def get_sensitivity(permpar):
"""
get_sensitivity(permpar)
Returns p-value sensitivity, i.e., the smallest non zero p-value that can
be measured given the number of permutations used and Bonferroni
corrections for number of tails and multiple comparisons.
Required args:
- permpar (PermPar or dict):
named tuple containing permutation parameters
Returns:
- sensitivity (float):
minimum theoretical p-value
"""
if isinstance(permpar, dict):
permpar = sess_ntuple_util.init_permpar(**permpar)
n_tails = 1 if permpar.tails in ["lo", "hi"] else int(permpar.tails)
sensitivity = n_tails / permpar.n_perms
if permpar.multcomp:
sensitivity *= permpar.multcomp
return sensitivity
def set_multcomp(permpar, sess_df, CIs=True, pairs=True, factor=1):
"""
set_multcomp(permpar)
Returns permpar updated with the number of comparisons computed from the
sessions.
Required args:
- permpar (PermPar):
named tuple containing permutation parameters
- sess_df (pd.DataFrame):
dataframe containing session information, including the following
keys: "sess_ns", "lines", "planes"
Optional args:
- CIs (bool):
include comparisons to CIs comparisons
default: True
- pairs (bool):
include paired comparisons
default: True
- factor (int):
additional factor by which to multiply the number of comparisons
default: 1
Returns:
- permpar (PermPar):
updated permutation parameter named tuple
"""
if isinstance(permpar, dict):
permpar = sess_ntuple_util.init_permpar(**permpar)
n_comps = 0
for _, sess_df_grp in sess_df.groupby(["lines", "planes"]):
n_sess = len(sess_df_grp)
# sessions compared to CIs
if CIs:
n_comps += n_sess
# session pair comparisons
if pairs:
k = 2
if n_sess >= k:
fact = np.math.factorial
n_comps += fact(n_sess) / (fact(k) * fact(n_sess - k))
# multiplied by specified factor
n_comps *= factor
permpar = sess_ntuple_util.get_modif_ntuple(
permpar, "multcomp", int(n_comps)
)
return permpar
def add_corr_p_vals(df, permpar, raise_multcomp=True):
"""
add_corr_p_vals(df, permpar)
Returns dataframe with p-values, corrected for tails and multiple
comparisons, added, if permpar.multcomp is True. If any case, original
"p_vals" column names are returned as "raw_p_vals" instead.
Required args:
- df (pd.DataFrame):
dataframe with p-value columns ("p_vals" in column names)
- permpar (PermPar or dict):
named tuple containing permutation parameters
Optional args:
- raise_multcomp (bool):
if True, an error is raised if permpar.multcomp is False
default: True
Returns:
- df (pd.DataFrame):
dataframe with raw p-value columns names changed to "raw_{}",
and corrected p-value columns added, if permpar.multcomp
"""
if isinstance(permpar, dict):
permpar = sess_ntuple_util.init_permpar(**permpar)
p_val_cols = [col for col in df.columns if "p_vals" in col]
if sum(["raw_p_vals" in col for col in p_val_cols]):
raise ValueError(
"Function converts 'p_vals' columns to 'raw_p_vals' columns. "
"Dataframe should not already contain columns with 'raw_p_vals' "
"in name.")
new_col_names = {
col: col.replace("p_vals", "raw_p_vals") for col in p_val_cols
}
df = df.rename(columns=new_col_names)
# define function with arguments for use with .map()
correct_p_val_fct = lambda x: get_corrected_p_val(
x, permpar, raise_multcomp
)
for corr_p_val_col in p_val_cols:
raw_p_val_col = corr_p_val_col.replace("p_vals", "raw_p_vals")
df[corr_p_val_col] = df[raw_p_val_col].map(correct_p_val_fct)
return df
def get_comp_info(permpar):
"""
get_comp_info(permpar)
Returns p-value correction information.
