Doc changes for ecosystem consistency

docs/_config.yml

@@ -5,6 +5,7 @@ title: Tstrait manual
 author: Tskit Developers
 copyright: "2023"
 only_build_toc_files: true
+favicon: favicon.ico
 
 execute:
   execute_notebooks: cache
@@ -22,14 +23,9 @@ html:
   use_repository_button: true
   use_edit_page_button: true
 
-  extra_footer: |
-    <div>
-    tstrait __TSTRAIT_VERSION__
-    </div>
 
 sphinx:
     extra_extensions:
-    - numpydoc
     - sphinx_copybutton
     - sphinx_design
     - sphinx.ext.autodoc
@@ -38,9 +34,17 @@ sphinx:
     - sphinx.ext.viewcode
     - sphinx.ext.intersphinx
     - sphinx_issues
+    - IPython.sphinxext.ipython_console_highlighting
 
     config:
       html_theme: sphinx_book_theme
+      html_theme_options:
+        pygments_dark_style: monokai
+        navigation_with_keys: false
+        logo:
+          text: |
+            tstrait<br/>
+            version __TSTRAIT_VERSION__
       myst_enable_extensions:
       - colon_fence
       - deflist

tstrait/genetic_value.py

@@ -49,13 +49,11 @@ def _accumulate_individual_values(
 class _GeneticValue:
     """GeneticValue class to compute genetic values of individuals.
 
-    Parameters
-    ----------
-    ts : tskit.TreeSequence
-        Tree sequence data with mutation
-    trait_df : pandas.DataFrame
-        Dataframe that includes causal site ID, causal allele, simulated effect
-        size, and trait ID.
+    :param ts: Tree sequence data with mutation
+    :type ts: tskit.TreeSequence
+    :param trait_df: Dataframe that includes causal site ID, causal allele,
+        simulated effect size, and trait ID.
+    :type trait_df: pandas.DataFrame
     """
 
     def __init__(self, ts, trait_df):
@@ -102,10 +100,8 @@ def _individual_genetic_values(self, tree, site, causal_allele, effect_size):
     def _run(self):
         """Computes genetic values of individuals.
 
-        Returns
-        -------
-        pandas.DataFrame
-            Dataframe with genetic value, individual ID, and trait ID.
+        :returns: Dataframe with genetic value, individual ID, and trait ID.
+        :rtype: pandas.DataFrame
         """
 
         num_ind = self.ts.num_individuals
@@ -139,56 +135,53 @@ def genetic_value(ts, trait_df):
     """
     Obtains genetic value from a trait dataframe.
 
-    Parameters
-    ----------
-    ts : tskit.TreeSequence
-        The tree sequence data that will be used in the quantitative trait
+    :param ts: The tree sequence data that will be used in the quantitative trait
         simulation.
-    trait_df : pandas.DataFrame
-        Trait dataframe.
-
-    Returns
-    -------
-    pandas.DataFrame
-        Pandas dataframe that includes genetic value of individuals in the
+    :type ts: tskit.TreeSequence
+    :param trait_df: Trait dataframe.
+    :type trait_df: pandas.DataFrame
+    :returns: Pandas dataframe that includes genetic value of individuals in the
         tree sequence.
+    :rtype: pandas.DataFrame
+
+    .. seealso::
+        :func:`trait_model` Return a trait model, which can be used as `model` input.
+
+        :func:`sim_trait` Return a trait dataframe, which can be used as a
+        `trait_df` input.
 
-    See Also
-    --------
-    trait_model : Return a trait model, which can be used as `model` input.
-    sim_trait : Return a trait dataframe, whch can be used as a `trait_df` input.
-    sim_env : Genetic value dataframe output can be used as an input to simulate
-        environmental noise.
+        :func:`sim_env` Genetic value dataframe output can be used as an input
+        to simulate environmental noise.
 
-    Notes
-    -----
-    The `trait_df` input has some requirements that will be noted below.
+    .. note::
+        The `trait_df` input has some requirements that will be noted below.
 
-    1. Columns
+        1. Columns
 
-    The following columns must be included in `trait_df`:
+        The following columns must be included in `trait_df`:
 
-        * **site_id**: Site IDs that have causal allele.
-        * **effect_size**: Simulated effect size of causal allele.
-        * **causal_allele**: Causal allele.
-        * **trait_id**: Trait ID.
+            * **site_id**: Site IDs that have causal allele.
+            * **effect_size**: Simulated effect size of causal allele.
+            * **causal_allele**: Causal allele.
+            * **trait_id**: Trait ID.
 
