def populatemetadata():
#%% save metadata from google drive if necessairy
lastmodify = online_notebook.fetch_lastmodify_time_animal_metadata()
with open(dj.config['locations.metadata_surgery_experiment'] +
'last_modify_time.json') as timedata:
lastmodify_prev = json.loads(timedata.read())
if lastmodify != lastmodify_prev:
print('updating surgery and WR metadata from google drive')
dj.config['locations.metadata_surgery_experiment']
df_surgery = online_notebook.fetch_animal_metadata()
df_surgery.to_csv(dj.config['locations.metadata_surgery_experiment'] +
'Surgery.csv')
IDs = df_surgery['ID'].tolist()
for ID in IDs:
df_wr = online_notebook.fetch_water_restriction_metadata(ID)
if type(df_wr) == pd.DataFrame:
df_wr.to_csv(
dj.config['locations.metadata_surgery_experiment'] + ID +
'.csv')
with open(
dj.config['locations.metadata_surgery_experiment'] +
'last_modify_time.json', "w") as write_file:
json.dump(lastmodify, write_file)
print('surgery and WR metadata updated')
lastmodify = online_notebook.fetch_lastmodify_time_lab_metadata()
with open(dj.config['locations.metadata_lab'] +
'last_modify_time.json') as timedata:
lastmodify_prev = json.loads(timedata.read())
if lastmodify != lastmodify_prev:
print('updating Lab metadata from google drive')
dj.config['locations.metadata_lab']
IDs = ['Experimenter', 'Rig', 'Virus']
for ID in IDs:
df_wr = online_notebook.fetch_lab_metadata(ID)
if type(df_wr) == pd.DataFrame:
df_wr.to_csv(dj.config['locations.metadata_lab'] + ID + '.csv')
with open(
dj.config['locations.metadata_lab'] + 'last_modify_time.json',
"w") as write_file:
json.dump(lastmodify, write_file)
print('Lab metadata updated')
#%% add users
df_experimenters = pd.read_csv(dj.config['locations.metadata_lab'] +
'Experimenter.csv')
experimenterdata = list()
for experimenter in df_experimenters.iterrows():
experimenter = experimenter[1]
dictnow = {
'username': experimenter['username'],
'fullname': experimenter['fullname']
}
experimenterdata.append(dictnow)
print('adding experimenters')
for experimenternow in experimenterdata:
try:
lab.Person().insert1(experimenternow)
except dj.errors.DuplicateError:
print('duplicate. experimenter: ', experimenternow['username'],
' already exists')
#%% add rigs
df_rigs = pd.read_csv(dj.config['locations.metadata_lab'] + 'Rig.csv')
rigdata = list()
for rig in df_rigs.iterrows():
rig = rig[1]
dictnow = {
'rig': rig['rig'],
'room': rig['room'],
'rig_description': rig['rig_description']
}
rigdata.append(dictnow)
print('adding rigs')
for rignow in rigdata:
try:
lab.Rig().insert1(rignow)
except dj.errors.DuplicateError:
print('duplicate. rig: ', rignow['rig'], ' already exists')
#%% add viruses
df_viruses = pd.read_csv(dj.config['locations.metadata_lab'] + 'Virus.csv')
virusdata = list()
serotypedata = list()
for virus in df_viruses.iterrows():
virus = virus[1]
if type(virus['remarks']) != str:
virus['remarks'] = ''
dictnow = {
'virus_id': virus['virus_id'],
'virus_source': virus['virus_source'],
'serotype': virus['serotype'],
'username': virus['username'],
'virus_name': virus['virus_name'],
'titer': virus['titer'],
'order_date': virus['order_date'],
'remarks': virus['remarks']
}
virusdata.append(dictnow)
dictnow = {'serotype': virus['serotype']}
serotypedata.append(dictnow)
print('adding rigs')
for virusnow, serotypenow in zip(virusdata, serotypedata):
try:
lab.Serotype().insert1(serotypenow)
except dj.errors.DuplicateError:
print('duplicate serotype: ', serotypenow['serotype'],
' already exists')
try:
lab.Virus().insert1(virusnow)
except dj.errors.DuplicateError:
print('duplicate virus: ', virusnow['virus_name'],
' already exists')
#%% populate subjects, surgeries and water restrictions
print('adding surgeries and stuff')
df_surgery = pd.read_csv(
dj.config['locations.metadata_surgery_experiment'] + 'Surgery.csv')
#%%
for item in df_surgery.iterrows():
if item[1]['project'] == dj.config['project'] and (
item[1]['status'] == 'training'
or item[1]['status'] == 'sacrificed'):
subjectdata = {
'subject_id': item[1]['animal#'],
'cage_number': item[1]['cage#'],
'date_of_birth': item[1]['DOB'],
'sex': item[1]['sex'],
'username': item[1]['experimenter'],
}
try:
lab.Subject().insert1(subjectdata)
except dj.errors.DuplicateError:
print('duplicate. animal :', item[1]['animal#'],
' already exists')
surgeryidx = 1
while 'surgery date (' + str(surgeryidx) + ')' in item[1].keys(
) and item[1]['surgery date (' + str(surgeryidx) + ')'] and type(
item[1]['surgery date (' + str(surgeryidx) + ')']) == str:
start_time = datetime.strptime(
item[1]['surgery date (' + str(surgeryidx) + ')'] + ' ' +
item[1]['surgery time (' + str(surgeryidx) + ')'],
'%Y-%m-%d %H:%M')
end_time = start_time + timedelta(
minutes=int(item[1]['surgery length (min) (' +
str(surgeryidx) + ')']))
surgerydata = {
'surgery_id':
surgeryidx,
'subject_id':
item[1]['animal#'],
'username':
item[1]['experimenter'],
'start_time':
start_time,
'end_time':
end_time,
'surgery_description':
item[1]['surgery type (' + str(surgeryidx) + ')'] +
':-: comments: ' +
str(item[1]['surgery comments (' + str(surgeryidx) + ')']),
}
try:
lab.Surgery().insert1(surgerydata)
except dj.errors.DuplicateError:
print('duplicate. surgery for animal ', item[1]['animal#'],
' already exists: ', start_time)
#checking craniotomies
#%
cranioidx = 1
while 'craniotomy diameter (' + str(
cranioidx) + ')' in item[1].keys() and item[1][
'craniotomy diameter (' + str(cranioidx) +
')'] and (
type(item[1]['craniotomy surgery id (' +
str(cranioidx) + ')']) == int or
type(item[1]['craniotomy surgery id (' +
str(cranioidx) + ')']) == float):
if item[1]['craniotomy surgery id (' + str(cranioidx) +
')'] == surgeryidx:
proceduredata = {
'surgery_id':
surgeryidx,
'subject_id':
item[1]['animal#'],
'procedure_id':
cranioidx,
'skull_reference':
item[1]['craniotomy reference (' + str(cranioidx) +
')'],
'ml_location':
item[1]['craniotomy lateral (' + str(cranioidx) +
')'],
'ap_location':
item[1]['craniotomy anterior (' + str(cranioidx) +
')'],
'surgery_procedure_description':
'craniotomy: ' + item[1]['craniotomy comments (' +
str(cranioidx) + ')'],
}
try:
lab.Surgery.Procedure().insert1(proceduredata)
except dj.errors.DuplicateError:
print('duplicate cranio for animal ',
item[1]['animal#'], ' already exists: ',
cranioidx)
cranioidx += 1
#%
virusinjidx = 1
while 'virus inj surgery id (' + str(
virusinjidx) + ')' in item[1].keys() and item[1][
'virus inj virus id (' + str(virusinjidx) +
')'] and item[1]['virus inj surgery id (' +
str(virusinjidx) + ')']:
if item[1]['virus inj surgery id (' + str(virusinjidx) +
')'] == surgeryidx:
# =============================================================================
# print('waiting')
# timer.sleep(1000)
# =============================================================================
if '[' in item[1]['virus inj lateral (' +
str(virusinjidx) + ')']:
virus_ml_locations = eval(
item[1]['virus inj lateral (' +
str(virusinjidx) + ')'])
virus_ap_locations = eval(
item[1]['virus inj anterior (' +
str(virusinjidx) + ')'])
virus_dv_locations = eval(
item[1]['virus inj ventral (' +
str(virusinjidx) + ')'])
virus_volumes = eval(
item[1]['virus inj volume (nl) (' +
str(virusinjidx) + ')'])
else:
virus_ml_locations = [
int(item[1]['virus inj lateral (' +
str(virusinjidx) + ')'])
]
virus_ap_locations = [
int(item[1]['virus inj anterior (' +
str(virusinjidx) + ')'])
]
virus_dv_locations = [
int(item[1]['virus inj ventral (' +
str(virusinjidx) + ')'])
]
virus_volumes = [
int(item[1]['virus inj volume (nl) (' +
str(virusinjidx) + ')'])
]
for virus_ml_location, virus_ap_location, virus_dv_location, virus_volume in zip(
virus_ml_locations, virus_ap_locations,
virus_dv_locations, virus_volumes):
injidx = len(lab.Surgery.VirusInjection()
& surgerydata) + 1
virusinjdata = {
'surgery_id':
surgeryidx,
'subject_id':
item[1]['animal#'],
'injection_id':
injidx,
'virus_id':
item[1]['virus inj virus id (' +
str(virusinjidx) + ')'],
'skull_reference':
item[1]['virus inj reference (' +
str(virusinjidx) + ')'],
'ml_location':
virus_ml_location,
'ap_location':
virus_ap_location,
'dv_location':
virus_dv_location,
'volume':
virus_volume,
'dilution':
item[1]['virus inj dilution (' +
str(virusinjidx) + ')'],
'description':
'virus injection: ' +
item[1]['virus inj comments (' +
str(virusinjidx) + ')'],
}
try:
lab.Surgery.VirusInjection().insert1(
virusinjdata)
except dj.errors.DuplicateError:
print('duplicate virus injection for animal ',
item[1]['animal#'], ' already exists: ',
injidx)
virusinjidx += 1
#%
surgeryidx += 1
#%
if item[1]['ID']:
#df_wr = online_notebook.fetch_water_restriction_metadata(item[1]['ID'])
try:
df_wr = pd.read_csv(
dj.config['locations.metadata_surgery_experiment'] +
item[1]['ID'] + '.csv')
except:
df_wr = None
if type(df_wr) == pd.DataFrame:
wrdata = {
'subject_id': item[1]['animal#'],
'water_restriction_number': item[1]['ID'],
'cage_number': item[1]['cage#'],
'wr_start_date': df_wr['Date'][0],
'wr_start_weight': df_wr['Weight'][0],
}
try:
lab.WaterRestriction().insert1(wrdata)
except dj.errors.DuplicateError:
print('duplicate. water restriction :',
item[1]['animal#'], ' already exists')
def mockdata():
print('populating with mock data')
reload(ccf)
reload(lab)
reload(experiment)
reload(ephys)
reload(publication)
try:
# TODO: these should be loaded in a more 'official' way
lab.Person().insert1({
'username': '******',
'fullname': 'Dave Liu'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification': 'VGAT-Chr2-EYFP Jax',
'gene_modification_description': 'VGAT'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification': 'PV-ires-Cre X Ai32',
'gene_modification_description': 'PV'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification':
'Rosa26 Cag lsl reachR-citrine 1A4 X PV-ires-Cre',
'gene_modification_description': 'reachR PV'
},
skip_duplicates=True)
# Subject 399752 / dl7
lab.Subject().insert1(
{
'subject_id': 399752,
'username': '******',
'cage_number': 145375,
'date_of_birth': '2017-08-03',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 399752,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 399752,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-03',
'end_time': '2017-11-03',
'surgery_description': 'Headbar anterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 399752,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -4,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 399752,
'water_restriction_number': 'dl7',
'cage_number': 148861,
'wr_start_date': '2017-11-07',
'wr_start_weight': 25
},
skip_duplicates=True)
# Subject 397853 / dl14
lab.Subject().insert1(
{
'subject_id': 397853,
'username': '******',
'cage_number': 144545,
'date_of_birth': '2017-07-15',
'sex': 'M',
'animal_source': 'Allen Institute'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 397853,
'gene_modification': 'PV-ires-Cre X Ai32'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 397853,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-20',
'end_time': '2017-11-20',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 397853,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 397853,
'water_restriction_number': 'dl14',
'cage_number': 149595,
'wr_start_date': '2017-11-27',
'wr_start_weight': 24.1
},
skip_duplicates=True)
# Subject 400480 / dl15
lab.Subject().insert1(
{
'subject_id': 400480,
'username': '******',
'cage_number': 145700,
'date_of_birth': '2017-08-09',
'sex': 'M',
'animal_source': 'Allen Institute'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 400480,
'gene_modification': 'PV-ires-Cre X Ai32'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 400480,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-21',
'end_time': '2017-11-21',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 400480,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 400480,
'water_restriction_number': 'dl15',
'cage_number': 149598,
'wr_start_date': '2017-11-27',
'wr_start_weight': 27.6
},
skip_duplicates=True)
# Subject 406680 / dl20
lab.Subject().insert1(
{
'subject_id': 406680,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-06',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 406680,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 406680,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-04',
'end_time': '2018-01-04',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 406680,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 406680,
'water_restriction_number': 'dl20',
'cage_number': 151282,
'wr_start_date': '2018-01-10',
'wr_start_weight': 22.7
