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jupyter nbconvert --to rst examples/*.ipynb
rm -r docs/Example*
mv examples/*rst examples/*_files docs/
cd docs/
perl -pi -w -e 's/.. code:: ipython3/.. code:: python/g;' *.rst

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# define a theano Op for our likelihood function
class LogLikeWithGrad(tt.Op):

    itypes = [tt.dvector] # expects a vector of parameter values when called
    otypes = [tt.dscalar] # outputs a single scalar value (the log likelihood)

    def __init__(self, loglike):
        """
        Initialise with various things that the function requires. Below
        are the things that are needed in this particular example.

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%%cython

import cython
cimport cython

import numpy as np
cimport numpy as np

import warnings

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import numpy as np
import sys
sys.path.append("../Lensing-PowerSpectra/Simulations/")
from units import *
import theano.tensor as tt

def deflection_sis(x, y, x0=0, y0=0, b=1.5):
    """ Deflection for singular isothermal ellipsoid, from astro-ph/0102341
    """ 
    # Go into shifted coordinats of the potential

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import numpy as np
from astropy.cosmology import Planck15

from units import *

import sys, os
if os.environ['DIFF']: # Whether to use Theano version of lensing gradients
    from profiles_theano import *
else:
    from profiles import *

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###############################################################################
# run_11bin.py
###############################################################################
#
# Perform scan to obtain best-fit disk and bulge PS population parameters
#
# Here using the 4D efficiency function and the new 11 binning
# 
###############################################################################

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#!/usr/bin/env bash

# rmate
# Copyright (C) 2011-2015 by Harald Lapp <[email protected]>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#

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