def stimulus_onset_sess123(sessions,
analyspar,
sesspar,
stimpar,
basepar,
figpar,
parallel=False):
"""
stimulus_onset_sess123(sessions, analyspar, sesspar, stimpar, basepar,
figpar)
Retrieves ROI responses to stimulus onset from sessions 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling stimulus onset sequences from session 1 to 3.",
extra={"spacing": "\n"})
split = "stim_onset"
trace_df = seq_analys.get_sess_grped_trace_df(sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
split=split,
parallel=parallel)
extrapar = {
"split": split,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"basepar": basepar._asdict(),
"extrapar": extrapar,
"trace_df": trace_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def nrois_sess123(sessions, analyspar, sesspar, figpar):
"""
nrois_sess123(sessions, analyspar, sesspar, figpar)
Retrieves number of ROIs for sessions 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- session (Session):
Session object
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- figpar (dict):
dictionary containing figure parameters
"""
logger.info("Compiling ROI numbers from session 1 to 3.",
extra={"spacing": "\n"})
nrois_df = misc_analys.get_check_sess_df(sessions, analyspar=analyspar)
extrapar = dict()
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"extrapar": extrapar,
"nrois_df": nrois_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def pupil_run_responses(sessions,
analyspar,
sesspar,
stimpar,
basepar,
figpar,
parallel=False):
"""
pupil_run_responses(sessions, analyspar, sesspar, stimpar, basepar, figpar)
Retrieves pupil and running responses to Gabor sequences for session 1.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling pupil and running sequences for session 1.",
extra={"spacing": "\n"})
split = "by_exp"
trace_df = behav_analys.get_pupil_run_trace_stats_df(sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
split=split,
parallel=parallel)
extrapar = {"split": split}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"basepar": basepar._asdict(),
"extrapar": extrapar,
"trace_df": trace_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def roi_overlays_sess123_enlarged(sessions,
analyspar,
sesspar,
figpar,
parallel=False):
"""
roi_overlays_sess123_enlarged(sessions, analyspar, sesspar, figpar)
Retrieves enlarged ROI mask overlay examples for sessions 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- session (Session):
Session object
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling enlarged ROI mask overlay examples.",
extra={"spacing": "\n"})
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
# remove incomplete session series and warn
sessions = misc_analys.check_sessions_complete(sessions)
roi_mask_df = roi_analys.get_roi_tracking_df(
sessions,
analyspar=analyspar,
reg_only=True,
proj=False,
crop_info="large",
parallel=parallel,
)
extrapar = dict()
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"extrapar": extrapar,
"roi_mask_df": roi_mask_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def mean_signal_sess123(sessions, analyspar, sesspar, figpar, parallel=False):
"""
mean_signal_sess123(sessions, analyspar, sesspar, figpar)
Retrieves ROI mean signal values for sessions 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- session (Session):
Session object
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling ROI signal means from session 1 to 3.",
extra={"spacing": "\n"})
logger.info("Calculating ROI signal means for each session...",
extra={"spacing": TAB})
all_signal_means = gen_util.parallel_wrap(misc_analys.get_snr,
sessions,
[analyspar, "signal_means"],
parallel=parallel)
sig_mean_df = misc_analys.get_check_sess_df(sessions, analyspar=analyspar)
sig_mean_df["signal_means"] = [
sig_mean.tolist() for sig_mean in all_signal_means
]
extrapar = dict()
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"extrapar": extrapar,
"sig_mean_df": sig_mean_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def roi_corr_sess123(sessions, analyspar, sesspar, figpar, parallel=False):
"""
roi_corr_sess123(sessions, analyspar, sesspar, figpar)
Retrieves ROI correlation values for sessions 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- session (Session):
Session object
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling ROI correlations from session 1 to 3.",
extra={"spacing": "\n"})
