def run_traces_by_qu_lock_sess(sessions,
analysis,
seed,
analyspar,
sesspar,
stimpar,
quantpar,
figpar,
datatype="roi"):
"""
run_traces_by_qu_lock_sess(sessions, analysis, analyspar, sesspar,
stimpar, quantpar, figpar)
Retrieves trace statistics by session x quantile at the transition of
expected to unexpected sequences (or v.v.) and plots traces across ROIs by
quantile with each session in a separate subplot.
Also runs analysis for one quantile (full data) with different unexpected
lengths grouped separated
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list) : list of Session objects
- analysis (str) : analysis type (e.g., "l")
- seed (int) : seed value to use. (-1 treated as None)
- analyspar (AnalysPar): named tuple containing analysis parameters
- sesspar (SessPar) : named tuple containing session parameters
- stimpar (StimPar) : named tuple containing stimulus parameters
- quantpar (QuantPar) : named tuple containing quantile analysis
parameters
- figpar (dict) : dictionary containing figure parameters
Optional args:
- datatype (str): type of data (e.g., "roi", "run")
"""
sessstr_pr = sess_str_util.sess_par_str(sesspar.sess_n, stimpar.stimtype,
sesspar.plane, stimpar.visflow_dir,
stimpar.visflow_size, stimpar.gabk,
"print")
dendstr_pr = sess_str_util.dend_par_str(analyspar.dend, sesspar.plane,
datatype, "print")
datastr = sess_str_util.datatype_par_str(datatype)
logger.info(
f"Analysing and plotting unexpected vs expected {datastr} "
f"traces locked to unexpected onset by quantile ({quantpar.n_quants}) "
f"\n({sessstr_pr}{dendstr_pr}).",
extra={"spacing": "\n"})
seed = rand_util.seed_all(seed, "cpu", log_seed=False)
# modify quantpar to retain all quantiles
quantpar_one = sess_ntuple_util.init_quantpar(1, 0)
n_quants = quantpar.n_quants
quantpar_mult = sess_ntuple_util.init_quantpar(n_quants, "all")
if stimpar.stimtype == "visflow":
pre_post = [2.0, 6.0]
elif stimpar.stimtype == "gabors":
pre_post = [2.0, 8.0]
else:
gen_util.accepted_values_error("stimpar.stimtype", stimpar.stimtype,
["visflow", "gabors"])
logger.warning("Setting pre to {}s and post to {}s.".format(*pre_post))
stimpar = sess_ntuple_util.get_modif_ntuple(stimpar, ["pre", "post"],
pre_post)
figpar = copy.deepcopy(figpar)
if figpar["save"]["use_dt"] is None:
figpar["save"]["use_dt"] = gen_util.create_time_str()
for baseline in [None, stimpar.pre]:
basestr_pr = sess_str_util.base_par_str(baseline, "print")
for quantpar in [quantpar_one, quantpar_mult]:
locks = ["unexp", "exp"]
if quantpar.n_quants == 1:
locks.append("unexp_split")
# get the stats (all) separating by session and quantiles
for lock in locks:
logger.info(
f"{quantpar.n_quants} quant, {lock} lock{basestr_pr}",
extra={"spacing": "\n"})
if lock == "unexp_split":
trace_info = quant_analys.trace_stats_by_exp_len_sess(
sessions,
analyspar,
stimpar,
quantpar.n_quants,
quantpar.qu_idx,
byroi=False,
nan_empty=True,
baseline=baseline,
datatype=datatype)
else:
trace_info = quant_analys.trace_stats_by_qu_sess(
sessions,
analyspar,
stimpar,
quantpar.n_quants,
quantpar.qu_idx,
byroi=False,
lock=lock,
nan_empty=True,
baseline=baseline,
datatype=datatype)
# for comparison, locking to middle of expected sample (1 quant)
exp_samp = quant_analys.trace_stats_by_qu_sess(
sessions,
analyspar,
stimpar,
quantpar_one.n_quants,
quantpar_one.qu_idx,
byroi=False,
lock="exp_samp",
nan_empty=True,
baseline=baseline,
datatype=datatype)
extrapar = {
"analysis": analysis,
"datatype": datatype,
"seed": seed,
}
xrans = [xran.tolist() for xran in trace_info[0]]
all_stats = [sessst.tolist() for sessst in trace_info[1]]
