def test_boundary_setter(self):
vocab.add_linked_art_boundary_check()
p = model.Person()
p2 = model.Person()
n = model.Name()
n.content = "Test"
p2.identified_by = n
p.exact_match = p2
# Now, Test should not appear in the resulting JSON of p
factory.linked_art_boundaries = True
js = factory.toJSON(p)
self.assertTrue(not 'identified_by' in js['exact_match'][0])
factory.linked_art_boundaries = False
js = factory.toJSON(p)
self.assertTrue('identified_by' in js['exact_match'][0])
def test_procurement_boundary(self): vocab.add_linked_art_boundary_check() a = model.Activity() p = vocab.ProvenanceEntry() a.caused = p js = factory.toJSON(a) self.assertTrue(not 'classified_as' in js['caused'][0])
def generate_example(self, egtext, resource):
# Yes really...
exec(egtext)
# Now in scope should be a top resource
factory.pipe_scoped_contexts = False
factory.toFile(top, compact=False)
factory.pipe_scoped_contexts = True
jsstr = factory.toString(top, compact=False, collapse=80)
factory.pipe_scoped_contexts = False
js = factory.toJSON(top)
# Generate all our serializations
nq = to_rdf(js, {"format": "application/nquads"})
g = ConjunctiveGraph()
for ns in ['crm', 'dc', 'schema', 'dcterms', 'skos', 'la']:
g.bind(ns, ctxt[ns])
g.parse(data=nq, format="nt")
out = g.serialize(format="turtle")
fp = js['id'][len(factory.base_url):]
fp2 = fp + ".ttl"
fh = open(os.path.join(factory.base_dir, fp2), 'w')
fh.write(out)
fh.close()
# And build mermaid description
mermaid = self.build_mermaid(js)
# Build index references
self.traverse(js, top.id, resource)
# And return the JSON plus links, to be substed by the top level filter
raw = top.id + ".json"
self.example_list.append(raw)
rawq = urllib.quote(raw).replace('/', "%2F")
playground = "http://json-ld.org/playground-dev/#startTab=tab-expanded©Context=true&json-ld=%s" % rawq
turtle = top.id + ".ttl"
turtle_play = "http://cdn.rawgit.com/niklasl/ldtr/v0.2.2/demo/?edit=true&url=%s" % turtle
egid = fp.replace('/', '_')
resp = """
<a id="%s"></a>
```json
%s
```
<div class="mermaid">
%s
</div>
Other Representations: [JSON-LD (Raw)](%s) |
[JSON-LD (Playground)](%s) |
[Turtle (Raw)](%s) |
[Turtle (Styled)](%s)
""" % (egid, jsstr, mermaid, raw, playground, turtle, turtle_play)
return resp
def test_destruction(self):
expect = OrderedDict([('id', u'http://lod.example.org/museum/Activity/1'),
('type', ['Destruction', 'Activity']), ('_label', "Test Destruction")])
mi.DestructionActivity._okayToUse = 1
da = mi.DestructionActivity("1")
da._label = "Test Destruction"
factory.context_uri = ""
dajs = factory.toJSON(da)
self.assertEqual(dajs, expect)
def test_payment(self):
factory.context_uri = "http://example.org/context.json",
expect = OrderedDict([
('@context', factory.context_uri), \
('id', u'http://lod.example.org/museum/Payment/1'), ('type', 'Payment'), \
('paid_to', 'http://lod.example.org/museum/Person/1')])
p = extra.Payment('1')
who = Person('1')
p.paid_to = who
pjs = factory.toJSON(p)
self.assertEqual(pjs, expect)
def test_linguistic_object_boundary(self):
vocab.add_linked_art_boundary_check()
jrnl = vocab.JournalText(label="journal")
issue = vocab.IssueText(label="issue")
issue.part_of = jrnl
issue.referred_to_by = vocab.MaterialStatement(content="Statement")
js = factory.toJSON(issue)
# Have not embedded journal in issue
self.assertTrue(not 'classified_as' in js['part_of'][0])
# Have embedded statement in issue
self.assertTrue('content' in js['referred_to_by'][0])
self.assertTrue('type' in js['referred_to_by'][0]['classified_as'][0]['classified_as'][0])
def main(ID):
data_folder = os.path.abspath('../GCI_API/data')
f = open(os.path.join(data_folder, "ref_col.csv"), "r")
r = csv.reader(f)
next(r) #Skip header
headers = [
"acq_by", "acq_from", "acq_date", "add_names", "avai_data", "cat",
"certified", "chem_comp", "CAS", "chem_form", "chem_name", "coll_name",
