@author: Anne Urai 15 January 2020 """ import seaborn as sns import os import matplotlib.pyplot as plt from paper_behavior_functions import (figpath, seaborn_style, group_colors, query_sessions_around_criterion, institution_map) # import wrappers etc from ibl_pipeline import reference, subject, behavior from dj_tools import plot_psychometric, dj2pandas # INITIALIZE A FEW THINGS seaborn_style() figpath = figpath() pal = group_colors() institution_map, col_names = institution_map() col_names = col_names[:-1] # ================================= # # GET DATA FROM TRAINED ANIMALS # ================================= # use_sessions, use_days = query_sessions_around_criterion( criterion='trained', days_from_criterion=[2, 0], as_dataframe=False) # restrict by list of dicts with uuids for these sessions b = use_sessions * subject.Subject * subject.SubjectLab * reference.Lab * \ behavior.TrialSet.Trial # reduce the size of the fetch
# (this is a tricky dependency, as is it can not be run in a python shell, it makes the whole file # need to run as an executable eg. >>> python figure1_training.py in windows command prompt) # sys.path.append(os.path.join(os.path.dirname(__file__), # '../IBL-pipeline/prelim_analyses/behavioral_snapshots/')) # import ibl_pipeline.prelim_analyses.behavioral_snapshots.behavior_plots # noqa # this only works if conda develop ./IBL-pipeline/prelim_analyses/behavioral_snapshots/ has been added to iblenv import load_mouse_data_datajoint, behavior_plots import dj_tools from paper_behavior_functions import seaborn_style, figpath # ================================= # # INITIALIZE A FEW THINGS # ================================= # seaborn_style() # noqa figpath = figpath() # noqa plt.close('all') # ================================= # # pick an example mouse # ================================= # mouse = 'KS014' lab = 'cortexlab' # ================================================== # CONTRAST HEATMAP # ================================= # plt.close('all')