exp_stats = [expst.tolist() for expst in exp_samp[1]]
trace_stats = {
"xrans": xrans,
"all_stats": all_stats,
"all_counts": trace_info[2],
"lock": lock,
"baseline": baseline,
"exp_stats": exp_stats,
"exp_counts": exp_samp[2]
}
if lock == "unexp_split":
trace_stats["unexp_lens"] = trace_info[3]
sess_info = sess_gen_util.get_sess_info(
sessions,
analyspar.fluor,
incl_roi=(datatype == "roi"),
rem_bad=analyspar.rem_bad)
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"quantpar": quantpar._asdict(),
"extrapar": extrapar,
"sess_info": sess_info,
"trace_stats": trace_stats
}
fulldir, savename = gen_plots.plot_traces_by_qu_lock_sess(
figpar=figpar, **info)
file_util.saveinfo(info, savename, fulldir, "json")
def run_traces_by_qu_unexp_sess(sessions,
analysis,
analyspar,
sesspar,
stimpar,
quantpar,
figpar,
datatype="roi"):
"""
run_traces_by_qu_unexp_sess(sessions, analysis, analyspar, sesspar,
stimpar, quantpar, figpar)
Retrieves trace statistics by session x unexp val x quantile and
plots traces across ROIs by quantile/unexpected with each session in a
separate subplot.
Also runs analysis for one quantile (full data).
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list) : list of Session objects
- analysis (str) : analysis type (e.g., "t")
- analyspar (AnalysPar): named tuple containing analysis parameters
- sesspar (SessPar) : named tuple containing session parameters
- stimpar (StimPar) : named tuple containing stimulus parameters
- quantpar (QuantPar) : named tuple containing quantile analysis
parameters
- figpar (dict) : dictionary containing figure parameters
Optional args:
- datatype (str): type of data (e.g., "roi", "run")
"""
sessstr_pr = sess_str_util.sess_par_str(sesspar.sess_n, stimpar.stimtype,
sesspar.plane, stimpar.visflow_dir,
stimpar.visflow_size, stimpar.gabk,
"print")
dendstr_pr = sess_str_util.dend_par_str(analyspar.dend, sesspar.plane,
datatype, "print")
datastr = sess_str_util.datatype_par_str(datatype)
logger.info(
f"Analysing and plotting unexpected vs expected {datastr} "
f"traces by quantile ({quantpar.n_quants}) \n({sessstr_pr}"
f"{dendstr_pr}).",
extra={"spacing": "\n"})
# modify quantpar to retain all quantiles
quantpar_one = sess_ntuple_util.init_quantpar(1, 0)
n_quants = quantpar.n_quants
quantpar_mult = sess_ntuple_util.init_quantpar(n_quants, "all")
figpar = copy.deepcopy(figpar)
if figpar["save"]["use_dt"] is None:
figpar["save"]["use_dt"] = gen_util.create_time_str()
for quantpar in [quantpar_one, quantpar_mult]:
logger.info(f"{quantpar.n_quants} quant", extra={"spacing": "\n"})
# get the stats (all) separating by session, unexpected and quantiles
trace_info = quant_analys.trace_stats_by_qu_sess(sessions,
analyspar,
stimpar,
quantpar.n_quants,
quantpar.qu_idx,
byroi=False,
by_exp=True,
datatype=datatype)
extrapar = {
"analysis": analysis,
"datatype": datatype,
}
xrans = [xran.tolist() for xran in trace_info[0]]
all_stats = [sessst.tolist() for sessst in trace_info[1]]
trace_stats = {
"xrans": xrans,
"all_stats": all_stats,
"all_counts": trace_info[2]
}
sess_info = sess_gen_util.get_sess_info(sessions,
analyspar.fluor,
incl_roi=(datatype == "roi"),
rem_bad=analyspar.rem_bad)
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"quantpar": quantpar._asdict(),
"extrapar": extrapar,
"sess_info": sess_info,
"trace_stats": trace_stats
}
fulldir, savename = gen_plots.plot_traces_by_qu_unexp_sess(
figpar=figpar, **info)
file_util.saveinfo(info, savename, fulldir, "json")
def run_regr(args):
"""
run_regr(args)
Does runs of a logistic regressions on the specified comparison and range
of sessions.
Required args:
- args (Argument parser): parser with analysis parameters as attributes:
alg (str) : algorithm to use ("sklearn" or "pytorch")
bal (bool) : if True, classes are balanced
batchsize (int) : nbr of samples dataloader will load per
batch (for "pytorch" alg)
visflow_dir (str) : visual flow direction to analyse
visflow_per (float) : number of seconds to include before visual
flow segments
visflow_size (int or list): visual flow square sizes to include
comp (str) : type of comparison
datadir (str) : data directory
dend (str) : type of dendrites to use ("allen" or "dend")
device (str) : device name (i.e., "cuda" or "cpu")
ep_freq (int) : frequency at which to log loss to
console
error (str) : error to take, i.e., "std" (for std
or quantiles) or "sem" (for SEM or MAD)
fluor (str) : fluorescence trace type
fontdir (str) : directory in which additional fonts are
located
gabfr (int) : gabor frame of reference if comparison
is "unexp"
gabk (int or list) : gabor kappas to include
gab_ori (list or str) : gabor orientations to include
incl (str or list) : sessions to include ("yes", "no", "all")
lr (num) : model learning rate (for "pytorch" alg)
mouse_n (int) : mouse number
n_epochs (int) : number of epochs
n_reg (int) : number of regular runs
n_shuff (int) : number of shuffled runs
scale (bool) : if True, each ROI is scaled
output (str) : general directory in which to save
output
parallel (bool) : if True, runs are done in parallel
plt_bkend (str) : pyplot backend to use
q1v4 (bool) : if True, analysis is trained on first and
tested on last quartiles
exp_v_unexp (bool) : if True, analysis is trained on
expected and tested on unexpected sequences
runtype (str) : type of run ("prod" or "pilot")
seed (int) : seed to seed random processes with
sess_n (int) : session number
stats (str) : stats to take, i.e., "mean" or "median"
stimtype (str) : stim to analyse ("gabors" or "visflow")
train_p (list) : proportion of dataset to allocate to
training
uniqueid (str or int) : unique ID for analysis
wd (float) : weight decay value (for "pytorch" arg)
"""
args = copy.deepcopy(args)
if args.datadir is None:
args.datadir = DEFAULT_DATADIR
else:
args.datadir = Path(args.datadir)
if args.uniqueid == "datetime":
args.uniqueid = gen_util.create_time_str()
elif args.uniqueid in ["None", "none"]:
args.uniqueid = None
reseed = False
if args.seed in [None, "None"]:
reseed = True
# deal with parameters
extrapar = {"uniqueid": args.uniqueid, "seed": args.seed}
techpar = {
"reseed": reseed,
"device": args.device,
"alg": args.alg,
"parallel": args.parallel,
"plt_bkend": args.plt_bkend,
"fontdir": args.fontdir,
"output": args.output,
"ep_freq": args.ep_freq,
"n_reg": args.n_reg,
"n_shuff": args.n_shuff,
}
mouse_df = DEFAULT_MOUSE_DF_PATH
stimpar = logreg.get_stimpar(args.comp,
args.stimtype,
args.visflow_dir,
args.visflow_size,
args.gabfr,
args.gabk,
gab_ori=args.gab_ori,
