class ShellHandler(CLIHandler):
def __init__(self):
CLIHandler.__init__(self)
def ProcessArgs(self, args):
cmdOptionsList = SessionOptions.GetOptParseOptions()
cmdOptionsList.append(
make_option("--context",
dest="context",
default='',
help=SUPPRESS_HELP))
_STR_USAGE = "%prog [options]"
cmdParser = OptionParser(option_list=cmdOptionsList,
usage=_STR_USAGE,
add_help_option=False)
# Get command line options
(options, remainingArgs) = cmdParser.parse_args(args)
sessionOptions = SessionOptions(options)
self.session = Session(sessionOptions)
self._ParseContext(options)
def ClearCmdState(self):
self.cmdNamespace = None
self.app = None
self.method = None
self.options = Values()
self.options._update_loose({'formatter': None, 'debug': False})
self.usage = ''
def ExecuteCommand(self, args):
try:
self.ClearCmdState()
result, message = self._HandleOneCmd(args)
if message:
self.Print(message)
message = ''
except CLIParseException as err:
# Parse error
if err.message:
logging.error(err.message)
message = self._FormatHelpNoRaise(self.cmdNamespace, self.app,
self.method, err)
except CLIExecuteException as err:
# Execution exception
message = err.message
except SessionException as err:
message = err.message
except vmodl.MethodFault as err:
message = "Runtime error: " + err.msg
except Exception as err:
LogException(err)
message = "Runtime error"
# Print message
self.Print(message)
def test_main_dicts_merge(request):
null, expected = read_csv_to_dict(
f'{request.config.rootdir}/{TEST_RESOURCES}/{MT_MERGE}')
sort_list_of_dict(expected)
options = Values()
options._update_loose({
'data_file':
f'{request.config.rootdir}/{TEST_RESOURCES}/{MT_PROCESSED_DATA}',
'metrics_file':
f'{request.config.rootdir}/{TEST_RESOURCES}/{MT_METRICS_DATA}',
'save_directory':
f'{request.config.rootdir}/{TEST_RESOURCES}/'
})
main_dicts_merge(options, None)
null, result = read_csv_to_dict(
f'{request.config.rootdir}/{TEST_RESOURCES}/{MT_RESULT_CSV_FILE}')
assert expected == result
def test_main_merge_pd_wo_type(request, cleanup):
expected = pd.read_csv(
f'{request.config.rootdir}/{TEST_RESOURCES}/{MT_MERGE_2}')
expected = expected.round(decimals=3)
expected.sort_values(by=expected.columns.to_list()[:-4], inplace=True)
expected.reset_index(drop=True, inplace=True)
options = Values()
options._update_loose({
'data_file':
f'{request.config.rootdir}/{TEST_RESOURCES}/{MT_PROCESSED_DATA}',
'metrics_file':
f'{request.config.rootdir}/{TEST_RESOURCES}/{MT_METRICS_DATA}',
'save_directory':
f'{request.config.rootdir}/{TEST_RESOURCES}/'
})
main_merge_pd(options)
result = pd.read_csv(f'{options.save_directory}{MT_RESULT_CSV_FILE}')
result = result.round(decimals=3)
assert expected.equals(result)
def generateGraph(self, filename, suggested_pathways=[], compound_data=None, gene_data=None, protein_data=None, format='svg'):
# Build options-like structure for generation of graph
# (compatibility with command line version, we need to fake it)
options = Values()
options._update_loose({
'file': None,
#'pathways': self.config.Read('/Pathways/Show'),
#'not_pathways':'',
'show_all': False, # self.config.ReadBool('/Pathways/ShowAll'),
'search': '',
'cluster_by': self.config.get('/App/ClusterBy'),
'show_enzymes': self.config.get('/App/ShowEnzymes'), # self.config.ReadBool('/App/ShowEnzymes'),
'show_secondary': self.config.get('/App/Show2nd'),
'show_molecular': self.config.get('/App/ShowMolecular'),
'show_network_analysis': self.config.get('/App/ShowAnalysis'),
'show_gibbs': self.config.get('/App/ShowGibbs'),
'highlightpathways': self.config.get('/App/HighlightPathways'),
'highlightregions': self.config.get('/App/HighlightRegions'),
'splines': 'true',
'focus': False,
'show_pathway_links': self.config.get('/Pathways/ShowLinks'),
# Always except when saving the file
'output': format,
})
#pathway_ids = self.config.value('/Pathways/Show').split(',')
if suggested_pathways:
pathway_ids = [p.id for p in suggested_pathways.entities[1]]
else:
pathway_ids = []
# Add the manually Shown pathways
pathway_ids_show = self.config.get('/Pathways/Show')
pathway_ids.extend(pathway_ids_show)
# Now remove the Hide pathways
pathway_ids_hide = self.config.get('/Pathways/Hide')
pathway_ids = [p for p in pathway_ids if p not in pathway_ids_hide]
# Convert pathways_ids to pathways
pathways = [self.m.db.pathway(pid) for pid in pathway_ids if self.m.db.pathway(pid) is not None]
if pathway_ids == []:
return None
if compound_data or gene_data or protein_data:
# Generate independent scales
node_colors = {}
for dsi in compound_data, gene_data, protein_data:
if dsi == None:
continue
#if self.m.data.analysis_timecourse:
