Exemple #1
0
Tyson Reimer
University of Manitoba
October 12th, 2019
"""

import os

from umbmid import get_proj_path, verify_path, get_script_logger
from umbmid.loadsave import save_pickle, save_mat, load_pickle
from umbmid.content import get_class_labels
from umbmid.ai.traintestsplit import split_to_train_test

###############################################################################

# Define the directory where the clean dataset is located
__DATA_DIR = os.path.join(get_proj_path(), 'datasets/gen-one/clean/')

###############################################################################

# Define the output directory where the train/test set files will
# be saved
__OUTPUT_DIR = os.path.join(get_proj_path(), 'datasets/gen-one/clean/')
verify_path(__OUTPUT_DIR)

###############################################################################

if __name__ == '__main__':

    logger = get_script_logger(__file__)  # Get logger

    # Load the dataset to be split and its metadata
Exemple #2
0
import os
import numpy as np
import matplotlib.pyplot as plt

from umbmid import get_proj_path, verify_path, get_script_logger
from umbmid.sigproc import iczt
from umbmid.loadsave import load_pickle

###############################################################################

# The generation of dataset to be explored - must be 'one' or 'two'
gen = 'two'

# The path to the data directory on your local PC
__DATA_DIR = os.path.join(get_proj_path(),
                          'datasets/gen-%s/simple-clean/python-data/' % gen)

__OUTPUT_DIR = os.path.join(get_proj_path(), 'output/simple-use-ex-output/')
verify_path(__OUTPUT_DIR)

###############################################################################


def plot_td_sinogram(td_data,
                     ini_t=0,
                     fin_t=6e-9,
                     title='',
                     save_fig=False,
                     save_str='',
                     transparent=False,
Exemple #3
0
Tyson Reimer
University of Manitoba
October 16th, 2019
"""

import os
import numpy as np
import matplotlib.pyplot as plt

from umbmid import get_proj_path, verify_path, get_script_logger
from umbmid.loadsave import load_pickle
from umbmid.content import report_metadata_content

###############################################################################

__DATA_DIR = os.path.join(get_proj_path(), 'datasets')
__OUTPUT_DIR = os.path.join(get_proj_path(), 'output/figs/')
verify_path(__OUTPUT_DIR)

###############################################################################

# Define the generation of data
gen = 'one'

# Define the type of data, must be in ['iczt', 'idft', 'fd']
# NOTE: for plotting the sinogram, 'iczt' is expected
data_type = 'iczt'

# Define the type of s-params to plot, must be in ['s11', 's21']
sparam = 's11'
Exemple #4
0
Tyson Reimer
University of Manitoba
July 17th, 2019
"""

import os
import numpy as np
import pandas as pd

from umbmid import null_logger, get_proj_path
from umbmid.loadsave import load_fd_data
from umbmid.sigproc import iczt

###############################################################################

__DATA_DIR = os.path.join(get_proj_path(), 'datasets\\')

###############################################################################

# This dict maps the headers for each info-piece contained in the
# metadata files to the corresponding Python datatype
dtypes_dict = {
    'n_expt': int,
    'id': int,
    'phant_id': str,
    'tum_rad': float,
    'tum_shape': str,
    'tum_x': float,
    'tum_y': float,
    'tum_z': float,
    'birads': int,
Exemple #5
0
"""
Tyson Reimer
University of Manitoba
October 2nd, 2019
"""

import os
import matplotlib.pyplot as plt

from umbmid import get_proj_path, verify_path

###############################################################################

# Define the output directory, used to save the figures
__OUTPUT_DIR = os.path.join(get_proj_path(), 'output/figs/')
verify_path(__OUTPUT_DIR)

###############################################################################

# Define the mean and stdev values for each metric, obtained from
# logistic regression performance on test set
acc = (85, 4)
roc = (94.4, 0.5)
sens = (95, 6)
spec = (80, 10)

# Define the reported value and its uncertainty for each metric from
# EuCAP 2019 paper, using simulations
simu_roc = (94, 1)
simu_acc = (87, 1)
simu_sens = (83, 2)
Exemple #6
0
Tyson Reimer
University of Manitoba
October 21st, 2019
"""

import os
import numpy as np

from umbmid import get_proj_path, verify_path, get_script_logger
from umbmid.loadsave import save_pickle, save_mat
from umbmid.build import (import_fd_dataset, import_metadata,
                          import_metadata_df)

###############################################################################

__OUTPUT_DIR = os.path.join(get_proj_path(), 'datasets/')
verify_path(__OUTPUT_DIR)

__DATA_DIR = os.path.join(get_proj_path(), 'datasets/')

###############################################################################

# The possible sparams for each generation of dataset
possible_sparams = {
    'one': ['s11'],
    'two': ['s11', 's21'],
}

###############################################################################

if __name__ == '__main__':