From e2c1f32e174368863e836e1c377fa5e16e426211 Mon Sep 17 00:00:00 2001
From: astroJLovell <87023162+astroJLovell@users.noreply.github.com>
Date: Tue, 25 Jun 2024 13:41:14 -0400
Subject: [PATCH] Added file

tutorial_v1 upload
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 C18O_tutorial.ipynb | 2957 +++++++++++++++++++++++++++++++++++++++++++
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "de23c815-1616-477e-a29c-71824a997612",
+   "metadata": {},
+   "source": [
+    "# Extracting extended line emission from SMA data of disks\n",
+    "\n",
+    "### Authors\n",
+    "Joshua Lovell (joshualovellastro@gmail.com); June 2024. \n",
+    "\n",
+    "_Special thanks to K. Monsch for providing access to scripts from Monsch et al. 2024 for verification purposes._\n",
+    "\n",
+    "### Aims & Objectives\n",
+    "In this tutorial, we are aiming to replicate various plots from the recent SMA paper: \n",
+    "'High-resolution Pan-STARRS and SMA observations of IRAS 23077+6707: A giant edge-on protoplanetary disk' [https://arxiv.org/abs/2402.01941]\n",
+    "\n",
+    "For demonstrating this example, we are just going to use the C18O line data -- the isotopologue of Carbon Monoxide -- which was the faintest\n",
+    "of the three CO lines measured in that work. \n",
+    "\n",
+    "Our aim is to:\n",
+    "\n",
+    "1-- understand the structure of a 'data cube', how we can access different information that this hosts, and how to stack this data up in \n",
+    "different ways (e.g., making 'moment' maps);\n",
+    "\n",
+    "2-- successfully replicate figures 1, and 2 (for the C18O line) from Monsch et al.;\n",
+    "\n",
+    "3-- understand how different choices made during the analyses of Monsch et al. would have impacted this line detection;\n",
+    "\n",
+    "4-- extend the analyses done in this work, and measure to see if the line hosts any level of emission asymmetry.\n",
+    "\n",
+    "So, like in all python scripts let's load in all libraries..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fe11b44-03c1-4a9f-a542-f80245fd2727",
+   "metadata": {},
+   "source": [
+    "### Requirements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "133703df-22af-4c10-b798-58817a120e46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##local copy of 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image.fits' -- this is the SMA data cube for the C18O line\n",
+    "#pip install astropy\n",
+    "#pip install gofish\n",
+    "#pip install matplotlib\n",
+    "#pip install bettermoments\n",
+    "#pip install seaborn\n",
+    "#pip install cmocean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a631f86-3a3a-4466-b65f-679a1a58dee5",
+   "metadata": {},
+   "source": [
+    "# Tutorial\n",
+    "\n",
+    "## Stage A: Setup our workspace\n",
+    "### Step A1: collect all relevent libraries\n",
+    "\n",
+    "We will start by importing all relevant libraries for this tutorial. All of the relevant packages should have been installed via pip in the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "95561c46-24f4-4ee4-97af-80ad9370d7bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import gofish\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from gofish import imagecube\n",
+    "import bettermoments as bm\n",
+    "import astropy.io.fits as pyfits\n",
+    "import cmocean\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab73c977-990b-475c-946e-2228ea774894",
+   "metadata": {},
+   "source": [
+    "### Step A2: import required data\n",
+    "\n",
+    "Next we want to import the data -- here we have chosen a C18O data cube from the SMA analysis of Monsch et al. 2024.\n",
+    "In that work a line spectrum is presented for three different CO cubes, including this (faintest) line.\n",
+    "\n",
+    "In this tutorial we will use 'bettermoments' and 'gofish' to re-extract the same detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4431f25d-a84d-448f-8b55-71136f667d62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loc='../data/'\n",
+    "file='DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image.fits' # this is the full data cube\n",
+    "cube = imagecube(loc+file, FOV=70.0) #FOV applies a cut on the data to speed up run time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9af1278-8155-4454-8942-460a15ac8650",
+   "metadata": {},
+   "source": [
+    "# Stage B: Imaging data in 2-dimensions\n",
+    "### Step B1: imaging channel maps\n",
+    "\n",
+    "Image cubes are 3 dimensional objects -- X and Y axes define pixels (each with some intensity), and the Z axis hosts the velocity/frequency\n",
+    "information.\n",
+    "\n",
+    "You should now trial plotting different frames of this cube (e.g., in runs of 5 consecutive frames). Note the cube is over 60 channels wide and you should run through every channel.\n",
+    "\n",
+    "The first 5 frames are setup already. What do you eventually notice?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e9f00822-242f-415f-a75f-7c977d4394db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Text(0.5, 0, 'Offset (arcsec)'),\n",
+       " Text(0.5, 0, 'Offset (arcsec)'),\n",
+       " Text(0.5, 0, 'Offset (arcsec)'),\n",
+       " Text(0.5, 0, 'Offset (arcsec)'))"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=5,figsize=(15,3))\n",
+    "ax[0].imshow(cube.data[0],cmap=cmocean.cm.ice,origin='lower',extent=cube.extent)\n",
+    "ax[1].imshow(cube.data[1],cmap=cmocean.cm.ice,origin='lower',extent=cube.extent)\n",
+    "ax[2].imshow(cube.data[2],cmap=cmocean.cm.ice,origin='lower',extent=cube.extent)\n",
+    "ax[3].imshow(cube.data[3],cmap=cmocean.cm.ice,origin='lower',extent=cube.extent)\n",
+    "ax[4].imshow(cube.data[4],cmap=cmocean.cm.ice,origin='lower',extent=cube.extent)\n",
+    "ax[0].set_xlabel('Offset (arcsec)'), ax[0].set_ylabel('Offset (arcsec)')\n",
+    "ax[1].set_xlabel('Offset (arcsec)'),ax[2].set_xlabel('Offset (arcsec)'),ax[3].set_xlabel('Offset (arcsec)'),ax[4].set_xlabel('Offset (arcsec)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db07e35-8068-4d0d-9d46-9090681ba127",
+   "metadata": {},
+   "source": [
+    "If you go far enough into the cube you will see emission appear over a wide range of channels, and then fade out again. This is due to the line having a velocity width that is many times wider than the velocity (or channel) width of our data. This line is therefore spectrally resolved. What you will have also seen is that the position and size of the emission changes with channel too. This is because the line is also spatially resolved.\n",
+    "\n",
+    "The channels containing line emission only spans a small range within the cube due to how this was imaged (using CASA's tclean package). When lines are imaged, we do not necessarily know a-priori where lines will appear within cubes due to these having different properties (e.g. line widths due to different rotation speeds and sky-projections, or different line centres due to having different recession velocities). So typically cubes are imaged to contain enough data to fully cover the line to avoid throwing emission away. It is also important often to understand the noise properties of our data in a region away from the line; and so cubes might be many times wider than the line width to provide better estimates of the typical uncertainties in the locations of emission.\n",
+    "\n",
+    "### Step B2: imaging moment maps -- part one Moment-0\n",
+    "\n",
+    "You might imagine that if you have lots of independent data points, stacking these to reduce their errors can enhance signal-to-noise estimates. This type of process can be done with 2-D data, and one way in which this is achieved is via the creation of 'moment' maps. Simply put, these are different ways to integrate the emission in a datacube to image different physical properties of the data.\n",
+    "\n",
+    "This might sound complicated, but there are lots of excellent public tools that allow you to do this.\n",
+    "We will first use the 'bettermoments' package to do this (Teague & Foreman-Mackey 2018: https://bettermoments.readthedocs.io/en/latest/index.html). \n",
+    "\n",
+    "We will start by making a 'Moment-0' map. This map is simply the 'velocity-integrated intensity map'. Given our maps contain channels of equal width, this is simply like collapsing all the channels in the z-axis into a single frame. Note the units will account for the total velocity width over which we are integrating (i.e. a map with a z axis of meters/second and x-y intensity units of Jy/beam, will yield a moment-0 map with units Jy/beam m/s).\n",
+    "\n",
+    "I have loaded in a first example of this for you to try. \n",
+    "Take a look at the code, and see how this runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "27429aca-e471-46b3-a730-f0b1e8d1ea59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Provided cube is only 2D. Shifting not available.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.contour.QuadContourSet at 0x2980d03d0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data, velax = bm.load_cube(loc+file) # this produces the bm (bettermoments) class object for our data, it produces data and velocities\n",
+    "rms = bm.estimate_RMS(data=data, N=5) #this estimates the error in the data (before applying the masks)\n",
+    "moments = bm.collapse_zeroth(velax=-velax, data=data, rms=rms) # this collapses the data to produce the moment 0 map ('zeroth') and its associated error map\n",
+    "bm.save_to_FITS(moments=moments, method='zeroth',path=loc+file) # this simply saves the object as a new fits file\n",
+    "\n",
+    "file2   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M0.fits' #this is just what bettermoments saved this file as...\n",
+    "cubeMom = imagecube(loc+file2, FOV=36.0) # this just re-opens the file, with some amount of field of view (FOV) clipping (to save some memory)\n",
+    "\n",
+    "# now we've loaded this data back in, let's plot our Moment 0 map:\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=1)\n",
+    "ax.imshow(cubeMom.data, origin='lower', extent=cubeMom.extent, cmap=cmocean.cm.ice)\n",
+    "ax.set_xlabel('Offset (arcsec)')\n",
+    "ax.set_ylabel('Offset (arcsec)')\n",
+    "mapPeak=np.amax(cubeMom.data)\n",
+    "ax.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[0.2*mapPeak,0.4*mapPeak,0.6*mapPeak,0.8*mapPeak],colors='w',linewidths=2)\n",
+    "ax.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[0.2*mapPeak,0.4*mapPeak,0.6*mapPeak,0.8*mapPeak],colors='k',linewidths=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b529ba01-adce-45bb-afd3-5e17bb119528",
+   "metadata": {},
+   "source": [
+    "So this shows that if we integrate over the complete cube, there is significant emission present at the center of the image.\n",
+    "\n",
+    "I have over-plotted contours at the level of 20, 40, 60 and 80% of the line peak (pixel) in the map. We can see some noisy regions, suggesting that the significance of this line is probably about 5-10sigma or so. Significant, but as we will see, well below what the emission would suggest when averaged in a smarter way.\n",
+    "\n",
+    "But what do you recall from plotting the separate channels? Do you think it makes sense to include *every* single channel in our moment map intergation?\n",
+    "\n",
+    "Let's see about whether there's a more sensible choice to maximise the SNR..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c3268d4c-3009-4e5e-9ef8-aefa1086f7fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Provided cube is only 2D. Shifting not available.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.contour.QuadContourSet at 0x29b711550>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#imaged line... this uses bettermoments to make a 'Moment 0 map' -- the velocity-integrated intensity map\n",
+    "data, velax = bm.load_cube(loc+file) # this produces the bm (bettermoments) class object for our data\n",
+    "\n",
+    "user_mask = bm.get_user_mask(data=data, user_mask_path=None)\n",
+    "smoothed_data = bm.smooth_data(data=data, smooth=1, polyorder=0)\n",
+    "rms_smoothed = bm.estimate_RMS(data=smoothed_data, N=5) # this smoothes over N=5 channels\n",
+    "rms = bm.estimate_RMS(data=data, N=5) #this estimates the error in the data (before applying the masks)\n",
+    "\n",
+    "threshold_mask = bm.get_threshold_mask(data=data,\n",
+    "                                       clip=2.0, # this is a 2sigma clip\n",
+    "                                       smooth_threshold_mask=0.0) # here we define a 2-sigma clip to throw away noise artefacts\n",
+    "\n",
+    "channel_mask = bm.get_channel_mask(data=data,\n",
+    "                                   firstchannel=8,\n",
+    "                                   lastchannel=29) # here we throw away all channels outside of the line width\n",
+    "\n",
+    "\n",
+    "# !!! if this choice of channel cuts doesn't make sense: trial plotting the cube frames, *after* we've applied the rms smoothing and threshold masks\n",
+    "\n",
+    "# Bettermoments gives us a quick way to combine masks into a single mask: \n",
+    "mask = bm.get_combined_mask(user_mask=user_mask,\n",
+    "                            threshold_mask=threshold_mask,\n",
+    "                            channel_mask=channel_mask,\n",
+    "                            combine='and')\n",
+    "\n",
+    "masked_data = smoothed_data * mask #now we mask the data accordingly (here to the smoothed data)\n",
+    "moments = bm.collapse_zeroth(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a moment 0 map...\n",
+    "bm.save_to_FITS(moments=moments, method='zeroth',path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "\n",
+    "file2   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M0.fits'\n",
+    "cubeMom = imagecube(loc+file2, FOV=24.0)\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=1)\n",
+    "ax.imshow(cubeMom.data, origin='lower', extent=cubeMom.extent, cmap=cmocean.cm.ice_r)\n",
+    "im = ax.imshow(cubeMom.data, origin='lower', extent=cubeMom.extent, cmap=cmocean.cm.ice_r)\n",
+    "ax.set_xlabel('Offset (arcsec)',fontsize=16)\n",
+    "ax.set_ylabel('Offset (arcsec)',fontsize=16)\n",
+    "fig.colorbar(im)\n",
+    "ax.text(x=-19,y=-6,s=r'Jy$\\,$beam$^{-1}\\,$m$\\,$s$^{-1}$',rotation=270,fontsize=16)\n",
+    "mapPeak=np.amax(cubeMom.data)\n",
+    "ax.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[0.1*mapPeak, 0.3*mapPeak,0.5*mapPeak,0.7*mapPeak,0.9*mapPeak],colors='w',linewidths=2)\n",
+    "ax.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[0.1*mapPeak, 0.3*mapPeak,0.5*mapPeak,0.7*mapPeak,0.9*mapPeak],colors='k',linewidths=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a160a1-8836-4c87-b6da-d49444b4f807",
+   "metadata": {},
+   "source": [
+    "Wow! The SNR of this map is way higher (more like 20-30 sigma!). How? Well, we ignored all the channels where there was no emission. We used those channels to measure the RMS. We also smoothed channels, and applied SNR clipping: all of which combined, preferentially boosts line signal. :)\n",
+    "\n",
+    "To convince yourself we've been able to replicate a published science product, take a look at Figure 1 in Monsch et al. 2024 (lower right).\n",
+    "\n",
+    "We've used a subtly different FOV trim, and we're also not over-plotting the orange continuum, but we are otherwise 100% reproducing that fig.\n",
+    "\n",
+    "If you want to trial out different clipping, RMS choices, channel cuts, go ahead! Remember to save this choice though since we are aiming\n",
+    "to replicate other figures, which come from this same map..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0c7d2bd-915d-4573-8363-1c3c0ddbc01c",
+   "metadata": {},
+   "source": [
+    "### Step B3: imaging moment maps -- part two Moment-1, 2, 8 and 9 maps\n",
+    "\n",
+    "None of these were presented in Monsch et al. 2024, but let's make a few other moment maps for fun here to see how else we can cut this data up...\n",
+    "\n",
+    "Note the units can get peculiar, and the integrations may be non-standard. I'd recommend taking a read through Rich's description of these on his web page to get a better feel for how pixels and velocities are being used here: https://bettermoments.readthedocs.io/en/latest/user/methods.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a0b25d19-30db-44c3-8fe6-0b52b40cf74c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Provided cube is only 2D. Shifting not available.\n",
+      "WARNING: Provided cube is only 2D. Shifting not available.\n",
+      "WARNING: Provided cube is only 2D. Shifting not available.\n",
+      "WARNING: Provided cube is only 2D. Shifting not available.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(Text(0.5, 0, 'Offset (arcsec)'),\n",
+       " Text(0.5, 0, 'Offset (arcsec)'),\n",
+       " Text(0.5, 0, 'Offset (arcsec)'))"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x300 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# previously we used the collapse_zeroth argument in bettermoments; we can also collapse first, second, eighth, ninth, and maximum. \n",
+    "# What do these look like?\n",
+    "moment1 = bm.collapse_first(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a moment 1 map...\n",
+    "moment2 = bm.collapse_second(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a moment 2 map...\n",
+    "moment8 = bm.collapse_eighth(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a moment 8 map...\n",
+    "moment9 = bm.collapse_ninth(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a moment 9 map...\n",
+    "\n",
+    "# save files & re-open, as it is good practice to do so\n",
+    "bm.save_to_FITS(moments=moment1, method='first',  path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "bm.save_to_FITS(moments=moment2, method='second', path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "bm.save_to_FITS(moments=moment8, method='eighth', path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "bm.save_to_FITS(moments=moment9, method='ninth',  path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "file_M1   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M1.fits'\n",
+    "file_M2   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M2.fits'\n",
+    "file_M8   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M8.fits'\n",
+    "file_M9   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M9.fits'\n",
+    "cubeMom_1 = imagecube(loc+file_M1, FOV=24.0)\n",
+    "cubeMom_2 = imagecube(loc+file_M2, FOV=24.0)\n",
+    "cubeMom_8 = imagecube(loc+file_M8, FOV=24.0)\n",
+    "cubeMom_9 = imagecube(loc+file_M9, FOV=24.0)\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=4,figsize=(16,3))\n",
+    "im1=ax[0].imshow(cubeMom_1.data, cmap='coolwarm', origin='lower', extent=cubeMom_1.extent)\n",
+    "im2=ax[1].imshow(cubeMom_2.data, cmap='coolwarm', origin='lower', extent=cubeMom_2.extent)\n",