logger.info("Calculating ROI correlations for each session...",
extra={"spacing": TAB})
rolling_win = 4
corr_df = misc_analys.get_all_correlations(sessions,
analyspar=analyspar,
rolling_win=rolling_win,
parallel=parallel)
extrapar = {
"rolling_win": rolling_win,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"extrapar": extrapar,
"corr_df": corr_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def imaging_planes(sessions, sesspar, figpar, parallel=False):
"""
imaging_planes(sessions, sesspar, figpar)
Retrieves imaging plane image examples.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- session (Session):
Session object
- sesspar (SessPar):
named tuple containing session parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling imaging plane projection examples.",
extra={"spacing": "\n"})
imaging_plane_df = misc_analys.get_check_sess_df(sessions, roi=False)
imaging_plane_df["max_projections"] = [
sess.max_proj.tolist() for sess in sessions
]
extrapar = dict()
info = {
"sesspar": sesspar._asdict(),
"extrapar": extrapar,
"imaging_plane_df": imaging_plane_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def pupil_run_block_diffs(sessions,
analyspar,
sesspar,
stimpar,
permpar,
figpar,
seed=None,
parallel=False):
"""
pupil_run_block_diffs(sessions, analyspar, sesspar, stimpar, permpar,
figpar)
Retrieves pupil and running block differences for Gabor sequences for
session 1.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling pupil and running block differences for session 1.",
extra={"spacing": "\n"})
permpar = sess_ntuple_util.get_modif_ntuple(permpar, "multcomp", False)
block_df = behav_analys.get_pupil_run_block_stats_df(sessions,
analyspar=analyspar,
stimpar=stimpar,
permpar=permpar,
randst=seed,
parallel=parallel)
extrapar = {"seed": seed}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"block_df": block_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def unexp_resp_stimulus_comp_sess1v3(sessions,
analyspar,
sesspar,
stimpar,
permpar,
figpar,
seed=None,
parallel=False):
"""
unexp_resp_stimulus_comp_sess1v3(sessions, analyspar, sesspar, stimpar,
permpar, figpar)
Retrieves changes in tracked ROI responses to unexpected sequences for
Gabors vs visual flow stimuli.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info(
("Compiling changes in unexpected responses to Gabor vs visual "
"flow stimuli."),
extra={"spacing": "\n"})
if analyspar.scale:
raise ValueError("analyspar.scale should be set to False.")
# calculate multiple comparisons
dummy_df = misc_analys.get_check_sess_df(
sessions, None, analyspar).drop_duplicates(subset=["lines", "planes"])
multcomp = len(dummy_df) + 1
permpar = sess_ntuple_util.get_modif_ntuple(permpar, "multcomp", multcomp)
comp_sess = [1, 3]
datatype = "rel_unexp_resp"
rel_sess = 1
pop_stats = True
unexp_comp_df = stim_analys.get_stim_stats_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
permpar=permpar,
comp_sess=comp_sess,
datatype=datatype,
rel_sess=rel_sess,
pop_stats=pop_stats,
randst=seed,
parallel=parallel,
)
extrapar = {
"comp_sess": comp_sess,
"datatype": datatype,
"rel_sess": rel_sess,
"pop_stats": pop_stats,
"seed": seed,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"unexp_comp_df": unexp_comp_df.to_dict(),
}
helper_fcts.plot_save_all(info, figpar)
def tracked_roi_usis_stimulus_comp_sess1v3(sessions,
analyspar,
sesspar,
stimpar,
basepar,
idxpar,
permpar,
figpar,
seed=None,
parallel=False):
"""
tracked_roi_usis_stimulus_comp_sess1v3(sessions, analyspar, sesspar,
stimpar, basepar, idxpar, permpar,
figpar)
Retrieves changes in tracked ROI USIs for Gabors vs visual flow stimuli.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info(
("Compiling changes in ROI USIs for Gabors vs visual flow stimuli."),
extra={"spacing": "\n"})
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
# remove incomplete session series and warn
sessions = misc_analys.check_sessions_complete(sessions)
# calculate multiple comparisons
dummy_df = misc_analys.get_check_sess_df(
sessions, None, analyspar).drop_duplicates(subset=["lines", "planes"])
multcomp = len(dummy_df) + 1
permpar = sess_ntuple_util.get_modif_ntuple(permpar, "multcomp", multcomp)
comp_sess = [1, 3]
datatype = "usis"
pop_stats = True
usi_comp_df = stim_analys.get_stim_stats_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
comp_sess=comp_sess,
datatype=datatype,
pop_stats=pop_stats,
randst=seed,
