Mercurial > repos > goeckslab > scimap_mcmicro_to_anndata

annotate scimap_plotting.py @ 3:8c55377d7f06 draft default tip

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit 49210b00535415865694ddbec16238d8cf5e6bb0
author goeckslab
date Wed, 26 Jun 2024 15:27:13 +0000
parents 4c767e1a9e7c
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1 import argparse
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2 import json
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3 import os
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4 import warnings
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6 import matplotlib.pylab as plt
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7 import numpy as np
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8 import scimap as sm
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9 import seaborn as sns
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10 from anndata import read_h5ad
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12 sns.set(color_codes=True)
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15 def main(inputs, anndata, output):
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16 """
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17 Parameter
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18 ---------
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19 inputs : str
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20 File path to galaxy tool parameter.
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21 anndata : str
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22 File path to anndata containing phenotyping info.
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23 output : str
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24 File path to output.
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25 """
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26 warnings.simplefilter('ignore')
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28 with open(inputs, 'r') as param_handler:
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29 params = json.load(param_handler)
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31 adata = read_h5ad(anndata)
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33 tool = params['analyses']['selected_tool']
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34 options = params['analyses']['options']
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36 if tool == 'stacked_barplot':
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38 # parse list text arguments
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39 for o in options.copy():
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40 opt_list = options.pop(o)
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41 if opt_list:
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42 options[o] = [x.strip() for x in opt_list.split(',')]
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43
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44 # add base args into options dict to pass to tool
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45 options['x_axis'] = params['analyses']['x_axis']
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46 options['y_axis'] = params['analyses']['y_axis']
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47 options['method'] = params['analyses']['method']
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48
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49 options['return_data'] = True
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50
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51 df = sm.pl.stacked_barplot(adata, **options)
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52
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53 # Pick cmap to use
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54 num_phenotypes = len(df.columns) - 1
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55 if num_phenotypes <= 9:
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56 matplotlib_cmap = "Set1"
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57 elif num_phenotypes > 9 and num_phenotypes <= 20:
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58 matplotlib_cmap = plt.cm.tab20
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59 else:
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60 matplotlib_cmap = plt.cm.gist_ncar
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61
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62 # Plotting
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63 sns.set_theme(style="white")
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64 ax = df.plot.bar(stacked=True, cmap=matplotlib_cmap)
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65 fig = ax.get_figure()
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66 handles, labels = ax.get_legend_handles_labels()
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67 ax.legend(
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68 reversed(handles),
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69 reversed(labels),
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70 bbox_to_anchor=(1, 1),
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71 loc='upper left'
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72 )
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73 plt.tight_layout()
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74
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75 # # save and close
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76 fig.savefig('out.png', dpi=300)
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77 plt.close(fig)
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78
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79 if tool == 'voronoi':
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80
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81 plt.style.use('fast')
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82
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83 tool_func = getattr(sm.pl, tool)
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84
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85 # x_lim/y_lim need to be parsed from comma-sep str to integer tuples
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86 for lim in ['x_lim', 'y_lim']:
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87 opt_list = options.pop(lim)
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88 if opt_list:
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89 options[lim] = [int(x.strip()) for x in opt_list.split(',')]
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90
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91 # parse list text arguments
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92 for cat in ['overlay_points_categories', 'overlay_drop_categories']:
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93 opt_list = options.pop(cat)
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94 if opt_list:
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95 options[cat] = [x.strip() for x in opt_list.split(',')]
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96
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97 # add base args into options dict to pass to tool
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98 options['color_by'] = params['analyses']['color_by']
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99 options['x_coordinate'] = params['analyses']['x_coordinate']
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100 options['y_coordinate'] = params['analyses']['y_coordinate']
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101
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102 # fill any missing params with None as tool expects
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103 for k, v in options.items():
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104 if v == '':
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105 options[k] = None
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106
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107 options['saveDir'] = os.getcwd()
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108 options['fileName'] = 'out.png'
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109
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110 if options['size_max'] is None:
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111 options['size_max'] = np.inf
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112
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113 # call the tool and unpack all options
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114 tool_func(adata, **options)
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115
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116
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117 if __name__ == '__main__':
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118 aparser = argparse.ArgumentParser()
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119 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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120 aparser.add_argument("-a", "--anndata", dest="anndata", required=True)
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121 aparser.add_argument("-e", "--output", dest="output", required=True)
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122
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123 args = aparser.parse_args()
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124
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125 main(args.inputs, args.anndata, args.output)