visflow_pre=args.visflow_pre)
analyspar = sess_ntuple_util.init_analyspar(args.fluor,
stats=args.stats,
error=args.error,
scale=not (args.no_scale),
dend=args.dend)
if args.q1v4:
quantpar = sess_ntuple_util.init_quantpar(4, [0, -1])
else:
quantpar = sess_ntuple_util.init_quantpar(1, 0)
logregpar = sess_ntuple_util.init_logregpar(args.comp, not (args.not_ctrl),
args.q1v4, args.exp_v_unexp,
args.n_epochs, args.batchsize,
args.lr, args.train_p, args.wd,
args.bal, args.alg)
omit_sess, omit_mice = sess_gen_util.all_omit(stimpar.stimtype,
args.runtype,
stimpar.visflow_dir,
stimpar.visflow_size,
stimpar.gabk)
sessids = sorted(
sess_gen_util.get_sess_vals(mouse_df,
"sessid",
args.mouse_n,
args.sess_n,
args.runtype,
incl=args.incl,
omit_sess=omit_sess,
omit_mice=omit_mice))
if len(sessids) == 0:
logger.warning(
f"No sessions found (mouse: {args.mouse_n}, sess: {args.sess_n}, "
f"runtype: {args.runtype})")
for sessid in sessids:
sess = sess_gen_util.init_sessions(sessid,
args.datadir,
mouse_df,
args.runtype,
full_table=False,
fluor=analyspar.fluor,
dend=analyspar.dend,
temp_log="warning")[0]
logreg.run_regr(sess, analyspar, stimpar, logregpar, quantpar,
extrapar, techpar)
plot_util.cond_close_figs()
def init_param_cont(args):
"""
init_param_cont(args)
Initializes parameter containers.
Returns args:
- in the following nametuples: analyspar, sesspar, stimpar, autocorr,
permpar, quantpar
- in the following dictionary: figpar
Required args:
- args (Argument parser): parser with the following attributes:
visflow_dir (str or list): visual flow direction values to include
("right", "left", ["right", "left"])
visflow_size (int or list): visual flow size values to include
(128, 256 or [128, 256])
closest (bool) : if False, only exact session number is
retained, otherwise the closest.
error (str) : error statistic parameter ("std" or "sem")
fontdir (str) : path to directory containing additional
fonts
gabfr (int) : gabor frame at which sequences start
(0, 1, 2, 3)
gabk (int or list) : gabor kappa values to include
(4, 16 or [4, 16])
gab_ori (int or list) : gabor orientation values to include
([0, 45, 90, 135, 180, 225])
incl (str) : sessions to include ("yes", "no", "all")
keepnans (str) : if True, the original running array is
used instead of the one where NaNs
are interpolated.
lag_s (num) : lag for autocorrelation (in sec)
line (str) : line ("L23", "L5", "any")
min_rois (int) : min number of ROIs
n_perms (int) : nbr of permutations to run
n_quants (int) : number of quantiles
ncols (int) : number of columns
no_datetime (bool) : if True, figures are not saved in a
subfolder named based on the date and time.
no_sharey (bool) : if True, sharey figure parameter is set to
False.
not_save_fig (bool) : if True, figures are not saved
output (str) : general directory in which to save output
overwrite (bool) : if False, overwriting existing figures
is prevented by adding suffix numbers.
pass_fail (str or list): pass/fail values of interest ("P", "F")
plane (str) : plane ("soma", "dend", "any")
plt_bkend (str) : mpl backend to use
post (num) : range of frames to include after each
reference frame (in s)
pre (num) : range of frames to include before each
reference frame (in s)
runtype (str or list) : runtype ("pilot" or "prod")
scale (bool) : whether to scale running data
sess_n (int) : session number
stats (str) : statistic parameter ("mean" or "median")
stimtype (str) : stimulus to analyse ("visflow" or "gabors")
tails (str or int) : which tail(s) to test ("hi", "lo", 2)
Returns:
- analysis_dict (dict): dictionary of analysis parameters
["analyspar"] (AnalysPar) : named tuple of analysis parameters
["sesspar"] (SessPar) : named tuple of session parameters
["stimpar"] (StimPar) : named tuple of stimulus parameters
["autocorrpar"] (AutocorrPar): named tuple of autocorrelation
parameters
["permpar"] (PermPar) : named tuple of permutation parameters
["quantpar"] (QuantPar) : named tuple of quantile parameters
["figpar"] (dict) : dictionary containing following
subdictionaries:
["init"]: dict with following inputs as attributes:
["ncols"] (int) : number of columns in the figures
["sharex"] (bool) : if True, x axis lims are shared
across subplots
["sharey"] (bool) : if True, y axis lims are shared
across subplots
["subplot_hei"] (num): height of each subplot (inches)
["subplot_wid"] (num): width of each subplot (inches)
["save"]: dict with the following inputs as attributes:
["datetime"] (bool) : if True, figures are saved in a
subfolder named based on the date and
time.
["fig_ext"] (str) : figure extension
["overwrite"] (bool): if True, existing figures can be
overwritten
["save_fig"] (bool) : if True, figures are saved
["use_dt"] (str) : datetime folder to use
["dirs"]: dict with the following attributes:
["figdir"] (str) : main folder in which to save figures
["roi"] (str) : subdirectory name for ROI analyses
["run"] (str) : subdirectory name for running analyses
["autocorr"] (str) : subdirectory name for autocorrelation
analyses
["locori"] (str) : subdirectory name for location and
orientation responses
["oridir"] (str) : subdirectory name for
orientation/direction analyses
["unexp_qu"] (str) : subdirectory name for unexpected,
quantile analyses
["tune_curv"] (str): subdirectory name for tuning curves
["grped"] (str) : subdirectory name for ROI grps data
["mags"] (str) : subdirectory name for magnitude
analyses
["mng"]: dict with the following attributes:
["plt_bkend"] (str): mpl backend to use
["linclab"] (bool) : if True, Linclab mpl defaults are used
["fontdir"] (str) : path to directory containing
additional fonts
"""
args = copy.deepcopy(args)
analysis_dict = dict()
# analysis parameters
analysis_dict["analyspar"] = sess_ntuple_util.init_analyspar(
"n/a", not (args.keepnans), args.stats, args.error, args.scale)
# session parameters
analysis_dict["sesspar"] = sess_ntuple_util.init_sesspar(
args.sess_n, args.closest, args.plane, args.line, args.min_rois,
args.pass_fail, args.incl, args.runtype)
# stimulus parameters
analysis_dict["stimpar"] = sess_ntuple_util.init_stimpar(
args.stimtype, args.visflow_dir, args.visflow_size, args.gabfr,
args.gabk, args.gab_ori, args.pre, args.post)
# SPECIFIC ANALYSES
# autocorrelation parameters
analysis_dict["autocorrpar"] = sess_ntuple_util.init_autocorrpar(
args.lag_s, byitem=False)
# permutation parameters
analysis_dict["permpar"] = sess_ntuple_util.init_permpar(
args.n_perms, 0.05, args.tails, False)
# quantile parameters
analysis_dict["quantpar"] = sess_ntuple_util.init_quantpar(
args.n_quants, [0, -1])
# figure parameters
analysis_dict["figpar"] = sess_plot_util.init_figpar(
ncols=int(args.ncols),
datetime=not (args.no_datetime),
overwrite=args.overwrite,
save_fig=not (args.not_save_fig),
runtype=args.runtype,
output=args.output,
plt_bkend=args.plt_bkend,
fontdir=args.fontdir,
sharey=not (args.no_sharey))
return analysis_dict
def init_param_cont(args):
"""
init_param_cont(args)
Initializes parameter containers.
