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# run with: gunicorn coreslicer:app --daemon

@app.route('/endpoint', methods = ['POST'])
  
def handle_endpoint_function():
  pass

def app(environ, start_response):
    port = 8000
    app.run(host='0.0.0.0', port=port)

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from flask import Flask, request, send_file
import numpy as np, os, json
from scipy.misc import imsave
from coreslicer import read_dcm

app = Flask(__name__)

APP_ROOT = os.path.dirname(os.path.abspath(__file__))
UPLOAD_FOLDER = os.path.join(APP_ROOT, '../tmp/coreslicer')
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

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Serves as a repository for images used in the CoreSlicer documentation

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# on remote machine

cd /home/coreslicer/www/
mkdir coreslicer && cd coreslicer
git init
git config receive.denyCurrentBranch updateInstead
cd .. && mkdir coreslicer_pipeline && cd coreslicer_pipeline
git init
git config receive.denyCurrentBranch updateInstead

louismullie

def window_level(arr, window_center, window_width, lut_min=0, lut_max=255):

    # Basic sanity checking.
    if np.isreal(arr).sum() != arr.size: raise ValueError
    if lut_max != 255: raise ValueError
    if arr.dtype != np.float64: arr = arr.astype(np.float64)

    # Get window information.
    window_width = max(1, window_width)
    wc, ww = np.float64(window_center), np.float64(window_width)

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#!/usr/bin/python
import sys, glob
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib import colors
from scipy.signal import convolve2d
import scipy
from scipy import ndimage
from scipy.misc import imread

louismullie

#!/usr/bin/python
import sys, glob
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib import colors
from scipy.signal import convolve2d
import scipy
from scipy import ndimage
from scipy.misc import imread

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class FalseClass
  def +(b); 0 + b.to_i; end
  def to_i; 0; end
end

class TrueClass
  def +(b); 1 + b.to_i; end
  def to_i; 1; end
end

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from __future__ import print_function
"""
A very simple 'animation' of a 3D plot
"""
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
import time

def generate(X, Y, phi):

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""" Fast algorithm for spectral analysis of unevenly sampled data

The Lomb-Scargle method performs spectral analysis on unevenly sampled
data and is known to be a powerful way to find, and test the 
significance of, weak periodic signals. The method has previously been
thought to be 'slow', requiring of order 10(2)N(2) operations to analyze
N data points. We show that Fast Fourier Transforms (FFTs) can be used
in a novel way to make the computation of order 10(2)N log N. Despite
its use of the FFT, the algorithm is in no way equivalent to