"color", "CI", "name", "comp_type", "contact", "email", "experiments",
"fire", "formulation", "fbarcode", "geo_org", "grid_loc", "health",
"manufacturer", "mass_vol", "mix_pigment", "mix_type", "MSDS",
"nat_syn", "notes", "obarcode", "org_date", "other_safe", "part_coll",
"phone", "phys_form", "prep", "reactivity", "samp_type", "typ_use",
"warning", "borrower", "inv_status"
]
for row in r:
rec = dict(zip(headers, row))
# Testing first two rows
if rec['fbarcode'] == ID:
s = ManMadeObject(rec['fbarcode'], label=rec['name'])
# Sample Identification/General Information
s.identified_by = Barcode(label="Full Barcode",
value=rec['fbarcode'])
s.identified_by = Barcode(label="Old Barcode",
value=rec['obarcode'])
s.identified_by = PrimaryName(label="Common Name",
value=rec['name'])
s.identified_by = Name(label="Additional Names",
value=rec['add_names'])
# Sample Type
if rec['samp_type']:
try:
s.classified_as = instances[rec['samp_type'].lower()]
except:
s.referred_to_by = Description(label="Sample Type",
value=rec['samp_type'])
# Typical use
if rec['typ_use']:
use = rec['typ_use'].split('/')
for i in range(len(use)):
try:
s.as_general_use = instances[use[i].lower().strip()]
except:
s.referred_to_by = Description(label="Typical Use",
value=use[i])
# Physical Form
if rec['phys_form']:
pf = rec['phys_form'].split()
if len(pf) < 4:
l = []
for i in range(len(pf)):
try:
s.classified_as = instances[pf[i].lower().replace(
',', '').strip()]
except:
l.append(pf[i])
l_join = " ".join(l)
s.referred_to_by = Description(label="Physical Form",
value=l_join)
else:
s.referred_to_by = Description(label="Physical Form",
value=rec['phys_form'])
# Color
if rec['color']:
try:
s.classified_as = instances[rec['color'].lower()]
except:
s.referred_to_by = Description(label="Color",
value=rec['color'])
# Index (CI) Color
# Natural/Synthetic
if rec['nat_syn']:
s.classified_as = instances[rec['nat_syn'].lower()]
elif rec['nat_syn'] == "Unknown":
s.classified_as = Type(label="Natural/Synthetic",
value="Unknown")
# Preparation : go to Production
# Certified Standard
# Grid Location: lab shelf/storage?
loc = Place()
if rec['grid_loc']:
loc.identified_by = Identifier(label="Grid Location",
value=rec['grid_loc'])
s.current_location = loc
# Chemical Information
# Chemical Formula
if rec['chem_form']:
s.identified_by = Formula(value=rec['chem_form'])
if rec['chem_name']:
s.identified_by = Name(label="Chemical Name",
value=rec['chem_name'])
if rec['CAS']:
s.identified_by = Identifier(label="Chemical (CAS) No.",
value=rec['CAS'])
if rec['comp_type']:
comp_type = rec['comp_type'].lower()
try:
s.classified_as = instances[comp_type]
except:
s.classified_as = Type(label="Compound Type",
value=rec['comp_type'])
if rec['mix_type']:
mix_type = rec['mix_type'].split('-')
for i in mix_type:
s.classified_as = instances[i.lower().strip()]
# Acquisition Information
acq = Acquisition()
s.changed_ownership_through = acq
acq.transferred_title_to = v.dept['GCI']
# Acquisition Date
if rec['acq_date']:
adate = rec['acq_date']
tspan = TimeSpan(label=adate)
if len(adate) == 4:
tspan.begin_of_the_begin = str(
parse(adate + 'January',
settings={'PREFER_DAY_OF_MONTH': 'first'}))
tspan.end_of_the_end = str(
parse(adate + 'January',
settings={'PREFER_DAY_OF_MONTH': 'first'}) +
timedelta(days=365))
elif 'ca.' in adate or 'Spring' in adate:
y = adate.split()[1]
tspan.begin_of_the_begin = str(
parse(y + 'January',
settings={'PREFER_DAY_OF_MONTH': 'first'}))
tspan.end_of_the_end = str(
parse(y + 'January',
settings={'PREFER_DAY_OF_MONTH': 'first'}) +
timedelta(days=365))
elif '-' in adate:
y = adate.split('-')
start_year = y[0].strip()
end_year = y[1].strip()
if len(start_year) == 4:
tspan.begin_of_the_begin = str(
parse(start_year + 'January',
settings={'PREFER_DAY_OF_MONTH': 'first'}))
