def prime_cache(args):
#
# TODO: relate survey chapter reference with language!
#
icons = {}
frequencies = {}
args.log.info('computing wals representation')
for feature in DBSession.query(common.Parameter).options(
joinedload(common.Parameter.valuesets)
):
feature.representation = len(feature.valuesets)
if feature.wals_id:
with open(path(apics.__file__).dirname().joinpath(
'static', 'wals', '%sA.json' % feature.wals_id
), 'r') as fp:
data = json.load(fp)
feature.wals_representation = sum([len(l['features']) for l in data['layers']])
args.log.info('computing language sources')
compute_language_sources((common.ContributionReference, 'contribution'))
compute_number_of_values()
for valueset in DBSession.query(common.ValueSet)\
.join(common.Parameter)\
.filter(common.Parameter.id == '0')\
.options(joinedload(common.ValueSet.language)):
if valueset.language.language_pk:
continue
if len(valueset.values) > 1:
valueset.language.lexifier = 'Other'
else:
if valueset.values[0].domainelement.name == 'Other':
valueset.language.lexifier = 'Other'
else:
valueset.language.lexifier = valueset.values[0].domainelement.name.replace('-based', '')
for lect in valueset.language.lects:
lect.lexifier = valueset.language.lexifier
args.log.info('creating icons')
for valueset in DBSession.query(common.ValueSet).options(
joinedload(common.ValueSet.parameter),
joinedload_all(common.ValueSet.values, common.Value.domainelement)
):
values = sorted(list(valueset.values), key=lambda v: v.domainelement.number)
assert abs(sum(v.frequency for v in values) - 100) < 1
fracs = []
colors = []
for v in values:
color = v.domainelement.jsondata['color']
frequency = round(v.frequency)
assert frequency
if frequency not in frequencies:
figure(figsize=(0.4, 0.4))
axes([0.1, 0.1, 0.8, 0.8])
coll = pie((int(100 - frequency), frequency), colors=('w', 'k'))
coll[0][0].set_linewidth(0.5)
save('freq-%s' % frequency)
frequencies[frequency] = True
v.jsondata = {'frequency_icon': 'freq-%s.png' % frequency}
fracs.append(frequency)
colors.append(color)
v.domainelement.jsondata = {
'color': color, 'icon': 'pie-100-%s.png' % color}
assert len(colors) == len(set(colors))
fracs, colors = tuple(fracs), tuple(colors)
basename = 'pie-'
basename += '-'.join('%s-%s' % (f, c) for f, c in zip(fracs, colors))
valueset.update_jsondata(icon=basename + '.png')
if (fracs, colors) not in icons:
figure(figsize=(0.4, 0.4))
axes([0.1, 0.1, 0.8, 0.8])
coll = pie(
tuple(reversed(fracs)),
colors=['#' + color for color in reversed(colors)])
for wedge in coll[0]:
wedge.set_linewidth(0.5)
save(basename)
icons[(fracs, colors)] = True
with open(str(icons_dir.joinpath(basename + '.svg')), 'w') as fp:
fp.write(svg.pie(fracs, ['#' + color for color in colors], width=40))
for de in DBSession.query(common.DomainElement):
if not de.jsondata.get('icon'):
de.update_jsondata(icon='pie-100-%s.png' % de.jsondata['color'])
gbs_func('update', args)
def test_compute_number_of_values(self):
from clld.db.util import compute_number_of_values
compute_number_of_values()
def prime_cache(args):
#
# TODO: relate survey chapter reference with language!
#
icons = {}
frequencies = {}
args.log.info('computing wals representation')
for feature in DBSession.query(common.Parameter).options(
joinedload(common.Parameter.valuesets)):
feature.representation = len(feature.valuesets)
if feature.wals_id:
data = jsonload(
path(apics.__file__).dirname().joinpath(
'static', 'wals', '%sA.json' % feature.wals_id))
feature.wals_representation = sum(
[len(l['features']) for l in data['layers']])
args.log.info('computing language sources')
compute_language_sources((common.ContributionReference, 'contribution'))
compute_number_of_values()
for valueset in DBSession.query(common.ValueSet)\
.join(common.Parameter)\
.filter(common.Parameter.id == '0')\
.options(joinedload(common.ValueSet.language)):
if valueset.language.language_pk:
continue
if len(valueset.values) > 1:
valueset.language.lexifier = 'Other'
else:
if valueset.values[0].domainelement.name == 'Other':
valueset.language.lexifier = 'Other'
else:
valueset.language.lexifier \
= valueset.values[0].domainelement.name.replace('-based', '')
for lect in valueset.language.lects:
lect.lexifier = valueset.language.lexifier
args.log.info('creating icons')
for valueset in DBSession.query(common.ValueSet).options(
joinedload(common.ValueSet.parameter),
joinedload_all(common.ValueSet.values,
common.Value.domainelement)):
values = sorted(list(valueset.values),
key=lambda v: v.domainelement.number)
assert abs(sum(v.frequency for v in values) - 100) < 1
fracs = []
colors = []
for v in values:
color = v.domainelement.jsondata['color']
frequency = round(v.frequency)
assert frequency
if frequency not in frequencies:
figure(figsize=(0.4, 0.4))
axes([0.1, 0.1, 0.8, 0.8])
coll = pie((int(100 - frequency), frequency),
colors=('w', 'k'))
coll[0][0].set_linewidth(0.5)
assert icons_dir.joinpath('freq-%s.png' % frequency).exists()
frequencies[frequency] = True
v.jsondata = {'frequency_icon': 'freq-%s.png' % frequency}
fracs.append(frequency)
colors.append(color)
v.domainelement.jsondata = {
'color': color,
'icon': 'pie-100-%s.png' % color
}
assert len(colors) == len(set(colors))
fracs, colors = tuple(fracs), tuple(colors)
basename = 'pie-'
basename += '-'.join('%s-%s' % (f, c) for f, c in zip(fracs, colors))
valueset.update_jsondata(icon=basename + '.png')
if (fracs, colors) not in icons:
figure(figsize=(0.4, 0.4))
axes([0.1, 0.1, 0.8, 0.8])
coll = pie(tuple(reversed(fracs)),
colors=['#' + _color for _color in reversed(colors)])
for wedge in coll[0]:
wedge.set_linewidth(0.5)
assert icons_dir.joinpath('%s.png' % basename).exists()
icons[(fracs, colors)] = True
assert icons_dir.joinpath(basename + '.svg').exists()
for de in DBSession.query(common.DomainElement):
if not de.jsondata.get('icon'):
de.update_jsondata(icon='pie-100-%s.png' % de.jsondata['color'])
gbs_func('update', args)