# # Generate the multiple views
# tps = sorted( self.m.data.analysis_timecourse.keys(), key=int )
# # Insert counter variable into the filename
# filename = self.get_filename_with_counter(filename)
# print "Generate timecourse..."
# for tp in tps:
# print "%s" % tp
# graph = generator( pathways, options, self.m.db, analysis=self.m.data.analysis_timecourse[ tp ]) #, layout=self.layout)
# graph.write(filename % tp, format=options.output, prog='neato')
# return tps
#else:
print("Generate map for single control:test...")
# Build analysis lookup dict; we want a single color for each metabolite
mini, maxi = min(abs(np.median(dsi.data)), 0), max(abs(np.median(dsi.data)), 0)
mini, maxi = -1.0, +1.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1], out=np.around) # rdbu9 scale
for n, m in enumerate(dsi.entities[1]):
if m is not None:
ecol = utils.calculate_rdbu9_color(scale, dsi.data[0, n])
#print xref, ecol
if ecol is not None:
node_colors[m.id] = ecol
graph = generator(pathways, options, self.m.db, analysis=node_colors) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None
else:
graph = generator(pathways, options, self.m.db) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None
def generateGraph(self,
filename,
suggested_pathways=[],
compound_data=None,
gene_data=None,
protein_data=None,
format='svg'):
# Build options-like structure for generation of graph
# (compatibility with command line version, we need to fake it)
options = Values()
options._update_loose({
'file':
None,
#'pathways': self.config.Read('/Pathways/Show'),
#'not_pathways':'',
'show_all':
False, # self.config.ReadBool('/Pathways/ShowAll'),
'search':
'',
'cluster_by':
self.config.get('/App/ClusterBy'),
'show_enzymes':
self.config.get('/App/ShowEnzymes'
), # self.config.ReadBool('/App/ShowEnzymes'),
'show_secondary':
self.config.get('/App/Show2nd'),
'show_molecular':
self.config.get('/App/ShowMolecular'),
'show_network_analysis':
self.config.get('/App/ShowAnalysis'),
'highlightpathways':
self.config.get('/App/HighlightPathways'),
'highlightregions':
self.config.get('/App/HighlightRegions'),
'splines':
'true',
'focus':
False,
'show_pathway_links':
self.config.get('/Pathways/ShowLinks'),
# Always except when saving the file
'output':
format,
})
#pathway_ids = self.config.value('/Pathways/Show').split(',')
if suggested_pathways:
pathway_ids = [p.id for p in suggested_pathways.entities[1]]
else:
pathway_ids = []
print(self.config.get('/Pathways/Show'))
# Add the manually Shown pathways
pathway_ids_show = self.config.get('/Pathways/Show')
pathway_ids.extend(pathway_ids_show)
# Now remove the Hide pathways
pathway_ids_hide = self.config.get('/Pathways/Hide')
pathway_ids = [p for p in pathway_ids if p not in pathway_ids_hide]
# Convert pathways_ids to pathways
pathways = [
self.m.db.pathways[pid] for pid in pathway_ids
if pid in list(self.m.db.pathways.keys())
]
if pathway_ids == []:
return None
if compound_data or gene_data or protein_data:
# Generate independent scales
node_colors = {}
for dsi in compound_data, gene_data, protein_data:
if dsi == None:
continue
#if self.m.data.analysis_timecourse:
# # Generate the multiple views
# tps = sorted( self.m.data.analysis_timecourse.keys(), key=int )
# # Insert counter variable into the filename
# filename = self.get_filename_with_counter(filename)
# print "Generate timecourse..."
# for tp in tps:
# print "%s" % tp
# graph = generator( pathways, options, self.m.db, analysis=self.m.data.analysis_timecourse[ tp ]) #, layout=self.layout)
# graph.write(filename % tp, format=options.output, prog='neato')
# return tps
#else:
print("Generate map for single control:test...")
# Build analysis lookup dict; we want a single color for each metabolite
mini, maxi = min(abs(np.median(dsi.data)),
0), max(abs(np.median(dsi.data)), 0)
mini, maxi = -1.0, +1.0 # Fudge; need an intelligent way to determine (2*median? 2*mean?)
scale = utils.calculate_scale([mini, 0, maxi], [9, 1],
out=np.around) # rdbu9 scale
for n, m in enumerate(dsi.entities[1]):
if m is not None:
ecol = utils.calculate_rdbu9_color(
scale, dsi.data[0, n])
#print xref, ecol
if ecol is not None:
node_colors[m.id] = ecol
graph = generator(pathways,
options,
self.m.db,
analysis=node_colors) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None
else:
graph = generator(pathways, options,
self.m.db) # , layout=self.layout)
self.status.emit('waiting')
self.progress.emit(0.5)
graph.write(filename, format=options.output, prog='neato')
return None
