--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scimap_plotting.py	Mon Jun 10 18:44:25 2024 +0000
@@ -0,0 +1,125 @@
+import argparse
+import json
+import os
+import warnings
+
+import matplotlib.pylab as plt
+import numpy as np
+import scimap as sm
+import seaborn as sns
+from anndata import read_h5ad
+
+sns.set(color_codes=True)
+
+
+def main(inputs, anndata, output):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter.
+    anndata : str
+        File path to anndata containing phenotyping info.
+    output : str
+        File path to output.
+    """
+    warnings.simplefilter('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    adata = read_h5ad(anndata)
+
+    tool = params['analyses']['selected_tool']
+    options = params['analyses']['options']
+
+    if tool == 'stacked_barplot':
+
+        # parse list text arguments
+        for o in options.copy():
+            opt_list = options.pop(o)
+            if opt_list:
+                options[o] = [x.strip() for x in opt_list.split(',')]
+
+        # add base args into options dict to pass to tool
+        options['x_axis'] = params['analyses']['x_axis']
+        options['y_axis'] = params['analyses']['y_axis']
+        options['method'] = params['analyses']['method']
+
+        options['return_data'] = True
+
+        df = sm.pl.stacked_barplot(adata, **options)
+
+        # Pick cmap to use
+        num_phenotypes = len(df.columns) - 1
+        if num_phenotypes <= 9:
+            matplotlib_cmap = "Set1"
+        elif num_phenotypes > 9 and num_phenotypes <= 20:
+            matplotlib_cmap = plt.cm.tab20
+        else:
+            matplotlib_cmap = plt.cm.gist_ncar
+
+        # Plotting
+        sns.set_theme(style="white")
+        ax = df.plot.bar(stacked=True, cmap=matplotlib_cmap)
+        fig = ax.get_figure()
+        handles, labels = ax.get_legend_handles_labels()
+        ax.legend(
+            reversed(handles),
+            reversed(labels),
+            bbox_to_anchor=(1, 1),
+            loc='upper left'
+        )
+        plt.tight_layout()
+
+        # # save and close
+        fig.savefig('out.png', dpi=300)
+        plt.close(fig)
+
+    if tool == 'voronoi':
+
+        plt.style.use('fast')
+
+        tool_func = getattr(sm.pl, tool)
+
+        # x_lim/y_lim need to be parsed from comma-sep str to integer tuples
+        for lim in ['x_lim', 'y_lim']:
+            opt_list = options.pop(lim)
+            if opt_list:
+                options[lim] = [int(x.strip()) for x in opt_list.split(',')]
+
+        # parse list text arguments
+        for cat in ['overlay_points_categories', 'overlay_drop_categories']:
+            opt_list = options.pop(cat)
+            if opt_list:
+                options[cat] = [x.strip() for x in opt_list.split(',')]
+
+        # add base args into options dict to pass to tool
+        options['color_by'] = params['analyses']['color_by']
+        options['x_coordinate'] = params['analyses']['x_coordinate']
+        options['y_coordinate'] = params['analyses']['y_coordinate']
+
+        # fill any missing params with None as tool expects
+        for k, v in options.items():
+            if v == '':
+                options[k] = None
+
+        options['saveDir'] = os.getcwd()
+        options['fileName'] = 'out.png'
+
+        if options['size_max'] is None:
+            options['size_max'] = np.inf
+
+        # call the tool and unpack all options
+        tool_func(adata, **options)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-a", "--anndata", dest="anndata", required=True)
+    aparser.add_argument("-e", "--output", dest="output", required=True)
+
+    args = aparser.parse_args()
+
+    main(args.inputs, args.anndata, args.output)