def populate(self, experiments=None):
self.items_by_pair_id = {}
self.session = db.Session()
if experiments is None:
experiments = db.list_experiments(session=self.session)
# preload all cells,pairs so they are not queried individually later on
pairs = self.session.query(db.Pair, db.Experiment, db.Cell, db.Slice).join(db.Experiment, db.Pair.experiment_id==db.Experiment.id).join(db.Cell, db.Cell.id==db.Pair.pre_cell_id).join(db.Slice).all()
experiments.sort(key=lambda e: e.acq_timestamp)
for expt in experiments:
date = expt.acq_timestamp
date_str = datetime.fromtimestamp(date).strftime('%Y-%m-%d')
slice = expt.slice
expt_item = pg.TreeWidgetItem(map(str, [date_str, '%0.3f'%expt.acq_timestamp, expt.rig_name, slice.species, expt.target_region, slice.genotype, expt.acsf]))
expt_item.expt = expt
self.addTopLevelItem(expt_item)
for pair in expt.pair_list:
if pair.n_ex_test_spikes == 0 and pair.n_in_test_spikes == 0:
continue
cells = '%d => %d' % (pair.pre_cell.ext_id, pair.post_cell.ext_id)
conn = {True:"syn", False:"-", None:"?"}[pair.synapse]
types = 'L%s %s => L%s %s' % (pair.pre_cell.target_layer or "?", pair.pre_cell.cre_type, pair.post_cell.target_layer or "?", pair.post_cell.cre_type)
pair_item = pg.TreeWidgetItem([cells, conn, types])
expt_item.addChild(pair_item)
pair_item.pair = pair
pair_item.expt = expt
self.items_by_pair_id[pair.id] = pair_item
# also allow select by ext id
self.items_by_pair_id[(expt.acq_timestamp, pair.pre_cell.ext_id, pair.post_cell.ext_id)] = pair_item
self.verticalScrollBar().setValue(self.verticalScrollBar().maximum())
def __init__(self):
s = db.Session()
self._signalHandler = SignalHandler()
self.sigOutputChanged = self._signalHandler.sigOutputChanged
self.pairs = None
self.acsf = None
projects = s.query(db.Experiment.project_name).distinct().all()
project_list = [{'name': str(record[0]), 'type': 'bool'} for record in projects]
acsf = s.query(db.Experiment.acsf).distinct().all()
acsf_list = [{'name': str(record[0]), 'type': 'bool'} for record in acsf]
internal = s.query(db.Experiment.internal).distinct().all()
internal_list = [{'name': str(record[0]), 'type': 'bool'} for record in internal]
self.params = Parameter.create(name='Data Filters', type='group', children=[
{'name': 'Projects', 'type': 'group', 'children':project_list},
{'name': 'ACSF', 'type': 'group', 'children':acsf_list, 'expanded': False},
{'name': 'Internal', 'type': 'group', 'children': internal_list, 'expanded': False},
])
self.params.sigTreeStateChanged.connect(self.invalidate_output)
def __init__(self, expt_browser=None):
if expt_browser is None:
expt_browser = ExperimentBrowser()
self.expt_browser = expt_browser
expt_browser.itemSelectionChanged.connect(self.expt_selection_changed)
self.session = db.Session()
self.pair = None
self.response_list = None
self.corrected_response_list = None
self._plot_items = []
self.params = pg.parametertree.Parameter(
name="crosstalk removal",
type="group",
children=[
dict(name='data',
type='group',
children=[
dict(name='pre mode',
type='list',
values=['ic', 'vc']),
dict(name='post mode',
type='list',
values=['ic', 'vc']),
dict(name='limit', type='int', value=100),
]),
dict(name='correction',
type='group',
children=[
dict(name='stimulus',
type='bool',
value=True,
children=[
dict(name='auto', type='action'),
dict(name='shift', type='int', value=0),
dict(name='scale',
type='float',
value=30e3,
dec=True,
step=0.5),
dict(name='lowpass',
type='float',
value=7e3,
dec=True,
step=0.5,
suffix='Hz',
siPrefix=True),
]),
dict(name='charging',
type='bool',
value=True,
children=[
dict(name='auto', type='action'),
dict(name='capacitance',
type='float',
value=10e-12,
suffix='F',
dec=True,
step=0.5,
siPrefix=True),
dict(name='resistance',
type='float',
value=10e6,
suffix=u'Ω',
dec=True,
step=0.5,
siPrefix=True),
dict(name='scale',
type='float',
value=30e3,
dec=True,
step=0.5),
dict(name='lowpass',
type='float',
value=7e3,
dec=True,
step=0.5,
suffix='Hz',
siPrefix=True),
]),
dict(name='spike',
type='bool',
value=False,
children=[
dict(name='scale',
type='float',
value=0.001,
dec=True,
step=0.5),
dict(name='lowpass',
type='float',
value=10e3,
dec=True,
step=0.5,
suffix='Hz',
siPrefix=True),
]),
dict(name='spike dv/dt',
type='bool',
value=True,
children=[
dict(name='plot only',
type='bool',
value=False),
dict(name='scale',
type='float',
value=0.03,
dec=True,
step=0.5),
dict(name='lowpass',
type='float',
value=550.,
dec=True,
step=0.5,
suffix='Hz',
siPrefix=True),
]),
]),
dict(name='display',
type='group',
children=[
dict(name='limit', type='int', value=20),
dict(name='plot spike aligned',
type='bool',
value=False),
dict(name='plot dv/dt', type='bool', value=False),
dict(name='plot lowpass',
type='float',
value=10e3,
dec=True,
step=0.5,
suffix='Hz',
siPrefix=True),
])
])
self.ptree = pg.parametertree.ParameterTree()
self.ptree.setParameters(self.params)
self.params.child('data').sigTreeStateChanged.connect(
self.data_filter_changed)
self.params.child('correction').sigTreeStateChanged.connect(
self.update_analysis)
self.params.child('display').sigTreeStateChanged.connect(
self.update_display)
self.params.child('correction', 'stimulus',
'auto').sigActivated.connect(self.auto_stim_clicked)
self.pw = pg.GraphicsLayoutWidget()
self.plt1 = self.pw.addPlot(0, 0)
self.plt2 = self.pw.addPlot(1, 0)
self.plt3 = self.pw.addPlot(2, 0)
self.plt2.setXLink(self.plt1)
self.plt3.setXLink(self.plt1)
# should result in chronological order
q = q.order_by(db.PulseResponse.id).all()
return (q)
# df = pandas.read_sql_query(q.statement, q.session.bind)
# recs = df.to_records()
# return recs
#uid=1527020350.517
uid = 1483748888.290
pre_cell_id = 3
post_cell_id = 1
# query a pair
session = db.Session()
expt = db.experiment_from_timestamp(uid, session=session)
pair = expt.pairs[(pre_cell_id, post_cell_id)]
# get pulse ids from connection strength
pulse_responses = get_single_pulse_data(session, pair)
for pr in pulse_responses:
if pr.id != 317966:
continue
print('pulse response id', pr.id)
out = afpf.fit_single_first_pulse(pr, pair)
if pr.clamp_mode == 'vc':
plt.plot(out['vc_psp_data'], 'b')
plt.plot(out['vc_psp_fit'], 'r')
plt.show()