Merge pull request #138 from ckmah/lp-fix

LP Fix

.devcontainer/devcontainer.json

@@ -7,7 +7,7 @@
 		"context": "..",
 		"args": { 
 			// Update 'VARIANT' to pick a Python version: 3, 3.6, 3.7, 3.8, 3.9
-			"VARIANT": "3.8",
+			"VARIANT": "3.11",
 			// Options
 			"INSTALL_NODE": "true",
 			"NODE_VERSION": "lts/*"
@@ -41,7 +41,7 @@
 	// "forwardPorts": [],
 
 	// Use 'postCreateCommand' to run commands after the container is created.
-	"postCreateCommand": "pip3 install poetry==1.2.0; pip3 install -e .",
+	"postCreateCommand": "curl -sSf https://rye.astral.sh/get | RYE_VERSION='0.38.0' RYE_INSTALL_OPTION='--yes' bash",
 
 	// Comment out connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
 	"remoteUser": "vscode"

bento/_utils.py

@@ -4,7 +4,10 @@
 import geopandas as gpd
 import numpy as np
 import pandas as pd
-from dask import dataframe as dd
+import dask
+
+dask.config.set({"dataframe.query-planning": False})
+import dask.dataframe as dd
 from spatialdata import SpatialData
 from spatialdata.models import PointsModel, ShapesModel, TableModel
 
@@ -34,12 +37,14 @@ def filter_by_gene(
     -------
     sdata : SpatialData
         .points[points_key] is updated to remove genes with low expression.
-        .table is updated to remove genes with low expression.
+        .tables["table"] is updated to remove genes with low expression.
     """
-    gene_filter = (sdata.table.X >= min_count).sum(axis=0) > 0
-    filtered_table = sdata.table[:, gene_filter]
+    gene_filter = (sdata.tables["table"].X >= min_count).sum(axis=0) > 0
+    filtered_table = sdata.tables["table"][:, gene_filter]
 
-    filtered_genes = list(sdata.table.var_names.difference(filtered_table.var_names))
+    filtered_genes = list(
+        sdata.tables["table"].var_names.difference(filtered_table.var_names)
+    )
     points = get_points(sdata, points_key=points_key, astype="pandas", sync=False)
     points = points[~points[feature_key].isin(filtered_genes)]
     points[feature_key] = points[feature_key].cat.remove_unused_categories()
@@ -52,10 +57,10 @@ def filter_by_gene(
     sdata.points[points_key] = points
 
     try:
-        del sdata.table
+        del sdata.tables["table"]
     except KeyError:
         pass
-    sdata.table = TableModel.parse(filtered_table)
+    sdata.tables["table"] = TableModel.parse(filtered_table)
 
     return sdata
 
@@ -126,7 +131,7 @@ def get_shape(sdata: SpatialData, shape_key: str, sync: bool = True) -> gpd.GeoS
     GeoSeries
         GeoSeries of Polygon objects
     """
-    instance_key = sdata.table.uns["spatialdata_attrs"]["instance_key"]
+    instance_key = sdata.tables["table"].uns["spatialdata_attrs"]["instance_key"]
 
     # Make sure shape exists in sdata.shapes
     if shape_key not in sdata.shapes.keys():

bento/io/_io.py

@@ -101,17 +101,17 @@ def prep(
             by=instance_key,
             value_key=feature_key,
             aggfunc="count",
-        ).table
+        ).tables["table"]
     )
 
     pbar.update()
 
     try:
-        del sdata.table
+        del sdata.tables["table"]
     except KeyError:
         pass
 
-    sdata.table = table
+    sdata.tables["table"] = table
     # Set instance key to cell_shape_key for all points and table
     sdata.points[points_key].attrs["spatialdata_attrs"]["instance_key"] = instance_key
     sdata.points[points_key].attrs["spatialdata_attrs"]["feature_key"] = feature_key

bento/plotting/_lp.py

@@ -15,8 +15,9 @@
 from ._utils import savefig
 from ._multidimensional import _radviz
 
+
 @savefig
-def lp_dist(sdata, show_counts="", show_percentages='{:.1%}', scale=1, fname=None):
+def lp_dist(sdata, show_counts="", show_percentages="{:.1%}", scale=1, fname=None):
     """Plot pattern combination frequencies as an UpSet plot.
 
