diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4f706840..ae835b43 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -41,11 +41,11 @@ repos:
             types: [text]
             stages: [commit, push, manual]
     - repo: https://github.com/pre-commit/mirrors-prettier
-      rev: v2.3.0
+      rev: v2.7.1
       hooks:
           - id: prettier
     - repo: https://github.com/pycqa/isort
-      rev: 5.8.0
+      rev: 5.10.1
       hooks:
           - id: isort
             name: isort (python)
diff --git a/batchglm/models/__init__.py b/batchglm/models/__init__.py
index fafadc42..fef02485 100644
--- a/batchglm/models/__init__.py
+++ b/batchglm/models/__init__.py
@@ -1 +1 @@
-from . import glm_beta, glm_nb, glm_norm, glm_poisson
+from . import base_glm, glm_beta, glm_nb, glm_norm, glm_poisson
diff --git a/batchglm/models/base_glm/__init__.py b/batchglm/models/base_glm/__init__.py
index 200e9d5d..ff20b82a 100644
--- a/batchglm/models/base_glm/__init__.py
+++ b/batchglm/models/base_glm/__init__.py
@@ -1,4 +1,4 @@
 # from .estimator import _EstimatorGLM
 from ...utils.input import InputDataGLM
-from .model import _ModelGLM
-from .utils import closedform_glm_mean, closedform_glm_scale, parse_design
+from .model import ModelGLM
+from .utils import closedform_glm_mean, closedform_glm_scale
diff --git a/batchglm/models/base_glm/model.py b/batchglm/models/base_glm/model.py
index 36aa8e33..4368bd2c 100644
--- a/batchglm/models/base_glm/model.py
+++ b/batchglm/models/base_glm/model.py
@@ -1,19 +1,22 @@
 import abc
 import logging
+import random
+import string
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
 
 import dask.array
 import numpy as np
+import pandas as pd
 import scipy
 
 from ...utils.input import InputDataGLM
 from .external import pkg_constants
-from .utils import generate_sample_description, parse_constraints, parse_design
+from .utils import generate_sample_description
 
 logger = logging.getLogger(__name__)
 
 
-class _ModelGLM(metaclass=abc.ABCMeta):
+class ModelGLM(metaclass=abc.ABCMeta):
     """
     Generalized Linear Model base class.
 
@@ -43,13 +46,15 @@ class _ModelGLM(metaclass=abc.ABCMeta):
     _cast_dtype: str = "float32"
     _chunk_size_cells: int
     _chunk_size_genes: int
+    _sample_description: pd.DataFrame
+    _features: List[str]
 
     def __init__(
         self,
         input_data: Optional[InputDataGLM] = None,
     ):
         """
-        Create a new _ModelGLM object.
+        Create a new ModelGLM object.
 
         :param input_data: Input data for the model
 
@@ -72,9 +77,14 @@ def extract_input_data(self, input_data: InputDataGLM):
         self._cast_dtype = input_data.cast_dtype
         self._chunk_size_genes = input_data.chunk_size_genes
         self._chunk_size_cells = input_data.chunk_size_cells
+        self._features = input_data.features
         self._xh_loc = np.matmul(self.design_loc, self.constraints_loc)
         self._xh_scale = np.matmul(self.design_scale, self.constraints_scale)
 
+    @property
+    def features(self) -> List[str]:
+        return self._features
+
     @property
     def chunk_size_cells(self) -> int:
         return self._chunk_size_cells
@@ -87,6 +97,10 @@ def chunk_size_genes(self) -> int:
     def cast_dtype(self) -> str:
         return self._cast_dtype
 
+    @property
+    def sample_description(self) -> pd.DataFrame:
+        return self._sample_description
+
     @property
     def design_loc(self) -> Union[np.ndarray, dask.array.core.Array]:
         """location design matrix"""
@@ -356,7 +370,7 @@ def generate_params(
         if rand_fn_scale is None:
             rand_fn_scale = rand_fn
 
-        _design_loc, _design_scale, _ = generate_sample_description(**kwargs)
+        _design_loc, _design_scale, _sample_description = generate_sample_description(**kwargs)
 
         self._theta_location = np.concatenate(
             [
@@ -366,8 +380,9 @@ def generate_params(
             axis=0,
         )
         self._theta_scale = np.concatenate([rand_fn_scale((_design_scale.shape[1], n_vars))], axis=0)
+        self._sample_description = _sample_description
 
-        return _design_loc, _design_scale
+        return _design_loc, _design_scale, _sample_description
 
     def generate_artificial_data(
         self,
@@ -379,6 +394,8 @@ def generate_artificial_data(
         shuffle_assignments: bool = False,
         sparse: bool = False,
         as_dask: bool = True,
+        theta_location_setter: Optional[Callable] = None,
+        theta_scale_setter: Optional[Callable] = None,
         **kwargs,
     ):
         """
@@ -391,9 +408,11 @@ def generate_artificial_data(
         :param shuffle_assignments: Depcreated. Does not do anything.
         :param sparse: If True, the simulated data matrix is sparse.
         :param as_dask: If True, use dask.
+        :param theta_location_setter: Override for parameter after generate_params, should return the parameter
+        :param theta_scale_setter: Override for parameter after generate_params, should return the parameter
         :param kwargs: Additional kwargs passed to generate_params.
         """
-        _design_loc, _design_scale = self.generate_params(
+        _design_loc, _design_scale, _ = self.generate_params(
             n_vars=n_vars,
             num_observations=n_obs,
             num_conditions=num_conditions,
@@ -402,6 +421,10 @@ def generate_artificial_data(
             shuffle_assignments=shuffle_assignments,
             **kwargs,
         )
+        if theta_location_setter is not None:
+            self._theta_location = theta_location_setter(self._theta_location)
+        if theta_scale_setter is not None:
+            self._theta_scale = theta_scale_setter(self._theta_scale)
 
         # we need to do this explicitly here in order to generate data
         self._constraints_loc = np.identity(n=_design_loc.shape[1])
@@ -413,8 +436,15 @@ def generate_artificial_data(
         data_matrix = self.generate_data().astype(self.cast_dtype)
         if sparse:
             data_matrix = scipy.sparse.csr_matrix(data_matrix)
-
-        input_data = InputDataGLM(data=data_matrix, design_loc=_design_loc, design_scale=_design_scale, as_dask=as_dask)
+        # generate random gene/feature names
+        feature_names = "".join("feature_" + str(i) for i in range(n_vars))
+        input_data = InputDataGLM(
+            data=data_matrix,
+            design_loc=_design_loc,
+            design_scale=_design_scale,
+            as_dask=as_dask,
+            feature_names=feature_names,
+        )
         self.extract_input_data(input_data)
 
     @abc.abstractmethod
diff --git a/batchglm/models/base_glm/utils.py b/batchglm/models/base_glm/utils.py
index cc45e07f..95c3e6fa 100644
--- a/batchglm/models/base_glm/utils.py
+++ b/batchglm/models/base_glm/utils.py
@@ -7,12 +7,22 @@
 import pandas as pd
 import patsy
 import scipy.sparse
+import sparse
 
 from .external import groupwise_solve_lm
 
 logger = logging.getLogger("batchglm")
 
 
+def densify(arr):
+    if isinstance(arr, dask.array.core.Array):
+        arr = arr.compute()
+    if isinstance(arr, sparse.COO) or isinstance(arr, scipy.sparse.csr_matrix):
+        return arr.todense()
+    else:
+        return arr
+
+
 def generate_sample_description(
     num_observations: int,
     num_conditions: int,
@@ -61,87 +71,6 @@ def generate_sample_description(
     return sim_design_loc, sim_design_scale, sample_description
 
