    B :
         Same as `A`, but `nPoints` can differ.
 
-    domain: tuple
+    domain : tuple
         Assume the domain is a **periodic** hyper-rectangle whose
         edges along dimension `i` span from 0 to `domain[i]`.
         NB: Behaviour not defined if `any(A.max(0) > domain)`, and likewise for `B`.
@@ -221,8 +221,8 @@ def rectangular_partitioning(shape, steps, do_ind=True):
 
     Parameters
     ----------
-    shape: (len(grid[dim]) for dim in range(ndim))
-    steps: (step_len[dim]  for dim in range(ndim))
+    shape : (len(grid[dim]) for dim in range(ndim))
+    steps : (step_len[dim]  for dim in range(ndim))
 
     Returns
     -------
diff --git a/dapper/tools/rounding.py b/dapper/tools/rounding.py
index 475160da..550e0bbd 100644
--- a/dapper/tools/rounding.py
+++ b/dapper/tools/rounding.py
@@ -124,12 +124,12 @@ def np_vectorize(f):
 
     Parameters
     ----------
-    f: callable
+    f : callable
         Your function.
 
     Returns
     -------
-    vectorized: callable
+    vectorized : callable
         Your function, now element-wise applicable to an iterable.
     """
     vectorized = np.vectorize(f)
@@ -192,7 +192,7 @@ def round2(x, prec=1.0):
     ----------
     x : array_like
         Value to be rounded.
-    prec: float
+    prec : float
         Precision, before prettify, which is given by
         $$ \text{prec} = 10^{\text{floor}(-\log_{10}|\text{prec}|)} $$
 
@@ -259,12 +259,12 @@ def is_whole(x, **kwargs):
 
     Parameters
     ----------
-    x: float or ndarray
+    x : float or ndarray
         Values to be checked
 
     Returns
     -------
-    l: bool
+    l : bool
         True if rounded x is close to x, otherwise False
     """
     return np.isclose(x, round(x), **kwargs)
diff --git a/dapper/tools/viz.py b/dapper/tools/viz.py
index e28635c4..33e14f56 100644
--- a/dapper/tools/viz.py
+++ b/dapper/tools/viz.py
@@ -27,9 +27,9 @@ def setup_wrapping(M, periodicity=None):
 
     Parameters
     ----------
-    M: int
+    M : int
         Length of the periodic domain
-    periodicity: bool, optional
+    periodicity : bool, optional
         The mode of the wrapping.
         "+1": the first element is appended after the last.
         "+/-05": adding the midpoint of the first and last elements.
@@ -37,9 +37,9 @@ def setup_wrapping(M, periodicity=None):
 
     Returns
     -------
-    ii: ndarray
+    ii : ndarray
         indices of periodic domain
-    wrap: func
+    wrap : func
         transform non-periodic data into periodic data
     """
     if periodicity in (None, True):
@@ -77,24 +77,24 @@ def amplitude_animation(
 
     Parameters
     ----------
-    EE: ndarray
+    EE : ndarray
         Ensemble arry of the shape (K, N, Nx).
         K is the length of time, N is the ensemble size, and
         Nx is the length of state vector.
-    dt: float
+    dt : float
         Time interval of each frame.
-    interval: float, optional
+    interval : float, optional
         Delay between frames in milliseconds. Defaults to 200.
-    periodicity: bool, optional
+    periodicity : bool, optional
         The mode of the wrapping.
         "+1": the first element is appended after the last.
         "+/-05": adding the midpoint of the first and last elements.
         Default: "+1"
-    blit: bool, optional
+    blit : bool, optional
         Controls whether blitting is used to optimize drawing. Default: True
-    fignum: int, optional
+    fignum : int, optional
         Figure index. Default: None
-    repeat: bool, optional
+    repeat : bool, optional
         If True, repeat the animation. Default: False
     """
     fig, ax = place.freshfig(fignum or "Amplitude animation")
@@ -133,15 +133,15 @@ def xtrema(xx, axis=None):
 
     Parameters
     ----------
-    xx: ndarray
-    axis: int, optional
+    xx : ndarray
+    axis : int, optional
         Specific axis for min and max. Defaults: None
 
     Returns
     -------
-    a: float
+    a : float
         min value
-    b: float
+    b : float
         max value
     """
     a = np.nanmin(xx, axis)
@@ -154,21 +154,21 @@ def stretch(a, b, factor=1, int_=False):
 
