File size: 12,933 Bytes
9d54b72
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
import json
import os
import stat
import sys
import tempfile
from typing import List

import biosimulators_utils.sedml.utils
import fire
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from biosimulators_utils.combine.data_model import CombineArchive
from biosimulators_utils.combine.io import CombineArchiveReader
from biosimulators_utils.combine.utils import get_sedml_contents
from biosimulators_utils.config import Config
from biosimulators_utils.log.data_model import Status, CombineArchiveLog, SedDocumentLog  # noqa: F401
from biosimulators_utils.combine.validation import validate
from biosimulators_utils.combine.data_model import CombineArchiveContentFormat

from biosimulators_utils.report.data_model import DataSetResults, ReportResults, ReportFormat  # noqa: F401
from biosimulators_utils.sedml.data_model import Report, Plot2D, Plot3D, DataSet, SedDocument
from biosimulators_utils.sedml.io import SedmlSimulationReader, SedmlSimulationWriter

# Move status PY code here
# Create temp directory
tmp_dir = tempfile.mkdtemp()

def validate_omex(omex_file_path: str, temp_dir_path: str, omex_json_report_path: str) -> str:
    if not os.path.exists(temp_dir_path):
        os.mkdir(temp_dir_path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)

    # defining archive
    config = Config(
        VALIDATE_OMEX_MANIFESTS=True,
        VALIDATE_SEDML=True,
        VALIDATE_SEDML_MODELS=True,
        VALIDATE_IMPORTED_MODEL_FILES=True,
        VALIDATE_OMEX_METADATA=True,
        VALIDATE_IMAGES=True,
        VALIDATE_RESULTS=True
    )

    reader = CombineArchiveReader()
    archive: CombineArchive = reader.run(in_file=omex_file_path, out_dir=temp_dir_path, config=config)
    print("errors: "+str(reader.errors)+"\n"+"warnings: "+str(reader.warnings))

    validator_errors: List[str] = []
    validator_warnings: List[str] = []
    if len(reader.errors) == 0:
        validator_errors, validator_warnings = validate(
            archive,
            temp_dir_path,
            formats_to_validate=list(CombineArchiveContentFormat.__members__.values()),
            config=config
        )

    results_dict = {
                "parse_errors": reader.errors,
                "parse_warnings": reader.warnings,
                "validator_errors": validator_errors,
                "validator_warnings": validator_warnings
            }
    with open(omex_json_report_path, "w") as file:
        file.write(json.dumps(results_dict, indent=2))
    return repr(results_dict)


def gen_sedml_2d_3d(omex_file_path, base_out_path) -> str:

    temp_path = os.path.join(base_out_path, "temp")
    if not os.path.exists(temp_path):
        os.mkdir(temp_path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)

    # defining archive
    config = Config(VALIDATE_OMEX_MANIFESTS=True)
    archive = CombineArchiveReader().run(in_file=omex_file_path, out_dir=temp_path,
                                         config=config)

    # determine files to execute
    sedml_contents = get_sedml_contents(archive)

    for i_content, content in enumerate(sedml_contents):
        content_filename = os.path.normpath(os.path.join(temp_path, content.location))
        starting_sedml_name_index = content.location.rfind("/") + 1 if '/' in content.location else 0
        ending_sedml_name_index = content.location.rfind(".")
        sedml_name = content.location[starting_sedml_name_index:ending_sedml_name_index]
#        sedml_name = Path(content.location).stem
        print("name: ", sedml_name, file=sys.stdout)
        print("sedml_name: ", sedml_name, file=sys.stdout)
        print("content.location: ", content.location, file=sys.stdout)
        print("content_filename: ", content_filename, file=sys.stdout)

        doc = SedmlSimulationReader().run(content_filename)
        for output in doc.outputs:
            if isinstance(output, (Plot2D, Plot3D)):
                report = Report(
                    id='__plot__' + output.id,
                    name=output.name)

                data_generators = {}
                if isinstance(output, Plot2D):
                    for curve in output.curves:
                        data_generators[curve.x_data_generator.id] = curve.x_data_generator
                        data_generators[curve.y_data_generator.id] = curve.y_data_generator