 
-def parse_design(
-    design_matrix: Union[pd.DataFrame, patsy.design_info.DesignMatrix, dask.array.core.Array, np.ndarray],
-    param_names: List[str] = None,
-) -> Tuple[np.ndarray, List[str]]:
-    r"""
-    Parser for design matrices.
-
-    :param design_matrix: Design matrix.
-    :param param_names:
-        Optional coefficient names for design_matrix.
-        Ignored if design_matrix is pd.DataFrame or patsy.design_info.DesignMatrix.
-    :return: Tuple[np.ndarray, List[str]] containing the design matrix and the parameter names.
-    :raise AssertionError: if the type of design_matrix is not understood.
-    :raise AssertionError: if length of provided param_names is not equal to number of coefficients in design_matrix.
-    :raise ValueError: if param_names is None when type of design_matrix is numpy.ndarray or dask.array.core.Array.
-    """
-    if isinstance(design_matrix, (pd.DataFrame, patsy.design_info.DesignMatrix)) and param_names is not None:
-        logger.warning(f"The provided param_names are ignored as the design matrix is of type {type(design_matrix)}.")
-
-    if isinstance(design_matrix, patsy.design_info.DesignMatrix):
-        dmat = np.asarray(design_matrix)
-        params = design_matrix.design_info.column_names
-    elif isinstance(design_matrix, pd.DataFrame):
-        dmat = np.asarray(design_matrix)
-        params = design_matrix.columns.tolist()
-    elif isinstance(design_matrix, dask.array.core.Array):
-        dmat = design_matrix.compute()
-        params = param_names
-    elif isinstance(design_matrix, np.ndarray):
-        dmat = design_matrix
-        params = param_names
-    else:
-        raise AssertionError(f"Datatype for design_matrix not understood: {type(design_matrix)}")
-    if params is None:
-        raise ValueError("Provide names when passing design_matrix as np.ndarray or dask.array.core.Array!")
-    assert len(params) == dmat.shape[1], (
-        "Length of provided param_names is not equal to " "number of coefficients in design_matrix."
-    )
-    return dmat, params
-
-
-def parse_constraints(
-    dmat: np.ndarray,
-    dmat_par_names: List[str],
-    constraints: Optional[Union[np.ndarray, dask.array.core.Array]] = None,
-    constraint_par_names: Optional[List[str]] = None,
-) -> Tuple[np.ndarray, List[str]]:
-    r"""
-    Parser for constraint matrices.
-
-    :param dmat: Design matrix.
-    :param constraints: Constraint matrix.
-    :param constraint_par_names: Optional coefficient names for constraints.
-    :return: Tuple[np.ndarray, List[str]] containing the constraint matrix and the parameter names.
-    :raise AssertionError: if the type of given design / contraint matrix is not np.ndarray or dask.array.core.Array.
-    """
-    assert isinstance(dmat, np.ndarray), "dmat must be provided as np.ndarray."
-    if constraints is None:
-        constraints = np.identity(n=dmat.shape[1])
-        constraint_params = dmat_par_names
-    else:
-        if isinstance(constraints, dask.array.core.Array):
-            constraints = constraints.compute()
-        assert isinstance(constraints, np.ndarray), "contraints must be np.ndarray or dask.array.core.Array."
-        # Cannot use all parameter names if constraint matrix is not identity: Make up new ones.
-        # Use variable names that can be mapped (unconstrained).
-        if constraint_par_names is not None:
-            assert len(constraint_params) == len(constraint_par_names)
-            constraint_params = constraint_par_names
-        else:
-            constraint_params = [
-                "var_" + str(i)
-                if np.sum(constraints[:, i] != 0) > 1
-                else dmat_par_names[np.where(constraints[:, i] != 0)[0][0]]
-                for i in range(constraints.shape[1])
-            ]
-        assert constraints.shape[0] == dmat.shape[1], "constraint dimension mismatch"
-
-    return constraints, constraint_params
-
-
 def closedform_glm_mean(
     x: Union[np.ndarray, scipy.sparse.csr_matrix, dask.array.core.Array],
     dmat: Union[np.ndarray, dask.array.core.Array],
@@ -168,8 +97,9 @@ def closedform_glm_mean(
         x = np.divide(x, size_factors)
 
     def apply_fun(grouping):
+
         groupwise_means = np.asarray(
-            np.vstack([np.mean(x[np.where(grouping == g)[0], :], axis=0) for g in np.unique(grouping)])
+            np.vstack([np.mean(densify(x[np.where(grouping == g)[0], :]), axis=0) for g in np.unique(grouping)])
         )
         if link_fn is None:
             return groupwise_means
@@ -218,7 +148,7 @@ def apply_fun(grouping):
         # Calculate group-wise means if not supplied. These are required for variance and MME computation.
         if provided_groupwise_means is None:
             gw_means = np.asarray(
-                np.vstack([np.mean(x[np.where(grouping == g)[0], :], axis=0) for g in np.unique(grouping)])
+                np.vstack([np.mean(densify(x[np.where(grouping == g)[0], :]), axis=0) for g in np.unique(grouping)])
             )
         else:
             gw_means = provided_groupwise_means
@@ -228,14 +158,14 @@ def apply_fun(grouping):
             expect_xsq = np.asarray(
                 np.vstack(
                     [
-                        np.asarray(np.mean(x[np.where(grouping == g)[0], :].power(2), axis=0))
+                        np.asarray(np.mean(densify(x[np.where(grouping == g)[0], :]).power(2), axis=0))
                         for g in np.unique(grouping)
                     ]
                 )
             )
         else:
             expect_xsq = np.vstack(
-                [np.mean(np.square(x[np.where(grouping == g)[0], :]), axis=0) for g in np.unique(grouping)]
+                [np.mean(np.square(densify(x[np.where(grouping == g)[0], :])), axis=0) for g in np.unique(grouping)]
             )
         expect_x_sq = np.square(gw_means)
         variance = expect_xsq - expect_x_sq
diff --git a/batchglm/models/glm_beta/external.py b/batchglm/models/glm_beta/external.py
index bc3a7bb1..855c6305 100644
--- a/batchglm/models/glm_beta/external.py
+++ b/batchglm/models/glm_beta/external.py
@@ -1,4 +1,4 @@
 import batchglm.utils.data as data_utils
 from batchglm import pkg_constants
-from batchglm.models.base_glm import _ModelGLM, closedform_glm_mean, closedform_glm_scale
+from batchglm.models.base_glm import ModelGLM, closedform_glm_mean, closedform_glm_scale
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/models/glm_beta/model.py b/batchglm/models/glm_beta/model.py
index f0af78d7..486c99b5 100644
--- a/batchglm/models/glm_beta/model.py
+++ b/batchglm/models/glm_beta/model.py
@@ -4,10 +4,10 @@
 import dask
 import numpy as np
 
-from .external import _ModelGLM
+from .external import ModelGLM
 
 
-class Model(_ModelGLM, metaclass=abc.ABCMeta):
+class Model(ModelGLM, metaclass=abc.ABCMeta):
     """
     Generalized Linear Model (GLM) with beta distributed noise, logit link for location and log link for scale.
     """
diff --git a/batchglm/models/glm_nb/external.py b/batchglm/models/glm_nb/external.py
index bc3a7bb1..855c6305 100644
--- a/batchglm/models/glm_nb/external.py
+++ b/batchglm/models/glm_nb/external.py
@@ -1,4 +1,4 @@
 import batchglm.utils.data as data_utils
 from batchglm import pkg_constants
-from batchglm.models.base_glm import _ModelGLM, closedform_glm_mean, closedform_glm_scale
+from batchglm.models.base_glm import ModelGLM, closedform_glm_mean, closedform_glm_scale
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/models/glm_nb/model.py b/batchglm/models/glm_nb/model.py
index 36c17df1..6d86d3c9 100644
--- a/batchglm/models/glm_nb/model.py
+++ b/batchglm/models/glm_nb/model.py
@@ -4,10 +4,10 @@
 import dask.array
 import numpy as np
 
-from .external import _ModelGLM
+from .external import ModelGLM
 
 
-class Model(_ModelGLM, metaclass=abc.ABCMeta):
+class Model(ModelGLM, metaclass=abc.ABCMeta):
     """
     Generalized Linear Model (GLM) with negative binomial noise.
     """
diff --git a/batchglm/models/glm_norm/external.py b/batchglm/models/glm_norm/external.py
index bc3a7bb1..855c6305 100644
--- a/batchglm/models/glm_norm/external.py
+++ b/batchglm/models/glm_norm/external.py
@@ -1,4 +1,4 @@
 import batchglm.utils.data as data_utils
 from batchglm import pkg_constants
-from batchglm.models.base_glm import _ModelGLM, closedform_glm_mean, closedform_glm_scale
+from batchglm.models.base_glm import ModelGLM, closedform_glm_mean, closedform_glm_scale
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/models/glm_norm/model.py b/batchglm/models/glm_norm/model.py
index dced5290..869aad28 100644
--- a/batchglm/models/glm_norm/model.py
+++ b/batchglm/models/glm_norm/model.py
@@ -4,10 +4,10 @@
 import dask
 import numpy as np
 
-from .external import _ModelGLM
+from .external import ModelGLM
 
 
-class Model(_ModelGLM, metaclass=abc.ABCMeta):
+class Model(ModelGLM, metaclass=abc.ABCMeta):
 
     """Generalized Linear Model (GLM) with normal noise."""
 
diff --git a/batchglm/models/glm_norm/utils.py b/batchglm/models/glm_norm/utils.py
index 481c3c20..10447d0a 100644
--- a/batchglm/models/glm_norm/utils.py
+++ b/batchglm/models/glm_norm/utils.py
@@ -61,7 +61,6 @@ def init_par(model, init_location: str, init_scale: str) -> Tuple[np.ndarray, np
             &= D \cdot x' = f^{-1}(\theta)
     $$
     """
-
     groupwise_means = None
 
     init_location_str = init_location.lower()
@@ -79,7 +78,7 @@ def init_par(model, init_location: str, init_scale: str) -> Tuple[np.ndarray, np
     elif init_location_str == "standard":
         overall_means = np.mean(model.x, axis=0)  # directly calculate the mean
         init_theta_location = np.zeros([model.num_loc_params, model.num_features])
-        init_theta_location[0, :] = np.log(overall_means)
+        init_theta_location[0, :] = overall_means  # identity linked.
     else:
         raise ValueError("init_location string %s not recognized" % init_location)
 
diff --git a/batchglm/models/glm_poisson/external.py b/batchglm/models/glm_poisson/external.py
index bc3a7bb1..855c6305 100644
--- a/batchglm/models/glm_poisson/external.py
+++ b/batchglm/models/glm_poisson/external.py
@@ -1,4 +1,4 @@
 import batchglm.utils.data as data_utils
 from batchglm import pkg_constants
-from batchglm.models.base_glm import _ModelGLM, closedform_glm_mean, closedform_glm_scale
+from batchglm.models.base_glm import ModelGLM, closedform_glm_mean, closedform_glm_scale
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/models/glm_poisson/model.py b/batchglm/models/glm_poisson/model.py
index bad489ee..764452cb 100644
--- a/batchglm/models/glm_poisson/model.py
+++ b/batchglm/models/glm_poisson/model.py
@@ -69,11 +69,10 @@ def bounds(self, sf, dmax, dtype) -> Tuple[Dict[str, Any], Dict[str, Any]]:
             "loc": np.nextafter(dmax, -np.inf, dtype=dtype) / sf,
             "scale": np.nextafter(dmax, -np.inf, dtype=dtype) / sf,
             "likelihood": dtype(1),
-            "ll": dtype(10000), # poisson models can have large log likelhoods initially
+            "ll": dtype(10000),  # poisson models can have large log likelhoods initially
             # Not used and should be removed: https://github.com/theislab/batchglm/issues/148
             "theta_scale": np.log(dmax) / sf,
             "eta_scale": np.log(dmax) / sf,
-
         }
         return bounds_min, bounds_max
 
@@ -85,7 +84,7 @@ def rand_fn_ave(self) -> Optional[Callable]:
 
     @property
     def rand_fn(self) -> Optional[Callable]:
-        return lambda shape: np.abs(np.random.uniform(2, 10, shape))
+        return lambda shape: np.abs(np.random.uniform(0.5, 2, shape))
 
     @property
     def rand_fn_loc(self) -> Optional[Callable]:
@@ -99,4 +98,5 @@ def generate_data(self) -> np.ndarray:
         """
         Sample random data based on poisson distribution and parameters.
         """
+        # see https://github.com/astronomyk/SimCADO/issues/59 for why we cast lam
         return np.random.poisson(lam=self.lam)
diff --git a/batchglm/train/base/__init__.py b/batchglm/train/base/__init__.py
new file mode 100644
index 00000000..39511c17
--- /dev/null
+++ b/batchglm/train/base/__init__.py
@@ -0,0 +1,2 @@
+from .estimator import BaseEstimatorGlm
+from .model_container import BaseModelContainer
diff --git a/batchglm/train/base/estimator.py b/batchglm/train/base/estimator.py
new file mode 100644
index 00000000..f0afd369
--- /dev/null
+++ b/batchglm/train/base/estimator.py
@@ -0,0 +1,19 @@
+import abc
+
+
+class BaseEstimatorGlm(metaclass=abc.ABCMeta):
+    @abc.abstractmethod
+    def model_container(self):
+        pass
+
+    @abc.abstractmethod
+    def initialize(self):
+        pass
+
+    @abc.abstractmethod
+    def train_sequence(self):
+        pass
+
+    @abc.abstractmethod
+    def finalize(self):
+        pass
diff --git a/batchglm/train/base/model_container.py b/batchglm/train/base/model_container.py
new file mode 100644
index 00000000..ef5996ae
--- /dev/null
+++ b/batchglm/train/base/model_container.py
@@ -0,0 +1,43 @@
+import abc
+
+from ...models.base_glm import ModelGLM
+
+
+class BaseModelContainer(metaclass=abc.ABCMeta):
+    @abc.abstractmethod
+    def error_codes(self):
+        pass
+
+    @abc.abstractmethod
+    def niter(self):
+        pass
+
+    @abc.abstractmethod
+    def ll(self):
+        pass
+
+    @abc.abstractmethod
+    def jac(self):
+        pass
+
+    @abc.abstractmethod
+    def hessian(self):
+        pass
+
+    @abc.abstractmethod
+    def fisher_inv(self):
+        pass
+
+    @abc.abstractmethod
+    def theta_location(self):
+        pass
+
+    @abc.abstractmethod
+    def model(self) -> ModelGLM:
+        pass
+
+    def theta_location_constrained(self):
+        pass
+
+    def theta_scale_constrained(self):
+        pass
diff --git a/batchglm/train/numpy/base_glm/__init__.py b/batchglm/train/numpy/base_glm/__init__.py
index 68cdae12..38af9069 100644
--- a/batchglm/train/numpy/base_glm/__init__.py
+++ b/batchglm/train/numpy/base_glm/__init__.py
@@ -1,2 +1,2 @@
 from .estimator import EstimatorGlm
-from .model_container import BaseModelContainer
+from .model_container import NumpyModelContainer
diff --git a/batchglm/train/numpy/base_glm/estimator.py b/batchglm/train/numpy/base_glm/estimator.py
index 0ffe9006..c7aef1b2 100644
--- a/batchglm/train/numpy/base_glm/estimator.py
+++ b/batchglm/train/numpy/base_glm/estimator.py
@@ -14,14 +14,15 @@
 import scipy.sparse
 import sparse
 
+from ...base import BaseEstimatorGlm
 from .external import pkg_constants
-from .model_container import BaseModelContainer
+from .model_container import NumpyModelContainer
 from .training_strategies import TrainingStrategies
 
 logger = logging.getLogger("batchglm")
 
 
-class EstimatorGlm(metaclass=abc.ABCMeta):
+class EstimatorGlm(BaseEstimatorGlm):
     """
     Estimator for Generalized Linear Models (GLMs).
 
@@ -34,11 +35,11 @@ class EstimatorGlm(metaclass=abc.ABCMeta):
 
     _train_loc: bool = False
     _train_scale: bool = False
-    _model_container: BaseModelContainer
+    _model_container: NumpyModelContainer
     lls: List[float] = []
     dtype: str = ""
 
-    def __init__(self, model_container: BaseModelContainer, dtype: str):
+    def __init__(self, model_container: NumpyModelContainer, dtype: str):
         """
         Performs initialisation and creates a new estimator.
         :param model:
@@ -63,7 +64,7 @@ def train_scale(self) -> bool:
         return self._train_scale
 
     @property
-    def model_container(self) -> BaseModelContainer:
+    def model_container(self) -> NumpyModelContainer:
         return self._model_container
 
     def train_sequence(self, training_strategy, **kwargs):
diff --git a/batchglm/train/numpy/base_glm/external.py b/batchglm/train/numpy/base_glm/external.py
index 7cb25668..f3008554 100644
--- a/batchglm/train/numpy/base_glm/external.py
+++ b/batchglm/train/numpy/base_glm/external.py
@@ -1,3 +1,3 @@
 from batchglm import pkg_constants
-from batchglm.models.base_glm import InputDataGLM, _ModelGLM
+from batchglm.models.base_glm import InputDataGLM, ModelGLM
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/train/numpy/base_glm/model_container.py b/batchglm/train/numpy/base_glm/model_container.py
index dc71172f..100550ad 100644
--- a/batchglm/train/numpy/base_glm/model_container.py
+++ b/batchglm/train/numpy/base_glm/model_container.py
@@ -4,16 +4,12 @@
 import dask.array
 import numpy as np
 
+from ....models.base_glm import ModelGLM
+from ....utils.data import dask_compute
+from ...base import BaseModelContainer
 
-def dask_compute(func: Callable):
-    def func_wrapper(*args, **kwargs):
-        result = func(*args, **kwargs)
-        return result.compute() if isinstance(result, dask.array.core.Array) else result
 
-    return func_wrapper
-
-
-class BaseModelContainer:
+class NumpyModelContainer(BaseModelContainer):
     """
     Build variables to be optimized.
 
@@ -63,7 +59,7 @@ def __init__(
             Precision used in tensorflow.
         """
 
-        self.model = model
+        self._model = model
         init_theta_location_clipped = model.np_clip_param(
             np.asarray(init_theta_location, dtype=dtype), "theta_location"
         )
@@ -90,8 +86,26 @@ def __init__(
         )
 
         # overriding the location and scale parameter by referencing the getter functions within the properties.
-        self.model._theta_location_getter = self._theta_location_getter
-        self.model._theta_scale_getter = self._theta_scale_getter
+        self._model._theta_location_getter = self._theta_location_getter
+        self._model._theta_scale_getter = self._theta_scale_getter
+
+    # Is this actually used in diffxpy? Why?
+    @property
+    def niter(self):
+        return None
+
+    # Is this actually used in diffxpy? Why?
+    @property
+    def error_codes(self):
+        return np.array()
+
+    @property
+    def model(self) -> ModelGLM:
+        return self._model
+
+    @property
+    def fisher_inv(self) -> np.ndarray:
+        return self._fisher_inv
 
     def _theta_location_getter(self) -> dask.array.core.Array:
         theta_location = self.params[0 : self.npar_location]
@@ -145,6 +159,16 @@ def theta_scale(self, value):
         else:
             self.params[self.npar_location :] = value
 
+    @property
+    def theta_location_constrained(self) -> Union[np.ndarray, dask.array.core.Array]:
+        """dot product of location constraints with location parameter giving new constrained parameters"""
+        return np.dot(self.constraints_loc, self.theta_location)
+
+    @property
+    def theta_scale_constrained(self) -> Union[np.ndarray, dask.array.core.Array]:
+        """dot product of scale constraints with scale parameter giving new constrained parameters"""
+        return np.dot(self.constraints_scale, self.theta_scale)
+
     def theta_scale_j(self, j) -> dask.array.core.Array:
         if isinstance(j, int) or isinstance(j, np.int32) or isinstance(j, np.int64):
             j = [j]
@@ -179,7 +203,7 @@ def jac(self) -> Union[np.ndarray, dask.array.core.Array]:
     @property
     def jac_location(self) -> Union[np.ndarray, dask.array.core.Array]:
         """
-
+        Location jacobian.
         :return: (features x inferred param)
         """
         w = self.fim_weight_location_location  # (observations x features)
@@ -190,7 +214,7 @@ def jac_location(self) -> Union[np.ndarray, dask.array.core.Array]:
 
     def jac_location_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
         """
-
+        Location jacobian indexed by j, the dependent variable of interest.
         :return: (features x inferred param)
         """
         # Make sure that dimensionality of sliced array is kept:
@@ -209,14 +233,10 @@ def jac_scale(self) -> Union[np.ndarray, dask.array.core.Array]:
         """
         w = self.jac_weight_scale  # (observations x features)
         xh = self.xh_scale  # (observations x inferred param)
-        return np.einsum("fob,of->fb", np.einsum("ob,of->fob", xh, w), xh)
-
-    @abc.abstractmethod
-    def jac_weight_scale_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
-        pass
+        return w.transpose() @ xh
 
     @dask_compute
-    def jac_scale_j(self, j) -> np.ndarray:
+    def jac_scale_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
         """
 
         :return: (features x inferred param)
@@ -226,7 +246,11 @@ def jac_scale_j(self, j) -> np.ndarray:
             j = [j]
         w = self.jac_weight_scale_j(j=j)  # (observations x features)
         xh = self.xh_scale  # (observations x inferred param)
-        return np.einsum("fob,of->fb", np.einsum("ob,of->fob", xh, w), xh)
+        return w.transpose() @ xh
+
+    @abc.abstractmethod
+    def jac_weight_scale_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
+        pass
 
     # hessians
 
@@ -238,7 +262,6 @@ def hessian_weight_location_location(self) -> Union[np.ndarray, dask.array.core.
     @property
     def hessian_location_location(self) -> Union[np.ndarray, dask.array.core.Array]:
         """
-
         :return: (features x inferred param x inferred param)
         """
         w = self.hessian_weight_location_location
@@ -342,10 +365,12 @@ def fim_weight(self) -> Union[np.ndarray, dask.array.core.Array]:
     @property
     @abc.abstractmethod
     def fim_weight_location_location(self) -> Union[np.ndarray, dask.array.core.Array]:
+        """
+        This is exactly W in (11) and in equation (7) as well and will be used as such in the
+        calculation of the Jacobian.
+        """
         pass
 
-    # ll
-
     @property
     @abc.abstractmethod
     def ll(self) -> Union[np.ndarray, dask.array.core.Array]:
@@ -364,13 +389,16 @@ def ll_byfeature(self) -> np.ndarray:
     def ll_byfeature_j(self, j) -> np.ndarray:
         return np.sum(self.ll_j(j=j), axis=0)
 
-    # bar
-
-    @property
     @abc.abstractmethod
     def ybar(self) -> Union[np.ndarray, dask.array.core.Array]:
+        """
+        This is Z in equation (8).
+        """
         pass
 
     @abc.abstractmethod
     def ybar_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
+        """
+        This is Z in equation (8) indexed by j i.e the dependent variable of interest.
+        """
         pass
diff --git a/batchglm/train/numpy/glm_nb/external.py b/batchglm/train/numpy/glm_nb/external.py
index c5a08c7a..b6abf2e6 100644
--- a/batchglm/train/numpy/glm_nb/external.py
+++ b/batchglm/train/numpy/glm_nb/external.py
@@ -5,5 +5,5 @@
 from batchglm.models.glm_nb.utils import init_par
 
 # import necessary base_glm layers
-from batchglm.train.numpy.base_glm import BaseModelContainer, EstimatorGlm
+from batchglm.train.numpy.base_glm import EstimatorGlm, NumpyModelContainer
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/train/numpy/glm_nb/model_container.py b/batchglm/train/numpy/glm_nb/model_container.py
index 699910f2..c1d5e74d 100644
--- a/batchglm/train/numpy/glm_nb/model_container.py
+++ b/batchglm/train/numpy/glm_nb/model_container.py
@@ -4,10 +4,11 @@
 import numpy as np
 import scipy
 
-from .external import BaseModelContainer
+from ....utils.data import dask_compute
+from .external import NumpyModelContainer
 
 
-class ModelContainer(BaseModelContainer):
+class ModelContainer(NumpyModelContainer):
     @property
     def fim_weight(self):
         raise NotImplementedError("This method is currently unimplemented as it isn't used by any built-in procedures.")
@@ -20,19 +21,19 @@ def fim_weight_location_location(self) -> Union[np.ndarray, dask.array.core.Arra
         """
         return self.location * self.scale / (self.scale + self.location)
 
-    @property
-    def ybar(self) -> Union[np.ndarray, dask.array.core.Array]:
+    def fim_weight_location_location_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
         """
+        Fisher inverse matrix weights at j
         :return: observations x features
         """
-        return np.asarray(self.x - self.location) / self.location
+        return self.location_j(j=j) * self.scale_j(j=j) / (self.scale_j(j=j) + self.location_j(j=j))
 
-    def fim_weight_location_location_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
+    @property
+    def ybar(self) -> Union[np.ndarray, dask.array.core.Array]:
         """
-        Fisher inverse matrix weights at j
         :return: observations x features
         """
-        return -self.location_j(j=j) * self.scale_j(j=j) / (self.scale_j(j=j) + self.location_j(j=j))
+        return np.asarray(self.x - self.location) / self.location
 
     def ybar_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
         """
diff --git a/batchglm/train/numpy/glm_norm/external.py b/batchglm/train/numpy/glm_norm/external.py
index bcaa8627..d30752ac 100644
--- a/batchglm/train/numpy/glm_norm/external.py
+++ b/batchglm/train/numpy/glm_norm/external.py
@@ -5,5 +5,5 @@
 from batchglm.models.glm_norm.utils import closedform_norm_glm_logsd, init_par
 
 # import necessary base_glm layers
-from batchglm.train.numpy.base_glm import BaseModelContainer, EstimatorGlm
+from batchglm.train.numpy.base_glm import EstimatorGlm, NumpyModelContainer
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/train/numpy/glm_norm/model_container.py b/batchglm/train/numpy/glm_norm/model_container.py
index f7308eba..b705c64e 100644
--- a/batchglm/train/numpy/glm_norm/model_container.py
+++ b/batchglm/train/numpy/glm_norm/model_container.py
@@ -4,7 +4,7 @@
 import dask
 import numpy as np
 
-from .external import BaseModelContainer
+from .external import NumpyModelContainer
 
 
 def ll(scale, loc, x):
@@ -13,7 +13,7 @@ def ll(scale, loc, x):
     return ll
 
 
-class ModelContainer(BaseModelContainer):
+class ModelContainer(NumpyModelContainer):
     @property
     def fim_weight(self):
         raise NotImplementedError("This method is currently unimplemented as it isn't used by any built-in procedures.")
@@ -59,27 +59,24 @@ def jac_weight_scale(self) -> Union[np.ndarray, dask.array.core.Array]:
     def jac_weight_scale_j(self, j) -> Union[np.ndarray, dask.array.core.Array]:
         return -np.ones_like(self.x[:, j]) - np.power((self.x[:, j] - self.location_j(j=j)) / self.scale_j(j=j), 2)
 
-    @property
-    def fim_location_location(self):
-        return np.power(self.location / self.scale, 2)
-
     @property
     def fim_location_scale(self) -> np.ndarray:
         return np.zeros([self.model.x.shape[1], self.theta_location.shape[0], self.theta_scale.shape[0]])
 
+    @property
+    def fim_weight_scale_scale(self) -> np.ndarray:
+        return np.full(self.scale.shape, 2)
+
     @property
     def fim_scale_scale(self) -> Union[np.ndarray, dask.array.core.Array]:
         """
+
         :return: (features x inferred param x inferred param)
         """
         w = self.fim_weight_scale_scale
         xh = self.xh_scale
         return np.einsum("fob,oc->fbc", np.einsum("ob,of->fob", xh, w), xh)
 
