def plot_from_dict(direc, plt_bkend=None, fontdir=None):
"""
plot_from_dict(direc)
Plots data from dictionaries containing analysis parameters and results, or
path to results.
Required args:
- direc (Path): path to directory in which dictionaries to plot data
from are located
Optional_args:
- plt_bkend (str): mpl backend to use for plotting (e.g., "agg")
default: None
- fontdir (Path) : directory in which additional fonts are stored
default: None
"""
logger.info(f"Plotting from hyperparameters in: {direc}",
extra={"spacing": "\n"})
direc = Path(direc)
plot_util.manage_mpl(plt_bkend, fontdir=fontdir)
hyperpars = file_util.loadfile("hyperparameters.json", fulldir=direc)
if "logregpar" in hyperpars.keys():
plot_traces_scores(hyperpars, savedir=direc)
plot_util.cond_close_figs()
def plot_from_dict(dict_path, plt_bkend=None, fontdir=None, parallel=False,
datetime=True, overwrite=False):
"""
plot_from_dict(dict_path)
Plots data from dictionaries containing analysis parameters and results.
Required args:
- dict_path (Path): path to dictionary to plot data from
Optional_args:
- plt_bkend (str) : mpl backend to use for plotting (e.g., "agg")
default: None
- fontdir (Path) : path to directory where additional fonts are stored
default: None
- parallel (bool) : if True, some of the analysis is parallelized
across CPU cores
default: False
- datetime (bool) : figpar["save"] datatime parameter (whether to
place figures in a datetime folder)
default: True
- overwrite (bool): figpar["save"] overwrite parameter (whether to
overwrite figures)
default: False
"""
logger.info(f"Plotting from dictionary: {dict_path}",
extra={"spacing": "\n"})
figpar = sess_plot_util.init_figpar(
plt_bkend=plt_bkend, fontdir=fontdir, datetime=datetime,
overwrite=overwrite
)
plot_util.manage_mpl(cmap=False, **figpar["mng"])
dict_path = Path(dict_path)
info = file_util.loadfile(dict_path)
savedir = dict_path.parent
analysis = info["extrapar"]["analysis"]
# 0. Plots the correlation between pupil and roi/run changes for each
# session
if analysis == "c": # difference correlation
plot_pup_diff_corr(figpar=figpar, savedir=savedir, **info)
# difference correlation per ROI between stimuli
elif analysis == "r":
plot_pup_roi_stim_corr(figpar=figpar, savedir=savedir, **info)
else:
warnings.warn(f"No plotting function for analysis {analysis}",
category=UserWarning, stacklevel=1)
plot_util.cond_close_figs()
def run_analyses(sessions,
analysis_dict,
analyses,
datatype="roi",
seed=None,
parallel=False):
"""
run_analyses(sessions, analysis_dict, analyses)
Runs requested analyses on sessions using the parameters passed.
Required args:
- sessions (list) : list of sessions, possibly nested
- analysis_dict (dict): analysis parameter dictionary
(see init_param_cont())
- analyses (str) : analyses to run
Optional args:
- datatype (str) : datatype ("run", "roi")
default: "roi"
- seed (int) : seed to use
default: None
- parallel (bool): if True, some analyses are parallelized
across CPU cores
default: False
"""
if len(sessions) == 0:
logger.warning("No sessions meet these criteria.")
return
# changes backend and defaults
plot_util.manage_mpl(cmap=False, **analysis_dict["figpar"]["mng"])
sess_plot_util.update_plt_linpla()
fct_dict = get_analysis_fcts()
args_dict = copy.deepcopy(analysis_dict)
for key, item in zip(["seed", "parallel", "datatype"],
[seed, parallel, datatype]):
args_dict[key] = item
# run through analyses
for analysis in analyses:
if analysis not in fct_dict.keys():
raise ValueError(f"{analysis} analysis not found.")
fct, datatype_req = fct_dict[analysis]
if datatype not in datatype_req:
continue
args_dict_use = gen_util.keep_dict_keys(
args_dict,
inspect.getfullargspec(fct).args)
fct(sessions=sessions, analysis=analysis, **args_dict_use)
plot_util.cond_close_figs()
def run_analyses(sessions, analysis_dict, analyses, seed=None,
parallel=False, plot_tc=True):
"""
run_analyses(sessions, analysis_dict, analyses)
Runs requested analyses on sessions using the parameters passed.