+    "im3=ax[2].imshow(cubeMom_8.data, cmap=cmocean.cm.ice, origin='lower', extent=cubeMom_8.extent)\n",
+    "im4=ax[3].imshow(cubeMom_9.data, cmap='coolwarm', origin='lower', extent=cubeMom_9.extent)\n",
+    "fig.colorbar(im1)\n",
+    "fig.colorbar(im2)\n",
+    "fig.colorbar(im3)\n",
+    "fig.colorbar(im4)\n",
+    "ax[0].set_xlabel('Offset (arcsec)'), ax[0].set_ylabel('Offset (arcsec)')\n",
+    "ax[1].set_xlabel('Offset (arcsec)'),ax[2].set_xlabel('Offset (arcsec)'),ax[3].set_xlabel('Offset (arcsec)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c40dc477-1489-45d7-96ab-8580d587c20d",
+   "metadata": {},
+   "source": [
+    "In brief:\n",
+    "\n",
+    "Moment 1 -- is the intensity weighted *average velocity*\n",
+    "\n",
+    "Moment 2 -- is the intensity-weighted average velocity *dispersion*\n",
+    "\n",
+    "Moment 8 -- is the *peak* value along the provided axis (here we use the velax)\n",
+    "\n",
+    "Moment 9 -- is the *velocity* of the peak intensity along the provided axis (here we also used the velax)\n",
+    "\n",
+    "\n",
+    "### Step B4: imaging moment maps -- arbitrary moment maps (extension exercise)\n",
+    "\n",
+    "Bettermoments also allows other methods to collapse data. I have loaded some of these in already for you. One in particular takes some time to run as this fits a model for every channel in the cube."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ba2547e0-6940-46cb-9d71-eee544f10551",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 99%|█████████▉| 128/129 [00:20<00:00,  5.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Provided cube is only 2D. Shifting not available.\n",
+      "WARNING: Provided cube is only 2D. Shifting not available.\n",
+      "WARNING: Provided cube is only 2D. Shifting not available.\n",
+      "WARNING: Provided cube is only 2D. Shifting not available.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 129/129 [00:20<00:00,  6.16it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(Text(0.5, 0, 'Offset (arcsec)'),\n",
+       " Text(0.5, 0, 'Offset (arcsec)'),\n",
+       " Text(0.5, 0, 'Offset (arcsec)'))"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x300 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Bettermoments also allows other collapses...\n",
+    "momentQ = bm.collapse_quadratic(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a moment map via a quadratic method...\n",
+    "momentM = bm.collapse_maximum(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a maximum moment map...\n",
+    "momentW = bm.collapse_width(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a width integrated moment map...\n",
+    "momentG = bm.collapse_gaussian(velax=-velax, data=masked_data, rms=rms) # and re-collapse into a Gaussian moment map...\n",
+    "\n",
+    "bm.save_to_FITS(moments=momentQ, method='quadratic', path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "bm.save_to_FITS(moments=momentM, method='maximum',   path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "bm.save_to_FITS(moments=momentW, method='width',     path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "bm.save_to_FITS(moments=momentG, method='gaussian',  path=loc+file) # we can save over the previous file as this is the one we care about keeping\n",
+    "file_MQ   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_Fnu.fits'\n",
+    "file_MM   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M9.fits' #note also produces a Fnu map too...\n",
+    "file_MW   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_dv.fits'\n",
+    "file_MG   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_gv0.fits' #note other maps also produced...\n",
+    "cubeMom_Q = imagecube(loc+file_MQ, FOV=24.0)\n",
+    "cubeMom_M = imagecube(loc+file_MM, FOV=24.0)\n",
+    "cubeMom_W = imagecube(loc+file_MW, FOV=24.0)\n",
+    "cubeMom_G = imagecube(loc+file_MG, FOV=24.0)\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=4,figsize=(16,3))\n",
+    "im1=ax[0].imshow(cubeMom_Q.data, cmap=cmocean.cm.ice, origin='lower', extent=cubeMom_Q.extent)\n",
+    "im2=ax[1].imshow(cubeMom_M.data, cmap=cmocean.cm.ice, origin='lower', extent=cubeMom_M.extent)\n",
+    "im3=ax[2].imshow(cubeMom_W.data, cmap=cmocean.cm.ice, origin='lower', extent=cubeMom_W.extent)\n",
+    "im4=ax[3].imshow(cubeMom_G.data, cmap=cmocean.cm.ice, origin='lower', extent=cubeMom_9.extent)\n",
+    "fig.colorbar(im1)\n",
+    "fig.colorbar(im2)\n",
+    "fig.colorbar(im3)\n",
+    "fig.colorbar(im4)\n",
+    "ax[0].set_xlabel('Offset (arcsec)'), ax[0].set_ylabel('Offset (arcsec)')\n",
+    "ax[1].set_xlabel('Offset (arcsec)'),ax[2].set_xlabel('Offset (arcsec)'),ax[3].set_xlabel('Offset (arcsec)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2847b6cb-2772-4497-b457-5cef101133b4",
+   "metadata": {},
+   "source": [
+    "In brief:\n",
+    "\n",
+    "Moment Q -- is the quadratic collapse method; provides the sub-channel precision of bettermoments.collapse_cube.collapse_first() with the robustness to noise from bettermoments.collapse_cube.collapse_ninth()\n",
+    "\n",
+    "Moment M -- is the result of both bettermoments.collapse_cube.collapse_eighth() and bettermoments.collapse_cube.collapse_ninth()... so this is provided to simplify getting both of those outputs!\n",
+    "\n",
+    "Moment W -- is the effective width, a rescaled ratio of the integrated intensity and the line peak\n",
+    "\n",
+    "Moment G -- is a collapse found by fitting a Gaussian line profile to each pixel. This function is a wrapper of collapse_analytical"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab93f3ed-b443-4c4d-bf92-ebc875576c42",
+   "metadata": {},
+   "source": [
+    "# Summary\n",
+    "Here we've successfully reproduced Fig 1 in Monsch et al. and also produced some brand new plots. Of interest is the peculiar tail feature that appears to extend southwards in the final moment maps... We'll return to investigate this later on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b332b0e-3434-483d-82c8-625fb1d40252",
+   "metadata": {},
+   "source": [
+    "# Stage C: Imaging data in 1-dimension\n",
+    "\n",
+    "### Step C1: Extracting line profiles\n",
+    "\n",
+    "Now that we've understood how to get moment maps from data cubes, we should trial extracting profiles from data cubes.\n",
+    "Gofish () is another excellent open source tool that allows us to do this, and usefully -- given this is a protoplanetary disk -- enables Keplerian masks to be utilized with reference to the expected kinematics of the source we are analysing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95f21def-6a4b-4bb1-b7ed-2b43e7f1836b",
+   "metadata": {},
+   "source": [
+    "To make sure we are working on the right data again, let's just re-load in the data cube (in case we over-wrote this earlier on)..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b00324fc-f6da-463f-ac32-09282dc75028",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loc='../data/'\n",
+    "file='DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image.fits' # this is the full data cube\n",
+    "cube = imagecube(loc+file, FOV=22.0) #FOV applies a cut on the data to speed up run time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbb8bd65-c229-4bad-81b9-21505ece2c90",
+   "metadata": {},
+   "source": [
+    "Although we didn't discuss where the imagecube function comes from -- this is in fact from the goFish package.\n",
+    "This produces a 'cube' class object on which the gofish package allows one to extract spectra.\n",
+    "The most common type of spectra we make are integrated line spectra, assuming some aperture through the cube -- which we can select with reference to our source geometry and data properties. In Monsch et al. the source is analysed as having an inclination of ~80 degrees, and a position angle of 342degrees.\n",
+    "\n",
+    "So, for example, if we want to extract a spectrum from a radius of 0--200 arcseconds we simply specify:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "728e39f3-41e0-4faa-9922-2a8a9d76a470",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x179486e50>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r_min, r_max = 0.0,200.0    # We will average the spectrum from 0--200'' (arcsec)\n",
+    "inc, PA      = 80.0, 342.0  # The source is highly inclined to the plane of the sky [degs], and has a large rotation on the sky from North [degs]\n",
+    "x0, y0       = 0.0, 0.0     # the central pixel for which r=0'' is measured from\n",
+    "xin, yin, dyin = cube.integrated_spectrum(r_min=r_min, r_max=r_max, inc=inc,\n",
+    "                                          PA=PA, x0=x0, y0=y0,empirical_uncertainty=True)\n",
+    "                                          # xin [m/s], yin [Jy], dyin [Jy] Note Jy as now integrated unit\n",
+    "\n",
+    "fig, ax0 = plt.subplots()\n",
+    "# given gofish auto-uses m/s and a more natural unit for our data (with channel widths 100s m/s) is km/s, we can simply divide x/1000. to convert to km/s (as below)...\n",
+    "ax0.errorbar(xin/1000., yin, dyin, fmt=' ', capsize=1.5, capthick=1.5, color='firebrick', lw=1.0) #note only works when mstar and dist are called\n",
+    "ax0.step(xin/1000., yin, where='mid', color='firebrick', lw=2.0,label=r'C$^{18}$O')\n",
+    "ax0.plot([-100,100],[0.0,0.0],color='k',ls=':')\n",
+    "ax0.plot([1.43,1.43],[-5,5],color='firebrick',ls='--',alpha=0.5,label='line centre')\n",
+    "plt.fill_between([-2.4,5.6],[-1,-1],[5,5],color='firebrick',alpha=0.1) #this spans the complete line width\n",
+    "ax0.set_title(r'Integrated Flux = {:.0f} mJy$\\,$km$\\,$/s'.format(np.round(np.trapz(yin, xin),-2)), fontsize=12)\n",
+    "\n",
+    "ax0.set_xlabel(r'Velocity [km$\\,$s$^{-1}$]', fontsize=12)\n",
+    "ax0.set_ylabel(r'Integrated line flux [Jy]', fontsize=12)\n",
+    "plt.xlim(-5.5,7.5),plt.ylim(-0.7,4.5)\n",
+    "plt.legend()\n",
+    "#print(xin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd2fa8af-2c89-4f50-9fed-1b07937fb782",
+   "metadata": {},
+   "source": [
+    "Now check out the same figure in Monsch et al. 2024 -- pretty convincing, huh? \n",
+    "If you are not yet convinced, you can re-color this with the blue 'C0' color and scale the axes to how this was presented in the paper.\n",
+    "\n",
+    "Note the features of this plot. \n",
+    "\n",
+    "-- we see the expected double peak velocity profile (for a disk in rotation, we expect a blue-shifted and red-shifted peak in the CO gas).\n",
+    "\n",
+    "-- the line centre (~1.6km/s) and width (~5km/s) can be used to estimate the recession velocity and rotation speed of the gas respectively.\n",
+    "\n",
+    "-- the total flux (which we integrate under the curve; 8.6 Jy km/s) can be used to estimate the total amount of C18O in the system (and by extension, and with many assumptions) the total CO mass, total gas mass, and then total disk mass.\n",
+    "\n",
+    "\n",
+    "### Step C2: Keplerian masks\n",
+    "Now, let's the try above exercise and see if we can find a sensible Keplerian mask to extract the same flux...\n",
+    "All we must specify in addition is the stellar mass and the distance to the source; along with the disk geometry, these uniquely define the velocity grid over which any disk in Keplerian rotation would be mapped to. A good map should return the same flux as our simple aperture extraction, and produce a much more sharply defined peak (at the line centre) as a result of the extraction which will effectively shift pixels from their red/blue shifted locations into a single velocity location (i.e., this process will alter the line shape)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a0fbbaff-fd7a-4db7-b2a0-de5467810ff3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: No finite pixels found between 82.05 and 83.08 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 83.08 and 84.10 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 84.10 and 85.13 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 85.13 and 86.15 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 86.15 and 87.18 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 87.18 and 88.21 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 88.21 and 89.23 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 89.23 and 90.26 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 90.26 and 91.28 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 91.28 and 92.31 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 92.31 and 93.33 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 93.33 and 94.36 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 94.36 and 95.38 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 95.38 and 96.41 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 96.41 and 97.44 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 97.44 and 98.46 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 98.46 and 99.49 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 99.49 and 100.51 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 100.51 and 101.54 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 101.54 and 102.56 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 102.56 and 103.59 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 103.59 and 104.62 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 104.62 and 105.64 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 105.64 and 106.67 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 106.67 and 107.69 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 107.69 and 108.72 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 108.72 and 109.74 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 109.74 and 110.77 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 110.77 and 111.79 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 111.79 and 112.82 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 112.82 and 113.85 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 113.85 and 114.87 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 114.87 and 115.90 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 115.90 and 116.92 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 116.92 and 117.95 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 117.95 and 118.97 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 118.97 and 120.00 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 120.00 and 121.03 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 121.03 and 122.05 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 122.05 and 123.08 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 123.08 and 124.10 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 124.10 and 125.13 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 125.13 and 126.15 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 126.15 and 127.18 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 127.18 and 128.21 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 128.21 and 129.23 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 129.23 and 130.26 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 130.26 and 131.28 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 131.28 and 132.31 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 132.31 and 133.33 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 133.33 and 134.36 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 134.36 and 135.38 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 135.38 and 136.41 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 136.41 and 137.44 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 137.44 and 138.46 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 138.46 and 139.49 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 139.49 and 140.51 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 140.51 and 141.54 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 141.54 and 142.56 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 142.56 and 143.59 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 143.59 and 144.62 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 144.62 and 145.64 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 145.64 and 146.67 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 146.67 and 147.69 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 147.69 and 148.72 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 148.72 and 149.74 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 149.74 and 150.77 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 150.77 and 151.79 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 151.79 and 152.82 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 152.82 and 153.85 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 153.85 and 154.87 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 154.87 and 155.90 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 155.90 and 156.92 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 156.92 and 157.95 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 157.95 and 158.97 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 158.97 and 160.00 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 160.00 and 161.03 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 161.03 and 162.05 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 162.05 and 163.08 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 163.08 and 164.10 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 164.10 and 165.13 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 165.13 and 166.15 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 166.15 and 167.18 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 167.18 and 168.21 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 168.21 and 169.23 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 169.23 and 170.26 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 170.26 and 171.28 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 171.28 and 172.31 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 172.31 and 173.33 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 173.33 and 174.36 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 174.36 and 175.38 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 175.38 and 176.41 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 176.41 and 177.44 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 177.44 and 178.46 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 178.46 and 179.49 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 179.49 and 180.51 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 180.51 and 181.54 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 181.54 and 182.56 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 182.56 and 183.59 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 183.59 and 184.62 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 184.62 and 185.64 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 185.64 and 186.67 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 186.67 and 187.69 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 187.69 and 188.72 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 188.72 and 189.74 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 189.74 and 190.77 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 190.77 and 191.79 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 191.79 and 192.82 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 192.82 and 193.85 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 193.85 and 194.87 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 194.87 and 195.90 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 195.90 and 196.92 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 196.92 and 197.95 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 197.95 and 198.97 arcsec. Skipping annulus.\n",
+      "WARNING: No finite pixels found between 198.97 and 200.00 arcsec. Skipping annulus.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x17cccee50>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mstar=3.5\n",
+    "dist = 400.0\n",
+    "xin, yin, dyin = cube.integrated_spectrum(r_min=r_min, r_max=r_max, inc=inc,\n",
+    "                                          PA=PA, x0=x0, y0=y0,empirical_uncertainty=True,\n",
+    "                                          mstar=mstar, dist=dist) #we have now provided the crucial ingredients to build a Keplerian mask\n",
+    "\n",
+    "fig, ax0 = plt.subplots()\n",
+    "ax0.errorbar(xin/1000., yin, dyin, fmt=' ', capsize=1.5, capthick=1.5, color='firebrick', lw=1.0) #note only works when mstar and dist are called\n",
+    "ax0.step(xin/1000., yin, where='mid', color='firebrick', lw=2.0,label=r'C$^{18}$O')\n",
+    "ax0.plot([-100,100],[0.0,0.0],color='k',ls=':')\n",
+    "ax0.plot([1.43,1.43],[-5,5],color='firebrick',ls='--',alpha=0.5,label='line centre')\n",
+    "plt.fill_between([-2.4,5.6],[-1,-1],[5,5],color='firebrick',alpha=0.1) #this spans the complete line width\n",
+    "ax0.set_title(r'Integrated Flux = {:.0f} mJy$\\,$km$\\,$/s'.format(np.round(np.trapz(yin, xin),-2)), fontsize=12)\n",
+    "\n",
+    "ax0.set_xlabel(r'Velocity [km$\\,$s$^{-1}$]', fontsize=12)\n",
+    "ax0.set_ylabel(r'Integrated line flux [Jy]', fontsize=12)\n",
+    "plt.xlim(-5.5,7.5),plt.ylim(-0.7,4.5)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a165f9d2-b95a-4137-9086-361c28770107",
+   "metadata": {},
+   "source": [
+    "Hm! Well that didn't work. We've lost about 90% of the flux! \n",
+    "\n",
+    "What could have gone wrong? Let's trial a few things to see...\n",
+    "\n",
+    "To start with, why would we integrate over such a large region of the sky? This is clearly causing some issues in the program, as it is throwing a bunch of errors out at large distances. Secondly, could the distance and stellar mass be wrong? Let's just alter the rmax first and see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "097153cc-fb8e-43b3-8a34-263b4470b75e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x178006e50>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r_min, r_max = 0.0,  15#,150.0    # min and max radial extents to extract emission from (arcsec)\n",
+    "inc, PA      = 80.0, 342.0 # Trialling a lower inclination, we find this improves the picture... Why?\n",
+    "mstar, dist  = 3.5,  400.   # Note that there is a linear degeneracy between mstar and dist. You can prove this to yourself for bodies with fixed rotating speeds about a central potential mass.\n",
+    "x0, y0       = 0.0,  0.0  # measured from the continuum data: 0.2, -0.65\n",
+    "\n",
+    "xin, yin, dyin = cube.integrated_spectrum(r_min=r_min, r_max=r_max, inc=inc,\n",
+    "                                          PA=PA, x0=x0, y0=y0,empirical_uncertainty=True,\n",
+    "                                          mstar=mstar, dist=dist) #we have now provided the crucial ingredients to build a Keplerian mask\n",
+    "\n",
+    "fig, ax0 = plt.subplots()\n",
+    "ax0.errorbar(xin/1000., yin, dyin, fmt=' ', capsize=1.5, capthick=1.5, color='firebrick', lw=1.0) #note only works when mstar and dist are called\n",
+    "ax0.step(xin/1000., yin, where='mid', color='firebrick', lw=2.0,label=r'C$^{18}$O')\n",
+    "ax0.plot([-100,100],[0.0,0.0],color='k',ls=':')\n",
+    "ax0.plot([1.43,1.43],[-5,5],color='firebrick',ls='--',alpha=0.5,label='line centre')\n",
+    "plt.fill_between([-2.4,5.6],[-1,-1],[5,5],color='firebrick',alpha=0.1) #this spans the complete line width\n",
+    "ax0.set_title(r'Integrated Flux = {:.0f} mJy$\\,$km$\\,$/s'.format(np.round(np.trapz(yin, xin),-2)), fontsize=12)\n",
+    "\n",
+    "ax0.set_xlabel(r'Velocity [km$\\,$s$^{-1}$]', fontsize=12)\n",
+    "ax0.set_ylabel(r'Integrated line flux [Jy]', fontsize=12)\n",
+    "plt.xlim(-5.5,7.5),plt.ylim(-0.7,5)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e132f45-3c33-4115-a0ec-cd4023bc3fb9",
+   "metadata": {},
+   "source": [
+    "Well, that's better. Not perfect, but better. \n",
+    "\n",
+    "Immediately you can see that gradually more of the more red-shifted and blue-shifted data are being pushed towards the line centre. Due to data imperfections at this resolution (and unresolved source structures which we are not accounting for), you can never make this perfect. But this is now within about ~80-90% of the total flux we measured earlier. \n",
+    "\n",
+    "### Step C3: getting the inclination right\n",
+    "\n",
+    "Let's now take a step back though and understand what gofish means when it asks for an inclination."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "e011d826-3a97-4497-818c-778d2023b7ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x299debb10>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r_min, r_max = 0.0,8.0    # We will average the spectrum from 0--8'' (arcsec)\n",
+    "inc, PA      = 45.0, 342.0  # Let's now use a much lower inclination...\n",
+    "x0, y0       = 0.0, 0.0     # the central pixel for which r=0'' is measured from\n",
+    "xin, yin, dyin = cube.integrated_spectrum(r_min=r_min, r_max=r_max, inc=inc,\n",
+    "                                          PA=PA, x0=x0, y0=y0,empirical_uncertainty=True)\n",
+    "                                          # xin [m/s], yin [Jy], dyin [Jy] Note Jy as now integrated unit\n",
+    "\n",
+    "fig, ax0 = plt.subplots()\n",
+    "# given gofish auto-uses m/s and a more natural unit for our data (with channel widths 100s m/s) is km/s, we can simply divide x/1000. to convert to km/s (as below)...\n",
+    "ax0.errorbar(xin/1000., yin, dyin, fmt=' ', capsize=1.5, capthick=1.5, color='firebrick', lw=1.0) #note only works when mstar and dist are called\n",
+    "ax0.step(xin/1000., yin, where='mid', color='firebrick', lw=2.0,label=r'C$^{18}$O')\n",
+    "ax0.plot([-100,100],[0.0,0.0],color='k',ls=':')\n",
+    "ax0.plot([1.43,1.43],[-5,5],color='firebrick',ls='--',alpha=0.5,label='line centre')\n",
+    "plt.fill_between([-2.4,5.6],[-1,-1],[5,5],color='firebrick',alpha=0.1) #this spans the complete line width\n",
+    "ax0.set_title(r'Integrated Flux = {:.0f} mJy$\\,$km$\\,$/s'.format(np.round(np.trapz(yin, xin),-2)), fontsize=12)\n",
+    "\n",
+    "ax0.set_xlabel(r'Velocity [km$\\,$s$^{-1}$]', fontsize=12)\n",
+    "ax0.set_ylabel(r'Integrated line flux [Jy]', fontsize=12)\n",
+    "plt.xlim(-5.5,7.5),plt.ylim(-0.7,4.5)\n",
+    "plt.legend()\n",
+    "#print(xin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c88db40-4a96-4fca-aa87-042b3e081f77",
+   "metadata": {},
+   "source": [
+    "Test what happens now if you alter the inclination with a 8'' aperture max radius with either 80 degrees inclination, or with 45 degrees inclination.\n",
+    "\n",
+    "...\n",
+    "\n",
+    "...\n",
+    "\n",
+    "...\n",
+    "\n",
+    "More flux is measured with the *non-physical* inclination! \n",
+    "\n",
+    "This is an important lesson in data analysis of low resolution interferometric cubes: the data resolution of our map is 4.1''x 2.7'', and there are only a few radial beams of data. As such, an 80-degree inclined disk can be smoothed into a morphology with a much smaller axis ratio (that would appear as a resolved ~45 degree inclined blob...).\n",
+    "\n",
+    "To accurately extract the total flux, one therefore needs to inspect the actual data with respect to the extraction mask. We will use our moment 0 map from earlier and over-plot these different gofish apertures to see which pixles this is picking up emission from... "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "1791838c-2865-45e7-8366-4ed04eb4a7a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Provided cube is only 2D. Shifting not available.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(11.5, 10, '80.0$^\\\\circ$')"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "file2   = 'DraChi.briggs0.C18O.velcor.fixVel.lsrk.im.image_M0.fits' #this is just what bettermoments saved our Mom-0 as...\n",
+    "cubeMom = imagecube(loc+file2, FOV=24.0)\n",
+    "\n",
+    "inc1,inc2=45.,80.\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
+    "ax[0].imshow(cubeMom.data, origin='lower', extent=cubeMom.extent, cmap=cmocean.cm.ice_r)\n",
+    "cube.plot_mask(ax=ax[0], r_min=r_min, r_max=r_max, PA_min=-180.0, PA_max=180.0, mask_frame='disk',inc=inc1, PA=PA)\n",
+    "ax[0].set_xlabel('Offset (arcsec)')\n",
+    "ax[0].set_ylabel('Offset (arcsec)')\n",
+    "\n",
+    "ax[1].imshow(cubeMom.data, origin='lower', extent=cubeMom.extent,cmap=cmocean.cm.ice_r)\n",
+    "cube.plot_mask(ax=ax[1], r_min=r_min, r_max=r_max, PA_min=-180.0, PA_max=180.0, mask_frame='disk',inc=inc2, PA=PA)\n",
+    "ax[1].set_xlabel('Offset (arcsec)')\n",
+    "ax[0].text(11.5,10,s=str(inc1)+r'$^\\circ$',color='w',fontsize=12)\n",
+    "ax[1].text(11.5,10,s=str(inc2)+r'$^\\circ$',color='w',fontsize=12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcb8b6a8-fbc3-47e9-ae40-6e7766712bc4",
+   "metadata": {},
+   "source": [
+    "So you can see that although in the case that we did not use a Keplerian mask, all the emission remained within the mask if we selected a aperture that covered the full disk extent. Due to the noise properties of interferometric maps, it is often better to integrate total fluxes in pixels as close in to the centre of the map as possible, since noise scales with the 'primary beam', which is a effectively a Gaussian map with FWHM ~ lambda/D (D=dish diameter). As such, although we might sum to get the same total flux using a much larger aperture (with i=80degrees, and rout=200'') we are likely to be less skewed by noise if here we used i=45degrees and rout=8''.\n",
+    "\n",
+    "Further complexity arises however if one then shifts this data into the disk frame, since a 45 degree inclined disk would not match the velocities well, and so much of the emission is then not picked up. In this instance, an 80 degree disk is required to recover as much flux as possible.\n",
+    "\n",
+    "So: BE CAREFUL how you specify your masks and be sure to check you are not over-clipping / under-masking. Always image your data and apertures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8c69e66-b3c1-4739-90aa-69643361ac14",
+   "metadata": {},
+   "source": [
+    "### Step C5: Image centering\n",
+    "gofish also lets us estimate the central pixel of the line to more accurately pull out total fluxes.\n",
+    "\n",
+    "This can be called with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "e6c0470d-2370-4105-b0fc-eccb05132969",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Peak SNR at (x0, y0) = (1.71\", -3.43\").\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x0s, y0s, SNR = cube.find_center(dx=4., dy=4., r_min=r_min, r_max=r_max,\n",
+    "                                 SNR='peak', inc=inc, PA=PA, mstar=mstar, dist=dist, resample=10)\n",
+    "\n",
+    "# Note this function can take time to run for progressively larger maps. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "16e1c4d3-ab09-4e0a-a2de-ce53c63e7680",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Offset (arcsec)')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# So now we can redo our earlier plots and use this updated set of values x0,y0\n",
+    "#print(SNR)\n",
+    "r_min, r_max = 0.0,8.0\n",
+    "inc, PA      = 45.0, 342.0\n",
+    "mstar, dist  = 3.0, 400.\n",
+    "x0, y0       = 1.71, -3.43  # above...\n",
+    "\n",
+    "xin, yin, dyin = cube.integrated_spectrum(r_min=r_min, r_max=r_max, inc=inc,\n",
+    "                                          PA=PA, x0=x0, y0=y0,empirical_uncertainty=True)#, mstar=mstar, dist=dist)\n",
+    "                                          # xin [m/s], yin [Jy], dyin [Jy] Note Jy as now integrated unit\n",
+    "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(12,6))\n",
+    "ax[0].errorbar(xin/1000., yin, dyin, fmt=' ', capsize=1.5, capthick=1.5, color='firebrick', lw=1.0) #note only works when mstar and dist are called\n",
+    "ax[0].step(xin/1000., yin, where='mid', color='firebrick', lw=2.0,label=r'C$^{18}$O')\n",
+    "ax[0].plot([-100,100],[0.0,0.0],color='k',ls=':')\n",
+    "ax[0].plot([1.43,1.43],[-5,5],color='firebrick',ls='--',alpha=0.5,label='line centre')\n",
+    "ax[0].fill_between([-2.4,5.6],[-1,-1],[5,5],color='firebrick',alpha=0.1) #this spans the complete line width\n",
+    "ax[0].set_title(r'Integrated Flux = {:.0f} Jy$\\,$km$\\,$/s'.format(np.round(np.trapz(yin, xin),-2)), fontsize=12)\n",
+    "\n",
+    "ax[0].set_xlabel(r'Velocity [km$\\,$s$^{-1}$]', fontsize=12)\n",
+    "ax[0].set_ylabel(r'Integrated line flux [Jy]', fontsize=12)\n",
+    "ax[0].set_xlim(-5.5,7.5),ax[0].set_ylim(-0.9,4.0)\n",
+    "#plt.legend()\n",
+    "ax[1].imshow(cubeMom.data, origin='lower', extent=cubeMom.extent, cmap=cmocean.cm.ice_r)\n",
+    "ax[1].contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[350, 1000,3500])\n",
+    "cube.plot_mask(ax=ax[1], r_min=r_min, r_max=r_max, PA_min=-180.0, PA_max=180.0, mask_frame='disk',inc=inc, PA=PA)\n",
+    "ax[1].set_xlabel('Offset (arcsec)')\n",
+    "ax[1].set_ylabel('Offset (arcsec)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2b9afc9-968d-4261-8dce-f2380235deb4",
+   "metadata": {},
+   "source": [
+    "Did this do a better job? It isn't clear. \n",
+    "\n",
+    "It looks a little low. \n",
+    "\n",
+    "And this doesn't match the continuum peak, nor what appears to be the peak pixel in the map either. So I might treat this output with suspicion for now and revert to the previous values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "bc70fda8-6b3a-4fe7-9356-107c54ef1d8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Offset (arcsec)')"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r_min, r_max = 0.0,8.0    # We will average the spectrum from 0--10'' (arcsec)\n",
+    "inc, PA      = 45.0, 342.0 # or with the lower inc...\n",
+    "mstar, dist  = 3.0, 330.   # The most controversial parameters. Here we take the values not presented in that paper. [Msolar], [parsecs]\n",
+    "x0, y0       = 0.0, 0.0  # above...\n",
+    "\n",
+    "xin, yin, dyin = cube.integrated_spectrum(r_min=r_min, r_max=r_max, inc=inc,\n",
+    "                                          PA=PA, x0=x0, y0=y0,empirical_uncertainty=True)\n",
+    "                                          # xin [m/s], yin [Jy], dyin [Jy] Note Jy as now integrated unit\n",
+    "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(12,6))\n",
+    "ax[0].errorbar(xin/1000., yin, dyin, fmt=' ', capsize=1.5, capthick=1.5, color='firebrick', lw=1.0) #note only works when mstar and dist are called\n",
+    "ax[0].step(xin/1000., yin, where='mid', color='firebrick', lw=2.0,label=r'C$^{18}$O')\n",
+    "ax[0].plot([-100,100],[0.0,0.0],color='k',ls=':')\n",
+    "ax[0].plot([1.43,1.43],[-5,5],color='firebrick',ls='--',alpha=0.5,label='line centre')\n",
+    "ax[0].fill_between([-2.4,5.6],[-1,-1],[5,5],color='firebrick',alpha=0.1) #this spans the complete line width\n",
+    "ax[0].set_title(r'Integrated Flux = {:.0f} Jy$\\,$km$\\,$/s'.format(np.round(np.trapz(yin, xin),-2)), fontsize=12)\n",
+    "\n",
+    "ax[0].set_xlabel(r'Velocity [km$\\,$s$^{-1}$]', fontsize=12)\n",
+    "ax[0].set_ylabel(r'Integrated line flux [Jy]', fontsize=12)\n",
+    "ax[0].set_xlim(-5.5,7.5),ax[0].set_ylim(-0.9,4.0)\n",
+    "ax[0].legend(loc='upper left')\n",
+    "ax[1].imshow(cubeMom.data, origin='lower', extent=cubeMom.extent, cmap=cmocean.cm.ice_r)\n",
+    "ax[1].contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 2500])\n",
+    "cube.plot_mask(ax=ax[1], r_min=r_min, r_max=r_max, PA_min=-180.0, PA_max=180.0, mask_frame='disk',inc=inc, PA=PA)\n",
+    "ax[1].set_xlabel('Offset (arcsec)')\n",
+    "ax[1].set_ylabel('Offset (arcsec)')\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5397a54-943d-4107-9d39-68713c55f8eb",
+   "metadata": {},
+   "source": [
+    "Overall -- it appears to be very hard at this resolution to truly get a good understanding of the kinematics. Likely this is due to unresolved sub-structure in the data that is not well accounted for by a single Keplerian mask. Whilst this comlicates the analysis, this might mean that some under-lying structure is present in this data that we can hunt for... Let's now give that a go with some of the other goFish tools available to us.\n",
+    "\n",
+    "# Stage D: Imaging asymmetric data\n",
+    "\n",
+    "### Step D1: plotting tools to image asymmetries & sub-structures\n",
+    "\n",
+    "goFish also allows us to plot tear-drop plots. Note that asymmetric emission can manifest in moment maps too (so take a look back at these and see if you think there are any significant asymmetries in our data).\n",
+    "\n",
+    "If we use these with physical parameters of the disk with a tear drop plot, we see that there is some evidence of asymmetric emission.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "4b555ee2-9edc-48e4-83e5-0e7d368673d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Velocity (km/s)', ylabel='Radius (arcsec)'>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cube.plot_teardrop(inc=80, PA=PA, mstar=mstar, dist=dist, x0=0.0, y0=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3775267d-5cf8-4f77-a820-eba9b8da54a6",
+   "metadata": {},
+   "source": [
+    "However -- this is probably not a great way to extract sub-structure/asymmetric emission, firstly this plot is 2-dimensional, and secondly the signal-to-noise isn't exceptionally high that any asymmetries are immediately obvious.\n",
+    "\n",
+    "### Stage D2: getting mean intensity 1-D profiles\n",
+    "\n",
