errors_correction6

src/troutpy/pl/plotting.py

@@ -11,9 +11,6 @@
 from matplotlib.colors import Colormap, Normalize
 from pathlib import Path
 
-
-
-
 def sorted_heatmap(celltype_by_feature, output_path:str='',filename:str="Heatmap_target_cells_by_gene",format='pdf',cmap='viridis',vmax=None,save=False,figsize=(10, 10)):
     """
     Plots the heatmap of target cells by gene.
@@ -195,47 +192,47 @@ def plot_crosstab(data, xvar: str = '', yvar: str = '', normalize=True, axis=1,
     -----------
     data : pd.DataFrame
         Input dataset containing the variables for the cross-tabulation.
-    
+
     xvar : str, optional (default: '')
         The variable to use on the x-axis for the cross-tabulation.
-    
+
     yvar : str, optional (default: '')
         The variable to use on the y-axis for the cross-tabulation.
-    
+
     normalize : bool, optional (default: True)
         Whether to normalize the cross-tabulated data (percentages). If True, the data will be normalized.
-    
+
     axis : int, optional (default: 1)
         The axis to normalize across. Use `1` for row normalization and `0` for column normalization.
-    
+
     kind : str, optional (default: 'barh')
         The kind of plot to generate. Options include:
         - 'barh': Horizontal bar plot
         - 'bar': Vertical bar plot
         - 'heatmap': Heatmap visualization
         - 'clustermap': Clustermap visualization
-    
+
     save : bool, optional (default: True)
         If True, the plot will be saved to a file.
-    
+
     figures_path : str, optional (default: '')
         The directory path where the figure should be saved. If not specified, the plot will be saved in the current directory.
-    
+
     stacked : bool, optional (default: True)
         If True, the bar plots will be stacked. Only applicable for 'barh' and 'bar' plot kinds.
-    
+
     figsize : tuple, optional (default: (6, 10))
         The size of the figure for the plot (width, height).
-    
+
     cmap : str, optional (default: 'viridis')
         The colormap to use for the plot, especially for heatmap and clustermap visualizations.
-    
+
     saving_format : str, optional (default: 'pdf')
         The format to save the plot in. Options include 'png', 'pdf', etc.
-    
+
     sortby : str, optional (default: None)
         The column or row to sort the cross-tabulated data by before plotting.
-    
+
     Returns:
     --------
     None
@@ -314,7 +311,7 @@ def pie_of_positive(data, groupby: str = '', figures_path: str = '', save: bool
     """
 
     plt.figure()
-    y = np.array([np.sum(data[groupby] == False), np.sum(data[groupby] )])
+    y = np.array([np.sum(~data[groupby]), np.sum(data[groupby] )])
     mylabels = [f"{groupby}=False", f"{groupby}=True"]
 
     plt.pie(y, labels=mylabels, colors=['#a0b7e0', '#c5e493'])
@@ -326,7 +323,7 @@ def pie_of_positive(data, groupby: str = '', figures_path: str = '', save: bool
 
