def populatemytables(paralel=True, cores=9):
IDs = {
k: v
for k, v in zip(*lab.WaterRestriction().fetch(
'water_restriction_number', 'subject_id'))
}
if paralel:
schema = dj.schema(pipeline_tools.get_schema_name('behavior_foraging'),
locals())
schema.jobs.delete()
ray.init(num_cpus=cores)
for runround in [1]:
arguments = {
'display_progress': False,
'reserve_jobs': True,
'order': 'random'
}
print('round ' + str(runround) + ' of populate')
result_ids = []
for coreidx in range(cores):
result_ids.append(
populatemytables_core_paralel.remote(arguments, runround))
ray.get(result_ids)
arguments = {'display_progress': True, 'reserve_jobs': False}
populatemytables_core(arguments, runround)
ray.shutdown()
else:
for runround in [1]:
arguments = {
'display_progress': True,
'reserve_jobs': False,
'order': 'random'
}
populatemytables_core(arguments, runround)
import datajoint as dj
import pipeline.lab as lab #, ccf
from pipeline.pipeline_tools import get_schema_name
import pipeline.experiment as experiment
import numpy as np
schema = dj.schema(get_schema_name('tracking'))
#[experiment] # NOQA flake8
@schema
class VideoTrialNum(dj.Manual):
definition = """
->experiment.Session
video_trial_num: smallint # how many trials for recorded because usually the last couple trials are not recorded
---
"""
@schema
class TrackingDevice(dj.Lookup):
definition = """
tracking_device: varchar(20) # device type/function
---
tracking_position: varchar(20) # device position
sampling_rate: decimal(8, 4) # sampling rate (Hz)
tracking_device_description: varchar(100) # device description
"""
contents = [('Camera 0', 'side_face', 1 / 0.0034,
'Chameleon3 CM3-U3-13Y3M-CS (FLIR)'),
('Camera 1', 'front_face', 1 / 0.0034,
import datajoint as dj
from pipeline.pipeline_tools import get_schema_name
schema = dj.schema(get_schema_name('lab'),locals())
#schema = dj.schema('rozmar_foraging_lab', locals())
@schema
class Person(dj.Manual):
definition = """
username : varchar(24)
----
fullname : varchar(255)
"""
@schema
class Rig(dj.Manual):
definition = """
rig : varchar(24)
---
room : varchar(20) # example 2w.342
rig_description : varchar(1024)
"""
@schema
class AnimalSource(dj.Lookup):
definition = """
animal_source : varchar(30)
"""
contents = zip(['Jackson Labs', 'Allen Institute', 'Charles River', 'MMRRC', 'Taconic', 'Other'])
import datajoint as dj
# =============================================================================
# import numpy as np
#
import pipeline.lab as lab #, ccf
from pipeline.pipeline_tools import get_schema_name
#from . import get_schema_name
#
schema = dj.schema(get_schema_name('experiment'), locals())
#schema = dj.schema('rozmar_foraging_experiment',locals())
# =============================================================================
#schema = dj.schema('rozmar_tutorial', locals())
@schema
class BrainLocation(dj.Manual):
definition = """
brain_location_name: varchar(32) # unique name of this brain location (could be hash of the non-primary attr)
---
-> lab.BrainArea
-> lab.Hemisphere
-> lab.SkullReference
"""
@schema
class Session(dj.Manual):
definition = """
-> lab.Subject
session : smallint # session number
import datajoint as dj
# =============================================================================
# import numpy as np
#
import pipeline.lab as lab#, ccf
import pipeline.experiment as experiment#, ccf
from pipeline.pipeline_tools import get_schema_name
#from . import get_schema_name
#
schema = dj.schema(get_schema_name('ephys_patch'),locals())
#schema = dj.schema('rozmar_foraging_experiment',locals())
# =============================================================================
#schema = dj.schema('rozmar_tutorial', locals())
@schema
class CellType(dj.Lookup):