Required args:
- permpar (PermPar or dict):
named tuple containing permutation parameters
Returns:
- full_comp_info (str):
string containing tails and multiple comparisons information
"""
if isinstance(permpar, dict):
permpar = sess_ntuple_util.init_permpar(**permpar)
if permpar.tails == "lo":
comp_info = "one-tailed"
elif permpar.tails == "hi":
comp_info = "one-tailed"
elif int(permpar.tails) == 2:
comp_info = "two-tailed"
else:
gen_util.accepted_values_error(
"permpar.tails", permpar.tails, ["lo", "hi", 2]
)
if permpar.multcomp:
comp_info = f"{int(permpar.multcomp)} comparisons, {comp_info}"
corr_str = "Corrected"
else:
corr_str = "Raw"
full_comp_info = f"{corr_str} p-values ({comp_info})"
return full_comp_info
def get_corrected_p_val(p_val, permpar, raise_multcomp=True):
"""
get_corrected_p_val(p_val, permpar)
Returns p-value, Bonferroni corrected for number of tails and multiple
comparisons.
Required args:
- p_val (float):
raw p-value
- permpar (PermPar or dict):
named tuple containing permutation parameters
Optional args:
- raise_multcomp (bool):
if True, an error is raised if permpar.multcomp is False
default: True
Returns:
- corr_p_val (float):
corrected p-value
"""
if isinstance(permpar, dict):
permpar = sess_ntuple_util.init_permpar(**permpar)
n_tails = 1 if permpar.tails in ["lo", "hi"] else int(permpar.tails)
corr_p_val = p_val * n_tails
if permpar.multcomp:
corr_p_val *= permpar.multcomp
elif raise_multcomp:
raise ValueError("permpar.multcomp is set to False.")
corr_p_val = np.min([corr_p_val, 1])
return corr_p_val
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_analysis(args):
"""
init_analysis(args)
Initializes analysis parameters based on input arguments containers.
Required args:
- args (dict):
parser argument dictionary
Returns:
- analysis_dict (dict):
dictionary of analysis parameters
["analyspar"] (AnalysPar): named tuple of analysis parameters
["sesspar"] (SessPar): named tuple with session parameters
["stimpar"] (StimPar): named tuple with stimulus parameters
["basepar"] (LatPar): named tuple with latency parameters
["idxpar"] (PermPar): named tuple with unexpected event index
parameters
["logregpar"] (LogRegPar):
named tuple with logistic regression parameters
["permpar"] (PermPar): named tuple with permutation parameters
["figpar"] (dict): dictionary containing subdictionaries
(see sess_plot_util.init_figpar), with fig_panel_analysis
added under the "fig_panel_analysis" key.
"""
args = copy.deepcopy(args)
fig_panel_analysis = paper_organization.FigurePanelAnalysis(
figure=args.figure,
panel=args.panel,
datadir=args.datadir,
mouse_df_path=args.mouse_df_path,
output=args.output,
full_power=args.full_power,
seed=args.seed,
parallel=args.parallel,
plt_bkend=args.plt_bkend,
fontdir=args.fontdir,
)
specific_params = fig_panel_analysis.specific_params
sess_n = reformat_sess_n(specific_params["sess_n"])
analysis_dict = dict()
# analysis parameters
analysis_dict["analyspar"] = sess_ntuple_util.init_analyspar(
fluor="dff", # type of fluorescence data to use (dF/F)
rem_bad=specific_params[
"rem_bad"], # whether to remove bad ROIs OR interpolate bad values in run or pupil data
stats="mean", # type of statistic to measure (mean/median)
error=specific_params["error"], # type of error to measure (std/SEM)
scale=specific_params[
"scale"], # whether to scale ROIs (robust scaling)
tracked=specific_params["tracked"], # whether to use only tracked ROIs
)
# session inclusion parameters
analysis_dict["sesspar"] = sess_ntuple_util.init_sesspar(
sess_n=sess_n, # session number(s)
plane=specific_params["plane"], # recording plane(s)
line=specific_params["line"], # mouse line(s)
pass_fail="P", # include sessions that passed QC
incl="all", # include all remaining sessions
runtype="prod", # production run data
mouse_n=specific_params["mouse_n"], # mouse numbers
)
# stimulus analysis parameters
analysis_dict["stimpar"] = sess_ntuple_util.init_stimpar(
stimtype=specific_params["stimtype"], # stimulus to analyse
visflow_dir=specific_params["visflow_dir"], # visual flow directions
visflow_size=specific_params[
"visflow_size"], # visual flow square sizes
gabfr=specific_params["gabfr"], # Gabor frame to center analyses on