},
skip_duplicates=True)
# Subject 408022 / dl21
lab.Subject().insert1(
{
'subject_id': 408022,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-19',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 408022,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 408022,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-05',
'end_time': '2018-01-05',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 408022,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 408022,
'water_restriction_number': 'dl21',
'cage_number': 151283,
'wr_start_date': '2018-01-10',
'wr_start_weight': 21.1
},
skip_duplicates=True)
# Subject 408021 / dl22
lab.Subject().insert1(
{
'subject_id': 408021,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-19',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 408021,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 408021,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-15',
'end_time': '2018-01-15',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 408021,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 408021,
'water_restriction_number': 'dl22',
'cage_number': 151704,
'wr_start_date': '2018-01-19',
'wr_start_weight': 21
},
skip_duplicates=True)
# Subject 407512 / dl24
lab.Subject().insert1(
{
'subject_id': 407512,
'username': '******',
'cage_number': 151629,
'date_of_birth': '2017-10-13',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407512,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407512,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-16',
'end_time': '2018-01-16',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407512,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407512,
'water_restriction_number': 'dl24',
'cage_number': 151793,
'wr_start_date': '2018-01-22',
'wr_start_weight': 26
},
skip_duplicates=True)
# 407513 / dl25
lab.Subject().insert1(
{
'subject_id': 407513,
'username': '******',
'cage_number': 148636,
'date_of_birth': '2017-10-13',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407513,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407513,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-17',
'end_time': '2018-01-17',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407513,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407513,
'water_restriction_number': 'dl25',
'cage_number': 151794,
'wr_start_date': '2018-01-22',
'wr_start_weight': 25.5
},
skip_duplicates=True)
# Subject 407986 / dl28
lab.Subject().insert1(
{
'subject_id': 407986,
'username': '******',
'cage_number': 152268,
'date_of_birth': '2017-10-18',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407986,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407986,
'surgery_id': 1,
'username': '******',
'start_time': '2018-02-01',
'end_time': '2018-02-01',
'surgery_description': 'Headbar anterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407986,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -4,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407986,
'water_restriction_number': 'dl28',
'cage_number': 152312,
'wr_start_date': '2018-02-05',
'wr_start_weight': 19.8
},
skip_duplicates=True)
# Subject 123457 / tw5
lab.Subject().insert1(
{
'subject_id': 123457,
'username': '******',
'cage_number': 145375,
'date_of_birth': '2017-08-03',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 123457,
'water_restriction_number': 'tw5',
'cage_number': 148861,
'wr_start_date': '2017-11-07',
'wr_start_weight': 20.5
},
skip_duplicates=True)
# Subject 412330 / tw34
lab.Subject().insert1(
{
'subject_id': 412330,
'username': '******',
'cage_number': 154522,
'date_of_birth': '2017-12-05',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 412330,
'water_restriction_number': 'tw34',
'cage_number': 154522,
'wr_start_date': '2018-03-18',
'wr_start_weight': 21.0
},
skip_duplicates=True)
# subject 432998 / dl55
lab.Subject().insert1(
{
'subject_id': 432998,
'username': '******',
'cage_number': 160920,
'date_of_birth': '2018-07-02',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 432998,
'water_restriction_number': 'dl55',
'cage_number': 160920,
'wr_start_date': '2018-09-05',
'wr_start_weight': 21.0
},
skip_duplicates=True)
# Subject 435884 / dl59
lab.Subject().insert1(
{
'subject_id': 435884,
'username': '******',
'cage_number': 161908,
'date_of_birth': '2018-08-06',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 435884,
'water_restriction_number': 'dl59',
'cage_number': 154522,
'wr_start_date': '2018-09-30',
'wr_start_weight': 21.0
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig1',
'room': '2w.334',
'rig_description': 'Training rig 1'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig2',
'room': '2w.334',
'rig_description': 'Training rig 2'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig3',
'room': '2w.334',
'rig_description': 'Training rig 3'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'RRig',
'room': '2w.334',
'rig_description': 'Recording rig'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'Ephys1',
'room': '2w.334',
'rig_description': 'Recording computer'
},
skip_duplicates=True)
# ---- Brain Location ----
experiment.BrainLocation.insert1(
{
'brain_location_name': 'left_alm',
'brain_area': 'ALM',
'hemisphere': 'left',
'skull_reference': 'Bregma'
},
skip_duplicates=True)
experiment.BrainLocation.insert1(
{
'brain_location_name': 'right_alm',
'brain_area': 'ALM',
'hemisphere': 'right',
'skull_reference': 'Bregma'
},
skip_duplicates=True)
experiment.BrainLocation.insert1(
{
'brain_location_name': 'both_alm',
'brain_area': 'ALM',
'hemisphere': 'both',
'skull_reference': 'Bregma'
},
skip_duplicates=True)
experiment.BrainLocation.insert1(
{
'brain_location_name': 'left_medulla',
'brain_area': 'Medulla',
'hemisphere': 'left',
'skull_reference': 'Bregma'
},
skip_duplicates=True)
experiment.BrainLocation.insert1(
{
'brain_location_name': 'right_medulla',
'brain_area': 'Medulla',
'hemisphere': 'right',
'skull_reference': 'Bregma'
},
skip_duplicates=True)
experiment.BrainLocation.insert1(
{
'brain_location_name': 'both_medulla',
'brain_area': 'Medulla',
'hemisphere': 'both',
'skull_reference': 'Bregma'
},
skip_duplicates=True)
# Probe (Neuropixel)
npx_probe_model = '15131808323' # using Model No. - SN TBD?
