411 lines
13 KiB
Python
411 lines
13 KiB
Python
import numpy as np
|
|
import datajoint as dj
|
|
import nixio as nix
|
|
import os
|
|
import glob
|
|
import util as ut
|
|
import uuid
|
|
|
|
from IPython import embed
|
|
|
|
schema = dj.schema("fish_book", locals())
|
|
|
|
|
|
@schema
|
|
class Dataset(dj.Manual):
|
|
definition = """ # Dataset
|
|
dataset_id : varchar(256)
|
|
----
|
|
data_source : varchar(512) # path to the dataset
|
|
experimenter : varchar(512)
|
|
recording_date : date
|
|
quality : varchar(512)
|
|
comment : varchar(1024)
|
|
has_nix : bool
|
|
"""
|
|
|
|
@staticmethod
|
|
def get_template_tuple(id=None):
|
|
if id is not None:
|
|
d = dict((Dataset() & {"dataset_id": id}).fetch1())
|
|
return d
|
|
return dict(dataset_id=None, data_source="", experimenter="", recording_date=None,
|
|
quality="", comment="", has_nix=False)
|
|
|
|
@staticmethod
|
|
def get_nix_file(key):
|
|
dset = (Dataset() & key).fetch1()
|
|
if dset["ignore"]:
|
|
return None
|
|
file_path = os.path.join(dset["data_source"], dset["dataset_id"] + ".nix")
|
|
if not (os.path.exists(file_path)):
|
|
print("\t No nix file found for path: %s" % dset["data_source"])
|
|
return None
|
|
if not Dataset.check_file_integrity(file_path):
|
|
return None
|
|
return file_path
|
|
|
|
@staticmethod
|
|
def check_file_integrity(nix_file):
|
|
sane = True
|
|
try:
|
|
f = nix.File.open(nix_file, nix.FileMode.ReadOnly)
|
|
b = f.blocks[0]
|
|
m = b.metadata
|
|
if "Recording" not in m.sections:
|
|
Warning("\t Could not find Recording section in dataset: %s" % nix_file)
|
|
sane = False
|
|
f.close()
|
|
except ():
|
|
f = None
|
|
print("file: %s is NOT SANE!")
|
|
sane = False
|
|
return sane
|
|
|
|
|
|
@schema
|
|
class Subject(dj.Manual):
|
|
definition = """
|
|
# Subject
|
|
subject_id : varchar(256)
|
|
----
|
|
species : varchar(256)
|
|
"""
|
|
|
|
@staticmethod
|
|
def get_template_tuple(subject_id=None):
|
|
tup = dict(subject_id=None, species="")
|
|
if subject_id is not None:
|
|
d = dict((Subject() & {"subject_id": subject_id}).fetch1())
|
|
return d
|
|
return tup
|
|
|
|
def make(self, key):
|
|
file_path = Dataset.get_nix_file(key)
|
|
if file_path is None:
|
|
return
|
|
nix_file = nix.File.open(file_path, nix.FileMode.ReadOnly)
|
|
m = nix_file.blocks[0].metadata
|
|
inserts = Subject.get_template_tuple()
|
|
subj_info = m["Recording"]["Subject"]
|
|
inserts["subject_id"] = subj_info["Identifier"]
|
|
inserts["species"] = subj_info["Species"][0]
|
|
inserts["weight"] = subj_info["Weight"]
|
|
inserts["size"] = subj_info["Size"]
|
|
inserts["eod_frequency"] = np.round(subj_info["EOD Frequency"] * 10) / 10
|
|
inserts.update(key)
|
|
self.insert1(inserts, skip_duplicates=True)
|
|
nix_file.close()
|
|
|
|
|
|
@schema
|
|
class SubjectDatasetMap(dj.Manual):
|
|
definition = """
|
|
# SubjectDatasetMap
|
|
-> Subject
|
|
-> Dataset
|
|
"""
|
|
|
|
|
|
@schema
|
|
class SubjectProperties(dj.Manual):
|
|
definition = """
|
|
# SubjectProperties
|
|
id : int auto_increment
|
|
----
|
|
-> Subject
|
|
recording_date : date
|
|
weight : float
|
|
size : float
|
|
eod_frequency : float
|
|
"""
|
|
|
|
def get_template_tuple(id=None):
|
|
tup = dict(id=None, subject_id=None, recording_date=None, weight=0.0, size=0.0,
|
|
eod_frequency=0.0)
|
|
if id is not None:
|
|
return dict((SubjectProperties() & {"id": id}).fetch1())
|
|
return tup
|
|
|
|
|
|
@schema
|
|
class Cell(dj.Manual):
|
|
definition = """
|
|
# Table that stores information about recorded cells.
|
|
cell_id : varchar(256)
|
|
----
|
|
-> Subject
|
|
cell_type : varchar(256)
|
|
firing_rate : float
|
|
structure : varchar(256)
|
|
region : varchar(256)
|
|
subregion : varchar(256)
|
|
depth : float
|
|
lateral_pos : float
|
|
transversal_section : float
|
|
"""
|
|
|
|
@staticmethod
|
|
def get_template_tuple(cell_id=None):
|
|
tup = dict(cell_id=None, subject_id=None, cell_type="", firing_rate=0.0,
|
|
depth=0.0, region="", subregion="", structure="",
|
|
lateral_pos=0.0, transversal_section=0.0)
|
|
if cell_id is not None:
|
|
d = dict((Cell() & {"cell_id": cell_id}).fetch1())
|
|
return d
|
|
return tup
|
|
|
|
|
|
@schema
|
|
class CellDatasetMap(dj.Manual):
|
|
definition = """
|
|
# Table that maps recorded cells to datasets
|
|
-> Dataset
|
|
-> Cell
|
|
"""
|
|
|
|
|
|
@schema
|
|
class Repro(dj.Manual):
|
|
definition = """
|
|
repro_id : varchar(512)
|
|
run : smallint
|
|
-> Dataset
|
|
----
|
|
repro_name : varchar(512)
|
|
settings : varchar(3000)
|
|
start : float
|
|
duration : float
|
|
"""
|
|
|
|
@staticmethod
|
|
def get_template_tuple(repro_id=None):
|
|
tup = dict(repro_id=None, dataset_id=None, run=0, repro_name="", settings=None, start=None, duration=None)
|
|
if repro_id is not None:
|
|
d = dict((Repro() & {"repro_id": repro_id}).fetch1())
|
|
return d
|
|
return tup
|
|
|
|
|
|
@schema
|
|
class Stimulus(dj.Manual):
|
|
definition = """
|
|
stimulus_id : varchar(50)
|
|
-> Repro
|
|
---
|
|
stimulus_index : int
|
|
stimulus_name : varchar(512)
|
|
start : float
|
|
duration : float
|
|
settings : varchar(3000)
|
|
"""
|
|
|
|
@staticmethod
|
|
def get_template_tuple(stimulus_id=None):
|
|
if stimulus_id is not None:
|
|
tup = dict((Stimulus & {"stimulus_id": stimulus_id}).fetch1())
|
|
else:
|
|
tup = dict(stimulus_id=None, stimulus_index=None, stimulus_name="", start=0.0, duration=0.0, settings=None)
|
|
return tup
|
|
|
|
|
|
def populate_datasets(data_path):
|
|
print("Importing dataset %s" % data_path)
|
|
if not os.path.exists(data_path):
|
|
return
|
|
dset_name = os.path.split(data_path)[-1]
|
|
experimenter, rec_date, quality, comment, has_nix = ut.read_dataset_info(os.path.join(data_path, 'info.dat'))
|
|
if not experimenter:
|
|
return False
|
|
|
|
inserts = Dataset.get_template_tuple()
|
|
inserts["dataset_id"] = dset_name
|
|
inserts["data_source"] = data_path
|
|
inserts["experimenter"] = experimenter
|
|
inserts["recording_date"] = rec_date
|
|
inserts["quality"] = quality
|
|
inserts["comment"] = comment
|
|
inserts["has_nix"] = has_nix
|
|
Dataset().insert1(inserts, skip_duplicates=True)
|
|
return True
|
|
|
|
|
|
def populate_subjects(data_path):
|
|
print("\tImporting subject(s) of %s" % data_path)
|
|
dset_name = os.path.split(data_path)[-1]
|
|
info_file = os.path.join(data_path, 'info.dat')
|
|
if not os.path.exists(info_file):
|
|
return None, None, False
|
|
info = ut.read_info_file(info_file)
|
|
p = []
|
|
ut.find_key_recursive(info, "Subject", p)
|
|
if len(p) > 0:
|
|
subj = ut.deep_get(info, p)
|
|
inserts = Subject.get_template_tuple()
|
|
inserts["subject_id"] = subj["Identifier"]
|
|
inserts["species"] = subj["Species"]
|
|
Subject().insert1(inserts, skip_duplicates=True)
|
|
|
|
# multi mach entry
|
|
dataset = dict((Dataset() & {"dataset_id": dset_name}).fetch1())
|
|
mm = dict(dataset_id=dataset["dataset_id"], subject_id=subj["Identifier"])
|
|
SubjectDatasetMap.insert1(mm, skip_duplicates=True)
|
|
|
|
# subject properties
|
|
props = SubjectProperties.get_template_tuple()
|
|
props["subject_id"] = subj["Identifier"]
|
|
props["recording_date"] = dataset["recording_date"]
|
|
if "Weight" in subj.keys():
|
|
props["weight"] = np.round(float(subj["Weight"][:-1]), 1)
|
|
if "Size" in subj.keys():
|
|
props["size"] = np.round(float(subj["Size"][:-2]), 1)
|
|
if "EOD Frequency" in subj.keys():
|
|
props["eod_frequency"] = np.round(float(subj["EOD Frequency"][:-2]))
|
|
p = props.copy()
|
|
p.pop("id")
|
|
if len(SubjectProperties & p) == 0:
|
|
SubjectProperties.insert1(props, skip_duplicates=True)
|
|
|
|
|
|
def populate_cells(data_path):
|
|
print("\tImporting cell(s) of %s" % data_path)
|
|
dset_name = os.path.split(data_path)[-1]
|
|
info_file = os.path.join(data_path, 'info.dat')
|
|
if not os.path.exists(info_file):
|
|
return None, None, False
|
|
info = ut.read_info_file(info_file)
|
|
p = []
|
|
ut.find_key_recursive(info, "Subject", p)
|
|
subject_info = ut.deep_get(info, p)
|
|
|
|
p = []
|
|
ut.find_key_recursive(info, "Cell", p)
|
|
cell_info = ut.deep_get(info, p)
|
|
|
|
p = []
|
|
ut.find_key_recursive(info, "Firing Rate1", p)
|
|
firing_rate = ut.deep_get(info, p, default=0.0)
|
|
if isinstance(firing_rate, str):
|
|
firing_rate = float(firing_rate[:-2])
|
|
|
|
dataset = dict((Dataset & {"dataset_id": dset_name}).fetch1())
|
|
subject = dict((Subject & {"subject_id": subject_info["Identifier"]}).fetch1())
|
|
|
|
dataset_id = dataset["dataset_id"]
|
|
cell_id = "-".join(dataset_id.split("-")[:4]) if len(dataset_id) > 4 else dataset_id
|
|
cell_props = Cell.get_template_tuple()
|
|
cell_props["subject_id"] = subject["subject_id"]
|
|
cell_props["cell_id"] = cell_id
|
|
cell_props["cell_type"] = cell_info["CellType"]
|
|
cell_props["firing_rate"] = firing_rate
|
|
if "Structure" in cell_info.keys():
|
|
cell_props["structure"] = cell_info["Structure"]
|
|
if "BrainRegion" in cell_info.keys():
|
|
cell_props["region"] = cell_info["BrainRegion"]
|
|
if "BrainSubRegion" in cell_info.keys():
|
|
cell_props["subregion"] = cell_info["BrainSubRegion"]
|
|
if "Depth" in cell_info.keys():
|
|
cell_props["depth"] = float(cell_info["Depth"][:-2])
|
|
if "Lateral position" in cell_info.keys():
|
|
cell_props["lateral_pos"] = float(cell_info["Lateral position"][:-2])
|
|
if "Transverse section" in cell_info.keys():
|
|
cell_props["transversal_section"] = float(cell_info["Transverse section"])
|
|
|
|
Cell.insert1(cell_props, skip_duplicates=True)
|
|
|
|
# multi mach entry
|
|
mm = dict(dataset_id=dataset["dataset_id"], cell_id=cell_props["cell_id"])
|
|
CellDatasetMap.insert1(mm, skip_duplicates=True)
|
|
|
|
|
|
def populate_repros(data_path):
|
|
print("\tImporting RePro(s) of %s" % data_path)
|
|
dset_name = os.path.split(data_path)[-1]
|
|
if len(Dataset & {"dataset_id": dset_name}) != 1:
|
|
return False
|
|
dataset = dict((Dataset & {"dataset_id": dset_name}).fetch1())
|
|
|
|
if dataset["has_nix"]:
|
|
print("\t\tscanning nix file")
|
|
nix_files = glob.glob(os.path.join(dataset["data_source"], "*.nix"))
|
|
for nf in nix_files:
|
|
if not Dataset.check_file_integrity(nf):
|
|
print("file is not sane!!!")
|
|
continue
|
|
f = nix.File.open(nf, nix.FileMode.ReadOnly)
|
|
b = f.blocks[0]
|
|
for t in b.tags:
|
|
if "relacs.repro_run" in t.type:
|
|
rs = t.metadata.find_sections(lambda x: "Run" in x.props)
|
|
if len(rs) == 0:
|
|
continue
|
|
rs = rs[0]
|
|
rp = Repro.get_template_tuple()
|
|
rp["run"] = rs["Run"]
|
|
rp["repro_name"] = rs["RePro"]
|
|
rp["dataset_id"] = dataset["dataset_id"]
|
|
rp["repro_id"] = t.name
|
|
settings = t.metadata.find_sections(lambda x: "settings" in x.type)
|
|
if len(settings) > 0:
|
|
rp["settings"] = ut.nix_metadata_to_yaml(settings[0])
|
|
else:
|
|
rp["settings"] = ut.nix_metadata_to_yaml(t.metadata)
|
|
rp["start"] = t.position[0]
|
|
rp["duration"] = t.extent[0]
|
|
Repro.insert1(rp, skip_duplicates=True)
|
|
repro = dict((Repro & dict(repro_id="BaselineActivity_2", dataset_id="2018-11-20-ae-invivo-1")).fetch1())
|
|
repro.pop("settings")
|
|
repro.pop("repro_name")
|
|
repro.pop("start")
|
|
repro.pop("duration")
|
|
# import Stimuli
|
|
mtags = ut.find_mtags_for_tag(b, t)
|
|
stim_index = 0
|
|
for mt in mtags:
|
|
mt_positions = np.atleast_2d(mt.positions[:]).T
|
|
mt_extents = np.atleast_2d(mt.extents[:]).T
|
|
for i in range(mt.positions.shape[0]):
|
|
settings = ut.mtag_settings_to_yaml(mt, i)
|
|
stim_start = mt_positions[i, 0]
|
|
stim_duration = mt_extents[i, 0]
|
|
|
|
stim = Stimulus.get_template_tuple()
|
|
stim["stimulus_id"] = str(uuid.uuid1())
|
|
stim["stimulus_index"] = stim_index
|
|
stim["start"] = stim_start
|
|
stim["duration"] = stim_duration
|
|
stim["settings"] = settings
|
|
stim.update(repro)
|
|
Stimulus.insert1(stim, skip_duplicates=True)
|
|
stim_index += 1
|
|
f.close()
|
|
f = None
|
|
else:
|
|
pass
|
|
return True
|
|
|
|
|
|
def drop_tables():
|
|
Dataset.drop()
|
|
Subject.drop()
|
|
|
|
|
|
def populate(datasets):
|
|
for d in datasets:
|
|
if not populate_datasets(d):
|
|
continue
|
|
populate_subjects(d)
|
|
populate_cells(d)
|
|
try:
|
|
populate_repros(d)
|
|
except ():
|
|
print("something went wrong! %s" % d)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
data_dir = "../../science/high_frequency_chirps/data"
|
|
# data_dir = "../high_freq_chirps/data"
|
|
datasets = glob.glob(os.path.join(data_dir, '2018-11-20*'))
|
|
drop_tables()
|
|
# populate(datasets)
|