def main():
"""
Entry point for script
"""
# first figure: betas for each predictor
fig, axes = plt.subplots(figsize=(8, 18), nrows=3)
image_paths = {
"3T": (f"{PATHS['figures_misc']}/figure_3_images/"
f"C1051_20161006_childVSall_depth_1.png"),
"7T": (f"{PATHS['figures_misc']}/figure_3_images/"
f"C1051_20160212_childVSall_depth_1_sim2pt4.png"),
"7T_noise": (f"{PATHS['figures_misc']}/figure_3_images/"
f"C1051_20160212_childVSall_depth_1_sim2pt4_noise.png"),
}
for ax, (_, image_path) in zip(axes, image_paths.items()):
assert os.path.isfile(image_path)
img = imread(image_path)
img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0
ax.imshow(img[200:-100, 50:-50, :])
ax.axis("off")
savefig(f"{PATHS['figures']}/figure_3ac.png")
plt.close(fig)
def make_plots(partition_weights, savepath):
# first figure: betas for each predictor
fig, axes = plt.subplots(figsize=(10, 6),
nrows=2,
sharey=True,
sharex=True)
plot_partition_weights(axes, partition_weights)
savefig(savepath)
plt.close(fig)
def plot_helper(matrices, aligned_embeddings, plot_params, savepath):
fig, axes = plt.subplots(figsize=(18, 12), nrows=2, ncols=3)
plot_utils.plot_rdms(
axes[0], matrices, cmap=plot_params["cmap"], clim=plot_params["clim"]
)
plot_utils.plot_embeddings(
axes[1], aligned_embeddings, point_size=250, alpha=0.8, plot_dashed_lines=True
)
savefig(savepath)
plt.close(fig)
def main():
"""
Wrapper for plotting functions and statistical functions
"""
# global plot params
plt.rcParams.update({"font.size": 34})
regressors = build_regressors()
fig, axes = plt.subplots(figsize=(12, 3), ncols=4)
for regressor, ax in zip(regressors, axes):
ax.imshow(regressor, cmap="gray_r", clim=[-0.5, 1])
ax.axis("off")
save_path = f"{PATHS['figures']}/figure_7a_rdm_fit_diagram.png"
savefig(save_path)
plt.close(fig)
def main():
fig, axes = plt.subplots(figsize=(24, 8), ncols=5, nrows=2)
path_base = f"{PATHS['figures_misc']}/figure_1_images"
# these numbers correspond to the images in the downloadable fLoc dataset
stim = [
"word-80",
"body-24",
"adult-28",
"car-64",
"corridor-62",
"number-59",
"limb-49",
"child-108",
"instrument-50",
"house-80",
]
# colors in order of categories
colors = (
np.array([
[0, 0, 0], # black
[252, 164, 0], # dark yellow
[248, 51, 60], # dark red
[0, 0, 255], # dark blue
[10, 255, 0], # dark green
[150, 150, 150], # gray
[252, 195, 88], # light yellow
[244, 110, 116], # light red
[0, 0, 150], # light blue
[10, 150, 10], # light green
]) / 255.0)
image_paths = [f"{path_base}/{x}.jpg" for x in stim]
border_width = 30 # px
for ax, image_path, border_color in zip(axes.ravel(), image_paths, colors):
assert os.path.isfile(image_path)
img = imread(image_path)
plot_img_with_border(ax, img, border_width, border_color)
ax.axis("off")
savefig(f"{PATHS['figures']}/figure_1a.png")
plt.close(fig)
def main():
"""
Entry point for script
"""
font = {"size": 18}
matplotlib.rc("font", **font)
save_dir = f"{PATHS['figures']}/figure_10c_metric_means"
mkdirquiet(save_dir)
# do stats for betas only
lateral_betas = load_means(
metric="beta",
partition="VTC_lateral",
abs_string="noabs",
r2_control=ARGS.r2_control,
)
medial_betas = load_means(
metric="beta",
partition="VTC_medial",
abs_string="noabs",
r2_control=ARGS.r2_control,
)
data = prepare_dataframe(lateral_betas, medial_betas)
fpath = f"{PATHS['r_scripts']}/data/raw_betas.csv"
data.to_csv(fpath)
subprocess.call(
f"Rscript --vanilla {PATHS['r_scripts']}/raw_betas_script.r {fpath}",
shell=True)
for abs_string in ["abs", "noabs"]:
for metric in METRICS + ["beta"]:
print(f"Plotting metric: {metric}")
fig, axes = plt.subplots(figsize=(16, 4), ncols=3, sharey=True)
for ax, partition in zip(axes, PARTITIONS):
partition_means = load_means(metric=metric,
partition=partition,
abs_string=abs_string)
plot_means(ax, partition_means, metric=metric)
savefig(f"{save_dir}/{metric}_{abs_string}.png")
plt.close(fig)
def main():
"""
Entry point for script
"""
font = {"size": 18}
matplotlib.rc("font", **font)
# create dictionary specifying data and plot options
metrics = {
"tSNR": {
"lateral": load_tsnr(partition="VTC_lateral"),
"medial": load_tsnr(partition="VTC_medial"),
"ylabel": "tSNR",
},
"R2": {
"lateral": load_R2(partition="VTC_lateral"),
"medial": load_R2(partition="VTC_medial"),
"ylabel": r"$R^2$",
},
}
# make plots
fig, axes = plt.subplots(figsize=(12, 4), ncols=2, gridspec_kw={"wspace": 0.4})
for (metric_name, metric_vals), ax in zip(metrics.items(), axes):
print(f"==============={metric_name}================")
lateral = metric_vals["lateral"]
medial = metric_vals["medial"]
plot_vals(ax, lateral, medial, ylabel=metric_vals["ylabel"])
report_range(lateral, medial, metric=metric_name)
data = prepare_dataframe(lateral, medial)
# call the R script
fpath = f"{PATHS['r_scripts']}/data/metrics_by_depth.csv"
data.to_csv(fpath)
subprocess.call(
f"Rscript --vanilla {PATHS['r_scripts']}/metrics_by_depth_script.r {fpath}",
shell=True,
)
savefig(f"{PATHS['figures']}/figure_10ab_tsnr_r2.png")
plt.close(fig)
def make_plots(partition_weights, save_dir):
"""
"""
# first figure: betas for each predictor
fig, axes = plt.subplots(figsize=(14, 7), ncols=2, sharey=True)
plot_partition_weights(axes, partition_weights)
savefig(f"{save_dir}/fit_weights.png")
plt.close(fig)
# second figure: betas for each predictor
fig, ax = plt.subplots(figsize=(10, 10))
plot_beta_scatter(ax, partition_weights)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
savefig(f"{save_dir}/partition_comparison.png")
plt.close(fig)
def main():
"""
Entry point for script
"""
save_dir = f"{PATHS['figures']}/figure_8af_rdms_by_depth"
mkdirquiet(save_dir)
# define some colormaps and limits here for each metric (maybe move to constants?)
plot_params = {
"tstat": {"cmap": plot_utils.blueblackred, "clim": [0.5, 1.5]},
"zscore": {"cmap": plot_utils.blueblackred, "clim": [0.5, 1.5]},
"znorm": {"cmap": plot_utils.blueblackred, "clim": [0.5, 1.5]},
}
for metric in plot_params.keys():
print(f"Plotting metric: {metric}")
rsms = load_rsms(metric=metric)
mean_rdms_by_depth = {
partition: np.mean(
[1 - split_by_depth(rsm) for rsm in partition_rsms], axis=0
)
for (partition, partition_rsms) in rsms.items()
}
base = 6
fig, axes = plt.subplots(figsize=(base * 3, base * 2), nrows=2, ncols=3)
for partition_idx, partition in enumerate(PARTITIONS):
if partition == "hOc1":
continue
rdm_dict = {
f"{partition}_{x}": mean_rdms_by_depth[partition][x] for x in range(3)
}
plot_utils.plot_rdms(
axes[partition_idx, :],
rdm_dict,
cmap=plot_params[metric]["cmap"],
clim=plot_params[metric]["clim"],
)
savefig(f"{save_dir}/{metric}.png")
plt.close(fig)
def main():
"""
Entry point for script
"""
# define bounding box for right VTC
width = 225
y_start = 245
x_start = 50
bbox = [y_start, x_start, width, width]
# first figure: betas for each predictor
fig, axes = plt.subplots(figsize=(27.7, 8), ncols=5, nrows=2)
# redefining contrast order to match figures
contrasts = [
"wordVSall",
"bodyVSall",
"adultVSall",
"carVSall",
"corridorVSall",
"numberVSall",
"limbVSall",
"childVSall",
"instrumentVSall",
"corridorVSall",
]
for contrast, ax in zip(contrasts, axes.ravel()):
image_path = (f"{PATHS['figures_misc']}/figure_4_images/"
f"C1051_20160212_{contrast}_depth_1.png")
assert os.path.isfile(image_path)
img = imread(image_path)
img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0
ax.imshow(img[bbox[0]:bbox[0] + bbox[2], bbox[1]:bbox[1] + bbox[3], :])
ax.axis("off")
savefig(f"{PATHS['figures']}/figure_4a.png")
plt.close(fig)
def main():
"""
Entry point for script
"""
# first figure: betas for each predictor
fig, axes = plt.subplots(figsize=(16, 8), ncols=3, nrows=1)
for depth, ax in zip(range(1, 4), axes.ravel()):
image_path = (f"{PATHS['figures_misc']}/figure_4_images/"
f"C1051_20160212_adultVSall_depth_{depth}.png")
assert os.path.isfile(image_path)
img = imread(image_path)
img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0
ax.imshow(img[200:-100, 50:-50, :])
ax.axis("off")
savefig(f"{PATHS['figures']}/figure_4b.png")
plt.close(fig)
def make_plots(partition_weight_dict, rdm_dict):
"""
"""
fig, axes = plt.subplots(figsize=(20, 20), nrows=3, ncols=3)
col_keys = ["high_resolution", "low_resolution", "low_resolution_noise"]
for col_idx, col_key in enumerate(col_keys):
plot_group_average_rdm(axes[0, col_idx],
rdm_dict[col_key]["VTC_lateral"])
plot_group_average_rdm(axes[1, col_idx],
rdm_dict[col_key]["VTC_medial"])
plot_beta_scatter(axes[2, col_idx], partition_weight_dict[col_key])
# add a ylabel to the leftmost plot
for ax in axes.ravel():
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
savefig(f"{PATHS['figures']}/figure_11_rescomp.png")
plt.close(fig)