     Parameters
@@ -32,7 +33,7 @@ def lp_dist(sdata, show_counts="", show_percentages='{:.1%}', scale=1, fname=Non
     fname : str, optional
         Save the figure to specified filename, by default None
     """
-    sample_labels = sdata.table.uns["lp"]
+    sample_labels = sdata.tables["table"].uns["lp"]
     sample_labels = sample_labels == 1
 
     # Sort by degree, then pattern name
@@ -60,12 +61,13 @@ def lp_dist(sdata, show_counts="", show_percentages='{:.1%}', scale=1, fname=Non
     upset.plot()
     plt.suptitle(f"Localization Patterns\n{sample_labels.shape[0]} samples")
 
+
 @savefig
 def lp_genes(
     sdata: SpatialData,
     groupby: str = "feature_name",
-    points_key = "transcripts",
-    instance_key = "cell_boundaries",
+    points_key="transcripts",
+    instance_key="cell_boundaries",
     annotate: Union[int, List[str], None] = None,
     sizes: Tuple[int] = (2, 100),
     size_norm: Tuple[int] = (0, 100),
@@ -101,17 +103,19 @@ def lp_genes(
 
     palette = dict(zip(PATTERN_NAMES, PATTERN_COLORS))
 
-    n_cells = sdata.table.n_obs
-    gene_frac = sdata.table.uns["lp_stats"][PATTERN_NAMES] / n_cells
+    n_cells = sdata.tables["table"].n_obs
+    gene_frac = sdata.tables["table"].uns["lp_stats"][PATTERN_NAMES] / n_cells
     genes = gene_frac.index
-    gene_expression_array = sdata.table[:,genes].X.toarray()
+    gene_expression_array = sdata.tables["table"][:, genes].X.toarray()
     gene_logcount = gene_expression_array.mean(axis=0, where=gene_expression_array > 0)
     gene_logcount = np.log2(gene_logcount + 1)
     gene_frac["logcounts"] = gene_logcount
-    
+
     cell_fraction = (
         100
-        * get_points(sdata, points_key, astype="pandas", sync=True).groupby(groupby, observed=True)[instance_key].nunique()
+        * get_points(sdata, points_key, astype="pandas", sync=True)
+        .groupby(groupby, observed=True)[instance_key]
+        .nunique()
         / n_cells
     )
     gene_frac["cell_fraction"] = cell_fraction
@@ -120,6 +124,7 @@ def lp_genes(
     scatter_kws.update(kwargs)
     _radviz(gene_frac, annotate=annotate, ax=ax, **scatter_kws)
 
+
 @savefig
 def lp_diff_discrete(sdata: SpatialData, phenotype: str, fname: str = None):
     """Visualize gene pattern frequencies between groups of cells. Plots the
@@ -134,7 +139,7 @@ def lp_diff_discrete(sdata: SpatialData, phenotype: str, fname: str = None):
     fname : str, optional
         Save the figure to specified filename, by default None
     """
-    diff_stats = sdata.table.uns[f"diff_{phenotype}"]
+    diff_stats = sdata.tables["table"].uns[f"diff_{phenotype}"]
 
     palette = dict(zip(PATTERN_NAMES, PATTERN_COLORS))
     g = sns.relplot(
@@ -162,4 +167,4 @@ def lp_diff_discrete(sdata: SpatialData, phenotype: str, fname: str = None):
         )  # line where FDR = 0.05
         sns.despine()
 
