remove deprecations

doc/long_vignettes/tskit_metadata_vignette.md

@@ -164,7 +164,7 @@ fwdpy11.evolvets(rng, pop, params,
                  simplification_interval=100,
                  suppress_table_indexing=True)
 
-ts = pop.dump_tables_to_tskit(demes_graph=graph)
+ts = pop.dump_tables_to_tskit()
 ```
 
 Now that we have some data, let's look at how the `fwdpy11` mutation and individual information got encoded as `tskit` metadata!

doc/long_vignettes/tskitconvert_vignette.md

@@ -161,84 +161,6 @@ ts = pop.dump_tables_to_tskit(model_params=params)
 assert fwdpy11.ModelParams(**eval(ts.metadata["model_params"])) == params
 ```
 
-### Including a `demes` graph
-
-You may include a {class}`demes.Graph` in the top-level metadata.
-If you also include a {class}`fwdpy11.ModelParams` (see above), including the `demes` graph gives redundant information.
-However, including the graph may be useful if downstream analysis will involve other tools compatible with the `demes` specification.
-With the graph as metadata, you can extract it and reconstruct the original `YAML` file, or send it to another Python package that understands it.
-
-The following hidden code block defines a function to return a {class}`demes.Graph` from `YAML` input stored in a string literal.
-
-```{code-cell} python
----
-tags: [ "hide-input" ]
----
-def gutenkunst():
-    import demes
-    yaml = """
-description: The Gutenkunst et al. (2009) OOA model.
-doi:
-- https://doi.org/10.1371/journal.pgen.1000695
-time_units: years
-generation_time: 25
-
-demes:
-- name: ancestral
-  description: Equilibrium/root population
-  epochs:
-  - {end_time: 220e3, start_size: 7300}
-- name: AMH
-  description: Anatomically modern humans
-  ancestors: [ancestral]
-  epochs:
-  - {end_time: 140e3, start_size: 12300}
-- name: OOA
-  description: Bottleneck out-of-Africa population
-  ancestors: [AMH]
-  epochs:
-  - {end_time: 21.2e3, start_size: 2100}
-- name: YRI
-  description: Yoruba in Ibadan, Nigeria
-  ancestors: [AMH]
-  epochs:
-  - start_size: 12300
-- name: CEU
-  description: Utah Residents (CEPH) with Northern and Western European Ancestry
-  ancestors: [OOA]
-  epochs:
-  - {start_size: 1000, end_size: 29725}
-- name: CHB
-  description: Han Chinese in Beijing, China
-  ancestors: [OOA]
-  epochs:
-  - {start_size: 510, end_size: 54090}
-
-migrations:
-- {demes: [YRI, OOA], rate: 25e-5}
-- {demes: [YRI, CEU], rate: 3e-5}
-- {demes: [YRI, CHB], rate: 1.9e-5}
-- {demes: [CEU, CHB], rate: 9.6e-5}
-"""
-    return demes.loads(yaml)
-```
-
-```{code-cell} python
-graph = gutenkunst()
-ts = pop.dump_tables_to_tskit(demes_graph=graph)
-```
-
-```{code-cell} python
-assert demes.Graph.fromdict(ts.metadata["demes_graph"]) == graph
-```
-
-Since this is an optional metadata field, accessing it will return `None` if no graph was provided:
-
-```{code-cell} python
-ts = pop.dump_tables_to_tskit()
-assert "demes_graph" not in ts.metadata
-```
-
 ### User-defined metadata
 
 It may be useful to store arbitrary data in the output.
@@ -279,51 +201,4 @@ Further, if the data are very large, then other output formats are likely more a
 
 ## Setting population table metadata
 
-You can also set the population table metadata when exporting data.
-Doing so requires a {class}`dict` mapping the integer label for each population to another {class}`dict`.
-
-For example, let's create an demographic model from the `demes` graph that used above:
-
-```{code-cell}python
-model = fwdpy11.discrete_demography.from_demes(gutenkunst())
-type(model)
-```
-
-This object contains a mapping from integer labels to the deme names:
-
-```{code-cell} python
-model.metadata['deme_labels']
-```
-
-We can make the required `dict` like this:
-
-```{code-cell} python
-pop_md = {}
-for key, value in model.metadata['deme_labels'].items():
-    pop_md[key] = {'name': value}
-```
-
-To actually illustrate the use of this metadata, we need to make sure that our `tskit` output actually has a population table:
-
-```{code-cell} python
-# initialize a population with the right number of demes...
-multideme_pop = fwdpy11.DiploidPopulation([100]*len(pop_md), 1.)
-ts = multideme_pop.dump_tables_to_tskit(population_metadata=pop_md)
-for pop in ts.populations():
-    print(pop.metadata)
-```
-
-We could also easily add the deme description to the metadata from the `demes` graph:
-
-```{code-cell} python
-graph = gutenkunst()
-graph.demes
-```
-
-```{code-cell} python
-pop_md = {}
-for i,deme in enumerate(graph.demes):
-    pop_md[i] = {'name': deme.name, "description": deme.description}
-
-pop_md
-```
+TODO

