def select_data_reduction_tag(self):
from pychron.processing.tagging.data_reduction_tags import SelectDataReductionTagModel
from pychron.processing.tagging.views import SelectDataReductionTagView
model = SelectDataReductionTagModel()
db = self.manager.db
with db.session_ctx():
items = self._get_selection()
uuids = [it.uuid for it in items] if items else None
tags = db.get_data_reduction_tags(uuids=uuids)
model.load_tags(tags)
v = SelectDataReductionTagView(model=model)
info = v.edit_traits()
if info.result:
stag = model.selected
self.debug('setting data reduction tag {}'.format(stag.name))
with db.session_ctx():
dbtag = db.get_data_reduction_tag(stag.id)
def func(ai, prog, i, n):
if prog:
prog.change_message('setting data reduction tag for {}'.format(ai.record_id))
dban = ai.analysis
dban.data_reduction_tag = dbtag
progress_iterator(dbtag.analyses, func)
def _checkout_analyses(self, ans, msg='Added'):
#make dbanalyses
ans = self.manager.make_analyses(ans, unpack=True, calculate_age=True)
#export to yaml files
exp=YAMLAnalysisExporter()
def func(ai, prog, i, n):
exp.add(ai)
p= os.path.join(self.workspace.path,'{}.yaml'.format(ai.record_id))
exp.destination.destination = p
exp.export()
# timethis(exp.export, msg='export')
exp.clear()
#update manifest
self.workspace.add_to_manifest(p)
# timethis(self.workspace.add_to_manifest, args=(p,), msg='a')
#add to repositiory
self.workspace.add_analysis(p, commit=False)
self.workspace.add_analysis_to_index(ai)
# timethis(self.workspace.add_analysis, args=(p,),
# kwargs={'commit':False},
# msg='add to git')
if prog:
prog.change_message('{} {} to workspace'.format(msg, ai.record_id))
progress_iterator(ans, func, threshold=1)
self.workspace.add_manifest_to_index()
self.workspace.commit('{} Analyses {} to {}'.format(msg, ans[0].record_id,
ans[-1].record_id))
def run(self, state):
po = self.plotter_options
keys = [fi.name for fi in list(reversed([pi for pi in po.get_plotable_aux_plots()]))]
def load_raw(x, prog, i, n):
x.load_raw_data(keys)
progress_iterator(state.unknowns, load_raw, threshold=1)
super(RegressionSeriesNode, self).run(state)
def run(self, state):
if state.saveable_irradiation_positions:
xs = [x for x in state.saveable_irradiation_positions if x.save]
if xs:
progress_iterator(xs,
lambda *args: self._save_j(state, *args),
threshold=1)
p = self.dvc.meta_repo.get_level_path(state.irradiation, state.level)
self.dvc.meta_repo.add(p)
self.dvc.meta_commit('fit flux for {}'.format(state.irradiation, state.level))
def run(self, state):
wrapper = lambda ai, prog, i, n: self._save_icfactors(ai, prog, i, n,
state.saveable_keys,
state.saveable_fits,
state.references)
progress_iterator(state.unknowns, wrapper, threshold=1)
msg = self.commit_message
if not msg:
f = ','.join('{}({})'.format(x, y) for x, y in zip(state.saveable_keys, state.saveable_fits))
msg = 'auto update ic_factors, fits={}'.format(f)
self._persist(state, msg)
def run(self, state):
# if not state.user_review:
# for ai in state.unknowns:
# self.dvc.save_blanks(ai, state.saveable_keys, state.references)
wrapper = lambda x, prog, i, n: self._save_blanks(x, prog, i, n,
state.saveable_keys, state.references)
progress_iterator(state.unknowns, wrapper, threshold=1)
msg = self.commit_message
if not msg:
f = ','.join('{}({})'.format(x, y) for x, y in zip(state.saveable_keys, state.saveable_fits))
msg = 'auto update blanks, fits={}'.format(f)
self._persist(state, msg)
def run(self, state):
if not state.saveable_keys:
return
wrapper = lambda x, prog, i, n: self._save_fit(x, prog, i, n, state.saveable_keys)
progress_iterator(state.unknowns, wrapper, threshold=1)
# for ai in state.unknowns:
# self.dvc.save_fits(ai, state.saveable_keys)
msg = self.commit_message
if not msg:
f = ','.join('{}({})'.format(x, y) for x, y in zip(state.saveable_keys, state.saveable_fits))
msg = 'fits={}'.format(f)
self._persist(state, msg)
def run(self, state):
# if not state.user_review:
# for ai in state.unknowns:
# self.dvc.save_blanks(ai, state.saveable_keys, state.references)
wrapper = lambda x, prog, i, n: self._save_blanks(
x, prog, i, n, state.saveable_keys, state.references)
progress_iterator(state.unknowns, wrapper, threshold=1)
msg = self.commit_message
if not msg:
f = ','.join(
'{}({})'.format(x, y)
for x, y in zip(state.saveable_keys, state.saveable_fits))
msg = 'auto update blanks, fits={}'.format(f)
self._persist(state, msg)
def run(self, state):
if not state.saveable_keys:
return
wrapper = lambda x, prog, i, n: self._save_fit(x, prog, i, n, state.saveable_keys)
progress_iterator(state.unknowns, wrapper, threshold=1)
# for ai in state.unknowns:
# self.dvc.save_fits(ai, state.saveable_keys)
msg = self.commit_message
if not msg:
f = ','.join('{}({})'.format(x, y) for x, y in zip(state.saveable_keys, state.saveable_fits))
msg = 'fits={}'.format(f)
self._persist(state, msg)
def _clone_repositories(self):
self.debug('clone repositories')
# check for selected repositories
if not self.selected_repositories:
return
# clone the repositories
def func(x, prog, i, n):
if prog is not None:
prog.change_message('Cloning {}'.format(x.name))
self.dvc.clone_repository(x.name)
progress_iterator(self.selected_repositories, func, threshold=0)
return True
def _clone_repositories(self):
self.debug('clone repositories')
# check for selected repositories
if not self.selected_repositories:
return
# clone the repositories
def func(x, prog, i, n):
if prog is not None:
prog.change_message('Cloning {}'.format(x.name))
self.dvc.clone_repository(x.name)
progress_iterator(self.selected_repositories, func, threshold=0)
return True
def generate_results(self):
self.debug('generate results')
dvc = self.dvc
db = dvc.db
positions = sorted([pp for p in self.positions for pp in p.positions])
wb = Workbook()
sh = wb.add_sheet('Results')
for i, attr in enumerate(
('Analysis', 'Position', 'Age', 'Error', 'Weight', 'Note')):
wb.sheet(0, i, attr)
wb.nrows = 1
def func(x, prog, i, n):
dbmps = db.get_measured_positions(self.load_name, x)
dbpos = db.get_load_position(self.load_name, x)
weight, note = dbpos.weight, dbpos.note
for dbmp in dbmps:
rid = dbmp.analysis.record_id
# rid = 1
if prog:
prog.change_message('Write results for {},{}'.format(
rid, x))
# ai = dvc.make_analyses((rid,))
age, error = 0, 0
sh.write(wb.nrows, 0, rid)
sh.write(wb.nrows, 1, x)
sh.write(wb.nrows, 2, age)
sh.write(wb.nrows, 3, error)
sh.write(wb.nrows, 4, weight)
sh.write(wb.nrows, 5, note)
wb.nrows += 1
with db.session_ctx():
progress_iterator(positions, func, threshold=1)
path, _ = unique_path(paths.load_results_dir,
self.load_name,
extension='.xls')
wb.save(path)
def run(self, state):
if state.saveable_irradiation_positions:
xs = [x for x in state.saveable_irradiation_positions if x.save]
if xs:
self.dvc.meta_repo.smart_pull()
progress_iterator(xs,
lambda *args: self._save_j(state, *args),
threshold=1)
p = self.dvc.meta_repo.get_level_path(state.irradiation, state.level)
self.dvc.meta_repo.add(p)
self.dvc.meta_commit('fit flux for {}'.format(state.irradiation, state.level))