tspan.end_of_the_end = str(
parse(end_year + 'January',
settings={'PREFER_DAY_OF_MONTH': 'first'}) +
timedelta(days=365))
else:
tspan.begin_of_the_begin = str(
parse(adate,
settings={'PREFER_DAY_OF_MONTH': 'first'}))
tspan.end_of_the_end = str(
parse(adate,
settings={'PREFER_DAY_OF_MONTH': 'last'}) +
timedelta(days=1))
else:
tspan.begin_of_the_begin = str(
parse(adate, settings={'PREFER_DAY_OF_MONTH':
'first'}))
tspan.end_of_the_end = str(
parse(adate, settings={'PREFER_DAY_OF_MONTH': 'last'})
+ timedelta(days=1))
acq.timespan = tspan
# Acquired by
if rec['acq_by']:
p = rec['acq_by'].split('-')
pname = p[0].split()
agent = " ".join(pname)
dpt = p[-1]
if agent in v.person:
emp = v.person[agent]
else:
emp = Person(label=agent)
acq.carried_out_by = emp
if dpt in v.dept:
emp.member_of = v.dept[dpt]
# Part of Collection
if rec['part_coll']:
s.referred_to_by = LinguisticObject(label='Part of Collection',
value=rec['part_coll'])
# Acquired from, debating whether it should be Actor or Group,
# so info of person in contact can be linked
global seller
if rec['acq_from']:
if rec['acq_from'] in v.sellers:
seller = v.sellers[rec['acq_from'].strip()]
else:
seller = Actor(label=rec['acq_from'].strip())
acq.transferred_title_from = seller
# Collection Name
if rec['coll_name']:
coll = v.collection[rec['coll_name']]
s.aggregated_by = coll
coll_creation = Creation()
coll.created_by = coll_creation
coll_creation.carried_out_by = seller
# Contact name
if rec['contact']:
if rec['contact'] in v.contact:
contact_name = v.contact[rec['contact'].strip()]
else:
s.related_entity = contact_name
# Email
if rec['email']:
contact_name.contact_point = EMail(label="E-Mail",
value=rec['email'])
# Phone number: could be either the number of the contact person,
# or of the acquired_from
# if rec['phone']:
prod = Production()
s.produced_by = prod
# Preparation
if rec['prep']:
prep = SamplePreparation(value=rec['prep'])
prod.technique = prep
if rec['formulation']:
prep.referred_to_by = Formulation(value=rec['formulation'])
# Geographic Origin
if rec['geo_org']:
origin = Place(label="Geographic Origin")
origin.identified_by = Name(value=rec['geo_org'])
prod.took_place_at = origin
# Manufacturer
if rec['manufacturer']:
if rec['manufacturer'] in v.manufacturers:
mfr = v.manufacturers[rec['manufacturer']]
elif rec['manufacturer'] in v.sellers:
mfr = v.sellers[rec['manufacturer']]
else:
mfr = Group(label=rec['manufacturer'])
prod.carried_out_by = mfr
# Catalog No.
if rec['cat']:
s.identified_by = CatalogNumber(label="Catalog No.",
value=rec['cat'])
# Miscellaneous
# Origination Date
# if rec['org_date']:
# odate = rec['org_date']
# t_span = TimeSpan(label=odate)
# if len(adate) == 4:
# t_span.begin_of_the_begin = str(parse(odate + 'January', settings={'PREFER_DAY_OF_MONTH': 'first'}))
# t_span.end_of_the_end = str(parse(odate + 'January', settings={'PREFER_DAY_OF_MONTH': 'first'}) + timedelta(days=365))
# elif 'ca.' in adate or 'pre' in adate:
# y = odate.split()[1]
# t_span.begin_of_the_begin = str(parse(y + 'January', settings={'PREFER_DAY_OF_MONTH': 'first'}))
# t_span.end_of_the_end = str(parse(y + 'January', settings={'PREFER_DAY_OF_MONTH': 'first'}) + timedelta(days=365))
# elif '-' in adate:
# y = odate.split('-')
# start_year = y[0].strip()
# end_year = y[1].strip()
# if len(start_year) == 4:
# t_span.begin_of_the_begin = str(parse(start_year + 'January', settings={'PREFER_DAY_OF_MONTH': 'first'}))
# t_span.end_of_the_end = str(parse(end_year + 'January', settings={'PREFER_DAY_OF_MONTH': 'first'}) + timedelta(days=365))
# else:
# t_span.begin_of_the_begin = str(parse(odate, settings={'PREFER_DAY_OF_MONTH': 'first'}))
# t_span.end_of_the_end = str(parse(odate, settings={'PREFER_DAY_OF_MONTH': 'last'}) + timedelta(days=1))
# else:
# t_span.begin_of_the_begin = str(parse(odate, settings={'PREFER_DAY_OF_MONTH': 'first'}))
# t_span.end_of_the_end = str(parse(odate, settings={'PREFER_DAY_OF_MONTH': 'last'}) + timedelta(days=1))
# acq.timespan = t_span
# Mass or Volume
if rec['mass_vol']:
