assert subject, type hints, pillow version

pyproject.toml

@@ -78,6 +78,7 @@ dependencies = [
     # * Plotting
     "matplotlib",
     "seaborn<=0.12.2", #!! 0.13 has issues with hue
+    "Pillow>=10.2.0", #!! github security risk
     #* Statistics
     "scipy",
     # "statannot",   #' Superseded by statannotations

requirements.txt

@@ -35,7 +35,7 @@ pandas-flavor==0.6.0
 parso==0.8.3
 patsy==0.5.3
 pexpect==4.8.0
-Pillow==10.1.0
+Pillow>=10.2.0
 pingouin==0.5.3
 platformdirs==4.0.0
 prompt-toolkit==3.0.41

src/plotastic/dimensions/dataframetool.py

@@ -494,7 +494,7 @@ def data_iter__allkeys_group_skip_empty(self) -> Generator:
     # == TRANSFORM =====================================================
 
     @staticmethod
-    def _rename_y(y: str, func: str) -> str:
+    def _rename_y(y: str, func: str | Callable) -> str:
         """Renames the y column to reflect the transformation
 
         Args:
@@ -514,7 +514,7 @@ def _add_transform_col(
         df: pd.DataFrame,
         y_raw: str,
         y_new: str,
-        func: str,
+        func: Callable,
     ) -> pd.DataFrame:
         """Adds a column to the dataframe that contains the transformed data
 
@@ -542,7 +542,7 @@ def _add_transform_col(
         return df
 
     def transform_y(
-        self, func: str | Callable, inplace=True
+        self, func: str | Callable, inplace=False
     ) -> "DataFrameTool | DataAnalysis":
         """DOC: Transforms the data, changes dv property"""
 

src/plotastic/stat/assumptions.py

@@ -154,6 +154,12 @@ def check_sphericity(
         :return: _description_
         :rtype: pd.DataFrame
         """
+        ### Make sure subject is specified
+        if self.subject is None:
+            raise ValueError(
+                "Testing sphericity requires a subject to be specified."
+            )
+
         # TODO: Add option to use x or hue as within-factors
         ### All
 

src/plotastic/stat/statresults.py

@@ -24,23 +24,23 @@ class StatResults:
     # == INIT ==========================================================================
     def __init__(self):
         ### Data Tables
-        self.DF_normality: pd.DataFrame = self.DEFAULT_UNCHECKED
-        self.DF_homoscedasticity: pd.DataFrame = self.DEFAULT_UNCHECKED
-        self.DF_sphericity: pd.DataFrame = self.DEFAULT_UNCHECKED
+        self.DF_normality: pd.DataFrame | str = self.DEFAULT_UNCHECKED
+        self.DF_homoscedasticity: pd.DataFrame | str = self.DEFAULT_UNCHECKED
+        self.DF_sphericity: pd.DataFrame | str = self.DEFAULT_UNCHECKED
 
-        self.DF_omnibus_anova: pd.DataFrame = self.DEFAULT_UNTESTED
-        self.DF_omnibus_rmanova: pd.DataFrame = self.DEFAULT_UNTESTED
-        self.DF_omnibus_kruskal: pd.DataFrame = self.DEFAULT_UNTESTED
-        self.DF_omnibus_friedman: pd.DataFrame = self.DEFAULT_UNTESTED
-        self.DF_posthoc: pd.DataFrame = self.DEFAULT_UNTESTED
-        self.DF_bivariate: pd.DataFrame = self.DEFAULT_UNTESTED
+        self.DF_omnibus_anova: pd.DataFrame | str = self.DEFAULT_UNTESTED
+        self.DF_omnibus_rmanova: pd.DataFrame | str = self.DEFAULT_UNTESTED
+        self.DF_omnibus_kruskal: pd.DataFrame | str = self.DEFAULT_UNTESTED
+        self.DF_omnibus_friedman: pd.DataFrame | str = self.DEFAULT_UNTESTED
+        self.DF_posthoc: pd.DataFrame | str = self.DEFAULT_UNTESTED
+        self.DF_bivariate: pd.DataFrame | str = self.DEFAULT_UNTESTED
 
         ### Assessments = Summarizing results from multiple groups
-        self._normal: bool = self.DEFAULT_UNASSESSED
-        self._homoscedastic: bool = self.DEFAULT_UNASSESSED
-        self._spherical: bool = self.DEFAULT_UNASSESSED
+        self._normal: bool | str = self.DEFAULT_UNASSESSED
+        self._homoscedastic: bool | str = self.DEFAULT_UNASSESSED
+        self._spherical: bool | str = self.DEFAULT_UNASSESSED
 
-        self._parametric: bool = self.DEFAULT_UNASSESSED
+        self._parametric: bool | str = self.DEFAULT_UNASSESSED
 
     # ==
     # == Summarize Results =============================================================
@@ -116,7 +116,7 @@ def assess_parametric(self):
     # ==
     # == EXPORT ========================================================================
 
-    def save(self, fname: str = "plotastic_results") -> None:
+    def save(self, fname: str | Path = "plotastic_results", verbose=True) -> None:
         """Exports all statistics to one excel file. Different sheets for different
         tests
 
@@ -138,6 +138,10 @@ def save(self, fname: str = "plotastic_results") -> None:
 
         ### Save
         writer.close()
+
+        ### Tell save location
+        if verbose:
+            print(f"Saved results to {fname.resolve()}")
 
 
 # !!

tests/DA_configs.py

@@ -132,6 +132,8 @@
 
 # %%
 # == Make Dataanalysis objects =========================================
+
+
 def make_DA_statistics(dataset: str = "qpcr") -> plst.DataAnalysis:
     """Makes a DA object with every possible data stored in it
 
@@ -170,6 +172,7 @@ def make_DA_statistics(dataset: str = "qpcr") -> plst.DataAnalysis:
 
 
 def make_DA_plot(dataset: str = "qpcr") -> plst.DataAnalysis:
+    """A DA that has a plot"""
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         ### Load example data
@@ -184,6 +187,7 @@ def make_DA_plot(dataset: str = "qpcr") -> plst.DataAnalysis:
 
 
 def make_DA_all(dataset: str) -> plst.DataAnalysis:
+    """A DA with all possible statistics and a plot"""
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
 

tests/DA_configs2.py

@@ -0,0 +1,9 @@
+
+### They are all non-empty
+StatTestCases = [
+    "all",
+    "paired", 
+    "unpaired",
+    "unpaired",
+    "parametric_paired",
+]

tests/DA_utils.py

@@ -0,0 +1,81 @@
+"""A utility class that creates DataAnalysis objects for testing"""
+# %%
+
+
+import pandas as pd
+
+import plotastic as plst
+from plotastic.dataanalysis.dataanalysis import DataAnalysis
+from plotastic.utils.subcache import SubCache
+
+import DA_configs as dac
+
+# %%
+# == Class CreateDA ====================================================
+
+
+class TestDA(DataAnalysis):
+    def __init__(
+        self,
+        data: pd.DataFrame,
+        dims: dict,
+        subject: str = None,
+        levels: list[tuple[str]] = None,
+        title: str = "untitled",
+        verbose=False,
+    ) -> DataAnalysis:
+        kws = dict(
+            data=data,
+            dims=dims,
+            subject=subject,
+            levels=levels, #' Introduced by DataFrameTool
+            title=title, #' Introduced by DataAnalysis
+            verbose=verbose, #' Introduced by DataAnalysis
+        )
+
+        super().__init__(**kws)
+
+    def perform_statistics_unpaired(self, parametric=True) -> "TestDA":
+        """Perform unpaired statistics"""
+        ### Assumptions
+        self.check_normality()
+        self.check_homoscedasticity()
+
+        ### Omnibus
+        if parametric:
+            self.omnibus_anova()
+        else:
+            self.omnibus_kruskal()
+
+        ### PostHoc
+        self.test_pairwise(parametric=parametric)
+
+        return self
+
+    def perform_statistics_paired(self, parametric=True) -> "TestDA":
+        """Perform unpaired statistics"""
+        ### Assumptions
+        self.check_normality()
+        self.check_homoscedasticity()
+        self.check_sphericity()
+
+        ### Omnibus
+        if parametric:
+            self.omnibus_anova()
+        else:
+            self.omnibus_kruskal()
+
+        ### PostHoc
+        self.test_pairwise(parametric=parametric)
+
+        return self
+
+
+
+if __name__ == "__main__":
+    pass
+    # %%
+    dims = dac.dims_withempty_tips[0]
+    data = dac.DF_tips
+    DA = TestDA(data=data, dims=dims)
+