Imports¶
In [1]:
from pathlib import Path
import pandas as pd
import json
import nibabel as nib
import numpy as np
from src.database import *
import matplotlib.pyplot as plt
plt.style.use('custom_matplotlibrc')
plt.rcParams["figure.figsize"] = (20,5)
fig_id = 0
import seaborn as sns
# Set the color palette
pal=sns.color_palette("Set2")
Configurations¶
In [2]:
database_path = Path('databases/3T_human_T1maps_database.pkl')
output_folder = Path("plots/08_wholedataset_scatter_Human/")
estimate_type = 'mean' # median or mean
Define functions¶
In [3]:
def plot_single_scatter(x, y, y_std,
title, x_label, y_label,
file_prefix, folder_path, fig_id,
y_type):
intra_bool = np.array([], dtype=bool)
for ind in x:
if round(np.double(ind))==9:
intra_bool = np.append(intra_bool, True)
else:
intra_bool = np.append(intra_bool, False)
plt.errorbar(x[~intra_bool][0:-2],y[~intra_bool][0:-2], y_std[~intra_bool][0:-2], fmt='o', solid_capstyle='projecting', color=pal.as_hex()[0])
plt.errorbar(x[intra_bool],y[intra_bool], y_std[intra_bool], fmt='o', solid_capstyle='projecting', color=pal.as_hex()[1])
plt.errorbar(x[-1],y[-1], y_std[-1], fmt='o', solid_capstyle='projecting', color=pal.as_hex()[0])
plt.title(title)
plt.xlabel(x_label)
plt.ylabel(y_label)
if y_type is 'site':
plt.xticks(np.arange(0, np.max(x), step=1))
elif y_type is 'meas':
plt.xticks(rotation=90)
fig = plt.gcf()
ax = plt.gca()
folder_path.mkdir(parents=True, exist_ok=True)
if fig_id<10:
filename = "0" + str(fig_id) + "_" + file_prefix + "_" + str(y_type)
else:
filename = str(fig_id) + "_" + file_prefix + "_" + str(y_type)
fig.savefig(folder_path / (str(filename) + '.svg'), facecolor='white')
fig.savefig(folder_path / (str(filename) + '.png'), facecolor='white')
fig_id = fig_id + 1
plt.show()
return fig_id
<>:20: SyntaxWarning: "is" with a literal. Did you mean "=="? <>:22: SyntaxWarning: "is" with a literal. Did you mean "=="? <>:20: SyntaxWarning: "is" with a literal. Did you mean "=="? <>:22: SyntaxWarning: "is" with a literal. Did you mean "=="? /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/4101298702.py:20: SyntaxWarning: "is" with a literal. Did you mean "=="? if y_type is 'site': /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/4101298702.py:22: SyntaxWarning: "is" with a literal. Did you mean "=="? elif y_type is 'meas':
In [4]:
def plot_both_scatter(x1, x2, y, y_std,
title, x1_label, x2_label, y_label,
file_prefix, folder_path, fig_id):
plt.rcParams["figure.figsize"] = (20,10)
fig, axs = plt.subplots(2)
fig.suptitle(title)
axs[0].errorbar(x1, y, y_std, fmt='o', solid_capstyle='projecting')
axs[0].set_xlabel(x1_label)
axs[0].set_ylabel(y_label)
axs[0].set_xticks(np.arange(0, np.max(x1), step=1))
axs[1].errorbar(x2, y, y_std, fmt='o', solid_capstyle='projecting')
axs[1].set_xlabel(x2_label)
axs[1].set_ylabel(y_label)
axs[1].set_xticklabels(labels=x2, rotation=90)
if fig_id<10:
filename = "0" + str(fig_id) + "_" + file_prefix
else:
filename = str(fig_id) + "_" + file_prefix
fig.savefig(folder_path / (str(filename) + '.svg'), facecolor='white')
fig.savefig(folder_path / (str(filename) + '.png'), facecolor='white')
fig_id = fig_id + 1
plt.show()
return fig_id
Load database¶
In [5]:
df = pd.read_pickle(database_path)
Initialize variables, store mean and standard deviations of T1 in the ROIs¶
In [6]:
genu_estimate = np.array([])
genu_std = np.array([])
splenium_estimate = np.array([])
splenium_std = np.array([])
deepgm_estimate = np.array([])
deepgm_std = np.array([])
cgm_estimate = np.array([])
cgm_std = np.array([])
intra_bool = np.array([])
ii = 0
for index, row in df.iterrows():
intra_bool = np.append(intra_bool, round(index)==9)
if estimate_type is 'mean':
genu_estimate = np.append(genu_estimate, np.mean(df.loc[index]['T1 - genu (WM)']))
genu_std = np.append(genu_std, np.std(df.loc[index]['T1 - genu (WM)']))
splenium_estimate = np.append(splenium_estimate, np.mean(df.loc[index]['T1 - splenium (WM)']))
splenium_std = np.append(splenium_std, np.std(df.loc[index]['T1 - splenium (WM)']))
deepgm_estimate = np.append(deepgm_estimate, np.mean(df.loc[index]['T1 - deep GM']))
deepgm_std = np.append(deepgm_std, np.std(df.loc[index]['T1 - deep GM']))
cgm_estimate = np.append(cgm_estimate, np.mean(df.loc[index]['T1 - cortical GM']))
cgm_std = np.append(cgm_std, np.std(df.loc[index]['T1 - cortical GM']))
elif estimate_type is 'median':
genu_estimate = np.append(genu_estimate, np.median(df.loc[index]['T1 - genu (WM)']))
genu_std = np.append(genu_std, np.std(df.loc[index]['T1 - genu (WM)']))
splenium_estimate = np.append(splenium_estimate, np.median(df.loc[index]['T1 - splenium (WM)']))
splenium_std = np.append(splenium_std, np.std(df.loc[index]['T1 - splenium (WM)']))
deepgm_estimate = np.append(deepgm_estimate, np.median(df.loc[index]['T1 - deep GM']))
deepgm_std = np.append(deepgm_std, np.std(df.loc[index]['T1 - deep GM']))
cgm_estimate = np.append(cgm_estimate, np.median(df.loc[index]['T1 - cortical GM']))
cgm_std = np.append(cgm_std, np.std(df.loc[index]['T1 - cortical GM']))
else:
Exception('Unsupported dataset estimate type.')
ii = ii +1
<>:14: SyntaxWarning: "is" with a literal. Did you mean "=="? <>:23: SyntaxWarning: "is" with a literal. Did you mean "=="? <>:14: SyntaxWarning: "is" with a literal. Did you mean "=="? <>:23: SyntaxWarning: "is" with a literal. Did you mean "=="? /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/2637949668.py:14: SyntaxWarning: "is" with a literal. Did you mean "=="? if estimate_type is 'mean': /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/2637949668.py:23: SyntaxWarning: "is" with a literal. Did you mean "=="? elif estimate_type is 'median': /Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, /Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide ret = ret.dtype.type(ret / rcount) /Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/numpy/core/_methods.py:269: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, /Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide arrmean = um.true_divide(arrmean, div, out=arrmean, /Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/numpy/core/_methods.py:261: RuntimeWarning: invalid value encountered in scalar divide ret = ret.dtype.type(ret / rcount)
In [7]:
# Store the IDs
indexes_numbers = df.index
indexes_strings = indexes_numbers.map(str)
In [8]:
fig_id = plot_single_scatter(x=indexes_numbers,
y=genu_estimate,
y_std=genu_std,
title='WM - Genu',
x_label='Site #',
y_label="T$_1$ (ms)",
file_prefix="WM_genu",
folder_path=output_folder,
fig_id=fig_id,
y_type='site')
In [9]:
fig_id = plot_single_scatter(x=indexes_strings,
y=genu_estimate,
y_std=genu_std,
title='WM - Genu',
x_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="WM_genu",
folder_path=output_folder,
fig_id=fig_id,
y_type='meas')
In [10]:
fig_id = plot_single_scatter(x=indexes_numbers,
y=splenium_estimate,
y_std=splenium_std,
title='WM - Splenium',
x_label='Site #',
y_label="T$_1$ (ms)",
file_prefix="WM_splenium",
folder_path=output_folder,
fig_id=fig_id,
y_type='site')
In [11]:
fig_id = plot_single_scatter(x=indexes_strings,
y=splenium_estimate,
y_std=splenium_std,
title='WM - Splenium',
x_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="WM_splenium",
folder_path=output_folder,
fig_id=fig_id,
y_type='meas')
In [12]:
fig_id = plot_single_scatter(x=indexes_numbers,
y=deepgm_estimate,
y_std=deepgm_std,
title='Deep GM',
x_label='Site #',
y_label="T$_1$ (ms)",
file_prefix="GM_deep",
folder_path=output_folder,
fig_id=fig_id,
y_type='site')
/Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/matplotlib/axes/_axes.py:1185: RuntimeWarning: All-NaN axis encountered miny = np.nanmin(masked_verts[..., 1]) /Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/matplotlib/axes/_axes.py:1186: RuntimeWarning: All-NaN axis encountered maxy = np.nanmax(masked_verts[..., 1])
In [13]:
fig_id = plot_single_scatter(x=indexes_strings,
y=deepgm_estimate,
y_std=deepgm_std,
title='Deep GM',
x_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="GM_deep",
folder_path=output_folder,
fig_id=fig_id,
y_type='meas')
In [14]:
fig_id = plot_single_scatter(x=indexes_numbers,
y=cgm_estimate,
y_std=cgm_std,
title='Cortical GM',
x_label='Site #',
y_label="T$_1$ (ms)",
file_prefix="GM_cortical",
folder_path=output_folder,
fig_id=fig_id,
y_type='site')
In [15]:
fig_id = plot_single_scatter(x=indexes_strings,
y=cgm_estimate,
y_std=cgm_std,
title='Cortical GM',
x_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="GM_cortical",
folder_path=output_folder,
fig_id=fig_id,
y_type='meas')
Subplots¶
In [16]:
fig_id = plot_both_scatter(x1=indexes_numbers,
x2=indexes_strings,
y=genu_estimate,
y_std=genu_std,
title='WM - Genu',
x1_label='Site #',
x2_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="WM_genu",
folder_path=output_folder,
fig_id=fig_id)
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/639034575.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator axs[1].set_xticklabels(labels=x2, rotation=90)
In [17]:
fig_id = plot_both_scatter(x1=indexes_numbers,
x2=indexes_strings,
y=splenium_estimate,
y_std=splenium_std,
title='WM - Splenium',
x1_label='Site #',
x2_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="WM_splenium",
folder_path=output_folder,
fig_id=fig_id)
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/639034575.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator axs[1].set_xticklabels(labels=x2, rotation=90)
In [18]:
fig_id = plot_both_scatter(x1=indexes_numbers,
x2=indexes_strings,
y=deepgm_estimate,
y_std=deepgm_std,
title='Deep GM',
x1_label='Site #',
x2_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="GM_deep",
folder_path=output_folder,
fig_id=fig_id)
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/639034575.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator axs[1].set_xticklabels(labels=x2, rotation=90)
In [19]:
fig_id = plot_both_scatter(x1=indexes_numbers,
x2=indexes_strings,
y=cgm_estimate,
y_std=cgm_std,
title='Cortical GM',
x1_label='Site #',
x2_label='Site#.Meas#',
y_label="T$_1$ (ms)",
file_prefix="GM_cortical",
folder_path=output_folder,
fig_id=fig_id)
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/639034575.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator axs[1].set_xticklabels(labels=x2, rotation=90)
In [20]:
x1_label='Site #'
x2_label='Site #.Meas #'
y_label="T$_1$ (ms)"
file_prefix = 'WM_and_GM'
folder_path=output_folder
x1=indexes_numbers
x2=indexes_strings
y=genu_estimate
y_std=genu_std
plt.rcParams["figure.figsize"] = (20,10)
fig, axs = plt.subplots(2)
axs[0].errorbar(x1, y, y_std, fmt='o', solid_capstyle='projecting')
axs[0].set_xlabel(x1_label)
axs[0].set_ylabel(y_label)
axs[0].set_xticks(np.arange(0, np.max(x1), step=1))
axs[1].errorbar(x2, y, y_std, fmt='o', solid_capstyle='projecting')
axs[1].set_xlabel(x2_label)
axs[1].set_ylabel(y_label)
axs[1].set_xticklabels(labels=x2, rotation=90)
x1=indexes_numbers
x2=indexes_strings
y=cgm_estimate
y_std=cgm_std
axs[0].errorbar(x1, y, y_std, color='#FF6961', fmt='o', solid_capstyle='projecting')
axs[0].set_xlabel(x1_label)
axs[0].set_ylabel(y_label)
axs[0].set_xticks(np.arange(0, np.max(x1), step=1))
axs[1].errorbar(x2, y, y_std, color='#FF6961', fmt='o', solid_capstyle='projecting')
axs[1].set_xlabel(x2_label)
axs[1].set_ylabel(y_label)
axs[1].set_xticklabels(labels=x2, rotation=90)
axs[0].legend((r'White matter (one 5x5 ROI, ~genu$\mathregular{^{*}}$)', 'Grey matter (three 3x3 ROIs, cortex)'))
if fig_id<10:
filename = "0" + str(fig_id) + "_" + file_prefix
else:
filename = str(fig_id) + "_" + file_prefix
fig.savefig(folder_path / (str(filename) + '.svg'), facecolor='white')
fig.savefig(folder_path / (str(filename) + '.png'), facecolor='white')
fig_id = fig_id + 1
plt.show()
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/630522227.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator axs[1].set_xticklabels(labels=x2, rotation=90) /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/630522227.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator axs[1].set_xticklabels(labels=x2, rotation=90)
In [21]:
x2_label='Site #.Meas #'
y_label="T$_1$ (ms)"
file_prefix = 'WM_and_GM'
folder_path=output_folder
x2=indexes_strings
y=genu_estimate
y_std=genu_std
plt.rcParams["figure.figsize"] = (20,10)
fig, ax = plt.subplots(1)
ax.errorbar(x2, y, y_std, fmt='o', solid_capstyle='projecting')
ax.set_xlabel(x2_label)
ax.set_ylabel(y_label)
ax.set_xticklabels(labels=x2, rotation=90)
x2=indexes_strings
y=cgm_estimate
y_std=cgm_std
ax.errorbar(x2, y, y_std, color='#FF6961', fmt='o', solid_capstyle='projecting')
ax.set_xlabel(x2_label)
ax.set_ylabel(y_label)
ax.set_xticklabels(labels=x2, rotation=90)
ax.legend((r'White matter (one 5x5 ROI, ~genu$\mathregular{^{*}}$)', 'Grey matter (three 3x3 ROIs, cortex)'), prop={'size': 30})
plt.show()
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/3877408191.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(labels=x2, rotation=90) /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36138/3877408191.py:26: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(labels=x2, rotation=90)
In [ ]: