iframe-proxy | Sunbelt Computer Software

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#!/usr/bin/env python
# Do *not* edit this script.
# These are helper functions that you can use with your code.
import os, numpy as np
# Check if a variable is a number or represents a number.
def is_number(x):
        float(x)
        return True
    except (ValueError, TypeError):
        return False
# Check if a variable is an integer or represents an integer.
def is_integer(x):
    if is_number(x):
        return float(x).is_integer()
        return False
# Check if a variable is a a finite number or represents a finite number.
def is_finite_number(x):
    if is_number(x):
        return np.isfinite(float(x))
        return False
# (Re)sort leads using the standard order of leads for the standard twelve-lead ECG.
def sort_leads(leads):
    x = ('I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6')
    leads = sorted(leads, key=lambda lead: (x.index(lead) if lead in x else len(x) + leads.index(lead)))
    return tuple(leads)
# Find header and recording files.
def find_challenge_files(data_directory):
    header_files = list()
    recording_files = list()
    for f in sorted(os.listdir(data_directory)):
        root, extension = os.path.splitext(f)
        if not root.startswith('.') and extension=='.hea':
            header_file = os.path.join(data_directory, root + '.hea')
            recording_file = os.path.join(data_directory, root + '.mat')
            if os.path.isfile(header_file) and os.path.isfile(recording_file):
                header_files.append(header_file)
                recording_files.append(recording_file)
    return header_files, recording_files
# Load header file as a string.
def load_header(header_file):
    with open(header_file, 'r') as f:
        header = f.read()
    return header
# Load recording file as an array.
def load_recording(recording_file, header=None, leads=None, key='val'):
    from scipy.io import loadmat
    recording = loadmat(recording_file)[key]
    if header and leads:
        recording = choose_leads(recording, header, leads)
    return recording
# Choose leads from the recording file.
def choose_leads(recording, header, leads):
    num_leads = len(leads)
    num_samples = np.shape(recording)[1]
    chosen_recording = np.zeros((num_leads, num_samples), recording.dtype)
    available_leads = get_leads(header)
    for i, lead in enumerate(leads):
        if lead in available_leads:
            j = available_leads.index(lead)
            chosen_recording[i, :] = recording[j, :]
    return chosen_recording
# Get recording ID.
def get_recording_id(header):
    recording_id = None
    for i, l in enumerate(header.split('\n')):
        if i==0:
            try:
                recording_id = l.split(' ')[0]
            except:
        else:
            break
    return recording_id
# Get leads from header.
def get_leads(header):
    leads = list()
    for i, l in enumerate(header.split('\n')):
        entries = l.split(' ')
        if i==0:
            num_leads = int(entries[1])
        elif i<=num_leads:
            leads.append(entries[-1])
        else:
            break
    return tuple(leads)
# Get age from header.
def get_age(header):
    age = None
    for l in header.split('\n'):
        if l.startswith('#Age'):
            try:
                age = float(l.split(': ')[1].strip())
            except:
                age = float('nan')
    return age
# Get sex from header.
def get_sex(header):
    sex = None
    for l in header.split('\n'):
        if l.startswith('#Sex'):
            try:
                sex = l.split(': ')[1].strip()
            except:
    return sex
# Get number of leads from header.
def get_num_leads(header):
    num_leads = None
    for i, l in enumerate(header.split('\n')):
        if i==0:
            try:
                num_leads = float(l.split(' ')[1])
            except:
        else:
            break
    return num_leads
# Get frequency from header.
def get_frequency(header):
    frequency = None
    for i, l in enumerate(header.split('\n')):
        if i==0:
            try:
                frequency = float(l.split(' ')[2])
            except:
        else:
            break
    return frequency
# Get number of samples from header.
def get_num_samples(header):
    num_samples = None
    for i, l in enumerate(header.split('\n')):
        if i==0:
            try:
                num_samples = float(l.split(' ')[3])
            except:
        else:
            break
    return num_samples
# Get analog-to-digital converter (ADC) gains from header.
def get_adc_gains(header, leads):
    adc_gains = np.zeros(len(leads))
    for i, l in enumerate(header.split('\n')):
        entries = l.split(' ')
        if i==0:
            num_leads = int(entries[1])
        elif i<=num_leads:
            current_lead = entries[-1]
            if current_lead in leads:
                j = leads.index(current_lead)
                    adc_gains[j] = float(entries[2].split('/')[0])
                except:
        else:
            break
    return adc_gains
# Get baselines from header.
def get_baselines(header, leads):
    baselines = np.zeros(len(leads))
    for i, l in enumerate(header.split('\n')):
        entries = l.split(' ')
        if i==0:
            num_leads = int(entries[1])
        elif i<=num_leads:
            current_lead = entries[-1]
            if current_lead in leads:
                j = leads.index(current_lead)
                    baselines[j] = float(entries[4].split('/')[0])
                except:
        else:
            break
    return baselines
# Get labels from header.
def get_labels(header):
    labels = list()
    for l in header.split('\n'):
        if l.startswith('#Dx'):
            try:
                entries = l.split(': ')[1].split(',')
                for entry in entries:
                    labels.append(entry.strip())
            except:
    return labels
# Save outputs from model.
def save_outputs(output_file, recording_id, classes, labels, probabilities):
    # Format the model outputs.
    recording_string = '#{}'.format(recording_id)
    class_string = ','.join(str(c) for c in classes)
    label_string = ','.join(str(l) for l in labels)
    probabilities_string = ','.join(str(p) for p in probabilities)
    output_string = recording_string + '\n' + class_string + '\n' + label_string + '\n' + probabilities_string + '\n'
    # Save the model outputs.
    with open(output_file, 'w') as f:
        f.write(output_string)
# Load outputs from model.
def load_outputs(output_file):
    with open(output_file, 'r') as f:
        for i, l in enumerate(f):
            if i==0:
                recording_id = l[1:] if len(l)>1 else None
            elif i==1:
                classes = tuple(entry.strip() for entry in l.split(','))
            elif i==2:
                labels = tuple(entry.strip() for entry in l.split(','))
            elif i==3:
                probabilities = tuple(float(entry) if is_finite_number(entry) else float('nan') for entry in l.split(','))
            else:
                break
    return recording_id, classes, labels, probabilities
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