parallel=parallel,
)
extrapar = {
"comp_sess": comp_sess,
"datatype": datatype,
"pop_stats": pop_stats,
"seed": seed,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"basepar": basepar._asdict(),
"idxpar": idxpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"usi_comp_df": usi_comp_df.to_dict(),
}
helper_fcts.plot_save_all(info, figpar)
def gabor_corrs_sess123_comps(sessions,
analyspar,
sesspar,
stimpar,
basepar,
idxpar,
permpar,
figpar,
seed=None,
parallel=False):
"""
gabor_corrs_sess123_comps(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar)
Retrieves tracked ROI Gabor USI correlations for session 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info(
"Compiling tracked ROI Gabor USI correlations for sessions 1 to 3.",
extra={"spacing": "\n"})
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
# remove incomplete session series and warn
sessions = misc_analys.check_sessions_complete(sessions)
consec_only = True
permpar = corr_analys.set_multcomp(permpar,
sessions,
analyspar,
consec_only=consec_only)
permute = PERMUTE
corr_type = CORR_TYPE
sig_only = SIG_ONLY
if "R_sqr" in corr_type:
permpar = sess_ntuple_util.get_modif_ntuple(permpar, "tails", "hi")
idx_corr_df = corr_analys.get_idx_corrs_df(sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
consec_only=consec_only,
permute=permute,
corr_type=corr_type,
sig_only=sig_only,
randst=seed,
parallel=parallel)
extrapar = {
"consec_only": consec_only,
"corr_type": corr_type,
"permute": permute,
"seed": seed,
"sig_only": sig_only,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"basepar": basepar._asdict(),
"idxpar": idxpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"idx_corr_df": idx_corr_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def corr_scatterplots(sessions,
analyspar,
sesspar,
stimpar,
basepar,
idxpar,
permpar,
figpar,
seed=None,
parallel=False):
"""
corr_scatterplots(sessions, analyspar, sesspar, stimpar, basepar, idxpar,
permpar, figpar)
Retrieves tracked ROI USI correlation scatterplot data for a session pair.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
# remove incomplete session series and warn
sessions = misc_analys.check_sessions_complete(sessions)
permpar = corr_analys.set_multcomp(permpar, sessions, analyspar, factor=2)
permute = PERMUTE
sig_only = SIG_ONLY
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
idx_corr_df = corr_analys.corr_scatterplots(sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
permute=permute,
sig_only=sig_only,
randst=seed,
parallel=parallel)
extrapar = {
"permute": permute,
"seed": seed,
"sig_only": sig_only,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"basepar": basepar._asdict(),
"idxpar": idxpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"idx_corr_df": idx_corr_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def gabor_sequence_diffs_sess123(sessions,
analyspar,
sesspar,
stimpar,
basepar,
permpar,
figpar,
seed=None,
parallel=False):
"""
gabor_sequence_diffs_sess123(sessions, analyspar, sesspar, stimpar,
basepar, permpar, figpar)
Retrieves differences in ROI responses to Gabor sequences from
sessions 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling Gabor sequence differences from session 1 to 3.",
extra={"spacing": "\n"})
# calculate multiple comparisons
dummy_df = misc_analys.get_check_sess_df(
sessions, None,
analyspar).drop_duplicates(subset=["lines", "planes", "sess_ns"])
permpar = misc_analys.set_multcomp(permpar, sess_df=dummy_df)
split = "by_exp"
diffs_df = seq_analys.get_sess_grped_diffs_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
permpar=permpar,
split=split,
randst=seed,
parallel=parallel,
)
extrapar = {
"split": split,
"seed": seed,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"basepar": basepar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"diffs_df": diffs_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def visual_flow_rel_resp_sess123(sessions,
analyspar,
sesspar,
stimpar,
permpar,
figpar,
seed=None,
parallel=False):
"""
visual_flow_rel_resp_sess123(sessions, analyspar, sesspar, stimpar,
permpar, figpar)
Retrieves ROI responses to expected and unexpected visual flow, relative
to session 1.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling ROI visual flow responses relative to session 1.",
extra={"spacing": "\n"})
if analyspar.scale:
raise ValueError("analyspar.scale should be set to False.")