Returns args:
- in the following nametuples: analyspar, sesspar, stimpar, autocorr,
permpar, quantpar, roigrppar, tcurvpar
- in the following dictionary: figpar
Required args:
- args (Argument parser): parser with the following attributes:
visflow_dir (str or list): visual flow direction values to include
("right", "left", ["right", "left"])
visflow_size (int or list): visual flow size values to include
(128, 256 or [128, 256])
closest (bool) : if False, only exact session number is
retained, otherwise the closest.
dend (str) : type of dendrites to use ("allen" or "dend")
error (str) : error statistic parameter ("std" or "sem")
fluor (str) : if "raw", raw ROI traces are used. If
"dff", dF/F ROI traces are used.
fontdir (str) : path to directory containing additional
fonts
gabfr (int) : gabor frame at which sequences start
(0, 1, 2, 3)
gabk (int or list) : gabor kappa values to include
(4, 16 or [4, 16])
gab_ori (int or list) : gabor orientation values to include
([0, 45, 90, 135, 180, 225])
grps (str or list) : set or sets of groups to return,
("all", "change", "no_change", "reduc",
"incr".)
incl (str) : sessions to include ("yes", "no", "all")
keepnans (str) : if True, ROIs with NaN/Inf values are
kept in the analyses.
lag_s (num) : lag for autocorrelation (in sec)
line (str) : "L23", "L5", "any"
min_rois (int) : min number of ROIs
n_perms (int) : nbr of permutations to run
n_quants (int) : number of quantiles
ncols (int) : number of columns
no_add_exp (bool) : if True, the group of ROIs showing no
significance in either is not added to
the groups returned
no_datetime (bool) : if True, figures are not saved in a
subfolder named based on the date and time.
not_byitem (bool) : if True, autocorrelation statistics are
taken across items (e.g., ROIs)
not_save_fig (bool) : if True, figures are not saved
op (str) : operation on values, if plotvals if "both"
("ratio" or "diff")
output (str) : general directory in which to save output
overwrite (bool) : if False, overwriting existing figures
is prevented by adding suffix numbers.
pass_fail (str or list): pass/fail values of interest ("P", "F")
plot_vals (str) : values to plot ("unexp", "exp", "both")
plane (str) : plane ("soma", "dend", "any")
plt_bkend (str) : mpl backend to use
post (num) : range of frames to include after each
reference frame (in s)
pre (num) : range of frames to include before each
reference frame (in s)
runtype (str or list) : runtype ("pilot" or "prod")
scale (bool) : whether to scale ROI data
sess_n (int) : session number
stats (str) : statistic parameter ("mean" or "median")
stimtype (str) : stimulus to analyse ("visflow" or "gabors")
tails (str or int) : which tail(s) to test ("hi", "lo", 2)
tc_gabfr (int or str) : gabor frame at which sequences start
(0, 1, 2, 3) for tuning curve analysis
(x_x, interpreted as 2 gabfrs)
tc_grp2 (str) : second group: either unexp, exp or rand
(random subsample of exp, the size of
unexp)
tc_post (num) : range of frames to include after each
reference frame (in s) for tuning curve
analysis
tc_vm_estim (bool) : runs analysis using von Mises parameter
estimation method
tc_test (bool) : if True, tuning curve analysis is run on a
small subset of ROIs and gabors
Returns:
- analysis_dict (dict): dictionary of analysis parameters
["analyspar"] (AnalysPar) : named tuple of analysis parameters
["sesspar"] (SessPar) : named tuple of session parameters
["stimpar"] (StimPar) : named tuple of stimulus parameters
["autocorrpar"] (AutocorrPar): named tuple of autocorrelation
parameters
["permpar"] (PermPar) : named tuple of permutation parameters
["quantpar"] (QuantPar) : named tuple of quantile parameters
["roigrppar"] (RoiGrpPar) : named tuple of roi grp parameters
["tcurvpar"] (TCurvPar) : named tuple of tuning curve
parameters
["figpar"] (dict) : dictionary containing following
subdictionaries:
["init"]: dict with following inputs as attributes:
["ncols"] (int) : number of columns in the figures
["sharex"] (bool) : if True, x axis lims are shared
across subplots
["sharey"] (bool) : if True, y axis lims are shared
across subplots
["subplot_hei"] (num): height of each subplot (inches)
["subplot_wid"] (num): width of each subplot (inches)
["save"]: dict with the following inputs as attributes:
["datetime"] (bool) : if True, figures are saved in a
subfolder named based on the date and
time.