-    return g
+    return g

bento/plotting/_multidimensional.py

@@ -205,8 +205,8 @@ def comp(
     """
 
     comp_key = f"{groupby}_comp_stats"
-    if groupby and comp_key in sdata.table.uns.keys():
-        comp_stats = sdata.table.uns[comp_key]
+    if groupby and comp_key in sdata.tables["table"].uns.keys():
+        comp_stats = sdata.tables["table"].uns[comp_key]
         if group_order is None:
             groups = list(comp_stats.keys())
         else:
@@ -240,7 +240,7 @@ def comp(
             ax.set_title(group, fontsize=12)
     else:
         comp_key = "comp_stats"
-        comp_stats = sdata.table.uns[comp_key]
+        comp_stats = sdata.tables["table"].uns[comp_key]
         return _radviz(
             comp_stats,
             annotate=annotate,

bento/plotting/_signatures.py

@@ -7,6 +7,7 @@
 from ._colors import red2blue, red_light
 from ._utils import savefig
 
+
 def colocation(
     sdata,
     rank,
@@ -42,9 +43,9 @@ def colocation(
     fname : str, optional
         Path to save figure, by default None
     """
-    factors = sdata.table.uns["factors"][rank].copy()
-    labels = sdata.table.uns["tensor_labels"].copy()
-    names = sdata.table.uns["tensor_names"].copy()
+    factors = sdata.tables["table"].uns["factors"][rank].copy()
+    labels = sdata.tables["table"].uns["tensor_labels"].copy()
+    names = sdata.tables["table"].uns["tensor_names"].copy()
 
     # Perform z-scaling upfront
     for i in range(len(factors)):
@@ -250,4 +251,4 @@ def _plot_loading(df, name, n_top, cut, show_labels, cluster, ax, **kwargs):
 
     ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
     ax.set_title(f"{name}: [{df.shape[0]} x {df.shape[1]}]")
-    sns.despine(ax=ax, right=False, top=False)
+    sns.despine(ax=ax, right=False, top=False)

bento/tools/_colocation.py

@@ -42,14 +42,14 @@ def colocation(
     Returns
     -------
     sdata : SpatialData
-        .table.uns['factors']: Decomposed tensor factors.
-        .table.uns['factors_error']: Decomposition error.
+        .tables["table"].uns['factors']: Decomposed tensor factors.
+        .tables["table"].uns['factors_error']: Decomposition error.
     """
 
     print("Preparing tensor...")
     _colocation_tensor(sdata, instance_key, feature_key)
 
-    tensor = sdata.table.uns["tensor"]
+    tensor = sdata.tables["table"].uns["tensor"]
 
     print(emoji.emojize(":running: Decomposing tensor..."))
     factors, errors = decompose(tensor, ranks, iterations=iterations)
@@ -61,8 +61,8 @@ def colocation(
         kl.plot_knee()
         sns.lineplot(data=errors, x="rank", y="rmse", ci=95, marker="o")
 
-    sdata.table.uns["factors"] = factors
-    sdata.table.uns["factors_error"] = errors
+    sdata.tables["table"].uns["factors"] = factors
+    sdata.tables["table"].uns["factors_error"] = errors
 
     print(emoji.emojize(":heavy_check_mark: Done."))
 
@@ -81,7 +81,7 @@ def _colocation_tensor(sdata: SpatialData, instance_key: str, feature_key: str):
         Key that specifies genes in sdata.
     """
 
-    clqs = sdata.table.uns["clq"]
+    clqs = sdata.tables["table"].uns["clq"]
 
     clq_long = []
     for shape, clq in clqs.items():
@@ -106,9 +106,9 @@ def _colocation_tensor(sdata: SpatialData, instance_key: str, feature_key: str):
     s = sparse.COO(label_orders, data=clq_long["log_clq"].values)
     tensor = s.todense()
 