mv = rec['mass_vol'].replace('appx.',
'').replace('approx.',
'').split()
if len(mv) == 2:
dim = MassVolume(value=mv[0])
s.dimension = dim
dim.unit = instances[mv[1].replace('.', '')]
else:
try:
u = instances[(mv[1] + " " + mv[2]).replace('.', '')]
dim = MassVolume(value=mv[0])
s.dimension = dim
dim.unit = u
except:
s.referred_to_by = DimensionStatement(
label="Mass or Volume", value=rec['mass_vol'])
# Experiments on Sample
if rec['experiments']:
s.referred_to_by = Description(label="Experiments on Sample",
value=rec['experiments'])
# Notes
if rec['notes']:
s.referred_to_by = Notes(value=rec['notes'])
# Available Data
# MSDS & Safety
# Safety: classified_as or referred_to_by properties
if rec['fire']:
s.classified_as = FireSafety(value=rec['fire'])
if rec['health']:
health_safe = Safety(label="Health Safety",
value=rec['health'])
s.classified_as = health_safe
health_safe.classified_as = instances['health']
if rec['reactivity']:
react_safe = Safety(label="Reactivity Safety",
value=rec['reactivity'])
s.classified_as = react_safe
react_safe.classified_as = instances['reactivity']
if rec['other_safe']:
other_safe = Safety(label="Other Safety",
value=rec['other_safe'])
s.classified_as = other_safe
other_safe.classified_as = instances['other']
js = factory.toJSON(s)
return js
def generate_example(self, egtext, resource):
# Yes really...
try:
exec(egtext)
except Exception as e:
print(">>> In %s" % resource.relative_path)
print("Caught Exception from example code at %s: %r" %
(sys.exc_info()[2].tb_lineno, e))
print("Failed to execute example:\n%s" % h[1])
return ""
try:
# Now in scope should be a top resource
factory.pipe_scoped_contexts = False
factory.toFile(top, compact=False)
js = factory.toJSON(top)
factory.pipe_scoped_contexts = True
jsstr = factory.toHtml(top)
factory.pipe_scoped_contexts = False
# Generate all our serializations
nq = to_rdf(js, {"format": "application/nquads"})
g = ConjunctiveGraph()
for ns in ['crm', 'dc', 'schema', 'dcterms', 'skos', 'la']:
g.bind(ns, ctxt[ns])
g.parse(data=nq, format="nt")
out = g.serialize(format="turtle")
fp = js['id'][len(factory.base_url):]
fp2 = fp + ".ttl"
fh = open(os.path.join(factory.base_dir, fp2), 'w')
fh.write(out)
fh.close()
except Exception as e:
print(">>> In %s" % resource.relative_path)
print("Caught Exception from serialization code at %s: %r" %
(sys.exc_info()[2].tb_lineno, e))
print("Failed to execute example:\n%s" % h[1])
return ""
try:
# And build mermaid description
mermaid = self.build_mermaid(js)
# Build index references
self.traverse(js, top.id, resource)
except Exception as e:
print(">>> In %s" % resource.relative_path)
print("Caught Exception from mermaid/indexing code at %s: %r" %
(sys.exc_info()[2].tb_lineno, e))
print("Failed to execute example:\n%s" % h[1])
return ""
# And return the JSON plus links, to be substed by the top level filter
raw = top.id
jsuri = raw + ".json"
self.example_list.append(raw)
rawq = urllib.quote(raw).replace('/', "%2F")
playground = "http://json-ld.org/playground-dev/#startTab=tab-expanded©Context=true&json-ld=%s" % rawq
turtle = raw + ".ttl"
turtle_play = "http://cdn.rawgit.com/niklasl/ldtr/v0.2.2/demo/?edit=true&url=%s" % turtle
egid = fp.replace('/', '_')
mmid = self.uri_to_label(raw)
resp = """
<a id="%s"></a>
<div class="jsonld">
%s
<div>
<div class="mermaid" id="mermaid_%s">
%s
</div>
<div id="mermaid_%s_svg"></div>
Other Representations: [JSON-LD (Raw)](%s) |
[JSON-LD (Playground)](%s) |
[Turtle (Raw)](%s) |
[Turtle (Styled)](%s)
""" % (egid, jsstr, mmid, mermaid, mmid, raw, playground, turtle, turtle_play)
return resp