-    @property
-    def fim_weight_scale_scale(self) -> np.ndarray:
-        return np.full(self.scale.shape, 2)
-
     @property
     def hessian_weight_location_scale(self) -> np.ndarray:
         scale = self.scale
diff --git a/batchglm/train/numpy/glm_norm/utils.py b/batchglm/train/numpy/glm_norm/utils.py
new file mode 100644
index 00000000..e69de29b
diff --git a/batchglm/train/numpy/glm_poisson/external.py b/batchglm/train/numpy/glm_poisson/external.py
index 5cd5bdfd..8268181b 100644
--- a/batchglm/train/numpy/glm_poisson/external.py
+++ b/batchglm/train/numpy/glm_poisson/external.py
@@ -5,5 +5,5 @@
 from batchglm.models.glm_poisson.utils import init_par
 
 # import necessary base_glm layers
-from batchglm.train.numpy.base_glm import BaseModelContainer, EstimatorGlm
+from batchglm.train.numpy.base_glm import EstimatorGlm, NumpyModelContainer
 from batchglm.utils.linalg import groupwise_solve_lm
diff --git a/batchglm/train/numpy/glm_poisson/model_container.py b/batchglm/train/numpy/glm_poisson/model_container.py
index a74994c8..547356cc 100644
--- a/batchglm/train/numpy/glm_poisson/model_container.py
+++ b/batchglm/train/numpy/glm_poisson/model_container.py
@@ -106,14 +106,10 @@ def hessian_weight_location_scale(self) -> np.ndarray:
     def jac_weight_scale_j(self) -> np.ndarray:
         raise NoScaleError("jac_weight_scale_j")
 
-    @property
-    def hessian(self) -> Union[np.ndarray, dask.array.core.Array]:
-        return self.hessian_location_location
-
     @property
     def fim(self) -> Union[np.ndarray, dask.array.core.Array]:
         return self.fim_location_location
 
     @property
     def jac(self) -> Union[np.ndarray, dask.array.core.Array]:
-        return self.jac_location
\ No newline at end of file
+        return self.jac_location
diff --git a/batchglm/utils/__init__.py b/batchglm/utils/__init__.py
index 16ed49d6..7bef4e1f 100644
--- a/batchglm/utils/__init__.py
+++ b/batchglm/utils/__init__.py
@@ -1 +1 @@
-from . import plotting
+from . import data, input, plotting
diff --git a/batchglm/utils/data.py b/batchglm/utils/data.py
index a8ca2e69..3db548e8 100644
--- a/batchglm/utils/data.py
+++ b/batchglm/utils/data.py
@@ -1,7 +1,8 @@
 import logging
 from functools import singledispatch
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Callable, Dict, List, Optional, Tuple, Union
 
+import dask
 import numpy as np
 import pandas as pd
 import patsy
@@ -9,6 +10,14 @@
 logger = logging.getLogger("batchglm")
 
 
+def dask_compute(func: Callable):
+    def func_wrapper(*args, **kwargs):
+        result = func(*args, **kwargs)
+        return result.compute() if isinstance(result, dask.array.core.Array) else result
+
+    return func_wrapper
+
+
 def design_matrix(
     sample_description: Optional[pd.DataFrame] = None,
     formula: Optional[str] = None,
@@ -45,10 +54,11 @@ def design_matrix(
         if sample_description is None:
             raise ValueError("Provide a sample_description if dmat is None.")
         if isinstance(as_categorical, bool):
-            as_categorical = [as_categorical] * sample_description.columns.size
+            as_categorical = sample_description.columns
         sample_description = sample_description.copy()
-        columns = sample_description.columns[as_categorical]
-        sample_description[columns] = sample_description[columns].apply(lambda col: col.astype("category"))
+        sample_description[as_categorical] = sample_description[as_categorical].apply(
+            lambda col: col.astype("category")
+        )
 
         dmat = patsy.dmatrix(formula, sample_description)
         coef_names = dmat.design_info.column_names
@@ -214,6 +224,8 @@ def constraint_system_from_star(
         coef_names = dmat.design_info.column_names
     else:
         raise TypeError(f"Type {type(dmat)} not recognized for argument dmat.")
+    if term_names is None:
+        term_names = coef_names
 
     # Test full design matrix for being full rank before returning:
     if cmat is None:
@@ -233,9 +245,14 @@ def constraint_system_from_star(
 @constraint_system_from_star.register
 def _constraint_system_from_dict(
     constraints: dict,
-    return_type: str = "patsy",
     **kwargs,
 ) -> Tuple:
+    if "return_type" in kwargs:
+        return_type = kwargs.pop("return_type")
+    else:
+        return_type = "patsy"
+    if "dmat" in kwargs:
+        kwargs.pop("dmat")  # not sure why dmat was an argument here but some things expect it to be part of the API.
     cmat, dmat, term_names = constraint_system_from_dict(constraints, **kwargs)
     return constraint_system_from_star(cmat, dmat=dmat, return_type=return_type, term_names=term_names, **kwargs)
 
diff --git a/batchglm/utils/input.py b/batchglm/utils/input.py
index 1957c4bd..62568381 100644
--- a/batchglm/utils/input.py
+++ b/batchglm/utils/input.py
@@ -1,6 +1,5 @@
 import logging
-from operator import indexOf
-from typing import List, Optional, Union
+from typing import List, Optional, Tuple, Union
 
 import anndata
 import dask.array
@@ -10,7 +9,88 @@
 import scipy.sparse
 import sparse
 
-from ..models.base_glm.utils import parse_constraints, parse_design
+logger = logging.getLogger("batchglm")
+
+
+def parse_design(
+    design_matrix: Union[pd.DataFrame, patsy.design_info.DesignMatrix, dask.array.core.Array, np.ndarray],
+    param_names: List[str] = None,
+) -> Tuple[np.ndarray, List[str]]:
+    r"""
+    Parser for design matrices.
+
+    :param design_matrix: Design matrix.
+    :param param_names:
+        Optional coefficient names for design_matrix.
+        Ignored if design_matrix is pd.DataFrame or patsy.design_info.DesignMatrix.
+    :return: Tuple[np.ndarray, List[str]] containing the design matrix and the parameter names.
+    :raise AssertionError: if the type of design_matrix is not understood.
+    :raise AssertionError: if length of provided param_names is not equal to number of coefficients in design_matrix.
+    :raise ValueError: if param_names is None when type of design_matrix is numpy.ndarray or dask.array.core.Array.
+    """
+    if isinstance(design_matrix, (pd.DataFrame, patsy.design_info.DesignMatrix)) and param_names is not None:
+        logger.warning(f"The provided param_names are ignored as the design matrix is of type {type(design_matrix)}.")
+
+    if isinstance(design_matrix, patsy.design_info.DesignMatrix):
+        dmat = np.asarray(design_matrix)
+        params = design_matrix.design_info.column_names
+    elif isinstance(design_matrix, pd.DataFrame):
+        dmat = np.asarray(design_matrix)
+        params = design_matrix.columns.tolist()
+    elif isinstance(design_matrix, dask.array.core.Array):
+        dmat = design_matrix.compute()
+        params = param_names
+    elif isinstance(design_matrix, np.ndarray):
+        dmat = design_matrix
+        params = param_names
+    else:
+        raise AssertionError(f"Datatype for design_matrix not understood: {type(design_matrix)}")
+    if params is None:
+        raise ValueError("Provide names when passing design_matrix as np.ndarray or dask.array.core.Array!")
+    assert len(params) == dmat.shape[1], (
+        "Length of provided param_names is not equal to " "number of coefficients in design_matrix."
+    )
+    return dmat, params
+
+
+def parse_constraints(
+    dmat: np.ndarray,
+    dmat_par_names: List[str],
+    constraints: Optional[Union[np.ndarray, dask.array.core.Array]] = None,
+    constraint_par_names: Optional[List[str]] = None,
+) -> Tuple[np.ndarray, List[str]]:
+    r"""
+    Parser for constraint matrices.
+
+    :param dmat: Design matrix.
+    :param constraints: Constraint matrix.
+    :param constraint_par_names: Optional coefficient names for constraints.
+    :return: Tuple[np.ndarray, List[str]] containing the constraint matrix and the parameter names.
+    :raise AssertionError: if the type of given design / contraint matrix is not np.ndarray or dask.array.core.Array.
+    """
+    assert isinstance(dmat, np.ndarray), "dmat must be provided as np.ndarray."
+    if constraints is None:
+        constraints = np.identity(n=dmat.shape[1])
+        constraint_params = dmat_par_names
+    else:
+        if isinstance(constraints, dask.array.core.Array):
+            constraints = constraints.compute()
+        assert isinstance(constraints, np.ndarray), "contraints must be np.ndarray or dask.array.core.Array."
+        # Cannot use all parameter names if constraint matrix is not identity: Make up new ones.
+        # Use variable names that can be mapped (unconstrained).
+        if constraint_par_names is not None:
+            assert len(constraint_params) == len(constraint_par_names)
+            constraint_params = constraint_par_names
+        else:
+            constraint_params = [
+                "var_" + str(i)
+                if np.sum(constraints[:, i] != 0) > 1
+                else dmat_par_names[np.where(constraints[:, i] != 0)[0][0]]
+                for i in range(constraints.shape[1])
+            ]
+        assert constraints.shape[0] == dmat.shape[1], "constraint dimension mismatch"
+
+    return constraints, constraint_params
 
 
 class InputDataGLM:
diff --git a/batchglm/utils/plotting.py b/batchglm/utils/plotting.py
index defe6375..6148c11e 100644
--- a/batchglm/utils/plotting.py
+++ b/batchglm/utils/plotting.py
@@ -24,7 +24,7 @@ def _input_checks(
 
     def _cast(data: Union[np.ndarray, dask.array.core.Array]) -> Tuple[np.ndarray, np.ndarray]:
         if isinstance(data, dask.array.core.Array):
-            to_return = data.coompute()
+            to_return = data.compute()
         elif isinstance(data, np.ndarray):
             to_return = data
         else:
diff --git a/docs/api/index.rst b/docs/api/index.rst
index 09b5476b..95d504d7 100644
--- a/docs/api/index.rst
+++ b/docs/api/index.rst
@@ -44,22 +44,20 @@ Negative Binomial
    models.glm_nb.Model
    train.numpy.glm_nb.Estimator
 
-Poisson
+Normal
 ~~~~~~~~~~~~~~~~~
-
 .. autosummary::
    :toctree: .
 
-   models.glm_poisson.Model
-   train.numpy.glm_poisson.Estimator
-
-Normal
+   models.glm_norm.Model
+   train.numpy.glm_norm.Estimator
+Poisson
 ~~~~~~~~~~~~~~~~~
 .. autosummary::
    :toctree: .
 