definition = """
#
cell_type : varchar(100)
---
cell_type_description : varchar(4000)
"""
contents = [
('pyr', 'putative pyramidal'),
('int', 'putative interneuron'),
('glia', 'astrocyte or oligodendrocyte precursor like cell'),
('unidentified', 'can''t tell based on electrophysiological recording')
]
dj.config["enable_python_native_blobs"] = True
import pipeline.lab as lab #, ccf
from pipeline.pipeline_tools import get_schema_name
import pipeline.experiment as experiment
import pipeline.tracking as tracking
import numpy as np
import math
import scipy as sp
import matplotlib
import matplotlib.pyplot as plt
import statistics as st
import math
import scipy.signal
import pandas as pd
schema = dj.schema(get_schema_name('bottom_tongue'))
#[experiment] # NOQA flake8
def movmean(
interval, window_size
): # calculate the moving average; the beginning and the end has some issues
window = np.ones(int(window_size)) / float(window_size)
return np.convolve(interval, window, 'same')
def cart2pol(x, y):
theta = np.nan
rho = []
for i in enumerate(x):
import datajoint as dj
import pipeline.lab as lab #, ccf
import pipeline.experiment as experiment
import pipeline.ephys_patch as ephys_patch
import pipeline.ephysanal as ephysanal
import pipeline.imaging as imaging
from pipeline.pipeline_tools import get_schema_name
schema = dj.schema(get_schema_name('imaging_gt'),
locals()) # TODO ez el van baszva
import numpy as np
import scipy
#%%
@schema
class CellMovieCorrespondance(dj.Computed):
definition = """
-> ephys_patch.Cell
-> imaging.Movie
---
sweep_numbers = null : longblob # sweep numbers that
"""
def make(self, key):
#%
# key = { 'subject_id': 462149, 'session':1,'cell_number':1,'movie_number':11}
session_time = (experiment.Session() & key).fetch('session_time')[0]
cell_time = (ephys_patch.Cell() & key).fetch('cell_recording_start')[0]
cell_sweep_start_times = (ephys_patch.Sweep()
& key).fetch('sweep_start_time')
import numpy as np
import matplotlib.pyplot as plt
import datajoint as dj
import pandas as pd
import scipy.signal as signal
import scipy.ndimage as ndimage
from pipeline import pipeline_tools, lab, experiment, behavioranal, ephys_patch
#dj.conn()
#%%
schema = dj.schema(pipeline_tools.get_schema_name('ephys-anal'),locals())
#%%
@schema
class ActionPotential(dj.Computed):
definition = """
-> ephys_patch.Sweep
ap_num : smallint unsigned # action potential number in sweep
---
ap_max_index=null : int unsigned # index of AP max on sweep
ap_max_time=null : decimal(8,4) # time of the AP max relative to recording start
"""
def make(self, key):
#%%
#key = {'subject_id': 454263, 'session': 1, 'cell_number': 1, 'sweep_number': 62}
#print(key)
keynow = key.copy()
if len(ActionPotential()&keynow) == 0:
pd_sweep = pd.DataFrame((ephys_patch.Sweep()&key)*(ephys_patch.SweepResponse()&key)*(ephys_patch.SweepStimulus()&key)*(ephys_patch.SweepMetadata()&key))
if len(pd_sweep)>0:
import datajoint as dj
import pipeline.lab as lab #, ccf
#import pipeline.experiment as experiment
#import pipeline.ephys_patch as ephys_patch
#import pipeline.ephysanal as ephysanal
from pipeline.pipeline_tools import get_schema_name
schema = dj.schema(get_schema_name('imaging'), locals())
from PIL import Image
import numpy as np
import os
#%%
@schema
class Movie(dj.Imported):
definition = """
-> experiment.Session
movie_number : smallint
---
movie_name : varchar(200) # movie name
movie_x_size : double # (pixels)
movie_y_size : double # (pixels)
movie_frame_rate : double # (Hz)
movie_frame_num : int # number of frames
movie_start_time : decimal(12, 6) # (s) from session start # maybe end_time would also be useful
movie_pixel_size : decimal(5,2) # in microns
"""
@schema
class MovieFrameTimes(dj.Imported):