Required args:
- sessions (list) : list of sessions, possibly nested
- analysis_dict (dict): analysis parameter dictionary
(see init_param_cont())
- analyses (str) : analyses to run
Optional args:
- seed (int) : seed to use
default: None
- parallel (bool): if True, some analyses are parallelized
across CPU cores
default: False
- plot_tc (bool) : if True, tuning curves are plotted for each ROI
default: True
"""
if len(sessions) == 0:
logger.warning("No sessions meet these criteria.")
return
comp = True if isinstance(sessions[0], list) else False
# changes backend and defaults
plot_util.manage_mpl(cmap=False, **analysis_dict["figpar"]["mng"])
fct_dict = get_analysis_fcts()
args_dict = copy.deepcopy(analysis_dict)
for key, item in zip(["seed", "parallel", "plot_tc"],
[seed, parallel, plot_tc]):
args_dict[key] = item
# run through analyses
for analysis in analyses:
if analysis not in fct_dict.keys():
raise ValueError(f"{analysis} analysis not found.")
fct, comp_req = fct_dict[analysis]
if comp_req != comp:
continue
args_dict_use = gen_util.keep_dict_keys(
args_dict, inspect.getfullargspec(fct).args)
fct(sessions=sessions, analysis=analysis, **args_dict_use)
plot_util.cond_close_figs()
def plot_from_dict(dict_path,
plt_bkend=None,
fontdir=None,
plot_tc=True,
parallel=False,
datetime=True,
overwrite=False):
"""
plot_from_dict(dict_path)
Plots data from dictionaries containing analysis parameters and results.
Required args:
- dict_path (Path): path to dictionary to plot data from
Optional_args:
- plt_bkend (str) : mpl backend to use for plotting (e.g., "agg")
default: None
- fontdir (Path) : path to directory where additional fonts are stored
default: None
- plot_tc (bool) : if True, tuning curves are plotted for each ROI
(dummy argument)
default: True
- parallel (bool) : if True, some of the analysis is parallelized
across CPU cores
default: False
- datetime (bool) : figpar["save"] datatime parameter (whether to
place figures in a datetime folder)
default: True
- overwrite (bool): figpar["save"] overwrite parameter (whether to
overwrite figures)
default: False
"""
logger.info(f"Plotting from dictionary: {dict_path}",
extra={"spacing": "\n"})
figpar = sess_plot_util.init_figpar(plt_bkend=plt_bkend,
fontdir=fontdir,
datetime=datetime,
overwrite=overwrite)
plot_util.manage_mpl(cmap=False, **figpar["mng"])
plt.rcParams["figure.titlesize"] = "xx-large"
plt.rcParams["axes.titlesize"] = "xx-large"
dict_path = Path(dict_path)
info = file_util.loadfile(dict_path)
savedir = dict_path.parent
analysis = info["extrapar"]["analysis"]
# 1. Plot average traces by quantile x unexpected for each session
if analysis == "t": # traces
gen_plots.plot_traces_by_qu_unexp_sess(figpar=figpar,
savedir=savedir,
modif=True,
**info)
# 2. Plot average traces by quantile, locked to unexpected for each session
elif analysis == "l": # unexpected locked traces
gen_plots.plot_traces_by_qu_lock_sess(figpar=figpar,
savedir=savedir,
modif=True,
**info)
else:
warnings.warn(f"No modified plotting option for analysis {analysis}",
category=UserWarning,
stacklevel=1)
plot_util.cond_close_figs()
def plot_from_dict(dict_path, plt_bkend=None, fontdir=None, parallel=False,
datetime=True, overwrite=False):
"""
plot_from_dict(dict_path)
Plots data from dictionaries containing analysis parameters and results.
Required args:
- dict_path (Path): path to dictionary to plot data from
Optional_args:
- plt_bkend (str) : mpl backend to use for plotting (e.g., "agg")
default: None
- fontdir (Path) : path to directory where additional fonts are stored
default: None
- parallel (bool) : if True, some of the plotting is parallelized
across CPU cores
default: False
- datetime (bool) : figpar["save"] datatime parameter (whether to
place figures in a datetime folder)
default: True
- overwrite (bool): figpar["save"] overwrite parameter (whether to
overwrite figures)
default: False
"""
logger.info(f"Plotting from dictionary: {dict_path}",
extra={"spacing": "\n"})
figpar = sess_plot_util.init_figpar(
plt_bkend=plt_bkend, fontdir=fontdir, datetime=datetime,
overwrite=overwrite
)
plot_util.manage_mpl(cmap=False, **figpar["mng"])
dict_path = Path(dict_path)
info = file_util.loadfile(dict_path)
savedir = dict_path.parent
analysis = info["extrapar"]["analysis"]
# 0. Plots the full traces for each session
if analysis == "f": # full traces
plot_full_traces(figpar=figpar, savedir=savedir, **info)
# 1. Plot average traces by quantile x unexpected for each session
elif analysis == "t": # traces
plot_traces_by_qu_unexp_sess(figpar=figpar, savedir=savedir, **info)
# 2. Plot average traces by quantile, locked to unexpected for each session
elif analysis == "l": # unexpected locked traces
plot_traces_by_qu_lock_sess(figpar=figpar, savedir=savedir, **info)
# 3. Plot magnitude of change in dF/F area from first to last quantile of
# unexpected vs no unexpected sequences, for each session
elif analysis == "m": # mag
plot_mag_change(figpar=figpar, savedir=savedir, **info)
# 4. Plot autocorrelations
elif analysis == "a": # autocorr
plot_autocorr(figpar=figpar, savedir=savedir, **info)
else:
warnings.warn(f"No plotting function for analysis {analysis}",
category=UserWarning, stacklevel=1)
plot_util.cond_close_figs()
def plot_summ(output, savename, stimtype="gabors", comp="unexp", ctrl=False,
visflow_dir="both", fluor="dff", scale=True, CI=0.95,
plt_bkend=None, fontdir=None, modif=False):
"""
plot_summ(output)
Plots summary data for a specific comparison, for each datatype in a
separate figure and saves figures.
Required args:
- output (str) : general directory in which summary dataframe
is saved (runtype and q1v4 values are inferred from
the directory name)
- savename (str): name of the dataframe containing summary data to plot
Optional args:
- stimtype (str) : stimulus type
default: "gabors"
- comp (str) : type of comparison
default: "unexp"
- ctrl (bool) : if True, control comparisons are analysed
default: False
- visflow_dir (str): visual flow direction
default: "both"
- fluor (str) : fluorescence trace type
default: "dff"
- scale (bool) : whether ROIs are scaled
default: True
- CI (num) : CI for shuffled data
default: 0.95
- plt_bkend (str) : mpl backend to use for plotting (e.g., "agg")
default: None
- fontdir (str) : directory in which additional fonts are located
default: None
- modif (bool) : if True, plots are made in a modified (simplified
way)
default: False
"""
plot_util.manage_mpl(plt_bkend, fontdir=fontdir)
summ_scores_file = Path(output, savename)
if summ_scores_file.is_file():
summ_scores = file_util.loadfile(summ_scores_file)
else:
warnings.warn(f"{summ_scores_file} not found.",
category=RuntimeWarning, stacklevel=1)
return
if len(summ_scores) == 0:
warnings.warn(f"No data in {summ_scores_file}.",
category=RuntimeWarning, stacklevel=1)
return
# drop NaN lines
summ_scores = summ_scores.loc[~summ_scores["epoch_n_mean"].isna()]
data_types = ["epoch_n", "test_acc", "test_acc_bal"]
data_titles = ["Epoch nbrs", "Test accuracy", "Test accuracy (balanced)"]
stats = ["mean", "sem", "sem"]
shuff_stats = ["median"] + math_util.get_percentiles(CI)[1]
q1v4, evu = False, False
if "_q1v4" in str(output):
q1v4 = True
elif "_evu" in str(output):
evu = True
runtype = "prod"
if "pilot" in str(output):
runtype = "pilot"
if stimtype == "gabors":
visflow_dir = "none"
stim_str = "gab"
stim_str_pr = "gabors"
else:
visflow_dir = sess_gen_util.get_params(stimtype, visflow_dir)[0]
if isinstance(visflow_dir, list) and len(visflow_dir) == 2:
visflow_dir = "both"
stim_str = sess_str_util.dir_par_str(visflow_dir, str_type="file")
stim_str_pr = sess_str_util.dir_par_str(visflow_dir, str_type="print")
scale_str = sess_str_util.scale_par_str(scale, "file")
scale_str_pr = sess_str_util.scale_par_str(scale, "file").replace("_", " ")
ctrl_str = sess_str_util.ctrl_par_str(ctrl)
ctrl_str_pr = sess_str_util.ctrl_par_str(ctrl, str_type="print")
modif_str = "_modif" if modif else ""
save_dir = Path(output, f"figures_{fluor}")
save_dir.mkdir(exist_ok=True)
cols = ["scale", "fluor", "visflow_dir", "runtype"]
cri = [scale, fluor, visflow_dir, runtype]
plot_lines = gen_util.get_df_vals(summ_scores, cols, cri)
cri_str = ", ".join([f"{col}: {crit}" for col, crit in zip(cols, cri)])
if len(plot_lines) == 0: # no data
warnings.warn(f"No data found for {cri_str}", category=RuntimeWarning,
stacklevel=1)
return
else: # shuffle or non shuffle missing
skip = False
for shuff in [False, True]:
if shuff not in plot_lines["shuffle"].tolist():
warnings.warn(f"No shuffle={shuff} data found for {cri_str}",
category=RuntimeWarning, stacklevel=1)
skip = True
if skip:
return
for data, data_title in zip(data_types, data_titles):
if not modif:
title = (f"{stim_str_pr.capitalize()} {comp}{ctrl_str_pr} - "
f"{data_title} for log regr on\n" +
u"{} {} ".format(scale_str_pr, fluor) +
f"data ({runtype})")
else:
title = (f"{stim_str_pr.capitalize()} {comp}{ctrl_str_pr}\n"
f"{data_title}")
if "_" in title:
title = title.replace("_", " ")
savename = (f"{data}_{stim_str}_{comp}{ctrl_str}{scale_str}"
f"{modif_str}.svg")
full_savename = Path(save_dir, savename)
plot_data_summ(plot_lines, data, stats, shuff_stats, title,
full_savename, CI, q1v4, evu, comp, modif)
plot_util.cond_close_figs()
def run_single_panel(args, sessions=None, new_fig=False):
"""
run_single_panel(args, sessions=None, new_fig=False)
Runs analyses and plots a single panel.
Required args:
- args (dict):
parser argument dictionary
Optional args:
- sessions (list):
preloaded Session objects
default: None
- new_fig (bool):
if True, a new figure is being plotted
(additional separator is logged)
default: False
Returns:
- sessions (list):
loaded Session objects
"""
analysis_dict = init_analysis(args)
fig_panel_analysis = analysis_dict["figpar"]["fig_panel_analysis"]
# changes backend and defaults
plot_util.manage_mpl(cmap=False, **analysis_dict["figpar"]["mng"])
sess_plot_util.update_plt_linpla()
action = "Running analysis and producing plot"
if args.plot_only:
action = "Producing plot"
sep = DOUBLE_SEP if new_fig else SEP
logger.info(
f"{sep}Fig. {fig_panel_analysis.figure}{fig_panel_analysis.panel}. "
f"{action}: {fig_panel_analysis.description}",
extra={"spacing": "\n"})
# Log any relevant warnings to the console
fig_panel_analysis.log_warnings(plot_only=args.plot_only)
# Check if analysis needs to be rerun, and if not, replots only.
run_analysis, data_path = \
helper_fcts.check_if_data_exists(
analysis_dict["figpar"], overwrite_plot_only=args.plot_only,
raise_no_data=False
)
if not run_analysis:
return
elif args.plot_only:
logger.warning(
f"Skipping plot, as no analysis data was found under {data_path}.",
extra={"spacing": "\n"})
return
sessions = init_sessions(
analyspar=analysis_dict["analyspar"],
sesspar=analysis_dict["sesspar"],
mouse_df=args.mouse_df_path,
datadir=args.datadir,
sessions=sessions,
roi=fig_panel_analysis.specific_params["roi"],
run=fig_panel_analysis.specific_params["run"],
pupil=fig_panel_analysis.specific_params["pupil"],
parallel=args.parallel,
)
analysis_dict["seed"] = fig_panel_analysis.seed
analysis_dict["parallel"] = bool(args.parallel * (not args.debug))
analysis_fct = fig_panel_analysis.analysis_fct
analysis_dict_use = gen_util.keep_dict_keys(
analysis_dict,
inspect.getfullargspec(analysis_fct).args)
analysis_fct(sessions=sessions, **analysis_dict_use)
return sessions