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fig_violin.py
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import pandas as pd
import matplotlib.pyplot as plt
plt.ion()
import matplotlib.gridspec as gridspec
import seaborn as sns
import os
import numpy as np
from os.path import join
from scipy.stats import mode
import matplotlib
font = {'weight' : 'bold',
'size' : 18}
matplotlib.rc('font', **font)
root_dir = "/home/hannaj/"
root_dir = "/home/jev/"
fig_dir = join(root_dir, "simnibs/figures")
data_dir = join(root_dir, "simnibs/3_results")
df_3 = pd.read_pickle(join(data_dir, "df_3.pickle"))
df_3["Version"] = ["3"] * len(df_3)
data_dir = join(root_dir, "simnibs/4_results")
df_4 = pd.read_pickle(join(data_dir, "df_4.pickle"))
df_4["Version"] = ["4"] * len(df_4)
df = pd.concat([df_3, df_4])
projects = np.sort(df["Project"].unique())
inner = None
dot_size = 9
# # v4: closest vs optimal
# best_radii = {}
# best_vals = {"Project":[], "Subject":[], "Condition":[], "Mags":[], "Focs":[]}
# this_df = df.query("Version=='4'")
# # get best radius for groups
# for project in projects:
# best_radii[project] = {}
# proj_df = this_df.query(f"Project=='{project}'")
# for cond in ["closest", "optimal"]:
# cond_df = proj_df.query(f"Condition=='{cond}'")
# rads = np.stack(cond_df["Mags"].values)
# meds = np.median(rads, axis=0)
# error = meds - 0.2
# error[error<0] = np.inf
# rad_idx = np.argmin(error)
# radii = cond_df["Radii"].values[0]
# best_radius = radii[rad_idx]
# best_radii[project][cond] = best_radius
#
# best_radius = radii[rad_idx]
# best_radii[project][cond] = best_radius
# subjs = np.sort(cond_df["Subject"].unique())
# for subj in subjs:
# line = cond_df.query(f"Subject=='{subj}'")
# rad_idx = list(line["Radii"].values[0]).index(best_radius)
# best_vals["Project"].append(project)
# best_vals["Subject"].append(subj)
# best_vals["Condition"].append(cond)
# best_vals["Mags"].append(line["Mags"].values[0][rad_idx])
# best_vals["Focs"].append(line["Focs"].values[0][rad_idx])
# best_vals = pd.DataFrame.from_dict(best_vals)
#
# fig, ax = plt.subplots(1, figsize=(38.4, 8))
# sns.violinplot(data=best_vals, x="Project", y="Mags", hue="Condition", ax=ax,
# inner=None)
# sns.stripplot(data=best_vals, x="Project", y="Mags", hue="Condition", ax=ax,
# dodge=True, color="black", legend=False, size=dot_size)
#
# plt.axhline(0.2, linestyle="--", color="gray")
# ax.set_title("Magnitude: V4 - Closest vs Optimal", fontsize=24, fontweight="bold")
# ax.set_xlabel("Project (closest/optimal best radius)", fontsize=24,
# fontweight="bold")
# ax.set_ylabel("Magnitude", fontsize=24, fontweight="bold")
# ax.set_xticklabels([f"{k} ({v['closest']}/{v['optimal']})"
# for k, v in best_radii.items()])
# plt.savefig(join(fig_dir, f"Mag_ClosestOptimal_violin.pdf"))
#
#
# fig, ax = plt.subplots(1, figsize=(38.4, 8))
# sns.violinplot(data=best_vals, x="Project", y="Focs", hue="Condition", ax=ax,
# inner=None)
# sns.stripplot(data=best_vals, x="Project", y="Focs", hue="Condition", ax=ax,
# dodge=True, color="black", legend=False, size=dot_size)
# ax.set_title("Focality: V4 - Closest vs Optimal", fontsize=24, fontweight="bold")
# ax.set_xlabel("Project (closest/optimal best radius)", fontsize=24,
# fontweight="bold")
# ax.set_ylabel("Focality", fontsize=24, fontweight="bold")
# ax.set_xticklabels([f"{k} ({v['closest']}/{v['optimal']})"
# for k, v in best_radii.items()])
# plt.savefig(join(fig_dir, f"Foc_ClosestOptimal_violin.pdf"))
#
# closest: 3 vs 4
best_radii = {}
best_vals = {"Project":[], "Subject":[], "Version":[], "Mags":[], "Focs":[]}
this_df = df.query("Condition=='closest'")
# get best radius for groups
for project in projects:
best_radii[project] = {}
proj_df = this_df.query(f"Project=='{project}'")
for version in [3, 4]:
vers_df = proj_df.query(f"Version=='{version}'")
rads = np.stack(vers_df["Mags"].values)
meds = np.median(rads, axis=0)
error = meds - 0.2
error[error<0] = np.inf
rad_idx = np.argmin(error)
radii = vers_df["Radii"].values[0]
best_radius = radii[rad_idx]
best_radii[project][version] = best_radius
subjs = np.sort(vers_df["Subject"].unique())
for subj in subjs:
line = vers_df.query(f"Subject=='{subj}'")
rad_idx = list(line["Radii"].values[0]).index(best_radius)
best_vals["Project"].append(project)
best_vals["Subject"].append(subj)
best_vals["Version"].append(version)
best_vals["Mags"].append(line["Mags"].values[0][rad_idx])
best_vals["Focs"].append(line["Focs"].values[0][rad_idx])
best_vals = pd.DataFrame.from_dict(best_vals)
fig, ax = plt.subplots(1, figsize=(38.4, 8))
sns.violinplot(data=best_vals, x="Project", y="Mags", hue="Version", ax=ax,
inner=inner)
sns.stripplot(data=best_vals, x="Project", y="Mags", hue="Version", ax=ax,
dodge=True, color="black", legend=False, size=dot_size)
plt.axhline(0.2, linestyle="--", color="gray")
ax.set_title("Magnitude: Closest - v3 vs. v4", fontsize=24, fontweight="bold")
ax.set_xlabel("Project (3/4 best radius)", fontsize=24,
fontweight="bold")
ax.set_ylabel("Magnitude", fontsize=24, fontweight="bold")
ax.set_xticklabels([f"{k} ({v[3]}/{v[4]})"
for k, v in best_radii.items()])
plt.savefig(join(fig_dir, f"Mag_3vs4_violin.pdf"))
fig, ax = plt.subplots(1, figsize=(38.4, 8))
sns.violinplot(data=best_vals, x="Project", y="Focs", hue="Version", ax=ax,
inner=inner)
sns.stripplot(data=best_vals, x="Project", y="Focs", hue="Version", ax=ax,
dodge=True, color="black", legend=False, size=dot_size)
ax.set_title("Focality: Closest - v3 vs. v4", fontsize=24, fontweight="bold")
ax.set_xlabel("Project (3/4 best radius)", fontsize=24,
fontweight="bold")
ax.set_ylabel("Focality", fontsize=24, fontweight="bold")
ax.set_xticklabels([f"{k} ({v[3]}/{v[4]})"
for k, v in best_radii.items()])
plt.savefig(join(fig_dir, f"Foc_3vs4_violin.pdf"))
df.to_excel(join(fig_dir, "simnibs_3vs4.xlsx"))
# # closest: 3 vs 4 bone
# best_radii = {}
# best_vals = {"Project":[], "Subject":[], "Version":[], "Mags":[], "Focs":[]}
# this_df = df.query("Condition=='closest' or Condition=='closest_bone'")
# this_df["Version"][this_df["Condition"] == "closest_bone"] = "4_bone"
# # get best radius for groups
# for project in projects:
# best_radii[project] = {}
# proj_df = this_df.query(f"Project=='{project}'")
# for version in [3, 4, "4_bone"]:
# vers_df = proj_df.query(f"Version=='{version}'")
# rads = np.stack(vers_df["Mags"].values)
# meds = np.median(rads, axis=0)
# error = meds - 0.2
# error[error<0] = np.inf
# rad_idx = np.argmin(error)
# radii = vers_df["Radii"].values[0]
# best_radius = radii[rad_idx]
# best_radii[project][version] = best_radius
# subjs = np.sort(vers_df["Subject"].unique())
# for subj in subjs:
# line = vers_df.query(f"Subject=='{subj}'")
# rad_idx = list(line["Radii"].values[0]).index(best_radius)
# best_vals["Project"].append(project)
# best_vals["Subject"].append(subj)
# best_vals["Version"].append(version)
# best_vals["Mags"].append(line["Mags"].values[0][rad_idx])
# best_vals["Focs"].append(line["Focs"].values[0][rad_idx])
# best_vals = pd.DataFrame.from_dict(best_vals)
#
#
#
# fig, ax = plt.subplots(1, figsize=(38.4, 8))
# sns.violinplot(data=best_vals, x="Project", y="Mags", hue="Version", ax=ax,
# inner=inner)
# sns.stripplot(data=best_vals, x="Project", y="Mags", hue="Version", ax=ax,
# dodge=True, color="black", legend=False, size=dot_size)
# plt.axhline(0.2, linestyle="--", color="gray")
# ax.set_title("Magnitude: Closest - v3 vs. v4", fontsize=24, fontweight="bold")
# ax.set_xlabel("Project (3/4 best radius)", fontsize=24,
# fontweight="bold")
# ax.set_ylabel("Magnitude", fontsize=24, fontweight="bold")
# ax.set_xticklabels([f"{k} ({v[3]}/{v[4]})"
# for k, v in best_radii.items()])
# plt.savefig(join(fig_dir, f"Mag_3vs4_violin_bone.pdf"))
#
#
# fig, ax = plt.subplots(1, figsize=(38.4, 8))
# sns.violinplot(data=best_vals, x="Project", y="Focs", hue="Version", ax=ax,
# inner=inner)
# sns.stripplot(data=best_vals, x="Project", y="Focs", hue="Version", ax=ax,
# dodge=True, color="black", legend=False, size=dot_size)
# ax.set_title("Focality: Closest - v3 vs. v4", fontsize=24, fontweight="bold")
# ax.set_xlabel("Project (3/4 best radius)", fontsize=24,
# fontweight="bold")
# ax.set_ylabel("Focality", fontsize=24, fontweight="bold")
# ax.set_xticklabels([f"{k} ({v[3]}/{v[4]}/{v['4_bone']})"
# for k, v in best_radii.items()])
# plt.savefig(join(fig_dir, f"Foc_3vs4_violin_bone.pdf"))
df.to_excel(join(fig_dir, "simnibs_3vs4.xlsx"))