def test_floating_point(sqlite):
compound = mo.Compound(name="foo", mono_isotopic_molecular_weight=1.0)
mo.store(compound)
compound.mono_isotopic_molecular_weight = 1.000007
mo.store(compound)
test = mo.retrieve("compound", name="foo")[-1]
assert test.mono_isotopic_molecular_weight == 1.000007, test.mono_isotopic_molecular_weight
def test_get_latest():
test = mo.Compound(name="hello")
mo.store(test)
test.name = "goodbye"
mo.store(test)
test = mo.retrieve("compound", unique_id=test.unique_id)
assert len(test) == 1
assert test[0].name == "goodbye"
def test_retrieve01(sqlite):
compound = mo.Compound(name="foo",
inchi=ADENOSINE_INCHI,
inchi_key="foobar")
mo.store(compound)
assert mo.retrieve("Compounds", inchi_key=[], username="******") == []
assert mo.retrieve("Compounds", inchi=[ADENOSINE_INCHI],
username="******")[0].inchi == ADENOSINE_INCHI
def make_atlas_df(atlas):
mz = []
rt = []
atlas_compound = []
label = []
for compound in atlas.compound_identifications:
label.append(compound.name)
if compound.mz_references:
mz.append(compound.mz_references[0])
else:
mz.append(metob.MzReference())
if compound.rt_references:
rt.append(compound.rt_references[0])
else:
rt.append(metob.RtReference())
if compound.compound:
atlas_compound.append(compound.compound[0])
else:
atlas_compound.append(metob.Compound())
compound_df = metob.to_dataframe(atlas_compound)
compound_df.rename(columns={
'name': 'compound_name',
'description': 'compound_description'
},
inplace=True)
#.rename(columns = {'name':'compound_name'}, inplace = True)
atlas_df = pd.concat(
[metob.to_dataframe(rt),
metob.to_dataframe(mz), compound_df], axis=1)
# atlas_df['label'] = label
atlas_keys = [
u'inchi_key', 'compound_name', u'rt_max', u'rt_min', u'rt_peak',
u'rt_units', u'detected_polarity', u'mz', u'mz_tolerance',
u'mz_tolerance_units', 'mono_isotopic_molecular_weight',
'pubchem_compound_id', 'synonyms', 'inchi', 'adduct'
]
# atlas_keys = [u'label','compound_name','compound_description',u'synonyms', u'num_free_radicals', u'number_components', u'permanent_charge', u'rt_max', u'rt_min', u'rt_peak',
# u'rt_units', u'detected_polarity', u'mz', u'mz_tolerance',u'mz_tolerance_units',
# u'inchi', u'inchi_key', u'neutralized_2d_inchi', u'neutralized_2d_inchi_key', u'neutralized_inchi',
# u'neutralized_inchi_key',u'chebi_id', u'hmdb_id', u'img_abc_id', u'kegg_id',u'lipidmaps_id', u'metacyc_id',
# u'mono_isotopic_molecular_weight', u'pubchem_compound_id', u'kegg_url', u'chebi_url', u'hmdb_url', u'lipidmaps_url', u'pubchem_url',u'wikipedia_url', u'source']
atlas_df = atlas_df[atlas_keys]
print((atlas_df.shape, len(label)))
atlas_df['label'] = label
return atlas_df
def make_atlas_df(atlas):
"""
inputs:
atlas: metatlas.datastructures.metatlas_objects.Atlas
output:
pandas DataFrame with one row per CompoundIdentification in atlas and each row also includes
the first RtReference, MzReference, and Compound from the CompoundIdentification
"""
mzs = [extract(aci, ['mz_references', 0], metob.MzReference()) for aci in atlas.compound_identifications]
rts = [extract(aci, ['rt_references', 0], metob.RtReference()) for aci in atlas.compound_identifications]
compounds = [extract(aci, ['compound', 0], metob.Compound()) for aci in atlas.compound_identifications]
ci_df = metob.to_dataframe(atlas.compound_identifications)
ci_df.rename(columns={'name': 'label'}, inplace=True)
compound_df = metob.to_dataframe(compounds)
compound_df.rename(columns={'name': 'compound_name', 'description': 'compound_description'}, inplace=True)
atlas_df = pd.concat([metob.to_dataframe(rts), metob.to_dataframe(mzs), compound_df, ci_df], axis=1)
atlas_keys = ['inchi_key', 'compound_name', 'rt_max', 'rt_min', 'rt_peak', 'rt_units',
'detected_polarity', 'mz', 'mz_tolerance', 'mz_tolerance_units',
'mono_isotopic_molecular_weight', 'pubchem_compound_id', 'synonyms', 'inchi', 'adduct',
'label', 'ms1_notes', 'ms2_notes', 'identification_notes']
return atlas_df[atlas_keys]