# calculate multiple comparisons
dummy_df = misc_analys.get_check_sess_df(
sessions, None,
analyspar).drop_duplicates(subset=["lines", "planes", "sess_ns"])
permpar = misc_analys.set_multcomp(permpar,
sess_df=dummy_df,
CIs=False,
factor=2)
rel_sess = 1
rel_resp_df = seq_analys.get_rel_resp_stats_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
permpar=permpar,
rel_sess=rel_sess,
randst=seed,
parallel=parallel,
)
extrapar = {
"rel_sess": rel_sess,
"seed": seed,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"rel_resp_df": rel_resp_df.to_dict(),
}
helper_fcts.plot_save_all(info, figpar)
def gabor_ex_roi_responses_sess1(sessions,
analyspar,
sesspar,
stimpar,
basepar,
figpar,
seed=None,
parallel=False):
"""
gabor_ex_roi_responses_sess1(sessions, analyspar, sesspar, stimpar,
basepar, figpar)
Retrieves example ROI Gabor sequence responses from session 1.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info(
"Compiling example ROI Gabor sequence responses from session 1.",
extra={"spacing": "\n"})
n_ex = 6
rolling_win = 4
ex_traces_df = seq_analys.get_ex_traces_df(
sessions,
analyspar,
stimpar,
basepar,
n_ex=n_ex,
rolling_win=rolling_win,
randst=seed,
parallel=parallel,
)
extrapar = {
"n_ex": n_ex,
"rolling_win": rolling_win,
"seed": seed,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"basepar": basepar._asdict(),
"extrapar": extrapar,
"ex_traces_df": ex_traces_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def gabor_example_roi_usis(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar, seed=None, parallel=False):
"""
gabor_example_roi_usis(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar)
Retrieves example ROI Gabor USI traces.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling Gabor example ROI USI data.",
extra={"spacing": "\n"}
)
# check stimpar.pre
if (not isinstance(stimpar.pre, list)) or (len(stimpar.pre) == 1):
pre_list = gen_util.list_if_not(stimpar.pre)
stimpar = sess_ntuple_util.get_modif_ntuple(stimpar, "pre", pre_list)
elif len(stimpar.pre) != 2:
raise ValueError("Expected 2 values for stimpar.pre: one for "
"index calculation and one for traces.")
# use first stimpar.pre for idx calculation
stimpar_idx = sess_ntuple_util.get_modif_ntuple(
stimpar, "pre", stimpar.pre[0]
)
chosen_rois_df = usi_analys.get_chosen_roi_df(
sessions,
analyspar=analyspar,
stimpar=stimpar_idx,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
target_idx_vals = [0.5, 0, -0.5],
target_idx_sigs = ["sig", "not_sig", "sig"],
randst=seed,
parallel=parallel
)
# use second stimpar.pre for traces
stimpar_tr = sess_ntuple_util.get_modif_ntuple(
stimpar, "pre", stimpar.pre[1]
)
chosen_rois_df = usi_analys.add_chosen_roi_traces(
sessions,
chosen_rois_df,
analyspar=analyspar,
stimpar=stimpar_tr,
basepar=basepar,
split=idxpar.feature,
parallel=parallel
)
extrapar = {"seed": seed}
info = {
"analyspar": analyspar._asdict(),
"stimpar": stimpar._asdict(),
"sesspar": sesspar._asdict(),
"basepar": basepar._asdict(),
"idxpar": idxpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"chosen_rois_df": chosen_rois_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def gabor_roi_usi_distr(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar, seed=None, parallel=False):
"""
gabor_roi_usi_distr(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar)
Retrieves ROI Gabor USI percentile distributions.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling Gabor ROI USI distributions.",
extra={"spacing": "\n"}
)
n_bins = 40
idx_df = usi_analys.get_idx_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
n_bins=n_bins,
randst=seed,
parallel=parallel
)
extrapar = {
"n_bins" : n_bins,
"seed" : seed,
}
info = {"analyspar": analyspar._asdict(),
"sesspar" : sesspar._asdict(),
"stimpar" : stimpar._asdict(),
"basepar" : basepar._asdict(),
"idxpar" : idxpar._asdict(),
"permpar" : permpar._asdict(),
"extrapar" : extrapar,
"idx_df" : idx_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def gabor_roi_usi_sig_by_mouse(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar, seed=None,
parallel=False):
"""