["fig_ext"] (str) : figure extension
["overwrite"] (bool): if True, existing figures can be
overwritten
["save_fig"] (bool) : if True, figures are saved
["use_dt"] (str) : datetime folder to use
["dirs"]: dict with the following attributes:
["figdir"] (str) : main folder in which to save figures
["roi"] (str) : subdirectory name for ROI analyses
["run"] (str) : subdirectory name for running analyses
["autocorr"] (str) : subdirectory name for autocorrelation
analyses
["locori"] (str) : subdirectory name for location and
orientation responses
["oridir"] (str) : subdirectory name for
orientation/direction analyses
["unexp_qu"] (str) : subdirectory name for unexpected,
quantile analyses
["tune_curv"] (str): subdirectory name for tuning curves
["grped"] (str) : subdirectory name for ROI grps data
["mags"] (str) : subdirectory name for magnitude
analyses
["mng"]: dict with the following attributes:
["plt_bkend"] (str): mpl backend to use
["linclab"] (bool) : if True, Linclab mpl defaults are used
["fontdir"] (str) : path to directory containing
additional fonts
"""
args = copy.deepcopy(args)
analysis_dict = dict()
# analysis parameters
analysis_dict["analyspar"] = sess_ntuple_util.init_analyspar(
args.fluor, not(args.keepnans), args.stats, args.error, args.scale,
dend=args.dend)
# session parameters
analysis_dict["sesspar"] = sess_ntuple_util.init_sesspar(
args.sess_n, args.closest, args.plane, args.line, args.min_rois,
args.pass_fail, args.incl, args.runtype)
# stimulus parameters
analysis_dict["stimpar"] = sess_ntuple_util.init_stimpar(
args.stimtype, args.visflow_dir, args.visflow_size, args.gabfr, args.gabk,
args.gab_ori, args.pre, args.post)
# SPECIFIC ANALYSES
# autocorrelation parameters
analysis_dict["autocorrpar"] = sess_ntuple_util.init_autocorrpar(
args.lag_s, not(args.not_byitem))
# permutation parameters
analysis_dict["permpar"] = sess_ntuple_util.init_permpar(
args.n_perms, 0.05, args.tails)
# quantile parameters
analysis_dict["quantpar"] = sess_ntuple_util.init_quantpar(
args.n_quants, [0, -1])
# roi grp parameters
analysis_dict["roigrppar"] = sess_ntuple_util.init_roigrppar(
args.grps, not(args.no_add_exp), args.op, args.plot_vals)
# tuning curve parameters
analysis_dict["tcurvpar"] = sess_ntuple_util.init_tcurvpar(
args.tc_gabfr, 0, args.tc_post, args.tc_grp2, args.tc_test,
args.tc_vm_estim)
# figure parameters
analysis_dict["figpar"] = sess_plot_util.init_figpar(
ncols=int(args.ncols), datetime=not(args.no_datetime),
overwrite=args.overwrite, save_fig=not(args.not_save_fig),
runtype=args.runtype, output=args.output, plt_bkend=args.plt_bkend,
fontdir=args.fontdir)
return analysis_dict