Added tasccoda example plots (#527)

* tasccoda plots

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* add tasccoda umap and small fixes

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

pertpy/plot/_coda.py

@@ -112,9 +112,6 @@ def stacked_barplot(  # pragma: no cover
             >>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label", \
                 sample_identifier="batch", covariate_obs=["condition"])
             >>> sccoda.plot_stacked_barplot(mdata, feature_name="samples")
-
-        Preview:
-            .. image:: ../_static/docstring_previews/sccoda_stacked_barplot.png
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -188,9 +185,6 @@ def effects_barplot(  # pragma: no cover
             >>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
             >>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
             >>> sccoda.plot_effects_barplot(mdata)
-
-        Preview:
-            .. image:: ../_static/docstring_previews/sccoda_effects_barplot.png
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -268,9 +262,6 @@ def boxplots(  # pragma: no cover
             >>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label", \
                 sample_identifier="batch", covariate_obs=["condition"])
             >>> sccoda.plot_boxplots(mdata, feature_name="condition", add_dots=True)
-
-        Preview:
-            .. image:: ../_static/docstring_previews/sccoda_boxplots.png
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -341,9 +332,6 @@ def rel_abundance_dispersion_plot(  # pragma: no cover
             >>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
             >>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
             >>> sccoda.plot_rel_abundance_dispersion_plot(mdata)
-
-        Preview:
-            .. image:: ../_static/docstring_previews/sccoda_rel_abundance_dispersion_plot.png
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -512,8 +500,6 @@ def draw_effects(  # pragma: no cover
             >>> )
             >>> tasccoda.run_nuts(mdata, num_samples=1000, num_warmup=100, rng_key=42)
             >>> tasccoda.plot_draw_effects(mdata, covariate="Health[T.Inflamed]", tree="lineage")
-
-        Preview: #TODO: Add preview
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -603,8 +589,6 @@ def effects_umap(  # pragma: no cover
             >>>                                      "effect_df_condition[T.Hpoly.Day10]"],
             >>>                                       cluster_key="nsbm_level_1",
             >>>                         )
-
-        Preview: #TODO: Add preview
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"

pertpy/tools/_coda/_base_coda.py

@@ -1864,6 +1864,9 @@ def plot_draw_tree(  # pragma: no cover
             >>> )
             >>> tasccoda.run_nuts(mdata, num_samples=1000, num_warmup=100, rng_key=42)
             >>> tasccoda.plot_draw_tree(mdata, tree="lineage")
+
+        Preview:
+            .. image:: /_static/docstring_previews/tasccoda_draw_tree.png
         """
         try:
             from ete3 import CircleFace, NodeStyle, TextFace, Tree, TreeStyle, faces
@@ -1954,7 +1957,10 @@ def plot_draw_effects(  # pragma: no cover
             >>>     mdata, formula="Health", reference_cell_type="automatic", tree_key="lineage", pen_args={"phi": 0}
             >>> )
             >>> tasccoda.run_nuts(mdata, num_samples=1000, num_warmup=100, rng_key=42)
-            >>> pt.pl.coda.draw_effects(mdata, covariate="Health[T.Inflamed]", tree="lineage")
+            >>> tasccoda.plot_draw_effects(mdata, covariate="Health[T.Inflamed]", tree="lineage")
+
+        Preview:
+            .. image:: /_static/docstring_previews/tasccoda_draw_effects.png
         """
         try:
             from ete3 import CircleFace, NodeStyle, TextFace, Tree, TreeStyle, faces
@@ -2124,15 +2130,17 @@ def plot_effects_umap(  # pragma: no cover
         Examples:
             Example with tascCODA:
             >>> import pertpy as pt
+            >>> import scanpy as sc
             >>> import schist
             >>> adata = pt.dt.haber_2017_regions()
-            >>> schist.inference.nested_model(adata, samples=100, random_seed=5678)
+            >>> sc.pp.neighbors(adata)
+            >>> schist.inference.nested_model(adata, n_init=100, random_seed=5678)
             >>> tasccoda_model = pt.tl.Tasccoda()
             >>> tasccoda_data = tasccoda_model.load(adata, type="cell_level",
             >>>                 cell_type_identifier="nsbm_level_1",
             >>>                 sample_identifier="batch", covariate_obs=["condition"],
             >>>                 levels_orig=["nsbm_level_4", "nsbm_level_3", "nsbm_level_2", "nsbm_level_1"],
-            >>>                 add_level_name=True)sccoda = pt.tl.Sccoda()
+            >>>                 add_level_name=True)
             >>> tasccoda_model.prepare(
             >>>     tasccoda_data,
             >>>     modality_key="coda",
@@ -2144,12 +2152,19 @@ def plot_effects_umap(  # pragma: no cover
             >>> tasccoda_model.run_nuts(
             ...     tasccoda_data, modality_key="coda", rng_key=1234, num_samples=10000, num_warmup=1000
             ... )
+            >>> tasccoda_model.run_nuts(
+            ...     tasccoda_data, modality_key="coda", rng_key=1234, num_samples=10000, num_warmup=1000
+            ... )
+            >>> sc.tl.umap(tasccoda_data["rna"])
             >>> tasccoda_model.plot_effects_umap(tasccoda_data,
             >>>                         effect_name=["effect_df_condition[T.Salmonella]",
             >>>                                      "effect_df_condition[T.Hpoly.Day3]",
             >>>                                      "effect_df_condition[T.Hpoly.Day10]"],
             >>>                                       cluster_key="nsbm_level_1",
             >>>                         )
+
+        Preview:
+            .. image:: /_static/docstring_previews/tasccoda_effects_umap.png
         """
         # TODO: Add effect_name parameter and cluster_key and test the example
         data_rna = data[modality_key_1]