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    Returns:
        if output_type == 'csv_zip':
            Flask File -- sends file contents to client using Flask send_from_directory()
        elif output_type == 'json':
            Flask File -- sends file contents to client using Flask send_from_directory()

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import os
import time
import pickle
import logging
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.models import Model
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.utils import plot_model

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import time
from lockfile import LockFile, LockTimeout

filepath = '/home/alex/Downloads/blah.txt'
lock = LockFile(filepath)

if lock.is_locked():
    print('Waiting to acquire the file lock...')

lock.acquire(timeout=10)

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#.*\n

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Compile

gcc -shared -o libsimpleopencvtest.so -fPIC ./simple_opencv_test.cpp -lopencv_core -lopencv_highgui -lopencv_objdetect -lopencv_imgproc -lopencv_features2d -lopencv_ml -lopencv_calib3d -lopencv_video

Run

python simple_opencv_test.py

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#!/bin/sh

# usage ./qsub_with_output.sh cnn_match.sh
# where cnn_match.sh is a qsub script

jobID=$(qsub $1)
jobIDNumeric=${jobID//[!0-9]/}

echo $jobIDNumeric
qstat -s -u am893

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Thunderbird Setup

Download and Install Theme

wget https://github.com/spymastermatt/thunderbird-monterail/archive/master.zip

unzip master.zip

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{"lastUpload":"2018-07-10T04:14:10.448Z","extensionVersion":"v2.9.2"}

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import numpy as np

def sample_pairs_siamese(train_X, train_y, batch_size):

    # Generate a random batch of batch_size samples
    rand_index = np.random.choice(len(train_X), size=batch_size)
    batch_xs, batch_ys = train_X[rand_index], train_y[rand_index]

    new_batch_xs, new_batch_ys = [], []

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import io
import pickle
import numpy as np
from nltk.tokenize import word_tokenize

def load_vectors(fname):
    fin = io.open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
    n, d = map(int, fin.readline().split())
    data = {}
    for line in fin: