Notebook

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 *
from src.nist import get_reference_NIST_values, get_NIST_ids
from src.tools import calc_error

import matplotlib.pyplot as plt
plt.style.use('custom_matplotlibrc')
plt.rcParams["figure.figsize"] = (10,10)
fig_id = 0

Configurations¶

In [2]:

database_path = Path('databases/3T_NIST_T1maps_database.pkl')
output_folder = Path("plots/03_singledataset_scatter_NIST/")

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='linear'):
    if y_type is 'linear':
        plt.errorbar(x,y, y_std, fmt='o', solid_capstyle='projecting')
        ax = plt.gca()
        ax.axline((1, 1), slope=1, linestyle='dashed')
        ax.set_ylim(ymin=0, ymax=2500)
        ax.set_xlim(xmin=0, xmax=2500)
    if y_type is 'log':
        plt.loglog(x,y,'o')
        ax = plt.gca()
        ax.set_ylim(ymin=20, ymax=2500)
        ax.set_xlim(xmin=20, xmax=2500)
    if y_type is 'error_t1':
        plt.errorbar(x,calc_error(x,y), fmt='o')
        ax = plt.gca()
        ax.axline((1, 0), slope=0, color='k')
        ax.axline((1, -10), slope=0, linestyle='dashed', color='k')
        ax.axline((1, 10), slope=0, linestyle='dashed', color='k')
        ax.set_ylim(ymin=-100, ymax=100)
        ax.set_xlim(xmin=0, xmax=2500)


    
    plt.title(title)
    plt.xlabel(x_label)
    plt.ylabel(y_label)

    fig = plt.gcf()
    

    folder_path.mkdir(parents=True, exist_ok=True)

    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

<>:5: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:11: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:16: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:5: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:11: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:16: SyntaxWarning: "is" with a literal. Did you mean "=="?
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/368739589.py:5: SyntaxWarning: "is" with a literal. Did you mean "=="?
  if y_type is 'linear':
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/368739589.py:11: SyntaxWarning: "is" with a literal. Did you mean "=="?
  if y_type is 'log':
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/368739589.py:16: SyntaxWarning: "is" with a literal. Did you mean "=="?
  if y_type is 'error_t1':

Load database¶

In [4]:

df = pd.read_pickle(database_path)

Initialize variables, store mean and standard deviations of T1 in the ROIs¶

In [5]:

dataset_estimate = np.array([])
dataset_std = np.array([])

index = 6.001

serial_number = df.loc[index]['phantom serial number']

for key in get_NIST_ids():
    if estimate_type is 'mean':
        dataset_estimate = np.append(dataset_estimate, np.mean(df.loc[index][key]))
    elif estimate_type is 'median':
        dataset_estimate = np.append(dataset_estimate, np.median(df.loc[index][key]))
    else:
        Exception('Unsupported dataset estimate type.')

    dataset_std = np.append(dataset_std, np.std(df.loc[index][key]))

ref_values = get_reference_NIST_values(serial_number)

<>:9: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:11: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:9: SyntaxWarning: "is" with a literal. Did you mean "=="?
<>:11: SyntaxWarning: "is" with a literal. Did you mean "=="?
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/3451975214.py:9: SyntaxWarning: "is" with a literal. Did you mean "=="?
  if estimate_type is 'mean':
/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/3451975214.py:11: SyntaxWarning: "is" with a literal. Did you mean "=="?
  elif estimate_type is 'median':

Plot figures¶

In [6]:

fig_id = plot_single_scatter(x=ref_values,
                             y=dataset_estimate,
                             y_std=dataset_std,
                             title='Linear axes',
                             x_label='Reference T$_1$ (ms)',
                             y_label="T$_1$ estimate (ms)",
                             file_prefix=str(index),
                             folder_path=output_folder,
                             fig_id=fig_id,
                             y_type='linear')

In [7]:

fig_id = plot_single_scatter(x=ref_values,
                             y=dataset_estimate,
                             y_std=dataset_std,
                             title='Log-log axes',
                             x_label='Reference T$_1$ (ms)',
                             y_label="T$_1$ estimate (ms)",
                             file_prefix=str(index),
                             folder_path=output_folder,
                             fig_id=fig_id,
                             y_type='log')

In [8]:

fig_id = plot_single_scatter(x=ref_values,
                             y=dataset_estimate,
                             y_std=dataset_std,
                             title='Error relative to reference',
                             x_label='Reference T$_1$ (ms)',
                             y_label="Error (%)",
                             file_prefix=str(index),
                             folder_path=output_folder,
                             fig_id=fig_id,
                             y_type='error_t1')