 def genes_over_noise(sdata, scores_by_genes,layer='extracellular_transcripts', output_path:str='',save=True,format:str='pdf'):
     """Function that plots log fold change per gene over noise using a boxplot.
-    
+ 
     Parameters:
     - data_quantified: DataFrame containing the extracellular transcript data, including feature names and codeword categories.
     - scores_by_genes: DataFrame containing gene scores with feature names and log fold ratios.
@@ -650,36 +647,36 @@ def paired_nmf_factors(
 ):
     """
     Plots the spatial distribution of NMF factors for extracellular transcripts and cells.
-    
+
     Parameters:
     ----------
     sdata : spatial data object
         The spatial data object containing both extracellular and cell data.
-    
+
     layer : str, optional
         Layer in sdata to extract the NMF data from (default: 'nmf_data').
-    
+
     n_factors : int, optional
         Number of NMF factors to plot (default: 5).
-    
+
     figsize : tuple, optional
         Size of the figure for each subplot (default: (12, 6)).
-    
+
     spot_size_exrna : float, optional
         Size of the spots for extracellular transcript scatter plot (default: 5).
-    
+
     spot_size_cells : float, optional
         Size of the spots for cell scatter plot (default: 10).
-    
+
     cmap_exrna : str, optional
         Colormap for the extracellular transcript NMF factors (default: 'YlGnBu').
-    
+
     cmap_cells : str, optional
         Colormap for the cell NMF factors (default: 'Reds').
-    
+
     vmax_exrna : str or float, optional
         Maximum value for extracellular transcript color scale (default: 'p99').
-    
+
     vmax_cells : str or float, optional
         Maximum value for cell color scale (default: None).
     """
@@ -797,61 +794,61 @@ def spatial_interactions(
     ----------
     sdata : AnnData
         An AnnData object containing the spatial omics data, including transcript expression and cell positions.
-    
+
     layer : str, optional, default: 'extracellular_transcripts_enriched'
         The layer in the AnnData object that contains the extracellular RNA transcript data.
-    
+
     gene : str, optional, default: 'Arc'
         The gene of interest to be visualized in terms of its spatial interaction with source and target cells.
-    
+
     gene_key : str, optional, default: 'feature_name'
         The column name in the AnnData object used to identify the gene.
-    
+
     cell_id_key : str, optional, default: 'cell_id'
         The column name in the AnnData object used to identify individual cells.
-    
+
     color_target : str, optional, default: 'blue'
         The color to be used for target cells in the plot.
-    
+
     color_source : str, optional, default: 'red'
         The color to be used for source cells in the plot.
-    
+
     color_transcript : str, optional, default: 'green'
         The color to be used for the RNA transcripts in the plot.
-    
+
     spatial_key : str, optional, default: 'spatial'
         The key in the AnnData object that stores the spatial coordinates of the cells.
-    
+
     img : Optional[Union[bool, Sequence]], optional, default: None
         A background image to overlay on the plot, such as a tissue section. Can be set to `None` to omit.
-    
+
     img_alpha : Optional[float], optional, default: None
         The transparency level of the background image. Ignored if `img` is `None`.
-    
+
     image_cmap : Optional[Colormap], optional, default: None
         The colormap to be used for the background image, if applicable.
-    
+
     size : Optional[Union[float, Sequence[float]]], optional, default: 8
         The size of the scatter plot points for the cells and transcripts.
-    
+
     alpha : float, optional, default: 0.6
         The transparency level for the scatter plot points.
-    
+
     title : Optional[Union[str, Sequence[str]]], optional, default: None
         The title of the plot. If `None`, the gene name is used.
-    
+
     legend_loc : Optional[str], optional, default: 'best'
         The location of the legend in the plot.
-    
+
     figsize : Tuple[float, float], optional, default: (10, 10)
         The dimensions of the plot in inches.
-    
+
     dpi : Optional[int], optional, default: 100
         The resolution (dots per inch) for the plot.
-    
+
     save : Optional[Union[str, Path]], optional, default: None
         The path to save the plot image. If `None`, the plot is displayed but not saved.
-    
+
     **kwargs : Additional keyword arguments
         Any additional arguments passed to the `scatter` or `imshow` functions for customizing plot appearance.
 