                elif isinstance(output, Plot3D):
                    for surface in output.surfaces:
                        data_generators[surface.x_data_generator.id] = surface.x_data_generator
                        data_generators[surface.y_data_generator.id] = surface.y_data_generator
                        data_generators[surface.z_data_generator.id] = surface.z_data_generator

                for data_generator in data_generators.values():
                    report.data_sets.append(DataSet(
                        id='__vcell_reserved_data_set_prefix__{}_{}'.format(
                            output.id, data_generator.id),
                        name=data_generator.name,
                        label=data_generator.id,
                        data_generator=data_generator,
                    ))

                report.data_sets.sort(key=lambda data_set: data_set.id)
                doc.outputs.append(report)

        filename_with_reports_for_plots = os.path.join(
            temp_path, f'simulation_{sedml_name}.sedml')
        SedmlSimulationWriter().run(doc, filename_with_reports_for_plots,
                                    validate_models_with_languages=False)
        if not os.path.exists(filename_with_reports_for_plots):
            raise FileNotFoundError("The desired pseudo-sedml failed to generate!")
    return temp_path


def transpose_vcml_csv(csv_file_path: str) -> str:
    df = pd.read_csv(csv_file_path, header=None)
    cols = list(df.columns)
    final_cols = [col for col in cols if col != '']
    df[final_cols].transpose().to_csv(csv_file_path, header=False, index=False)
    print("Success!")
    return csv_file_path


def get_all_dataref_and_curves(sedml_path):
    all_plot_curves = {}
    all_report_dataref = {}

    sedml: SedDocument = SedmlSimulationReader().run(sedml_path)

    for output in sedml.outputs:
        if isinstance(output, Plot2D):
            all_curves = {}
            for curve in output.curves:
                all_curves[curve.getId()] = {
                    'x': curve.getXDataReference(),
                    'y': curve.getYDataReference()
                }
            all_plot_curves[output.id] = all_curves
        if isinstance(output, Report):
            for dataset in output.data_sets():

                ######
                if output.id in all_report_dataref:
                    all_report_dataref[output.id].append({
                        'data_reference': dataset.getDataReference(),
                        'data_label': dataset.getLabel()
                    })
                else:
                    all_report_dataref[output.id] = []
                    all_report_dataref[output.id].append({
                        'data_reference': dataset.getDataReference(),
                        'data_label': dataset.getLabel()
                    })

    return all_report_dataref, all_plot_curves


def get_report_label_from_data_ref(dataref: str, all_report_dataref):
    for report in all_report_dataref.keys():
        for data_ref in all_report_dataref[report]:
            if dataref == data_ref['data_reference']:
                return report, data_ref['data_label']




# Update plots dict

def update_dataref_with_report_label(all_report_dataref, all_plot_curves):

    for plot, curves in all_plot_curves.items():
        for curve_name, datarefs in curves.items():
            new_ref = dict(datarefs)
            new_ref['x'] = get_report_label_from_data_ref(
                datarefs['x'], all_report_dataref)[1]
            new_ref['y'] = get_report_label_from_data_ref(
                datarefs['y'], all_report_dataref)[1]
            new_ref['report'] = get_report_label_from_data_ref(
                datarefs['y'], all_report_dataref)[0]
            curves[curve_name] = new_ref

    return all_report_dataref, all_plot_curves


def get_report_dataframes(all_report_dataref, result_out_dir):
    report_frames = {}
    reports_list = list(set(all_report_dataref.keys()))
    for report in reports_list:
        report_frames[report] = pd.read_csv(str(os.path.join(result_out_dir, report + ".csv"))).T.reset_index()
        report_frames[report].columns = report_frames[report].iloc[0].values
        report_frames[report].drop(index=0, inplace=True)
    return report_frames

# PLOTTING


def plot_and_save_curves(all_plot_curves, report_frames, result_out_dir) -> str:
    all_plots = dict(all_plot_curves)
    for plot, curve_dat in all_plots.items():
        dims = (12, 8)
        fig, ax = plt.subplots(figsize=dims)
        for curve, data in curve_dat.items():
            df = report_frames[data['report']]
            df.to_csv(os.path.join(result_out_dir, plot + '.csv'),
                      index=False, header=True)
            transpose_vcml_csv(os.path.join(result_out_dir, plot + '.csv'))
            sns.lineplot(x=df[data['x']].astype(
                np.float64), y=df[data['y']].astype(np.float64), ax=ax, label=curve)
            ax.set_ylabel('')
        #         plt.show()
        plt.savefig(os.path.join(result_out_dir, plot + '.pdf'), dpi=300)
        return os.path.join(result_out_dir, plot + '.pdf')


def gen_plot_pdfs(sedml_path, result_out_dir):
    all_report_dataref, all_plot_curves = get_all_dataref_and_curves(
        sedml_path)
    all_report_dataref, all_plot_curves = update_dataref_with_report_label(
        all_report_dataref, all_plot_curves)
    report_frames = get_report_dataframes(all_report_dataref, result_out_dir)
    print("Success!")
    return plot_and_save_curves(all_plot_curves, report_frames, result_out_dir)


def gen_plots_for_sed2d_only(sedml_path, result_out_dir):
    sedml: SedDocument = SedmlSimulationReader().run(sedml_path)

    # Generate all_plot_curves
    all_plot_curves = {}
    for output in sedml.outputs:
        if not isinstance(output, Plot2D):
            continue
        sed_plot_2d: Plot2D = output
        all_curves = {}

        for curve in sed_plot_2d.curves:
            all_curves[curve.id] = {
                'x': curve.x_data_generator,
                'y': curve.y_data_generator,
                'name': curve.name
            }
        all_plot_curves[sed_plot_2d.id] = all_curves


    all_plots = dict(all_plot_curves)
    for plot_id, curve_dat_dict in all_plots.items(): # curve_dat_dict <--> all_curves
        dims = (12, 8)
        fig, ax = plt.subplots(figsize=dims)

        labelMap = {}
        df = pd.read_csv(os.path.join(result_out_dir, plot_id + '.csv'), header=None).T
        
        # create mapping from task to all repeated tasks (or just itself)
        for elem in df.iloc[1]:
            if elem not in labelMap:
                labelMap[elem] = []
            labelMap[elem].append(str(elem) + "_" + str(len(labelMap[elem])))

        # Prepare new labels
        labels = []
        for key in labelMap:
            if len(labelMap[key]) == 1:
                labelMap[key][0] = key      # If there wasn't repeated tasks, restore the old name
            for elem in labelMap[key]:
                labels.append(elem)

        # format data frame
        df.columns = labels
        labels_df = df.copy()

        df.drop(df.index[:3], inplace=True)
        labels_df.drop(labels_df.index[:2], inplace=True)
        labels_df.drop(labels_df.index[1:], inplace=True)

        df.reset_index(inplace=True)
        df.drop('index', axis=1, inplace=True)

        for curve_id, data in curve_dat_dict.items(): # data <--> (dict)all_curves.values()
            shouldLabel = True
            x_axis_id = data['x'].id
            y_axis_id = data['y'].id
            x_data_sets = labelMap[x_axis_id]
            y_data_sets = labelMap[y_axis_id]

            for i in range(len(y_data_sets)):
                series_name = y_data_sets[i]
                x_data_set = x_data_sets[0] if len(x_data_sets) == 1 else x_data_sets[i]
                label_name = data['name'] if data['name'] is not None else curve_id
                sns.lineplot(data=df, x=x_data_set, y=series_name, ax=ax, label=(label_name if shouldLabel else None))
                ax.set_ylabel('')
                ax.set_xlabel(labels_df.at[labels_df.index[0], x_data_set])
                shouldLabel = False
            plt.savefig(os.path.join(result_out_dir, plot_id + '.pdf'), dpi=300)
    print("Success!")
    return result_out_dir


if __name__ == "__main__":
    fire.Fire({
        'genSedml2d3d': gen_sedml_2d_3d,
        'genPlotsPseudoSedml': gen_plots_for_sed2d_only,
        'transposeVcmlCsv': transpose_vcml_csv,
        'genPlotPdfs': gen_plot_pdfs,
    })