-    sdata.table.uns["tensor"] = tensor
-    sdata.table.uns["tensor_labels"] = labels
-    sdata.table.uns["tensor_names"] = label_names
+    sdata.tables["table"].uns["tensor"] = tensor
+    sdata.tables["table"].uns["tensor_labels"] = labels
+    sdata.tables["table"].uns["tensor_names"] = label_names
 
 
 def coloc_quotient(
@@ -145,7 +145,7 @@ def coloc_quotient(
     Returns
     -------
     sdata : SpatialData
-        .table.uns['clq']: Pairwise gene colocalization similarity within each cell formatted as a long dataframe.
+        .tables["table"].uns['clq']: Pairwise gene colocalization similarity within each cell formatted as a long dataframe.
     """
 
     all_clq = dict()
@@ -191,7 +191,7 @@ def coloc_quotient(
         # Save to uns['clq'] as adjacency list
         all_clq[shape] = cell_clqs
 
-    sdata.table.uns["clq"] = all_clq
+    sdata.tables["table"].uns["clq"] = all_clq
 
 
 def _cell_clq(cell_points, radius, min_points, feature_key):

bento/tools/_composition.py

@@ -71,7 +71,7 @@ def comp(sdata: SpatialData, points_key: str, shape_names: list):
     Returns
     -------
     sdata : spatialdata.SpatialData
-        Updates `sdata.table.uns` with average gene compositions for each shape.
+        Updates `sdata.tables["table"].uns` with average gene compositions for each shape.
     """
     points = get_points(sdata, points_key=points_key, astype="pandas")
 
@@ -83,7 +83,7 @@ def comp(sdata: SpatialData, points_key: str, shape_names: list):
         points, shape_names, instance_key=instance_key, feature_key=feature_key
     )
 
-    sdata.table.uns["comp_stats"] = comp_stats
+    sdata.tables["table"].uns["comp_stats"] = comp_stats
 
 
 def comp_diff(
@@ -132,4 +132,4 @@ def comp_diff(
             index=ref_comp.index,
         )
 
-    sdata.table.uns[f"{groupby}_comp_stats"] = comp_stats
+    sdata.tables["table"].uns[f"{groupby}_comp_stats"] = comp_stats

bento/tools/_flux.py

@@ -80,9 +80,9 @@ def flux(
             flux values: <gene_name> for each gene used in embedding.
             embeddings: flux_embed_<i> for each component of the embedding.
             colors: hex color codes for each pixel.
-        .table.uns["flux_genes"] : list
+        .tables["table"].uns["flux_genes"] : list
             List of genes used for embedding.
-        .table.uns["flux_variance_ratio"] : np.ndarray
+        .tables["table"].uns["flux_variance_ratio"] : np.ndarray
             [components] array of explained variance ratio for each component.
     """
 
@@ -145,7 +145,7 @@ def flux(
     # points_grouped = dask.delayed(points_grouped)
     # rpoints_grouped = dask.delayed(rpoints_grouped)
 
-    cell_composition = sdata.table[cells, gene_names].X.toarray()
+    cell_composition = sdata.tables["table"][cells, gene_names].X.toarray()
 
     # Compute cell composition
     cell_composition = cell_composition / (cell_composition.sum(axis=1).reshape(-1, 1))
@@ -268,8 +268,8 @@ def process_cell(bag):
         columns=metadata.columns,
     )
 
-    sdata.table.uns["flux_variance_ratio"] = variance_ratio
-    sdata.table.uns["flux_genes"] = gene_names  # gene names
+    sdata.tables["table"].uns["flux_variance_ratio"] = variance_ratio
+    sdata.tables["table"].uns["flux_genes"] = gene_names  # gene names
 
     pbar.set_description(emoji.emojize("Done. :bento_box:"))
     pbar.update()
@@ -523,7 +523,7 @@ def fluxmap(
         del sdata.shapes[key]
 
     sd_attrs = sdata.shapes[instance_key].attrs
-    fluxmap_df = fluxmap_df.reindex(sdata.table.obs_names).where(
+    fluxmap_df = fluxmap_df.reindex(sdata.tables["table"].obs_names).where(
         fluxmap_df.notna(), other=Polygon()
     )
     fluxmap_names = fluxmap_df.columns.tolist()