@@ -895,7 +892,7 @@ def interactions_with_arrows(
     cell_id_key: str = 'cell_id',
     color_target: str = 'blue',
     color_source: str = 'red',
-    color_transcript:str='green',
+    color_transcript: str = 'green',
     spatial_key: str = 'spatial',
     img: Optional[Union[bool, Sequence]] = None,
     img_alpha: Optional[float] = None,
@@ -909,6 +906,40 @@ def interactions_with_arrows(
     save: Optional[Union[str, Path]] = None,
     **kwargs
 ):
+    """
+    Visualizes interactions between source and target cells using arrows, along with transcript locations.
+    
+    The function plots arrows from source to target cells based on transcript proximity, color-coding source and target cells, and transcript locations. An optional image layer can be overlaid behind the plot. 
+
+    Parameters:
+    sdata (AnnData): The AnnData object containing the spatial omics data.
+    layer (str, optional): The key in `sdata` for the extracellular transcript layer to analyze. Default is 'extracellular_transcripts_enriched'.
+    gene (str, optional): The gene of interest. Default is 'Arc'.
+    gene_key (str, optional): The key for gene names in the data. Default is 'feature_name'.
+    cell_id_key (str, optional): The key for cell IDs. Default is 'cell_id'.
+    color_target (str, optional): Color for the target cells. Default is 'blue'.
+    color_source (str, optional): Color for the source cells. Default is 'red'.
+    color_transcript (str, optional): Color for the transcript locations. Default is 'green'.
+    spatial_key (str, optional): The key for spatial coordinates in `sdata`. Default is 'spatial'.
+    img (Optional[Union[bool, Sequence]], optional): Optional background image (e.g., tissue section) to display behind the plot.
+    img_alpha (Optional[float], optional): Transparency level for the background image. Default is None (no image).
+    image_cmap (Optional[Colormap], optional): Colormap for the image. Default is None.
+    size (Optional[Union[float, Sequence[float]]], optional): Size of the plotted points (cells and transcripts). Default is 8.
+    alpha (float, optional): Transparency level for plotted points. Default is 0.6.
+    title (Optional[Union[str, Sequence[str]]], optional): Title of the plot. Default is the gene name.
+    legend_loc (Optional[str], optional): Location of the legend on the plot. Default is 'best'.
+    figsize (Tuple[float, float], optional): Size of the plot. Default is (10, 10).
+    dpi (Optional[int], optional): Resolution of the plot. Default is 100.
+    save (Optional[Union[str, Path]], optional): If provided, the path where the plot will be saved.
+    **kwargs: Additional arguments passed to the `scatter` and `imshow` functions for customization.
+
+    Returns:
+    None: The function displays or saves a plot of interactions between cells and transcripts.
+
+    Notes:
+    The plot will show arrows from source to target cells, with different colors for source, target, and transcript points.
+    """
+
     # Extract relevant data
     transcripts = sdata.points[layer]
     trans_filt = transcripts[transcripts[gene_key] == gene]
@@ -924,7 +955,7 @@ def interactions_with_arrows(
     # Plot arrows between each paired source and target cell
     for source, target in zip(source_cells, target_cells):
         if source in cell_positions.index and target in cell_positions.index:
-            if source!=target:
+            if source != target:
                  x_start, y_start = cell_positions.loc[source, 'x'], cell_positions.loc[source, 'y']
                  x_end, y_end = cell_positions.loc[target, 'x'], cell_positions.loc[target, 'y']
                  plt.arrow(x_start, y_start, x_end - x_start, y_end - y_start, color='black', alpha=0.8, head_width=8, head_length=8)
@@ -933,7 +964,7 @@ def interactions_with_arrows(
     plt.scatter(cell_positions['x'], cell_positions['y'], c='grey', s=0.6, alpha=alpha, **kwargs)
     plt.scatter(cell_positions.loc[target_cells, 'x'], cell_positions.loc[target_cells, 'y'], c=color_target, s=size, label='Target Cells', **kwargs)
     plt.scatter(cell_positions.loc[source_cells, 'x'], cell_positions.loc[source_cells, 'y'], c=color_source, s=size, label='Source Cells', **kwargs)
-    plt.scatter(trans_filt['x'], trans_filt['y'], c=color_transcript, s=size*0.4, label='Transcripts', **kwargs)
+    plt.scatter(trans_filt['x'], trans_filt['y'], c=color_transcript, s=size * 0.4, label='Transcripts', **kwargs)
 
     # Titles and Legends
     plt.title(title or gene)
@@ -944,4 +975,4 @@ def interactions_with_arrows(
     # Save the plot if path provided
     if save:
         plt.savefig(save)
-    plt.show()
+    plt.show()