Refactor

ehrapy/plot/__init__.py

@@ -1,5 +1,4 @@
 from ehrapy.plot._missingno_pl_api import *  # noqa: F403
-from ehrapy.plot._qc import qc_metrics
 from ehrapy.plot._scanpy_pl_api import *  # noqa: F403
 from ehrapy.plot._survival_analysis import kmf, ols
 from ehrapy.plot._util import *  # noqa: F403

ehrapy/preprocessing/_encode.py

@@ -114,8 +114,6 @@ def undo_encoding(
     else:
         raise ValueError(f"Cannot decode object of type {type(data)}. Can only decode AnnData objects!")
 
-    return None
-
 
 def _encode(
     adata: AnnData,
@@ -787,8 +785,7 @@ def _reorder_encodings(adata: AnnData, new_encodings: dict[str, list[list[str]]
                 # if encoding mode is
             if not encoded_categoricals_with_mode:
                 del adata.uns["encoding_to_var"][encode_mode]
-    logg.info("Re-encoded the AnnData object.")
-    # return updated encodings
+
     return _update_new_encode_modes(new_encodings, adata.uns["encoding_to_var"])
 
 

ehrapy/preprocessing/_quality_control.py

@@ -9,8 +9,6 @@
 from rich import print
 from thefuzz import process
 
-from ehrapy import logging as logg
-
 if TYPE_CHECKING:
     from collections.abc import Collection
 
@@ -36,44 +34,32 @@ def qc_metrics(
 
         Observation level metrics include:
 
-        `missing_values_abs`
-            Absolute amount of missing values.
-        `missing_values_pct`
-            Relative amount of missing values in percent.
+        - `missing_values_abs`: Absolute amount of missing values.
+        - `missing_values_pct`: Relative amount of missing values in percent.
 
         Feature level metrics include:
 
-        `missing_values_abs`
-            Absolute amount of missing values.
-        `missing_values_pct`
-            Relative amount of missing values in percent.
-        `mean`
-            Mean value of the features.
-        `median`
-            Median value of the features.
-        `std`
-            Standard deviation of the features.
-        `min`
-            Minimum value of the features.
-        `max`
-            Maximum value of the features.
+        - `missing_values_abs`: Absolute amount of missing values.
+        - `missing_values_pct`: Relative amount of missing values in percent.
+        - `mean`: Mean value of the features.
+        - `median`: Median value of the features.
+        - `std`: Standard deviation of the features.
+        - `min`: Minimum value of the features.
+        - `max`: Maximum value of the features.
 
         Examples:
             >>> import ehrapy as ep
             >>> import seaborn as sns
-            >>> import matplotlib.pyplot as plt
             >>> adata = ep.dt.mimic_2(encoded=True)
             >>> ep.pp.qc_metrics(adata)
             >>> sns.displot(adata.obs["missing_values_abs"])
-            >>> plt.show()
     """
     obs_metrics = _obs_qc_metrics(adata, layer, qc_vars)
     var_metrics = _var_qc_metrics(adata, layer)
 
     if inplace:
         adata.obs[obs_metrics.columns] = obs_metrics
         adata.var[var_metrics.columns] = var_metrics
-        logg.info("Added the calculated metrics to AnnData's `obs` and `var`.")
 
     return obs_metrics, var_metrics
 
@@ -91,10 +77,8 @@ def _missing_values(
     Returns:
         Absolute or relative amount of missing values.
     """
-    # Absolute number of missing values
     if shape is None:
         return pd.isnull(arr).sum()
-    # Relative number of missing values in percent
     else:
         n_rows, n_cols = shape
         if df_type == "obs":
@@ -256,7 +240,7 @@ def qc_lab_measurements(
 
     If you want to specify your own table as a Pandas DataFrame please examine the existing default table.
     Ethnicity and age columns can be added.
-    https://github.com/theislab/ehrapy/ehrapy/preprocessing/laboratory_reference_tables/laposata.tsv
+    https://github.com/theislab/ehrapy/blob/main/ehrapy/preprocessing/laboratory_reference_tables/laposata.tsv
 