lab.Probe.insert1(
{
'probe': npx_probe_model,
'probe_type': 'neuropixel'
},
skip_duplicates=True,
)
lab.Probe.Electrode.insert(
({
'probe': npx_probe_model,
'electrode': x
} for x in range(1, 961)),
skip_duplicates=True,
)
except Exception as e:
print("error creating mock data: {e}".format(e=e), file=sys.stderr)
raise
def mockdata():
print('populating with mock data')
reload(ccf)
reload(lab)
reload(experiment)
reload(ephys)
reload(publication)
try:
# TODO: these should be loaded in a more 'official' way
lab.Person().insert1({
'username': '******',
'fullname': 'Dave Liu'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification': 'VGAT-Chr2-EYFP Jax',
'gene_modification_description': 'VGAT'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification': 'PV-ires-Cre X Ai32',
'gene_modification_description': 'PV'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification':
'Rosa26 Cag lsl reachR-citrine 1A4 X PV-ires-Cre',
'gene_modification_description': 'reachR PV'
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 399752,
'username': '******',
'cage_number': 145375,
'date_of_birth': '2017-08-03',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 399752,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 399752,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-03',
'end_time': '2017-11-03',
'surgery_description': 'Headbar anterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 399752,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -4,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 399752,
'water_restriction_number': 'dl7',
'cage_number': 148861,
'wr_start_date': '2017-11-07',
'wr_start_weight': 25
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 397853,
'username': '******',
'cage_number': 144545,
'date_of_birth': '2017-07-15',
'sex': 'M',
'animal_source': 'Allen Institute'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 397853,
'gene_modification': 'PV-ires-Cre X Ai32'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 397853,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-20',
'end_time': '2017-11-20',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 397853,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 397853,
'water_restriction_number': 'dl14',
'cage_number': 149595,
'wr_start_date': '2017-11-27',
'wr_start_weight': 24.1
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 400480,
'username': '******',
'cage_number': 145700,
'date_of_birth': '2017-08-09',
'sex': 'M',
'animal_source': 'Allen Institute'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 400480,
'gene_modification': 'PV-ires-Cre X Ai32'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 400480,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-21',
'end_time': '2017-11-21',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 400480,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 400480,
'water_restriction_number': 'dl15',
'cage_number': 149598,
'wr_start_date': '2017-11-27',
'wr_start_weight': 27.6
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 406680,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-06',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 406680,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 406680,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-04',
'end_time': '2018-01-04',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 406680,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 406680,
'water_restriction_number': 'dl20',
'cage_number': 151282,
'wr_start_date': '2018-01-10',
'wr_start_weight': 22.7
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 408022,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-19',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 408022,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 408022,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-05',
'end_time': '2018-01-05',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 408022,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 408022,
'water_restriction_number': 'dl21',
'cage_number': 151283,
'wr_start_date': '2018-01-10',
'wr_start_weight': 21.1
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 408021,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-19',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 408021,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 408021,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-15',
'end_time': '2018-01-15',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 408021,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 408021,
'water_restriction_number': 'dl22',
'cage_number': 151704,
'wr_start_date': '2018-01-19',
'wr_start_weight': 21
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 407512,
'username': '******',
'cage_number': 151629,
'date_of_birth': '2017-10-13',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407512,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407512,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-16',
'end_time': '2018-01-16',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407512,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407512,
'water_restriction_number': 'dl24',
'cage_number': 151793,
'wr_start_date': '2018-01-22',
'wr_start_weight': 26
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 407513,
'username': '******',
'cage_number': 148636,
'date_of_birth': '2017-10-13',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407513,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407513,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-17',
'end_time': '2018-01-17',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407513,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407513,
'water_restriction_number': 'dl25',
'cage_number': 151794,
'wr_start_date': '2018-01-22',
'wr_start_weight': 25.5
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 407986,
'username': '******',
'cage_number': 152268,
'date_of_birth': '2017-10-18',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407986,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407986,
'surgery_id': 1,
'username': '******',
'start_time': '2018-02-01',
'end_time': '2018-02-01',
'surgery_description': 'Headbar anterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407986,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -4,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407986,
'water_restriction_number': 'dl28',
'cage_number': 152312,
'wr_start_date': '2018-02-05',
'wr_start_weight': 19.8
},
skip_duplicates=True)
lab.Subject().insert1(
{
'subject_id': 123457,
'username': '******',
'cage_number': 145375,
'date_of_birth': '2017-08-03',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 123457,
'water_restriction_number': 'tw5',
'cage_number': 148861,
'wr_start_date': '2017-11-07',
'wr_start_weight': 20.5
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig1',
'room': '2w.334',
'rig_description': 'Training rig 1'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig2',
'room': '2w.334',
'rig_description': 'Training rig 2'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig3',
'room': '2w.334',
'rig_description': 'Training rig 3'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'RRig',
'room': '2w.334',
'rig_description': 'Recording rig'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'Ephys1',
'room': '2w.334',
'rig_description': 'Recording computer'
},
skip_duplicates=True)
except Exception as e:
print("error creating mock data: {e}".format(e=e), file=sys.stderr)
raise
def mockdata():
print('populating with mock data')
reload(lab)
reload(ccf)
reload(experiment)
reload(ephys)
reload(histology)
reload(tracking)
try:
lab.Person().insert1({
'username': '******',
'fullname': 'Unknown'
},
skip_duplicates=True)
lab.Person().insert1({
'username': '******',
'fullname': 'Dave Liu'
},
skip_duplicates=True)
lab.Person().insert1({
'username': '******',
'fullname': 'Susu Chen'
},
skip_duplicates=True)
lab.Person().insert1({
'username': '******',
'fullname': 'Thinh Nguyen'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification': 'VGAT-Chr2-EYFP Jax',
'gene_modification_description': 'VGAT'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification': 'PV-ires-Cre X Ai32',
'gene_modification_description': 'PV'
},
skip_duplicates=True)
lab.ModifiedGene().insert1(
{
'gene_modification':
'Rosa26 Cag lsl reachR-citrine 1A4 X PV-ires-Cre',
'gene_modification_description': 'reachR PV'
},
skip_duplicates=True)
# Subject 399752 / dl7
lab.Subject().insert1(
{
'subject_id': 399752,
'username': '******',
'cage_number': 145375,
'date_of_birth': '2017-08-03',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 399752,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 399752,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-03',
'end_time': '2017-11-03',
'surgery_description': 'Headbar anterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 399752,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -4,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 399752,
'water_restriction_number': 'dl7',
'cage_number': 148861,
'wr_start_date': '2017-11-07',
'wr_start_weight': 25
},
skip_duplicates=True)
# Subject 397853 / dl14
lab.Subject().insert1(
{
'subject_id': 397853,
'username': '******',
'cage_number': 144545,
'date_of_birth': '2017-07-15',
'sex': 'M',
'animal_source': 'Allen Institute'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 397853,
'gene_modification': 'PV-ires-Cre X Ai32'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 397853,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-20',
'end_time': '2017-11-20',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 397853,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 397853,
'water_restriction_number': 'dl14',
'cage_number': 149595,
'wr_start_date': '2017-11-27',
'wr_start_weight': 24.1
},
skip_duplicates=True)
# Subject 400480 / dl15
lab.Subject().insert1(
{
'subject_id': 400480,
'username': '******',
'cage_number': 145700,
'date_of_birth': '2017-08-09',
'sex': 'M',
'animal_source': 'Allen Institute'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 400480,
'gene_modification': 'PV-ires-Cre X Ai32'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 400480,
'surgery_id': 1,
'username': '******',
'start_time': '2017-11-21',
'end_time': '2017-11-21',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 400480,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 400480,
'water_restriction_number': 'dl15',
'cage_number': 149598,
'wr_start_date': '2017-11-27',
'wr_start_weight': 27.6
},
skip_duplicates=True)
# Subject 406680 / dl20
lab.Subject().insert1(
{
'subject_id': 406680,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-06',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 406680,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 406680,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-04',
'end_time': '2018-01-04',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 406680,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 406680,
'water_restriction_number': 'dl20',
'cage_number': 151282,
'wr_start_date': '2018-01-10',
'wr_start_weight': 22.7
},
skip_duplicates=True)
# Subject 408022 / dl21
lab.Subject().insert1(
{
'subject_id': 408022,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-19',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 408022,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 408022,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-05',
'end_time': '2018-01-05',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 408022,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 408022,
'water_restriction_number': 'dl21',
'cage_number': 151283,
'wr_start_date': '2018-01-10',
'wr_start_weight': 21.1
},
skip_duplicates=True)
# Subject 408021 / dl22
lab.Subject().insert1(
{
'subject_id': 408021,
'username': '******',
'cage_number': 148859,
'date_of_birth': '2017-10-19',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 408021,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 408021,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-15',
'end_time': '2018-01-15',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 408021,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 408021,
'water_restriction_number': 'dl22',
'cage_number': 151704,
'wr_start_date': '2018-01-19',
'wr_start_weight': 21
},
skip_duplicates=True)
# Subject 407512 / dl24
lab.Subject().insert1(
{
'subject_id': 407512,
'username': '******',
'cage_number': 151629,
'date_of_birth': '2017-10-13',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407512,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407512,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-16',
'end_time': '2018-01-16',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407512,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407512,
'water_restriction_number': 'dl24',
'cage_number': 151793,
'wr_start_date': '2018-01-22',
'wr_start_weight': 26
},
skip_duplicates=True)
# 407513 / dl25
lab.Subject().insert1(
{
'subject_id': 407513,
'username': '******',
'cage_number': 148636,
'date_of_birth': '2017-10-13',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407513,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407513,
'surgery_id': 1,
'username': '******',
'start_time': '2018-01-17',
'end_time': '2018-01-17',
'surgery_description': 'Headbar posterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407513,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -1.75,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407513,
'water_restriction_number': 'dl25',
'cage_number': 151794,
'wr_start_date': '2018-01-22',
'wr_start_weight': 25.5
},
skip_duplicates=True)
# Subject 407986 / dl28
lab.Subject().insert1(
{
'subject_id': 407986,
'username': '******',
'cage_number': 152268,
'date_of_birth': '2017-10-18',
'sex': 'F',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.Subject.GeneModification().insert1(
{
'subject_id': 407986,
'gene_modification': 'VGAT-Chr2-EYFP Jax'
},
skip_duplicates=True)
lab.Surgery().insert1(
{
'subject_id': 407986,
'surgery_id': 1,
'username': '******',
'start_time': '2018-02-01',
'end_time': '2018-02-01',
'surgery_description': 'Headbar anterior'
},
skip_duplicates=True)
lab.Surgery.Procedure().insert1(
{
'subject_id': 407986,
'surgery_id': 1,
'procedure_id': 1,
'skull_reference': 'Bregma',
'ml_location': 0,
'ap_location': -4,
'surgery_procedure_description': 'Fiducial marker'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 407986,
'water_restriction_number': 'dl28',
'cage_number': 152312,
'wr_start_date': '2018-02-05',
'wr_start_weight': 19.8
},
skip_duplicates=True)
# Subject 123457 / tw5
lab.Subject().insert1(
{
'subject_id': 123457,
'username': '******',
'cage_number': 145375,
'date_of_birth': '2017-08-03',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 123457,
'water_restriction_number': 'tw5',
'cage_number': 148861,
'wr_start_date': '2017-11-07',
'wr_start_weight': 20.5
},
skip_duplicates=True)
# Subject 412330 / tw34
lab.Subject().insert1(
{
'subject_id': 412330,
'username': '******',
'cage_number': 154522,
'date_of_birth': '2017-12-05',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 412330,
'water_restriction_number': 'tw34',
'cage_number': 154522,
'wr_start_date': '2018-03-18',
'wr_start_weight': 21.0
},
skip_duplicates=True)
# subject 432998 / dl55
lab.Subject().insert1(
{
'subject_id': 432998,
'username': '******',
'cage_number': 160920,
'date_of_birth': '2018-07-02',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 432998,
'water_restriction_number': 'dl55',
'cage_number': 160920,
'wr_start_date': '2018-09-05',
'wr_start_weight': 21.0
},
skip_duplicates=True)
# Subject 435884 / dl59
lab.Subject().insert1(
{
'subject_id': 435884,
'username': '******',
'cage_number': 161908,
'date_of_birth': '2018-08-06',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 435884,
'water_restriction_number': 'dl59',
'cage_number': 154522,
'wr_start_date': '2018-09-30',
'wr_start_weight': 21.0
},
skip_duplicates=True)
# Subject 432572 / dl56
lab.Subject().insert1(
{
'subject_id': 432572,
'username': '******',
'cage_number': 161125,
'date_of_birth': '2018-06-28',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 432572,
'water_restriction_number': 'dl56',
'cage_number': 161125,
'wr_start_date': '2018-09-10',
'wr_start_weight': 21.0
},
skip_duplicates=True)
# Subject 412753 / dl36
lab.Subject().insert1(
{
'subject_id': 412753,
'username': '******',
'cage_number': 154570,
'date_of_birth': '2017-12-07',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 412753,
'water_restriction_number': 'dl36',
'cage_number': 154570,
'wr_start_date': '2017-03-30',
'wr_start_weight': 21.0
},
skip_duplicates=True)
# Subject 440010 / dl62
lab.Subject().insert1(
{
'subject_id': 440010,
'username': '******',
'cage_number': 163782,
'date_of_birth': '2018-09-24',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 440010,
'water_restriction_number': 'dl62',
'cage_number': 163782,
'wr_start_date': '2018-11-24',
'wr_start_weight': 23.0
},
skip_duplicates=True)
# Subject 55004 / DL004
lab.Subject().insert1(
{
'subject_id': 550004,
'username': '******',
'cage_number': 163782,
'date_of_birth': '2018-09-24',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 550004,
'water_restriction_number': 'DL004',
'cage_number': 163782,
'wr_start_date': '2018-11-24',
'wr_start_weight': 23.0
},
skip_duplicates=True)
# Subject 550009 / DL009
lab.Subject().insert1(
{
'subject_id': 550009,
'username': '******',
'cage_number': 163782,
'date_of_birth': '2018-09-24',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 550009,
'water_restriction_number': 'DL009',
'cage_number': 163782,
'wr_start_date': '2018-11-24',
'wr_start_weight': 23.0
},
skip_duplicates=True)
# Subject 550010 / DL010
lab.Subject().insert1(
{
'subject_id': 550010,
'username': '******',
'cage_number': 163782,
'date_of_birth': '2018-09-24',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 550010,
'water_restriction_number': 'DL010',
'cage_number': 163782,
'wr_start_date': '2018-11-24',
'wr_start_weight': 23.0
},
skip_duplicates=True)
# Subject 550022 / DL022
lab.Subject().insert1(
{
'subject_id': 550022,
'username': '******',
'cage_number': 163782,
'date_of_birth': '2018-09-24',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 550022,
'water_restriction_number': 'DL022',
'cage_number': 163782,
'wr_start_date': '2018-11-24',
'wr_start_weight': 23.0
},
skip_duplicates=True)
# Subject 440959 / SC011
lab.Subject().insert1(
{
'subject_id': 440959,
'username': '******',
'cage_number': 440959,
'date_of_birth': '2018-10-09',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 440959,
'water_restriction_number': 'SC011',
'cage_number': 440959,
'wr_start_date': '2018-12-21',
'wr_start_weight': 22.8
},
skip_duplicates=True)
# Subject 442571 / SC022
lab.Subject().insert1(
{
'subject_id': 442571,
'username': '******',
'cage_number': 442571,
'date_of_birth': '2018-10-29',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 442571,
'water_restriction_number': 'SC022',
'cage_number': 442571,
'wr_start_date': '2019-01-02',
'wr_start_weight': 26.5
},
skip_duplicates=True)
# Subject 460432 / SC030
lab.Subject().insert1(
{
'subject_id': 456773,
'username': '******',
'cage_number': 171857,
'date_of_birth': '2019-05-29',
'sex': 'M',
'animal_source': 'Jackson Labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 456773,
'water_restriction_number': 'SC030',
'cage_number': 171857,
'wr_start_date': '2019-08-08',
'wr_start_weight': 25.700
},
skip_duplicates=True)
# Subject 460432 / SC032
lab.Subject().insert1(
{
'subject_id': 460432,
'username': '******',
'cage_number': 173167,
'date_of_birth': '2019-07-15',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 460432,
'water_restriction_number': 'SC032',
'cage_number': 173167,
'wr_start_date': '2019-09-20',
'wr_start_weight': 22.8
},
skip_duplicates=True)
for num in range(1, 20):
lab.Subject().insert1(
{
'subject_id': 777000 + num,
'username': '******',
'cage_number': 173167,
'date_of_birth': '2019-07-15',
'sex': 'M',
'animal_source': 'Jackson labs'
},
skip_duplicates=True)
lab.WaterRestriction().insert1(
{
'subject_id': 777000 + num,
'water_restriction_number': 'FOR' + f'{num:02}',
'cage_number': 173167,
'wr_start_date': '2019-09-20',
'wr_start_weight': 22.8
},
skip_duplicates=True)
# Rig
lab.Rig().insert1(
{
'rig': 'TRig1',
'room': '2w.334',
'rig_description': 'Training rig 1'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig2',
'room': '2w.334',
'rig_description': 'Training rig 2'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'TRig3',
'room': '2w.334',
'rig_description': 'Training rig 3'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'RRig',
'room': '2w.334',
'rig_description': 'Recording rig'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'RRig2',
'room': '2w.334',
'rig_description': 'Recording rig2'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'RRig3',
'room': '2w.334',
'rig_description': 'Recording rig3 for multi-target-licking'
},
skip_duplicates=True)
lab.Rig().insert1(
{
'rig': 'Ephys1',
'room': '2w.334',
'rig_description': 'Recording computer'
},
skip_duplicates=True)
lab.ProbeType.create_neuropixels_probe()
except Exception as e:
print("error creating mock data: {e}".format(e=e), file=sys.stderr)
raise
'Rin':list(),
'AP_amplitude':list(),
'AP_halfwidth':list(),
'AP_threshold':list(),
'resting':list()}
cells = ephys_patch.Cell()
APs_needed = 50
AP_max_RS = 40
AP_max_baseline = -40 #mV
max_baseline_current = 100*10**-12 #A
max_v0 = -.04 #V
min_current = -40*10**-12 #A
min_pulse_time = 0.3 #s
integration_window = .04 #sec
for cell in cells:
if len((lab.Surgery()&'subject_id = {}'.format(cell['subject_id'])).fetch('start_time'))==1:
continue # if there is only one surgery, we don't care
expression_time = np.diff((lab.Surgery()&'subject_id = {}'.format(cell['subject_id'])).fetch('start_time'))[0].days
virus_id = (lab.Surgery.VirusInjection()&'subject_id = {}'.format(cell['subject_id'])).fetch('virus_id')[0]
if virus_id == 238:
virus = 'Voltron 1'
elif virus_id == 240:
virus = 'Voltron 2'
else:
virus = '??'
squarepulses = ephysanal.SquarePulse()&cell&'square_pulse_amplitude < {}'.format(min_current)&'square_pulse_length > {}'.format(min_pulse_time)
Rins = list()
RSs = list()
v0s = list()
for squarepulse in squarepulses:
#%% metadata for methods
subject_ids = np.unique(imaging.Movie().fetch('subject_id'))
movie_num_list = list()
sex_list = list()
age_first_surgery_list = list()
expression_time_list = list()
ml_list = list()
ap_list=list()
depth_list = list()
for subject_id in subject_ids:
virus_ids = (lab.Surgery.VirusInjection()&'subject_id = {}'.format(subject_id)).fetch('virus_id')
virus_id = virus_ids[0]
virus_name = (lab.Virus()&'virus_id = {}'.format(virus_id)).fetch1('virus_name')
if virus_name != 'syn-FLEX-Voltron-ST':
continue
if len(lab.Surgery()&'subject_id = {}'.format(subject_id))!=2:
print('only {} surgery for {}'.format(len(lab.Surgery()&'subject_id = {}'.format(subject_id)),subject_id))
continue
movie_num_list.append(len(imaging.Movie()&'subject_id = {}'.format(subject_id)))
date_of_birth,sex = (lab.Subject()&'subject_id = {}'.format(subject_id)).fetch1('date_of_birth','sex')
surgery_times = (lab.Surgery()&'subject_id = {}'.format(subject_id)).fetch('start_time')
ml,ap = (lab.Surgery.Procedure()&'subject_id = {}'.format(subject_id)).fetch1('ml_location','ap_location')
ml_list.append(int(ml))
ap_list.append(int(ap))
age_first_surgery_list.append((np.min(surgery_times).date()-date_of_birth).days)
expression_time_list.append(np.diff(surgery_times)[0].days)
sex_list.append(sex)
depth_list.extend((ephys_patch.Cell()&'subject_id = {}'.format(subject_id)).fetch('depth'))
depth_list = np.asarray(depth_list)
ap_list = np.asarray(ap_list)
def plot_cell_SN_ratio_APwise(roi_type='VolPy',
v0_max=-35,
holding_min=-600,
frame_rate_min=300,
frame_rate_max=1800,
F0_min=50,
bin_num=10):
#%% Show S/N ratios for each AP
bin_num = 10
holding_min = -600 #pA
v0_max = -35 #mV
roi_type = 'VolPy_raw' #'Spikepursuit'#'VolPy_denoised'#'SpikePursuit'#'VolPy_dexpF0'#'VolPy'#'SpikePursuit_dexpF0'#'VolPy_dexpF0'#''Spikepursuit'#'VolPy'#
F0_min = 50
frame_rate_min = 200
frame_rate_max = 800
cmap = cm.get_cmap('jet')
key = {'roi_type': roi_type}
gtdata = pd.DataFrame((imaging_gt.GroundTruthROI() & key))
cells = gtdata.groupby([
'session', 'subject_id', 'cell_number', 'motion_correction_method',
'roi_type'
]).size().reset_index(name='Freq')
snratio = list()
v0s = list()
holdings = list()
rss = list()
threshs = list()
mintreshs = list()
f0s_all = list()
snratios_all = list()
peakamplitudes_all = list()
noise_all = list()
for cell in cells.iterrows():
cell = cell[1]
key_cell = dict(cell)
del key_cell['Freq']
snratios, f0, peakamplitudes, noises = (
imaging.Movie() * imaging_gt.GroundTruthROI() *
imaging_gt.ROIAPWave() * ephysanal.ActionPotentialDetails()
& key_cell & 'ap_real = 1'
& 'movie_frame_rate > {}'.format(frame_rate_min)
& 'movie_frame_rate < {}'.format(frame_rate_max)
& 'apwave_f0 > {}'.format(F0_min)).fetch('apwave_snratio',
'apwave_f0',
'apwave_peak_amplitude',
'apwave_noise')
#f0 = (imaging_gt.GroundTruthROI()*imaging.ROI()&key_cell).fetch('roi_f0')
sweep = (imaging_gt.GroundTruthROI() * imaging_gt.ROIAPWave()
& key_cell).fetch('sweep_number')[0]
thresh = (imaging_gt.GroundTruthROI() * imaging_gt.ROIAPWave() *
ephysanal.ActionPotentialDetails() & key_cell
& 'ap_real = 1').fetch('ap_threshold')
trace = (ephys_patch.SweepResponse() * imaging_gt.GroundTruthROI()
& key_cell
& 'sweep_number = {}'.format(sweep)).fetch('response_trace')
trace = trace[0]
stimulus = (ephys_patch.SweepStimulus() * imaging_gt.GroundTruthROI()
& key_cell &
'sweep_number = {}'.format(sweep)).fetch('stimulus_trace')
stimulus = stimulus[0]
RS = (ephysanal.SweepSeriesResistance() * imaging_gt.GroundTruthROI()
& key_cell
& 'sweep_number = {}'.format(sweep)).fetch('series_resistance')
RS = RS[0]
medianvoltage = np.median(trace) * 1000
holding = np.median(stimulus) * 10**12
#print(np.mean(snratios[:100]))
snratio.append(np.mean(snratios[:50]))
v0s.append(medianvoltage)
holdings.append(holding)
rss.append(RS)
threshs.append(thresh)
mintreshs.append(np.min(thresh))
f0s_all.append(f0)
snratios_all.append(snratios)
peakamplitudes_all.append(peakamplitudes)
noise_all.append(noises)
#plot_AP_waveforms(key_cell,AP_tlimits)
#%%
# =============================================================================
# virus_list=list()
# subject_ids = list()
# for cell in cells.iterrows():
# cell = cell[1]
# key_cell = dict(cell)
# del key_cell['Freq']
# virus_id = (lab.Surgery.VirusInjection()&'subject_id = {}'.format(key_cell['subject_id'])).fetch('virus_id')[0]
# if virus_id == 238:
# virus = 'Voltron 1'
# elif virus_id == 240:
# virus = 'Voltron 2'
# virus_list.append(virus)
# subject_ids.append(key_cell['subject_id'])
# # order = np.argsort(virus_list)
# order = np.lexsort((virus_list, subject_ids))
# snratio = np.asarray(snratio)[order]
# v0s = np.asarray(v0s)[order]
# holdings = np.asarray(holdings)[order]
# rss = np.asarray(rss)[order]
# threshs =np.asarray(threshs)[order]
# mintreshs = np.asarray(mintreshs)[order]
# f0s_all = np.asarray(f0s_all)[order]
# snratios_all = np.asarray(snratios_all)[order]
# peakamplitudes_all = np.asarray(peakamplitudes_all)[order]
# noise_all = np.asarray(noise_all)[order]
# virus_list = np.asarray(virus_list)[order]
# cells = cells.set_index(order, append=True).sort_index(level=1).reset_index(1, drop=True)
# =============================================================================
#%%
apnum = 50
fig = plt.figure(figsize=[10, 10])
ax_exptime_f0 = fig.add_subplot(221)
ax_exptime_dff = fig.add_subplot(222)
ax_exptime_noise = fig.add_subplot(223)
ax_exptime_snration = fig.add_subplot(224)
for loopidx, (f0, snratio_now, noise_now, peakampl_now,
cell_now) in enumerate(
zip(f0s_all, snratios_all, noise_all, peakamplitudes_all,
cells.iterrows())):
if len(
f0
) > 0: # and cell_now[1]['subject_id']==466774:# and cell_now[1]['cell_number']==1:
coloridx = loopidx / len(cells)
cell_now = cell_now[1]
virus_id = (lab.Surgery.VirusInjection()
& 'subject_id = {}'.format(
cell_now['subject_id'])).fetch('virus_id')[0]
if virus_id == 238:
virus = 'Voltron 1'
elif virus_id == 240:
virus = 'Voltron 2'
else:
virus = '??'
label_now = 'Subject:{}'.format(
cell_now['subject_id']) + ' Cell:{} - {}'.format(
cell_now['cell_number'], virus)
expression_time = np.diff(
(lab.Surgery() & 'subject_id = {}'.format(
cell_now['subject_id'])).fetch('start_time'))[0].days
ax_exptime_f0.plot(expression_time,
np.mean(f0[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_f0.errorbar(expression_time,
np.mean(f0[:apnum]),
np.std(f0[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_f0.set_xlabel('expression time (days)')
ax_exptime_f0.set_ylabel('F0 (pixel intensity)')
ax_exptime_dff.plot(expression_time,
np.mean(peakampl_now[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_dff.errorbar(expression_time,
np.mean(peakampl_now[:apnum]),
np.std(peakampl_now[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_dff.set_xlabel('expression time (days)')
ax_exptime_dff.set_ylabel('AP peak amplitude (dF/F)')
ax_exptime_noise.plot(expression_time,
np.mean(noise_now[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_noise.errorbar(expression_time,
np.mean(noise_now[:apnum]),
np.std(noise_now[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_noise.set_xlabel('expression time (days)')
ax_exptime_noise.set_ylabel('noise (dF/F)')
ax_exptime_snration.plot(expression_time,
np.mean(snratio_now[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_snration.errorbar(expression_time,
np.mean(snratio_now[:apnum]),
np.std(snratio_now[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_snration.set_xlabel('expression time (days)')
ax_exptime_snration.set_ylabel('S/N ratio')
#%% for each AP
fig = plt.figure()
ax_sn_f0 = fig.add_axes([0, 0, 1, 1])
ax_sn_f0_binned = fig.add_axes([0, -1.2, 1, 1])
ax_noise_f0 = fig.add_axes([2.6, 0, 1, 1])
ax_noise_f0_binned = fig.add_axes([2.6, -1.2, 1, 1])
ax_peakampl_f0 = fig.add_axes([1.3, 0, 1, 1])
ax_peakampl_f0_binned = fig.add_axes([1.3, -1.2, 1, 1])
for loopidx, (f0, snratio_now, noise_now, peakampl_now,
cell_now) in enumerate(
zip(f0s_all, snratios_all, noise_all, peakamplitudes_all,
cells.iterrows())):
if len(
f0
) > 0: # and cell_now[1]['subject_id']==466774:# and cell_now[1]['cell_number']==1:
coloridx = loopidx / len(cells)
cell_now = cell_now[1]
virus_id = (lab.Surgery.VirusInjection()
& 'subject_id = {}'.format(
cell_now['subject_id'])).fetch('virus_id')[0]
if virus_id == 238:
virus = 'Voltron 1'
elif virus_id == 240:
virus = 'Voltron 2'
else:
virus = '??'
label_now = 'Subject:{}'.format(
cell_now['subject_id']) + ' Cell:{} - {}'.format(
cell_now['cell_number'], virus)
ax_sn_f0.plot(f0,
snratio_now,
'o',
ms=1,
color=cmap(coloridx),
label=label_now)
ax_noise_f0.plot(f0,
noise_now,
'o',
ms=1,
color=cmap(coloridx),
label=label_now)
ax_peakampl_f0.plot(f0,
peakampl_now,
'o',
ms=1,
color=cmap(coloridx),
label=label_now)
lows = np.arange(np.min(f0), np.max(f0),
(np.max(f0) - np.min(f0)) / (bin_num + 1))
highs = lows + (np.max(f0) - np.min(f0)) / (bin_num + 1)
mean_f0 = list()
sd_f0 = list()
mean_sn = list()
sd_sn = list()
mean_noise = list()
sd_noise = list()
mean_ampl = list()
sd_ampl = list()
for low, high in zip(lows, highs):
idx = (f0 >= low) & (f0 < high)
if len(idx) > 10:
mean_f0.append(np.mean(f0[idx]))
sd_f0.append(np.std(f0[idx]))
mean_sn.append(np.mean(snratio_now[idx]))
sd_sn.append(np.std(snratio_now[idx]))
mean_noise.append(np.mean(noise_now[idx]))
sd_noise.append(np.std(noise_now[idx]))
mean_ampl.append(np.mean(peakampl_now[idx]))
sd_ampl.append(np.std(peakampl_now[idx]))
ax_sn_f0_binned.errorbar(mean_f0,
mean_sn,
sd_sn,
sd_f0,
'o-',
color=cmap(coloridx),
label=label_now)
ax_noise_f0_binned.errorbar(mean_f0,
mean_noise,
sd_noise,
sd_f0,
'o-',
color=cmap(coloridx),
label=label_now)
ax_peakampl_f0_binned.errorbar(mean_f0,
mean_ampl,
sd_ampl,
sd_f0,
'o-',
color=cmap(coloridx),
label=label_now)
ax_sn_f0.set_xlabel('F0')
ax_sn_f0.set_ylabel('S/N ratio')
ax_sn_f0_binned.set_xlabel('F0')
ax_sn_f0_binned.set_ylabel('S/N ratio')
#ax_sn_f0_binned.legend()
ax_sn_f0_binned.legend(loc='upper center',
bbox_to_anchor=(-.45, 1.5),
shadow=True,
ncol=1)
ax_noise_f0.set_xlabel('F0')
ax_noise_f0.set_ylabel('Noise (std(dF/F))')
ax_noise_f0_binned.set_xlabel('F0')
ax_noise_f0_binned.set_ylabel('Noise (std(dF/F))')
ax_peakampl_f0.set_xlabel('F0')
ax_peakampl_f0.set_ylabel('Peak amplitude (dF/F)')
ax_peakampl_f0_binned.set_xlabel('F0')
ax_peakampl_f0_binned.set_ylabel('Peak amplitude (dF/F)')
#%%
cells['SN'] = snratio
cells['V0'] = v0s
cells['holding'] = holdings
cells['RS'] = np.asarray(rss, float)
print(cells)
cells = cells[cells['V0'] < v0_max]
cells = cells[cells['holding'] > holding_min]
print(cells)
#% S/N ratio histogram
fig = plt.figure()
ax_hist = fig.add_axes([0, 0, 1, 1])
ax_hist.hist(cells['SN'].values)
ax_hist.set_xlabel('S/N ratio of first 50 spikes')
ax_hist.set_ylabel('# of cells')
ax_hist.set_title(roi_type.replace('_', ' '))
ax_hist.set_xlim([0, 15])