-    2. Data requirements
+        2. Data requirements
 
-        * Site IDs in **site_id** column must be sorted in an ascending order. Please
-          refer to :py:meth:`pandas.DataFrame.sort_values` for details on sorting
-          values in a :class:`pandas.DataFrame`.
+            * Site IDs in **site_id** column must be sorted in an ascending order. Please
+              refer to :py:meth:`pandas.DataFrame.sort_values` for details on sorting
+              values in a :class:`pandas.DataFrame`.
 
-        * Trait IDs in **trait_id** column must start from zero and be consecutive.
+            * Trait IDs in **trait_id** column must start from zero and be consecutive.
 
-    The genetic value dataframe contains the following columns:
+        The genetic value dataframe contains the following columns:
 
-        * **trait_id**: Trait ID.
-        * **individual_id**: Individual ID inside the tree sequence input.
-        * **genetic_value**: Genetic values that are obtained from the trait dataframe.
+            * **trait_id**: Trait ID.
+            * **individual_id**: Individual ID inside the tree sequence input.
+            * **genetic_value**: Genetic values that are obtained from the trait
+              dataframe.
+
+    .. rubric:: Examples
 
-    Examples
-    --------
     See :ref:`genetic_value` for worked examples.
     """
 
@@ -217,44 +210,34 @@ def genetic_value(ts, trait_df):
 def normalise_genetic_value(genetic_df, mean=0, var=1, ddof=1):
     """Normalise genetic value dataframe.
 
-    Parameters
-    ----------
-    genetic_df : pandas.DataFrame
-        Genetic value dataframe.
-    mean : float, default 0
-        Mean of the resulting genetic value.
-    var : float, default 1
-        Variance of the resulting genetic value.
-    ddof : int, default 1
-        Delta degrees of freedom. The divisor used in computing the variance
+    :param genetic_df: Genetic value dataframe.
+    :type genetic_df: pandas.DataFrame
+    :param mean: Mean of the resulting genetic value.
+    :type mean: float
+    :param var: Variance of the resulting genetic value.
+    :type var: float
+    :param ddof: Delta degrees of freedom. The divisor used in computing the variance
         is N - ddof, where N represents the number of elements.
+    :type ddof: int
+    :returns: Dataframe with normalised genetic value.
+    :rtype: pandas.DataFrame
+    :raises ValueError: If `var` <= 0.
 
-    Returns
-    -------
-    pandas.DataFrame
-        Dataframe with normalised genetic value.
-
-    Raises
-    ------
-    ValueError
-        If `var` <= 0.
+    .. note::
+        The following columns must be included in `genetic_df`:
 
-    Notes
-    -----
-    The following columns must be included in `genetic_df`:
+            * **trait_id**: Trait ID.
+            * **individual_id**: Individual ID inside the tree sequence input.
+            * **genetic_value**: Simulated genetic values.
 
-        * **trait_id**: Trait ID.
-        * **individual_id**: Individual ID inside the tree sequence input.
-        * **genetic_value**: Simulated genetic values.
+        The dataframe output has the following columns:
 
-    The dataframe output has the following columns:
+            * **trait_id**: Trait ID.
+            * **individual_id**: Individual ID inside the tree sequence input.
+            * **genetic_value**: Normalised genetic values.
 
-        * **trait_id**: Trait ID.
-        * **individual_id**: Individual ID inside the tree sequence input.
-        * **genetic_value**: Normalised genetic values.
+    .. rubric:: Examples
 
-    Examples
-    --------
     See :ref:`normalise_genetic_value` section for worked examples.
     """
     if var <= 0:

tstrait/simulate_effect_size.py

@@ -15,12 +15,10 @@ class _FreqResult:
     """
     Data class that contains simulated effect size and allele frequency.
 
-    Attributes
-    ----------
-    beta_array : numpy.array
-        Numpy array that includes simulated effect size.
-    allele_freq : numpy.array
-        Allele frequency of each causal mutation.
+    :ivar beta_array: Numpy array that includes simulated effect size.
+    :vartype beta_array: numpy.array
+    :ivar allele_freq: Allele frequency of each causal mutation.
+    :vartype allele_freq: numpy.array
     """
 
     beta_array: np.array
@@ -31,18 +29,16 @@ class _TraitSimulator:
     """Simulator class to select causal alleles and simulate effect sizes of causal
     mutations.
 
-    Parameters
-    ----------
-    ts : tskit.TreeSequence
-        Tree sequence data with mutation.
-    causal_sites : list
-        List of causal site IDs.
-    model : TraitModel
-        Trait model that will be used to simulate effect sizes.
-    alpha : float
-        Parameter that determines the degree of the frequency dependence model.
-    rng : numpy.random.Generator
-        Generator object that will be used to generate random numbers.
+    :param ts: Tree sequence data with mutation.
+    :type ts: tskit.TreeSequence
+    :param causal_sites: List of causal site IDs.
+    :type causal_sites: list
+    :param model: Trait model that will be used to simulate effect sizes.
+    :type model: TraitModel
+    :param alpha: Parameter that determines the degree of the frequency dependence model.
+    :type alpha: float
+    :param rng: Generator object that will be used to generate random numbers.
+    :type rng: numpy.random.Generator
     """
 
     def __init__(self, ts, causal_sites, model, alpha, rng):
@@ -169,62 +165,53 @@ def sim_trait(
     """
     Simulates traits.
 
