# Sold by the estate of Jeanne Baptiste d'Albert de Luynes
from cromulent.model import factory, TimeSpan, Aggregation, Actor, Place
from cromulent.vocab import Painting, add_art_setter, LotNumber, Purchase, Description
add_art_setter()
acq = Purchase()
lot = Aggregation(label="Verrue sale, lot 87")
acq2 = Purchase()
obj = Painting("http://www.getty.edu/art/collection/objects/882/rembrandt-harmensz-van-rijn-the-abduction-of-europa-dutch-1632/", label="The Abduction of Europa", art=1)
est = Actor(label="The estate of Jeanne Baptiste d'Albert de Luynes, comtesse de Verrue")
lot.identified_by = LotNumber(value="87")
lot.aggregates = obj
# Date of Acquisition
date = TimeSpan()
date.begin_of_the_begin = "1737-03-27T00:00:00Z"
date.end_of_the_end = "1737-03-27T23:59:59Z"
# Description
des = Description(value="1736 - 1737: Estate of Jeanne Baptiste d'Albert de Luynes, comtesse de Verrue, 1670 - 1736 [sold, Verrue sale, Paris, March 27, 1737, lot 87.]")
acq.used_specific_object = lot
acq.part = acq2
acq2.timespan = date
acq2.transferred_title_from = est
acq2.transferred_title_of = obj
dept = {
'GCI': GCI,
'JPGM': JPGM,
'JPGM Antiquities': JPGM_Anti,
'JPGM Painting Conservation': JPGM_Paint,
'JPGM Dec Arts Conservation': JPGM_Dec,
'JPGM Antiquities Conservation': JPGM_Anti_Con
}
# ACQUIRED FROM SELLERS
CSchoettlin = Actor(label="Charles Schoettlin")
Kremer= Group(label="Kremer Pigments Inc.")
Dick_Blick = Group(label="Dick Blick Art Materials")
World_Timbers = Group(label="World Timbers")
Forbes = Group(label="E. W. Forbes Collection")
Tate = Group(label="Tate Britain")
Pearl_Art = Group("Pearl Art and Craft Supply")
McCrone = Group(label="McCrone Accessories & Components")
Schweppe = Actor(label="H. Schweppe")
US_Customs = Group(label="U.S.Customs Lab, Long Beach")
Standard_Brands = Group(label="Standard Brands")
Daniel_Smith = Group(label="Daniel Smith Artist Materials")
Arbidar = Group(label="The Arbidar Co.")
Harvard = Group(label="Harvard Art Museums")
BH_Photo = Group(label="B&H Photo Video")
Sam_Francis = Group(label="Sam Francis Foundation")
def main():
f = open("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 counter, row in enumerate(r):
rec = dict(zip(headers, row))
# Testing first two rows
if counter < 2:
s = ManMadeObject(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']
print(factory.toString(s, compact=False))
def main():
f = open("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", "col_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_col", "phone", "phys_form", "prep", "reactivity", "samp_type", "typ_use",
"warning", "borrower", "inv_status"]
for counter, row in enumerate(r):
rec = dict(zip(headers, row))
# Testing first two rows
if counter < 2:
s = ManMadeObject(label="Sample Object")
# Sample Identification/General Information
# AccessionNumber is used for now, will a new class Barcode - aat:300343361
s.identified_by = Barcode(label="Full Barcode", value=rec['fbarcode'])
s.identified_by = Barcode(label="Old Barcode", value=rec['obarcode'])
s.identified_by = Name(label="Common Name", value=rec['name'])
s.identified_by = Name(label="Additional Names", value=rec['add_names'])
# Sample Type
if rec['samp_type']:
s.classified_as = instances[rec['samp_type'].lower()]
# Typical use
if rec['typ_use']:
s.as_general_use = instances[rec['typ_use'].lower()]
# Physical Form
if rec['phys_form']:
pf = rec['phys_form'].split()
if len(pf) < 4:
i = 0
while i <= 2:
s.classified_as = instances[pf[i].lower().replace(',', '')]
i += 1
break
else:
s.referred_to_by = Description(label="Sample Type", value=rec['phys_form'])
# Color
if rec['color']:
s.classified_as = instances[rec['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")
# 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
# Index (CI) No., Preparation, Certified Standard
# Acquisition Information
acq = Acquisition()
gci = Department("http://www.getty.edu/conservation/", label="Getty Research Institute")
s.changed_ownership_through = acq
acq.transferred_title_to = gci
# Acquisition Date
if rec['acq_date']:
acq.timespan = TimeSpan(label=rec['acq_date'])
# Acquired by
if rec['acq_by']:
emp = Actor(label=rec['acq_by'])
acq.carried_out_by = emp
emp.member_of = gci
# Acquired from, debating whether it should be Actor or Group,
# so info of person in contact can be linked
if rec['acq_from']:
acq.transferred_title_from = Actor(label=rec['acq_from'])
# Geographic Origin
if rec['geo_org']:
prod = Production()
origin = Place(label="Geographic Origin")
origin.identified_by = Name(value=rec['geo_org'])
prod.took_place_at = origin
s.produced_by = prod
# Catalog No.
if rec['cat']:
s.identified_by = CatalogNumber(label="Catalog No.", value=rec['cat'])
# Miscellaneous
# Notes
if rec['notes']:
io = InformationObject(value=rec['notes'])
io.classified_as = instances["notes"]
print(factory.toString(s, compact=False))
# Title
obj.identified_by = PrimaryName(label="Title", value="The Abduction of Europa")
obj.identified_by = Name(label="ALternate Title",
value="El rapto de Europa (Published Title)")
prod = Production()
obj.produced_by = prod
# Collection
col = CollectionSet("http://www.getty.edu/art/collection/",
label="The J. Paul Getty Museum Collection")
obj.aggregated_by = col
# Artist info
artist = Actor(
"http://www.getty.edu/art/collection/artists/469/rembrandt-harmensz-van-rijn-dutch-1606-1669/",
label="Artist")
artist.identified_by = PrimaryName(value="Rembrandt Harmensz. van Rijn")
artist.classified_as = instances["dutch nationality"]
birth = BeginningOfExistence()
btime = TimeSpan(label="1606")
btime.begin_of_the_begin = "1606-01-01T00:00:00Z"
btime.end_of_the_end = "1607-01-01T00:00:00Z"
birth.timespan = btime
death = EndOfExistence()
dtime = TimeSpan(label="1669")
dtime.begin_of_the_begin = "1669-01-01T00:00:00Z"
dtime.end_of_the_end = "1670-01-01T00:00:00Z"
death.timespan = dtime
artist.brought_into_existence_by = birth
artist.taken_out_of_existence_by = death