     Args:
         adata: Annotated data matrix.
@@ -267,13 +251,13 @@ def qc_lab_measurements(
         threshold: Minimum required matching confidence score of the fuzzysearch.
                    0 = no matches, 100 = all must match. Defaults to 20.
         age_col: Column containing age values.
-        age_range: The inclusive age-range to filter for. e.g. 5-99
+        age_range: The inclusive age-range to filter for such as 5-99.
         sex_col: Column containing sex values. Column must contain 'U', 'M' or 'F'.
         sex: Sex to filter the reference values for. Use U for unisex which uses male values when male and female conflict.
-             Defaults to 'U|M'
+             Defaults to 'U|M'.
         ethnicity_col: Column containing ethnicity values.
         ethnicity: Ethnicity to filter for.
-        copy: Whether to return a copy. Defaults to False .
+        copy: Whether to return a copy. Defaults to False.
         verbose: Whether to have verbose stdout. Notifies user of matched columns and value ranges.
 
     Returns:
@@ -323,7 +307,6 @@ def qc_lab_measurements(
                 f"ethnicity columns and their values."
             )
 
-        # Fetch reference values
         try:
             if age_col:
                 min_age, max_age = age_range.split("-")
@@ -344,7 +327,6 @@ def qc_lab_measurements(
         except TypeError:
             print(f"[bold yellow]Unable to find specified reference values for {measurement}.")
 
-        # Check whether the measurements are inside the reference ranges
         check = reference_values[reference_column].values
         check_str: str = np.array2string(check)
         check_str = check_str.replace("[", "").replace("]", "").replace("'", "")

ehrapy/tools/__init__.py

@@ -1,7 +1,7 @@
 from ehrapy.tools._sa import anova_glm, glm, kmf, ols, test_kmf_logrank, test_nested_f_statistic
 from ehrapy.tools._scanpy_tl_api import *  # noqa: F403
 from ehrapy.tools.causal._dowhy import causal_inference
-from ehrapy.tools.feature_ranking._rank_features_groups import rank_features_groups
+from ehrapy.tools.feature_ranking._rank_features_groups import filter_rank_features_groups, rank_features_groups
 
 try:  # pragma: no cover
     from ehrapy.tools.nlp._medcat import (

ehrapy/tools/_scanpy_tl_api.py

@@ -679,52 +679,3 @@ def ingest(
         inplace=inplace,
         **kwargs,
     )
-
-
-def filter_rank_features_groups(
-    adata: AnnData,
-    key="rank_features_groups",
-    groupby=None,
-    key_added="rank_features_groups_filtered",
-    min_in_group_fraction=0.25,
-    min_fold_change=1,
-    max_out_group_fraction=0.5,
-) -> None:  # pragma: no cover
-    """Filters out features based on fold change and fraction of features containing the feature within and outside the `groupby` categories.
-
-    See :func:`~ehrapy.tl.rank_features_groups`.
-
-    Results are stored in `adata.uns[key_added]`
-    (default: 'rank_genes_groups_filtered').
-
-    To preserve the original structure of adata.uns['rank_genes_groups'],
-    filtered genes are set to `NaN`.
-
-    Args:
-        adata: Annotated data matrix.
-        key: Key previously added by :func:`~ehrapy.tl.rank_features_groups`
-        groupby: The key of the observations grouping to consider.
-        key_added: The key in `adata.uns` information is saved to.
-        min_in_group_fraction: Minimum in group fraction (default: 0.25).
-        min_fold_change: Miniumum fold change (default: 1).
-        max_out_group_fraction: Maximum out group fraction (default: 0.5).
-
-    Returns:
-        Same output as :func:`ehrapy.tl.rank_features_groups` but with filtered feature names set to `nan`
-
-    Examples:
-        >>> import ehrapy as ep
-        >>> adata = ep.dt.mimic_2(encoded=True)
-        >>> ep.tl.rank_features_groups(adata, "service_unit")
-        >>> ep.pl.rank_features_groups(adata)
-    """
-    return sc.tl.filter_rank_genes_groups(
-        adata=adata,
-        key=key,
-        groupby=groupby,
-        use_raw=False,
-        key_added=key_added,
-        min_in_group_fraction=min_in_group_fraction,
-        min_fold_change=min_fold_change,
-        max_out_group_fraction=max_out_group_fraction,
-    )