-    Parameters
-    ----------
-    ts : tskit.TreeSequence
-        The tree sequence data that will be used in the quantitative trait
+    :param ts: The tree sequence data that will be used in the quantitative trait
         simulation.
-    model : tstrait.TraitModel
-        Trait model that will be used to simulate effect sizes.
-    num_causal : int, default None
-        Number of causal sites that will be randomly selected . If both `num_causal` and
-        `causal_sites` are None, number of causal sites will be 1.
-    causal_sites : list, default None
-        List of site IDs that have causal allele. If None, causal site IDs will be
-        chosen randomly according to `num_causal`.
-    alpha : float, default None
-        Parameter that determines the degree of the frequency dependence model. Please
-        see :ref:`frequency_dependence` for details on how this parameter influences
-        effect size simulation. If None, alpha will be 0.
-    random_seed : int, default None
-        Random seed of simulation. If None, simulation will be conducted randomly.
-
-    Returns
-    -------
-    pandas.DataFrame
-        Trait dataframe that includes simulated effect sizes.
-
-    Raises
-    ------
-    ValueError
-        If the number of mutations in `ts` is smaller than `num_causal`.
-    ValueError
-        If both `num_causal` and `causal_sites` are specified.
-    ValueError
-        If there are repeated values in `causal_sites`.
-
-    See Also
-    --------
-    trait_model : Return a trait model, which can be used as `model` input.
-    genetic_value : The trait dataframe output can be used as an input to obtain
-        genetic values.
-
-    Notes
-    -----
-    The simulation output is given as a :py:class:`pandas.DataFrame` and contains the
-    following columns:
-
-        * **position**: Position of sites that have causal allele in genome coordinates.
-        * **site_id**: Site IDs that have causal allele. The output dataframe has sorted
-          site IDs.
-        * **effect_size**: Simulated effect size of causal allele.
-        * **causal_allele**: Causal allele.
-        * **allele_freq**: Allele frequency of causal allele. It is described in detail
-          in :ref:`trait_frequency_dependence`.
-        * **trait_id**: Trait ID.
-
-    Examples
-    --------
+    :type ts: tskit.TreeSequence
+    :param model: Trait model that will be used to simulate effect sizes.
+    :type model: tstrait.TraitModel
+    :param num_causal: Number of causal sites that will be randomly selected.
+        If both `num_causal` and `causal_sites` are None, number of causal sites
+        will be 1.
+    :type num_causal: int
+    :param causal_sites: List of site IDs that have causal allele. If None,
+        causal site IDs will be chosen randomly according to `num_causal`.
+    :type causal_sites: list
+    :param alpha: Parameter that determines the degree of the frequency
+        dependence model. Please see :ref:`frequency_dependence` for details on how
+        this parameter influences effect size simulation. If None, alpha will be 0.
+    :type alpha: float
+    :param random_seed: Random seed of simulation. If None, simulation will be
+        conducted randomly.
+    :type random_seed: int
+    :returns: Trait dataframe that includes simulated effect sizes.
+    :rtype: pandas.DataFrame
+    :raises ValueError: If the number of mutations in `ts` is smaller than `num_causal`.
+    :raises ValueError: If both `num_causal` and `causal_sites` are specified.
+    :raises ValueError: If there are repeated values in `causal_sites`.
+
+    .. seealso::
+        :func:`trait_model` Return a trait model, which can be used as `model` input.
+
+        :func:`genetic_value` The trait dataframe output can be used as an input
+        to obtain genetic values.
+
+    .. note::
+        The simulation output is given as a :py:class:`pandas.DataFrame` and contains the
+        following columns:
+
+            * **position**: Position of sites that have causal allele in genome
+              coordinates.
+            * **site_id**: Site IDs that have causal allele. The output dataframe
+              has sorted site IDs.
+            * **effect_size**: Simulated effect size of causal allele.
+            * **causal_allele**: Causal allele.
+            * **allele_freq**: Allele frequency of causal allele. It is described
+              in detail in :ref:`trait_frequency_dependence`.
+            * **trait_id**: Trait ID.
+
+    .. rubric:: Examples
+
     See :ref:`sim_trait` for worked examples.
     """
     ts = _check_instance(ts, "ts", tskit.TreeSequence)