+    "In general, for low-resolution data, we might be better off first trialing to see if an average 1D profile of the data (imaged back into 2D) and then subtracted off the 2D map shows evidence of asymmetric structure. Gofish also allows us to do just this..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "28a760e6-1922-4c57-8e2e-3d4c1b6c2c36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Integrated Intensity (Jy/beam m/s)')"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x0,y0=0.0, 0.0\n",
+    "x, y, dy = cubeMom.radial_profile(inc=45, PA=PA, x0=x0, y0=y0)\n",
+    "fig, ax = plt.subplots(constrained_layout=True)\n",
+    "ax.errorbar(x, y, dy)\n",
+    "ax.plot([0,12],[0,0],'--k')\n",
+    "ax.set_yscale('linear')\n",
+    "ax.set_xlabel('Radius (arcsec)')\n",
+    "ax.set_ylabel('Integrated Intensity (Jy/beam m/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52777d2f-ec61-4b0d-918f-ee657e797949",
+   "metadata": {},
+   "source": [
+    "This produces a nice brightness profile, centred on (0,0) which shows the disk is bright out to ~7-8''. goFish has a tool background_residual which then takes this, maps it back to a 2D structure and then subtracts this from the data.\n",
+    "\n",
+    "### Step D3: from profiles to 2-D empirical models\n",
+    "\n",
+    "To visualise this, let's plot this empirical model (overlaid with the data contours) side by side with the residual map (and also overlaid with these contours) to see what we find..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "7eeaa4e7-5581-4116-8e25-c48be33491fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "Net flux in map:  100.0 Jy/beam m/s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x0,y0=0.0, 0.0\n",
+    "divpal=sns.diverging_palette(220, 20, as_cmap=True)\n",
+    "linpal=sns.color_palette(\"rocket\", as_cmap=True)\n",
+    "\n",
+    "residual   = cubeMom.background_residual(inc=45., PA=PA, x0=x0, y0=y0)\n",
+    "background = cubeMom.background_residual(inc=45., PA=PA, x0=x0, y0=y0, background_only=True)\n",
+    "#background = cubeMom.convolve_with_beam(data=background)\n",
+    "vmin,vmax=-850,850\n",
+    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,constrained_layout=True,figsize=(12,6))\n",
+    "im1 = ax1.imshow(background, origin='lower', extent=cubeMom.extent, cmap=linpal)\n",
+    "ax1.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 1500],colors='w',alpha=0.5)\n",
+    "im2 = ax2.imshow(residual, origin='lower', extent=cubeMom.extent, cmap=divpal, vmin=vmin, vmax=vmax)\n",
+    "cb = plt.colorbar(im2, pad=0.02)\n",
+    "ax2.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 1500],colors='k',alpha=0.5)\n",
+    "ax2.contour(residual, origin='lower', extent=cubeMom.extent, levels=[-400,400],colors='w',linestyle='--')\n",
+    "ax2.set_xlabel('Offset (arcsec)')\n",
+    "ax2.set_ylabel('Offset (arcsec)')\n",
+    "cb.set_label('Residiual (Jy/beam m/s)', rotation=270, labelpad=13)\n",
+    "cube.plot_beam(ax=ax2)\n",
+    "\n",
+    "pixPerBeam = (4.08/0.5)*(2.68/0.5)\n",
+    "beamsInMap = np.count_nonzero(~np.isnan(residual)) / pixPerBeam # this accounts for beam smoothing in the data, cannot simply sum pixel vals\n",
+    "print('Net flux in map: ',np.round(np.nansum(residual)/beamsInMap,-2), 'Jy/beam m/s')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29ea51de-f56f-491a-a4dd-83fafa422dac",
+   "metadata": {},
+   "source": [
+    "Well -- there are certainly some large residuals. But these could well be due to use defining a poor model. \n",
+    "\n",
+    "The only way to check this is by altering the model parameters; inc, PA, x0 and y0. Note this is not a Keplerian model, so inc can be played with readily, but I would suggest keeping PA fixed since this is relatively well known.\n",
+    "\n",
+    "As a first trial, I would recommend trialling different x0 y0 values in steps of +-0.5 (the pixel sizes are 0.5''). As an example (0.5,0.5):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "cdc86e1f-cc4f-44b5-ab12-d4112af89d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "Net flux in map:  100.0 Jy/beam m/s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x0,y0=0.5, 0.5\n",
+    "divpal=sns.diverging_palette(220, 20, as_cmap=True)\n",
+    "linpal=sns.color_palette(\"rocket\", as_cmap=True)\n",
+    "inc,PA = 45,342\n",
+    "residual   = cubeMom.background_residual(inc=inc, PA=PA, x0=x0, y0=y0)\n",
+    "background = cubeMom.background_residual(inc=inc, PA=PA, x0=x0, y0=y0, background_only=True)\n",
+    "#background = cubeMom.convolve_with_beam(data=background)\n",
+    "vmin,vmax=-850,850\n",
+    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,constrained_layout=True,figsize=(12,6))\n",
+    "im1 = ax1.imshow(background, origin='lower', extent=cubeMom.extent, cmap=linpal)\n",
+    "ax1.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 1500],colors='w',alpha=0.5)\n",
+    "im2 = ax2.imshow(residual, origin='lower', extent=cubeMom.extent, cmap=divpal, vmin=vmin, vmax=vmax)\n",
+    "cb = plt.colorbar(im2, pad=0.02)\n",
+    "ax2.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 1500],colors='k',alpha=0.5)\n",
+    "ax2.contour(residual, origin='lower', extent=cubeMom.extent, levels=[-400,400],colors='w',linestyle='--')\n",
+    "ax2.set_xlabel('Offset (arcsec)')\n",
+    "ax2.set_ylabel('Offset (arcsec)')\n",
+    "cb.set_label('Residiual (Jy/beam m/s)', rotation=270, labelpad=13)\n",
+    "cube.plot_beam(ax=ax2)\n",
+    "\n",
+    "pixPerBeam = (4.08/0.5)*(2.68/0.5)\n",
+    "beamsInMap = np.count_nonzero(~np.isnan(residual)) / pixPerBeam # this accounts for beam smoothing in the data, cannot simply sum pixel vals\n",
+    "print('Net flux in map: ',np.round(np.nansum(residual)/beamsInMap,-2), 'Jy/beam m/s')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7666cffb-9c06-409f-8caf-34ec5d5dc01b",
+   "metadata": {},
+   "source": [
+    "Well, as a first trial, that looked worse. Can you find a solution that only yields residuals in the southern 'tail' of the disk?...\n",
+    "\n",
+    "Overall there are 9 permutations, of which you have trialled 2 so far. Which one do you think demonstrates there is asymmetric emission in the disk most convincingly?\n",
+    "\n",
+    "x0, y0 =  0.0,  0.0\n",
+    "\n",
+    "x0, y0 =  0.0, -0.5\n",
+    "\n",
+    "x0, y0 =  0.0,  0.5\n",
+    "\n",
+    "x0, y0 =  0.5, -0.5\n",
+    "\n",
+    "x0, y0 =  0.5,  0.5\n",
+    "\n",
+    "x0, y0 =  0.5,  0.0\n",
+    "\n",
+    "x0, y0 = -0.5, -0.5\n",
+    "\n",
+    "x0, y0 = -0.5,  0.5\n",
+    "\n",
+    "x0, y0 = -0.5,  0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e752f25-0fc2-4dc4-9c27-a41cfe5b36ac",
+   "metadata": {},
+   "source": [
+    "### Step D4: Finding a best-fit model [*Advanced exercise*]\n",
+    "\n",
+    "We have assumed inc=45, PA=342. But the beam properties could have altered the apparent disk orientation as well as its inclination. This could be subtle, but this is a check we should make. \n",
+    "\n",
+    "Is there a combination of inc and PA which fully removes all contours? How could you go about finding this? What values do the best job? What might this tell you about the possibility of there being asymmetric emission in these data? What might you expect the net flux in the map to be? What about the error properties? What might you have to do to the residual maps to fairly compare?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76687998-2791-4137-bcb4-78613428acfc",
+   "metadata": {},
+   "source": [
+    "## Only proceed past here once you've had a proper think through the above..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a4b51dc4-13dc-492b-a475-18465a10b3d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "Net flux in map:  800.0 Jy/beam m/s\n",
+      "Res RMS:  121.74529184098373 Jy/beam m/s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1idcuREREFC5WrlyJ48ePw2q14vTp07j77rt7PS8IAn75y1/CaDTCarVi06ZNKCgo6DUmLi4O//73v9HW1gaz2Yx//vOfiIyMDOSnMSh+Xca4bNkyLFu2rM/nJEnC73//e/zsZz/DqlWrAAAvv/wykpOT8d577+G2227zZ2hEREREl+C1CxEREckiCJ4jkOcj2YLWoL68vBxGoxGLFy/ueSw6OhqzZ8/G7t27+32dzWaDxWLpdRARERH5G69diIiIiIaHoCW7jEYjACA5ObnX48nJyT3P9eWpp55CdHR0z5GZmenXOImIiIgAXrsQERHRRUQx8AfJNuzerUceeQRms7nnqKqqCnZIRERERP3itQsRERFRYAUt2ZWSkgIAqK+v7/V4fX19z3N90Wg0MBgMvQ4iIiIif+O1CxEREdHw4NcG9ZeTm5uLlJQUbN68GVOmTAEAWCwW7N27F9/73veCFRaRz6jUGbLGSZD8HAmR95TqdJ/O57TX+HQ+omDgtQuFuzb2k6NhzNdfv1G8MUEDEAQBQgCbxgfyXOHAr8mu9vZ2nDt3rufj8vJyHDlyBHFxccjKysL999+PJ554Avn5+cjNzcWjjz6KtLQ03HDDDf4Mi4iIiKhPvHYhIiIiGv78muw6cOAArrzyyp6PH3zwQQDA2rVr8a9//QsPPfQQOjo68O1vfxsmkwnz58/Hhg0boNVq/RkWERERUZ947UJERESyCKLnCOT5SDZBkqRhvYbKYrEgOjoaZrOZPTAopHAZI9EXuIxx5OHv5/7xvaFQxWWMRF/gMsaRR+7v5+5xu3/1ICK1moDF1261oejR3/H6Qaag9ewiIiIiIiIiIhqO2LMrtLEOjoiIiIiIiIiIwgaTXUREREREREREFDa4jJGIiIiIiIiIyBtsUB/S+G4REREREREREVHYYGUXEREREREREZE3RMFzBPJ8JBuTXUReUqrTgx0C0bAj9/vGaa/xcyRERCNPm8US7BCIhh253zdRBoOfIyGiwWCyi4iIiIiIiIjIC4IgQBACV20VyHOFA/bsIiIiIiIiIiKisMFkFxERERERERERhQ0uYyQiIiIiIiIi8oYgAEIA64e4jNErrOwiIiIiIiIiIqKwwcouIiIiIiIiIiKvCAGutmJllzdY2UVERERERERERGGDlV1ERERERERERF4QRBGCGLj6oUCeKxww2UV0EaU6PdghDEuSJAU7hGFJYJPJS8j5HnTaawIQCRHR8NBmsQQ7BKIRTc73YJTBEIBIiOhiTA0SERERERER0Yjhcrlgt9uDHQb5ESu7iIiIiIiIiChsSZKE5uZmlJaWorS0FOfPn8fChQsxb968wU8qBLhBPVeFeIXJLiIiIiIiIiIKK52dnSgvL+9JcJnN5l7PV1dXBykyCgQmu4iIiIiIiIho2HO5XNi1axeKi4tRW1vbq7ewQqFAdnY2Ro0ahVGjRiE5OXlI5xIEEYIQwAb1ATxXOGCyi4iIiIiIiIiGNbvdjnfffRenT5/ueSwpKaknuZWdnQ2VShXECCmQmOwiIiIiIiIiomGroaEBb7zxBpqamqBQKLB48WKMHz8eBn/uhMmeXSGNyS4iIiIiIiIiGpaOHj2KdevWweFwwGAw4KabbkJWVlaww6IgY7KLiIiIiIiIiIYVh8OB9evX49ChQwCAUaNGYfXq1dDr9YEJQBA9R6CwZ5dXmOwiIiIiIiIiomGjubkZb775JoxGIwRBwKJFi7BgwQKIIhNC5MFkF40ISnV6sEMImIt3HKHQFqx/K2GYr/eX+/3stNf4ORIiIv9ps1iCHQIR+Yjc7+cof/aXCiOnTp3C+++/D5vNBr1ejzVr1iAvLy/YYVGIYbKLiIiIiIiIiEKay+XCxo0bsWfPHgBAdnY2brrpJkRFRQUlHkEUIIiBu4kcyHOFAya7iIiIiIiIiChkOZ1O/N///R8qKioAAPPnz8dVV13FZYvULya7iIiIiIiIiChkffrpp6ioqIBWq8WNN96IMWPGBDskQBA8RyDPR7Ix2UVEREREREREIenkyZPYt28fAGDNmjXIz88PckQ0HDDZRUREREREREQhx2q14oMPPgDgWbroq0RXY2MjtFrt0Pp9CaLnCJRAnisMMNlFRERERERERCFHEAS43W4AGPKOi5Ikoby8HLt27cK5c+cwd+5cLFmyxBdhUghisouIiIiIiIiIQo5Go8GUKVOwf/9+7N69e1AJL6fTiRMnTmD37t2or68H4EmidXV1+TpcCiFMdhERERERERFRSCoqKsKBAwdQUlKChoYGJCUlyXpdZ2cnDhw4gH379qG9vR0AoFarMXXqVMyePRtxcXFDiksQBAgBbBofyHOFAya7iIiIiIiIiCgkxcXFYezYsTh9+jQ2b96MyZMnQ6FQQBRFiKJ4yf+7XC4cOXIER48ehcPhAAAYDAbMnj0b06ZNg06nC/JnRIHAZBcNa0p1erBDGDJJkoIdAo0wcr7mwuHOkdyfD057jZ8jISL6QpvFEuwQiChEyf35EGUw+DmS0DNv3jycPn0aZ86cwZkzZ2S/LjU1FUVFRRg/fjwUCoVvgxJFzxEogTxXGGCyi4iIiIiIiIhCVkZGBhYtWoSKigq4XC643e6e/178/y6XC5IkIT09HUVFRcjOzg6Lm7jkPSa7iIiIiIiIiCikLVq0KNgh9MKeXaGNdXBERERERERERBQ2mOwiIiIiIiIiIqKwwWWMRERERERERETeEARACGD9EJcxeoWVXUREREREREREFDZY2UVERERERERE5A1BCGy1FSu7vMLKLiIiIiIiIiIiChus7CIiIiIiIiIi8oYgQhAD2bOLtUreYLKLQpJSnR7sEIZMkqRghzBkAktl/SqUv0Z8HVsofy3J/XnjtNf4ORIiGs7aLJZgh0BEI4TcnzdRBoOfIyEKXUwNEhERERERERFR2GBlFxERERERERGRNwQxsEsLuYzRK3y3iIiIiIiIiIgobDDZRURERERERETkBUEQAn4M1m9+8xsIgoD777+/5zGr1Yp77rkH8fHxiIyMxJo1a1BfX9/rdZWVlVixYgUiIiKQlJSEH//4x3A6nYOOI5CY7CIiIiIiIiIiCkP79+/HCy+8gEmTJvV6/IEHHsCHH36IN998E1u3bkVtbS1Wr17d87zL5cKKFStgt9uxa9cuvPTSS/jXv/6Fxx57LNCfwqAw2UVERERERERE5A1RCPzhpfb2dtx+++3429/+htjY2J7HzWYz/vGPf+B3v/sdrrrqKkyfPh0vvvgidu3ahT179gAAPv30U5w6dQqvvPIKpkyZgmXLluFXv/oVnnvuOdjtdp+9jf7CZBcRERERERER0TBgsVh6HTabrd+x99xzD1asWIHFixf3evzgwYNwOBy9Hh87diyysrKwe/duAMDu3bsxceJEJCcn94xZunQpLBYLTp486ePPyve4GyMRERERERERkTeCtBtjZmZmr4cff/xx/PznP79k+GuvvYZDhw5h//79lzxnNBqhVqsRExPT6/Hk5GQYjcaeMRcnurqf734u1DHZRUREREREREQ0DFRVVcFgMPR8rNFo+hzzgx/8ABs3boRWqw1keCGDyS4KOKU6PdghDIkkScEO4bKGskvHJXNB3lxyzyn6MLZgccv495f9NeLjtyOUvzblxubLr19fU6kzZI1z2Kv9HAkRBVqbxRLsEIiIvCb3Z1fURYkTCn0Gg6FXsqsvBw8eRENDA6ZNm9bzmMvlwrZt2/CnP/0Jn3zyCex2O0wmU6/qrvr6eqSkpAAAUlJSsG/fvl7zdu/W2D0mlLFnFxERERERERGRFwRBCPgh19VXX43jx4/jyJEjPceMGTNw++239/y/SqXC5s2be15z5swZVFZWoqioCABQVFSE48ePo6GhoWfMxo0bYTAYUFhY6Ls30k9Y2UVEREREREREFCaioqIwYcKEXo/p9XrEx8f3PP7Nb34TDz74IOLi4mAwGHDfffehqKgIc+bMAQAsWbIEhYWF+PrXv45nnnkGRqMRP/vZz3DPPff0uXQy1DDZRURERERERETkjSA1qPeVZ599FqIoYs2aNbDZbFi6dCn+/Oc/9zyvUCiwbt06fO9730NRURH0ej3Wrl2LX/7ylz6Nw1+Y7CIiIiIiIiIiCmOff/55r4+1Wi2ee+45PPfcc/2+Jjs7Gx9//LGfI/MPJruIiIiIiIiIiLwhCp4jkOcj2dignoiIiIiIiIiIwgaTXUREREREREREFDa4jJGIiIiIiIiIyAuCIEIIYIP6QJ4rHDDZRT6jVKcHO4QhkyQp2CH0SxDkrdEWMPA4uXMpZP5AlT2f6Lsf0KKMzxMA3PDtv6mcrxG3zK8jt+SWNc7lljdO5lsiSyh/LwSL5OOvJSIKvjaLJdghEBERkR8w2UVERERERERE5A1B8ByBPB/Jxjo4IiIiIiIiIiIKG6zsIiIiIiIiIiLygiAIAe7Zxcoub7Cyi4iIiIiIiIiIwgaTXUREREREREREFDa4jJGIiIiIiIiIyBtsUB/SWNlFRERERERERERhg5VdRERERERERETeEAXPEcjzkWys7CIiIiIiIiIiorDByi4aESRJCnYI/ZK7hawAeeOUomLAMQpRXp5bIXMrXbnzyYlN7ucpynzf3DL/7SXIG+dyuwcc43S7ZM3llnm/QYC8+VzSwLEBgFvGOLlfl77+3pIzX6hvu6xUpw84xmmvCUAkRERERANrs1gGHBNlMAQgkuFFEEQIMv9e8tX5SD6+W0REREREREREFDaY7CIiIiIiIiIiorDBZYxERERERERERN4QBM8RyPORbKzsIiIiIiIiIiKisMHKLiIiIiIiIiIibwii5wjk+Ug2vltERERERERERBQ2WNlFREREREREROQFAQKEAPbREsCeXd5gZRcREREREREREYUNJruIiIiIiIiIiChscBkjDUipTg92CP2SJCnYIfRLbkmrKLPRoFJUyBqnEAeeTy3K+9ZXK2SOkzmfnM9Bbnmu6OOSYZfkljXO6XYNOMYhDTwGAGxOh6xxosz3xCEjNgBwyvhU3TLfj2CQ+30fyLJyIgotbRZLsEOgEGc+uEPWOLdj4N/Vro42WXMlXH29rHFENEyIoucI5PlINia7iCgkaLQaRMcYEG2IQlR0FAzdhyEKHe2d2LZlJ1qaTcEOk4iIiAiSJMFmt8Pc3oHWxgZYOjpg6ejsOSJ1WlwxdRLiow3BDpWIaERisouIAkYQBMTGxSAhMQ4JSQmIT4j1JLYMUVBrVJ4xfVQxxSfG4ba1a7D9s904fvhUoMMmIiKiEcrtdqPV0oYmkwmNLSY0m82wtHfA0tEB+4WqL7fdfsnrms0WvLbxMyyYMhETR+Wy2pgoDAlCgBvU8+eIV5jsIiK/UKtVSEiMR2JyAhKTEpCQFI/kpAQolP0vZezs6ESbuR1mkwUWczsslja0t7VjyvSJyM7NxJXXLEB2biY2b9gKW5ctgJ8NERERhTub3YGm1lY0tLSisaUVTa0mNDQ3w+Xqv01AhFaHqKhIGCL1MOgjYNBHIFKnw5Gz51BhbMCWA0dwvq4ei2dODeBnQkRETHYRkU9lZqfjqmsWIDY+9pLnREGA0+FEU2MLGhua0NTYDIupDRZzG9os7XA6nX1WdlWUVWHK9ImYt2g28kbnIPnOJGxe/zkqy6sD8SkRERFRGDtfU4fP9x1Ai/nSXm+S2wWVUon4mBgkxcYgITYG0ZGe5FaUPgIqpbLPnl05qck4crYUO46eQFlNHV5tbsEtY6dj1KhRgfiUiCgQBMFzBPJ8JFvQk10///nP8Ytf/KLXY2PGjEFxcXGQIiKiwdJo1Fixagm0Oi0AoL2tHY31zWhsaEJjYzNaG1thNlkGtbHAkYPHUV1Vi2tXXo24hFisumk5zpwqwfbPdqOr0+rrT4WIqF+8diEKH1a7HR9t3Q6b3bMkMVIfgcS4WCTFxiIhNgYJhkjEREV5vXxIEARMHTMaGUkJWL9nP1rMbXj9jTcwccIEXHXVVdDr9f74dIiI6IKgJ7sAYPz48di0aVPPx0plSIRFRF6aOmMStDotTC0mvPHv99DZ0dXrebk7SvanqaEZr738DuZeMQtTpk/AmMJ8ZOVmYsdnu1F8smRIcxMReYPXLkTh4fCpYtjsDsQaonDLsmug1+l6PS9nN8bLSYyNwVeuuRK7jp3CWZeA4ydOoLS0FFdffTUmTJjAHjxEw5kgeo5Ano9kC4krM6VSiZSUFFljbTYbbLYvevVYuLU0UUjQaDWYOmMSAGDn9n2XJLp8xel0YtuWXSg5fQ5XXbsQCYlxuGb5lRg7vgBbN+1Ea4vJL+clIroYr12Ihr8umw0HT3kqMudOnXxJomswVIYYGPIKAAhoOXagZxnkwmmTMGfsdKxfvx4NjY34cN06HD9xAkuXLEF8fPyQz0tERL2FRGqwpKQEaWlpyMvLw+23347Kysp+xz711FOIjo7uOTIzMwMYKRH1Z9qMSdBoNWhubEFJcanfz1dvbMTrL7+D3dv3welyITM7HV/75i1Y/ZXrMG5CAVSqkMjlE1GY4rUL0fB36GQx7HYHEmJjUJCTNaS5dMlpSJl3NTKuXglDbgEMuflInLWgV4+d9PR03HnnnVi0cCGUSiXOnz+PF/76V7zyyis4fvw4HEOsIiMioi8I0mCa5/jQ+vXr0d7ejjFjxqCurg6/+MUvUFNTgxMnTiAqKuqS8X3dHc3MzITZbIbBYAhk6COGUp0e7BD6FeQv38uSW5YuyixHlbsEUCEOPJ9alJcIUivkjYuK0OOb3/0a1Bo1PnrvE5ScKetznJzPoa8G9X0RL3p/Y2KjMW/RHOSOyup53+12B86ePodTx4pRb2wccD6X5JZ1Xqe7/x2ZujmkgccAgM0p76JWzjkBwCFznJz53DLfj3D4HgwGp70m2CH4ncViQXR0dFj+fua1S+hrY/UcDcC4azP+8fb7sNsdWLloQb/JrsstYxREBfRpGYjMzIU6KhoAIEludBproUtKgahQoq2yFE0HdwMAEq6+vue1zS0t2LJlC86dO9fzu1SjVqOwsBCTJ09GampqSP8eo5EnagT8vpJ77dI97tSHbyBKHxGw+No6OlF43S28fpAp6KUPy5Yt6/n/SZMmYfbs2cjOzsYbb7yBb37zm5eM12g00Gg0gQyRqJdgJbFUMsdplKoBx+gUanlzKQaeCwDmF81ElC4CzY0tMJbVIUrV9zIArTjweVWCvM+zV1KsXcLedbtxPPIoCsaPRsGEfMTERGLWtCmYOW0KWptacfZECc6dKoO1q+9m9nITVFa3fcAxdrdT1lxyP9cu18DnBADIO628qeTluuDG8E+KEXmL1y5Ew9/+Yydgs9mQGBuDURmpcLv6/iVqra245DGFLgIxYychZsxEKLSeax6X3QbTqcNoObofjjYTInPykbn8FkRljYK9uQH1Ozeh6fN1vea5MkGLGRF5OF1egZNlFWht78DOsydwYNcOJMbHYWJhIcaPG4uIfpZXqhPkLaUmIhqJgp7s+rKYmBgUFBTg3LlzwQ6FiGTQ6rSYPH0CAGD/rkNBjaWzvRNH9h7Dkb3HkJKRjDETC5BTkI3YhFjMXjQLMxdMR2VpFc6dLkP1+Rq4nPISXEREl8NrF6LhpbOzE0eKzwIA5kyeKPtGpjLSgPgps2HILYCg8NywcrSZ0XxkL0ynDsPt+OLmVPv5EtRu+RDpi1chfmoRnF2d6Gyqv2TOqIgIzBo/DjMLx6K6oREnyypQZZPQ2NyCLdt3YOvOXRidl4vCMWOQm50FlUrejUgiCgA2qA9pIZfsam9vR2lpKb7+9a8HOxQikmHazElQqVRorG9GWcn5YIfTw1hdD2N1PXZt2YO8MbkYMzEfCSkJyCnIQU5BDpwOJ6rP1+B8SQWqyqrhsPqnoT4RhT9euxANL3v37oXD6URSXCxGZcpr16GKikbmsjVQRkQCALrqa9F66jDaK8vgbDP1+Rpz8TEotDqkzF+C5LlXo/HIXrRX9t3XVBAEZCYnITM5Cer8iTh15gyOnzwNY0MDzpwrxZlzpVAplcjNzkbB6DyMys2FvDp9IqKRKejJrh/96Ee47rrrkJ2djdraWjz++ONQKBT4yle+EuzQiGgAuggtJk0bDwDYv+tgkKPpm91mR/GxMyg+dgaxCbHIHz8Kufk5iIyORE5+NnLys+F2uVBVWYvykvM4X1LZ71JHIiKA1y5Ew1lHRwcOHvRcs8yZPEFWVZdSH4mMpTdCGREJW2sT6nduhrWPKq2+tBzZC6VOj4Tp85AweSbcDhs666ov+xqtRoNpkyZh2qRJqG9sxMnTxThbWgqzpQ1nS0txtrQUClFEXuEEjCkoQEFBAfR6vax4iMiHBPTahCIg5yPZgp7sqq6uxle+8hU0NzcjMTER8+fPx549e5CYmBjs0IhoAFNnToZKpUJDfRPOl/a/E1moaG1qxb6tB7Bv6wHEJ8X1JLti4mOQmZOOzJx0LFg8F8aaBpw6WozS4r4b7RPRyMZrF6Lha8+ePbA7HEiOj0NexsBVXaJag4ylq6GKNMBubkX1J+/C5WU1eMPuLVBodYgdPw1J0+ahbvcW2FoG3jwHAJITE5GcmIgrF8xHfWMjzp4rRUlpGZpaWlBWVoaysjJs+OQTZGVmYurUqRg3bhwb2xMRIQSSXa+99lqwQyCiQYiNj8GU6RMBAPt3hmZV1+U0N7SguaEFB3ceRnRcNDJGZyA3PxuJyQlIzUhGakYy9JEROHbgRLBDJaIQw2sXouGpqakJB7ys6tIlp0NtiIHbYR9Uoqtb3ecfQx2XCH1qJmLyC1G/d6tXrxcEASlJSUhJSsIVc4vQ3NKC8iYTzpw5gzqjERWVlaiorERbeztmz5o1qBiJiMJJ0JNdRDQ8XX3tQigUIspLK1FRVhXscIbE3GJG094WHNl7DJGGSEyeMQHjp47D9LlTce50KTo72M+LiIhoOJMkCR9//DFcLhdGjx6N3PQEWa+zNtRBcrshqobYIUuSYDpzAvrUTGjjkjxLn4awU3F8XBxSCwoxd+5cmEwm7Nm7F4cOHcL2bdtQOG4coqKihhYvEQ1IEEQIAWwaH8hzhQO+W0TktYlTCpGalgyHw4HPN+4Idjg+1W5px84te1Bf2wCVSonZC2cGOyQiIiIaooOHDqG6pgYatRrXLl0qe6mfy9aFrsY6AIA+M3dIMdgtJrjsdogqFdTRsUOa62IxMTFYumQJ0tPSYHc48Nlnn/lsbiKi4YrJLiLySmRUJOYunA0A2LV1H9rb2oMckX/s3LIHkiQhf9woJKcnBTscIiIiGiSz2YzPLySAFi1aBIPB4NXrOyrLAQCRWXlDjESCtbkBAKCLTx7iXL0JgoAlS5ZAEAScOHkSVVXDu+qeaFgQhMAfJBuXMY5gSrW8rZaDQRpCWfdQyLnLJ8jcBkMpKmSNU8kcp1XKK5+PUGoGHKNTyJsrSqm75LElyxbDoNOjvrYBVScqEKuKhEHUyotNVA04Rg1574fcH/Vyv5LscPV+oMWJxtM1GDVxNFYuuQqfvLoBkiShw20fcC6LS95ujm2CvOWRPm806xx4iNzvQdnfqzI/BV9+78udKxiNfOX+/HXaa/wcCdHw0maxBDsECnGO1qZeH0uShI8/+BBWixnpaamYmJt1yZjLUegieiq7IlIyoIyKhuR09BrTVVUqay5tSgasDTXQp2ZAG5cA02lbn+M6yoplzaeMiun1cXJcLCaOG4ujx47jk48/xh3f+DpEUYSokXedRnQ5cn/+RnmZTCbyF1Z2EZFseWNykZWXAbfLje2f7Ax2OH53bOdROGx2xCTGYtTE0cEOh4iIiLxUfPYsSsvKoVCIuHbx1YO6weFoM8PeZoagUCAiZWg3i7sajQAAbUKyX6o0Fi5YAK1Wg/qGBhw9dszn8xPRFwRRCPhB8jHZRUSyaHVazL3Ks3zxyN5jMLWYgxyR/9m6bDi203OhOGneJKi1A1fNERERUWjo6OzEps89ux4WzZqFhPj4Qc/VWVMBANCnZw8pJrupFS67DaJKDU3M4OPpj16vx4J58wAAW7dtR2dnp8/PQUQ0HDDZRUQDEgQBV1+3CNoILUzNJhzddzzYIQXMuWMlMDW2Qq3VYNK8ScEOh4iIiGRwuVx4/6OP0dnZhYSEeMyZOWNI83XUVgIAIlIzh1iRJcHaVA8A0CamDCmm/kybOhVJiYnoslqxfWf4V+ITEfWFyS4iGtDshTORmpkCh92JTR98BpfLNfCLwoQkSTj0+UEAwKiJoxGXFBfkiIiIiGggn2/fgarqGqjVaqxavgwKhbyeoP2xNtXDZbNCodF6liAOZa4LPcB0fkp2iaKIxVdfBQA4fOQo6uvr/XIeohFPEAN/kGx8t4joskaNzcOE6YUAgK3rt4+I5Ytf1lDdgMozFRAEAbOunBnscIiIiOgyThWfwYHDRwAAK5YuGdLyxR6ShK76WgAYcrKrV98u2VvueCc7KwuF48ZCkiRs3LgxaJs/EREFC5NdRNSvqOhIzF9cBAA4vOcozp+rCHJEwXN42yG4HE4kpSchLSct2OEQERFRH0xmMz7ZvAUAUDR7JgpGj/LZ3NbmC8sPE4ZYkdW9DFIU/ZXrAgBcuWgRlEoFKquqUF5e7r8TEY1UghD4g2RjsouI+iQIAhYtuwIqjQr1NQ04tOtIsEMKqq72Lpw7dg4AMLmIvbuIiIhCjdvtxroNn8ButyMjPQ3z58zx6fxdDd3LD4e2k2JUlicB11lbBfix4soQFYVpU6YCAHbs2MHqLiIaUZjsIqI+TZ8zBcnpSXDYHPjs4228QAJw+sApOB1OJKYmsrqLiIgoxOzatQs1tXVQq9VYuXQpRNG3f+rYTS1w2ayenRTjEgc5iwB9Ri4AoL2y1HfB9WP2rFlQKpWorqlhdReRjwmCGPCD5OO7RUSXSE5Nwsy50wAAOzfvRrulPcgRhQZrpxVnj50FwOouIiKiUFJTU4MdF3YeXHLVlYiONvjlPN39tnRJqYN6vS4pFUpdBFw2KzqNNb4MrU+RkXpMm8rqLiIaeZTBDoAolAgyGicoZN4llDtOo1TJGheh1Mgap5cxLl4Z1e9zKrUK119/LSKVOjScrUFHSRNSlZe/YIwR1LJiixEG/lwNksz3V2aTCwfkXdSZBXk7TDYdqoBuygTkpqVjct5oNFQYLxmjk/F5AoBSkLszlLxko9wLWJfbPfAYaeAxACDJfH+d7pGzgycREXmnq6pswDG6zLx+n7PZbHj/gw/gdrsxJicTYzLT4Gy3XHY+USHvzyBRre19ruZGRGbkICIlA5bSMwAAdVySrLkU+ihE5Y0BAHTW10ARoe9znEZmTzBRox14EAB1Wjbq27ai9mQxonfsRVpm1iVjZvq4Yr2zrFjWuIi8sT49LxENb3feeaescS+++OKAY1jZRUS9zFo0E1ExBnS0deDAlv3BDifk2DqtKD/mWXYwrmhCkKMhIiKijRs3wmQyITo6GouvWODXc3VXdmniEj0N5r0giCIiUtIBAB21lT6PrT+6CD3GjJ8IADiyfx+ru4h8RRAAMYDHCGhQbzabex21tbV45ZVXej5uaGjASy+9JGsuVnYRUY+cgmzkTxgNSZKw7ePtcNgcwQ4pJJ09UIzcSaMQlxKPpOyUPqu7/CVCr0NOXhZKzpTBYee/DxERjWynT5/GsePHIQgCrlu5ElrB6dfzOdrMcNmsUGi00MTGw9bcKPu1uqQ0iEoVnJ0dsLU2+zHKS02YOhVnTh5HY70RddVVfVZ3+UtHZydKKyoxZlQeNGp5qwGIaGR65513en1cXl6OSZMm9Tze2NiIlBSZ1a8+j46IhqWIyAgULfbsWnR8/wnU1zQEOSIPhVoJlU7eEs5ACVZ1V0paMm5duwZXXnsFlt9wDYQRcHeHiIioP2azGevXrwcAzC0qQlZWYBI4XY31AABdP8sN7Q4nuuz2Sx7Xp3ni66ir8l9w/QhWdVeNsR4vvfUOPtm6He9/sgluGa0UiIYNQQz8McK0t7fD6fziJobdbodCIa8VzMh7t4hGEEOMAWMK85Gdl4mklEToInT9jl2wbB40Wg2a65txZNfRAEbZP61Bj9nfWIK5dy9H1owxIZXcOXugGC6nE3Ep8cgqzPH7+QonjcWNt61EhN7zb5iRnY4ZRVP9fl4iIqJAajWbcbL4DErPV6CuvgEdnZ19JmbcbjfWrVsHq82GtNRUzJ8/P2AxWi8sZdQmJl/ynLmzC//ethf/2rwTB0vP9yR3FBoddIme5FgglzBebMLUqVAoFGisN6L0jLyeWkNx9NRpvP7BOnR0dgEAKmpqsOfQEb+fl4jCgyRJePbZZ2Gz2XDixAkAwMGDB5GRkSHr9VzGSBSmomMNuHXtaiiVvb/NrVYbHCYrTM1mmFtMMDWbkZyRjNTMVDidTmz9eHtI3HVT67WYvHo+NJGe5M6o+ROQPWsMTNWNaK1sgLmyEZ0tbUGLz9ZpRfHeUxg/bxKmXDUdHaZ2NNc2+eVcefk5uHKppwdJdUUNzpdWYv5VRZg5dxrOnjoHs+nyTXiJiIiGgxaTCS+9/lavu/gAoNVqkDqqAPHx8YhPSEBCfDyqqqpQUVkJtUqF666/Xvadfl+wNnkqu7TxiYhIy4S1tgIA0GG14YN9R9ButQIAdheX4uC5CmTEx2L+tcshiCKcXR1wDNA83190EXpMmjETh/fuwd4d22CIiUFSyuB2lRzI2bJyfLptBwAgOz0deVmZ+Gz3Huw6eAjj8kchNjraL+clCiRBEAJ6Mz6Ubvz7k8vlwptvvolf//rXmDNnDhQKBZYsWYK5c+di06ZN+M53viNrHlZ2EYWpKTMnQalUwm53oKG+Ce1tHZAkCVqtBklpSSiYmI+ZC2fimtWLMWmWp6z94PZDsLQGP3Gi0mkwZc0CRMREwmrpQMnWo7B32aBUq5CQl4b8RVMw8xvXoOju5Rh77QwkF2ZDHdl/1Zq/lBwoRn1FHRRKJebeuBDxaQl+OU9HeyecDs+Ff1pGKsZPHgcAsNvssPexTIKIiGg42nf4CJxOJ9RqFZKTEhEVqYcgCLBabaiqrsaRo0exefNmvP7GG9i1ezcAYOGiRYiPiwtonI52CzrrayGICiTPXoiUK65Fl8uN9/cehrmjE4YIHRYUFkCnVsPudKKsvhE7DxyC2+2GUqeHURGBk6XlaOvsDGjcADBhyjSkZWbB6XBg47oPUF9X65fz6CMioLyQgKwxGnGs2FNJplGroVGHVnsKonD11FNPYebMmYiKikJSUhJuuOEGnDlzptcYq9WKe+65B/Hx8YiMjMSaNWtQX1/fa0xlZSVWrFiBiIgIJCUl4cc//vElNyV85emnn0ZOTg4efPBBPPDAA/jrX/+KN998Ezk5OTh//jy+973v4Ze//KWsuVjZRRSGdBE6jBmfDwD46J1PUFftKbdXKBSIiYtGblI6YuKjERMfg+i4aETHGmButeDs8ZJghg0AUGpUmLx6PvRxBtg6unD4re2wWjpQc6QUkYnRiM1KRmxmImIzEqHWa5E8NgvJYz09MMw1TTi39RjaG0wBiVWSJOz9cCfmXL8ASVnJmHvjQux6dytM1TU+PU99XQP+8+JbWHjNfGTlZiA2PgZ2mwPvv/ExujqtPj0XERFRMHR0duLUmbMAgDUrlyMj1VNx5HQ60WIyo0OjR1NTk+dobkZLSwvi4uIwdcqUoMRbv/tzxBZORkzBeMROnA6bIR7qk2WIdLqwavZUREfoMDE7HU2WdlQ1t6C6vgbvv/oybvz6HZi9ZDlePluCT99Zh/SkRCycPhnJ8YFJ2ImiiCuXLsPm9R/BWFONTR99iMUrrgNy0nx6nvSUZNx5603YtH0nyquq0dxqgkalws0rliFCp/XpuYiob1u3bsU999yDmTNnwul04qc//SmWLFmCU6dOQa/XAwAeeOABfPTRR3jzzTcRHR2Ne++9F6tXr8bOnTsBeKqsVqxYgZSUFOzatQt1dXX4xje+AZVKhV//+tc+j/nZZ5/FQw89hHvuuQcajScxfsMNN+CGG27wei5BGuZ7z1osFkRHR8NsNsNgMAQ7nGFFqU4Pdgj9CtaXpSij6Z9C5jbTKoW8XLJOKW9XGr1S3oWBXqnBrPnTMWPOVNTXNeDtVz+4ZEy8MqrXx4Ig9PmexyrkVUvFCPI+hxhBddnnFWolFtx4BaJT4mHvsuHwG1vR2dr3UkWlQoHotHjEZiUhNjMJUcmxgABAAoynKlC28wQcnTY4IO9rySy4ZI0zSZfugKhQKnoSXk6HExve3ojG2oF3Z2p2ybuj2+ps7/n//LGjMHpMHg7tO4r6ut6bCHQ6bbLma3cMnCCzueTt9Oh0y3vf5I4Lxvd+KJeEO+2+TZwGEn8/94/vzeC1WYJffUy+11VVhm179mLvwcNIS0nGV1ffcMnPZl1mXq+PXS4XRFG8ZFxXVZmsc9ob5FU0OUwtl31eGZeI2CmzERkdA7vNisotH8NRdrrPsers0dDmjkH6+ClwOh34y1NPoLT4NARBQGFeDuZNmQC9TgdtqrxG+xF5Y2WN23/+0s/V6XBgy4aPUFddDaVKhR9+51vIzMyUNZ8cnWWeSi5JklBcWoYzpWWYPXUyUpOSeo2T+znQ8BE1jH+vyf393D3u7OcbERWpD1h8be0dKFh0zaCvHxobG5GUlIStW7fiiiuugNlsRmJiIv7973/jpptuAgAUFxdj3Lhx2L17N+bMmYP169dj5cqVqK2tRXKyp0/h888/j5/85CdobGyE2sc7rHZ1dUGn06G9vR2nTp1CZGQkRo8ePajzcBkjUZhRqpSYMKUQAHB43zFZrwmFnLeoVGD69fMQnRIPh9WOI29v6zfRBQCSyw1TVSPKd57Eodc+w+5/rEd9cSUgACnjszH7jqXInFEAUeH/H3Mupwt7PtiOhsp6KFVKLFp9JRLTE/1yrpLiUqx/f+MliS4iIqLhyma348iJkwCAmVMmy7oJoVAogn6zwuF04j9vvo0nv/9NnDlyEGqNFqOXrUbakhsg9rFUT6lQwFl5Dp3GGiiVKnzvJz/FnJkzIEkSTpaW418fbMCBk8V+Wx7UKxaVCldduwKpGRlwOhx4/fXXUVXl+10iBUHAuNGjcMPSay5JdBHR4Fgsll6HzSbvhrfZbAYAxF1Y+n3w4EE4HA4sXry4Z8zYsWORlZWF3ReWiu/evRsTJ07sSXQBwNKlS2GxWHDy5ElffUo9dDodHn30USQmJmLOnDmYMGEC4uLi8OSTT3r9NyuXMYahUK7YAkK7ckPOOKUorwGqSuY4nUJellruuKKp0xAbYUCbyQLL+ZZLqrgAIEkh7w5EiiivmizHffmKrZ75HH3/2wsKETnXzUVkahJUnXZUv74N4+tNl53rknfX2g68txdS2jkkXDUZ2pQ4ZM0Zj+ljc9Cw9Tjaz13+Dm6rQt7nUKPs58emE2j+4ABSry9CdFYcVqy+Bnve247mmoErvAbigrwNA1ySvHEOaeAqK7mVWHJjEyDzDxIZw0IhOUtEvsWKrZHtZEkp7E4X4uLjUVBQALGPKnqTVV6PSp3MflDajFxZ45Qx8X0+7nQ6sW7DRjQ43BDVOphPHkarTo2Y/PGIzh+PiPRsNBzcBWfHF9XZXZXnAABVtZXIWnkbdMlpuPEbdyHZZsb2E8VoaG7FZ1sbUNxowqJ5czAqO/uy16b1bV2yPodGS0e/z02cdyXaNm1ASZ0Rv3nuBSy6dgWSUvtf0piTGCvrnNms2KIRIFgN6r9chfn444/j5z//+WVf63a7cf/992PevHmYMGECAMBoNEKtViMmJqbX2OTkZBiNxp4xFye6up/vfs7X/vjHP+KFF17A3//+d2RnZ2P58uXYuHEj7rrrLoiiiEceeUT2XKzsIgojoihi/HRPVdeJA6eGRVJAEAVkrZyFyJwkuB0u1Ly9A7b61kHPZ61tRvUrW1D/8T4427ugjo1Exg1FyLx5AdQJ/i2rdjtdOPHBbjRUeCq85tywAPF+qvAiIiIKBy6XCweOHAEAzJo6pc9EV6hxuVz4cNMWVFRXQ6lU4oYrFyA5Pg6W8hIY926Fs7MDqohIpMxeBE0fyTLJ5UT1J+/Aae2EJi4BY2YV4eb5s3D1lPGI0KjRarHg3fWf4q1169HYfPlllEOlVCoxd/G1SEn3VHh9vuEjNPipaT0R+UZVVRXMZnPPIScBdM899+DEiRN47bXXAhDh4P3lL3/B//zP/+D2229HWloaJEnC7Nmz8b//+7/429/+5tVcof/bhIhkGzUmF/ooPaydVpw7VRrscAYmCMhYNhOG0WmQnG5UvLcL1ppmn0zddqoSFf/YgOY9xZCcLuizk5D7jauRfPUUiBp5VVyD4Xa6sO/DHV4nvJQqJXSRuqAvySAiIgqk06dPw9LWjgidDuPHjgl2OANyu91Y/9lWlJ6vgEKpwOplS5Ge9MXvebu5FXW7P4PN1AyFWo3kmfMRkXLpqguXtRMtR/cBABKmzYMgihiXmYavXTkPc6ZNgUIUcb66Gi+/+TY2bd8Jq8xlSoOhVCpxxdJlSMnIlJ3wkiQJDrsd7W1tcLvlVXkThR1RCPwBwGAw9Dq6G7n3595778W6devw2WefISMjo+fxlJQU2O12mEymXuPr6+uRkpLSM+bLuzN2f9w9xpfKysowf/78Sx4fPXq015VkXMZIFEamzZoMADh1+DRcTnnL0IIpY8k0xIzNgORyo+LDPWivbES0D+eXHC407jgJ0/HzSFo0EVH56YidOgq69HhUvr4Nbpu8Juzecl1IeM28bh6Ss1NQdOMVOL3rOERRhDpCC41ODXWEFg6tCI1OC02EBooL23NXFJ/HrvW7/BIXERFRKJEkCXv37gUATJs8Ccr+WgWECEmS8MnW7Sg+VwpRocCqJYuRlZ4GW33vzUTcDjvq9+1AwqQZiEhJR+KU2WgtPt6zjLFb68nDiJs0E+roWETnj4f57AmoVUosmD0LE8eNxee79qCk/DwOnziJmjojbl21EtoB/qgdLKVShSuWXIttn26AsboKn2/4CJOmz4LL7YKtqws2qxVWaxf0SgHWri50dXbC5fJca+aPGYtrli33S1xENHiSJOG+++7Du+++i88//xy5ub2Xb0+fPh0qlQqbN2/GmjVrAABnzpxBZWUlioqKAABFRUV48skn0dDQgKQLffc2btwIg8GAwsJCn8ccExPT01vsYtu2bcOYMd7dEGFlF1GYyMhOQ0JSPJxOJ84cPRvscAaUduVkxE7IBiSg8qP9aCvz/Zrvbg5zB2re34PKN7bB2WGFNikGmWvmQVDJ66s2GC6nC/s+2IH6CiMUSgUmXDEFhfMnYfS0AmSOy0Fydgpik+IQERXRk+gCgOyxOUjJ9v1dEiIiolBTXl6O+oYGqFQqTJ04IdjhXJYkSdiyazdOnjkLURSx4uorkZfV/66JktuFxiN7YTnvSXDFjp2I5LlXAxdVcEtOB1qOeKq74qfNBS7aFTzGYMAN1y7BLdetQIROh4bmZrzz0QbYHf65UQd8kfDqrvA6tGcnju7bg+LjR1FecgZ1VZVoamhAe1tbT6ILAErOFKOqssJvcRGFLEHwfN8G7PBuBcg999yDV155Bf/+978RFRUFo9EIo9GIri5Pv7/o6Gh885vfxIMPPojPPvsMBw8exJ133omioiLMmTMHALBkyRIUFhbi61//Oo4ePYpPPvkEP/vZz3DPPfcMWFE2GNOmTcPOnTt7PnY4HLj77rvx3e9+Fz/72c+8miu0b58QkWyFEz2NQEuOl8Bm9V+p+1AJooC0xVMRNzEHAFC1fj8sJTWXf5GPdFY2ourN7ci6bSF0afHIXD0PVe/shOTwTxWc2+XGvg92YNzciYiKN8DeZYOt0wZblxX2Ljvq2s2wddlg7bLB1mnF5HmTUTB1DCbMmQhjhf+Sf0RERKHg6NGjAICJ48ZBp5W3KU4wuFwubNy+EyeKzwAArl10BcbkyWtw31p8DM6uTsSOnYjYCdMhanSo+/wj4EJf1dbTR5AwcwHUhhjoM7LRUVXe6/XZGem45brleO29D1FTX493Pt6ANcuXQaXyz59x3QmvYwf2w2JqhUajhUang1ang0arxai0FGh1OugidIjQRWDPrp04fuQwDuzZjcysbL/ERESD85e//AUAsGjRol6Pv/jii7jjjjsAAM8++yxEUcSaNWtgs9mwdOlS/PnPf+4Zq1AosG7dOnzve99DUVER9Ho91q5di1/+8pd+ifmnP/0pyss9Pwc1Gg2mTZuGrq4ufPLJJ1iwYIFXczHZRRQmYuI8CwBrK+uCHEn/FFoVsq6bg8isREACajYdhum077e5vhxbkwVVb+1A1s0LEJGZiMwb56LqnV09F52+5na5cXL70T6fa3Rbe31cfKgYBVPHICElAUqVEk6H/7ceJyIiCpbWVs+GNLlZmQOMDJ4uqxUffLoJVbV1EAQBVy+Yh8KCfK/maKs4B5etCwmTZiA6vxCCKKJ2yzpAciMyexREpRJuhx3Wpvo+X58YH481K5fhrXUfo6q2Du+s34DVy671xafXJ6VShWlz5vb53Jd3Y5w8bRqOHzkMo9EIh90OlVre7uFE5H9yNivTarV47rnn8Nxzz/U7Jjs7Gx9//LEvQ+vX/Pnze3p2paenY9euwbd34TJGojARGaUHAHRcZnvpYNLGRGLUV69EZFYi3HYnzr+3Cy3Hygd+oR9Yja2oensH3HYnIrKSkHHjXAhK/y1plKvD0oEOSwcEUUAid3EkIqIwZ7FYAABRUZFBjqRvLa0m/Pu9D1BVWwe1Wo0bly3BlMJxg5qr01iDmo3vw+1ywjBqLNKvWQVRpUbSrIUAgOYje+Hq6uz39WnJyVizYhnUKhUqa2rx3oZP4XQG/6aYwRCNKIMBktsNI3dxpBFGEISAH+HuySefxOOPP97z8YYNG7Bq1Srce++9lzTSHwiTXURhQKlUQBehAwC0t4VessuQkYCJX1kETWwkHJZOlP7nc7/26JKjq7alJ+Glz05C3vVzICiC/yOxodpzVzcxPSnIkRAREfmPw+FA54W+MVGRoZfsqqyuwStvvoVWkxlRkZH4yqqVl+3RJUd7xTnUfPIu3E4nonLykXvzN6GKioajvQ0tx/YP+Pr0lJSehNf56mp89P57IZHwSkv37DZZU10d5EiIaLj7z3/+g+nTpwMAmpubsWbNGiQmJmL37t245557vJor+H/ZEdGQ6S/cEXU6nLBb7UGOprfEwiwUrp4PpVaNzrpWnHv1M1ibLMEOCwDQVdOMqnd2wu1wwpCTjNzrZgc94VVf1QAASM5IDmocRERE/mRpawMAqFUqv+0wOFjHTp3Cm+9/AKvVitTkJHxt9Sokxsf7ZO6O6nJUb3gLbqcDqsgoAEDj/m2QXPKSVhmpKbhx2VKolEpUnC/Hxx9+EPSEV1qGZxlqLZNdNNIEtDm92GsTi3B1/vx5jB8/HgDw8ccfo6CgAH//+9/x97//HRs3bvRqLvbsIrqIQsYPEDljAECjUPl0XLQyot/nUmPioRFVsHZ0Il6hG3CudHHgMQAwzinvR8QoR98JtrgrJiJm1lhAktB+8jyUOz7DRLiA/j8VGOKs/T95EYXaLWuc1TLA+2tvhHJbK7BwKZLy4pGxcjJaPtgJuPueP9p2meB7YpPXZNcpXrqO3l7bCp2gRHpqEhI0ergcTtghr4F+p0vexgR2ceCLYpsob1mnS5L37yB3nJzeAr4m95wjoXSciChQLBe2lo8yGKDQDPx7s8subxdCXUKarHFK26WV8JIkYev27dizbz+gVKFw/HhcNb4AKqUSUj/XOgCgS5fXmP3i3yONh3YjYfJs2NtMcNrtiMj+og+Yw9R82XlS9RpcN3823jtyBjWV57Hlk49x3aobeu3ufLEJmQPv8qwQ5f2Oa267dKllZFwirA4nKquq0GSyePp2xRlkzUdEdDGdTger1fP34MaNG7FkyRIAQHx8PNrb272aK/xTg0QjQKTBU9nVbvHuB4C/CEoFkq4v8iS6ALTuOYWGD/cATv/sejhUzhojWt7bDsnpgm5UOuKumwuIwfnx2GnpRKelA6IoIj7VN3eRiYiIQk13vy6DITSSIg6HA+9+8IEn0QVg7pzZWLVyJVRK/9QG2FqbUbN1PRoPDq75clZqCm5ccxMUCgVKz53Dug8+gMsVnOusKIMBkZFRcEsSGoyhu1ESkc+JQuCPMLdgwQI88sgj+Otf/4q33noLN9xwAwDg3LlzyMz0bjMTJruIwkBPc/r2/hubBooYoUHaVxYhsiADksuNho/3oXXHyWCHNSBbZT2auxNeozMQt6IoaAmvpppGAEBCJvt2ERFReGq7sIwxOgSSXe0dHXj1tddxtuQcFKKIlcuX4Yr58/1f0TvEaubsnBysunE1FAoFzpWcxUcffhiUhJcgCEi50LfLWMsm9UQ0eM8++yyamprw0EMP4d5770VRUREAoKurCz/96U+9movLGInCgD7Ks7SuPcg7MQpKBVJWz4cmOQ6uLhuM7+2ErebypfihxFZhRPMHOxC/agF0BZlI0GnQ8uFOuLvkLQ/0labqBmSNy0FCBpNdREQUnr7YiTEqqHE4HA68/e57MNbXQ6fTYfWqVcjMSA9qTN7IzcvD9TfciPffexclZ8/g7Te7sPK66xGh1wc0jtT0dJw7U4y6WvbtIqLBy87Oxq5dl1a8rlixwuu5WNlFFAa6G9R3tAV3GWPi0hnQpsTBZbWj9j+fDatEVzdbeR2a398Ot90JTWYSEr+2BKrEmIDG0HihSX1schwUKt6TICKi8GMOgWWMkiTh408+QZ3RCK1Wi69/5bZhlejqljdqFFbdcCNUKhWqKivxyv+9jIb6+oDGkJKWAQBoqq+Hwx5amyUR+YsgiAE/wl1FRcVlD8Dzs7v7/y8n/N8tohEgQu9pON/Z3hW0GPRjMhA5LguS243693fB0dIWtFiGylZeh8ZXP4XT1AalQY+EW6+GOj0xYOfvtHT09O3KGDO0bc6JiIhCUceFRsORkZFBi6H47FmcLj4DhShi9arrERcXF7RYhipv1Ch89WtfR0xMDNosFrzx2n9QXVUVsPN39+1yud0oO1cSsPMSUXjJy8tDbm7uJUf34wDQ2NjY8/+Xw2QXURhQKD277ziD1ABeUCkQv2gyAMC05zSsVY1BicOXnC0WNLzyKWzVDRA1KiTctAjaUfJ2ePKF0iNnAQDj5oyHqOCPaiIiCi+uC7seq1TydqX2Nbvdji2ffQ4AKJozG1leNj4ORQmJifjq17+BjIwM2Gw2vPXG6ygtPReQcwuCgPGTpwAADu/bC6dz4J2fiYY9QQj8EeYOHz6MI0eOXHJ0Pw4ACQkJPf9/OfwLiigMiBcaqbvdwUl2xcwZB2VUBByWDpj2nQlKDP4g2RxofnsrukprICgViLt+PqLHy9tefKjKjp5DV3sndJERGDN5TEDOSUREFCjdjdQVCkVQzr977160tbcjJjoas2fODEoM/qDT6bD65lswavRouFwufPDuuyg+dSog5x47YSL0+kh0dLTj0OHDATknEYWXSZMmXfYAPH/7dv//5TDZRRQGxAvb0Lpd7oCfWxMTiZgZnmRM85YjkIJUXeYvktOFlvd3oONkOQRRRNqyGYibnu/387pdbpze49nFcsKs8VCpg3Pnm4iIyB/cF5JdYhB2Pm5pacG+/QcAAFdfuSho1WX+olKpcN2qG1A4fjzcbjc2f7IeRw4e9Pt5lUolpsycBQDYtXMnbLbAbvBDFGiCIAT8GAmqqqrw//7f/8Pq1auxevVq/PSnP0XVIJZlM9lFFAbEC3dFXUFIdmVdNRmCQkRnuRGd58J0u2lJgmnDXrQfKAYAJF85CYnzx/v9tJUny9FuaoNGp8W4aeP8fj4iIqJACVZllyRJ2LxpI1xuN/JyczF61KiAnj9QFAoFrl2+AtNneKrWdm77HHt27oAkSX49b8G4QkTHxKCzqwv79+/367mIKPx8/PHHKCgowIcffoi4uDjExcXhww8/REFBAT7++GOv5uI2XzSsyc1uC5A5TsZ8Cpl3INWivG8vjSjvbmK0QtfvcxFKDTSCEpFQI0oY+KJxtEtebGMd1ss+rxuVhqTMeMQZ2tBxbhNicy2XHR81ST3gORXpKbJig1LexXFky+Vj6hZV3DLwIMsWtO7uQtT8KYibl4cMgxuWzQeAL1042h3ytlE3qzWXHyABjTvPIH3FNEyeMQHVR8tgt/a/w1GEYoD5LuhyDbxLklKU9/7a3fJ6cogyv1fd/r0GH3ZU6gxZ4xz28N7q3WazQaOR9/VNRMPDxcmuFlXEgOO7bPJ2+KtzXP73fnnpORw7dRqRETrMveZadKn6v74CAGHUhAHPKTdhF50iry+YrUHezUOljB2bly65Bkq1Brt2bMPxQwcgOR24avE1l1TUxWvlXY+2DLAZkkKhwPTZRTi5exv27t2LadOmISJi4H9fCh9tFpnX3kHciZVC1wMPPID77rsPzzzzTK/HH3roITzwwANYvny57LlY2UUUBrobmLulwFV2CUoF4q6eCgCwHz8Jt8xfbMNdx/7TMG/aB0mSEDFxNGJWzoMg42JzsOpLqtHa0AKVWoVxMwv9dh6iULJ+/XqsXbsWeXl5UKlUiIiIgMFgwMKFC/Hkk0+itjZMq0iJRhD3hQb1gVzG6HA4sGPr5wCAGTNnDuvdF+USBAGz5szB1dcshSAIOHHsKD5e9yHsdnnJw8HIHZ2P5KQk2Ox27N6922/nIQo6QQz8EeaqqqrwrW9965LH7777bq+XMob/u0U0Aii6k10BXMZomDUWSoMerrZO2I4cC9h5Q0HX8VKYPtoJyeWGdnQm4r+6FMrEGL+d78ROz/s7ekoBdJGXvwNNNJy9++67KCgowF133QWlUomf/OQneOedd/DJJ5/g73//OxYuXIhNmzYhLy8P3/3ud9HYOPx3fiUaqbp3YwzkMsYjBw+gzWJBVJQBs+cUBey8oWDi5MlYvvJ6iKIC586ewX9e+T80NtT75VyCIGDhokUAgIOHDsEyQm6IEtHQzZo1CwcOHLjk8QMHDqCoyLuf21zGSBQGenZjdLkB+P+iURmtR/TssQCAls+PIG0Ebi9tK6lCS+cWxCybC2WcAfFfWYq2bYfReeSsz89Vd74OjdUNSMxIwvg5E3BgE3tgUHh65pln8Oyzz2LZsmV9VnvccsstAICamhr88Y9/xCuvvIIHHngg0GES0RBJktS7Z1cA7tVZzCYcPuD5/Tn3iiugVg/cWiHc5I8ZA11EBNZ/9CFaW5rxn1dewRVXXonJU6b6/Fyj8vKQmZGBqupq7Ny5E8uWLfP5OYiCThQ8RyDPF+buvPNO/OhHP8Lx48d7klu7d+/Gv/71L/zmN7/B1q1be8YuXLjwsnMx2UU0zF3cZ8yzJMDPyS5RRNziaRAUClgr69F5phrI9e8pQ5WjphFNr6xH9JI50I5Kh+HK6VBnJkF4/zgkH1fZHd95FFfdeg1yx+eheP9ptJvbfTo/USiQu9wlPT0dv/nNb/wcDRH5S/cSRuDCDTs/J7tcLhe2fbYFTqcTGVlZyBvt/12VQ1VGZia+9o078OmG9SgvK8XnmzehuqoKt6++AUql7/40FAQBCxcuxCuvvoqjx45h9uzZI2LZKBENzV133QUAl/Tsuvg5wHPT5OLfJX3hMkaiYU4boQXg+YZ3OvxcYaUQkbiqCLrcVEguN1q2HPbv+YYByWqH6YNtsHx2sGdZY/aNcyHIbKAvV1NtE4zn6yCIInIn5Pl0biIiokDq7OwE4EmI+LvCyul04pOP1qHy/HkoFArMX3il7A2OwpUuIgLX37gaC6+6umdZ41tvvw2Hw+HT82RlZSE3NxdutxvHjx/36dxEoUCACEEI4DEC0jetra2yDpPJNOBc4f9uEYW56PhoAECbqd2/PbsUIpJXL0DEqHRILhca39sBRxN7MHTrPHIWre98BrfdicjsJOTcOBeiyrcJr/KTZQCAzIIsn85LFIrWrFmDp59++pLHn3nmGdx8881BiIiIfKWpqQkAEBsb69eeXQ6HAx+9/y7Ol5VCqVTi2pXXIy4+3m/nG04EQcDUadNx4003QalUoay8HG+89ZbPG9dPmjgRAHDq9GlIErdcJqLLMxgMso+BcBkj0TAXHedJdplbzH49T9xVU6HNTobkcKL+ne2wVbEx9JfZqxvQ+u5ncK9aCn1WIrJXz0PFu7vgtvum4q6uvBYuhxORMVGITY5Da32LT+YlCkXbtm3Dz3/+80seX7ZsGX77298GPiAi8pnGC8muxIQEv55n57bPUVNVBZVKjeWrViE9I9Ov5xuOMrOyccOam7Dxg3dRUVGBN958EzffdBM0Go1P5s/Pz4dKqURrayuMRiNSU1N9Mi8RhaeXXnrpss+vXbsWNpsNr732GtauXXvZsUx2EQ1zMQkxAABzs/+SXfpxWYiaPAqQJDS8t5OJrstw1Dbh/Js7kHvTfOgzEpCzZh7Ov7MLbtvQlwY4HU7UltcisyALOeNymOyisNbe3t7n8iaVSsWdvYiGueYLya4EPya7zhYX49Tx4xAEAdeuXMlE12VkZGbitltvxetvvIHKqiq8/sYbuOXmm6HVaoc8t1qtxujRo3G6uBjHT5xgsovCiyB4jkCeL8xdbuMhSZKwdu1atLW14YEHHmCyiwInlEuT5fZmEGWMU4ryyu3ljotQyLtzFiX03dMiOT4OakEBR3MHogQ1UjFw74tMu0vWObNTTRBjDIhaMx6CygrrgaNItp8BvnSdYiiKkjWfYtrEAccIKTIvRhXyfnwJrQ2yxukMJ2SNS7PWDDjGdMKIjtc2I+WWhYhKjUHMmjmof2s73NbeSwNqJXn/9rXCF59r3YnzyCvIQcH40SjdfQrOi6rGIkR5fU9MwsAr2BV97ITXFxHh/0s3mCSE7s9Vf5s4cSJef/11PPbYY70ef+2111BYWBikqIjIF5q+lOxyuAa+LrHJ3Pl5TFwkmpubcXTH54jWKDFv7lzMmzD2knGiWt51hK1+4N/7CkOMrLkc6ghZ43SZ8npztnbJW3Io5/o2JSYKN1+3Am+88w4qy0vx6sv/wi033gidrnfCK9Ggl3XO4zVf3BjVJmegYd9BbNyxG/Gjx0Gt/uL6Z2J6oqz5iGhkaGkZ+GZ+QkKCrHHs2UU0zBku9Oyy+GMZo1IB/TULIahUcNTUwXrwmO/PEabs9a0wvv45XF02aFLikLR6vk/mbaisR1urBUq1ClnjcnwyJ1EoevTRR/GrX/0Ka9euxUsvvYSXXnoJ3/jGN/Dkk0/i0UcfDXZ4RDRIkiShsdGTCElM9H2iw+Fw4N3334fdYUd2Vhbmz53r83OEq7TUFNx2003QabWoMxrx1vvv++RmdnpWNqJjY+Gw23GuuNgHkRKFCEEM/EGysbKLaBjT6nVQa9WQJAmWFt8v69HNmwVFfCzcnV3o3LQdCOHqvVBkbzDB+MZWpH7lSmjT4qHLS0VXWd2Q5y07UoLJV05H3pR8lB0954NIiULPddddh/feew+//vWv8dZbb0Gn02HSpEnYtGkTFi5cGOzwiGiQ2tvbYbXZIAgC4uLifD7/p5s2obGpCZF6Pa5fuRKizEpl8khJTsJXbr4Jr7z2Bmpqa1F2/jxG5eYOaU5BEFA4cTJ2b/scp44fxbiJk0b8jphE1L9PP/0UW7ZsQWNjI9zu3huwvfjii7Ln4U9/omHMEO/ZhaLdDzsxGgqzoBmXD0mS0LlpO6Quq0/nHykcjWa0HfHsohg9a4xP5qw8fR5OuwNRsQYkZaf4ZE6iULRixQrs3LkTHR0daGpqwpYtW5joIhrmLt6JUan07X33s8WncexCn67rVq5EZGSkT+cfKZISEzFlkqf1xN79B3wy5+ixY6FSq2FubUVNVaVP5iQKOsGTzA3UMRK6h/ziF7/A8uXL8fnnn6OlpQVms7nX4Q1WdhENY4YLOzFamn1b1aWOj0LKkmkAbLAeOApnrdGn8480lkMlMEzPhzYjEZrUONjqhtZY3ml34vzJcoyeWoBRU/LRUMF/HwpPJpMJb731FsrKyvCjH/0IcXFxOHToEJKTk5Genh7s8IhoEPy1E2NrSzN2frYF0SoB8+fOQ052tk/nH2lmTJuKg4ePoLK6GrV1RqSlDu3mmlqtQf7YQpw6dgSnjh1FRhb/fYjoUi+88AJefPFFfP3rXx/yXKzsIhrGuiu7fNqvSxCQtnI2BKUCjuo62A4d993cI5SrvQsdxZ67mIbpBT6Zs+xICSBJSMlJhTZS55M5iULJsWPHUFBQgKeffhr//d//DZPJBAB455138MgjjwQ3OCIaNH/sxOh2u7Flw3o4nQ7k5uRgbtEcn809UhmiolA41lORvv/QIZ/MWThpEgCguuI8OjrafTInUVCJYuCPMGe1WlFUVOSTucL/3SIKYxqdZzcba4fvlhhG5adBkxgNl9XOPl0+1HasHACgyfDNxb3TcWFXqu6yZqIw8+CDD+KOO+5ASUkJtNovdgNbvnw5tm3bFsTIiGgourq6AAB6Hy4xPF96Di3NTdBotLhuxQr26fKRyRMnAACqqgfekVIOlUoNQRA8Te95fUlEfbjrrrvwyiuv+GQuLmMkCgc+vGCIm+3Zntt0uBR6K/t0+Yq9wQQAUEbqIEZo4O60DWm+9PwMQBDQamxGV1unDyIkCi379+/HCy+8cMnj6enpMBq5dJdouOre3c9Xt2kkScKRA/sBAOMnT4Fer/fRzNS91LS9ox2dXV2I0A2tkvx86TlIkoTElBToI6N8ESIRhRmHw4Hf//732LRpE6ZMmQKVStXr+WeffVb2XEx2EQ1j3RU9vtgWGgD0OcnQJsfA7XCi9dA5pMf4ZFoCIDmccJjaoYqJhDohGtbKhiHNl16QBQCoPssmrxSeNBoNLJZL+xGePXsWiYmJQYiIiEJRVcV5NDc1QqVSYfykycEOJ6xoNBrEREfDZDajsbEJ2VmZQ5qvrOQsACBvdL4vwiMKOiHAKyxGwmqO48ePY9q0aQCAU6dO9XrO2795mewiCgO+quuKn3OhqutoOVxddiDGRxMTAMDeaPYkuxKHluzSRuqQkOa521pTUu2r8IhCyvXXX49f/vKXeOONNwB4LvAqKyvxk5/8BGvWrAlydEQ0WD2VXT76o627qmvshEnQDrHyiC6VmJAAk9mMhqbGISW7OtrbUF9XCwDIZbKLiPqxZcsWn83FZBeNCKLMCyo5F14KQV4fCKWgkDVOK6gGHgRA18d8akGECiK0EHueT3APHF+SeOmyN3VGIhIyYyG5HbAcOoEk0YboAqes2BQT5DVdF8ZOG3iuzAny5lLI+/HlqiuRNc5tl7dkU1vVNOCYmJK+lyhqTI3QKlMQm6aHeMIGg0sj65xqZe9/05z8LCgEES21TXC1WaG+0H5RKbMNo5yvYdHHexsLI2GvZPKp3/72t7jpppuQlJSErq4uLFy4EEajEUVFRXjyySeDHR4RDVL3DbqLr7nqTAPvKj02LemSx6qrqtBlakFMpB7XXrUQkZFRkFzy2gTYm1rlxetwDDzGKe96SelqkzXO3tUhaxwi4+XN53QNOEah73tZYUpmFkorq9Hc1gGFPgoau1vWOSem967A3buvHEkGPTIzMjBnTJ6sOYhCniB4jkCebwSQJAmtra2Ii4sb0jxMdhENY75cxmiYNQ4A0HmiHK72riHPR5dyNJkAAMrEmCHNkz7Gs4Sx5mzVECMiCl3R0dHYuHEjdu7ciaNHj6K9vR3Tpk3D4sWLgx0aEYWIvXt2AwAmTJyISPaA8oukC8vGGxoahzRP8enTAIBxhYVDjomIwteWLVtw6623orm5GQUFBVi3bh1Gjx6Nd955B3q9HkuXLpU9F7cqIQoDQ011qZJioc1NBSQJbfuLfRITXcrZaAIAKOOiB31nRmeIQGxKPCBJqCthsovC37x58/D9738fDz30EGbMmBHscIhoiHy1jLHeaMT58nIIgoCZs2b7IjTqQ1KSp6KuqakRbre8qq4vM5lMqKmthSAIGDtmjC/DIwouQQz8Eeb+67/+C8uXL8f27duRm5uLxx57DAAgiiKeeOIJr+YK/3eLKIz5qt9F1CxPr67OM5Vwmtp9MiddymVuh+RwQlAqoIgd3B3otHxPv4zmmkZYO7hbJoWvp59+Gq+//nrPx7fccgvi4+ORnp6Oo0ePBjEyIvKFoV7D7Nu7FwAwdtw4xMTE+CAi6ktMTAyUKiWcThdaW+Ut/fyyM2fOAACysrIQGRnpy/CIKMyUlZXhsccew7x58/DQQw9h74Wf9ZMnT8aJEye8movJLqLhrPtCcQjLGJXxBkQUeBIobXtP+yIquozupYyqhJhBvT76whLIhop63wREFKKef/55ZGZ6fjZt3LgRGzduxPr167Fs2TL8+Mc/DnJ0RDRYvmi90NzUhJKzngTKrNlzhjwf9U8URSQmDG0pY32955olLzfXZ3ERUXgaM2YMKioqAABpaWloavL0S25ra4NCIa8ndjcmu4iGMVH0fAsP5cIxaloBIAjoKqmGo8nsq9CoH85mTxNeZXz0oF6vi/TsNNXVdukmA0ThxGg09iS71q1bh1tuuQVLlizBQw89hP379wc5OiIaLJfL0yx9KJVdBw8egCRJGJ1fgITExIFfQEOSmOjZAbqpeeBNevrS1uZpzG8wGHwWE1EoEEQh4Ee4+8Mf/oBHHnkEO3bsgNvthtvtRlNTEx577DEUFRV5NRcb1BMNU4IoIjbFs0NFW/PAuxj1R5Pl6cXQcbzMJ3HR5Yk6zw6M7i55u0V9mdPh2fFJVPJeBYW32NhYVFVVITMzExs2bOjp0yBJUs8fy0Q0vLhcLtTV1gIAEoeQpKo877nrP3HSJJ/ERZfX2em5wRah0w3q9UqVZ+dxh4ydLYloZFu0aBEA4IorrgDguTGSlJSEiRMn4t133/VqLia7iIapuJQ4qNQq2LpsMDUMroeCIlIHZUwUIEmw1Q7ubh15R3VhGaLzwnJGb1kv7JSpi4rwUUREoWn16tX46le/ivz8fDQ3N2PZsmUAgMOHD2P06NFBjo6IBqO2thY2ux0ROh1SUlIGNUdbmwVmswmCICA9I8PHEVJfupcvJl5oVu+tqAt9urorvIjCRqCbxo+ABvVfTmip1WpkZWWhcBA7uTLZRTRMJWV7LhIbq+oHvYxRne65q2pvNEGy8W6bvwkaNRQGPQDA0WgCoPV6jvZWz4Vi9CB7fhENF88++yxycnJQVVWFZ555pqepcV1dHb7//e8HOToiGozy8nIAQE5OzqCXMVZXVQMAkpKTodFofBYb9a2rqwsWi2cFQVJiIgazNU58gmcZZENDgw8jI6JwdP311/tsLia7iIap7mRXQ4Vx0HNoMi4ku6oH13CUvKNM8PTpclk6INnsGEyyy1TfAgCISY71ZWhEIUelUuFHP/rRJY8/8MADQYiGiHyhrMzTMiEvL2/Qc1RXVwEAMjIyfRITXV5Do+caMTo6GlqtFla72+s5Ui9U8dUZB3/NShSSBOGLDcMCdb4w192cXo7s7OzLPs9k1zCjVKcHO4RhSYC8HwyijHEKmeWjcsepBXm7Sqgu2k9CpVEjITkegICWioZez0XKuAaJ0nn6RUXnxUKpcsLaWNPz2MWUKXpZsQlJqbLGKVLzBxyjThkjay65JKdd3riYM7LGCdED96vQavs+pzY9CkqlC25zM7RaB1Qdsk4J5UVfl+0NZoiShIjICOgjdLB1fnGPVRwBvwBp5HjqqaeQnJyMu+66q9fj//znP9HY2Iif/OQnQYqMvNVmGXxfSRredpRU9vy/zWrF3pNnIEkSjC4lLBc9lxozcONyxYXGzLXV1RAEICsrs+exiwnqSFmxOeuqZI0TZOz+FZvu28RbS9V5WeNcbnmJp9rWgTcgMnep+3z8REk5mrtsiEyJREmzBdoL/bcGcqyyruf/HXY7KpvNkJpM2HD4NHQRX7RiWDjm8n+sEtHIkpeXB0mSIAhCv6uXup9zD/AzkMkuomEoISsJEAS0tVh6ejh5S9BqenYEtNeyrDwQlBeWHjqbBtdjDQBcDifaW9sQGWdAdHIsGsrrBn4R0TD0wgsv4N///vclj48fPx633XYbk11Ew4yxphqSJCEmLg4RkfISUl/W0dGB5gs7ArJfV2B0v9/dSxEHQ6VWwxATC3NrC1qbGqHLYoKLiPp2+PBhn83FZBfRMJQ6ylPh1ziEJYyqNM8SRmeLGdIgdwYk7ygSPEsPh5LsAgBTfSsi4wyIYbKLwpjRaERq6qVVo4mJiair49c90XBTXXEeAJA6hOWHNdWefl0JCYmIiOBGLYHQfGEZY3zC4HfPBIC4hESYW1vQ3NSINCa7KEwIggghgE3jA3muYJnkw112w//dIgozSrUSqaM9ya6aM5UDjO6fOt2zo46jhlVdASEIUMbFAPBFssvTtys6OW6oURGFrMzMTOzcufOSx3fu3Im0tLQgREREg2W32VBV7unXlT1q8Lup9vTrymS/rkBwu91oaW4GAMQNobILAOISPcmylkZedxJRYLCyi2iYSSvIhEKpRHurBa3GlkHPo7qQ7LIz2RUQipgoCCoFJIcLbnP7kOYy13uSZWxST+Hs7rvvxv333w+Hw4GrrroKALB582Y89NBD+OEPfxjk6IjIG5XlpXA6nYiOjUV8YtKg5+neiTEjk0sYA8FiMsHlckGpVMIQHT2kueIu/Lu3NHFTJAojbFAf0pjsIhpm0guyAACVJ88PaR5lkidR4qhtGmpIJIMyOR4A4GxuBfpptiiXtcPTp00ToYWoVMDtdA05PqJQ8+Mf/xjNzc34/ve/D7vds+mDVqvFT37yEzzyyCNBjo6IvHH+XAkAIK9gLIRB/rEmSRIaGuoBAGlp3LApEBrqPe0y4hMSIIpDWxCk03mWnXZ1dMDpdECplNfonohosJjsIhpGlGol4jM8ZeB156oHP5Eg9FxsSnaHL0KjAaizPRfmjur6Ic+VdiHh2VrXzEQXhSWXy4WdO3fi4YcfxqOPPorTp09Dp9MhPz8fGo0m2OERkRccdjsajJ4+e5k5uYOex+129+zMpVb3vXMg+Vbl+fMAgPTMrCHPVVHqSXgmJKcw0UVhQxAFCH3sCuvP85F8THYRDSMJmUkQRRGd5nZ0mIa2FI4CSBCgyvY02raXDyFJeUH2xDwAQMXxsiHPRRSKFAoFlixZgtOnTyM3NxczZ84MdkhENEjG2mq4XW5ERUfDEBPjkzkHWx1G8rndblRXVgAAMnNyhjSXJEkoPXMaADB6XOFQQyMikoXJLqJhJOlCwqT+/BB3IuNFYkApUxMhatRwd9ngrG8e0lwJmUmIiNbDYXOg9myVjyIkCj0TJkxAWVkZcnMHXwlCRMFXW+nZTCdtCLswUuDV19XCZrNBq9UiKTllSHMZa6rRbrFApdEgK2+UjyIkonDkcrmwfv16fPLJJzh8+DCampqg0+mQnZ2NBQsW4Oabb0ZWlrxqU+7GSDSMJOd6kl0N542+m3SI/aNoYOoczxJGe2XtkN/v9LGeH+41Zyq5hJHC2hNPPIEf/ehHWLduHerq6mCxWHodRBT6JElCbdWFZFdW9pDnosCpKC8HAGRm5wy5X1dF6TkAQM6ofC5hpPAiiIE/wpTL5cKf//xn5OTk4O6770ZNTQ0WLVqEu+++GzfddBPS09PxxhtvoKCgAGvWrMGZM2cGnJOVXURBJsqssoqJi4Y+KgJulwutVY1Qou/XyfmmVirdEEWp5/8lt7vvgSqZPTGUMn+UKIJwgeOS2ZPM6ZQ3zjHwOKez9y8iZVYG3G4BXaV1vZ6TeUY48cUFfmxmEtwAaktrej0OAG7+IUBhZPny5QCA66+/vteSJUmSIAgCXC4me4lCnWDvgttuQ2SEFuPyR0Op6vs6oKWjc8C5EqN0X8x7uWsna4es2ByWVlnjEISfNU59jKxxFlObrHGxet2AY4xf2im6+MxZdNkdiE5OQ0t7V8/jHTazrHNePe6LqtwTmzuRnRCDGxfNxahRQ0t6ElF4euaZZ7Bt2zb885//xOLFi/v9OV9XV4eXXnoJixYtQl3d5Vc7MdlFNEwk5XpKyJurm4Ze0cNVjAEj6nVQxkdDkiTYh7j8NMKgR4RBD8ntRjN30aQw99lnnwU7BCIaouoKT8+n1PSMfhNdFHo6Ozpgam2BIAhDXn7a2toKs9kMURSRmcmlrBRuhAC3hwnPP+Lef/993HvvvbJ2205NTcXDDz+M++67b8CxTHYRDRNJ2Z5kV8NQ+3V9GYuB/EqZGAsAcLVaINnsQ5orITMJANBa3wKXjAozouFs4cKFwQ6BiIaoquI8ACBziEsYAS5jDKSWpkYAQHRsHDRa7ZDmqriQ8ExPS+MumkTUp69//evYs2cPCgvlb2Ch1+sHHMNkF9EwoFQpEZeRCMBH/brYoD5gupNdzkaZyyUuozvZ1VTVMOS5iIaLzs5OVFZWwm7vnSyeNGlSkCIiIjlsNhvq62oBePo++RJ3Y/SvliZP9XhcfMKQ5+pOdmVnc/kihR9BECAEsI9WuP7smz9/Pr7zne/gnnvuQXJysqw+gXJuijLZRTQMpGSmQBRFdJo70NEqrz+DXBJLu/xKmcBkF9FgNDY24s4778T69ev7fJ49u4hCW0VFBVwuNwzRBhhiYoIdDnmhtdlT2RU7xGSXJElMdhHRgP7617/iBz/4Ab7zne+gvb19wPGSJMHdX8/pizDZRTQMREZHAgAsTSbfTOhyQ3JLEEQBolYDN5fE+YcgQJXuSVA56luGNFVCZhK0eh1cTida2K+LRoD7778fJpMJe/fuxaJFi/Duu++ivr4eTzzxBH77298GOzwiGkCryQTAUx3ki2oEURQhiiLcbjesViuXxPmJ2+1Gg9HTMiMuMXFIc1VUVKC9owNqlQrp6em+CI+IwlBGRgbefvttAJ6KfqfcjcMGEBJ7Vz733HPIycmBVqvF7NmzsW/fvmCHRBRSzC2enW+i4g2+mVCS4GryVBopk+J9MyddQpWaAIVeC7fNAUdt45DmypuSDwCoOnUebtfAdzKIhrstW7bgd7/7HWbMmAFRFJGdnY2vfe1reOaZZ/DUU08FOzxeuxANICHec31hah16ZTMAKBQKJCZ6biDV1db6ZE66VFO9EV2dnVCrNUhKSR3SXPsPHAAATJg4EUq5O3cTDSeiEPjDS8PteiUiIgIGg6HPQ6lU9vy/HEFPdr3++ut48MEH8fjjj+PQoUOYPHkyli5dioYGLtMh6tbS4KkK0kdHQqHyzcWCo74ZAKBMZrLLXzR5GQAAe3kNIKPUtj8RBj1S8tIAAGVHzvkkNqJQ19HRgaQkzx+2sbGxaGz0JIwnTpyIQ4cOBTM0XrsQyZCcnAwAMJtMcNiHtkFLt5RUT/LFOMB28zR4leVlAICM7BwoFIoBx2tUyj73h2ttbcW5c55rlpkzZvgyRCKSabhcr/ziF79AV1dXv8/v378f3/nOd5Ca6l0CPujJrt/97ne4++67ceedd6KwsBDPP/88IiIi8M9//jPYoRGFDFuXDdaOLkAQYEiI9smczgvJLhWTXX6jGe3ZYtt2rmpI8+ROGQ0IAhorjGhvsfgiNKKQN2bMGJw5cwYAMHnyZLzwwguoqanB888/7/XFjq/x2oVoYJGRkYjQ6yFJElqafbP8vvt7v47JLr+QJKkn2ZWVm3fZsQKACVlpWDZtPKaNyrrk+YMHD0KSJOTl5SE+nteaFKYEMfCHF4bL9cqTTz6J2i9V7JpMJvzpT3/ClClTMG/ePNTW1uLFF1/0at6g1pPa7XYcPHgQjzzySM9joihi8eLF2L17d5+vsdlssNlsPR9bLPzDj0YGS6MJWr0OhsQYtNY1D3k+Z73nwlOZHO/ZnZFbevuUMiEWiuhISE4X7JWDvygXVQpkjc8FAJQePuur8IhC3g9+8IOeP2gff/xxXHvttXj11VehVqvxr3/9K2hx8dqFSL74xER0dnSguakJyalpQ56vu7Kr3miE2+2WtWMXydfa3IT2NguUSiXSMi9NYHVTKRWYOTobSdFRAIDMhFicra1HW5fn55zDbsfJo0cBADNY1UXkc1++jtBoNNBoNL0eG8z1SrAsWrQIq1atwk9+8hNkZGTgH//4B959911kZ2fjjjvuwNq1a3uqhb0R1GRXU1MTXC7XJYEnJyejuLi4z9c89dRT+MUvfhGI8IgCwi0zydTS0IqEnFREJsXAeZkdFB0y5rJaVUBdF/SdEkS1Bk59Apx9NL+XzP2Xk/YaZ5KXfHM3Vgw4xqGU13BWcslrXOiqK5E3X4O8hJTT2DHgmLauOBgy82BzKNF1zgiLRQHg0qUA7TJ+AidPyIZCo4LFZEHN+cv3KLFL8nanc0kDL6mUMwYA3DJ38+Sun4PjtNcEO4Sg+drXvtbz/9OnT0dFRQWKi4uRlZWFhISh7RA2FLx2IZJPFxUNm9OJqppaZIwe0+84hYw+NK0dXRC1EXBBgKWjE2VVNYjv42dBEuQtmRRV8q43OipODTjmzD+flTVX5JhJssZFFU6VN04r73NobBv42qWxrR0nT55Ep82OtJR0tFptgNV2ybhTVfX4/rULkBQdBZvDiZb2DqTGRiM6IgJbTniWLdaWnkWK3Y64uDiMyrt8hRiFnyiZ/ZTCgVsQ4PbBBhzenA8AMjMzez3++OOP4+c//3mvxwZzvRIs7733Hn7zm9/ghz/8IZqamqBWq/Hf//3fuO+++4Y077C7HfLII4/AbDb3HFVVQ1seRDRcmBtNAIDoxBjfTChJXyxlTInzzZzUQzfas+tQV8nQkhUpU0YBAEqPyEvaEYWriIgITJs2LaiJrsHitQuNVLEXvl9bfbSMURRFJF34w63eaPTJnIOlMsQgMicfCdPmInPZTci75ZtBjccXaivOAwDSsrP7HSMB2HTsDJraOvDCpzvx/v7jAIAJWalQKRSQJAk15zxL0GfMmOGTnTiJqLeqqqpe1xUXV28NRxEREfjlL3+J2tpavPXWW1i8eDEefPBBTJo0Cb/73e96+rZ6K6iVXQkJCVAoFKivr+/1eH19PVJSUvp8TV8lekQjgaXBBAAwJERDEEVIQ2h43s1ubIY6MxmqlHh0nSgb8nzkoYyJhCoxBnC7YS0b/I5R0TnJ0MVFodHeicpT5b4LkChE/eY3v8EPfvAD6HS6Acfu3bsXTU1NWLFiRQAi+wKvXYjki0tIBAC0Njf7bNlhUnIqaqqqUW+sQ+GECUOezxu6lAzETZwBTVwiFOpLq6pUhhg4LKaAxuQrbWYTLK0tEEQRqV9awigAiNRq0Hah0utMbQPOGRvhcnuqtj/Yfxwnq4xwuFxora9DV3sbNOoUTAzwvw9RoLklzxHI8wGQtSPhYK5Xgk2pVGL16tVYvXo1amtr8fLLL+P555/Hww8/jOXLl+OOO+7ADTfcIHu+oFZ2qdVqTJ8+HZs3b+55zO12Y/PmzSgqKgpiZEShp8PcDqfdAVGhQGRspE/mdBi7K7vYONSXdPmeXRit1Y1wWwe/A1V3VVfFyXI47fKWbRINZ6dOnUJWVha+//3vY/369b3u5DmdThw7dgx//vOfMXfuXNx6662IiooKeIy8diGSL8oQDZVKDZfLCbOp1SdzJl/4I60+wE3qNbEJSLtyOSJS0qFQq+F2OtDVaISp+Bjqtn+C8+/9Hxxt5oDG5Eu1lZ5WE4mpaVBrtD2PqxQKXFGYj2umjINe80WCz3XRX/h7SyrQfiERVnuhqmvS5MlM8hMF0XC/XklLS8PDDz+Ms2fPYsuWLYiLi+vV4kKOoFZ2AcCDDz6ItWvXYsaMGZg1axZ+//vfo6OjA3feeWewQyMKOeZGE+LTExGdFIu25qE3OO5OdinjoyGolJAcTKj4Qneyq+ts9aDn0MToEZubAkmSUMYljDRCvPzyyzh69Cj+9Kc/4atf/SosFgsUCgU0Gg06OzsBAFOnTsW3vvUt3HHHHdBqtQPM6B+8diGSRxAExMbHo8FYh9amJsTGDf3mWneyq7m5GXa7Heo+Kqx8TRAVSF20DKJShY6aCjQd2gW7ubVncx+3o/8bW4JCgYi0bHRUhXYFfW2Fp4I8LSun1+OLJ41FbGQEnC43ovWXr7rtbLOgpb4OAgRMnzbNX6ESkUzD6XqlpKQEcXFxfe7eOn/+fMyfPx9//OMfvZoz6MmuW2+9FY2NjXjsscdgNBoxZcoUbNiwYVDd9onCndPuaT+vUPrmW9fd3gWnqQ3KmCho8zPRxaVyQyZEGqBOjQckCV3nBp/sis31XMxbqhrRYW73VXhEIW/y5Mn429/+hhdeeAHHjh1DRUUFurq6kJCQgClTpoREzy5euxDJp75Q3eN0+uaGWmRUFAzR0bCYzSg7V4KxheN9Mu/lRKRlQRVpgLOzA8btn142uXUxQaFE1spboY1PQtWGt/0c5eCZW1vR0tAACEKvfl2CIPQkuLaeKkG9qf8brddOGYfkSA1ebK3DyYoaxMWxHyyFP0mSZG825qvzeWM4Xa98+umnePDBBzF79mysWrUK119/PfLz83uN0ev1Xs0ZEg3q7733XlRUVMBms2Hv3r2YPXt2sEMiCkm6qAgAQJdl4F115Oo66Ulw6SaM8tmcI5kiZzQAwHreCHeHddDzRGclAQBMFfUDjCQKT6IoYsqUKVi1ahVuu+02LF68OCQSXd147UIkT3tbGwBAH+mbFgyCIGDchV5QJ48f98mcA4m88Lu97XyJ7EQX4Nk52npht+fUK66FqFL5Jb6hOlvs2XEyJT0Tuogv/piUJAltXfKuZTITYlGQlQGNRo3Y5FS/xElE3hsu1yv33HMPqqurcccdd2D79u2YOnUqCgsL8fDDD2PXrl1eJ/qAEEl2EZE8EQbPBUhnW6fP5uw6WQZJkqBOT4Qi2rtsOX2JIECR7UkadpwYQpWcIMCQ4fmj3lI1uN1HiIiIgk2SJHRcSHZFRl2+mbI3xhWOhyAIqKupgdlk8tm8fRFEBfTpOQCA9opzXr++Yd9W2FqboIzQI3Z0oY+jGzq3242S4mIAQHZBwSXPWzo9ya7oiMsvG0+K9iQzz1fVICYx9KpGiPxBkqSAH+EsMTERd911F9577z00NTXh6aefRnNzM9asWYPU1FR861vfwocffgirVV4SnskuomFCqVFBqfbcEezyYbLL3dEFe3UDAEAzOtNn845EYlIahAg93FY7ukprBj1PZHIsFGoVnFY7OhqGb7NbIiIa2ew2GxwXKqF8VdkFeJYypmWkAwDKzvm3r2VEWhYUajUcHW2wNnlfbS25XKj9/GO4nU5o4xKgTw2ta62aqkp0tLdDrdEgNSPrkufNnV0AgOiI/vt16bVqRGjUcLncMDY1Qx8T67d4iWhk0Gq1uO666/C3v/0NdXV1+OCDD5CUlIRHHnlEdqV/0Ht2EQWCBN9lwd0y53JJblnjnJA3TmXQwQUJ9i4bui5TQm9RDDyXydZ7dxzXqXrEp6ZBys2FadcXFUnWcnmJFv1ZeXc63eqBm0lLTTL7XLnk9f6QjFXypjtxVtY489n+f2xGZBVAahZRe6oWDY6Blyo0iX3/2+uz49EpSGioaUCT4EKX5JIVm1VyyBrnlDGf3K9fuXeYAtnPgIiIQoNedCNSo4YuIgIZCf0nQM43DbxTo6mjq9fHSRnZKC0rx/ETJ5E7bkLP46kJ8irIJJe83636FE9SzVx8HPZ+kl2S6/K/fx0tDTB+/hHSFq9CdPYotJWchN3U3P85c8fIik2llLdJx8U7J35Z8cmTkCQJ6bmjIIgKuL801nzhfY/SauF2Sz0VXBfLiI8BABgbG5CcmorkmMDvlEsUDJLUs09FwM43Us2aNQuzZs3Cr3/9a5SVydvwg8kuomGiu19Xpw/7dXXrPFuNuKumQJMaD0WkDq72roFfRL0IGjVUOZ47opYTFUOaKzbT06+rlUsYiYhoGGuzeBqaR0X5PvmRM2oUdm/7HA31RnS0t0Ef6YcEiyAiKs+TeLKcPTGkqVqO7EH02MnQZ+QguehKVH/6HiSZN+78xWa1oqLc80djTn7fCbaaFhM+2Hfssr274iI916hVNXWIZ78uIhqCX/ziF7LHPv7445d9nskuomFC74d+Xd1cHVbYapuhTU9AREE62g5535NipFONzoWgVMDV3Apr/cB3qPsjKkTEpHu23G2tavBVeETDzl133YX//d//veSP5I6ODtx333345z//GaTIiEiutgv9uqIMvuvX1U2vj0RSSioajHU4X1qK8ZOn+Pwc2th4KDRa2C0mdBkHv8Nyt4b925EZlwi1IQYJ04rQuH+7D6IcvLKSs3C7XIhLSEB0XHyfY+xOF+zOy1fBxeo9ya6KmlrEp6T5PE4iGjnef/99lJSUwGazISvLU0hQWVkJjUbTa3dGSZIGTHaxZxfRMOHPyi4A6Czx9JjSF2T4Zf5wpx7jaUxvPzO0RKEhNQ6iQgFbpxWdLW2+CI1oWHrppZfQ1XVplWlXVxdefvnlIERERN7qruzyZXP6i+WO8uySeL6s1C/z6xI8ldaWklM+mc9tt6F+7+eQJAmGvDHQZ+b6ZN7BOnva83nljyuEIAiDnqetzYKGpmbUNTRB74fEJlGocktSwI9w953vfAdz585FZWUlzp07h3PnzqGyshJz5szBd7/7XRw6dAiHDh3C4cOHB5yLyS6iYcIfOzFerPOs546lJj0BigF23KHexNgYKJMSILndsJ8dwi6MuHgJI6u6aGSyWCwwm82eLe/b2mCxWHqO1tZWfPzxx0hKSgp2mEQkQ3vbhWWMfkqA5FxIdhlra9DV6ePrI0GENi4RwNCXMF7M2lAH06kjAIDEGfOhjPBd435vtDY3o6mhHoIoYlQ/Sxi7ZSXEYu6YPGT203fttXUb8I3/+iGOlVcMKWlGRPSrX/0K//3f/42UlJSex1JSUvC73/0OTzzxhFdzcRkj0TChi/TsguOw9d+cfiiclk7Y6luhSY6FvjALlgPyGrYToJ02EQDgrKiGZLUCGFzfEK0hAinjPOW6rZVMdtHIFBMTA0EQIAgCCgoKLnleEASv+jkQUfC0t7cDANRqtV/mjzIYkJCUjKaGepScKcakqdN8NrcmOgaiUglHm9knSxgv1nLyEHTJadAmJCNu8kw07P7Mp/MPRJIkHDmwDwCQlZMLXUQEcJl+rQmGSOSlJCAvJQGTc9JRb25Hg7kNDeY2VNQaUVvuqaxjvy4aaSRIsjdr8tX5wl1rayvM5ks3STObzWhu7n9jj74w2UU0TLQYWxCXmoCCGWNRVVwJyS1vtzxvtB0phWbpDMQUFaL91NCarI8UmonjoM7PheR2w3p08MscNFE6TL91ETR6HbosHWg8V+vDKImGj88++wySJOGqq67C22+/jbi4uJ7n1Go1srOzkZbGnjBEw0FySirq6+pw5MAB5I8ZC4VCxpbRXho3fgK2N9TjyIF9yB8zFoBvqsi6/4D1x/UWJKlnXleXfyr2L+fksSMoKzkLQRQxUUaCsLy+GRnxsTBEaBGjj0CMPgJj0r6osP3svbegi4xEUkaWP8MmohHguuuuw913343//d//RVFREQBg9+7duO+++7By5Uqv5mKyi2iYKN5zApljsxAVF4386WNwdv9pn5+j/cR5RE0ZBU1yLGIXTARqqnx+jnCiTEuBtmg6AMC6+yBcxsFXY+UvnAyNXoeOFguOvLMDTtvltzEnClcLFy4EAJSXlyMrK4tLYoiGsRmz56DkTDFaWppx9NBBTJs5y+fnyB9XiFMnjqO5sQEH9uzCtKzVPpnX0e7pm6mOjoVCo4PL5rudqrUJydAlpUJyuWA6c9xn88pRW12FfTt3AABmzZuP5NSBbx60dnTig/3HoFYqoVIqkBQdhaToKMRolCguOYcF84ogxaVCrdH4O3yikCJJAa7sGgE9u1544QXcc889WLlyJVwuz+YYoijiK1/5Cv785z97NRd7dhENEw6bA8e3HgEAjJ1d2NPDy6ek/9/encdHWZ39H//MJJnsK1lZEhLCFpawYxAQEAXE9VGsdam2aqvVulbFuqG41Sr+tNVqn7Zira37Y1UQ2URBwr4vYQ8JgSyQfZ3MzP37IxJNWTKBWZLJ983rfmlmzpxzzT1kOHPNua9jULqkqdhf2MCemGNjXT+GjzCHhxFy4XhMZjPW3ftp2HrmycfoHvHEp3fDcDjYNn81Dae5lECks0hJSWHFihVcf/31jBkzhoKCpk003nnnHVasWOHl6ETEGUFBQYwZNx6AdWtWU1l54qUpZ8tsNpM1rilJvnvnDo4UFrqkX8Nuw1bftOoqKD6xldZtE9V/CABVuXs8urKrsqKCr7/6EsPhIL1ffwYMHtKmx1ttNvKOlrFuXx7/mLeQG+68l+dfewNrWAxBISHuCVpEOpXIyEj++c9/cvjwYb755hu++eYbDh8+zDvvvHPCDt2tUbJLpAPJzznI0UPF+Pn7kznRdXUpfqzh8DGqtzcVGLWMGu2WMTq8gABCp03CHBSEreQYtd9mn3FXJrOJPhMzATi0ZT81RytdFaVIh/bxxx8zZcoUgoOD2bBhAw0NDUBTzYZnn33Wy9GJiLP69OtPt+49sDU2smLZMreMkdi1K+l9+mEYBosWLXbZ6ofjq7uC4l136bQluguhXXtgOByU7dzssn5bY7U2sHj+59TX1REbn8C5500845WzDrudneuban5179WH8KiTF64XETlTcXFx9O3bl+HDh5/xxkS6jLGDMdH6P0qdoXCduzicOHd2J2s32A3n2tU6nCs4X2u2AZC9ZDUXXj+NLqmJdOmdRP7ulpcaFpvsrfZ1xHz6QrElK3Lo0zeZoKAeVAQMoW7H6XcY7B7kXOHWwMqaVtuYY5ystWFr/XkC2AucK2RYtcW51+FgYSzxV5wLAYnYj9Rx5J8bsFe3/JYh3+JcTZIj1JM6uDeOmFCO1dWSnb2ZRk6Mo8pw8u+IvcGpdjZH6+fOmTaA01sgt+dl17pMrn16+umneeONN/jZz37Ge++913z7ueee2+bdeETEO3p2iQTguisv569/+xvlhQXUFR+mf//+LdodPFreal+ttUnsN5BN27azv+AIq7buIGPAwNO2D4yIanVMu61p7hWc1B1z8KlXLjn774h/RBQxg0cCUFt4CPz88D9JHHWHnNvZ+WiMczWy9haWsGrpIgrzDxEUEkL6yCzyylp+uRbo79zHwl+MG8aatWspjgohJKkLv/rVTQQHBzv1WBFf4zCaDk+O1xn87W9/44knnuDw4aYaxr169WLWrFlcd911bepHK7tEOpjK0kpy1jVdMjfygnOI+H4i6Uq2mnqKs3MACBubiSkwwOVjdFRR4wYSnNYVw26n+NPvsJ/FJYeW4ED6ZQ0AYOd3W92206ZIR7Rr1y7Gjx9/wu2RkZGUl5d7PiAROWOxsbGcM7pptfi8efMoKSlx+RjBIaFkDGla9b78m2+aV4OejcbvLzEM6pJw1n0B+IeGE5LQDYCKfTku6dMZ2zespTA/D7OfH+dMuoDg0LAz7qumpoYVy5cDTTUWlegSEVd67733uPvuu7n99tv517/+RUhICE8//TQPPfQQf/3rX9vUl5JdIh3QjlXbKM4vwt/iz9hLx2EJdP2W3sfW78FWVolfaDBho0//7Whn4Z+eRuSopm+jjy1Yi7Ww9Kz66z9mIAGBFiqKy8jb7ty3uCKdRWJiInv37j3h9hUrVpCWluaFiETkbIwbN46U5GSsjY189PHH1NW5vj5lesZAoqKjqa2pYXX2yrPuz/Z9sssSFYPJyZVPpxOZ1heTyURtYQGN1Z4pW7B75w72bN0CwPCx44mJO7PLgY775ptvqG9oIDEhgczMTFeEKNJhGcYPReo9c3j7GbvfCy+8wHPPPccjjzzCqFGjMJlM/OQnP+G1115jzpw5bepLyS6RDsjhcLDyixXUVNYQFhVO1vQxLr8Uy3AYVH69AYCQoX3wc/byQh9ljosleNwYACpW76RmZ95Z9ReaEEXKwKYP7FuXbWzXl/mJeMOtt97K3XffzerVqzGZTBw+fJh3332X3/72t9x+++3eDk9E2sjPz4/LL7+cyMhIysrK+PQ//8HhZGkIp8fw92fCpEkAbFy/ntJjzpUyOBWHrRFbTTUms5nAmLNLEgWERxLatemyw4r9nlnVVXTkCN8sWQxA38FD6JGWflb9lR4tYfOWpsTZBRdcgNmsj5Ii4lo7d+5k6tSpJ9w+ZMgQDhxo2+IAvUOJdFDWeivfffYt9kYbCSlJDB7r+m/XrAePUL+vAJPZTMQE9xTE7whMISGEXDAJ/Pyo3VdA+fKz3yY8eUImmEwcyjlI6eGjLohSxLfMnDmTa6+9lvPPP5/q6mrGjx/PLbfcwq9+9St+85vfeDs8ETkDoaGhXHXllQT4+3PgwAGWuaFgfWpaL1J79cLhcLBs6ZKz/jKp/lgRAMFxZ3cpY0zmKExmM3VHi7BWlJ1VX86oqqrkq3mfY3fYSUpOIWPYiLPqzzAMNq36DsMwGDhgAD169HBRpCIdlwPD44evCw0NPell6Bs3biQ1NbVNfSnZJdKBlZeUs2bhKgD6juhPhBtWX1V9swHD7iAwJQlLzySX998RBE8YhykkGEdZOUfnrT7r/iKS4wjv2gVbo40dK7a4IEIR32MymXjkkUcoLS1l27ZtrFq1ipKSEmbPnu3t0ETkLCQkJDB9+nQAVq1ezdGjrv/CZ8LESZj9/DiYm8vB3LMrE1B/tCnZFRh7dsmu8NQ+APiHhDat8HLj5iiGYbBs0UJqa2vo0iWWEeMmnPUVAEWHD3G0qAhLQAATJ050UaQiIi0NGjSIdevWNf9st9t55plnuPXWW9v8ZaeSXSIdXP7ufA7vKwCg5wDX17GxV1RTu2k3AOFjBru8//YuoF8f/LomYths1C5aimG1nXWfMX26A1CQc5D6syhwL9IZWCwWMjIy6NevH4sXL2bnzp3eDklEzlJGRga9e/cGYMsW13/pExUdzZChQwHI/u67s1rdVX+sGICgmLiziqlkzbfYrQ0EhIQRmzmKruOnENq9p1uSXju3baXgUD7+/gFcOP0SAixnX9v10PeXDw0YOJDw8PBWWouInJl77rmneSMiPz8/oqKimD9/PnPmzGlzGQslu0R8wIHt+wDo2b+ny2t3AdSs3YHDaiMgIYbAtG4u77+9MoWGEjiqadm/dd1GjMoqF3RqIrpXVwAO7zl09v2J+Kirr76aP/3pTwDU1dUxcuRIrr76agYPHszHH3/s5ehE5GwNHjQIgG3btrm8dhfAyFGj8Q/wp/DIEfbv23fG/TR8n+wKPMtkV8WurRQs+5KyXVt/SHoNGkHiORMwmf3Oqu8fq6qqZNV3TbsljsoaQ2RU1Fn36XA4KDjYlOzq17fvWfcn4is8W5ze6BQ1fi+77DLuueceAFJSUjh8+DDfffcdN9xwQ5v7UrJLxAccOXCEhtp6gkKD6dLTNdtj/5ijroHazU2ru8I60equoLFZmCwB2ItLsG53zWqS8G5dCAgJxFZv5egh12+9LuIrvv32W8aNGwfA//3f/+FwOCgvL+fVV1/l6aef9nJ0InK20tPTCQkOprqmhsJD+S7vPyQ0lKHDhgOQ/d2KM/6QaK0ow2FrxBxgISAi6qxiMuw2KvfvomDZfEp3bsbeaCUwqgtdMkeeVb/N/RsGy5cuxWq1kpjUlYGZQ1zS79HCIzTU1xMYFERycrJL+hQROZXPP/+cOXPmkJ2dfVb9KNkl4gMcDgcHcw4C0DWjp1vGqFm7E0eDlYC4KIL6uWeM9sQ/vRf+PbqB3UH9t9/hqr1+Y3o3rYwr23cYww3fZIv4ioqKCmJiYgBYsGABV155JSEhIUyfPp09e/Z4OToROVt+fn4MGDgQgH273LM74fARI7EEWigpLiZn544z68QwaChtqisW1OXsdmRs7tJupyp3DyXrV2I4HIQmdieq94Cz7ndPzk7yDh7A38+f88533W6Jh3L3A9A1OQU/P9etQhPp6AzD84evmzlzJldddRV//vOfGT9+PO+//z4Azz77LDNnzmxTX/7uCFDcp9Ha+mVP/pbOc5mZsxxOvjM4862fzWF3qi+b4Vy7WvuJu02cTLWf9bT3b9uWQ8+h6YT0iudYkIG1/tTtIwOCnRrTYg374QcrlGXnEj9uII2jR7B/eymGvSlZY93o3GQqLr+m1TZBERVO9WW3OjdmZWmQU+0OV/1weYJfSCDdp0+kpsxC6fKtlG+0AE337wtove7FXr9T1/Xq3ieRUrODjfvyKDOce+0r7M7V9WpwNDrVzupove6Y3clEnLPflBtO7h7TGZZni3N69OhBdnY2MTExLFiwgPfeew+AsrIygoKc+72W9iE8ovXNU6oqKz0QibQ3gwcNYu3atZQWFhDiZyIo+NTzk0a7k/OvH7ULCAxk2IiRrFyxnBXLl5OW3rs5WeNXWe5UfwERUTRWVRAcn0RwUo/mGl4/VrHJudUHtQf3nnBbff5+es74BZHp/Snb8B2lm1aTMO1q52Lz/yHxVFtTw6rvluNnNjMqK4v4+B/mNc7+m34yhmFw6Psi/917ur42rIjIj/3973/nH//4Bz/5yU94/vnn+eMf/8hPfvITzj//fH72s5/x/PPPO92XVnaJ+Ijyo+WUFpdiNpvp3i/FLWOUrt+LrbqOgMhQoga3bevXjqTLpCH4BVloKCqjfO0ul/Ub1bULQWHBNDZYKc0/cbIsIj+45557uO666+jevTtdu3ZlwoQJQNPljYO+r/UjIh1bQkICiQkJOOx29u/Z7ZYxMocOIyQ0lMqKcrZu3nxGfTSUlwIQGNXFlaEBULoxm8JvvgQg+X9uJLTHmSWUVnyzjIb6emLj4xkyfITL4jtWVEh9XR0Wi4WErvpCXdrG4XCQl5fnlrp87YHDMDx++LqGhgZGjmy6tPvCCy9k3/c1F5OSkjh0qG31jpXsEvEh+74vVJ+S4Z5ElGGzU7KyqXZV3Jj+mC2+tzg0KDmOsH7JGIZByVfrwOG6f1QSezftwliy/wgOu2/+oy9yNip/tLrn17/+NatWreLvf/87K1asaL4cJy0tTTW7RHzIoMFNtUB3n+llhq2wWCyMzhoDwJrV2TQ0OLeq+ses5cea+op2fbIL4PDC/6N8+0bM/gGk3XAHfoFtW72an3eQvbt3YTKZmDj5Apdeanh8VVfX5BTMuoRR2mjevHn8/e9/Z/ny5d4ORTqI6dOn88UXXwAQGRlJdXU1AFu3biUxMbFNfSnZJeJDcnNycTgcRMVHExkX5ZYxyrfmYi2rwi84kC4j+7hlDK8xm4g9fxgAlZv2YS0ud2n3CelN34gW7i1wab8iviI6Opri4qZVj5MmTaJXr15cccUVhIX9cEn19OnTOffcc70Vooi42ICMDMx+fhwrKab06FH3jDFwEFHR0dTV1rJx/bo2P77h+2RXQFgEJn83fNFnGOR++DdqD+cREBZBlwFDMDmZWLLb7axY9jUAAwZnEhfvuo2Kmi5hbKrX1T1VlzBK2+Tn57N+/XoAVq9eTWOjc+U2pHM755xzeOqpp7jlllv497//jc1m46WXXuLWW2/luuuua1NfSnaJ+JCG+gaO7GtKpCS7aXUXhkHx8u0AxAzvjV9IoHvG8YLIYelYukRgr62n7Lttru07IZrg8BBsVhtHDxa5tG8RXxEWFsaxY00fKpctW6aJsUgnEBISQnLPpjnL7p3b3TKGn58fWeeOBWDDunXU1LReQ/THHA312OpqMJlMWCJj3BEiDmsD+/7xJxorywkIDSe670CnHrd100bKSksJDg5h9JgxLo2ptKSY2poa/AMCSOja3aV9i28zDKN5dQ5AbW0tW7du9WJE7mEYhscPXzdr1iwcDgeffPIJc+bMITIykvfff5/77ruPJ554ok19+d41SCKd3MHtB+jWuwcpA1LJWbWdxobTF7Y/E1W7C6g7UkpwUgwxQ3tBdtu/JW1vzCGBRI9p2gmpdPk2HPWu/ZDdbUDTRL7kwBEcNueK7Ip0NpMnT2bixIn0798fgCuuuAKL5eSbQixdutSToYmIG/Xun0Huvr3sydnJ0FHnEBjo+i/Sevfpy/qEtRQXFbJl00YmJEU6/Vi/oGBMfk0fm8z+AS6P7bjGyjL2vfMafX/9MMFd4vELDMLeUH/K9rW1taxbvQqAc8aOJbCNlz+25sDuprqlXXsk4+eOFW3is+rr6ykpKWlxW1GRvuyV1pWWlrqsL63sEvExRblHqDxWQYAlgF5De7ttnGPrmgrJRg1OBRdtbe1N0VkZmC0BNBSVUbX1gEv7Dgiy0K1/MgD5W/e7tG8RX/LPf/6TWbNmMWJEU3HlAQMGkJmZedJDRHxHj5SeRHfpgrWhge2bN7plDJPJxLDv31u2bd2C3cndHQFih43BzxJIfWkJdcWH3RLfcbUFudi/ryvmZzl90m/dqmysViux8fH0yxjg0jga6us4uLdprterv2v7Ft8XHBzMtddeS3Jy0/x34MCBnHfeeV6OyvUMDxen7wwru1xJKXoRH7Rz1TZGTz+X9KF92LdxN40Nrr8UqGrPYWzVdfiHBRPcuzt1u/JcPoan+EVHEJHZVIvi2LIz26npdLoPTMUvwJ/K4jJKD5W0/gCRTqqxsZHbbrsNgHXr1vH73/+eqKgo7wYlIm5nMpkYMmIUX3/1Jds3b2JA5lC3rO5K792HkNBQamtq2JuXT9/Unq0+JiSpB+E9e2M4HJSs/gY88GHTYW2AoGDMp1jZCnCsvJzt25ouCzt3/HmYTCaXxrB/Vw52u53oLrHEJrStKLQIQHp6Ounp6TQ2NhIQ4L4VkeJbnnzyydPe35ZLGZXsEvFBh/ccovJYBRFdIuk1tA85q9xQA8NhULblAHFjMggdkt6hk12hY4dhNZup2XeY+nzXJqNMZhMpmb0AyN24x6V9i/ia6Ohojhw5Qnx8vMs/uIlI+5aa3ptN69ZQduwYO7ZsYujI0S4fw8/Pj4GDBrNmVTabduS0muwymf2IGzUegPJdW2koc08B/f9mtzaVoDjdyq5v127AcDhI7dWLbt17uHR8h93O3h1NtUt7Dxik92M5K76c6DIMj+S/W4zn6/7zn/+0+LmxsZHc3FxMJhO9evVqU7Kr4197JCIndTzBlT60DwGBp/5m8GyUb8nFcDgI7B6Pf6zztS/ak4DuCQSmdsNwOCj9ZovL+0/s04Og8BAaauop3H3I5f2L+JIfF6j/5ptvVKBepBM5vroLYNumjTTUn7pW1dkYODgTs9nMoaJiSkrLTts2IqUXAaHhNFZXUrplrVviORlHY1Oyyxxw8vnbwcNH2J9/CJPZTNbY8S4fP2//PupqawkKDqaHdmEUEQ/asGFDi2Pr1q0UFRUxbdo07rrrrjb1pZVd4jLOfuvjjWuNnR3T7nC02sZmcq7Gg9Vhc6pdg8O5D3Pl9jqn2llo2qq6bNdekkf3JbJLJOkTB7Ny/nfNbfaanXut7K0VOm2sx5Z7hLRe3Tk2dBD5izedtnl0cUirYwYVtf4aADhbaaPidFt3m0z0Hj2OusYg9mzfR25NA7SSGMw1t/56FTp+mKBnDEul0mhk68bt5De23P3pmJOvaYWt1ql2DXbn/i45085uOPk6ONlOxBk/LlBvGIYK1It0EoO6xQEwsGssxXt2UHL0KIe2rueyyy5rnl8GBpzm3/MfieH0/8bFhAcxuFcqmw/tZ8O6tUwaOeyk7SyRMYR+vwPhofkfUntw7yn7tHRJcCq2xorTJ9eOs9VUAU1za3ttdYv7HA4Hy75bidFoZfiwYXRNiG+1v0kD0lttExXU9F5rGAZ/W72M9IQYJpx3HmOG9nMqZpHOyNM7JHbWml0hISE8+eSTTJkyhZ///OdOP04ru0R82OqFqzAcBil9U0gb4J5v5go27wMgun8yfoEda5ly9IBkguIjsTc0cmhVjsv7T0hJJCouCpvVxv7Np54ki0gTFagX6dxMJhMXXXQRZrOZHTt3snXrVreMM2xI03vIzgN5NJxsBanJRGzmSEwmM2XbN1Kd69kyBHbr9wXqT/Kl4479uZSUlhFosTA6K8vlYx84cIDikhIsAQEMHTrU5f2LiJyJffv2tXmnRq3sEvFhxwqPsSV7C5nnZjJ84giOHj5KZVmlS8eoOHSUumOVBHeJICYjmZKN+1zav7uYA/xIOLdpd6HiVTnY6q0uH6Pv8KZvQw9s34+1wfX9i/ia4OBgFagX6eS6devG+HHjWPbNNyxcuJBu3bvTJSbGpWMk9+hBTGQEpRWV7DxwkCF9Wq58ikjriyUyGru1gSNff+HSsZ1xqt0YrY2NrNzclAAcNSiD4ODWV8y31eo1awDIHDKE4OBgl/cvInI6/71yyzAMioqK+Prrr7nlllva1JdWdon4uJ1rd1CUX4R/gD9jLhqD2c/1v/ZHN+0HIDaz49R1iB3Rh4CwIKzlNRxzQ4IuKi6KhJREDMNgz8ZdLu9fxNd9/fXXREVFcfToUY4e9UxRaBFpH8455xxSUlKwNjbyn08/xWZzrjSEs0wmE4N7N81ZtuzZ3+LSIP+QMKL6DgSgdPtG7LU1J+3DnezWpnII/72ya/2OXdTU1hEZHsaQvr1dPm5RUREHDhzAbDYz8vsVtiJyag7D8Pjh6yoqKlocx44dY/fu3UycOJFXX321TX0p2SXi4wzDIPvLlTTUNRAdH8PA0QNdPkbpzjzsjTaCYsIJT45zef+u5h8WTNzIPgAULt+GYXd93ak+w5pWdR3anU9NhecnyiIdWXl5OXfccQexsbEkJCSQkJBAbGwsd955J+Xl5d4OT0TczGw2c8nFFxMSHExhURHfffdd6w9qo/6pKQT4+1NaUcmhouM7MZvokjkSs58/dSWF1BzKdfm4znB8v7LL/KOVXVU1tazb0VRyYezQTPxPV5P0DK35flVXv759tapWRLzik08+aXF8/vnn7Nu3j759+/Liiy+2qS8lu0Q6gbqaOjZ8swGAbr26u7x/h9VG6Y48oGOs7uo2KRNzgB+1h49RsbvA5f37W/zp3qdpG/DdG7SqS6QtSktLGT16NG+//TZXXnklL730Ei+99BL/8z//w9y5c8nKyqKszLkizyLScUVERDBp0iQA9uxxfc2swIAA+qcmA7B5b9MK76i+AwmOTcBhs3FsyzqXj+ms4zW7zP7+mMxNSa1l6zZgs9noGh9H72TXz+Xq6+vZmdOUTBs1apTL+xfxRccL1Hvy6KzuuOMOXn755TY9RjW7RDqJ0sJjAIRFhrml/6Ob9hOXmUZkryT8Q4Ow1bhny/CzFZ6WSETvrhgOg4JFG90yRvf0Hvj5+1FVWtl83kXEOU899RQWi4V9+/aRkJBwwn0XXnghTz31VJsnPCLS8XTt2hVouqzFMAynd/521qD0NLbs2c/+Q4cxRcUS1aepluexzWuw/dcuiJ5k2G3Nz9fk78/efXnszTuE2Wxm0qjhLj8PALt27cJmsxEbG0tSUpLL+xcRORvfffcdDkfbrsZRskukk6iubJq0+Qf4YwmyYHVxQfb6Y5VUHzpKWPdYYgenUpi906X9u4I5wI+uk4YAcHT9HuqPurZY/3HJ/XsCkLsz1y39i/iyTz/9lDfffPOERBdAYmIiL7zwArfddpuSXSKdQGRkJAANVisN9fUEubhgelx0FF3jumD1CyBp5FgAKvfvouZwnkvHOROOxkb8LBbsJhNfr21anT+sfx/ioqPcMt627dsBGDhggFuSaSK+yPj+8OR4vm7SpEktVrAdL1C/a9cunn766Tb1pWSXSCfhsDuoq6kjODSYkMgwrPVt27rVGUe37CeseyxdBvWkcFUOtKOltiaziR7TR2GJDMFaUUuxm5JxQWHBxPeIByAv56BbxhDxZUeOHGHAgAGnvH/gwIEUFhZ6MCIR8ZaAgADCw8Koqq6mqrLS5ckugKEZ/Rl08ZVYgoKoP1ZM6Y7NLh/jTDhsVvwsFtbs2ENVTQ0RYaGMHnTq98azUVVVRV5eU4IvIyPDLWOIiDhjyJAhLX42m83Ex8czceJERo4c2aa+lOySDs3p65ad/ILKcCJfbjecWz7ZYGt0ql2AybkCo5WmWqfa+Z2mFF9hWSmJIfE0hPtTcqT1ywwbzc6d3+KAAADMuQeZ2DAQS2QghRkJHNmd36JdpH/rzzUA586H3cnvNipNdgAGTx1FdZ9EKmw21i1ZTZnJCpYf2pUbzr1e5Y7Tr4jr1yeFWsNGUX4RuWXFp217zFbl1Jh1dudW4TnbrtFhb7WNzYk24PzvoDdqDLTnb6ZtVtfXivMVsbGx5Obm0r37yWvSHDhwgJiYGA9HJSLeEhkZSVV1NTH+0Ce+9d/9slVfO9Vvbe5uAEZfdDUR3XtQfrSE1f/7/+gR1nIHRMPe+k6QFRtXOjVmeL9Mp9o5rA04rFYIgdKqKswYXDByKP6GA8f39bwAYoOc+yh3vO7XqeTs2YVhGCT36KHC9HJK4RER3g5BOoE5c+a4rC8VqBfpRKormi5ldFfdLofdQf62AwAMumAEcT0T3TJOW2VMGkbXfskYDgeb5q2irOCo28bq2a8nAPt27nfbGCK+bMqUKTzyyCNYrScmbxsaGnjssceYOnWqFyITEW84nnxxx06scedMJKJXP2yNjbw5+zHmf7eag8Xer7VpGAZHjzXFERoWxsXjsuge777dro9fwni6VbUiciKHYXj8EOdpZZdIJ1JV0bSSKNRNyS6AfWt3EhEfRVzPRIZfei5bF6+jYIf3Lufrc+5AkgenYRgGmxesoeTAEbeNFRkbRVRcFDW2BnL36BJGkTPx1FNPMWLECHr37s0dd9xBv379MAyDnTt38vrrr9PQ0MA777zj7TBFxEOO1+0qr6hwab8RvQcQN3IcAAWL/wNlJdjsDuat38rEQX3p3917Rdq/27yNgQk96darN8MGZBBS5b4EXHFxMcUlJfj5+9OvXz+3jSMi4mlKdol0IseL1IdFhLptDHujnfWffcegC0bQrX8Kgy8cSWBIEPvX7XLbmKeSPKIv3Uc2Tdy2L9lA4e5Dbh2v5/eF6fP3F7h8AwCRzqJ79+5kZ2fz61//mocffrj5EliTycQFF1zAn/70J3r06OHlKEXEU9yxsisgPJKEEecCcHT9Smr27uCi4YNYujWH3QVFLN2SQ521kaGpnn+v2ZB3hC0NJfQ6dyIASYkJVLgx2bV12zYA0nv1ItgNNdFEfJlhGB4t1eGNsiAdmZJdIp1IZXnTyi53XcZ4nOEw2PLVWhpq6kkb0Ze+YwcRGBrE4WXb3Druj3UdnEavsQNpwCDn2y0c+v7ySncxmUyk9E0BYP8OXcIocjZSU1P58ssvKSsrY8+ePQCkp6erVpdIJ3R8ZVeFi1Z2mS2BxA0djTkggKrcPRRnLwHAz2xm8uD+hFgsbDqQT3bOPmrqGzinR7zHakBuKyhizYFDBHXtSZfwpi8mzf4BbhvP4XCwY8cOoGnzDxERX6Jkl0gncnxlV0hEKGazGYfDuWL7Z2rXiq001NbTf3wmPYf2JjoklJ1frcVhd++4cb270XfSUAD2rt5B7obdbh0PIK5bHCHhIVgbGjl0wL0ryEQ6i+joaEaNGuXtMETEi46v7KqoqMBut+Pn59xGNqfSZcAQ/IOCaSg7SsFXn7TYOdpkMnFu/3RCAi2szNnHltxDVFdUMDEjHX8/95Y63ldcyoq9TTsijh7Yn7jjyS5LoNvGzMvPp6q6muCgIHr16uW2cUR8lafraKlmV9so2SXSiVRX1lBbU0dwWAQJyYkcyT3s9jFzN+yhoaaewReOJKFPdywhgWz9PBtbg3O7H7ZVeEI0GVObtqU9tHkfe7N3uGWc/9Y1tRsABfsOYXdzMk9ERKSziIyMJCw0lOqaGnJzc88qKRMQHklwXCKGw0H+vA9a7Gz4Y0PTkgkJtLBkSw57i45Sa7UydXA/AgPc89GpuLKapbuaVqAP6BrPOYMyaKyqBCAoJtYtYwLs27cPgN69e+Pvr4+FIuJ9P//5z097/1tvveV0X9qNUaSTObin6VvD5D7JHhvzyK581n26Apu1kejucQy7+jwCw11fFyIwPJjBl43B7OfH0QNH2Ltss8vHOJXElKadJwtz3VcAX0REpLMxmUz06dsXgJycnLPqK6JnOgC1hQVYy06/M3PfbolcPGIwFn8/DpdV8un6bVTVnzw5djaq6htYsH0vdoeDlJgozk1PxmQyUVdSiOGwExAWgX9ouMvHBThwoCnBlpaa6pb+RUTaqqKiosVx9OhRvvnmGz755JM2125Uskukk9n//TeH3Xt1x2z23FvAsfxiNnz4DQ01dYR1iWT4TyYSGhvhsv79AvwYdOkYLCFBVB+tYMeXazxWxDEoNJiouCgMw+DIwUKPjCkiItJZ9Ps+2bV7927sdvsZ9eEfHEJIYtMq7MrcvU49JjkuhsuGDyQk0EJpdS3/t24rx6pqzmj8k7Ha7Hy5bS+11kZiQkM4v38a5u/rgxl2G/XHSgAISejqsjGPq6qqorikBJPJRM+ePV3ev0hnYBieP3zdJ5980uL4/PPP2b9/PzfeeGObS1so2SXSyRQVFFNfW48lyEJCcoJHx64uqWD9+8uoKa0kKCyY4VdPoPeETCISz67odHhCNAMvziI8LgprXQNbP1uJ3WpzUdStS/p+VVdZcRlWN3zrKyIi0pklJycTGhJCXX09ubm5Z9RHeEp604qpo0U0Vlc6/bjY8FCuGDGQqNBgauqtfLp+Gyt2HaCoouqMv1QzDIOiymoW7thHaU0tIZYApg1Mx+Lfsh5ZbVEBAMFuSHYd+P48JiYkEBIS4vL+RURc6Te/+Q2vvPJKmx6ji7NFfsTuRMF2E87tyGMzOffNY53d6lQ753cCqm61xdacXWQM6U9Er3g27T118fY6P+fqalWaLU61s+AHFTXsfG8+Yy4dR1z3OIKHJNNzSDI1FdXk5RwkL+cgVaWnn4SaTCZiu8fRLb0H3Xp1Izg8BDtQaKtj2adLKa34YYtuq+Hc61DrcPK5OupPuK1f90iqHA1s37+PI7ZKqmx1TvXl7Gtfa3MugdZgc+45OPP33Jk2AAad4CsmERHxKrPZTFpCHJu2bmXb6pV0Dws6ZVtrafEJt/kFBhHatQcApVvWYi0tprH82AntTsZWUUYwcHFqAgt35XKkspJNVZVs2r2XiCAL6bFRpCekEB1y6pigqbB0QXk1+4+WkXu0jJqGpjmAn9mPKQP7EB78Q3mH47FV7mmky8DhBMXEYa+pwtHYct5QvXurU88huMeJdc727tyBw9pAStdEbDVV+IdHOtWXiPzAMAyPXUlyfLzO6rvvvqOhoW2LCpTsEumE9u/KJWNIf3qmJ7Nikft3Zfxv1gYr33y0lISURJL7pdCtV3dCI8PoP3oAGaMHUF5STl5OLnm78qirqgXAz9+P+OREuqd3p2uvbgQE/ZBgs1ltHMk9zN6NuyktdG7y6krdejZdFnHoQIHHxxYREekM+qb3YtPWrezet58LJpzXpl0ZI3sPwOzvT/2xYuqKz6y2ZlCAPxcP6MWh8ir2Hi0nt7SCynorGw4Vs6GghC6hwaTHRZMeF01YYNMcxWZ3cKi8itzScnJLK2k0fvjiMsDPj+QuUQzqFk9CRNhJx7RVV9JQXkpgVAwhXZOpPujc5ZetMQyD3IMHAUhNSXFJnyIirjBp0qQWST3DMCgqKmLXrl089dRTbepLyS6RTqiwoIjamjpCQoPpmpzEoVzPJ2kMw6Aw9wiFuUfwD/AnKbUryf1SSErtSlRcFFFxQxg8bgglh0poqKsnMSUJf8sPb1n1dQ0c3ldAwd5DFOUV4vDSDoixCV0ICg6k0Wqj+EiJV2IQERHxdT26dSUkJJja2jryCgpITXZuox2Tvz+RvTMAKNu55axiMJtMJEdHkBwdQaPdzsGySvaWlJNfUc2xmjqO1dSxOvcwSZFhBAf4k19WReOPaoyFBIfQMzaK1NhoukVH4O9E7dSaQ7kERsUQltzLZcmuwqIiauvqCAy00DUpySV9inRGDgwcHlxt5egEV1QMGTKkxc+NjU1XrvTs2ZOHH364TX0p2SXSCRmGQe6eptVdqX16eiXZ9WO2Rhv5u/PI351HYJCFbr17kNw3hbjuccR1j2tuV1tZS8G+QxTsPURJQUm7WMrbPaVpVdeR/CMeXyEnIiLSWZjNZvr06sWmrdvYtWev08muiLS++AUGYa2qoKYg12XxBPj5kR4bTXpsNPUOgwPHytlbUsbhimqOVPxQUiIs0EJql0h6domkW3x8cwF6Z1Xl7iZm4DDCklPxDwnFVnv2BfKPr+pK7pHcphVyIiLuNmfOnJPe/vzzz/Pwww/zwgsvON2Xkl0indSB3Qebkl29U/hucXa7SdRY660c2LqPA1v3ERwWTPc+yQRYAjhy4DBlRaXN7byf5mrSPbUp2ZWvSxhFRETcqm96Opu2bnP+UkaTiai+gwAoz9nitq3MggL86Z8YS//EWKobrOw/Wk6j3UFydASxYcHNdVfbmugCsJYdo7awgJDEbkT2GcSxTavOOt79ubqEUUQ6lhkzZjBq1Cglu0SkdUcOFVJXW0dwSDBxSbEUFZxY0NXb6qrr2LNhl7fDOKWI6AgSusYDUHBQyS4RERF36tGtK8HBwdTV1VFYXEy3Vi7BC4pNICA0HIetkarcPR6JMSzQwuBu8S7tszxnCyGJ3QhP7XPWya7SsjIKDh8GlOwSOVsqUO8ZDoeD9957r807xyrZJdJJGYZBXU1TssvfX28FZ2Ls5CxMZhOHDhRQWV7l7XBERER8mtlsJiw0lLq6Ohobba22dxzfucsAw+7c7sztka229Z22nWEYBl8tXozD4SCtZ0+io6Nc0q+IiKukpaWdUKD+2LFjWK1W3nzzzTb1pU+4ItJpvyU4G4NHDqRrchK2Rhsrl579JQUiIiLSurbMWaxV5TjsNswBAQSERdBYXenGyNzHLzAIAHtD/Vn1s3rdOg7m5RMQEMAFkya5IjSRTs0wwOHBj1Gd4SPbvffe2+J93mw2Ex8fz9ixY+natWub+mp9CxAR8V3Ha0d0gjdOV4pLjGXk2OEArPp6jVZ1iYiIeMjxD0EmZ+pfGQbWijIALFFd3BmWWx1PdjmsZ57sOnykkG9XfAfA5IkTtKpLRJrl5uZy8803k5qaSnBwML169eKJJ57AarW2aLdlyxbGjRtHUFAQPXr0OGn9rA8//JB+/foRFBTEoEGDmD9/vlMx7Nmzh2PHjvGb3/yGu+66q/m48847ufrqq9uc6AIlu0Q6teMTRa3scl6AJYBJ08/DZDaxf1cuOVt3ezskERGRTsepZBfQUHYMgMDoGHeG41bms1zZ1dDQwGfz5uFwOOjXtw+DBw50ZXginZbhhT/ukJOTg8Ph4M0332T79u28/PLLvPHGG/zud79rblNZWcmFF15ISkoK69ev5w9/+AOzZs3iL3/5S3OblStX8tOf/pSbb76ZjRs3cvnll3P55Zezbdu2VmNYuHAhXbt2Zfz48bz00kvs2XP2dRZ1GaMPslmdK5Ttb+nm5khOzpnJiauTL07358S8yW44t2tho8PJ2hCtl5xoE2ee6/HnUG9YqXdYqbTXUm47cSvrWkeDU2MGmS1OtQswtb69tcmZFwGcfrNvNJx7Heod1tYbARMuGE9AeCDFZcf4fP5CrI0nPq7B3uhUX3V258ZssDnXn7N/52xOtHP2/HojUersBxxvcfY9WER+EB4R4VS7qsqOeRmanD3rsSIAbNWV2OtqsJUfxRp84kcZ/7CWf5dsdbVAU7H6H9/XWF6KM5xZTWWJTXCqL7/gMKfamfwDWvzsH9RUlNnR2NjiPqOx9fmBYRgs/S6byvoGouMTuPiKKwkICnIqDuk8nH0PFt80depUpk6d2vxzWloau3bt4s9//jMvvvgiAO+++y5Wq5W///3vWCwWBgwYwKZNm5gzZw6//OUvAXjllVeYOnUqDzzwAACzZ89m0aJF/OlPf+KNN944bQx33HEHV199NZ9//jmfffYZTzzxBMnJyVx66aVceumlZGVltfkzgFZ2iXRiWtnVNn0zetO3fzoOh8FXny/F2uBcskpERERc4/iXMc5+6LFWfn8ZY0SUu0JyO3NgIHBmK7t27N3P9h07MJvNXHbppQQp0SXS4VVWVrY4GhqcW6DQFhUVFcTE/LAiNjs7m/Hjx2Ox/LDIYcqUKezatYuysrLmNpMnT27Rz5QpU8jOznZqzLi4OH7xi1/w6aefcvToUX7/+99z7NgxrrzySpKSkrjlllv4/PPPqa937r1QyS4R6RTFDs9WZHQE4y8YA8DqlesoPFzk5YhEREQ6n7Z+QWetrADAPzgUc4BzK9HbGz9LU7LLYW3bB9qyikqWrloDwPhx4+jevbvLYxPpzAzD8PgB0KNHDyIjI5uP5557zqXPa+/evfzxj3/kV7/6VfNthYWFJCS0XMV6/OfCwsLTtjl+f1sEBQVxySWX8L//+78cOXKEzz77jPj4eB5++GFiY2Od6kPJLpHOTCu7nOLn58eFF59PQEAAh/KPsH7VJm+HJCIi0ik1z1mcXNll2BpprKkGOu7qruaaXW0oUG+z2Zm37FusjY2kpKRwzjnnuCs8EfGw/Px8Kioqmo+HH374pO1mzpyJyWQ67ZGTk9PiMQUFBUydOpUZM2Zw6623euLptNDY2HjSlVujRo3i2WefZdu2bWzZssWpvlSzS6QTa++1j9qLc8aPJC6hC3V19Sz8YqmSgyIiIh2ItaqcgNAwLBFR1B8r9nY4bXZ8ZZe9DZcqLV+/gaJjpQQHBXHJxRdjNmuNg4irOQwDhwc/FxwfKyIigggn6qzdf//93HTTTadtk5aW1vz/hw8fZuLEiYwZM6ZF4XmAxMREiopaXtly/OfExMTTtjl+vzNuvPFGunTpwh//+EcAnnzySebMmUNycjLvvvsugwcPbhHz6ehdT6QTq69typpPmjKe6C5R3g2mnRo6cjCZw5t2Lfp6wbfUVJ9YyF9EREQ8I+j7+lWLVqyktLyiTY/1C+x49ar8Q8KwRDbVzbF/X2y/NWu2bGPD9p0ATB03xqkPxSLie+Li4ujXr99pj+M1uAoKCpgwYQLDhw/nrbfeOiFBnpWVxbfffkvjjzbGWLRoEX379iU6Orq5zZIlS1o8btGiRWRlZTkd88qVK7nqqquApsspn3nmGf70pz8xbNgw7rnnnjY9fyW7RDqxb5Z8R01NHV1io7nyp5cSGBTo7ZDalcHDB5J13igAVi1fR+6+PC9HJCIi0rlNGnMOocHBHCsr570v5lNXf/rVTpaIaEITu2MYBtUFBz0UpevEjRqH2d+fuuLDNJQdbbX9+m07WL5uAwBjhw8lrYfqdIm4i2F4/nCH44mu5ORkXnzxRUpKSigsLGxRa+vaa6/FYrFw8803s337dt5//31eeeUV7rvvvuY2d999NwsWLOCll14iJyeHWbNmsW7dOu68806nYykqKiI1NRWAefPmcd5553HDDTfw6KOPsm7dujY9LyW7RDqxoiPFvPf2R5SVlhMcHMSgoRneDqndGDikP2MnNtW3WJu9kQ2rN3k3IBEREaFrfBw3XHEp0ZER1NU3sHlnzmnbR/UbBEDN4Twaq9q2EszbwlN7E5rUA4fdRvGa5a2237RzF8vWNH0YzBqayejMQe4OUUR8wKJFi9i7dy9Lliyhe/fuJCUlNR/HRUZGsnDhQg4cOMDw4cO5//77efzxx/nlL3/Z3GbMmDH861//4i9/+QuZmZl89NFHfPrppwwcONDpWGJjY8nPzwdg/vz5zbs7ms3mNpfgUbJLpJOrraljbXbTN4BDhg/C31+l/DIG92P85HMB2LhmC2u/W+/liEREROS40JBgzhmaCcCG7TtotNlO2s4SGUNIfBKGw0H5rq2eDPGs+QUFEzusaRfo0q3rW03Ubc7ZzZLs1QCMGjyQrCGD3R6jSGfXtNrKk7sxuud53HTTTafd/fG4wYMHs3z5curr6zl06BAPPfTQCX3NmDGDXbt20dDQwLZt27jooovaFMuVV17JTTfdxLXXXsu3337LjBkzANi0aRN9+vRpU19KdokIu3fuo6KiiuDgIDIG9/N2OF41dFQmEy4cC8Dm9dvI/naNlyMSERGR/9YvLZWIsDDq6hvYumv3SduEde8JNK3qstV2rJqbcSPG4mcJpP5YCeU5p995bM2WbSxeuQqAYQP6M3b4UG1CJCId0gsvvMCMGTOora3lvffeay5G36dPH95888029aUlHCI/4swuew4cTvVlc66Z807+peUJ7I7WB2407CfctmLlGi6Ych4Dhmewat0GHA4HASY/p8asM1udaudncl1+3dmdTwyca2dz2Bk74RyGjR5Kg8PG2uwNfPft6hPaWR2tvxBWu3MvljN9gXOvKTQ9B2c4jNb7046TIiLSnoRnDDvhtvMaTHy1cCFbiysY+z+Z+Pn5kT93TtOdJjPdJ10MwLH1K6g9uLfFY51NBlli4l3SBsAvNNypdpH9MgnrkYrhcFCas4WAqC4nbRc+eBRff/01aw4VE5jYnTFZWZx33nlKdIlIh2WxWHj22WdPuH3QoLZflq2VXSICwPatOdTU1BIREUb/jLYtEe3oTCYTk6dOYMTooQAs/3rlSRNdIiIi0n5kZmYSFhpKZWUl27dvb3FfaI9U/ENCsdXWUJO/30sRtp1fYBAxGU2XaFbs33XKyxcdDgdffvklq1Y3zVfOnzSJCRMmKNEl4kEOw/D44avWr1/PsWPHnG7/1VdftdpGyS4RAcBut7N+7WYARmUN6zSTJT8/P6ZdOpmBmf0xDIPFXy5j/ZrN3g5LREREWuHv78/IkSMByF61qsWq5Mi+TTWrKvdsBydXSLcH8aMn4BcYhLWqgop9u07axma3M39FNps2b8ZkMjH9oosYPXq0hyMVEXGd/fv3069fP2677TaWLVtGQ8OJO+3m5+fz+uuvM3r0aGbOnNlqn0p2iUizzRu30dBgJSYmivQ+ad4Ox+0CAvy55H+mkt43DYfdzrxPv2Lblp3eDktEREScNGzYMIICAzl27Bi7dzfV7jL5BxDeq6kGaUUHKkwf2j2ViPSmL9+ObdsAJyk7YG1s5D9fL2dP3iH8/Pz4nyuuIDMz0wvRiohni9OfWDDel8yYMYN169ZhNpu57LLLiIiIICMjg7FjxzJ8+HCSkpJISUlh7ty53HXXXWzcuLHVPlWzS0SaWa2NbFy/lXPGDGfUOcPI3Z3r7ZDcJiIynGmXTiYhKZ5GayOffjyf/IMF3g5LRERE2iAwMJBhw4axMjublStXcj4GEal98LMEYq0oo64w39shtsoSFUNYci+iM5rKKVTm7sFaUXZCu/KqKr5csYrCY6VY/P25esYMUlNTPR2uiIhbpKSk8PrrrzNnzhyys7PZuHEjJSUlhISEkJKSwrhx49r0nqdkl4i0sGH9FoaPyiQxMY4+/dPZvXNv6w/qYAYNyWDshHMIsARQX9fAZx/N51DBEW+HJSIiImdg5MiRrF27liOFheyjglFDmwrBN5SWeDmyUzP5+xMzaCQRaX2xREY3395QfoyKPS1XmRuGwdY9+1i+YTNWm40gi4UrJo1XokvEyxxG0+HJ8TqDoKAgJk6cyMSJE8+qHyW7RKSFuto61mRv4Nxxoxh73jns230Au925Xf7au+iYKMafP4aU1B4AFOQdZtGXy6isqPJyZCIiInKmQkNDOeecc1i+YgUrd+2n/6BcEoGQbilgNre7ml1BcYkkjZ/anORy2G3UHs6nOm8fVQd2Y4lNbG5bWlHJsnUbOHikCIDuCXFcmDWayLBQr8QuItJRKNklIidYv3Yzg4cMIDIynKEjBrNudevXRLdn4RFhjD53BP0H9sFkMmG32fnum9VsWt9x6niIiIjIqY0ePZpNmzZRUFfPqjVrSb3iBvyDQwlJ7E7t4Txvh9fEZKbLkFF0yRyNyWymsaaKkrXLqc7bj2FrbNG0orqGVVu2sfPAQQzDwN/Pj3OHDGZov96dZhMhEZGzoWSXiJygsbGRFd+uYvrFFzAqaxg7tuVQW1Pn7bDaLDQshBHnDGVgZn/Mfk37cRzYe5AVy1ZRVlru3eBERETEZSwWCxMmTODdFYvYsC+Xiw7spUtGJqHJ6e0i2WWJ6kK3qVcSHNe0aqtyXw5F2UtxWFvuOFZdV8/KNevZtnc/9u9XpKV178q4oZnEREZ4PG4ROTVPF4335QL17qBkl4ic1I5tuxgxIpOExHiyxo5iyVffeDskpwUFBzFi9BAGDxuAn78fAPm5BWQvX0PhkWIvRyciIiLuMHDgQOIiwympqGLT6pWcn5FJWEovSlYt9Wpc0YNGkHDuBZgDArA31FO0cglVB3a3aFPXYGX9ngNsPZAHoU01x5ITExiTOYikuC7eCFtEpENTskvaJWeXZ7fn7LbjJNtFn4zNyTISzj5XB623s1mdq8H15Vdfc+31V9JnYG9Wrl5HScmxk7bzM5ud6s9E66+r2cnX3nGS8xEYaGHEqCEMH5mJxWLBjkFeXj7Lv1lFft7pd1q0OZw7J3Yn6n7YnXztnekLnI/NcOK1h/b9e9OeL82wWbVbp4hIe2YymTh/wgQ+Wrqcb1at5fyf305QfFdCUvudsILKEt/NuU6d+DfTbAk86e1+QcHEDT+XkMSmsWw2G/XWRsKHjyN8+DgA6uvrWbNmDWvXrqUhMJrgftF079aN8847j5SUFOdiFDmF8AitBnQnrexq35TsEpFTOpR/mF05e+nbL51Jk8fx/r8/9XZIJxUUFEjm0IGMHD2UoKCmCWdRUQkrvlnN/v253g1OREREPKZbXCzpPbqxN7+A4oJ84rv1IDi+KzWHDng0jtBuKcQOy8LPEojDbqN063oC0/o3319bW8umTZtYvXo1dfX1ACQmJDD+vPPolZbWrr/8ERHpCJTsEpHTWvb1d6T3TiWlZw96pfdk395cb4fULC4+lmHDB5ExoG/z5YrHjpbx3fLV7N61z8vRiYiIiDecOyiD/QVH2LpuHed360Fk7wz8AgOxVpbTWFmOvaHe9YOaTARGdSEoLoHghK6ExHcFoKHsKMVrl9NYVUlcWn+KiopYv34927Zvx2azARAbG8v4cePo27evklwiHYgDw6mralw5njhPyS4ROa2K8krWr93EqHOGM3HSWA7sz8PhxS28zWYz6X3SGDJsIN17dG2+vbj4KOvWbGLn9t1a4isiItKJRYWHMaR3L7atX8v5l11BUEwsQTGxzffbG+qxVpT9cFSWYa0sx9HYiOGwO3XposnPj8DoWILjkwhO7EZQl3jM/gHN9xsOB+U5WyjL2YLdZmff4SMseOcd8g8dam6TEB/PqFGjGDBgAGYnS0KIiIhzlOwSkVZlr1zHwMH9iekSzYyfXMqunL0cOJBHRXmlR+OwWAK4+qeXk5AUj2EYOBwGu3ftZeP6rRQcOuLRWERERKT9GpXRh7nzF/OX559m8LBhpKSmEZOQhCU8Ar/AoKYkVXzSKR9v2O0YDgeG4/h/m/7fZDLhFxiEOcBywmPs1gbqjxZRX1JEbeEhGqsraWhs5JNl31FcWkZIr/6YzWb69e3L8OHD6d69u1ZyiYi4iZJdItKqhgYrXy/5jumXXEBKzx6k9OwBQHl5BbkH8sg/WEDewUPU1ze00tPZmXbxZBKS4qmvq2fD+i1s3rSd6qoat44pIiIiHU+gxcK4zAEsWrOO7RvWNd/eJSaaQRkDSEvvRdceKYTEdMESGY1/aHiLxJPJzw+Tnx8QcJLem9jr66grKaT+WDH1JUVYK8ta3G8YBgtXr6e4tIwgi4Wx557L0KFDCQ8Pd/nzFRHPMwynFoK6dDxxnpJdIuKU7dtyKCk+SnrvVHqm9qBrtySioiIZMnQQw4YNxjAMCguLyT2Qx8HcQ5SWllNbU+uySwrPHT+a9D5p2G02PvnwCwoKCl3Sr4iIiPimjNQUYqMiOXC4kLzCYgpLyzhWWsayFSv4+ttvMGEiISaKHvFxJCfGERURSWhwEH7+/mD2w2Q2YzKZmv77/c+YTNjr67A31ONotAKn3o0xe9tO9hccwc/Pj0vHZzF4/HhPPn0RkU5NyS4RcVpx8VGKi4+y8ru1WCwB9EjuRs/UZNJSk+kSG0NSUgJJSQlkjRkJgMPhoKamlurqGqqqqqmpqqWqqprq6hpqqmuoqqympqaWhgbracft2z+dc8aMAGDhgmUcOVzk9ucqIiIiHV98dBTx0VGMHtCPhsZGCoqPkldUQt6RI5RWVlFYWkZhaRlrc3YDYDKZCQ0KJDQoiLCQIEIDAwkLDiY0+PvbgoMJDQrEEuB/2ksQd+UdYu2OXQBMHjGUpC4xHnm+IuI5hmF4tFaw6hK3jVeTXT179uTgwYMtbnvuueeYOXOmlyISEWdZrY3s25vLvr25LDObCQsPpWfPHvRMTaZb9yTCw8Mwm82Eh4cRHh5GUlLCKfuy2WxUV9VQU1NLTXUtNdU1zUkyh8PBlGmTAFi7agM7tu3y1FMUETmB5i4iHVdgQABp3ZJI65aEo7EfVbV15BcfJa+omCPHSqmurccwHFTX1VFdV0dRGZxq8zN/fz9Cg4IICQokPCyM0OAgQoOCCA0OwoSJxes2AjC8X2/6fV/+QUREPMfrK7ueeuopbr311uafdQ2759isBU6187d0c3MkZ87Zop6uzII725ezsTkM53Y2dHZcw4ktac0m53b8sTsZm8lhoq7MSklZGWs3bmm6zWQiNDTk+2RXKOHhYYSFhxEeFkp4RBhhYU23BwYGgtlEaGQYoZFhnOysGUDO7r18tfib5vPg6q137U7uMOnM6+DseXPla9qW/rxBBXjFl2ju4j3hERFOtauq9OwGKtJ+xF1whdNtE4D0H/3ctCK9hqqqKqqrq6msqqL6x/9fXU1VZSUN1qYV6Tag8vsDO1DjgJpaAAJ79qFXr15cdtVV2mlRxEcZhoFDK7vaLa8nu8LDw0lMTPR2GCLiYoZhUF1dQ3V1DUdOs1Giv78/4eGhhIaGEhYeSkRYGGFhIYSFhREWHkpYWChVVdV89ukCvcGLSLuguYuIb2pakR7eagK7sbGRqqoqampqmhJg1dXUVFc3/391dTXh4eFcdumlSnSJiHiJ15Ndzz//PLNnzyY5OZlrr72We++9F3//U4fV0NBAQ8MPO75V6ps7kQ7NZrNRVlZBWVkFAOaTru0SEWk/NHcR6dwCAgKIiYkhJkZ1uERE2iuvJrvuuusuhg0bRkxMDCtXruThhx/myJEjzJkz55SPee6553jyySc9GKWIiIhIE81dREREBFSgvr0zGS4+YzNnzuT3v//9advs3LmTfv36nXD73//+d371q19RXV3dVMfnJE727WiPHj2oqKggwsk6DtI27blml7O88cbg6hpFJidXPPk5sVze2ZpdZiefgyufq7Mru1Sz68z78wZfqNnlbJ1DaVJZWUlkZGSH+PdZcxffo5pdIiLO1zmUJs7OXY63++DrlYSEhXksvtrqaq6eOEbzBye5fGXX/fffz0033XTaNmlpaSe9ffTo0dhsNnJzc+nbt+9J2wQGBp5yMikiIiLSVpq7iIiISFs5jKbDk+OJ81ye7IqLiyMuLu6MHrtp0ybMZjPx8fEujkpERETk5DR3EREREfEtXqvZlZ2dzerVq5k4cSLh4eFkZ2dz7733cv311xMdHe2tsEREREROSnMXEREROU41u9o3ryW7AgMDee+995g1axYNDQ2kpqZy7733ct9993krJBEREZFT0txFREREpGPwWrJr2LBhrFq1ylvDi4iIiLSJ5i4iIiJynIGHV3a5eIMuX+e1ZJd0HM7uAqhfvpZc/sbn5GZ2Nofdia6c2y3Q2R30nN21sT1zOLszohPtXP270J6XLGuXRRERERHP0i6LIq0zezsAERERERERERERV9HKLhERERERERGRNnAYhtNXh7hqPHGeVnaJiIiIiIiIiIjP0MouEREREREREZE2MIymw5PjifO0sktERERERERERHyGVnaJiIiIiIiIiLSBAwOHi3dhb208cZ5WdomIiIiIiIiIiM9QsktERERERERERHyGLmMUEREREREREWkDwzAwPFg13pNj+QIlu6RVjdZDTrXzt3RzcyRnzmQyOdWuPb+BuDQ2506H02M62u9p84r2/PfIWc7+zrRnNmuBt0MQES8Jj4hwql1VZaWbIxERcZ6z710i0jolu0RERERERERE2kAru9o31ewSERERERERERGfoZVdIiIiIiIiIiJt4DA8W85FpWPaRiu7RERERERERETEZyjZJSIiIiIiIiIiPkOXMYqIiIiIiIiItIEK1LdvWtklIiIiIiIiIiI+Qyu7RERERERERETaQCu72jet7BIREREREREREZ+hlV3iMjZrgVPt/C3d3BzJmTOZTC7rqz1n3ttzbOJ+rvx77g3OvteIiLQmPCLCqXZVlZVujkREfJmz7zXSsRiGgUMru9otrewSERERERERERGfoWSXiIiIiIiIiIj4DF3GKCIiIiIiIiLSBobRdHhyPHGeVnaJiIiIiIiIiIjP0MouEREREREREZE2cBh4tEC9Qyu72kQru0RERERERERExGdoZZeIiIiIiIiISBsY3//x5HjiPK3sEhERERERERERn6Fkl3iczVrQ6uELTCaTU4dIa5z9u+QLf+c6y/uDiHQs4RERrR4i0jnp/UF8SUNDA0OGDMFkMrFp06YW923ZsoVx48YRFBREjx49eOGFF054/Icffki/fv0ICgpi0KBBzJ8/30ORn0jJLhERERERERGRNjAMw+OHuz344IN07dr1hNsrKyu58MILSUlJYf369fzhD39g1qxZ/OUvf2lus3LlSn76059y8803s3HjRi6//HIuv/xytm3b5va4T0bJLhERERERERGRDqCysrLF0dDQ4JJ+v/zySxYuXMiLL754wn3vvvsuVquVv//97wwYMIBrrrmGu+66izlz5jS3eeWVV5g6dSoPPPAA/fv3Z/bs2QwbNow//elPLomvrZTsEhERERERERFpA4fh+QOgR48eREZGNh/PPffcWT+XoqIibr31Vt555x1CQkJOuD87O5vx48djsViab5syZQq7du2irKysuc3kyZNbPG7KlClkZ2efdXxnQrsxioiIiIiIiIh0APn5+UT8qBZcYGDgWfVnGAY33XQTt912GyNGjCA3N/eENoWFhaSmpra4LSEhofm+6OhoCgsLm2/7cZvCwsKziu9MaWWXiIiIiIiIiEgbeKtmV0RERIvjVMmumTNntrpxVU5ODn/84x+pqqri4Ycf9uTpczut7BIRERERERER8SH3338/N91002nbpKWlsXTpUrKzs09Imo0YMYLrrruOt99+m8TERIqKilrcf/znxMTE5v+erM3x+z1NyS4RERERERERkTbw1A6JPx6vLeLi4oiLi2u13auvvsrTTz/d/PPhw4eZMmUK77//PqNHjwYgKyuLRx55hMbGRgICAgBYtGgRffv2JTo6urnNkiVLuOeee5r7WrRoEVlZWW2K21WU7BIRERERERER6YSSk5Nb/BwWFgZAr1696N69OwDXXnstTz75JDfffDMPPfQQ27Zt45VXXuHll19uftzdd9/Neeedx0svvcT06dN57733WLduHX/5y18892R+RDW7RERERERERETkpCIjI1m4cCEHDhxg+PDh3H///Tz++OP88pe/bG4zZswY/vWvf/GXv/yFzMxMPvroIz799FMGDhzolZi1skvaJZu1wKl2/pZubo7E/Uwmk8fH9ORyW1/jjdfLFzj7Oy0i0lGF/2hnrNOpqqx0cyQi4grO/k5L5+UwDBwe/FzlqbF69ux50s+LgwcPZvny5ad97IwZM5gxY4a7QmsTrewSERERERERERGfoZVdIiIiIiIiIiJt4Ksru3yFVnaJiIiIiIiIiIjP0MouEREREREREZE2MAzDo7WQVXe5bbSyS0REREREREREfIaSXSIiIiIiIiIi4jN0GaOIiIiIiIiISBs4AIcHryx0eG4on6CVXSIiIiIiIiIi4jO0sktERESkk2tsbCQ7O5uVK1eye/dudu3aRUNDA4GBgfj5+REYGEjfvn258MILmTBhAhEREd4OWURExKtUoL59U7JLOjSbtcCpdv6Wbm6OpGMxmUzeDkF8hLO/gyLSPuXk5PDSSy/x8ccfU1ZWhr+/P6GhoVx44YVYLBb+85//YBgG48ePZ/78+bz22mv4+fmRlZXFlClTuOiiixg2bJi3n0aHEu5korCqstLNkYh0Ts7+DopIx6bLGEVEREQ6mX379vE///M/ZGRkMH/+fC6//HJCQ0MZOXIk+fn5vPjii6xcuZK4uDh27NjB+++/T1JSEmFhYdx///3ExMTw9NNPM3z4cKZOncqWLVu8/ZREREQ86/uVXZ460MquNlGyS0RERKSTMAyDt956i0GDBrF48WLeeOMN3n//fT7++GMyMzP56quvKCsrY8KECQAsW7aM6Ohopk6dyrZt21iyZAnPPvss4eHhNDY2cs8997Bv3z6GDBnCjTfeSF5ennefoIiIiAhKdomIiIh0CseOHePKK6/kF7/4BV27diUvL48hQ4Ywffp0Bg4cyIIFC06b6Fq0aBHDhw/nxhtv5L333uPf//43L7/8Mtdffz2GYfB///d/9OnThwcffJDy8nKvPlcRERHp3JTsEhEREfFxFRUVjB8/ns8++4xx48axa9cudu/ezQUXXHBGia5//etfXH311Tz99NPMmjWL2bNnU1BQwMUXX8ycOXPIzMxk48aN3n3SIiIibuQwDI8f4jwlu0RERER8WGNjI1deeSW7du1i2rRpfP3116xfv94lia7HHnuM2bNn8+ijj/Lll1/y6aefcvHFFxMbG8uYMWN4++23vfvkRUREpFNSsktERETERxmGwa9+9SuWLl3KxIkT+fTTT92S6Prggw+49tprueaaa/j44495+eWXsdvt3HTTTTz00EM4HA7vnggREREXO14z3pOHOM/f2wGIiIiIiHs8++yzvPXWW2RlZbFgwQK3J7refvtt1q9fzyWXXMKIESO45JJLeOSRR8jPz2fu3LlYLBbvnhARERHpFLSyS0RERMQHzZs3j0cffZRBgwaxfPlyjyW6jo/x1Vdfcd111xEXF8f777/PpZdeSmNjo3dPioiIiIuoZlf7pmSXiIiIiI+x2+3cd999JCUlsWHDBo8nun48RmhoKO+88w5Lly7l9ttvx9BkXURERNxMlzFKp2CzFjjVzt/Szc2RiHifCVOrbRqthzwQiYi4y7vvvsvu3bvJzs5mw4YNXkt0/XiMlJQU/va3v9GnTx8efPBB752cDiI8IsKpdlWVlW6ORKRjcPZ3RkQ6B63sEhEREfEhVquVRx99lMsvvxyz2dwuEl1Tp06luLiYn//85zz00EN8/PHH3jtBIiIiLmB44Y84T8kuERERER/y17/+lUOHDvGTn/yk3SS6jo/x5ptv0rNnT6699lrWrFnjvZMkIiIiPk3JLhEREREfUVtby+zZs5k2bRq/+tWv2lWia/jw4fz85z8nLy+P1NRULrnkEg4ePOi9kyUiInIWDMPw+CHOU7JLRERExEd8+OGHFBUV8e2337a7RNfxMf7973+zfPlybDYbN9xwgybvIiIi4nJKdomIiIj4iI8++oiAgAAGDx7cLhNdx8d48803KS0tZfny5cydO9dr50tERORMOQzPH+I8JbtEREREfEBFRQULFiyga9eu7TrR9eMxrr/+eu6//35KSkq8d+JERETE5yjZJSIiIuIDvvzyS2w2G/Pnz+8Qia5HH32U8847j8rKSu69917vnTgRERHxOUp2iYiIiPiARYsW0a9fP0JDQztEouuDDz7gtttuY+TIkbz77rssXrzYeydPRESkjVSgvn1TsktERETEByxdupRzzjmnwyS6jo+xfPlyhg8fzi233EJdXZ33TqCIiIj4DH9vByDSntisBS7ry9/SzWV9SedmwuRUu0brITdHIiLtVW5uLrm5ucyfP5/Q0NAOk+g6PkZOTg719fX84Q9/4PHHH/fuyexgwiMiXNZXVWWly/oScYYr//6KeJrDMHB4cLWVJ8fyBVrZJSIiItLB7dy5E4DAwMAOl+i64IILyMzM5NZbb+WVV16htrbWi2dSREREfIGSXSIiIiIdXF5eHiaTqUMmuo6Pcf3111NWVsbcuXO9ei5FREScoZpd7ZuSXSIiIiIdXF5eHklJScTFxXXIRFdZWRk33HADoaGhzJkzB7vd7t0TKiIiIh2akl0iIiIiHVxeXh7JyckdNtF1fIx///vf7Nu3j//85z/eO5kiIiJOMAzPH+I8FagXERER6eDy8vLYu3cvVqu1wya6jo8RFRXF888/zxVXXIHJ5NwGHSIiIiI/ppVdIiIiIh3c/v37qays7PCJrqlTp9LQ0MDatWtZuXKl906oiIiIdGhKdomIiIh0YHa7nSNHjnDPPfd0+ETXtm3bWLp0Kf369eMPf/iD906qiIhIKxyG4fFDnKdkl4iIiEgH1tDQgN1uZ9CgQR0+0bVo0SJGjhxJaGgo8+bNo7y83GvnVURERDou1ewScRObtcAr4/pbunllXPmBt157EemcAgMDAXjttddYu3Zth050HR9j48aNOBwOPv/8c2644QYvndnOJzwiwivjVlVWemVc+YG3XnuRjqypaLznVltpYVfbaGWXiIiISAfm5+eHv78/q1ev9olE13vvvce///1vsrKy+PDDD713YkVERKTDUrJLREREpIPz9/fnhhtu8IlE1/ExIiMjWbBgAdXV1d47sSIiIqdiHF/d5ZkDrexqEyW7RERERDq4kJAQ+vfv7zOJrqeffpoFCxbQ2NjI119/7b0TKyIiIh2Skl0iIiIiHVxQUBALFy70mUTXY489xlNPPUVaWhoLFizw3okVERGRDknJLhEREZEOLiAggK+//tpnEl2zZ8/mscceIz09nc8++8yjBYBFRESc4cDw+CHOU7JLREREpIOzWCycc845PpPoOj7G4sWLOXToEAcOHPDeyRUREZEOR8kuERERkQ6uT58+REdH+1Si69prr+Wyyy4DYMuWLV46syIiIidnGIbHD3Gekl0iIiIiHdzAgQNZv369TyW6rrnmGj744AMiIiLYsWOH906uiIiIdDj+3g5ARERERM5ORkYGxcXFXH311T6T6Do+Rn19Pdu3b/feyRURETkJh2Hg8OBqK0+O5QuU7BLxMTZrgbdDEBERD8vIyADgnnvu8alE1wUXXEB0dLRWdvm48IgIb4cgIiI+xm2XMT7zzDOMGTOGkJAQoqKiTtomLy+P6dOnExISQnx8PA888AA2m81dIYmIiIicUkeeu/Tv3x+AL7/80qcSXQMHDuTee+8lJycHu93upbMrIiIiHY3bkl1Wq5UZM2Zw++23n/R+u93O9OnTsVqtrFy5krfffpu5c+fy+OOPuyskERERkVPqyHOX0NBQUlJS+P3vf+9Tia4FCxbQvXt36uvrtSOjiIi0K4bh+UOc57Zk15NPPsm9997LoEGDTnr/woUL2bFjB//85z8ZMmQI06ZNY/bs2bz22mtYrVZ3hSUiIiJyUh197pKRkUFoaKhPJbrKysp4+OGHAXQpo4iIiDjNa7sxZmdnM2jQIBISEppvmzJlCpWVlactQtrQ0EBlZWWLQ0RERMTd2vvcJTMzE4vFQmlpqc8kuiZMmICfnx+hoaHs2bPHLedNRETkTBiG4fFDnOe1ZFdhYWGLySLQ/HNhYeEpH/fcc88RGRnZfPTo0cOtcYqIiIhA+5+7nH/++RQVFXHuuecCvpHoAvjmm28ICwujrq7OLedNREREfE+bkl0zZ87EZDKd9sjJyXFXrAA8/PDDVFRUNB/5+fluHU9EREQ6rs40dxk7dixBQUHU1tb6TKLr+BjV1dXU19e75byJiIicCQcGDsODB1rZ1Rb+bWl8//33c9NNN522TVpamlN9JSYmsmbNmha3FRUVNd93KoGBgQQGBjo1hoiIiHRunWnuEhQUxMSJE6moqPCpRNfUqVOx2WxKdomIiIjT2pTsiouLIy4uziUDZ2Vl8cwzz1BcXEx8fDwAixYtIiIigoyMDJeMISIiIp1bZ5u7TJ8+nXvvvZfJkyeTk5PjE4mubdu2kZycTENDg/dOrIiIiHQobqvZlZeXx6ZNm8jLy8Nut7Np0yY2bdpEdXU1ABdeeCEZGRnccMMNbN68ma+++opHH32UO+64Qyu3RERExON8Ye5y+eWX09jYyJYtW3wm0bVo0SIiIyO1sktERNoVFahv39yW7Hr88ccZOnQoTzzxBNXV1QwdOpShQ4eybt06APz8/Pjiiy/w8/MjKyuL66+/np/97Gc89dRT7gpJRERE5JR8Ye7SrVs3Ro0axahRo3wm0TV8+HAOHTqkZJeIiIgbzZs3j9GjRxMcHEx0dDSXX355i/vz8vKYPn06ISEhxMfH88ADD2Cz2Vq0WbZsGcOGDSMwMJD09HTmzp3ruSfwX9p0GWNbzJ07t9UnlpKSwvz5890VgoiIiIjTfGXucs011/Dwww9z7bXX8vHHH3f4RNeNN95IcXGxLmMUEZF2xTCaDk+O5y4ff/wxt956K88++yyTJk3CZrOxbdu25vvtdjvTp08nMTGRlStXcuTIEX72s58REBDAs88+C8CBAweYPn06t912G++++y5LlizhlltuISkpiSlTprgv+FNwW7JLRERERDxvxowZPPDAA3z44Ye89957HT7R9d5779G7d+92c6moiIiIL7HZbNx999384Q9/4Oabb26+/cf1SBcuXMiOHTtYvHgxCQkJDBkyhNmzZ/PQQw8xa9YsLBYLb7zxBqmpqbz00ksA9O/fnxUrVvDyyy97JdnltssYRURERMTzunfvzowZM4iPj+fKK6/s8Imuf/3rX9TU1JCSkuK9kyoiIvJfHIbh8QOgsrKyxXG2K583bNhAQUEBZrOZoUOHkpSUxLRp01qs7MrOzmbQoEEkJCQ03zZlyhQqKyvZvn17c5vJkye36HvKlClkZ2efVXxnSskuERERER/z29/+lqKiIq677roOn+i64oorKCgoULJLREQE6NGjB5GRkc3Hc889d1b97d+/H4BZs2bx6KOP8sUXXxAdHc2ECRMoLS0FoLCwsEWiC2j+ubCw8LRtKisrqaurO6sYz4SSXSIiIiI+Zvjw4UyYMIGPPvqIp556qsMmuq6++mpmzpyJYRgkJyd774SKiIj8F8MLfwDy8/OpqKhoPh5++OGTxjdz5kxMJtNpj5ycHBwOBwCPPPIIV155JcOHD+ett97CZDLx4Ycfeux8uppqdomIiIj4oAceeIBly5YxYcKEDpvoevrpp5kzZw6Akl0iIiJAREQEERERrba7//77uemmm07bJi0tjSNHjgAta3QFBgaSlpZGXl4eAImJiaxZs6bFY4uKiprvO/7f47f9uE1ERATBwcGtxutqSnaJiIiI+KCpU6eSkZHBfffdx8aNGztkouuxxx7jyiuv5OOPP1ayS0REpA3i4uKIi4trtd3w4cMJDAxk165djB07FoDGxkZyc3ObSwhkZWXxzDPPUFxcTHx8PACLFi0iIiKiOUmWlZV1wo7VixYtIisry5VPy2m6jFFERETEB5nNZu6//37WrVvHRRdd1CETXbNnz2bYsGHExMQQGhrqvZMpIiLyX7xVoN7VIiIiuO2223jiiSdYuHAhu3bt4vbbbweadngGuPDCC8nIyOCGG25g8+bNfPXVVzz66KPccccdzbsl33bbbezfv58HH3yQnJwcXn/9dT744APuvfdet8TdGiW7RERERHzUddddR2JiImazuUMmuh599FHmzZtHWlqa906iiIiIj/vDH/7ANddcww033MDIkSM5ePAgS5cuJTo6GgA/Pz+++OIL/Pz8yMrK4vrrr+dnP/sZTz31VHMfqampzJs3j0WLFpGZmclLL73EX//6V6ZMmeKV56TLGEVERER8VGBgIK+++ipXX301CxcuZOjQoR0q0fX++++zZs0aHnjgAe+dRBERkZMwjKbDk+O5S0BAAC+++CIvvvjiKdukpKSccJnif5swYQIbN250dXhnRCu7RERERHzYVVddxbRp0wD48MMPO0yi6/gYNpuNSZMmeensiYiISEekZJeIiIiIDzOZTPz5z3/GZDLx6KOPdqhE15AhQwgMDOTcc8/13gkUERE5GcPA8ODh0WVkPkDJLhEREREfl5KSwlNPPcVbb71FQ0NDh0h0XXPNNVgsFqZMmeKVLctFRESk41KyS0RERKQTuPvuuxk0aBDR0dGEh4e3+0TXs88+y6pVq7jqqqu8e+JERESkw1GBehEREZFOwN/fn7/97W+cc845ZGZmUlFR0W4TXW+//Tb33Xcf/v7+XHLJJd49cSIiIifhMAwcHry00JNj+QKt7BIRERHpJEaOHMnjjz9Ofn4+Dz74YLtNdK1YsYLXX3+da6+9lqioKK+eMxEREel4tLJLREREpBN5/PHH2bt3L7Nnz+bbb79lyZIl7SrRtX79eqZMmQLAk08+6cUzJSIicmrNheM9OJ44T8kuERERkU7EZDLx17/+lQMHDrBkyRJefvnldpXomjx5MoZhcNttt9GzZ0+vnisRERHpmJTsEhEREelkAgMD+c9//sPo0aN5/fXXKSws5Nlnn/V6ouuCCy4gIiKCsrIyHnnkEe+eJBERkdNwGE2HJ8cT56lml4iIiEgn1KVLF+bPn09RURHPP/88TzzxhNcTXX369KGsrIy7776bxMRE754gERER6bCU7BIRERHppPr06cOnn36Kv78/33zzDW+99ZbXEl0ZGRkEBgYSEhLCAw884N0TIyIiIh2aLmMUERER6cTGjx/PkiVLuOSSS1ixYgWXXHKJxxNdAwYMoEePHvznP/9hyZIlREdHe/ekiIiItEIF6ts3rewSERER6eTGjh3LypUrSUxMZNWqVfztb3/zWKJr4MCBjBs3jg8++IB//OMfnHvuud49GSIiItLhKdklIiIiIvTv35+1a9fSu3dvbrvtNmJiYvjyyy/dnui64YYbeOGFF3juuee4+uqrvXsSREREnHR8ZZcnD3Gekl0iIiIiAkBiYiJLlizhnnvuITc3l2nTppGVlQW459LF888/nzvvvJObb76Zhx56yMvPXkRERHyFkl0iIiIi0iwgIIA5c+Ywb948SkpKOHz4MIMGDaK8vNxlia709HQMw+CZZ57hoYce4s9//jMmk8nbT11ERMRpDsPw+CHOU4F6ERERETnBRRddxIUXXsjcuXN54oknGDp0KP7+/nz22WdnnOiaPHky0dHR7Nmzh5iYGJYtW8a4ceO8/VRFRETEx2hll4iIiIiclL+/P7fccgt79uxh9uzZBAYGcvHFF9O9e3f+9a9/MXv2bK666qrTJrreeust/vnPfzJu3DgaGhooLCzkuuuuY/PmzUp0iYiIiFso2SUiIiIipxUSEsLvfvc79u/fzyuvvMLIkSMJDQ3ld7/7HZGRkTz22GOMGTOGmpoarrrqKq655hpSU1M5fPgwCQkJ3HTTTYSGhnLnnXeyf/9+/vznPxMZGentpyUiInLGDC8c4jxdxigiIiIiTomNjeXXv/41v/71r2lsbGTVqlUsXLiQxYsXU1ZWxvvvv09tbS0xMTGEhobSpUsX7rzzTs4//3zOPfdc/P019RQRERH304xDRERERNosICCAcePGMW7cOGbPnu3tcERERDzKMAwMDxaN9+RYvkCXMYqIiIiIiIiIiM/Qyi4RERERERERkTZwGAYOD6628uRYvkAru0RERERERERExGco2SUiIiIiIiIiIj5DlzGKiIiIiIiIiLSBCtS3b1rZJSIiIiIiIiIiPqPDr+w6nt2srKz0ciQiIiJy3PF/l/Ut5Ik0dxEREWl/2jp3cRhNh6d4cixf0OGTXVVVVQD06NHDy5GIiIjIf6uqqiIyMtLbYbQrmruIiIi0X5q7+IYOn+zq2rUr+fn5hIeHYzKZvB2OUyorK+nRowf5+flERER4O5xOT69H+6HXon3R69G+dLTXwzAMqqqq6Nq1q7dDaXc0d5Gzpdej/dBr0b7o9WhfOtrr0da5i2F4dgW7Fsu3TYdPdpnNZrp37+7tMM5IREREh/il7yz0erQfei3aF70e7UtHej30rejJae4irqLXo/3Qa9G+6PVoXzrS66G5i+/o8MkuERERERERERHP8uxujKClXW2h3RhFRERERERERMRnKNnlBYGBgTzxxBMEBgZ6OxRBr0d7oteifdHr0b7o9RBv0t+/9kWvR/uh16J90evRvuj1EG8yGdoTXERERERERESkVZWVlURGRnLV828QEBTssXEb6+v4aOZtVFRUdJgaaN6klV0iIiIiIiIiIuIzVKBeRERERERERKQNDKPp8OR44jyt7BIREREREREREZ+hlV0iIiIiIiIiIm1gfP/Hk+OJ87SyS0REREREREREfIaSXW707bffcskll9C1a1dMJhOffvppi/sNw+Dxxx8nKSmJ4OBgJk+ezJ49e7wTbCcza9YsTCZTi6Nfv37eDqvTe+211+jZsydBQUGMHj2aNWvWeDsk+V7Pnj1P+J15/vnnvR1Wp/DMM88wZswYQkJCiIqKOmmbvLw8pk+fTkhICPHx8TzwwAPYbDbPBio+QXOX9ktzl/ZJc5f2S3MX79HcRdoDJbvcqKamhszMTF577bWT3v/CCy/w6quv8sYbb7B69WpCQ0OZMmUK9fX1Ho60cxowYABHjhxpPlasWOHtkDq1999/n/vuu48nnniCDRs2kJmZyZQpUyguLvZ2aPK9p556qsXvzG9+8xtvh9QpWK1WZsyYwe23337S++12O9OnT8dqtbJy5Urefvtt5s6dy+OPP+7hSMUXaO7Svmnu0r5o7tL+ae7iHZ1l7uIwDI8f4jzV7HKjadOmMW3atJPeZxgG/+///T8effRRLrvsMgD+8Y9/kJCQwKeffso111zjyVA7JX9/fxITE70dhnxvzpw53Hrrrfz85z8H4I033mDevHn8/e9/Z+bMmV6OTgDCw8P1O+MFTz75JABz58496f0LFy5kx44dLF68mISEBIYMGcLs2bN56KGHmDVrFhaLxYPRSkenuUv7prlL+6K5S/unuYt3aO4i7YFWdnnJgQMHKCwsZPLkyc23RUZGMnr0aLKzs70YWeexZ88eunbtSlpaGtdddx15eXneDqnTslqtrF+/vsXvg9lsZvLkyfp9aEeef/55unTpwtChQ/nDH/6gpebtRHZ2NoMGDSIhIaH5tilTplBZWcn27du9GJn4Gs1dvE9zl/ZDc5eOQXOX9slX5i6GYXj8EOdpZZeXFBYWArT4BT/+8/H7xH1Gjx7N3Llz6du3L0eOHOHJJ59k3LhxbNu2jfDwcG+H1+kcPXoUu91+0t+HnJwcL0UlP3bXXXcxbNgwYmJiWLlyJQ8//DBHjhxhzpw53g6t0yssLDzp787x+0RcRXMX79LcpX3R3KX909yl/dLcRTxBK7ukU5o2bRozZsxg8ODBTJkyhfnz51NeXs4HH3zg7dBEPGbmzJknFG797+P4hP2+++5jwoQJDB48mNtuu42XXnqJP/7xjzQ0NHj5WXRMbTn3IiKguYsIaO7iTZq7nMhheP4Q52lll5ccv3a8qKiIpKSk5tuLiooYMmSIl6LqvKKioujTpw979+71diidUmxsLH5+fhQVFbW4vaioSHUW3Oj+++/npptuOm2btLS0k94+evRobDYbubm59O3b1w3R+bazOff/LTEx8YTdv47/Lun3R1xJc5f2RXMX79LcxTs0d/EezV2ko1Gyy0tSU1NJTExkyZIlzRPEyspKVq9efcpdK8R9qqur2bdvHzfccIO3Q+mULBYLw4cPZ8mSJVx++eUAOBwOlixZwp133und4HxYXFwccXFxZ/TYTZs2YTabiY+Pd3FUncPZnPv/lpWVxTPPPENxcXHz67Fo0SIiIiLIyMhwyRgioLlLe6O5i3dp7uIdmrt4j+Yu0tEo2eVG1dXVLb5tO3DgAJs2bSImJobk5GTuuecenn76aXr37k1qaiqPPfYYXbt2bf4HU9znt7/9LZdccgkpKSkcPnyYJ554Aj8/P3760596O7RO67777uPGG29kxIgRjBo1iv/3//4fNTU1zTscifdkZ2ezevVqJk6cSHh4ONnZ2dx7771cf/31REdHezs8n5eXl0dpaSl5eXnY7XY2bdoEQHp6OmFhYVx44YVkZGRwww038MILL1BYWMijjz7KHXfcQWBgoHeDlw5Hc5f2S3OX9kdzl/ZLcxfv6ixzF08XjVeB+rZRssuN1q1bx8SJE5t/vu+++wC48cYbmTt3Lg8++CA1NTX88pe/pLy8nLFjx7JgwQKCgoK8FXKncejQIX76059y7Ngx4uLiGDt2LKtWrXLZtxXSdj/5yU8oKSnh8ccfp7CwkCFDhrBgwYITileK5wUGBvLee+8xa9YsGhoaSE1N5d57721+TxP3evzxx3n77bebfx46dCgAX3/9NRMmTMDPz48vvviC22+/naysLEJDQ7nxxht56qmnvBWydGCau7Rfmru0P5q7tF+au3iX5i7SHpgMpQdFRERERERERFpVWVlJZGQk05/6IwFBwR4bt7G+jnmP/4aKigoiIiI8Nm5Hpd0YRURERERERETEZ+gyRhERERERERGRNnAYBg4PXijnybF8gVZ2iYiIiIiIiIiIz1CyS0REREREREREfIYuYxQRERERERERaQPDaDo8OZ44Tyu7RERERERERETEZ2hll4iIiIiIiIhIG6hAffumlV0iIiIiIiIiIuIzlOwSEREREZF2rba2liuvvJKIiAhMJhPl5eUnvc2TbrjhBp599lmPjulq55xzDh9//LG3wxDpkAzD8PjhLrt37+ayyy4jNjaWiIgIxo4dy9dff92iTV5eHtOnTyckJIT4+HgeeOABbDZbizbLli1j2LBhBAYGkp6ezty5c90Wc2uU7BIREREREa/Iz8/nF7/4BV27dsVisZCSksLdd9/NsWPHWrR7++23Wb58OStXruTIkSNERkae9LazsWzZMqeTZps3b2b+/PncddddZzWmtz366KPMnDkTh8Ph7VBExIsuvvhibDYbS5cuZf369WRmZnLxxRdTWFgIgN1uZ/r06VitVlauXMnbb7/N3Llzefzxx5v7OHDgANOnT2fixIls2rSJe+65h1tuuYWvvvrKK89JyS4REREREfG4/fv3M2LECPbs2cO///1v9u7dyxtvvMGSJUvIysqitLS0ue2+ffvo378/AwcOJDExEZPJdNLbPOWPf/wjM2bMICws7Iz7MAzjhFURnjZt2jSqqqr48ssvvRqHiHjP0aNH2bNnDzNnzmTw4MH07t2b559/ntraWrZt2wbAwoUL2bFjB//85z8ZMmQI06ZNY/bs2bz22mtYrVYA3njjDVJTU3nppZfo378/d955J1dddRUvv/yyV56Xkl0iIiIiIuJxd9xxBxaLhYULF3LeeeeRnJzMtGnTWLx4MQUFBTzyyCMATJgwgZdeeolvv/0Wk8nEhAkTTnobwOuvv07v3r0JCgoiISGBq666qnk8h8PBc889R2pqKsHBwWRmZvLRRx8BkJuby8SJEwGIjo7GZDJx0003nTRuu93ORx99xCWXXNLi9nfeeYcRI0YQHh5OYmIi1157LcXFxc33H1859uWXXzJ8+HACAwNZsWIFDoeDF154gfT0dAIDA0lOTuaZZ54BwGq1cuedd5KUlERQUBApKSk899xzzX2Wl5dzyy23EBcXR0REBJMmTWLz5s0t4vr8888ZOXIkQUFBxMbGcsUVVzTf5+fnx0UXXcR7773XlpdORADDC38AKisrWxwNDQ1n9Ty6dOlC3759+cc//kFNTQ02m40333yT+Ph4hg8fDkB2djaDBg0iISGh+XFTpkyhsrKS7du3N7eZPHlyi76nTJlCdnb2WcV3prQbo4iIiIiIeFRpaSlfffUVzzzzDMHBwS3uS0xM5LrrruP999/n9ddf55NPPmHmzJls27aNTz75BIvFAnDCbevWreOuu+7inXfeYcyYMZSWlrJ8+fLmfp977jn++c9/8sYbb9C7d2++/fZbrr/+euLi4hg7diwff/wxV155Jbt27SIiIuKEuI7bsmULFRUVjBgxosXtjY2NzJ49m759+1JcXMx9993HTTfdxPz581u0mzlzJi+++CJpaWlER0fz8MMP87//+7+8/PLLjB07liNHjpCTkwPAq6++ymeffcYHH3xAcnIy+fn55OfnN/c1Y8YMgoOD+fLLL4mMjOTNN9/k/PPPZ/fu3cTExDBv3jyuuOIKHnnkEf7xj39gtVpPiGfUqFE8//zzbXwFRcRbevTo0eLnJ554glmzZp1xfyaTicWLF3P55ZcTHh6O2WwmPj6eBQsWEB0dDUBhYWGLRBfQ/PPxSx1P1aayspK6urpTvqe6i5JdIiIiIiLiUXv27MEwDPr373/S+/v3709ZWRklJSXEx8cTEhKCxWIhMTGxuc1/37Zs2TJCQ0O5+OKLCQ8PJyUlhaFDhwLQ0NDAs88+y+LFi8nKygIgLS2NFStW8Oabb3LeeecRExMDQHx8PFFRUaeM/eDBg/j5+REfH9/i9l/84hfN/5+Wlsarr77KyJEjqa6ubnG541NPPcUFF1wAQFVVFa+88gp/+tOfuPHGGwHo1asXY8eOBZoKQvfu3ZuxY8diMplISUlp7mfFihWsWbOG4uJiAgMDAXjxxRf59NNP+eijj/jlL3/JM888wzXXXMOTTz7Z/LjMzMwWcXft2pX8/HwcDgdmsy78EXGWYYDDfTXjTzoeNNU6jIiIaL79+O//f5s5cya///3vT9vnzp076du3L3fccQfx8fEsX76c4OBg/vrXv3LJJZewdu1akpKSXPYcPEnJLhERERER8QpX7i52wQUXkJKSQlpaGlOnTmXq1KlcccUVhISEsHfvXmpra5uTTMdZrdbmhJiz6urqCAwMPKFG2Pr165k1axabN2+mrKysueh7Xl4eGRkZze1+vCJs586dNDQ0cP755590rJtuuokLLriAvn37MnXqVC6++GIuvPBCoKlIfnV1NV26dDkhvn379gGwadMmbr311tM+n+DgYBwOBw0NDR5feSEibRcREdEi2XUq999//ykvxz4uLS2NpUuX8sUXX1BWVtbc7+uvv86iRYt4++23mTlzJomJiaxZs6bFY4uKigCav3BITExsvu3HbU63UtadlOwSERERERGPSk9Px2QysXPnzhY1pI7buXMn0dHRxMXFOd1neHg4GzZsYNmyZSxcuJDHH3+cWbNmsXbtWqqrqwGYN28e3bp1a/G4U62KOJXY2Fhqa2uxWq3Nl1TW1NQwZcoUpkyZwrvvvktcXBx5eXlMmTKluXjzcaGhoc3/39oHwGHDhnHgwAG+/PJLFi9ezNVXX83kyZP56KOPqK6uJikpiWXLlp3wuOMr05z5gFlaWkpoaKgSXSJtZBiGSxP2zozXFnFxcU69h9bW1gKcsLLTbDY3J+2zsrJ45plnKC4ubl7VumjRIiIiIpqT+VlZWSdcJr1o0aLm1bSepnWqIiIiIiLiUV26dOGCCy7g9ddfp66ursV9hYWFvPvuu/zkJz9p8w6L/v7+TJ48mRdeeIEtW7aQm5vL0qVLycjIIDAwkLy8PNLT01scx+vfHE9c2e32044xZMgQAHbs2NF8W05ODseOHeP5559n3Lhx9OvXr0Vx+lPp3bs3wcHBLFmy5JRtIiIi+MlPfsL//u//8v777/Pxxx9TWlrKsGHDKCwsxN/f/4TnFBsbC8DgwYNP2zfAtm3b2ry6TUR8R1ZWFtHR0dx4441s3ryZ3bt388ADD3DgwAGmT58OwIUXXkhGRgY33HADmzdv5quvvuLRRx/ljjvuaP7C4LbbbmP//v08+OCD5OTk8Prrr/PBBx9w7733euV5aWWXiIiIiIh43J/+9CfGjBnDlClTePrpp0lNTWX79u088MADdOvWrXlHQmd98cUX7N+/n/HjxxMdHc38+fNxOBz07duX8PBwfvvb33LvvfficDgYO3YsFRUVfPfdd0RERHDjjTeSkpKCyWTiiy++4KKLLiI4OLhFra3j4uLiGDZsGCtWrGhOfCUnJ2OxWPjjH//IbbfdxrZt25g9e3arMQcFBfHQQw/x4IMPYrFYOPfccykpKWH79u3cfPPNzJkzh6SkJIYOHYrZbObDDz8kMTGRqKgoJk+eTFZWFpdffjkvvPACffr04fDhw81F6UeMGMETTzzB+eefT69evbjmmmuw2WzMnz+fhx56qDmG5cuXN18aKSLOa+8ru5wVGxvLggULeOSRR5g0aRKNjY0MGDCA//znP801/vz8/Pjiiy+4/fbbycrKIjQ0lBtvvJGnnnqquZ/U1FTmzZvHvffeyyuvvEL37t3561//ypQpU9wSd2uU7BIREREREY/r3bs369at44knnuDqq6+mtLSUxMRELr/8cp544onmgvHOioqK4pNPPmHWrFnU19fTu3dv/v3vfzNgwAAAZs+eTVxcHM899xz79+8nKiqKYcOG8bvf/Q6Abt268eSTTzJz5kx+/vOf87Of/Yy5c+eedKxbbrmFf/zjH9x5551AUwJs7ty5/O53v+PVV19l2LBhvPjii1x66aWtxv3YY4/h7+/P448/zuHDh0lKSuK2224Dmi7NfOGFF9izZw9+fn6MHDmS+fPnN19uNH/+fB555BF+/vOfU1JSQmJiIuPHj2/eEW3ChAl8+OGHzJ49m+eff56IiAjGjx/fPHZBQQErV67kn//8Z5vOtYj4lhEjRvDVV1+dtk1KSsoJlyn+twkTJrBx40ZXhnbGTIYnU5EiIiIiIiIdXF1dHX379uX999/3Wj0aV3jooYcoKyvjL3/5i7dDEekwKisriYyMZOIjL+Ef5Llad7b6Or5+5n4qKiqcKlDf2Wlll4iIiIiISBsEBwfzj3/8g6NHj3o7lLMSHx/Pfffd5+0wRDokh2Hg8ODaIU+O5QuU7BIREREREWmjCRMmeDuEs3b//fd7OwQREbdQsktEREREREREpA0Mo+nw5HjiPLO3AxAREREREREREXEVrewSEREREREREWkD1exq37SyS0REREREREREfIaSXSIiIiIiIiIi4jN0GaOIiIiIiIiISBsYGBgevLTQQJcxtoWSXSIiIiIiIiIibWBvqPfp8To6k+HJVKSIiIiIiIiISAdVX19PamoqhYWFHh87MTGRAwcOEBQU5PGxOxolu0REREREREREnFRfX4/VavX4uBaLRYkuJynZJSIiIiIiIiIiPkO7MYqIiIiIiIiIiM9QsktERERERERERHyGkl0iIiIiIiIiIuIzlOwSERERERERERGfoWSXiIiIiIiIiIj4DCW7RERERERERETEZyjZJSIiIiIiIiIiPuP/A9oOMpqjpRMuAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x0,y0=-0.5, 0.5\n",
+    "divpal=sns.diverging_palette(220, 20, as_cmap=True)\n",
+    "linpal=sns.color_palette(\"rocket\", as_cmap=True)\n",
+    "\n",
+    "inc,PA = 25, 340\n",
+    "\n",
+    "residual   = cubeMom.background_residual(inc=inc, PA=PA, x0=x0, y0=y0)\n",
+    "background = cubeMom.background_residual(inc=inc, PA=PA, x0=x0, y0=y0, background_only=True)\n",
+    "#background = cubeMom.convolve_with_beam(data=background)\n",
+    "mask = np.where(cubeMom.data>500,1.0,0.0)\n",
+    "residual = np.multiply(residual,mask)\n",
+    "\n",
+    "\n",
+    "vmin,vmax=-850,850\n",
+    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,constrained_layout=True,figsize=(12,6))\n",
+    "im1 = ax1.imshow(background, origin='lower', extent=cubeMom.extent, cmap=linpal)\n",
+    "ax1.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 1500],colors='w',alpha=0.5)\n",
+    "im2 = ax2.imshow(residual, origin='lower', extent=cubeMom.extent, cmap=divpal, vmin=vmin, vmax=vmax)\n",
+    "cb = plt.colorbar(im2, pad=0.02)\n",
+    "ax2.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 1500],colors='k',alpha=0.5)\n",
+    "ax2.contour(residual, origin='lower', extent=cubeMom.extent, levels=[-400,400],colors='w',linestyle='--')\n",
+    "ax2.set_xlabel('Offset (arcsec)')\n",
+    "ax2.set_ylabel('Offset (arcsec)')\n",
+    "cb.set_label('Residiual (Jy/beam m/s)', rotation=270, labelpad=13)\n",
+    "cube.plot_beam(ax=ax2)\n",
+    "\n",
+    "pixPerBeam = (4.08/0.5)*(2.68/0.5)\n",
+    "beamsInMap = np.count_nonzero(~np.isnan(residual)) / pixPerBeam # this accounts for beam smoothing in the data, cannot simply sum pixel vals\n",
+    "print('Net flux in map: ',np.round(np.nansum(residual)/beamsInMap,-2), 'Jy/beam m/s')\n",
+    "print('Res RMS: ', np.nanstd(residual), 'Jy/beam m/s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "760c2d0c-63a2-4162-a220-142d715b468e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "47.5 352.5\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "WARNING: Attached data is not 3D, so shifting cannot be applied.\n",
+      "\t Reverting to standard azimuthal averaging; will ignore `unit` argument.\n",
+      "Net flux in map:  -200.0 Jy/beam m/s\n",
+      "Res RMS:  138.30529545057362 Jy/beam m/s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#One such example... finding the minima in the rms of the reisidual map, after masking all pixels beyond the outer contour boundary...\n",
+    "mask = np.where(cubeMom.data>500,1.0,0.0)\n",
+    "minResVal = 10000000000\n",
+    "incs = np.linspace(20.,75.,25)\n",
+    "PAs  = np.linspace(330.,360.,25)\n",
+    "for i in range(len(incs)):\n",
+    "  for j in range(len(PAs)):\n",
+    "    residual   = cubeMom.background_residual(inc=incs[i], PA=PAs[j], x0=x0, y0=y0)\n",
+    "    residual   = np.multiply(residual,mask)\n",
+    "    resval     = np.nanstd(residual)\n",
+    "    if resval<minResVal:\n",
+    "      minvals = [i,j]\n",
+    "      minResVal = resval\n",
+    "print(incs[minvals[0]],PAs[minvals[1]])\n",
+    "\n",
+    "inc,PA = incs[minvals[0]],PAs[minvals[1]]\n",
+    "\n",
+    "residual   = cubeMom.background_residual(inc=inc, PA=PA, x0=x0, y0=y0)\n",
+    "background = cubeMom.background_residual(inc=inc, PA=PA, x0=x0, y0=y0, background_only=True)\n",
+    "#background = cubeMom.convolve_with_beam(data=background)\n",
+    "mask = np.where(cubeMom.data>500,1.0,0.0)\n",
+    "#residual = np.multiply(residual,mask)\n",
+    "\n",
+    "\n",
+    "vmin,vmax=-850,850\n",
+    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,constrained_layout=True,figsize=(12,6))\n",
+    "im1 = ax1.imshow(background, origin='lower', extent=cubeMom.extent, cmap=linpal)\n",
+    "ax1.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[500, 1500],colors='w',alpha=0.5)\n",
+    "im2 = ax2.imshow(residual, origin='lower', extent=cubeMom.extent, cmap=divpal, vmin=vmin, vmax=vmax)\n",
+    "cb = plt.colorbar(im2, pad=0.02)\n",
+    "ax2.contour(cubeMom.data, origin='lower', extent=cubeMom.extent, levels=[370, 1500],colors='k',alpha=0.5)\n",
+    "ax2.contour(residual, origin='lower', extent=cubeMom.extent, levels=[-400,400],colors='w',linestyle='--')\n",
+    "ax2.set_xlabel('Offset (arcsec)')\n",
+    "ax2.set_ylabel('Offset (arcsec)')\n",
+    "cb.set_label('Residiual (Jy/beam m/s)', rotation=270, labelpad=13)\n",
+    "cube.plot_beam(ax=ax2)\n",
+    "\n",
+    "pixPerBeam = (4.08/0.5)*(2.68/0.5)\n",
+    "beamsInMap = np.count_nonzero(~np.isnan(residual)) / pixPerBeam # this accounts for beam smoothing in the data, cannot simply sum pixel vals\n",
+    "print('Net flux in map: ',np.round(np.nansum(residual)/beamsInMap,-2), 'Jy/beam m/s')\n",
+    "print('Res RMS: ', np.nanstd(residual), 'Jy/beam m/s')\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbca1f8a-b57c-49be-93cd-b0319215fc14",
+   "metadata": {},
+   "source": [
+    "So there could well be some ~significant level of emission asymmetry in this system. \n",
+    "\n",
+    "It looks like a relatively low brightness asymmetry as expected, but in comparison with the moment maps produced earlier suggests this could well be real. Pretty cool!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35f547df-1867-4b77-bcb9-1d60ed9eb6e0",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "Hopefully by this stage you have thoroughly exhausted how to cut up this single C18O map!\n",
+    "\n",
+    "However: *nothing* that we have done here is particular about the choice of line, only that we sensibly account for system properties in orrder to accurately extract emission from data. As such, with any data cube with line emission (or non-detections even) you should be well-versed now to reproduce these types of analysis for different systems and/or different emission lines."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}