ehrapy/tools/feature_ranking/_rank_features_groups.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from collections.abc import Iterable
-from typing import TYPE_CHECKING, Literal, Optional, Union
+from typing import TYPE_CHECKING, Literal
 
 import numpy as np
 import pandas as pd
@@ -346,8 +346,11 @@ def rank_features_groups(
                             Used only for statistical tests (e.g. doesn't work for "logreg" `num_cols_method`)
         tie_correct: Use tie correction for `'wilcoxon'` scores. Used only for `'wilcoxon'`.
         layer: Key from `adata.layers` whose value will be used to perform tests on.
-        field_to_rank: Set to `layer` to rank variables in `adata.X` or `adata.layers[layer]` (default), `obs` to rank `adata.obs`, or `layer_and_obs` to rank both. Layer needs to be None if this is not 'layer'.
-        columns_to_rank: Subset of columns to rank. If 'all', all columns are used. If a dictionary, it must have keys 'var_names' and/or 'obs_names' and values must be iterables of strings. E.g. {'var_names': ['glucose'], 'obs_names': ['age', 'height']}.
+        field_to_rank: Set to `layer` to rank variables in `adata.X` or `adata.layers[layer]` (default), `obs` to rank `adata.obs`, or `layer_and_obs` to rank both.
+                       Layer needs to be None if this is not 'layer'.
+        columns_to_rank: Subset of columns to rank. If 'all', all columns are used.
+                         If a dictionary, it must have keys 'var_names' and/or 'obs_names' and values must be iterables of strings
+                         such as {'var_names': ['glucose'], 'obs_names': ['age', 'height']}.
         **kwds: Are passed to test methods. Currently this affects only parameters that
                 are passed to :class:`sklearn.linear_model.LogisticRegression`.
                 For instance, you can pass `penalty='l1'` to try to come up with a
@@ -568,7 +571,6 @@ def rank_features_groups(
     adata_orig.uns[key_added] = adata.uns[key_added]
     adata = adata_orig
 
-    # Adjust p values
     if "pvals" in adata.uns[key_added]:
         adata.uns[key_added]["pvals_adj"] = _adjust_pvalues(
             adata.uns[key_added]["pvals"], corr_method=correction_method
@@ -581,3 +583,52 @@ def rank_features_groups(
     _sort_features(adata, key_added)
 
     return adata if copy else None
+
+
+def filter_rank_features_groups(
+    adata: AnnData,
+    key="rank_features_groups",
+    groupby=None,
+    key_added="rank_features_groups_filtered",
+    min_in_group_fraction=0.25,
+    min_fold_change=1,
+    max_out_group_fraction=0.5,
+) -> None:  # pragma: no cover
+    """Filters out features based on fold change and fraction of features containing the feature within and outside the `groupby` categories.
+
+    See :func:`~ehrapy.tl.rank_features_groups`.
+
+    Results are stored in `adata.uns[key_added]`
+    (default: 'rank_genes_groups_filtered').
+
+    To preserve the original structure of adata.uns['rank_genes_groups'],
+    filtered genes are set to `NaN`.
+
+    Args:
+        adata: Annotated data matrix.
+        key: Key previously added by :func:`~ehrapy.tl.rank_features_groups`
+        groupby: The key of the observations grouping to consider.
+        key_added: The key in `adata.uns` information is saved to.
+        min_in_group_fraction: Minimum in group fraction (default: 0.25).
+        min_fold_change: Miniumum fold change (default: 1).
+        max_out_group_fraction: Maximum out group fraction (default: 0.5).
+
+    Returns:
+        Same output as :func:`ehrapy.tl.rank_features_groups` but with filtered feature names set to `nan`
+
+    Examples:
+        >>> import ehrapy as ep
+        >>> adata = ep.dt.mimic_2(encoded=True)
+        >>> ep.tl.rank_features_groups(adata, "service_unit")
+        >>> ep.pl.rank_features_groups(adata)
+    """
+    return sc.tl.filter_rank_genes_groups(
+        adata=adata,
+        key=key,
+        groupby=groupby,
+        use_raw=False,
+        key_added=key_added,
+        min_in_group_fraction=min_in_group_fraction,
+        min_fold_change=min_fold_change,
+        max_out_group_fraction=max_out_group_fraction,
+    )