comparison scimap_plotting.py @ 4:2983eb1d7362 draft default tip

planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit c39cea5ae2ebf61b3d51d687fd9d2930c907b72d

3:472780baf150

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

    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()

4:2983eb1d7362

import argparse
import json
import os
import warnings

import matplotlib.pyplot as plt
import numpy as np
import scimap as sm
import seaborn as sns
from anndata import read_h5ad

    if tool == 'stacked_barplot':

        # parse list text arguments
        for o in options.copy():
            opt = options.pop(o)
            if o == 'matplotlib_cmap':
                matplotlib_cmap = opt
            elif opt != "":
                options[o] = [x.strip() for x in opt.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)

        # Plotting
        sns.set_theme(style="white")
        ax = df.plot.bar(stacked=True, cmap=matplotlib_cmap)
        fig = ax.get_figure()