doc/misc/changelog.md

@@ -641,7 +641,7 @@ Bug fixes
 
 Breaking changes
 
-* {func}`fwdpy11.TableCollection.fs` no longer accepts more than two sample sets.
+* `fwdpy11.TableCollection.fs` no longer accepts more than two sample sets.
   This change allowed us to drop `sparse` as a dependency that was causing
   headaches when new Python point releases come out.
   PR {pr}`924`. Issues {issue}`876`, {issue}`919`.
@@ -1055,7 +1055,7 @@ Fixes:
 This is a point release adding more documentation:
 
 * {ref}`Demes vignette <demes_vignette>` updated.
-* {func}`fwdpy11.TableCollection.fs` docstring updated regarding some perhaps unexpected behavior of `sparse.COO`.
+* `fwdpy11.TableCollection.fs` docstring updated regarding some perhaps unexpected behavior of `sparse.COO`.
 
 ## 0.14.0
 
@@ -1494,7 +1494,7 @@ release candidates (see below) plus the following:
   {issue}`389`
   {issue}`390`
   {issue}`392`
-* {func}`fwdpy11.TableCollection.fs` added.  See `tablefs`.
+* `fwdpy11.TableCollection.fs` added.  See `tablefs`.
   PR {pr}`387`
   PR {pr}`399`
 * Creating populations from `msprime` input improved.

doc/pages/functions.md

@@ -14,10 +14,6 @@
     :rtype: int
 ```
 
-```{eval-rst}
-.. autofunction:: fwdpy11.simplify
-```
-
 ```{eval-rst}
 .. autofunction:: fwdpy11.simplify_tables
 ```

doc/pages/genetic_values.md

@@ -23,8 +23,6 @@ For some background, see {ref}`definitions`.
 ```
 
 ```{eval-rst}
-.. autoclass:: fwdpy11.StrictAdditiveMultivariateEffects
+.. autoclass:: fwdpy11.AdditivePleiotropy
     :members: asdict, fromdict, asblack, shape, genetic_values, maps_to_fitness, maps_to_trait_value
 ```
-
-

doc/short_vignettes/incomplete_sweep.md

@@ -76,6 +76,7 @@ def setup(prune_selected=False):
         "rates": (0, 0, None),
         "prune_selected": False,
         "simlen": 200,
+        "demography": fwdpy11.ForwardDemesGraph.tubes([pop.N], burnin=10)
     }
     params = fwdpy11.ModelParams(**pdict)
 

doc/short_vignettes/selective_sweep.md

@@ -59,6 +59,7 @@ def setup(prune_selected=False):
         "rates": (0, 0, None),
         "prune_selected": False,
         "simlen": 200,
+        "demography": fwdpy11.ForwardDemesGraph.tubes([pop.N], burnin=10)
     }
     params = fwdpy11.ModelParams(**pdict)
 

doc/short_vignettes/trackmutationfates.md

@@ -58,6 +58,7 @@ def setup(prune_selected=False):
         "rates": (0, 0, None),
         "prune_selected": False,
         "simlen": 200,
+        "demography": fwdpy11.ForwardDemesGraph.tubes([pop.N], burnin=10)
     }
     params = fwdpy11.ModelParams(**pdict)
 

