Commit 10eab075 authored by Andrei Roibu's avatar Andrei Roibu
Browse files

commented out calls to matplotlib.plt to prevent cluster errors

parent 76c6a42a
......@@ -9,7 +9,6 @@ setup(
maintainer_email='andrei-claudiu.roibu@dtc.ox.ac.uk',
install_requires=[
'pip',
'matplotlib',
'numpy',
'pandas',
'torch==1.4',
......
......@@ -17,7 +17,6 @@ Usage:
import numpy as np
from fsl.data.image import Image
from fsl.utils.image.resample import resampleToPixdims
import matplotlib.pyplot as plt
from preprocessor import directory_reader, regression_weight_calculator
from tempfile import TemporaryFile
from datetime import datetime
......
......@@ -21,7 +21,6 @@ import torch
import logging
import utils.data_utils as data_utils
from utils.common_utils import create_folder
import matplotlib.pyplot as plt
import pandas as pd
from fsl.data.image import Image
from fsl.utils.image.roi import roi
......@@ -511,39 +510,3 @@ def _pearson_correlation(volume, target):
np.sum(np.power(np.subtract(volume, volume.mean()), 2)), np.sum(np.power(np.subtract(target, target.mean()), 2))))
return r
def _generate_correlation_matrix(correlation_matrix, title, prediction_output_path, normalize=False):
"""Visual correlation matrix generator
This function generates a visual representation of the correlation matrix
Args:
correaltion_matrix (np.array): Array containing the correlation matrix
title (str): Title of the correlation matrix figure (also the experiment name)
normalize (bool): Flag indicating if the values of the correlation matrix should be normalized
prediction_output_path (str): Output prediction path
"""
# THIS FUNCTION NEEDS TO BE VERIFIED!
if normalize:
correlation_matrix = correlation_matrix.astype(
'float') / correlation_matrix.sum(axis=1)[:, np.newaxis]
plt.imshow(correlation_matrix, interpolation='nearest', cmap=plt.cm.Blues)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(correlation_matrix))
plt.xticks(tick_marks, correlation_matrix)
plt.yticks(tick_marks, correlation_matrix)
threshold = correlation_matrix.max() / 2.0
for i, j in itertools.product(range(correlation_matrix.shape[0]), range(correlation_matrix.shape[1])):
plt.text(j, i, format(correlation_matrix[i, j], '.4f'), horizontalalignment='center',
color="white" if correlation_matrix[i, j] > threshold else "black")
plt.tight_layout()
plt.ylabel('Predicted')
plt.xlabel('Targets')
plt.savefig(prediction_output_path + '/' + title + '.png')
......@@ -13,8 +13,6 @@ Usage:
"""
import os
import matplotlib
import matplotlib.pyplot as plt
import shutil
import logging
import numpy as np
......@@ -24,11 +22,8 @@ import torch
# More here: https://tensorboardx.readthedocs.io/en/latest/tensorboard.html
from tensorboardX import SummaryWriter
import utils.data_evaluation_utils as evaluation
plt.axis('scaled')
class LogWriter():
......@@ -46,7 +41,7 @@ class LogWriter():
confusion_matrix_cmap (class): Colour Map to be used for the Conusion Matrix
"""
def __init__(self, number_of_classes, logs_directory, experiment_name, use_last_checkpoint=False, labels=None, confusion_matrix_cmap=plt.cm.Blues):
def __init__(self, number_of_classes, logs_directory, experiment_name, use_last_checkpoint=False, labels=None):
self.number_of_classes = number_of_classes
training_logs_directory = os.path.join(
......@@ -66,8 +61,6 @@ class LogWriter():
'validation': SummaryWriter(logdir=validation_logs_directory)
}
self.confusion_matrix_color_map = confusion_matrix_cmap
self.current_iteration = 1
self.labels = ['rsfMRI']
......
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