gabor_roi_usi_sig_by_mouse(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar)
Retrieves percentage of signifiant ROI Gabor USIs by mouse.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info(
f"Compiling percentages of significant Gabor USIs by mouse.",
extra={"spacing": "\n"}
)
by_mouse = True
idx_df = usi_analys.get_idx_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
by_mouse=by_mouse,
randst=seed,
parallel=parallel,
)
permpar = misc_analys.set_multcomp(
permpar, sess_df=idx_df, pairs=False, factor=2
)
perc_sig_df = usi_analys.get_perc_sig_df(idx_df, analyspar, permpar, seed)
extrapar = {
"by_mouse": by_mouse,
"seed" : seed,
}
info = {"analyspar" : analyspar._asdict(),
"sesspar" : sesspar._asdict(),
"stimpar" : stimpar._asdict(),
"basepar" : basepar._asdict(),
"idxpar" : idxpar._asdict(),
"permpar" : permpar._asdict(),
"extrapar" : extrapar,
"perc_sig_df": perc_sig_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def gabor_tracked_roi_abs_usi_means_sess123_by_mouse(
sessions, analyspar, sesspar, stimpar, basepar, idxpar, figpar,
parallel=False):
"""
gabor_tracked_roi_abs_usi_means_sess123_by_mouse(
sessions, analyspar, sesspar, stimpar, basepar, idxpar, figpar)
Retrieves mean absolute for tracked ROI Gabor USIs, for each mouse, for
session 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info(
("Compiling absolute means per mouse of tracked ROI Gabor USIs for "
"sessions 1 to 3."),
extra={"spacing": "\n"})
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
# remove incomplete session series and warn
sessions = misc_analys.check_sessions_complete(sessions)
absolute = True
by_mouse = True
idx_stats_df = usi_analys.get_idx_stats_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
absolute=absolute,
by_mouse=by_mouse,
parallel=parallel,
)
extrapar = {
"absolute": absolute,
"by_mouse": by_mouse,
}
info = {"analyspar" : analyspar._asdict(),
"sesspar" : sesspar._asdict(),
"stimpar" : stimpar._asdict(),
"basepar" : basepar._asdict(),
"idxpar" : idxpar._asdict(),
"extrapar" : extrapar,
"idx_stats_df": idx_stats_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def visual_flow_tracked_roi_abs_usi_means_sess123(
sessions, analyspar, sesspar, stimpar, basepar, idxpar, permpar,
figpar, seed=None, parallel=False):
"""
visual_flow_tracked_roi_abs_usi_means_sess123(
sessions, analyspar, sesspar, stimpar, basepar, idxpar, permpar,
figpar)
Retrieves mean absolute for tracked ROI visual flow USIs for session 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info(
("Compiling absolute means of tracked ROI Gabor USIs for "
"sessions 1 to 3."),
extra={"spacing": "\n"})
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
# remove incomplete session series and warn
sessions = misc_analys.check_sessions_complete(sessions)
# calculate multiple comparisons
dummy_df = misc_analys.get_check_sess_df(
sessions, None, analyspar).drop_duplicates(
subset=["lines", "planes", "sess_ns"])
permpar = misc_analys.set_multcomp(permpar, sess_df=dummy_df, CIs=False)
absolute = True
by_mouse = False
idx_stats_df = usi_analys.get_idx_stats_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
absolute=absolute,
by_mouse=by_mouse,
randst=seed,
parallel=parallel,
)
extrapar = {
"absolute": absolute,
"by_mouse": by_mouse,
"seed": seed,
}
info = {"analyspar" : analyspar._asdict(),
"sesspar" : sesspar._asdict(),
"stimpar" : stimpar._asdict(),
"basepar" : basepar._asdict(),
"idxpar" : idxpar._asdict(),
"permpar" : permpar._asdict(),
"extrapar" : extrapar,
"idx_stats_df": idx_stats_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def visual_flow_tracked_roi_usis_sess123(sessions, analyspar, sesspar, stimpar,
basepar, idxpar, figpar,
parallel=False):
"""
visual_flow_tracked_roi_usis_sess123(sessions, analyspar, sesspar, stimpar,
basepar, idxpar, figpar)
Retrieves tracked ROI visual flow USIs for session 1 to 3.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
logger.info("Compiling tracked ROI visual flow USIs for sessions 1 to 3.",
extra={"spacing": "\n"})
if not analyspar.tracked:
raise ValueError("analyspar.tracked should be set to True.")
# remove incomplete session series and warn
sessions = misc_analys.check_sessions_complete(sessions)
idx_only_df = usi_analys.get_idx_only_df(
sessions,
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
parallel=parallel
)
extrapar = dict()
info = {"analyspar" : analyspar._asdict(),
"sesspar" : sesspar._asdict(),
"stimpar" : stimpar._asdict(),
"basepar" : basepar._asdict(),
"idxpar" : idxpar._asdict(),
"extrapar" : extrapar,
"idx_only_df": idx_only_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
def gabor_decoding_sess123(sessions,
analyspar,
sesspar,
stimpar,
logregpar,
permpar,
figpar,
seed=None,
parallel=False):
"""
gabor_decoding_sess123(sessions, analyspar, sesspar, stimpar, logregpar,
permpar, figpar)
Runs decoding analyses (D and U orientations).
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- logregpar (LogRegPar):
named tuple containing logistic regression parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
- parallel (bool):
if True, some of the analysis is run in parallel across CPU cores
default: False
"""
comp_str = logregpar.comp.replace("ori", " orientation")
logger.info(
f"Compiling Gabor {comp_str} decoder performances for sessions 1 to 3.",
extra={"spacing": "\n"})
if not analyspar.scale:
raise ValueError("analyspar.scale should be True.")
# calculate multiple comparisons
dummy_df = misc_analys.get_check_sess_df(
sessions, None,
analyspar).drop_duplicates(subset=["lines", "planes", "sess_ns"])
permpar = misc_analys.set_multcomp(permpar,
sess_df=dummy_df,
pairs=False,
factor=2)
n_splits = 100
score_df = decoding_analys.run_sess_logregs(
sessions,
analyspar=analyspar,
stimpar=stimpar,
logregpar=logregpar,
permpar=permpar,
n_splits=n_splits,
seed=seed,
parallel=parallel,
)
extrapar = {
"n_splits": n_splits,
"seed": seed,
}
info = {
"analyspar": analyspar._asdict(),
"sesspar": sesspar._asdict(),
"stimpar": stimpar._asdict(),
"logregpar": logregpar._asdict(),
"permpar": permpar._asdict(),
"extrapar": extrapar,
"scores_df": score_df.to_dict(),
}
helper_fcts.plot_save_all(info, figpar)
def gabor_example_roi_usi_sig(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar, seed=None):
"""
gabor_example_roi_usi_sig(sessions, analyspar, sesspar, stimpar, basepar,
idxpar, permpar, figpar)
Retrieves example ROI Gabor USI null distribution for significance
assessment.
Saves results and parameters relevant to analysis in a dictionary.
Required args:
- sessions (list):
Session objects (singleton)
- analyspar (AnalysPar):
named tuple containing analysis parameters
- sesspar (SessPar):
named tuple containing session parameters
- stimpar (StimPar):
named tuple containing stimulus parameters
- basepar (BasePar):
named tuple containing baseline parameters
- idxpar (IdxPar):
named tuple containing index parameters
- permpar (PermPar):
named tuple containing permutation parameters
- figpar (dict):
dictionary containing figure parameters
Optional args:
- seed (int):
seed value to use. (-1 treated as None)
default: None
"""
target_roi_perc = TARGET_ROI_PERC
logger.info(
("Compiling Gabor ROI USIs, and identifying an example at or near "
f"the {target_roi_perc} percentile."),
extra={"spacing": "\n"}
)
if len(sessions) != 1:
raise ValueError("Expected to receive only 1 session.")
ex_idx_df = usi_analys.get_ex_idx_df(
sessions[0],
analyspar=analyspar,
stimpar=stimpar,
basepar=basepar,
idxpar=idxpar,
permpar=permpar,
randst=seed,
target_roi_perc=target_roi_perc
)
extrapar = {"seed": seed}
info = {
"analyspar": analyspar._asdict(),
"stimpar" : stimpar._asdict(),
"sesspar" : sesspar._asdict(),
"basepar" : basepar._asdict(),
"idxpar" : idxpar._asdict(),
"permpar" : permpar._asdict(),
"extrapar" : extrapar,
"ex_idx_df": ex_idx_df.to_dict()
}
helper_fcts.plot_save_all(info, figpar)