-   models.glm_norm.Model
-   train.numpy.glm_norm.Estimator
+   models.glm_poisson.Model
+   train.numpy.glm_poisson.Estimator
 
 Planned or Incomplete Models:
 
diff --git a/poetry.lock b/poetry.lock
index 5cea3795..ac86e750 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -60,7 +60,7 @@ python-dateutil = ">=2.7.0"
 
 [[package]]
 name = "aspy.refactor-imports"
-version = "2.2.1"
+version = "2.3.0"
 description = "Utilities for refactoring imports in python-like syntax."
 category = "dev"
 optional = false
@@ -71,7 +71,7 @@ cached-property = "*"
 
 [[package]]
 name = "atomicwrites"
-version = "1.4.0"
+version = "1.4.1"
 description = "Atomic file writes."
 category = "dev"
 optional = false
@@ -93,11 +93,11 @@ tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>
 
 [[package]]
 name = "babel"
-version = "2.9.1"
+version = "2.10.3"
 description = "Internationalization utilities"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.6"
 
 [package.dependencies]
 pytz = ">=2015.7"
@@ -134,26 +134,24 @@ chardet = ">=3.0.2"
 
 [[package]]
 name = "black"
-version = "21.10b0"
+version = "22.6.0"
 description = "The uncompromising code formatter."
 category = "dev"
 optional = false
 python-versions = ">=3.6.2"
 
 [package.dependencies]
-click = ">=7.1.2"
+click = ">=8.0.0"
 mypy-extensions = ">=0.4.3"
-pathspec = ">=0.9.0,<1"
+pathspec = ">=0.9.0"
 platformdirs = ">=2"
-regex = ">=2020.1.8"
-tomli = ">=0.2.6,<2.0.0"
-typing-extensions = ">=3.10.0.0"
+tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
+typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
 d = ["aiohttp (>=3.7.4)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
-python2 = ["typed-ast (>=1.4.3)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
@@ -166,15 +164,15 @@ python-versions = "*"
 
 [[package]]
 name = "certifi"
-version = "2021.10.8"
+version = "2022.6.15"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 
 [[package]]
 name = "cffi"
-version = "1.15.0"
+version = "1.15.1"
 description = "Foreign Function Interface for Python calling C code."
 category = "dev"
 optional = false
@@ -193,37 +191,37 @@ python-versions = ">=3.6.1"
 
 [[package]]
 name = "chardet"
-version = "4.0.0"
-description = "Universal encoding detector for Python 2 and 3"
+version = "5.0.0"
+description = "Universal encoding detector for Python 3"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.6"
 
 [[package]]
 name = "charset-normalizer"
-version = "2.0.12"
+version = "2.1.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 category = "main"
 optional = false
-python-versions = ">=3.5.0"
+python-versions = ">=3.6.0"
 
 [package.extras]
 unicode_backport = ["unicodedata2"]
 
 [[package]]
 name = "click"
-version = "8.0.4"
+version = "8.1.3"
 description = "Composable command line interface toolkit"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cloudpickle"
-version = "2.0.0"
+version = "2.1.0"
 description = "Extended pickling support for Python objects"
 category = "main"
 optional = false
@@ -231,7 +229,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "colorama"
-version = "0.4.4"
+version = "0.4.5"
 description = "Cross-platform colored terminal text."
 category = "main"
 optional = false
@@ -282,7 +280,7 @@ six = ">=1.10"
 
 [[package]]
 name = "cookietemple"
-version = "1.4.0"
+version = "1.4.1"
 description = "A cookiecutter based project template creation tool supporting several domains and languages with advanced linting, syncing and standardized workflows to get your project kickstarted in no time."
 category = "dev"
 optional = false
@@ -320,7 +318,7 @@ toml = ["toml"]
 
 [[package]]
 name = "cryptography"
-version = "36.0.1"
+version = "36.0.2"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 category = "dev"
 optional = false
@@ -355,27 +353,26 @@ python-versions = ">=3.6,<4.0"
 
 [[package]]
 name = "dask"
-version = "2022.2.1"
+version = "2021.4.1"
 description = "Parallel PyData with Task Scheduling"
 category = "main"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 
 [package.dependencies]
 cloudpickle = ">=1.1.1"
 fsspec = ">=0.6.0"
-packaging = ">=20.0"
 partd = ">=0.3.10"
-pyyaml = ">=5.3.1"
+pyyaml = "*"
 toolz = ">=0.8.2"
 
 [package.extras]
-array = ["numpy (>=1.18)"]
-complete = ["bokeh (>=2.1.1)", "distributed (==2022.02.1)", "jinja2", "numpy (>=1.18)", "pandas (>=1.0)"]
-dataframe = ["numpy (>=1.18)", "pandas (>=1.0)"]
-diagnostics = ["bokeh (>=2.1.1)", "jinja2"]
-distributed = ["distributed (==2022.02.1)"]
-test = ["pytest", "pytest-rerunfailures", "pytest-xdist", "pre-commit"]
+array = ["numpy (>=1.16)"]
+complete = ["bokeh (>=1.0.0,!=2.0.0)", "distributed (>=2021.04.1)", "numpy (>=1.16)", "pandas (>=0.25.0)"]
+dataframe = ["numpy (>=1.16)", "pandas (>=0.25.0)"]
+diagnostics = ["bokeh (>=1.0.0,!=2.0.0)"]
+distributed = ["distributed (>=2021.04.1)"]
+test = ["pytest", "pytest-rerunfailures", "pytest-xdist"]
 
 [[package]]
 name = "deprecated"
@@ -425,7 +422,7 @@ pipenv = ["pipenv"]
 
 [[package]]
 name = "filelock"
-version = "3.6.0"
+version = "3.7.1"
 description = "A platform independent file lock."
 category = "dev"
 optional = false
@@ -502,11 +499,11 @@ flake8 = "*"
 
 [[package]]
 name = "flake8-rst-docstrings"
-version = "0.2.5"
+version = "0.2.6"
 description = "Python docstring reStructuredText (RST) validator"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 flake8 = ">=3.0.0"
@@ -515,19 +512,20 @@ restructuredtext-lint = "*"
 
 [[package]]
 name = "fonttools"
-version = "4.29.1"
+version = "4.34.4"
 description = "Tools to manipulate font files"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"]
+all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"]
 graphite = ["lz4 (>=1.7.4.2)"]
 interpolatable = ["scipy", "munkres"]
 lxml = ["lxml (>=4.0,<5)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
 type1 = ["xattr"]
 ufo = ["fs (>=2.2.0,<3)"]
@@ -536,7 +534,7 @@ woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
 
 [[package]]
 name = "fsspec"
-version = "2022.2.0"
+version = "2022.5.0"
 description = "File-system specification"
 category = "main"
 optional = false
@@ -563,6 +561,7 @@ s3 = ["s3fs"]
 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
+tqdm = ["tqdm"]
 
 [[package]]
 name = "gitdb"
@@ -588,7 +587,7 @@ gitdb = ">=4.0.1,<5"
 
 [[package]]
 name = "h5py"
-version = "3.6.0"
+version = "3.7.0"
 description = "Read and write HDF5 files from Python"
 category = "main"
 optional = false
@@ -599,7 +598,7 @@ numpy = ">=1.14.5"
 
 [[package]]
 name = "identify"
-version = "2.4.11"
+version = "2.5.1"
 description = "File identification library for Python"
 category = "dev"
 optional = false
@@ -618,7 +617,7 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "imagesize"
-version = "1.3.0"
+version = "1.4.1"
 description = "Getting image size from png/jpeg/jpeg2000/gif file"
 category = "main"
 optional = false
@@ -626,7 +625,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "importlib-metadata"
-version = "4.11.2"
+version = "4.12.0"
 description = "Read metadata from Python packages"
 category = "main"
 optional = false
@@ -638,7 +637,7 @@ zipp = ">=0.5"
 [package.extras]
 docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
 perf = ["ipython"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
 
 [[package]]
 name = "iniconfig"
@@ -650,11 +649,11 @@ python-versions = "*"
 
 [[package]]
 name = "jinja2"
-version = "3.0.3"
+version = "3.1.2"
 description = "A very fast and expressive template engine."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 MarkupSafe = ">=2.0"
@@ -676,7 +675,7 @@ jinja2 = "*"
 
 [[package]]
 name = "kiwisolver"
-version = "1.3.2"
+version = "1.4.3"
 description = "A fast implementation of the Cassowary constraint solver"
 category = "main"
 optional = false
@@ -704,15 +703,15 @@ python-versions = ">=3.6,<3.10"
 
 [[package]]
 name = "locket"
-version = "0.2.1"
-description = "File-based locks for Python for Linux and Windows"
+version = "1.0.0"
+description = "File-based locks for Python on Linux and Windows"
 category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "markupsafe"
-version = "2.1.0"
+version = "2.1.1"
 description = "Safely add untrusted strings to HTML/XML markup."
 category = "main"
 optional = false
@@ -720,7 +719,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "matplotlib"
-version = "3.5.1"
+version = "3.5.2"
 description = "Python plotting package"
 category = "main"
 optional = false
@@ -784,11 +783,11 @@ icu = ["PyICU (>=1.0.0)"]
 
 [[package]]
 name = "nodeenv"
-version = "1.6.0"
+version = "1.7.0"
 description = "Node.js virtual environment builder"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
 
 [[package]]
 name = "nox"
@@ -835,7 +834,7 @@ numpy = ">=1.15"
 
 [[package]]
 name = "numpy"
-version = "1.22.3"
+version = "1.23.1"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
@@ -854,7 +853,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
 [[package]]
 name = "pandas"
-version = "1.4.1"
+version = "1.4.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 category = "main"
 optional = false
@@ -912,7 +911,7 @@ test = ["pytest", "pytest-cov", "scipy"]
 
 [[package]]
 name = "pbr"
-version = "5.8.1"
+version = "5.9.0"
 description = "Python Build Reasonableness"
 category = "main"
 optional = false
@@ -931,23 +930,27 @@ flake8-polyfill = ">=1.0.2,<2"
 
 [[package]]
 name = "pillow"
-version = "9.0.1"
+version = "9.2.0"
 description = "Python Imaging Library (Fork)"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+
 [[package]]
 name = "platformdirs"
-version = "2.5.1"
+version = "2.5.2"
 description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["Sphinx (>=4)", "furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)"]
-test = ["appdirs (==1.4.4)", "pytest (>=6)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)"]
+docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)", "sphinx (>=4)"]
+test = ["appdirs (==1.4.4)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)", "pytest (>=6)"]
 
 [[package]]
 name = "pluggy"
@@ -971,11 +974,11 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "pre-commit"
-version = "2.17.0"
+version = "2.20.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 category = "dev"
 optional = false
-python-versions = ">=3.6.1"
+python-versions = ">=3.7"
 
 [package.dependencies]
 cfgv = ">=2.0.0"
@@ -987,19 +990,19 @@ virtualenv = ">=20.0.8"
 
 [[package]]
 name = "pre-commit-hooks"
-version = "4.1.0"
+version = "4.3.0"
 description = "Some out-of-the-box hooks for pre-commit."
 category = "dev"
 optional = false
-python-versions = ">=3.6.1"
+python-versions = ">=3.7"
 
 [package.dependencies]
 "ruamel.yaml" = ">=0.15"
-toml = "*"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.28"
+version = "3.0.30"
 description = "Library for building powerful interactive command lines in Python"
 category = "dev"
 optional = false
@@ -1073,15 +1076,15 @@ integrations = ["cryptography"]
 
 [[package]]
 name = "pygments"
-version = "2.11.2"
+version = "2.12.0"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [[package]]
 name = "pyjwt"
-version = "2.3.0"
+version = "2.4.0"
 description = "JSON Web Token implementation in Python"
 category = "dev"
 optional = false
@@ -1110,14 +1113,14 @@ tests = ["pytest (>=3.2.1,!=3.3.0)", "hypothesis (>=3.27.0)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.0.7"
-description = "Python parsing module"
+version = "3.0.9"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.6.8"
 
 [package.extras]
-diagrams = ["jinja2", "railroad-diagrams"]
+diagrams = ["railroad-diagrams", "jinja2"]
 
 [[package]]
 name = "pytest"
@@ -1153,7 +1156,7 @@ six = ">=1.5"
 
 [[package]]
 name = "python-slugify"
-version = "6.1.1"
+version = "6.1.2"
 description = "A Python slugify application that also handles Unicode"
 category = "dev"
 optional = false
@@ -1167,7 +1170,7 @@ unidecode = ["Unidecode (>=1.1.1)"]
 
 [[package]]
 name = "pytz"
-version = "2021.3"
+version = "2022.1"
 description = "World timezone definitions, modern and historical"
 category = "main"
 optional = false
@@ -1195,42 +1198,34 @@ prompt_toolkit = ">=2.0,<4.0"
 [package.extras]
 docs = ["Sphinx (>=3.3,<4.0)", "sphinx-rtd-theme (>=0.5.0,<0.6.0)", "sphinx-autobuild (>=2020.9.1,<2021.0.0)", "sphinx-copybutton (>=0.3.1,<0.4.0)", "sphinx-autodoc-typehints (>=1.11.1,<2.0.0)"]
 
-[[package]]
-name = "regex"
-version = "2022.3.2"
-description = "Alternative regular expression module, to replace re."
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
 [[package]]
 name = "reorder-python-imports"
-version = "2.7.1"
+version = "2.8.0"
 description = "Tool for reordering python imports"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-"aspy.refactor-imports" = ">=2.2.1"
+"aspy.refactor-imports" = ">=2.3.0,<3"
 
 [[package]]
 name = "requests"
-version = "2.27.1"
+version = "2.28.1"
 description = "Python HTTP for Humans."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = ">=3.7, <4"
 
 [package.dependencies]
 certifi = ">=2017.4.17"
-charset-normalizer = {version = ">=2.0.0,<2.1.0", markers = "python_version >= \"3\""}
-idna = {version = ">=2.5,<4", markers = "python_version >= \"3\""}
+charset-normalizer = ">=2,<3"
+idna = ">=2.5,<4"
 urllib3 = ">=1.21.1,<1.27"
 
 [package.extras]
-socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
-use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "restructuredtext-lint"
@@ -1298,7 +1293,7 @@ requests = "*"
 
 [[package]]
 name = "scipy"
-version = "1.8.0"
+version = "1.8.1"
 description = "SciPy: Scientific Library for Python"
 category = "main"
 optional = false
@@ -1323,15 +1318,16 @@ scipy = ">=1.0"
 
 [[package]]
 name = "setuptools-scm"
-version = "6.4.2"
+version = "7.0.5"
 description = "the blessed package to manage your versions by scm tags"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 packaging = ">=20.0"
 tomli = ">=1.0.0"
+typing-extensions = "*"
 
 [package.extras]
 test = ["pytest (>=6.2)", "virtualenv (>20)"]
@@ -1363,26 +1359,26 @@ python-versions = "*"
 
 [[package]]
 name = "sparse"
-version = "0.13.0"
+version = "0.9.1"
 description = "Sparse n-dimensional arrays"
 category = "main"
 optional = false
-python-versions = ">=3.6, <4"
+python-versions = ">=3.5, <4"
 
 [package.dependencies]
-numba = ">=0.49"
-numpy = ">=1.17"
+numba = ">=0.45"
+numpy = "*"
 scipy = ">=0.19"
 
 [package.extras]
-all = ["dask", "pytest (>=3.5)", "pytest-black", "pytest-cov", "tox", "sphinx", "sphinx-rtd-theme"]
+all = ["pytest (>=3.5)", "pytest-black", "pytest-cov", "tox", "sphinx", "sphinx-rtd-theme"]
 docs = ["sphinx", "sphinx-rtd-theme"]
-tests = ["dask", "pytest (>=3.5)", "pytest-black", "pytest-cov"]
-tox = ["dask", "pytest (>=3.5)", "pytest-black", "pytest-cov", "tox"]
+tests = ["pytest (>=3.5)", "pytest-black", "pytest-cov"]
+tox = ["pytest (>=3.5)", "pytest-black", "pytest-cov", "tox"]
 
 [[package]]
 name = "sphinx"
-version = "4.4.0"
+version = "4.5.0"
 description = "Python documentation generator"
 category = "main"
 optional = false
@@ -1430,18 +1426,18 @@ test = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "sphinx-autodoc-typehints"
-version = "1.17.0"
+version = "1.18.3"
 description = "Type hints (PEP 484) support for the Sphinx autodoc extension"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-Sphinx = ">=4"
+Sphinx = ">=4.5"
 
 [package.extras]
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-type_comments = ["typed-ast (>=1.4.0)"]
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+type_comments = ["typed-ast (>=1.5.2)"]
 