     Parameters
     ----------
-    a: float
+    a : float
         Lower bound of domain.
-    b: float
+    b : float
         Upper bound of domain.
-    factor: float, optional
+    factor : float, optional
         Streching factor. Defaults: 1
-    int_: bool, optional
+    int_ : bool, optional
         If True, the domain bounds are integer.
         Defaults: False
 
     Returns
     -------
-    a: float
+    a : float
         Lower bound of domain.
-    b: float
+    b : float
         Upper bound of domain.
     """
     c = (a + b) / 2
@@ -185,12 +185,12 @@ def set_ilim(ax, i, Min=None, Max=None):
 
     Parameters
     ----------
-    ax: matplotlib.axes
-    i: int
+    ax : matplotlib.axes
+    i : int
         1: x-axis; 2: y-axis; 3: z-axis
-    Min: float, optional
+    Min : float, optional
         Lower bound limit. Defaults: None
-    Max: float, optional
+    Max : float, optional
         Upper bound limit. Defaults: None
     """
     if i == 0:
@@ -210,16 +210,16 @@ def estimate_good_plot_length(xx, tseq=None, mult=100):
 
     Parameters
     ----------
-    xx: ndarray
+    xx : ndarray
         Plotted array
-    tseq: [`tools.chronos.Chronology`][], optional
+    tseq : [`tools.chronos.Chronology`][], optional
         object with property dko. Defaults: None
-    mult: int, optional
+    mult : int, optional
         Number of waves for plotting. Defaults: 100
 
     Returns
     -------
-    K: int
+    K : int
         length for plotting
 
     Example
@@ -319,9 +319,9 @@ def plot_hovmoller(xx, tseq=None):
 
     Parameters
     ----------
-    xx: ndarray
+    xx : ndarray
         Plotted array
-    tseq: [`tools.chronos.Chronology`][], optional
+    tseq : [`tools.chronos.Chronology`][], optional
         object with property dko. Defaults: None
     """
     fig, ax = place.freshfig("Hovmoller", figsize=(4, 3.5))
@@ -352,15 +352,15 @@ def integer_hist(E, N, centrd=False, weights=None, **kwargs):
 
     Parameters
     ----------
-    E: ndarray
+    E : ndarray
         Ensemble array.
-    N: int
+    N : int
         Number of histogram bins.
-    centrd: bool, optional
+    centrd : bool, optional
         If True, each bin is centered in the midpoint. Default: False
-    weights: float, optional
+    weights : float, optional
         Weights for histogram. Default: None
-    kwargs: dict
+    kwargs : dict
         keyword arguments for matplotlib.hist
     """
     ax = plt.gca()
@@ -374,10 +374,10 @@ def not_available_text(ax, txt=None, fs=20):
 
     Parameters
     ----------
-    ax: matplotlib.axes
-    txt: str, optional
+    ax : matplotlib.axes
+    txt : str, optional
         Printed text. Defaults: '[Not available]'
-    fs: float, optional
+    fs : float, optional
         Font size. Defaults: 20.
     """
     if txt is None:
@@ -401,8 +401,7 @@ def plot_err_components(stats):
 
     Parameters
     ----------
-    stats:
-        [`stats.Stats`][]
+    stats : stats.Stats
 
     Note
     ----
@@ -468,7 +467,7 @@ def plot_rank_histogram(stats):
 
     Parameters
     ----------
-    stats: [`stats.Stats`][]
+    stats: stats.Stats
     """
     tseq = stats.HMM.tseq
 
@@ -514,12 +513,12 @@ def axis_scale_by_array(ax, arr, axis="y", nbins=3):
 
     Parameters
     ----------
-    ax: matplotlib.axes
-    arr: ndarray
+    ax : matplotlib.axes
+    arr : ndarray
         Array for plotting
-    axis: str, optional
+    axis : str, optional
         Scaled axis, which can be 'x', 'y' or 'z'. Defaults: 'y'
-    nbins: int, optional
+    nbins : int, optional
         Number of major ticks. Defaults: 3
     """
     yy = array([y for y in arr if y is not None], dtype=float)  # rm None
diff --git a/dapper/xp_launch.py b/dapper/xp_launch.py
index d084ddec..1aa700f6 100644
--- a/dapper/xp_launch.py
+++ b/dapper/xp_launch.py
@@ -39,9 +39,9 @@ def seed_and_simulate(HMM, xp):
 
     Parameters
     ----------
-    HMM: HiddenMarkovModel
+    HMM : HiddenMarkovModel
         Container defining the system.
-    xp: object
+    xp : object
         Type: a [`da_methods.da_method`][]-decorated class.
 
         !!! warning "`xp.seed` should be set (and `int`)."
@@ -86,25 +86,25 @@ def run_experiment(
 
     Parameters
     ----------
-    xp: object
+    xp : object
         Type: a [`da_methods.da_method`][]-decorated class.
-    label: str
+    label : str
         Name attached to progressbar during assimilation.
-    savedir: str
+    savedir : str
         Path of folder wherein to store the experiment data.
-    HMM: HiddenMarkovModel
+    HMM : HiddenMarkovModel
         Container defining the system.
-    free: bool
+    free : bool
         Whether (or not) to `del xp.stats` after the experiment is done,
         so as to free up memory and/or not save this data
         (just keeping `xp.avrgs`).
-    statkeys: list
+    statkeys : list
         A list of names (possibly in the form of abbreviations) of the
         statistical averages that should be printed immediately afther
         this xp.
-    fail_gently: bool
+    fail_gently : bool
         Whether (or not) to propagate exceptions.
-    setup: function
+    setup : function
         This function must take two arguments: `HMM` and `xp`, and return the `HMM` to
         be used by the DA methods (typically the same as the input `HMM`, but could be
         modified), and the (typically synthetic) truth and obs time series.
@@ -218,10 +218,10 @@ class xpList(list):
 
     Parameters
     ----------
-    args: entries
+    args : entries
         Nothing, or a list of `xp`s.
 
-    unique: bool
+    unique : bool
         Duplicates won't get appended. Makes `append` (and `__iadd__`) relatively slow.
         Use `extend` or `__add__` or `combinator` to bypass this validation.
 
@@ -299,7 +299,7 @@ def prep_table(self, nomerge=()):
 
         Parameters
         ----------
-        nomerge: list
+        nomerge : list
             Attributes that should always be seen as distinct.
         """
 
diff --git a/dapper/xp_process.py b/dapper/xp_process.py
index e86589d6..7635cf40 100644
--- a/dapper/xp_process.py
+++ b/dapper/xp_process.py
@@ -90,7 +90,7 @@ def __init__(self, dims):
 
         Parameters
         ----------
-        dims: list or tuple
+        dims : list or tuple
             The attributes defining the coordinate system.
         """
         # Define coordinate system
@@ -587,10 +587,10 @@ def print(
 
         Parameters
         ----------
-        statkey: str
+        statkey : str
             The statistic to extract from the `xp.avrgs` for each `xp`.
             Examples: `"rmse.a"` (i.e. `"err.rms.a"`), `"rmse.ocean.a"`, `"duration"`.
-        dims: dict
+        dims : dict
             Allots (maps) the dims of `xpSpace` to different roles in the tables.
 
             - The "role" `outer` should list the dims/attributes
@@ -610,7 +610,7 @@ def print(
 
             Equivalently, use `mean=("seed",)`.
             It is acceptible to leave this empty: `mean=()` or `mean=None`.
-        subcols: bool
+        subcols : bool
             If `True`, then subcolumns are added to indicate
 
             - `1σ`: the confidence interval. If `mean=None` is used, this simply reports
@@ -621,16 +621,16 @@ def print(
               as defined by `costfun`.
             - `☠`: the number of failures (non-finite values) at that point.
             - `✓`: the number of successes that go into the value
-        decimals: int
+        decimals : int
             Number of decimals to print.
             If `None`, this is determined for each statistic by its uncertainty.
-        costfun: str or function
+        costfun : str or function
             Use `'increasing'` (default) or `'decreasing'` to indicate that the optimum
             is defined as the lowest or highest value of the `statkey` found.
-        squeeze_labels: bool
+        squeeze_labels : bool
             Don't include redundant attributes in the line labels.
             Caution: `get_style` will not be able to access the eliminated attrs.
-        colorize: bool
+        colorize : bool
             Add color to tables for readability.
         """
         # Title
@@ -768,16 +768,16 @@ def plot(
 
         Parameters
         ----------
-        get_style: function
+        get_style : function
             A function that takes an object, and returns a dict of line styles,
             usually as a function of the object's attributes.
-        title1: anything
+        title1 : anything
             Figure title (in addition to the the defaults).
-        title2: anything
+        title2 : anything
             Figure title (in addition to the defaults). Goes on a new line.
-        unique_labels: bool
+        unique_labels : bool
             Only show a given line label once, even if it appears in several panels.
-        squeeze_labels:
+        squeeze_labels :
             Don't include redundant attributes in the labels.
         """