def add_USI_boxes(ax, chosen_rois_df, sorted_target_idxs):
"""
add_USI_boxes(ax, chosen_rois_df, sorted_target_idxs)
Adds boxes with USI values to individual plots (e.g., trace plots).
Required args:
- ax (subplot array):
pyplot axis array
- chosen_rois_df (pd.DataFrame):
chosen ROIs dataframe with, in addition to the basic sess_df
columns, "target_idxs".
- sorted_target_idxs (list):
order in which different target_idxs should be added to subplots
"""
props = dict(
boxstyle="round", facecolor="white", edgecolor="black", alpha=0.5,
lw=1.5)
for (line, plane), lp_df in chosen_rois_df.groupby(["lines", "planes"]):
li, pl, _, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
for r, row_val in enumerate(sorted_target_idxs):
rows = lp_df.loc[lp_df["target_idxs"] == row_val]
if len(rows) == 0:
continue
elif len(rows) > 1:
raise RuntimeError(
"Expected row_order instances to be unique per line/plane."
)
row = rows.loc[rows.index[0]]
sub_ax = ax[r + pl * len(sorted_target_idxs), li]
# place a text box in upper left in axes coords
sub_ax.text(0.1, 0.9, f"USI = {row['roi_idxs']:.2f}",
transform=sub_ax.transAxes, fontsize=15, va="center",
bbox=props)
def plot_idx_correlations(idx_corr_df,
permpar,
figpar,
permute="sess",
corr_type="corr",
title=None,
small=True):
"""
plot_idx_correlations(idx_corr_df, permpar, figpar)
Plots ROI USI index correlations across sessions.
Required args:
- idx_corr_df (pd.DataFrame):
dataframe with one row per line/plane, and the
following columns, in addition to the basic sess_df columns:
for session comparisons, e.g. 1v2
- {}v{}{norm_str}_corrs (float): intersession ROI index correlations
- {}v{}{norm_str}_corr_stds (float): bootstrapped intersession ROI
index correlation standard deviation
- {}v{}_null_CIs (list): adjusted null CI for intersession ROI
index correlations
- {}v{}_raw_p_vals (float): p-value for intersession correlations
- {}v{}_p_vals (float): p-value for intersession correlations,
corrected for multiple comparisons and tails
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- permute (bool):
type of permutation to due ("tracking", "sess" or "all")
default: "sess"
- corr_type (str):
type of correlation run, i.e. "corr" or "R_sqr"
default: "corr"
- title (str):
plot title
default: None
- small (bool):
if True, smaller subplots are plotted
default: True
Returns:
- ax (2D array):
array of subplots
"""
norm = False
if permute in ["sess", "all"]:
corr_type = f"diff_{corr_type}"
if corr_type == "diff_corr":
norm = True
title = title.replace("Correlations", "Normalized correlations")
norm_str = "_norm" if norm else ""
sess_pairs = get_sorted_sess_pairs(idx_corr_df, norm=norm)
n_pairs = int(np.ceil(len(sess_pairs) / 2) * 2) # multiple of 2
figpar = sess_plot_util.fig_init_linpla(figpar,
kind="reg",
n_sub=int(n_pairs / 2))
figpar["init"]["ncols"] = n_pairs
figpar["init"]["sharey"] = "row"
figpar["init"]["gs"] = {"hspace": 0.25}
if small:
figpar["init"]["subplot_wid"] = 2.7
figpar["init"]["subplot_hei"] = 4.21
figpar["init"]["gs"]["wspace"] = 0.2
else:
figpar["init"]["subplot_wid"] = 3.3
figpar["init"]["subplot_hei"] = 4.71
figpar["init"]["gs"]["wspace"] = 0.3
fig, ax = plot_util.init_fig(n_pairs * 2, **figpar["init"])
if title is not None:
fig.suptitle(title, y=0.98, weight="bold")
plane_pts = get_idx_corr_ylims(idx_corr_df, norm=norm)
lines = [None, None]
comp_info = misc_analys.get_comp_info(permpar)
logger.info(f"{comp_info}:", extra={"spacing": "\n"})
for (line, plane), lp_df in idx_corr_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
linpla_name = plot_helper_fcts.get_line_plane_name(line, plane)
lines[li] = line.split("-")[0].replace("23", "2/3")
if len(lp_df) != 1:
raise RuntimeError("Expected only one row per line/plane.")
row = lp_df.loc[lp_df.index[0]]
lp_sig_str = f"{linpla_name:6}:"
for s, sess_pair in enumerate(sess_pairs):
sub_ax = ax[pl, s]
if s == 0:
sub_ax.set_ylim(plane_pts[pl])
col_base = f"{sess_pair[0]}v{sess_pair[1]}"
CI = row[f"{col_base}_null_CIs"]
extr = np.asarray([CI[0], CI[2]])
plot_util.plot_CI(sub_ax,
extr,
med=CI[1],
x=li,
width=0.45,
med_rat=0.025)
y = row[f"{col_base}{norm_str}_corrs"]
err = row[f"{col_base}{norm_str}_corr_stds"]
plot_util.plot_ufo(sub_ax,
x=li,
y=y,
err=err,
color=col,
capsize=8)
# add significance markers
p_val = row[f"{col_base}_p_vals"]
side = np.sign(y - CI[1])
sensitivity = misc_analys.get_sensitivity(permpar)
sig_str = misc_analys.get_sig_symbol(p_val,
sensitivity=sensitivity,
side=side,
tails=permpar["tails"],
p_thresh=permpar["p_val"])
if len(sig_str):
high = np.max([CI[-1], y + err])
plot_util.add_signif_mark(sub_ax,
li,
high,
rel_y=0.1,
color=col,
fontsize=24,
mark=sig_str)
sess_str = f"S{sess_pair[0]}v{sess_pair[1]}: "
lp_sig_str = f"{lp_sig_str}{TAB}{sess_str}{p_val:.5f}{sig_str:3}"
logger.info(lp_sig_str, extra={"spacing": TAB})
# Add plane, line info to plots
sess_plot_util.format_linpla_subaxes(ax,
datatype="roi",
lines=["", ""],
xticks=[0, 1],
ylab="",
xlab="",
kind="traces")
xs = np.arange(len(lines))
pad_x = 0.6 * (xs[1] - xs[0])
for row_n in range(len(ax)):
for col_n in range(len(ax[row_n])):
sub_ax = ax[row_n, col_n]
sub_ax.tick_params(axis="x", which="both", bottom=False)
sub_ax.set_xticks(xs)
sub_ax.set_xticklabels(lines, weight="bold")
sub_ax.set_xlim([xs[0] - pad_x, xs[-1] + pad_x])
sub_ax.set_ylim(plane_pts[row_n])
sub_ax.set_yticks(plane_pts[row_n])
if row_n == 0:
if col_n < len(sess_pairs):
s1, s2 = sess_pairs[col_n]
sess_pair_title = f"Session {s1} v {s2}"
sub_ax.set_title(sess_pair_title,
fontweight="bold",
y=1.07)
sub_ax.spines["bottom"].set_visible(True)
plot_util.set_interm_ticks(ax[row_n],
3,
axis="y",
weight="bold",
share=False,
update_ticks=True)
return ax
def get_idx_corr_ylims(idx_corr_df, norm=False):
"""
get_idx_corr_ylims(idx_corr_df)
Returns data edges (min and max data to be plotted).
Required args:
- idx_corr_df (pd.DataFrame):
dataframe with one row per line/plane, and the
following columns, in addition to the basic sess_df columns:
for session comparisons, e.g. 1v2
- {}v{}{norm_str}_corrs (float): intersession ROI index
correlations
- {}v{}{norm_str}_corr_stds (float): bootstrapped intersession ROI
index correlation standard deviation
- {}v{}_null_CIs (list): adjusted null CI for normalized
intersession ROI index correlations
Returns:
- plane_pts (list):
[low_pt, high_pt] for each plane, in plane order, based on plane
indices
"""
sess_pairs = get_sorted_sess_pairs(idx_corr_df, norm=norm)
norm_str = "_norm" if norm else ""
plane_pts = []
plane_idxs = []
for plane, plane_df in idx_corr_df.groupby("planes"):
_, pl, _, _ = plot_helper_fcts.get_line_plane_idxs(plane=plane)
plane_idxs.append(pl)
low_pts, high_pts = [], []
for sess_pair in sess_pairs:
base = f"{sess_pair[0]}v{sess_pair[1]}"
# get null_CIs low
null_CI_low = np.min(
[null_CI[0] for null_CI in plane_df[f"{base}_null_CIs"]])
null_CI_high = np.max(
[null_CI[2] for null_CI in plane_df[f"{base}_null_CIs"]])
# get data
data_low = (plane_df[f"{base}{norm_str}_corrs"] -
plane_df[f"{base}{norm_str}_corr_stds"]).min()
data_high = (plane_df[f"{base}{norm_str}_corrs"] +
plane_df[f"{base}{norm_str}_corr_stds"]).max()
low_pts.extend([null_CI_low, data_low])
high_pts.extend([null_CI_high, data_high])
low_pt = np.min(low_pts)
high_pt = np.max(high_pts)
pt_range = high_pt - low_pt
low_pt -= pt_range / 10
high_pt += pt_range / 10
plane_pts.append([low_pt, high_pt])
plane_pts = [plane_pts[i] for i in np.argsort(plane_idxs)]
return plane_pts
def plot_rand_corr_ex_data(idx_corr_norm_df, title=None):
"""
plot_rand_corr_ex_data(idx_corr_norm_df)
Plots example random correlation data in a scatterplot and histogram to
show how normalized residual correlations are calculated.
Required args:
- idx_corr_norm_df (pd.DataFrame):
dataframe with one row for a line/plane, and the
following columns, in addition to the basic sess_df columns:
for a specific session comparison, e.g. 1v2
- {}v{}_corrs (float): unnormalized intersession ROI index
correlations
- {}v{}_norm_corrs (float): normalized intersession ROI index
correlations
- {}v{}_rand_ex_corrs (float): unnormalized intersession
ROI index correlations for an example of randomized data
- {}v{}_rand_corr_meds (float): median of randomized correlations
- {}v{}_corr_data (list): intersession values to correlate
- {}v{}_rand_ex (list): intersession values for an example of
randomized data
- {}v{}_rand_corrs_binned (list): binned random unnormalized
intersession ROI index correlations
- {}v{}_rand_corrs_bin_edges (list): bins edges
Optional args:
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
plot_types = ["scatter", "hist"]
fig, ax = plt.subplots(nrows=len(plot_types),
figsize=[8.7, 9.3],
gridspec_kw={"hspace": 0.7})
if len(idx_corr_norm_df) != 1:
raise ValueError("Expected idx_corr_norm_df to contain only one row.")
sorted_pairs = get_sorted_sess_pairs(idx_corr_norm_df, norm=True)
if len(sorted_pairs) != 1:
raise RuntimeError(
"Expected to find only one pair of sessions for which to plot data."
)
sess_pair = sorted_pairs[0]
row = idx_corr_norm_df.loc[idx_corr_norm_df.index[0]]
corr_name = f"{sess_pair[0]}v{sess_pair[1]}"
_, _, col, _ = plot_helper_fcts.get_line_plane_idxs(
row["lines"], row["planes"])
if title is not None:
fig.suptitle(title, y=0.95, weight="bold")
# plot scatterplot
scatt_ax = ax[0]
plot_corr_ex_data_scatterplot(scatt_ax, row, corr_name=corr_name, col=col)
# plot histogram
hist_ax = ax[1]
plot_corr_ex_data_histogram(hist_ax, row, corr_name=corr_name, col=col)
plot_util.set_interm_ticks(ax,
n_ticks=4,
axis="y",
share=False,
fontweight="bold")
return ax
def plot_imaging_planes(imaging_plane_df, figpar, title=None):
"""
plot_imaging_planes(imaging_plane_df, figpar)
Plots imaging planes.
Required args:
- imaging_plane_df (pd.DataFrame in dict format):
dataframe with a row for each mouse, and the following
columns, in addition to the basic sess_df columns:
- "max_projections" (list): pixel intensities of maximum projection
for the plane (hei x wid)
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
figpar = sess_plot_util.fig_init_linpla(figpar)
figpar["init"]["sharex"] = False
figpar["init"]["sharey"] = False
figpar["init"]["subplot_hei"] = 2.4
figpar["init"]["subplot_wid"] = 2.4
figpar["init"]["gs"] = {"wspace": 0.25, "hspace": 0.2}
# MUST ADJUST if anything above changes [right, bottom, width, height]
new_axis_coords = [0.91, 0.115, 0.15, 0.34]
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
sub_ax_scale = fig.add_axes(new_axis_coords)
plot_util.remove_axis_marks(sub_ax_scale)
sub_ax_scale.spines["left"].set_visible(True)
if title is not None:
fig.suptitle(title, y=1, weight="bold")
hei_lens = []
raster_zorder = -12
for (line, plane), lp_mask_df in imaging_plane_df.groupby(["lines", "planes"]):
li, pl, _, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
sub_ax = ax[pl, li]
if len(lp_mask_df) != 1:
raise RuntimeError("Expected only one row per line/plane.")
lp_row = lp_mask_df.loc[lp_mask_df.index[0]]
# add projection
imaging_plane = np.asarray(lp_row["max_projections"])
hei_lens.append(imaging_plane.shape[0])
add_imaging_plane(sub_ax, imaging_plane, alpha=0.98,
zorder=raster_zorder - 1
)
# add scale marker
hei_lens = np.unique(hei_lens)
if len(hei_lens) != 1:
raise NotImplementedError(
"Adding scale bar not implemented if ROI mask image heights are "
"different for different planes."
)
add_scale_marker(
sub_ax_scale, side_len=hei_lens[0], ori="vertical", quadrant=3,
fontsize=16
)
logger.info("Rasterizing imaging plane images...", extra={"spacing": TAB})
for sub_ax in ax.reshape(-1):
sub_ax.set_rasterization_zorder(raster_zorder)
# Add plane, line info to plots
sess_plot_util.format_linpla_subaxes(ax, ylab="", kind="map")
for sub_ax in ax.reshape(-1):
plot_util.remove_axis_marks(sub_ax)
return ax
def plot_stim_data_df(stim_data_df, stimpar, permpar, figpar, pop_stats=True,
title=None):
"""
plot_stim_data_df(stim_data_df, stimpar, permpar, figpar)
Plots stimulus comparison data.
Required args:
- stim_stats_df (pd.DataFrame):
dataframe with one row per line/plane and one for all line/planes
together, and the basic sess_df columns, in addition to,
for each stimtype:
- stimtype (list): absolute fractional change statistics (me, err)
- raw_p_vals (float): uncorrected p-value for data differences
between stimulus types
- p_vals (float): p-value for data differences between stimulus
types, corrected for multiple comparisons and tails
- stimpar (dict):
dictionary with keys of StimPar namedtuple
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- pop_stats (bool):
if True, analyses are run on population statistics, and not
individual tracked ROIs
default: True
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
(does not include added subplot for all line/plane data together)
"""
figpar = sess_plot_util.fig_init_linpla(figpar, kind="reg")
figpar["init"]["subplot_wid"] = 2.1
figpar["init"]["subplot_hei"] = 4.2
figpar["init"]["gs"] = {"hspace": 0.20, "wspace": 0.3}
figpar["init"]["sharey"] = "row"
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
fig.suptitle(title, y=0.98, weight="bold")
sub_ax_all = fig.add_axes([1.05, 0.11, 0.3, 0.77])
stimtypes = stimpar["stimtype"][:] # deep copy
# indicate bootstrapped error with wider capsize
capsize = 8 if pop_stats else 6
lp_data = []
cols = []
for (line, plane), lp_df in stim_data_df.groupby(["lines", "planes"]):
x = [0, 1]
data = np.vstack(
[lp_df[stimtypes[0]].tolist(), lp_df[stimtypes[1]].tolist()]
).T
y = data[0]
err = data[1:]
if line != "all" and plane != "all":
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(
line, plane
)
alpha = 0.5
sub_ax = ax[pl, li]
lp_data.append(y)
cols.append(col)
else:
col = plot_helper_fcts.NEARBLACK
dash = None
alpha = 0.2
sub_ax = sub_ax_all
sub_ax.set_title("all", fontweight="bold")
plot_util.plot_bars(
sub_ax, x, y=y, err=err, width=0.5, lw=None, alpha=alpha,
color=col, ls=dash, capsize=capsize
)
# add dots to the all subplot
x_vals = np.asarray([-0.17, 0.25, -0.25, 0.17]) # to spread dots out
lw = 4
ms = 200
for s, _ in enumerate(stimtypes):
lp_stim_data = [data[s] for data in lp_data]
sorter = np.argsort(lp_stim_data)
for i, idx in enumerate(sorter):
x_val = s + x_vals[i]
# white behind
sub_ax_all.scatter(
x=x_val, y=lp_stim_data[idx], s=ms, linewidth=lw, alpha=0.8,
color="white", zorder=10
)
# colored dots
sub_ax_all.scatter(
x=x_val, y=lp_stim_data[idx], s=ms, alpha=0.6, linewidth=0,
color=cols[idx], zorder=11
)
# dot borders
sub_ax_all.scatter(
x=x_val, y=lp_stim_data[idx], s=ms, color="None",
edgecolor=cols[idx], linewidth=lw, alpha=1, zorder=12
)
# add between stim significance
add_between_stim_sig(ax, sub_ax_all, stim_data_df, permpar)
# add plane, line info to plots
sess_plot_util.format_linpla_subaxes(ax, datatype="roi", lines=None,
planes=["", ""], xticks=[0, 1], ylab="Absolute fractional change",
kind="reg", xlab=""
)
# adjust plot details
stimtype_names = stimtypes[:]
stimtype_names[stimtypes.index("visflow")] = "visual\nflow"
for sub_ax in fig.axes:
y_max = sub_ax.get_ylim()[1]
sub_ax.set_ylim([0, y_max])
sub_ax.set_xticks([0, 1])
sub_ax.set_xticklabels(
stimtypes, weight="bold", rotation=45, ha="right"
)
sub_ax.tick_params(axis="x", bottom=False)
sub_ax_all.set_xlim(ax[0, 0].get_xlim())
plot_util.set_interm_ticks(
np.asarray(sub_ax_all), 4, axis="y", share=False, weight="bold"
)
return ax
def plot_ex_gabor_traces(ex_traces_df, stimpar, figpar, title=None):
"""
plot_ex_gabor_traces(ex_traces_df, stimpar, figpar)
Plots example Gabor traces.
Required args:
- ex_traces_df (pd.DataFrame):
dataframe with a row for each ROI, and the following columns,
in addition to the basic sess_df columns:
- time_values (list): values for each frame, in seconds
- roi_ns (list): selected ROI number
- traces_sm (list): selected ROI sequence traces, smoothed, with
dims: seq x frames
- trace_stat (list): selected ROI trace mean or median
- stimpar (dict):
dictionary with keys of StimPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
if stimpar["stimtype"] != "gabors":
raise ValueError("Expected stimpar['stimtype'] to be 'gabors'.")
group_columns = ["lines", "planes"]
n_per = np.max(
[len(lp_df) for _, lp_df in ex_traces_df.groupby(group_columns)]
)
per_rows, per_cols = math_util.get_near_square_divisors(n_per)
n_per = per_rows * per_cols
figpar = sess_plot_util.fig_init_linpla(
figpar, kind="traces", n_sub=per_rows
)
figpar["init"]["subplot_hei"] = 1.36
figpar["init"]["subplot_wid"] = 2.47
figpar["init"]["ncols"] = per_cols * 2
fig, ax = plot_util.init_fig(
plot_helper_fcts.N_LINPLA * n_per, **figpar["init"]
)
if title is not None:
fig.suptitle(title, y=1.03, weight="bold")
ylims = np.full(ax.shape + (2, ), np.nan)
logger.info("Plotting individual traces...", extra={"spacing": TAB})
raster_zorder = -12
for (line, plane), lp_df in ex_traces_df.groupby(["lines", "planes"]):
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(line, plane)
for i, idx in enumerate(lp_df.index):
row_idx = int(pl * per_rows + i % per_rows)
col_idx = int(li * per_cols + i // per_rows)
sub_ax = ax[row_idx, col_idx]
ylims[row_idx, col_idx] = plot_ex_gabor_roi_traces(
sub_ax,
lp_df.loc[idx],
col=col,
dash=dash,
zorder=raster_zorder - 1
)
time_values = np.asarray(lp_df.loc[lp_df.index[-1], "time_values"])
sess_plot_util.format_linpla_subaxes(ax, fluor="dff",
area=False, datatype="roi", sess_ns=None, xticks=None, kind="traces",
modif_share=False)
# fix x ticks and lims
for sub_ax in ax.reshape(-1):
xlims = [time_values[0], time_values[-1]]
xticks = np.linspace(*xlims, 6)
sub_ax.set_xticks(xticks)
plot_util.set_interm_ticks(ax, 3, axis="x", fontweight="bold", skip=False)
for sub_ax in ax.reshape(-1):
sub_ax.set_xlim(xlims)
# reset y limits
for r in range(ax.shape[0]):
for c in range(ax.shape[1]):
if not np.isfinite(ylims[r, c].sum()):
continue
ax[r, c].set_ylim(ylims[r, c])
plot_util.set_interm_ticks(
ax, 2, axis="y", share=False, weight="bold", update_ticks=True
)
# rasterize the gray lines
logger.info("Rasterizing individual traces...", extra={"spacing": TAB})
for sub_ax in ax.reshape(-1):
sub_ax.set_rasterization_zorder(raster_zorder)
return ax
def add_between_sess_sig(ax, data_df, permpar, data_col="diff_stats",
highest=None, ctrl=False, p_val_prefix=False,
dry_run=False):
"""
add_between_sess_sig(ax, data_df, permpar)
Plot significance markers for significant comparisons between sessions.
Required args:
- ax (plt Axis):
axis
- data_df (pd.DataFrame):
dataframe with one row per session/line/plane, and the following
columns, in addition to the basic sess_df columns:
- {data_col} (list): data stats (me, err)
for session comparisons, e.g. 1v2:
- p_vals_{}v{} (float): p-value for differences between sessions,
corrected for multiple comparisons and tails
- permpar (dict):
dictionary with keys of PermPar namedtuple
Optional args:
- data_col (str):
data column name in data_df
default: "diff_stats"
- highest (list):
highest point for each line/plane, in order
default: None
- ctrl (bool):
if True, significance symbols should use control colour and symbol
default: False
- p_val_prefix (bool):
if True, p-value columns start with data_col as a prefix
"{data_col}_p_vals_{}v{}".
default: False
- dry_run (bool):
if True, a dry-run is done to get highest values, but nothing is
plotted or logged.
default: False
Returns:
- highest (list):
highest point for each line/plane, in order, after plotting
"""
sensitivity = misc_analys.get_sensitivity(permpar)
comp_info = misc_analys.get_comp_info(permpar)
prefix = f"{data_col}_" if p_val_prefix else ""
if not dry_run:
logger.info(f"{comp_info}:", extra={"spacing": "\n"})
for pass_n in [0, 1]: # add significance markers on the second pass
linpla_grps = list(data_df.groupby(["lines", "planes"]))
if highest is None:
highest = [0] * len(linpla_grps)
elif len(highest) != len(linpla_grps):
raise ValueError("If highest is provided, it must contain as "
"many values as line/plane groups in data_df.")
for l, ((line, plane), lp_df) in enumerate(linpla_grps):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
line_plane_name = plot_helper_fcts.get_line_plane_name(line, plane)
sub_ax = ax[pl, li]
if ctrl:
col = "gray"
lp_sess_ns = np.sort(lp_df["sess_ns"].unique())
for sess_n in lp_sess_ns:
rows = lp_df.loc[lp_df["sess_ns"] == sess_n]
if len(rows) != 1:
raise RuntimeError("Expected 1 row per line/plane/session.")
sig_p_vals, sig_strs, sig_xs = [], [], []
lp_sig_str = f"{line_plane_name:6} (between sessions):"
for i, sess_n1 in enumerate(lp_sess_ns):
row_1s = lp_df.loc[lp_df["sess_ns"] == sess_n1]
for sess_n2 in lp_sess_ns[i + 1: ]:
row_2s = lp_df.loc[lp_df["sess_ns"] == sess_n2]
if len(row_1s) != 1 or len(row_2s) != 1:
raise RuntimeError(
"Expected exactly one row per session."
)
row1 = row_1s.loc[row_1s.index[0]]
row2 = row_2s.loc[row_2s.index[0]]
row1_highest = row1[data_col][0] + row1[data_col][-1]
row2_highest = row2[data_col][0] + row2[data_col][-1]
highest[l] = np.nanmax(
[highest[l], row1_highest, row2_highest]
)
if dry_run:
continue
p_val = row1[
f"{prefix}p_vals_{int(sess_n1)}v{int(sess_n2)}"
]
side = np.sign(row2[data_col][0] - row1[data_col][0])
sig_str = misc_analys.get_sig_symbol(
p_val, sensitivity=sensitivity, side=side,
tails=permpar["tails"], p_thresh=permpar["p_val"],
ctrl=ctrl
)
if len(sig_str):
sig_p_vals.append(p_val)
sig_strs.append(sig_str)
sig_xs.append([sess_n1, sess_n2])
lp_sig_str = (
f"{lp_sig_str}{TAB} S{sess_n1}v{sess_n2}: "
f"{p_val:.5f}{sig_str:3}"
)
if dry_run:
continue
n = len(sig_p_vals)
ylims = sub_ax.get_ylim()
prop = np.diff(ylims)[0] / 8.0
if pass_n == 0:
logger.info(lp_sig_str, extra={"spacing": TAB})
if n == 0:
continue
# count number of significant comparisons, and adjust y limits
ylims = [
ylims[0], np.nanmax(
[ylims[1], highest[l] + prop * (n + 1)])
]
sub_ax.set_ylim(ylims)
else:
if n == 0:
continue
if ctrl:
mark_rel_y = 0.22
fontsize = 14
else:
mark_rel_y = 0.18
fontsize = 20
# add significance markers sequentially, on second pass
y_pos = highest[l]
for s, (p_val, sig_str, sig_x) in enumerate(
zip(sig_p_vals, sig_strs, sig_xs)
):
y_pos = highest[l] + (s + 1) * prop
plot_util.plot_barplot_signif(
sub_ax, sig_x, y_pos, rel_y=0.11, color=col, lw=3,
mark_rel_y=mark_rel_y, mark=sig_str, fontsize=fontsize
)
highest[l] = np.nanmax([highest[l], y_pos])
if y_pos > ylims[1]:
sub_ax.set_ylim([ylims[0], y_pos * 1.1])
return highest
def plot_idxs(idx_df, sesspar, figpar, plot="items", density=True, n_bins=40,
title=None, size="reg"):
"""
plot_idxs(idx_df, sesspar, figpar)
Returns exact color for a specific line.
Required args:
- idx_df (pd.DataFrame):
dataframe with indices for different line/plane combinations, and
the following columns, in addition to the basic sess_df columns:
- rand_idx_binned (list): bin counts for the random ROI indices
- bin_edges (list): first and last bin edge
- CI_edges (list): confidence interval limit values
- CI_perc (list): confidence interval percentile limits
if plot == "items":
- roi_idx_binned (list): bin counts for the ROI indices
if plot == "percs":
- perc_idx_binned (list): bin counts for the ROI index percentiles
optionally:
- n_signif_lo (int): number of significant ROIs (low)
- n_signif_hi (int): number of significant ROIs (high)
- sesspar (dict):
dictionary with keys of SessPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- plot (str):
type of data to plot ("items" or "percs")
default: "items"
- density (bool):
if True, histograms are plotted as densities
default: True
- n_bins (int):
number of bins to use in histograms
default: 40
- title (str):
plot title
default: None
- size (str):
plot size ("reg", "small" or "tall")
default: "reg"
Returns:
- ax (2D array):
array of subplots
"""
if plot == "items":
data_key = "roi_idx_binned"
CI_key = "CI_edges"
elif plot == "percs":
data_key = "perc_idx_binned"
CI_key = "CI_perc"
else:
gen_util.accepted_values_error("plot", plot, ["items", "percs"])
sess_ns = misc_analys.get_sess_ns(sesspar, idx_df)
n_plots = len(sess_ns) * 4
figpar["init"]["sharey"] = "row"
figpar = sess_plot_util.fig_init_linpla(figpar, kind="idx",
n_sub=len(sess_ns), sharex=(plot == "percs"))
y = 1
if size == "reg":
subplot_hei = 3.2
subplot_wid = 5.5
elif size == "small":
y = 1.04
subplot_hei = 2.40
subplot_wid = 3.75
figpar["init"]["gs"] = {"hspace": 0.25, "wspace": 0.30}
elif size == "tall":
y = 0.98
subplot_hei = 5.3
subplot_wid = 5.55
else:
gen_util.accepted_values_error("size", size, ["reg", "small", "tall"])
figpar["init"]["subplot_hei"] = subplot_hei
figpar["init"]["subplot_wid"] = subplot_wid
figpar["init"]["sharey"] = "row"
fig, ax = plot_util.init_fig(n_plots, **figpar["init"])
if title is not None:
fig.suptitle(title, y=y, weight="bold")
for (line, plane), lp_df in idx_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
for s, sess_n in enumerate(sess_ns):
rows = lp_df.loc[lp_df["sess_ns"] == sess_n]
if len(rows) == 0:
continue
elif len(rows) > 1:
raise RuntimeError(
"Expected sess_ns to be unique per line/plane."
)
row = rows.loc[rows.index[0]]
sub_ax = ax[s + pl * len(sess_ns), li]
# get percentage significant label
perc_label = None
if "n_signif_lo" in row.keys() and "n_signif_hi" in row.keys():
n_sig_lo, n_sig_hi = row["n_signif_lo"], row["n_signif_hi"]
nrois = np.sum(row["nrois"])
perc_signif = np.sum([n_sig_lo, n_sig_hi]) / nrois * 100
perc_label = (f"{perc_signif:.2f}% sig\n"
f"({n_sig_lo}-/{n_sig_hi}+ of {nrois})")
plot_stim_idx_hist(
sub_ax, row[data_key], row[CI_key], n_bins=n_bins,
rand_data=row["rand_idx_binned"],
orig_edges=row["bin_edges"],
plot=plot, col=col, density=density, perc_label=perc_label)
if size == "small":
sub_ax.legend(fontsize="small")
# Add plane, line info to plots
y_lab = "Density" if density else f"N ROIs"
sess_plot_util.format_linpla_subaxes(ax, datatype="roi", ylab=y_lab,
xticks=None, sess_ns=None, kind="idx", modif_share=False,
xlab="Index", single_lab=True)
# Add indices after setting formatting
if plot == "percs":
nticks = 5
xticks = [int(np.around(x, 0)) for x in np.linspace(0, 100, nticks)]
for sub_ax in ax[-1]:
sub_ax.set_xticks(xticks)
sub_ax.set_xticklabels(xticks, weight="bold")
elif plot == "items":
nticks = 3
plot_util.set_interm_ticks(
ax, nticks, axis="x", weight="bold", share=False, skip=False
)
else:
gen_util.accepted_values_error("plot", plot, ["items", "percs"])
return ax
def plot_roi_correlations(corr_df, figpar, title=None, log_scale=True):
"""
plot_roi_correlations(corr_df, figpar)
Plots correlation histograms.
Required args:
- corr_df (pd.DataFrame):
dataframe with one row per session/line/plane, and the
following columns, in addition to the basic sess_df columns:
- bin_edges (list): first and last bin edge
- corrs_binned (list): number of correlation values per bin
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
- log_scale (bool):
if True, a near logarithmic scale is used for the y axis (with a
linear range to reach 0, and break marks to mark the transition
from linear to log range)
default: True
Returns:
- ax (2D array):
array of subplots
"""
sess_ns = np.arange(corr_df.sess_ns.min(), corr_df.sess_ns.max() + 1)
n_sess = len(sess_ns)
figpar = sess_plot_util.fig_init_linpla(figpar,
kind="prog",
n_sub=len(sess_ns))
figpar["init"]["subplot_hei"] = 3.0
figpar["init"]["subplot_wid"] = 2.8
figpar["init"]["sharex"] = log_scale
if log_scale:
figpar["init"]["sharey"] = True
fig, ax = plot_util.init_fig(4 * len(sess_ns), **figpar["init"])
if title is not None:
fig.suptitle(title, y=1.02, weight="bold")
sess_plot_util.format_linpla_subaxes(ax,
datatype="roi",
ylab="Density",
xlab="Correlation",
sess_ns=sess_ns,
kind="prog",
single_lab=True)
log_base = 2
for (line, plane), lp_df in corr_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
for s, sess_n in enumerate(sess_ns):
sess_rows = lp_df.loc[lp_df["sess_ns"] == sess_n]
if len(sess_rows) == 0:
continue
elif len(sess_rows) > 1:
raise RuntimeError("Expected exactly one row.")
sess_row = sess_rows.loc[sess_rows.index[0]]
sub_ax = ax[pl, s + li * n_sess]
weights = np.asarray(sess_row["corrs_binned"])
bin_edges = np.linspace(*sess_row["bin_edges"], len(weights) + 1)
sub_ax.hist(bin_edges[:-1],
bin_edges,
weights=weights,
color=col,
alpha=0.6,
density=True)
sub_ax.axvline(0,
ls=plot_helper_fcts.VDASH,
c="k",
lw=3.0,
alpha=0.5)
sub_ax.spines["bottom"].set_visible(True)
sub_ax.tick_params(axis="x", which="both", bottom=True, top=False)
if log_scale:
sub_ax.set_yscale("log", base=log_base)
sub_ax.set_xlim(-1, 1)
else:
sub_ax.autoscale(axis="x", tight=True)
sub_ax.autoscale(axis="y", tight=True)
if log_scale: # update x ticks
set_symlog_scale(ax, log_base=log_base, col_per_grp=n_sess, n_ticks=4)
else: # update x and y ticks
for i in range(ax.shape[0]):
for j in range(int(ax.shape[1] / n_sess)):
sub_axes = ax[i, j * n_sess:(j + 1) * n_sess]
plot_util.set_interm_ticks(sub_axes,
4,
axis="y",
share=True,
update_ticks=True)
plot_util.set_interm_ticks(ax,
4,
axis="x",
share=log_scale,
update_ticks=True,
fontweight="bold")
return ax
def plot_snr_sigmeans_nrois(data_df,
figpar,
datatype="snrs",
title="ROI SNRs"):
"""
plot_snr_sigmeans_nrois(data_df, figpar)
Plots SNR, signal means or number of ROIs, depending on the case.
Required args:
- data_df (pd.DataFrame):
dataframe with SNR, signal mean or number of ROIs data for each
session, in addition to the basic sess_df columns
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- datatype (str):
type of data to plot, also corresponding to column name
default: "snrs"
- title (str):
plot title
default: "ROI SNRs"
Returns:
- ax (2D array):
array of subplots
"""
sess_ns = np.arange(data_df.sess_ns.min(), data_df.sess_ns.max() + 1)
figpar = sess_plot_util.fig_init_linpla(figpar, kind="reg")
figpar["init"]["sharey"] = "row"
figpar["init"]["subplot_hei"] = 4.4
figpar["init"]["gs"] = {"wspace": 0.2, "hspace": 0.2}
if datatype != "nrois":
figpar["init"]["subplot_wid"] = 3.2
else:
figpar["init"]["subplot_wid"] = 2.5
fig, ax = plot_util.init_fig(4, **figpar["init"])
if title is not None:
fig.suptitle(title, y=0.97, weight="bold")
for (line, plane), lp_df in data_df.groupby(["lines", "planes"]):
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(line, plane)
sub_ax = ax[pl, li]
if datatype == "snrs":
sub_ax.axhline(y=1,
ls=plot_helper_fcts.HDASH,
c="k",
lw=3.0,
alpha=0.5)
elif datatype == "signal_means":
sub_ax.axhline(y=0,
ls=plot_helper_fcts.HDASH,
c="k",
lw=3.0,
alpha=0.5)
elif datatype != "nrois":
gen_util.accepted_values_error("datatype", datatype,
["snrs", "signal_means", "nrois"])
if datatype == "nrois":
plot_nrois(sub_ax, lp_df, sess_ns=sess_ns, col=col, dash=dash)
continue
data = []
use_sess_ns = []
for sess_n in sess_ns:
rows = lp_df.loc[lp_df["sess_ns"] == sess_n]
if len(rows) > 0:
use_sess_ns.append(sess_n)
data.append(np.concatenate(rows[datatype].tolist()))
sub_ax.boxplot(data,
positions=use_sess_ns,
notch=True,
patch_artist=True,
whis=[5, 95],
widths=0.6,
boxprops=dict(facecolor="white",
color=col,
linewidth=3.0),
capprops=dict(color=col, linewidth=3.0),
whiskerprops=dict(color=col, linewidth=3.0),
flierprops=dict(color=col,
markeredgecolor=col,
markersize=8),
medianprops=dict(color=col, linewidth=3.0))
sess_plot_util.format_linpla_subaxes(ax,
datatype="roi",
ylab="",
xticks=sess_ns,
kind="reg",
single_lab=True)
return ax
def plot_roi_tracking(roi_mask_df, figpar, title=None):
"""
plot_roi_tracking(roi_mask_df, figpar)
Plots ROI tracking examples, for different session permutations, and union
across permutations.
Required args:
- roi_mask_df (pd.DataFrame in dict format):
dataframe with a row for each mouse, and the following
columns, in addition to the basic sess_df columns:
- "roi_mask_shapes" (list): shape into which ROI mask indices index
(sess x hei x wid)
- "union_n_conflicts" (int): number of conflicts after union
for "union", "fewest" and "most" tracked ROIs:
- "{}_registered_roi_mask_idxs" (list): list of mask indices,
registered across sessions, for each session
(flattened across ROIs) ((sess, hei, wid) x val),
ordered by {}_sess_ns if "fewest" or "most"
- "{}_n_tracked" (int): number of tracked ROIs
for "fewest", "most" tracked ROIs:
- "{}_sess_ns" (list): ordered session number
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
if len(roi_mask_df) != 1:
raise ValueError("Expected only one row in roi_mask_df")
roi_mask_row = roi_mask_df.loc[roi_mask_df.index[0]]
columns = ["fewest", "most", "", "union"]
figpar["init"]["ncols"] = len(columns)
figpar["init"]["sharex"] = False
figpar["init"]["sharey"] = False
figpar["init"]["subplot_hei"] = 5.05
figpar["init"]["subplot_wid"] = 5.05
figpar["init"]["gs"] = {"wspace": 0.06}
# MUST ADJUST if anything above changes [right, bottom, width, height]
new_axis_coords = [0.905, 0.125, 0.06, 0.74]
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
sub_ax_scale = fig.add_axes(new_axis_coords)
plot_util.remove_axis_marks(sub_ax_scale)
sub_ax_scale.spines["left"].set_visible(True)
if title is not None:
fig.suptitle(title, y=1.05, weight="bold")
sess_cols = get_sess_cols(roi_mask_df)
alpha = 0.6
for c, column in enumerate(columns):
sub_ax = ax[0, c]
if c == 0:
lp_col = plot_helper_fcts.get_line_plane_idxs(
roi_mask_row["lines"], roi_mask_row["planes"]
)[2]
lp_name = plot_helper_fcts.get_line_plane_name(
roi_mask_row["lines"], roi_mask_row["planes"]
)
sub_ax.set_ylabel(lp_name, fontweight="bold", color=lp_col)
log_info = f"Conflicts and matches for a {lp_name} example:"
if column == "":
sub_ax.set_axis_off()
subplot_title = \
" Union - conflicts\n... ====================>"
sub_ax.set_title(subplot_title, fontweight="bold", y=0.5)
continue
else:
plot_util.remove_axis_marks(sub_ax)
for spine in ["right", "left", "top", "bottom"]:
sub_ax.spines[spine].set_visible(True)
if column in ["fewest", "most"]:
y = 1.01
ord_sess_ns = roi_mask_row[f"{column}_sess_ns"]
ord_sess_ns_str = ", ".join([str(n) for n in ord_sess_ns])
n_matches = int(roi_mask_row[f"{column}_n_tracked"])
subplot_title = f"{n_matches} matches\n(sess {ord_sess_ns_str})"
log_info = (f"{log_info}\n{TAB}"
f"{column.capitalize()} matches (sess {ord_sess_ns_str}): "
f"{n_matches}")
elif column == "union":
y = 1.04
ord_sess_ns = roi_mask_row["sess_ns"]
n_union = int(roi_mask_row[f"{column}_n_tracked"])
n_conflicts = int(roi_mask_row[f"{column}_n_conflicts"])
n_matches = n_union - n_conflicts
subplot_title = f"{n_matches} matches"
log_info = (f"{log_info}\n{TAB}"
"Union - conflicts: "
f"{n_union} - {n_conflicts} = {n_matches} matches"
)
sub_ax.set_title(subplot_title, fontweight="bold", y=y)
roi_masks = create_sess_roi_masks(
roi_mask_row,
mask_key=f"{column}_registered_roi_mask_idxs"
)
for sess_n in roi_mask_row["sess_ns"]:
col = sess_cols[int(sess_n)]
s = ord_sess_ns.index(sess_n)
add_roi_mask(sub_ax, roi_masks[s], col=col, alpha=alpha)
# add scale marker
hei_len = roi_mask_row["roi_mask_shapes"][1]
add_scale_marker(
sub_ax_scale, side_len=hei_len, ori="vertical", quadrant=3, fontsize=20
)
logger.info(log_info, extra={"spacing": "\n"})
# add legend
add_sess_col_leg(
ax[0, columns.index("")],
sess_cols,
bbox_to_anchor=(0.67, 0.3),
alpha=alpha
)
return ax
def plot_roi_masks_overlayed(roi_mask_df, figpar, title=None):
"""
plot_roi_masks_overlayed(roi_mask_df, figpar)
Plots ROI masks overlayed across sessions, optionally cropped.
Required args:
- roi_mask_df (pd.DataFrame in dict format):
dataframe with a row for each mouse, and the following
columns, in addition to the basic sess_df columns:
- "registered_roi_mask_idxs" (list): list of mask indices,
registered across sessions, for each session
(flattened across ROIs) ((sess, hei, wid) x val)
- "roi_mask_shapes" (list): shape into which ROI mask indices index
(sess x hei x wid)
and optionally, if cropping:
- "crop_fact" (num): factor by which to crop masks (> 1)
- "shift_prop_hei" (float): proportion by which to shift cropped
mask center vertically from left edge [0, 1]
- "shift_prop_wid" (float): proportion by which to shift cropped
mask center horizontally from left edge [0, 1]
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
crop = "crop_fact" in roi_mask_df.columns
figpar = sess_plot_util.fig_init_linpla(figpar)
figpar["init"]["sharex"] = False
figpar["init"]["sharey"] = False
figpar["init"]["subplot_hei"] = 5.2
figpar["init"]["subplot_wid"] = 5.2
figpar["init"]["gs"] = {"wspace": 0.03, "hspace": 0.32}
# MUST ADJUST if anything above changes [right, bottom, width, height]
new_axis_coords = [0.885, 0.11, 0.1, 0.33]
if crop: # move to the left
new_axis_coords[0] = 0.04
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
sub_ax_scale = fig.add_axes(new_axis_coords)
plot_util.remove_axis_marks(sub_ax_scale)
sub_ax_scale.spines["left"].set_visible(True)
if title is not None:
fig.suptitle(title, y=0.95, weight="bold")
sess_cols = get_sess_cols(roi_mask_df)
alpha = 0.6
hei_lens = []
for (line, plane), lp_mask_df in roi_mask_df.groupby(["lines", "planes"]):
li, pl, _, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
sub_ax = ax[pl, li]
if len(lp_mask_df) != 1:
raise RuntimeError("Expected only one row per line/plane.")
lp_row = lp_mask_df.loc[lp_mask_df.index[0]]
roi_masks = create_sess_roi_masks(lp_row, crop=crop)
hei_lens.append(roi_masks.shape[1])
for s, sess_n in enumerate(lp_row["sess_ns"]):
col = sess_cols[int(sess_n)]
add_roi_mask(sub_ax, roi_masks[s], col=col, alpha=alpha)
# add legend
add_sess_col_leg(
ax[0, 1], sess_cols, bbox_to_anchor=(0.7, -0.01), alpha=alpha
)
# add scale marker
hei_lens = np.unique(hei_lens)
if len(hei_lens) != 1:
raise NotImplementedError(
"Adding scale bar not implemented if ROI mask image heights are "
"different for different planes."
)
quadrant = 1 if crop else 3
add_scale_marker(
sub_ax_scale, side_len=hei_lens[0], ori="vertical", quadrant=quadrant,
fontsize=22
)
# Add plane, line info to plots
sess_plot_util.format_linpla_subaxes(ax, ylab="", kind="map")
return ax
def plot_roi_masks_overlayed_with_proj(roi_mask_df, figpar, title=None):
"""
plot_roi_masks_overlayed_with_proj(roi_mask_df, figpar)
Plots ROI mask contours overlayed over imaging planes, and ROI masks
overlayed over each other across sessions.
Required args:
- roi_mask_df (pd.DataFrame in dict format):
dataframe with a row for each mouse, and the following
columns, in addition to the basic sess_df columns:
- "max_projections" (list): pixel intensities of maximum projection
for the plane (hei x wid)
- "registered_roi_mask_idxs" (list): list of mask indices,
registered across sessions, for each session
(flattened across ROIs) ((sess, hei, wid) x val)
- "roi_mask_idxs" (list): list of mask indices for each session,
and each ROI (sess x (ROI, hei, wid) x val) (not registered)
- "roi_mask_shapes" (list): shape into which ROI mask indices index
(sess x hei x wid)
- "crop_fact" (num): factor by which to crop masks (> 1)
- "shift_prop_hei" (float): proportion by which to shift cropped
mask center vertically from left edge [0, 1]
- "shift_prop_wid" (float): proportion by which to shift cropped
mask center horizontally from left edge [0, 1]
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
n_lines = len(roi_mask_df["lines"].unique())
n_planes = len(roi_mask_df["planes"].unique())
sess_cols = get_sess_cols(roi_mask_df)
n_sess = len(sess_cols)
n_cols = n_sess * n_lines
figpar = sess_plot_util.fig_init_linpla(figpar)
figpar["init"]["sharex"] = False
figpar["init"]["sharey"] = False
figpar["init"]["subplot_hei"] = 2.3
figpar["init"]["subplot_wid"] = 2.3
figpar["init"]["gs"] = {"wspace": 0.2, "hspace": 0.2}
figpar["init"]["ncols"] = n_cols
fig, ax = plot_util.init_fig(n_cols * n_planes * 2, **figpar["init"])
if title is not None:
fig.suptitle(title, y=0.93, weight="bold")
crop = "crop_fact" in roi_mask_df.columns
sess_cols = get_sess_cols(roi_mask_df)
alpha = 0.6
raster_zorder = -12
for (line, plane), lp_mask_df in roi_mask_df.groupby(["lines", "planes"]):
li, pl, _, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
lp_col = plot_helper_fcts.get_line_plane_idxs(line, plane)[2]
lp_name = plot_helper_fcts.get_line_plane_name(line, plane)
if len(lp_mask_df) != 1:
raise RuntimeError("Expected only one row per line/plane.")
lp_row = lp_mask_df.loc[lp_mask_df.index[0]]
# identify subplots
base_row = (pl % n_planes) * n_planes
base_col = (li % n_lines) * n_lines
ax_grp = ax[base_row : base_row + 2, base_col : base_col + n_sess + 1]
# add imaging planes and masks
imaging_planes = add_proj_and_roi_masks(
ax_grp, lp_row, sess_cols, crop=crop, alpha=alpha,
proj_zorder=raster_zorder - 1
)
# add markings
shared_row = base_row + 1
shared_col = base_col + int((n_sess - 1) // 2)
shared_sub_ax = ax[shared_row, shared_col]
if shared_col == 0:
shared_sub_ax.set_ylabel(lp_name, fontweight="bold", color=lp_col)
else:
lp_sub_ax = ax[shared_row, 0]
lp_sub_ax.set_xlim([0, 1])
lp_sub_ax.set_ylim([0, 1])
lp_sub_ax.text(
0.5, 0.5, lp_name, fontweight="bold", color=lp_col,
ha="center", va="center", fontsize="x-large"
)
# add scale bar
if n_sess < 2:
raise NotImplementedError(
"Scale bar placement not implemented for fewer than 2 "
"sessions."
)
scale_ax = ax[shared_row, -1]
wid_len = imaging_planes[0].shape[-1]
add_scale_marker(
scale_ax, side_len=wid_len, ori="horizontal", quadrant=1,
fontsize=20
)
logger.info("Rasterizing imaging plane images...", extra={"spacing": TAB})
for i in range(ax.shape[0]):
for j in range(ax.shape[1]):
sub_ax = ax[i, j]
plot_util.remove_axis_marks(sub_ax)
if not(i % 2):
sub_ax.set_rasterization_zorder(raster_zorder)
# add legend
if n_sess < 2:
raise NotImplementedError(
"Legend placement not implemented for fewer than 2 sessions."
)
add_sess_col_leg(
ax[-1, -1], sess_cols, bbox_to_anchor=(1, 0.6), alpha=alpha,
fontsize="small"
)
return ax
def plot_idx_corr_scatterplots(idx_corr_df,
permpar,
figpar,
permute="sess",
title=None):
"""
plot_idx_corr_scatterplots(idx_corr_df, permpar, figpar)
Plots ROI USI index correlation scatterplots for individual session
comparisons.
Required args:
- idx_corr_df (pd.DataFrame):
dataframe with one row per line/plane, and the
following columns, in addition to the basic sess_df columns:
for correlation data (normalized if corr_type is "diff_corr") for
session comparisons (x, y), e.g. 1v2
- binned_rand_stats (list): number of random correlation values per
bin (xs x ys)
- corr_data_xs (list): USI values for x
- corr_data_ys (list): USI values for y
- corrs (float): correlation between session data (x and y)
- p_vals (float): p-value for correlation, corrected for
multiple comparisons and tails
- rand_corr_meds (float): median of the random correlations
- raw_p_vals (float): p-value for intersession correlations
- regr_coefs (float): regression correlation coefficient (slope)
- regr_intercepts (float): regression correlation intercept
- x_bin_mids (list): x mid point for each random correlation bin
- y_bin_mids (list): y mid point for each random correlation bin
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- permute (bool):
type of permutation to due ("tracking", "sess" or "all")
default: "sess"
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
diffs = False
if permute in ["sess", "all"]:
diffs = True
figpar = sess_plot_util.fig_init_linpla(figpar, kind="reg")
figpar["init"]["sharex"] = False
figpar["init"]["sharey"] = False
figpar["init"]["subplot_hei"] = 4
figpar["init"]["subplot_wid"] = 4
figpar["init"]["gs"] = {"hspace": 0.4, "wspace": 0.4}
fig, ax = plot_util.init_fig(4, **figpar["init"])
if title is not None:
fig.suptitle(title, fontweight="bold", y=0.97)
sess_ns = None
# first pass to plot
for (line, plane), lp_df in idx_corr_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
sub_ax = ax[pl, li]
if len(lp_df) != 1:
raise RuntimeError("Expected exactly one row.")
lp_row = lp_df.loc[lp_df.index[0]]
if sess_ns is None:
sess_ns = lp_row["sess_ns"]
xlabel = f"Session {sess_ns[0]} USIs"
ylabel = f"Session {sess_ns[1]} USIs"
if diffs:
ylabel = f"Session {sess_ns[1]} - {sess_ns[0]} USIs"
elif sess_ns != lp_row["sess_ns"]:
raise RuntimeError("Expected all sess_ns to match.")
density_data = [
lp_row["x_bin_mids"], lp_row["y_bin_mids"],
np.asarray(lp_row["binned_rand_stats"]).T
]
sub_ax.contour(*density_data,
levels=6,
cmap="Greys",
zorder=-13,
linewidths=4)
alpha = 0.3**(len(lp_row["corr_data_xs"]) / 300)
sub_ax.scatter(lp_row["corr_data_xs"],
lp_row["corr_data_ys"],
color=col,
alpha=alpha,
lw=2,
s=35)
# Add plane, line info to plots
sess_plot_util.format_linpla_subaxes(ax,
datatype="roi",
xticks=None,
ylab=ylabel,
xlab=xlabel,
kind="reg")
# second pass to add plot markings
comp_info = misc_analys.get_comp_info(permpar)
logger.info(f"{comp_info}:", extra={"spacing": "\n"})
sig_str = ""
for (line, plane), lp_df in idx_corr_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
line_plane_name = plot_helper_fcts.get_line_plane_name(line, plane)
sub_ax = ax[pl, li]
# add markers back in (removed due to kind='reg')
sub_ax.tick_params(axis="x", which="both", bottom=True, top=False)
sub_ax.spines["bottom"].set_visible(True)
lp_row = lp_df.loc[lp_df.index[0]]
p_val_corr = lp_row["p_vals"]
lp_sig_str = add_scatterplot_markers(sub_ax,
permpar,
lp_row["corrs"],
lp_row["rand_corr_meds"],
lp_row["regr_coefs"],
lp_row["regr_intercepts"],
lp_row["p_vals"],
col=col,
diffs=diffs)
sig_str = (f"{sig_str}{TAB}{line_plane_name}: "
f"{p_val_corr:.5f}{lp_sig_str:3}")
logger.info(sig_str, extra={"spacing": TAB})
return ax
def plot_perc_sig_usis(perc_sig_df, analyspar, permpar, figpar, by_mouse=False,
title=None):
"""
plot_perc_sig_usis(perc_sig_df, analyspar, figpar)
Plots percentage of significant USIs.
Required args:
- perc_sig_df (pd.DataFrame):
dataframe with one row per session/line/plane, and the following
columns, in addition to the basic sess_df columns:
for sig in ["lo", "hi"]: for low vs high ROI indices
- perc_sig_{sig}_idxs (num): percent significant ROIs (0-100)
- perc_sig_{sig}_idxs_stds (num): bootstrapped standard deviation
over percent significant ROIs
- perc_sig_{sig}_idxs_CIs (list): adjusted CI for percent sig. ROIs
- perc_sig_{sig}_idxs_null_CIs (list): adjusted null CI for percent
sig. ROIs
- perc_sig_{sig}_idxs_raw_p_vals (num): uncorrected p-value for
percent sig. ROIs
- perc_sig_{sig}_idxs_p_vals (num): p-value for percent sig.
ROIs, corrected for multiple comparisons and tails
- analyspar (dict):
dictionary with keys of AnalysPar namedtuple
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- by_mouse (bool):
if True, plotting is done per mouse
default: False
- title (str):
plot title
default: None
Returns:
- ax (2D array):
array of subplots
"""
perc_sig_df = perc_sig_df.copy(deep=True)
nanpol = None if analyspar["rem_bad"] else "omit"
sess_ns = perc_sig_df["sess_ns"].unique()
if len(sess_ns) != 1:
raise NotImplementedError(
"Plotting function implemented for 1 session only."
)
figpar = sess_plot_util.fig_init_linpla(figpar, kind="idx", n_sub=1,
sharex=True, sharey=True)
figpar["init"]["sharey"] = True
figpar["init"]["subplot_wid"] = 3.4
figpar["init"]["gs"] = {"wspace": 0.18}
if by_mouse:
figpar["init"]["subplot_hei"] = 8.4
else:
figpar["init"]["subplot_hei"] = 3.5
fig, ax = plot_util.init_fig(2, **figpar["init"])
if title is not None:
y = 0.98 if by_mouse else 1.07
fig.suptitle(title, y=y, weight="bold")
tail_order = ["Low tail", "High tail"]
tail_keys = ["lo", "hi"]
chance = permpar["p_val"] / 2 * 100
ylims = get_perc_sig_ylims(perc_sig_df, high_pt_min=40)
n_linpla = plot_helper_fcts.N_LINPLA
comp_info = misc_analys.get_comp_info(permpar)
logger.info(f"{comp_info}:", extra={"spacing": "\n"})
for t, (tail, key) in enumerate(zip(tail_order, tail_keys)):
sub_ax = ax[0, t]
sub_ax.set_title(tail, fontweight="bold")
sub_ax.set_ylim(ylims)
# replace bottom spine with line at 0
sub_ax.spines['bottom'].set_visible(False)
sub_ax.axhline(y=0, c="k", lw=4.0)
data_key = f"perc_sig_{key}_idxs"
CIs = np.full((plot_helper_fcts.N_LINPLA, 2), np.nan)
CI_meds = np.full(plot_helper_fcts.N_LINPLA, np.nan)
tail_sig_str = f"{tail:9}:"
linpla_names = []
for (line, plane), lp_df in perc_sig_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
x_index = 2 * li + pl
linpla_name = plot_helper_fcts.get_line_plane_name(line, plane)
linpla_names.append(linpla_name)
if len(lp_df) == 0:
continue
elif len(lp_df) > 1 and not by_mouse:
raise RuntimeError("Expected a single row per line/plane.")
lp_df = lp_df.sort_values("mouse_ns") # sort by mouse
df_indices = lp_df.index.tolist()
if by_mouse:
# plot means or medians per mouse
mouse_data = lp_df[data_key].to_numpy()
mouse_cols = plot_util.get_hex_color_range(
len(lp_df), col=col,
interval=plot_helper_fcts.MOUSE_COL_INTERVAL
)
mouse_data_mean = math_util.mean_med(
mouse_data, stats=analyspar["stats"], nanpol=nanpol
)
CI_dummy = np.repeat(mouse_data_mean, 2)
plot_util.plot_CI(sub_ax, CI_dummy, med=mouse_data_mean,
x=x_index, width=0.6, med_col=col, med_rat=0.01)
else:
# collect confidence interval data
row = lp_df.loc[df_indices[0]]
mouse_cols = [col]
CIs[x_index] = np.asarray(row[f"{data_key}_null_CIs"])[
np.asarray([0, 2])
]
CI_meds[x_index] = row[f"{data_key}_null_CIs"][1]
if by_mouse:
perc_p_vals = []
rel_y = 0.05
else:
tail_sig_str = f"{tail_sig_str}{TAB}{linpla_name}: "
rel_y = 0.1
for df_i, mouse_col in zip(df_indices, mouse_cols):
# plot UFOs
err = None
no_line = True
if not by_mouse:
err = perc_sig_df.loc[df_i, f"{data_key}_stds"]
no_line = False
# indicate bootstrapped error with wider capsize
plot_util.plot_ufo(
sub_ax, x_index, perc_sig_df.loc[df_i, data_key], err,
color=mouse_col, capsize=8, no_line=no_line
)
# add significance markers
p_val = perc_sig_df.loc[df_i, f"{data_key}_p_vals"]
perc = perc_sig_df.loc[df_i, data_key]
nrois = np.sum(perc_sig_df.loc[df_i, "nrois"])
side = np.sign(perc - chance)
sensitivity = misc_analys.get_binom_sensitivity(
nrois, null_perc=chance, side=side
)
sig_str = misc_analys.get_sig_symbol(
p_val, sensitivity=sensitivity, side=side,
tails=permpar["tails"], p_thresh=permpar["p_val"]
)
if len(sig_str):
perc_high = perc + err if err is not None else perc
plot_util.add_signif_mark(sub_ax, x_index, perc_high,
rel_y=rel_y, color=mouse_col, fontsize=24,
mark=sig_str)
if by_mouse:
perc_p_vals.append(
(int(np.around(perc)), p_val, sig_str)
)
else:
tail_sig_str = (
f"{tail_sig_str}{p_val:.5f}{sig_str:3}"
)
if by_mouse: # sort p-value logging by percentage value
tail_sig_str = f"{tail_sig_str}\n\t{linpla_name:6}: "
order = np.argsort([vals[0] for vals in perc_p_vals])
for i in order:
perc, p_val, sig_str = perc_p_vals[i]
perc_str = f"(~{perc}%)"
tail_sig_str = (
f"{tail_sig_str}{TAB}{perc_str:6} "
f"{p_val:.5f}{sig_str:3}"
)
# add chance information
if by_mouse:
sub_ax.axhline(
y=chance, ls=plot_helper_fcts.VDASH, c="k", lw=3.0, alpha=0.5,
zorder=-12
)
else:
plot_util.plot_CI(sub_ax, CIs.T, med=CI_meds,
x=np.arange(n_linpla), width=0.45, med_rat=0.025, zorder=-12)
logger.info(tail_sig_str, extra={"spacing": TAB})
for sub_ax in fig.axes:
sub_ax.tick_params(axis="x", which="both", bottom=False)
plot_util.set_ticks(
sub_ax, min_tick=0, max_tick=n_linpla - 1, n=n_linpla, pad_p=0.2)
sub_ax.set_xticklabels(linpla_names, rotation=90, weight="bold")
ax[0, 0].set_ylabel("%", fontweight="bold")
plot_util.set_interm_ticks(ax, 3, axis="y", weight="bold", share=True)
# adjustment if tick interval is repeated in the negative
if ax[0, 0].get_ylim()[0] < 0:
ax[0, 0].set_ylim([ylims[0], ax[0, 0].get_ylim()[1]])
return ax
def plot_sess_traces(data_df, analyspar, sesspar, figpar,
trace_col="trace_stats", row_col="sess_ns",
row_order=None, split="by_exp", title=None, size="reg"):
"""
plot_sess_traces(data_df, analyspar, sesspar, figpar)
Plots traces from dataframe.
Required args:
- data_df (pd.DataFrame):
traces data frame with, in addition to the basic sess_df columns,
columns specified by trace_col, row_col, and a "time_values" column
- analyspar (dict):
dictionary with keys of AnalysPar namedtuple
- sesspar (dict):
dictionary with keys of SessPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- trace_col (str):
dataframe column containing trace statistics, as
split x ROIs x frames x stats
default: "trace_stats"
- row_col (str):
dataframe column specifying the variable that defines rows
within each line/plane
default: "sess_ns"
- row_order (list):
ordered list specifying the order in which to plot from row_col.
If None, automatic sorting order is used.
default: None
- split (str):
data split, e.g. "exp_lock", "unexp_lock", "stim_onset" or
"stim_offset"
default: False
- title (str):
plot title
default: None
- size (str):
subplot sizes
default: "reg"
Returns:
- ax (2D array):
array of subplots
"""
# retrieve session numbers, and infer row_order, if necessary
sess_ns = None
if row_col == "sess_ns":
sess_ns = row_order
if row_order is None:
row_order = misc_analys.get_sess_ns(sesspar, data_df)
elif row_order is None:
row_order = data_df[row_col].unique()
figpar = sess_plot_util.fig_init_linpla(
figpar, kind="traces", n_sub=len(row_order), sharey=False
)
if size == "small":
figpar["init"]["subplot_hei"] = 1.51
figpar["init"]["subplot_wid"] = 3.7
elif size == "wide":
figpar["init"]["subplot_hei"] = 1.36
figpar["init"]["subplot_wid"] = 4.8
figpar["init"]["gs"] = {"wspace": 0.3, "hspace": 0.5}
elif size == "reg":
figpar["init"]["subplot_hei"] = 1.36
figpar["init"]["subplot_wid"] = 3.4
else:
gen_util.accepted_values_error("size", size, ["small", "wide", "reg"])
fig, ax = plot_util.init_fig(len(row_order) * 4, **figpar["init"])
if title is not None:
fig.suptitle(title, y=1.0, weight="bold")
for (line, plane), lp_df in data_df.groupby(["lines", "planes"]):
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(line, plane)
for r, row_val in enumerate(row_order):
rows = lp_df.loc[lp_df[row_col] == row_val]
if len(rows) == 0:
continue
elif len(rows) > 1:
raise RuntimeError(
"Expected row_order instances to be unique per line/plane."
)
row = rows.loc[rows.index[0]]
sub_ax = ax[r + pl * len(row_order), li]
if line == "L2/3-Cux2":
exp_col = "darkgray" # oddly, lighter than gray
else:
exp_col = "gray"
plot_traces(
sub_ax, row["time_values"], row[trace_col], split=split,
col=col, ls=dash, exp_col=exp_col, lab=False
)
for sub_ax in ax.reshape(-1):
plot_util.set_minimal_ticks(sub_ax, axis="y")
sess_plot_util.format_linpla_subaxes(ax, fluor=analyspar["fluor"],
area=False, datatype="roi", sess_ns=sess_ns, xticks=None,
kind="traces", modif_share=False)
# fix x ticks and lims
plot_util.set_interm_ticks(ax, 3, axis="x", fontweight="bold")
xlims = [np.min(row["time_values"]), np.max(row["time_values"])]
if split != "by_exp":
xlims = [-xlims[1], xlims[1]]
sub_ax.set_xlim(xlims)
return ax
def plot_ex_roi_hists(ex_idx_df, sesspar, permpar, figpar, title=None):
"""
plot_ex_roi_hists(ex_idx_df, sesspar, permpar, figpar)
Plot example ROI histograms.
Required args:
- ex_idx_df (pd.DataFrame):
dataframe with a row for the example ROI, and the following
columns, in addition to the basic sess_df columns:
- rand_idx_binned (list): bin counts for the random ROI indices
- bin_edges (list): first and last bin edge
- CI_edges (list): confidence interval limit values
- CI_perc (list): confidence interval percentile limits
- sesspar (dict):
dictionary with keys of SessPar namedtuple
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- plot (str):
type of data to plot ("items" or "percs")
default: "items"
Returns:
- ax (2D array):
array of subplots
"""
ex_idx_df = copy.deepcopy(ex_idx_df) # add dummy binned_roi_idxs
ex_idx_df["roi_idx_binned"] = [
np.zeros_like(rand_idx_binned)
for rand_idx_binned in ex_idx_df["rand_idx_binned"].tolist()
]
with gen_util.TempWarningFilter("invalid value", RuntimeWarning):
ax = plot_idxs(
ex_idx_df, sesspar, figpar, plot="items", title=title, size="tall",
density=True, n_bins=40)
# adjust x axes
for sub_ax in ax.reshape(-1):
sub_ax.set_xticks([-0.5, 0, 0.5])
sub_ax.set_xticklabels(["-0.5", "0", "0.5"])
# add lines and labels
for (line, plane), lp_df in ex_idx_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
if len(lp_df) == 0:
continue
elif len(lp_df) > 1:
raise RuntimeError("Expected at most one row per line/plane.")
row = lp_df.loc[lp_df.index[0]]
sub_ax = ax[pl, li]
xlims = sub_ax.get_xlim()
# add CI markers
for c, (CI_val, CI_perc) in enumerate(
zip(row["CI_edges"], row["CI_perc"])
):
sub_ax.axvline(
CI_val, ls=plot_helper_fcts.VDASH, c="red", lw=3.0, alpha=1.0,
label=f"p{CI_perc:0.2f}")
sub_ax.axvspan(
CI_val, xlims[c], color=plot_helper_fcts.DARKRED, alpha=0.1,
lw=0, zorder=-13
)
ex_perc = row["roi_idx_percs"]
sensitivity = misc_analys.get_sensitivity(permpar)
sig_str = misc_analys.get_sig_symbol(
ex_perc, percentile=True, sensitivity=sensitivity,
p_thresh=permpar["p_val"]
)
sub_ax.axvline(
x=row["roi_idxs"], ls=plot_helper_fcts.VDASH, c=col, lw=3.0,
alpha=0.8, label=f"p{ex_perc:0.2f}{sig_str}"
)
sub_ax.axvline(
x=0, ls=plot_helper_fcts.VDASH, c="k", lw=3.0, alpha=0.5
)
# reset the x limits
sub_ax.set_xlim(xlims)
sub_ax.legend()
return ax
def plot_sess_data(data_df, analyspar, sesspar, permpar, figpar,
between_sess_sig=True, data_col="diff_stats",
decoder_data=False, title=None, wide=False):
"""
plot_sess_data(data_df, analyspar, sesspar, permpar, figpar)
Plots errorbar data across sessions.
Required args:
- data_df (pd.DataFrame):
dataframe with one row per session/line/plane, and the following
columns, in addition to the basic sess_df columns:
- {data_key} (list): data stats (me, err)
- null_CIs (list): adjusted null CI for data
- raw_p_vals (float): uncorrected p-value for data within
sessions
- p_vals (float): p-value for data within sessions,
corrected for multiple comparisons and tails
for session comparisons, e.g. 1v2:
- raw_p_vals_{}v{} (float): uncorrected p-value for data
differences between sessions
- p_vals_{}v{} (float): p-value for data between sessions,
corrected for multiple comparisons and tails
- analyspar (dict):
dictionary with keys of AnalysPar namedtuple
- sesspar (dict):
dictionary with keys of SessPar namedtuple
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- between_sess_sig (bool):
if True, significance between sessions is logged and plotted
default: True
- data_col (str):
dataframe column in which data to plot is stored
default: "diff_stats"
- decoder_data (bool):
if True, data plotted is decoder data
default: False
- title (str):
plot title
default: None
- wide (bool):
if True, subplots are wider
default: False
Returns:
- ax (2D array):
array of subplots
"""
sess_ns = misc_analys.get_sess_ns(sesspar, data_df)
figpar = sess_plot_util.fig_init_linpla(figpar)
sharey = True if decoder_data else "row"
figpar["init"]["sharey"] = sharey
figpar["init"]["subplot_hei"] = 4.4
figpar["init"]["gs"] = {"hspace": 0.2}
if wide:
figpar["init"]["subplot_wid"] = 3.0
figpar["init"]["gs"]["wspace"] = 0.3
else:
figpar["init"]["subplot_wid"] = 2.6
figpar["init"]["gs"]["wspace"] = 0.3
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
if title is not None:
fig.suptitle(title, y=0.97, weight="bold")
sensitivity = misc_analys.get_sensitivity(permpar)
comp_info = misc_analys.get_comp_info(permpar)
for pass_n in [0, 1]: # add significance markers on the second pass
if pass_n == 1:
logger.info(f"{comp_info}:", extra={"spacing": "\n"})
for (line, plane), lp_df in data_df.groupby(["lines", "planes"]):
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(
line, plane
)
line_plane_name = plot_helper_fcts.get_line_plane_name(line, plane)
sub_ax = ax[pl, li]
sess_indices = []
lp_sess_ns = []
for sess_n in sess_ns:
rows = lp_df.loc[lp_df["sess_ns"] == sess_n]
if len(rows) == 1:
sess_indices.append(rows.index[0])
lp_sess_ns.append(sess_n)
elif len(rows) > 1:
raise RuntimeError("Expected 1 row per line/plane/session.")
data = np.asarray([lp_df.loc[i, data_col] for i in sess_indices])
if pass_n == 0:
# plot errorbars
plot_util.plot_errorbars(
sub_ax, data[:, 0], data[:, 1:].T, lp_sess_ns, color=col,
alpha=0.8, xticks="auto", line_dash=dash
)
if pass_n == 1:
# plot CIs
CIs = np.asarray(
[lp_df.loc[i, "null_CIs"] for i in sess_indices]
)
CI_meds = CIs[:, 1]
CIs = CIs[:, np.asarray([0, 2])]
plot_util.plot_CI(sub_ax, CIs.T, med=CI_meds, x=lp_sess_ns,
width=0.45, color="lightgrey", med_col="gray", med_rat=0.03,
zorder=-12)
# add significance markers within sessions
y_maxes = data[:, 0] + data[:, -1]
sides = [
np.sign(sub[0] - CI_med)
for sub, CI_med in zip(data, CI_meds)
]
p_vals_corr = [lp_df.loc[i, "p_vals"] for i in sess_indices]
lp_sig_str = f"{line_plane_name:6} (within session):"
for s, sess_n in enumerate(lp_sess_ns):
sig_str = misc_analys.get_sig_symbol(
p_vals_corr[s], sensitivity=sensitivity, side=sides[s],
tails=permpar["tails"], p_thresh=permpar["p_val"]
)
if len(sig_str):
plot_util.add_signif_mark(sub_ax, sess_n, y_maxes[s],
rel_y=0.15, color=col, mark=sig_str)
lp_sig_str = (
f"{lp_sig_str}{TAB} S{sess_n}: "
f"{p_vals_corr[s]:.5f}{sig_str:3}"
)
logger.info(lp_sig_str, extra={"spacing": TAB})
if between_sess_sig:
add_between_sess_sig(ax, data_df, permpar, data_col=data_col)
area, ylab = True, None
if decoder_data:
area = False
if "balanced" in data_col:
ylab = "Balanced accuracy (%)"
else:
ylab = "Accuracy %"
sess_plot_util.format_linpla_subaxes(ax, fluor=analyspar["fluor"],
area=area, ylab=ylab, datatype="roi", sess_ns=sess_ns, kind="reg",
xticks=sess_ns, modif_share=False)
return ax
def plot_tracked_idxs(idx_only_df, sesspar, figpar, title=None, wide=False):
"""
plot_tracked_idxs(idx_only_df, sesspar, figpar)
Plots tracked ROI USIs as individual lines.
Required args:
- idx_only_df (pd.DataFrame):
dataframe with one row per (mouse/)session/line/plane, and the
following columns, in addition to the basic sess_df columns:
- roi_idxs (list): index for each ROI
- sesspar (dict):
dictionary with keys of SessPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
- wide (bool):
if True, subplots are wider
default: False
Returns:
- ax (2D array):
array of subplots
"""
sess_ns = misc_analys.get_sess_ns(sesspar, idx_only_df)
figpar = sess_plot_util.fig_init_linpla(figpar)
figpar["init"]["sharey"] = "row"
figpar["init"]["subplot_hei"] = 4.1
figpar["init"]["subplot_wid"] = 2.5
figpar["init"]["gs"] = {"wspace": 0.25, "hspace": 0.2}
if wide:
figpar["init"]["subplot_wid"] = 3.3
figpar["init"]["gs"]["wspace"] = 0.25
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
if title is not None:
fig.suptitle(title, y=0.98, weight="bold")
for (line, plane), lp_df in idx_only_df.groupby(["lines", "planes"]):
li, pl, col, _ = plot_helper_fcts.get_line_plane_idxs(line, plane)
sub_ax = ax[pl, li]
# mouse_ns
lp_mouse_ns = sorted(lp_df["mouse_ns"].unique())
lp_data = []
for mouse_n in lp_mouse_ns:
mouse_df = lp_df.loc[lp_df["mouse_ns"] == mouse_n]
nrois = mouse_df["nrois"].unique()
if len(nrois) != 1:
raise RuntimeError(
"Each mouse in idx_stats_df should retain the same number "
" of ROIs across sessions.")
mouse_data = np.full((len(sess_ns), nrois[0]), np.nan)
for s, sess_n in enumerate(sess_ns):
rows = mouse_df.loc[mouse_df["sess_ns"] == sess_n]
if len(rows) == 1:
mouse_data[s] = rows.loc[rows.index[0], "roi_idxs"]
elif len(rows) > 1:
raise RuntimeError(
"Expected 1 row per line/plane/session/mouse."
)
lp_data.append(mouse_data)
lp_data = np.concatenate(lp_data, axis=1)
sub_ax.axhline(
y=0, ls=plot_helper_fcts.HDASH, c="k", lw=3.0, alpha=0.5,
zorder=-13
)
sub_ax.plot(sess_ns, lp_data, color=col, lw=2, alpha=0.3)
# Add plane, line info to plots
sess_plot_util.format_linpla_subaxes(
ax, datatype="roi", xticks=sess_ns, ylab="", kind="reg"
)
for sub_ax in ax.reshape(-1):
xticks = sub_ax.get_xticks()
plot_util.set_ticks(
sub_ax, "x", np.min(xticks), np.max(xticks), n=len(xticks),
pad_p=0.2
)
return ax
def plot_rel_resp_data(rel_resp_df, analyspar, sesspar, stimpar, permpar,
figpar, title=None, small=True):
"""
plot_rel_resp_data((rel_resp_df, analyspar, sesspar, stimpar, permpar,
figpar)
Plots relative response errorbar data across sessions.
Required args:
- rel_resp_df (pd.DataFrame):
dataframe with one row per session/line/plane, and the following
columns, in addition to the basic sess_df columns:
- rel_reg or rel_exp (list):
data stats for regular or expected data (me, err)
- rel_unexp (list): data stats for unexpected data (me, err)
for reg/exp/unexp data types, session comparisons, e.g. 1v2:
- {data_type}_raw_p_vals_{}v{} (float): uncorrected p-value for
data differences between sessions
- {data_type}_p_vals_{}v{} (float): p-value for data between
sessions, corrected for multiple comparisons and tails
- analyspar (dict):
dictionary with keys of AnalysPar namedtuple
- sesspar (dict):
dictionary with keys of SessPar namedtuple
- stimpar (dict):
dictionary with keys of StimPar namedtuple
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
- small (bool):
if True, subplots are smaller
default: False
Returns:
- ax (2D array):
array of subplots
"""
sess_ns = misc_analys.get_sess_ns(sesspar, rel_resp_df)
figpar = sess_plot_util.fig_init_linpla(figpar)
figpar["init"]["sharey"] = "row"
if small:
figpar["init"]["subplot_hei"] = 4.1
figpar["init"]["subplot_wid"] = 2.6
figpar["init"]["gs"] = {"hspace": 0.2, "wspace": 0.25}
figpar["init"]["gs"]["wspace"] = 0.25
else:
figpar["init"]["subplot_hei"] = 4.4
figpar["init"]["subplot_wid"] = 3.0
figpar["init"]["gs"] = {"wspace": 0.3}
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
if title is not None:
fig.suptitle(title, y=0.98, weight="bold")
if stimpar["stimtype"] == "gabors":
data_types = ["rel_reg", "rel_unexp"]
elif stimpar["stimtype"] == "visflow":
data_types = ["rel_exp", "rel_unexp"]
else:
gen_util.accepted_values_error(
"stimpar['stimtype']", stimpar["stimtype"], ["gabors", "visflow"]
)
for (line, plane), lp_df in rel_resp_df.groupby(["lines", "planes"]):
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(line, plane)
sub_ax = ax[pl, li]
sess_indices = []
lp_sess_ns = []
for sess_n in sess_ns:
rows = lp_df.loc[lp_df["sess_ns"] == sess_n]
if len(rows) == 1:
sess_indices.append(rows.index[0])
lp_sess_ns.append(sess_n)
elif len(rows) > 1:
raise RuntimeError("Expected 1 row per line/plane/session.")
sub_ax.axhline(
y=1, ls=plot_helper_fcts.HDASH, c="k", lw=3.0, alpha=0.5, zorder=-13
)
colors = ["gray", col]
fmts = ["-d", "-o"]
alphas = [0.6, 0.8]
ms = [12, None]
for d, data_type in enumerate(data_types):
data = np.asarray([lp_df.loc[i, data_type] for i in sess_indices])
plot_util.plot_errorbars(
sub_ax, data[:, 0], data[:, 1:].T, lp_sess_ns, color=colors[d],
alpha=alphas[d], ms=ms[d], fmt=fmts[d], line_dash=dash)
highest = None
for dry_run in [True, False]: # to get correct data heights
for data_type in data_types:
ctrl = ("unexp" not in data_type)
highest = add_between_sess_sig(
ax, rel_resp_df, permpar, data_col=data_type, highest=highest,
ctrl=ctrl, p_val_prefix=True, dry_run=dry_run)
if not dry_run:
highest = [val * 1.05 for val in highest] # increment a bit
sess_plot_util.format_linpla_subaxes(ax, fluor=analyspar["fluor"],
datatype="roi", sess_ns=sess_ns, kind="reg", xticks=sess_ns,
modif_share=False)
return ax
def plot_tracked_idx_stats(idx_stats_df, sesspar, figpar, permpar=None,
absolute=True, between_sess_sig=True,
by_mouse=False, bootstr_err=None, title=None,
wide=False):
"""
plot_tracked_idx_stats(idx_stats_df, sesspar, figpar)
Plots tracked ROI USI statistics.
Required args:
- idx_stats_df (pd.DataFrame):
dataframe with one row per session, and the following columns, in
addition to the basic sess_df columns:
- roi_idxs (list): index statistics
or
- abs_roi_idxs (list): absolute index statistics
- sesspar (dict):
dictionary with keys of SessPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- permpar (dict):
dictionary with keys of PermPar namedtuple. Required if
between_sess_sig is True.
default: None
- absolute (bool):
if True, data statistics are on absolute ROI indices
default: True
- between_sess_sig (bool):
if True, significance between sessions is logged and plotted
default: True
- by_mouse (bool):
if True, plotting is done per mouse
default: False
- bootstr_err (bool):
if True, error is bootstrapped standard deviation
default: False
- title (str):
plot title
default: None
- wide (bool):
if True, subplots are wider
default: False
Returns:
- ax (2D array):
array of subplots
"""
sess_ns = misc_analys.get_sess_ns(sesspar, idx_stats_df)
figpar = sess_plot_util.fig_init_linpla(figpar)
figpar["init"]["sharey"] = "row"
figpar["init"]["subplot_hei"] = 4.1
figpar["init"]["subplot_wid"] = 2.6
figpar["init"]["gs"] = {"wspace": 0.25, "hspace": 0.2}
if wide:
figpar["init"]["subplot_wid"] = 3.3
fig, ax = plot_util.init_fig(plot_helper_fcts.N_LINPLA, **figpar["init"])
if title is not None:
fig.suptitle(title, y=0.98, weight="bold")
data_col = "roi_idx_stats"
if absolute:
data_col = f"abs_{data_col}"
if data_col not in idx_stats_df.columns:
raise KeyError(f"Expected to find {data_col} in idx_stats_df columns.")
for (line, plane), lp_df in idx_stats_df.groupby(["lines", "planes"]):
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(line, plane)
sub_ax = ax[pl, li]
sub_ax.axhline(
y=0, ls=plot_helper_fcts.HDASH, c="k", lw=3.0, alpha=0.5,
zorder=-13
)
mouse_ns = ["any"]
mouse_cols = [col]
if by_mouse:
mouse_ns = sorted(lp_df["mouse_ns"].unique())
mouse_cols = plot_util.get_hex_color_range(
len(mouse_ns), col=col,
interval=plot_helper_fcts.MOUSE_COL_INTERVAL
)
for mouse_n, mouse_col in zip(mouse_ns, mouse_cols):
sub_df = lp_df
if by_mouse:
sub_df = lp_df.loc[lp_df["mouse_ns"] == mouse_n]
sess_indices = []
sub_sess_ns = []
for sess_n in sess_ns:
rows = sub_df.loc[sub_df["sess_ns"] == sess_n]
if len(rows) == 1:
sess_indices.append(rows.index[0])
sub_sess_ns.append(sess_n)
data = np.asarray([sub_df.loc[i, data_col] for i in sess_indices])
# plot errorbars
alpha = 0.6 if by_mouse else 0.8
capsize = 8 if bootstr_err else None
plot_util.plot_errorbars(
sub_ax, data[:, 0], data[:, 1:].T, sub_sess_ns, color=mouse_col,
alpha=alpha, xticks="auto", line_dash=dash, capsize=capsize,
)
if between_sess_sig:
if permpar is None:
raise ValueError(
"If 'between_sess_sig' is True, must provide permpar."
)
if by_mouse:
raise NotImplementedError(
"Plotting between session statistical signifiance is not "
"implemented if 'by_mouse' if True."
)
seq_plots.add_between_sess_sig(
ax, idx_stats_df, permpar, data_col=data_col
)
# Add plane, line info to plots
sess_plot_util.format_linpla_subaxes(
ax, datatype="roi", xticks=sess_ns, ylab="", kind="reg"
)
return ax
def add_between_stim_sig(ax, sub_ax_all, data_df, permpar):
"""
add_between_stim_sig(ax, sub_ax_all, data_df, permpar)
Plot significance markers for significant comparisons between stimulus
types.
Required args:
- ax (plt Axis):
axis
- sub_ax_all (plt subplot):
all line/plane data subplot
- data_df (pd.DataFrame):
dataframe with one row per session/line/plane, and the following
columns, in addition to the basic sess_df columns:
- {data_col} (list): data stats (me, err)
for session comparisons, e.g. 1v2:
- p_vals_{}v{} (float): p-value for differences between sessions,
corrected for multiple comparisons and tails
- permpar (dict):
dictionary with keys of PermPar namedtuple
"""
sensitivity = misc_analys.get_sensitivity(permpar)
comp_info = misc_analys.get_comp_info(permpar)
logger.info(f"{comp_info}:", extra={"spacing": "\n"})
stimtypes = ["gabors", "visflow"]
stim_sig_str = f"Gabors vs visual flow: "
for (line, plane), lp_df in data_df.groupby(["lines", "planes"]):
if len(lp_df) != 1:
raise RuntimeError("Expected 1 row per line/plane/session.")
row_idx = lp_df.index[0]
x = [0, 1]
data = np.vstack(
[lp_df[stimtypes[0]].tolist(), lp_df[stimtypes[1]].tolist()]
).T
y = data[0]
err = data[1:]
highest = np.max(y + err[-1])
if line != "all" and plane != "all":
li, pl, col, dash = plot_helper_fcts.get_line_plane_idxs(
line, plane
)
linpla_name = plot_helper_fcts.get_line_plane_name(line, plane)
sub_ax = ax[pl, li]
mark_rel_y = 0.18
else:
col = plot_helper_fcts.NEARBLACK
linpla_name = "All"
sub_ax = sub_ax_all
all_data_max = np.concatenate(
[data_df[stimtypes[0]].tolist(),
data_df[stimtypes[1]].tolist()],
axis=0
)[:, 0].max()
highest = np.max([data[0].max(), all_data_max])
mark_rel_y = 0.15
p_val = lp_df.loc[row_idx, "p_vals"]
side = np.sign(y[1] - y[0])
sig_str = misc_analys.get_sig_symbol(
p_val, sensitivity=sensitivity, side=side, tails=permpar["tails"],
p_thresh=permpar["p_val"]
)
stim_sig_str = \
f"{stim_sig_str}{TAB}{linpla_name}: {p_val:.5f}{sig_str:3}"
if len(sig_str):
plot_util.plot_barplot_signif(
sub_ax, x, highest, rel_y=0.11, color=col, lw=3,
mark_rel_y=mark_rel_y, mark=sig_str,
)
logger.info(stim_sig_str, extra={"spacing": TAB})
def plot_pupil_run_block_diffs(block_df, analyspar, permpar, figpar,
title=None, seed=None):
"""
plot_pupil_run_trace_stats(trace_df, analyspar, permpar, figpar)
Plots pupil and running block differences.
Required args:
- block_df (pd.DataFrame):
dataframe with one row per session/line/plane, and the following
columns, in addition to the basic sess_df columns:
- run_block_diffs (list):
running velocity differences per block
- run_raw_p_vals (float):
uncorrected p-value for differences within sessions
- run_p_vals (float):
p-value for differences within sessions,
corrected for multiple comparisons and tails
- pupil_block_diffs (list):
for pupil diameter differences per block
- pupil_raw_p_vals (list):
uncorrected p-value for differences within sessions
- pupil_p_vals (list):
p-value for differences within sessions,
corrected for multiple comparisons and tails
- analyspar (dict):
dictionary with keys of AnalysPar namedtuple
- permpar (dict):
dictionary with keys of PermPar namedtuple
- figpar (dict):
dictionary containing the following figure parameter dictionaries
["init"] (dict): dictionary with figure initialization parameters
["save"] (dict): dictionary with figure saving parameters
["dirs"] (dict): dictionary with additional figure parameters
Optional args:
- title (str):
plot title
default: None
- seed (int):
seed value to use. (-1 treated as None)
default: None
Returns:
- ax (2D array):
array of subplots
"""
if analyspar["scale"]:
raise NotImplementedError(
"Expected running and pupil data to not be scaled."
)
if len(block_df["sess_ns"].unique()) != 1:
raise NotImplementedError(
"'block_df' should only contain one session number."
)
nanpol = None if analyspar["rem_bad"] else "omit"
sensitivity = misc_analys.get_sensitivity(permpar)
comp_info = misc_analys.get_comp_info(permpar)
datatypes = ["run", "pupil"]
datatype_strs = ["Running velocity", "Pupil diameter"]
n_datatypes = len(datatypes)
fig, ax = plt.subplots(
1, n_datatypes, figsize=(12.7, 4), squeeze=False,
gridspec_kw={"wspace": 0.22}
)
if title is not None:
fig.suptitle(title, y=1.2, weight="bold")
logger.info(f"{comp_info}:", extra={"spacing": "\n"})
corr_str = "corr." if permpar["multcomp"] else "raw"
for d, datatype in enumerate(datatypes):
datatype_sig_str = f"{datatype_strs[d]:16}:"
sub_ax = ax[0, d]
lp_names = [None for _ in range(plot_helper_fcts.N_LINPLA)]
xs, all_data, cols, dashes, p_val_texts = [], [], [], [], []
for (line, plane), lp_df in block_df.groupby(["lines", "planes"]):
x, col, dash = plot_helper_fcts.get_line_plane_idxs(
line, plane, flat=True
)
line_plane_name = plot_helper_fcts.get_line_plane_name(
line, plane
)
lp_names[int(x)] = line_plane_name
if len(lp_df) == 1:
row_idx = lp_df.index[0]
elif len(lp_df) > 1:
raise RuntimeError("Expected 1 row per line/plane/session.")
lp_data = lp_df.loc[row_idx, f"{datatype}_block_diffs"]
# get p-value information
p_val_corr = lp_df.loc[row_idx, f"{datatype}_p_vals"]
side = np.sign(
math_util.mean_med(
lp_data, stats=analyspar["stats"], nanpol=nanpol
)
)
sig_str = misc_analys.get_sig_symbol(
p_val_corr, sensitivity=sensitivity, side=side,
tails=permpar["tails"], p_thresh=permpar["p_val"]
)
p_val_text = f"{p_val_corr:.2f}{sig_str}"
datatype_sig_str = (
f"{datatype_sig_str}{TAB}{line_plane_name}: "
f"{p_val_corr:.5f}{sig_str:3}"
)
# collect information
xs.append(x)
all_data.append(lp_data)
cols.append(col)
dashes.append(dash)
p_val_texts.append(p_val_text)
plot_violin_data(
sub_ax, xs, all_data, palette=cols, dashes=dashes, seed=seed
)
# edit ticks
sub_ax.set_xticks(range(plot_helper_fcts.N_LINPLA))
sub_ax.set_xticklabels(lp_names, fontweight="bold")
sub_ax.tick_params(axis="x", which="both", bottom=False)
plot_util.expand_lims(sub_ax, axis="y", prop=0.1)
plot_util.set_interm_ticks(
np.asarray(sub_ax), n_ticks=3, axis="y", share=False,
fontweight="bold", update_ticks=True
)
for i, (x, p_val_text) in enumerate(zip(xs, p_val_texts)):
ylim_range = np.diff(sub_ax.get_ylim())
y = sub_ax.get_ylim()[1] + ylim_range * 0.08
ha = "center"
if d == 0 and i == 0:
x += 0.2
p_val_text = f"{corr_str} p-val. {p_val_text}"
ha = "right"
sub_ax.text(
x, y, p_val_text, fontsize=20, weight="bold", ha=ha
)
logger.info(datatype_sig_str, extra={"spacing": TAB})
# add labels/titles
for d, datatype in enumerate(datatypes):
sub_ax = ax[0, d]
sub_ax.axhline(
y=0, ls=plot_helper_fcts.HDASH, c="k", lw=3.0, alpha=0.5
)
if d == 0:
ylabel = "Trial differences\nU-G - D-G"
sub_ax.set_ylabel(ylabel, weight="bold")
if datatype == "run":
title = "Running velocity (cm/s)"
elif datatype == "pupil":
title = "Pupil diameter (mm)"
sub_ax.set_title(title, weight="bold", y=1.2)
return ax