doc/short_vignettes/workingexample_fitness.md

@@ -32,6 +32,7 @@ p = {
     "prune_selected": True,
     "demography": None,
     "simlen": 10 * N,
+    "demography": fwdpy11.ForwardDemesGraph.tubes([N], burnin=10)
 }
 params = fwdpy11.ModelParams(**p)
 ```

fwdpy11/__init__.py

@@ -49,16 +49,11 @@
     PleiotropicOptima,
     Optimum,
     GaussianStabilizingSelection,
-    GSS,
-    GSSmo,
-    MultivariateGSS,
-    MultivariateGSSmo,
     NoNoise,
     GaussianNoise,
     Additive,
     Multiplicative,
     GBR,
-    StrictAdditiveMultivariateEffects,
     AdditivePleiotropy,
     PyDiploidGeneticValue,
     TimingError,
@@ -84,8 +79,6 @@
 from ._functions import (  # NOQA
     data_matrix_from_tables,
     infinite_sites,
-    make_data_matrix,
-    simplify,
     simplify_tables,
     _validate_regions,
 )  # NOQA

fwdpy11/_evolvets.py

@@ -121,16 +121,7 @@ def evolvets(
         except Exception as e:
             raise e
     else:
-        # Build a default model of "tubes"
-        from fwdpy11 import ForwardDemesGraph
-
-        sizes = pop.deme_sizes()[1].tolist()
-        msg = "Applying a default demographic model "
-        msg += f"where deme sizes are {sizes} "
-        msg += f"and the burn-in length is 10*{sum(sizes)}. "
-        msg += "This will raise an error in future releases."
-        warnings.warn(msg, DeprecationWarning, stacklevel=2)
-        demographic_model = ForwardDemesGraph.tubes(sizes, 10)
+        raise ValueError("params.demography cannot be None")
 
     for r in params.sregions:
         if isinstance(r, fwdpy11.mvDES):

fwdpy11/_functions/__init__.py

@@ -1,6 +1,6 @@
-from .data_matrix_from_tables import data_matrix_from_tables, make_data_matrix  # NOQA
+from .data_matrix_from_tables import data_matrix_from_tables  # NOQA
 from .import_demes import demography_from_demes  # NOQa
-from .simplify_tables import simplify, simplify_tables  # NOQA
+from .simplify_tables import simplify_tables  # NOQA
 
 from fwdpy11._fwdpy11 import _infinite_sites
 from fwdpy11 import GSLrng, DiploidPopulation

fwdpy11/_functions/data_matrix_from_tables.py

@@ -25,39 +25,6 @@
 from .._types import DataMatrix, DiploidPopulation, TableCollection
 
 
-def make_data_matrix(
-    pop: DiploidPopulation,
-    samples: Union[List, np.ndarray],
-    record_neutral: bool,
-    record_selected: bool,
-) -> DataMatrix:
-    """
-    Create a :class:`fwdpy11.DataMatrix` from a table collection.
-
-    :param pop: A population
-    :type pop: :class:`fwdpy11.PopulationBase`
-    :param samples: A list of sample nodes
-    :type samples: list
-    :param record_neutral: If True, generate data for neutral variants
-    :type record_neutral: bool
-    :param record_selected: If True, generate data for selected variants
-    :type record_selected: bool
-
-    .. deprecated:: 0.3.0
-
-       Prefer :func:`fwdpy11.data_matrix_from_tables`.
-
-    """
-    from warnings import warn
-
-    warn(
-        "fwdpy11.make_data_matrix is deprecated."
-        " Please use fwdpy11.data_matrix_from_tables.",
-        FutureWarning,
-    )
-    return DataMatrix(_make_data_matrix(pop, samples, record_neutral, record_selected))
-
-
 def data_matrix_from_tables(
     tables: TableCollection,
     samples: Union[List, np.ndarray],

fwdpy11/_functions/simplify_tables.py

@@ -5,61 +5,6 @@
 import numpy as np
 
 
-def simplify(pop, samples):
-    """
-    Simplify a TableCollection stored in a Population.
-
-    :param pop: A :class:`fwdpy11.DiploidPopulation`
-    :param samples: A list of samples (node indexes).
-
-    :return: The simplified tables and array mapping input sample
-             IDs to output IDS
-
-    :rtype: tuple
-
-    Note that the samples argument is agnostic with respect to the time of
-    the nodes in the input tables. Thus, you may do things like simplify
-    to a set of "currently-alive" nodes plus some or all ancient samples by
-    including some node IDs from
-    :attr:`fwdpy11.DiploidPopulation.ancient_sample_metadata`.
-
-    If the input contains ancient samples,
-    and you wish to include them in the output,
-    then you need to include their IDs in the samples argument.
-
-    .. note::
-
-        Due to node ID remapping, the metadata corresponding
-        to nodes becomes a bit more difficult to look up.
-        You need to use the output ID map, the original IDs, and
-        the population's metadata containers.
-
-    .. deprecated:: 0.3.0
-
-        Prefer :func:`fwdpy11.simplify_tables`
-
-    .. versionchanged:: 0.3.0
-
-        Ancient samples are no longer kept by default
-
-    .. versionchanged:: 0.5.0
-
-        No longer requires a :class:`MutationVector` argument.
-
-    """
-    import warnings
-
-    warnings.warn(
-        "This function is deprecated and will be removed soon."
-        "Please use fwdpy11.simplify_tables instead",
-        category=FutureWarning,
-        stacklevel=2,
-    )
-
-    ll_t, idmap = fwdpy11._fwdpy11._simplify(pop, samples)
-    return fwdpy11._types.TableCollection(ll_t), idmap
-
-
 def simplify_tables(
     tables: fwdpy11._types.TableCollection, samples: Union[List, np.ndarray]
 ) -> Tuple[fwdpy11._types.TableCollection, np.ndarray]: