[frontend] a lot of docstrings
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@ -7,6 +7,8 @@ from IPython import embed
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from fishbook.backend.util import progress
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class Cell:
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"""The Cell class represents a recorded cell. It is characterized by *id*, the cell *type*, the *firing_rate*, and the recording *location*.
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"""
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def __init__(self, cell_id=None, tuple=None):
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if tuple:
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self.__tuple = tuple
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@ -20,18 +22,40 @@ class Cell:
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@property
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def id(self):
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"""The id of the cell. Unsually the same as the dataset name.
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Returns:
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str: the id
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"""
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return self.__tuple["cell_id"] if "cell_id" in self.__tuple.keys() else ""
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@property
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def type(self):
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"""The cell type.
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Returns:
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str: the cell type
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"""
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return self.__tuple["cell_type"] if "cell_type" in self.__tuple.keys() else ""
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@property
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def firing_rate(self):
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"""The firing rate of the neuron, if applicable, else 0.0.
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Returns:
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float: the firing rate.
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"""
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return self.__tuple["firing_rate"] if "firing_rate" in self.__tuple.keys() else 0.0
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@property
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def location(self):
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"""The recording location which consists of the structure (e.g. brain), the region (e.g. ELL), the subregion (e.g. lateral segment),
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the depth below surface, the lateral position and the transversal section if applicable. The latter two are given according to the
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[Apteronotus brain atlas](http://nelson.beckman.illinois.edu/atlas.html).
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Returns:
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dict: the recording location
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"""
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keys = ["structure", "region", "subregion", "depth", "lateral_pos", "transversal_section"]
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loc = {}
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for k in keys:
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@ -43,23 +67,55 @@ class Cell:
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@property
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def subject(self):
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"""The subject in which this cell was recorded.
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Returns:
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fishbook.Subject: the subject
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"""
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return Subject(tuple=(Subjects & {"subject_id": self.__tuple["subject_id"]}).fetch(limit=1, as_dict=True)[0])
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@property
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def datasets(self):
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"""All datasets that have been collected from this cell.
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Returns:
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list of fishbook.Dataset: the linked datasets
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"""
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return [Dataset(tuple=d) for d in (Datasets & (CellDatasetMap & {"cell_id": self.id})).fetch(as_dict=True)]
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@property
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def repro_runs(self):
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"""All relacs RePros that have been run while recording this cell.
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Returns:
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list of fishbook.RePro: the repros
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"""
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repros = (Repros & "cell_id = '%s'" % self.id)
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return [RePro(tuple=r) for r in repros]
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@staticmethod
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def unique_cell_types():
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"""List of unique cell types stored in the database.
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Returns:
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np.array of str: the cell types
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"""
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return np.unique(Cells.fetch("cell_type"))
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@staticmethod
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def find(cell_type=None, species=None, quality="good"):
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def find(cell_type=None, species=None, quality="good", test=False):
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"""Find cells that match with respect to cell type, subject species, and recording quality.
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Args:
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cell_type (str, optional): The desired cell type, case insensitive. Defaults to None.
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species (str, optional): The species name, or parts of it. Defaults to None.
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quality (str, optional): The desired qulality. Defaults to "good".
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test (bool, optional): Whether or not this query is a test run. Defaults to False.
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Returns:
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list of fishbook.Cell: the matching cells, empty it test==True.
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int : the number of matches
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"""
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cs = Cells * CellDatasetMap * Datasets * Subjects
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if cell_type:
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cs = cs & "cell_type like '{0:s}'".format(cell_type)
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@ -67,11 +123,19 @@ class Cell:
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cs = cs & "species like '%{0:s}%'".format(species)
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if quality:
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cs = cs & "quality like '{0:s}'".format(quality)
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return [Cell(tuple=c) for c in cs]
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results = []
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total = len(cs)
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if not test:
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for i, c in enumerate(cs):
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progress(i+1, total, "fetching %i matches" % total)
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results.append(Cell(tuple=c))
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return results, total
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def __str__(self):
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str = ""
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str += "Cell: %s \t type: %s\n" % (self.id, self.type)
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str += "Baseline firing: %.3f\n" % self.firing_rate
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str += "structure: %s \t region: %s\t subregion: %s\n" % (self.__tuple["structure"], self.__tuple["region"], self.__tuple["subregion"])
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return str
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@property
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@ -286,15 +350,15 @@ class Dataset:
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return self.__tuple.copy()
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def __str__(self):
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str = "id: %s\n" % self.id
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str = "dataset id: %s\n" % self.id
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str += "recorded: %s \t by: %s\n" % (self.recording_date, self.experimenter)
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str += "duration: %ss \t quality: %s\n" % (self.recording_duration, self.quality)
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str += "duration: %s min \t quality: %s\n" % (self.recording_duration, self.quality)
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str += "comment: %s" % self.comment
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return str
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class RePro:
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"""Repro class represents an entry in the repro table. This is a run of a certain relacs "Research Protocol".
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"""The RePro class represents an entry in the repro table. This is a run of a certain relacs "Research Protocol".
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"""
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def __init__(self, repro_id=None, tuple=None):
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@ -406,6 +470,12 @@ class RePro:
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def _tuple(self):
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return self.__tuple.copy()
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def __str__(self):
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str = "RePro: %s\t id: %s\n" % (self.name, self.id)
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str += "run: %i\t on cell: %s\n" %(self.run, self.cell_id)
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str += "start time: %s\t duration: %s\n" % (self.start, self.duration)
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return str
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class Stimulus:
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"""The stimulus class represents a Stimulus that was presented. A Stimulus has several properties
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@ -510,6 +580,11 @@ class Stimulus:
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class Subject:
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"""Representation of the recorded subject's properties.
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A subject is defined by the its *id*, and its *species*.
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The
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"""
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def __init__(self, subject_id=None, tuple=None):
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if tuple:
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self.__tuple = tuple
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@ -544,6 +619,15 @@ class Subject:
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@staticmethod
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def find(species=None, test=False):
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"""Find all subjects matching the given species.
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Args:
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species ([type], optional): [description]. Defaults to None.
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test (bool, optional): [description]. Defaults to False.
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Returns:
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[type]: [description]
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"""
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subjs = Subjects & True
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if species:
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subjs = (Subjects & "species like '%{0:s}%'".format(species))
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@ -559,9 +643,18 @@ class Subject:
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@staticmethod
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def unique_species():
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"""Unique list of species names.
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Returns:
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np.array: unique names
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"""
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all_species = (Subjects & True).fetch("species")
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return np.unique(all_species)
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def __str__(self):
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str = "Subject: %s\n" % self.id
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str += ""
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if __name__ == "__main__":
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from IPython import embed
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