analysis_configuration.TEMPLATE

# DATA_DIRS IS NO LONGER USED, LEFT FOR HISTORICAL REASONS
[data_dirs]
root = "/home/markowitzmeister_gmail_com/jeff_win_share/reinforcement_data" # root directory
reinforcement = [
	"_aggregate_results_arhmm_03",
	"_aggregate_results_arhmm_04",
	"_aggregate_results_arhmm_05",
	"_aggregate_results_arhmm_06",
	"_aggregate_results_arhmm_07",
	"_aggregate_results_arhmm_08",
	"_aggregate_results_arhmm_09",
#	"_aggregate_results_arhmm_10",
	"_aggregate_results_arhmm_11",
#	"_aggregate_results_arhmm_12",
]
reinforcement_photometry = [
	"_aggregate_results_arhmm_photometry_02/",
	"_aggregate_results_arhmm_photometry_03/",
]
reinforcement_scalar = [
	"_aggregate_results_arhmm_scalar_01/",
	"_aggregate_results_arhmm_scalar_03/",
]
excitation = [
	"_aggregate_results_arhmm_excitation_01/",
	"_aggregate_results_arhmm_excitation_02/",
	"_aggregate_results_arhmm_excitation_03/",
#	"_aggregate_results_arhmm_excitation_04/",
]
excitation_photometry = ["_aggregate_results_arhmm_photometry_excitation_02/"]
excitation_pulsed = ["_aggregate_results_arhmm_excitation_pulsed_01/"]
excitation_pulsed_photometry = [
	"_aggregate_results_arhmm_photometry_excitation_pulsed_01/",
]
photometry = [
	"_aggregate_results_arhmm_photometry_06/",
	"_aggregate_results_arhmm_photometry_07/",
	"_aggregate_results_arhmm_photometry_08/",
]

# REPLACE BASEDIR WITH WHERE YOU DOWNLOAD DATA -
# ALTERNATIVELY USE _reformat_zenodo_download.ipynb TO REPLACE BASEDIR PROGRAMATICALLY
[raw_data]
dlight = "/BASEDIR/dlight_raw_data/"
rl_modeling = "/BASEDIR/rl_raw_data/"
closed_loop_behavior = "/BASEDIR/optoda_raw_data/"
realtime_package = "/BASEDIR/realtime_package/"
hek_cells = "/BASEDIR/hek_raw_data/"
misc = "/BASEDIR/misc_raw_data/"
keypoints = "/BASEDIR/keypoints_raw_data/"


[intermediate_results]
dlight = "/BASEDIR/dlight_intermediate_results/"
closed_loop_behavior = "/BASEDIR/optoda_intermediate_results/"
rl_modeling = "/BASEDIR/rl_intermediate_results/"
misc = "/BASEDIR/misc_intermediate_results/"
hek_cells = "/BASEDIR/hek_intermediate_results/"


[figures]
store_dir = "/BASEDIR/panels/"


[common]
fs = 30.0
missing_frame_cutoff = 0.05
largest_gap_cutoff = 300
last_timestamp_cutoff = 1750
first_timestamp_cutoff = 180


[dlight_common]
dff_threshold = 1.5
dlight_reference_corr_threshold = 0.6

[dlight_snippet]
dataframe_filename = "_jeff-cache/merged_feedback_dataframe_debounce11.parquet" # NO LONGER USED
scalars.hampel_filter.threshold = 10.0
reference.filter.corner_fs = 3.0
reference.filter.order = 2
rolling_z.window = 600
rolling_z.min_periods = 30
rolling_z.center = true
# HERE YOU WILL WANT TO RUN MULTIPLE TIMES
snippet_grab.window_bounds = [-3, 3] # short window
# snippet_grab.window_bounds = [-10, 10] # long window, save appended to longwin
# snippet_grab.gb_key = "uuid"
snippet_grab.label_key = "predicted_syllable" # online 
# snippet_grab.label_key= "predicted_syllable (offline)" # offline
# snippet_grab.label_key = "movement_initiations" # movement initiations
# LONG WINDOW KEYS, USE THESE KEYS FOR [-10, 10]
# data_keys = [
# 	"signal_reref_dff",
# 	"signal_reref_dff_z",
# 	"velocity_2d_mm",
# 	"height_ave_mm",
# 	"feedback_status",
# 	"acceleration_2d_mm",
# 	"jerk_2d_mm",
# 	"angle",
# 	"angle_unwrapped",
# 	"velocity_angle",
# 	"velocity_height",
# 	"timestamp",
# ]
# SHORT WINDOW KEYS, USE THESE KEYS FOR [-3, 3]
data_keys = [
	"signal_reref_dff",
	"signal_reref_dff_z",	
	"velocity_2d_mm",
	"height_ave_mm",
	"centroid_x_mm",
	"centroid_y_mm",
	"feedback_status",
	"acceleration_2d_mm",
	"jerk_2d_mm",
	"angle",
	"angle_unwrapped",
	"velocity_angle",
	"velocity_height",
	"pc00",
	"pc01",
	"pc02",
	"pc03",
	"pc04",
	"pc05",
	"pc06",
	"pc07",
	"pc08",
	"pc09",
	"timestamp",
]
meta_keys = [
	"uuid",
	"mouse_id",
	"session_number",
	"target_syllable",
	"stim_duration",
	"date",
	"trial_count",
	"opsin",
	"area",
]
convs.target_syllable = "int16"
convs.session_number = "int8"
convs.signal_reference_corr = "float32"
convs.signal_max = "float32"
convs.reference_max = "float32"
convs.uuid = "category"
convs.mouse_id = "category"
convs.area = "category"

