{
"cells": [
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"cell_type": "code",
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"id": "301f8708",
"metadata": {
"tags": [
"hide-input"
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"text/markdown": [
""
],
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""
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"source": [
"from IPython.display import Markdown\n",
"from shrbk.interact import get_url, make_html_binder_button\n",
"\n",
"# Provide binder badge\n",
"Markdown(make_html_binder_button(get_url('rho-statistics.ipynb')))"
]
},
{
"cell_type": "markdown",
"id": "bf5fc085",
"metadata": {},
"source": [
"# Rho Statistics"
]
},
{
"cell_type": "markdown",
"id": "25f05695",
"metadata": {},
"source": [
"## Introduction to Rho statistics\n",
"\n",
"Rho statistics are a way to quantify the error on shear correlations due to the PSF. The first two were introduced by Rowe in 2010 ({cite}`Rowe_2010`), although they were named $D_1$ and $D_2$. In this section, we will use the same formalism as Jarvis et al. did in a paper for DES {cite}`Jarvis_2016`, with 5 rho statistics, denoted $\\rho_1$ through $\\rho_5$. \n",
"When measuring the PSF, one can only get measurements at the positions of stars, and thus not directly at the positions of the galaxies for which the shape is measured. This means that the PSF must be interpolated. This interpolation will lead to correlated errors on galaxies which are close to each other. As these errors are not random, independent errors, they cannot be considered as simply an additional source of noise that can be added to the uncertainty on the shear. We expect that they will introduce a systematic error on the shear, linked to the two-point correlation functions of these errors.\n",
"\n",
"Here after, we will denote the true ellipticity of the PSF (defined using the second moments) $\\epsilon_\\text{PSF}$, and the true size of the PSF $T_\\text{PSF}$ (see the derivation for an exact definition) ; we also define $\\delta \\epsilon_\\text{PSF}$ and $T_\\text{PSF}$ the errors in ellipticity and size between the model and the true PSF.\n",
"\n",
"We define the five rho statistics as :\n",
"$$\\begin{align*}\n",
"\\rho_1(\\theta)& = <\\delta \\epsilon_\\text{PSF}^* (x) \\delta \\epsilon_\\text{PSF} (x+\\theta)> \\\\\n",
"\\rho_2(\\theta)& = <\\epsilon_\\text{PSF}^* (x) \\delta \\epsilon_\\text{PSF} (x+\\theta)> \\\\\n",
"\\rho_3(\\theta)& = <\\left(\\epsilon_\\text{PSF}^* \\frac{\\delta T_\\text{PSF}}{T_\\text{PSF}}\\right) (x) \\left(\\epsilon_\\text{PSF} \\frac{\\delta T_\\text{PSF}}{T_\\text{PSF}}\\right) (x+\\theta)> \\\\\n",
"\\rho_4(\\theta)& = <\\delta \\epsilon_\\text{PSF}^* (x) \\left(\\epsilon_\\text{PSF} \\frac{\\delta T_\\text{PSF}}{T_\\text{PSF}}\\right) (x+\\theta)> \\\\\n",
"\\rho_5(\\theta)& = <\\epsilon_\\text{PSF}^* (x) \\left(\\epsilon_\\text{PSF} \\frac{\\delta T_\\text{PSF}}{T_\\text{PSF}}\\right) (x+\\theta)>\n",
"\\end{align*}$$\n",
"\n",
"They are computed at the location of the stars only, as we can only observe the PSF at these locations."
]
},
{
"cell_type": "markdown",
"id": "881d7a67",
"metadata": {},
"source": [
"## Derivation of the Rho statistics\n",
"You can find below a derivation of the rho statistics.\n",
"```{admonition} Derivation\n",
":class: dropdown\n",
"Under certain assumptions, we can assume that the second order moments of the observed galaxy ($I_{obs}$) correspond to the addition of the second order moments of the \"true\" galaxy ($I_{\\text{gal}}$) and of the PSF ($I_{\\text{PSF}}$), meaning that we can write \n",
"$\\begin{equation}\n",
"I_{\\text{gal}} = I_{obs} - I_{\\text{PSF}}.\n",
"\\end{equation}$\n",
"We can then see how the ellipticity of the galaxy relates to the ellipticity of the PSF, by writing\n",
"$\\begin{equation} \n",
"\\epsilon_{\\text{gal}} = \\frac{I_{11,\\text{gal}} - I_{22,\\text{gal}} + 2iI_{12,\\text{gal}}}{I_{11,\\text{gal}} + I_{22,\\text{gal}}}\n",
"\\end{equation}$\n",
"and replacing the moments with the previous formula we can obtain\n",
"$\\begin{equation} \n",
"\\epsilon_{\\text{gal}} = \\frac{\\epsilon_{obs}T_{obs} - \\epsilon_{\\text{PSF}}T_{\\text{PSF}}}{T_{obs} - T_{\\text{PSF}}}\n",
"\\end{equation}$\n",
"where $T = I_{11}+I_{22}$ ($\\sqrt{T}$ can be considered to be a sort of radius).\n",
"To get the contribution of the PSF, we can do a partial differentiation, considering that errors relative to $T$ and $\\epsilon_{\\text{PSF}}$ will be small :\n",
"$\\begin{equation}\n",
"\\delta\\epsilon_{\\text{gal}} = \\frac{\\partial \\epsilon_{\\text{gal}}}{\\partial T_{\\text{PSF}}} \\delta T_{\\text{PSF}}\n",
" + \\frac{\\partial \\epsilon_{\\text{gal}}}{\\partial \\epsilon_{\\text{PSF}}} \\delta \\epsilon_{\\text{PSF}}\n",
"\\end{equation}$\n",
" \n",
"We can compute the derivatives:\n",
"$\\begin{equation}\n",
"\\frac{\\partial \\epsilon_{\\text{gal}}}{\\partial T_{\\text{PSF}}} = \n",
"\\frac{\\epsilon_{obs}T_{obs} - \\epsilon_{\\text{PSF}}T_{obs}}{(T_{obs}-T_{\\text{PSF}})^2}\n",
"= \\frac{\\epsilon_{\\text{gal}}-\\epsilon_{\\text{PSF}}}{T_{\\text{gal}}}\n",
"\\end{equation}$\n",
"\n",
"(the second step is done by adding $-\\epsilon_{\\text{PSF}}T_{\\text{PSF}}+\\epsilon_{\\text{PSF}}T_{\\text{PSF}}$ to the numerator) and\n",
"$\\begin{equation}\n",
"\\frac{\\partial \\epsilon_{\\text{gal}}}{\\partial \\epsilon_{\\text{PSF}}} = -\\frac{T_{\\text{PSF}}}{T_{obs}-T_{\\text{PSF}}} = -\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\n",
"\\end{equation}$\n",
"\n",
"We can thus write\n",
"\n",
"$\\begin{equation}\n",
"\\delta\\epsilon_{\\text{gal}} = (\\epsilon_{\\text{gal}}-\\epsilon_{\\text{PSF}})\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}} - \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\delta \\epsilon_{\\text{PSF}}\n",
"\\end{equation}$\n",
"\n",
"\n",
"From there, we use the estimator $\\hat{\\epsilon}_{\\text{gal}} = \\epsilon_{\\text{gal}} + \\delta \\epsilon_{\\text{gal}} + \\alpha \\epsilon_{\\text{PSF}}$ ($\\delta \\epsilon_{\\text{gal}}$ represents the systematic error, while $\\alpha \\epsilon_{\\text{PSF}}$ represents the impact of the \\text{PSF} shape in the shape measurement algorithm, which can be non-zero even if the \\text{PSF} is perfectly modelled). We can then have an estimator for the shear correlation :\n",
"\n",
"$$\\begin{align}\n",
"\\hat{\\xi}^+ (\\theta) &= <\\hat{\\epsilon}_{\\text{gal}}^*(x) \\hat{\\epsilon}_{\\text{gal}}(x+\\theta)> \\\\\n",
"\t &= <(\\epsilon_{\\text{gal}}^*(x) + \\delta \\epsilon_{\\text{gal}}^*(x) + \\alpha \\epsilon_{\\text{PSF}}^*)(\\epsilon_{\\text{gal}}(x+\\theta) + \\delta \\epsilon_{\\text{gal}}(x+\\theta) + \\alpha \\epsilon_{\\text{PSF}})> \\\\\n",
"\t &= \\underbrace{<\\epsilon_{\\text{gal}}^*(x)\\epsilon_{\\text{gal}}(x+\\theta)>}_{\\xi^+(\\theta)} + <\\epsilon_{\\text{gal}}^*(x) \\alpha \\epsilon_{\\text{PSF}}(x+\\theta)> + <\\epsilon_{\\text{gal}}^*(x) \\delta \\epsilon_{\\text{gal}}(x+\\theta)> \\\\\n",
"\t & \\quad + <\\alpha \\epsilon_{\\text{PSF}}^*(x) \\epsilon_{\\text{gal}}(x+\\theta)> + <\\alpha^2 \\epsilon_{\\text{PSF}}^*(x) \\epsilon_{\\text{PSF}}(x+\\theta)> + <\\alpha \\epsilon_{\\text{PSF}}^*(x) \\delta\\epsilon_{\\text{gal}}(x+\\theta)> \\\\\n",
"\t & \\quad + <\\delta \\epsilon_{\\text{gal}}^*(x)\\epsilon_{\\text{gal}}(x+\\theta)> + <\\delta \\epsilon_{\\text{gal}}^*(x) \\alpha \\epsilon_{\\text{PSF}}(x+\\theta)> + <\\delta \\epsilon_{\\text{gal}}^*(x)\\delta \\epsilon_{\\text{gal}}(x+\\theta)>\n",
"\\end{align}$$\n",
"\n",
"We thus wish to estimate $\\delta \\xi^+(\\theta)$. From now on we will omit the $x$ and $x+\\theta$ so as to avoid cluttering the computations. We will also make two hypothesis :\n",
"- H1 : the galaxy and the PSF are not correlated, ie $<\\epsilon_{\\text{gal}}\\epsilon_{\\text{PSF}}>$ and $<\\epsilon_{\\text{gal}}\\delta\\epsilon_{\\text{PSF}}>$ terms are $0$.\n",
"- H2 : separation of T and $\\epsilon$\n",
"\n",
"Following H1, we can already eliminate 2 terms. Since $\\alpha$ is supposed to be small, we will also drop the term containing $\\alpha^2$. Finally, the symmetry of the correlation reduces the number of terms to three. Let us compute the three remaining terms :\n",
"\n",
"$$\\begin{align*}\n",
"<\\epsilon_{\\text{gal}}^* \\delta \\epsilon_{\\text{gal}}> &= <\\epsilon_{\\text{gal}}^* ((\\epsilon_{\\text{gal}}-\\epsilon_{\\text{PSF}})\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}- \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\delta \\epsilon_{\\text{PSF}}) \\\\\n",
"&= <\\epsilon^*_{\\text{gal}} \\epsilon_{\\text{gal}} \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}> - \\underbrace{<\\epsilon^*_{\\text{gal}} \\epsilon_{\\text{PSF}} \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}>}_{0~ (H1)} - \\underbrace{<\\epsilon^*_{\\text{gal}} \\delta \\epsilon_{\\text{PSF}} \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}>}_{0~ (H1)} \\\\\n",
"&= <\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}> \\xi^+(\\theta) \\\\\n",
"<\\delta \\epsilon^*_{\\text{gal}}\\epsilon_{\\text{gal}}> &= <\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}> \\xi^+(\\theta)\\\\\n",
"<\\alpha \\epsilon_{\\text{PSF}}^* \\delta \\epsilon_{\\text{gal}}> &= <\\alpha\\epsilon_{\\text{PSF}}^* ((\\epsilon_{\\text{gal}}-\\epsilon_{\\text{PSF}})\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}- \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\delta \\epsilon_{\\text{PSF}}) \\\\\n",
"&= \\underbrace{<\\alpha\\epsilon_{\\text{PSF}}^* \\epsilon_{\\text{gal}} \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}>}_{0~ (H1)} - <\\alpha \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\underbrace{\\epsilon_{\\text{PSF}}^* \\epsilon_{\\text{PSF}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}}_{\\rho_5}> - <\\alpha \\underbrace{\\epsilon_{\\text{PSF}}^* \\delta \\epsilon_{\\text{PSF}}}_{\\rho_2} \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}> \\\\\n",
"&= - \\alpha \\left< \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right> \\rho_5(\\theta) - \\alpha \\left< \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right> \\rho_2(\\theta)\\\\\n",
"<\\alpha \\epsilon_{\\text{PSF}}^* \\delta \\epsilon_{\\text{gal}}> &= - \\alpha \\left< \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right> \\rho_5(\\theta) - \\alpha \\left< \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right> \\rho_2(\\theta)\\\\\n",
"<\\delta \\epsilon^*_{\\text{gal}} \\delta \\epsilon_{\\text{gal}}> &= <((\\epsilon_{\\text{gal}}^*-\\epsilon_{\\text{PSF}}^*)\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}- \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\delta \\epsilon^*_{\\text{PSF}})((\\epsilon_{\\text{gal}}-\\epsilon_{\\text{PSF}})\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}- \\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\delta \\epsilon_{\\text{PSF}})> \\\\\n",
"&= <\\left(\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right)^2 (\\underbrace{\\epsilon_{\\text{gal}}^* \\epsilon_{\\text{gal}}}_{\\xi^+} - \\underbrace{\\epsilon_{\\text{PSF}}^* \\epsilon_{\\text{gal}}}_{0~ (H1)} - \\underbrace{\\epsilon_{\\text{gal}}^* \\epsilon_{\\text{PSF}}}_{0~ (H1)} + \\underbrace{\\epsilon_{\\text{PSF}}^* \\epsilon_{\\text{PSF}})\\left(\\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}\\right)^2}_{\\rho_3}> \\\\\n",
"& \\quad - <(\\underbrace{\\epsilon_{\\text{gal}}^* \\delta \\epsilon_{\\text{PSF}}}_{0~ (H1)} - \\underbrace{\\epsilon_{\\text{PSF}}^* \\delta \\epsilon_{\\text{PSF}}) \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}}_{\\rho_4} \\left(\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right)^2> \\\\\n",
"& \\quad -< (\\underbrace{\\delta \\epsilon_{\\text{PSF}}^* \\epsilon_{\\text{gal}}}_{0~ (H1)} - \\underbrace{\\delta \\epsilon_{\\text{PSF}}^* \\epsilon_{\\text{PSF}}) \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}}_{\\rho_4} \\left(\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right)^2> \\\\\n",
"& \\quad + <\\left(\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right)^2 \\underbrace{\\delta \\epsilon_{\\text{PSF}}^* \\delta \\epsilon_{\\text{PSF}}}_{\\rho_1}> \\\\\n",
"& = \\left<\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right>^2 \\rho_1(\\theta) + \\left<\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right>^2 \\rho_3(\\theta) + 2 \\left<\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right>^2 \\rho_4(\\theta) \\\\\n",
"& \\quad + \\underbrace{<\\left(\\frac{\\delta T_{\\text{PSF}}}{T_{\\text{gal}}}\\right)^2> \\xi_+(\\theta)}_{\\text{negligible}}\n",
"\\end{align*}$$\n",
"Finally, we get \n",
"\n",
"$\\begin{align*}\n",
"\\delta \\xi^+(\\theta) &= 2<\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}} \\frac{\\delta T_{\\text{PSF}}}{T_{\\text{PSF}}}> \\xi^+(\\theta) + \\left<\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right>^2 \\rho_1(\\theta) + \\left<\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right>^2 \\rho_3(\\theta) + 2 \\left<\\frac{T_{\\text{PSF}}}{T_{\\text{gal}}}\\right>^2 \\rho_4(\\theta) \\\\ \n",
"&\\quad - 2\\alpha \\left< \\frac{ T_{\\text{PSF}}}{T_{\\text{gal}}}\\right> \\rho_5(\\theta) - 2\\alpha \\left< \\frac{ T_{\\text{PSF}}}{T_{\\text{gal}}}\\right> \\rho_2(\\theta)\n",
"\\end{align*}$\n",
"\n",
"We can notice that the formula here is slightly different from the one in the Jarvis paper, as there are factors of two in front of $\\rho_2$,$\\rho_4$ and $\\rho_5$; however these are ultimately unimportant since the precise coefficients will be measured from the data anyway.\n",
"```\n",
"\n",
"```{note} \n",
"Some papers do not use the exact same statistics ; for instance, {cite}`Giblin_2021` makes a similar derivation and obtains a consistent result but regroups the terms differently ; they also do not have the terms involving $\\alpha$.\n",
"```\n",
"\n"
]
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"id": "e738c09c",
"metadata": {},
"source": [
"## Interpretation\n",
"\n",
"To get a better sense of what the statistics mean and how they differ from each other, we will be making experiments using the [Piff package](https://github.com/rmjarvis/Piff), which is used for PSF modelling and allows us to easily compute the rho statistics. We will try to show how introducing an error in the model is repercuted in the rho statistics."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a9775ba1",
"metadata": {},
"outputs": [],
"source": [
"import galsim\n",
"import os\n",
"import numpy as np\n",
"import fitsio\n",
"import piff\n",
"import tempfile\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams.update({'font.size': 22})\n",
"\n",
"output_dir = tempfile.mkdtemp()\n",
"\n",
"def setup(sigma = 0.4,g1 = 0.15,g2 = 0,du = 0,dv = 0,flux = 123.45,stamp_size=2048,n_stars=5000,point_array=None,plot=False,randomize_ell=False):\n",
" \"\"\"Build an input image and catalog used by a few tests below.\n",
" \"\"\"\n",
" wcs = galsim.PixelScale(0.263)\n",
" image = galsim.Image(stamp_size, stamp_size, wcs=wcs)\n",
"\n",
" # Where to put the stars.\n",
" if point_array is None:\n",
" point_array = np.round(np.random.uniform(33,stamp_size-33,size=(n_stars,2)),2)\n",
" \n",
" x_list = point_array[:,0]\n",
" y_list = point_array[:,1]\n",
" \n",
" # Draw a Gaussian PSF at each location on the image.\n",
" for x, y in zip(x_list, y_list):\n",
" psf = galsim.Gaussian(sigma=sigma).shear(g1=g1+randomize_ell*np.random.normal(0,0.01), g2=g2).shift(du,dv) * flux #*(stamp_size-x)/stamp_size\n",
" image2 = galsim.Image(stamp_size, stamp_size, wcs=wcs)\n",
" bounds = galsim.BoundsI(int(x-31), int(x+32), int(y-31), int(y+32))\n",
" offset = galsim.PositionD(x-int(x)-0.5, y-int(y)-0.5)\n",
" psf.drawImage(image=image2[bounds], method='no_pixel', offset=offset)\n",
" image[bounds] += image2[bounds]\n",
" image.addNoise(galsim.GaussianNoise(rng=galsim.BaseDeviate(1234), sigma=1e-6))\n",
" if plot:\n",
" plt.figure(figsize=(15,15))\n",
" plt.imshow(image.array)\n",
" plt.show()\n",
" # Write out the image to a file\n",
" image_file = os.path.join(output_dir,'test_stats_image.fits')\n",
" image.write(image_file)\n",
"\n",
" # Write out the catalog to a file\n",
" dtype = [ ('x','f8'), ('y','f8') ]\n",
" data = np.empty(len(x_list), dtype=dtype)\n",
" data['x'] = x_list\n",
" data['y'] = y_list\n",
" cat_file = os.path.join(output_dir,'test_stats_cat.fits')\n",
" fitsio.write(cat_file, data, clobber=True)"
]
},
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"cell_type": "markdown",
"id": "be19fbdc",
"metadata": {},
"source": [
"First, we need to generate a field containing stars. Here, we will have a uniform PSF over the field so that each star is identical."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "332e75f1",
"metadata": {
"scrolled": false
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"stamp_size = 13500\n",
"n_stars = 5000\n",
"point_array = np.round(np.random.uniform(33,stamp_size-33,size=(n_stars,2)),2)\n",
"setup(stamp_size=stamp_size,n_stars=n_stars,point_array=point_array,plot=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "37db1c3d",
"metadata": {},
"outputs": [],
"source": [
"logger = piff.config.setup_logger(verbose=2)\n",
"image_file = os.path.join(output_dir,'test_stats_image.fits')\n",
"cat_file = os.path.join(output_dir,'test_stats_cat.fits')\n",
"config = {\n",
" 'input' : {\n",
" 'image_file_name' : image_file,\n",
" 'cat_file_name' : cat_file,\n",
" 'stamp_size' : 30\n",
" }\n",
"}\n",
"\n",
"# Test rho statistics directly.\n",
"min_sep = 20\n",
"max_sep = 10000\n",
"bin_size = 0.5"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cbe21353",
"metadata": {},
"outputs": [],
"source": [
"def plot_single(ax, rho, color, marker, offset=0., num=1):\n",
" # Add a single rho stat to the plot.\n",
" meanr = rho.meanr * (1. + rho.bin_size * offset)\n",
" xip = rho.xip\n",
" sig = np.sqrt(rho.varxip)\n",
" ax.plot(meanr, xip, color=color,label=r'$\\rho_{}(\\theta)$'.format(num))\n",
" ax.plot(meanr, -xip, color=color, ls=':')\n",
" ax.errorbar(meanr[xip>0], xip[xip>0], yerr=sig[xip>0], color=color, ls='', marker=marker)\n",
" ax.errorbar(meanr[xip<0], -xip[xip<0], yerr=sig[xip<0], color=color, ls='', marker=marker,\n",
" fillstyle='none', mfc='white')\n",
" \n",
"def compute_rho_stats_mean(sigma,g1,g2,du,dv,flux,plot=False):\n",
" galsim_psf = galsim.Gaussian(sigma=sigma).shear(g1=g1, g2=g2).shift(du,dv) * flux\n",
" model_psf = piff.GSObjectModel(galsim_psf)\n",
" model_interp = piff.Mean()\n",
" psf = piff.SimplePSF(model_psf,model_interp)\n",
" orig_stars, wcs, pointing = piff.Input.process(config['input'], logger)\n",
" stats = piff.RhoStats(min_sep=min_sep, max_sep=max_sep, bin_size=bin_size, sep_units=\"arcsec\")\n",
" stats.compute(psf, orig_stars)\n",
"\n",
" rhos = [stats.rho1, stats.rho2, stats.rho3, stats.rho4, stats.rho5]\n",
"\n",
" if plot:\n",
" fig, (ax1,ax2) = plt.subplots(1,2,figsize=(20,10))\n",
" ax1.set_xlabel(r'$\\theta$ (arcsec)')\n",
" ax1.set_ylabel(r'$\\rho(\\theta)$')\n",
" ax1.set_xlim(min_sep,max_sep)\n",
" ax1.set_xscale('log')\n",
" ax1.set_yscale('log', nonposy='clip')\n",
" ax2.set_xlabel(r'$\\theta$ (arcsec)')\n",
" ax2.set_ylabel(r'$\\rho(\\theta)$')\n",
" ax2.set_xlim(min_sep,max_sep)\n",
" ax2.set_xscale('log')\n",
" ax2.set_yscale('log', nonposy='clip')\n",
" plot_single(ax1,rhos[0],\"red\",\"x\",num=1)\n",
" plot_single(ax1,rhos[2],\"blue\",\"x\",0.1,num=3)\n",
" plot_single(ax1,rhos[3],\"green\",\"x\",0.2,num=4)\n",
" plot_single(ax2,rhos[1],\"red\",\"x\",num=2)\n",
" plot_single(ax2,rhos[4],\"blue\",\"x\",num=5)\n",
" ax1.legend()\n",
" ax2.legend()\n",
" plt.show()\n",
" \n",
" return [np.abs(np.mean(rho.xip)) for rho in rhos]"
]
},
{
"cell_type": "markdown",
"id": "4e8d9559",
"metadata": {},
"source": [
"First, let's compute the rho statistics with a reference PSF (slightly different from the original graph so as to get a cleaner plot). To simplify, we will average the rho statistics over all scales in order to compare different situations ; since the true PSF and the model should be the same everywhere, we expect them to be approximateley constant anyway."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e09ddccd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading in 1 images\n",
"Getting wcs from image file ./output/test_stats_image.fits\n",
"Reading image file ./output/test_stats_image.fits\n",
"Reading star catalog ./output/test_stats_cat.fits.\n",
"Processing catalog 0 with 5000 stars\n",
"Read a total of 5000 stars from 1 image\n",
"/home/thuiop/Documents/stageAPC/Shear-and-PSF-Reading-Group/env/lib64/python3.7/site-packages/ipykernel_launcher.py:29: MatplotlibDeprecationWarning: The 'nonposy' parameter of __init__() has been renamed 'nonpositive' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
"/home/thuiop/Documents/stageAPC/Shear-and-PSF-Reading-Group/env/lib64/python3.7/site-packages/ipykernel_launcher.py:34: MatplotlibDeprecationWarning: The 'nonposy' parameter of __init__() has been renamed 'nonpositive' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean rho statistics : [2.5590926591492516e-05, 0.0006785157602205058, 8.469582860874487e-05, 3.877320601654588e-05, 0.0013022010557144023]\n"
]
}
],
"source": [
"sigma = 0.42\n",
"g1 = 0.15\n",
"g2 = 0\n",
"du = 0. # in arcsec\n",
"dv = 0.\n",
"flux = 123.45\n",
"ref_rho_stats = compute_rho_stats_mean(sigma,g1,g2,du,dv,flux,plot=True)\n",
"print(\"Mean rho statistics : \",ref_rho_stats)"
]
},
{
"cell_type": "markdown",
"id": "608d27db",
"metadata": {},
"source": [
"Now, let's see what happens if we give it a higher ellipticity, say by increasing the first component of the ellipticity (denoted g1 in the code). We expect that this will increase the $\\delta\\epsilon_\\text{PSF}$ terms, while the $\\epsilon_\\text{PSF}$ will not change (as the real PSF stays the same) ; thus we should expect that only $\\rho_1$, $\\rho_2$ and $\\rho_4$ will increase, and $\\rho_1$ the most of all (please note that $\\delta T_\\text{PSF}$ should not change by much either)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "143e888c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading in 1 images\n",
"Getting wcs from image file ./output/test_stats_image.fits\n",
"Reading image file ./output/test_stats_image.fits\n",
"Reading star catalog ./output/test_stats_cat.fits.\n",
"Processing catalog 0 with 5000 stars\n",
"Read a total of 5000 stars from 1 image\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean rho statistics : [0.00019727433668392385, 0.0020020268022177042, 8.529093619015561e-05, 0.00013013887123203773, 0.001306930731654299]\n",
"Ratio compared to reference : [7.708760993027349, 2.9505973473144977, 1.0070264095786803, 3.3564124456590703, 1.0036320627441835]\n"
]
}
],
"source": [
"sigma = 0.42\n",
"g1 = 0.17\n",
"g2 = 0\n",
"du = 0. # in arcsec\n",
"dv = 0.\n",
"flux = 123.45\n",
"high_ellipticity_rho_stats = compute_rho_stats_mean(sigma,g1,g2,du,dv,flux,plot=False)\n",
"print(\"Mean rho statistics : \",high_ellipticity_rho_stats)\n",
"print(\"Ratio compared to reference : \",[high_ellipticity_rho_stats[i]/ref_rho_stats[i] for i in range(5)])"
]
},
{
"cell_type": "markdown",
"id": "22365c49",
"metadata": {},
"source": [
"Indeed, we obtain a result that matches expectations. Let's now see the impact of changing the size of the PSF (without changing the ellipticity). This should only affect $\\rho_3$, $\\rho_4$ and $\\rho_5$ since $\\rho_1$ and $\\rho_2$ do not contain the $\\delta T_\\text{PSF}$ factor, which evaluates the error in size (and again, $\\rho_3$ should be the most impacted)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dfe20b2c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading in 1 images\n",
"Getting wcs from image file ./output/test_stats_image.fits\n",
"Reading image file ./output/test_stats_image.fits\n",
"Reading star catalog ./output/test_stats_cat.fits.\n",
"Processing catalog 0 with 5000 stars\n",
"Read a total of 5000 stars from 1 image\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean rho statistics : [2.1938800858592285e-05, 0.0006197287874065584, 0.00026221554669173186, 6.523429271697407e-05, 0.0023192674711321488]\n",
"Ratio compared to reference : [0.8572882572327667, 0.913359458599985, 3.095967664512087, 1.6824580533561324, 1.781036392924572]\n"
]
}
],
"source": [
"sigma = 0.44\n",
"g1 = 0.15\n",
"g2 = 0\n",
"du = 0. # in arcsec\n",
"dv = 0.\n",
"flux = 123.45\n",
"high_size_rho_stats = compute_rho_stats_mean(sigma,g1,g2,du,dv,flux,plot=False)\n",
"print(\"Mean rho statistics : \",high_size_rho_stats)\n",
"print(\"Ratio compared to reference : \",[high_size_rho_stats[i]/ref_rho_stats[i] for i in range(5)])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cc58aa81",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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3JiL48MMPee211/jqq6+YPn06Xbp0yek5Rx11FNdccw0XX3wxQ4YMYbvttmP77benTZs2zJ07l1dffZVFixZx0003rbM+UVn9+vXjrrvu4vTTT+ekk07i0ksvZaeddmLTTTdl3rx5TJ06lf/85z9ccsklVZ5Ul4+SkhIOPfRQnn/+eXr37s2+++5Lp06dmDNnDq+99hrt2rXjww8/XDO+Lj5LqZjkk1zaHhi/jsQSwFIyhb4lSZIkSQ3E97//fW688Ubef/997rzzTs444wzuu+8+Tj75ZG6//XYmT57M66+/ziabbEK3bt046aSTOOqoo9h6663zfs7gwYMZM2YM48eP56mnnqJZs2Z069aN/fffnyOPPJJjjz027/hPPPFE9thjD0aPHs1TTz3FhAkTAOjatSt9+/bl8MMPZ/jw4XnPW5lNN92U5557jttuu4177rmHyZMn89VXX9GlSxf2228/vvnNb5Yb37x58zr5LKViERWXC1Y5MGIpMC6lNKxM22qgJKV0epm2Z4DdUkodCh1sLvr165emTJlSH4+WJEmSVA+mT5/OjjvuWN9hSFKDk8/vnxHxckqpX2V9+dRc+gDYNSKqvCciWgNfI7M1TpIkSZIkSY1cPsmlR4DuwAXVjLkY6AA8XJugJEmSJEmS1DDkU3PpWuA04JcRsRtQesZjx4gYChwPjABmAjcWNEpJkiRJkiQVpZyTSymlTyPiUDKrkk4ETgAScHj2FcAs4MiU0tI6iFWSJEmSJElFJp+VS6SUpkXETmRWMA0FegNNySSVHgduTSl9XvAoJUmSJEmSVJTySi4BpJS+BG7KviRJkiRJkrQBy6egtyRJkiRJklROzsmliOgTEVdki3lXNebr2TE7FCY8SZIkSZIkFbN8Vi6dDVwOzKtmzH+AK4Dv1iYoSZIkSZIkNQz5JJcOAF5LKc2uakC2bypwYC3jkiRJkiRJUgOQT3JpC+D9HMZ9AHSvWTiSJEmSJElqSPJJLjXNcXwALWsWjiRJkiRJkhqSfJJLM4C9IqLKe7J9ewGzahuYJEmSJEmSil8+yaW/A5sDl1Qz5mIy2+f+XpugJEmSJEmS1DDkk1y6FlgK/Cwi7o6IAyNi8+zrgIi4G/h5dsw1dRGsJEmSJCk/vXr1IiIYP358teMGDRpERFBSUlKuvaSkhIjg1FNPLUg848ePJyIYNGhQQebTf5X+Gq7r1zoXH374IRFBr169aj3XulT1M1ZdDKU/1x9++GGdx1cqIoiIWs9z6qmnVvrfWkOWc3IpexLcN4DPgZOAp8hsf5sF/CPb9jlwQkppRuFDlSRJkiRJKrxiSPgUOpG7PjXLZ3BK6cmI6ANcAAwBtgQSMJPMVrhrU0ozCx6lJEmSJKleHHPMMey99960a9euvkOR1nj66adZsWIFW2yxxXp75vTp0wsyzy9+8QsuvfRSunXrVpD5ikFeySWAlNIs4PzChyJJkiRJKjbt2rUzsaSis/XWW6/3Z+6www4Fmadbt26NKrEE+dVckiRJkiSVWrwYZszIXBuxdW3VeeCBB9hnn31o06YNHTp04JBDDuG5557LqbbSihUr+PnPf84OO+xAq1at6Ny5MyeffDIzZ1a9IWbWrFmcd955bL/99rRu3Zq2bdsyYMAASkpKSCmtNb5sHaJnn32Www8/nI4dO9KkSRMeeuihdX7/ZbdLvfHGGxx33HF06tSJNm3asO+++zJu3Lg1Y//2t78xcOBA2rVrR9u2bRk2bBj//ve/q5z7hRde4LjjjqNr1660aNGCrl27Mnz4cF588cUq75k/fz4jR46ke/futGzZkt69e/ODH/yAL774otrvI6XEvffeyyGHHELHjh1p2bIlPXv25Mwzzyx43aLPP/+cX/3qV+yxxx60bduW1q1b06dPH370ox/x2WefFeQZVdVcKvvrPW7cOA466CA6dOiw5tfrkUceKTe+tK7TnXfeCcBpp522prZSxW1y1dVcWrFiBbfeeisHHHAAm2666ZrP94gjjuDuu+8uN7ayLXi9evXitNNOA+DOO+8sF8Opp57KZ599Rrt27WjWrBmzZ8+u8nPZfffdiQgee+yxdX2EBWVySZIkSZLyMXs2XHwxHHIInHBC5nrJJZn2DcyVV165JhnSt29fhg4dyieffMIBBxyw1j/iK1qxYgVDhw7ll7/8Jdtssw1Dhw6lSZMm3H333ey7774sWrRorXvGjRvHLrvswujRo1m9ejWHHnooe+21F6+//jqnnXYaI0aMqPJ5999/PwcccAAzZ87k4IMPZvDgwTRv3jzn73XKlCnsueeevPPOOwwePJjtt9+eiRMnMmTIEJ577jnGjBnDUUcdRUqJIUOGsOmmm/LXv/6V/fffnwULFqw130033cR+++3HX/7yF3r27Mnw4cPp2bMnDzzwAAMGDOC2225b6565c+ey1157ccMNN7B8+XKGDRtGnz59GDNmDIMHD2b58uVVftbDhw/npJNO4vnnn2ennXZi2LBhbLzxxvzud7/j61//OlOmTMn5s6jO7Nmz2XPPPbnkkkuYMWMG/fv355BDDmHhwoX8+Mc/ZsCAASxcuLAgz6rOgw8+yEEHHcR//vMfhg4dyq677srEiRM56qijuPbaa9eMa9OmDSNGjFizEmrAgAGMGDFizWubbbZZ57MWLlzIwIED+c53vsOkSZPYbbfdOPbYY9lqq62YOHEil1122TrnGD58OAMGDAAyq7LKxrDvvvvSpk0bTjvtNFatWsWtt95a6Rwvvvgir7zyCr179+bQQw/N5WMqnJRSzi+gLfADMgW83wTer+L1Xj7zFvK1++67J0mSJEkbjjfffHP9PWzWrJQOPDClPfZIaejQlI44InPdY4+UBg/O9BeZLbfcMgFp3Lhx1Y4bOHBgAtIdd9xRrv2OO+5IQBoxYkS59ilTpqQmTZqk5s2bp8cff7xc3/XXX5/I1OdNAwcOLNc3bty4NX39+vVLn3zyyZq+RYsWpa9//esJSD/72c/K3TdnzpzUoUOH1LRp01RSUpJWr169pm/mzJmpb9++lcZf+n0B6ZZbbqn2M6jMiBEj1tx/zTXXlOu7+OKLE5C222671LZt2/Tss8+u6Vu2bFnab7/9EpB+8pOflLtv6tSpqVmzZqlJkyZp7Nix5fruueeeNZ/rtGnTyvUde+yxCUgHHXRQWrJkyZr22bNnp+22225NnBV/rS+55JIEpP333z/NqvAzOmbMmASkrbfeOq1YsWJN+wcffJCAtOWWW+b8Wa1evTr1798/AWnkyJHpiy++WNP3xRdfpJNPPrnSn6Wqfsaqi6H05/qDDz4o11721/vXv/51ub5HHnkkNWvWLDVt2jS99tpr5fpKf50r/vyUVTpvRUcddVQCUv/+/dNHH31Urm/ZsmXpsccey+lZVX0Opd55550UEalbt25p+fLla/WfcsoplX7f1cnn909gSqoiF5PzyqWI6AG8DvwMOBDYAehVzUuSJEmSGpfRo2HpUujSBZo2zbQ1bZp5v3gxjBlTv/FV44ADDii31abia8KECXnNd8MNN7B69Wq+/e1vr7VKYtSoUey1117V3h8R/P73v6dz585r2tq1a8cll1wCZAo2l3XdddexcOFCLrjgAkaMGFFue1KPHj3WrPQZU8WvwcEHH8xZZ52V+zdYQf/+/fn+979fru3SSy8F4J133uGcc85hv/32W9PXqlUrvve97wGU2zoHMHr0aFauXMmJJ57I8ccfX66vtG3FihVcf/31a9pnzpzJgw8+SNOmTbn55pvZZJNN1vRtscUWXH311ZXG/emnnzJ69GjatGnD/fffT/fu3cv1jxw5ksMPP5z33nuPxx9/PNePo1JPPPEEkyZNYu+99+b666+ndevWa/pat27NzTffTOfOnbn77rvrfPVSv379uPDCC8u1HXnkkXzzm99k1apVVf6c5Gvq1Kk8/PDDbLLJJjz88MNsvvnm5fpbtWrF0KFDC/KsbbfdlkMPPZSPP/6YBx98sFzf/PnzGTt2LK1ateL0008vyPPykc+2uCuBnsCrwAnArsBWVbx6FzZMSZIkSapnixfDhAnQsWPl/Z06wfjxsGTJeg0rV0OGDCm31abiq0uXLnnNV5qM+uY3v1lp/0knnVTt/T179mSXXXZZq720aPKcOXPKtZfWkKmYjCm1++6706ZNG6ZOncqXX365Vv+xxx5bbTzrUtk2ow4dOrDZZptV2b/tttsCa38vpZ9dVXWsSpMD48ePX9P27LPPklJi7733rrSY9ZFHHkn79u3Xah83bhzLli1j4MCB5RJ5ZQ0cOBCASZMmVdqfq9Jfo+OOO44mTdZON2y88cb069ePlStX8s9//rNWz1qXb33rW5W2n3LKKUD5z7Y2nnjiCQCGDRtGp06dCjJndc4991wAbrzxxnLtt99+O1999RUnnngim266aZ3HUVE+p8UdAswFDkgpLa2jeCRJkiSpOC1aBBH/XbFUUdOm0KQJLFwIbduu19Bycemll1ZbXHvQoEF88sknOc/30UcfAbDllltW2l9Ve6mePXtW2t42+9lVTBC9//77AOyxxx7rjG3BggVrHVG/rnjWpeKKn1Jt2rRhwYIFlfa3adMGWPt7Kf3sttpqq0rn7N27d7lxwJoizlXdA5nvsWKtqtLP7dFHH62yGHWpefPmVdu/LqXPuuiii7jooovq9FnrUtXn1KtXL4Bqi2LnY8aMGUDhTpJbl0MPPZRtt92WCRMm8Oabb7LTTjuxevVqbr75ZgDOOeec9RJHRfkkl9oCj5lYkiRJkrRBat8eUoJVqypPMK1aBatXQ4cO6z20+lRVwqKylSv59Fe0atUqAE444QRatWpV7diWLVuu1VZ2i1ZNFPr7gao/u0Iq/dy233579t5772rHrmsrY67PGjhw4JokTlVqm+wrFuvj17Di80aOHMl5553HjTfeyG9/+1see+wxPvzwQ/bYYw/69eu3XuMplU9y6UMg91L6kiRJktSYtGsHAwdmtr5VtoVs3jw48MCiXLVUFzbffHM++OADZsyYsWalTVmFPt6+R48evPvuu1x++eX06dOnoHOvb1tssQXvvfce77//fqVb3EpXAJVdfVX6dXWfa+kqmrJ69OgBwC677EJJSUktol630mcdf/zx9baCplRVn1Npe8WVbTVVugLv7bffLsh8uTj11FO57LLL+MMf/sAvf/nLNVvk6vMzzye1+kdgYERsVuggImJQRKQcX5WvnZQkSZKkujZqVCZ5NHduZqUSZK5z52aST9l6KBuC/fffH4B77rmn0v577723oM8rLYp8//33F3Te+lBa4+iuu+6qtP+OO+4AKLeNcb/99iMimDRp0prkU1mPPvroWlviAA466CCaN2/OP/7xj0r7C6mYfo3uvvvuatsrbhFt0aIFACtXrszrOUOGDAHg4YcfZv78+XlGWV6uMbRt25YRI0awZMkSfvKTn/D3v/+dzTbbjBNOOKFWz6+NfJJLVwGTgcciYqcCxzEXuLOa1/TsuPeAWQV+tiRJkiTlpnt3KCnJrFBasADmz89cBw/OtFdRl6cxOuecc4gI7rzzTp566qlyfTfccEOti0NXdNFFF9G2bVuuvPJKbrjhhkr/Af7GG2/wl7/8paDPrQujRo2iWbNm3HPPPWud+nX//fczduxYmjdvzqhRo9a09+rVi2HDhrFq1Sq++93v8vnnn6/pmzNnzlono5Xq0qUL55xzDosWLWLYsGG89dZba435/PPP+dOf/pRXza3KHH300ey+++5MmDCB//3f/+XTTz9da8zcuXPXnOxXl/75z3/ym9/8plzbY489xh//+EeaNm3KyJEjy/WVrmSaPn06+dhtt9048sgjWbp0Kccccwwff/xxuf4vv/wy51P48olh5MiRRAS//vWvWb16Naeffvo6t4vWpXy2xT1JZlvcHsDrETETmAmsrmRsSikNznXilNJbwKlV9UfEm9kvf59SSjlHLEmSJEmF1r07XHVV5lS4hQszNZY2kK1wZe2xxx78+Mc/5oorrmDIkCEMGDCAnj178sYbbzBt2jTOO+88rr/++jWrMWqrR48ePPTQQwwfPpyRI0fy85//nD59+tC5c2cWLVrEtGnTmDVrFieccEKtT4ara7vuuivXX389I0eO5Nhjj2WvvfZi66235t1332Xy5Mk0adKE3/72t2udpnfjjTfy2muv8eSTT7LVVlsxcOBAvvrqK5555hl23nln+vfvX2lS71e/+hVz5sxh7Nix7LzzzvTt25fevXsTEXz44Ye89tprfPXVV0yfPj3vUwPLatKkCQ899BCHHXYYt9xyC3/605/Ydddd6dGjB19++SXvvPMOb775Jp07d+bMM8+s8XNyMWrUKC688EJKSkro06cPM2fOZOLEiUDm8+jbt2+58UcddRQ/+clPuO666/jXv/5F9+7diQhOP/109tlnn2qfVVJSwqGHHsrzzz9P79692XfffenUqRNz5szhtddeo127djltE917773p2rUrr7zyCv369aNPnz40b96cAQMGcNppp5Ubu8MOO3DwwQfz5JNP0qRJE7773e/m9fkUWj7JpUFlvm4C9Mq+KlOwBFBE9Ad2BFYBJYWaV5IkSZJqpW3bDTKpVNbll1/ODjvswLXXXssrr7zCtGnT6NevH08//fSa07g6duxYsOcdcMABvPHGG4wZM4ZHH32UF198kRUrVtC1a1d69+7N2WefzfHHH1+w59Wls88+m1133ZVrrrmGiRMn8vLLL7Ppppty7LHHcuGFF9K/f/+17tl8882ZPHkyP/zhD3n44Yd55JFH2HzzzTn77LP54Q9/yOGHH17ps5o3b859993HySefzO23387kyZN5/fXX2WSTTejWrRsnnXQSRx11VKX1n/LVvXt3Jk+ezO23387YsWOZNm0aL730EpttthlbbLEFF1xwAcccc0ytn7MuxxxzDEceeSRXXnkljz76KCtXrmSfffbhoosu4uijj15rfN++fbnvvvu4+uqreeGFF/jss88A2HfffdeZXNp000157rnnuO2227jnnnuYPHkyX331FV26dGG//fbjm9/8Zk4xt2zZkieeeILLLruMSZMm8eqrr7J69WpWrly5VnIJWJNcGjp0aLWnCK4PketCoIgYmM/EKaUJNYpo7efeBvwP8GhK6Yh1je/Xr1+aMmVKIR4tSZIkqQGYPn06O+64Y32HoQrOOOMMfv/733P11VdzwQUX1Hc42kAMGjSICRMmMG7cuLXqKjU2u+22G1OnTuWxxx5bU+8qX/n8/hkRL6eUKj2OLueVS4VKFuUjIjYCSitS3b6+ny9JkiRJqto777xD586dad++/Zq2lBIlJSXccccdtGzZkpNOOqn+ApQaqQcffJCpU6ey4447cuihh9Z3OHlti6sPxwObAP8B/lbPsUiSJEmSyrjrrrv49a9/zW677UaPHj1YtmwZb775Jh988AFNmjRhzJgxbL755vUdptQoLFiwgEsuuYRPP/2Uxx57DIBf//rXREQ9R1aD5FJENAGGAv2BTsBLKaXfZ/s6AR2A91JKqwoQ3+nZ610ppRXVxHQWcBZAz549C/BYSZIkSdK6HHbYYbz33nu89NJLvPHGG3z11Vd06tSJ4cOHc/755zNgwID6DlFqNJYuXcrtt99Os2bN2GabbfjBD35QZZ2t9S3nmksAEfF14F5gayDIFO6+M6V0erb/JOCPwNEppb/WKrCIbYB/Z9/ulFLK6TxAay5JkiRJGxZrLklSzRSq5lKTXB8YEVsCTwHbAI8BF5NJMJX1MLAcODrXeatRumppUq6JJUmSJEmSJK1fOSeXgMvIbHkbmVI6MqV0dcUBKaUvgNeAPWoTVEQ0Bb6dfWshb0mSJEmSpCKVT3JpCDA9pXTjOsZ9CHSrcUT/fdYWwGfAfbWcS5IkSZIkSXUkn+RSF+BfOYwLMie81cYZ2evYlNJntZxLkiRJkiRJdSSf5NJSMgmmdekNzK9ZOBARHYEjs2/dEidJkiRJklTE8kkuvQr0i4gqt7xFxPZAX2ByLWI6BWgOvJVSeqEW80iSJEmSJKmO5ZNc+j2wEXB3RGxWsTMi2gK3ZueszYqj08o8T5IkSZIkSUWsWa4DU0r3RsTxwDHA+xExIdu1d0TcBxxE5jS5+1JKj9Y0oJTS12p6ryRJkiRJktavfFYuAZwA/IpMUuqIbNsOwPFAG+A6MtvaJEmSJEmStAHIeeUSQEppJXBpRFwFHECmeHdTYBbwj5TSfwofoiRJkiRJkopVziuXIuIvEXEjQEppYUrpLymlq1NKV6WU/mRiSZIkSZKKT69evYgIxo8fX+24QYMGERGUlJSUay8pKSEiOPXUUwsSz/jx44kIBg0aVJD59F+lv4br+rXOxYcffkhE0KtXr1rPVZVC/2yp/uSzLe5wYK1C3pIkSZIkSdpw5bMt7iOgeV0FIkmSJEkqPscccwx777037dq1q+9Q1Mj4s9V45JNc+htwUkRslFL6oq4CkiRJkiQVj3bt2vmPf9UJf7Yaj3y2xf0IWAL8OSJ61E04kiRJktRALAZmZK+N2Lrq4jzwwAPss88+tGnThg4dOnDIIYfw3HPP5VRbacWKFfz85z9nhx12oFWrVnTu3JmTTz6ZmTNnVnnPrFmzOO+889h+++1p3bo1bdu2ZcCAAZSUlJBSWmt82TpEzz77LIcffjgdO3akSZMmPPTQQ+v8/k899dQ1tajeeOMNjjvuODp16kSbNm3Yd999GTdu3Jqxf/vb3xg4cCDt2rWjbdu2DBs2jH//+99Vzv3CCy9w3HHH0bVrV1q0aEHXrl0ZPnw4L774YpX3zJ8/n5EjR9K9e3datmxJ7969+cEPfsAXX1S/BiSlxL333sshhxxCx44dadmyJT179uTMM8/kww8/XOfnkKu3336bESNGsOWWW9KiRQs22WQTevXqxTHHHMMDDzxQbmxVP1sRsc5XxdpgAH//+98ZNmwYXbp0oUWLFnTr1o2TTjqJadOmFez7U+XyWbl0NfAGcATw74h4hcxvpcsqGZtSSmcUID5JkiRJKi6zgdHABCCABAwCzgW6119Y9eHKK6/ksssuIyLYZ5996NmzJ2+88QYHHHAAo0aNqvbeFStWMHToUF566SUGDhzIjjvuyKRJk7j77rt59tlnef3112nfvn25e8aNG8cxxxzD4sWL2WabbTj00EP57LPPePHFFznttNN45plnuOuuuyp93v3338/NN9/MTjvtxMEHH8z8+fNp3jz3yi9TpkzhnHPOoXfv3gwePJh///vfTJw4kSFDhvD0008zdepUzj//fAYMGMCQIUOYPHkyf/3rX/nnP//Jv/71LzbbrHwJ45tuuomRI0eyevVq9thjDw488EDeffddHnjgAR588EFuvvlmzjzzzHL3zJ07lwEDBvD+++/TqVMnhg0bxpdffsmYMWPWJPOq+qxPPPFE/vKXv9C6dWv69etHly5d+Ne//sXvfvc7HnjgAZ588kn69euX8+dRmWnTpjFgwACWLl3KDjvswJFHHklE8NFHH/H3v/+dZcuWcdxxx61znhEjRlTavnLlSu69915WrVpF06ZNy/Wdd955jB49mmbNmrHHHnvQvXt33n33Xe69914eeughHnjgAQ477LBafX+qRkoppxewGliVva7rtSrXeQv92n333ZMkSZKkDcebb765/h42K6V0YEppj5TS0JTSEdnrHimlwdn+IrPlllsmII0bN67acQMHDkxAuuOOO8q133HHHQlII0aMKNc+ZcqU1KRJk9S8efP0+OOPl+u7/vrrE5m0Wxo4cGC5vnHjxq3p69evX/rkk0/W9C1atCh9/etfT0D62c9+Vu6+OXPmpA4dOqSmTZumkpKStHr16jV9M2fOTH379q00/tLvC0i33HJLtZ9BZUaMGLHm/muuuaZc38UXX5yAtN1226W2bdumZ599dk3fsmXL0n777ZeA9JOf/KTcfVOnTk3NmjVLTZo0SWPHji3Xd88996z5XKdNm1au79hjj01AOuigg9KSJUvWtM+ePTttt912a+Ks+Gt9ySWXJCDtv//+adas8j+kY8aMSUDaeuut04oVK9a0f/DBBwlIW265Zc6f1WmnnZaAdOWVV67Vt3Tp0vTCCy+Ua6vqZ6sqZ555ZgLSPvvsk5YtW7am/aabbkpA6tOnT5o+fXq5ex588MHUrFmz1L59+/Tpp5/m/L1sKPL5/ROYkqrIxeSzLe404PTsdV2v0/OYV5IkSZIahtHAUqALULpwomn2/WJgTD3FlYMDDjig2m1GEyZMyGu+G264gdWrV/Ptb3+bQw89tFzfqFGj2Guvvaq9PyL4/e9/T+fOnde0tWvXjksuuQSAp59+utz46667joULF3LBBRcwYsSIcqt0evTowW233QbAmDGV/yIcfPDBnHXWWbl/gxX079+f73//++XaLr30UgDeeecdzjnnHPbbb781fa1ateJ73/seQLmtcwCjR49m5cqVnHjiiRx//PHl+krbVqxYwfXXX7+mfebMmTz44IM0bdqUm2++mU022WRN3xZbbMHVV19dadyffvopo0ePpk2bNtx///10715+ed3IkSM5/PDDee+993j88cdz/Tgq9cknnwAwdOjQtfratGlD//79azz3L37xC2677Ta23XZbHnnkEVq1agXAqlWr+MlPfgLA2LFj2WGHHcrdd/TRR/Od73yHRYsW8cc//rHGz1f1ct4Wl1K6sy4DkSRJkqSitpjMVriOVfR3AsaTqVTbdj3FlIchQ4bQtWvXKvufeOKJNcmBXJQmo775zW9W2n/SSSfx0ksvVXl/z5492WWXXdZqL00OzJkzp1z7Y489BrBWMqbU7rvvTps2bZg6dSpffvnlmuRDqWOPPbbKWHJRMYEG0KFDBzbbbDMWLFhQaf+2224LrP29lH52VdWxOv3007nvvvsYP378mrZnn32WlBJ77703W2+99Vr3HHnkkbRv355FixaVax83bhzLli3j8MMPL5fIK2vgwIE8+uijTJo0iSOPPLLSMbnYc889eeyxx/jf//1ffvrTn7L//vvTsmXLGs9X6t577+Wyyy6jY8eOPPbYY+W2GE6dOpWPP/6YPn36sNNOO1V6/8CBA7nhhhuYNGkS5557bq3j0dryqbkkSZIkSRuuRWRqLDWtor8pmSOTFlKUyaVLL7202uLagwYNyiu59NFHHwGw5ZZbVtpfVXupnj17Vtretm3mw/vyyy/Ltb///vsA7LHHHuuMbcGCBWyxxRZ5xbMuFVf8lGrTpg0LFiyotL9NmzbA2t9L6We31VZbVTpn7969y40DmD17drX3QOZ7rJhcKv3cHn300SprMpWaN29etf3rctFFF/Hcc8/x9NNPc8ghh9CyZUv69u3LwIEDOfnkkytNJq7Lc889x6mnnkrLli15+OGH2Wabbcr1l35/b7zxRp1/f6pajZJLEdEH6E8mN/9GSumRbHsToFlKaXnhQpQkSZKkItCeTEWbVVSeYCqtUNthPcZUBKr6B32TJtVXYVlXf0WrVq0C4IQTTlhrVVJFla2Wad26dV7Pq6jQ3w9U/dkVUunntv3227P33ntXO3ZdWxnXZaONNuIf//gHL730Ek888QQTJ05k0qRJvPTSS/zqV7/ixz/+MVdccUXO873zzjscffTRLF++nLFjx7LPPvusNab0+9tiiy046KCDqp2v4pY5FU5eyaWI6AmUAAPLNN8JPJL9+n+AmyLikJTS00iSJElSY9GOzL+ExpOpsVTRPOBAinLVUl3YfPPN+eCDD5gxY8aalTZlFfJ4e8jUVXr33Xe5/PLL6dOnT0HnXt+22GIL3nvvPd5///1Kt7iVrsYpu/qq9OvqPtcZM2as1dajRw8AdtllF0pKSmoRde722muvNYmq5cuX86c//YkzzzyTH/3oR5xwwglsv/3265xj3rx5HHbYYXz66adcffXVDB8+vNJxpd9ft27d1tv3p7XlnFqNiI7As2QO2fwXcBOZRaFl3U8mV39UgeKTJEmSpOIxikzyaC6ZlUpkr3PJJJ82oHIu+++/PwD33HNPpf333ntvQZ9XWiT6/vvvL+i89WHgwMx6jbvuuqvS/jvuuAOg3DbG/fbbj4hg0qRJa5JPZT366KNrbYkDOOigg2jevDn/+Mc/Ku2vay1atODUU09l7733JqXE66+/vs57li1bxrBhw3jvvfc4++yzueCCC6ocu+eee7LZZpvx6quv8u677xYydOUhn3V7PwB6AlcBfVNKIysOSCktBF4H9i1MeJIkSZJURLqT2ctxILAAmJ+9Ds62V16Wp1E655xziAjuvPNOnnrqqXJ9pcWTC+miiy6ibdu2XHnlldxwww2sXLlyrTFvvPEGf/nLXwr63LowatQomjVrxj333MODDz5Yru/+++9n7NixNG/enFGjRq1p79WrF8OGDWPVqlV897vf5fPPP1/TN2fOHC688MJKn9WlSxfOOeccFi1axLBhw3jrrbfWGvP555/zpz/9Ka+aW5W58cYbefvtt9dqf//993njjTeAdde+Silxyimn8OKLL3LEEUcwevToasc3b96cyy+/nFWrVnH00UczefLktcYsX76cRx55pNLvXYWRz7a4I4EPgP9LKaVqxr0P7FdNvyRJkiQ1XN3J/C/3JWSKd3dgg9kKV9Yee+yxpobOkCFDGDBgAD179uSNN95g2rRpnHfeeVx//fW0aNGiIM/r0aMHDz30EMOHD2fkyJH8/Oc/p0+fPnTu3JlFixYxbdo0Zs2axQknnFDrk+Hq2q677sr111/PyJEjOfbYY9lrr73Yeuuteffdd5k8eTJNmjTht7/97VoFsG+88UZee+01nnzySbbaaisGDhzIV199xTPPPMPOO+9M//79K03q/epXv2LOnDmMHTuWnXfemb59+9K7d28igg8//JDXXnuNr776iunTp9OlS2V7PnNz6623cs4559C7d2923nln2rRpw9y5c3n++edZvnw5J554InvuuWe1czz//PM88MADALRq1Yozzjij0nH/8z//w777Zta1nHfeecyYMYPf/OY37LXXXnzta19j6623pkWLFnz00Ue8+uqrfP755zz++OPWXaoj+SSXegB/W0diCWAlG1wJO0mSJEkbnLZskEmlsi6//HJ22GEHrr32Wl555RWmTZtGv379ePrpp9ecbtaxY8eCPe+AAw7gjTfeYMyYMTz66KO8+OKLrFixgq5du9K7d2/OPvtsjj/++II9ry6dffbZ7LrrrlxzzTVMnDiRl19+mU033ZRjjz2WCy+8kP79+691z+abb87kyZP54Q9/yMMPP8wjjzzC5ptvztlnn80Pf/hDDj/88Eqf1bx5c+677z5OPvlkbr/9diZPnszrr7/OJptsQrdu3TjppJM46qijKq3/lI+f/exn/O1vf+Oll17ihRdeYMmSJXTp0oWBAwdy5plnctxxx61zjtIC3QB//vOfqxw3aNCgNcklgGuvvZajjz6am266iYkTJ/Loo4/SunVrunXrxhFHHMGwYcPYbz/XwdSVWHeuKDsw4lPg5ZTSwWXaVgMlKaXTy7RNAnqnlGqe7qyFfv36pSlTptTHoyVJkiTVg+nTp7PjjjvWdxiq4IwzzuD3v/89V199dbU1cyTVn3x+/4yIl1NK/Srry6fm0r+A3SOiXTUP2gLYFXglj3klSZIkSQ3QO++8s1aR6JQSd9xxB3fccQctW7bkpJNOqp/gJK03+WyL+xNwI3BLRHw7pbS8bGdENAFGAy2BPxYuREmSJElSMbrrrrv49a9/zW677UaPHj1YtmwZb775Jh988AFNmjRhzJgxbL755vUdpqQ6lk9y6XfAt4BvAHtExKPZ9p0j4irgaGBbYDyZRJQkSZIkqRE77LDDeO+993jppZd44403+Oqrr+jUqRPDhw/n/PPPZ8CAAfUdoqT1IOfkUkppZUQcBtxGJsE0MtvVL/sCeAgYkUPRb0mSJElSA7fPPvuwzz771HcYkupZPiuXSCktBU6MiB8DQ4HeQFNgFvB4SunVwocoSZIkSZKkYlVlcikirgUmp5TurdiXUpoOTK/LwCRJkiRJklT8qjst7nzgkNI3EbEqIm6v84gkSZIkKU9W5pCk/BTy983qkkurgOZl3kf2JUmSJElFo0mTJqxevbq+w5CkBmX16tU0aVJdWih31c3yH6BvRJhQkiRJklS0WrVqxRdffFHfYUhSg/LFF1/QunXrgsxVXUHv8cBJwPsR8UG27dCIeCaHeVNKaXBtg5MkSZKkdWnTpg2LFi2iTZs2+P/GJWndUkosWrSIjTfeuCDzVZdcugTYFugHbJlt65p9rYsbniVJkiStFx06dGDJkiV8/PHHbLbZZrRo0cIkkyRVIqXE8uXLWbBgAStXrqRDhw4FmbfK5FJKaTawZ0T0AnqSWcn0BHBVQZ4sSZIkSQXQpEkTevTowaeffsrMmTNZuXJlfYckSUWrWbNmtGvXjs6dOxes5lJ1K5cASCl9CHyYzfzPTSlNKMiTJUmSJKlAmjVrRufOnencuXN9hyJJG5x1JpfK2Ar4rK4CkSRJkiRJUsOTc3IppTSj7PuI2AboBCxIKb1T6MAkSZIkSZJU/PLaXBcRzSLiioj4BHgbeB64tEz/tyLihYjYucBxSpIkSZIkqQjlnFyKiGbAY8APgfbAdKDiEQwTgb2B4woUnyRJkiRJkopYPiuXRgIHAU8DvVJKa61Oyhb/fhc4pCDRSZIkSZIkqajlk1w6BVgAfCOl9HE146YDPWoVlSRJkiRJkhqEfJJL2wMvpZQWrWPcUjKFviVJkiRJktTI5ZNcSsDqHMZtDnxZs3AkSZIkSZLUkOSTXPoA2DUiqrwnIloDXyOzNU6SJEmSJEmNXD7JpUeA7sAF1Yy5GOgAPFyboCRJkiRJktQwNMtj7LXAacAvI2I34M/Z9o4RMRQ4HhgBzARuLGiUkiRJkiRJKko5J5dSSp9GxKFkViWdCJxApg7T4dlXALOAI1NKS+sgVkmSJEmSJBWZfFYukVKaFhE7kVnBNBToDTQlk1R6HLg1pfR5waOUJEmSJElSUco5uRQRw4AVKaXHgZuyL0mSJEmSJG3A8ino/SBwfh3FIUmSJEmSpAYon+TSp8D8ugpEkiRJkiRJDU8+yaXJwM51FYgkSZIkSZIannySS1cBfSLijLoKRpIkSZIkSQ1LXqfFATcDt0bEcDI1mGYAyyobmFJ6tiYBRURr4FzgeGBboAXwCTAFuC6lNLEm80qSJEmSJKnw8kkujQcSEMAQ4JBqxqY85wYgIrYCngS2AT4GxgErgS2Bo4HXAJNLkiRJkiRJRSKfBNCzZJJGdSIiNgaeAnoDlwJXp5RWlenfDNisrp4vSZIkSZKk/OWcXEopDarDOAD+H7A18NuU0lWVPH8BsKCOY5AkSZIkSVIe8inoXWciogVwZvbttfUZiyRJkiRJknKXd12kOrI7mS1vH6WUPoiIrwPHAJ3JFPN+MqX0fH0GKEmSJEmSpLUVS3Jpl+z1o4i4GrigQv/lEfEQcHJK6fP1GpkkSZIkSZKqVBTb4oBNs9fdyCSWriNzYlwH4CjgIzKnxd1YD7FJkiRJkiSpCsWSXCqNoznwx5TS91JK76WUFqWUHiGTWErAKRGxdcWbI+KsiJgSEVPmzZu3/qKWJEmSJEnawBVLcmlpma9vq9iZUpoCvAwEMLCS/ltTSv1SSv06depUd1FKkiRJkiSpnGJJLn1QxdeVjelax7FIkiRJkiQpR8WSXHq1zNebVTGmY/b6WR3HIkmSJEmSpBwVRXIppfQR8FL27eCK/RHRAfh69u2U9RWXJEmSJEmSqpd3cikidoqIWyLi7Yj4LPt6OyJujog+tYjl59nr/0VEvzLPawXcBLQjU3dpUi2eIUmSJEmSpAJqls/giDgDuIHMqW5Rpmvb7OvUiDgnpXR7voGklP4aEdcAFwAvRMSLwAJgT2Bz4CPgpJRSynduSZIkSZIk1Y2cVy5FxF7ALWQSUmOBIfw3qTQEuC/bd3N2bN5SShcCxwHPA7sAhwFfANcCu6WU/l2TeSVJkiRJklQ38lm5dCGZ1UonpZTGVuh7D3gqIv5CJsl0AfCNmgSUUvoL8Jea3CtJkiRJkqT1K5+aS/sC/6wksbRGSul+YDKwX20DkyRJkiRJUvHLJ7m0KfBuDuPezY6VJEmSJElSI5dPculTYJscxm2dHStJkiRJkqRGLp/k0gvAHhFxbFUDIuJoYC9gYi3jkiRJkiRJUgOQT3LpGiAB90XEXRExOCJ6R8RW2a/vJHOK3OrsWEmSJEmSJDVyOZ8Wl1J6ISLOBa4HvpV9lRXASuDclNKkwoUoSZIkSZKkYpXPyiVSSjcB/YAS4H3gq+zrfeD3QL/sGEmSJEmSJG0Acl65VCql9DpwRh3EIkmSJEmSpAYm55VLEbF/RGyXw7htI2L/2oUlSZIkSZKkhiCfbXHjgUtyGHcxMK5G0UiSJEmSJKlByavmEpmi3ZIkSZIkSRKQf3IpF52BZXUwryRJkiRJkopMtQW9K6md1LWaekrNgB2BQ4DpBYhNkiRJkiRJRW5dp8WNB1KZ90Oyr+oEcEstYpIkSZIkSVIDsa7k0rP8N7k0EPgP8FYVY5cDHwEPppT+WpjwJEmSJEmSVMyqTS6llAaVfh0Rq4HHU0qn13VQkiRJkiRJahjWtXKprAOAuXUViCRJkiRJkhqenJNLKaUJdRmIJEmSJEmSGp4m9R2AJEmSJEmSGi6TS5IkSZIkSaoxk0uSJEmSJEmqMZNLkiRJkiRJqjGTS5IkSZIkSaoxk0uSJEmSJEmqMZNLkiRJkiRJqrFmVXVERM/aTJxSmlmb+yVJkiRJklT8qkwuAR8CqYbzpnXMLUmSJEmSpEagugTQTCpPLm1Z5uvF2Wu7Mm0zahuUJEmSJEmSGoYqay6llHqllLYqfQFbAy8D84BRQIeUUoeUUgegA3Au8AkwJTtWkiRJkiRJjVw+W9cuAA4Hvp5Sml62I6W0GLghIp4BXgUuAq4qWJSSJEmSJEkqSvmcFncqML5iYqmsbN84YEQt45IkSZIkSVIDkE9yaStgYQ7jFgG9ahKMJEmSJEmSGpZ8kktLgH0iosqtdNm+/tmxkiRJkiRJauTySS49CfQAbouITSp2RkQb4JbsmL8XJjxJkiRJkiQVs3wKev8/YCjwbeCoiPgb8EG2rxdwBNAe+BS4onAhSpIkSZIkqVjlnFxKKc2MiIHAH4DdgJOBlO2O7HUqcEpKaUYhg5QkSZIkSVJxymflEimlN4HdI2JfYCDQPdv1ETAhpfRcgeOTJEmSJElSEcsruVQqpfQ88HyBY5EkSZIkSVIDk09Bb0mSJEmSJKmcvJNLETEwIu6PiNkR8VVE3F6m7+CIuDIiuhY2TEmSJEmSJBWjvLbFRcSPgMv5bwFvKny9CLiETA2mG2oZmyRJkiRJkopcziuXIuJI4ApgNnA80KXimJTSP4F5wBGFClCSJEmSJEnFK5+VS6OAr4BDU0rTASKisnFTgW1qHZkkSZIkSZKKXj41l3YHXixNLFVjHmDNJUmSJEmSpA1APsml1mQSR+uyaQ1jkSRJkiRJUgOTT3LpY2CHHMbtBMyoWTiSJEmSJElqSPJJLo0D+kTEIVUNiIgTgC2Bp2obmCRJkiRJkopfPsmlXwMrgPsj4qyI2Ky0IyI2iohvA7cAXwCjCxumJEmSJEmSilHOyaWU0lvAqUBL4CbgEyABJwNLgTvI1GU6PaX0QcEjlSRJkiRJUtHJZ+USKaV7gT2APwOfAQE0A74E/gbsk1IaW+ggJUmSJEmSVJya5TowItoCKaU0DTghIgLYDGgKzE8praqjGCVJkiRJklSk8lm5tAj4R+mblDE/pfRJIRJLEVESEama11u1fYYkSZIkSZIKK+eVS2TqKv27rgIpYyLwbiXtH6+HZ0uSJEmSJCkP+SSXpgPd6yqQMn6XUipZD8+RJEmSJElSLeWzLe42YN+I2L2ugpEkSZIkSVLDknNyKaV0O3Aj8FREXBIR20VEy7oLTZIkSZIkScUun9PiyhbtvjL7InNo3FpSSimfLXdlHRARXwPaAJ8AzwNPpZRW13A+SZIkSZIk1ZF8EkCVZpEKMLaib1fS9mZEnJhSmlaLeSVJkiRJklRg+WyLa5LPqwaxTAVGATuRWbW0OXAE8Fq27R8RsUVlN0bEWRExJSKmzJs3rwaPliRJkiRJUk1ESqm+Y6hWRLQAJgB7AzeklEZWN75fv35pypQp6yU2SZIkSZKkDUFEvJxS6ldZX01WGK1XKaXlwC+ybw+rz1gkSZIkSZJUXtEnl7Leyl4r3RYnSZIkSZKk+pFXcikiWkTERRHxUkQsjIhVVbxWFjjOzbLXzwo8ryRJkiRJkmoh59PiIqIVMA7Yk3WfBleb0+Iq843s9Z8FnleSJEmSJEm1kM/Kpe8DewFPANsBdwEJaAn0IVMX6Uvg5/meFhcRfSPiiIhoWqG9WURcQOYUOYDf5DOvJEmSJEmS6lbOK5eA4cAS4KSU0pKISAAppRXAdOCyiHgOeDQi3kgp3ZvH3L2AB4FPI+IV4D9ktsLtAmwOrAYuTin9PY85JUmSJEmSVMfyWWG0LfBSSmlJ9n0CKLvaKKX0BJmtayPzjOM14HrgbWAn4DhgIPAFcAewZ0rp13nOKUmSJEmSpDqWz8qlJsCCMu+XZa/tK7S/BxyeTxAppQ+A8/O5R5IkSZIkSfUvn5VLc8hsUSs1O3v9WoVxvciuapIkSZIkSVLjlk9y6V/A9mXeP0vmVLgfRcQmABFxEtAfeLNgEUqSJEmSJKlo5ZNcehzoEhGDAFJKE4FJwH7AgohYAPyRzKqlqwsbpiRJkiRJkopRPsmlP5FJJP27TNsxwKPZeToAi4CLUkoPFipASZIkSZIkFa+cC3qnlD4DJlZo+w9wZERsBLQDPkkprS5siJIkSZIkSSpW+ZwWV6WU0hfAF4WYS5IkSZIkSQ1HjZNLEdEN6J59+1FKaU5hQpIkSZIkSVJDkU/NJQAi4n8j4m1gNvBi9jUrIt6JiLMLHaAkSZIkSZKKV87JpYhoGhEPADcA25I5FW5O9pWAbYAxEfFgRDSti2AlSZIkSZJUXPJZuXQemdPh5gCnAxullHqklHoArYHTgI+AYdmxkiRJkiRJauTySS6dDiwDBqWUSlJKy0s7UkorUkp3AgcAXwFnFDZMSZIkSZIkFaN8kktbA+NTSu9VNSDbNw7oXdvAJEmSJEmSVPzySS4tBpbkMG5pdqwkSZIkSZIauXySS/8A9o2IFlUNyPYNAJ6pbWCSJEmSJEkqfvkkl/4fmcLdf4iIjhU7I2JT4C6gFfB/hQlPkiRJkiRJxaxZHmO/Dfwtez0sIp4CPsj29QIOATYC/gB8OyLK3ptSSj+tdbSSJEmSJEkqKvkkl34EpOzXGwNHVzHuFKA0s5SyXyfA5JIkSZIkSVIjk09y6Sf8N7kkSZIkSZIk5Z5cSin9qA7jkCRJkiRJUgOUT0FvSZIkSZIkqRyTS5IkSZIkSaqxfGouARARA4ADgM2BVlUMSymlM2oTmCRJkiRJkopfzsmliGgD3A8cUtpUzfAEmFySJEmSJElq5PJZufRLYAjwKfBH4N/AZ3URlCRJkiRJkhqGfJJLxwELgb4ppdl1FI8kSZIkSZIakHwKercFnjWxJEmSJEmSpFL5JJfeowYFwCVJkiRJktR45ZNcugMYFBFd6yoYSZIkSZIkNSz5JJeuA54AnomIwRFR3WlxkiRJkiRJ2gDkvM0tpZQi4jvAeOBJYEVEzAVWVzF868KEKEmSJEmSpGKVc3IpInoBzwJbAAG0AHpWMTzVOjJJkiRJkiQVvXwKdP8a6A5MILNF7l3gszqISZIkSZIkSQ1EPsmlA4APgCEppeV1FI8kSZIkSZIakHwKejcH/mliSZIkSZIkSaXySS69BnSpq0AkSZIkSZLU8OSTXPo1sF9E9K+rYCRJkiRJktSw5FNz6TXgGuCpiPgN8HdgNrC6ssEppZm1D0+SJEmSJEnFLJ/k0gfZawD/l31VJeU5tyRJkiRJkhqgfBJAs8gkjSRJkiRJkiQgj+RSSqlXHcYhSZIkSZKkBiifgt6SJEmSJElSOSaXJEmSJEmSVGN5F92OiI7AmcAgYIts80fAOOD2lNK8gkUnSZIkSZKkopZXcikihgJ3A+3InBpXaifgIOCiiDg5pfR44UKUJEmSJElSsco5uRQROwAPAK2AF4E7gPez3b2B04C9gT9HxO4ppbcKHKskSZIkSZKKTD41ly4lk1i6KKW0T0rptpTS09nXbSmlfYALgdbAJXURrCRJkiRJkopLPsmlA4F/pZSuqWpASula4F/A4NoGJkmSJEmSpOKXT3KpC/B6DuOmAZ1rFo4kSZIkSZIaknySS0v47+lw1dkcWFqzcCRJkiRJktSQ5JNcmgLsGxEDqhoQEfsA+wH/rG1gkiRJkiRJKn75JJd+CzQFHo+In0TE1hHRLCKaZr/+MfA4ENmxtRIRV0ZEyr4urO18kiRJkiRJKryck0sppUeBq4A2wGXAO8Ay4Mvs1/8P2AS4KqX0WG2Ciog9gIuBVJt5JEmSJEmSVLfyWblESukHwBHAeGA5mZVMTbNfjwOOSCn9X20CioiWwJ3AJ8DDtZlLkiRJkiRJdatZvjdkVyU9FhFNgc2yzQtSSqsKFNNPgB2BYcBxBZpTkiRJkiRJdSCvlUtlpZRWpZT+k30VJLEUEXsBFwB/Sin9tRBzSpIkSZIkqe7knFyKiA4RsX9EbF7NmC2yY9rnG0hEtCKzHe5T4Lx875ckSZIkSdL6l8/KpfPI1FXqVs2YrtkxI2sQy8+B7YFzU0rza3C/JEmSJEmS1rN8kkuHA++mlF6uakC27z0yRb9zFhH7AOcDD6WU7svnXkmSJEmSJNWffJJLvYB3chj3NrBVrpNGRGugBFgCnJ1HPGXnOCsipkTElHnz5tVkCkmSJEmSJNVAPsmlTYClOYxbCrTLY94rgW2B76eUPs7jvjVSSremlPqllPp16tSpJlNIkiRJkiSpBprlMXYusHMO4/oA+dRMOgZYDYyIiBEV+nbIXr8bEUeQ2Zb3P3nMLUmSJEmSpDqUT3JpInBiRByWUnqssgERMRTYBRibZxxNgIHV9PfOvtrnOa8kSZIkSZLqUD7b4q7PXu+JiDMjomVpR0S0jIgzgXuABIzOddKUUq+UUlT2Au7MDrso29Y3j3glSZIkSZJUx3JOLqWUJgP/j0ztpZuBxRHxTkS8AyzKtrUFfphSeqEOYpUkSZIkSVKRyWflEimlXwDHAdOAFsA22VfLbNtxKaWfFTpISZIkSZIkFad8ai4BkFJ6EHgwIroAW5LZBjczpfRJoYOTJEmSJElSccs7uVQqm0yq04RSSulU4NS6fIYkSZIkSZJqLq9tcZIkSZIkSVJZVSaXIuLKiGhXm8kjol1EXFmbOSRJkiRJklS8qlu5dAnwfkT8MCJ65jNpRPSMiB8B7wMX1yI+SZIkSZIkFbHqkksDgPeAH5JJMv0jIn4QEYMioktENAOIiGbZ9wdExP9FxDNkkkpXAP8G9qnrb0KSJEmSJEn1o8qC3imlF4E9I+KbwPnAgcABZcdExFdAy7JN2euLwPUppfsKGq0kSZIkSZKKyjpPi0sp/Qn4U0T0A44GBgF9gY2AVtlhnwOvAOOAh1JKUwsfqiRJkiRJkorNOpNLpVJKU4Appe8jYiOgHbAopbSsDmKTJEmSJElSkcs5uVRRSukL4IsCxiJJkiRJkqQGprqC3pIkSZIkSVK1TC5JkiRJkiSpxkwuSZIkSZIkqcZMLkmSJEmSJKnGTC5JkiRJkiSpxkwuSZIkSZIkqcZMLkmSJEmSJKnGTC5JkiRJkiSpxnJOLkXEZhGxZ0R0rNC+RUT8MSKmRcRfI2K3wocpSZIkSZKkYpTPyqUfAJOAzUsbIqIl8DxwEtAHOBx4JiJ6FDJISZIkSZIkFad8kksHAO+nlF4v03YisCUwDjgIGA20A0YWLEJJkiRJkiQVrXySS1sA71VoOxxIwJkppWdSSucD7wOHFiY8SZIkSZIkFbN8kksdgPkV2voDb6eUPijT9irgtjhJkiRJkqQNQD7JpWXAmmLeEdGTzGqmiRXGLQda1D40SZIkSZIkFbt8kktvAvuWOS3uW2S2xD1bYVwP4JMCxCZJkiRJkqQil09y6S5gI2BKRPwF+BGwFHi4dEBEtAK+DrxVwBglSZIkSZJUpJrlMfZWYG/g20BPMomlM1JKS8qMGUYmATWhYBFKkiRJkiSpaOWcXEoprQZOjYgrgM7AWymlzyoMewc4BnixcCFKkiRJkiSpWOWzcgmAlNJMYGYVfVOBqbULSZIkSZIkSQ1F3sklgIhoB+wBdAJmpJReKGhUkiRJkiRJahDyKehNRLSLiN8D/wH+DvwR+J8y/f8TEXMiYu/ChilJkiRJkqRilHNyKSI2BsYDpwILgceBqDDsb0AX4OiCRCdJkiRJjcXixTBjRuYqSY1IPtviLgR2JbNa6X9TSl9ExOqyA1JKcyPiTeDAAsYoSZIkSQ3X7NkwejRMmAARkBIMGgTnngvdu9d3dJJUa/kkl44H5gBnppS+qmbcO4Db4iRJkiRp9mwYMQKWLoWOHaFpU1i1CsaNg5dfhpISE0ySGrx8ai71Bv65jsQSwJfAZjUPSZIkSZIaidGjM4mlLl0yiSXIXLt0yWyPGzOmfuOTpALIJ7m0AmiVw7gewGc1C0eSJEmSGonFizNb4Tp2rLy/UycYPx6WLFmvYUlSoeWTXHob2C0iWlY1ICI6kKnLNK22gUmSJElSg7ZoUabGUumKpYqaNoUmTWDhwvUaliQVWj7JpT8DnYGrqhlzJdAGGFuboCRJkiSpwWvfPlO8e9WqyvtXrYLVq6FDh/UaliQVWj7Jpd8C04FzI+L5iPh+tr1XRHw3Ip4BziKzaun2AscpSZIkSQ1Lu3YwcCDMn195/7x5mVPj2rZdr2FJUqHlfFpcSumLiDgEuB/YB+if7RqYfQXwMnB0Sml5oQOVJEmSpAZn1Ch45RWYOzdTY6n0tLh58zLJp3PPre8IJanWck4uAaSUPgL2iYhDgcPInCDXFJgFPA48lFJKBY9SkiRJkhqi7t2hpCRzKtz48ZkaS6tXw+DBMHJkpl+SGrhobLmgfv36pSlTptR3GJIkSZJU3pIlmeLdHTq4FU5SgxMRL6eU+lXWl9fKJUmSJElSDbVta1JJUqOUT0FvSZIkSZIkqZycVy5FRBXnZ1YqpZRcFSVJkiRJktTI5ZMAijoaK0mSJEmSpAYq521xKaUmlb3InBbXGzgX+BT4abZdkiRJkiRJjVytt66lzHFzHwI3RMRrwLiImJ5Sure2c0uSJEmSJKm4FXSFUUrpeeAV4PxCzitJkiRJkqTiVBfb12YAfepgXkmSpMZjMZm/NS2u70AkSZJqpy5OdOsDrK6DeSVJkhq+2cBoYAKZI1ASMIhM9cru9ReWJElSTRUsuRQRmwE/BnYAni7UvJIkSY3GbGAEsBToSOZYlFXAOOBloAQTTJIkqcHJObkUEe9X090G2IzM/39bDvyodmFJkiQ1QqPJJJa6lGlrmn0/FxgDXFUPcUmSJNVCPiuXeq2jfznwHHBFSmlSvoFExLnAfsAuQGegLbAIeI3M/8e7O3synSRJUsOzmMxWuI5V9HcCxgNLyPwtSJIkqYHIJ7m0VTV9y4F5KaWVtYjlEjJJpX8BLwCfA1sCBwKDgeERcWxKyXpOkiSp4VlEZo130yr6m5I5amUhJpckSVKDknNyKaU0oy4DAU4EXk0pfV62MSL6kKnhdBSZKgV31HEckiRJhdeeTPHuVVSeYFpF5kiUDusxJkmSpAJoUt8BlEopPV8xsZRtfwO4Ifv24PUblSRJUoG0AwYC86von0fm1DhXLUmSpAamaJJL61C63e6reo1CkiSpNkaRSR7NJbNSiex1Lpnk07n1FJckSVItVLktbh2nw61LSiltXYv7y8axFfC/2bePFGJOSZKketGdzDElY8gU725CZivcYGBktl+SJKmBqa7mUq9azFvjU90i4jQyi8abk/kr1j5k/up1ZUrpwVrEJEnFbfFiWLQI2reHdu3qOxpJdaU7cBWZU+EWkqmx5FY4SZLUgFWXXKrudLi6NIBM4e5SK4HLgWuruiEizgLOAujZs2edBidJBTd7NoweDRMmQASkBIMGwbnnQneXMUiNVltMKkmSpEYhUqrxIqM6FRGtySS4TgPOA94EDkspzanuvn79+qUpU6ashwglqQBmz4YRI2DpUujYEZo2hVWrYP58aNsWSkpMMEmSJEmqdxHxckqpX2V9RVvQO6W0LKX0ZkrpIuAHwK7Ab+s5LEkqrNGjM4mlLl0yiSXIXLt0yWyTGzOmfuOTJEmSpHUo2uRSBSXZ65ER0bw+A5Gkglm8OLMVrmPHyvs7dYLx42HJkvUaliRJkiTlo7qaS5WKiD2A4cB2ZCoFRCXDUkppcC1jK2shmdpLzYBNgU8KOLck1Y9FizI1lkpXLFXUtCk0aQILF2a2yEmSJElSEcoruRQR1wHn8t+EUqJ8cqn0faELOe1PJtZFwPwCzy1J9aN9+0zx7lWrKk8wrVoFq1dDhw7rPTRJkiRJylXO2+Ii4iRgFDCbzMlsT2a7hgDnAC+QSSxdBRyYTxARsW9EHBERayW7ImIAcHv27e0ppVX5zC1JRatdOxg4MFO8uzLz5mVOjXPVkiRJkqQils/KpTPJbE07MKX0XjbpQ0rpKeAp4KaIuAK4DHggzzi2Ae4AFkXEK8BcYBNga2Cn7JhHgcvznFeSituoUfDKKzB3bqbGUulpcfPmZZJP555b3xFKkiRJUrXyKei9K/BiSum9asb8FJhFJsGUjwnZe6cC2wLHAocAG5NJVB2TUjoipbQsz3klqbh17w4lJXDggbBgQWYV04IFMHhwpr179/qOUJIkSZKqlc/KpY3JbIkr9RVARGySUloKmSreEfFP8twWl1L6ALgin3skqdHo3h2uuipzKtzChZkaS26FkyRJktRA5JNc+g+wWZn387LXbYBXy7S3A9rUMi5J2vC0bWtSSZIkSVKDk8+2uHeBrcq8/yeZAt7/W9oQEdsDBwDVbZ2TJEmSJElSI5FPcukpYOuI2DH7/u/AR8D/RMTkiHgAeBFoAfyhsGFKkiRJkiSpGOWzLe5uMsmojQBSSl9FxDeAB4F+2RfA34DfFDJISZIkSZIkFaeck0sppZnAzyu0TYqIrYD9gU2Bt1JKr1Z2vyRJkiRJkhqffFYuVSqltIzMFjlJkiRJkiRtYHKuuRQRZ0dE+zqMRZIkSZIkSQ1MPgW9fwt8HBH3RcTQiMjnXkmSJEmSJDVC+SSI/gIEcDyZot2zIuKqiNipTiKTJEmSJElS0cs5uZRSGg50A0YCL2e/vgiYFhEvRcR33TYnSZIkSZK0Yclra1tKaWFK6caU0p7ATsCvgDnAHvx329zYiDis8KFKkiRJkiSp2NS4blJK6a2U0qVAT2AocB+wGhgOPFKY8CRJkiRJklTMmtV2gpRSAv4eEbOBpcCZZGozSZIkSZIkqZGrVXIpIjoA3wROBb5epmtibeaVJEmSJElSw5B3cikimgCHkUkoHQ60ILNSaTZwF1CSUnq3gDFKkiRJkiSpSOWcXIqIXcgklL4JdCaTUFoG3AuUAP/IbpGTJEmSJEnSBiKflUuvAYlMUulFMgmle1NKS+ogLkmSJEmSJDUA+SSXPua/297erqN4JEmSJEmS1IDkk1zqkVJaXWeRSJIkSZIkqcFpkutAE0uSJEmSJEmqKOfkkiRJkiRJklSRySVJkiRJkiTVmMklSZIkSZIk1ZjJJUmSJEmSJNWYySVJkiRJkiTVmMklSZIkSZIk1VjBkksRsVlENC3UfJIkSZIkSSp+OSeXIqJvRFwcETtUaD8kImYB/wHmRcSZhQ5SkiRJkiRJxSmflUvnAlcCS0obIqIL8BdgCyAB7YGbImKPAsYoSZIkSZKkIpVPcmkf4PWU0pwybd8GNgKuA1oBx2bnPLdQAUqSJEmSJKl45ZNc6gzMqtB2ELAC+HFKaWVK6SFgCrBXYcKTJEmSJElSMcsnubQJ8FmFtj2BV1JKi8u0vUdmm5wkSZIkSZIauXySSwuBLUvfRERfoB0wsZI5V9Q6MkmSJEmSJBW9fJJLU4C9IqJ0y9v3yBTxfqbCuG2BjwsQmyRJkiRJkopcPsml64GmwAsRsQA4BXgf+HvpgIjoCOwCTC1gjJIkSZIkSSpSOSeXUkpPAqcDM4CWwHjgyJTSqjLDTiGTgBpfuBAlSZIkSZJUrJrlMzilVAKUVDPkZuD3rF34W5IkSZIkSY1QXsmldUkpLQOWFXJOSZIkSZIkFa+ct8VFRI+I+HZEbF/NmO2zY7oXJjxJkiRJkiQVs3wKeo8C7shhXAlwTo2ikSRJkiRJUoOST3LpEOCNlNLbVQ3I9r0BDKltYJIkSZIkSSp++SSXegDv5jDuXaBnzcKRJEmSJElSQ5JPcqkVsDyHccuBjWsWjiRJkiRJkhqSfJJLHwG75zDu68DcmoUjSZIkSZKkhiSf5NI4oHdEnFrVgIgYAWwNPFPLuCRJkiRJktQA5JNcuhZYAdwaET+PiN6lHRGxVUT8HLg1O+bawoYpSZIkSZKkYpRzciml9BZwVvbtpcC/I+KriPiKTBHvS7PzfSel9EbBI5UkSZIkSVLRyWflEimlu4B9gL8Cy4Dm2deybNs+KaWSAscoSZIkSZKkItUs3xtSSlOAoyOiCdARSMCClNLqQgcnSZIkSZKk4pZ3cqlUNpn0nwLGIkmSJEmSpAYmr21xdSUimkfE4Ii4JiKmRMSSiFgeER9FxJ8jYlB9xyhJkiRJkqS1VblyKSKuyH7525TSp2Xe5yKllH6ax/iBwFPZr+cCzwKfAzsBxwHHRcRPU0r5xCBJkiRJkqQ6Vt22uB+Rqad0L/BpmfdRzT2l/QnIJ7m0GngAuD6l9FzZjog4AbgbuDwixqWUxuUxryRJkiRJkupQdcmln5BJEs2v8L7gUkrPAM9U0XdfRBwMnAGcDJhckiRJkiRJKhJVJpdSSj+q7v169mr22r0eY5AkSZIkSVIFRVHQOwfbZq8f12sUkiRJkiRJKifn5FJEvB8RV+Uw7hcR8V7twio3X1fg1OzbBwo1ryRJkiRJkmovn5VLvYBOOYzrmB1baxHRDPgj0A54OqX010LMK0mSJEmSpMKoi21xrYGVBZrrZmAwMItMMe9KRcRZETElIqbMmzevQI+WJEmSJEnSuhQ0uRQR7YABwNwCzHU9mRPi5gKDU0pVzplSujWl1C+l1K9Tp1wWV0mSJEmSJKkQqjwtDjJ1lio0DY+IQdXM1SV7vb02QUXENcAoYB6ZxNK/azOfJEmSJEmS6ka1ySXK105KQJvsqyrLgYeAS2oaUET8Cvg+sAA4KKX0Zk3nkiRJkiRJUt1aV3Jpq+w1gPeBPwMXVTF2OTAvpVTjeksR8cvs/AuBg1NKr9d0LkmSJEmSJNW9apNLKaUZpV9HxJ3Ac2XbCikifkZmxdMiMomlV+viOZIkSZIkSSqcda1cWiOldFpdBRERw4DLsm/fBc6NiMqGvpVS+mVdxSFJkiRJkqT85JxcqkpENAFOB3YFZgC3pJSW5jnNpmW+7pd9VWYCYHJJkiRJkiSpSDTJdWBEXBoRX1RyWtyjwC3AOcBVwKSI2DifIFJKJSmlyOFV8dmSJEmSJEmqRzknl4AhwBIyq4cAiIhDsu0fAT8DJgM7klnJJEmSJEmSpEYun+TSNsCbKaVUpu04IAEnppSuAA4kc9LbNwsXoiRJkiRJUgO0mEwBocX1HUjdyqfmUkfg2Qpt+wJzU0ovAKSUlkXEC8AeBYpPkiRJkiSpYZkNjCaz9yvILMsZBJwLdK+/sOpKPsml1cCaWkoR0Q7YAXigwrjFQPtaRyZJkiRJktTQzAZGAEvJLNNpCqwCxgEvAyU0ugRTPtviPgD2yp4OB3AEmfzb8xXGdQLmFyA2SZIkSZKkhmU0mcRSFzKJJbLXLmSW44ypp7jqUD7JpUfIfBQPRsQo4Ndkcm8Plw6IiAB2I5OIkiRJkiRJ2nAsJrMVrmMV/Z2A8WSOS2tE8kkuXQVMB44ErgO6AlenlGaUGbMvmY+q4momSZIkSZKkxm0RmT1eTavob0omE7NwfQW0fuRccymltDgi+gHDyaxg+mdKaUKFYZsB1wP3Fi5ESZIkSWoEFpP5h2d7oF29RiKprrQnU7x7FZUnmFaRqWjdYT3GtB7kU9CblNIy4A/V9D8EPFS7kCRJkiSpEdnATo2SNmjtgIFktr51qaR/HnAg0HY9xrQe5LMtTpIkSZKUj9JTo8aT2efRKXsdB5ya7ZfUuIwikzyaS2alEtnrXDLJp3PrKa46VOXKpYjomf3yo5TSqjLvc5JSmlmryCRJkiSpoSt7alSp0lOj5pI5NeqqeohLUt3pDpSQ+e97PJllPauBwcBIGuWKxeq2xX1I5tvfCXgn+z7lOG9ax9ySJEmS1Ljlc2pUI9siI23wupNJHC8hU7y7A436v/PqEkAzySSJVlR4L0mSJElal0XkfmpUI/5Hp7RBa8sG8d93lcmllFKv6t5LkiRJkqrRng3y1ChJGx4LekuSJElSXSg9NWp+Ff3zyJwatwGsapDUuOWcXIqIKyJiWA7jjoyIK2oXliRJkiQ1AhvgqVGSNjz5rFz6EXB0DuOGAT+sSTCSJEmS1KiUnhp1ILCAzCqmBWROjSqhUZ4aJWnDUxcnujXFwt+SJEmSlLGBnRolacNTF8mlrcn8tilJkiRJKrWBnBolacNTbXKpktpJfaupp9QM2BHYFxhf+9AkSZIkSZJU7Na1culHZLa4RfZ93+yrOl8AP6lNUJIkSZIkSWoY1pVc+gn/TS5dAUwFHq5i7HLgI+DvKaVPChWgJEmSJEmSile1yaWU0o9Kv85uh5uaUvpxXQclSZIkSZKkhiHngt4ppSZ1GYgkSZIkSZIaHhNGkiRJkiRJqrGcVy6VFRE7AtuROUgzKhuTUrqrFnFJkiRJkiSpAcgruRQR+wC3AjtWN4xMEXCTS5IkSZIkSY1czsmliNgBeBLYCHgB6ApsBdwLbAPsBjQFHgIWFzpQSZIkSZIkFZ98ai5dSiax9J2U0r7AcwAppW+llPYCdgVeJrNdblShA5UkSZIkSVLxySe5NAj4d0rptso6U0rTgSOAnsDltQ9NkiRJkiRJxS6f5FJX4F9l3q8CiIiWpQ0ppf8AE4BjChKdJEmSJEmSilo+yaXPKrxfkr12q9C+DNiixhFJkiRJkiSpwcgnuTSbzJa3Um9lrweUNkREc2AvYF7tQ5MkSZIkSVKxy/m0OGAicFpEtE0pLQEeJbM17jcR0YpM8ulMoDuZE+QkSZIkSZLUyOWzcukvwEdkCnuTUvoI+AXQFvgt8BCZgt6Lgf8rZJCSJEmSJEkqTjmvXEopPQ1sW6HthxHxOjAc2JTMVrnrUkozChqlJEmSJEmSilI+2+IqlVJ6AHigALFIkiRJkiSpgcl5W1xEfDsi9slh3N4R8e3ahSVJkiRJkqSGIJ+aSyXA/+Qw7gzgjhpFI0mSJEmSpAYln+RSrqIO5pQkSZIkSVIRqovkUnfgszqYV5IkSZIkSUWm2oLeldRO2qaaekrNgB2BwcA/CxCbJEmSJEmSity6TosrAVKZ9wOyr6oEsBq4unZhSZIkSZIkqSFYV3LpLv6bXBoBvAdMrGLscuAj4OGU0muFCU+SJEmSJEnFrNrkUkrp1NKvI2IE8HxK6fS6DkqSJEmSJEkNw7pWLpW1FRbqliRJkiRJUhk5J5dSSjOq64+IzYBFKaVVtY5KkiRJkiRJDUKTXAdGRN+IuDgidqjQfkhEzAL+A8yLiDMLHaQkSZIkSZKKU87JJeBc4EpgSWlDRHQB/gJsQabwd3vgpojYo4AxSpIkSZIkqUjlk1zaB3g9pTSnTNu3gY2A64BWwLHZOc8tVICSJEmSJEkqXvkklzoDsyq0HQSsAH6cUlqZUnoImALsVZjwJEmSJEmSVMzySS5twtqnxe0JvJJSWlym7T0y2+TyEhHbR8R5EfHHiHgrIlZHRIqI4fnOJUmSJEmSpPUj59PigIXAlqVvIqIv0A6YWGFcEzKrmfL1XeC8GtwnSZIkSZKkepLPyqUpwF4RUbrl7Xtking/U2HctsDHNYjlX8CvgROAbYAJNZhDkiSpYVi8GGbMyFwlSVLjtIH8eZ/PyqXrgaHACxGxCOhAZgvc30sHRERHYBfg/nwDSSn9ruz7iMh3CkmSpOI3ezaMHg0TJkAEpASDBsG550L37vUdnSRJKoQN7M/7nFcupZSeBE4HZgAtgfHAkSmlVWWGnQI0zfZJkiSprNmzYcQIGD8eNtsMOnXKXMeNg1NPzfRLkqSGbQP88z6fbXGklEpSSr1TSm1SSgemlN6qMORmMiuabi9YhJIkSY3F6NGwdCl06QJNm2bamjbNvF+8GMaMqd/4JElS7W2Af97nsy1unVJKy4BlhZwzX2+//TaDBg0q1/aNb3yDs88+my+++ILDDjtsrXtOPfVUTj31VObPn8/w4WsfTvfd736XE044gVmzZnHKKaes1X/BBRdw5JFH8vbbb/Od73xnrf7/9//+HwcddBBTp07l/PPPX6v/yiuvZJ999uGFF17g//7v/9bqv+666+jbty//+Mc/+NnPfrZW/y233ML222/PX//6V6655pq1+v/whz/Qo0cP7rvvPm666aa1+v/85z/TsWNHSkpKKCkpWav/scceY6ONNuLGG29k7Nixa/WPHz8egKuvvpq//e1v5fpat27N448/DsBPf/pTnn766XL9m222GQ888AAAP/jBD5g0aVK5/u7du/PHP/4RgPPPP5+pU6eW699uu+249dZbATjrrLN45513yvX37duX6667DoCTTz6Z2RUyxP379+cXv/gFAMcddxwLFiwo1z948GAuv/xyAIYOHcqyZeV/vI844gguvPBCgLV+7sCfPX/2rgP82fNnz5+9sjbon72VK/nzF1/QsWtXSmbNomTWrLXufywlNlqyhBv/+Ed/9vzZK8ff9/zZ82fPn72K/Nkr0p+9lSvZ7M03eWDvvQH4wfTpTFq4sNz93T/4gD9edhm0bdsgf/Yqk9fKpWIVEWdFxJSImLJiRU0OqpMkSapjK1dmai6U/h/MyjRpAhX+AipJkhqQlSsz1+r+vI9odH/eR0qpvmOoVESMBwYCx6eU/pzrff369UtTpkyps7gkqc4sBhYB7YF29RqJpLqweDEcckim5kJlf+FctQoWLICnnoK2bdd/fJIkqfYa8Z/3EfFySqlfZX2NYuWSJDVos4GLgUOAE7LXS7LtkhqPdu1g4ECYP7/y/nnzMqfINLC/aEqSpDI20D/vTS5JUn2aDYwgc8bmZkCn7HUccCommKTGZtSozF8m587N/J9LyFznzs38ZfTcc+s3PkmSVHsb4J/3JpckqT6NBpYCXYDSVbNNs+8XA43vIAlpw9a9O5SUwIEHZpbEz5+fuQ4enGnv3r2+I5QkSbW1Af55X9DT4lRA1l6RGr/FwASgYxX9ncisaFoCNK5Vs9KGrXt3uOoqWLIkU8yzQ4dGtzRekqQN3gb2573JpWIzm8xKhglAAAkYBJwLNL7kprRhW0Tmv/OqDpJoSmZ96UJMLkmNUdu2jfovmZIkiQ3mz/saJZciYgtgf2CLbNNHwLMppY9qGkhEfB24sUzTTtnrlRFxYWljSmnvmj6j6JXWXllKZiVDU2AVmdorLwMlmGCSGpP2ZBLIq6g8wbQKWA10WI8xSZIkSVKe8kouRUR74AbgG6xdr2l1RNwHjEwpLapBLG2BvSpp37YGczVMZWuvlCqtvTKXTO2Vq+ohLkl1ox0wkMzWty6V9M8DDsRVS5IkSZKKWs7JpYhoDTwD7Erm/7W/CLyf7e5NJjF0ErBjROybUlqWTyAppfFkNohsmKy9Im2YRgGvkEkgd+K/KxbnkUk+Nb6DJCRJkiQ1MvmcFnc+0BeYBOySUhqQUjol+xoA7AJMzI4ZVeA4G79F5F57RVLj0Z3MltcDgQXA/Ox1MG6FlSRJktQg5LMt7htkUhuHp5QWV+xMKU2PiGHAe8CJuIErP+2x9oq0oepO5nfMJWR+l+2AKxQlSZIkNRj5rFzaFhhXWWKpVLbW0jg2pDpJhVJae2V+Ff3zyJwa5z84pcarLbAl/ncuSZIkqUHJJ7mkujaKzD8q55JZqUT2Ohdrr0iSJEmSpKKUT3LpXWBQRGxS1YCIaEtmfc27tYxrw2TtFUmSJEmS1MDkU3PpfuCnwCMRcWZKqVwCKSK2AW4hUy3k2sKFuIGx9ookSZIkSWpA8kku/QY4gUxloOkR8SLwAZky1L2BvcmUop4GXFfYMDdAbTGpJEmSJEmSil7OyaWU0hcRcQBwE3AcMCD7WjME+DPw3ZTSFwWNUpIkSZIkSUUpn5VLpJQWAN+IiJ7AfsAW2a6PgOdSSjMLHJ8kSZIkSZKKWF7JpVLZJNLdBY5FkiRJkiRJDUw+p8VJkiRJkiRJ5eS9cikiWgH9gM2BVlWNSyndVYu4JEmSJEmS1ADklVyKiO8BV5DbOWYmlyRJkiRJkhq5nJNLEXE6cE327XTgLWBJXQQlSZIkSZKkhiGflUujgAScklL6Ux3FI0mSJEmSpAYkn4Le2wMvmFiSJEmSJElSqXySS58DM+sqEEmSJEmSJDU8+SSXXgB2rqtAJEmSJEmS1PDkk1z6MbBDRIyoq2AkSZIkSZLUsFRZ0Dsi9q+k+Vrg9xFxGPAomW1yqyu7P6X0bEEilCRJkiRJUtGq7rS48WROh6sogOHZV1XSOuaWJEmSJElSI1BdAuhZKk8uSZIkSZIkSUA1yaWU0qD1GIckSZIkSZIaoHwKekuSJEmSJEnlFDy5FBFNIuK0Qs8rSZIkSZKk4lOw5FI2qTQCeBv4XaHmlSRJkiRJUvFa54luEbE5cAjQBfgEeDKlNKfCmG8CPwK2JnOa3CcFj1TS/2/vzmIlKcswjv+fgIgwiBEzQRxF3C4EiQsajEEk7ntQNAZ3jQuiAVFcEhFETVwILkEEEo1RuQFRlAshqMNiNMogqOAGyuYy6iAgqCM4vF5UHXM4mXOcrlPd1T39/yUn1dVfnZNnkn6nut/+6itJkiRJkqbOis2lJEcBHwN2WvT0nUmOqqozkjwMOBN4Ek1T6XbgJODkMeWVJEmSJEnSFFm2uZTkqcCn2t3bgd8AuwP7AKcmuQ74Ms2MpruAU4GPVtWmsSaWJEmSJEnS1Fhp5tKR7fZU4N1VtRkgyb7AOcA3gZ2BnwMvr6pfjzOoJEmSJEmSps9KC3ofCNwIHLXQWAKoqquBd9I0lv4FPMvGkiRJkiRJ0nxaqbm0FriiqrZsZeyH7faSqnLxbkmSJEmSpDm1UnPp3sAtWxuoqlvbhxv7DiRJkiRJkqTZsVJzaVtULykkSZIkSZI0k1Za0Btgz/aucSOPV9Ul3WNJkiRJkiRpFvy/5tKz25+tqRXGaxv+tiRJkiRJkmbcSg2gG/GyN0mSJEmSJK1g2eZSVT10gjkkSZIkSZI0g1a7oLckSZIkSZLmmM0lSZIkSZIkdWZzSZIkSZIkSZ3ZXJIkSZIkSVJnNpckSZIkSZLUmc0lSZIkSZIkdWZzSZIkSZIkSZ3ZXJIkSZIkSVJnNpckSZIkSZLUmc0lSZIkSZIkdWZzSZIkSZIkSZ3ZXJIkSZIkSVJnU9dcSnJ4kkuT3JbkjiQbkhyZZOqySpIkSZIkzbupatgk+RxwJnAAcClwIfAo4BTgazaYJEmSJEmSpsvUNGuSvBR4G7AR2L+qXlBVhwKPBH4JHAq8Y8CIkiRJkiRJWmJqmkvA+9vte6vqmoUnq+rPwBHt7vucvSRJkiRJkjQ9pqJRk2Qd8ATgTuDspeNVdTHwB2BP4MDJppMkSZIkSdJypqK5BDyu3V5dVf9a5pjLlhwrSZIkSZKkgU1Lc2mfdnvDCsfcuORYSZIkSZIkDWxamktr2u0/Vjjmjna725izSJIkSZIkaRvtOHSAPiR5M/DmdveOJL8eMk+PHgBsGjqEpImy7qX5Ys1L88e6l+bP9lL3ey83MC3NpYVZSbuucMzC7Kbblw5U1RnAGX2HGlqSDVV1wNA5JE2OdS/NF2temj/WvTR/5qHup+WyuOvb7bJdMODBS46VJEmSJEnSwKaluXRFu903yX2WOeaJS46VJEmSJEnSwKaiuVRVNwE/AXYCXrZ0PMnBwDpgI/DDyaYb1HZ3qZ+k/8u6l+aLNS/NH+temj/bfd2nqobOAECSw4CzaRpIB1XVte3za4H1wKOBo6vqM8OllCRJkiRJ0mJT01wCSHIqcASwGfgOcBfwdOC+wLnAYVW1ZbCAkiRJkiRJuoepai4BJDkcOBJ4DLAD8Cvgi8Dnq+ruIbNJkiRJkiTpnqauuSRJkiRJkqTZMRULes+7JDsmOSbJL5L8M8ldSa5N8v4kOwydT1L/rHtpvljz0vyx7qX5Mu8178ylgSXZGziP5jLAq4GfA3sBBwEBTq6qdw2XUFLfrHtpvljz0vyx7qX5Ys3bXBpUkvsDPwXWAm+sqq8uGnsl8FWaRc3XVdVfhkkpqU/WvTRfrHlp/lj30nyx5hteFjesk4F1wPGLX4AAVXUmcC1wL+DAJI9IclqSK5P8J8lVA+SVtHqj1P3Lkpyb5KYk/0jysyRHJPH/bml2jFLzL0ny/SSbkmxO8tskJyXZfYDckrrb5rpfPJZkTZLfJ6kkB0wsraTVGuVc/7q2xpf+nDJA7l7tOHSAeZXkQcBrgD8An1jmsOuARwC7AvsCzwd+RNMU9MOlNGM61P1RwA3AscCfgUOAzwIPa5+TNMU61PyuwCU0b1L/BuwPnNBunzXmuJJ60KHuFzsBP59JM2UVNf8c4LZF+xvHEnCC/M9rOIfSXHv59aq6e5lj9mi3G4GLq+qbAEm+BPhthjR7Rq37F1bVXxeNrU+yBnh7kg9U1b/HmFXS6o1U81W1fsnYRUk2A6cn2auq/jiuoJJ6M+q5HoAk+wFvBY4BTh9rQkl9GrXm924fX15Vm8YdbpKc/TKcZ7bbS7Y2mOR+wH7AFuCKFV6okmbHqHX/160cdgWwM3D/cQSU1KuRan6Zv7HwxnOnXpNJGpeudf854BTgN+MMJ6l3fZzrtws2l4bzxHa7tQ+PAC+meSO5vqpunUgiSePWR90fRHO5zHa7GKC0HelU80l2SLJzkicAHwS+VVXXjzOopN6MXPdJXk1zycxHxp5OUt+6vr+/KsmWJNclOT7JzF9VNvP/gFnUXpf5wHZ3762M7w6c2O6eNKlcksanj7pvF/d8PfChqtoyjpyS+rHKmr8ZWFjE+3zg8HFklNSvLnXfPvdJ4F1VdUeSSUSV1IOO5/o/AccDP6aZzfRc4DhgH+B1Y4w7ds5cGsbi9ZKOSbLbwk6SBwBnAQ8BvlJVF0w6nKSxWFXdJ9kTOIfmRPTxMWeVtHqrqfmnAU8B3kJzQ4/zkuww3riSetCl7j8CXNPeUUrSbBm55qvqgqo6sarOr6oLq+oY4MPAa5M8fJLh++bMpWEsTJ07HXg1cE2Si4FdaN5QrgG+AbxpkHSSxqFz3bffenwb+Cfwoqq6axKBJa1K55qvqivbhz9IcjmwgWbB0K+NN7KkVRqp7pPsS7OI9zPbdVlojwFYk2S3qrp9MtElddDX5/qzaO4W+Xjgt+MIOgnOXBrGQofzHOAZwK+A59HcZvynNC/Ml3onKGm70qnuk+wMfAtYCzynqm6eWGJJq9HXuf5K4G6a9VgkTbdR6/6RNF/2rwduaX/Oa8fWA5dOJrakjvxcv4gzl4ax8CLcUFW30HQ1JW3fRq77dmG/s4D9gYOr6obxxZPUs77O9U+m+TLwd32EkjRWo9b992k+hC72WOBTNDOaLu8znKTe9XWufwVQzHjN21yasCT7AHsA17YvwG39vV1ouqDQLBZ23ySHtfuX+aFTml5d657mtsQvBN4D7JLkwEVjv6iqv/cYU1JPVnGuvwD4LnA1sJnmQ+axwM+Ac3sPKqk3Xeq+qjYBFy35OwsPL6+qDX1mlNSfVZ7rvwdcRTMz+bnA24AvVNVMf5Fkc2nyFq7LvGzE31sLnL3kuYX91wNfWkUmSePVte6f3W4/sZWxQ1jyhlTS1Oha8z8GXkVzxxiA64HTgJOr6s5+okkak651L2k2da35XwJvANbR9GOuAd4LfLq3ZAOxuTR5/5s6N8ovVdX1gPcmlWZT17p/aP9RJE1A15o/juZ2xJJmT6e6X6qqLsL3/NIs6HquPxo4uu8w0yBVNXQGSZIkSZIkzSjvFidJkiRJkqTObC5JkiRJkiSpM5tLkiRJkiRJ6szmkiRJkiRJkjqzuSRJkiRJkqTObC5JkiRJkiSpM5tLkiRJkiRJ6szmkiRJkiRJkjr7L57PLT5aBVNfAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = [r\"$\\rho_1$\",r\"$\\rho_2$\",r\"$\\rho_3$\",r\"$\\rho_4$\",r\"$\\rho_5$\"]\n",
"plt.figure(figsize=(20,10))\n",
"plt.axhline(1,color=\"black\",linestyle=\"--\",label=\"Reference\")\n",
"#plt.scatter(x,[1.0 for i in range(5)],marker=\"o\",s=100,color=\"blue\",label=\"Reference\")\n",
"plt.scatter(x,[high_ellipticity_rho_stats[i]/ref_rho_stats[i] for i in range(5)],marker=\"o\",s=70,color=\"red\",label=\"Higher model ellipticity\",alpha=0.7)\n",
"plt.scatter(x,[high_size_rho_stats[i]/ref_rho_stats[i] for i in range(5)],marker=\"o\",s=70,color=\"magenta\",label=\"Higher model size\",alpha=0.7)\n",
"plt.ylabel(\"Rho statistics values (compared to reference)\")\n",
"plt.ylim(0)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53c3f361",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Aucun(e)",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
},
"metadata": {
"execution": {
"timeout": 30
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}