gabk=specific_params["gabk"], # Gabor orientation kappas
gab_ori=specific_params["gab_ori"], # mean Gabor orientations
pre=specific_params["pre"], # number of seconds pre reference frame
post=specific_params["post"] # number of seconds post reference frame
)
# baseline parameters
analysis_dict["basepar"] = sess_ntuple_util.init_basepar(
baseline=0, # sequence baselining (None)
)
# USI analysis parameters
analysis_dict["idxpar"] = sess_ntuple_util.init_idxpar(
op="d-prime", # USI measure
feature=specific_params["idx_feature"], # how to select sequences
)
# logistic regression parameters
analysis_dict["logregpar"] = sess_ntuple_util.init_logregpar(
comp=specific_params["comp"], # classes
ctrl=True, # control for dataset size
n_epochs=1000, # number of training epochs
batchsize=200, # batch size
lr=0.0001, # learning rate
train_p=0.75, # train:test split
wd=0, # weight decay to use (None)
)
# permutation analysis parameters
analysis_dict["permpar"] = sess_ntuple_util.init_permpar(
n_perms=fig_panel_analysis.n_perms, # number of permutations to run
p_val=0.05, # significance threshold to consider
tails=specific_params["tails"], # number of tails
multcomp=False # multiple comparisons
)
# figure plotting parameters
analysis_dict["figpar"] = sess_plot_util.init_figpar(
datetime=False,
overwrite=args.overwrite,
runtype="prod",
output=args.output,
plt_bkend=args.plt_bkend,
fontdir=args.fontdir,
paper=True,
)
analysis_dict["figpar"]["fig_panel_analysis"] = fig_panel_analysis
return analysis_dict
def init_param_cont(args):
"""
init_param_cont(args)
Initializes parameter containers.
Returns args:
- in the following nametuples: analyspar, sesspar, stimpar, permpar,
basepar, idxpar, latpar
- in the following dictionary: figpar
Required args:
- args (Argument parser): parser with the following attributes:
base (float) : baseline value to use
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])
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])
idx_feature (str) : feature used to calculate index
("by_exp", "unexp_lock", "prog_unexp")
idx_op (str) : type of index to use
("d-prime", "rel_diff", "diff")
idx_position (int) : position to use if using a "prog" feature
to calculate index (e.g., 0)
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)
lat_method (str) : latency calculation method
(ratio or ttest)
lat_not_unexp_resp (bool): if False, only unexpected event
responsive ROIs are used for latency
analysis
lat_p_val_thr (float) : p-value threshold for ttest latency
method
lat_std (float) : standard deviation threshold for ratio
latency method
line (str) : "L23", "L5", "any"
min_rois (int) : min number of ROIs
n_perms (int) : nbr of permutations to run
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_scale (bool) : if True, data is not scaled
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")
p_val (float) : p-value threshold for significane tests
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)
rel_std (float) : relative st. dev. threshold for ratio
latency method
runtype (str or list) : runtype ("pilot" or "prod")
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
["permpar"] (PermPar) : named tuple of permutation parameters
["basepar"] (BasePar) : named tuple of baseline parameters
["idxpar"] (IdxPar) : named tuple of unexpected index parameters
["latpar"] (LatPar) : named tuple of latency 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,
scale=not (args.no_scale),
dend=args.dend)
# session parameters
analysis_dict["sesspar"] = sess_ntuple_util.init_sesspar(
args.sess_n, False, args.plane, args.line, args.min_rois,
args.pass_fail, args.incl, args.runtype, args.mouse_ns)
# 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
analysis_dict["permpar"] = sess_ntuple_util.init_permpar(
args.n_perms, args.p_val, args.tails, False)
analysis_dict["basepar"] = sess_ntuple_util.init_basepar(args.base)
analysis_dict["idxpar"] = sess_ntuple_util.init_idxpar(
args.idx_op, args.idx_feature, args.idx_position)
analysis_dict["latpar"] = sess_ntuple_util.init_latpar(
args.lat_method, args.lat_p_val_thr, args.lat_rel_std,
not (args.lat_not_unexp_resp))
# 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
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