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 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 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_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)