 [[package]]
 name = "sphinx-click"
@@ -1555,11 +1551,11 @@ test = ["pytest"]
 
 [[package]]
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-version = "3.5.0"
+version = "4.0.0"
 description = "Manage dynamic plugins for Python applications"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 
 [package.dependencies]
 pbr = ">=2.0.0,<2.1.0 || >2.1.0"
@@ -1598,7 +1594,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "toolz"
-version = "0.11.2"
+version = "0.12.0"
 description = "List processing tools and functional utilities"
 category = "main"
 optional = false
@@ -1606,11 +1602,11 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "tornado"
-version = "6.1"
+version = "6.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 category = "dev"
 optional = false
-python-versions = ">= 3.5"
+python-versions = ">= 3.7"
 
 [[package]]
 name = "typeguard"
@@ -1642,7 +1638,7 @@ python-versions = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.27.11"
+version = "2.28.1"
 description = "Typing stubs for requests"
 category = "dev"
 optional = false
@@ -1653,7 +1649,7 @@ types-urllib3 = "<1.27"
 
 [[package]]
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-version = "1.26.10"
+version = "1.26.16"
 description = "Typing stubs for urllib3"
 category = "dev"
 optional = false
@@ -1661,28 +1657,28 @@ python-versions = "*"
 
 [[package]]
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-version = "4.1.1"
-description = "Backported and Experimental Type Hints for Python 3.6+"
-category = "dev"
+version = "4.3.0"
+description = "Backported and Experimental Type Hints for Python 3.7+"
+category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
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+version = "1.26.10"
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 category = "main"
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+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4"
 
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 [[package]]
 name = "virtualenv"
-version = "20.13.3"
+version = "20.15.1"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
@@ -1708,7 +1704,7 @@ python-versions = "*"
 
 [[package]]
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+version = "1.14.1"
 description = "Module for decorators, wrappers and monkey patching."
 category = "dev"
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@@ -1744,20 +1740,20 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
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 python-versions = ">=3.7"
 
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+    {file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"},
+]
+xdoctest = []
 xlrd = [
     {file = "xlrd-1.2.0-py2.py3-none-any.whl", hash = "sha256:e551fb498759fa3a5384a94ccd4c3c02eb7c00ea424426e212ac0c57be9dfbde"},
     {file = "xlrd-1.2.0.tar.gz", hash = "sha256:546eb36cee8db40c3eaa46c351e67ffee6eeb5fa2650b71bc4c758a29a1b29b2"},
 ]
-zipp = [
-    {file = "zipp-3.7.0-py3-none-any.whl", hash = "sha256:b47250dd24f92b7dd6a0a8fc5244da14608f3ca90a5efcd37a3b1642fac9a375"},
-    {file = "zipp-3.7.0.tar.gz", hash = "sha256:9f50f446828eb9d45b267433fd3e9da8d801f614129124863f9c51ebceafb87d"},
-]
+zipp = []
diff --git a/pyproject.toml b/pyproject.toml
index 95e5306b..0c625d62 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,13 +24,13 @@ python = ">=3.8.0, <3.10.0"
 click = "^8.0.0"
 rich = "^10.3.0"
 PyYAML = "^5.4.1"
-dask = "^2022.1.1"
+dask = "2021.4.1" # https://github.com/theislab/diffxpy/issues/194
 numpy = ">=1.22.2"
 patsy = "^0.5.2"
 scipy = "^1.7.3"
 pandas = "^1.4.0"
 anndata = "^0.7.8"
-sparse = "^0.13.0"
+sparse = "0.9.1" # https://github.com/theislab/diffxpy/issues/194
 matplotlib = "^3.5.1"
 sphinx-autodoc-typehints = "^1.16.0"
 seaborn = "^0.11.2"
@@ -46,7 +46,7 @@ sphinx = "^4.0.2"
 sphinx-autobuild = "^2021.3.14"
 pre-commit = "^2.11.1"
 flake8 = "^3.8.4"
-black = ">=20.8b1, <=21.10b0"
+black = ">=21.12b0"
 flake8-bandit = "^2.1.2"
 flake8-bugbear = "^21.4.3"
 flake8-docstrings = "^1.5.0"
diff --git a/tests/numpy/test_accuracy.py b/tests/numpy/test_accuracy.py
index 40921c00..542a0ae0 100644
--- a/tests/numpy/test_accuracy.py
+++ b/tests/numpy/test_accuracy.py
@@ -5,7 +5,7 @@
 from utils import get_estimator, get_generated_model
 
 from batchglm import pkg_constants
-from batchglm.models.base_glm import _ModelGLM
+from batchglm.models.base_glm import ModelGLM
 from batchglm.train.numpy.base_glm import EstimatorGlm
 
 logger = logging.getLogger("batchglm")
@@ -165,6 +165,7 @@ def test_accuracy_rand_theta(self) -> bool:
 
         sparse_estimator = get_estimator(noise_model="norm", model=sparse_model)
         assert self._test_accuracy(sparse_estimator)
+        return True
 
     def test_accuracy_const_theta(self) -> bool:
         """
diff --git a/tests/numpy/utils.py b/tests/numpy/utils.py
index bf12a8fe..cc3c6eb1 100644
--- a/tests/numpy/utils.py
+++ b/tests/numpy/utils.py
@@ -2,12 +2,11 @@
 
 import numpy as np
 
-from batchglm.models.base_glm import _ModelGLM
+from batchglm.models.base_glm import ModelGLM
 from batchglm.models.glm_beta import Model as BetaModel
 from batchglm.models.glm_nb import Model as NBModel
 from batchglm.models.glm_norm import Model as NormModel
 from batchglm.models.glm_poisson import Model as PoissonModel
-
 from batchglm.train.numpy.base_glm import EstimatorGlm
 from batchglm.train.numpy.glm_nb import Estimator as NBEstimator
 from batchglm.train.numpy.glm_norm import Estimator as NormEstimator
@@ -28,7 +27,7 @@ def get_estimator(noise_model: str, **kwargs) -> EstimatorGlm:
     raise ValueError(f"Noise model {noise_model} not recognized.")
 
 
-def get_model(noise_model: str) -> _ModelGLM:
+def get_model(noise_model: str) -> ModelGLM:
     if noise_model is None:
         raise ValueError("noise_model is None")
     if noise_model == "nb":
@@ -44,7 +43,7 @@ def get_model(noise_model: str) -> _ModelGLM:
 
 def get_generated_model(
     noise_model: str, num_conditions: int, num_batches: int, sparse: bool, mode: Optional[str] = None
-) -> _ModelGLM:
+) -> ModelGLM:
     model = get_model(noise_model=noise_model)
 
     def random_uniform(low: float, high: float):
@@ -62,7 +61,8 @@ def const(offset: float):
     elif mode == "randTheta":
 
         if noise_model in ["nb", "norm", "poisson"]:
-            rand_fn_ave = random_uniform(10, 1000)
+            # too large mean breaks poisson
+            rand_fn_ave = random_uniform(10, 1000 if noise_model != "poisson" else 15)
             rand_fn_loc = random_uniform(1, 3)
             rand_fn_scale = random_uniform(1, 3)
         elif noise_model == "beta":
@@ -75,7 +75,8 @@ def const(offset: float):
     elif mode == "constTheta":
 
         if noise_model in ["nb", "norm", "poisson"]:
-            rand_fn_ave = random_uniform(10, 1000)
+            # too large mean breaks poisson
+            rand_fn_ave = random_uniform(10, 1000 if noise_model != "poisson" else 15)
             rand_fn_loc = const(1.0)
             rand_fn_scale = const(1.0)
         elif noise_model == "beta":
diff --git a/tests/run_data_utils_test.py b/tests/run_data_utils_test.py
index 3a6d9b5d..80b080e4 100644
--- a/tests/run_data_utils_test.py
+++ b/tests/run_data_utils_test.py
@@ -55,10 +55,10 @@ class TestConstraintSystemFromStar(unittest.TestCase):
     true_terms = ["condition", "batch"]
     true_coefs = ["condition[0]", "condition[1]", "batch[0]", "batch[1]", "batch[2]", "batch[3]"]
 
-    true_terms_list = None
+    true_terms_list = ["condition[0]", "condition[1]", "batch[T.1]", "batch[T.2]", "batch[T.3]"]
     true_coefs_list = ["condition[0]", "condition[1]", "batch[T.1]", "batch[T.2]", "batch[T.3]"]
 
-    true_terms_array = None
+    true_terms_array = ["condition[0]", "condition[1]", "batch[T.1]"]
     true_coefs_array = ["condition[0]", "condition[1]", "batch[T.1]"]
 
     # dict tests
diff --git a/tests/test_types_dmat.py b/tests/test_types_dmat.py
index 7d857241..3c797e79 100644
--- a/tests/test_types_dmat.py
+++ b/tests/test_types_dmat.py
@@ -6,7 +6,7 @@
 import pandas as pd
 import patsy
 
-from batchglm.models.base_glm.utils import parse_design
+from batchglm.utils.input import parse_design
 
 
 def check_np_dask(dmat: Union[np.ndarray, dask.array.core.Array], params: List[str]) -> bool: