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