🐛 make all imports explicit w.r.t. to pkg

vaep/__init__.py

@@ -9,10 +9,7 @@
 from importlib import metadata
 
 import njab
-import pandas as pd
-import pandas.io.formats.format as pf
 
-# from . import logging, nb, pandas, plotting
 import vaep.logging
 import vaep.nb
 import vaep.pandas

vaep/analyzers/__init__.py

@@ -2,7 +2,7 @@
 """
 from types import SimpleNamespace
 
-from . import compare_predictions, diff_analysis
+from vaep.analyzers import compare_predictions, diff_analysis
 
 __all__ = ['diff_analysis', 'compare_predictions', 'Analysis']
 

vaep/data_handling.py

@@ -4,8 +4,6 @@
 import numpy as np
 import pandas as pd
 
-# coverage
-
 
 def coverage(X: pd.DataFrame, coverage_col: float, coverage_row: float):
     """Select proteins by column depending on their coverage.

vaep/filter.py

@@ -4,8 +4,6 @@
 
 logger = logging.getLogger(__name__)
 
-# ! use in data selection and tutorial
-
 
 def select_features(df: pd.DataFrame,
                     feat_prevalence: float = .2,

vaep/models/__init__.py

@@ -16,8 +16,7 @@
 from fastcore.foundation import L
 
 import vaep
-
-from . import ae, analysis, collab, vae
+from vaep.models import ae, analysis, collab, vae
 
 logger = logging.getLogger(__name__)
 

vaep/models/ae.py

@@ -21,7 +21,7 @@
 import vaep.models
 import vaep.transform
 
-from . import analysis
+from vaep.models import analysis
 
 logger = logging.getLogger(__name__)
 

vaep/models/collab.py

@@ -11,8 +11,7 @@
 
 import vaep.io.dataloaders
 import vaep.io.datasplits
-
-from . import analysis
+from vaep.models import analysis
 
 logger = logging.getLogger(__name__)
 

vaep/pandas/__init__.py

@@ -7,7 +7,30 @@
 import omegaconf
 import pandas as pd
 
-from .calc_errors import calc_errors_per_feat, get_absolute_error
+from vaep.pandas.calc_errors import calc_errors_per_feat, get_absolute_error
+
+__all__ = [
+    'calc_errors_per_feat',
+    'get_absolute_error',
+    'unique_cols',
+    'get_unique_non_unique_columns',
+    'prop_unique_index',
+    'replace_with',
+    'index_to_dict',
+    'get_columns_accessor',
+    'get_columns_accessor_from_iterable',
+    'select_max_by',
+    'get_columns_namedtuple',
+    'highlight_min',
+    '_add_indices',
+    'interpolate',
+    'flatten_dict_of_dicts',
+    'key_map',
+    'parse_query_expression',
+    'length',
+    'get_last_index_matching_proportion',
+    'get_lower_whiskers',
+    'get_counts_per_bin']
 
 
 def unique_cols(s: pd.Series) -> bool:
@@ -285,16 +308,15 @@ def get_lower_whiskers(df: pd.DataFrame, factor: float = 1.5) -> pd.Series:
     return ret
 
 
-def get_counts_per_bin(df: pd.DataFrame, bins: range, columns: Optional[List[str]] = None) -> pd.DataFrame:
+def get_counts_per_bin(df: pd.DataFrame,
+                       bins: range,
+                       columns: Optional[List[str]] = None) -> pd.DataFrame:
     """Return counts per bin for selected columns in DataFrame."""
     counts_per_bin = dict()
     if columns is None:
         columns = df.columns.to_list()
     for col in columns:
-        _series = (pd.cut(df[col], bins=bins)
-                   .to_frame()
-                   .groupby(col)
-                   .size())
+        _series = (pd.cut(df[col], bins=bins).to_frame().groupby(col).size())
         _series.index.name = 'bin'
         counts_per_bin[col] = _series
     counts_per_bin = pd.DataFrame(counts_per_bin)

vaep/plotting/__init__.py

@@ -11,11 +11,8 @@
 import seaborn
 
 import vaep.pandas
-
-from . import data, defaults, errors, plotly
-from .errors import plot_rolling_error
-
-# from . defaults import order_categories, labels_dict, IDX_ORDER
+from vaep.plotting import data, defaults, errors, plotly
+from vaep.plotting.errors import plot_rolling_error
 
 seaborn.set_style("whitegrid")
 # seaborn.set_theme()