[dlight_basic_analysis]
scalars = [
	"velocity_2d_mm",
	"acceleration_2d_mm",
	"jerk_2d_mm",
	"velocity_angle",
	"velocity_height",
]
timescale_correlation.bins = [0.25, 60.1, 0.5]
timescale_correlation.nshuffles = 1000
dlight_key = "signal_reref_dff_z"

[dlight_regression]
scalars = [
	"velocity_2d_mm",
	"acceleration_2d_mm",
	"velocity_angle",
	"velocity_height",
]

[dlight_transition_features]
use_offline = true # run feature computation once set to true and once set to false
renormalize = false
pre_window = [-0.5, 5.0]
windows = [
	[
		-0.2,
		0.3,
	],
	[
		-0.1,
		0.4,
	],
	[
		-0.3,
		0.3,
	],
	[
		0.0,
		0.5,
	],
	[
		0.0,
		0.3,
	],
	[
		0.0,
		0.6,
	],
	[
		0.0,
		1.0,
	],
	[
		0.0,
		inf,
	],
]
# scalars included along with the features
scalars = [
	"velocity_2d_mm",
	"acceleration_2d_mm",
	"jerk_2d_mm",
	"velocity_angle",
	"velocity_height",
	"centroid_x_mm",
	"centroid_y_mm",
]
# metadata included in the features
meta_keys = [
	"uuid",
	"timestamp",
	"syllable",
	"mouse_id",
	"session_number",
	"stim_duration",
	"duration",
	"target_syllable",
	"signal_max",
	"reference_max",
	"signal_reference_corr",
	"date",
	"area",
	"opsin",
]
# channels to compute features over
proc_keys = [
	"signal_reref_dff_z",
]

[dlight_encoding_features]
label_key = "predicted_syllable (offline)"
window_sizes = [5, 10, 25, 50, 100, 200, 300, 400, 800, 1600]
average_variant.bins = [5, 10, 25, 50, 100, 200, 300, 400, 800, 1600]

[dlight_lagged_correlations]
nshuffles = 1000
use_offline = true
use_renormalized = false
estimate_within_bin = true
use_neural_features = [
	"signal_reref_dff_z_max",
]
use_windows = ["(0.0, 0.3)"] # what we're using in paper

correlation_method = "pandas"
correlation_kwargs.method = "pearson"
usage_and_scalars.bins = [10, 400, 20]
usage_and_scalars.scalars = [
	"velocity_2d_mm",
]
usage_and_scalars.correlation_keys = ["syllable", "mouse_id", "bin"]
entropy.bins = [5, 52, 5]
entropy.ndlight_bins = 10 # bin dlight then aggregate tm stats
entropy.dlight_bin_keys = [
	"mouse_id",
	"syllable",
] # compute dlight bin cutoffs over these keys
entropy.pre_agg_keys = [
	"uuid",
	"mouse_id",
	"bin",
	"dlight_bin_feature",
	"dlight_bin",
] # this first aggregate just reduces data size
entropy.agg_keys = [
	"mouse_id",
	"bin",
	"dlight_bin_feature",
	"dlight_bin",
] # compute entropy with these keys, last key is chopped off and used in correlation
entropy.correlation_keys = [
	"dlight_bin_feature",
	"bin",
] # final groupby, then correlation is averaged
entropy.corr_kwargs.method = "spearman"
entropy.tm_truncate = 36
clustering.nclusters = 4
clustering.bin_size = 60
clustering.use_features = [
	"velocity_2d_mm",
	"acceleration_2d_mm",
	"jerk_2d_mm",
	"velocity_angle",
	"velocity_height",
]
clustering.display_features = [
	"velocity_2d_mm",
	"acceleration_2d_mm",
	"jerk_2d_mm",
	"velocity_angle",
	"velocity_height",
]
usage_and_scalars_shifted.bins = [10]
stim.use_windows = ["(0.0, 0.6)", "(0.0, 1.0)"]
stim.estimate_within_bin = true


[closed_loop_behavior]
partition_cols = ["experiment_type", "area", "mouse_id"]
dataframe_filename = "_jeff-cache/feedback_dataframe_debounce11.parquet" # not used, left for historical reasons
learning_timecourse.bin_size = 30
learning_timecourse.bin_overlap = 0
learning_timecourse.baseline = "m"
learning_timecourse.meta_keys = [
	"mouse_id",
	"session_number",
	"syllable_group",
	"target_syllable",
	"stim_duration",
	"area (pooled)",
	"experiment_type",
	"area",
	"genotype",
	"uuid",
	"date",
	"opsin",
	"power",
	"sex",
	"cohort",
]

[dask]
# address = "tcp://10.10.0.21:35679" # only uncomment if you are going to use a dask scheduler for long-running computations