if confirmation_dialog('Would you like to share your changes?'):
self.dvc.meta_repo.smart_pull()
self.dvc.meta_repo.push()
def generate_results(self):
self.debug('generate results')
dvc = self.dvc
db = dvc.db
positions = sorted([pp for p in self.positions
for pp in p.positions])
wb = Workbook()
sh = wb.add_sheet('Results')
for i, attr in enumerate(('Analysis', 'Position', 'Age',
'Error', 'Weight', 'Note')):
wb.sheet(0, i, attr)
wb.nrows = 1
def func(x, prog, i, n):
dbmps = db.get_measured_positions(self.load_name, x)
dbpos = db.get_load_position(self.load_name, x)
weight, note = dbpos.weight, dbpos.note
for dbmp in dbmps:
rid = dbmp.analysis.record_id
# rid = 1
if prog:
prog.change_message('Write results for {},{}'.format(rid, x))
# ai = dvc.make_analyses((rid,))
age, error = 0, 0
sh.write(wb.nrows, 0, rid)
sh.write(wb.nrows, 1, x)
sh.write(wb.nrows, 2, age)
sh.write(wb.nrows, 3, error)
sh.write(wb.nrows, 4, weight)
sh.write(wb.nrows, 5, note)
wb.nrows += 1
with db.session_ctx():
progress_iterator(positions, func, threshold=1)
path, _ = unique_path(paths.load_results_dir, self.load_name, extension='.xls')
wb.save(path)
def run(self, state):
wrapper = lambda ai, prog, i, n: self._save_icfactors(ai, prog, i, n,
state.saveable_keys,
state.saveable_fits,
state.references)
progress_iterator(state.unknowns, wrapper, threshold=1)
# for ai in state.unknowns:
# self.dvc.save_icfactors(ai, state.saveable_keys,
# state.saveable_fits,
# state.references)
msg = self.commit_message
if not msg:
f = ','.join('{}({})'.format(x, y) for x, y in zip(state.saveable_keys, state.saveable_fits))
msg = 'auto update ic_factors, fits={}'.format(f)
self._persist(state, msg)
def set_data_reduction_tag(self):
items = self._get_analyses_to_tag()
if items:
model = self._get_dr_tagname(items)
if model is not None:
db = self.manager.db
with db.session_ctx():
dbtag = db.add_data_reduction_tag(model.tagname, model.comment)
self.debug('added data reduction tag: {}'.format(model.tagname))
def func(ai, prog, i, n):
dban = db.get_analysis_uuid(ai.uuid)
db.add_data_reduction_tag_set(dbtag, dban, dban.selected_histories.id)
dban.data_reduction_tag = dbtag
if prog:
prog.change_message(
'Applying data reduction tag "{}" to {}'.format(model.tagname, ai.record_id))
progress_iterator(model.items, func)
def run(self, state):
if state.saveable_irradiation_positions:
xs = [x for x in state.saveable_irradiation_positions if x.save]
if xs:
self.dvc.meta_repo.smart_pull()
progress_iterator(xs,
lambda *args: self._save_j(state, *args),
threshold=1)
p = self.dvc.meta_repo.get_level_path(state.irradiation,
state.level)
self.dvc.meta_repo.add(p, commit=False)
self.dvc.meta_commit('fit flux for {}{}'.format(
state.irradiation, state.level))
if confirmation_dialog(
'Would you like to share your changes?'):
self.dvc.meta_repo.smart_pull()
self.dvc.meta_repo.push()
def set_data_reduction_tag(self):
items = self._get_analyses_to_tag()
if items:
model = self._get_dr_tagname(items)
if model is not None:
db = self.manager.db
with db.session_ctx():
dbtag = db.add_data_reduction_tag(model.tagname,
model.comment)
self.debug('added data reduction tag: {}'.format(
model.tagname))
def func(ai, prog, i, n):
dban = db.get_analysis_uuid(ai.uuid)
db.add_data_reduction_tag_set(
dbtag, dban, dban.selected_histories.id)
dban.data_reduction_tag = dbtag
if prog:
prog.change_message(
'Applying data reduction tag "{}" to {}'.
format(model.tagname, ai.record_id))
progress_iterator(model.items, func)
