view scimap_plotting.py @ 1:c42e97dc5a3a draft

planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit 49210b00535415865694ddbec16238d8cf5e6bb0

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)