From a8334f8fe7e4e9b5497fd92f4ba900e23f90288d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 17 Oct 2024 08:24:59 -0400 Subject: [PATCH 01/92] Initial attempt at including diffusion Signed-off by: David Rowenhorst --- pyebsdindex/nlpar.py | 6 +++++- pyebsdindex/nlpar_cpu.py | 19 ++++++++++++++----- pyebsdindex/opencl/clnlpar.cl | 18 +++++++++++++++--- pyebsdindex/opencl/nlpar_cl.py | 12 +++++++++--- pyebsdindex/opencl/nlpar_clray.py | 15 +++++++++++---- 5 files changed, 54 insertions(+), 16 deletions(-) diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index ca23681..eeb00f7 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -60,4 +60,8 @@ __all__ = [ "NLPAR", -] \ No newline at end of file +] + +class DIFF_NLPAR(NLPAR): + def __init__(self, **kwargs): + pass diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 2b00fc3..a188181 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -44,10 +44,12 @@ class NLPAR: - def __init__(self, filename=None, lam=0.7, searchradius=3,dthresh=0.0, nrows = None, ncols = None, **kwargs): + def __init__(self, filename=None, lam=0.7, searchradius=3,dthresh=0.0, diff_offset=1.0, + nrows = None, ncols = None, **kwargs): self.lam = lam self.searchradius = searchradius self.dthresh = dthresh + self.diff_offset = diff_offset, self.filepath = None self.hdfdatapath = None self.filepathout = None @@ -304,7 +306,7 @@ def d2norm(d2, n2, dij, sigma): return np.mean(lamopt_values, axis = 0).flatten() def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, - filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, + filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, diff_offset=None, **kwargs): if lam is not None: @@ -313,12 +315,16 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, if dthresh is not None: self.dthresh = dthresh + if diff_offset is not None: + self.diff_offset = diff_offset + if searchradius is not None: self.searchradius = searchradius lam = np.float32(self.lam) dthresh = np.float32(self.dthresh) sr = np.int64(self.searchradius) + diff_offset = np.float32(self.diff_offset) if filename is not None: self.setfile(filepath=filename) @@ -429,7 +435,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, #dataout = data dataout = self.nlpar_nb(data,lam, sr, dthresh, sigchunk, - rowcountread,ncols,indices,saturation_protect) + rowcountread,ncols,indices,saturation_protect, diff_offset=diff_offset) dataout = dataout.reshape(rowcountread, ncols, phw) dataout = dataout[j-rowstartread:, :, : ] @@ -646,7 +652,7 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) - def nlpar_nb(data,lam, sr, dthresh, sigma, nrows,ncols,indices,saturation_protect=True): + def nlpar_nb(data,lam, sr, dthresh, sigma, nrows,ncols,indices,saturation_protect=True, diff_offset = np.float32(1.0)): def getpairid(idx0, idx1): idx0_t = int(idx0) idx1_t = int(idx1) @@ -661,7 +667,7 @@ def getpairid(idx0, idx1): shpdata = data.shape shpind = indices.shape winsz = np.int32((2*sr+1)**2) - + diff_step = np.ones((winsz), dtype=np.float32) mxval = np.max(data) if saturation_protect == False: @@ -690,7 +696,9 @@ def getpairid(idx0, idx1): if indx_nn == indx_0: weights[counter] = np.float32(-1.0e6) + diff_step[counter] = 1.0 / diff_offset else: + diff_step[counter] = diff_offset pairid = getpairid(indx_0, indx_nn) if pairid in pairdict: weights[counter] = pairdict[pairid] @@ -720,6 +728,7 @@ def getpairid(idx0, idx1): weights[i_nn] = np.maximum(weights[i_nn]-dthresh, numba.float32(0.0)) weights[i_nn] = np.exp(-1.0 * weights[i_nn] * lam2) + weights[i_nn] *= diff_step[i_nn] sum += weights[i_nn] for i_nn in range(winsz): diff --git a/pyebsdindex/opencl/clnlpar.cl b/pyebsdindex/opencl/clnlpar.cl index 6289dc7..6972e84 100644 --- a/pyebsdindex/opencl/clnlpar.cl +++ b/pyebsdindex/opencl/clnlpar.cl @@ -268,7 +268,8 @@ __kernel void calcnlpar( const long npatpoint, const float maxlim, const float lam2, - const float dthresh){ + const float dthresh, + const float diff_offset){ //IDs of work-item represent x and y coordinates in image //const long4 calclim = crlimits[0]; const long x = get_global_id(0)+crlimits[0]; @@ -293,11 +294,13 @@ __kernel void calcnlpar( float d[512]; // taking a risk here that noone will want a SR > 10 float n[512]; + float diff_step[512]; for(j=0; j < nnn; ++j){ d[j] = 0.0; n[j] = 1.0e-6; + diff_step[j] = 1.0 ; } @@ -340,7 +343,14 @@ __kernel void calcnlpar( d1 *= mask1; dd = sum16(&d1); - dd = (indx_ij == indx0) ? -1.0 : dd; // mark the center point + if (indx_ij == indx0) { + dd = -1.0; + diff_step[count] = 1.0 / diff_offset; + } else{ + dd = dd; + diff_step[count] = diff_offset; + } + //dd = (indx_ij == indx0) ? -1.0 : dd; // mark the center point d[count] += dd; n[count] += sum16(&mask1); @@ -388,10 +398,12 @@ __kernel void calcnlpar( nn = 1.0; } - dd -= dthresh; + dd -= dthresh; + dd = dd >= 0.0 ? dd : 0.0; dd = exp(-1.0*dd*lam2); + dd *= diff_step[count]; sum += dd; d[count] = dd; diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index 1c7f9cf..1796ed6 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -304,8 +304,8 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, normalize_d=Fa def calcnlpar_cl(self, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, - filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False, - gpu_id = None, verbose=2, **kwargs): + filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,diff_offset= None, + gpu_id = None, verbose=2, **kwargs): class OpenCLClalcError(Exception): pass @@ -315,9 +315,13 @@ class OpenCLClalcError(Exception): if dthresh is not None: self.dthresh = dthresh + if self.dthresh is None: self.dthresh = 0.0 + if diff_offset is not None: + self.diff_offset = diff_offset + if searchradius is not None: self.searchradius = searchradius @@ -330,6 +334,7 @@ class OpenCLClalcError(Exception): lam = np.float32(self.lam) dthresh = np.float32(self.dthresh) sr = np.int64(self.searchradius) + diff_offset = np.float32(self.diff_offset) if filename is not None: self.setfile(filepath=filename) @@ -561,7 +566,8 @@ class OpenCLClalcError(Exception): np.int64(npat_point), np.float32(mxval), np.float32(1.0/lam**2), - np.float32(dthresh) ) + np.float32(dthresh), + np.float32(diff_offset)) data = data.astype(np.float32) # prepare to receive data back from GPU data.reshape(-1)[:] = 0.0 diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index 6ecacb2..808abbb 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -370,8 +370,8 @@ def _sigmachunkcalc_cl(self, data, calclim, clparams=None, saturation_protect=Tr - def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, - filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False, + def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask = True, + filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,diff_offset = None, verbose = 2, gpu_id = None, **kwargs): if lam is not None: @@ -384,6 +384,9 @@ def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturat if self.dthresh is None: self.dthresh = 0.0 + if diff_offset is not None: + self.diff_offset = diff_offset + if searchradius is not None: self.searchradius = searchradius @@ -396,6 +399,7 @@ def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturat lam = np.float32(self.lam) dthresh = np.float32(self.dthresh) sr = np.int64(self.searchradius) + diff_offset = np.float32(self.diff_offset) if filename is not None: self.setfile(filepath=filename) @@ -477,7 +481,8 @@ def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturat reset_sigma=reset_sigma, backsub = backsub, rescale = rescale, - gpu_id= gpu_id) + gpu_id= gpu_id, + diff_offset=diff_offset) target_mem = clparams.gpu[gpu_id].max_mem_alloc_size//6 max_mem = clparams.gpu[gpu_id].global_mem_size*0.4 @@ -601,6 +606,7 @@ def _nlparchunkcalc_cl(self, data, calclim, clparams=None): sr = np.int64(self.searchradius) nnn = int((2 * sr + 1) ** 2) dthresh = np.float32(self.dthresh) + diff_offset = np.float32(self.diff_offset) #print(chunks[2], chunks[3]) #print(lam, sr, dthresh) @@ -662,7 +668,8 @@ def _nlparchunkcalc_cl(self, data, calclim, clparams=None): np.int64(npat_point), np.float32(mxval), np.float32(1.0 / lam ** 2), - np.float32(dthresh)) + np.float32(dthresh), + np.float32(diff_offset)) data = data.astype(np.float32) # prepare to receive data back from GPU data.reshape(-1)[:] = 0.0 From 6854fbfa0fee6e5609db30f8fdae70decc85201a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 22 Oct 2024 18:13:58 -0400 Subject: [PATCH 02/92] First attempt to add a scale bar to ipf images Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 89 +++++++++++++++++++++++- pyebsdindex/EBSDImage/OpenSans-Bold.ttf | Bin 0 -> 130860 bytes 2 files changed, 86 insertions(+), 3 deletions(-) create mode 100644 pyebsdindex/EBSDImage/OpenSans-Bold.ttf diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index 15ad00a..24c967a 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -21,7 +21,8 @@ The US Naval Research Laboratory Date: 21 Aug 2020''' - +from PIL import Image, ImageDraw, ImageFont +import os import matplotlib.colors as pltcolors import matplotlib.pyplot as plt import numpy as np @@ -29,7 +30,7 @@ from pyebsdindex import rotlib -def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None): +def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, addscalebar=False): nphase = len(indexer.phaseLib) npoints = ebsddata.shape[-1] @@ -70,6 +71,8 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = # npts = int(xsize*ysize) ipf_out[0:npts*3] = ipfout[0:npts,:].flatten() ipf_out = ipf_out.reshape(ysize, xsize, 3) + if addscalebar == True: + ipf_out = add_scalebar(ipf_out, indexer.fID.xStep, rescale=False) return ipf_out @@ -357,4 +360,84 @@ def ipf_ledgend_hex(size=512): anno011 = plt.text(size - 10*fsize*size/512/figsz,triOrigin[1] - 9.5*fsize*size/512.0/figsz,r'$2\bar{1}\bar{1}0$',fontsize=0.9*fsize) anno111 = plt.text(size - 25*fsize*size/512/figsz,(triangleWY+triOrigin[1])*0.85,r'$10\bar{1}0$',fontsize=0.9*fsize) fig.savefig("IPFHex.pdf",bbox_inches=0, transparent=True) - plt.close(1001) \ No newline at end of file + plt.close(1001) + +def add_scalebar(image, stepsize, rescale=True): + # image: grayscale or color image to add scale bar to. + # stepsize: size of a pixel in microns. + imshape = image.shape + channels = 1 + if len(imshape) > 2: + channels = imshape[-1] + + scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize, imfract=0.33) + #print(scale_bar_size, scale_bar_width_px, units) + #scale_bar_height_px = np.int64(scale_bar_width_px / (16.18/2) ) # use golden ratio. + #underbar_size = (scale_bar_height_px*3, imshape[1]) + underbar_size = (int(imshape[1] * 0.04) , imshape[1]) + scale_bar_height_px = int(underbar_size[0] * 0.5) + + + underbar = np.zeros(underbar_size, dtype=np.uint8)+255 + yxoffset = (int(0.25*underbar_size[0]), int(0.01*imshape[1])) + # add our scale bar. + underbar[yxoffset[0]:yxoffset[0]+scale_bar_height_px, + yxoffset[1]:yxoffset[1]+scale_bar_width_px] = 0 + + underbarim = Image.fromarray(underbar, mode='L') + draw = ImageDraw.Draw(underbarim) + fontsize = scale_bar_height_px * 1.4 + imfont = ImageFont.truetype(os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf'), fontsize) + imtext = ' ' + str(scale_bar_size) + ' ' + units + text_color = 0 + text_length = draw.textlength(imtext, imfont) + txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.35) + draw.text(txoffset, imtext, fill=text_color, font=imfont) + + underbar = np.array(underbarim, dtype=np.float32) + underbar -= underbar.min() + underbar *= 1.0/underbar.max() + + + + newshp = (imshape[0]+underbar_size[0], imshape[1], channels) + scalebarim = np.zeros(newshp, dtype=np.float32) + + rescaleim = image.astype(np.float32) + if rescale == True: + rescaleim -= rescaleim.min() + rescaleim *= (1.0 / (rescaleim.max()))*0.999 + + + rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) + scalebarim[0:imshape[0], 0:imshape[1], :] = rescaleim + for i in range(channels): + scalebarim[imshape[0]:, 0:imshape[1], i] = underbar + scalebarim = np.squeeze(scalebarim) + return scalebarim + + + + +def _round_scalebar(image_width, pixel_width, imfract=0.333): + # image width is the width of the image in pixels (steps) in microns. + # pixel_width is the width of one pixel (aka step size). + # imfract is the maximum fraction of the image width that is desired for the scale bar size. + units = 'μm' + # these are the acceptable scale bar sizes + sequence = np.array([1,2,5,10,20,25,50,100,200,250,500], dtype=np.int64) + max_scale_bar_size = image_width*pixel_width*imfract + if max_scale_bar_size < 1: + units = 'nm' + max_scale_bar_size *= 1000.0 + pixel_width *= 1000.0 + if max_scale_bar_size > 1000: + units = 'mm' + max_scale_bar_size *= 0.001 + pixel_width *= 0.001 + scale_bar_size = sequence[sequence < max_scale_bar_size].max() + scale_bar_size_px = np.int64(np.float32(scale_bar_size/np.float32(pixel_width))) + return scale_bar_size, scale_bar_size_px, units + + + 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Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 22 Oct 2024 18:14:56 -0400 Subject: [PATCH 03/92] Set diffusion offset to be a scalar added to the center weighted kernel. Default = 0.0 Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 19 +++++++++++++------ pyebsdindex/opencl/clnlpar.cl | 8 ++++---- pyebsdindex/opencl/nlpar_clray.py | 2 +- 3 files changed, 18 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index a188181..f9a2a1b 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -44,7 +44,7 @@ class NLPAR: - def __init__(self, filename=None, lam=0.7, searchradius=3,dthresh=0.0, diff_offset=1.0, + def __init__(self, filename=None, lam=0.7, searchradius=3,dthresh=0.0, diff_offset=0.0, nrows = None, ncols = None, **kwargs): self.lam = lam self.searchradius = searchradius @@ -520,6 +520,13 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): return sigma + def auto_nlpar(self, filename = None, fileout=None, searchradius=None, lindex = 1, **kwargs): + if filename is not None: + self.setfile(filename) + lam = self.opt_lambda( automask = True, autoupdate=True, backsub = False, **kwargs) + nlparfile = self.calcnlpar(searchradius = searchradius, lam = lam[int(lindex)], saturation_protect=True, automask=True, + fileout=fileout, backsub=False, **kwargs) + return nlparfile def backsub(self, data): # This function will fit a 2D gaussian on top of a plane to the averaged set of patterns (data) that is provided. # It will automatically use whatever mask is defined for valid data. @@ -652,7 +659,7 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) - def nlpar_nb(data,lam, sr, dthresh, sigma, nrows,ncols,indices,saturation_protect=True, diff_offset = np.float32(1.0)): + def nlpar_nb(data,lam, sr, dthresh, sigma, nrows,ncols,indices,saturation_protect=True, diff_offset = np.float32(0.0)): def getpairid(idx0, idx1): idx0_t = int(idx0) idx1_t = int(idx1) @@ -667,7 +674,7 @@ def getpairid(idx0, idx1): shpdata = data.shape shpind = indices.shape winsz = np.int32((2*sr+1)**2) - diff_step = np.ones((winsz), dtype=np.float32) + diff_step = np.zeros((winsz), dtype=np.float32) mxval = np.max(data) if saturation_protect == False: @@ -696,9 +703,9 @@ def getpairid(idx0, idx1): if indx_nn == indx_0: weights[counter] = np.float32(-1.0e6) - diff_step[counter] = 1.0 / diff_offset + diff_step[counter] += diff_offset else: - diff_step[counter] = diff_offset + diff_step[counter] = 0.0 pairid = getpairid(indx_0, indx_nn) if pairid in pairdict: weights[counter] = pairdict[pairid] @@ -728,7 +735,7 @@ def getpairid(idx0, idx1): weights[i_nn] = np.maximum(weights[i_nn]-dthresh, numba.float32(0.0)) weights[i_nn] = np.exp(-1.0 * weights[i_nn] * lam2) - weights[i_nn] *= diff_step[i_nn] + weights[i_nn] += diff_step[i_nn] sum += weights[i_nn] for i_nn in range(winsz): diff --git a/pyebsdindex/opencl/clnlpar.cl b/pyebsdindex/opencl/clnlpar.cl index 6972e84..4dcd022 100644 --- a/pyebsdindex/opencl/clnlpar.cl +++ b/pyebsdindex/opencl/clnlpar.cl @@ -300,7 +300,7 @@ __kernel void calcnlpar( for(j=0; j < nnn; ++j){ d[j] = 0.0; n[j] = 1.0e-6; - diff_step[j] = 1.0 ; + diff_step[j] = 0.0 ; } @@ -345,10 +345,10 @@ __kernel void calcnlpar( dd = sum16(&d1); if (indx_ij == indx0) { dd = -1.0; - diff_step[count] = 1.0 / diff_offset; + diff_step[count] += diff_offset; } else{ dd = dd; - diff_step[count] = diff_offset; + diff_step[count] = 0.0; //diff_offset; } //dd = (indx_ij == indx0) ? -1.0 : dd; // mark the center point @@ -403,7 +403,7 @@ __kernel void calcnlpar( dd = dd >= 0.0 ? dd : 0.0; dd = exp(-1.0*dd*lam2); - dd *= diff_step[count]; + dd += diff_step[count]; sum += dd; d[count] = dd; diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index 808abbb..0f373d8 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -668,7 +668,7 @@ def _nlparchunkcalc_cl(self, data, calclim, clparams=None): np.int64(npat_point), np.float32(mxval), np.float32(1.0 / lam ** 2), - np.float32(dthresh), + np.float32(dthresh), np.float32(diff_offset)) data = data.astype(np.float32) # prepare to receive data back from GPU From 983e43b8958fff167aa948b636d3988310ce3eab Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 22 Oct 2024 19:23:15 -0400 Subject: [PATCH 04/92] Include basic font in manifest Signed-off by: David Rowenhorst --- MANIFEST.in | 1 + 1 file changed, 1 insertion(+) diff --git a/MANIFEST.in b/MANIFEST.in index 0694e84..f187483 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -11,6 +11,7 @@ include README.md include RELEASE.rst include setup.cfg include setup.py +include pyebsdindex/EBSDImage/*.tff recursive-include pyebsdindex *.png *.cl *.py recursive-include doc Makefile make.bat *.rst *.py *.ipynb *.png *.css \ No newline at end of file From bb4a0c6fa7de66bae433ff3f9d7260edf557daab Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 23 Oct 2024 09:38:39 -0400 Subject: [PATCH 05/92] Attempt to fix manifest Signed-off by: David Rowenhorst --- MANIFEST.in | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/MANIFEST.in b/MANIFEST.in index f187483..6aa7e24 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -11,7 +11,7 @@ include README.md include RELEASE.rst include setup.cfg include setup.py -include pyebsdindex/EBSDImage/*.tff +include ./pyebsdindex/EBSDImage/*.ttf recursive-include pyebsdindex *.png *.cl *.py recursive-include doc Makefile make.bat *.rst *.py *.ipynb *.png *.css \ No newline at end of file From dff1e36d672fa9039b7653c39267d929a2b48569 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 23 Oct 2024 16:43:46 -0400 Subject: [PATCH 06/92] Some improvements to scalebar attachment. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 59 ++++++++++++++++++++++++++++++- 1 file changed, 58 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index 24c967a..c14a083 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -21,10 +21,20 @@ The US Naval Research Laboratory Date: 21 Aug 2020''' +'''This package uses the Google Font Open Sans. +Copyright 2020 The Open Sans Project Authors (https://github.com/googlefonts/opensans) +This Font Software is licensed under the SIL Open Font License, Version 1.1 . +This license available with a FAQ at: https://openfontlicense.org +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +''' + from PIL import Image, ImageDraw, ImageFont import os import matplotlib.colors as pltcolors import matplotlib.pyplot as plt +from matplotlib.font_manager import findfont, FontProperties +FONT = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) + import numpy as np from pyebsdindex import rotlib @@ -75,6 +85,50 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = ipf_out = add_scalebar(ipf_out, indexer.fID.xStep, rescale=False) return ipf_out +def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, addscalebar=False, cmap='viridis', datafieldindex=0): + npoints = ebsddata.shape[-1] + ipfout = ebsddata[-1][datafield] + if len(ipfout.shape) > 1: + ipfout = ipfout[:,datafieldindex] + + ipfout = ipfout.astype(np.float32) + if datafield == 'fit': + mn = ipfout[ipfout < 179].mean() + std = ipfout[ipfout < 179].std() + norm = plt.Normalize(vmin=max(0.0, mn-3*std), vmax=mn+3*std) + else: + norm = plt.Normalize() + ipfout = np.array(norm(ipfout)) + #ipfout -= ipfout.min() + #ipfout *= 1.0/ipfout.max() + + cm = plt.colormaps[cmap] + ipfout = cm(ipfout) + + if xsize is not None: + xsize = int(xsize) + # if ysize is None: + # print(ysize) + else: + xsize = indexer.fID.nCols + # xsize = int(npoints) + # ysize = 1 + + if ysize is not None: + ysize = int(ysize) + else: + ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) + + ipf_out = np.zeros((ysize, xsize, 3), dtype=np.float32) + ipf_out = ipf_out.flatten() + npts = min(int(npoints), int(xsize * ysize)) + # if int(xsize*ysize) < npoints: + # npts = int(xsize*ysize) + ipf_out[0:npts * 3] = ipfout[0:npts, 0:3].flatten() + ipf_out = ipf_out.reshape(ysize, xsize, 3) + if addscalebar == True: + ipf_out = add_scalebar(ipf_out, indexer.fID.xStep, rescale=False) + return ipf_out def qu2ipf_cubic(quats, vector=np.array([0,0,1.0])): @@ -379,7 +433,8 @@ def add_scalebar(image, stepsize, rescale=True): underbar = np.zeros(underbar_size, dtype=np.uint8)+255 - yxoffset = (int(0.25*underbar_size[0]), int(0.01*imshape[1])) + #yxoffset = (int(0.25*underbar_size[0]), int(0.01*imshape[1])) + yxoffset = (int(0.25 * underbar_size[0]), 0) # add our scale bar. underbar[yxoffset[0]:yxoffset[0]+scale_bar_height_px, yxoffset[1]:yxoffset[1]+scale_bar_width_px] = 0 @@ -387,7 +442,9 @@ def add_scalebar(image, stepsize, rescale=True): underbarim = Image.fromarray(underbar, mode='L') draw = ImageDraw.Draw(underbarim) fontsize = scale_bar_height_px * 1.4 + imfont = ImageFont.truetype(os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf'), fontsize) + #imfont = ImageFont.truetype(FONT, fontsize) imtext = ' ' + str(scale_bar_size) + ' ' + units text_color = 0 text_length = draw.textlength(imtext, imfont) From dbd2e72cac220a663802df0b82da9fa5abac07c0 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 24 Oct 2024 10:35:43 -0400 Subject: [PATCH 07/92] Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 130 +-------------------- pyebsdindex/EBSDImage/scalarimage.py | 163 +++++++++++++++++++++++++++ 2 files changed, 166 insertions(+), 127 deletions(-) create mode 100644 pyebsdindex/EBSDImage/scalarimage.py diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index c14a083..da5f01f 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -28,16 +28,13 @@ SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 ''' -from PIL import Image, ImageDraw, ImageFont -import os import matplotlib.colors as pltcolors import matplotlib.pyplot as plt -from matplotlib.font_manager import findfont, FontProperties -FONT = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) import numpy as np from pyebsdindex import rotlib +from pyebsdindex.EBSDImage import scalarimage def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, addscalebar=False): @@ -82,53 +79,10 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = ipf_out[0:npts*3] = ipfout[0:npts,:].flatten() ipf_out = ipf_out.reshape(ysize, xsize, 3) if addscalebar == True: - ipf_out = add_scalebar(ipf_out, indexer.fID.xStep, rescale=False) + ipf_out = scalarimage.add_scalebar(ipf_out, indexer.fID.xStep, rescale=False) return ipf_out -def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, addscalebar=False, cmap='viridis', datafieldindex=0): - npoints = ebsddata.shape[-1] - ipfout = ebsddata[-1][datafield] - if len(ipfout.shape) > 1: - ipfout = ipfout[:,datafieldindex] - - ipfout = ipfout.astype(np.float32) - if datafield == 'fit': - mn = ipfout[ipfout < 179].mean() - std = ipfout[ipfout < 179].std() - norm = plt.Normalize(vmin=max(0.0, mn-3*std), vmax=mn+3*std) - else: - norm = plt.Normalize() - ipfout = np.array(norm(ipfout)) - #ipfout -= ipfout.min() - #ipfout *= 1.0/ipfout.max() - - cm = plt.colormaps[cmap] - ipfout = cm(ipfout) - - if xsize is not None: - xsize = int(xsize) - # if ysize is None: - # print(ysize) - else: - xsize = indexer.fID.nCols - # xsize = int(npoints) - # ysize = 1 - - if ysize is not None: - ysize = int(ysize) - else: - ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) - ipf_out = np.zeros((ysize, xsize, 3), dtype=np.float32) - ipf_out = ipf_out.flatten() - npts = min(int(npoints), int(xsize * ysize)) - # if int(xsize*ysize) < npoints: - # npts = int(xsize*ysize) - ipf_out[0:npts * 3] = ipfout[0:npts, 0:3].flatten() - ipf_out = ipf_out.reshape(ysize, xsize, 3) - if addscalebar == True: - ipf_out = add_scalebar(ipf_out, indexer.fID.xStep, rescale=False) - return ipf_out def qu2ipf_cubic(quats, vector=np.array([0,0,1.0])): @@ -416,85 +370,7 @@ def ipf_ledgend_hex(size=512): fig.savefig("IPFHex.pdf",bbox_inches=0, transparent=True) plt.close(1001) -def add_scalebar(image, stepsize, rescale=True): - # image: grayscale or color image to add scale bar to. - # stepsize: size of a pixel in microns. - imshape = image.shape - channels = 1 - if len(imshape) > 2: - channels = imshape[-1] - - scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize, imfract=0.33) - #print(scale_bar_size, scale_bar_width_px, units) - #scale_bar_height_px = np.int64(scale_bar_width_px / (16.18/2) ) # use golden ratio. - #underbar_size = (scale_bar_height_px*3, imshape[1]) - underbar_size = (int(imshape[1] * 0.04) , imshape[1]) - scale_bar_height_px = int(underbar_size[0] * 0.5) - - - underbar = np.zeros(underbar_size, dtype=np.uint8)+255 - #yxoffset = (int(0.25*underbar_size[0]), int(0.01*imshape[1])) - yxoffset = (int(0.25 * underbar_size[0]), 0) - # add our scale bar. - underbar[yxoffset[0]:yxoffset[0]+scale_bar_height_px, - yxoffset[1]:yxoffset[1]+scale_bar_width_px] = 0 - - underbarim = Image.fromarray(underbar, mode='L') - draw = ImageDraw.Draw(underbarim) - fontsize = scale_bar_height_px * 1.4 - - imfont = ImageFont.truetype(os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf'), fontsize) - #imfont = ImageFont.truetype(FONT, fontsize) - imtext = ' ' + str(scale_bar_size) + ' ' + units - text_color = 0 - text_length = draw.textlength(imtext, imfont) - txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.35) - draw.text(txoffset, imtext, fill=text_color, font=imfont) - - underbar = np.array(underbarim, dtype=np.float32) - underbar -= underbar.min() - underbar *= 1.0/underbar.max() - - - - newshp = (imshape[0]+underbar_size[0], imshape[1], channels) - scalebarim = np.zeros(newshp, dtype=np.float32) - - rescaleim = image.astype(np.float32) - if rescale == True: - rescaleim -= rescaleim.min() - rescaleim *= (1.0 / (rescaleim.max()))*0.999 - - - rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) - scalebarim[0:imshape[0], 0:imshape[1], :] = rescaleim - for i in range(channels): - scalebarim[imshape[0]:, 0:imshape[1], i] = underbar - scalebarim = np.squeeze(scalebarim) - return scalebarim - - - - -def _round_scalebar(image_width, pixel_width, imfract=0.333): - # image width is the width of the image in pixels (steps) in microns. - # pixel_width is the width of one pixel (aka step size). - # imfract is the maximum fraction of the image width that is desired for the scale bar size. - units = 'μm' - # these are the acceptable scale bar sizes - sequence = np.array([1,2,5,10,20,25,50,100,200,250,500], dtype=np.int64) - max_scale_bar_size = image_width*pixel_width*imfract - if max_scale_bar_size < 1: - units = 'nm' - max_scale_bar_size *= 1000.0 - pixel_width *= 1000.0 - if max_scale_bar_size > 1000: - units = 'mm' - max_scale_bar_size *= 0.001 - pixel_width *= 0.001 - scale_bar_size = sequence[sequence < max_scale_bar_size].max() - scale_bar_size_px = np.int64(np.float32(scale_bar_size/np.float32(pixel_width))) - return scale_bar_size, scale_bar_size_px, units + diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py new file mode 100644 index 0000000..a610dee --- /dev/null +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -0,0 +1,163 @@ +'''This software was developed by employees of the US Naval Research Laboratory (NRL), an +agency of the Federal Government. Pursuant to title 17 section 105 of the United States +Code, works of NRL employees are not subject to copyright protection, and this software +is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no +responsibility whatsoever for its use by other parties, and makes no guarantees, +expressed or implied, about its quality, reliability, or any other characteristic. We +would appreciate acknowledgment if the software is used. To the extent that NRL may hold +copyright in countries other than the United States, you are hereby granted the +non-exclusive irrevocable and unconditional right to print, publish, prepare derivative +works and distribute this software, in any medium, or authorize others to do so on your +behalf, on a royalty-free basis throughout the world. You may improve, modify, and +create derivative works of the software or any portion of the software, and you may copy +and distribute such modifications or works. Modified works should carry a notice stating +that you changed the software and should note the date and nature of any such change. +Please explicitly acknowledge the US Naval Research Laboratory as the original source. +This software can be redistributed and/or modified freely provided that any derivative +works bear some notice that they are derived from it, and any modified versions bear +some notice that they have been modified. + +Author: David Rowenhorst; +The US Naval Research Laboratory Date: 21 Aug 2020''' + + +'''This package uses the Google Font Open Sans. +Copyright 2020 The Open Sans Project Authors (https://github.com/googlefonts/opensans) +This Font Software is licensed under the SIL Open Font License, Version 1.1 . +This license available with a FAQ at: https://openfontlicense.org +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +''' + +from PIL import Image, ImageDraw, ImageFont +import os +import matplotlib.colors as pltcolors +import matplotlib.pyplot as plt +#from matplotlib.font_manager import findfont, FontProperties +#FONT = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) + +FONT = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') + + +import numpy as np + +def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, addscalebar=False, cmap='viridis', datafieldindex=0): + npoints = ebsddata.shape[-1] + imagedata = ebsddata[-1][datafield] + if len(imagedata.shape) > 1: + imagedata = imagedata[:,datafieldindex] + + imagedata = imagedata.astype(np.float32) + if datafield == 'fit': + mn = imagedata[imagedata < 179].mean() + std = imagedata[imagedata < 179].std() + norm = plt.Normalize(vmin=max(0.0, mn-3*std), vmax=mn+3*std) + else: + norm = plt.Normalize() + imagedata = np.array(norm(imagedata)) + #imagedata -= imagedata.min() + #imagedata *= 1.0/imagedata.max() + + cm = plt.colormaps[cmap] + imagedata = cm(imagedata) + + if xsize is not None: + xsize = int(xsize) + # if ysize is None: + # print(ysize) + else: + xsize = indexer.fID.nCols + # xsize = int(npoints) + # ysize = 1 + + if ysize is not None: + ysize = int(ysize) + else: + ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) + + image_out = np.zeros((ysize, xsize, 3), dtype=np.float32) + image_out = image_out.flatten() + npts = min(int(npoints), int(xsize * ysize)) + # if int(xsize*ysize) < npoints: + # npts = int(xsize*ysize) + image_out[0:npts * 3] = imagedata[0:npts, 0:3].flatten() + image_out = image_out.reshape(ysize, xsize, 3) + if addscalebar == True: + image_out = add_scalebar(image_out, indexer.fID.xStep, rescale=False) + return image_out + +def add_scalebar(image, stepsize, rescale=True): + # image: grayscale or color image to add scale bar to. + # stepsize: size of a pixel in microns. + imshape = image.shape + channels = 1 + if len(imshape) > 2: + channels = imshape[-1] + + scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize, imfract=0.33) + #print(scale_bar_size, scale_bar_width_px, units) + #scale_bar_height_px = np.int64(scale_bar_width_px / (16.18/2) ) # use golden ratio. + #underbar_size = (scale_bar_height_px*3, imshape[1]) + underbar_size = (int(imshape[1] * 0.04) , imshape[1]) + scale_bar_height_px = int(underbar_size[0] * 0.5) + + + underbar = np.zeros(underbar_size, dtype=np.uint8)+255 + #yxoffset = (int(0.25*underbar_size[0]), int(0.01*imshape[1])) + yxoffset = (int(0.25 * underbar_size[0]), 0) + # add our scale bar. + underbar[yxoffset[0]:yxoffset[0]+scale_bar_height_px, + yxoffset[1]:yxoffset[1]+scale_bar_width_px] = 0 + + underbarim = Image.fromarray(underbar, mode='L') + draw = ImageDraw.Draw(underbarim) + fontsize = scale_bar_height_px * 1.4 + + imfont = ImageFont.truetype(FONT, fontsize) + #imfont = ImageFont.truetype(FONT, fontsize) + imtext = ' ' + str(scale_bar_size) + ' ' + units + text_color = 0 + text_length = draw.textlength(imtext, imfont) + txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.35) + draw.text(txoffset, imtext, fill=text_color, font=imfont) + + underbar = np.array(underbarim, dtype=np.float32) + underbar -= underbar.min() + underbar *= 1.0/underbar.max() + + + + newshp = (imshape[0]+underbar_size[0], imshape[1], channels) + scalebarim = np.zeros(newshp, dtype=np.float32) + + rescaleim = image.astype(np.float32) + if rescale == True: + rescaleim -= rescaleim.min() + rescaleim *= (1.0 / (rescaleim.max()))*0.999 + + + rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) + scalebarim[0:imshape[0], 0:imshape[1], :] = rescaleim + for i in range(channels): + scalebarim[imshape[0]:, 0:imshape[1], i] = underbar + scalebarim = np.squeeze(scalebarim) + return scalebarim + +def _round_scalebar(image_width, pixel_width, imfract=0.333): + # image width is the width of the image in pixels (steps) in microns. + # pixel_width is the width of one pixel (aka step size). + # imfract is the maximum fraction of the image width that is desired for the scale bar size. + units = 'μm' + # these are the acceptable scale bar sizes + sequence = np.array([1,2,5,10,20,25,50,100,200,250,500], dtype=np.int64) + max_scale_bar_size = image_width*pixel_width*imfract + if max_scale_bar_size < 1: + units = 'nm' + max_scale_bar_size *= 1000.0 + pixel_width *= 1000.0 + if max_scale_bar_size > 1000: + units = 'mm' + max_scale_bar_size *= 0.001 + pixel_width *= 0.001 + scale_bar_size = sequence[sequence < max_scale_bar_size].max() + scale_bar_size_px = np.int64(np.float32(scale_bar_size/np.float32(pixel_width))) + return scale_bar_size, scale_bar_size_px, units \ No newline at end of file From 44760be7e25fa765bc37d7c3d2f24b787616c380 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 24 Oct 2024 16:18:32 -0400 Subject: [PATCH 08/92] add_scalebar --> addscalebar. Rearrange to be able to rescale any image. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 7 +++-- pyebsdindex/EBSDImage/scalarimage.py | 39 ++++++++++++++++++---------- 2 files changed, 31 insertions(+), 15 deletions(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index da5f01f..d84e054 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -32,12 +32,14 @@ import matplotlib.pyplot as plt import numpy as np +import scipy.ndimage as scipyndim from pyebsdindex import rotlib from pyebsdindex.EBSDImage import scalarimage -def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, addscalebar=False): +def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, + addscalebar=False, **kwargs): nphase = len(indexer.phaseLib) npoints = ebsddata.shape[-1] @@ -78,8 +80,9 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = # npts = int(xsize*ysize) ipf_out[0:npts*3] = ipfout[0:npts,:].flatten() ipf_out = ipf_out.reshape(ysize, xsize, 3) + if addscalebar == True: - ipf_out = scalarimage.add_scalebar(ipf_out, indexer.fID.xStep, rescale=False) + ipf_out = scalarimage.addscalebar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) return ipf_out diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index a610dee..0fe44bd 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -30,8 +30,9 @@ from PIL import Image, ImageDraw, ImageFont import os -import matplotlib.colors as pltcolors + import matplotlib.pyplot as plt +import scipy.ndimage as scipyndim #from matplotlib.font_manager import findfont, FontProperties #FONT = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) @@ -40,7 +41,8 @@ import numpy as np -def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, addscalebar=False, cmap='viridis', datafieldindex=0): +def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, + addscalebar=False, cmap='viridis', datafieldindex=0, **kwargs): npoints = ebsddata.shape[-1] imagedata = ebsddata[-1][datafield] if len(imagedata.shape) > 1: @@ -81,11 +83,13 @@ def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, a # npts = int(xsize*ysize) image_out[0:npts * 3] = imagedata[0:npts, 0:3].flatten() image_out = image_out.reshape(ysize, xsize, 3) + # perform desired image resize + if addscalebar == True: - image_out = add_scalebar(image_out, indexer.fID.xStep, rescale=False) + image_out = addscalebar(image_out, indexer.fID.xStep, rescale=False, **kwargs) return image_out -def add_scalebar(image, stepsize, rescale=True): +def addscalebar(image, stepsize, rescale=True, upscale_xsize=None): # image: grayscale or color image to add scale bar to. # stepsize: size of a pixel in microns. imshape = image.shape @@ -93,7 +97,24 @@ def add_scalebar(image, stepsize, rescale=True): if len(imshape) > 2: channels = imshape[-1] - scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize, imfract=0.33) + rescaleim = image.astype(np.float32) + if rescale == True: + rescaleim -= rescaleim.min() + rescaleim *= (1.0 / (rescaleim.max())) * 0.999 + + rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) + stepadjust = 1.0 + if upscale_xsize is not None: + upscale_xsize = np.int64(upscale_xsize) + #aspect = np.float32(imshape[1]) / np.float32(imshape[0]) + #upscale_ysize = np.int64(upscale_xsize / aspect) + zoomfact = upscale_xsize / np.float32(imshape[1]) + rescaleim = scipyndim.zoom(rescaleim, (zoomfact, zoomfact, 1)).clip(0.0, 1.0) + stepadjust = 1.0 / zoomfact + + imshape = rescaleim.shape + + scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize*stepadjust, imfract=0.33) #print(scale_bar_size, scale_bar_width_px, units) #scale_bar_height_px = np.int64(scale_bar_width_px / (16.18/2) ) # use golden ratio. #underbar_size = (scale_bar_height_px*3, imshape[1]) @@ -124,18 +145,10 @@ def add_scalebar(image, stepsize, rescale=True): underbar -= underbar.min() underbar *= 1.0/underbar.max() - - newshp = (imshape[0]+underbar_size[0], imshape[1], channels) scalebarim = np.zeros(newshp, dtype=np.float32) - rescaleim = image.astype(np.float32) - if rescale == True: - rescaleim -= rescaleim.min() - rescaleim *= (1.0 / (rescaleim.max()))*0.999 - - rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) scalebarim[0:imshape[0], 0:imshape[1], :] = rescaleim for i in range(channels): scalebarim[imshape[0]:, 0:imshape[1], i] = underbar From 92f942ba800e0e4a8ab396d99fe2b5c544daecf1 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 24 Oct 2024 16:28:22 -0400 Subject: [PATCH 09/92] Changed default cpu core usage behavior to be more optimal. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 2a78087..71b94c8 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -276,10 +276,8 @@ def index_pats_distributed( npats = npatsTotal - patstart # Now set up the cluster with the indexer - n_cpu_nodes = int(os.cpu_count()) - # int(sum([ r['Resources']['CPU'] for r in ray.nodes()])) - if ncpu != -1: - n_cpu_nodes = int(ncpu) + + ngpu = None @@ -308,6 +306,15 @@ def index_pats_distributed( if indexer.bandDetectPlan.useCPU == True: ngpu = 0 + if ncpu == 0: + ncpu = os.cpu_count() + if ncpu <= 0: + if ngpu > 0: + ncpu = min(os.cpu_count(), len(indexer.phaseLib)*10) # this is a heuristic, and may be highly dependent on hardware + else: + ncpu = os.cpu_count() + if ncpu != -1: + n_cpu_nodes = int(ncpu) if ngpu > 0: gpuratio = (12, ngpu*4) From af526f7c6bb40576c467726319392c0efa309d1c Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 24 Oct 2024 16:54:57 -0400 Subject: [PATCH 10/92] another function name change addscalebar --> _addscalebar Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 2 +- pyebsdindex/EBSDImage/scalarimage.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index d84e054..dda0489 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -82,7 +82,7 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = ipf_out = ipf_out.reshape(ysize, xsize, 3) if addscalebar == True: - ipf_out = scalarimage.addscalebar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) + ipf_out = scalarimage._addscalebar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) return ipf_out diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index 0fe44bd..62cf5c9 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -86,10 +86,10 @@ def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, # perform desired image resize if addscalebar == True: - image_out = addscalebar(image_out, indexer.fID.xStep, rescale=False, **kwargs) + image_out = _addscalebar(image_out, indexer.fID.xStep, rescale=False, **kwargs) return image_out -def addscalebar(image, stepsize, rescale=True, upscale_xsize=None): +def _addscalebar(image, stepsize, rescale=True, upscale_xsize=None): # image: grayscale or color image to add scale bar to. # stepsize: size of a pixel in microns. imshape = image.shape From 0eae9392ae69b6ebce2ba3826d33dfb1b778e994 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 29 Oct 2024 18:03:26 -0400 Subject: [PATCH 11/92] Another code rearrangement and some documentation. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 5 +- pyebsdindex/EBSDImage/scalarimage.py | 173 ++++++------------ pyebsdindex/EBSDImage/scalebar.py | 252 +++++++++++++++++++++++++++ 3 files changed, 307 insertions(+), 123 deletions(-) create mode 100644 pyebsdindex/EBSDImage/scalebar.py diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index dda0489..ce548e1 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -32,10 +32,9 @@ import matplotlib.pyplot as plt import numpy as np -import scipy.ndimage as scipyndim from pyebsdindex import rotlib -from pyebsdindex.EBSDImage import scalarimage +from pyebsdindex.EBSDImage import scalebar, scalarimage def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, @@ -82,7 +81,7 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = ipf_out = ipf_out.reshape(ysize, xsize, 3) if addscalebar == True: - ipf_out = scalarimage._addscalebar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) + ipf_out = scalebar.addscalebar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) return ipf_out diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index 62cf5c9..dad7b61 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -18,17 +18,9 @@ some notice that they have been modified. Author: David Rowenhorst; -The US Naval Research Laboratory Date: 21 Aug 2020''' +The US Naval Research Laboratory Date: 29 Oct 2024''' -'''This package uses the Google Font Open Sans. -Copyright 2020 The Open Sans Project Authors (https://github.com/googlefonts/opensans) -This Font Software is licensed under the SIL Open Font License, Version 1.1 . -This license available with a FAQ at: https://openfontlicense.org -SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 -''' - -from PIL import Image, ImageDraw, ImageFont import os import matplotlib.pyplot as plt @@ -38,29 +30,24 @@ FONT = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') - import numpy as np - -def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, - addscalebar=False, cmap='viridis', datafieldindex=0, **kwargs): +from pyebsdindex.EBSDImage import scalebar + +def scalarimage(ebsddata, indexer, + datafield='pq', + xsize = None, + ysize = None, + addscalebar=False, + cmap='viridis', + norescalegray=False, + datafieldindex=0, + **kwargs): npoints = ebsddata.shape[-1] imagedata = ebsddata[-1][datafield] if len(imagedata.shape) > 1: imagedata = imagedata[:,datafieldindex] imagedata = imagedata.astype(np.float32) - if datafield == 'fit': - mn = imagedata[imagedata < 179].mean() - std = imagedata[imagedata < 179].std() - norm = plt.Normalize(vmin=max(0.0, mn-3*std), vmax=mn+3*std) - else: - norm = plt.Normalize() - imagedata = np.array(norm(imagedata)) - #imagedata -= imagedata.min() - #imagedata *= 1.0/imagedata.max() - - cm = plt.colormaps[cmap] - imagedata = cm(imagedata) if xsize is not None: xsize = int(xsize) @@ -70,107 +57,53 @@ def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, xsize = indexer.fID.nCols # xsize = int(npoints) # ysize = 1 - if ysize is not None: ysize = int(ysize) else: ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) - image_out = np.zeros((ysize, xsize, 3), dtype=np.float32) - image_out = image_out.flatten() - npts = min(int(npoints), int(xsize * ysize)) - # if int(xsize*ysize) < npoints: - # npts = int(xsize*ysize) - image_out[0:npts * 3] = imagedata[0:npts, 0:3].flatten() - image_out = image_out.reshape(ysize, xsize, 3) - # perform desired image resize + + if datafield == 'fit': + mn = imagedata[imagedata < 179].mean() + std = imagedata[imagedata < 179].std() + norm = plt.Normalize(vmin=max(0.0, mn-3*std), vmax=mn+3*std) + elif datafield == 'phase': + norm = plt.Normalize(vmin=-1) + else: + norm = plt.Normalize() + + if cmap == 'gray' and norescalegray == True: + if datafield == 'fit': + imagedata = np.array(imagedata).clip(max(0.0, mn-4*std),mn+4*std ) + imagedata = np.array(imagedata) + else: + imagedata = np.array(norm(imagedata)) + cm = plt.colormaps[cmap] + imagedata = cm(imagedata) + if cmap == 'gray': + imagedata = (imagedata[:,0]).squeeze() + + + + if len(imagedata.shape) > 1: + image_out = np.zeros((ysize, xsize, 3), dtype=np.float32) + image_out = image_out.flatten() + npts = min(int(npoints), int(xsize * ysize)) + # if int(xsize*ysize) < npoints: + # npts = int(xsize*ysize) + image_out[0:npts * 3] = imagedata[0:npts, 0:3].flatten() + image_out = image_out.reshape(ysize, xsize, 3) + # perform desired image resize + else: + image_out = np.zeros((ysize, xsize), dtype=np.float32) + image_out = image_out.flatten() + npts = min(int(npoints), int(xsize * ysize)) + # if int(xsize*ysize) < npoints: + # npts = int(xsize*ysize) + image_out[0:npts] = imagedata[0:npts].flatten() + image_out = image_out.reshape(ysize, xsize) if addscalebar == True: - image_out = _addscalebar(image_out, indexer.fID.xStep, rescale=False, **kwargs) + image_out = scalebar.addscalebar(image_out, indexer.fID.xStep, rescale=False, **kwargs) return image_out -def _addscalebar(image, stepsize, rescale=True, upscale_xsize=None): - # image: grayscale or color image to add scale bar to. - # stepsize: size of a pixel in microns. - imshape = image.shape - channels = 1 - if len(imshape) > 2: - channels = imshape[-1] - - rescaleim = image.astype(np.float32) - if rescale == True: - rescaleim -= rescaleim.min() - rescaleim *= (1.0 / (rescaleim.max())) * 0.999 - - rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) - stepadjust = 1.0 - if upscale_xsize is not None: - upscale_xsize = np.int64(upscale_xsize) - #aspect = np.float32(imshape[1]) / np.float32(imshape[0]) - #upscale_ysize = np.int64(upscale_xsize / aspect) - zoomfact = upscale_xsize / np.float32(imshape[1]) - rescaleim = scipyndim.zoom(rescaleim, (zoomfact, zoomfact, 1)).clip(0.0, 1.0) - stepadjust = 1.0 / zoomfact - - imshape = rescaleim.shape - - scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize*stepadjust, imfract=0.33) - #print(scale_bar_size, scale_bar_width_px, units) - #scale_bar_height_px = np.int64(scale_bar_width_px / (16.18/2) ) # use golden ratio. - #underbar_size = (scale_bar_height_px*3, imshape[1]) - underbar_size = (int(imshape[1] * 0.04) , imshape[1]) - scale_bar_height_px = int(underbar_size[0] * 0.5) - - - underbar = np.zeros(underbar_size, dtype=np.uint8)+255 - #yxoffset = (int(0.25*underbar_size[0]), int(0.01*imshape[1])) - yxoffset = (int(0.25 * underbar_size[0]), 0) - # add our scale bar. - underbar[yxoffset[0]:yxoffset[0]+scale_bar_height_px, - yxoffset[1]:yxoffset[1]+scale_bar_width_px] = 0 - - underbarim = Image.fromarray(underbar, mode='L') - draw = ImageDraw.Draw(underbarim) - fontsize = scale_bar_height_px * 1.4 - - imfont = ImageFont.truetype(FONT, fontsize) - #imfont = ImageFont.truetype(FONT, fontsize) - imtext = ' ' + str(scale_bar_size) + ' ' + units - text_color = 0 - text_length = draw.textlength(imtext, imfont) - txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.35) - draw.text(txoffset, imtext, fill=text_color, font=imfont) - - underbar = np.array(underbarim, dtype=np.float32) - underbar -= underbar.min() - underbar *= 1.0/underbar.max() - - newshp = (imshape[0]+underbar_size[0], imshape[1], channels) - scalebarim = np.zeros(newshp, dtype=np.float32) - - - scalebarim[0:imshape[0], 0:imshape[1], :] = rescaleim - for i in range(channels): - scalebarim[imshape[0]:, 0:imshape[1], i] = underbar - scalebarim = np.squeeze(scalebarim) - return scalebarim - -def _round_scalebar(image_width, pixel_width, imfract=0.333): - # image width is the width of the image in pixels (steps) in microns. - # pixel_width is the width of one pixel (aka step size). - # imfract is the maximum fraction of the image width that is desired for the scale bar size. - units = 'μm' - # these are the acceptable scale bar sizes - sequence = np.array([1,2,5,10,20,25,50,100,200,250,500], dtype=np.int64) - max_scale_bar_size = image_width*pixel_width*imfract - if max_scale_bar_size < 1: - units = 'nm' - max_scale_bar_size *= 1000.0 - pixel_width *= 1000.0 - if max_scale_bar_size > 1000: - units = 'mm' - max_scale_bar_size *= 0.001 - pixel_width *= 0.001 - scale_bar_size = sequence[sequence < max_scale_bar_size].max() - scale_bar_size_px = np.int64(np.float32(scale_bar_size/np.float32(pixel_width))) - return scale_bar_size, scale_bar_size_px, units \ No newline at end of file diff --git a/pyebsdindex/EBSDImage/scalebar.py b/pyebsdindex/EBSDImage/scalebar.py new file mode 100644 index 0000000..b071ecd --- /dev/null +++ b/pyebsdindex/EBSDImage/scalebar.py @@ -0,0 +1,252 @@ +'''This software was developed by employees of the US Naval Research Laboratory (NRL), an +agency of the Federal Government. Pursuant to title 17 section 105 of the United States +Code, works of NRL employees are not subject to copyright protection, and this software +is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no +responsibility whatsoever for its use by other parties, and makes no guarantees, +expressed or implied, about its quality, reliability, or any other characteristic. We +would appreciate acknowledgment if the software is used. To the extent that NRL may hold +copyright in countries other than the United States, you are hereby granted the +non-exclusive irrevocable and unconditional right to print, publish, prepare derivative +works and distribute this software, in any medium, or authorize others to do so on your +behalf, on a royalty-free basis throughout the world. You may improve, modify, and +create derivative works of the software or any portion of the software, and you may copy +and distribute such modifications or works. Modified works should carry a notice stating +that you changed the software and should note the date and nature of any such change. +Please explicitly acknowledge the US Naval Research Laboratory as the original source. +This software can be redistributed and/or modified freely provided that any derivative +works bear some notice that they are derived from it, and any modified versions bear +some notice that they have been modified. + +Author: David Rowenhorst; +The US Naval Research Laboratory Date: 29 Oct 2024''' + + +'''This package uses the Google Font Open Sans. +Copyright 2020 The Open Sans Project Authors (https://github.com/googlefonts/opensans) +This Font Software is licensed under the SIL Open Font License, Version 1.1 . +This license available with a FAQ at: https://openfontlicense.org +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +''' + + + +from PIL import Image, ImageDraw, ImageFont +import os +import numpy as np +import matplotlib.pyplot as plt +import scipy.ndimage as scipyndim +FONT = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') + +def addscalebar(image, + stepsize=1.0, + addminor = False, + rescale=True, + upscale_xsize=None, + **kwargs): + """Automatically add a scalebar to the bottom of a micrograph + when given the dimension of a pixel within the image in microns. It will automatically + choose the appropriate length of the scale bar, and will autoscale the units from + nm, μm, or mm depending on the image width and provided ``stepsize``. + The microbar is burned-in within the rasterized array, or in otherwords, it is not + a vectorized shapes/text. + + Parameters + ---------- + image : numpy.ndarray + numpy array, of shape + ``(n image rows, n image columns)``, + or ``(n image rows, n image columns, n image channels)``. + If no channel dimension is given, it is assumed that the image + is a gray-scale image. + stepsize : float + The width of one pixel in microns. Default is ``1.0``. + addminor: bool, optional [``False``] + Set equal to ``True`` if you want to automatically add minor tick lines + to the scale bar. Default is ``False`` + rescale: bool, optional [``False``] + Set to ``True`` to scale the output image values between + ``[0,1.0]``. If set to ``False`` the background of the scale + bar area will be set to the maximum data value, and the bar/text + will be set to the minumum value. + upscale_xsize: int, optional + Set this to be the output array number of columns in pixels. This can be useful + for small images, with < 500 pixels across the image. In these cases the text + for the scalebar can become pixelated. The image will be interpolated + (using bi-cubic interpolation) to the entered xsize (keeping the aspect ratio + the same). We're not going to tell you how to live your life + but this will only use nearest-neighbor interpolation for the resizing, + thus even muliples of the original image size (2x, 3x, ...) is ideal for up-sizing. + Note: setting ``upscale_xsize`` to a value that is smaller + than the input image number of columns will provide a down-sized image, + and obviously data points will be removed. + + Returns + ------- + numpy.ndarray + A copy of the numpy image-like array where the scalebar and notation is appended to + the bottom of the image. The output image will have dimensions, + ``(round(1.04*image.shape[0], image.shape[1], {image.shape[2]})``, depending on if + the original image had multiple channels. + + Example: + ------- + Make a random micrograph that has a stepsize of 0.15 microns, add a scale bar, + and write it out to a png and show it. + >>> import os + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> from pyebsdindex.EBSDImage import scalebar + >>> a = np.random.random((800, 1024)) + >>> abar = scalebar.addscalebar(a, 0.15) + >>> plt.imsave(os.path.expanduser('~/random_image.png'), abar, cmap='gray') + >>> plt.imshow(abar) + + Notes: + ------- + If the input image has four channels, with the notion that the channels + represnet ``[R,G,B,A]`` where ``A`` is the alpha channel, the current behavior of + ```addscalebar``` will output the bar/text as ``[0.0,0.0,0.0,0.0]`` (black, transparent) + and the background as ``[1.0,1.0,1.0,1.0]`` (white, opaque). Currently, this is seen as a + feature and not a bug. The behavior can easily be altered as in this example: + >>> import os + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> from pyebsdindex.EBSDImage import scalebar + >>> a = np.random.random((800, 1024, 4)) + >>> abar = scalebar.addscalebar(a, 0.15) + >>> abar[a.shape[0]:, :, 3] = 1.0 # if ``a`` is [R,G,B,A] ubyte8, replace with 255 + >>> plt.imsave(os.path.expanduser('~/random_image.png'), abar, cmap='gray') + >>> plt.imshow(abar) + """ + + imshape = image.shape + channels = 1 + if len(imshape) > 2: + channels = imshape[-1] + + rescaleim = image.astype(np.float32) + if rescale == True: + rescaleim -= rescaleim.min() + rescaleim *= (1.0 / (rescaleim.max())) * 0.999 + + rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) + stepadjust = 1.0 + if upscale_xsize is not None: + upscale_xsize = np.int64(upscale_xsize) + #aspect = np.float32(imshape[1]) / np.float32(imshape[0]) + #upscale_ysize = np.int64(upscale_xsize / aspect) + zoomfact = upscale_xsize / np.float32(imshape[1]) + rescaleim = scipyndim.zoom(rescaleim, (zoomfact, zoomfact, 1), + order=0, grid_mode=True, mode='mirror') + stepadjust = 1.0 / zoomfact + + imshape = rescaleim.shape + + scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize*stepadjust)#, imfract=0.33) + #print(scale_bar_size, scale_bar_width_px, units) + #scale_bar_height_px = np.int64(scale_bar_width_px / (16.18/2) ) # use golden ratio. + #underbar_size = (scale_bar_height_px*3, imshape[1]) + underbar_size = (max(int(np.round(imshape[1] * 0.04)), 10) , imshape[1]) + scale_bar_height_px = int(underbar_size[0] * 0.5) + + + underbar = np.zeros(underbar_size, dtype=np.uint8)+255 + # set the scalebar to start slightly off of the edge of the image. + bump = max(1, int(np.floor(0.001* underbar_size[1]))) + yxoffset = (int(0.25 * underbar_size[0]), bump) + # add our scale bar. + underbar[yxoffset[0]:yxoffset[0]+scale_bar_height_px, + yxoffset[1]:yxoffset[1]+scale_bar_width_px] = 0 + + if addminor == True: + minorgray = 64 + minorw = int(np.floor(0.002*scale_bar_width_px)) + + underbar[yxoffset[0]:yxoffset[0] + scale_bar_height_px, yxoffset[1]: yxoffset[1]+minorw] = minorgray + underbar[yxoffset[0]:yxoffset[0] + scale_bar_height_px, + yxoffset[1]+scale_bar_width_px-minorw:yxoffset[1]+scale_bar_width_px] = minorgray + + underbar[yxoffset[0]:yxoffset[0] + minorw, + yxoffset[1]:yxoffset[1] + scale_bar_width_px] = minorgray + + underbar[yxoffset[0] + scale_bar_height_px - minorw: yxoffset[0] + scale_bar_height_px, + yxoffset[1]:yxoffset[1] + scale_bar_width_px] = minorgray + + scalefact = 1 - np.int64(np.floor(np.log10(scale_bar_size))) + if ((scale_bar_size*10.0**scalefact) % 4) == 0: + minorlines = np.int64(scale_bar_width_px*np.array([ 0.25, 0.5, 0.75, ])) + else: + minorlines = np.int64(scale_bar_width_px*np.array([ 0.2, 0.4, 0.6, 0.8 ])) + for l in minorlines: + underbar[yxoffset[0]:yxoffset[0] + scale_bar_height_px, l-minorw:l+minorw+1] = minorgray + #print(scale_bar_width_px, minorw) + + underbarim = Image.fromarray(underbar, mode='L') + draw = ImageDraw.Draw(underbarim) + fontsize = scale_bar_height_px * 1.4 + + imfont = ImageFont.truetype(FONT, fontsize) + #imfont = ImageFont.truetype(FONT, fontsize) + imtext = ' ' + str(scale_bar_size) + ' ' + units + text_color = 0 + text_length = draw.textlength(imtext, imfont) + txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.35) + draw.text(txoffset, imtext, fill=text_color, font=imfont) + + underbar = np.array(underbarim, dtype=np.float32) + underbar -= underbar.min() + underbar *= 1.0/underbar.max() + underbar *= rescaleim.max()-rescaleim.min() + underbar += rescaleim.min() + + + newshp = (imshape[0]+underbar_size[0], imshape[1], channels) + scalebarim = np.zeros(newshp, dtype=np.float32) + + + scalebarim[0:imshape[0], 0:imshape[1], :] = rescaleim + for i in range(channels): + scalebarim[imshape[0]:, 0:imshape[1], i] = underbar + scalebarim = np.squeeze(scalebarim) + return scalebarim + +def _round_scalebar(image_width, pixel_width, imfract=0.333): + """ Internal function that when given the number of columns in an image, and the + width of a pixel in microns, will return the appropriate scalebar for the image from + 1 nm -- 500 mm. + + + Parameters + ---------- + image_width: int + The width (number of columns) of the image in pixels (steps). + pixel_width: float + The width of one pixel (aka step size) in microns. + imfract: float, optional [0.333] + The maximum fraction of the image width that is allowed for the scale bar size. + Default value is 0.333 of the image width. + + Returns + ------- + (np.int64, np.int64, str) + scale_bar_size: size of the scale bar in the ``units`` provided. + scale_bar_size_px: size of the scale bar on the image in pixels. + units: the units for the scale bar: {nm, μm, mm}. + """ + + units = 'μm' + # these are the acceptable scale bar sizes + sequence = np.array([1,2,5,10,20,25,50,100,200,250,500], dtype=np.int64) + + max_scale_bar_size = image_width*pixel_width*imfract + if max_scale_bar_size < 1: + units = 'nm' + max_scale_bar_size *= 1000.0 + pixel_width *= 1000.0 + if max_scale_bar_size > 1000: + units = 'mm' + max_scale_bar_size *= 0.001 + pixel_width *= 0.001 + scale_bar_size = sequence[sequence < max_scale_bar_size].max() + scale_bar_size_px = np.int64(np.float32(scale_bar_size/np.float32(pixel_width))) + return scale_bar_size, scale_bar_size_px, units \ No newline at end of file From 447621b94bc548816a55a9f99ad94eed2101a6c9 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 29 Oct 2024 18:13:05 -0400 Subject: [PATCH 12/92] Change behavior for norescale if no scale bar. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/scalarimage.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index dad7b61..7ef75f7 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -72,7 +72,7 @@ def scalarimage(ebsddata, indexer, else: norm = plt.Normalize() - if cmap == 'gray' and norescalegray == True: + if (cmap == 'gray' and norescalegray == True) or (addscalebar==False and norescalegray == True): if datafield == 'fit': imagedata = np.array(imagedata).clip(max(0.0, mn-4*std),mn+4*std ) imagedata = np.array(imagedata) @@ -80,8 +80,7 @@ def scalarimage(ebsddata, indexer, imagedata = np.array(norm(imagedata)) cm = plt.colormaps[cmap] imagedata = cm(imagedata) - if cmap == 'gray': - imagedata = (imagedata[:,0]).squeeze() + From 00f297706d8462b15cc8c46594c76618e6467ea6 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 30 Oct 2024 08:10:25 -0400 Subject: [PATCH 13/92] Added grayscale option to IPF maps. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 14 +++++++++++++- pyebsdindex/EBSDImage/scalarimage.py | 18 ++++++++++++++++-- 2 files changed, 29 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index ce548e1..7fecf3f 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -38,7 +38,7 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, - addscalebar=False, **kwargs): + addscalebar=False, graychannel=None, **kwargs): nphase = len(indexer.phaseLib) npoints = ebsddata.shape[-1] @@ -80,6 +80,18 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = ipf_out[0:npts*3] = ipfout[0:npts,:].flatten() ipf_out = ipf_out.reshape(ysize, xsize, 3) + if graychannel is not None: + if graychannel == 'fit': + gchan = 'fitinv' + else: + gchan = graychannel + gray = scalarimage.scalarimage(ebsddata, indexer, + addscalebar=False, + datafield=gchan, + cmap='gray', + rescalenice=True) + ipf_out *= gray + if addscalebar == True: ipf_out = scalebar.addscalebar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) return ipf_out diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index 7ef75f7..e89dfec 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -40,10 +40,15 @@ def scalarimage(ebsddata, indexer, addscalebar=False, cmap='viridis', norescalegray=False, + rescalenice = False, datafieldindex=0, **kwargs): npoints = ebsddata.shape[-1] - imagedata = ebsddata[-1][datafield] + if datafield != 'fitinv': + imagedata = ebsddata[-1][datafield] + else: + imagedata = ebsddata[-1]['fit'] + if len(imagedata.shape) > 1: imagedata = imagedata[:,datafieldindex] @@ -67,8 +72,17 @@ def scalarimage(ebsddata, indexer, mn = imagedata[imagedata < 179].mean() std = imagedata[imagedata < 179].std() norm = plt.Normalize(vmin=max(0.0, mn-3*std), vmax=mn+3*std) + elif datafield == 'fitinv': + mn = imagedata[imagedata < 179].mean() + std = imagedata[imagedata < 179].std() + imagedata *= -1 + norm = plt.Normalize(vmin= (-mn-3*std), vmax=min(0.0, -mn+3*std)) elif datafield == 'phase': norm = plt.Normalize(vmin=-1) + elif rescalenice == True: + mn = imagedata.mean() + std = imagedata.std() + norm = plt.Normalize(vmin= (mn - 4 * std), vmax= (mn + 3 * std)) else: norm = plt.Normalize() @@ -80,7 +94,7 @@ def scalarimage(ebsddata, indexer, imagedata = np.array(norm(imagedata)) cm = plt.colormaps[cmap] imagedata = cm(imagedata) - + From 628cbf4da47c7bd915cc797e093c99da96c2f3fa Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 30 Oct 2024 08:25:35 -0400 Subject: [PATCH 14/92] More pleasing contrast for fitinv. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/scalarimage.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index e89dfec..bebad4d 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -76,7 +76,7 @@ def scalarimage(ebsddata, indexer, mn = imagedata[imagedata < 179].mean() std = imagedata[imagedata < 179].std() imagedata *= -1 - norm = plt.Normalize(vmin= (-mn-3*std), vmax=min(0.0, -mn+3*std)) + norm = plt.Normalize(vmin= (-mn-4*std), vmax=min(0.0, -mn+3*std)) elif datafield == 'phase': norm = plt.Normalize(vmin=-1) elif rescalenice == True: From 52fb338ab37f4d07d3f5934fe429735fbfd1042f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 30 Oct 2024 15:25:28 -0400 Subject: [PATCH 15/92] Clean up some code. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/scalarimage.py | 3 --- pyebsdindex/EBSDImage/scalebar.py | 10 ++++++++-- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index bebad4d..a758888 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -25,10 +25,7 @@ import matplotlib.pyplot as plt import scipy.ndimage as scipyndim -#from matplotlib.font_manager import findfont, FontProperties -#FONT = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) -FONT = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') import numpy as np from pyebsdindex.EBSDImage import scalebar diff --git a/pyebsdindex/EBSDImage/scalebar.py b/pyebsdindex/EBSDImage/scalebar.py index b071ecd..82757c3 100644 --- a/pyebsdindex/EBSDImage/scalebar.py +++ b/pyebsdindex/EBSDImage/scalebar.py @@ -35,6 +35,10 @@ import numpy as np import matplotlib.pyplot as plt import scipy.ndimage as scipyndim +from matplotlib.font_manager import findfont, FontProperties +#FONT = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) +#FONT = findfont(FontProperties(family='Dejavu Sans', style='normal', weight='bold'), fontext='ttf', ) + FONT = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') def addscalebar(image, @@ -183,14 +187,16 @@ def addscalebar(image, underbarim = Image.fromarray(underbar, mode='L') draw = ImageDraw.Draw(underbarim) - fontsize = scale_bar_height_px * 1.4 + fontsize = scale_bar_height_px * 1.4 #Open sans + #fontsize = scale_bar_height_px * 1.32 #dejavu sans imfont = ImageFont.truetype(FONT, fontsize) #imfont = ImageFont.truetype(FONT, fontsize) imtext = ' ' + str(scale_bar_size) + ' ' + units text_color = 0 text_length = draw.textlength(imtext, imfont) - txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.35) + txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.35) #Open sans + #txoffset = (yxoffset[1]+scale_bar_width_px, yxoffset[0]-fontsize*0.19) #dejavu sans draw.text(txoffset, imtext, fill=text_color, font=imfont) underbar = np.array(underbarim, dtype=np.float32) From 7fc579d9a09742885c7f5591e919b5be75a53961 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 31 Oct 2024 09:42:53 -0400 Subject: [PATCH 16/92] Changed upscale_xsize --> zoom_xsize. Added zoom_kwargs. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/scalebar.py | 46 ++++++++++++++++++++++--------- 1 file changed, 33 insertions(+), 13 deletions(-) diff --git a/pyebsdindex/EBSDImage/scalebar.py b/pyebsdindex/EBSDImage/scalebar.py index 82757c3..f7f0bce 100644 --- a/pyebsdindex/EBSDImage/scalebar.py +++ b/pyebsdindex/EBSDImage/scalebar.py @@ -35,24 +35,35 @@ import numpy as np import matplotlib.pyplot as plt import scipy.ndimage as scipyndim -from matplotlib.font_manager import findfont, FontProperties -#FONT = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) -#FONT = findfont(FontProperties(family='Dejavu Sans', style='normal', weight='bold'), fontext='ttf', ) -FONT = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') + +from matplotlib.font_manager import findfont, FontProperties +FONT = 'OpenSans-Bold' +if FONT != 'OpenSans-Bold': + FONTPATH = findfont(FontProperties(family='Dejavu Sans', style='normal', weight='bold'), fontext='ttf', ) + FONTPATH = findfont(FontProperties(family='sans-serif', weight='bold'), fontext='ttf', ) +else: + FONTPATH = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') def addscalebar(image, stepsize=1.0, addminor = False, rescale=True, - upscale_xsize=None, + zoom_xsize=None, + zoom_kwargs = None, **kwargs): - """Automatically add a scalebar to the bottom of a micrograph + """Automatically add a scale bar to the bottom of a micrograph when given the dimension of a pixel within the image in microns. It will automatically choose the appropriate length of the scale bar, and will autoscale the units from nm, μm, or mm depending on the image width and provided ``stepsize``. The microbar is burned-in within the rasterized array, or in otherwords, it is not - a vectorized shapes/text. + a vectorized shapes/text. ``scalebar`` has the opinion that micron/scale bars should not + cover up the image, and thus will always place the bar under the image. + + For closer integrration with matplotlib, and many more options includeing putting + the scale bar on top of the image, the user is pointed towards + the ``matplotlib-scalebar`` package. + Parameters ---------- @@ -72,7 +83,7 @@ def addscalebar(image, ``[0,1.0]``. If set to ``False`` the background of the scale bar area will be set to the maximum data value, and the bar/text will be set to the minumum value. - upscale_xsize: int, optional + zoom_xsize: int, optional Set this to be the output array number of columns in pixels. This can be useful for small images, with < 500 pixels across the image. In these cases the text for the scalebar can become pixelated. The image will be interpolated @@ -83,6 +94,11 @@ def addscalebar(image, Note: setting ``upscale_xsize`` to a value that is smaller than the input image number of columns will provide a down-sized image, and obviously data points will be removed. + zoom_kwargs: dict, optional + Optional set of kwargs that are compatible with scipy.ndimage.zoom function. The + deafult value is ``zoom_kwargs = {'order':0, 'grid_mode':True, 'mode':'mirror'}`` + which sets the output image to be a nearest-neighbor interpolation. Changing order + to > 0 (int) will use bicubic interpolation for the output image. Returns ------- @@ -107,6 +123,7 @@ def addscalebar(image, Notes: ------- + If the input image has four channels, with the notion that the channels represnet ``[R,G,B,A]`` where ``A`` is the alpha channel, the current behavior of ```addscalebar``` will output the bar/text as ``[0.0,0.0,0.0,0.0]`` (black, transparent) @@ -135,13 +152,16 @@ def addscalebar(image, rescaleim = rescaleim.reshape((imshape[0], imshape[1], channels)) stepadjust = 1.0 - if upscale_xsize is not None: - upscale_xsize = np.int64(upscale_xsize) + if zoom_xsize is not None: + zoom_xsize = np.int64(zoom_xsize) #aspect = np.float32(imshape[1]) / np.float32(imshape[0]) #upscale_ysize = np.int64(upscale_xsize / aspect) - zoomfact = upscale_xsize / np.float32(imshape[1]) + zoomfact = zoom_xsize / np.float32(imshape[1]) + if zoom_kwargs is None: + zoom_kwargs = {'order':0, 'grid_mode':True, 'mode':'mirror'} rescaleim = scipyndim.zoom(rescaleim, (zoomfact, zoomfact, 1), - order=0, grid_mode=True, mode='mirror') + **zoom_kwargs) + #order=0, grid_mode=True, mode='mirror') stepadjust = 1.0 / zoomfact imshape = rescaleim.shape @@ -190,7 +210,7 @@ def addscalebar(image, fontsize = scale_bar_height_px * 1.4 #Open sans #fontsize = scale_bar_height_px * 1.32 #dejavu sans - imfont = ImageFont.truetype(FONT, fontsize) + imfont = ImageFont.truetype(FONTPATH, fontsize) #imfont = ImageFont.truetype(FONT, fontsize) imtext = ' ' + str(scale_bar_size) + ' ' + units text_color = 0 From 8bbf4c38bce280bebc42c2b728ca1d1d5d4a70f9 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 31 Oct 2024 10:46:22 -0400 Subject: [PATCH 17/92] Refactor to micronbar to avoid confusion with matplotlib_scalebar package. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 10 +++---- .../EBSDImage/{scalebar.py => micronbar.py} | 26 +++++++++---------- pyebsdindex/EBSDImage/scalarimage.py | 12 ++++----- 3 files changed, 23 insertions(+), 25 deletions(-) rename pyebsdindex/EBSDImage/{scalebar.py => micronbar.py} (95%) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index 7fecf3f..a13af3e 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -34,11 +34,11 @@ import numpy as np from pyebsdindex import rotlib -from pyebsdindex.EBSDImage import scalebar, scalarimage +from pyebsdindex.EBSDImage import micronbar, scalarimage def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, - addscalebar=False, graychannel=None, **kwargs): + addmicronbar=False, graychannel=None, **kwargs): nphase = len(indexer.phaseLib) npoints = ebsddata.shape[-1] @@ -86,14 +86,14 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = else: gchan = graychannel gray = scalarimage.scalarimage(ebsddata, indexer, - addscalebar=False, + addmicronbar=False, datafield=gchan, cmap='gray', rescalenice=True) ipf_out *= gray - if addscalebar == True: - ipf_out = scalebar.addscalebar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) + if addmicronbar == True: + ipf_out = micronbar.addmicronbar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) return ipf_out diff --git a/pyebsdindex/EBSDImage/scalebar.py b/pyebsdindex/EBSDImage/micronbar.py similarity index 95% rename from pyebsdindex/EBSDImage/scalebar.py rename to pyebsdindex/EBSDImage/micronbar.py index f7f0bce..8c86fcc 100644 --- a/pyebsdindex/EBSDImage/scalebar.py +++ b/pyebsdindex/EBSDImage/micronbar.py @@ -45,13 +45,13 @@ else: FONTPATH = os.path.join(os.path.dirname(__file__), 'OpenSans-Bold.ttf') -def addscalebar(image, - stepsize=1.0, - addminor = False, - rescale=True, - zoom_xsize=None, - zoom_kwargs = None, - **kwargs): +def addmicronbar(image, + stepsize=1.0, + addminor = False, + rescale=True, + zoom_xsize=None, + zoom_kwargs = None, + **kwargs): """Automatically add a scale bar to the bottom of a micrograph when given the dimension of a pixel within the image in microns. It will automatically choose the appropriate length of the scale bar, and will autoscale the units from @@ -115,9 +115,9 @@ def addscalebar(image, >>> import os >>> import numpy as np >>> import matplotlib.pyplot as plt - >>> from pyebsdindex.EBSDImage import scalebar + >>> from pyebsdindex.EBSDImage import micronbar >>> a = np.random.random((800, 1024)) - >>> abar = scalebar.addscalebar(a, 0.15) + >>> abar = micronbar.addmicronbar(a, 0.15) >>> plt.imsave(os.path.expanduser('~/random_image.png'), abar, cmap='gray') >>> plt.imshow(abar) @@ -132,9 +132,9 @@ def addscalebar(image, >>> import os >>> import numpy as np >>> import matplotlib.pyplot as plt - >>> from pyebsdindex.EBSDImage import scalebar + >>> from pyebsdindex.EBSDImage import micronbar >>> a = np.random.random((800, 1024, 4)) - >>> abar = scalebar.addscalebar(a, 0.15) + >>> abar = micronbar.addmicronbar(a, 0.15) >>> abar[a.shape[0]:, :, 3] = 1.0 # if ``a`` is [R,G,B,A] ubyte8, replace with 255 >>> plt.imsave(os.path.expanduser('~/random_image.png'), abar, cmap='gray') >>> plt.imshow(abar) @@ -166,7 +166,7 @@ def addscalebar(image, imshape = rescaleim.shape - scale_bar_size, scale_bar_width_px, units = _round_scalebar(imshape[1], stepsize*stepadjust)#, imfract=0.33) + scale_bar_size, scale_bar_width_px, units = _round_micronbar(imshape[1], stepsize * stepadjust)#, imfract=0.33) #print(scale_bar_size, scale_bar_width_px, units) #scale_bar_height_px = np.int64(scale_bar_width_px / (16.18/2) ) # use golden ratio. #underbar_size = (scale_bar_height_px*3, imshape[1]) @@ -236,7 +236,7 @@ def addscalebar(image, scalebarim = np.squeeze(scalebarim) return scalebarim -def _round_scalebar(image_width, pixel_width, imfract=0.333): +def _round_micronbar(image_width, pixel_width, imfract=0.333): """ Internal function that when given the number of columns in an image, and the width of a pixel in microns, will return the appropriate scalebar for the image from 1 nm -- 500 mm. diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index a758888..f544b5d 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -28,13 +28,13 @@ import numpy as np -from pyebsdindex.EBSDImage import scalebar +from pyebsdindex.EBSDImage import micronbar def scalarimage(ebsddata, indexer, datafield='pq', xsize = None, ysize = None, - addscalebar=False, + addmicronbar=False, cmap='viridis', norescalegray=False, rescalenice = False, @@ -83,9 +83,7 @@ def scalarimage(ebsddata, indexer, else: norm = plt.Normalize() - if (cmap == 'gray' and norescalegray == True) or (addscalebar==False and norescalegray == True): - if datafield == 'fit': - imagedata = np.array(imagedata).clip(max(0.0, mn-4*std),mn+4*std ) + if (cmap == 'gray' and norescalegray == True) or (addmicronbar == False and norescalegray == True): imagedata = np.array(imagedata) else: imagedata = np.array(norm(imagedata)) @@ -113,7 +111,7 @@ def scalarimage(ebsddata, indexer, image_out[0:npts] = imagedata[0:npts].flatten() image_out = image_out.reshape(ysize, xsize) - if addscalebar == True: - image_out = scalebar.addscalebar(image_out, indexer.fID.xStep, rescale=False, **kwargs) + if addmicronbar == True: + image_out = micronbar.addmicronbar(image_out, indexer.fID.xStep, rescale=False, **kwargs) return image_out From 55e21650717ff2d4f0db1126d8ff94d5edf47aa9 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 31 Oct 2024 18:38:58 -0400 Subject: [PATCH 18/92] Fix PQ Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 2 +- pyebsdindex/_ebsd_index_single.py | 71 ++++++++++++++++++++++++------- pyebsdindex/band_detect.py | 19 +++++++-- pyebsdindex/opencl/clkernels.cl | 13 ++++-- 4 files changed, 82 insertions(+), 23 deletions(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index a13af3e..d99bcfe 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -93,7 +93,7 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = ipf_out *= gray if addmicronbar == True: - ipf_out = micronbar.addmicronbar(ipf_out, indexer.fID.xStep, rescale=False, **kwargs) + ipf_out = micronbar.addmicronbar(ipf_out, indexer.fID.xStep, rescale=True, **kwargs) return ipf_out diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index d11647f..af174e0 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -375,6 +375,7 @@ def __init__( rSigma=rSigma, rhoMaskFrac=rhoMaskFrac, nBands=nBands, + nPhases = len(self.phaseLib), **kwargs ) else: @@ -532,16 +533,23 @@ def index_pats( return indxData, banddata, patstart, npats - def getmatchedpole(self, banddata, float_out=False): + def getmatchedpole(self, ebsddata, banddata, phasenumber = -1, float_out=False): """Return the pole from the library that was matched to the detected band. Parameters ---------- + ebsddata: numpy.ndarray + Output structured ebsd data array from + :meth:`~pyebsdindex.ebsd_index.index_pats` or + :meth:`~pyebsdindex.ebsd_index.index_pats_distributed`. banddata : numpy.ndarray Output structured band data array from :meth:`~pyebsdindex.ebsd_index.index_pats` or :meth:`~pyebsdindex.ebsd_index.index_pats_distributed`. + phasenumber: int, optional + Default( -1). Set this to which phase the poles should be returned for. + The default is to return the match to the best-fit phase. float_out : bool, optional Default (False) is to return an array of ints with Miller indices. If set to True, then floats, with unit length, will @@ -557,6 +565,14 @@ def getmatchedpole(self, banddata, float_out=False): the float_out is set to True, then the output will be floating point vectors of length one, within the sample Cartesian reference frame. + + If the pole was unindexed, then for that entry, this will return + [0,0,0]. Note - this might be a single band that was unindexed. + If all bands return unindexed, this might be because the pattern did + not have enough bands to index, or because another phase forced an early + exit (solution was good enough that no other phases were tested). + Setting ``EBSDIndexer.nband_earlyexit`` to a value that is greater than the + number of bands will avoid this. """ nphases = len(self.phaseLib) @@ -577,13 +593,31 @@ def getmatchedpole(self, banddata, float_out=False): else: polekey = 'polesCart' - for ph in range(nphases): - wh = np.nonzero(bnddat['band_match_index'][:,0,0] == ph)[0] - if len(wh) == 0: - continue - pindex = bnddat['band_match_index'][wh,:, 1] - poles = self.phaseLib[ph].completelib[polekey][pindex,:] - polesout[wh, :, :] = poles + if phasenumber == -1: # use the best-fit phase ... + for ph in range(nphases): + #wh = np.nonzero(bnddat['band_match_index'][:,0,0] == ph)[0] + wh = np.nonzero(ebsddata[-1, :]['phase'] == ph)[0] + if len(wh) == 0: + continue + pindex = (bnddat[wh]['band_match_index'][:,:, ph]).flatten() + wh2 = np.nonzero(pindex >= 0)[0] + if len(wh2) == 0: + continue + + poles = self.phaseLib[ph].completelib[polekey][pindex[wh2],:] + temp = np.zeros((pindex.shape[0],3)) + + temp[wh2,:] = poles + polesout[wh, :, :] = temp.reshape(wh.shape[0], nbands, 3) + else: + pindex = (bnddat[:]['band_match_index'][:,:, phasenumber]).flatten() + wh2 = np.nonzero(pindex >= 0)[0] + if len(wh2) > 0: + poles = self.phaseLib[phasenumber].completelib[polekey][pindex[wh2], :] + temp = np.zeros((npoints*nbands, 3)) + temp[wh2, :] = poles + polesout[:, :, :] = temp.reshape(npoints,nbands, 3) + if float_out is False: polesout = np.round(polesout).astype(int) @@ -637,7 +671,8 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): nPhases = len(self.phaseLib) q = np.zeros((nPhases, npoints, 4)) indxData = np.zeros((nPhases + 1, npoints), dtype=self.dataTemplate) - bandmatchindex = np.zeros((nPhases, npoints,shpBandDat[-1],2), dtype=np.int32)-100 + #bandmatchindex = np.zeros((nPhases, npoints,shpBandDat[-1],2), dtype=np.int32)-100 + bandmatchindex = np.zeros((npoints,shpBandDat[-1], nPhases), dtype=np.int32)-100 banddataout = banddata.copy() indxData["phase"] = -1 @@ -660,7 +695,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): else: earlyexit = self.nband_earlyexit - + # the adj_intensity is used to weight the peaks in the quest fit. adj_intensity = (-1 * np.abs(banddata["rho"]) * 0.5 / rhomax + 1) * banddata["max"] adj_intensity *= ((banddata["theta"] > (2 * np.pi / 180)).astype(np.float32) + 0.5) / 2 adj_intensity *= ((banddata["theta"] < (178.0 * np.pi / 180)).astype(np.float32) + 0.5) / 2 @@ -672,7 +707,9 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): for j in range(len(self.phaseLib)): - indxData['pq'][j, :] = np.sum(banddata['max'] * banddata['valid'], axis=1) + indxData['pq'][j, :] = np.sum(banddata['max'] * banddata['valid'], axis=1) / shpBandDat[-1] + + p2do = np.ravel(np.nonzero(np.max(indxData["nmatch"], axis=0) < earlyexit)[0]) if p2do.size ==0: @@ -686,7 +723,11 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): matchAttempts, totvotes, ) = self.phaseLib[j].bandindex( - bandnorm[p2do, ...], band_intensity=adj_intensity[p2do, ...], band_widths=banddata["width"][p2do, ...], verbose=verbose) + bandnorm[p2do, ...], + band_intensity=adj_intensity[p2do, ...], + band_widths=banddata["width"][p2do, ...], + verbose=verbose) + whgood = np.nonzero(nMatch >= 3 )[0] if whgood.size > 0: whgood2 = p2do[whgood] @@ -697,7 +738,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): indxData["nmatch"][j, whgood2] = nMatch[whgood] indxData["matchattempts"][j, whgood2] = matchAttempts[whgood, ...] indxData["totvotes"][j, whgood2] = totvotes[whgood] - bandmatchindex[j, whgood2, ..., 1] = bandmatch[whgood, ...] + bandmatchindex[whgood2, ..., j] = bandmatch[whgood, ...] @@ -709,7 +750,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): q = q.reshape(nPhases, npoints, 4) indxData["quat"][0:nPhases, :, :] = q indxData[-1, :] = indxData[0, :] - banddataout['band_match_index'][:,:,:] = bandmatchindex[0,:,:,:].squeeze() + banddataout['band_match_index'][:,:, 0:nPhases] = bandmatchindex[:,:,:].squeeze() if nPhases > 1: for j in range(1, nPhases): # indxData[-1, :] = np.where( @@ -721,7 +762,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): > ((3.0 - indxData[-1, :]["fit"]) * indxData[-1, :]["nmatch"]) whbetter = np.nonzero(phasetest) indxData[-1, whbetter] = indxData[j, whbetter] - banddataout['band_match_index'][whbetter,:] = bandmatchindex[j,whbetter,:,:].squeeze() + #banddataout['band_match_index'][whbetter,:] = bandmatchindex[j,whbetter,:,:].squeeze() return indxData, banddataout def _indexbandsphase_old(self, banddata, bandnorm, verbose=0): diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index cbf02f3..09fc500 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -70,6 +70,9 @@ def __init__( rSigma=None, rhoMaskFrac=0.1, nBands=9, + nPhases = 10, # this is needed for later storage of the band index after indexing. + # in the normal process, this will get initiated to the number of phases the user has + # specified. Using 10 as a "this should be big enough" placeholder. **kwargs ): self.patDim = None @@ -91,6 +94,10 @@ def __init__( self.rhomask1 = None self.nBands = nBands + if nPhases is None: + nPhases = 10 + self.nPhases = nPhases + self.EDAXIQ = False self.backgroundsub = None self.patternmask = None @@ -99,7 +106,7 @@ def __init__( self.dataType = np.dtype([('id', np.int32), ('max', np.float32), \ ('maxloc', np.float32, (2)), ('avemax', np.float32), ('aveloc', np.float32, (2)),\ ('pqmax', np.float32), ('width', np.float32), ('theta', np.float32), ('rho', np.float32), - ('valid', np.int8),('band_match_index', np.int32, (2))]) + ('valid', np.int8),('band_match_index', np.int64, (self.nPhases))]) if (patterns is None) and (patDim is None): @@ -253,6 +260,7 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ kernel = np.zeros(ksz, dtype=np.float32) kernel[(ksz[0]/2).astype(int),(ksz[1]/2).astype(int) ] = 1 kernel = -1.0*scipyndim.gaussian_filter(kernel, [self.rSigma, self.tSigma], order=[2,0]) + kernel *= 1.0/np.sum(kernel).clip(1e-12) self.kernel = kernel.reshape((1,ksz[0], ksz[1])) #self.peakPad = np.array(np.around([ 4*ksz[0], 20.0/self.dTheta]), dtype=np.int64) self.peakPad = np.array(np.around([2 * ksz[0], 2 * ksz[1]]), dtype=np.int64) @@ -606,6 +614,7 @@ def band_label_numba(nBands,nPats,nRho,nTheta,rdnConv,rdnPad,lMaxRdn): dtype=np.float32) # location of the max based on the nearest neighbor interpolation bandData_width = np.zeros((nP,nB),dtype=np.float32) # a metric of the band width + #nnc = np.array([-2,-1,0,1,2,-2,-1,0,1,2,-2,-1,0,1,2],dtype=np.float32) #nnr = np.array([-1,-1,-1,-1,-1,0,0,0,0,0,1,1,1,1,1],dtype=np.float32) #nnN = numba.float32(15) @@ -613,6 +622,9 @@ def band_label_numba(nBands,nPats,nRho,nTheta,rdnConv,rdnPad,lMaxRdn): nnr = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1], dtype=np.float32) nnN = numba.float32(9) for q in range(nPats): + averdnpat = np.float32(np.mean(rdnConv[:,:,q])) + if averdnpat < np.float32(1.0e-12): + averdnpat = np.float32(1.0e-12) # rdnConv_q = np.copy(rdnConv[:,:,q]) # rdnPad_q = np.copy(rdnPad[:,:,q]) # lMaxRdn_q = np.copy(lMaxRdn[:,:,q]) @@ -626,7 +638,7 @@ def band_label_numba(nBands,nPats,nRho,nTheta,rdnConv,rdnPad,lMaxRdn): r = np.int32(peakLoc[0][srt[-1 - i]]) c = np.int32(peakLoc[1][srt[-1 - i]]) bandData_maxloc[q,i,:] = np.array([r,c]) - bandData_max[q,i] = rdnPad[r,c,q] + bandData_max[q,i] = rdnPad[r,c,q] / averdnpat bandData_width[q, i] = 1.0 / (bandData_max[q,i] - 0.5* (rdnPad[r+1, c, q] + rdnPad[r-1, c, q]) + 1.0e-12) #center of mass peak localization @@ -641,7 +653,7 @@ def band_label_numba(nBands,nPats,nRho,nTheta,rdnConv,rdnPad,lMaxRdn): nn = rdnConv[r - 1:r + 2,c - 1:c + 2,q].copy() sumnn = (np.sum(nn) + 1.e-12) nn /= sumnn - bandData_avemax[q,i] = sumnn / nnN + bandData_avemax[q,i] = (sumnn / nnN)/ averdnpat # rnn = np.sum(nn * (np.float32(r) + nnr)) # cnn = np.sum(nn * (np.float32(c) + nnc)) #dx = 0.125 * (2.0 * (nn[1,2] - nn[1,0]) + (nn[0,2] - nn[0,0]) + (nn[2,2] - nn[2,0])) @@ -671,6 +683,7 @@ def band_label_numba(nBands,nPats,nRho,nTheta,rdnConv,rdnPad,lMaxRdn): cnn = c - dc rnn = r - rc bandData_aveloc[q,i,:] = np.array([rnn,cnn]) + bandData_valid[q,i] = 1 return bandData_max,bandData_avemax,bandData_maxloc,bandData_aveloc, bandData_valid, bandData_width diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index e473c85..5c11d07 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -597,7 +597,8 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max maxval[i+lnmax*z] = -1.0e12f; } - + float aveint = 0.0; + long int avecounter = 0; //maxval1d[lnmax] = 1.0e12f; // prime the pump for sorting for(j = pady; j< imszy - pady; ++j){ indy = j*imszx; @@ -605,11 +606,14 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max indxy = indy+i; imVal1 = maxlocin[(indxy)*nImChunk+z]; + imVal2 = maxvalin[(indxy)*nImChunk+z]; + avecounter += 1; + aveint += imVal2; if (imVal1 == 0){ continue; } else{ - imVal2 = maxvalin[(indxy)*nImChunk+z]; + //imVal2 = maxvalin[(indxy)*nImChunk+z]; dirtsort(&(maxval[z*lnmax]), &(maxloc[z*lnmax]), indxy, imVal2, lnmax); } @@ -617,10 +621,11 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max } } - + aveint /= avecounter; // now place them in the output arrays for (i=0; i< lnmax; ++i){ if (maxval[z*lnmax+i] > -1.0e6){ + maxval[z*lnmax+i] *= 1.0/aveint; //maxval[z*lnmax + i] = maxval1d[lnmax-i-1]; indxy = maxloc[z*lnmax+i]; x = ( indxy % imszx ); @@ -753,7 +758,7 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max iy = (float) y - iy; aveloc[z*lnmax + i] = (float2) (iy, ix); - aveval[z*lnmax + i] = avetempweight/9.0; + aveval[z*lnmax + i] = (avetempweight/9.0); // band width metric width[z*lnmax + i] = 1.0 / (w - 0.5 * (imValyp1 + imValym1) + 1e-12) ; From 94c60a00e587274297536dadbc4fbd0137a9768e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 31 Oct 2024 19:20:09 -0400 Subject: [PATCH 19/92] Updated CL programs for new PQ metric. 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b/pyebsdindex/opencl/band_detect_cl.py @@ -458,13 +458,14 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = # for each radon, get the min value mns = cl.Buffer(ctx,mf.READ_WRITE,size=nImCL * 4) + ave = cl.Buffer(ctx, mf.READ_WRITE, size=nImCL * 4) - prg.imageMin(queue,(nImChunk,1,1),None, - rdnConv_gpu, mns,np.uint32(shp[1]),np.uint32(shp[0]), + prg.imageMinAve(queue,(nImChunk,1,1),None, + rdnConv_gpu, mns, ave, np.uint32(shp[1]),np.uint32(shp[0]), np.uint32(self.padding[1]),np.uint32(self.padding[0])) # subtract the min value, clipping to 0. - prg.imageSubMinWClip(queue,(np.int32(shp[1]), np.int32(shp[0]),nImChunk),None, - rdnConv_gpu,mns,np.uint32(shp[1]),np.uint32(shp[0]), + prg.imageSubMinNormWClip(queue,(np.int32(shp[1]), np.int32(shp[0]),nImChunk),None, + rdnConv_gpu,mns, ave, np.uint32(shp[1]),np.uint32(shp[0]), np.uint32(0),np.uint32(0)) diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index 5c11d07..74c3357 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -402,30 +402,39 @@ __kernel void morphDilateKernelBF( __global const float16 *in, __global float16 out[(y*imszx + x)*nImChunk+z] = extremePxVal; } -//find the minimum value in each image. This probably could be sped up by using a work group store. -__kernel void imageMin( __global const float16 *im1, __global float16 *imMin, +//find the minimum and average intensity value in each image. This probably could be sped up by using a work group store. +__kernel void imageMinAve( __global const float16 *im1, __global float16 *imMin, __global float16 *imAve, const unsigned int imszx, const unsigned int imszy, const unsigned int padx, const unsigned int pady) { const unsigned long int z = get_global_id(0); const unsigned long int nImChunk = get_global_size(0); long int indx,i, j; float16 cmin = (float16) (1.0e12); + float16 cave = (float16) (0.0); float16 imVal; + //indxz = z*imszx*imszy; for(j = pady; j<= imszy - pady-1; ++j){ indx = j*imszx; for(i = padx; i<= imszx - padx-1; ++i){ imVal = im1[(indx+i)*nImChunk+z]; cmin = select(cmin, imVal, (imVal < cmin)); + cave += imVal; } } + + cave /= (float) ( (imszy - 2*pady) * (imszx - 2*padx)); + cave -= cmin; + cave = select(cave, (float16) (1.0f), (cave < (float16) (1.0e-8f)) ); + imAve[z] = cave; imMin[z] = cmin; } -// Subtract a value from an image stack, with clipping. +// Subtract a value from an image stack, divide by average intesnsity, with clipping at 0.0. // The value to be subtracted are unique to each image, stored in an array in imMin -__kernel void imageSubMinWClip( __global float16 *im1, __global const float16 *imMin, +__kernel void imageSubMinNormWClip( __global float16 *im1, + __global const float16 *imMin, __global const float16 *imAve, const unsigned int imszx, const unsigned int imszy, const unsigned int padx, const unsigned int pady) { @@ -440,9 +449,11 @@ __kernel void imageSubMinWClip( __global float16 *im1, __global const float16 *i const long int indx = (x+y*imszx)*nImChunk + z; const float16 im1val = im1[indx]; float16 value = im1val - imMin[z]; - + value = select(value, (float16) (0.0f), (value < (float16) (0.0f)) ); + value *= 1.0/imAve[z]; + //im1[indx] = (value < (float16) (0.0f)) ? (float16) (0.0f) : value; - im1[indx] = select(value, (float16) (0.0f), (value < (float16) (0.0f)) ); + im1[indx] = value; } @@ -597,8 +608,7 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max maxval[i+lnmax*z] = -1.0e12f; } - float aveint = 0.0; - long int avecounter = 0; + //maxval1d[lnmax] = 1.0e12f; // prime the pump for sorting for(j = pady; j< imszy - pady; ++j){ indy = j*imszx; @@ -606,14 +616,14 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max indxy = indy+i; imVal1 = maxlocin[(indxy)*nImChunk+z]; - imVal2 = maxvalin[(indxy)*nImChunk+z]; - avecounter += 1; - aveint += imVal2; + //imVal2 = maxvalin[(indxy)*nImChunk+z]; + + if (imVal1 == 0){ continue; } else{ - //imVal2 = maxvalin[(indxy)*nImChunk+z]; + imVal2 = maxvalin[(indxy)*nImChunk+z]; dirtsort(&(maxval[z*lnmax]), &(maxloc[z*lnmax]), indxy, imVal2, lnmax); } @@ -621,11 +631,10 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max } } - aveint /= avecounter; + // now place them in the output arrays for (i=0; i< lnmax; ++i){ if (maxval[z*lnmax+i] > -1.0e6){ - maxval[z*lnmax+i] *= 1.0/aveint; //maxval[z*lnmax + i] = maxval1d[lnmax-i-1]; indxy = maxloc[z*lnmax+i]; x = ( indxy % imszx ); From c69483555fe64952454731aefe0a99e46fc426c1 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 31 Oct 2024 19:46:12 -0400 Subject: [PATCH 20/92] Try and correct a rare divide by 0 Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 09fc500..223f254 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -719,8 +719,9 @@ def _display_radon_pattern(self, rdnConvarray, bandData, patterns): zorder=1, aspect='auto' ) - width = bandData['width'][-1, :] - width /= width.min() + width = (bandData['width'][-1, :]).clip(1e-4) + width /= (width.min()) + width *= 2.0 xplt = np.squeeze( 180.0 - np.interp(bandData['aveloc'][-1, :, 1] + 0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) From 1e01ab10e84b3a412186a96607201fc91e5a5170 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 1 Nov 2024 11:52:07 -0400 Subject: [PATCH 21/92] Fix bandindex arrays Signed-off by: David Rowenhorst --- doc/tutorials/ebsd_index_demo.ipynb | 27 +++++++++++++++++---------- pyebsdindex/_ebsd_index_single.py | 10 ++++++---- pyebsdindex/band_detect.py | 2 +- pyebsdindex/opencl/clkernels.cl | 6 +++--- 4 files changed, 27 insertions(+), 18 deletions(-) diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index ef073c8..41936f1 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -22,17 +22,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "fuzzy-imaging", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pyebsdindex'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mh5py\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcopy\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyebsdindex\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tripletvote, ebsd_pattern, ebsd_index, ebsdfile, pcopt\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyebsdindex\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mEBSDImage\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mEBSDImage\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyebsdindex'" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import h5py\n", "import copy\n", "from pyebsdindex import tripletvote, ebsd_pattern, ebsd_index, ebsdfile, pcopt\n", - "from pyebsdindex.EBSDImage import IPFcolor\n" + "import pyebsdindex.EBSDImage as EBSDImage\n" ] }, { @@ -526,7 +538,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('PyEBSDIndex')", + "display_name": "PyEBSDIndexUpdate", "language": "python", "name": "python3" }, @@ -540,12 +552,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "vscode": { - "interpreter": { - "hash": "3cacad8e052162ebde31eae56cfe36e34759a2ea87d5d6503dd4028aeda06101" - } + "version": "3.11.9" } }, "nbformat": 4, diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index af174e0..799a4f7 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -669,10 +669,11 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): shpBandDat = banddata.shape npoints = int(banddata.size/(shpBandDat[-1])+0.1) nPhases = len(self.phaseLib) + nBands = shpBandDat[-1] q = np.zeros((nPhases, npoints, 4)) indxData = np.zeros((nPhases + 1, npoints), dtype=self.dataTemplate) #bandmatchindex = np.zeros((nPhases, npoints,shpBandDat[-1],2), dtype=np.int32)-100 - bandmatchindex = np.zeros((npoints,shpBandDat[-1], nPhases), dtype=np.int32)-100 + bandmatchindex = np.zeros((npoints,nBands, nPhases), dtype=np.int32)-100 banddataout = banddata.copy() indxData["phase"] = -1 @@ -696,6 +697,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): earlyexit = self.nband_earlyexit # the adj_intensity is used to weight the peaks in the quest fit. + #adj_intensity = banddata["max"].copy() adj_intensity = (-1 * np.abs(banddata["rho"]) * 0.5 / rhomax + 1) * banddata["max"] adj_intensity *= ((banddata["theta"] > (2 * np.pi / 180)).astype(np.float32) + 0.5) / 2 adj_intensity *= ((banddata["theta"] < (178.0 * np.pi / 180)).astype(np.float32) + 0.5) / 2 @@ -707,7 +709,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): for j in range(len(self.phaseLib)): - indxData['pq'][j, :] = np.sum(banddata['max'] * banddata['valid'], axis=1) / shpBandDat[-1] + indxData['pq'][j, :] = np.mean(banddata['max'] * banddata['valid'], axis=1) #/ shpBandDat[-1] p2do = np.ravel(np.nonzero(np.max(indxData["nmatch"], axis=0) < earlyexit)[0]) @@ -738,7 +740,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): indxData["nmatch"][j, whgood2] = nMatch[whgood] indxData["matchattempts"][j, whgood2] = matchAttempts[whgood, ...] indxData["totvotes"][j, whgood2] = totvotes[whgood] - bandmatchindex[whgood2, ..., j] = bandmatch[whgood, ...] + bandmatchindex[whgood2, ..., j] = bandmatch[whgood, ...].reshape(whgood.size,nBands ) @@ -750,7 +752,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): q = q.reshape(nPhases, npoints, 4) indxData["quat"][0:nPhases, :, :] = q indxData[-1, :] = indxData[0, :] - banddataout['band_match_index'][:,:, 0:nPhases] = bandmatchindex[:,:,:].squeeze() + banddataout['band_match_index'][:,:, 0:nPhases] = bandmatchindex[:,:,:]#.squeeze() if nPhases > 1: for j in range(1, nPhases): # indxData[-1, :] = np.where( diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 223f254..0869b7d 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -106,7 +106,7 @@ def __init__( self.dataType = np.dtype([('id', np.int32), ('max', np.float32), \ ('maxloc', np.float32, (2)), ('avemax', np.float32), ('aveloc', np.float32, (2)),\ ('pqmax', np.float32), ('width', np.float32), ('theta', np.float32), ('rho', np.float32), - ('valid', np.int8),('band_match_index', np.int64, (self.nPhases))]) + ('valid', np.int8),('band_match_index', np.int64, (self.nPhases, ))]) if (patterns is None) and (patDim is None): diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index 74c3357..c7039be 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -350,7 +350,7 @@ __kernel void convolution3d2d( __global const float16 *in, __constant float *ker { pxVal = in[(yIndx+(startx - i)) * nImChunk + z]; kVal = kern[yKIndx + i]; - sum += pxVal*kVal; + sum += pxVal * ((float16) kVal); } } // sum = current < sum ? sum : current; @@ -424,7 +424,7 @@ __kernel void imageMinAve( __global const float16 *im1, __global float16 *imMin, } } - cave /= (float) ( (imszy - 2*pady) * (imszx - 2*padx)); + cave /= (float16) ( (imszy - 2*pady) * (imszx - 2*padx)); cave -= cmin; cave = select(cave, (float16) (1.0f), (cave < (float16) (1.0e-8f)) ); imAve[z] = cave; @@ -450,7 +450,7 @@ __kernel void imageSubMinNormWClip( __global float16 *im1, const float16 im1val = im1[indx]; float16 value = im1val - imMin[z]; value = select(value, (float16) (0.0f), (value < (float16) (0.0f)) ); - value *= 1.0/imAve[z]; + value *= ((float16) (1.0))/imAve[z]; //im1[indx] = (value < (float16) (0.0f)) ? (float16) (0.0f) : value; im1[indx] = value; From 51a883916e54884a238afe0391894562d37d267f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 1 Nov 2024 15:04:35 -0400 Subject: [PATCH 22/92] Improved QUEST weighting. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 2 +- pyebsdindex/tripletvote.py | 8 ++++++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 799a4f7..9397e08 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -709,7 +709,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): for j in range(len(self.phaseLib)): - indxData['pq'][j, :] = np.mean(banddata['max'] * banddata['valid'], axis=1) #/ shpBandDat[-1] + indxData['pq'][j, :] = np.mean(banddata['avemax'] * banddata['valid'], axis=1) #/ shpBandDat[-1] p2do = np.ravel(np.nonzero(np.max(indxData["nmatch"], axis=0) < earlyexit)[0]) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 1b39eb4..69548dc 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -842,7 +842,7 @@ def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, polematch, ba npats = accumulator.shape[0] nbands = polematch.shape[-1] weights = np.zeros((npats, nbands), dtype=np.float32) - + #print(band_intensity) for p in range(npats): score = np.full((nbands), -1.0, np.float32) pmatch = np.ravel(polematch[p, :]).astype(np.int64) @@ -862,9 +862,13 @@ def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, polematch, ba srt = np.flip(np.argsort(score)) srt6 = srt[0:min(nfit, whGood.size)] + #print(srt6) for s in srt6: weights[p, s] = band_intensity[p, s] - + weights[p, :] /= weights[p,:].max() + weights[p, :] = np.exp(2*weights[p, :])-1.0 + weights[p, :] /= weights[p, :].max() + #print(weights[p,:]/weights[p,:].max()) return weights def _refine_orientation_quest(self, libpolecart, bandnorms, polesmatch, weights = None): From b92c26ea9d829ca18b0e7a71e02fb3be32bcf32a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 1 Nov 2024 15:04:54 -0400 Subject: [PATCH 23/92] Gamma function for images. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 5 +++-- pyebsdindex/EBSDImage/scalarimage.py | 3 +++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index d99bcfe..7082763 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -38,7 +38,7 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, - addmicronbar=False, graychannel=None, **kwargs): + addmicronbar=False, graychannel=None, gamma=1.0, **kwargs): nphase = len(indexer.phaseLib) npoints = ebsddata.shape[-1] @@ -90,7 +90,8 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = datafield=gchan, cmap='gray', rescalenice=True) - ipf_out *= gray + ipf_out *= gray**gamma + if addmicronbar == True: ipf_out = micronbar.addmicronbar(ipf_out, indexer.fID.xStep, rescale=True, **kwargs) diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index f544b5d..9c76e58 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -39,6 +39,7 @@ def scalarimage(ebsddata, indexer, norescalegray=False, rescalenice = False, datafieldindex=0, + gamma=1.0, **kwargs): npoints = ebsddata.shape[-1] if datafield != 'fitinv': @@ -110,6 +111,8 @@ def scalarimage(ebsddata, indexer, # npts = int(xsize*ysize) image_out[0:npts] = imagedata[0:npts].flatten() image_out = image_out.reshape(ysize, xsize) + + image_out = image_out**gamma if addmicronbar == True: image_out = micronbar.addmicronbar(image_out, indexer.fID.xStep, rescale=False, **kwargs) From 3217f226e40508eade2302a7ab7d21336cd9df18 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 1 Nov 2024 16:36:54 -0400 Subject: [PATCH 24/92] More consistent scaling for PQ value. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/scalarimage.py | 2 +- pyebsdindex/opencl/band_detect_cl.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index 9c76e58..e2c4191 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -111,7 +111,7 @@ def scalarimage(ebsddata, indexer, # npts = int(xsize*ysize) image_out[0:npts] = imagedata[0:npts].flatten() image_out = image_out.reshape(ysize, xsize) - + image_out = image_out**gamma if addmicronbar == True: diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 322be2a..a62db8a 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -155,10 +155,10 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU blabeltime += timer() - tic1 - bandData['avemax'] *= pscale[1] - bandData['avemax'] += pscale[0] - bandData['max'] *= pscale[1] - bandData['max'] += pscale[0] + #bandData['avemax'] *= pscale[1] + #bandData['avemax'] += pscale[0] + #bandData['max'] *= pscale[1] + #bandData['max'] += pscale[0] tottime = timer() - tic0 # going to manually clear the clparams queue -- this should clear the memory of the queue off the GPU From 6d5168c43ce6a80781d07ed8637f8e67180ee249 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 1 Nov 2024 16:49:12 -0400 Subject: [PATCH 25/92] Better automated CPU allocation. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 71b94c8..39cfa19 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -312,7 +312,7 @@ def index_pats_distributed( if ngpu > 0: ncpu = min(os.cpu_count(), len(indexer.phaseLib)*10) # this is a heuristic, and may be highly dependent on hardware else: - ncpu = os.cpu_count() + ncpu = max(1,os.cpu_count()//2) if ncpu != -1: n_cpu_nodes = int(ncpu) From 98b80e87e6738a143fb13ab03751b0ab81a6bc2d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 4 Nov 2024 08:07:39 -0500 Subject: [PATCH 26/92] Fixed pole assignment issue Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 48 +++++++++++++++++++++++++------------- 1 file changed, 32 insertions(+), 16 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 69548dc..14e25f3 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -552,11 +552,11 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose n_band_early = np.int64(self.nband_earlyexit) # this will check the vote, and return the exact band matching to specific poles of the best fitting solution. - fit, polematch, nMatch, whGood, ij, R, fitb = \ + fit, polematch, polevalid, nMatch, whGood, ij, R, fitb = \ self._assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band_early, bandnorms, bandRank_arg, bandFam) # check how often the indexed band matched the top voting band family. - acc_correct = np.sum(np.array((polematch >= 0) & (self.completelib['familyid'][polematch] == bandFam), dtype=int),axis=1).astype(np.int32) + acc_correct = np.sum(np.array((polevalid > 0) & (self.completelib['familyid'][polematch] == bandFam), dtype=int),axis=1).astype(np.int32) # accumulator = accumulator[0, ...] @@ -596,13 +596,15 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose if self.high_fidelity == True: - weights = self._calc_quest_weights(libFamID, accumulator, accumulator_nw, polematch, band_intensity, nfit=6) - avequat, fit = self._refine_orientation_quest(libPolesCart, bandnorms, polematch, weights = weights) + weights = self._calc_quest_weights(libFamID, accumulator, accumulator_nw, + polematch, polevalid, band_intensity, nfit=6) + avequat, fit = self._refine_orientation_quest(libPolesCart, bandnorms, + polematch, polevalid, weights = weights) fit = np.arccos(np.clip(fit, -1.0, 1.0))*RADEG else: avequat = rotlib.om2qu(R) - cm2 = self._calc_cm(accumulator, polematch, libFamID) + cm2 = self._calc_cm(accumulator, polematch, polevalid, libFamID) if verbose > 2: print('refinement: ', timer() - tic) @@ -838,7 +840,8 @@ def _refine_orientation(self, bandnorms, whGood, polematch): @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) - def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, polematch, band_intensity, nfit=6): + def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, + polematch, polevalid, band_intensity, nfit=6): npats = accumulator.shape[0] nbands = polematch.shape[-1] weights = np.zeros((npats, nbands), dtype=np.float32) @@ -846,7 +849,8 @@ def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, polematch, ba for p in range(npats): score = np.full((nbands), -1.0, np.float32) pmatch = np.ravel(polematch[p, :]).astype(np.int64) - whGood = (np.nonzero(pmatch >= 0)[0]).astype(np.int64) + pvalid = np.ravel(polevalid[p, :]) + whGood = (np.nonzero(pvalid > 0)[0]).astype(np.int64) if whGood.size < 2: continue @@ -865,13 +869,19 @@ def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, polematch, ba #print(srt6) for s in srt6: weights[p, s] = band_intensity[p, s] - weights[p, :] /= weights[p,:].max() + + + #weights[p, :] *= 2.0/weights[p,:].max() + #weights[p, :] = 0.5*(1+np.tanh(8.0 * (weights[p, :] - 1.0))) + weights[p, :] *= 1.0 / weights[p, :].max() weights[p, :] = np.exp(2*weights[p, :])-1.0 + weights[p, :] /= weights[p, :].max() #print(weights[p,:]/weights[p,:].max()) return weights - def _refine_orientation_quest(self, libpolecart, bandnorms, polesmatch, weights = None): + def _refine_orientation_quest(self, libpolecart, bandnorms, + polesmatch, polesvalid, weights = None): tic = timer() npats = bandnorms.shape[0] nbands = bandnorms.shape[-1] @@ -879,7 +889,7 @@ def _refine_orientation_quest(self, libpolecart, bandnorms, polesmatch, weights if weights is None: weights = np.ones((npats, nbands), dtype=np.float64) - weights *= (polesmatch > 0).astype(np.float32) + weights *= (polesvalid > 0).astype(np.float32) weightsn = np.asarray(weights, dtype=np.float64) weightsn /= np.maximum(np.sum(weightsn, axis=1), 1e-12).reshape(-1, 1) @@ -1220,7 +1230,8 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band whGood_out = np.zeros((npats, nBnds), dtype=np.int64)-1 Rout = np.zeros((npats,3,3), dtype=np.float32) Rout[:,0,0] = 1.0 ; Rout[:,1,1] = 1.0 ; Rout[:,2,2] = 1.0 ; - polematch_out = np.full((npats, nBnds),-1, dtype=np.int64) - 1 + polematch_out = np.full((npats, nBnds),-1000, dtype=np.int64) + polevalid_out = np.full((npats, nBnds),0, dtype=np.uint8) fitout = np.full(npats, 360.0, dtype=np.float32) fitbout = np.full((npats, nBnds),360.0, dtype=np.float32) @@ -1237,7 +1248,8 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band for ii in range(nBnds-1): for jj in range(ii+1,nBnds): #print(ii,jj) - polematch = np.zeros((nBnds),dtype=np.int64) - 1 + polematch = np.full((nBnds),-1, dtype=np.int64) + polevalid = np.zeros((nBnds), dtype=np.uint8) bnd1 = bandRank_arg[p, -1 - ii] bnd2 = bandRank_arg[p, -1 - jj] @@ -1325,7 +1337,8 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band score = scoreTry angFit = angfitTry for j in range(nBnds): - polematch[j] = np.argmax(test[:,j]) * ( 2*np.int32(angfitTry[j] < angTol)-1) + polematch[j] = np.argmax(test[:,j]) + polevalid[j] = np.uint8(angfitTry[j] < angTol) R[0, :,:] = Rtry[i,:,:] @@ -1355,6 +1368,7 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band nMatch[p] = nGood whGood_out[p,0:nGood] = whGood[:] polematch_out[p,...] = polematch[:] + polevalid_out[p, ...] = polevalid[:] Rout[p,:,:] = R[0,:,:] ij[p,:] = np.asarray((ii,jj,bnd1,bnd2), dtype=np.int64) break @@ -1368,6 +1382,7 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band nMatch[p] = nGood whGood_out[p, 0:nGood] = whGood[:] polematch_out[p, ...] = polematch[:] + polevalid_out[p, ...] = polevalid[:] Rout[p, :, :] = R[0, :, :] ij[p, :] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) @@ -1380,6 +1395,7 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band nMatch[p] = nGood whGood_out[p, 0:nGood] = whGood[:] polematch_out[p, ...] = polematch[:] + polevalid_out[p, ...] = polevalid[:] Rout[p, :, :] = R[0, :, :] ij[p, :] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) @@ -1394,7 +1410,7 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band #print(testout.T) #print(pflt[polematch_out,:]) #print(dave) - return fitout, polematch_out,nMatch, whGood_out, ij, Rout, fitbout + return fitout, polematch_out,polevalid_out, nMatch, whGood_out, ij, Rout, fitbout @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True,parallel=False) @@ -1755,14 +1771,14 @@ def _fitcheck(self, q, bandnorms, cartxstalpoles): @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) - def _calc_cm(accumulator, polematch, libFamIndx): + def _calc_cm(accumulator, polematch, polevalid, libFamIndx): npats = accumulator.shape[0] cm2 = -1 * np.ones(npats, dtype=np.float32) for p in range(npats): - whmatch = (np.nonzero(polematch[p, :] >= 0)[0]).astype(np.int64) + whmatch = (np.nonzero(polevalid[p, :] > 0)[0]).astype(np.int64) if whmatch.size < 2: continue # cm = np.mean(band_cm[whmatch]) From 2e28d3fc9f24b8a112a607c22936b61339f30e5c Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 4 Nov 2024 09:08:37 -0500 Subject: [PATCH 27/92] Fixed array indexing issue. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 14e25f3..2c5871a 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -556,7 +556,9 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose self._assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band_early, bandnorms, bandRank_arg, bandFam) # check how often the indexed band matched the top voting band family. - acc_correct = np.sum(np.array((polevalid > 0) & (self.completelib['familyid'][polematch] == bandFam), dtype=int),axis=1).astype(np.int32) + acc_correct = np.sum(np.array((polevalid > 0) & #take valid poles + (self.completelib['familyid'][polematch.clip(0)] == bandFam), # AND with matching correctly + dtype=int),axis=1).astype(np.int32) # and sum. # accumulator = accumulator[0, ...] @@ -870,11 +872,11 @@ def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, for s in srt6: weights[p, s] = band_intensity[p, s] - #weights[p, :] *= 2.0/weights[p,:].max() #weights[p, :] = 0.5*(1+np.tanh(8.0 * (weights[p, :] - 1.0))) + weights[p, :] *= 1.0 / weights[p, :].max() - weights[p, :] = np.exp(2*weights[p, :])-1.0 + weights[p, :] = np.exp(2 * weights[p, :])-1.0 weights[p, :] /= weights[p, :].max() #print(weights[p,:]/weights[p,:].max()) From a6413138c190f75b64329800597dc652d43db810 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 4 Nov 2024 10:28:58 -0500 Subject: [PATCH 28/92] Using un-weighted fit. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 37 ++++++++++++++++++++++++------------- 1 file changed, 24 insertions(+), 13 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 2c5871a..339e18e 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -558,7 +558,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose # check how often the indexed band matched the top voting band family. acc_correct = np.sum(np.array((polevalid > 0) & #take valid poles (self.completelib['familyid'][polematch.clip(0)] == bandFam), # AND with matching correctly - dtype=int),axis=1).astype(np.int32) # and sum. + dtype=int),axis=1).astype(np.int32) # and sum. # accumulator = accumulator[0, ...] @@ -898,9 +898,9 @@ def _refine_orientation_quest(self, libpolecart, bandnorms, #print(weightsn) pflt = np.asarray(libpolecart[polesmatch, :], dtype=np.float64) bndnorm = np.asarray(bandnorms, dtype=np.float64) - avequat, fit = self._orientation_quest_nb(pflt, bndnorm, weightsn) - - return avequat, fit + avequat, fit, fit_unweight = self._orientation_quest_nb(pflt, bndnorm, weightsn) + #fit = self._fitcheck(avequat, bndnorm, pflt) + return avequat, fit_unweight @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) @@ -914,6 +914,7 @@ def _orientation_quest_nb(polescart, bandnorms, weights): qout = np.zeros((npats, 4), dtype=np.float64) qout[:, 0] = 1.0 fitout = np.full((npats), np.pi, dtype=np.float64) + fitout_unweight = np.full((npats), np.pi, dtype=np.float64) for p in range(npats): @@ -983,9 +984,10 @@ def _orientation_quest_nb(polescart, bandnorms, weights): qout[p, :] = q fitout[p] = lam - # polesrot = rotlib.quat_vectorL1N(q, pflt, npoles, np.float64, p=1) - # pdot = np.sum(polesrot*bndnorm, axis = 1, dtype=np.float64) - return qout, fitout + + polesrot = rotlib.quat_vectorL1N(q, bndnorm, npoles, np.float64, p=1) + fitout_unweight[p] = np.mean(np.sum(polesrot*pflt, axis = 1, dtype=np.float64)) + return qout, fitout, fitout_unweight @staticmethod @@ -1763,13 +1765,22 @@ def _pairvote_nb(bandnorms, bandangs, qsym, angTableReduce, poles, polesReduce, solSrt = np.argsort(solutionVotes) return solutions, nsolutions, solutionVotes, solSrt - def _fitcheck(self, q, bandnorms, cartxstalpoles): - bandnorms = np.atleast_2d(bandnorms) - cartxstalpoles = np.atleast_2d(cartxstalpoles) - bandnorms_xstal = rotlib.quat_vector(q, bandnorms) - mean_dot = np.mean(np.sum(bandnorms_xstal*cartxstalpoles, axis = 1)) + def _fitcheck(self, quat, bandnorms, cartxstalpoles): + + npat = np.int64(quat.size//4) + quat = quat.reshape(npat, 4) + + nbands = bandnorms.shape[-2] + bandnorms = bandnorms.reshape(npat, nbands, 3) + cartxstalpoles = cartxstalpoles.reshape(npat, nbands, 3) + + fitout = np.zeros((npat), dtype=np.float64) + + for p in range(npat): + bandnorms_xstal = rotlib.quat_vector(quat[p,:], bandnorms[p,:, :]) + fitout[p] = np.mean(np.sum(bandnorms_xstal * cartxstalpoles [p,:, :], axis = 1)) # mean_ang = np.degrees(np.arccos(mean_dot)) - return mean_dot + return fitout @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) From 145817b0b46e0e54cf511ad7199150c184e131d5 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 4 Nov 2024 11:32:36 -0500 Subject: [PATCH 29/92] Another rare exception caught. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 339e18e..ddfbf78 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -854,7 +854,7 @@ def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, pvalid = np.ravel(polevalid[p, :]) whGood = (np.nonzero(pvalid > 0)[0]).astype(np.int64) - if whGood.size < 2: + if whGood.size < 3: continue acc = accumulator[p, ...] @@ -896,7 +896,7 @@ def _refine_orientation_quest(self, libpolecart, bandnorms, weightsn = np.asarray(weights, dtype=np.float64) weightsn /= np.maximum(np.sum(weightsn, axis=1), 1e-12).reshape(-1, 1) #print(weightsn) - pflt = np.asarray(libpolecart[polesmatch, :], dtype=np.float64) + pflt = np.asarray(libpolecart[polesmatch.clip(0), :], dtype=np.float64) # using clip 0 here --> weights SHOULD be 0.0 for all unmatched bndnorm = np.asarray(bandnorms, dtype=np.float64) avequat, fit, fit_unweight = self._orientation_quest_nb(pflt, bndnorm, weightsn) #fit = self._fitcheck(avequat, bndnorm, pflt) @@ -919,7 +919,7 @@ def _orientation_quest_nb(polescart, bandnorms, weights): for p in range(npats): whgood = (np.nonzero(weights[p, :] > eps)[0]).astype(np.int64) - if whgood.size < 2: + if whgood.size < 3: continue wn = np.zeros((whgood.size, 1), dtype=np.float64) @@ -1792,7 +1792,7 @@ def _calc_cm(accumulator, polematch, polevalid, libFamIndx): for p in range(npats): whmatch = (np.nonzero(polevalid[p, :] > 0)[0]).astype(np.int64) - if whmatch.size < 2: + if whmatch.size < 3: continue # cm = np.mean(band_cm[whmatch]) From 0eafdb996775d0068a137144417b4eca8a00c38a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 4 Nov 2024 17:07:07 -0500 Subject: [PATCH 30/92] Update tutorial for indexing. Introduced the IQ metric. Signed-off by: David Rowenhorst --- doc/tutorials/ebsd_index_demo.ipynb | 110 +++++++++++++++++++++------ pyebsdindex/EBSDImage/IPFcolor.py | 5 +- pyebsdindex/_ebsd_index_parallel.py | 5 +- pyebsdindex/_ebsd_index_single.py | 4 +- pyebsdindex/band_detect.py | 14 +++- pyebsdindex/opencl/band_detect_cl.py | 22 ++++-- pyebsdindex/opencl/clkernels.cl | 2 +- 7 files changed, 121 insertions(+), 41 deletions(-) diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index 41936f1..bbce4a3 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -22,29 +22,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "fuzzy-imaging", "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'pyebsdindex'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mh5py\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcopy\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyebsdindex\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tripletvote, ebsd_pattern, ebsd_index, ebsdfile, pcopt\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyebsdindex\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mEBSDImage\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mEBSDImage\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyebsdindex'" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import h5py\n", "import copy\n", "from pyebsdindex import tripletvote, ebsd_pattern, ebsd_index, ebsdfile, pcopt\n", - "import pyebsdindex.EBSDImage as EBSDImage\n" + "from pyebsdindex.EBSDImage import IPFcolor, scalarimage" ] }, { @@ -71,7 +59,7 @@ "PC = np.array([0.46, 0.70, 0.64]) # this is pulled from the .ang/ctf/h5 file, but only is a rough guess. We will refine in a later. \n", "cam_elev = 5.3 # The tilt of the camera from horizontal -- positive angles are tilted below the horizontal. See diagrams in PyEBSDIndex paper for full description. \n", "sampleTilt = 70.0 # sample tilt \n", - "vendor = 'EDAX' # notes the conventions for pattern center and orientations. " + "vendor = 'EDAX' # notes the conventions for pattern center and orientations. " ] }, { @@ -224,12 +212,60 @@ "imshape = (indxer.fID.nRows, indxer.fID.nCols)\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "decdc6a8", + "metadata": {}, + "outputs": [], + "source": [ + "dat1" + ] + }, { "cell_type": "markdown", "id": "d1241ddf-06ba-4c89-b3a7-b4ba25c9fd22", "metadata": {}, "source": [ - "The data output *dat1* here, is a complex numpy array (or array of structured data), that is `[nphases+1, npoints]`. The data is stored for each phase used in indexing and the dat1\\[-1\\] layer uses the best guess on which is the most likely phase, based on the fit, and number of bands matched for each phase. Each data entry contains the orientation expressed as a quaternion (quat) (using EDAX convention by default), Pattern Quality (pq), Confidence Metric (cm), Phase ID (phase), Fit (fit) and Number of Bands Matched (nmatch). There are some other metrics reported, but these are mostly for debugging purposes. " + "### Indexed ebsd data\n", + "The data output `dat1` here, is a compound numpy array (or array of structured data), that is `[nphases+1, npoints]`. The data is stored for each phase used in indexing and the dat1\\[-1\\] layer uses the best guess on which is the most likely phase, based on the fit, and number of bands matched for each phase. Each data entry contains:\n", + "\n", + "\n", + "`'quat'`: the orientation expressed as a quaternion with [q0, q1\\*i, q2\\*j, q3\\*k ] using EDAX rotation/reference frame convention by default.\n", + "\n", + "`'iq;`: pattern Image Quality, here expressed as the mean normalized peak intensity (e.g. peak intensity relative to average convolved radon intensity.)\n", + "Tends to be a consistent value from scan-to-scan between 1.5 - 2.0. Values near 1.0 indicate very little contrast between the peaks and the background, and are an indicator that the pattern is not very informative. \n", + "\n", + "`'pq'`: Pattern Quality, here defined as the mean peak intensity of the detected bands as measured on the convolved radon.\n", + "\n", + "`'cm'`: Confidence Metric, a measure between `[0,1.0]` of the confidence of the index solution. \n", + "\n", + "`'phase'`: Phase ID index indicating the phase (as it appears in the phase list), with -1 reserved for unindexed patterns assigned to no phase. \n", + "\n", + "`'fit'`: The Fit, or MAD, with units of degrees. \n", + "\n", + "`'nmatch'`: Number of bands matched. \n", + "\n", + "\n", + "\n", + "### Band data \n", + "The second output, `bnd1` is also a compound numpy array, with dimensions `[npoints, nbands]`. Each entry these values for each band:\n", + "\n", + "`'max'`: convolved radon peak maximum value. \n", + "\n", + "`'normmax'`: convolved radon normalized (by average convolved radon intensity) peak hights. Better for making IPF images. Normally between values 1.0-2.0\n", + "\n", + "`'maxloc'`: the integer location of that max in the randon space as `(int)[rho_indx, theta_index]`.\n", + "\n", + "`'avemax'`: the nearest neighbor average max for the peak. This is what is later used to calculate the pattern quality. \n", + "\n", + "`'aveloc'`: the sub-pixel location of the max `(float)[rho_indx, theta_index]`.\n", + "\n", + "`'theta'`and`'rho'`: the equivalent Radon values for theta and rho for the sub-pixel max.\n", + "\n", + "`'valid'`: a 0,1 which indicates if the band is valid or not. \n", + "\n", + "There are some other metrics reported, but these are mostly for debugging purposes. Also - these fields may be added onto in the future, but those listed here are expected to be stable. " ] }, { @@ -265,7 +301,7 @@ "source": [ "Now use that indexer object to index the whole file. Setting `npats = -1` will index to the end of the file/array (latter on will be an example of using an array as input). \n", "\n", - "The defaults will be to detect all the GPUs on your machine, and use them. Scheduling is dynamic, so it does not matter if the GPUs are matched. After radon processing/peak finding, the cpus take over for performing the index voting -- thus the number of CPUs needed will depend highly on the number of phases that need to be indexed. The number of CPUs needed also is dependent on how fast your GPUs are - on a 2019 MacPro with a Radeon 6800 GPU there are diminishing returns of including more than 32 CPUs when using the above conditions. \n", + "The defaults will be to detect all the GPUs on your machine, and use them. Scheduling is dynamic, so it does not matter if the GPUs are matched. After radon processing/peak finding, the cpus take over for performing the index voting -- thus the number of CPUs needed will depend highly on the number of phases that need to be indexed. Using the default with `ncpu = -1` will automatically allocate min(10 cpu processes/phase, number of cpu cores on machine). \n", "\n", "The first time this executes, it will take longer as the JIT compilers need to do the initial compile. Currently, the program cache is set to the system `/tmp` directory, so after reboots, many of the programs will need to be recompiled (which happens automatically with the first run)" ] @@ -306,6 +342,24 @@ "ipfim = IPFcolor.makeipf(data, indxer); plt.imshow(ipfim)" ] }, + { + "cell_type": "markdown", + "id": "69ec109e", + "metadata": {}, + "source": [ + "There are some options for using other data metrics for decorating the IPF maps: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d948199a", + "metadata": {}, + "outputs": [], + "source": [ + "ipfim = IPFcolor.makeipf(data, indxer, graychannel='iq', addmicronbar=True, gamma=0.75); plt.imshow(ipfim); plt.axis('off')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -313,7 +367,15 @@ "metadata": {}, "outputs": [], "source": [ - "fit = (data[-1]['fit']).reshape(imshape[0],imshape[1]); plt.imshow(fit.clip(0, 2.0))" + "fit = scalarimage.scalarimage(data, indxer, datafield='fit'); plt.imshow(fit);" + ] + }, + { + "cell_type": "markdown", + "id": "81902e97", + "metadata": {}, + "source": [ + "Or if you would rather do it yourself, something like this would work:" ] }, { @@ -323,7 +385,8 @@ "metadata": {}, "outputs": [], "source": [ - "pq = (data[-1]['pq']).reshape(imshape[0],imshape[1]); plt.imshow(pq)" + "pq = (data[-1]['pq']).reshape(imshape[0],imshape[1]); plt.imshow(pq, cmap='gray')\n", + "print(pq.min(), pq.max())" ] }, { @@ -432,7 +495,7 @@ "metadata": {}, "outputs": [], "source": [ - "ipfim = IPFcolor.makeipf(datasm, indxer, xsize = 200); plt.imshow(ipfim) # xsize needs to be defined for array inputs. " + "ipfim = IPFcolor.makeipf(datasm, indxer, xsize = 200, graychannel='nmatch'); plt.imshow(ipfim) # xsize needs to be defined for array inputs. " ] }, { @@ -513,6 +576,7 @@ "h5file = '/Path/to/hdf5/file.h5'\n", "f = h5py.File(h5file, 'r') # this is an HDF5 file type used by EDAX. \n", "h5pats = f['/Scan 1/EBSD/Data/Pattern'] # location of the pattern array within the HDF5 file. \n", + "\n", "# index the first 1000\n", "h5data, h5bnddata, indxer=ebsd_index.index_pats(patsin = h5pats[0:1000,:,:],\n", " patstart = 0, npats = 1000,return_indexer_obj = True,\n", @@ -522,9 +586,9 @@ " phaselist = phaselist, \n", " PC = PC, camElev=cam_elev, sampleTilt=sampleTilt, \n", " vendor = vendor, \n", - " verbose = 2)\n", + " verbose = 0)\n", "#now index them all\n", - "h5data, h5banddata = ebsd_index.index_pats_distributed(patsin = h5pats, ebsd_indexer_obj = indxer, ncpu = 28)" + "h5data, h5banddata = ebsd_index.index_pats_distributed(patsin = h5pats, ebsd_indexer_obj = indxer, ncpu = -1, verbose=2)" ] }, { diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index 7082763..e1ade33 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -86,10 +86,13 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = else: gchan = graychannel gray = scalarimage.scalarimage(ebsddata, indexer, + xsize=xsize, + ysize=ysize, addmicronbar=False, datafield=gchan, cmap='gray', - rescalenice=True) + rescalenice=True, **kwargs + ) ipf_out *= gray**gamma diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 39cfa19..069fba5 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -307,10 +307,11 @@ def index_pats_distributed( ngpu = 0 if ncpu == 0: - ncpu = os.cpu_count() + ncpu = max(1,os.cpu_count()//2) if ncpu <= 0: if ngpu > 0: - ncpu = min(os.cpu_count(), len(indexer.phaseLib)*10) # this is a heuristic, and may be highly dependent on hardware + ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*10))) + # this is a heuristic, and may be highly dependent on hardware else: ncpu = max(1,os.cpu_count()//2) if ncpu != -1: diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 9397e08..06c0000 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -709,8 +709,8 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): for j in range(len(self.phaseLib)): - indxData['pq'][j, :] = np.mean(banddata['avemax'] * banddata['valid'], axis=1) #/ shpBandDat[-1] - + indxData['pq'][j, :] = np.mean(banddata['max'] * banddata['valid'], axis=1) #/ shpBandDat[-1] + indxData['iq'][j, :] = np.mean(banddata['normmax'] * banddata['valid'], axis=1) # / shpBandDat[-1] p2do = np.ravel(np.nonzero(np.max(indxData["nmatch"], axis=0) < earlyexit)[0]) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 0869b7d..95f3633 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -103,8 +103,8 @@ def __init__( self.patternmask = None self.useCPU = True - self.dataType = np.dtype([('id', np.int32), ('max', np.float32), \ - ('maxloc', np.float32, (2)), ('avemax', np.float32), ('aveloc', np.float32, (2)),\ + self.dataType = np.dtype([('id', np.int32), ('max', np.float32), ('normmax', np.float32), + ('maxloc', np.float32, (2)), ('avemax', np.float32), ('aveloc', np.float32, (2)), ('pqmax', np.float32), ('width', np.float32), ('theta', np.float32), ('rho', np.float32), ('valid', np.int8),('band_match_index', np.int64, (self.nPhases, ))]) @@ -419,14 +419,16 @@ def find_bands(self, patternsIn, verbose=0, chunksize=-1, **kwargs): rdnNorm = self.radonPlan.radon_faster(patterns[chnk[0]:chnk[1],:,:], self.padding, fixArtifacts=False, background=self.backgroundsub) rdntime += timer() - tic1 tic1 = timer() - rdnConv = self.rdn_conv(rdnNorm) + rdnConv, imageave = self.rdn_conv(rdnNorm) convtime += timer()-tic1 tic1 = timer() lMaxRdn= self.rdn_local_max(rdnConv) lmaxtime += timer()-tic1 tic1 = timer() bandDataChunk= self.band_label(chnk[1]-chnk[0], rdnConv, rdnNorm, lMaxRdn) + bandDataChunk['normmax'] /= imageave.clip(1e-7) bandData[chnk[0]:chnk[1]] = bandDataChunk + if (verbose > 1) and (chnk[1] == nPats): # need to pull the radonconv off the gpu rdnConv = rdnConv[:,:,0:chnk[1]-chnk[0] ] @@ -542,11 +544,14 @@ def rdn_conv(self, radonIn): #print(rdnConv.min(),rdnConv.max()) mns = (rdnConv[self.padding[0]:shprdn[1]-self.padding[0],self.padding[1]:shprdn[1]-self.padding[1],:]).min(axis=0).min(axis=0) + ave = np.mean(rdnConv[self.padding[0]:shprdn[1] - self.padding[0], self.padding[1]:shprdn[1] - self.padding[1],:], axis=(0,1)) + + ave -= mns rdnConv -= mns.reshape((1,1, shp[2])) rdnConv = rdnConv.clip(min=0.0) - return rdnConv + return rdnConv, ave def rdn_local_max(self, rdn, clparams=None, rdn_gpu=None, use_gpu=False): @@ -587,6 +592,7 @@ def band_label(self,nPats,rdnConvIn,rdnNormIn,lMaxRdnIn): ) bandData['max'] = bdat[0][0:nPats, :] + bandData['normmax'] = bdat[0][0:nPats, :] bandData['avemax'] = bdat[1][0:nPats, :] bandData['maxloc'] = bdat[2][0:nPats, :, :] bandData['aveloc'] = bdat[3][0:nPats, :, :] diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index a62db8a..7b24c17 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -116,7 +116,7 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU rdntime += timer() - tic1 tic1 = timer() - rdnConv, clparams = self.rdn_convCL2(rdnNorm, clparams=clparams, returnBuff=True, separableKernel=True) + rdnConv, imageave, clparams = self.rdn_convCL2(rdnNorm, clparams=clparams, returnBuff=True, separableKernel=True) rdnNorm.release() convtime += timer()-tic1 @@ -128,6 +128,8 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU bandDataChunk = self.band_labelCL(rdnConv, lMaxRdn, clparams=clparams) lMaxRdn.release() bandData['max'][chnk[0]:chnk[1]] = bandDataChunk[0][0:nPatsChunk, :] + bandData['normmax'][chnk[0]:chnk[1]] = (bandDataChunk[0][0:nPatsChunk, :] / + imageave[0:nPatsChunk].reshape(nPatsChunk, 1).clip(1e-7)) bandData['avemax'][chnk[0]:chnk[1]] = bandDataChunk[1][0:nPatsChunk, :] bandData['maxloc'][chnk[0]:chnk[1]] = bandDataChunk[2][0:nPatsChunk, :, :] bandData['aveloc'][chnk[0]:chnk[1]] = bandDataChunk[3][0:nPatsChunk, :, :] @@ -154,11 +156,11 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU rdnConv = None blabeltime += timer() - tic1 - - #bandData['avemax'] *= pscale[1] - #bandData['avemax'] += pscale[0] - #bandData['max'] *= pscale[1] - #bandData['max'] += pscale[0] + # correct any scaling that happened due to float-int conversion. + bandData['avemax'] *= pscale[1] + bandData['avemax'] += pscale[0] + bandData['max'] *= pscale[1] + bandData['max'] += pscale[0] tottime = timer() - tic0 # going to manually clear the clparams queue -- this should clear the memory of the queue off the GPU @@ -472,6 +474,10 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = #rdn_gpu.release() mns.release() + + imageave = np.ones((nImCL), dtype=np.float32) + cl.enqueue_copy(queue, imageave, ave, is_blocking=True) + if kern_gpu is None: kern_gpu_y.release() kern_gpu_x.release() @@ -485,9 +491,9 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = cl.enqueue_copy(queue, resultConv, rdnConv_gpu, is_blocking=True) rdnConv_gpu.release() rdnConv_gpu = None - return resultConv, clparams + return resultConv, imageave, clparams else: - return rdnConv_gpu, clparams + return rdnConv_gpu, imageave, clparams diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index c7039be..aaa7ffd 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -450,7 +450,7 @@ __kernel void imageSubMinNormWClip( __global float16 *im1, const float16 im1val = im1[indx]; float16 value = im1val - imMin[z]; value = select(value, (float16) (0.0f), (value < (float16) (0.0f)) ); - value *= ((float16) (1.0))/imAve[z]; + //value *= ((float16) (1.0))/imAve[z]; //im1[indx] = (value < (float16) (0.0f)) ? (float16) (0.0f) : value; im1[indx] = value; From a4e11dd2b14b8918f782127387c1dda272cd03d3 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 5 Nov 2024 11:02:21 -0500 Subject: [PATCH 31/92] Fixed array slicing on CPU band detection. Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 95f3633..6fd01f2 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -426,7 +426,7 @@ def find_bands(self, patternsIn, verbose=0, chunksize=-1, **kwargs): lmaxtime += timer()-tic1 tic1 = timer() bandDataChunk= self.band_label(chnk[1]-chnk[0], rdnConv, rdnNorm, lMaxRdn) - bandDataChunk['normmax'] /= imageave.clip(1e-7) + bandDataChunk['normmax'] /= imageave.clip(1e-7).reshape(chnk[1]-chnk[0], 1) bandData[chnk[0]:chnk[1]] = bandDataChunk if (verbose > 1) and (chnk[1] == nPats): # need to pull the radonconv off the gpu From eb8055d5a244a2b70bcb117d302e8f01dd92dc8e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 5 Nov 2024 17:25:34 -0500 Subject: [PATCH 32/92] Fixed keeping pattern mask with indexer object. Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 6fd01f2..3795623 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -118,7 +118,7 @@ def __init__( self.patDim = np.asarray(patDim) patternmask = None if 'patternmask' in kwargs : - patternmask = kwargs.get('patternmask') + self.patternmask = kwargs.get('patternmask') patternmaskindex = None if 'patternmaskindex' in kwargs: @@ -126,7 +126,7 @@ def __init__( #print(patternmask) self.band_detect_setup(patterns, self.patDim,self.nTheta,self.nRho,\ self.tSigma, self.rSigma,self.rhoMaskFrac,self.nBands, - patternmask = patternmask,patternmaskindex = patternmaskindex, + patternmask = self.patternmask,patternmaskindex = patternmaskindex, **kwargs) def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ @@ -165,6 +165,10 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ if self.dRho is None: recalc_radon = True + if patternmask is not None: + self.patternmask = patternmask + + #recalc_radon = True if recalc_radon == True: if (self.rhoMaskFrac < 1) and (self.rhoMaskFrac > 0): @@ -178,10 +182,10 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ self.radonPlan = radon_fast.Radon(imageDim=self.patDim, nTheta=self.nTheta, nRho=self.nRho, rhoMax=self.rhoMax, - mask=patternmask, maskindex=patternmaskindex) + mask=self.patternmask, maskindex=patternmaskindex) - if patternmask is not None: - back = np.array(patternmask > 0).astype(np.float32) + if self.patternmask is not None: + back = np.array(self.patternmask > 0).astype(np.float32) else: back = np.ones(self.patDim[-2:], dtype=np.float32) From c3c68f0e756bf066d64412d04962258904b11d66 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 27 Nov 2024 17:06:21 -0500 Subject: [PATCH 33/92] Start Adding DM5 file type. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 208 +++++++++++++++++++++++++++++++++++- 1 file changed, 207 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 7db8fee..c244244 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -55,6 +55,8 @@ def get_pattern_file_obj(path,file_type=str('')): ftype = 'H5' elif (extension == '.h5oina'): ftype = 'H5OINA' + elif (extension == '.dm5'): + ftype = 'DM5' else: raise ValueError('Error: extension not recognized') @@ -72,6 +74,11 @@ def get_pattern_file_obj(path,file_type=str('')): if hdf5path is None: #automatically chose the first data group ebsdfileobj.get_data_paths() ebsdfileobj.set_data_path(pathindex=0) + if (ftype.upper() == 'DM5'): + ebsdfileobj = DM5(path) + if hdf5path is None: #automatically chose the first data group + ebsdfileobj.get_data_paths() + ebsdfileobj.set_data_path(pathindex=0) if (ftype.upper() == 'H5'): ebsdfileobj = HDF5PatFile(path) # if the path variable is a list, # the second item is set to be the hdf5 path to the patterns. @@ -1507,4 +1514,203 @@ def pat_reader(self, patStart=0, nPatToRead=1): except: print("File Not Found:",str(Path(self.filepath))) - return patterns, xyloc \ No newline at end of file + return patterns, xyloc + + +class DM5(HDF5PatFile): + def __init__(self, path=None): + HDF5PatFile.__init__(self, path) + self.vendor = 'GATAN' + # OXFORDOINA only attributes + self.filedatatype = None # np.uint8 + self.patternh5id = 'Data' # Could also be 'Raw Patterns' + + if self.filepath is not None: + self.get_data_paths() + + def set_data_path(self, datapath=None, pathindex=0): # overloaded from parent - will default to first group. + if datapath is not None: + self.h5patdatpth = datapath + else: + if len(self.h5datagroups) > 0: + # self.activegroupid = pathindex + self.h5patdatpth = self.h5datagroups[pathindex] + self.patternh5id + + def get_data_paths(self, verbose=0, getraw=False): + '''Based on the DM5 spec this will search for viable Pattern Datasets ''' + try: + f = h5py.File(self.filepath, 'r') + except: + print("File Not Found:", str(Path(self.filepath))) + return -1 + self.h5datagroups = [] + self.h5othergrps = [] + if 'ImageList' in f.keys(): + groupsets = list(f['ImageList'].keys()) + for grpset in groupsets: + try: + if self.patternh5id in f['/ImageList/'+grpset + '/ImageData/'].keys(): + if len(f['/ImageList/'+grpset + '/ImageData/'+self.patternh5id].shape) == 4: + if ('/ImageList/'+grpset + '/ImageData/' not in self.h5datagroups): + self.h5datagroups.append('/ImageList/'+grpset + '/ImageData/') + except KeyError: + pass + f.close() + + if len(self.h5datagroups) < 1: + print("No viable STEM patterns found:", str(Path(self.filepath))) + return -2 + else: + if verbose > 0: + print(self.h5datagroups) + return len(self.h5datagroups) + + def read_header(self, path=None): + + if path is not None: + self.filepath = path + + try: + f = h5py.File(Path(self.filepath).expanduser(), 'r') + except: + print("File Not Found:", str(Path(self.filepath))) + return -1 + + self.version = '5' #str(f['Format Version'][()][0].decode('UTF-8')) + + if self.version >= '5': + ngrp = self.get_data_paths() + if ngrp <= 0: + f.close() + return -2 # no data groups with patterns found. + if self.h5patdatpth is None: # default to the first datagroup + self.set_data_path(pathindex=0) + + dset = f[self.h5patdatpth] + shp = np.array(dset.shape) + self.patternW = shp[-1] + self.patternH = shp[-2] + self.nPatterns = np.int64(shp[-3]*shp[-4]) + self.filedatatype = dset.dtype.type + #headerpath = (f[self.h5patdatpth].parent.parent)["Header"] + self.nCols = np.int64(shp[1]) + self.nRows = np.int64(shp[0]) + # self.hexflag = np.int32(headerpath['Grid Type'][()][0] == 'HexGrid') + + ####### PLACE HOLDERS ################# + self.xStep = 1.0 #np.float32(headerpath['X Step'][()][0]) + self.yStep = 1.0 #np.float32(headerpath['Y Step'][()][0]) + + return 0 # note this function uses multiple returns + + def read_data(self, path=None, convertToFloat=False, patStartCount=[0, -1], returnArrayOnly=False): + ''' We modify the read_data function here to account for the 4D array layout''' + + if path is not None: + self.set_filepath(path) + self.read_header() + if self.version is None: + self.read_header() + patStartCount = np.array(patStartCount, dtype=np.int64) + + try: + f = h5py.File(Path(self.filepath).expanduser(), 'r') + except: + print("File Not Found:", str(Path(self.filepath))) + return -1 + + if convertToFloat == True: + typeout = np.float32 + else: + typeout = self.filedatatype + + pStartEnd = np.asarray(patStartCount, dtype=np.int64) + if pStartEnd.ndim == 1: # read a continuous set of patterns. + patStart = np.int64(patStartCount[0]) + nPatToRead = np.int64(patStartCount[-1]) + if nPatToRead == -1: + nPatToRead = np.int64(self.nPatterns - patStart) + if nPatToRead == 0: + nPatToRead = 1 + if (patStart + nPatToRead) > self.nPatterns: + nPatToRead = np.int64(self.nPatterns - patStart) + + + readpats = np.zeros((nPatToRead, self.patternH, self.patternW)) + readindex = np.arange(patStart, nPatToRead, dtype=np.int64) + readindexX = readindex % self.nCols + readindexY = readindex // self.nCols + patterndset = f[self.h5patdatpth] + for indx in range(nPatToRead): + readpats[indx, :, :] = np.array(patterndset[readindexY[indx], readindexX[indx], :, :]) + + readpats = readpats.reshape(nPatToRead, self.patternH, self.patternW) + f.close() + yx = np.unravel_index(np.arange(patStart, patStart + nPatToRead), (self.nRows, self.nCols)) + + xyloc = np.array([yx[1], yx[0]]).T.copy().astype(np.float32) + xyloc[:, 0] -= self.nCols * 0.5 + xyloc[:, 1] -= self.nRows * 0.5 + xyloc[:, 0] *= self.xStep + xyloc[:, 1] *= self.yStep + + patterns = readpats.astype(typeout) + + elif pStartEnd.ndim == 2: # read a slab of patterns. + colstart = np.int64(pStartEnd[0, 0]) + ncolread = np.int64(pStartEnd[1, 0]) + rowstart = np.int64(pStartEnd[0, 1]) + nrowread = np.int64(pStartEnd[1, 1]) + + patStart = [colstart, rowstart] + if ncolread < 0: + ncolread = np.int64(self.nCols - colstart) + if nrowread < 0: + nrowread = np.int64(self.nRows - rowstart) + + if (colstart + ncolread) > self.nCols: + ncolread = np.int64(self.nCols - colstart) + + if (rowstart + nrowread) > self.nRows: + nrowread = np.int64(self.nRows - rowstart) + nrowread = np.uint64(nrowread) + ncolread = np.uint64(ncolread) + nPatToRead = [ncolread, nrowread] + + #patterns = np.zeros([np.int64(ncolread , nrowread), self.patternH, self.patternW], dtype=typeout) + xyloc = np.zeros([np.int64(ncolread * nrowread), 2], dtype=np.float32) + + patterndset = f[self.h5patdatpth] + + patterns = np.array(patterndset[rowstart:rowstart+nrowread,colstart:colstart+ncolread, :, :]) + f.close() + patterns = patterns.astype(typeout) + + rng = ncolread*nrowread + colstart+(rowstart*self.nCols) + yx = np.unravel_index(np.arange(rng), (self.nRows, self.nCols)) + + xyloc = np.array([yx[1], yx[0]]).T.copy().astype(np.float32) + xyloc[:, 0] -= self.nCols * 0.5 + xyloc[:, 1] -= self.nRows * 0.5 + xyloc[:, 0] *= self.xStep + xyloc[:, 1] *= self.yStep + + if returnArrayOnly == True: + return patterns, xyloc + else: # package this up in an EBSDPatterns Object + patsout = EBSDPatterns() + patsout.vendor = self.vendor + patsout.file = Path(self.filepath).expanduser() + patsout.filetype = self.filetype + patsout.patternW = self.patternW + patsout.patternH = self.patternH + patsout.nFileCols = np.uint64(self.nCols) + patsout.nFileRows = np.uint64(self.nRows) + patsout.nPatterns = np.array(nPatToRead) + patsout.hexflag = self.hexflag + patsout.xStep = self.xStep + patsout.yStep = self.yStep + patsout.patStart = np.array(patStart) + patsout.patterns = patterns + patsout.xyLocations = xyloc + return patsout # note this function uses multiple return statements \ No newline at end of file From f8123005f9f9e479d4ea35b52188f6017b0b6db3 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 5 Dec 2024 12:29:28 -0500 Subject: [PATCH 34/92] Fix numba typing error Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index f9a2a1b..cf7e19c 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -326,6 +326,9 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, sr = np.int64(self.searchradius) diff_offset = np.float32(self.diff_offset) + if type(diff_offset) is np.ndarray: + diff_offset = np.float32(diff_offset[0]) + if filename is not None: self.setfile(filepath=filename) From 3d506143b3915b0c062bef4650b905db2d1871a2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 6 Dec 2024 16:56:51 -0500 Subject: [PATCH 35/92] Suppress bogus warnings on NVIDIA Opencl Signed-off by: David Rowenhorst --- pyebsdindex/opencl/openclparam.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index fa0206d..3716815 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -24,6 +24,7 @@ import numpy as np from os import path import pyopencl as cl +import warnings from os import environ environ['PYOPENCL_COMPILER_OUTPUT'] = '0' @@ -104,7 +105,9 @@ def get_context(self, gpu_id=None, kfile = 'clkernels.cl' ): self.ctx = cl.Context(devices = [self.gpu[self.gpu_id]]) kernel_location = path.dirname(__file__) - self.prg = cl.Program(self.ctx,open(path.join(kernel_location,kfile)).read()).build() + warnings.filterwarnings("ignore") + self.prg = cl.Program(self.ctx,open(path.join(kernel_location,kfile)).read()).build(options=['-cl-std=CL1.2', '-w']) + warnings.resetwarnings() #print('ctx', self.gpu_id) return self.ctx def get_queue(self, gpu_id=None): From dd4c02b4ff4f67935902597f461343f1935bb657 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 6 Dec 2024 16:58:01 -0500 Subject: [PATCH 36/92] Fix for Windows needing integer valued font size. Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/micronbar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/EBSDImage/micronbar.py b/pyebsdindex/EBSDImage/micronbar.py index 8c86fcc..c87e1c4 100644 --- a/pyebsdindex/EBSDImage/micronbar.py +++ b/pyebsdindex/EBSDImage/micronbar.py @@ -209,7 +209,7 @@ def addmicronbar(image, draw = ImageDraw.Draw(underbarim) fontsize = scale_bar_height_px * 1.4 #Open sans #fontsize = scale_bar_height_px * 1.32 #dejavu sans - + fontsize = int(round(fontsize)) imfont = ImageFont.truetype(FONTPATH, fontsize) #imfont = ImageFont.truetype(FONT, fontsize) imtext = ' ' + str(scale_bar_size) + ' ' + units From 0cc0088deb6e1d9a075bddc4b1b43ef8048c772e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 6 Dec 2024 17:06:55 -0500 Subject: [PATCH 37/92] Keep from resetting warnings Signed-off by: David Rowenhorst --- pyebsdindex/opencl/openclparam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index 3716815..7d1be91 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -107,7 +107,7 @@ def get_context(self, gpu_id=None, kfile = 'clkernels.cl' ): kernel_location = path.dirname(__file__) warnings.filterwarnings("ignore") self.prg = cl.Program(self.ctx,open(path.join(kernel_location,kfile)).read()).build(options=['-cl-std=CL1.2', '-w']) - warnings.resetwarnings() + #warnings.resetwarnings() #print('ctx', self.gpu_id) return self.ctx def get_queue(self, gpu_id=None): From a93402225954dc36ae69ae99f9f7b584883d2855 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 9 Dec 2024 11:37:19 -0500 Subject: [PATCH 38/92] Better warning handling around OpenCL builds. Signed-off by: David Rowenhorst --- pyebsdindex/opencl/openclparam.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index 7d1be91..10280e5 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -105,8 +105,9 @@ def get_context(self, gpu_id=None, kfile = 'clkernels.cl' ): self.ctx = cl.Context(devices = [self.gpu[self.gpu_id]]) kernel_location = path.dirname(__file__) - warnings.filterwarnings("ignore") - self.prg = cl.Program(self.ctx,open(path.join(kernel_location,kfile)).read()).build(options=['-cl-std=CL1.2', '-w']) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + self.prg = cl.Program(self.ctx,open(path.join(kernel_location,kfile)).read()).build(options=['-cl-std=CL1.2', '-w']) #warnings.resetwarnings() #print('ctx', self.gpu_id) return self.ctx From baa818e0b26dbaadd2cab333c4670f73d42b53b8 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 9 Dec 2024 12:00:41 -0500 Subject: [PATCH 39/92] Better warning handling around OpenCL builds. Signed-off by: David Rowenhorst --- pyebsdindex/opencl/openclparam.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index 10280e5..ef31067 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -105,10 +105,10 @@ def get_context(self, gpu_id=None, kfile = 'clkernels.cl' ): self.ctx = cl.Context(devices = [self.gpu[self.gpu_id]]) kernel_location = path.dirname(__file__) - with warnings.catch_warnings(): + with warnings.catch_warnings(): # put in to supress OpenCL build warnings -- especially on NVIDIA platforms. warnings.simplefilter("ignore") self.prg = cl.Program(self.ctx,open(path.join(kernel_location,kfile)).read()).build(options=['-cl-std=CL1.2', '-w']) - #warnings.resetwarnings() + #print('ctx', self.gpu_id) return self.ctx def get_queue(self, gpu_id=None): From 2d4f7e13accafd03b4f906f08e9a5d146d204d8a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 12 Dec 2024 07:54:31 -0500 Subject: [PATCH 40/92] First attempt at NLPAR for 4D STEM. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 110 +++++++++++++++++++++++++++--- pyebsdindex/nlpar_cpu.py | 28 ++++++-- pyebsdindex/opencl/nlpar_cl.py | 22 ++++-- pyebsdindex/opencl/nlpar_clray.py | 21 ++++-- 4 files changed, 157 insertions(+), 24 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index c244244..c714f38 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -1598,8 +1598,10 @@ def read_header(self, path=None): # self.hexflag = np.int32(headerpath['Grid Type'][()][0] == 'HexGrid') ####### PLACE HOLDERS ################# - self.xStep = 1.0 #np.float32(headerpath['X Step'][()][0]) - self.yStep = 1.0 #np.float32(headerpath['Y Step'][()][0]) + #self.xStep = 1.0 #np.float32(headerpath['X Step'][()][0]) + #self.yStep = 1.0 #np.float32(headerpath['Y Step'][()][0]) + self.xStep = ((f[self.h5patdatpth].parent)["Calibrations/Dimension/[2]"]).attrs['Scale'] #np.float32(headerpath['X Step'][()][0]) + self.yStep = ((f[self.h5patdatpth].parent)["Calibrations/Dimension/[3]"]).attrs['Scale'] return 0 # note this function uses multiple returns @@ -1637,15 +1639,16 @@ def read_data(self, path=None, convertToFloat=False, patStartCount=[0, -1], retu readpats = np.zeros((nPatToRead, self.patternH, self.patternW)) - readindex = np.arange(patStart, nPatToRead, dtype=np.int64) + readindex = np.arange(patStart, patStart+nPatToRead, dtype=np.int64) readindexX = readindex % self.nCols readindexY = readindex // self.nCols patterndset = f[self.h5patdatpth] + for indx in range(nPatToRead): readpats[indx, :, :] = np.array(patterndset[readindexY[indx], readindexX[indx], :, :]) readpats = readpats.reshape(nPatToRead, self.patternH, self.patternW) - f.close() + yx = np.unravel_index(np.arange(patStart, patStart + nPatToRead), (self.nRows, self.nCols)) xyloc = np.array([yx[1], yx[0]]).T.copy().astype(np.float32) @@ -1681,20 +1684,22 @@ def read_data(self, path=None, convertToFloat=False, patStartCount=[0, -1], retu xyloc = np.zeros([np.int64(ncolread * nrowread), 2], dtype=np.float32) patterndset = f[self.h5patdatpth] + #print(rowstart, nrowread, colstart, ncolread) + patterns = np.array(patterndset[int(rowstart):int(rowstart+nrowread), + int(colstart):int(colstart+ncolread), :, :]) - patterns = np.array(patterndset[rowstart:rowstart+nrowread,colstart:colstart+ncolread, :, :]) - f.close() patterns = patterns.astype(typeout) + patterns = patterns.reshape(ncolread*nrowread, self.patternH, self.patternW) rng = ncolread*nrowread + colstart+(rowstart*self.nCols) - yx = np.unravel_index(np.arange(rng), (self.nRows, self.nCols)) + yx = np.unravel_index(np.arange(int(rng)), (self.nRows, self.nCols)) xyloc = np.array([yx[1], yx[0]]).T.copy().astype(np.float32) xyloc[:, 0] -= self.nCols * 0.5 xyloc[:, 1] -= self.nRows * 0.5 xyloc[:, 0] *= self.xStep xyloc[:, 1] *= self.yStep - + f.close() if returnArrayOnly == True: return patterns, xyloc else: # package this up in an EBSDPatterns Object @@ -1713,4 +1718,91 @@ def read_data(self, path=None, convertToFloat=False, patStartCount=[0, -1], retu patsout.patStart = np.array(patStart) patsout.patterns = patterns patsout.xyLocations = xyloc - return patsout # note this function uses multiple return statements \ No newline at end of file + return patsout # note this function uses multiple return statements + + def write_data(self, newpatterns=None, patStartCount = [0,-1], writeHead=False, + flt2int='None', scalevalue = 0.98, maxScale = None): + writeblank = False + + if not os.path.isfile(Path(self.filepath).expanduser().resolve()): # file does not exist + writeHead = True + writeblank = True + + if writeHead==True: + self.write_header(writeBlank=writeblank) + + try: + f = h5py.File(Path(self.filepath).expanduser(), 'r+') + except: + print("File Not Found:", str(Path(self.filepath))) + return -1 + + if isinstance(newpatterns,EBSDPatterns): + pats = newpatterns.patterns + npats = newpatterns.nPatterns + elif isinstance(newpatterns, np.ndarray): + shp = newpatterns.shape + ndim = newpatterns.ndim + if ndim == 2: + pats = newpatterns.reshape(1,shp[0], shp[1]) + elif ndim == 3: + pats = newpatterns + npats = pats.shape[0] + max = pats.max() + + if maxScale is not None: + max = maxScale + + pStartEnd = np.asarray(patStartCount) + # npats == number of patterns in the newpatterns + # self.nPatterns == number of patterns in the file + # nPats to write == number of patterns to write out + typewrite = self.filedatatype + pat2write = pat_flt2int(pats, typeout=typewrite, method=flt2int, scalevalue=scalevalue, maxScale=None) + + if pStartEnd.ndim == 1: # write a continuous set of patterns. + patStart = np.int64(patStartCount[0]) + nPatToWrite = np.int64(patStartCount[-1]) + if nPatToWrite == -1: + nPatToWrite = npats + if nPatToWrite == 0: + nPatToWrite = 1 + if (patStart + nPatToWrite) > self.nPatterns: + nPatToWrite = self.nPatterns - patStart + + + + readindex = np.arange(patStart, patStart + nPatToWrite, dtype=np.int64) + readindexX = readindex % self.nCols + readindexY = readindex // self.nCols + patterndset = f[self.h5patdatpth] + + for indx in range(nPatToWrite): + patterndset[readindexY[indx], readindexX[indx], :, :] = pat2write[indx, :, :] + + + elif pStartEnd.ndim == 2: # write a slab of patterns. + colstart = np.int64(pStartEnd[0,0]) + ncolwrite = np.int64(pStartEnd[1,0]) + rowstart = np.int64(pStartEnd[0,1]) + nrowwrite = np.int64(pStartEnd[1,1]) + + patStart = [colstart, rowstart] + if ncolwrite < 0: + ncolwrite = np.int64(self.nCols - colstart) + if nrowwrite < 0: + nrowwrite = np.int64(self.nRows - rowstart) + + if (colstart+ncolwrite) > self.nCols: + ncolwrite = np.int64(self.nCols - colstart) + + if (rowstart+nrowwrite) > self.nRows: + nrowwrite = np.int64(self.nRows - rowstart) + + patterndset = f[self.h5patdatpth] + + pat2write = pat2write.reshape(nrowwrite, ncolwrite, self.patternH, self.patternW) + + patterndset[int(rowstart):int(rowstart + nrowwrite), + int(colstart):int(colstart + ncolwrite), :, :] = pat2write + f.close() \ No newline at end of file diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index cf7e19c..acf1a42 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -191,7 +191,9 @@ def getoutfileobj(self): return None def opt_lambda(self,chunksize=0,saturation_protect=True,automask=True, backsub = False, - target_weights=(0.5, 0.34, 0.25), dthresh=0.0, autoupdate=True, verbose = 2, **kwargs): + target_weights=(0.5, 0.34, 0.25), dthresh=0.0, autoupdate=True, + stem_scale = False, + verbose = 2, **kwargs): target_weights = np.asarray(target_weights) @@ -271,6 +273,9 @@ def d2norm(d2, n2, dij, sigma): #back = np.mean(data, axis=0) #back -= np.mean(back) #data -= back + if stem_scale is True: + data = data-data.min() + 1 + data = np.log(data) data = data.reshape(shp[0], phw) rowstartcount = np.asarray([0,rowcountread],dtype=np.int64) @@ -286,7 +291,7 @@ def d2norm(d2, n2, dij, sigma): for tw in target_weights: lam = 1.0 lambopt1 = opt.minimize(loptfunc,lam,args=(d2,tw,dthresh),method='Nelder-Mead', - bounds = [[0.001, 10.0]],options={'fatol': 0.0001}) + bounds = [[0.001, 20.0]],options={'fatol': 0.0001}) lamopt_values_chnk.append(lambopt1['x']) @@ -305,7 +310,8 @@ def d2norm(d2, n2, dij, sigma): self.sigma = sigma return np.mean(lamopt_values, axis = 0).flatten() - def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, + def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, + saturation_protect=True, automask=True, stem_scale = False, filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, diff_offset=None, **kwargs): @@ -417,6 +423,10 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, rowcountread = np.int64(rowend-rowstartread) data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], convertToFloat=True,returnArrayOnly=True) + if stem_scale is True: + datamin = data.min() + data = data - datamin + 1 + data = np.log(data) shpdata = data.shape @@ -444,6 +454,10 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, dataout = dataout[j-rowstartread:, :, : ] shpout = dataout.shape dataout = dataout.reshape(shpout[0]*shpout[1], pheight, pwidth) + if stem_scale is True: + dataout = np.exp(dataout)-1+datamin + + if rescale == True: for i in range(dataout.shape[0]): temp = dataout[i,:,:] @@ -466,7 +480,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, numba.set_num_threads(nthreadpos) return str(patternfileout.filepath) - def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): + def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True, stem_scale=False): self.sigmann = nn patternfile = self.getinfileobj() @@ -499,13 +513,17 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): colstartcount = np.asarray([0,ncols],dtype=np.int64) for j in range(0,nrows,chunksize): - rowstartread = np.int64(max(0, j-nn)) + rowstartread = np.int64(max(0, np.int64(j)-np.int64(nn))) rowend = min(j + chunksize+nn,nrows) if (rowend - rowstartread) < (3): rowstartread = np.int64(max(0, rowend - (3))) rowcountread = np.int64(rowend-rowstartread) + print(rowstartread, ncols, rowcountread, rowstartread+rowcountread) data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], convertToFloat=True,returnArrayOnly=True) + if stem_scale is True: + data = data - data.min() + 1 + data = np.log(data) shp = data.shape data = data.reshape(data.shape[0], phw) diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index 1796ed6..2fe2147 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -55,7 +55,8 @@ def calcnlpar(self, **kwargs): return self.calcnlpar_cl(**kwargs) - def calcsigma(self,nn=1, saturation_protect=True,automask=True, return_nndist=False, **kwargs): + def calcsigma(self,nn=1, saturation_protect=True,automask=True, stem_scale=False, + return_nndist=False, **kwargs): self.sigmann = nn if self.sigmann > 7: print("Sigma optimization search limited to a search radius <= 7") @@ -65,7 +66,9 @@ def calcsigma(self,nn=1, saturation_protect=True,automask=True, return_nndist=Fa sig = self.calcsigma_cl(nn=nn, saturation_protect=saturation_protect, - automask=automask, **kwargs) + automask=automask, + stem_scale = stem_scale, + **kwargs) if return_nndist == True: return sig else: @@ -81,7 +84,9 @@ def calcsigma_cpu(self,nn=1, saturation_protect=True,automask=True, **kwargs): saturation_protect=saturation_protect, automask=automask, **kwargs) def opt_lambda_cl(self, saturation_protect=True, automask=True, backsub=False, - target_weights=[0.5, 0.34, 0.25], dthresh=0.0, autoupdate=True, **kwargs): + target_weights=[0.5, 0.34, 0.25], dthresh=0.0, autoupdate=True, + stem_scale = False, + **kwargs): target_weights = np.asarray(target_weights) @@ -105,7 +110,8 @@ def loptfunc(lam, d2, tw, dthresh): dthresh = np.float32(dthresh) lamopt_values = [] - sigma, d2, n2 = self.calcsigma(nn=1, saturation_protect=saturation_protect, automask=automask, normalize_d=True, + sigma, d2, n2 = self.calcsigma(nn=1, saturation_protect=saturation_protect, automask=automask, + stem_scale=stem_scale, normalize_d=True, return_nndist=True, **kwargs) #sigmapad = np.pad(sigma, 1, mode='reflect') @@ -133,7 +139,9 @@ def loptfunc(lam, d2, tw, dthresh): return lamopt_values.flatten() - def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, normalize_d=False, gpu_id = None, verbose = 2, **kwargs): + def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, + stem_scale = False, + normalize_d=False, gpu_id = None, verbose = 2, **kwargs): self.sigmann = nn if self.sigmann > 7: print("Sigma optimization search limited to a search radius <= 7") @@ -236,6 +244,10 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, normalize_d=Fa data, xyloc = patternfile.read_data(patStartCount=[[cstart, rstart], [ncolchunk, nrowchunk]], convertToFloat=False, returnArrayOnly=True) + if stem_scale is True: + data = data - data.min() + 1 + data = np.log(data) + mxval = data.max() if saturation_protect == False: diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index 0f373d8..217729a 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -53,7 +53,9 @@ def __init__( self,filename=None, **kwargs): def calcnlpar(self, **kwargs): return self.calcnlpar_clray(**kwargs) - def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist=False, **kwargs): + def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist=False, + stem_scale=False, + **kwargs): if self.sigmann > 7: print("Sigma optimization search limited to a search radius <= 7") print("The search radius has been clipped to 7") @@ -62,7 +64,9 @@ def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist= sig = self.calcsigma_clray(nn=nn, saturation_protect=saturation_protect, - automask=automask, **kwargs) + automask=automask, + stem_scale = stem_scale, + **kwargs) if return_nndist == True: return sig else: @@ -75,7 +79,8 @@ def calcnlpar_clsq(self, **kwargs): def calcsigma_clsq(self, **kwargs): return nlpar_cl.NLPAR.calcsigma_cl(self, **kwargs) - def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, normalize_d=False, + def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, + stem_scale = False, normalize_d=False, gpu_id = None, verbose=2, **kwargs): self.patternfile = self.getinfileobj() self.sigmann = nn @@ -117,6 +122,7 @@ def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, normaliz return nlpar_cl.NLPAR.calcsigma_cl(self, nn=nn, saturation_protect=saturation_protect, automask=automask, normalize_d=normalize_d, + stem_scale=stem_scale, gpu_id=gpu_id, **kwargs) target_mem = clparams.gpu[gpu_id].max_mem_alloc_size // 2 @@ -217,7 +223,9 @@ def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, normaliz wrker = idlewrker.pop() job = jobqueue.pop() - tasks.append(wrker.runsigma_chunk.remote(job, nlparobj=nlpar_remote, saturation_protect=saturation_protect)) + tasks.append(wrker.runsigma_chunk.remote(job, nlparobj=nlpar_remote, + saturation_protect=saturation_protect, + stem_scale = stem_scale)) busywrker.append(wrker) if len(tasks) > 0: donetasks, stillbusy = ray.wait(tasks, num_returns=len(busywrker), timeout=0.1) @@ -712,7 +720,7 @@ def __init__(self, actorid=0, gpu_id=None, cudavis = '0'): #elf.openCLParams = None - def runsigma_chunk(self,gpujob, nlparobj=None, **kwargs): + def runsigma_chunk(self,gpujob, nlparobj=None, stem_scale = False, **kwargs): if gpujob is None: #time.sleep(0.001) return 'Bored', (None, None, None) @@ -726,6 +734,9 @@ def runsigma_chunk(self,gpujob, nlparobj=None, **kwargs): data, xyloc = nlparobj.patternfile.read_data(patStartCount=[[gpujob.cstart, gpujob.rstart], [gpujob.ncolchunk, gpujob.nrowchunk]], convertToFloat=False, returnArrayOnly=True) + if stem_scale == True: + data = data - data.min() + 1 + data = np.log(data) newdata = nlparobj._sigmachunkcalc_cl(data, gpujob, clparams=self.openCLParams, **kwargs) From 10bacbfb77fdc5bb188a607e66ef778d50b9ded8 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 13 Dec 2024 14:48:47 -0500 Subject: [PATCH 41/92] Default oh5 files to OIM 9.1 specification. Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 31 +++++++++++++++++++++---------- 1 file changed, 21 insertions(+), 10 deletions(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 7fe6137..a470b18 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -101,9 +101,17 @@ def writeang(filename, indexer, data, def writeoh5(filename, indexer, data, gridtype='SqrGrid', xstep=1.0, ystep=1.0, - ncols=None, nrows=None, datasetname='Scan 1'): + ncols=None, nrows=None, datasetname='Scan 1', version='9.1'): fpath = Path(filename).expanduser() + nphase = data.shape[0] - 1 + phaseIDadd = 0 + if version == '8.6': + if nphase == 1: + phaseIDadd = 1 + else: + phaseIDadd = 1 + with h5py.File(fpath, 'w') as f: f.create_dataset(datasetname +'/EBSD/Header/Camera Azimuthal Angle', data=np.array([np.float64(0.0)])) @@ -131,7 +139,9 @@ def writeoh5(filename, indexer, data, f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/zAdjCoeff0', data=np.array([np.float64(0.0)])) f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/zAdjCoeff1', data=np.array([np.float64(0.0)])) f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/zAdjCoeff2', data=np.array([np.float64(0.0)])) - pcount = 1 + + + pcount = phaseIDadd nphase = len(indexer.phaseLib) @@ -223,11 +233,7 @@ def writeoh5(filename, indexer, data, npoints = data[-1].shape[-1] - nphase = data.shape[0] - 1 - if nphase == 1: - phaseIDadd = 0 - else: - phaseIDadd = 1 + eulers = rotlib.qu2eu(data[-1]['quat']) phi1 = np.squeeze(eulers[:,0]).astype(np.float32) phi = np.squeeze(eulers[:, 1]).astype(np.float32) @@ -240,6 +246,8 @@ def writeoh5(filename, indexer, data, f.create_dataset(datasetname + '/EBSD/Data/Phi2', data=phi2) f.create_dataset(datasetname + '/EBSD/Data/IQ', + data=(data[-1]['iq']).astype(np.float32)) + f.create_dataset(datasetname + '/EBSD/Data/Pattern Quality', data=(data[-1]['pq']).astype(np.float32)) f.create_dataset(datasetname + '/EBSD/Data/Fit', @@ -269,9 +277,12 @@ def writeoh5(filename, indexer, data, f.create_dataset(datasetname + '/EBSD/Data/Valid', data=np.zeros(npoints, dtype=np.int8)) f.create_dataset(datasetname + '/EBSD/Data/SEM Signal', data=np.zeros(npoints, dtype=np.int32)) - version = 'OIM Analysis 8.6.103 x64 [29 Sep 2022]' - chararray = np.chararray(1, itemsize=len(version)+1) - chararray[:] = version + if version == '8.6': + versiontxt = 'OIM Analysis 8.6.103 x64 [29 Sep 2022]' + else: + versiontxt = 'OIM Analysis 9.1.0' + chararray = np.chararray(1, itemsize=len(versiontxt)+1) + chararray[:] = versiontxt f.create_dataset('Version', data=chararray) man = 'EDAX' chararray = np.chararray(1, itemsize=len(man)+1 ) From 92d02685c80bc96d23ee5a06f78810d7101690a5 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 13 Dec 2024 14:53:56 -0500 Subject: [PATCH 42/92] Default oh5 to OIM 8.6 spec, 9.1 spec enabled with version='9.1' Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index a470b18..20a3918 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -101,7 +101,7 @@ def writeang(filename, indexer, data, def writeoh5(filename, indexer, data, gridtype='SqrGrid', xstep=1.0, ystep=1.0, - ncols=None, nrows=None, datasetname='Scan 1', version='9.1'): + ncols=None, nrows=None, datasetname='Scan 1', version='8.6'): fpath = Path(filename).expanduser() nphase = data.shape[0] - 1 From 96e674ffe80ca55e518dc1f62e13b8de657287ea Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 3 Feb 2025 10:51:49 -0500 Subject: [PATCH 43/92] Update tests to oldest python 3.8 Signed-off by: David Rowenhorst --- .github/workflows/tests.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 4f7d5a2..96e7d40 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -42,10 +42,10 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ['3.10', '3.11'] + python-version: ['3.10', '3.11', '3.12'] include: - os: ubuntu-latest - python-version: 3.7 + python-version: 3.8 DEPENDENCIES: matplotlib==3.3 numba==0.52 numpy==1.19 ray[default]==1.13 LABEL: -oldest steps: From 5236216cfbd79032c06b292d4191e54dea1739fd Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 5 Feb 2025 19:25:55 -0500 Subject: [PATCH 44/92] Added SpaceGroupNumber to oh5 metadata Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 20a3918..3275815 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -146,7 +146,11 @@ def writeoh5(filename, indexer, data, for phase in indexer.phaseLib: - f.create_dataset(datasetname + '/EBSD/Header/Phase/'+str(pcount)+'/LGsymID', data=np.array([np.int32(phase.lauecode)])) + f.create_dataset(datasetname + '/EBSD/Header/Phase/'+str(pcount)+'/LGsymID', + data=np.array([np.int32(phase.lauecode)])) + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/SpaceGroupNumber', + data=np.array([np.int32(phase.spacegroup)])) + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant a', data=np.array([np.float32(phase.latticeparameter[0]*10)])) From 6e4e85077268e8d99743e670d320c9b694fee90a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 6 Feb 2025 15:51:09 -0500 Subject: [PATCH 45/92] Add helper functions for when no opencl is found. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index acf1a42..05e5520 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -768,6 +768,12 @@ def getpairid(idx0, idx1): #print('_______', '\n') return dataout + def opt_lambda_cpu(self,**kwargs): # helper function + return self.opt_lambda(**kwargs) + + def calcnlpar_cpu(self, **kwargs): # helper function + return self.calcnlpar(**kwargs) + def _calcchunks(self, patdim, ncol, nrow, target_bytes=2e9, col_overlap=0, row_overlap=0, col_offset=0, row_offset=0): col_overlap = min(col_overlap, ncol - 1) From 2b4c3989a127a21b6d0904fcadd541b76a719b55 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 3 Feb 2025 10:51:49 -0500 Subject: [PATCH 46/92] Update tests to oldest python 3.8 Signed-off by: David Rowenhorst --- .github/workflows/tests.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 4f7d5a2..96e7d40 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -42,10 +42,10 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ['3.10', '3.11'] + python-version: ['3.10', '3.11', '3.12'] include: - os: ubuntu-latest - python-version: 3.7 + python-version: 3.8 DEPENDENCIES: matplotlib==3.3 numba==0.52 numpy==1.19 ray[default]==1.13 LABEL: -oldest steps: From bbe3f62056f3fb9c44f4f31f6daf86e40531f239 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 5 Feb 2025 19:25:55 -0500 Subject: [PATCH 47/92] Added SpaceGroupNumber to oh5 metadata Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 20a3918..3275815 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -146,7 +146,11 @@ def writeoh5(filename, indexer, data, for phase in indexer.phaseLib: - f.create_dataset(datasetname + '/EBSD/Header/Phase/'+str(pcount)+'/LGsymID', data=np.array([np.int32(phase.lauecode)])) + f.create_dataset(datasetname + '/EBSD/Header/Phase/'+str(pcount)+'/LGsymID', + data=np.array([np.int32(phase.lauecode)])) + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/SpaceGroupNumber', + data=np.array([np.int32(phase.spacegroup)])) + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant a', data=np.array([np.float32(phase.latticeparameter[0]*10)])) From 30b05f16acc5f3791410d3465d1175d80755eb01 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 10 Apr 2025 10:09:05 -0400 Subject: [PATCH 48/92] Slightly improved error handling if things go very wrong. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 84 ++++++++++++++++------------- 1 file changed, 47 insertions(+), 37 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index c0d0c89..7a15952 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -485,6 +485,8 @@ def index_pats_distributed( gpuwrker_cycles = -500 cpuwrker_cycles = 0 + ngpu_retry = 0 + ncpu_retry = 0 while ncpudone < njobs: @@ -564,37 +566,41 @@ def index_pats_distributed( - except: + except Exception as e: gjob = gtaskindex[jid] print('A GPU death has occured', gjob.pstart, gjob.pend) - del gpuworkers[jid] - del gputask[jid] - del gtaskindex[jid] - gpujobs.append(gjob) - if len(gpuworkers) == 0: - - gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. - # These actors are read/write, thus can initialize the GPU queues - #GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=0).remote( - GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( - actorid=0, clparammodule=clparamfunction, gpu_id=gpu_id, cudavis=cudagpuvis - ) - ) - gjob = gpujobs.pop(0) - if inputmode == "filemode": - gputask.append( - gpuworkers[0].findbands.remote(gjob, pats=None, - indexer=remote_indexer + if ngpu_retry < 5: + ngpu_retry +=1 + del gpuworkers[jid] + del gputask[jid] + del gtaskindex[jid] + gpujobs.append(gjob) + if len(gpuworkers) == 0: + + gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + #GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=0).remote( + GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + actorid=0, clparammodule=clparamfunction, gpu_id=gpu_id, cudavis=cudagpuvis ) ) - else: - gputask.append( - gpuworkers[0].findbands.remote(gjob, - pats=pats[gjob.pstart:gjob.pend, :, :], - indexer=remote_indexer, + gjob = gpujobs.pop(0) + if inputmode == "filemode": + gputask.append( + gpuworkers[0].findbands.remote(gjob, pats=None, + indexer=remote_indexer + ) ) - ) - gtaskindex.append(gjob) + else: + gputask.append( + gpuworkers[0].findbands.remote(gjob, + pats=pats[gjob.pstart:gjob.pend, :, :], + indexer=remote_indexer, + ) + ) + gtaskindex.append(gjob) + else: + raise e # toc = timer() if gpuwrker_cycles > 100: # a gpu worker got stuck -- see if I can unstick it. @@ -705,17 +711,21 @@ def index_pats_distributed( print(e) cjob = ctaskindex[jid] print('A CPU death has occured') - ray.kill(cpuworkers[jid]) - del cpuworkers[jid] - del cputask[jid] - del ctaskindex[jid] - cpujobs.append(cjob) - if len(cpuworkers) == 0: - cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. - # These actors are read/write, thus can initialize the GPU queues - CPUWorker.options(num_cpus=1, num_gpus=0).remote(0)) - cputask.append(cpuworkers[0].indexpoles.remote(None, None, None)) - ctaskindex.append(None) + if ncpu_retry < 5: + ncpu_retry += 1 + ray.kill(cpuworkers[jid]) + del cpuworkers[jid] + del cputask[jid] + del ctaskindex[jid] + cpujobs.append(cjob) + if len(cpuworkers) == 0: + cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + CPUWorker.options(num_cpus=1, num_gpus=0).remote(0)) + cputask.append(cpuworkers[0].indexpoles.remote(None, None, None)) + ctaskindex.append(None) + else: + raise e if verbose > 0: print('\n...') ray.shutdown() From d98155d14d1e4e4ec572d1ebd71bf80647f80ed5 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 16 May 2025 10:35:40 -0400 Subject: [PATCH 49/92] Preliminary support for version 6 ebsp files. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 4 +-- pyebsdindex/ebsdfile.py | 60 ++++++++++++++++++------------------- pyebsdindex/nlpar_cpu.py | 7 ++++- pyebsdindex/pcopt.py | 6 ++-- 4 files changed, 42 insertions(+), 35 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index c1a0891..c4a35a1 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -734,7 +734,7 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO loc0 = 0 counter = 0 while loc0 == 0: # check for non-stored points. - loc0 = np.int64(np.fromfile(f, dtype=np.uint64, count=1)) + loc0 = np.int64(np.fromfile(f, dtype=np.uint64, count=1))[0] counter += 1 f.seek(-8*counter, 1) # move back 8 bytes (or however far we needed to move into the file to find a legitamte offset. @@ -753,7 +753,7 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO self.nPatterns = np.int64((counter)) - elif self.version == 5: + elif self.version >= 5: f.seek(loc02N[0], 0) patdata = np.fromfile(f, dtype=np.uint32, count=per_pat_header) if patdata[0] == 1: diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index aba7f9f..7a2f04a 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -8,48 +8,48 @@ def writeang(filename, indexer, data, gridtype = 'SqrGrid', xstep=1.0, ystep=1.0, ncols = None, nrows=None): fpath = Path(filename).expanduser() - with open(fpath,'w',encoding = 'utf-8') as f: - f.write('# HEADER: Start \r\n') - f.write('# TEM_PIXperUM 1.000000\r\n') - f.write('# x-star ' + str(indexer.PC[0])+'\r\n') - f.write('# y-star ' + str(indexer.PC[1])+'\r\n') - f.write('# z-star ' + str(indexer.PC[2])+'\r\n') - f.write('# SampleTiltAngle ' + str(indexer.sampleTilt)+'\r\n') - f.write('# CameraElevationAngle ' + str(indexer.camElev)+'\r\n') - f.write('# '+'\r\n') + with open(fpath,'w',encoding = 'utf-8', newline='\r\n') as f: + f.write('# HEADER: Start \n') + f.write('# TEM_PIXperUM 1.000000\n') + f.write('# x-star ' + str(indexer.PC[0])+'\n') + f.write('# y-star ' + str(indexer.PC[1])+'\n') + f.write('# z-star ' + str(indexer.PC[2])+'\n') + f.write('# SampleTiltAngle ' + str(indexer.sampleTilt)+'\n') + f.write('# CameraElevationAngle ' + str(indexer.camElev)+'\n') + f.write('# '+'\n') pcount = 1 nphase = len(indexer.phaseLib) for phase in reversed(indexer.phaseLib): - f.write('# Phase '+str(nphase - pcount + 1)+'\r\n') - f.write('# MaterialName \t' + str(phase.phasename)+'\r\n') - f.write('# Formula '+'\t \r\n') - f.write('# Info '+'\t\t \r\n') - f.write('# Symmetry ' + str(phase.lauecode) + '\r\n') - #f.write('# PointGroupID ' + str(phase.pointgroupid) + '\r\n') + f.write('# Phase '+str(nphase - pcount + 1)+'\n') + f.write('# MaterialName \t' + str(phase.phasename)+'\n') + f.write('# Formula '+'\t \n') + f.write('# Info '+'\t\t \n') + f.write('# Symmetry ' + str(phase.lauecode) + '\n') + #f.write('# PointGroupID ' + str(phase.pointgroupid) + '\n') latticeparameter = np.array(phase.latticeparameter).astype(float) * np.array([10.0, 10.0, 10.0, 1.0, 1.0, 1.0]) - f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in latticeparameter)+'\r\n') - f.write('# NumberFamilies ' + str(phase.npolefamilies) + '\r\n') + f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in latticeparameter)+'\n') + f.write('# NumberFamilies ' + str(phase.npolefamilies) + '\n') poles = np.array(phase.polefamilies).astype(int) if (phase.lauecode == 62) or (phase.lauecode == 6): if poles.shape[-1] == 4: poles = poles[:,[0,1,3]] for i in range(phase.npolefamilies): - f.write('# hklFamilies \t' + (' '.join(str(x).rjust(2,' ') for x in poles[i, :])) + ' 1 0.00000 1' + '\r\n') + f.write('# hklFamilies \t' + (' '.join(str(x).rjust(2,' ') for x in poles[i, :])) + ' 1 0.00000 1' + '\n') - f.write('# '+'\r\n') + f.write('# '+'\n') pcount += 1 - f.write('# '+'\r\n') - f.write('# GRID: '+gridtype+'\r\n') + f.write('# '+'\n') + f.write('# GRID: '+gridtype+'\n') if indexer.fID is not None: if indexer.fID.xStep > 1e-6: xstep = indexer.fID.xStep ystep = indexer.fID.yStep - f.write('# XSTEP: ' + str(xstep)+'\r\n') - f.write('# YSTEP: ' + str(ystep)+'\r\n') + f.write('# XSTEP: ' + str(xstep)+'\n') + f.write('# YSTEP: ' + str(ystep)+'\n') if ncols is None: ncols = 1 nrows = data.shape[-1] @@ -64,12 +64,12 @@ def writeang(filename, indexer, data, ncols = int(ncols) nrows = int(nrows) - f.write('# NCOLS_ODD: ' + str(ncols)+'\r\n') - f.write('# NCOLS_EVEN: ' + str(ncols)+'\r\n') - f.write('# NROWS: ' + str(nrows)+'\r\n') - f.write('# VERSION 7'+'\r\n') - f.write('# COLUMN_COUNT: 10'+'\r\n') - f.write('# HEADER: End'+'\r\n') + f.write('# NCOLS_ODD: ' + str(ncols)+'\n') + f.write('# NCOLS_EVEN: ' + str(ncols)+'\n') + f.write('# NROWS: ' + str(nrows)+'\n') + f.write('# VERSION 7'+'\n') + f.write('# COLUMN_COUNT: 10'+'\n') + f.write('# HEADER: End'+'\n') nphase = data.shape[0]-1 if nphase == 1: @@ -97,7 +97,7 @@ def writeang(filename, indexer, data, line += ' {:}'.format(phase) + '' line += '1'.rjust(7, ' ')+'' line += ('{:.3f}'.format(fit)).rjust(7, ' ') - f.write(line+'\r\n') + f.write(line+'\n') def writeoh5(filename, indexer, data, gridtype='SqrGrid', xstep=1.0, ystep=1.0, diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 1bff137..a9262a5 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -134,7 +134,12 @@ def setoutfile(self, patternfile, filepath=None): p = Path(patternfile.filepath) appnd = "_NLPAR_l{:1.2f}".format(self.lam) + "sr{:d}".format(self.searchradius) newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) - patternfile.copy_file(newfilepath,empty_data=True) + emptydata = True + if patternfile.filetype in ['EBSP']: + if patternfile.version > 5: + emptydata = False + #print(emptydata) + patternfile.copy_file(newfilepath,empty_data=emptydata) if patternfile.filetype == 'HDF5': hdf5path_tmp = str(patternfile.h5patdatpth).split('/') diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 05cb5f0..2db925c 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -60,7 +60,9 @@ def _optfunction(PC_i, indexer=None, banddat=None): - fit =indexdata[-1]['fit'] + fit = indexdata[-1]['fit'] + iq = np.clip(np.array(indexdata[-1]['iq'])-1.5, 0, None) + nmatch = indexdata[-1]['nmatch'] average_fit = fit + 1.0*(nbands - nmatch) #average_fit = -1.0*(3.0-fit)*nmatch @@ -73,7 +75,7 @@ def _optfunction(PC_i, indexer=None, banddat=None): average_fit = 1000 else: average_fit = np.sum(average_fit[whgood[0]]) + 4.0*(nbands+1)*(npoints - n_averages) - average_fit /= npoints + average_fit /= npoints #average_fit /= n_averages #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) result[q] = average_fit From a695fbf9f3e900f3f96d35f1a5ec9684fc56c126 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 21 Jul 2025 13:21:47 -0400 Subject: [PATCH 50/92] Allow for PC to be an array of values, one for each pattern to be indexed. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 30 +++++++++++--- pyebsdindex/_ebsd_index_single.py | 19 ++++++++- pyebsdindex/pcopt.py | 61 +++++++++++++++++++---------- pyebsdindex/tripletvote.py | 20 ++++++---- 4 files changed, 96 insertions(+), 34 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 7a15952..f8ec4de 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -319,12 +319,14 @@ def index_pats_distributed( ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*10))) # this is a heuristic, and may be highly dependent on hardware else: - ncpu = max(1,os.cpu_count()//2) + ncpu = max(1,os.cpu_count()//4) if ncpu != -1: n_cpu_nodes = int(ncpu) + gpucycletimeout = 1000 if ngpu > 0: gpuratio = (12, ngpu*4) + gpucycletimeout = 100 if (platform.machine(), platform.system()) == ('x86_64', 'Darwin'): gpuratio = (6, ngpu*6) ngpupro = min(max(gpuratio), 12) # number of processes that will serve data to the gpu @@ -360,6 +362,8 @@ def index_pats_distributed( chunksize = 1000 ncpuwrker = n_cpu_nodes + PCpat = indexer._fillPCarray(PC, npats) + ray.shutdown() @@ -401,6 +405,8 @@ def index_pats_distributed( p_indx_start_end[-1][1] = npats + patstart p_indx_start_end[-1][2] = p_indx_start_end[-1][1] - p_indx_start_end[-1][0] + + gpujobs = [] cpujobs = [] jid = 1 @@ -469,7 +475,8 @@ def index_pats_distributed( gputask.append( gpuworkers[i].findbands.remote(gjob, pats=None, - indexer=remote_indexer + indexer=remote_indexer, + PC = PCpat[gjob.pstart:gjob.pend, :] ) ) else: @@ -477,6 +484,7 @@ def index_pats_distributed( gpuworkers[i].findbands.remote(gjob, pats=pats[gjob.pstart:gjob.pend, :, :], indexer=remote_indexer, + PC=PCpat[gjob.pstart:gjob.pend, :] ) ) gtaskindex.append(gjob) @@ -543,11 +551,14 @@ def index_pats_distributed( if inputmode == "filemode": gputask[jid] = gpuworkers[jid].findbands.remote(gjob, pats=None, + PC=PCpat[gjob.pstart:gjob.pend, :], indexer=remote_indexer + ) else: gputask[jid] = gpuworkers[jid].findbands.remote(gjob, pats=pats[gjob.pstart:gjob.pend, :, :], + PC=PCpat[gjob.pstart:gjob.pend, :], indexer=remote_indexer, ) gtaskindex[jid] = gjob @@ -567,6 +578,7 @@ def index_pats_distributed( except Exception as e: + print(e) gjob = gtaskindex[jid] print('A GPU death has occured', gjob.pstart, gjob.pend) if ngpu_retry < 5: @@ -587,14 +599,17 @@ def index_pats_distributed( gjob = gpujobs.pop(0) if inputmode == "filemode": gputask.append( - gpuworkers[0].findbands.remote(gjob, pats=None, - indexer=remote_indexer + gpuworkers[0].findbands.remote(gjob, + pats=None, + PC=PCpat[gjob.pstart:gjob.pend, :], + indexer=remote_indexer ) ) else: gputask.append( gpuworkers[0].findbands.remote(gjob, pats=pats[gjob.pstart:gjob.pend, :, :], + PC=PCpat[gjob.pstart:gjob.pend, :], indexer=remote_indexer, ) ) @@ -603,7 +618,7 @@ def index_pats_distributed( raise e # toc = timer() - if gpuwrker_cycles > 100: # a gpu worker got stuck -- see if I can unstick it. + if gpuwrker_cycles > gpucycletimeout: # a gpu worker got stuck -- see if I can unstick it. wrker = busy[0] gpuwrker_cycles = 0 jid = gputask.index(wrker) @@ -627,13 +642,15 @@ def index_pats_distributed( gputask.append( gpuworkers[0].findbands.remote(gjob, pats=None, - indexer=remote_indexer + indexer=remote_indexer, + PC = PCpat[gjob.pstart:gjob.pend, :], ) ) else: gputask.append( gpuworkers[0].findbands.remote(gjob, pats=pats[gjob.pstart:gjob.pend, :, :], + PC=PCpat[gjob.pstart:gjob.pend, :], indexer=remote_indexer, ) ) @@ -804,6 +821,7 @@ def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): return chunk + @ray.remote(num_cpus=1, num_gpus=1) class GPUWorker: def __init__(self, actorid=0, clparammodule=None, gpu_id=None, cudavis = '0'): diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 5cb8032..d965874 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -248,6 +248,7 @@ def index_pats( patsin=pats, patstart=patstart, npats=npats, + PC = PC, clparams=clparams, verbose=verbose, chunksize=chunksize, @@ -539,13 +540,15 @@ def index_pats( if npats == -1: npats = npoints + PCpat = self._fillPCarray(PC, npats) + gpuid = gpu_id try: # just in case the user sends in the gpu_id as a list/array gpuid = gpu_id[0] except: pass - banddata, bandnorm = self._detectbands(pats, PC, xyloc=xyloc, clparams=clparams, verbose=verbose, + banddata, bandnorm = self._detectbands(pats, PCpat, xyloc=xyloc, clparams=clparams, verbose=verbose, chunksize=chunksize, gpu_id=gpuid) tic = timer() @@ -808,6 +811,20 @@ def _detector2refframe(self): return quatref2detect + def _fillPCarray(self, PC, npats): + if PC is None: + PC = np.array(self.PC) + else: + PC = np.array(PC) + shpPC = PC.shape + if len(shpPC) == 1: + PC = PC.reshape(-1, shpPC[0]) + shpPC = PC.shape + + PCpat = np.zeros((npats, shpPC[1])) + PCpat[:, :] = PC[-1, :] + PCpat[0:shpPC[0], :] = PC + return PCpat # def pcCorrect(self, xy=[[0.0, 0.0]]): # # TODO: At somepoint we will put some methods here for # # correcting the PC depending on the location within the scan. diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 2db925c..c8bb2a8 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -21,13 +21,18 @@ # The US Naval Research Laboratory Date: 21 Aug 2020 """Optimization of the pattern center (PC) of EBSD patterns.""" - +import os import numpy as np -import multiprocessing +#import multiprocessing + + import functools import scipy.optimize as opt +import scipy.stats.qmc as scipyqmc from timeit import default_timer as timer +from pyebsdindex import _ray_installed + __all__ = [ "optimize", @@ -61,23 +66,32 @@ def _optfunction(PC_i, indexer=None, banddat=None): fit = indexdata[-1]['fit'] - iq = np.clip(np.array(indexdata[-1]['iq'])-1.5, 0, None) + iq = np.array(indexdata[-1]['iq']) + #if iq.max() > 1.5: + # iq = np.clip(iq - 1.5, 0.0, None) + #print(iq) nmatch = indexdata[-1]['nmatch'] average_fit = fit + 1.0*(nbands - nmatch) #average_fit = -1.0*(3.0-fit)*nmatch whgood = np.nonzero(fit < 90.0) - + #average_fit *= iq n_averages = len(whgood[0]) if n_averages < 0.9: average_fit = 1000 else: - average_fit = np.sum(average_fit[whgood[0]]) + 4.0*(nbands+1)*(npoints - n_averages) - average_fit /= npoints - #average_fit /= n_averages - #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) + iq = iq[whgood[0]] # weight averages by the iq value + if iq.max() > 1.5: + iq -= 1.0 + iq = np.clip(iq, 0.0001, None) + iq /= iq.max() + average_fit = np.sum(average_fit[whgood[0]]*iq) + average_fit /= sum(iq) + average_fit += (4.0*(nbands+1)*(npoints - n_averages))/n_averages + #average_fit /= npoints + result[q] = average_fit #print(timer()-tic) return result @@ -438,9 +452,13 @@ def initializeswarm(self, start=None, bounds=None): self.bounds = bounds self.range = self.bounds[1] - self.bounds[0] - self.pos = np.random.uniform(low=bounds[0], high=bounds[1], size=(self.n_particles, self.dimensions)) + #self.pos = np.random.uniform(low=bounds[0], high=bounds[1], size=(self.n_particles, self.dimensions)) + samppler = scipyqmc.Halton(self.dimensions) + self.pos = samppler.random(self.n_particles) * self.range + self.bounds[0] + self.pos[0, :] = start + self.vel = np.random.normal(size=(self.n_particles, self.dimensions), loc=0.0, scale=1.0) meanv = np.mean(np.sqrt(np.sum(self.vel**2, axis=1))) self.vel *= np.sqrt(np.sum(self.range**2))/(20. * meanv) @@ -465,18 +483,20 @@ def updateswarmbest(self, fun2opt, pool, **kwargs): temp = self.pos[part_i, :] val[part_i] = fun2opt(temp, **kwargs) #print(timer()-tic) - #pos = self.pos.copy() - #tic = timer() - #results = pool.map(functools.partial(fun2opt, **kwargs),list(pos) ) - #print(timer()-tic) - #print(len(results[0]), type(results[0])) - #print(len(results)) - #val = np.concatenate(results) - wh_newpbest = np.nonzero(val < self.pbest)[0] + # pos = list(self.pos.copy()) + # + # tic = timer() + # results = pool.map(functools.partial(fun2opt, **kwargs),pos ) + # #print(timer()-tic) + # #print(len(results[0]), type(results[0])) + # #print(len(results)) + # val = np.concatenate(results) - self.pbest[wh_newpbest] = val[wh_newpbest] - self.pbest_loc[wh_newpbest, :] = self.pos[wh_newpbest, :] + wh_newpbest = np.nonzero(val < self.pbest)[0] + if wh_newpbest.size > 0: + self.pbest[wh_newpbest] = val[wh_newpbest] + self.pbest_loc[wh_newpbest, :] = self.pos[wh_newpbest, :] wh_minpbest = np.argmin(self.pbest) if self.pbest[wh_minpbest] < self.gbest: @@ -572,7 +592,8 @@ def optimize(self, function, start=None, bounds=None, niter=50, verbose = 1, **k #f.write("interation, pnum, posx, posy, posz \n") # in theory the below should work -- find it to be unstable and buggy, and not actaully quicker when it does work. - #with multiprocessing.get_context("spawn").Pool(min(multiprocessing.cpu_count(), self.n_particles)) as pool: + + #with multiprocessing.Pool() as pool: pool = None if verbose >= 1: print('n_particles:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index ddfbf78..ca2fc8a 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -189,6 +189,7 @@ def __init__(self, self.angTol = angTol self.nband_earlyexit = nband_earlyexit self.high_fidelity = True + self.simpleqweights = False # many objects to hold the information about the reflecting poles, angles between them ... self.angpairs = None # dictionary that will store the possible unique angles between all pole families. @@ -599,7 +600,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose if self.high_fidelity == True: weights = self._calc_quest_weights(libFamID, accumulator, accumulator_nw, - polematch, polevalid, band_intensity, nfit=6) + polematch, polevalid, band_intensity, nfit=6, simpleqweights=bool(self.simpleqweights)) avequat, fit = self._refine_orientation_quest(libPolesCart, bandnorms, polematch, polevalid, weights = weights) fit = np.arccos(np.clip(fit, -1.0, 1.0))*RADEG @@ -843,7 +844,7 @@ def _refine_orientation(self, bandnorms, whGood, polematch): @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, - polematch, polevalid, band_intensity, nfit=6): + polematch, polevalid, band_intensity, nfit=6, simpleqweights=True): npats = accumulator.shape[0] nbands = polematch.shape[-1] weights = np.zeros((npats, nbands), dtype=np.float32) @@ -859,11 +860,16 @@ def _calc_quest_weights( libComFamID, accumulator, accumulator_nw, acc = accumulator[p, ...] acc_nw = accumulator_nw[p,...] - for q in range(whGood.size): - whg = np.uint64(whGood[q]) - a1indx = np.uint64(libComFamID[pmatch[whg]]) - score[whg] = acc[a1indx, whg] - score[whg] /= max(acc_nw[a1indx, whg], 1.0e-12) + if simpleqweights is False: + for q in range(whGood.size): + whg = np.uint64(whGood[q]) + a1indx = np.uint64(libComFamID[pmatch[whg]]) + score[whg] = acc[a1indx, whg] + score[whg] /= max(acc_nw[a1indx, whg], 1.0e-12) + else: + for q in range(whGood.size): + whg = np.uint64(whGood[q]) + score[whg] = band_intensity[p, q] srt = np.flip(np.argsort(score)) From d9e7d94a97c69a0a93661cef2b51fb7fd81fd67c Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 4 Aug 2025 10:04:04 -0400 Subject: [PATCH 51/92] Set up kernel object reuse - should be more efficient, and eliminate warnings from latest versions of pyopencl. Signed-off by: David Rowenhorst --- pyebsdindex/opencl/band_detect_cl.py | 81 ++++++++++++++-------------- pyebsdindex/opencl/nlpar_cl.py | 20 +++---- pyebsdindex/opencl/nlpar_clray.py | 20 +++---- pyebsdindex/opencl/openclparam.py | 7 +++ pyebsdindex/opencl/radon_fast_cl.py | 9 ++-- 5 files changed, 73 insertions(+), 64 deletions(-) diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 5a3fcd0..2f9bbda 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -242,6 +242,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b ctx = clparams.ctx prg = clparams.prg queue = clparams.queue + clkern = clparams.kernels mf = clparams.memflags else: clparams = openclparam.OpenClParam() @@ -251,8 +252,10 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b ctx = clparams.ctx prg = clparams.prg queue = clparams.queue + clkern = clparams.kernels mf = clparams.memflags + shapeIm = np.shape(image) if image.ndim == 2: nIm = 1 @@ -284,11 +287,11 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b if image.dtype.type is np.float32: - prg.loadfloat32(queue, (shapeIm[2], shapeIm[1], nIm), None, image_gpu, image_gpuflt, nImCL) + clkern['loadfloat32'](queue, (shapeIm[2], shapeIm[1], nIm), None, image_gpu, image_gpuflt, nImCL) if image.dtype.type is np.ubyte: - prg.loadubyte8(queue, (shapeIm[2], shapeIm[1], nIm), None, image_gpu, image_gpuflt, nImCL) + clkern['loadubyte8'](queue, (shapeIm[2], shapeIm[1], nIm), None, image_gpu, image_gpuflt, nImCL) if image.dtype.type is np.uint16: - prg.loaduint16(queue, (shapeIm[2], shapeIm[1], nIm), None, image_gpu, image_gpuflt, nImCL) + clkern['loaduint16'](queue, (shapeIm[2], shapeIm[1], nIm), None, image_gpu, image_gpuflt, nImCL) queue.flush() image_gpu.release() image_gpu = None @@ -297,7 +300,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b if background is not None: back_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=background.astype(np.float32)) - prg.backSub(queue,(imstep, 1, 1),None,image_gpuflt,back_gpu,nImChunk) + clkern['backSub'](queue,(imstep, 1, 1),None,image_gpuflt,back_gpu,nImChunk) #imBack = np.zeros((shapeIm[1], shapeIm[2], nImCL),dtype=np.float32) #cl.enqueue_copy(queue,imBack,image_gpu,is_blocking=True) @@ -311,14 +314,14 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b rdnIndx_gpu = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=self.radonPlan.indexPlan) cl.enqueue_fill_buffer(queue, radon_gpu, np.float32(self.radonPlan.missingval), 0, radon_gpu.size) - prg.radonSum(queue,(nImChunk,rdnstep),None,rdnIndx_gpu,image_gpuflt,radon_gpu, + clkern['radonSum'](queue,(nImChunk,rdnstep),None,rdnIndx_gpu,image_gpuflt,radon_gpu, imstep, indxstep, shpRdn[0], shpRdn[1], padRho, padTheta, np.uint64(self.nTheta)) if (fixArtifacts == True): - prg.radonFixArt(queue,(nImChunk,shpRdn[0]),None,radon_gpu, + clkern['radonFixArt'](queue,(nImChunk,shpRdn[0]),None,radon_gpu, shpRdn[0],shpRdn[1],padTheta) rdnIndx_gpu.release() @@ -349,21 +352,17 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = if clparams is not None: if clparams.queue is None: clparams.get_queue() - gpu = clparams.gpu - gpu_id = clparams.gpu_id - ctx = clparams.ctx - prg = clparams.prg - queue = clparams.queue - mf = clparams.memflags else: clparams = openclparam.OpenClParam() clparams.get_queue() - gpu = clparams.gpu - gpu_id = clparams.gpu_id - ctx = clparams.ctx - prg = clparams.prg - queue = clparams.queue - mf = clparams.memflags + + gpu = clparams.gpu + gpu_id = clparams.gpu_id + ctx = clparams.ctx + clkern = clparams.kernels + prg = clparams.prg + queue = clparams.queue + mf = clparams.memflags nT = self.nTheta nTp = nT + 2 * self.padding[1] @@ -415,10 +414,11 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = #prg.radonPadRho2(queue,(shp[2],shp[1],1),None,rdn_gpu, # np.uint64(shp[0]),np.uint64(shp[1]),np.uint64(self.padding[0]+1)) - prg.radonPadRho2(queue, (shp[2], shp[1], 1), None, rdn_gpu, + clkern['radonPadRho2'](queue, (shp[2], shp[1], 1), None, rdn_gpu, np.uint64(shp[0]),np.uint64(shp[1]),np.uint64(shp[0]//2-1)) kern_gpu = None + if separableKernel == False: # for now I will assume that the kernel(s) can fit in local memory on the GPU # also going to assume that there is only one kernel -- this will be something to fix at some point. @@ -426,7 +426,7 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = kshp = np.asarray(k0.shape, dtype=np.int32) pad = kshp/2 kern_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=k0) - prg.convolution3d2d(queue,(np.int32((shp[1]-2*pad[1])),np.int32((shp[0]-2*pad[0])), nImChunk),None, + clkern['convolution3d2d'](queue,(np.int32((shp[1]-2*pad[1])),np.int32((shp[0]-2*pad[0])), nImChunk),None, rdn_gpu, kern_gpu,np.int32(shp[1]),np.int32(shp[0]),np.int32(shp[2]), np.int32(kshp[1]), np.int32(kshp[0]), np.int32(pad[1]), np.int32(pad[0]), rdnConv_gpu) @@ -447,7 +447,7 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = kshp = np.asarray(k0x.shape,dtype=np.int32) kern_gpu_x = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=k0x) - prg.convolution3d2d(queue,(np.int32((shp[1]-2*pad[1])),np.int32((shp[0]-2*pad[0])), nImChunk),None, + clkern['convolution3d2d'](queue,(np.int32((shp[1]-2*pad[1])),np.int32((shp[0]-2*pad[0])), nImChunk),None, rdn_gpu,kern_gpu_x,np.int32(shp[1]),np.int32(shp[0]),np.int32(shp[2]), np.int32(kshp[1]),np.int32(kshp[0]),np.int32(pad[1]),np.int32(pad[0]),tempConvbuff) @@ -458,7 +458,7 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = kshp = np.asarray(k0y.shape,dtype=np.int32) kern_gpu_y = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=k0y) - prg.convolution3d2d(queue,(np.int32((shp[1]-2*pad[1])),np.int32((shp[0]-2*pad[0])), nImChunk),None, + clkern['convolution3d2d'](queue,(np.int32((shp[1]-2*pad[1])),np.int32((shp[0]-2*pad[0])), nImChunk),None, tempConvbuff,kern_gpu_y,np.int32(shp[1]),np.int32(shp[0]),np.int32(shp[0]), np.int32(kshp[1]),np.int32(kshp[0]),np.int32(pad[1]),np.int32(pad[0]),rdnConv_gpu) @@ -466,11 +466,11 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = mns = cl.Buffer(ctx,mf.READ_WRITE,size=nImCL * 4) ave = cl.Buffer(ctx, mf.READ_WRITE, size=nImCL * 4) - prg.imageMinAve(queue,(nImChunk,1,1),None, + clkern['imageMinAve'](queue,(nImChunk,1,1),None, rdnConv_gpu, mns, ave, np.uint32(shp[1]),np.uint32(shp[0]), np.uint32(self.padding[1]),np.uint32(self.padding[0])) # subtract the min value, clipping to 0. - prg.imageSubMinNormWClip(queue,(np.int32(shp[1]), np.int32(shp[0]),nImChunk),None, + clkern['imageSubMinNormWClip'](queue,(np.int32(shp[1]), np.int32(shp[0]),nImChunk),None, rdnConv_gpu,mns, ave, np.uint32(shp[1]),np.uint32(shp[0]), np.uint32(0),np.uint32(0)) @@ -514,21 +514,16 @@ def rdn_local_maxCL(self,radonIn, clparams=None, returnBuff = True): if clparams is not None: if clparams.queue is None: clparams.get_queue() - gpu = clparams.gpu - gpu_id = clparams.gpu_id - ctx = clparams.ctx - prg = clparams.prg - queue = clparams.queue - mf = clparams.memflags else: clparams = openclparam.OpenClParam() clparams.get_queue() - gpu = clparams.gpu - gpu_id = clparams.gpu_id - ctx = clparams.ctx - prg = clparams.prg - queue = clparams.queue - mf = clparams.memflags + gpu = clparams.gpu + gpu_id = clparams.gpu_id + ctx = clparams.ctx + prg = clparams.prg + clkern = clparams.kernels + queue = clparams.queue + mf = clparams.memflags @@ -585,12 +580,14 @@ def rdn_local_maxCL(self,radonIn, clparams=None, returnBuff = True): # np.int64(self.padding[1]),np.int64(self.padding[0]), # np.int64(self.peakPad[1]),np.int64(1)) # calculate the max in the x direction - prg.morphDilateKernelBF(queue, (np.uint32(shp[1]), np.uint32(nR), nImChunk), None, rdn_gpu, lmaxX, + + + clkern['morphDilateKernelBF'](queue, (np.uint32(shp[1]), np.uint32(nR), nImChunk), None, rdn_gpu, lmaxX, np.int64(shp[1]), np.int64(shp[0]), np.int64(0), np.int64(self.padding[0]), np.int64(self.peakPad[1]), np.int64(1)) # take the max in the x output, use as input, and calculate in the y direction - prg.morphDilateKernelBF(queue, (np.uint32(nT), np.uint32(nR), nImChunk), None, lmaxX, lmaxXY, + clkern['morphDilateKernelBF'](queue, (np.uint32(nT), np.uint32(nR), nImChunk), None, lmaxX, lmaxXY, np.int64(shp[1]), np.int64(shp[0]), np.int64(self.padding[1]), np.int64(self.padding[0]), np.int64(1), np.int64(self.peakPad[0])) @@ -598,7 +595,7 @@ def rdn_local_maxCL(self,radonIn, clparams=None, returnBuff = True): local_max = np.zeros((shp),dtype=np.ubyte) local_max_gpu = cl.Buffer(ctx,mf.WRITE_ONLY,size=local_max.nbytes) - prg.im1EQim2(queue,(np.uint32(nT),np.uint32(nR),nImCL),None, lmaxXY, rdn_gpu, local_max_gpu, + clkern['im1EQim2'](queue,(np.uint32(nT),np.uint32(nR),nImCL),None, lmaxXY, rdn_gpu, local_max_gpu, np.uint64(shp[1]),np.uint64(shp[0]), np.uint64(self.padding[1]),np.uint64(self.padding[0])) @@ -608,7 +605,7 @@ def rdn_local_maxCL(self,radonIn, clparams=None, returnBuff = True): maskrnd = maskrnd.astype(np.ubyte) maskrnd_gpu = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=maskrnd) - prg.maxmask(queue, (np.uint32(nT), np.uint32(nR)), None, local_max_gpu, maskrnd_gpu, + clkern['maxmask'](queue, (np.uint32(nT), np.uint32(nR)), None, local_max_gpu, maskrnd_gpu, np.uint64(shp[1]), np.uint64(nImChunk), np.uint64(self.padding[1]), np.uint64(self.padding[0])) @@ -662,7 +659,7 @@ def band_labelCL(self,rdnConvIn, lMaxRdnIn,clparams=None): prg = clparams.prg queue = clparams.queue mf = clparams.memflags - + clkern = clparams.kernels nT = self.nTheta nTp = nT + 2 * self.padding[1] @@ -719,7 +716,7 @@ def band_labelCL(self,rdnConvIn, lMaxRdnIn,clparams=None): #rhoMaskTrim = np.int64((shp[0] - 2 * self.padding[0]) * self.rhoMaskFrac + self.padding[0]) rhoMaskTrim = np.int64(self.padding[0]) - prg.maxlabel(queue,(nIm, 1,1),(1,1,1), + clkern['maxlabel'](queue,(nIm, 1,1),(1,1,1), lMaxRdn_gpu,rdnConv_gpu, maxloc_gpu, maxval_gpu, aveloc_gpu,aveval_gpu, diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index 1796ed6..f4b41ca 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -166,6 +166,7 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, normalize_d=Fa target_mem = min(clparams.queue.device.max_mem_alloc_size//2, np.int64(4e9)) ctx = clparams.ctx prg = clparams.prg + clkern = clparams.kernels queue = clparams.queue mf = clparams.memflags clvectlen = 16 @@ -253,11 +254,11 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, normalize_d=Fa data_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=data) if data.dtype.type is np.float32: - prg.nlloadpat32flt(queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) + clkern['nlloadpat32flt'](queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) if data.dtype.type is np.ubyte: - prg.nlloadpat8bit(queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) + clkern['nlloadpat8bit'](queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) if data.dtype.type is np.uint16: - prg.nlloadpat16bit(queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) + clkern['nlloadpat16bit'](queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) toc = timer() #print(toc - tic) @@ -267,14 +268,14 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, normalize_d=Fa sigmachunk_gpu = cl.Buffer(ctx, mf.WRITE_ONLY, size=sigmachunk.nbytes) cl.enqueue_barrier(queue) - prg.calcsigma(queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, + clkern['calcsigma'](queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, datapad_gpu, mask_gpu,sigmachunk_gpu, dist_local, count_local, np.int64(nn), np.int64(npatsteps), np.int64(npat_point), np.float32(mxval) ) if normalize_d is True: cl.enqueue_barrier(queue) - prg.normd(queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, + clkern['normd'](queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, sigmachunk_gpu, count_local, dist_local, np.int64(nn)) @@ -414,6 +415,7 @@ class OpenCLClalcError(Exception): #target_mem = min(clparams.queue.device.max_mem_alloc_size*3, np.int64(18e9)) ctx = clparams.ctx prg = clparams.prg + clkern = clparams.kernels queue = clparams.queue mf = clparams.memflags clvectlen = 16 @@ -541,11 +543,11 @@ class OpenCLClalcError(Exception): data_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=data) # print("data", data_gpu.size) if data.dtype.type is np.float32: - prg.nlloadpat32flt(queue, (np.uint64(data.size),1), None, data_gpu, datapad_gpu, wait_for=[filldatain]) + clkern['nlloadpat32flt'](queue, (np.uint64(data.size),1), None, data_gpu, datapad_gpu, wait_for=[filldatain]) if data.dtype.type is np.ubyte: - prg.nlloadpat8bit(queue, (np.uint64(data.size),1), None, data_gpu, datapad_gpu, wait_for=[filldatain]) + clkern['nlloadpat8bit'](queue, (np.uint64(data.size),1), None, data_gpu, datapad_gpu, wait_for=[filldatain]) if data.dtype.type is np.uint16: - prg.nlloadpat16bit(queue, (np.uint64(data.size),1), None, data_gpu, datapad_gpu, wait_for=[filldatain]) + clkern['nlloadpat16bit'](queue, (np.uint64(data.size),1), None, data_gpu, datapad_gpu, wait_for=[filldatain]) @@ -554,7 +556,7 @@ class OpenCLClalcError(Exception): cl.enqueue_barrier(queue) data_gpu.release() try: - envt = prg.calcnlpar(queue, (np.uint32(ncolcalc), np.uint32(nrowcalc)), None, + envt = clkern['calcnlpar'](queue, (np.uint32(ncolcalc), np.uint32(nrowcalc)), None, #prg.calcnlpar(queue, (1, 1), None, datapad_gpu, mask_gpu, diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index dfd0382..6f7e2e8 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -265,6 +265,7 @@ def _sigmachunkcalc_cl(self, data, calclim, clparams=None, saturation_protect=Tr data = np.ascontiguousarray(data) ctx = clparams.ctx prg = clparams.prg + clkern = clparams.kernels clparams.get_queue() @@ -328,11 +329,11 @@ def _sigmachunkcalc_cl(self, data, calclim, clparams=None, saturation_protect=Tr data_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=data) if data.dtype.type is np.float32: - prg.nlloadpat32flt(clparams.queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) + clkern['nlloadpat32flt'](clparams.queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) if data.dtype.type is np.ubyte: - prg.nlloadpat8bit(clparams.queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) + clkern['nlloadpat8bit'](clparams.queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) if data.dtype.type is np.uint16: - prg.nlloadpat16bit(clparams.queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) + clkern['nlloadpat16bit'](clparams.queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) toc = timer() #print(toc - tic) @@ -341,14 +342,14 @@ def _sigmachunkcalc_cl(self, data, calclim, clparams=None, saturation_protect=Tr sigmachunk_gpu = cl.Buffer(ctx, mf.WRITE_ONLY, size=sigmachunk.nbytes) cl.enqueue_barrier(clparams.queue) - prg.calcsigma(clparams.queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, + clkern['calcsigma'](clparams.queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, datapad_gpu, mask_gpu,sigmachunk_gpu, dist_local, count_local, np.int64(nn), np.int64(npatsteps), np.int64(npat_point), np.float32(mxval) ) cl.enqueue_barrier(clparams.queue) - prg.normd(clparams.queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, + clkern['normd'](clparams.queue, (np.uint32(ncolchunk), np.uint32(nrowchunk)), None, sigmachunk_gpu, count_local, dist_local, np.int64(nn)) @@ -581,6 +582,7 @@ def _nlparchunkcalc_cl(self, data, calclim, clparams=None): data = np.ascontiguousarray(data) ctx = clparams.ctx prg = clparams.prg + clkern = clparams.kernels clparams.get_queue() @@ -648,17 +650,17 @@ def _nlparchunkcalc_cl(self, data, calclim, clparams=None): cl.enqueue_barrier(clparams.queue) data_gpu = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=data) if data.dtype.type is np.float32: - prg.nlloadpat32flt(clparams.queue, (np.uint64(data.size), 1), None, data_gpu, datapad_gpu)#, wait_for=[fill1,fill2]) + clkern['nlloadpat32flt'](clparams.queue, (np.uint64(data.size), 1), None, data_gpu, datapad_gpu)#, wait_for=[fill1,fill2]) if data.dtype.type is np.ubyte: - prg.nlloadpat8bit(clparams.queue, (np.uint64(data.size), 1), None, data_gpu, datapad_gpu)#, wait_for=[fill1,fill2]) + clkern['nlloadpat8bit'](clparams.queue, (np.uint64(data.size), 1), None, data_gpu, datapad_gpu)#, wait_for=[fill1,fill2]) if data.dtype.type is np.uint16: - prg.nlloadpat16bit(clparams.queue, (np.uint64(data.size), 1), None, data_gpu, datapad_gpu)#, wait_for=[fill1,fill2]) + clkern['nlloadpat16bit'](clparams.queue, (np.uint64(data.size), 1), None, data_gpu, datapad_gpu)#, wait_for=[fill1,fill2]) calclim = np.array([cstartcalc, rstartcalc, ncolchunk, nrowchunk], dtype=np.int64) crlimits_gpu = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=calclim) cl.enqueue_barrier(clparams.queue) data_gpu.release() - prg.calcnlpar(clparams.queue, (np.uint32(ncolcalc), np.uint32(nrowcalc)), None, + clkern['calcnlpar'](clparams.queue, (np.uint32(ncolcalc), np.uint32(nrowcalc)), None, # prg.calcnlpar(clparams.queue, (1, 1), None, datapad_gpu, mask_gpu, diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index 8a4cc33..4d517ac 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -41,6 +41,7 @@ def __init__(self, gpu_id=0): self.gpu_id = gpu_id self.ctx = None self.prg = None + self.kernels = None self.queue = None self.memflags = cl.mem_flags @@ -109,6 +110,12 @@ def get_context(self, gpu_id=None, kfile = 'clkernels.cl' ): warnings.simplefilter("ignore") self.prg = cl.Program(self.ctx,open(path.join(kernel_location,kfile)).read()).build(options=['-cl-std=CL1.2', '-w']) + kernels = self.prg.all_kernels() + self.kernels = {} + for k in kernels: + self.kernels.update({k.function_name: k}) + + #print('ctx', self.gpu_id) return self.ctx def get_queue(self, gpu_id=None): diff --git a/pyebsdindex/opencl/radon_fast_cl.py b/pyebsdindex/opencl/radon_fast_cl.py index fd8b3b9..a32c2c8 100644 --- a/pyebsdindex/opencl/radon_fast_cl.py +++ b/pyebsdindex/opencl/radon_fast_cl.py @@ -73,6 +73,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, prg = clparams.prg queue = clparams.queue mf = clparams.memflags + clkern = clparams.kernels shapeIm = np.shape(image) if image.ndim == 2: @@ -111,21 +112,21 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, if background is not None: back_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=background.astype(np.float32)) if str.upper(background_method) == 'DIVIDE': - prg.backDiv(queue,(imstep, 1, 1),None,image_gpu,back_gpu,nImChunk) + clkern['backDiv'](queue,(imstep, 1, 1),None,image_gpu,back_gpu,nImChunk) else: - prg.backSub(queue,(imstep, 1, 1),None,image_gpu,back_gpu,nImChunk) + clkern['backSub'](queue,(imstep, 1, 1),None,image_gpu,back_gpu,nImChunk) #imBack = np.zeros((shapeIm[1], shapeIm[2], nImCL),dtype=np.float32) #cl.enqueue_copy(queue,imBack,image_gpu,is_blocking=True) cl.enqueue_fill_buffer(queue, radon_gpu, np.float32(self.missingval), 0, radon_gpu.size) - prg.radonSum(queue,(nImChunk,rdnstep),None,rdnIndx_gpu,image_gpu,radon_gpu, + clkern['radonSum'](queue,(nImChunk,rdnstep),None,rdnIndx_gpu,image_gpu,radon_gpu, imstep, indxstep, shpRdn[0], shpRdn[1], padRho, padTheta, np.uint64(self.nTheta)) if (fixArtifacts == True): - prg.radonFixArt(queue,(nImChunk,shpRdn[0]),None,radon_gpu, + clkern['radonFixArt'](queue,(nImChunk,shpRdn[0]),None,radon_gpu, shpRdn[0],shpRdn[1],padTheta) From 111d5c8833c7f34dc231b61a67d1a577da1adeea Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 12 Aug 2025 05:18:50 +0000 Subject: [PATCH 52/92] Bump actions/checkout from 4 to 5 Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 5. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/publish.yml | 2 +- .github/workflows/tests.yml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 63f1cd4..082d75d 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -29,7 +29,7 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing: id-token: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 - name: Set up Python uses: actions/setup-python@v5 diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 430caaf..d924625 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -20,7 +20,7 @@ jobs: name: check manifest runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 - uses: actions/setup-python@v5 with: @@ -49,7 +49,7 @@ jobs: DEPENDENCIES: matplotlib==3.3 numba==0.55.1 ray[default]==2.9 LABEL: -oldest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 From 531cd43f87422022d0fb13ee846b0d9972968def Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 20:22:39 +0000 Subject: [PATCH 53/92] Bump actions/setup-python from 5 to 6 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/setup-python dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/publish.yml | 2 +- .github/workflows/tests.yml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 082d75d..c57b3b1 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -32,7 +32,7 @@ jobs: - uses: actions/checkout@v5 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: '3.x' diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d924625..ba4d5a1 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -22,7 +22,7 @@ jobs: steps: - uses: actions/checkout@v5 - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: '3.11' @@ -52,7 +52,7 @@ jobs: - uses: actions/checkout@v5 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} From f62a332c7d79f430387c7e6c85a2156586ee15ae Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 1 Oct 2025 13:36:24 -0400 Subject: [PATCH 54/92] Provide ability to set a user defined background pattern. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 12 +++++++++--- pyebsdindex/_ebsd_index_single.py | 15 +++++++++------ pyebsdindex/band_detect.py | 3 +++ 3 files changed, 21 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index f8ec4de..8e8a257 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -316,7 +316,7 @@ def index_pats_distributed( ncpu = max(1,os.cpu_count()//2) if ncpu <= 0: if ngpu > 0: - ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*10))) + ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*16))) # this is a heuristic, and may be highly dependent on hardware else: ncpu = max(1,os.cpu_count()//4) @@ -566,8 +566,7 @@ def index_pats_distributed( else: # no more gpu tasks to submit - #del gpuworkers[jid] - #ray.kill(gpuworkers[jid]) + gpuworkers[jid].exit.remote() del gpuworkers[jid] del gputask[jid] del gtaskindex[jid] @@ -701,6 +700,7 @@ def index_pats_distributed( #cputask[jid] = None #ctaskindex[jid] = None #ray.kill(cpuworkers[jid]) + cpuworkers[jid].exit.remote() del cpuworkers[jid] del cputask[jid] del ctaskindex[jid] @@ -912,6 +912,9 @@ def _getstats(self): } return stats + def exit(self): + ray.actor.exit_actor() + @@ -937,6 +940,8 @@ def indexpoles(self, cpujob, banddata, bandnorm, indexer=None): print(e) cpujob.rate = None return "Error", (None,None, cpujob) + def exit(self): + ray.actor.exit_actor() class CPUGPUJob: @@ -956,3 +961,4 @@ def _endtime(self): self.extime += self.endtime - self.starttime self.rate = self.npat/(self.extime + 1e-12) + diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index d965874..ac87106 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -130,9 +130,9 @@ def index_pats( nBands : int, optional Number of detected bands to use in triplet voting. Default is 9. Unused if ``ebsd_indexer_obj`` is passed. - backgroundSub : bool, optional + backgroundSub : bool, ndarray optional Whether to subtract a static background prior to indexing. - Default is ``False``. + Default is ``False``. Set to a ndarray to use your own background. patstart : int, optional Starting index of the patterns to index. Default is ``0``. npats : int, optional @@ -237,10 +237,13 @@ def index_pats( if not np.all(indexer.bandDetectPlan.patDim == np.array(pdim)): indexer.update_file(patDim=pats.shape[-2:]) - if backgroundSub: - indexer.bandDetectPlan.collect_background( - fileobj=indexer.fID, patsIn=pats, nsample=1000 - ) + if type(backgroundSub) is np.ndarray: + indexer.bandDetectPlan.backgroundsub = backgroundSub + else: + if backgroundSub: + indexer.bandDetectPlan.collect_background( + fileobj=indexer.fID, patsIn=pats, nsample=1000 + ) #indexer.bandDetectPlan.radonPlan.masksetup(mask=patternmask, maskindex=patternmaskindex) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 3795623..8a8fd29 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -746,6 +746,9 @@ def _display_radon_pattern(self, rdnConvarray, bandData, patterns): # subrdn.ylim(-self.rhoMax, self.rhoMax) subpat = fig.add_subplot(122) pat1 = patterns[-1, :, :].copy().squeeze().astype(float) + if self.backgroundsub is not None: + pat1 -= self.backgroundsub + pat1 -= pat1.min() minpat = pat1.min() pdim = pat1.shape pat1 = np.concatenate((pat1.flatten(), [minpat])) From c78cd0cee1ebe5feb8454a34f594b57afa0365e4 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 2 Oct 2025 16:00:24 -0400 Subject: [PATCH 55/92] Fixed issue that prevented autonlpar from using user defined lambda. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 6 +++++- pyebsdindex/tripletvote.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index a9262a5..8ad8fde 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -532,7 +532,11 @@ def auto_nlpar(self, filename = None, fileout=None, searchradius=None, lindex = if filename is not None: self.setfile(filename) lam = self.opt_lambda( automask = True, autoupdate=True, backsub = False, **kwargs) - nlparfile = self.calcnlpar(searchradius = searchradius, lam = lam[int(lindex)], saturation_protect=True, automask=True, + if 'lam' in kwargs: + pass + else: + kwargs['lam'] = lam[int(lindex)] + nlparfile = self.calcnlpar(searchradius = searchradius, saturation_protect=True, automask=True, fileout=fileout, backsub=False, **kwargs) return nlparfile def backsub(self, data): diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index ca2fc8a..6ad3f8f 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -142,7 +142,7 @@ def addphase(libtype=None, phasename=None, else: latticeparameter = np.array(latticeparameter) if polefamilies is None: - polefamilies = np.array([ [1, 0, -1, 0], [0, 0, 0, 2],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], + polefamilies = np.array([ [1, 0, -1, 0], [0, 0, 0, 2],[1, 0, -1, 1],[1, 0, -1, 2], [1, 1, -2, 0], [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]).astype(np.int32) else: polefamilies = np.atleast_2d(np.array(polefamilies)) From ce26c29f140d8d2ad10510381d341c4ed3eabb3a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 10 Oct 2025 15:20:24 -0400 Subject: [PATCH 56/92] Fixed bug in reading step size in ebsp files. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 2 +- pyebsdindex/ebsdfile.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index c4a35a1..adfc601 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -866,7 +866,7 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO self.nCols = np.uint64(ncol) - self.yStep = yall[0] - yall[self.nCols] + self.yStep = np.abs(yall[0] - yall[self.nCols]) if self.yStep > 1e-6: nrow = (yall.max() - yall.min()) / self.yStep diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 7a2f04a..be2e076 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -272,8 +272,8 @@ def writeoh5(filename, indexer, data, f.create_dataset(datasetname + '/EBSD/Data/CI', data=(ci).astype(np.float32)) - x = (np.arange(ncols[0] * nrows[0], dtype=int) % ncols[0]).astype(np.float32) * xstep[0] - y = (np.arange(ncols[0] * nrows[0], dtype=int) // ncols[0]).astype(np.float32) * ystep[0] + x = (np.arange(ncols[0] * nrows[0], dtype=int) % int(ncols[0])).astype(np.float32) * xstep[0] + y = (np.arange(ncols[0] * nrows[0], dtype=int) // int(ncols[0])).astype(np.float32) * ystep[0] f.create_dataset(datasetname + '/EBSD/Data/X Position', data=x) f.create_dataset(datasetname + '/EBSD/Data/Y Position', data=y) From 9432a49597657e8da935e1e6a5911f836f82ff38 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 18 Nov 2025 16:14:10 -0500 Subject: [PATCH 57/92] Merge develop Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 5ec5ae4..4aa67c4 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -310,14 +310,11 @@ def d2norm(d2, n2, dij, sigma): self.sigma = sigma return np.mean(lamopt_values, axis = 0).flatten() -<<<<<<< HEAD def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, stem_scale = False, -======= - def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, ->>>>>>> main - filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, diff_offset=None, - **kwargs): + filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, + diff_offset=None, + **kwargs): if lam is not None: self.lam = lam From 29793d518848afe3139950ad615bb8b5db0877ef Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 18 Nov 2025 16:47:55 -0500 Subject: [PATCH 58/92] Include helper functions Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 8ad8fde..b86a397 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -759,6 +759,13 @@ def getpairid(idx0, idx1): #print('_______', '\n') return dataout + def opt_lambda_cpu(self,**kwargs): # helper function + return self.opt_lambda(**kwargs) + + def calcnlpar_cpu(self, **kwargs): # helper function + return self.calcnlpar(**kwargs) + + def _calcchunks(self, patdim, ncol, nrow, target_bytes=2e9, col_overlap=0, row_overlap=0, col_offset=0, row_offset=0): col_overlap = min(col_overlap, ncol - 1) From 2c6db692c3c704b2ddd1d0e95c887cb6a7485bed Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 18 Nov 2025 16:52:06 -0500 Subject: [PATCH 59/92] Finish merge with develop Signed-off by: David Rowenhorst --- .github/workflows/tests.yml | 5 ----- pyebsdindex/ebsdfile.py | 4 ---- 2 files changed, 9 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 601acb5..430caaf 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -45,13 +45,8 @@ jobs: python-version: ['3.10', '3.11', '3.12'] include: - os: ubuntu-latest -<<<<<<< HEAD - python-version: 3.8 - DEPENDENCIES: matplotlib==3.3 numba==0.52 numpy==1.19 ray[default]==1.13 -======= python-version: 3.9 DEPENDENCIES: matplotlib==3.3 numba==0.55.1 ray[default]==2.9 ->>>>>>> main LABEL: -oldest steps: - uses: actions/checkout@v4 diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index ab7a19e..01a05a4 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -111,11 +111,7 @@ def writeoh5(filename, indexer, data, phaseIDadd = 1 else: phaseIDadd = 1 -<<<<<<< HEAD - with h5py.File(fpath, 'w') as f: -======= ->>>>>>> main with h5py.File(fpath, 'w') as f: # Write standard Header Information From a0c43d21013f58a501e85a5313014074364475db Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Wed, 19 Nov 2025 10:57:10 -0500 Subject: [PATCH 60/92] Begin block cpu NLPAR Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index b86a397..af8f4b4 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -402,6 +402,9 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, else: # not int, so no rescale. rescale = False + #chunks = self._calcchunks( [pwidth, pheight], ncols, nrows, target_bytes=target_mem, + # col_overlap=sr, row_overlap=sr) + nthreadpos = numba.get_num_threads() #numba.set_num_threads(18) #numba.set_num_threads(18) From 7e9ff54ff6649f58621d81202c9fa65774f1f37a Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Wed, 19 Nov 2025 11:01:22 -0500 Subject: [PATCH 61/92] Continuing interrupted merge from develop Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 7e3f38f..4dceb14 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -737,12 +737,10 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): for j in range(len(self.phaseLib)): -<<<<<<< HEAD + indxData['pq'][j, :] = np.mean(banddata['max'] * banddata['valid'], axis=1) #/ shpBandDat[-1] indxData['iq'][j, :] = np.mean(banddata['normmax'] * banddata['valid'], axis=1) # / shpBandDat[-1] -======= ->>>>>>> main p2do = np.ravel(np.nonzero(np.max(indxData["nmatch"], axis=0) < earlyexit)[0]) if (p2do.size ==0) is None: @@ -772,10 +770,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): indxData["matchattempts"][j, whgood2] = matchAttempts[whgood, ...] indxData["totvotes"][j, whgood2] = totvotes[whgood] bandmatchindex[whgood2, ..., j] = bandmatch[whgood, ...].reshape(whgood.size,nBands ) -<<<<<<< HEAD -======= ->>>>>>> main From 5838a5335b051007b29f1d5b6eaca296daad72c5 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Wed, 19 Nov 2025 15:59:17 -0500 Subject: [PATCH 62/92] calcnlpar_cpu now block chunked Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 279 ++++++++++++++++++++++++++++++--------- 1 file changed, 213 insertions(+), 66 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index af8f4b4..6cb5be6 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -24,7 +24,7 @@ # Patrick T. Brewick, Stuart I. Wright, David J. Rowenhorst. Ultramicroscopy, 200:50–61, May 2019. - +import psutil from pathlib import Path from timeit import default_timer as timer @@ -255,7 +255,7 @@ def d2norm(d2, n2, dij, sigma): dthresh = np.float32(dthresh) lamopt_values = [] - + for j in range(0,nrows,chunksize): if verbose >= 2: @@ -360,20 +360,30 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, pheight = np.int64(patternfile.patternH) phw = pheight*pwidth + # if chunksize <= 0: + # chunksize = np.maximum(1, np.round(1e9 / phw / ncols) ) # keeps the chunk at about 8GB + # chunksize = np.minimum(nrows, chunksize) + # print("Chunk size set to nrows:", int(chunksize)) + # chunksize = np.int64(chunksize) if chunksize <= 0: - chunksize = np.maximum(1, np.round(1e9 / phw / ncols) ) # keeps the chunk at about 8GB - chunksize = np.minimum(nrows, chunksize) - print("Chunk size set to nrows:", int(chunksize)) - chunksize = np.int64(chunksize) + sysram = (psutil.virtual_memory()).total + chunksize = np.minimum(32e9, sysram/4) + chunks = self._calcchunks( [pwidth, pheight], ncols, nrows, target_bytes=chunksize, + col_overlap=sr, row_overlap=sr) + chunksize = (chunks[2][:, 1] - chunks[2][:, 0]).reshape(1, -1) * \ + (chunks[3][:, 1] - chunks[3][:, 0]).reshape(-1, 1) + nchunks = chunksize.size + # return chunks, chunksize + mxchunk = np.int64(chunksize.max()) if (automask is True) and (self.mask is None): self.mask = (self.automask(pheight,pwidth)) if self.mask is None: self.mask = np.ones((pheight,pwidth), dtype=np.uint8) - indices = np.asarray( (self.mask.flatten().nonzero())[0], np.uint64) + indices = np.asarray( (self.mask.flatten().nonzero())[0], np.int64) calcsigma = False if self.sigma is None: calcsigma = True @@ -402,8 +412,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, else: # not int, so no rescale. rescale = False - #chunks = self._calcchunks( [pwidth, pheight], ncols, nrows, target_bytes=target_mem, - # col_overlap=sr, row_overlap=sr) + nthreadpos = numba.get_num_threads() #numba.set_num_threads(18) @@ -412,60 +421,170 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, if verbose >= 1: print("lambda:", self.lam, "search radius:", self.searchradius, "dthresh:", self.dthresh) - for j in range(0,nrows,chunksize): - #print('Row start', j) - if verbose >= 2: - print("begin row: ", j, "/", nrows, sep='', end='\r') - - rowstartread = np.int64(max(0, j-sr)) - rowend = min(j + chunksize+sr,nrows) - - if (rowend - rowstartread) < (2*sr+1): - rowstartread = np.int64(max(0, rowend - (2*sr+1))) - rowcountread = np.int64(rowend-rowstartread) - data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], - convertToFloat=True,returnArrayOnly=True) - - shpdata = data.shape - - if backsub is True: - data = self.backsub(data) - - - data = data.reshape(shpdata[0], phw) - - rowstartcount = np.asarray([0,rowcountread],dtype=np.int64) - if calcsigma is True: - sigchunk, tmp = self.sigma_numba(data,1,rowcountread,ncols,rowstartcount,colstartcount,indices,saturation_protect) - del tmp - tmp = (sigma[rowstartread:rowend,:] < sigchunk).choose(sigchunk,sigma[rowstartread:rowend,:]) - sigma[rowstartread:rowend,:] = tmp - else: - sigchunk = sigma[rowstartread:rowend,:] - - #dataout = data - - dataout = self.nlpar_nb(data,lam, sr, dthresh, sigchunk, - rowcountread,ncols,indices,saturation_protect, diff_offset=diff_offset) - - dataout = dataout.reshape(rowcountread, ncols, phw) - dataout = dataout[j-rowstartread:, :, : ] - shpout = dataout.shape - dataout = dataout.reshape(shpout[0]*shpout[1], pheight, pwidth) - if rescale == True: - for i in range(dataout.shape[0]): - temp = dataout[i,:,:] - temp -= temp.min() - temp *= np.float32(mxval)/temp.max() - dataout[i,:,:] = temp - - patternfileout.write_data(newpatterns=dataout,patStartCount = [[0,j], [ncols, shpout[0]]], - flt2int='clip',scalevalue=1.0 ) - #self.patternfileout.write_data(newpatterns=dataout,patStartCount=[j*ncols,shpout[0]*shpout[1]], - # flt2int='clip',scalevalue=1.0 ) - #return dataout - #sigma[j:j+rowstartcount[1],:] += \ - # sigchunk[rowstartcount[0]:rowstartcount[0]+rowstartcount[1],:] + # set up a job queue + ndone = 0 + jqueue = [] + jobid = 0 + # if verbose >= 2: + # print('\n', end='') + for rowchunk in range(chunks[1]): + rstart = chunks[3][rowchunk, 0] + rend = chunks[3][rowchunk, 1] + nrowchunk = rend - rstart + + rstartcalc = sr if (rowchunk > 0) else 0 + rendcalc = nrowchunk - sr if (rowchunk < (chunks[1] - 1)) else nrowchunk + nrowcalc = np.int64(rendcalc - rstartcalc) + + for colchunk in range(chunks[0]): + cstart = chunks[2][colchunk, 0] + cend = chunks[2][colchunk, 1] + ncolchunk = cend - cstart + + cstartcalc = sr if (colchunk > 0) else 0 + cendcalc = ncolchunk - sr if (colchunk < (chunks[0] - 1)) else ncolchunk + ncolcalc = np.int64(cendcalc - cstartcalc) + + job = {"rstart": rstart, + "rend": rend, + "nrowchunk": nrowchunk, + "rstartcalc": rstartcalc, + "rendcalc": rendcalc, + "nrowcalc": nrowcalc, + "cstart": cstart, + "cend": cend, + "ncolchunk": ncolchunk, + "cstartcalc": cstartcalc, + "cendcalc": cendcalc, + "ncolcalc": ncolcalc, + "nattempts": -1, + "jobid": jobid} + jobid += 1 + jqueue.append(job) + + + + while len(jqueue) > 0: + j = jqueue.pop(0) + j["nattempts"] += 1 + + rstart = j["rstart"] + cstart = j["cstart"] + rend = j["rend"] + cend = j["cend"] + cstartcalc = j["cstartcalc"] + rstartcalc = j["rstartcalc"] + ncolchunk = j["ncolchunk"] + nrowchunk = j["nrowchunk"] + ncolcalc = j["ncolcalc"] + nrowcalc = j["nrowcalc"] + + calclim = np.array([cstartcalc, rstartcalc, ncolcalc, nrowcalc], dtype=np.int64) + + data, xyloc = patternfile.read_data(patStartCount=[[ cstart, rstart], [ncolchunk, nrowchunk]], + convertToFloat=False, returnArrayOnly=True) + + shpdata = data.shape + + if backsub is True: + data = self.backsub(data) + + data = data.reshape(shpdata[0], phw) + + if calcsigma is True: + sigchunk, tmp = self.sigma_numba(data, 1, nrowchunk, ncolchunk, + [0,nrowchunk], [0,ncolchunk], + indices, saturation_protect) + del tmp + tmp = np.minimum(sigma[rstart:rend,cstart:cend], sigchunk) + sigma[rstart:rend,cstart:cend] = tmp + else: + sigchunk = sigma[rstart:rend, cstart:cend ] + + + + dataout = self.nlpar_nb(data, lam, sr, dthresh, sigchunk, + nrowchunk, ncolchunk, calclim, indices, saturation_protect, diff_offset=diff_offset) + + # nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim=np.array([-1, -1, -1, -1], dtype= np.int64), + # indices=np.array([-1], dtype= np.int64), saturation_protect=True, + # diff_offset=np.float32(0.0)) + + dataout = dataout[rstartcalc: rstartcalc + nrowcalc, + cstartcalc:cstartcalc + ncolcalc, :] + shpout = dataout.shape + dataout = dataout.reshape(shpout[0] * shpout[1], pheight, pwidth) + if rescale == True: + for i in range(dataout.shape[0]): + temp = dataout[i, :, :] + temp -= temp.min() + temp *= np.float32(mxval) / temp.max() + dataout[i, :, :] = temp + + patternfileout.write_data(newpatterns=dataout, + patStartCount=[[np.int64(cstart + cstartcalc), np.int64(rstart + rstartcalc)], + [ncolcalc, nrowcalc]], + flt2int='clip', scalevalue=1.0) + ndone += 1 + if verbose >= 2: + print("tiles complete: ", ndone, "/", nchunks, sep='', end='\r') + + + # for j in range(0,nrows,chunksize): + # #print('Row start', j) + # if verbose >= 2: + # print("begin row: ", j, "/", nrows, sep='', end='\r') + # + # rowstartread = np.int64(max(0, j-sr)) + # rowend = min(j + chunksize+sr,nrows) + # + # if (rowend - rowstartread) < (2*sr+1): + # rowstartread = np.int64(max(0, rowend - (2*sr+1))) + # rowcountread = np.int64(rowend-rowstartread) + # data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], + # convertToFloat=True,returnArrayOnly=True) + # + # shpdata = data.shape + # + # if backsub is True: + # data = self.backsub(data) + # + # + # data = data.reshape(shpdata[0], phw) + # + # rowstartcount = np.asarray([0,rowcountread],dtype=np.int64) + # if calcsigma is True: + # sigchunk, tmp = self.sigma_numba(data,1,rowcountread,ncols,rowstartcount,colstartcount,indices,saturation_protect) + # del tmp + # tmp = (sigma[rowstartread:rowend,:] < sigchunk).choose(sigchunk,sigma[rowstartread:rowend,:]) + # sigma[rowstartread:rowend,:] = tmp + # else: + # sigchunk = sigma[rowstartread:rowend,:] + # + # #dataout = data + # + # dataout = self.nlpar_nb(data,lam, sr, dthresh, sigchunk, + # rowcountread,ncols,indices,saturation_protect, diff_offset=diff_offset) + # + # + # dataout = dataout.reshape(rowcountread, ncols, phw) + # dataout = dataout[j-rowstartread:, :, : ] + # shpout = dataout.shape + # dataout = dataout.reshape(shpout[0]*shpout[1], pheight, pwidth) + # if rescale == True: + # for i in range(dataout.shape[0]): + # temp = dataout[i,:,:] + # temp -= temp.min() + # temp *= np.float32(mxval)/temp.max() + # dataout[i,:,:] = temp + # + # patternfileout.write_data(newpatterns=dataout,patStartCount = [[0,j], [ncols, shpout[0]]], + # flt2int='clip',scalevalue=1.0 ) + # #self.patternfileout.write_data(newpatterns=dataout,patStartCount=[j*ncols,shpout[0]*shpout[1]], + # # flt2int='clip',scalevalue=1.0 ) + # #return dataout + # #sigma[j:j+rowstartcount[1],:] += \ + # # sigchunk[rowstartcount[0]:rowstartcount[0]+rowstartcount[1],:] if verbose >= 2: @@ -674,7 +793,9 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) - def nlpar_nb(data,lam, sr, dthresh, sigma, nrows,ncols,indices,saturation_protect=True, diff_offset = np.float32(0.0)): + def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim = np.array([-1, -1, -1, -1], dtype= np.int64), + indices_in=np.array([-1], dtype= np.int64), saturation_protect=True, + diff_offset=np.float32(0.0)): def getpairid(idx0, idx1): idx0_t = int(idx0) idx1_t = int(idx1) @@ -684,9 +805,33 @@ def getpairid(idx0, idx1): pairid = idx1_t + (idx0_t << 32) return numba.uint64(pairid) + # set some defaults + # calclim = np.array([cstartcalc, rstartcalc, ncolcalc, nrowcalc], dtype=np.int64) + if calclim[0] <= -1: + calclim[0] = 0 + if calclim[1] <= -1: + calclim[1] = 0 + if calclim[2] <= -1: + calclim[2] = ncols-calclim[0] + if calclim[3] <= -1: + calclim[3] = nrows-calclim[1] + + + lam2 = 1.0 / lam**2 dataout = np.zeros_like(data, np.float32) shpdata = data.shape + + # set defaults ... normally will not be needed. + if indices_in.size == 1: + if indices_in[0] == -1: + indices = np.arange(shpdata[1], dtype=np.int64) + else: + indices = indices_in.astype(np.int64) + else: + indices = indices_in.astype(np.int64) + + shpind = indices.shape winsz = np.int32((2*sr+1)**2) diff_step = np.zeros((winsz), dtype=np.float32) @@ -696,11 +841,13 @@ def getpairid(idx0, idx1): mxval += np.float32(1.0) else: mxval *= np.float32(0.999) - for i in numba.prange(ncols): + for ii in numba.prange(calclim[2]): + i = calclim[0]+ii winstart_x = max((i - sr),0) - max((i + sr - (ncols - 1)),0) winend_x = min((i + sr),(ncols - 1)) + max((sr - i),0) + 1 pairdict = numba.typed.Dict.empty(key_type=numba.core.types.uint64,value_type=numba.core.types.float32) - for j in range(nrows): + for jj in range(calclim[2]): + j = calclim[1]+jj winstart_y = max((j - sr),0) - max((j + sr - (nrows - 1)),0) winend_y = min((j + sr),(nrows - 1)) + max((sr - j),0) + 1 From 466c998248f43328a30558cb12ba4c196c0f04b8 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Wed, 19 Nov 2025 16:27:48 -0500 Subject: [PATCH 63/92] Check point Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 6cb5be6..62323d4 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -482,7 +482,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, calclim = np.array([cstartcalc, rstartcalc, ncolcalc, nrowcalc], dtype=np.int64) data, xyloc = patternfile.read_data(patStartCount=[[ cstart, rstart], [ncolchunk, nrowchunk]], - convertToFloat=False, returnArrayOnly=True) + convertToFloat=True, returnArrayOnly=True) shpdata = data.shape @@ -509,7 +509,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, # nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim=np.array([-1, -1, -1, -1], dtype= np.int64), # indices=np.array([-1], dtype= np.int64), saturation_protect=True, # diff_offset=np.float32(0.0)) - + dataout = dataout.reshape(nrowchunk, ncolchunk, -1) dataout = dataout[rstartcalc: rstartcalc + nrowcalc, cstartcalc:cstartcalc + ncolcalc, :] shpout = dataout.shape @@ -846,7 +846,7 @@ def getpairid(idx0, idx1): winstart_x = max((i - sr),0) - max((i + sr - (ncols - 1)),0) winend_x = min((i + sr),(ncols - 1)) + max((sr - i),0) + 1 pairdict = numba.typed.Dict.empty(key_type=numba.core.types.uint64,value_type=numba.core.types.float32) - for jj in range(calclim[2]): + for jj in range(calclim[3]): j = calclim[1]+jj winstart_y = max((j - sr),0) - max((j + sr - (nrows - 1)),0) winend_y = min((j + sr),(nrows - 1)) + max((sr - j),0) + 1 @@ -880,7 +880,7 @@ def getpairid(idx0, idx1): d1 = data[indx_nn,indices[q]] if (d1 < mxval) and (d0 < mxval): n2 += np.float32(1.0) - d2 += (d0 - d1) ** np.int32(2) + d2 += np.float32(d0 - d1) ** np.int32(2) d2 -= n2*(sigma0+sigma1) dnorm = (sigma1 + sigma0)*np.sqrt(np.float32(2.0)*n2) if dnorm > np.float32(1.e-8): From b815dd358bb879d82d3c64b31ca7e491369381ed Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Thu, 20 Nov 2025 13:04:45 -0500 Subject: [PATCH 64/92] Moved sigma calculation (and also lambda optimization) to block-based chunks vs. row based. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 191 +++++++++++++++--------------- pyebsdindex/opencl/nlpar_cl.py | 24 ++-- pyebsdindex/opencl/nlpar_clray.py | 10 +- 3 files changed, 118 insertions(+), 107 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 62323d4..1a2bc15 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -201,28 +201,13 @@ def opt_lambda(self,chunksize=0,saturation_protect=True,automask=True, backsub = target_weights = np.asarray(target_weights) def loptfunc(lam,d2,tw,dthresh): - temp = (d2 > dthresh).choose(dthresh, d2) + temp = np.maximum(d2, dthresh) dw = np.exp(-(temp) / lam ** 2) w = np.sum(dw, axis=2) + 1e-12 metric = np.mean(np.abs(tw - 1.0 / w)) return metric - - @numba.njit(fastmath=True, cache=True,parallel=True) - def d2norm(d2, n2, dij, sigma): - shp = d2.shape - s2 = sigma**2 - for j in numba.prange(shp[0]): - for i in range(shp[1]): - for q in range(shp[2]): - if n2[j,i,q] > 0: - ii = dij[j,i,q,1] - jj = dij[j,i,q,0] - s2_12 = (s2[j,i]+s2[jj,ii]) - d2[j,i,q] -= n2[j,i,q] * s2_12 - d2[j,i,q] /= s2_12*np.sqrt(2.0*n2[j,i,q]) - patternfile = self.getinfileobj() patternfile.read_header() nrows = np.uint64(self.nrows) #np.uint64(patternfile.nRows) @@ -233,12 +218,6 @@ def d2norm(d2, n2, dij, sigma): phw = pheight * pwidth nn = 1 - if chunksize <= 0: - chunksize = np.maximum(1, np.round(1e9 / phw / ncols) ) # keeps the chunk at about 4GB - chunksize = np.minimum(nrows,chunksize) - print("Chunk size set to nrows:", int(chunksize)) - chunksize = np.int64(chunksize) - nn = np.uint64(nn) if (automask is True) and (self.mask is None): @@ -247,68 +226,67 @@ def d2norm(d2, n2, dij, sigma): self.mask = np.ones((pheight,pwidth),dtype=np.uint8) self.mask = (self.mask).astype(np.uint8) - indices = np.asarray((self.mask.flatten().nonzero())[0],np.uint64) - - sigma = np.zeros((nrows,ncols),dtype=np.float32)+1e24 - colstartcount = np.asarray([0,ncols],dtype=np.int64) - dthresh = np.float32(dthresh) - lamopt_values = [] - - for j in range(0,nrows,chunksize): - - if verbose >= 2: - print("begin row: ", j, "/", nrows, sep='', end='\r') - #print('Block',j) - #rowstartread = np.int64(max(0,j - nn)) - rowstartread = np.int64(j) - rowend = min(j + chunksize + nn,nrows) - rowcountread = np.int64(rowend - rowstartread) - data, xyloc = patternfile.read_data(patStartCount=[[0,rowstartread],[ncols,rowcountread]], - convertToFloat=True,returnArrayOnly=True) - shp = data.shape + # for j in range(0,nrows,chunksize): + # + # if verbose >= 2: + # print("begin row: ", j, "/", nrows, sep='', end='\r') + # #print('Block',j) + # + # #rowstartread = np.int64(max(0,j - nn)) + # rowstartread = np.int64(j) + # rowend = min(j + chunksize + nn,nrows) + # rowcountread = np.int64(rowend - rowstartread) + # data, xyloc = patternfile.read_data(patStartCount=[[0,rowstartread],[ncols,rowcountread]], + # convertToFloat=True,returnArrayOnly=True) + # + # shp = data.shape + # + # if backsub is True: + # data = self.backsub(data) + # #back = np.mean(data, axis=0) + # #back -= np.mean(back) + # #data -= back + # data = data.reshape(shp[0], phw) + # + # rowstartcount = np.asarray([0,rowcountread],dtype=np.int64) + # sigchunk, (d2,n2, dij) = self.sigma_numba(data,nn,rowcountread,ncols,rowstartcount,colstartcount,indices,saturation_protect) + # tmp = (sigma[j:j + rowstartcount[1],:] < sigchunk).choose( sigchunk, sigma[j:j + rowstartcount[1],:]) + # sigma[j:j + rowstartcount[1],:] = tmp - if backsub is True: - data = self.backsub(data) - #back = np.mean(data, axis=0) - #back -= np.mean(back) - #data -= back - data = data.reshape(shp[0], phw) - rowstartcount = np.asarray([0,rowcountread],dtype=np.int64) - sigchunk, (d2,n2, dij) = self.sigma_numba(data,nn,rowcountread,ncols,rowstartcount,colstartcount,indices,saturation_protect) - tmp = (sigma[j:j + rowstartcount[1],:] < sigchunk).choose( sigchunk, sigma[j:j + rowstartcount[1],:]) - sigma[j:j + rowstartcount[1],:] = tmp + sigma, (d2,n2) = self.calcsigma(chunksize=chunksize, nn=nn, saturation_protect = saturation_protect, automask = automask) + #print(d2.max(), d2.min()) + #d2 = d2norm(d2, n2, dij, sigma) + #print(d2.max(), d2.min()) - d2norm(d2, n2, dij, sigchunk) + lamopt_values = [] + stride = 1 if sigma.size < 1e6 else 2 #for large scans cut down on the optimization time. + for tw in target_weights: - lamopt_values_chnk = [] - for tw in target_weights: lam = 1.0 - lambopt1 = opt.minimize(loptfunc,lam,args=(d2,tw,dthresh),method='Nelder-Mead', + lambopt1 = opt.minimize(loptfunc,lam,args=(d2[0::stride,0::stride,:],tw,dthresh),method='Nelder-Mead', bounds = [[0.001, 10.0]],options={'fatol': 0.0001}) - lamopt_values_chnk.append(lambopt1['x']) + lamopt_values.append(lambopt1['x']) - lamopt_values.append(lamopt_values_chnk) if verbose >= 2: print('', end='') lamopt_values = np.asarray(lamopt_values) - if verbose >=1: - print("Range of lambda values: ", np.mean(lamopt_values, axis = 0).flatten()) - print("Optimal Choice: ", np.median(np.mean(lamopt_values, axis = 0))) + print("Range of lambda values: ", lamopt_values.flatten()) + print("Optimal Choice: ", np.median(lamopt_values)) if autoupdate == True: - self.lam = np.median(np.mean(lamopt_values, axis = 0)) + self.lam = np.median(lamopt_values) if self.sigma is None: self.sigma = sigma - return np.mean(lamopt_values, axis = 0).flatten() + return lamopt_values.flatten() def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, diff_offset=None, @@ -593,7 +571,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, numba.set_num_threads(nthreadpos) return str(patternfileout.filepath) - def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): + def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True, verbose = 2, **kwargs): self.sigmann = nn patternfile = self.getinfileobj() @@ -605,14 +583,16 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): pheight = np.uint64(patternfile.patternH) phw = pheight*pwidth + nn = np.uint64(nn) + nnn = np.uint64((2*nn+1)**2) if chunksize <= 0: - chunksize = np.round(2e9/phw/ncols) # keeps the chunk at about 8GB - chunksize = np.minimum(nrows,chunksize) - print("Chunk size set to nrows:", int(chunksize)) + sysram = (psutil.virtual_memory()).total + chunksize = np.minimum(32e9, sysram // 4) chunksize = np.int64(chunksize) + chunks = self._calcchunks([pwidth, pheight], ncols, nrows, target_bytes=chunksize, + col_overlap=nn, row_overlap=nn) - nn = np.uint64(nn) if (automask is True) and (self.mask is None): self.mask = (self.automask(pheight,pwidth)) @@ -621,34 +601,46 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): indices = np.asarray( (self.mask.flatten().nonzero())[0], np.uint64) - sigma = np.zeros((nrows, ncols), dtype=np.float32) - #d_nn = np.zeros((nrows, ncols, int((2*nn+1)**2)), dtype=np.float32) - colstartcount = np.asarray([0,ncols],dtype=np.int64) + sigma = np.zeros((nrows, ncols), dtype=np.float32)+1e18 - for j in range(0,nrows,chunksize): - rowstartread = np.int64(max(0, j-nn)) - rowend = min(j + chunksize+nn,nrows) - if (rowend - rowstartread) < (3): - rowstartread = np.int64(max(0, rowend - (3))) - rowcountread = np.int64(rowend-rowstartread) - data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], - convertToFloat=True,returnArrayOnly=True) - - shp = data.shape - data = data.reshape(data.shape[0], phw) - - #data = None - if rowend == nrows: - rowstartcount = np.asarray([j-rowstartread,rowcountread - (j-rowstartread) ], dtype=np.int64) - else: - rowstartcount = np.asarray([j-rowstartread,chunksize ], dtype=np.int64) + n2 = np.zeros((nrows, ncols, nnn), dtype=np.float32) + d2 = np.zeros((nrows, ncols, nnn), dtype=np.float32) - sigchunk, temp = self.sigma_numba(data,nn, rowcountread,ncols,rowstartcount,colstartcount,indices,saturation_protect) + ndone = 0 + nchunks = int(chunks[1] * chunks[0]) + + for rowchunk in range(chunks[1]): + rstart = chunks[3][rowchunk, 0] + rend = chunks[3][rowchunk, 1] + nrowchunk = rend - rstart + + for colchunk in range(chunks[0]): + cstart = chunks[2][colchunk, 0] + cend = chunks[2][colchunk, 1] + ncolchunk = cend - cstart + data, xyloc = patternfile.read_data(patStartCount=[[cstart, rstart], [ncolchunk, nrowchunk]], + convertToFloat=True, returnArrayOnly=True) + + shp = data.shape + data = data.reshape(data.shape[0], phw) - sigma[j:j+rowstartcount[1],:] += \ - sigchunk[rowstartcount[0]:rowstartcount[0]+rowstartcount[1],:] - return sigma + + sigchunk, (d2chunk, n2chunk) = self.sigma_numba(data,nn, nrowchunk,ncolchunk, + np.array([0,nrowchunk ], dtype=np.uint64), + np.array([0,ncolchunk],dtype=np.uint64), + indices,saturation_protect) + + sigma[rstart:rend, cstart:cend] = np.minimum(sigma[rstart:rend, cstart:cend], sigchunk) + # temp = (d2 > thresh).choose(dthresh, d2) + n2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [n2chunk], default=n2[rstart:rend, cstart:cend,:]) + d2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [d2chunk], default=d2[rstart:rend, cstart:cend,:]) + #dij[rstart:rend, cstart:cend, :] = dijchunk + ndone += 1 + if verbose >= 2: + print("tiles complete: ", ndone, "/", nchunks, sep='', end='\r') + + return sigma, (d2, n2) def auto_nlpar(self, filename = None, fileout=None, searchradius=None, lindex = 1, **kwargs): if filename is not None: @@ -787,9 +779,20 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s count += 1 sigma[j,i] = np.sqrt(mind) - #if sigma[j,i] > 1e12: - # print(sigma[j,i], dout[j,i,:], nout[i,j,:]) - return sigma,( dout, nout, dij) + + # normalize the distances for lambda optimization. + shp = dout.shape + s2 = sigma ** 2 + for j in numba.prange(shp[0]): + for i in range(shp[1]): + for q in range(shp[2]): + if nout[j, i, q] > 0: + ii = dij[j, i, q, 1] + jj = dij[j, i, q, 0] + s2_12 = (s2[j, i] + s2[jj, ii]) + dout[j, i, q] -= nout[j, i, q] * s2_12 + dout[j, i, q] /= s2_12 * np.sqrt(2.0 * nout[j, i, q]) + return sigma,(dout, nout) @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index f4b41ca..8ab5457 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -111,15 +111,13 @@ def loptfunc(lam, d2, tw, dthresh): #sigmapad = np.pad(sigma, 1, mode='reflect') #d2normcl(d2, n2, sigmapad) - #print(d2.min(), d2.max(), d2.mean()) - - lamopt_values_chnk = [] + stride = 1 if sigma.size < 1e6 else 2 for tw in target_weights: - stride = 1 if sigma.size < 1e6 else 10 lam = 1.0 - lambopt1 = sp_opt.minimize(loptfunc, lam, args=(d2[0::stride, :], tw, dthresh), method='Nelder-Mead', + lambopt1 = sp_opt.minimize(loptfunc, lam, args=(d2[0::stride,0::stride, :], tw, dthresh), method='Nelder-Mead', bounds=[[0.001, 10.0]], options={'fatol': 0.0001}) + lamopt_values.append(lambopt1['x']) #lamopt_values.append(lamopt_values_chnk) @@ -127,7 +125,7 @@ def loptfunc(lam, d2, tw, dthresh): print("Range of lambda values: ", lamopt_values.flatten()) print("Optimal Choice: ", np.median(lamopt_values)) if autoupdate == True: - self.lam = np.median(np.mean(lamopt_values, axis=0)) + self.lam = np.median(lamopt_values) if self.sigma is None: self.sigma = sigma return lamopt_values.flatten() @@ -288,12 +286,20 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, normalize_d=Fa #sigmachunk_gpu.release() queue.finish() - countnn[rstart:rend, cstart:cend] = countchunk[0:int(ncolchunk*nrowchunk), :].reshape(nrowchunk, ncolchunk, nnn) - dist[rstart:rend, cstart:cend] = distchunk[0:int(ncolchunk*nrowchunk), :].reshape(nrowchunk, ncolchunk, nnn) + #countnn[rstart:rend, cstart:cend] = countchunk[0:int(ncolchunk*nrowchunk), :].reshape(nrowchunk, ncolchunk, nnn) + countchunkt = countchunk[0:int(ncolchunk*nrowchunk)].reshape(nrowchunk, ncolchunk, nnn) + distchunkt = distchunk[0:int(ncolchunk*nrowchunk)].reshape(nrowchunk, ncolchunk, nnn) + countnn[rstart:rend, cstart:cend] = np.select([countchunkt >0], + [countchunkt], default=countnn[rstart:rend, cstart:cend] ) + dist[rstart:rend, cstart:cend] = np.select([countchunkt > 0], + [distchunkt], default=dist[rstart:rend, cstart:cend]) + + #dist[rstart:rend, cstart:cend] = distchunk[0:int(ncolchunk*nrowchunk), :].reshape(nrowchunk, ncolchunk, nnn) sigma[rstart:rend, cstart:cend] = np.minimum(sigma[rstart:rend, cstart:cend], sigmachunk) + ndone += 1 if verbose >= 2: print("tiles complete: ", ndone, "/", nchunks, sep='', end='\r') - ndone +=1 + dist_local.release() count_local.release() datapad_gpu.release() diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index 6f7e2e8..3266bef 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -244,10 +244,12 @@ def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, normaliz nrowcalc = job.nrowcalc ncolcalc = job.ncolcalc - countnn[rstart:rend, cstart:cend] = countchunk[0:int(ncolchunk * nrowchunk), :].reshape(nrowchunk, - ncolchunk, nnn) - dist[rstart:rend, cstart:cend] = distchunk[0:int(ncolchunk * nrowchunk), :].reshape(nrowchunk, ncolchunk, - nnn) + countchunkt = countchunk[0:int(ncolchunk*nrowchunk)].reshape(nrowchunk, ncolchunk, nnn) + distchunkt = distchunk[0:int(ncolchunk*nrowchunk)].reshape(nrowchunk, ncolchunk, nnn) + countnn[rstart:rend, cstart:cend] = np.select([countchunkt > 0], + [countchunkt], default=countnn[rstart:rend, cstart:cend]) + dist[rstart:rend, cstart:cend] = np.select([countchunkt > 0], + [distchunkt], default=dist[rstart:rend, cstart:cend]) sigma[rstart:rend, cstart:cend] = np.minimum(sigma[rstart:rend, cstart:cend], sigmachunk) idlewrker.append(busywrker.pop(indx)) From f6ba08d9916bf7b4806329345a5b4c160023f1cf Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Fri, 21 Nov 2025 16:34:42 -0500 Subject: [PATCH 65/92] More consistent return format. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 1a2bc15..b1f5cc3 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -258,7 +258,7 @@ def loptfunc(lam,d2,tw,dthresh): # sigma[j:j + rowstartcount[1],:] = tmp - sigma, (d2,n2) = self.calcsigma(chunksize=chunksize, nn=nn, saturation_protect = saturation_protect, automask = automask) + sigma, d2,n2 = self.calcsigma(chunksize=chunksize, nn=nn, saturation_protect = saturation_protect, automask = automask) #print(d2.max(), d2.min()) #d2 = d2norm(d2, n2, dij, sigma) @@ -470,12 +470,12 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, data = data.reshape(shpdata[0], phw) if calcsigma is True: - sigchunk, tmp = self.sigma_numba(data, 1, nrowchunk, ncolchunk, + sigchunk = self.sigma_numba(data, 1, nrowchunk, ncolchunk, [0,nrowchunk], [0,ncolchunk], - indices, saturation_protect) - del tmp - tmp = np.minimum(sigma[rstart:rend,cstart:cend], sigchunk) - sigma[rstart:rend,cstart:cend] = tmp + indices, saturation_protect)[0] + + sigchunk = np.minimum(sigma[rstart:rend,cstart:cend], sigchunk) + sigma[rstart:rend,cstart:cend] = sigchunk else: sigchunk = sigma[rstart:rend, cstart:cend ] @@ -626,7 +626,7 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True, verbo - sigchunk, (d2chunk, n2chunk) = self.sigma_numba(data,nn, nrowchunk,ncolchunk, + sigchunk, d2chunk, n2chunk = self.sigma_numba(data,nn, nrowchunk,ncolchunk, np.array([0,nrowchunk ], dtype=np.uint64), np.array([0,ncolchunk],dtype=np.uint64), indices,saturation_protect) @@ -640,7 +640,7 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True, verbo if verbose >= 2: print("tiles complete: ", ndone, "/", nchunks, sep='', end='\r') - return sigma, (d2, n2) + return sigma, d2, n2 def auto_nlpar(self, filename = None, fileout=None, searchradius=None, lindex = 1, **kwargs): if filename is not None: @@ -792,7 +792,7 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s s2_12 = (s2[j, i] + s2[jj, ii]) dout[j, i, q] -= nout[j, i, q] * s2_12 dout[j, i, q] /= s2_12 * np.sqrt(2.0 * nout[j, i, q]) - return sigma,(dout, nout) + return sigma, dout, nout @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) From 9e5e4a584436907746212c44a3ebf600a042e0b1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 24 Nov 2025 20:28:22 +0000 Subject: [PATCH 66/92] Bump actions/checkout from 5 to 6 Bumps [actions/checkout](https://github.com/actions/checkout) from 5 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/publish.yml | 2 +- .github/workflows/tests.yml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index c57b3b1..dac4d85 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -29,7 +29,7 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing: id-token: write steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 - name: Set up Python uses: actions/setup-python@v6 diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index ba4d5a1..d8d8710 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -20,7 +20,7 @@ jobs: name: check manifest runs-on: ubuntu-latest steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 - uses: actions/setup-python@v6 with: @@ -49,7 +49,7 @@ jobs: DEPENDENCIES: matplotlib==3.3 numba==0.55.1 ray[default]==2.9 LABEL: -oldest steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v6 From 37b9d38e6468faca7aeceea6edd9f91aac63cff8 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Mon, 24 Nov 2025 16:24:40 -0500 Subject: [PATCH 67/92] Adjusted stem_scale scaling to sqrt() [not log()]. Corrected method inheritance issues. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 42 ++++++++++++++++++------------- pyebsdindex/opencl/nlpar_cl.py | 30 +++++++++++----------- pyebsdindex/opencl/nlpar_clray.py | 11 +++++--- 3 files changed, 48 insertions(+), 35 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index f34553b..f1d5e90 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -34,6 +34,7 @@ from pyebsdindex import ebsd_pattern + #from os import environ #environ["NUMBA_CACHE_DIR"] = str(tempdir) @@ -195,9 +196,8 @@ def getoutfileobj(self): else: return None - def opt_lambda(self,chunksize=0,saturation_protect=True,automask=True, backsub = False, - target_weights=(0.5, 0.34, 0.25), dthresh=0.0, autoupdate=True, - stem_scale = False, + def opt_lambda_cpu(self, target_weights=(0.5, 0.34, 0.25), dthresh=0.0, autoupdate=True, + automask = True, verbose = 2, **kwargs): target_weights = np.asarray(target_weights) @@ -260,8 +260,7 @@ def loptfunc(lam,d2,tw,dthresh): # sigma[j:j + rowstartcount[1],:] = tmp - sigma, d2,n2 = self.calcsigma_cpu(chunksize=chunksize, nn=nn, - saturation_protect = saturation_protect, automask = automask,stem_scale = stem_scale ) + sigma, d2,n2 = self.calcsigma_cpu(**kwargs) #print(d2.max(), d2.min()) #d2 = d2norm(d2, n2, dij, sigma) @@ -291,7 +290,7 @@ def loptfunc(lam,d2,tw,dthresh): self.sigma = sigma return lamopt_values.flatten() - def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, + def calcnlpar_cpu(self, chunksize=0, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, stem_scale = False, filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, diff_offset=None, @@ -468,8 +467,10 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, convertToFloat=True, returnArrayOnly=True) if stem_scale is True: datamin = data.min() - data = data - datamin + 1 - data = np.log(data) + # data = data - datamin + 1 + # data = np.log(data) + data = data - datamin + data = np.sqrt(data) shpdata = data.shape @@ -500,7 +501,8 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, dataout = dataout[rstartcalc: rstartcalc + nrowcalc, cstartcalc:cstartcalc + ncolcalc, :] if stem_scale is True: - dataout = np.exp(dataout) - 1 + datamin + #dataout = np.exp(dataout) - 1 + datamin + dataout = dataout**2 + datamin shpout = dataout.shape dataout = dataout.reshape(shpout[0] * shpout[1], pheight, pwidth) if rescale == True: @@ -582,7 +584,8 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, numba.set_num_threads(nthreadpos) return str(patternfileout.filepath) - def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True, stem_scale=False, verbose = 2, **kwargs): + def calcsigma_cpu(self,chunksize=0,nn=1,saturation_protect=True,automask=True, stem_scale=False, verbose = 2, **kwargs): + self.sigmann = nn patternfile = self.getinfileobj() @@ -633,9 +636,10 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True, stem_ convertToFloat=True, returnArrayOnly=True) if stem_scale is True: - data = data - data.min() + 1 - data = np.log(data) - + #data = data - data.min() + 1 + #data = np.log(data) + data = data - data.min() + data = np.sqrt(data) shp = data.shape data = data.reshape(data.shape[0], phw) @@ -927,11 +931,15 @@ def getpairid(idx0, idx1): #print('_______', '\n') return dataout - def opt_lambda_cpu(self,**kwargs): # helper function - return self.opt_lambda(**kwargs) + def calcsigma(self,**kwargs): # helper function + return self.calcsigma_cpu(**kwargs) + + + def opt_lambda(self, **kwargs): # helper function + return self.opt_lambda_cpu(**kwargs) - def calcnlpar_cpu(self, **kwargs): # helper function - return self.calcnlpar(**kwargs) + def calcnlpar(self, **kwargs): # helper function + return self.calcnlpar_cpu(**kwargs) def _calcchunks(self, patdim, ncol, nrow, target_bytes=2e9, col_overlap=0, row_overlap=0, col_offset=0, row_offset=0): diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index 936eec6..76accd8 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -56,7 +56,7 @@ def calcnlpar(self, **kwargs): def calcsigma(self,nn=1, saturation_protect=True,automask=True, stem_scale=False, - return_nndist=False, **kwargs): + **kwargs): self.sigmann = nn if self.sigmann > 7: print("Sigma optimization search limited to a search radius <= 7") @@ -64,23 +64,24 @@ def calcsigma(self,nn=1, saturation_protect=True,automask=True, stem_scale=False nn = 7 self.sigmann = nn - sig = self.calcsigma_cl(nn=nn, + sig, dnn, cnn = self.calcsigma_cl(nn=nn, saturation_protect=saturation_protect, automask=automask, stem_scale = stem_scale, **kwargs) - if return_nndist == True: - return sig - else: - return sig[0] + + + return sig, dnn, cnn + def opt_lambda_cpu(self, **kwargs): - return nlpar_cpu.NLPAR.opt_lambda(self, **kwargs) + return nlpar_cpu.NLPAR.opt_lambda_cpu(self, **kwargs) + def calcnlpar_cpu(self, **kwargs): - return nlpar_cpu.NLPAR.calcnlpar(self, **kwargs) + return nlpar_cpu.NLPAR.calcnlpar_cpu(self, **kwargs) def calcsigma_cpu(self,nn=1, saturation_protect=True,automask=True, **kwargs): - return nlpar_cpu.NLPAR.calcsigma(self, nn=nn, + return nlpar_cpu.NLPAR.calcsigma_cpu(self, nn=nn, saturation_protect=saturation_protect, automask=automask, **kwargs) def opt_lambda_cl(self, saturation_protect=True, automask=True, backsub=False, @@ -139,7 +140,7 @@ def loptfunc(lam, d2, tw, dthresh): def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, stem_scale = False, - normalize_d=False, gpu_id = None, verbose = 2, **kwargs): + normalize_d=True, gpu_id = None, verbose = 2, **kwargs): self.sigmann = nn if self.sigmann > 7: print("Sigma optimization search limited to a search radius <= 7") @@ -301,10 +302,10 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, #countnn[rstart:rend, cstart:cend] = countchunk[0:int(ncolchunk*nrowchunk), :].reshape(nrowchunk, ncolchunk, nnn) countchunkt = countchunk[0:int(ncolchunk*nrowchunk)].reshape(nrowchunk, ncolchunk, nnn) distchunkt = distchunk[0:int(ncolchunk*nrowchunk)].reshape(nrowchunk, ncolchunk, nnn) - countnn[rstart:rend, cstart:cend] = np.select([countchunkt >0], - [countchunkt], default=countnn[rstart:rend, cstart:cend] ) - dist[rstart:rend, cstart:cend] = np.select([countchunkt > 0], - [distchunkt], default=dist[rstart:rend, cstart:cend]) + countnn[rstart:rend, cstart:cend,:] = np.select([countchunkt >0], + [countchunkt], default=countnn[rstart:rend, cstart:cend,:] ) + dist[rstart:rend, cstart:cend,:] = np.select([countchunkt > 0], + [distchunkt], default=dist[rstart:rend, cstart:cend,:]) #dist[rstart:rend, cstart:cend] = distchunk[0:int(ncolchunk*nrowchunk), :].reshape(nrowchunk, ncolchunk, nnn) sigma[rstart:rend, cstart:cend] = np.minimum(sigma[rstart:rend, cstart:cend], sigmachunk) @@ -318,6 +319,7 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, queue.flush() queue = None self.sigma = sigma + return sigma, dist, countnn diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index bc4f54c..d368ead 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -55,7 +55,7 @@ def __init__( self,filename=None, **kwargs): def calcnlpar(self, **kwargs): return self.calcnlpar_clray(**kwargs) - def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist=False, + def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist=True, stem_scale=False, **kwargs): if self.sigmann > 7: @@ -82,7 +82,7 @@ def calcsigma_clsq(self, **kwargs): return nlpar_cl.NLPAR.calcsigma_cl(self, **kwargs) def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, - stem_scale = False, normalize_d=False, + stem_scale = False, normalize_d=True, gpu_id = None, verbose=2, **kwargs): self.patternfile = self.getinfileobj() self.sigmann = nn @@ -741,8 +741,11 @@ def runsigma_chunk(self,gpujob, nlparobj=None, stem_scale = False, **kwargs): [gpujob.ncolchunk, gpujob.nrowchunk]], convertToFloat=False, returnArrayOnly=True) if stem_scale == True: - data = data - data.min() + 1 - data = np.log(data) + #data = data - data.min() + 1 + #data = np.log(data) + data = data - data.min() + data = np.sqrt(data) + newdata = nlparobj._sigmachunkcalc_cl(data, gpujob, clparams=self.openCLParams, **kwargs) From 52a63c8d40a5cb8dd04db0f6b153594e6a4de5cc Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Mon, 24 Nov 2025 17:26:59 -0500 Subject: [PATCH 68/92] Decide on best NLPAR version on import. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar.py | 11 ++++++++++- pyebsdindex/nlpar_cpu.py | 2 +- pyebsdindex/opencl/nlpar_cl.py | 12 ++++-------- pyebsdindex/opencl/nlpar_clray.py | 18 +++++++++--------- 4 files changed, 24 insertions(+), 19 deletions(-) diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index eeb00f7..34c8e09 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -34,6 +34,7 @@ from pyebsdindex import _pyopencl_installed gpuisthere = False +gpusharedmem = True if _pyopencl_installed: # check for at least one gpu import pyopencl as cl @@ -44,6 +45,10 @@ g = p.get_devices(device_type=cl.device_type.GPU) if len(g) > 0: gpuisthere = True + + for gpu in g: + if gpu.host_unified_memory == False: + gpusharedmem = False g = None break plt = None @@ -52,7 +57,11 @@ if _ray_installed and gpuisthere: - from pyebsdindex.opencl.nlpar_clray import NLPAR + if gpusharedmem: + from pyebsdindex.opencl.nlpar_cl import NLPAR + else: + from pyebsdindex.opencl.nlpar_clray import NLPAR + elif gpuisthere and not _ray_installed: from pyebsdindex.opencl.nlpar_cl import NLPAR else: diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index f1d5e90..deedf93 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -197,7 +197,7 @@ def getoutfileobj(self): return None def opt_lambda_cpu(self, target_weights=(0.5, 0.34, 0.25), dthresh=0.0, autoupdate=True, - automask = True, + saturation_protect=True, automask=True, stem_scale = False, backsub=False, verbose = 2, **kwargs): target_weights = np.asarray(target_weights) diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index 76accd8..00a298b 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -43,14 +43,10 @@ def __init__( self, filename=None, **kwargs): self.useCPU = False - def opt_lambda(self,saturation_protect=True, automask=True, backsub=False, - target_weights=[0.5, 0.34, 0.25], dthresh=0.0, autoupdate=True, **kwargs): - return self.opt_lambda_cl(saturation_protect=saturation_protect, - automask=automask, - backsub=backsub, - target_weights=target_weights, - dthresh=dthresh, - autoupdate=autoupdate, **kwargs) + def opt_lambda(self, target_weights=[0.5, 0.34, 0.25], dthresh=0.0, autoupdate=True, + saturation_protect=True, automask=True, stem_scale = False, backsub=False, **kwargs): + return self.opt_lambda_cl(**kwargs) + def calcnlpar(self, **kwargs): return self.calcnlpar_cl(**kwargs) diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index d368ead..a78cbc6 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -64,15 +64,15 @@ def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist= nn = 7 self.sigmann = nn - sig = self.calcsigma_clray(nn=nn, + sig, dnn, cnn = self.calcsigma_clray(nn=nn, saturation_protect=saturation_protect, automask=automask, stem_scale = stem_scale, **kwargs) if return_nndist == True: - return sig + return sig, dnn, cnn else: - return sig[0] + return sig def calcnlpar_clsq(self, **kwargs): @@ -120,12 +120,12 @@ def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, ngpuwrker = 6 clparams.get_context(gpu_id=gpu_id, kfile = 'clnlpar.cl') clparams.get_queue() - if clparams.gpu[gpu_id].host_unified_memory: - return nlpar_cl.NLPAR.calcsigma_cl(self, nn=nn, saturation_protect=saturation_protect, - automask=automask, - normalize_d=normalize_d, - stem_scale=stem_scale, - gpu_id=gpu_id, **kwargs) + # if clparams.gpu[gpu_id].host_unified_memory: + # return nlpar_cl.NLPAR.calcsigma_cl(self, nn=nn, saturation_protect=saturation_protect, + # automask=automask, + # normalize_d=normalize_d, + # stem_scale=stem_scale, + # gpu_id=gpu_id, **kwargs) target_mem = clparams.gpu[gpu_id].max_mem_alloc_size // 2 max_mem = clparams.gpu[gpu_id].global_mem_size * 0.5 From 9c9b9b4b56872b25ee2ed1c90f2d65ae51faf962 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Mon, 24 Nov 2025 17:53:35 -0500 Subject: [PATCH 69/92] Better flexibility on choosing NLPAR implementation. Signed-off by: David Rowenhorst --- pyebsdindex/opencl/nlpar_cl.py | 2 +- pyebsdindex/opencl/nlpar_clray.py | 20 ++++++++++---------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index 00a298b..218b9e4 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -427,7 +427,7 @@ class OpenCLClalcError(Exception): #print(gpu_id) clparams.get_context(gpu_id=gpu_id, kfile ='clnlpar.cl') clparams.get_queue() - target_mem = min(clparams.queue.device.max_mem_alloc_size//4, np.int64(2e9)) + target_mem = min(clparams.queue.device.max_mem_alloc_size//4, np.int64(4e9)) #target_mem = min(clparams.queue.device.max_mem_alloc_size*3, np.int64(18e9)) ctx = clparams.ctx prg = clparams.prg diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index a78cbc6..dd2d139 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -486,16 +486,16 @@ def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturat # print(gpu_id) clparams.get_context(gpu_id=gpu_id, kfile = 'clnlpar.cl') clparams.get_queue() - if clparams.gpu[gpu_id].host_unified_memory: - return nlpar_cl.NLPAR.calcnlpar_cl(self, saturation_protect=saturation_protect, - automask=automask, - filename=filename, - fileout=fileout, - reset_sigma=reset_sigma, - backsub = backsub, - rescale = rescale, - gpu_id= gpu_id, - diff_offset=diff_offset) + # if clparams.gpu[gpu_id].host_unified_memory: + # return nlpar_cl.NLPAR.calcnlpar_cl(self, saturation_protect=saturation_protect, + # automask=automask, + # filename=filename, + # fileout=fileout, + # reset_sigma=reset_sigma, + # backsub = backsub, + # rescale = rescale, + # gpu_id= gpu_id, + # diff_offset=diff_offset) target_mem = clparams.gpu[gpu_id].max_mem_alloc_size//6 max_mem = clparams.gpu[gpu_id].global_mem_size*0.4 From deb7eda80c55e40dc5eef4d878e20d4d15619a4a Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 25 Nov 2025 09:08:34 -0500 Subject: [PATCH 70/92] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index deedf93..78a56f1 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -492,7 +492,9 @@ def calcnlpar_cpu(self, chunksize=0, searchradius=None, lam = None, dthresh = No dataout = self.nlpar_nb(data, lam, sr, dthresh, sigchunk, - nrowchunk, ncolchunk, calclim, indices, saturation_protect, diff_offset=diff_offset) + nrowchunk, ncolchunk, + calclim=calclim, indices_in=indices, + saturation_protect=saturation_protect, diff_offset=diff_offset) # nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim=np.array([-1, -1, -1, -1], dtype= np.int64), # indices=np.array([-1], dtype= np.int64), saturation_protect=True, @@ -815,7 +817,7 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) - def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim = np.array([-1, -1, -1, -1], dtype= np.int64), + def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim = np.array([-1, -1, -1, -1], dtype=np.int64), indices_in=np.array([-1], dtype= np.int64), saturation_protect=True, diff_offset=np.float32(0.0)): def getpairid(idx0, idx1): From 3d48e9e304a869f3a1f3ceb0fe22025db620ca9f Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 25 Nov 2025 09:18:15 -0500 Subject: [PATCH 71/92] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 78a56f1..01252c1 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -817,8 +817,8 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) - def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim = np.array([-1, -1, -1, -1], dtype=np.int64), - indices_in=np.array([-1], dtype= np.int64), saturation_protect=True, + def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim =[-1, -1, -1, -1], + indices_in=[-1], saturation_protect=True, diff_offset=np.float32(0.0)): def getpairid(idx0, idx1): idx0_t = int(idx0) @@ -847,13 +847,13 @@ def getpairid(idx0, idx1): shpdata = data.shape # set defaults ... normally will not be needed. - if indices_in.size == 1: + if np.array(indices_in).size == 1: if indices_in[0] == -1: indices = np.arange(shpdata[1], dtype=np.int64) else: - indices = indices_in.astype(np.int64) + indices = np.array(indices_in).astype(np.int64) else: - indices = indices_in.astype(np.int64) + indices = np.array(indices_in).astype(np.int64) shpind = indices.shape From 678fbf54e6d615da3ed56769e56df5dae621543d Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 25 Nov 2025 09:26:22 -0500 Subject: [PATCH 72/92] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 01252c1..78a56f1 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -817,8 +817,8 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) - def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim =[-1, -1, -1, -1], - indices_in=[-1], saturation_protect=True, + def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim = np.array([-1, -1, -1, -1], dtype=np.int64), + indices_in=np.array([-1], dtype= np.int64), saturation_protect=True, diff_offset=np.float32(0.0)): def getpairid(idx0, idx1): idx0_t = int(idx0) @@ -847,13 +847,13 @@ def getpairid(idx0, idx1): shpdata = data.shape # set defaults ... normally will not be needed. - if np.array(indices_in).size == 1: + if indices_in.size == 1: if indices_in[0] == -1: indices = np.arange(shpdata[1], dtype=np.int64) else: - indices = np.array(indices_in).astype(np.int64) + indices = indices_in.astype(np.int64) else: - indices = np.array(indices_in).astype(np.int64) + indices = indices_in.astype(np.int64) shpind = indices.shape From 1420226c251d064e6e297149381c36a3d443dbbc Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Wed, 26 Nov 2025 09:17:22 -0500 Subject: [PATCH 73/92] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 98 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 94 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 78a56f1..a71069f 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -647,15 +647,15 @@ def calcsigma_cpu(self,chunksize=0,nn=1,saturation_protect=True,automask=True, s - sigchunk, d2chunk, n2chunk = self.sigma_numba(data,nn, nrowchunk,ncolchunk, + sigchunk, d2chunk, n2chunk = self.sigma_numba2(data,nn, nrowchunk,ncolchunk, np.array([0,nrowchunk ], dtype=np.uint64), np.array([0,ncolchunk],dtype=np.uint64), indices,saturation_protect) sigma[rstart:rend, cstart:cend] = np.minimum(sigma[rstart:rend, cstart:cend], sigchunk) # temp = (d2 > thresh).choose(dthresh, d2) - n2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [n2chunk], default=n2[rstart:rend, cstart:cend,:]) - d2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [d2chunk], default=d2[rstart:rend, cstart:cend,:]) + n2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 1], [n2chunk], default=n2[rstart:rend, cstart:cend,:]) + d2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 1], [d2chunk], default=d2[rstart:rend, cstart:cend,:]) #dij[rstart:rend, cstart:cend, :] = dijchunk ndone += 1 if verbose >= 2: @@ -786,7 +786,7 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s for q in range(shpind[0]): d0 = data[indx_0, indices[q]] d1 = data[indx_nn, indices[q]] - if (d1 < mxval) and (d0 < mxval): + if (d1 < mxval) and (d0 < mxval): # this is a saturation protection n2 += 1.0 d2 += (d0 - d1)**2 nout[j,i,count] = n2 @@ -815,6 +815,96 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s dout[j, i, q] /= s2_12 * np.sqrt(2.0 * nout[j, i, q]) return sigma, dout, nout + @staticmethod + @numba.jit(nopython=True, cache=True, fastmath=False, parallel=True) + def sigma_numba2(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, saturation_protect=True): + sigma = np.zeros((nrows, ncols), dtype=np.float32) + dout = np.zeros((nrows, ncols, (nn * 2 + 1) ** 2), dtype=np.float32) + nout = np.zeros((nrows, ncols, (nn * 2 + 1) ** 2), dtype=np.float32) + dij = np.zeros((nrows, ncols, (nn * 2 + 1) ** 2, 2), dtype=np.uint64) + ncount = np.zeros((nrows, ncols), dtype=np.uint64) + shpdata = data.shape + + n0 = np.float32(shpdata[-1]) + shpind = indices.shape + mxval = np.max(data) + if saturation_protect == False: + mxval += 1.0 + else: + mxval *= 0.9961 + + for j in numba.prange(rowstartcount[0], rowstartcount[0] + rowstartcount[1]): + nn_r_start = j - nn if (j - nn) >= 0 else 0 + nn_r_end = (j + nn if (j + nn) < nrows else nrows - 1) + 1 + for i in numba.prange(colstartcount[0], colstartcount[0] + colstartcount[1]): + nn_c_start = i - nn if (i - nn) >= 0 else 0 + nn_c_end = (i + nn if (i + nn) < ncols else ncols - 1) + 1 + + mind = np.float32(1e24) + indx_0 = i + ncols * j + count = 0 + for j_nn in range(nn_r_start, nn_r_end): + for i_nn in range(nn_c_start, nn_c_end): + dij[j, i, count, 0] = np.uint64(j_nn) + dij[j, i, count, 1] = np.uint64(i_nn) # want to save this for labmda optimization + indx_nn = i_nn + ncols * j_nn + d2 = np.float32(0.0) + n2 = np.float32(1.0e-12) + nout[j, i, count] = n0 # want to save this for labmda optimization + if not ((i == i_nn) and (j == j_nn)): + for q in range(shpind[0]): + d0 = data[indx_0, indices[q]] + d1 = data[indx_nn, indices[q]] + if (d1 < mxval) and (d0 < mxval): # this is a saturation protection + n2 += 1.0 + d2 += (d0 - d1) ** 2 + + + if d2 >= 1.e-3: # sometimes EDAX collects the same pattern twice + s0 = d2 / np.float32(n2 * 2.0) + if s0 < mind: + mind = s0 + dout[j, i, count] = d2 # want to save this for labmda optimization + nout[j, i, count] = n2 + count += 1 + #else: + # d2 = 1e12 + + + #ncount[j,i] = count + #print('------') + dtemp = dout[j,i,0:count].ravel() + #print(dtemp) + ntemp = (nout[j,i,0:count].ravel()).astype(np.float32) + #print(ntemp) + dtemp = dtemp/(ntemp * 2.0) + #print(dtemp) + + mediandtemp = np.median(dtemp) + madtemp = np.median(np.absolute(dtemp-mediandtemp)) + #print(mediandtemp, madtemp) + #print('------') + zsc_mod = np.absolute(0.6745*(dtemp-mediandtemp)/madtemp) + #print([j, i], dtemp, zsc_mod) + if zsc_mod.min() >= 3.5: + sigma[j, i] = np.sqrt(mind) + else: + sigma[j, i] = np.sqrt(np.mean(dtemp[zsc_mod < 3.5 ])) + #print(sigma[j, i], dtemp, zsc_mod) + # normalize the distances for lambda optimization. + shp = dout.shape + s2 = sigma ** 2 + for j in numba.prange(shp[0]): + for i in range(shp[1]): + for q in range(shp[2]): + if nout[j, i, q] > 0: + ii = dij[j, i, q, 1] + jj = dij[j, i, q, 0] + s2_12 = (s2[j, i] + s2[jj, ii]) + dout[j, i, q] -= nout[j, i, q] * s2_12 + dout[j, i, q] /= s2_12 * np.sqrt(2.0 * nout[j, i, q]) + return sigma, dout, nout + @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) def nlpar_nb(data, lam, sr, dthresh, sigma, nrows, ncols, calclim = np.array([-1, -1, -1, -1], dtype=np.int64), From cb9786c9c43e57d9bfb26a61c979807f587ef1bf Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Wed, 26 Nov 2025 11:03:40 -0500 Subject: [PATCH 74/92] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index a71069f..c41db96 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -647,7 +647,7 @@ def calcsigma_cpu(self,chunksize=0,nn=1,saturation_protect=True,automask=True, s - sigchunk, d2chunk, n2chunk = self.sigma_numba2(data,nn, nrowchunk,ncolchunk, + sigchunk, d2chunk, n2chunk = self.sigma_numba(data,nn, nrowchunk,ncolchunk, np.array([0,nrowchunk ], dtype=np.uint64), np.array([0,ncolchunk],dtype=np.uint64), indices,saturation_protect) From ad1b2732d8899e6e59618bd2a6fa534ae25a9c86 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 2 Dec 2025 09:42:16 -0500 Subject: [PATCH 75/92] Removed alternate sigma calculation. That effort is stored on NLPAR2 branch. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 88 ---------------------------------------- 1 file changed, 88 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index c41db96..aafd1bc 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -815,95 +815,7 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s dout[j, i, q] /= s2_12 * np.sqrt(2.0 * nout[j, i, q]) return sigma, dout, nout - @staticmethod - @numba.jit(nopython=True, cache=True, fastmath=False, parallel=True) - def sigma_numba2(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, saturation_protect=True): - sigma = np.zeros((nrows, ncols), dtype=np.float32) - dout = np.zeros((nrows, ncols, (nn * 2 + 1) ** 2), dtype=np.float32) - nout = np.zeros((nrows, ncols, (nn * 2 + 1) ** 2), dtype=np.float32) - dij = np.zeros((nrows, ncols, (nn * 2 + 1) ** 2, 2), dtype=np.uint64) - ncount = np.zeros((nrows, ncols), dtype=np.uint64) - shpdata = data.shape - - n0 = np.float32(shpdata[-1]) - shpind = indices.shape - mxval = np.max(data) - if saturation_protect == False: - mxval += 1.0 - else: - mxval *= 0.9961 - - for j in numba.prange(rowstartcount[0], rowstartcount[0] + rowstartcount[1]): - nn_r_start = j - nn if (j - nn) >= 0 else 0 - nn_r_end = (j + nn if (j + nn) < nrows else nrows - 1) + 1 - for i in numba.prange(colstartcount[0], colstartcount[0] + colstartcount[1]): - nn_c_start = i - nn if (i - nn) >= 0 else 0 - nn_c_end = (i + nn if (i + nn) < ncols else ncols - 1) + 1 - mind = np.float32(1e24) - indx_0 = i + ncols * j - count = 0 - for j_nn in range(nn_r_start, nn_r_end): - for i_nn in range(nn_c_start, nn_c_end): - dij[j, i, count, 0] = np.uint64(j_nn) - dij[j, i, count, 1] = np.uint64(i_nn) # want to save this for labmda optimization - indx_nn = i_nn + ncols * j_nn - d2 = np.float32(0.0) - n2 = np.float32(1.0e-12) - nout[j, i, count] = n0 # want to save this for labmda optimization - if not ((i == i_nn) and (j == j_nn)): - for q in range(shpind[0]): - d0 = data[indx_0, indices[q]] - d1 = data[indx_nn, indices[q]] - if (d1 < mxval) and (d0 < mxval): # this is a saturation protection - n2 += 1.0 - d2 += (d0 - d1) ** 2 - - - if d2 >= 1.e-3: # sometimes EDAX collects the same pattern twice - s0 = d2 / np.float32(n2 * 2.0) - if s0 < mind: - mind = s0 - dout[j, i, count] = d2 # want to save this for labmda optimization - nout[j, i, count] = n2 - count += 1 - #else: - # d2 = 1e12 - - - #ncount[j,i] = count - #print('------') - dtemp = dout[j,i,0:count].ravel() - #print(dtemp) - ntemp = (nout[j,i,0:count].ravel()).astype(np.float32) - #print(ntemp) - dtemp = dtemp/(ntemp * 2.0) - #print(dtemp) - - mediandtemp = np.median(dtemp) - madtemp = np.median(np.absolute(dtemp-mediandtemp)) - #print(mediandtemp, madtemp) - #print('------') - zsc_mod = np.absolute(0.6745*(dtemp-mediandtemp)/madtemp) - #print([j, i], dtemp, zsc_mod) - if zsc_mod.min() >= 3.5: - sigma[j, i] = np.sqrt(mind) - else: - sigma[j, i] = np.sqrt(np.mean(dtemp[zsc_mod < 3.5 ])) - #print(sigma[j, i], dtemp, zsc_mod) - # normalize the distances for lambda optimization. - shp = dout.shape - s2 = sigma ** 2 - for j in numba.prange(shp[0]): - for i in range(shp[1]): - for q in range(shp[2]): - if nout[j, i, q] > 0: - ii = dij[j, i, q, 1] - jj = dij[j, i, q, 0] - s2_12 = (s2[j, i] + s2[jj, ii]) - dout[j, i, q] -= nout[j, i, q] * s2_12 - dout[j, i, q] /= s2_12 * np.sqrt(2.0 * nout[j, i, q]) - return sigma, dout, nout @staticmethod @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) From 2bc4ef2db52da6ae45a4e5a2aef078d92faaf67b Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Thu, 4 Dec 2025 09:13:27 -0500 Subject: [PATCH 76/92] Significant code clean-up, removing redundant keywords within NLPAR. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar_cpu.py | 474 ++++++++++++++++-------------- pyebsdindex/opencl/nlpar_cl.py | 92 ++++-- pyebsdindex/opencl/nlpar_clray.py | 129 +++++--- 3 files changed, 417 insertions(+), 278 deletions(-) diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index 78a56f1..e97bebf 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -45,12 +45,22 @@ class NLPAR: - def __init__(self, filename=None, lam=0.7, searchradius=3,dthresh=0.0, diff_offset=0.0, + def __init__(self, filename=None, + lam=0.7, + searchradius=3, + dthresh=0.0, + diff_offset=0.0, + saturation_protect=True, + stem_scale=False, + automask=True, nrows = None, ncols = None, **kwargs): self.lam = lam self.searchradius = searchradius self.dthresh = dthresh self.diff_offset = diff_offset, + self.saturation_protect = saturation_protect, + self.stem_scale = stem_scale, + self.automask = automask, self.filepath = None self.hdfdatapath = None self.filepathout = None @@ -68,140 +78,31 @@ def __init__(self, filename=None, lam=0.7, searchradius=3,dthresh=0.0, diff_off if ncols is not None: self.ncols = ncols - - def setfile(self,filepath=None): - self.filepath = None - self.hdfdatapath = None - pathtemp = np.atleast_1d(filepath) - fpath = pathtemp[0] - hdf5path = None - if pathtemp.size > 1: - hdf5path = pathtemp[1] - if fpath is not None: - self.filepath = Path(fpath) - self.hdfdatapath = hdf5path - - def setoutfile(self, patternfile, filepath=None): - """Set the output file. - - Parameters - ---------- - patternfile - Input pattern file object from ebsd_pattern. - filepath - String. - - Notes - ----- - In the future I want to be able to specify the HDF5 data path to - store the output data, but that is proving to be a bit of a mess. - For now, a copy of the original HDF5 is made, and the NLPAR patterns - will be overwritten on top of the originals. - """ - self.filepathout = None - self.hdfdatapathout = None - pathtemp = np.atleast_1d(filepath) - fpath = pathtemp[0] - hdf5path = None - #if pathtemp.size > 1: - # hdf5path = pathtemp[1] - #print(fpath, hdf5path) - if fpath is not None: # the user has set an output file path. - self.filepathout = Path(fpath).expanduser().resolve() - self.hdfdatapathout = hdf5path - if patternfile.filetype != 'HDF5': #check that the input and output are not the same. - pathok = self.filepathout.exists() - if pathok: - pathok = not self.filepathout.samefile(patternfile.filepath) - if not pathok: - raise ValueError('Error: File input and output are exactly the same.') - return - - patternfile.copy_file([self.filepathout,self.hdfdatapathout], empty_data=True) - return # fpath and (maybe) hdf5 path were set manually. - else: # this is a hdf5 file - if self.hdfdatapathout is None: - patternfile.copy_file(self.filepathout, empty_data=True) - self.hdfdatapathout = patternfile.h5patdatpth - return - else: - patternfile.copy_file([self.filepathout, self.hdfdatapathout], empty_data=True) - return - - if patternfile is not None: # the user has set no path. - hdf5path = None - - if patternfile.filetype in ['UP', 'EBSP', 'TFPAT']: - p = Path(patternfile.filepath) - appnd = "_NLPAR_l{:1.2f}".format(self.lam) + "sr{:d}".format(self.searchradius) - newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) - emptydata = True - if patternfile.filetype in ['EBSP']: - if patternfile.version > 5: - emptydata = False - #print(emptydata) - patternfile.copy_file(newfilepath,empty_data=emptydata) - - if patternfile.filetype == 'HDF5': - hdf5path_tmp = str(patternfile.h5patdatpth).split('/') - if hdf5path_tmp[0] == '': - hdf5path_org = hdf5path_tmp[1] - else: - hdf5path_org = hdf5path_tmp[0] - p = Path(patternfile.filepath) - appnd = "_NLPAR_l{:1.2f}".format(self.lam) + "sr{:d}".format(self.searchradius) - hdf5path = hdf5path_org+appnd - newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) - #patternfile.copy_file([newfilepath, hdf5path_org], newh5path=hdf5path) - patternfile.copy_file([newfilepath], empty_data=True) - hdf5path = patternfile.h5patdatpth - - self.filepathout = newfilepath - self.hdfdatapathout = hdf5path - return - - def getinfileobj(self): - if self.filepath is not None: - fID = ebsd_pattern.get_pattern_file_obj([self.filepath, self.hdfdatapath]) - if (fID.nRows is not None): - if (self.nrows is None): - self.nrows = fID.nRows - else: - fID.nRows = self.nrows - - if (fID.nCols is not None): - if (self.ncols is None): - self.ncols = fID.nCols - else: - fID.nCols = self.ncols - - return fID - + def auto_nlpar(self, filename = None, fileout=None, lindex = 1, **kwargs): + if filename is not None: + self.setfile(filename) + lam = self.opt_lambda(autoupdate=True, **kwargs) + if 'lam' in kwargs: + pass else: - return None + kwargs['lam'] = lam[int(lindex)] + nlparfile = self.calcnlpar(fileout=fileout, **kwargs) + return nlparfile - def getoutfileobj(self): - if self.filepathout is not None: - fID = ebsd_pattern.get_pattern_file_obj([self.filepathout, self.hdfdatapathout]) - if self.nrows is not None: - fID.nRows = self.nrows - else: - self.nrows = fID.nRows - if self.ncols is not None: - fID.nCols = self.ncols - else: - self.ncols = fID.nCols - return fID - else: - return None - def opt_lambda_cpu(self, target_weights=(0.5, 0.34, 0.25), dthresh=0.0, autoupdate=True, - saturation_protect=True, automask=True, stem_scale = False, backsub=False, + def opt_lambda_cpu(self, target_weights=(0.5, 0.34, 0.25), autoupdate=True, dthresh=None, + # see __init__ for default dthresh values verbose = 2, **kwargs): + # will accept all keywords to calcsigma_cpu. See NLPAR __init__ for default values target_weights = np.asarray(target_weights) + if dthresh is not None: + self.dthresh = dthresh + dthresh = self.dthresh if np.isscalar(self.dthresh) else self.dthresh[0] + dthresh = np.float64(dthresh) + def loptfunc(lam,d2,tw,dthresh): temp = np.maximum(d2, dthresh) dw = np.exp(-(temp) / lam ** 2) @@ -221,14 +122,6 @@ def loptfunc(lam,d2,tw,dthresh): nn = 1 nn = np.uint64(nn) - - if (automask is True) and (self.mask is None): - self.mask = (self.automask(pheight,pwidth)) - if self.mask is None: - self.mask = np.ones((pheight,pwidth),dtype=np.uint8) - - self.mask = (self.mask).astype(np.uint8) - dthresh = np.float32(dthresh) @@ -260,7 +153,7 @@ def loptfunc(lam,d2,tw,dthresh): # sigma[j:j + rowstartcount[1],:] = tmp - sigma, d2,n2 = self.calcsigma_cpu(**kwargs) + sigma, d2,n2 = self.calcsigma_cpu(nn=nn, **kwargs) #print(d2.max(), d2.min()) #d2 = d2norm(d2, n2, dij, sigma) @@ -290,31 +183,136 @@ def loptfunc(lam,d2,tw,dthresh): self.sigma = sigma return lamopt_values.flatten() + def calcsigma_cpu(self,chunksize=0,nn=1,saturation_protect=None,automask=None, stem_scale=None, + # See NLPAR __init__ for default values + verbose = 2, **kwargs): + + self.sigmann = nn + + if saturation_protect is not None: + self.saturation_protect = saturation_protect + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] + + if automask is not None: + self.automask = automask + automask = self.automask if np.isscalar(self.automask) else self.automask[0] + + if stem_scale is not None: + self.stem_scale = stem_scale + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] + + + + patternfile = self.getinfileobj() + + + nrows = np.int64(self.nrows)#np.uint64(patternfile.nRows) + ncols = np.int64(self.ncols)#np.uint64(patternfile.nCols) + + pwidth = np.uint64(patternfile.patternW) + pheight = np.uint64(patternfile.patternH) + phw = pheight*pwidth + + nn = np.uint64(nn) + nnn = np.uint64((2*nn+1)**2) + if chunksize <= 0: + sysram = (psutil.virtual_memory()).total + chunksize = np.minimum(32e9, sysram // 4) + chunksize = np.int64(chunksize) + chunks = self._calcchunks([pwidth, pheight], ncols, nrows, target_bytes=chunksize, + col_overlap=nn, row_overlap=nn) + + + + if (automask is True) and (self.mask is None): + self.mask = (self.makeautomask(pheight,pwidth)) + if self.mask is None: + self.mask = np.ones((pheight,pwidth), dtype=np.uint8) + + indices = np.asarray( (self.mask.flatten().nonzero())[0], np.uint64) + + sigma = np.zeros((nrows, ncols), dtype=np.float32)+1e18 + + n2 = np.zeros((nrows, ncols, nnn), dtype=np.float32) + d2 = np.zeros((nrows, ncols, nnn), dtype=np.float32) + + ndone = 0 + nchunks = int(chunks[1] * chunks[0]) + + for rowchunk in range(chunks[1]): + rstart = chunks[3][rowchunk, 0] + rend = chunks[3][rowchunk, 1] + nrowchunk = rend - rstart + + for colchunk in range(chunks[0]): + cstart = chunks[2][colchunk, 0] + cend = chunks[2][colchunk, 1] + ncolchunk = cend - cstart + data, xyloc = patternfile.read_data(patStartCount=[[cstart, rstart], [ncolchunk, nrowchunk]], + convertToFloat=True, returnArrayOnly=True) + + if stem_scale is True: + #data = data - data.min() + 1 + #data = np.log(data) + data = data - data.min() + data = np.sqrt(data) + shp = data.shape + data = data.reshape(data.shape[0], phw) + + + sigchunk, d2chunk, n2chunk = self.sigma_numba(data,nn, nrowchunk,ncolchunk, + np.array([0,nrowchunk ], dtype=np.uint64), + np.array([0,ncolchunk],dtype=np.uint64), + indices,saturation_protect) + + sigma[rstart:rend, cstart:cend] = np.minimum(sigma[rstart:rend, cstart:cend], sigchunk) + # temp = (d2 > thresh).choose(dthresh, d2) + n2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [n2chunk], default=n2[rstart:rend, cstart:cend,:]) + d2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [d2chunk], default=d2[rstart:rend, cstart:cend,:]) + #dij[rstart:rend, cstart:cend, :] = dijchunk + ndone += 1 + if verbose >= 2: + print("tiles complete: ", ndone, "/", nchunks, sep='', end='\r') + + return sigma, d2, n2 + def calcnlpar_cpu(self, chunksize=0, searchradius=None, lam = None, dthresh = None, - saturation_protect=True, automask=True, stem_scale = False, + saturation_protect=None, automask=None, stem_scale = None, # see NLPAR __init__ for default values filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,verbose=2, diff_offset=None, **kwargs): + if searchradius is not None: + self.searchradius = searchradius + sr = self.searchradius if np.isscalar(self.searchradius) else self.searchradius[0] + sr = np.int64(sr) + if lam is not None: self.lam = lam + lam = self.lam if np.isscalar(self.lam) else self.lam[0] + lam = np.float32(lam) + if dthresh is not None: self.dthresh = dthresh + dthresh = self.dthresh if np.isscalar(self.dthresh) else self.dthresh[0] + dthresh = np.float32(dthresh) if diff_offset is not None: self.diff_offset = diff_offset + diff_offset = self.diff_offset if np.isscalar(self.diff_offset) else self.diff_offset[0] - if searchradius is not None: - self.searchradius = searchradius + if saturation_protect is not None: + self.saturation_protect = saturation_protect + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] - lam = np.float32(self.lam) - dthresh = np.float32(self.dthresh) - sr = np.int64(self.searchradius) - diff_offset = np.float32(self.diff_offset) + if automask is not None: + self.automask = automask + automask = self.automask if np.isscalar(self.automask) else self.automask[0] - if type(diff_offset) is np.ndarray: - diff_offset = np.float32(diff_offset[0]) + if stem_scale is not None: + self.stem_scale = stem_scale + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] if filename is not None: self.setfile(filepath=filename) @@ -361,7 +359,7 @@ def calcnlpar_cpu(self, chunksize=0, searchradius=None, lam = None, dthresh = No mxchunk = np.int64(chunksize.max()) if (automask is True) and (self.mask is None): - self.mask = (self.automask(pheight,pwidth)) + self.mask = (self.makeautomask(pheight,pwidth)) if self.mask is None: self.mask = np.ones((pheight,pwidth), dtype=np.uint8) @@ -586,94 +584,138 @@ def calcnlpar_cpu(self, chunksize=0, searchradius=None, lam = None, dthresh = No numba.set_num_threads(nthreadpos) return str(patternfileout.filepath) - def calcsigma_cpu(self,chunksize=0,nn=1,saturation_protect=True,automask=True, stem_scale=False, verbose = 2, **kwargs): - - self.sigmann = nn - patternfile = self.getinfileobj() - + def setfile(self,filepath=None): + self.filepath = None + self.hdfdatapath = None + pathtemp = np.atleast_1d(filepath) + fpath = pathtemp[0] + hdf5path = None + if pathtemp.size > 1: + hdf5path = pathtemp[1] + if fpath is not None: + self.filepath = Path(fpath) + self.hdfdatapath = hdf5path - nrows = np.int64(self.nrows)#np.uint64(patternfile.nRows) - ncols = np.int64(self.ncols)#np.uint64(patternfile.nCols) + def setoutfile(self, patternfile, filepath=None): + """Set the output file. - pwidth = np.uint64(patternfile.patternW) - pheight = np.uint64(patternfile.patternH) - phw = pheight*pwidth + Parameters + ---------- + patternfile + Input pattern file object from ebsd_pattern. + filepath + String. - nn = np.uint64(nn) - nnn = np.uint64((2*nn+1)**2) - if chunksize <= 0: - sysram = (psutil.virtual_memory()).total - chunksize = np.minimum(32e9, sysram // 4) - chunksize = np.int64(chunksize) - chunks = self._calcchunks([pwidth, pheight], ncols, nrows, target_bytes=chunksize, - col_overlap=nn, row_overlap=nn) + Notes + ----- + In the future I want to be able to specify the HDF5 data path to + store the output data, but that is proving to be a bit of a mess. + For now, a copy of the original HDF5 is made, and the NLPAR patterns + will be overwritten on top of the originals. + """ + self.filepathout = None + self.hdfdatapathout = None + pathtemp = np.atleast_1d(filepath) + fpath = pathtemp[0] + hdf5path = None + #if pathtemp.size > 1: + # hdf5path = pathtemp[1] + #print(fpath, hdf5path) + if fpath is not None: # the user has set an output file path. + self.filepathout = Path(fpath).expanduser().resolve() + self.hdfdatapathout = hdf5path + if patternfile.filetype != 'HDF5': #check that the input and output are not the same. + pathok = self.filepathout.exists() + if pathok: + pathok = not self.filepathout.samefile(patternfile.filepath) + if not pathok: + raise ValueError('Error: File input and output are exactly the same.') + return + patternfile.copy_file([self.filepathout,self.hdfdatapathout], empty_data=True) + return # fpath and (maybe) hdf5 path were set manually. + else: # this is a hdf5 file + if self.hdfdatapathout is None: + patternfile.copy_file(self.filepathout, empty_data=True) + self.hdfdatapathout = patternfile.h5patdatpth + return + else: + patternfile.copy_file([self.filepathout, self.hdfdatapathout], empty_data=True) + return + if patternfile is not None: # the user has set no path. + hdf5path = None + + if patternfile.filetype in ['UP', 'EBSP', 'TFPAT']: + p = Path(patternfile.filepath) + appnd = "_NLPAR_l{:1.2f}".format(self.lam) + "sr{:d}".format(self.searchradius) + newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) + emptydata = True + if patternfile.filetype in ['EBSP']: + if patternfile.version > 5: + emptydata = False + #print(emptydata) + patternfile.copy_file(newfilepath,empty_data=emptydata) - if (automask is True) and (self.mask is None): - self.mask = (self.automask(pheight,pwidth)) - if self.mask is None: - self.mask = np.ones((pheight,pwidth), dtype=np.uint8) + if patternfile.filetype == 'HDF5': + hdf5path_tmp = str(patternfile.h5patdatpth).split('/') + if hdf5path_tmp[0] == '': + hdf5path_org = hdf5path_tmp[1] + else: + hdf5path_org = hdf5path_tmp[0] + p = Path(patternfile.filepath) + appnd = "_NLPAR_l{:1.2f}".format(self.lam) + "sr{:d}".format(self.searchradius) + hdf5path = hdf5path_org+appnd + newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) + #patternfile.copy_file([newfilepath, hdf5path_org], newh5path=hdf5path) + patternfile.copy_file([newfilepath], empty_data=True) + hdf5path = patternfile.h5patdatpth - indices = np.asarray( (self.mask.flatten().nonzero())[0], np.uint64) + self.filepathout = newfilepath + self.hdfdatapathout = hdf5path + return - sigma = np.zeros((nrows, ncols), dtype=np.float32)+1e18 + def getinfileobj(self): + if self.filepath is not None: + fID = ebsd_pattern.get_pattern_file_obj([self.filepath, self.hdfdatapath]) + if (fID.nRows is not None): + if (self.nrows is None): + self.nrows = fID.nRows + else: + fID.nRows = self.nrows - n2 = np.zeros((nrows, ncols, nnn), dtype=np.float32) - d2 = np.zeros((nrows, ncols, nnn), dtype=np.float32) + if (fID.nCols is not None): + if (self.ncols is None): + self.ncols = fID.nCols + else: + fID.nCols = self.ncols - ndone = 0 - nchunks = int(chunks[1] * chunks[0]) + return fID - for rowchunk in range(chunks[1]): - rstart = chunks[3][rowchunk, 0] - rend = chunks[3][rowchunk, 1] - nrowchunk = rend - rstart + else: + return None - for colchunk in range(chunks[0]): - cstart = chunks[2][colchunk, 0] - cend = chunks[2][colchunk, 1] - ncolchunk = cend - cstart - data, xyloc = patternfile.read_data(patStartCount=[[cstart, rstart], [ncolchunk, nrowchunk]], - convertToFloat=True, returnArrayOnly=True) + def getoutfileobj(self): + if self.filepathout is not None: + fID = ebsd_pattern.get_pattern_file_obj([self.filepathout, self.hdfdatapathout]) + if self.nrows is not None: + fID.nRows = self.nrows + else: + self.nrows = fID.nRows - if stem_scale is True: - #data = data - data.min() + 1 - #data = np.log(data) - data = data - data.min() - data = np.sqrt(data) - shp = data.shape - data = data.reshape(data.shape[0], phw) + if self.ncols is not None: + fID.nCols = self.ncols + else: + self.ncols = fID.nCols + return fID + else: + return None - sigchunk, d2chunk, n2chunk = self.sigma_numba(data,nn, nrowchunk,ncolchunk, - np.array([0,nrowchunk ], dtype=np.uint64), - np.array([0,ncolchunk],dtype=np.uint64), - indices,saturation_protect) - sigma[rstart:rend, cstart:cend] = np.minimum(sigma[rstart:rend, cstart:cend], sigchunk) - # temp = (d2 > thresh).choose(dthresh, d2) - n2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [n2chunk], default=n2[rstart:rend, cstart:cend,:]) - d2[rstart:rend, cstart:cend,:] = np.select( [n2chunk > 0], [d2chunk], default=d2[rstart:rend, cstart:cend,:]) - #dij[rstart:rend, cstart:cend, :] = dijchunk - ndone += 1 - if verbose >= 2: - print("tiles complete: ", ndone, "/", nchunks, sep='', end='\r') - return sigma, d2, n2 - def auto_nlpar(self, filename = None, fileout=None, searchradius=None, lindex = 1, **kwargs): - if filename is not None: - self.setfile(filename) - lam = self.opt_lambda( automask = True, autoupdate=True, backsub = False, **kwargs) - if 'lam' in kwargs: - pass - else: - kwargs['lam'] = lam[int(lindex)] - nlparfile = self.calcnlpar(searchradius = searchradius, saturation_protect=True, automask=True, - fileout=fileout, backsub=False, **kwargs) - return nlparfile def backsub(self, data): # This function will fit a 2D gaussian on top of a plane to the averaged set of patterns (data) that is provided. # It will automatically use whatever mask is defined for valid data. @@ -732,7 +774,7 @@ def fit_gauss(M, *args): return data @staticmethod - def automask( h,w ): + def makeautomask(h, w): r = (min(h,w)*0.98*0.5) x = np.arange(w, dtype=np.float32) x = np.minimum(x , (w-x)) diff --git a/pyebsdindex/opencl/nlpar_cl.py b/pyebsdindex/opencl/nlpar_cl.py index 218b9e4..a60b68d 100644 --- a/pyebsdindex/opencl/nlpar_cl.py +++ b/pyebsdindex/opencl/nlpar_cl.py @@ -43,8 +43,9 @@ def __init__( self, filename=None, **kwargs): self.useCPU = False - def opt_lambda(self, target_weights=[0.5, 0.34, 0.25], dthresh=0.0, autoupdate=True, - saturation_protect=True, automask=True, stem_scale = False, backsub=False, **kwargs): + def opt_lambda(self, target_weights=[0.5, 0.34, 0.25], dthresh=None, autoupdate=True, + # see __init__ nlpar_cpu for default dthresh value + **kwargs): return self.opt_lambda_cl(**kwargs) def calcnlpar(self, **kwargs): @@ -80,13 +81,19 @@ def calcsigma_cpu(self,nn=1, saturation_protect=True,automask=True, **kwargs): return nlpar_cpu.NLPAR.calcsigma_cpu(self, nn=nn, saturation_protect=saturation_protect, automask=automask, **kwargs) - def opt_lambda_cl(self, saturation_protect=True, automask=True, backsub=False, - target_weights=[0.5, 0.34, 0.25], dthresh=0.0, autoupdate=True, - stem_scale = False, + def opt_lambda_cl(self, target_weights=[0.5, 0.34, 0.25], dthresh=None, + autoupdate=True, + # accepts all keywords that calcsigma accepts. **kwargs): target_weights = np.asarray(target_weights) + if dthresh is not None: + self.dthresh = dthresh + dthresh = self.dthresh if np.isscalar(self.dthresh) else self.dthresh[0] + dthresh = np.float64(dthresh) + + def loptfunc(lam, d2, tw, dthresh): temp = np.maximum(d2, dthresh)#(d2 > dthresh).choose(dthresh, d2) dw = np.exp(-(temp) / lam ** 2) @@ -107,9 +114,7 @@ def loptfunc(lam, d2, tw, dthresh): dthresh = np.float32(dthresh) lamopt_values = [] - sigma, d2, n2 = self.calcsigma(nn=1, saturation_protect=saturation_protect, automask=automask, - stem_scale=stem_scale, normalize_d=True, - return_nndist=True, **kwargs) + sigma, d2, n2 = self.calcsigma(nn=1, normalize_d=True, return_nndist=True, **kwargs) #sigmapad = np.pad(sigma, 1, mode='reflect') #d2normcl(d2, n2, sigmapad) @@ -134,8 +139,9 @@ def loptfunc(lam, d2, tw, dthresh): return lamopt_values.flatten() - def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, - stem_scale = False, + def calcsigma_cl(self,nn=1, + saturation_protect=None,automask=None, stem_scale = None, + # for defaults see __init__ in nlpar_cpu normalize_d=True, gpu_id = None, verbose = 2, **kwargs): self.sigmann = nn if self.sigmann > 7: @@ -144,6 +150,19 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, nn = 7 self.sigmann = nn + if saturation_protect is not None: + self.saturation_protect = saturation_protect + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] + + if automask is not None: + self.automask = automask + automask = self.automask if np.isscalar(self.automask) else self.automask[0] + + if stem_scale is not None: + self.stem_scale = stem_scale + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] + + if gpu_id is None: clparams = openclparam.OpenClParam() clparams.get_gpu() @@ -187,7 +206,7 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, col_overlap=1, row_overlap=1) if (automask is True) and (self.mask is None): - self.mask = (self.automask(pheight, pwidth)) + self.mask = (self.makeautomask(pheight, pwidth)) if self.mask is None: self.mask = np.ones((pheight, pwidth), dtype=np.uint8) @@ -241,8 +260,11 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, data, xyloc = patternfile.read_data(patStartCount=[[cstart, rstart], [ncolchunk, nrowchunk]], convertToFloat=False, returnArrayOnly=True) if stem_scale is True: - data = data - data.min() + 1 - data = np.log(data) + #data = data - data.min() + 1 + #data = np.log(data) + dmin = data.min() + data = data - dmin + data = np.sqrt(data).astype(np.float32) mxval = data.max() @@ -258,7 +280,7 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, tic = timer() #datapad = np.zeros((npad), dtype=np.float32) + np.float32(mxval + 10) #datapad[0:szdata] = data.reshape(-1) - data_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=data) + data_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.USE_HOST_PTR, hostbuf=data) if data.dtype.type is np.float32: clkern['nlloadpat32flt'](queue, (np.uint64(data.size),), None, data_gpu, datapad_gpu, wait_for=[evnt]) @@ -320,8 +342,8 @@ def calcsigma_cl(self,nn=1,saturation_protect=True,automask=True, - def calcnlpar_cl(self, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, - filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,diff_offset= None, + def calcnlpar_cl(self, searchradius=None, lam = None, dthresh = None, saturation_protect=None, automask=None, + filename=None, fileout=None, reset_sigma=False, stem_scale=None, rescale = False,diff_offset= None, gpu_id = None, verbose=2, **kwargs): class OpenCLClalcError(Exception): @@ -329,15 +351,21 @@ class OpenCLClalcError(Exception): if lam is not None: self.lam = lam + lam = self.lam if np.isscalar(self.lam) else self.lam[0] + lam = np.float32(lam) if dthresh is not None: self.dthresh = dthresh if self.dthresh is None: self.dthresh = 0.0 + dthresh = self.dthresh if np.isscalar(self.dthresh) else self.dthresh[0] + dthresh = np.float32(dthresh) if diff_offset is not None: self.diff_offset = diff_offset + diff_offset = self.diff_offset if np.isscalar(self.diff_offset) else self.diff_offset[0] + diff_offset = np.float32(diff_offset) if searchradius is not None: self.searchradius = searchradius @@ -347,11 +375,20 @@ class OpenCLClalcError(Exception): print("The search radius has been clipped to 10") searchradius = 10 self.searchradius = searchradius + sr = self.searchradius if np.isscalar(self.searchradius) else self.searchradius[0] + sr = np.int64(sr) + + if saturation_protect is not None: + self.saturation_protect = saturation_protect + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] + + if automask is not None: + self.automask = automask + automask = self.automask if np.isscalar(self.automask) else self.automask[0] - lam = np.float32(self.lam) - dthresh = np.float32(self.dthresh) - sr = np.int64(self.searchradius) - diff_offset = np.float32(self.diff_offset) + if stem_scale is not None: + self.stem_scale = stem_scale + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] if filename is not None: self.setfile(filepath=filename) @@ -381,14 +418,15 @@ class OpenCLClalcError(Exception): self.sigma = None if self.sigma is None: - self.sigma = self.calcsigma_cl(nn=1, saturation_protect=saturation_protect, automask=automask, gpu_id=gpu_id)[0] + self.sigma = self.calcsigma_cl(nn=1, saturation_protect=saturation_protect, + automask=automask, stem_scale=stem_scale, gpu_id=gpu_id)[0] sigma = np.asarray(self.sigma).astype(np.float32) if (automask is True) and (self.mask is None): - self.mask = (self.automask(pheight, pwidth)) + self.mask = (self.makeautomask(pheight, pwidth)) if self.mask is None: self.mask = np.ones((pheight, pwidth), dtype=np.uint8) @@ -537,7 +575,9 @@ class OpenCLClalcError(Exception): if mnval0 < 0: data -= mnval0 mxval = mxval - mnval0 - + + if stem_scale is True: + data = np.sqrt(data).astype(np.float32) if saturation_protect == False: mxval += 1.0 @@ -594,6 +634,9 @@ class OpenCLClalcError(Exception): cl.enqueue_copy(queue, data, datapadout_gpu, is_blocking=True) #print(envt.command_execution_status) queue.finish() + + + data = data[rstartcalc: rstartcalc + nrowcalc, cstartcalc:cstartcalc + ncolcalc, :, :] mxout = data.max(axis=(-1,-2)) @@ -603,6 +646,9 @@ class OpenCLClalcError(Exception): # nothing. It will attempt to reprocess the data 3 times before just writing out # whatever it has. if (mxval0 < np.float32(1.e-8)) or ( mxtest < 0.5 ) or (j["nattempts"] >= 3): + if stem_scale is True: + data = data ** 2 + if mnval0 < 0: data += mnval0 diff --git a/pyebsdindex/opencl/nlpar_clray.py b/pyebsdindex/opencl/nlpar_clray.py index dd2d139..7f260a9 100644 --- a/pyebsdindex/opencl/nlpar_clray.py +++ b/pyebsdindex/opencl/nlpar_clray.py @@ -55,9 +55,7 @@ def __init__( self,filename=None, **kwargs): def calcnlpar(self, **kwargs): return self.calcnlpar_clray(**kwargs) - def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist=True, - stem_scale=False, - **kwargs): + def calcsigma(self, nn=1, **kwargs): if self.sigmann > 7: print("Sigma optimization search limited to a search radius <= 7") print("The search radius has been clipped to 7") @@ -65,14 +63,9 @@ def calcsigma(self, nn=1, saturation_protect=True, automask=True, return_nndist= self.sigmann = nn sig, dnn, cnn = self.calcsigma_clray(nn=nn, - saturation_protect=saturation_protect, - automask=automask, - stem_scale = stem_scale, **kwargs) - if return_nndist == True: - return sig, dnn, cnn - else: - return sig + + return sig, dnn, cnn def calcnlpar_clsq(self, **kwargs): @@ -81,12 +74,25 @@ def calcnlpar_clsq(self, **kwargs): def calcsigma_clsq(self, **kwargs): return nlpar_cl.NLPAR.calcsigma_cl(self, **kwargs) - def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, - stem_scale = False, normalize_d=True, + def calcsigma_clray(self, nn=1, saturation_protect=None, automask=None, + stem_scale = None, gpu_id = None, verbose=2, **kwargs): self.patternfile = self.getinfileobj() self.sigmann = nn + if saturation_protect is not None: + self.saturation_protect = saturation_protect + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] + + if automask is not None: + self.automask = automask + automask = self.automask if np.isscalar(self.automask) else self.automask[0] + + if stem_scale is not None: + self.stem_scale = stem_scale + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] + + if self.sigmann > 7: print("Sigma optimization search limited to a search radius <= 7") print("The search radius has been clipped to 7") @@ -143,7 +149,7 @@ def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, npat_point = int(pwidth * pheight) if (automask is True) and (self.mask is None): - self.mask = (self.automask(pheight, pwidth)) + self.mask = (self.makeautomask(pheight, pwidth)) if self.mask is None: self.mask = np.ones((pheight, pwidth), dtype=np.uint8) @@ -225,9 +231,7 @@ def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, wrker = idlewrker.pop() job = jobqueue.pop() - tasks.append(wrker.runsigma_chunk.remote(job, nlparobj=nlpar_remote, - saturation_protect=saturation_protect, - stem_scale = stem_scale)) + tasks.append(wrker.runsigma_chunk.remote(job, nlparobj=nlpar_remote)) busywrker.append(wrker) if len(tasks) > 0: donetasks, stillbusy = ray.wait(tasks, num_returns=len(busywrker), timeout=0.1) @@ -269,10 +273,21 @@ def calcsigma_clray(self, nn=1, saturation_protect=True, automask=True, print('\n', end='') return sigma, dist, countnn - def _sigmachunkcalc_cl(self, data, calclim, clparams=None, saturation_protect=True): + def _sigmachunkcalc_cl(self, data, calclim, clparams=None): nn = self.sigmann + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] data = np.ascontiguousarray(data) + + + if stem_scale == True: + # data = data - data.min() + 1 + # data = np.log(data) + dmin = data.min() + data = data - dmin + data = np.sqrt(data).astype(np.float32) + ctx = clparams.ctx prg = clparams.prg clkern = clparams.kernels @@ -298,7 +313,7 @@ def _sigmachunkcalc_cl(self, data, calclim, clparams=None, saturation_protect=Tr pwidth = data.shape[2] npat_point = pheight*pwidth - mask = self.mask + mask = np.array(self.mask) npad = clvectlen * int(np.ceil(mask.size/clvectlen)) maskpad = np.zeros((npad) , np.float32) @@ -383,22 +398,26 @@ def _sigmachunkcalc_cl(self, data, calclim, clparams=None, saturation_protect=Tr - def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask = True, + def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, + saturation_protect=None, automask = None, stem_scale = None, filename=None, fileout=None, reset_sigma=False, backsub = False, rescale = False,diff_offset = None, verbose = 2, gpu_id = None, **kwargs): if lam is not None: self.lam = lam - - self.saturation_protect = saturation_protect + lam = self.lam if np.isscalar(self.lam) else self.lam[0] + lam = np.float32(lam) if dthresh is not None: self.dthresh = dthresh if self.dthresh is None: self.dthresh = 0.0 + dthresh = self.dthresh if np.isscalar(self.dthresh) else self.dthresh[0] + dthresh = np.float32(dthresh) if diff_offset is not None: self.diff_offset = diff_offset + diff_offset = np.float32(self.diff_offset) if searchradius is not None: self.searchradius = searchradius @@ -408,11 +427,21 @@ def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturat print("The search radius has been clipped to 10") searchradius = 10 self.searchradius = searchradius + sr = self.searchradius if np.isscalar(self.searchradius) else self.searchradius[0] + sr = np.int64(sr) + + if saturation_protect is not None: + self.saturation_protect = saturation_protect + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] + + if automask is not None: + self.automask = automask + automask = self.automask if np.isscalar(self.automask) else self.automask[0] + + if stem_scale is not None: + self.stem_scale = stem_scale + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] - lam = np.float32(self.lam) - dthresh = np.float32(self.dthresh) - sr = np.int64(self.searchradius) - diff_offset = np.float32(self.diff_offset) if filename is not None: self.setfile(filepath=filename) @@ -442,14 +471,14 @@ def calcnlpar_clray(self, searchradius=None, lam = None, dthresh = None, saturat self.sigma = None if self.sigma is None: - self.sigma = self.calcsigma_cl(nn=1, saturation_protect=saturation_protect, automask=automask, gpu_id=gpu_id)[0] + self.sigma = self.calcsigma_cl(nn=1, gpu_id=gpu_id)[0] sigma = np.asarray(self.sigma).astype(np.float32) if (automask is True) and (self.mask is None): - self.mask = (self.automask(pheight, pwidth)) + self.mask = (self.makeautomask(pheight, pwidth)) if self.mask is None: self.mask = np.ones((pheight, pwidth), dtype=np.uint8) @@ -614,20 +643,34 @@ def _nlparchunkcalc_cl(self, data, calclim, clparams=None): pheight = data.shape[1] pwidth = data.shape[2] + lam = self.lam if np.isscalar(self.lam) else self.lam[0] + lam = np.float32(lam) + sr = self.searchradius if np.isscalar(self.searchradius) else self.searchradius[0] + sr = np.int64(sr) + nnn = int((2 * sr + 1) ** 2) + dthresh = self.dthresh if np.isscalar(self.dthresh) else self.dthresh[0] + dthresh = np.float32(dthresh) + diff_offset = self.diff_offset if np.isscalar(self.diff_offset) else self.diff_offset[0] + diff_offset = np.float32(diff_offset) + saturation_protect = self.saturation_protect if np.isscalar(self.saturation_protect) else self.saturation_protect[0] + + stem_scale = self.stem_scale if np.isscalar(self.stem_scale) else self.stem_scale[0] + + if stem_scale == True: + # data = data - data.min() + 1 + # data = np.log(data) + mndat = data.min() + data = data - mndat + data = np.sqrt(data).astype(np.float32) - lam = np.float32(self.lam) - sr = np.int64(self.searchradius) - nnn = int((2 * sr + 1) ** 2) - dthresh = np.float32(self.dthresh) - diff_offset = np.float32(self.diff_offset) #print(chunks[2], chunks[3]) #print(lam, sr, dthresh) # precalculate some needed arrays for the GPU mxval = data.max() - if self.saturation_protect == False: + if saturation_protect == False: mxval += 1.0 else: mxval *= 0.9961 @@ -691,6 +734,14 @@ def _nlparchunkcalc_cl(self, data, calclim, clparams=None): cl.enqueue_copy(clparams.queue, data, datapadout_gpu, is_blocking=True) sigmachunk_gpu.release() clparams.queue.finish() + + if stem_scale == True: + # data = data - data.min() + 1 + # data = np.log(data) + data = data**2 + data += mndat + + if self.rescale == True: for i in range(data.shape[0]): temp = data[i, :, :] @@ -726,7 +777,7 @@ def __init__(self, actorid=0, gpu_id=None, cudavis = '0'): #elf.openCLParams = None - def runsigma_chunk(self,gpujob, nlparobj=None, stem_scale = False, **kwargs): + def runsigma_chunk(self,gpujob, nlparobj=None, **kwargs): if gpujob is None: #time.sleep(0.001) return 'Bored', (None, None, None) @@ -740,11 +791,8 @@ def runsigma_chunk(self,gpujob, nlparobj=None, stem_scale = False, **kwargs): data, xyloc = nlparobj.patternfile.read_data(patStartCount=[[gpujob.cstart, gpujob.rstart], [gpujob.ncolchunk, gpujob.nrowchunk]], convertToFloat=False, returnArrayOnly=True) - if stem_scale == True: - #data = data - data.min() + 1 - #data = np.log(data) - data = data - data.min() - data = np.sqrt(data) + + newdata = nlparobj._sigmachunkcalc_cl(data, gpujob, clparams=self.openCLParams, **kwargs) @@ -778,6 +826,8 @@ def runnlpar_chunk(self, gpujob, nlparobj=None): [gpujob.ncolchunk, gpujob.nrowchunk]], convertToFloat=False, returnArrayOnly=True) + + newdata = nlparobj._nlparchunkcalc_cl(data, gpujob, clparams=self.openCLParams) if self.openCLParams.queue is not None: @@ -785,6 +835,7 @@ def runnlpar_chunk(self, gpujob, nlparobj=None): self.openCLParams.queue.finish() self.openCLParams.queue = None + nlparobj.patternfileout.write_data(newpatterns=newdata, patStartCount=[[gpujob.cstart + gpujob.cstartcalc, gpujob.rstart + gpujob.rstartcalc], From ddc1bb1c0ea5c04c53a562d594ec09c23439009b Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Fri, 5 Dec 2025 17:52:20 -0500 Subject: [PATCH 77/92] First steps to numba parallel Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 238 ++++++++++++++++++++----------------- 1 file changed, 127 insertions(+), 111 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 6ad3f8f..3a5d988 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -909,7 +909,7 @@ def _refine_orientation_quest(self, libpolecart, bandnorms, return avequat, fit_unweight @staticmethod - @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) + @numba.jit(nopython=True, cache=True, fastmath=True, parallel=True) def _orientation_quest_nb(polescart, bandnorms, weights): # this uses the Quaternion Estimator AKA quest algorithm. # this has been adjusted to work with a batch of matching vectors. @@ -922,9 +922,12 @@ def _orientation_quest_nb(polescart, bandnorms, weights): fitout = np.full((npats), np.pi, dtype=np.float64) fitout_unweight = np.full((npats), np.pi, dtype=np.float64) - for p in range(npats): + for p in numba.prange(npats): - whgood = (np.nonzero(weights[p, :] > eps)[0]).astype(np.int64) + polescart_p = polescart[p, ...] + bandnorms_p = bandnorms[p, ...] + weights_p = weights[p, ...] + whgood = (np.nonzero(weights_p > eps)[0]).astype(np.int64) if whgood.size < 3: continue @@ -933,12 +936,11 @@ def _orientation_quest_nb(polescart, bandnorms, weights): pflt = np.zeros((whgood.size, 3), dtype=np.float64) for j in range(whgood.size): whg = np.uint64(whgood[j]) - wn[j, 0] = weights[p, whg] - pflt[j,:] = np.asarray(polescart[p, whg, :], dtype=np.float64) - bndnorm[j,:] = np.asarray(bandnorms[p, whg, :], dtype=np.float64) + wn[j, 0] = weights_p[whg] + pflt[j, :] = np.asarray(polescart_p[whg, :], dtype=np.float64) + bndnorm[j, :] = np.asarray(bandnorms_p[whg, :], dtype=np.float64) wn /= np.sum(wn) - npoles = pflt.shape[0] # wn = np.ones((nGood,1), dtype=np.float32)/np.float32(nGood) #(weights[whGood]).reshape(nGood,1) @@ -992,12 +994,11 @@ def _orientation_quest_nb(polescart, bandnorms, weights): fitout[p] = lam polesrot = rotlib.quat_vectorL1N(q, bndnorm, npoles, np.float64, p=1) - fitout_unweight[p] = np.mean(np.sum(polesrot*pflt, axis = 1, dtype=np.float64)) + fitout_unweight[p] = np.mean(np.sum(polesrot * pflt, axis=1, dtype=np.float64)) return qout, fitout, fitout_unweight - @staticmethod - @numba.jit(nopython=True, cache=True,fastmath=True,parallel=False) + @numba.jit(nopython=True, cache=True, fastmath=True, parallel=True) def _tripvote_numba(bandnorms, band_intensity, LUT, angTol, tripAngles, tripID, nfam): timing1 = 0.0 timing2 = 0.0 @@ -1010,33 +1011,37 @@ def _tripvote_numba(bandnorms, band_intensity, LUT, angTol, tripAngles, tripID, accumulator = np.zeros((npats, nfam, n_bands), dtype=np.float32) accumulatorW = np.zeros((npats, nfam, n_bands), dtype=np.float32) - mxvote = np.zeros((npats,n_bands), dtype=np.int32) - tvotes = np.zeros((npats,n_bands), dtype=np.int32) - band_cm = np.zeros((npats,n_bands), dtype=np.float32) - bandRank = np.zeros((npats,n_bands), dtype=np.float32) - bandFam = np.zeros((npats,n_bands), dtype=np.int32) - - #count = 0.0 - #angTest2 = np.zeros(ntrip, dtype=numba.boolean) - #angTest2 = np.empty(ntrip,dtype=numba.boolean) - for p in range(npats): - bandangs = np.abs(bandnorms[p,...].dot(bandnorms[p,...].T)) + + band_cm = np.zeros((npats, n_bands), dtype=np.float32) + bandRank = np.zeros((npats, n_bands), dtype=np.float32) + bandFam = np.zeros((npats, n_bands), dtype=np.int32) + + for p in numba.prange(npats): + accumulator_p = np.zeros((nfam, n_bands), dtype=np.float32) + accumulatorW_p = np.zeros((nfam, n_bands), dtype=np.float32) + + mxvote = np.zeros((n_bands), dtype=np.int32) + tvotes = np.zeros((n_bands), dtype=np.int32) + bandFam_p = np.zeros((n_bands), dtype=np.int32) + band_cm_p = np.zeros((n_bands), dtype=np.float32) + + bandangs = np.abs(bandnorms[p, ...].dot(bandnorms[p, ...].T)) bandangs = np.clip(bandangs, -1.0, 1.0) bandangs = np.arccos(bandangs) * RADEG - for i in range(n_bands): - if band_intensity[p,i] < 1e-6: # invalid band - bandangs[i,:] = 10000.0 - bandangs[:, i] = 10000.0 + for q in range(n_bands): + if band_intensity[p, q] < 1e-6: # invalid band + bandangs[q, :] = 10000.0 + bandangs[:, q] = 10000.0 angTest0 = np.zeros((3), dtype=np.float32) for i in range(n_bands): - for j in range(i + 1,n_bands): - for k in range(j + 1,n_bands): + for j in range(i + 1, n_bands): + for k in range(j + 1, n_bands): # tic = ntime() - angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=np.float32) - #srt = np.array(np.argsort(angtri), dtype=numba.int64) + angtri = np.array([bandangs[i, j], bandangs[i, k], bandangs[j, k]], dtype=np.float32) + # srt = np.array(np.argsort(angtri), dtype=numba.int64) # I am doing the above, but is MUCH faster for just the three numbers to hard code - srt = np.array([0,1,2], dtype=np.uint64) + srt = np.array([0, 1, 2], dtype=np.uint64) if angtri[srt[0]] > angtri[srt[2]]: srt[2], srt[0] = srt[0], srt[2] if angtri[srt[0]] > angtri[srt[1]]: @@ -1045,10 +1050,10 @@ def _tripvote_numba(bandnorms, band_intensity, LUT, angTol, tripAngles, tripID, srt[2], srt[1] = srt[1], srt[2] ##### end hard code argsrt ###### - srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=np.int64).copy() - #unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) - #again, hard coding in the above for speed. - unsrtFID = np.array([0,1,2], dtype=np.uint64) + srt2 = np.asarray(LUTTemp[:, srt[0], srt[1], srt[2]], dtype=np.int64).copy() + # unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) + # again, hard coding in the above for speed. + unsrtFID = np.array([0, 1, 2], dtype=np.uint64) if srt2[unsrtFID[0]] > srt2[unsrtFID[2]]: unsrtFID[2], unsrtFID[0] = unsrtFID[0], unsrtFID[2] if srt2[unsrtFID[0]] > srt2[unsrtFID[1]]: @@ -1056,110 +1061,121 @@ def _tripvote_numba(bandnorms, band_intensity, LUT, angTol, tripAngles, tripID, if srt2[unsrtFID[1]] > srt2[unsrtFID[2]]: unsrtFID[2], unsrtFID[1] = unsrtFID[1], unsrtFID[2] ##### end hard code argsrt ###### - angtriSRT = np.asarray(angtri[srt], dtype = np.float32) - - #angTest0 = (np.abs(tripAngles - angtriSRT)).astype(np.float32) - #print(angTest0.shape) - #angTest = (angTest0 <= angTol)#.astype(np.int) - # toc = ntime() - # timing1 += toc - tic - # toc = ntime() - for q in range(ntrip): - #print('____') - #print(tripAngles[q,:], angtriSRT) - - test1 = np.abs(tripAngles[q,0] - angtriSRT[0]) + angtriSRT = np.asarray(angtri[srt], dtype=np.float32) + + for qq in range(ntrip): + # print('____') + # print(tripAngles[q,:], angtriSRT) + + test1 = np.abs(tripAngles[qq, 0] - angtriSRT[0]) if test1 > angTol: continue else: angTest0[0] = test1 - test2 = np.abs(tripAngles[q, 1] - angtriSRT[1]) + test2 = np.abs(tripAngles[qq, 1] - angtriSRT[1]) if test2 > angTol: continue else: angTest0[1] = test2 - test3 = np.abs(tripAngles[q, 2] - angtriSRT[2]) + test3 = np.abs(tripAngles[qq, 2] - angtriSRT[2]) if test3 > angTol: continue else: angTest0[2] = test3 - #print('here') - #angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 - #if angTest2: - f = tripID[q,:] + f = tripID[qq, :] f = f[unsrtFID] - #print(angTest0[q,:]) - w1 = ( angTol - 0.5*(angTest0[0] + angTest0[1]) ) - w2 = ( angTol - 0.5*(angTest0[0] + angTest0[2]) ) - w3 = ( angTol - 0.5*(angTest0[1] + angTest0[2]) ) - #print(w1, w2, w3) - accumulatorW[p,f[0],i] += w1 - accumulatorW[p,f[1],j] += w2 - accumulatorW[p,f[2],k] += w3 - accumulator[p,f[0], i] += 1 - accumulator[p,f[1], j] += 1 - accumulator[p,f[2], k] += 1 + + w1 = (angTol - 0.5 * (angTest0[0] + angTest0[1])) + w2 = (angTol - 0.5 * (angTest0[0] + angTest0[2])) + w3 = (angTol - 0.5 * (angTest0[1] + angTest0[2])) + + accumulatorW_p[f[0], i] += w1 + accumulatorW_p[f[1], j] += w2 + accumulatorW_p[f[2], k] += w3 + accumulator_p[f[0], i] += 1 + accumulator_p[f[1], j] += 1 + accumulator_p[f[2], k] += 1 t1 = False t2 = False t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - accumulatorW[p,f[0],j] += w1 - accumulatorW[p,f[1],i] += w2 - accumulatorW[p,f[2],k] += w3 - accumulator[p,f[0], j] += 1 - accumulator[p,f[1], i] += 1 - accumulator[p,f[2], k] += 1 + accumulatorW_p[f[0], j] += w1 + accumulatorW_p[f[1], i] += w2 + accumulatorW_p[f[2], k] += w3 + accumulator_p[f[0], j] += 1 + accumulator_p[f[1], i] += 1 + accumulator_p[f[2], k] += 1 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulatorW[p,f[0],i] += w1 - accumulatorW[p,f[1],k] += w2 - accumulatorW[p,f[2],j] += w3 - accumulator[p,f[0], i] += 1 - accumulator[p,f[1], k] += 1 - accumulator[p,f[2], j] += 1 + accumulatorW_p[f[0], i] += w1 + accumulatorW_p[f[1], k] += w2 + accumulatorW_p[f[2], j] += w3 + accumulator_p[f[0], i] += 1 + accumulator_p[f[1], k] += 1 + accumulator_p[f[2], j] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulatorW[p,f[0],k] += w1 - accumulatorW[p,f[1],j] += w2 - accumulatorW[p,f[2],i] += w3 - accumulator[p,f[0], k] += 1 - accumulator[p,f[1], j] += 1 - accumulator[p,f[2], i] += 1 + accumulatorW_p[f[0], k] += w1 + accumulatorW_p[f[1], j] += w2 + accumulatorW_p[f[2], i] += w3 + accumulator_p[f[0], k] += 1 + accumulator_p[f[1], j] += 1 + accumulator_p[f[2], i] += 1 t3 = True if (t1 and t2 and t3): - accumulatorW[p,f[0],k] += w1 - accumulatorW[p,f[1],i] += w2 - accumulatorW[p,f[2],j] += w3 - - accumulatorW[p,f[0], j] += w1 - accumulatorW[p,f[1], k] += w2 - accumulatorW[p,f[2], i] += w3 - - accumulator[p,f[0], k] += 1 - accumulator[p,f[1], i] += 1 - accumulator[p,f[2], j] += 1 - - accumulator[p,f[0], j] += 1 - accumulator[p,f[1], k] += 1 - accumulator[p,f[2], i] += 1 - - # timing2 += ntime() - toc - - for q in range(n_bands): - mxvote[p,q] = np.amax(accumulatorW[p,:,q]) - tvotes[p,q] = np.sum(accumulatorW[p,:,q]) - #for i in range(n_bands): - if tvotes[p,q] < 1: - band_cm[p,q] = 0.0 + accumulatorW_p[f[0], k] += w1 + accumulatorW_p[f[1], i] += w2 + accumulatorW_p[f[2], j] += w3 + + accumulatorW_p[f[0], j] += w1 + accumulatorW_p[f[1], k] += w2 + accumulatorW_p[f[2], i] += w3 + + accumulator_p[f[0], k] += 1 + accumulator_p[f[1], i] += 1 + accumulator_p[f[2], j] += 1 + + accumulator_p[f[0], j] += 1 + accumulator_p[f[1], k] += 1 + accumulator_p[f[2], i] += 1 + + for qqq in range(n_bands): + accumW_col = accumulatorW_p[:, qqq].flatten() + # numba does not like max function here when in parallel=True. No idea why + # mxvote[qqq] = np.max(accumW_col)#accumulatorW_p[:,qqq].max() + # tvotes[qqq] = np.sum(accumW_col) + mxval = np.float32(-1.0e12) + sumval = np.float32(0.0) + for qq in accumW_col: + sumval += qq + if qq > mxval: + mxval = qq + mxvote[qqq] = mxval + tvotes[qqq] = sumval + + if tvotes[qqq] < 1: + band_cm_p[qqq] = 0.0 else: - srt = np.argsort(accumulatorW[p,:,q]) - band_cm[p,q] = (accumulatorW[p,srt[-1],q] - accumulatorW[p,srt[-2],q]) / (tvotes[p,q]) - #for q in range(n_bands): - bandFam[p,q] = np.argmax(accumulatorW[p,:,q]) - bandRank[p,:] = (n_bands - np.arange(n_bands)) / n_bands * band_cm[p,:] * mxvote[p,:] + srt = np.argsort(accumW_col) + band_cm_p[qqq] = (accumW_col[srt[-1]] - accumW_col[srt[-2]]) / (tvotes[qqq]) + + # And same strange numba error with argmax + # bandFam_p[qqq] = np.argmax(accumulatorW_p[:,qqq]) + mxval = np.float32(-1.0e12) + for qq in range(accumW_col.size): + if accumW_col[qq] > mxval: + mxval = accumW_col[qq] + bandFam_p[qqq] = qq + + bandRank[p, :] = (n_bands - np.arange(n_bands)) / n_bands * band_cm_p * mxvote + bandFam[p, :] = bandFam_p + band_cm[p, :] = band_cm_p + accumulator[p, :, :] = accumulator_p + accumulatorW[p, :, :] = accumulatorW_p + # print(timing1, timing2) return accumulatorW, bandFam, bandRank, band_cm, accumulator From 7dfede4a9eb77c8d987c726b7a4846216c6aac17 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Sat, 6 Dec 2025 15:34:53 -0500 Subject: [PATCH 78/92] Appears to be working - numba multi-threading Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 369 ++++++++++++++++++++++++++++++------- 1 file changed, 301 insertions(+), 68 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 3a5d988..a62e151 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -554,7 +554,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose # this will check the vote, and return the exact band matching to specific poles of the best fitting solution. fit, polematch, polevalid, nMatch, whGood, ij, R, fitb = \ - self._assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band_early, bandnorms, bandRank_arg, bandFam) + self._assign_bands_nb2(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band_early, bandnorms, bandRank_arg, bandFam) # check how often the indexed band matched the top voting band family. acc_correct = np.sum(np.array((polevalid > 0) & #take valid poles @@ -1243,7 +1243,7 @@ def _pairvote_numba(bandnorms,band_intensity, angTol, pairAngs, pairID, nfam): return accumulatorW, bandFam, bandRank, band_cm, accumulator @staticmethod - @numba.jit(nopython=True, cache=True, fastmath=True,parallel=False) + @numba.jit(nopython=True, cache=True, fastmath=True,parallel=True) def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band_early, bandnorms, bandRank_arg, bandFam ): eps = np.float32(1.0e-12) @@ -1264,26 +1264,46 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band nMatch = np.zeros(npats, dtype=np.int64) #np.int64(0) ij = np.full((npats, 4), -1, np.int64) - for p in range(npats): + for p in numba.prange(npats): + bandnorms_p = bandnorms[p,...] bndnorm = np.transpose(np.asarray(bandnorms[p,...], dtype=np.float32)) - R = np.zeros((1, 3, 3), dtype=np.float32) - fit = np.float32(360.0) + + bandRank_arg_p = bandRank_arg[p,...] + bandFam_p = bandFam[p,...] + + fitout_p = np.float32(360.0) + fitbout_p = np.full((nBnds), 360.0, dtype=np.float32) + nMatch_p = np.int64(0) # np.int64(0) + whGood_out_p = np.zeros((nBnds), dtype=np.int64)-1 + polematch_out_p = np.full((nBnds),-1000, dtype=np.int64) + polevalid_out_p = np.full((nBnds),0, dtype=np.uint8) + Rout_p = np.zeros((3, 3), dtype=np.float32) + ij_p = np.full((4), -1, np.int64) + + pflt = np.asarray(libPolesCart, dtype=np.float32) + nFam_p = nFam + + libAngTable_p = libAngTable.copy() + libFamIndx_p = libFamIndx.copy() + #fit = np.float32(360.0) #whGood = np.zeros(nBnds, dtype=np.int64) - 1 for ii in range(nBnds-1): for jj in range(ii+1,nBnds): + R = np.zeros((1, 3, 3), dtype=np.float32) + fit = np.float32(360.0) #print(ii,jj) polematch = np.full((nBnds),-1, dtype=np.int64) polevalid = np.zeros((nBnds), dtype=np.uint8) - bnd1 = bandRank_arg[p, -1 - ii] - bnd2 = bandRank_arg[p, -1 - jj] + bnd1 = bandRank_arg_p[ -1 - ii] + bnd2 = bandRank_arg_p[ -1 - jj] - v1 = bandnorms[p, bnd1,:] - f1 = bandFam[p, bnd1] - v2 = bandnorms[p, bnd2,:] - f2 = bandFam[p, bnd2] + v1 = bandnorms_p[bnd1,:] + f1 = bandFam_p[ bnd1] + v2 = bandnorms_p[ bnd2,:] + f2 = bandFam_p[bnd2] ang01 = (np.dot(v1,v2)) #if ang01 < 0: # v2 *= -1 @@ -1299,14 +1319,14 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band if paralleltest < angTol: # the two poles are parallel, send in another two poles if available. continue ang01 = np.arccos(ang01) * RADEG - wh12 = np.nonzero(np.abs(libAngTable[libFamIndx[f1],libFamIndx[f2]:np.int64(libFamIndx[f2] + nFam[f2])] - ang01) < angTol)[0] + wh12 = np.nonzero(np.abs(libAngTable_p[libFamIndx_p[f1],libFamIndx_p[f2]:np.int64(libFamIndx_p[f2] + nFam_p[f2])] - ang01) < angTol)[0] n12 = wh12.size if n12 == 0: continue - wh12 += libFamIndx[f2] - p1 = pflt[libFamIndx[f1], :] + wh12 += libFamIndx_p[f2] + p1 = pflt[libFamIndx_p[f1], :] n12 = wh12.size v1v2c = np.cross(v1,v2) @@ -1371,73 +1391,286 @@ def _assign_bands_nb(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band whGood = (np.nonzero(angFit < angTol)[0]).astype(np.int64) nGood = max(np.int64(whGood.size), np.int64(0)) - if nGood < 3: + if nGood < 3: # less than three poles matched the library. Move on. continue - #return 360.0,-1,-1,-1 - #whGood = -1*np.ones((1), dtype=np.int64) - #fit = np.float32(360.0) - #polematch[:] = -1 - #nGood = np.int64(-1) - else: + else: # calculate the matching metrics. fitb = angFit #fit = np.mean(fitb[whGood]) fit = np.float32(0.0) - for q in range(nGood): + for q in range(nGood): # numba did not like the np.mean function here. fit += np.float32(fitb[whGood[q]]) fit /= np.float32(nGood) - - if nGood >= (n_band_early): - testout = testp - fitout[p] = np.float32(fit) - fitbout[p,...] = fitb - nMatch[p] = nGood - whGood_out[p,0:nGood] = whGood[:] - polematch_out[p,...] = polematch[:] - polevalid_out[p, ...] = polevalid[:] - Rout[p,:,:] = R[0,:,:] - ij[p,:] = np.asarray((ii,jj,bnd1,bnd2), dtype=np.int64) + if nGood >= (n_band_early): # we matched A LOT of bands. Assume we can exit. + fitout_p = np.float32(fit) + fitbout_p[...] = fitb + nMatch_p = np.int64(nGood) + whGood_out_p[0:nGood] = whGood[:] + polematch_out_p[...] = polematch[:] + polevalid_out_p[ ...] = polevalid[:] + Rout_p[:,:] = R[0,:,:] + ij_p[:] = np.asarray((ii,jj,bnd1,bnd2), dtype=np.int64) break else: - if nMatch[p] < nGood: - #print((nMatch*(3.0-fitout)) , (nGood*(3.0-fit))) - #if (nMatch*(2.0-fitout)) < (nGood*(2.0-fit)): - testout = testp - fitout[p] = np.float32(fit) - fitbout[p, ...] = fitb - nMatch[p] = nGood - whGood_out[p, 0:nGood] = whGood[:] - polematch_out[p, ...] = polematch[:] - polevalid_out[p, ...] = polevalid[:] - Rout[p, :, :] = R[0, :, :] - ij[p, :] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) - - elif nMatch[p] == nGood: - #elif (nMatch*(2.0-fitout)) == (nGood*(2.0-fit)): - if fitout[p] > fit: - testout = testp - fitout[p] = np.float32(fit) - fitbout[p, ...] = fitb - nMatch[p] = nGood - whGood_out[p, 0:nGood] = whGood[:] - polematch_out[p, ...] = polematch[:] - polevalid_out[p, ...] = polevalid[:] - Rout[p, :, :] = R[0, :, :] - ij[p, :] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) - - #print('----') - #print(ij) - - #print(testout.T) - #print(pflt[polematch_out, :]) - if nMatch[p] >= (n_band_early): + if nMatch_p < nGood: + fitout_p = np.float32(fit) + fitbout_p[...] = fitb + nMatch_p = np.int64(nGood) + whGood_out_p[0:nGood] = whGood[:] + polematch_out_p[...] = polematch[:] + polevalid_out_p[...] = polevalid[:] + Rout_p[:, :] = R[0, :, :] + ij_p[ :] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) + + elif nMatch_p == nGood: + if fitout_p > fit: + #testout = testp + fitout_p = np.float32(fit) + fitbout_p[...] = fitb + nMatch_p = np.int64(nGood) + whGood_out_p[0:nGood] = whGood[:] + polematch_out_p[...] = polematch[:] + polevalid_out_p[...] = polevalid[:] + Rout_p[:, :] = R[0, :, :] + ij_p[:] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) + + if nMatch_p >= (n_band_early): break + fitout[p] = fitout_p + fitbout[p,...] = fitbout_p + nMatch[p] = nMatch_p + whGood_out[p,...] = whGood_out_p + polematch_out[p,...] = polematch_out_p + polevalid_out[p, ...] = polevalid_out_p + Rout[p, ...] = Rout_p + ij[p,...] = ij_p + - #print(testout.T) - #print(pflt[polematch_out,:]) - #print(dave) return fitout, polematch_out,polevalid_out, nMatch, whGood_out, ij, Rout, fitbout + + + + @staticmethod + @numba.jit(nopython=True, cache=True, fastmath=True, parallel=True) + def _assign_bands_nb2(libPolesCart, libAngTable, libFamIndx, nFam, angTol, n_band_early, bandnorms, bandRank_arg, + bandFam): + + + def __assign_triadfit(ii, jj, nBnds, bandnorms_p, bandRank_arg_p, bandFam_p, + pflt, libAngTable_p, libFamIndx_p, nFam_p, angTol, bndnorm): + + eps = np.float32(1.0e-12) + + R = np.zeros((1, 3, 3), dtype=np.float32) + fit = np.float32(360.0) + angFit = np.zeros(nBnds, dtype=np.float32) + whGood = np.zeros(nBnds, dtype=np.int64) + nGood = np.int64(0) + # print(ii,jj) + polematch = np.full(nBnds, -1, dtype=np.int64) + polevalid = np.zeros(nBnds, dtype=np.uint8) + + bnd1 = bandRank_arg_p[-1 - ii] + bnd2 = bandRank_arg_p[-1 - jj] + + v1 = bandnorms_p[bnd1, :] + f1 = bandFam_p[bnd1] + v2 = bandnorms_p[bnd2, :] + f2 = bandFam_p[bnd2] + ang01 = (np.dot(v1, v2)) + # if ang01 < 0: + # v2 *= -1 + # ang01 *= -1 + + if ang01 > np.float32(1.0): + ang01 = np.float32(1.0 - eps) + if ang01 < np.float32(-1.0): + ang01 = np.float32(-1.0 + eps) + + paralleltest = np.arccos(np.fabs(ang01)) * RADEG + + if paralleltest > angTol: # if not the two poles are parallel, send in another two poles if available. + ang01 = np.arccos(ang01) * RADEG + wh12 = np.nonzero(np.abs( + libAngTable_p[libFamIndx_p[f1], libFamIndx_p[f2]:np.int64(libFamIndx_p[f2] + nFam_p[f2])] - ang01) < angTol)[0] + + n12 = wh12.size + if n12 > 0: + wh12 += libFamIndx_p[f2] + p1 = pflt[libFamIndx_p[f1], :] + + n12 = wh12.size + v1v2c = np.cross(v1, v2) + v1v2c /= np.linalg.norm(v1v2c) + # attempt to see which solution gives the best match to all the poles + # best is measured as the number of poles that are within tolerance, + # divided by the angular deviation. + # Use the TRIAD method for finding the rotation matrix + + Rtry = np.zeros((n12, 3, 3), dtype=np.float32) + + # score = np.zeros((n01), dtype = np.float32) + A = np.zeros((3, 3), dtype=np.float32) + B = np.zeros((3, 3), dtype=np.float32) + # AB = np.zeros((3,3),dtype=np.float32) + b2 = np.cross(v1, v1v2c) + B[0, :] = v1 + B[1, :] = v1v2c + B[2, :] = b2 + A[:, 0] = p1 + score = -1.0 + + for i in range(n12): + p2 = pflt[wh12[i], :] + ntemp = np.linalg.norm(p2) + 1.0e-35 + p2 = p2 / ntemp + p1p2c = np.cross(p1, p2) + ntemp = np.linalg.norm(p1p2c) + 1.0e-35 + p1p2c = p1p2c / ntemp + A[:, 1] = p1p2c + A[:, 2] = np.cross(p1, p1p2c) + AB = (A.dot(B)) + Rtry[i, :, :] = AB + + testp = (AB.dot(bndnorm)) + test = (pflt.dot(testp)) + # print(test.shape) + angfitTry = np.zeros((nBnds), dtype=np.float32) + # angfitTry = np.max(test,axis=0) + # print(test.shape) + for j in range(nBnds): + angfitTry[j] = np.max(test[:, j]) + angfitTry[j] = -1.0 if angfitTry[j] < -1.0 else angfitTry[j] + angfitTry[j] = 1.0 if angfitTry[j] > 1.0 else angfitTry[j] + + # angfitTry = np.clip(np.amax(test,axis=0),-1.0,1.0) + + angfitTry = np.arccos(angfitTry) * RADEG + whMatch = np.nonzero(angfitTry < angTol)[0] + nmatch = whMatch.size + # scoreTry = np.float32(nmatch) * np.mean(np.abs(angTol - angfitTry[whMatch])) + scoreTry = np.float32(nmatch) / (np.mean(angfitTry[whMatch]) + 1e-6) + if scoreTry > score: + score = scoreTry + angFit[:] = angfitTry + for j in range(nBnds): + polematch[j] = np.argmax(test[:, j]) + polevalid[j] = np.uint8(angfitTry[j] < angTol) + R[0, :, :] = Rtry[i, :, :] + + whGood = (np.nonzero(angFit < angTol)[0]).astype(np.int64) + nGood = max(np.int64(whGood.size), np.int64(0)) + + return whGood, nGood, angFit, polematch, polevalid, R, bnd1, bnd2 + + eps = np.float32(1.0e-12) + pflt = np.asarray(libPolesCart, dtype=np.float32) + + npats = bandnorms.shape[0] + nBnds = bandnorms.shape[1] + + whGood_out = np.zeros((npats, nBnds), dtype=np.int64) - 1 + Rout = np.zeros((npats, 3, 3), dtype=np.float32) + Rout[:, 0, 0] = 1.0; + Rout[:, 1, 1] = 1.0; + Rout[:, 2, 2] = 1.0; + polematch_out = np.full((npats, nBnds), -1000, dtype=np.int64) + polevalid_out = np.full((npats, nBnds), 0, dtype=np.uint8) + + fitout = np.full(npats, 360.0, dtype=np.float32) + fitbout = np.full((npats, nBnds), 360.0, dtype=np.float32) + nMatch = np.zeros(npats, dtype=np.int64) # np.int64(0) + ij = np.full((npats, 4), -1, np.int64) + + for p in numba.prange(npats): + bandnorms_p = bandnorms[p, ...] + bndnorm = np.transpose(np.asarray(bandnorms[p, ...], dtype=np.float32)) + + bandRank_arg_p = bandRank_arg[p, ...] + bandFam_p = bandFam[p, ...] + + fitout_p = np.float32(360.0) + fitbout_p = np.full((nBnds), 360.0, dtype=np.float32) + nMatch_p = np.int64(0) # np.int64(0) + whGood_out_p = np.zeros((nBnds), dtype=np.int64) - 1 + polematch_out_p = np.full((nBnds), -1000, dtype=np.int64) + polevalid_out_p = np.full((nBnds), 0, dtype=np.uint8) + Rout_p = np.zeros((3, 3), dtype=np.float32) + ij_p = np.full((4), -1, np.int64) + + pflt = np.asarray(libPolesCart, dtype=np.float32) + nFam_p = nFam + + libAngTable_p = libAngTable.copy() + libFamIndx_p = libFamIndx.copy() + + # fit = np.float32(360.0) + # whGood = np.zeros(nBnds, dtype=np.int64) - 1 + + for ii in range(nBnds - 1): + for jj in range(ii + 1, nBnds): + whGood, nGood, angFit, polematch, polevalid, R, bnd1, bnd2 = __assign_triadfit(ii, jj, nBnds, + bandnorms_p, bandRank_arg_p, bandFam_p, + pflt, libAngTable_p, libFamIndx_p, nFam_p, angTol, bndnorm) + + if nGood < 3: # less than three poles matched the library. Move on. + continue + else: # calculate the matching metrics. + fitb = angFit + # fit = np.mean(fitb[whGood]) + fit = np.float32(0.0) + for q in range(nGood): # numba did not like the np.mean function here. + fit += np.float32(fitb[whGood[q]]) + fit /= np.float32(nGood) + + if nGood >= (n_band_early): # we matched A LOT of bands. Assume we can exit. + fitout_p = np.float32(fit) + fitbout_p[...] = fitb + nMatch_p = np.int64(nGood) + whGood_out_p[0:nGood] = whGood[:] + polematch_out_p[...] = polematch[:] + polevalid_out_p[...] = polevalid[:] + Rout_p[:, :] = R[0, :, :] + ij_p[:] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) + break + else: + if nMatch_p < nGood: + fitout_p = np.float32(fit) + fitbout_p[...] = fitb + nMatch_p = np.int64(nGood) + whGood_out_p[0:nGood] = whGood[:] + polematch_out_p[...] = polematch[:] + polevalid_out_p[...] = polevalid[:] + Rout_p[:, :] = R[0, :, :] + ij_p[:] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) + + elif nMatch_p == nGood: + if fitout_p > fit: + # testout = testp + fitout_p = np.float32(fit) + fitbout_p[...] = fitb + nMatch_p = np.int64(nGood) + whGood_out_p[0:nGood] = whGood[:] + polematch_out_p[...] = polematch[:] + polevalid_out_p[...] = polevalid[:] + Rout_p[:, :] = R[0, :, :] + ij_p[:] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) + + if nMatch_p >= (n_band_early): + break + fitout[p] = fitout_p + fitbout[p, ...] = fitbout_p + nMatch[p] = nMatch_p + whGood_out[p, ...] = whGood_out_p + polematch_out[p, ...] = polematch_out_p + polevalid_out[p, ...] = polevalid_out_p + Rout[p, ...] = Rout_p + ij[p, ...] = ij_p + + return fitout, polematch_out, polevalid_out, nMatch, whGood_out, ij, Rout, fitbout + + @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True,parallel=False) def _orientation_refine_loops_triad(nGood, whGood, poles, bandnorms, polematch, n2Fit): From c44257184906c23f16475a21272e59ac45587100 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 6 Dec 2025 17:57:09 -0500 Subject: [PATCH 79/92] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index a62e151..43b943c 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -1583,6 +1583,12 @@ def __assign_triadfit(ii, jj, nBnds, bandnorms_p, bandRank_arg_p, bandFam_p, nMatch = np.zeros(npats, dtype=np.int64) # np.int64(0) ij = np.full((npats, 4), -1, np.int64) + pflt = np.asarray(libPolesCart, dtype=np.float32) + nFam_p = nFam + + libAngTable_p = libAngTable.copy() + libFamIndx_p = libFamIndx.copy() + for p in numba.prange(npats): bandnorms_p = bandnorms[p, ...] bndnorm = np.transpose(np.asarray(bandnorms[p, ...], dtype=np.float32)) @@ -1599,15 +1605,6 @@ def __assign_triadfit(ii, jj, nBnds, bandnorms_p, bandRank_arg_p, bandFam_p, Rout_p = np.zeros((3, 3), dtype=np.float32) ij_p = np.full((4), -1, np.int64) - pflt = np.asarray(libPolesCart, dtype=np.float32) - nFam_p = nFam - - libAngTable_p = libAngTable.copy() - libFamIndx_p = libFamIndx.copy() - - # fit = np.float32(360.0) - # whGood = np.zeros(nBnds, dtype=np.int64) - 1 - for ii in range(nBnds - 1): for jj in range(ii + 1, nBnds): whGood, nGood, angFit, polematch, polevalid, R, bnd1, bnd2 = __assign_triadfit(ii, jj, nBnds, From b68fb3907f07e90800c4c4934189c537f37b6d31 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 6 Dec 2025 18:25:31 -0500 Subject: [PATCH 80/92] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 43b943c..74ea14f 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -1621,17 +1621,6 @@ def __assign_triadfit(ii, jj, nBnds, bandnorms_p, bandRank_arg_p, bandFam_p, fit += np.float32(fitb[whGood[q]]) fit /= np.float32(nGood) - if nGood >= (n_band_early): # we matched A LOT of bands. Assume we can exit. - fitout_p = np.float32(fit) - fitbout_p[...] = fitb - nMatch_p = np.int64(nGood) - whGood_out_p[0:nGood] = whGood[:] - polematch_out_p[...] = polematch[:] - polevalid_out_p[...] = polevalid[:] - Rout_p[:, :] = R[0, :, :] - ij_p[:] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) - break - else: if nMatch_p < nGood: fitout_p = np.float32(fit) fitbout_p[...] = fitb @@ -1654,6 +1643,8 @@ def __assign_triadfit(ii, jj, nBnds, bandnorms_p, bandRank_arg_p, bandFam_p, Rout_p[:, :] = R[0, :, :] ij_p[:] = np.asarray((ii, jj, bnd1, bnd2), dtype=np.int64) + if nMatch_p >= (n_band_early): + break if nMatch_p >= (n_band_early): break fitout[p] = fitout_p From 2e63e46884dcdae5c5b6d4e3b4aec4a1c1de463e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 8 Dec 2025 09:03:04 -0500 Subject: [PATCH 81/92] Adjust multiprocessor settings. Add addphaselist method. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 19 +++++++++--------- pyebsdindex/_ebsd_index_single.py | 31 ++++++++++++++++++++--------- 2 files changed, 32 insertions(+), 18 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 8e8a257..b810f3d 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -313,7 +313,8 @@ def index_pats_distributed( ngpu = 0 if ncpu == 0: - ncpu = max(1,os.cpu_count()//2) + ncpu = 1 + #ncpu = max(1,os.cpu_count()//2) if ncpu <= 0: if ngpu > 0: ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*16))) @@ -330,13 +331,12 @@ def index_pats_distributed( if (platform.machine(), platform.system()) == ('x86_64', 'Darwin'): gpuratio = (6, ngpu*6) ngpupro = min(max(gpuratio), 12) # number of processes that will serve data to the gpu - #ngpupro = 8 - if n_cpu_nodes < 8: - ngpupro = min(ngpupro, n_cpu_nodes) - if n_cpu_nodes < 2: - ngpupro = 2 - #if OSPLATFORM == 'Linux': - # ngpupro = 2 + + # if n_cpu_nodes < 8: + # ngpupro = min(ngpupro, n_cpu_nodes) + # if n_cpu_nodes < 2: + # ngpupro = 2 + n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) @@ -374,7 +374,8 @@ def index_pats_distributed( os.environ["CUDA_VISIBLE_DEVICES"] = cudagpuvis rayclust = ray.init( - num_cpus=int(np.round(n_cpu_nodes)), + #num_cpus=int(np.round(n_cpu_nodes)), + num_cpus=int(np.round(os.cpu_count())), num_gpus=ngpu, _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 4dc5e24..c98658d 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -349,15 +349,16 @@ def __init__( else: self.fID = None - self.phaselist = phaselist - self.phaseLib = [] - for ph in self.phaselist: - if ph is None: - self.phaseLib.append(None) - if isinstance(ph, str): - self.phaseLib.append(bandindexer.addphase(libtype=ph)) - if isinstance(ph, BandIndexer): - self.phaseLib.append(ph) + self.addphaselist(phaselist) + # self.phaselist = phaselist + # self.phaseLib = [] + # for ph in self.phaselist: + # if ph is None: + # self.phaseLib.append(None) + # if isinstance(ph, str): + # self.phaseLib.append(bandindexer.addphase(libtype=ph)) + # if isinstance(ph, BandIndexer): + # self.phaseLib.append(ph) self.vendor = "EDAX" if vendor is None: @@ -562,6 +563,18 @@ def index_pats( return indxData, banddata, patstart, npats + def addphaselist(self, phaselist=[None]): + + self.phaselist = phaselist + self.phaseLib = [] + for ph in self.phaselist: + if ph is None: + self.phaseLib.append(None) + if isinstance(ph, str): + self.phaseLib.append(bandindexer.addphase(libtype=ph)) + if isinstance(ph, BandIndexer): + self.phaseLib.append(ph) + def getmatchedpole(self, ebsddata, banddata, phasenumber = -1, float_out=False): """Return the pole from the library that was matched to the detected band. From 5e3e1d386a591df068020b387202fdaf8b9f7c4d Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Mon, 8 Dec 2025 14:23:12 -0500 Subject: [PATCH 82/92] SubPrograms in triplevote to assist numba parallel. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 205 +++++++++++++++++++++++++++++++------ 1 file changed, 175 insertions(+), 30 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 74ea14f..0c16a11 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -1000,39 +1000,10 @@ def _orientation_quest_nb(polescart, bandnorms, weights): @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=True) def _tripvote_numba(bandnorms, band_intensity, LUT, angTol, tripAngles, tripID, nfam): - timing1 = 0.0 - timing2 = 0.0 - npats = bandnorms.shape[0] - n_bands = bandnorms.shape[1] - LUTTemp = np.asarray(LUT).copy() - - tshape = np.shape(tripAngles) - ntrip = int(tshape[0]) - - accumulator = np.zeros((npats, nfam, n_bands), dtype=np.float32) - accumulatorW = np.zeros((npats, nfam, n_bands), dtype=np.float32) - band_cm = np.zeros((npats, n_bands), dtype=np.float32) - bandRank = np.zeros((npats, n_bands), dtype=np.float32) - bandFam = np.zeros((npats, n_bands), dtype=np.int32) - - for p in numba.prange(npats): + def __trivoteloops(bandangs, LUTTemp, tripAngles, ntrip, n_bands, nfam): accumulator_p = np.zeros((nfam, n_bands), dtype=np.float32) accumulatorW_p = np.zeros((nfam, n_bands), dtype=np.float32) - - mxvote = np.zeros((n_bands), dtype=np.int32) - tvotes = np.zeros((n_bands), dtype=np.int32) - bandFam_p = np.zeros((n_bands), dtype=np.int32) - band_cm_p = np.zeros((n_bands), dtype=np.float32) - - bandangs = np.abs(bandnorms[p, ...].dot(bandnorms[p, ...].T)) - bandangs = np.clip(bandangs, -1.0, 1.0) - bandangs = np.arccos(bandangs) * RADEG - for q in range(n_bands): - if band_intensity[p, q] < 1e-6: # invalid band - bandangs[q, :] = 10000.0 - bandangs[:, q] = 10000.0 - angTest0 = np.zeros((3), dtype=np.float32) for i in range(n_bands): for j in range(i + 1, n_bands): @@ -1142,6 +1113,14 @@ def _tripvote_numba(bandnorms, band_intensity, LUT, angTol, tripAngles, tripID, accumulator_p[f[1], k] += 1 accumulator_p[f[2], i] += 1 + return accumulator_p, accumulatorW_p + + def __trivotemetriccalc(accumulator_p, accumulatorW_p, n_bands): + mxvote = np.zeros((n_bands), dtype=np.int32) + tvotes = np.zeros((n_bands), dtype=np.int32) + bandFam_p = np.zeros((n_bands), dtype=np.int32) + band_cm_p = np.zeros((n_bands), dtype=np.float32) + for qqq in range(n_bands): accumW_col = accumulatorW_p[:, qqq].flatten() # numba does not like max function here when in parallel=True. No idea why @@ -1170,6 +1149,172 @@ def _tripvote_numba(bandnorms, band_intensity, LUT, angTol, tripAngles, tripID, mxval = accumW_col[qq] bandFam_p[qqq] = qq + return mxvote,tvotes, band_cm_p, bandFam_p + + npats = bandnorms.shape[0] + n_bands = bandnorms.shape[1] + + + tshape = np.shape(tripAngles) + ntrip = int(tshape[0]) + + accumulator = np.zeros((npats, nfam, n_bands), dtype=np.float32) + accumulatorW = np.zeros((npats, nfam, n_bands), dtype=np.float32) + + band_cm = np.zeros((npats, n_bands), dtype=np.float32) + bandRank = np.zeros((npats, n_bands), dtype=np.float32) + bandFam = np.zeros((npats, n_bands), dtype=np.int32) + + for p in numba.prange(npats): + LUTTemp = np.asarray(LUT).copy() + tripAnglestemp = tripAngles.copy() + + bandangs = np.abs(bandnorms[p, ...].dot(bandnorms[p, ...].T)) + bandangs = np.clip(bandangs, -1.0, 1.0) + bandangs = np.arccos(bandangs) * RADEG + for q in range(n_bands): + if band_intensity[p, q] < 1e-6: # invalid band + bandangs[q, :] = 10000.0 + bandangs[:, q] = 10000.0 + accumulator_p, accumulatorW_p = __trivoteloops(bandangs,LUTTemp, tripAnglestemp, ntrip, n_bands, nfam) + # angTest0 = np.zeros((3), dtype=np.float32) + # for i in range(n_bands): + # for j in range(i + 1, n_bands): + # for k in range(j + 1, n_bands): + # # tic = ntime() + # angtri = np.array([bandangs[i, j], bandangs[i, k], bandangs[j, k]], dtype=np.float32) + # # srt = np.array(np.argsort(angtri), dtype=numba.int64) + # # I am doing the above, but is MUCH faster for just the three numbers to hard code + # srt = np.array([0, 1, 2], dtype=np.uint64) + # if angtri[srt[0]] > angtri[srt[2]]: + # srt[2], srt[0] = srt[0], srt[2] + # if angtri[srt[0]] > angtri[srt[1]]: + # srt[1], srt[0] = srt[0], srt[1] + # if angtri[srt[1]] > angtri[srt[2]]: + # srt[2], srt[1] = srt[1], srt[2] + # ##### end hard code argsrt ###### + # + # srt2 = np.asarray(LUTTemp[:, srt[0], srt[1], srt[2]], dtype=np.int64).copy() + # # unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) + # # again, hard coding in the above for speed. + # unsrtFID = np.array([0, 1, 2], dtype=np.uint64) + # if srt2[unsrtFID[0]] > srt2[unsrtFID[2]]: + # unsrtFID[2], unsrtFID[0] = unsrtFID[0], unsrtFID[2] + # if srt2[unsrtFID[0]] > srt2[unsrtFID[1]]: + # unsrtFID[1], unsrtFID[0] = unsrtFID[0], unsrtFID[1] + # if srt2[unsrtFID[1]] > srt2[unsrtFID[2]]: + # unsrtFID[2], unsrtFID[1] = unsrtFID[1], unsrtFID[2] + # ##### end hard code argsrt ###### + # angtriSRT = np.asarray(angtri[srt], dtype=np.float32) + # + # for qq in range(ntrip): + # # print('____') + # # print(tripAngles[q,:], angtriSRT) + # + # test1 = np.abs(tripAngles[qq, 0] - angtriSRT[0]) + # if test1 > angTol: + # continue + # else: + # angTest0[0] = test1 + # + # test2 = np.abs(tripAngles[qq, 1] - angtriSRT[1]) + # if test2 > angTol: + # continue + # else: + # angTest0[1] = test2 + # + # test3 = np.abs(tripAngles[qq, 2] - angtriSRT[2]) + # if test3 > angTol: + # continue + # else: + # angTest0[2] = test3 + # + # f = tripID[qq, :] + # f = f[unsrtFID] + # + # w1 = (angTol - 0.5 * (angTest0[0] + angTest0[1])) + # w2 = (angTol - 0.5 * (angTest0[0] + angTest0[2])) + # w3 = (angTol - 0.5 * (angTest0[1] + angTest0[2])) + # + # accumulatorW_p[f[0], i] += w1 + # accumulatorW_p[f[1], j] += w2 + # accumulatorW_p[f[2], k] += w3 + # accumulator_p[f[0], i] += 1 + # accumulator_p[f[1], j] += 1 + # accumulator_p[f[2], k] += 1 + # t1 = False + # t2 = False + # t3 = False + # if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: + # accumulatorW_p[f[0], j] += w1 + # accumulatorW_p[f[1], i] += w2 + # accumulatorW_p[f[2], k] += w3 + # accumulator_p[f[0], j] += 1 + # accumulator_p[f[1], i] += 1 + # accumulator_p[f[2], k] += 1 + # t1 = True + # if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: + # accumulatorW_p[f[0], i] += w1 + # accumulatorW_p[f[1], k] += w2 + # accumulatorW_p[f[2], j] += w3 + # accumulator_p[f[0], i] += 1 + # accumulator_p[f[1], k] += 1 + # accumulator_p[f[2], j] += 1 + # t2 = True + # if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: + # accumulatorW_p[f[0], k] += w1 + # accumulatorW_p[f[1], j] += w2 + # accumulatorW_p[f[2], i] += w3 + # accumulator_p[f[0], k] += 1 + # accumulator_p[f[1], j] += 1 + # accumulator_p[f[2], i] += 1 + # t3 = True + # if (t1 and t2 and t3): + # accumulatorW_p[f[0], k] += w1 + # accumulatorW_p[f[1], i] += w2 + # accumulatorW_p[f[2], j] += w3 + # + # accumulatorW_p[f[0], j] += w1 + # accumulatorW_p[f[1], k] += w2 + # accumulatorW_p[f[2], i] += w3 + # + # accumulator_p[f[0], k] += 1 + # accumulator_p[f[1], i] += 1 + # accumulator_p[f[2], j] += 1 + # + # accumulator_p[f[0], j] += 1 + # accumulator_p[f[1], k] += 1 + # accumulator_p[f[2], i] += 1 + + # for qqq in range(n_bands): + # accumW_col = accumulatorW_p[:, qqq].flatten() + # # numba does not like max function here when in parallel=True. No idea why + # # mxvote[qqq] = np.max(accumW_col)#accumulatorW_p[:,qqq].max() + # # tvotes[qqq] = np.sum(accumW_col) + # mxval = np.float32(-1.0e12) + # sumval = np.float32(0.0) + # for qq in accumW_col: + # sumval += qq + # if qq > mxval: + # mxval = qq + # mxvote[qqq] = mxval + # tvotes[qqq] = sumval + # + # if tvotes[qqq] < 1: + # band_cm_p[qqq] = 0.0 + # else: + # srt = np.argsort(accumW_col) + # band_cm_p[qqq] = (accumW_col[srt[-1]] - accumW_col[srt[-2]]) / (tvotes[qqq]) + # + # # And same strange numba error with argmax + # # bandFam_p[qqq] = np.argmax(accumulatorW_p[:,qqq]) + # mxval = np.float32(-1.0e12) + # for qq in range(accumW_col.size): + # if accumW_col[qq] > mxval: + # mxval = accumW_col[qq] + # bandFam_p[qqq] = qq + + mxvote,tvotes, band_cm_p, bandFam_p = __trivotemetriccalc(accumulator_p, accumulatorW_p, n_bands) bandRank[p, :] = (n_bands - np.arange(n_bands)) / n_bands * band_cm_p * mxvote bandFam[p, :] = bandFam_p band_cm[p, :] = band_cm_p From 46460feea8f9adb60cf5cd1fe55c1c2c73edfd0f Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Mon, 8 Dec 2025 16:33:54 -0500 Subject: [PATCH 83/92] Changing multiprocessing optimal values for number of indexing processes. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 7 +- pyebsdindex/tripletvote.py | 139 +--------------------------- 2 files changed, 6 insertions(+), 140 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index b810f3d..5dec96c 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -313,11 +313,12 @@ def index_pats_distributed( ngpu = 0 if ncpu == 0: - ncpu = 1 + ncpu = 2 #ncpu = max(1,os.cpu_count()//2) if ncpu <= 0: if ngpu > 0: - ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*16))) + ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*2))) + #ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*16))) # this is a heuristic, and may be highly dependent on hardware else: ncpu = max(1,os.cpu_count()//4) @@ -372,6 +373,7 @@ def index_pats_distributed( # workers do not know where to find the PyEBSDIndex module. cudagpuvis0 = os.getenv("CUDA_VISIBLE_DEVICES") os.environ["CUDA_VISIBLE_DEVICES"] = cudagpuvis + os.environ["RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO"] ='0' rayclust = ray.init( #num_cpus=int(np.round(n_cpu_nodes)), @@ -381,6 +383,7 @@ def index_pats_distributed( runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__)), "CUDA_VISIBLE_DEVICES": cudagpuvis, + "RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO":'0' }}, logging_level=logging.WARNING, log_to_driver=False, ) # Supress INFO messages from ray. diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 0c16a11..560cbd3 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -1150,7 +1150,7 @@ def __trivotemetriccalc(accumulator_p, accumulatorW_p, n_bands): bandFam_p[qqq] = qq return mxvote,tvotes, band_cm_p, bandFam_p - + npats = bandnorms.shape[0] n_bands = bandnorms.shape[1] @@ -1177,143 +1177,6 @@ def __trivotemetriccalc(accumulator_p, accumulatorW_p, n_bands): bandangs[q, :] = 10000.0 bandangs[:, q] = 10000.0 accumulator_p, accumulatorW_p = __trivoteloops(bandangs,LUTTemp, tripAnglestemp, ntrip, n_bands, nfam) - # angTest0 = np.zeros((3), dtype=np.float32) - # for i in range(n_bands): - # for j in range(i + 1, n_bands): - # for k in range(j + 1, n_bands): - # # tic = ntime() - # angtri = np.array([bandangs[i, j], bandangs[i, k], bandangs[j, k]], dtype=np.float32) - # # srt = np.array(np.argsort(angtri), dtype=numba.int64) - # # I am doing the above, but is MUCH faster for just the three numbers to hard code - # srt = np.array([0, 1, 2], dtype=np.uint64) - # if angtri[srt[0]] > angtri[srt[2]]: - # srt[2], srt[0] = srt[0], srt[2] - # if angtri[srt[0]] > angtri[srt[1]]: - # srt[1], srt[0] = srt[0], srt[1] - # if angtri[srt[1]] > angtri[srt[2]]: - # srt[2], srt[1] = srt[1], srt[2] - # ##### end hard code argsrt ###### - # - # srt2 = np.asarray(LUTTemp[:, srt[0], srt[1], srt[2]], dtype=np.int64).copy() - # # unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) - # # again, hard coding in the above for speed. - # unsrtFID = np.array([0, 1, 2], dtype=np.uint64) - # if srt2[unsrtFID[0]] > srt2[unsrtFID[2]]: - # unsrtFID[2], unsrtFID[0] = unsrtFID[0], unsrtFID[2] - # if srt2[unsrtFID[0]] > srt2[unsrtFID[1]]: - # unsrtFID[1], unsrtFID[0] = unsrtFID[0], unsrtFID[1] - # if srt2[unsrtFID[1]] > srt2[unsrtFID[2]]: - # unsrtFID[2], unsrtFID[1] = unsrtFID[1], unsrtFID[2] - # ##### end hard code argsrt ###### - # angtriSRT = np.asarray(angtri[srt], dtype=np.float32) - # - # for qq in range(ntrip): - # # print('____') - # # print(tripAngles[q,:], angtriSRT) - # - # test1 = np.abs(tripAngles[qq, 0] - angtriSRT[0]) - # if test1 > angTol: - # continue - # else: - # angTest0[0] = test1 - # - # test2 = np.abs(tripAngles[qq, 1] - angtriSRT[1]) - # if test2 > angTol: - # continue - # else: - # angTest0[1] = test2 - # - # test3 = np.abs(tripAngles[qq, 2] - angtriSRT[2]) - # if test3 > angTol: - # continue - # else: - # angTest0[2] = test3 - # - # f = tripID[qq, :] - # f = f[unsrtFID] - # - # w1 = (angTol - 0.5 * (angTest0[0] + angTest0[1])) - # w2 = (angTol - 0.5 * (angTest0[0] + angTest0[2])) - # w3 = (angTol - 0.5 * (angTest0[1] + angTest0[2])) - # - # accumulatorW_p[f[0], i] += w1 - # accumulatorW_p[f[1], j] += w2 - # accumulatorW_p[f[2], k] += w3 - # accumulator_p[f[0], i] += 1 - # accumulator_p[f[1], j] += 1 - # accumulator_p[f[2], k] += 1 - # t1 = False - # t2 = False - # t3 = False - # if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - # accumulatorW_p[f[0], j] += w1 - # accumulatorW_p[f[1], i] += w2 - # accumulatorW_p[f[2], k] += w3 - # accumulator_p[f[0], j] += 1 - # accumulator_p[f[1], i] += 1 - # accumulator_p[f[2], k] += 1 - # t1 = True - # if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - # accumulatorW_p[f[0], i] += w1 - # accumulatorW_p[f[1], k] += w2 - # accumulatorW_p[f[2], j] += w3 - # accumulator_p[f[0], i] += 1 - # accumulator_p[f[1], k] += 1 - # accumulator_p[f[2], j] += 1 - # t2 = True - # if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - # accumulatorW_p[f[0], k] += w1 - # accumulatorW_p[f[1], j] += w2 - # accumulatorW_p[f[2], i] += w3 - # accumulator_p[f[0], k] += 1 - # accumulator_p[f[1], j] += 1 - # accumulator_p[f[2], i] += 1 - # t3 = True - # if (t1 and t2 and t3): - # accumulatorW_p[f[0], k] += w1 - # accumulatorW_p[f[1], i] += w2 - # accumulatorW_p[f[2], j] += w3 - # - # accumulatorW_p[f[0], j] += w1 - # accumulatorW_p[f[1], k] += w2 - # accumulatorW_p[f[2], i] += w3 - # - # accumulator_p[f[0], k] += 1 - # accumulator_p[f[1], i] += 1 - # accumulator_p[f[2], j] += 1 - # - # accumulator_p[f[0], j] += 1 - # accumulator_p[f[1], k] += 1 - # accumulator_p[f[2], i] += 1 - - # for qqq in range(n_bands): - # accumW_col = accumulatorW_p[:, qqq].flatten() - # # numba does not like max function here when in parallel=True. No idea why - # # mxvote[qqq] = np.max(accumW_col)#accumulatorW_p[:,qqq].max() - # # tvotes[qqq] = np.sum(accumW_col) - # mxval = np.float32(-1.0e12) - # sumval = np.float32(0.0) - # for qq in accumW_col: - # sumval += qq - # if qq > mxval: - # mxval = qq - # mxvote[qqq] = mxval - # tvotes[qqq] = sumval - # - # if tvotes[qqq] < 1: - # band_cm_p[qqq] = 0.0 - # else: - # srt = np.argsort(accumW_col) - # band_cm_p[qqq] = (accumW_col[srt[-1]] - accumW_col[srt[-2]]) / (tvotes[qqq]) - # - # # And same strange numba error with argmax - # # bandFam_p[qqq] = np.argmax(accumulatorW_p[:,qqq]) - # mxval = np.float32(-1.0e12) - # for qq in range(accumW_col.size): - # if accumW_col[qq] > mxval: - # mxval = accumW_col[qq] - # bandFam_p[qqq] = qq - mxvote,tvotes, band_cm_p, bandFam_p = __trivotemetriccalc(accumulator_p, accumulatorW_p, n_bands) bandRank[p, :] = (n_bands - np.arange(n_bands)) / n_bands * band_cm_p * mxvote bandFam[p, :] = bandFam_p From f455481f22a086c744655a65770865b0291909ef Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Mon, 8 Dec 2025 16:57:41 -0500 Subject: [PATCH 84/92] Upped max number of patterns per chunk in distributed indexing. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 5dec96c..a60f9c0 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -820,7 +820,7 @@ def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): # finally - I am unsure how to check for integrated graphics that report system memory, so I am going # throw an arbitrary cap on this: - chunk = min(2032, chunk) + chunk = min(2032*2, chunk) return chunk From ff6a0608359070bb4d72f8980d54527d4ffe7fd5 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Tue, 9 Dec 2025 13:49:52 -0500 Subject: [PATCH 85/92] More scheduling adjustments. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 28 ++++++++++++++-------------- pyebsdindex/opencl/openclparam.py | 3 +++ 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index a60f9c0..67d30e8 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -339,15 +339,15 @@ def index_pats_distributed( # ngpupro = 2 - n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) + n_cpu_per_gpu = (0.5/ngpu) #max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) ngpuwrker = ngpupro - ngpu_per_wrker = ngpu/ngpuwrker - 1.0e-6 # fraction of a GPU to give to each worker (band finding worker) - ncpugpu_per_wrker = n_cpu_per_gpu/ngpuwrker - 1.0e-6 # fraction of a cpu to allocate to each gpu worker + ngpu_per_wrker = ngpu/ngpuwrker - 1.0e-3 # fraction of a GPU to give to each worker (band finding worker) + ncpugpu_per_wrker = n_cpu_per_gpu/ngpuwrker - 1.0e-3 # fraction of a cpu to allocate to each gpu worker # amount of cpu to allocate to each cpu worker (indexing worker) - ncpucpu_per_worker = (n_cpu_nodes - ncpugpu_per_wrker * ngpuwrker)/n_cpu_nodes + ncpucpu_per_worker = (n_cpu_nodes - ncpugpu_per_wrker * ngpuwrker)/n_cpu_nodes - 1.0e-3 if chunksize <= 0: @@ -357,8 +357,8 @@ def index_pats_distributed( usegpu = False ngpu_per_wrker = 0 ngpuwrker = n_cpu_nodes - ncpucpu_per_worker = 0.5 - 1.0e-6 - ncpugpu_per_wrker = 0.5 - 1.0e-6 + ncpucpu_per_worker = 0.5 - 1.0e-3 + ncpugpu_per_wrker = 0.5 - 1.0e-3 if chunksize <= 0: chunksize = 1000 ncpuwrker = n_cpu_nodes @@ -377,7 +377,7 @@ def index_pats_distributed( rayclust = ray.init( #num_cpus=int(np.round(n_cpu_nodes)), - num_cpus=int(np.round(os.cpu_count())), + num_cpus=min(int(np.round(os.cpu_count())), int(ncpuwrker+ngpuwrker) ), num_gpus=ngpu, _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": @@ -495,7 +495,7 @@ def index_pats_distributed( #gpu_launched += 1 - gpuwrker_cycles = -500 + gpuwrker_cycles = -1000 cpuwrker_cycles = 0 ngpu_retry = 0 ncpu_retry = 0 @@ -583,7 +583,7 @@ def index_pats_distributed( except Exception as e: print(e) gjob = gtaskindex[jid] - print('A GPU death has occured', gjob.pstart, gjob.pend) + print('A GPU Process death has occurred', gjob.pstart, gjob.pend) if ngpu_retry < 5: ngpu_retry +=1 del gpuworkers[jid] @@ -626,7 +626,7 @@ def index_pats_distributed( gpuwrker_cycles = 0 jid = gputask.index(wrker) gjob = gtaskindex[jid] - print('A GPU death has occured. Attempting to restart.', gjob.pstart, gjob.pend) + print('A GPU Process death has occurred. Attempting to restart.', gjob.pstart, gjob.pend) ray.kill(gpuworkers[jid]) del gpuworkers[jid] del gputask[jid] @@ -731,7 +731,7 @@ def index_pats_distributed( except Exception as e: print(e) cjob = ctaskindex[jid] - print('A CPU death has occured') + print('A Indexing Process death has occurred') if ncpu_retry < 5: ncpu_retry += 1 ray.kill(cpuworkers[jid]) @@ -818,9 +818,9 @@ def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): if OSPLATFORM == 'Darwin': # I don't know why, but AMD/Intel macOS does not like powers of two. chunk = max(48, chunk-16) - # finally - I am unsure how to check for integrated graphics that report system memory, so I am going - # throw an arbitrary cap on this: - chunk = min(2032*2, chunk) + if clparam.gpusharedmem == True: # The GPU is an integrated GPU, so memory reporting might be strange. + # Put a hard cap on number of patterns to process. + chunk = min(2032*2, chunk) return chunk diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index 4d517ac..fd4d9f0 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -39,6 +39,7 @@ def __init__(self, gpu_id=0): self.gpu = None self.ngpu = 0 self.gpu_id = gpu_id + self.gpusharedmem = None self.ctx = None self.prg = None self.kernels = None @@ -79,12 +80,14 @@ def get_gpu(self): pgpudiscrete[i] = -1 gpu = [] if pgpudiscrete.max() > 0: # discrete graphics found + self.gpusharedmem = False self.platform = [self.platform[pgpudiscrete.argmax()]] g = self.platform[0].get_devices(device_type=cl.device_type.GPU) for g1 in g: if g1.host_unified_memory == False: gpu.append(g1) elif pgpudiscrete.max() == 0: # only integrated graphics available + self.gpusharedmem = True self.platform = [self.platform[pgpudiscrete.argmax()]] gpu.extend(self.platform[0].get_devices(device_type=cl.device_type.GPU)) else: From 51c394c920a5ae1db6a79a9c741cb100c1afab67 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Thu, 11 Dec 2025 10:52:22 -0500 Subject: [PATCH 86/92] Final performance tweaking ... for now. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 15 ++++++++------- pyebsdindex/_ebsd_index_single.py | 1 + pyebsdindex/opencl/band_detect_cl.py | 9 +++++---- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 67d30e8..bc02a1b 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -317,7 +317,7 @@ def index_pats_distributed( #ncpu = max(1,os.cpu_count()//2) if ncpu <= 0: if ngpu > 0: - ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*2))) + ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*3))) #ncpu = max(1,min(os.cpu_count(), int(len(indexer.phaseLib)*16))) # this is a heuristic, and may be highly dependent on hardware else: @@ -329,9 +329,10 @@ def index_pats_distributed( if ngpu > 0: gpuratio = (12, ngpu*4) gpucycletimeout = 100 - if (platform.machine(), platform.system()) == ('x86_64', 'Darwin'): - gpuratio = (6, ngpu*6) - ngpupro = min(max(gpuratio), 12) # number of processes that will serve data to the gpu + if (platform.system()) == ('Darwin'): + gpuratio = (12, ngpu*6) + #ngpupro = min(max(gpuratio), 12) # number of processes that will serve data to the gpu + ngpupro = min(max(gpuratio), 12) # if n_cpu_nodes < 8: # ngpupro = min(ngpupro, n_cpu_nodes) @@ -815,12 +816,12 @@ def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): twocheck = np.log2(float(chunk)) if np.abs((twocheck) - np.round(twocheck)) < 0.2: chunk = int(2**int(np.round(twocheck))) - if OSPLATFORM == 'Darwin': # I don't know why, but AMD/Intel macOS does not like powers of two. - chunk = max(48, chunk-16) + #if OSPLATFORM == 'Darwin': # I don't know why, but AMD/Intel macOS does not like powers of two. + # chunk = max(48, chunk-16) if clparam.gpusharedmem == True: # The GPU is an integrated GPU, so memory reporting might be strange. # Put a hard cap on number of patterns to process. - chunk = min(2032*2, chunk) + chunk = min(2048*2, chunk) return chunk diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index c98658d..41173c0 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -79,6 +79,7 @@ def index_pats( verbose=0, chunksize=528, gpu_id=None, + **kwargs, ): """Index EBSD patterns on a single thread. diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 2f9bbda..6c89a92 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -385,8 +385,9 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = nImCL = np.int32(clvtypesize * (np.int64(np.ceil(nIm / clvtypesize)))) # there is something very strange that happens if the number of images # is an exact multiple of the max group size (typically 256) - mxGroupSz = gpu[gpu_id].get_info(cl.device_info.MAX_WORK_GROUP_SIZE) - nImCL += np.int64(16 * (1 - np.int64(np.mod(nImCL,mxGroupSz) > 0))) + #mxGroupSz = gpu[gpu_id].get_info(cl.device_info.MAX_WORK_GROUP_SIZE) + #nImCL += np.int64(16 * (1 - np.int64(np.mod(nImCL,mxGroupSz) > 0))) + radonCL = np.zeros( (nRp , nTp, nImCL), dtype = np.float32) radonCL[:,:,0:shp[2]] = radon rdn_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=radonCL) @@ -547,8 +548,8 @@ def rdn_local_maxCL(self,radonIn, clparams=None, returnBuff = True): nImCL = np.int32(clvtypesize * (np.int64(np.ceil(nIm / clvtypesize)))) # there is something very strange that happens if the number of images # is a exact multiple of the max group size (typically 256) - mxGroupSz = gpu[gpu_id].get_info(cl.device_info.MAX_WORK_GROUP_SIZE) - nImCL += np.int64(16 * (1 - np.int64(np.mod(nImCL,mxGroupSz) > 0))) + #mxGroupSz = gpu[gpu_id].get_info(cl.device_info.MAX_WORK_GROUP_SIZE) + #nImCL += np.int64(16 * (1 - np.int64(np.mod(nImCL,mxGroupSz) > 0))) radonCL = np.zeros((nRp,nTp,nImCL),dtype=np.float32) radonCL[:,:,0:shp[2]] = radon rdn_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=radonCL) From b0ed8203d4ca27b48ab672da4dc483f665b19ccf Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Thu, 11 Dec 2025 14:47:18 -0500 Subject: [PATCH 87/92] removed unused attribute phaseName (was actually using phasename) Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 560cbd3..9997406 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -102,7 +102,7 @@ def addphase(libtype=None, phasename=None, #set up generic FCC if str(libtype).upper() == 'FCC': nband_earlyexit=8 - if phasename is None: + if (phasename is None) or (phasename ==''): phasename = 'FCC' if spacegroup is None: spacegroup = 225 @@ -165,7 +165,7 @@ def addphase(libtype=None, phasename=None, return triplib class BandIndexer(): - #def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): + #def __init__(self, libType='FCC', phasename=None, laticeParameter = None): def __init__(self, phasename=None, spacegroup = None, @@ -173,7 +173,7 @@ def __init__(self, polefamilies = None, angTol=2.0, nband_earlyexit = 8): - self.phaseName = None # User provided name of the phase. + self.phasename = None # User provided name of the phase. self.spacegroup = None # space group id 1-230 self.latticeparameter = None # 6 element array for the lattice parameter. self.polefamilies = None # array of integer pole normals that should have reflections @@ -206,7 +206,7 @@ def __init__(self, self.lut = np.asarray(lut).copy() if phasename is None: - self.phasename = ' ' + self.phasename = '' else: self.phasename = str(phasename) @@ -222,7 +222,7 @@ def __init__(self, def setlatticeparameter(self, latticeparameter): self.latticeparameter = np.array(latticeparameter) - self.crystalmats = crystallometry.Crystal(self.phaseName, + self.crystalmats = crystallometry.Crystal(self.phasename, self.latticeparameter[0], self.latticeparameter[1], self.latticeparameter[2], @@ -259,8 +259,8 @@ def setpolefamilies(self, reflectors): self.polefamilies = np.rint(poles * (1.+ 1e-6)).astype(int) # def build_fcc(self): - # if self.phaseName is None: - # self.phaseName = 'FCC' + # if self.phasename is None: + # self.phasename = 'FCC' # self.pointgroup = "Cubic m3m" # self.pointgroupid = 131 # self.spacegroup = 225 @@ -270,8 +270,8 @@ def setpolefamilies(self, reflectors): # self.build_trip_lib(poles) # # def build_dc(self): - # if self.phaseName is None: - # self.phaseName = 'Diamond Cubic' + # if self.phasename is None: + # self.phasename = 'Diamond Cubic' # self.pointgroup = "Cubic m3m" # self.pointgroupid = 131 # self.spacegroup = 227 @@ -281,8 +281,8 @@ def setpolefamilies(self, reflectors): # self.build_trip_lib(poles) # # def build_bcc(self): - # if self.phaseName is None: - # self.phaseName = 'BCC' + # if self.phasename is None: + # self.phasename = 'BCC' # self.pointgroup = "Cubic m3m" # self.pointgroupid = 131 # self.spacegroup = 229 @@ -294,8 +294,8 @@ def setpolefamilies(self, reflectors): # def build_hcp(self): - # if self.phaseName is None: - # self.phaseName = 'HCP' + # if self.phasename is None: + # self.phasename = 'HCP' # self.pointgroup = "Hexagonal 6/mmm" # self.spacegroup = 194 # self.lauecode = crystal_sym.spacegroup2lauenumber(self.spacegroup) From 5438f56a469e4f27a18e43d4b17bcb57f9f61811 Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Thu, 11 Dec 2025 15:58:46 -0500 Subject: [PATCH 88/92] Beginning of a pyebsdindex save/restore. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 53 ++++++++++++++++++++++++++++++- 1 file changed, 52 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 41173c0..d850210 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -32,7 +32,8 @@ """ from timeit import default_timer as timer - +from pathlib import Path +import pickle, gzip import numpy as np import h5py @@ -853,3 +854,53 @@ def _fillPCarray(self, PC, npats): # # Need to correct band_detect.radon2pole to accept a PC for # # each point. # pass + + + def saveindexer(self, filename='indexer.pyindx'): + fpath = Path(filename).expanduser() + #with gzip.open(fpath, 'wb') as file: + # pickle.dump(self, file) + + + excludedpaths = ["fID", + "bandDetectPlan", + "phaseLib", "phaselist", + 'PCcorrectMethod', 'PCcorrectParam'] + + savedict = {} + for item in vars(self).keys(): + if item not in excludedpaths: + savedict[item] = getattr(self, item) + + savedict['bandDetectPlan'] = {} + excludedpaths = ['radonPlan'] + for item in vars(self.bandDetectPlan).keys(): + if item not in excludedpaths: + savedict['bandDetectPlan'][item] = getattr(self.bandDetectPlan, item) + + savedict['bandDetectPlan']['radonPlan'] = {} + excludedpaths = ['indexPlan'] + + for item in vars(self.bandDetectPlan.radonPlan).keys(): + if item not in excludedpaths: + savedict['bandDetectPlan']['radonPlan'][item] = getattr(self.bandDetectPlan.radonPlan, item) + + + includpaths = ['phasename', 'spacegroup', 'latticeparameter', + 'polefamilies', 'lauecode', 'pointgroup', 'pointgroupid', + 'angTol', 'nband_earlyexit'] + + savedict['phaseLib'] = [] + savedict['phaselist'] = [] + for phase in range(len(self.phaseLib)): + savedict['phaseLib'][phase] = {} + savedict['phaselist'][phase] = self.phaseLib[phase].phasename + for item in vars(self.phaseLib[phase]).keys(): + if item in includpaths: + savedict['phaseLib'][phase][item] = getattr(self.phaseLib[phase], item) + + + + + + From 1d919e8f10d04eb9deb4742a708f861d18a451ca Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Fri, 12 Dec 2025 16:37:50 -0500 Subject: [PATCH 89/92] First attempt at a indexer file save. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 70 +++++++++++++++++++++++++++---- pyebsdindex/tripletvote.py | 17 +++++--- 2 files changed, 73 insertions(+), 14 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index d850210..2f2a22e 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -43,6 +43,7 @@ ebsd_pattern, rotlib, _pyopencl_installed, + __version__, ) if _pyopencl_installed: @@ -857,9 +858,7 @@ def _fillPCarray(self, PC, npats): def saveindexer(self, filename='indexer.pyindx'): - fpath = Path(filename).expanduser() - #with gzip.open(fpath, 'wb') as file: - # pickle.dump(self, file) + excludedpaths = ["fID", @@ -868,12 +867,14 @@ def saveindexer(self, filename='indexer.pyindx'): 'PCcorrectMethod', 'PCcorrectParam'] savedict = {} + savedict['version'] = __version__ + for item in vars(self).keys(): if item not in excludedpaths: savedict[item] = getattr(self, item) savedict['bandDetectPlan'] = {} - excludedpaths = ['radonPlan'] + excludedpaths = ['radonPlan', 'rdnNorm'] for item in vars(self.bandDetectPlan).keys(): if item not in excludedpaths: savedict['bandDetectPlan'][item] = getattr(self.bandDetectPlan, item) @@ -893,14 +894,65 @@ def saveindexer(self, filename='indexer.pyindx'): savedict['phaseLib'] = [] savedict['phaselist'] = [] for phase in range(len(self.phaseLib)): - savedict['phaseLib'][phase] = {} - savedict['phaselist'][phase] = self.phaseLib[phase].phasename + savedict['phaseLib'].append({}) + savedict['phaselist'].append(self.phaseLib[phase].phasename) for item in vars(self.phaseLib[phase]).keys(): if item in includpaths: savedict['phaseLib'][phase][item] = getattr(self.phaseLib[phase], item) + fpath = Path(filename).expanduser() + with gzip.open(fpath, 'wb') as file: + pickle.dump(savedict, file) + def restoreindexer(self, filename='indexer.pyindx'): - - - + fpath = Path(filename).expanduser() + with gzip.open(fpath, 'rb') as file: + savedict = pickle.load(file) + + + ver = savedict['version'] + if ver >= "0.3.8": + # Reconstruct the indexer object. + cam_elev = savedict['camElev'] + sampleTilt = savedict['sampleTilt'] + PC = savedict['PC'] + vendor = savedict['vendor'] + patDim = savedict['bandDetectPlan']['patDim'] + ntheta = savedict['bandDetectPlan']['nTheta'] + nrho = savedict['bandDetectPlan']['nRho'] + tSigma = savedict['bandDetectPlan']['tSigma'] + rSigma = savedict['bandDetectPlan']['rSigma'] + kernel = savedict['bandDetectPlan']['kernel'] + rhoMaskFrac = savedict['bandDetectPlan']['rhoMaskFrac'] + nBands = savedict['bandDetectPlan']['nBands'] + patternmask = savedict['bandDetectPlan']['patternmask'] + rdnmask = savedict['bandDetectPlan']['rdnmask'] + backgroundsub = savedict['bandDetectPlan']['backgroundsub'] + useCPU = savedict['bandDetectPlan']['useCPU'] + + phaselist = [] + + for phase in savedict['phaseLib']: + phasename = phase['phasename'] + spacegroup = phase['spacegroup'] + latticeparameter = phase['latticeparameter'] + pointgroup = phase['pointgroup'] + pointgroupid = phase['pointgroupid'] + angTol = phase['angTol'] + nband_earlyexit = phase['nband_earlyexit'] + polefamilies = phase['polefamilies'] + newphase = bandindexer.addphase(phasename=phasename, + spacegroup=spacegroup, + latticeparameter=latticeparameter, + polefamilies=polefamilies, + pointgroup=pointgroup, + pointgroupid=pointgroupid, + nband_earlyexit=nband_earlyexit + ) + newphase.angTol = angTol + phaselist.append(newphase) + + + + #newindexer diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 9997406..1718d3f 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -70,7 +70,10 @@ def addphase(libtype=None, phasename=None, spacegroup=None, latticeparameter=None, - polefamilies=None, nband_earlyexit = 10): + polefamilies=None, + nband_earlyexit = 10, + pointgroup = None, + pointgroupid = None): """Return a band indexer for a phase. Parameters @@ -159,7 +162,9 @@ def addphase(libtype=None, phasename=None, spacegroup=spacegroup, latticeparameter=latticeparameter, polefamilies=np.atleast_2d(polefamilies), - nband_earlyexit=nband_earlyexit) + nband_earlyexit=nband_earlyexit, + pointgroup = pointgroup, + pointgroupid = pointgroupid) triplib.build_trip_lib() return triplib @@ -172,7 +177,9 @@ def __init__(self, latticeparameter=None, polefamilies = None, angTol=2.0, - nband_earlyexit = 8): + nband_earlyexit = 8, + pointgroup = ' ', + pointgroupid = None): self.phasename = None # User provided name of the phase. self.spacegroup = None # space group id 1-230 self.latticeparameter = None # 6 element array for the lattice parameter. @@ -183,8 +190,8 @@ def __init__(self, self.lauecode = None # Laue code for the space group (following DREAM.3D notation. self.qsymops = None # array of quaternions that represent proper symmetry operations for the laue group - self.pointgroup = ' ' # point group nomenclature - self.pointgroupid = None + self.pointgroup = pointgroup # point group nomenclature + self.pointgroupid = pointgroupid self.angTol = angTol self.nband_earlyexit = nband_earlyexit From 79688efd8e6ab01fd7d02f8ac1bb673d484966ac Mon Sep 17 00:00:00 2001 From: Dave Rowenhorst Date: Mon, 26 Jan 2026 10:44:37 -0500 Subject: [PATCH 90/92] Small bug fixes to allow for saving indexer object in an HDF5 Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 222 +++++++++++++++++++----------- pyebsdindex/band_detect.py | 36 +++-- pyebsdindex/ebsd_index.py | 3 +- pyebsdindex/opencl/clkernels.cl | 2 +- pyebsdindex/radon_fast.py | 52 +++---- 5 files changed, 197 insertions(+), 118 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 2f2a22e..efa08d6 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -33,7 +33,6 @@ from timeit import default_timer as timer from pathlib import Path -import pickle, gzip import numpy as np import h5py @@ -711,7 +710,7 @@ def _detectbands(self, pats, PC, xyloc=None, clparams=None, verbose=0, chunksize def _indexbandsphase(self, banddata, bandnorm, verbose=0): # - rhomax = self.bandDetectPlan.rhoMax * (1-self.bandDetectPlan.rhoMaskFrac) + rhomax = self.bandDetectPlan.rhoMax #* (1-self.bandDetectPlan.rhoMaskFrac) shpBandDat = banddata.shape npoints = int(banddata.size/(shpBandDat[-1])+0.1) nPhases = len(self.phaseLib) @@ -743,6 +742,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): # the adj_intensity is used to weight the peaks in the quest fit. #adj_intensity = banddata["max"].copy() + adj_intensity = (-1 * np.abs(banddata["rho"]) * 0.5 / rhomax + 1) * banddata["max"] adj_intensity *= ((banddata["theta"] > (2 * np.pi / 180)).astype(np.float32) + 0.5) / 2 adj_intensity *= ((banddata["theta"] < (178.0 * np.pi / 180)).astype(np.float32) + 0.5) / 2 @@ -857,91 +857,127 @@ def _fillPCarray(self, PC, npats): # pass - def saveindexer(self, filename='indexer.pyindx'): + def saveindexer(self, filename='myindexer.pyindx'): + def h5getatrib(h5grp, obj, item): + thisitem = getattr(obj, item) + if thisitem is not None: + if type(thisitem) is str: + h5grp[item] = str(thisitem).encode('utf-8') + else: + h5grp[item] = thisitem - excludedpaths = ["fID", - "bandDetectPlan", - "phaseLib", "phaselist", - 'PCcorrectMethod', 'PCcorrectParam'] - - savedict = {} - savedict['version'] = __version__ - - for item in vars(self).keys(): - if item not in excludedpaths: - savedict[item] = getattr(self, item) - - savedict['bandDetectPlan'] = {} - excludedpaths = ['radonPlan', 'rdnNorm'] - for item in vars(self.bandDetectPlan).keys(): - if item not in excludedpaths: - savedict['bandDetectPlan'][item] = getattr(self.bandDetectPlan, item) - - savedict['bandDetectPlan']['radonPlan'] = {} - excludedpaths = ['indexPlan'] - - for item in vars(self.bandDetectPlan.radonPlan).keys(): - if item not in excludedpaths: - savedict['bandDetectPlan']['radonPlan'][item] = getattr(self.bandDetectPlan.radonPlan, item) - - - includpaths = ['phasename', 'spacegroup', 'latticeparameter', - 'polefamilies', 'lauecode', 'pointgroup', 'pointgroupid', - 'angTol', 'nband_earlyexit'] - - savedict['phaseLib'] = [] - savedict['phaselist'] = [] - for phase in range(len(self.phaseLib)): - savedict['phaseLib'].append({}) - savedict['phaselist'].append(self.phaseLib[phase].phasename) - for item in vars(self.phaseLib[phase]).keys(): - if item in includpaths: - savedict['phaseLib'][phase][item] = getattr(self.phaseLib[phase], item) - - fpath = Path(filename).expanduser() - with gzip.open(fpath, 'wb') as file: - pickle.dump(savedict, file) - - def restoreindexer(self, filename='indexer.pyindx'): - fpath = Path(filename).expanduser() - with gzip.open(fpath, 'rb') as file: - savedict = pickle.load(file) - - - ver = savedict['version'] + with h5py.File(fpath, 'w') as hfile: + excludedpaths = ["fID", + "bandDetectPlan", + "phaseLib", "phaselist", + 'PCcorrectMethod', 'PCcorrectParam', 'dataTemplate'] + + savedict = {} + hfile['version'] = str(__version__).encode('ascii') + for item in vars(self).keys(): + if item not in excludedpaths: + # hfile[item] = getattr(self, item) + h5getatrib(hfile, self, item) + + + bdp = hfile.create_group('bandDetectPlan') + excludedpaths = ['radonPlan', 'rdnNorm', 'dataType', 'useCPU'] + for item in vars(self.bandDetectPlan).keys(): + if item not in excludedpaths: + #bdp[item] = getattr(self.bandDetectPlan, item) + h5getatrib(bdp, self.bandDetectPlan, item) + + rdnp = bdp.create_group('radonPlan') + excludedpaths = ['indexPlan'] + for item in vars(self.bandDetectPlan.radonPlan).keys(): + if item not in excludedpaths: + #rdnp[item] = getattr(self.bandDetectPlan.radonPlan, item) + h5getatrib(rdnp, self.bandDetectPlan.radonPlan, item) + + includpaths = ['phasename', 'spacegroup', 'latticeparameter', + 'polefamilies', 'lauecode', 'pointgroup', 'pointgroupid', + 'angTol', 'nband_earlyexit'] + + #savedict['phaseLib'] = [] + #savedict['phaselist'] = [] + phasesgroup = hfile.create_group('phases') + + for phase in range(len(self.phaseLib)): + thisphasegroup = phasesgroup.create_group(str(phase).encode('utf-8')) + for item in vars(self.phaseLib[phase]).keys(): + if item in includpaths: + #thisphasegroup[item] = getattr(self.phaseLib[phase], item) + h5getatrib(thisphasegroup, self.phaseLib[phase], item) + + +def restoreindexer(filename='indexer.pyindx'): + + # fpath = Path(filename).expanduser() + # with gzip.open(fpath, 'rb') as file: + # savedict = pickle.load(file) + def get_subgroups(parent_group): + subgroups = [] + for name, item in parent_group.items(): + if isinstance(item, h5py.Group): + subgroups.append(item) + return subgroups + + + + fpath = Path(filename).expanduser().resolve() + + with h5py.File(fpath, 'r') as hfile: + ver = str(hfile['version'][()]) if ver >= "0.3.8": # Reconstruct the indexer object. - cam_elev = savedict['camElev'] - sampleTilt = savedict['sampleTilt'] - PC = savedict['PC'] - vendor = savedict['vendor'] - patDim = savedict['bandDetectPlan']['patDim'] - ntheta = savedict['bandDetectPlan']['nTheta'] - nrho = savedict['bandDetectPlan']['nRho'] - tSigma = savedict['bandDetectPlan']['tSigma'] - rSigma = savedict['bandDetectPlan']['rSigma'] - kernel = savedict['bandDetectPlan']['kernel'] - rhoMaskFrac = savedict['bandDetectPlan']['rhoMaskFrac'] - nBands = savedict['bandDetectPlan']['nBands'] - patternmask = savedict['bandDetectPlan']['patternmask'] - rdnmask = savedict['bandDetectPlan']['rdnmask'] - backgroundsub = savedict['bandDetectPlan']['backgroundsub'] - useCPU = savedict['bandDetectPlan']['useCPU'] + cam_elev = hfile['camElev'][()] + sampleTilt = hfile['sampleTilt'][()] + PC = hfile['PC'][()] + vendor = (hfile['vendor'][()]).decode('utf-8') + patDim = hfile['bandDetectPlan/patDim'][:] + ntheta = hfile['bandDetectPlan/nTheta'][()] + nrho = hfile['bandDetectPlan/nRho'][()] + tSigma = hfile['bandDetectPlan/tSigma'][()] + rSigma = hfile['bandDetectPlan/rSigma'][()] + kernel = hfile['bandDetectPlan/kernel'][:] + rhoMaskFrac = hfile['bandDetectPlan/rhoMaskFrac'][()] + nBands = hfile['bandDetectPlan/nBands'][()] + + patternmask = hfile['bandDetectPlan/radonPlan/mask'][...] + patternmaskindex = hfile['bandDetectPlan/radonPlan/maskindex'][...] + + + if 'rdnmask' in hfile['bandDetectPlan']: + rdnmask = hfile['bandDetectPlan/rdnmask'][...] + else: + rdnmask = None + if 'backgroundsub' in hfile['bandDetectPlan']: + backgroundsub = hfile['bandDetectPlan/backgroundsub'][...] + else: + backgroundsub = None - phaselist = [] - for phase in savedict['phaseLib']: - phasename = phase['phasename'] - spacegroup = phase['spacegroup'] - latticeparameter = phase['latticeparameter'] - pointgroup = phase['pointgroup'] - pointgroupid = phase['pointgroupid'] - angTol = phase['angTol'] - nband_earlyexit = phase['nband_earlyexit'] - polefamilies = phase['polefamilies'] + phasegroup = get_subgroups(hfile['phases']) + + phaselist = [] + for phase in phasegroup: + phasename = (phase['phasename'][()]).decode('utf-8') + spacegroup = phase['spacegroup'][()] + latticeparameter = phase['latticeparameter'][:] + if 'pointgroup' in phase: + pointgroup = str(phase['pointgroup'][()]) + else: + pointgroup = None + if 'pointgroupid' in phase: + pointgroupid = phase['pointgroupid'][()] + else: + pointgroupid = None + angTol = phase['angTol'][()] + nband_earlyexit = phase['nband_earlyexit'][()] + polefamilies = phase['polefamilies'][...] newphase = bandindexer.addphase(phasename=phasename, spacegroup=spacegroup, latticeparameter=latticeparameter, @@ -955,4 +991,30 @@ def restoreindexer(self, filename='indexer.pyindx'): - #newindexer + newindexer = EBSDIndexer( + filename=None, + phaselist=phaselist, + vendor=vendor, + PC=PC, + sampleTilt=sampleTilt, + camElev=cam_elev, + nRho=nrho, + nTheta=ntheta, + tSigma=tSigma, + rSigma=rSigma, + rhoMaskFrac=rhoMaskFrac, + nBands=nBands, + patDim=patDim, + nband_earlyexit=nband_earlyexit, + patternmask = patternmask, + patternmaskindex = patternmaskindex, + + ) + newindexer.bandDetectPlan.kernel = kernel + newindexer.bandDetectPlan.backgroundsub = backgroundsub + newindexer.bandDetectPlan.rdnmask = rdnmask + + return newindexer + + + diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 8a8fd29..8a237f6 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -81,7 +81,7 @@ def __init__( self.dTheta = None self.dRho = None self.rhoMax = None - self.radonPlan = None + self.radonPlan = radon_fast.Radon() self.rdnNorm = None self.tSigma = tSigma self.rSigma = rSigma @@ -100,7 +100,6 @@ def __init__( self.EDAXIQ = False self.backgroundsub = None - self.patternmask = None self.useCPU = True self.dataType = np.dtype([('id', np.int32), ('max', np.float32), ('normmax', np.float32), @@ -118,15 +117,15 @@ def __init__( self.patDim = np.asarray(patDim) patternmask = None if 'patternmask' in kwargs : - self.patternmask = kwargs.get('patternmask') + self.radonPlan.mask = kwargs.get('patternmask') patternmaskindex = None if 'patternmaskindex' in kwargs: - patternmaskindex = kwargs.get('patternmaskindex') + self.radonPlan.maskindex = kwargs.get('patternmaskindex') #print(patternmask) self.band_detect_setup(patterns, self.patDim,self.nTheta,self.nRho,\ self.tSigma, self.rSigma,self.rhoMaskFrac,self.nBands, - patternmask = self.patternmask,patternmaskindex = patternmaskindex, + patternmask = self.radonPlan.mask,patternmaskindex = self.radonPlan.maskindex, **kwargs) def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ @@ -166,7 +165,13 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ recalc_radon = True if patternmask is not None: - self.patternmask = patternmask + self.radonPlan.mask = patternmask + recalc_radon = True + + + if patternmaskindex is not None: + self.radonPlan.maskindex = patternmaskindex + recalc_radon = True #recalc_radon = True @@ -179,13 +184,17 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ self.rhomask1thresh = np.float32(self.patDim.min())*0.1 self.dRho = self.rhoMax/np.float32(self.nRho) - self.radonPlan = radon_fast.Radon(imageDim=self.patDim, + #self.radonPlan = radon_fast.Radon(imageDim=self.patDim, + # nTheta=self.nTheta, nRho=self.nRho, + # rhoMax=self.rhoMax, + # mask=self.patternmask, maskindex=self.patternmaskindex) + self.radonPlan.radon_plan_setup(imageDim=self.patDim, nTheta=self.nTheta, nRho=self.nRho, rhoMax=self.rhoMax, - mask=self.patternmask, maskindex=patternmaskindex) + mask=patternmask, maskindex=patternmaskindex) - if self.patternmask is not None: - back = np.array(self.patternmask > 0).astype(np.float32) + if self.radonPlan.mask is not None: + back = np.array(self.radonPlan.mask > 0).astype(np.float32) else: back = np.ones(self.patDim[-2:], dtype=np.float32) @@ -198,10 +207,10 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ rdnmask = rdnmask > 0 rdnmask = rdnmask.squeeze() - if self.rhoMaskFrac >= 1: + if self.rhoMaskFrac >= (1- 1.0e-12): # apply a erosion mask to the full radon space of size rhoMaskFrac s = np.ones(( 3, 1)) rdnmask = scipyndim.binary_erosion(rdnmask, structure=s, iterations = int(self.rhoMaskFrac) ) - else: + else: # normal rho mask that will sum over the whole image, but ignore peaks outside the rhomask. cmask = self._circmask(back) rdncmask = rdnmask.copy() if self.rhoMaskFrac > 0: @@ -213,7 +222,7 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ #thresh = 0.5 * (np.min(back.shape[-2:]) * (1.0 - self.rhoMaskFrac)) #cmask = (cmask < thresh).astype(np.float32) - elif self.rhoMaskFrac < 0: + elif self.rhoMaskFrac < 0: # set the radon to operate on the max rho possible, and errode by a fractional amount. mskinx = int(-1*self.nRho * self.rhoMaskFrac) #rdncmask[0:mskinx, :] = 0 #rdncmask[-mskinx:, :] = 0 @@ -650,6 +659,7 @@ def band_label_numba(nBands,nPats,nRho,nTheta,rdnConv,rdnPad,lMaxRdn): bandData_maxloc[q,i,:] = np.array([r,c]) bandData_max[q,i] = rdnPad[r,c,q] / averdnpat bandData_width[q, i] = 1.0 / (bandData_max[q,i] - 0.5* (rdnPad[r+1, c, q] + rdnPad[r-1, c, q]) + 1.0e-12) + #FWHM = 2 * sqrt(ln(2)) / sqrt(2*ln(y_peak) - ln(y_minus) - ln(y_plus)) #center of mass peak localization #nn = rdnConv[r - 1:r + 2,c - 2:c + 3,q].ravel() diff --git a/pyebsdindex/ebsd_index.py b/pyebsdindex/ebsd_index.py index 1eaf443..8c95f47 100644 --- a/pyebsdindex/ebsd_index.py +++ b/pyebsdindex/ebsd_index.py @@ -23,7 +23,7 @@ """Setup and handling of Radon indexing runs of EBSD patterns.""" from pyebsdindex import _ray_installed -from pyebsdindex._ebsd_index_single import EBSDIndexer, index_pats +from pyebsdindex._ebsd_index_single import EBSDIndexer, index_pats, restoreindexer if _ray_installed: from pyebsdindex._ebsd_index_parallel import index_pats_distributed @@ -33,4 +33,5 @@ "EBSDIndexer", "index_pats", "index_pats_distributed", + "restoreindexer" ] diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index aaa7ffd..b7cbfa5 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -770,7 +770,7 @@ __kernel void maxlabel( __global const uchar *maxlocin,__global const float *max aveval[z*lnmax + i] = (avetempweight/9.0); // band width metric width[z*lnmax + i] = 1.0 / (w - 0.5 * (imValyp1 + imValym1) + 1e-12) ; - + //FWHM = 2 * sqrt(ln(2)) / sqrt(2*ln(y_peak) - ln(y_minus) - ln(y_plus)) } else{ break; // no more detected peaks diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index 300d4fc..6ebfc66 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -58,10 +58,10 @@ def __init__(self, image=None, imageDim=None, nTheta=180, nRho=90, rhoMax=None, self.imDim = np.asarray(image.shape[-2:]) else: self.imDim = np.asarray(imageDim[-2:]) - self.masksetup() - #self.radon_plan_setup(imageDim=self.imDim, nTheta=self.nTheta, nRho=self.nRho, rhoMax=self.rhoMax) + #self._masksetup(mask=mask, maskindex=maskindex) + self.radon_plan_setup() - def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rhoMax=None): + def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rhoMax=None, mask=None, maskindex=None): if (image is None) and (imageDim is not None): self.imDim = np.asarray(imageDim, dtype=np.int64) elif (image is not None): @@ -71,7 +71,12 @@ def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rh return -1 imDim = self.imDim + if mask is not None: + self.mask = mask + if maskindex is not None: + self.maskindex = maskindex + self._masksetup() if (nTheta is not None) : self.nTheta = nTheta if (nRho is not None): self.nRho = nRho @@ -160,31 +165,32 @@ def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rh self.indexPlan.sort(axis = -1) - def masksetup(self,mask=None, maskindex=None): + def _masksetup(self, mask=None, maskindex=None): if mask is not None: self.mask = np.array(mask).astype(int) if maskindex is not None: self.maskindex = np.array(maskindex).astype(np.int64) - nPx = int(self.imDim[0]) * int(self.imDim[1]) - - if self.mask is None: - self.mask = np.ones(self.imDim) - if self.maskindex is None: - self.maskindex = np.arange(nPx, dtype = np.int64) - self.maskindex = self.maskindex.reshape(self.imDim) - - #if (self.mask.shape != self.imDim).all(): - # raise Exception("mask and image must have same size") - # #return -1 - #if (self.maskindex.shape != self.imDim).all(): - # raise Exception("mask index array and image must have same size") - # #return -2 - if self.maskindex.max() >= nPx: - raise Exception("max of index array must be less than the total number of pixel in the array") - #return -3 - - self.radon_plan_setup() + if self.imDim is not None: + nPx = int(self.imDim[0]) * int(self.imDim[1]) + + if self.mask is None: + self.mask = np.ones(self.imDim) + if self.maskindex is None: + self.maskindex = np.arange(nPx, dtype = np.int64) + self.maskindex = self.maskindex.reshape(self.imDim) + + #if (self.mask.shape != self.imDim).all(): + # raise Exception("mask and image must have same size") + # #return -1 + #if (self.maskindex.shape != self.imDim).all(): + # raise Exception("mask index array and image must have same size") + # #return -2 + if self.maskindex.max() >= nPx: + raise Exception("max of index array must be less than the total number of pixel in the array") + #return -3 + + #self.radon_plan_setup() def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, background = None, background_method = 'SUBTRACT'): From f8299d8571f605b3695e43328925a7a38b99417f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 26 Jan 2026 13:41:51 -0500 Subject: [PATCH 91/92] Prepare for 0.3.9 release Signed-off by: David Rowenhorst --- CHANGELOG.rst | 50 +++- doc/tutorials/NLPAR_demo.ipynb | 74 +++--- doc/tutorials/ebsd_index_demo.ipynb | 384 ++++++++++++++++++++++++--- pyebsdindex/EBSDImage/IPFcolor.py | 54 ++-- pyebsdindex/EBSDImage/scalarimage.py | 48 ++-- pyebsdindex/__init__.py | 2 +- pyebsdindex/nlpar_cpu.py | 5 + 7 files changed, 487 insertions(+), 130 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index e2b3c48..27ed305 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -5,18 +5,56 @@ Changelog All notable changes to PyEBSDIndex will be documented in this file. The format is based on `Keep a Changelog `_. + +0.3.9 (2026-01-26) +================== + +Added +----- +- Now have the ability to save an indexer object to an HDF5 file using + the ``ebsd_index.indexer.saveindexer(filename='indexer.pyindx')`` method. It can similarly be + restored using the ``indexer_obj = ebsd_index.restoreindexer(filename='indexer.pyindx')`` function. + This should be considered a beta-level capability, as there might be situations that yield incompatible + values for HDF5 and variable that are defined as ``None``. + +- Some preliminary support for DM5 files for use in the NLPAR algorithm. + +Changed +------- +- The band indexing steps in the ``triplevote`` module are now mostly multi-threaded using Numba. + This causes a long delay on the first index process, but should be much faster overall, espeically when + indexing using a single process mode (non-distributed). + +- Rebalanced the default number of processes used when using the distributed (aka multi-process) indexing, + given that the band indexing is now multi-threaded. + +- NLPAR now always breaks a scan into blocks of patterns, rather than defaulting to always using a full + row of patterns. This was always the case for the GPU version of NLPAR, but now also works for the CPU version, + and hopefully prevents memory issues for larger scans on machines with lower RAM totals. + +- the ``EBSDImage.IPFcolor`` and ``EBSDImage.scalarimage`` now use the keywords ``ncols`` and ``nrows`` to + keep some consistency with the rest of the package. ``xsize`` and ``ysize`` will still be respected for backwards + compatibility, but at some point in the future will be depreciated. + + +Fixed +----- +- openCL should now stop spewing warning messages about reuse of programming objects. + + + 0.3.8 (2025-04-01) ================== Added ----- - Ability to add micron bars to IPF and scalar values maps. Use the ``addmicronbar`` keyword -to ``makeipf`` and ``scalarimage`` functions. + to ``makeipf`` and ``scalarimage`` functions. - When using ``ebsd_index`` function, if the machine has multiple GPUs, the desired GPU -can be chosen using the ``gpu_id`` keyword. + can be chosen using the ``gpu_id`` keyword. - When making IPF maps, a grayscale mix can be added using ``graychannel`` keyword. - New pattern quality parameter, ``iq`` which is the mean intensity of the convolved peaks -divided by the mean intensity of the radon. Typical values are 1.8--2.0 + divided by the mean intensity of the radon. Typical values are 1.8--2.0 - Initial support for Thermo-Fisher ``.pat`` files. Changed @@ -24,11 +62,11 @@ Changed - Minimum official support is now python 3.9 - pyebsdinex[parallel] now uses a minimum Ray v2.9 - oh5 files are currently written with OIM 8.6. -OIM 9.1 oh5 files can be specified using ``version=9.1`` + OIM 9.1 oh5 files can be specified using ``version=9.1`` - The ``fit`` value now is the _unweighted_ mean angular deviation. Previously this was -the weighted eigen value from the QUEST algorithm. + the weighted eigen value from the QUEST algorithm. - Automatic CPU scheduling is changed for distributed indexing, avoiding spinning up many -processes on large workstations. + processes on large workstations. Fixed diff --git a/doc/tutorials/NLPAR_demo.ipynb b/doc/tutorials/NLPAR_demo.ipynb index c32da4d..1848bb7 100644 --- a/doc/tutorials/NLPAR_demo.ipynb +++ b/doc/tutorials/NLPAR_demo.ipynb @@ -10,17 +10,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "257eb666-089b-45c8-9350-70d5bbd1c2ed", "metadata": {}, "outputs": [], "source": [ - "from pyebsdindex import nlpar" + "from pyebsdindex import nlpar\n", + "#from pyebsdindex import nlpar_cpu as nlpar" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "307a4120-d677-4a3b-a496-9d247839c852", "metadata": {}, "outputs": [], @@ -30,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "8207251c-fd5a-41ae-a43e-ef8e4f05fb49", "metadata": {}, "outputs": [], @@ -66,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "83a724b4-120f-408b-834e-f05e17cda4b4", "metadata": {}, "outputs": [ @@ -74,27 +75,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Chunk size set to nrows: 278\n", - "Block 0\n" + "The number of scan columns is set to one, which is unusual, and may indicate that\n", + "the number of columns is not saved as metadata in the pattern file. Consider manually\n", + "entering the number of columns/rows with ``nlobj.ncols={number of your scan columns}`` and \n", + "``nlobj.nrows={number of your scan rows}``.\n", + "The number of scan columns is set to one, which is unusual, and may indicate that\n", + "the number of columns is not saved as metadata in the pattern file. Consider manually\n", + "entering the number of columns/rows with ``nlobj.ncols={number of your scan columns}`` and \n", + "``nlobj.nrows={number of your scan rows}``.\n", + "Range of lambda values: [0.001 0.001 0.41923828]\n", + "Optimal Choice: 0.001\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Block 278\n", - "Block 556\n", - "Block 834\n", - "Range of lambda values: [0.65239258 0.90292969 1.15952148]\n", - "Optimal Choice: 0.9029296874999998\n" - ] + "data": { + "text/plain": [ + "array([0.001 , 0.001 , 0.41923828])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -125,13 +126,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Chunk size set to nrows: 278\n", - "0.90292966 4 0.0\n", - "Block 0\n", - "Block 278\n", - "Block 556\n", - "Block 834\n" + "lambda: 0.9039062 search radius: 4 dthresh: 0.0\n", + "tiles complete: 36/36\r" ] + }, + { + "data": { + "text/plain": [ + "'/Users/dave/Desktop/SLMtest/scan2v3_NLPAR_l0.90sr4.up1'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -199,7 +206,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('PyEBSDIndex')", + "display_name": "PyEBSDIndex", "language": "python", "name": "python3" }, @@ -213,12 +220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "3cacad8e052162ebde31eae56cfe36e34759a2ea87d5d6503dd4028aeda06101" - } + "version": "3.11.11" } }, "nbformat": 4, diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index bbce4a3..cec26f1 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "fuzzy-imaging", "metadata": {}, "outputs": [], @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "5b23e165", "metadata": {}, "outputs": [], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "30a6ae6e", "metadata": {}, "outputs": [], @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "da0c7317", "metadata": {}, "outputs": [], @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "62139aac", "metadata": {}, "outputs": [], @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "e57c71b9", "metadata": {}, "outputs": [], @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "47ed2c34-9aab-44fc-b288-7dd75ca94cee", "metadata": {}, "outputs": [], @@ -195,10 +195,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "dental-singapore", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radon Time: 0.010932042030617595\n", + "Convolution Time: 0.0972222089767456\n", + "Peak ID Time: 0.01805804204195738\n", + "Band Label Time: 0.03955824999138713\n", + "Total Band Find Time: 0.16610112483613193\n", + "Band Vote Time: 0.11271612485870719\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ipfim = IPFcolor.makeipf(data, indxer); plt.imshow(ipfim)" ] @@ -352,20 +455,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "d948199a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(-0.5), np.float64(1000.5), np.float64(893.5), np.float64(-0.5))" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ipfim = IPFcolor.makeipf(data, indxer, graychannel='iq', addmicronbar=True, gamma=0.75); plt.imshow(ipfim); plt.axis('off')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "3ee4b685-5a7c-4eae-a175-b2e86a5afcf0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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i9OEBJxRRFhjGAr1WzOMaO+84XI2plwa9UUz18TfEXCnqdxnbxCOcQB4EdhIwrw1+FuimICtJ/zyweTNDCfAa1HVMKDzuywz3tIdOobIAU4fcSPy9RtoAUmBKz/T5nsMXY3okcgCmHqsE8YPBBYkewEfQ3klqP8acd5gmkFjLZj0lIEAGZGHJhUWdDVTWYHLo5p6fffOMj17c4HrJD0fveBNPcVcF2YuSdZ2j68D80JO989jZQN+lSOuxhwhnBJwJbr6J8SPBv/rjTxGDIBCIt4IhNrhtYG8K9FVE7yPawmNuJCoEZBKQPzeEqSX0BjlAdhXwI7AFDEZwICEISRb3DE7RVAYbKXTvEcNxQZ6uf7F6s19aGv93fud3+Ht/7+/xV/7KX+Gv/bW/xr/4F/+CN2/e8Pf//t//zz6HqQEraOqIBPAK4sGjUks9RIgA0RuFcNA2EWLi0NYTULhp4EGliIPmfLbh5bMN129mHB7mRIPFnTmmxYH91wU6gEOx6TKclzztd2Rjy4OcMY1rNocCag1nPX4OtBIbJDM/YBY1Way4W01pUoE9REQ15FnPfLHjJp4wK0oOWnGXFCjrmMgWKRw3mznZyYHX2xMoFW0aSFaBMk5ReCJtGQrBNG34YThw/4HCbw2bZsy7P3mCsILQgBsfA7583tGVMacfLdnuU0qpuHIphWkYjwcOq5S9NOhKMjE9PHUoF7h88UBSBpabgiJqObxN+TKdYRFUyRixU9yuZujTjlneME08G5nTO428TxFWUiuF85LJK09oJQTgWY+8UvRS08cSsUpQY081M6jY0UcS3Qa604D+qCPNLad5T3onuV6lDIMmGZf0eSBEmnzfs59oeNmjJp6bTY4cOZLWYYMlOelYi4y206Ah3kJzNUI10OUBWYMSgbd+ii4843eCL8IUuZbUc412Hrc0uL1BHQQ+d7T/bsr2k4HrOib+wLH7ekTw4Caed//+nLiHXilkG2h+OmJdjUlMz2GfYXeGk5MDdjDoWY9tBasog8RT3Dvslwk6C4zymtYIkqeOqkoQKewTzWp9ipp6npU7bl2O7j15PjCSA62TdFoxDS2Jt+xUihUeFh7uNAqPiT1hrdmWM4wM1E3E0GviywZRgvw6Rn3XgvMMYziXJYcnmv3bgjaXyO9WFJVgXRUsfU4ydpSrAr9o+YYc+aDpdxqvAnWbERUK+2JAB9hvs2NW4L0aGwTyacP+dYYuLHfvpkSXPeIA84/2PIxnhFKylDPUpCN4gcFz6Q60dYSdai6mFV99u6Dr4DcmS37cPkG/qNH/Nubb8RydDfRfF8h5QBeOmhRdBXg+oFWP8R4pHZfRnu3UwINielFyeDumdjHv8hHtVzH5RcWb/QRbaW62c+bfWyO/ylmHBVZp9GmHTzxp7RhfS+wuon9mkTuJuzfUmSbee/xYEIIgSA9CcHg1YkgUIrVkYiAfVXgrqOuC2HqyeUM9Euy1JwyWebFn9rzl1TBj+DZDSc8kNJy8v+H21Qy/NXQTiXmj0ftAGaWETtErCHOH2ClUCAxjyVyW+FzS7qLjZujeEk4Hwkqh8oFoaQgerBaY2qNWGjUP8L1A5lvEOkcOHq+hHyuGmee+mpA9H8gKRyViBmkobEd1JggtiF6grwwHFaGf1NStYfSyJkl7mm/nqMSR0jMUElMH/hc//Bm///CEt6/O+LKakU467r4+QdlAd9LgG4XILelgKWqLlI43vkA4gw8eFTsYDwy1wK4S1BCI55rhoFCZxWUBdQCEwPSeYSTwbUTSS4aFAx3wFwGzknTTQPIgaIXC9OBNQDiBFQKvPLoLeCcJXtJ1MSEGoQPRLqBOLeFBM+SC5KT+C4mt/9/4pQX7v/t3/y6r1Yp/+k//KTc3N3z/+9/nX/2rf8V77733n32O08+W3K2fonaSfhFQ7TG9xb3BPfPITuBLgYsU/RjEoDBvFTYDEdQxle1gdT2j0waXC7pM4NbHmvzrNEee9byf74nSgeuvJ+RJy9VuxNh1DFrQtAlDDmoSEI1G7hV26smWgdVpzFR5Dp9PcIWi2ea4LNAngSEKbL+4RKc9aRyo+xQz6dFLyep2gjYeazXv9BxjBooPdxg89blBR55m0KirCL1omco9/+HrZzjn6ISk+EqjlSR81BLfSuaTim0Ss3A1652mXqecmJZQSeom4iAMPBgOU8UuNUR5z34TEw8dPre0JDyvD/i9ZPUypn2IWBSOTZWzqzJ8AqOnB+JFi3sVM88busjSryI+eXrFm2HOps9RfSCa9iw3GeKkx60MzfNA6jq08oSJwAZJ4hy0kkl0YPjMsV5OGLueUkueNC11JFl4T59GrF2KuVZMzzcw8xBb5tM9y9WEvpeYnWcrR3gTyKc9H7kl1/sTpLJEseNgU8zEggK7MhySFFXD6XjPlZ0RDx5OB/ROYQaBjyRCgP+0I1sJiCA+OOKzgd2mYPSsoX6bE99AEg/UhSFUMGTgR8fdylBGiBBwEqpdTG4G9g8xrtUkcc/0vR1+CPQLj9gZjOnp7xLOfrTiqlrQ1CmdjBjQyLXkK5tCZTHvBFEfuE9y/MwROs1SRURFj0s47thvU/KTCjsLEMBNNX4vaGqNTz3utAcRyE8rSnJ2yxFVoXip73jYLjjJKjZDwX6TkQ2BxeWefAhsuwS0Y3gZqFvFB2bNu8WEskxZJCUuarn3E051y7gZ4Lcamibm4VAgPGgvUAtLWnlkCKh4gAj6z491Z2slVZfi7xPCyvBXn3+JfeL42X94j0pGVJ1CS8+cirdv51z8xpr+25jDB57FpOK6nyC+1+MjQf82IjpvCb1kkpb82sfX/NHNEzZvRzyMLId1QqN71q9GxL2HQbJpEnTw7O8KeNaRRT3+Au5fz1GDQBhHcBK3jvEjS781RIUlFo6uiYj34BcWuVfsvxdIvwG/8NA4bCYY0GjlKbwlnXfsvpnx/JMb0k8dai0J9IRmhE49Tycb+jLioZ9QPAgmkzX644rydcGprzhcJngv8HXKJNlR+/SoP8kCqgtEraMzkrP3H3j45oRDSJCNQO0FIihcFNA/haTocLsEqwXDCEzniTbgEoXaB7ovc5KVJ6SB8GxA7BVe/tlOXkrK3BAdNDaSCALrE0O8EgQt8FnAxgErJN0mZdQOLFLLfR9zMd/SKM3+7YhPWSFTx3/7+iNmz7ec3+y52kw5yVvsy8Dmxws+/eyGVz8+oxtJDiomeVajG6hFis4sn3x6z5tyzL5OMHeS+F4QCo8sFdJ4QiNRJQQnUJcteMXQa/QWROyPC/HbGNEIbO6PGTIpEeGokxkmAtUFhAdzCBxvLIkzHl17QqVQHXgVMMFRTTXBSnbjX2z4/aX12f8PYb/fM5lM+NH/4X9Nt1ogNxJVWORwFOiFpcHF0M8D6U1ACsfwNCBuNMJJEIF+CjJ4cBLVQvlBIF4L4u/via8UpYqQOwlrjXjZMZ8dWBcRo71l8zBien6g2SeYk542Ffi9oXubMZmUlFVK3DiqU0F61uB/ltMphVYO0YF3xwXHZFoSS4sMcBdlSDxJE2hXKXoyYL0kjXraZYqPA2neos57JnTcv5kR9Z755Y5hErh7mJGWDnuT4LQkn9c0xXEH5Lzk7HzL8G2CdxL6wDbP0MbSec1oUjOZlTTrFCU8IXdIL2j7iFE9sEwTTqf7Y226jJEy4KxBOIdD8NCOGC0qxqMa7QLVqxG+8IiVQj3pGMqI7XKE3EpQATlA/p096zbHHARKOUQPUQe9EqQXDUMmiHtLeVUgnGQ+3vPQjsAETGwZvXeg/zIne1HRvsrZlylm6xnmkvTlgdOTHXdfnPDe2ZI3fka/jpGZR94p+iaiuDzQHGKkD7h1TEgCLy7v+bZboFqIS0evFYOSyFaSrRztB57OSC6frFleT4mCQ0WOYVD0iUBfa9JpQ1vGiCcD4suY7LzisC6QkSNqAqwkQUM3l6gahkuH2krciUPWEmGhHwnSxqPw8KwnALEY6F7nPD1fciPGiBtDpzTqWiAmnmoikBUoJLIGPw5o4fCtQhlHNdKIxFGEgbqMKE5qmnVEO9MUusWVmm6I8KeWvHKY3tEbTVdAHgY+Hq+xh0BLyubtCFsJxNjz/R9c8fnDgptmwvdP3vHt3Rmlijgva5KPKpavZ5htIOhAMOoo5vSWcdZyntX87GbB09mWVVUwn+5Ra0NlNIwHrNe8yJf85OcvGIxmHNVw5qCRnNJy99UYPwM5tTwzFVcyxX9tKIqO7TjC15of/uAV21VOU8WsbEa2c6jTntHBcr0d4xOFnzhO0z3+2rCXGaOPDxz+IKH8IIJWkbaelx/fUH8x4lCldKlELQbGdBxcROQCu12GV0DuUZUgROAGyShvENeGOpPkPxe4SaB/AUOvkLOBsDYwdgxOMZ8dMNajMs9k37HLDT2KdpkyKSoe0pSLXYPqBKtRRDtE/PrLd1RlyoOLmcuSoYnZTjXiXtMdYs7Pt7y5XaCXin4eeGlWXDdTIm9p+5hkB2eTLZt1QUOMzQKqPX6OwQjMHoYC4g3Yice4gKqhSTTxJpAWPZSOvjPULyVmJbGZIGktJAHpjn8f+zrCPfWEB4MajucUwiMbgfCBi+8/MHxo2byd8F625MbPiNrAu37EYlwiZaAZw2Vb8WZzQjZpKYnwG8NkUrHfZczjiirTdDtDOni6qxRZOFTh8DZQRRGqHpBak80anpQ1X1YLBCBbgTpIhAioWU/fGaZVR2c0fulBmT8XAtZnElOCN+K4sIk9zgBWooueoTYIHYjLAJVA9eANcDHg7w3N+x4zwLDp+On/8X/DbrdjPB7/hcfNv9Te+LttjhPgogHWEgeEj1tcevxyVSVAiKPyvBe4DNpzcAXYEdTnAhcJggpkt8fMQP+nI4oXB3RqEQ6GcSBcGe5+coq/ijh/skEZR+IdexszfJvif5aipGOiG4pRw+Jih/9w4OPTFXJp8AtLLAf6VECQCA9kjl5qhkgyWpQkckC/jlBDIN15hr2hEDWn0Q5x1pOUlr86fYt5q6k3GU9ONnRngcObEc1PJ+ilQl8ZIixuYek6jdgpPtVLTroWXQwczhXd7JhuUoklyXo+niyZ7S1z0aBaEIuBxhhibbmcLEnsQDpqua/H7N+NKKIW6QObNwVEnsPVmKxzhAdNWEXUPx9hzEAbNCEGv47Y+wQhPYkY+H58y+LDFYwsqgI5soyeHhi1lmef3ZLvB0QtaTcxh1cTQpAMVrG8ndIHjbrR2LuYzY8XtNuEzT5nY2JC6sh+bc+zT+/Iop5XD2f0J4JvdmfEWzDvNOGrBIdAhkD3OkNtFP5gEAESM/D67Tk2SEQF1VwRwp8tRHwgiECy8Yw3jofVBBsBuaNrDEOr4T5iuPDUIT7+3+0lfS6o1xmqhnYiqJ4IuonCfbcnflEzm+2J3xx3F9FKEC4HmieBOO5p0GzHhs5pkBArSz+Bb9dntN8UDOuIwSqCDDC35LVD9RLpAjYW0EiUBRcHXKlRXSBeB4bXMSPdMtwmBKuJcER9IFiJ6TzjnaO3ivc/uSWqAvkyMHyZ83Y75vPNE958ucDOAu+92KJfdvzp3Rlhp1joktv9hFRZPovv+fj5mqubEy5OD/hnjjKLqaVmMimJnzQMjSTpe0InWc5iDuEoRNtZja4dfhVxamt2X53gzgJiEBgc9RChYsvDNkO0gvdGDzw/3fD19QLVgykFi5Oa3/jkFWfPV2zvRujaQ/A0bcxHv35FLi2xEMzHHSr2hKXhQ7Fi9Lwlv2xQrxKCjUnfKtJrwft+x9X9CctZzPt//ZrkvCXpHc+rlnnoaQeF6T0uD6gBvBCIPZg7RfttgW49abCU7x/V5SIbCMWAvNeI+QDK43NPt48YvGLrYt6+XnBVTYlmPU8+fCDKLN87u+ZyeuBWF9hp4PL5kt0q4W1fMJo2REuBHA2kWc/4/T1nT7d0SjHOG/Tzltn4gHqvJZ70eKd49skd/TPHGzOh/tHAcGnRZx3jiz26sIhOojqIDuBjEK3EZWDzQHbn6WaSQ4hZP08IBZiDwI4E0gZcELSFpC4UZaLxBrR2iIuOIEFYUAcIMmBHsG1zmvuE9ZDxzfac9Sqnf9ER9wNp3FFJRbNOebU7wdaG8HkC7wy2l7jrmPidZKgNhzpBpg7dBIrf2KLmPf3GMIwE748eULlkYVo+kGvEi4GQHj9bd+oZFh5/cezukQ7WZ5pqLPGpwaUwjCTdQiDt8TXGWaR3dOcB5T3puCG3A/Eu4AyEXqIGT9Ce/gT8g0YuLLKXzHyDOe/+fwe4/8L8Ulvv/ocyiWvqPsHlEpkPyA785ynSBYYxx5Xa6YDcGIYgkcWAGjvi6YBZG+S8pysinDu2SblIoozj7bfnyFJgn1hE70h2Hu8D+l5zk5/wfHH8IRutPC//62vu/uiUQ6vovaAxBh1ZLustNmhcrUhrRxdLsmsYEgEK0jrQtjEdEW3QfP/5W35WvaDLQT3AtGg49wfeyRFPL9aEEPGTwwWz6Z6rfsaF6TDfarq1xqYC7Ydjzew9x3vJhut8zJPZlpWL2VtD/zAnTXv8ylDbnPje4lTM0+/c8k2YsNtlNFJzeSjJBoe3hodsQnrWM+4Gol7gcsHdYcpwMHA5UMWa+EnNICUTUfNwPcNHcDLrsTtNKDqaoHA7STrraHLDKzFmv8wYURITsJ1glLe0nwbmYWATWYL1nO096yFHbyWiBpcEbC5QT3uKznK4L/AyYP67FPkiUCwDvk15aAsGb4i9P+5KxpJDHKFaRWws7MDtHcNMIK3EG44/UCuN1h4qCAkYHHEcaGyEy6A+AdkrpllNbVMWtmZfH1PMwYFuPW6jcE7g8wBlTN4PhIPEqWO70SKUrE4L5HXMMHM8JBG68HS5RE8G1L1CGMnQGdJxg/UaIzwj03H4qiCbdhSLlvv6hKj2+FLTvwzoRmOWkvhlh7uJiDuIXtSELBCWGbYQ6MEi15pwNmBvEvqXHrEKyGuNPO140a9Yv5ewmB1498WCu3KM1hZrBKMPt9ztp5inDToJ+MjxpRvTXcUo6THO00aBydAxFg12EXhwCfPFgfp1AlYjo0DwEm6OQtqdT/iD7QhhPfU6ITmvEBLU2UDnJM2g+cGzHd/4Y+ts855ld5NxNl9x7ivejsZEm4hiZNkOBrVocUVg8j9dk44PSAKtj3iQx5ZVMffIycCf3j/F3iUoBj747pLDlYLgWM5Tmk5SCokdNJO04ezZhrpJyAZL0hjan+V8u75gE8fka8fbqcN1mrOkZP7ZnjrXPJQj9mVOWGp06Oi9Ya8ixE4iTaAbK0INeupJRUMULMtmxPtn9+y3Ofm4xd4XtC88p0lD+eWEXQJDqzkYQ54NuCqQrQQi1dzsRvROsW1G1G80WrfU72J2GUyKmm6bMIsrbsKYzcOI/HnJ+5dL3pyMWR4KUtOBg8YqkrOGkDlmoy2HLy4gWOS8Jx0sPpLYSjBsYoaRJJwd78mQBJLKEwqBHwQpA94qBqmJrxz2zBH/XDEYcG8iMAFhA3YBURnwkSQEwTpNGJeQBIcdBfK+ZfvjGQyKTZHTlgkvX9xRZ5J4LSlHCbqMmUwrmm8yfOYYTUoOqwR1AvaZxX5RwInDaYH+OuZddk7UeB7GCcNszHjT895sydJn1DZi8rwirxzLcUzatPQR9JWhvVDoPUQbsItjJ5DaSWysUM8aiigwtCl6KSkvNWLuEUEiZgO9MnQzMHWguZAUSY8uFUMuke5/JAK9/xLYgyF1HmclnTRk7wIyD4iKY01Ue/pDRMgcWdpTDxGtCagvclzh0W/+rL4EOPVnKRkrSO/AxQJZG0w+oDOL3Sj6aaB9PWV9muMTQVwI3vyHS549XdKtJpQmomkyPpk+8PAwxu0lJnJ88oM3fP5HLxgWAdFp8AK3V0Qe3PsWcVDcf76gyWAie+qnElcKmicK0UFcOW5jw/TkwLpOka813y6f4EtzFNlMPciAcB69FqyfpYzWHSqVyHmHET2h1+h/n9ApySxt0M8a1puCf/unz4gXgQ9eLunymg7JtstRAaL7luVsTFdG6K1gnDX0LiYePGbcUVARcs3t2zm1ATPrGL7IGaYaNenpSs2iOFBLDcGTmp6ulSSi54k6EKYtr5oRd9czimvH5zeXVM8ktAJfa6wSJHFLv1DwYIi2MJQxDJqgA7oBYTzxrSJXHcNO0CWSeOmpn0hCcvRuMEvBcOKRnaQvQA0CrCQqWspcIyy0HwT0jSSYcCy1rDVNDL7wqIOAg0SVgv16jJw5SpmiTMA1EgTog0c87UmuBXzYse9SbBkYpCK5daitYsUYHwUCAbmWJMlAmFs+eHnHz1+9IAyQOI++7DgNFW92J7gqIp3UJNmAV4JWGqJxT1p0hCqF6wifBepTQW4CYj5gW424jWmfgCkDkoCIwWaBoZCoTqC7gNACKwNuEvALz/auwN/EuMHQNjGLpGc3FoiNQucDci/pBkNnNbISRNOBcdyxmOz55mqBF4YQWa6+OsX3knzUIGeWeVaz+skJJ9MN67sRTkiiZz3qyuB7xbjr2c0T2vcH7AR8LzkTOzZNTHcaCF/ExInHXAWWuzPa90uixjF+WfKHb5+hCnsUfd4otjph408IscdFkt5r+lYQXTuME/TJMZNyOd3gamDkef9iyTJKGb7JELFCO3j+yR0T2VKankOkKd50lC9juhN4mqzYmRErnaJ7T/0w4qEuYK9RY8voomb8vZr1NqcZUqJ7SZ1HmMgy6lvcSNLvDF3iyCc9adfTNDGm8WTNgC0HpNf4keKkrzHzgapOELvAej3lSVbxnbNrDmXBcpph72J2bYYYS8yrBOEDo7hlE08x0nF/MqLZx6hG4L9N+Mlhgswt2liKvWfbZmTjGhsUXivKbYYdNHpkcSrAeEAcNMFH2CIgnUNHgb6NCUJggoc00BiNc4bkoiN65+lmCrWX+MQT7cDOwEtJl0N857HPPCJ2JL0nPERsnsZQK9KhIyQOGSQuC8xHe8T8wFqkaGuPnRiNwkw6HuqC0UcNJ/uBsey5GQSj0pLMG+5cxHhSw7eGbiTR44FhiIjvYK0mVJUnPW2JZ8fuCXeQPCwz+khwURyoYqAN9CJheO4x3uO9RKwNqON9LA4S7iT2FJqJpIsU0S7gjWDQktFFTV/l9HPPdHFgvS9ITgbceU/90+QXGi//Ugf7BsOzUcudT4lfS6pLgfKOpALvBUln8VZiB0njEqQLRHfHoBitAlHuqFSEm3rUTpLeOlwCwgvMwTNMwaPpW4kJAfmVwkno2ujYglFDO1J8M8zxDuyJJ/aO67sxjALdVYzPAl+9vcC8bOhfjwkGdBUQ3VH0Im419mTg9mEKcWAnIvLOE5833A4j/meXP+cPvv2QYtqw3YzwTjAIRRwsXgT6J47iac35tqYzhpVLieKB7/7XV9SHgq9//wn2xLEXMeI7NbJW9LFnojraOoNpoNA9334z5eBSulhzdrLjs8mSL9YzTvqW8XhLXSisMGQrmMUdTWVYHqY4rRmnLeHKYHaSdhpIdoF2UAyHiC7T0EieXqzpv844PBTE3zuwXufcigLdCvqDIB7X5O+VyLGkfpVhn1pCr+i3ki4oZC7I3ivZDilDJcEGzBBo5wK1EeybFKdBSscwkkQHkAOYRU94iDC3Dqs0UoBqIZoMyMjjBoVU7pjOftJTK02IAzxEOA8hswwRpK8kxbTBDhJyiz6AtxJmHe4qQn234cn5luV4xPA6gVTgNgbVB7pTgSkDduahVwxZIGdgCIoWyTebM0It8Xi8V7R1QkjE0bxoMTBYw3CdYl968ltPlRt8I0ith02gKTzy5UCzNSSjgbBXyNIRaoUbW4T36B7M8igcUg2EWGCnR+OgxA+82p3iIti1CdGTlvou423sCa80Lg+MDz211GRPK4aDRs48vza5Z9fHXH89wxnFvjF0E0FyH2gXjn6nSU46RqZl6Tx30wz5YYu7SkgGj/5sT/nlmINNobXE1lGXGfpec3jPc+1G6K1gNKuOu+Vzj3wQVJuE9rVhE0+QGjoiTp9uObwrOJd7bpYLRucHxF4xhJh8XHGYK9LOkl0L7LnjazPHtYrF+IBNQB8EtrTMJjXLuGC5ncAc3m5PWFDxdLGingkK1dL4CMbHRfpMNKzLCYWp2R4y5DZCb6EqDOOnDUOdUJxXTE1J8+Mxs/nAOo7xThLdG7bl+Fj33mr2xnA636ATSwgBGSR6LzkEw/iiQkYOYwNXjLn75lOCChgskXNkswP+VJI2jq6OcN4TTyz1XUbXG0SvwMH65yeE7/d0g0beGTY7hZ042jonvhGEWaDUR3OvKFgoHPW3Y/S0QzQCnXosktJIBJ5oC31j8IMnOXiap4JDaoiLgGxAxoF43FOLFBuDbiHknjaSjCct5klLVPR0X58QEUgWDcp5DrsMM3KoycDlZMvPtk9Je4sSgoaIymiGOiHtHK5MiD/Y8827c6InDeDYNhmeiO2VIaQSvReEWiFDwMYSvRPEt2B3CdZkuBGUhSe1nvy8RqQdw7cT+rHGCkH8AOOPD4RKs0nH2NxDI5G1hk8G1J0mf1FjDxlx5nFOI3eKMk0Y65a+F9htRBIs0cuGUV4hpv0vNF7+pQ72IrNsTzRx39E8TxmpjvYuoV94bCRIy0ByWbHejbEnHrlRx3Y0A2LsqAuJvgakROFxiWAoBMId1ZVOCpyAuHOERtHMA8IFhACXglCeUdTRvdM4JGNT00tJ2xv6rUHkgaddSVUoklFHPeuJaoUQAmEl9onD1xqdBi4+XHL77ZzhIhDd9Wy3Gf088P/cfIQbYoprydAliMJhUot60lDeaU4u9vRCUU016axm+MMpo+kee5Ny/e0E9aLlxNbIzRRXxjSTQLpW8L5HVZ7Zck/9IqU+U4SlJO8H6jLmj8sL3Kml/smUKw96PPBrp3fc309YpjFDKhikRL5T7MYGeWZZRBW7MGboNKOoRckAW0O2CtxNx6Bjwhz2P1ngFczpsCn0naEfCdo3M9LLhnTR8CIpuRcjducR+XXAnjiqYIiygcW4ZP/FFHsekDOHbRUfvbzm1X6BCQ63igljfyy/tNA/DZh7RfeRY8gD6lqgZx1qI9GJJz9vSPaezTonagTNs0DIIdl7/O5YvhABWq8JmUfuDfqDinVTYK4VUkGkHDcPc/pRQAtJXAZkNKAvB/rXGfYkoA+CvgioBpyBLmhkHfCrFJN5mheW7EoSNw5lAtUTT7TS+KsJzZOjpuRQS5AeqyStEvjTgJGW7tYQG4uX0H1kibseX2ao1MFO0Zw5Zhcl3SrHP3eIicX3Gi89oxrWW8nF95YoKQlTx1U0JTaW7Lxk6COm1Hib0n6VogrHs23Dq9UZ3vSMLyrE0lMdRvQion3uUR70bGDlMh7WBR9+fMfivOHf7p6T6Y5f/+FrfnL9BDWztIVnNj2wXE54ulizOc2InDt+X0Gjw8DZByvWd2PMaU8bDOqDgfiNok8kshOs300YCsGbbo5/39L2ORpB/GGJHw1MWtjplPhFx9BrPn7Yc/0kIctaXr86Z/F1jf9Mcbuc43LB2y7FHxRuF1EvBsrDCcFpylmCXxuyeKBNBOl5zfPnDbqBdZIy01vCSHHYZJxc16TPV1w9nJAXPS9++54rMi6XDa9yjZ94RrVjHpU89Dkvlg1vm3OkBdcIxBPLLtccljF/5YNrfmrnpE9qZlcKnga0dFBL8ruBGEsYeYg92yjHNYr9esRkXLNqCiIsjAN+kPiVIWoD6hCQeU9UgnswR5FZCySB0Er6TUbYWWTskCtJlHdU1Zi0aImWmiEoVBMYLiy+U7hIovdAL1EHS78Qx539TqEGj0cRtKe4qKhkxHvTNas+pvqDCVEPOmmJJi2LZeCVNoAgqjw3mznlmxFV5ojmHZOlw571x0Un0MwCV//+CclJS9727BPDfpejJx5xUJjI4S8cgxLMNh16D9UmI2gBE4e+lwQfCJmnNobmxlA1I6RzxyzixB/F1UsFQjK/3LF3MaLU2EwSh0D/suPhMOKUGtsaUJJuBkJIVJC0sUDOAn6pGd0EtvsZoWl/ofHyL3Wwp1c0tymRPBqZVNagvcBrSVQ7qtQQ0gHrAnEY8Ba8krgYhkgT3wa8CgQnkaUkSAgIVB8Q7s8UlED4ZMBdS9SBY90Yh96JY020VAwuOr5fVDA5KwmLnm2rSIXF5wEDPO1ryq8XtKkgKMF01MBPctpTkHngPs3Ixh067dlNcuwAherprlKiy5Z2JTGjjlHacb8eI3+aMh63iKUi+aCjf53yvCi56T0rEvos4D91nNqOL8o55rkl/zeCXioS1yJrKH8YqD8fIVbHlXK2DLQLjQ7QeMNU74l7yyfFA9dnKV8PY/wnA+m2p+xHqDvN7OM1q+2IhSoprzOenu8RhWR3lTHx7mh5ejYwfF0gtMMeDOSe2HrKOiK9HzBCIHYR4SQgEkvXGl69uiD0Cq8FbXcMtung6FPFVkTokw6pBKESiNOeb/yCZqQ471uGicKfBNZFxDRqkJXAvxCQB06Khv17CbaMePbZA6v7M/Y2prMQIiBznN1Z1iIjjAJpPaAajYw9yStoxgY38iybHH2lUZGjHyvWbc6LdsftdkxcerpEohcDg9M073t0KcAJggY79wxSoZKBiW/J5cBsduDnzRnJhx1irRmqiLQ+miRJG4hFQEWOroiRE8v8xZa0tcR3gfJScv3lGX4nkL/WkV7B9y6uuD0Zs1cRlch4//mK280YMe/Q14qT8z0hh9uvTukXng+fXXN/O6EOEVN5IBaOrLMkytMp+HpzSqJ7PmHJN7enHOIYM+4wo56H9Yhax4RUMN46jBlQ5x2HEGOcpbvNeJedMBw2xK97oimc+BbzxtBHCrV3bPdjkicNlY/Y1Rm7PkV0gg8/u2KoDH6QyK1kNK6J4p5KpvBhT54M7G/GxBW8XDxQnktWbUHcezJtObijUFVsJdlBslUjso3l6tkYo1o2uwI/CYz/Jwc+zCx/0D0h7xwHFbG1KU9frvlqdYYRlqls2d3lR1e39rhIfY0miY7q7YvJjlB4hAvYaaBKA8O3Y4p5x05qtss50WSgeVITf+M5tRUG6KaB3Jc0WUz0WtCPA5GDxPfsspTGSf7b9fvkruN2yMhqi563lG3CPqQ8OTmgRh0+SPoqY9tmvLB7DnFC0HB2uqWqEnLRU0YJ40rgpcQh6VpDtBWY1kERaGaKUKeoDjI/0CYQbiL6HtwuIppbJkPLpkiRDxocRG8kPoJgArIRxBuPTRWIo1CvzCOinUCEgGggqsC/aLhr06PV7Lzm4W6KbBTdTcZV6slMT6Mj6quMpcnIJg1xammXCRtliOoBowZ0DXoXI7pAZSO26xRjLKboiQ/QSoUQAWPcUaz9xNPMJSruGdYR7YmCHOIlqKXCdGAqD1IiBkkYQ34FxIbVS0V0r6AfYKUZRkDs2cuIpGiQO0W9NyTjlkEZ9BAwicXmnkh6nsxKVj4nTgNDYdnXjzX7/2x0BSr29Eoje4i8IwiPPB0opUGVgmInib2j15rpuKFeZwyJxGwcIUiKuMW/1cip45DHmMYdfcmlJ/IQrMB9leClhNijalCDwj8dsDuDMS2yjYiTgbaOGF6lhJ2jeNJi1zHuItC8i/hmWNBPIM56xrKjfMjpcsmo7HFO0zmBvBKEtSK2nuj5QPLKIWJDITqGdc46Smjlsd0vngb0omXvUuKlR50O/OlhgbzoSKYd3T5Ch8A3uxNYHZ2nht/quTyU3LsRrZgy9R31p4q61KiNQE4cc91S3WT4Fxa9E/iZpcoF7RcZp0+2iEGwixJk7pDX8IQO9U7DDxzKgdhJrt0EO3X0g8J6yfRLycEZ7AeB2dDQbBIgEFQgjCSR7DkUBicFTZ8TP0gu33tg9+9OsWON8JDUFj/+s5vQWNAec9AMgyI56ZmKhuYhZVNNkGOH+kZQ/KBm6iqGQrFfFWh/VLq7OkMVjuK8Jl/1VKsE34JsJXInKJUhLMJRhR8F7ABhdOzN9VtJGg9Umxg3CqhSEpUCJTzNBSSHDrs3+AyUBZN2iKWkEwrdBuJhwD1omplknFZ0HMVgP7t+ineSfuqRXsJXBvFpT7IEn3jEIEDD+Idrmj8c4y4UOy05ndfcvjrjVDaM36t4dzXhIj/w+zfvQRIY5Q2ji4o8abhU8PlwQphL/HpMv9Ho+cDWZxy0wTpFLBybH5/QnQZMHmhfFfjvtHx0WHKZ1bjvW4rXCV0Vcf604TaLiIoW907TT6HbRfQ3EShNO4f3xmu2O4ndacohJYkDE93xf/6//zptKvng7Ja73YSLizWNVdzvZggH4qInrCPuHmZEbqD6yYh+ZEhUIHEDGkFTJzQy5uNPrtje5YjvNKy/viBSAx9dLnn9zRlcGeSoJ2wlrdYUvaVLFdwY+nVMyDwxjuXHOZ/fjLg43TNxLbmWXL075dVPLtEBSAQ7DD4J6PqY4o5eVPzGi2v+5O0TxOuO4TuC2AYmWQ13kvOnW94IxXI1IRwEZtozbCLW1YQhUuxHmoMzRDcpInPM44Y4KtklEePf2HD3Zs6TyZKdS9ncTmjXKfKsJz1v6RW8mK25CiP2X+aE30uoXxy7W5LO8a2e8iTf46eehSppbYI/dVBakmgg+sJw/zIheIn81lBqjek9qg/QCqInDbIJmK9S6rnAtIFmJlAHxbYuEP5YMjWVgADeHnVORJ56fuxyUr3AdD1KOepZQj+SJDvPRiekTcPN1QQ5tpyEA+e/fs/6bszwbUR4afEKooOg+7TlZBPY2JRmn5CpAdFDUydMaBmMOmZhk4DpAvGoowwRi7qhOyToYjhmIuyAetrRfl5gjWA4sQyZImpAro6vT24FzQeWou05VDmqPfqwuFjgjWD6B45+FBh8hPqz0phIHJXQiDeG6adbzIPkdj8irXtG0xZbR0RZyzyvqHxEfNJwvRyzCA2L/WMa/z+bSdqwnsSYJdiFpc2PJgaiVBS+p3eGQ5cgE4+oFPsiQgkwTUCeWGoZkQ+e4QJkIjCbgOw89YUmaEVSe/zUEw+eYCV9r1C9p3kKyit0bNltc1CCaNGRVZKhNDSxxt8YTic7aATPn95zZ8ec53se+oLroiDl6F++byL0AOYW/GKgKxO6k0CsO56+3PLt1xntIcE9dYy2LbNQYb1kN4+Z5AOxsmy3GaZxvAxbrscFy88XiNwhD4L56MA6HjPkoCvD7swR1z3OSfpVzIt5yf0A1cQSL3rO7nuuphqtHdXbAqUDUQrJpMV6w/PRga2MeG4bvv5kyjd+TP7EsrQThighlIrzDzYsH8aIrSaWgf4MuAZXK5oculIROLYoqQ4y1VK3Bj0dqCuF0QNoSflEknyww36ZU6WKSHm46LGV5PT5ju3/4xRrNG2p4C5C5xYzOBolSBz0b1NWQiNVOA6fOOm5DTP6TPDidM2b+wVloknTluQe6kOKywXZi4rexehsQN4JlIJYd7SJRt5Dc4gwHUjt0SGQPK+o1xnNIaO0EUoJvPS01uDWCaoCvwj0E0EwCjkOxNOO/X2BLgVfv4xQvSSZt5z4hm6XsvugJ513zD44sKtzTkPP+nrE+5uKu49AHxzv7JhVP+HUtdis5+bS0L5T3PoCVMAqQTprWa9GfGPmzIoWIS2uVgyvNNlpy+AiGq/x65gibtivChQcHdH2hmEeMHvJ5Htb7puUh/WUk493XN8t+Op+jmwkp6dbLs9v+fbrc7qJItoI9E8ixKcDu6FgtKgJsaYsY9pdTB9HDKcOpSybTc4QS642M5IVaCHwJw5eRxjpqNoM4XtCLhiNK4onB17EFa+XE64aycdlzXZQ7E402z+94Cyt6XeGtSjotMA/t5zke8o459nphs4Z7OdTouzY3njy4ZqwM6zqHLaGnUhZLA58/fkL0nnL+ye3/OTmCaNRy/56xNlozbaaYN7vSeKW2yHBxAOjFx7bSlpi/E2MMYEnt4FvS00qB06yAzEDb/0ZUROQSU9xUrH/+TnJ+YHT6Z6rL2Z0MmHWVbz5/IL3ig2fpAf+w+uM/KxGKoUeD7jCsW8yqjpG20DytGHVjpEHMMsY88meF6uGyWWLXtTcfL4g7CTj2R5hBStfMLtsCXhCJynPBJlt8QlEDJR9jKkU20EjJwG/OHZlmD2o1jLMJGYF1RNF8uBwqSR9CBA8xANRfMyS+lQgPu3YJxEXYc19NaZNJHHvae5T8q7DNopwiHlbTzk3e/ZjhSl6eqcRC8+s8WzezJAnDjV4hkSRVJ6TuGE0r1jeTolmHaU5DmbKraURBj9owiCRiaWsEjpbEMUt84uW5suUzz67oo8USRe4vcy4ej3FjQXCQ6UipHD0p4EQBHov0bmlywVhL7AFmINEtY5wpdGFhMawux4RaQcpnEYlk3GPmlT80eqChyTlbLol3Ef8cHbPl82USkW/0Hj5lzrYt3XEfNxQk8KdJrloGGpNXymSwdOfetR9wFWK2Fv0ZcduMGS3YLcRWWqpN0dF7TBEyMLhYom0ghBAlALdS3x6NFKQayBIvD/2iMoa+g+OzlHb5QibAwUUbqCtFNnUIh4U1ZOEISjudILVYILHnwdYalACLLz47JaH+ymVCzAIbGt4+yLj/PktbWNo2ghpJLciwy5jRrOKV9WMuAkoHyhDROtjwpMBnQ2IUmJzz64qCI0kSMVwMnD6uuPteE48bpG55Wf3C36QPXBdFeyuCq6+0nQTwVAJROyYnh54sx3T9jE/vPyWbRRx8/UJq2mC3yd4FSiFpHeaMLNwFVj+fI5LBcI7/KVDnHaIc8f0AbY2IR0PCO3paoNtJas4RewFg4iICNhC8VCOyMctpgpUI3tUsa9hsIqnH6yoqoRk2uJxxNOeSAeStePmg4zkZ5r+UhKsQliQ95JCdsTPD/RBs6oKVn9yghg7ivcqonggJBFcBkobMz44st5ShYjZezsOt1PCiUDFPV54uI8QOsDMoYOlvwhkdcupqbkqMoZtihxZgnLEq8BwIYhHlmzRoYMg6h0PQ0HxvT3JyrFMUtK9ZKQbsrOaMArIKGG3SXl62HKzi7i43CHzhOvdmNYZ0nnD5WjDzXqOP/UcfI6/Bb0NiEyQlR4iS/WnY6JO0aQxVZYzmjR0VmBlxKYdcSZL+j5HxJ59kx49HoLESYlNHFHcY95JvhVPcAfDdFHyZDtwKz3Zecf2j094G81JXwfkWiNOA6oCZCBaw/OTDbtDzkEo9qkmzjvixsHE46LA7nUB8YDcaCoDopSkrju6Xm5jlHS0IqIzCjcaeLU64WGase4KvqdvqKsZm22E+wRMJfjO5QM/WT9hX6V88uyGL8tTbrZTPn16zztXMOp7ds87Xpyv+WY9wyYCmXWk14ZD4rHrmKvDKR+lG8Z0/LtwxtPJnsP1CFkJqqsccTrQvcqp85Tlg2deVGyH7NhD3wv6WvFbH19jjWdXpoiDYn09ox8JTi83RE2gKw0bm6H6gCwc5rTlZFsx/fCa3ddTVJXx7nCCeDDM8wGRWlZJwbPswO1yzDxv6HvDyPVc7yf82geveb7Y8m/3z4HAZ0+v+fFPX/C9Rclbb7iVBeWd4eWnN6zKgnKZ4oUkdh6uJVbG2FiiEotxYL0iHg0QSdI1x/KEh+494HWMTQTFO0+7EJA4GqPwErJaUTuFdAE96+mGiC5WLG2OGg2k1jMMmtwfjZyGIlCvYnSiGL4HprM09xl27tkrSfZGkZ2X+FbQzyTDVUp54qj3Bfs3KWJhiUaWUdrTvcpYTWK0tDg8ftYxbGNU4gkC4go2V1NkEnjz83PO39/x323OgEAmHPY8wKmlSGsOX00QTjJtB5rG0BtNkIKsd6T3jiEz9LlEt57gQHQe+bOY/pkl3sD9k4Iqq3m6spg9mAGy+cCNHNEfNFHvqG8eHfT+//IfHfS+97/6J8RpxMjU3DHiTNSsdwWB4wpN9YFuETA7QYjCcQJRHkgbj7UK03j8C4vaAWtNVAz4StJpjf+Pwyg0qMih9wIhBHYQ2FQdU7w6UMxqahsTfavoC8FkXNGlkjgMrPsR9tTy/L17+uuUdiQp73OkB1VB2GricUcowlHZnTtcremNYDCSWVEyoue6GROygLLQOU3GgNoK9MQSD47Dt2MEEL3XsMdQTBsmquH2bs4otDTXguJjRz+C9jbDSQinlovhwK0b8dFtw7pP0POeQ5siXhmIA4ORhKknfb+kfkiZvdiz2x4XGzkDMjgm45rtN1PcBz3NXcqsqFnvC8g8pgyET1pyP5AJjwD6zLNajVkcWg4qOraqRD1+a2BpUDagpwPCeIbbGHti0cqTih6RCKqJxBjHpKhYHUb02wiReuane1QpOawyBqspXpTUdYxYKgahOFns6LVEC88ir/jq1TnBCBI50GpJEjz7bUYsLNOn+6OzWeqoPx/RG0/3VJGZnsg7kn5gKA31E0l+8Az7mFlWsa4z7KBIT2v6zpB4y3tJyc/s9GgkExQfx1tef3NCnwqezA5UTrB8mDI6rVnM9tAKvnl7gdrLo2e8CgxaQBo4ue+xLxwMgq7TzFVNecjYZ4ZUDfhX8XFx40C1Hik9Tmra5x6d2OP41YOmeS+QNY7Iesa+52y+5a2d0l5l9E8c/l3C6P09ejTAVlN/OSL+sESNLSdrx0EJtj6jXmdHW2PdMOw1gz66DVot0TjsxFMULdF3Sty7lNYIYmF5mjZsrwp2heLXL97x4/VThnWM6CSjpmetE9CQjzrsXsNBUD8HM+1xm4jUD5zMDkjnuf3qFGsE3/3oLduQME4Hbjc5+yonnrW0uxSLQKWWvzp+Q7UQfLU6J997oleBya81fLk94aOLO97dz5mU/bHEMqr5fvLAm4dT7vcRaTHw7foctZWIxKEqQdJbXKtwShLNetRJT5cIcjlQDjFzWizwyWdv+eM/+YBs0YAV7P/dBP2ypxKGSHsKO1BqwyyuIQmk8XEozL4w7L8Y88HzO27dmMt8w2Bjvr5ZYGzAbiWnn67YDjnPDjX1XCJcR5Wl2E3MaGVZiuzoXugEYZAMwUDiOLnc4dNAvBJk44ZlX1BWKaGViL3EzT2LyZ7qpxNG5yX3qymqFSACNvNI/2ftmyeWqILDXCJiz6zrWKkU7wWXu4qhENQTSdcZoirw4eSB0ias3o74+Lfe8Cefv09ueppcEg09opd0dynti4AfOxargdGi4u7tCemsobnKcWkgFB7dOvLBsVEZZ6Zks8/RO0F2UrOLY4q4o5OSUGqcVaRhwJ8dF5b9YDh9vqX5IiWed7RVRFdIjPNsZYx2oG8ll7+24v7rCfFFSznEuJVBdIGRt9gE2kijZj3R1xrOLAebgDyaVHHaE7YG4WCqG6xTrKeGl2bF9icjmonlJ//7/+YX5qD3l3pnnxYNg49hHAgHwc7Fx6Duju1zNjlaFXLRExCku8A2jegigbk7zr0f6qMy1S4CfmOwk6PiXjYCGQTxuOMwJERbx3AB1kjUqGMmW+o6Yfg6gzFwajFLxXKUo5eBwzRGqONUvMOfzAlxj71J+OC3rnn99Tlx7uidYJhAqDTioidaQmM1p6Mdh3VOkIr1bozyGnV1nNesJoHOaHQraYeYNhuO/au5R8RwcrKjW6bsyhFnlxu2b8fMPqm49yOiB4+IHOm6gRNNF2LMFxHf6ISkdTgZYftjutyPA93Y853onmtbYFJLvUnoBsP0vGT/UKCUYvtQkJy1sIqQqWUTxajU4jOgc6i9pDER7flAuDfM8y3FfUXVp7hLSBqHuTH0QRFii5wcLWit0+RPDxgfWGYpNmgmQ8eZqxlQbP9wjJ7CaF8xfznw5tsxkbQ4J8npGS8tGzuieFlRPeRQKvoh5pAJpBX86Nlrfro9p3+VU5y0RGWgMHviy473Th/4gy8/onoR+O0fXvF6nbHeTpGZoL0pGE56rJRM9J5apTRo2rsps7SiJzCsIppcIwT8rJ5jG42oBUJZ/sPinPffW3K3n7CsUtpgEPea3WFCS8Fo6MlXkioz+LGD3KO3EmsFy2nEQpXY1mCXMfeFRncCU0pkbUiGgWaqkHtB934g2zuEhEINFEPHbmH4sNhzsxpR66PAdL9OSA4pPQZ11hPNHIGA2cKeFO8U6kVPH0kO1zPua0lxcrRG5l1MHwRDKo8/bgUYNSB2GiFhGAnKXYK6kqSbgDjzPJvt2H89410zRlaeH/tnfDDdInygfKZQtWK5TlDTAY/n9GzD/WnB6T4wm+15M5xQ6whNzGlR8uJ8yzem4OtugW01z6dXfLG6wCSW8r5g/HJHfZMzUS1vqxnlKoUcsknF6qOcqe4JTlDeZmjtiJKe/KzF3cb83s0nDLVhGpXcNyOmswPCKNovC8gtXh+dOCNhicY95VWGGwXMs4ZIS5JJzes/vOR1cpza5raag0+IfljjM8Fp3bEzMVlcka41+8wgSsloHdje59QvHfbS8badcT7esY8j3qwX6HbATySxseziozPhITpqitZvTpFLKFNNuTYY62jOJMk92EQQpT3iraF/W9CdBuocSiJIPEIeJ2ZOn1e0sWT9zYzi/RJRQi57qjgCxVHE9rQheQjsuoSk8ojTgMwdgxWkN4LyAh5GKRfLim56FAmuNzO6SLK9HuEuBl5tT3CNwteGJG9RS033kJJ/UjH4GL2XdBjcrsBIh1xqZm1Lk4BbavpMUc/AvAvsn2rUtGU+tDRjcBvDXhrCbYQaD+Shoziv2e0yZOKwbcRDm5Dh2R4yprQM65R2JhEHhWgDbux4+9MFpvTEeWAx33Gjc7rrmO2lQIuAWnvy3tLlGpzAB44asSKQbAJdF4gjR38whK0m7WBvp3SZAbX/hcbLv9R2uVYLvBKU+5TEDMixAy+IZI8cWWZxTdI48gdP/LVi6BXJBpTwMHHYDOKNBwFiOPpWByHBSlwicAjqcFSPD3NBk0tU55CdZ70aIY1jyI8GLV5DfaEICoIUaOmhlbDTVBOJtgppLMvDCHFnaKxhnpQo5fEXA6kYMH3AZoHD6xFuE9F+PYJ3EaY+jpJMooFJXB/tTYNAxz3prIXLnolp+E5yh9hq5EahbeDh8wWDUNR9wnQ9wErht55+XNB+m1N9XRDyQLoLx/+7jTzW0j8cSP3A+KzklZ3z0e6AXRrkXnDytqV5lYIEfX8U5CAEPOshCXgvYOSIuoEXH97jI0920hCuY/K4o3ozoT1L6V9ALTVDY6gyxXDimU4quhlsbEY9GJpIoy56dOvxrcAuJft1xk09gXOLLzX7POfr5YwuExy0QYjAwcVs6wIlHGKtiLbHzMlkWvHdkxvO8po3uwWZD5x+vKbaprRCs/I5m89n/OHrl/SdJl0G/vTfPcNuYoIXyP+QIkuJuNVEXxva/+sc/jgl2UORNbTbmEOXUNsYcytwpcbVGnvij0KdUSA6wPV+hnSB/TbHvk4xHLMeQ2nYpBHhoiebViySGlMJ5EHiRwF9MnBY5kf9gRfI5jhgxAwe0R8VvzGO+L0KGVvKEwMfdLRjQXcqyHuH1R53o5gtDrTbhF4rVj6nMoaXL1ewNHQ3KXeHKSJANmuIi6OCPw4WaQM2FVw/nDB9tmMSVaQri8+OnQsBwWjcENKAvlekB0juJFjB86bB3yWsRESwgjQ4zl3PvKiZTB1z27MojoN4dGaPbWQLjendccrfNudyfGCcNPh3ipvNlPVIYr7VTGg5rw78/NUZ751vsEjGec3lukHIgK80VZ0yTmv6XXxsk0Qzntb8QNzQa0VdJyyHMa9eXbBqC1Tk/6wrR5ImA8XGoy5bxPst7twz5ILRtMH3kvbLFOOPNfD7n59S32Y0dcz8kw2Jt0er4tbgIhif1cyzik3ICCvD/Zs5D+sR/U9yyjbhOk+opOLltma6swgV+GJ5Qd3GpHmHPzE8OdnxmyevCbuI9+yBh6GgvCsQy+NER1UKJB5Sx2WyI/6kJNqB20eo4NCpJ6wiunVC+y6jf5Uh3xlMKShKC1tNHBwq9bRjwbOnD8TOES4GvBFgAs2pRK0UlTAY58k6R93F+Klj5AakCHTf9fRCcXg3whWBBsP7kzVCCUaVQwtLKxSUirgYkC874rTHZ8c2QSdhcAqvYZdG1O97KECf9mgLbRXhn1lcZbjId6weRjR1jGlg7muytOPlJ7d0I4ltNJNJTWkM4UVPEBLpj5uozWmEWlhOdt2xYyZWRJ07focfDpT7lC+Y0iwj5h9uEcYTvYHRZkApR6EaimlNXFvCwjM731N8WqIKj680TXp0OhXGwa1Cek/28n8kU+/+SxCCQp+3iIOkjTSxE4jgERpCKynrFGygmcQkHuJ5S7XPECX4OBCtA9JKMA4XFFHe03uFaQXCezpl0PcSGQl8OKpwbS6JVxF2ImnvU4gB4eBO4j+zmHeSeO+wXqKEw1uNMYKaGDF2uPsEM+/prGa3UNRCoSpBeZVgDgLxIqCmA26t/jwl6ypJNO0wYw8ucLHY0owjJosDeBj+4AnNs8BP758Rz1rqQjKb15jXEUncMDxE7BeSi+zA7f0MuQVpBT4DIS0idfjv9fTCoK4UM1kRDYJqpTkEwVc3C9KPG8QgePk/f8dPfvo+ZidR73VIYRhnB3I5sH2Y4I2jsTHpac2oceRmYHc/Ij5pqXpDoxIkDh8HstARfWjxB42oJLsqh0ahS4lUFiaB7nVMm0SokaVpi2MgcA5rFQHB4BXOSuhA2sBktqNuDOmoR9Qx+3djhlPHshphXI8WnvtqRF/FJHnH06FDrg3VTCI6QSkF6k8i2rPA6HODNZLdbnysxZ4GrIHkQREEyMHhpseWzHAdM3ziEHUg3gYmH+54287JTMfZ2Z7eJyy7HNVC0g6czXd4F9FnHnaCOOupZUwWHPY+oXwZSLKB5NbRPbecFQ3DXcz5eMlX9pxwYim6gR8+v+VP7s7ZtSl1pzHzlvSnMC0qql9XHPqEuIdx0rCY9LwdMsKvtzTSkOUd2z5BW03wktXPpiTFQO8T0nhArxT1tiA8KE7jPfrG0vxmTz4MDE2CO+F4P8UWHXkqGZGtPZ2IYRrQ2qHHHbSS/JMdpRIst2NCkCxmJdV1TLRwPNiMLtHcfTunjyQn04rMNjw8TAhbgz5riel49uSO6x8/IaAZTSt0ZLn55gw3D7RdRJcpnkY7rh5OkLkjqRzf3jxBji3bIuN7p++4Wc5BgWs040XNt39yyfaQkxdHn/xqF6NnA+UmI550TOOWyFnS3FI/GNQgGBpNEg84rY6vlR39icQ3BnkQqAEMgeV6wfByoCdmlLVEL/Ycrk44fFmQPO2QryJU7FDFQGgU/fPjOSsfkV9askVF4jwPDyco43l/tqN9HXOT5ly/m7Ga5thB0Ywkw9KQqhb3cYuWgZHrUZcO3XsOZUo3Cgxnx8l1dIJypPFeEO3+zNHRKpqLo0HV8n6O8h71YUP9TcYwFuTZQLuAkWgZophuGzO9ONC9B8mkw/7cUC1ijA9YdzTl8aMA30RARCePNtSt01TRQOQCnVD4WCJOevpK0a1j7KVlklmSW/B6IG5BDwG3V4Sxw14lJD+o8GvD2QdLXj+cwSDJ5g1v352SvIPWGOampRMGP3XcLGdMxyVuE4ELRGGgoMcuE3Yhh3zAanV04DQSYwM+eKwwhJElznuexDU/bU9Rk57lUBDeJQzGUnaGSdJS65j2kBJOj7NU7EvF+dbShYHuo555Z1mqEeag6KcC3Xra6+wXGi//Utfsv/u//W+YPD+arbhvcnzsiR+O9c32uSW5F8SDpZeacNDHXbcKyBBIdM/hTDEqPV0O7iHCOIe3CuMCLveEXuIlBOTRDcqB7B3NuUbagK6O5wsCCAGfCfACPXhcAqoKDCOFUo75fM+dK45p17MWeaNonwIrjS8CZg1MHHno2T5MkDbgNZjI0klJMuqYnpdsSNCVwDuJ3ED2pKHfxeh9oFmnNB8GwiCIi56sdJjes6mKo3vVgyLkA05oUm3pdzHPf/OO6zcTOiOJ7hQ2h7hoiVNL2Sa4XlHojvGkpuk0baIZUNi3CSHzzJMakXjG+8DNJmOyqFinx7GmzbuUAYM7t/ReHeuGnSSynjgdOH++4qac0H4xxieBZHC4wh8tgBtJvOhJZj1iL7lbTcjdgNcC3Xm6TBEOEmHC0Y+7PWodrFSY2hKCQuqAzxyfnD5wty9wO4W9CHS3GWno2U4M6iBJvCf5f7H3H7+yrGt6J/b7TPiItCuX2/74c+65vOWaxe4W0EU1CAgQyZH+DBHiVEBDoCAOJA6oSY0000ickYAgCVJrIJVoRFaTVdccf/beZ5vl02f4+IwGWd1AAxqQkHCBCzCmmUCuBUTE+73v+zzPb3xE0/aFQEuL7DzztOKhGRFuQZ4OuBCwnq7WxIUhyxsOLqTbxoQPR81FPz7mM2RZSzVESOXI8pa2Dmj/ikK2uclxXsPckIQd4jqkTiVhZhhuIlQt6M8sUT7QbGMuFyvOZnuuN1MCY4mSgWVdcJ5vKYTjez+hX0bkVx2lTnELRyIMttQ0gcIHMH504OfJin/z9ROmi8Ox2M0bgjfwth0TpwarFP4hoAskfpDgIdiCFwL1ccNehYSzjqjylGXC08WS/SqnfZdQX0JyL9DKIncCJwTdROARRDsHv6iZ6Ia+1finA0EpWb8d0UvFhxf3rF9O8LOBbZsxHR+w7wJM4Dl7dMBEgmYTs2wLtPVE0UDThgSRodMSOR3Io5ayiwmdZe4aeB8iM8t6kyP9QHUakvoB1XjGsmP1ZkLzsUOsFeJRT3gD8UXDfohRwqE8RIOjjwRhPiAiRxQMiLcBu6eSapUSXB8hRUFgGBJgpwhaz6Al+nlDvzlGSxfnJdZpnjy55f5uyrt2gnaeSVjjjMTPDKoV5HnD/W9OSHpDOdeEgeHR6YrrzZQnjx54vFjx8l8/ZvF0x+t6zoAkjnr8Q4BMLDuTkPaGxfmWS3fg26+f8OQP7vnLzQUj3TERDdeHKeOgYdVlLJqaVVngw6NOprlPmTQd5Sahm0OwF7iRI91auueWse/oHkLkxcD5kzWtDkhfa/rUkzwIXiYF5i6mP3MU9DQ2IHqlqD81sNdE6YDda2QL7vnAcBdBK4kPR+3U03jNTZ9RBRFnqqRZR5RRxCSpsZHn8K44Jlc+NqQMVDc5JuH4rpvXxA8OGXu6RKIHcbyfdhlp2qFKweE6p4sVSWWwKfReA8dMhCxviTpHaSNEJdDGoj3UPkSHhvP5gYc6JUgHzLsEKwS+OtJL5XQglBbxY4g8OVqyh5nDSckoauiFYh7WvLcFOrCkWcfw6wyKkn/3D//hb21n/ztd7H/xX/0vkOcaU2rEVYSPPDbyJAePd2BOHcp5jJHQScI9eAv9WCClwySerDqiS+V3EVpb5N7igmOWsuw9gTAMM/CHAFmBKTyik8ds9goEUJ9CuPME0qKzAX8XHb28ePzMwkEhLKhHLbqDVkvEQaCfdthNgCv8kYA1MfSNJnqjsTHozsFHHXnW0iM5i/c8iIz4SnBbTZmcHmiFxv8UkVxWWC85yBDbaaJoYLIe8I8Hqh8KBqnoM3AjR9JY8r5HjQwX+YaHoaDcxnSBphUByainPYSoAbS3JEUDvw6QwnN4GhKmA/ogqFRIurM0E8Hn8oHbIqbuYrR2tFJhhWf0YDnMAuwy4sUHN7zfzJFLgX7Sob6P6KUk/uLAdp2TZS2jvWGfhHw5veXOJVy9WyA7GKTA2+MUJF45qmcQVHA62VLZiG5yRLwKB2JusA8Rn31wR2cDXtZjnHawUdQThd9pAuuIBottNJNxyaFLENcBQwo2deTvgOc9fRXiMsfgFVED7mIgnrcMbxNUZmjvEoBj6M4ne/xa09+kJE8rTtID7/Zz3E4fKVjdUXRZ2ohOKegkcdLxebykHjtubucEiUF3sHtbkEw6mnM4kRX338159nxNuthjNSzfTHhS7Mnylls3ZreMj9YfYSijALlXqHmPNYInwYbX/QnxuMNbwYuTe55mDX9RnSDWgiejA1+vFwy3CboCxPEQi3KoVqJftFQy4OnFkqtqzODl8SXdW+TI8rjb8011QXrlEcIz5ILgrCXYSlqvSc9qfCVY5gnRrSQ5WAYlmaiG+MMaVx4PPtsuob1OmKuKnYuZjiqeP1vy6805LvScDT27txn7JCBYtMT6SEhc9RmxNBTjmu0+Bi9JvGM8aihUx+27CclHB+7eTwnSge42xU0M9ArRw8njLVSSZh9hnhjsm5Qk6FBzQ19qPIKztEaNWlQxsG9j3i/nhNcaXxzDh8TM0NQhyY3EtIL+DMbPdxzeFgQrifuiRQ1wLg9smpRGBqh5T7YyVD4me35AW2i8pvtuRD9xmF1IuqhwVtKtIqZP9ojvA9TcsXEx066jHwvqu5TppKbxGlWBmPW4zKHt8d4pR4pgK3AS5s8O3H0/5z48imxFKY+R4nOPvBOkwuLiAWRIMq9Zvp0SSPNXFmZDZBzPoyU+kKwmIWoTEOQ91w9TktOG8seCUVTjzyz725xxNeASBx72IibOe3xqESODvzkihIMtdKf+KEadOMwhQDrQ+YBbB4hWEouBeoiQyhBWHnvq8U5wNt0BUC4zhk4iM0thWg4uZHF5YLMuiMYt0h/zM8LB02hJU8f4rSLQhmTUU2aK5FrSXVjEVuH2mugwUH0gcaVGlZLIGPTaY9Ij7EoIGKRCnPSIQaJXApd6irRl3WZEFTSBQgpHKAwm91BLkqgjtGCblv/3/+p//R8Fev8+Vz/yTLyhukuPHXsxIJQgtgOVDHEcgSpubIgri8w9vVf0U4j2AjkyHHKFaAWhgm4GiAjdQlIa2tlxPysA3XpsIPBOHsEbB01wGBAjeNTVbESOXkm81xgp0aEl3FhaFyCcp1lA9FNEl3uEh2zSontL8qzk+mYG0hNYhyslJhZEiwamBnrBow8eWD8UXF2dHB+AHqQ/Th8ukiVvNpfYE4m9j3CtIrv29BNN13q2y5TIHkfQwcFjlGWS1pyJDTfVCeWZplmGmCpAn1viK0jjAeEFZtDEk47MDtSfSLpdyM9+7zXf/+op/dySmpY6PrLRd8uQR8mGt+mM5MFzsCN87KnnIO408VJwf3+KiqHNFPpHjjn/Lwy+jAjFQDjuCEJLVCpeVlO6LkDW0IwVei1gYhGRp08FPoUoqtmcavxWYu1REBZIw/AQk8U9D7sUlfbEtUXkhlYKgpvwyH1/0TPND9weJqxuCyLX4wKI/YCPHPIDz/hyz/0PJyhlSbKWWibowdO+zlDO0wwh85+tWNoU5TzxzrEaIqYf7SgPCZVNSMY1VRHyaFSy/XHEbhIQNYbOKMZnB9wg+LVbcPq6Iy482zdjLkTJKDtgooFF2jPZDtwuJrzbjrncGm5nBVnU81qNcPsJ/U8Z6lmDXIHPJT4QhKWlX8AkaZjScxt2qF9GVJeCn+4v+DEQBJctYex400xg5NDDgCXAB55AtgilmTzbs80CFHCznqAtUCsoLMNDgneG+9uAovSAw31g8OqIGyV0mPD43CSFIdhCsBHUY41qoX6IaD8yPBmX/LhZEC9qgouObgOjtGLyaE9/HTP0ITK0rJ3EFKDfaEaJoQ0VnZJEynB2skemPbOzHU0XwsuI1UNGeSHpYsH+9YxpUB0RxtYwGWoOUcDmRKO0Y3kYEeU9jy9XPHSeOTVvzQTVSn7/0Xu++fERmfCsr08YpTUvwjWrFymPZ2uafcRexQinmXy5hUZQHRLybiB5uqGbRZiDgkFw3c8YtMQkkBiLcZ6T8Z6L4cB0XvLr6ozZh3eICjZxxrCL2A0xSdrRrmKqLCbzHfSS5GnJ9qszhkDw0Ka4wvF0tub65QI/tmRJy1fBKT9br3h3GBNHLctXGdxqzv/6iujHgenPW3bLlIdyTHbZcng/YvHZmnMxIN4HHOKUwyoj3VXkT3qq05ClSDhLS8QuZnOb0T+JjsE2aw3iqE1QD4Jw1uFnA7oSDBeWp1cb8lDybihotyGknnDWMOQh4kGjL1v624gsHZhN99ytJszCinYuCGqB2UmyrCX+qGZ1PYHIE4wNl2HJjfB8P0wZRz2/ePSWf/f//Iir6xPSpyUj2XJTTmAtiZ8fWL+b4xTE457i8sChTjC7gHZkcTcRUTJgPIjMcl6UrK5nWO/pvcY8NvjMo98rVGGOK8leYFpF88SSVo7mfUr2rMHepPgPLKpxqD2Mxh3LMKG3IWrSkpjht1ovf6eLvV8HNIdjxrELwLeapHKUMiLXLfs6Pe68Y4V1nj4GGTo0ApsL4g4GcRwrixct6R7MyZFRLm8VUd8zFCFKW4ZMM2QguyNvvpOG8jNBbBzg0XtPOzmKwdR4YIgkzQtQ154hgqwe8K1mGDvoNFsTkylHe5sTekuvAuru6KdevRoRTDuqNqbJJD988xidGDqtCIuGqg85zVakYcf1V+eoyKN/CJk8PdBUEcMsQClQGII6IH1RcnlSsvuhoE+ge1DYF55h47m6WpC9KLEPCdHWQzJQ3WWYSBBFPb3XbIUguhc8/WjJpsxJnMF4aG9SmFuq1zl9NnC9LQhuFFzUqI1E3P23/GfF9KykzhT9LiLPWxg7ZqOWrQxwtcSdWNoupPwhwz/p6VYFwkCYGsKiYwg0gTmm7uVhTzqxLNyOwzZHzjuaXDASHaUL6UNJ/SakmLQMm5i06NlXEdp7kqKFVOBaweZ2yjzu6R911DcpOjTEUc+2SfHKcHU9JxQOt4zQgcEkAjf3FKKibkNkpzgMEVna0+xidqQEiaEIWwYRsu1SPuluufMFJ+3AeuZQh4B0VBOsPeI7gX7qScqemyxhMdRE85pNrwlTxxDFpD+kGN/xxWzJ9TjhICLipSOYGs7iHd99+5RgL6iDjPDRQGv1MQGS44GwDDVf7Re4XUjQWZQW+LlhNivZbArkuKf8YYSdWsaNoekF8dZSfalJGsuwDHFa4qzEHjTuYmAS12z7lPxpidlp9mlIVHt41DB53LC7GtEdQoLU4hLDgKRb55xc7BmnPT/dn2Fzx7BwRDcBqzzFVIpBh5hO4y8c1nlevj6nmNXMo5rk4FmEJf9m9xj5yLAOY9xViI0FMrKs7nLCcYexCmslkfWEycCT+Yqvbp9hQ4E+G+ibiOznW2Jr6R8cCWDK4DgKpqf+MadfRVwTkZ90iA8GvnZTROtZbUf41PHh8xv6TcZwiHh7c8Ji1/HHTx74fjQiHjVsbU4xrViZBC8E07AmDg33aUriOwplMVLCfYCNJfuZRjzkbOucxiW03vPFbEn3ZwW3JPi5QF8eR+DWgKgk8nTgp9WCRDmyk4E6kohS8f7NCSK1CAebKkd0gpf9KRQOb2Oq5REeU91lrE8kL4KX7IuUj92O0LdcJYLl2ynvlikj0VGLiA9+8Z47U3D/9QRnBeefr/nhq4sjXTHypFvPsNTUTUDWWsqZRhvNYBxCwuTpgf3bGbEU7PcaMXFIJWg2EWYb8uFsw9VFhhhbgsGSXZToRjC/LLn7acysa9n5GHrB0IewVVipkZWnLEL+/O0LRqOWZC8w55qrtmA9DekTQfh9Svp7O+K9oSs023ejY+CaVzghab4d0WeK3A8EvaU9tYS30GWWehvTbTWucHx8ccP7+xNKE5G6njbVhAfNPDtwQ4ofWaaqxV46uiSiGhJ4Lgikw0rFoC3hDoJOEghH9TBiP/3txuX+To/x//B/9l/RP0rxewX1cd+c9QbTKnRsabVClQJfOIJFd/RkVxIcyMCRPz3Qo2AZUNcR2aKkQ9PvYiIME1GzHnL8mWFoNOOdoepDVAcuEkdhS2HxW01QO3zkUZ1gmHlkKYiKlsbER9znaU19myKER5egGkd/IQmGDnsiiNOe5hSK0lKWMSe+JPmo4rBJ2dcpVv1VsMWtJLiB5ycVb+cxVgnUtaIPFLq3GKmpA0WydVghSX52QPSCapWgzjpmaU19l1KXIf4+IvCGbiowWkLiGFc9ZP7Yld1HDHXAkEDQevSiJywd4WXD/pDRbyPs1BHUnrFrUXODf1DskgjbKYKtxOQWFwgWck97k3I41+jxgA4tuhbsdUC4EZxmB66TFHWnkaGjmNS0r3MW8w03RYbYSz47u2epQoLGc21GDEpwahv8iaU1AbEfqFYJrtMMM0ekBzSOpLfcPoxRraMfa6SBKDDIfOCxP1DeJzTnYB9iquwI7RAKhk1EpAecl8Sjju4uJhy1dI8ccKTaTc/2mFpTrjLUvCeQDqUtjVL0twnP5J546+lPLe+bCeOnO7oqpGsDkh80T79c8up6RnUG4/ua6Sc1fR/SB2BT2L8ZkxwMXRsRnHXIvaR4fCC9V0xdy7ffnSC/7Ck3Oem8oVlFiA68hFmy5/RJw7fvzrChRzcCOzMUP1iGC4mRikgP9O/TIyCoOwqHTj7eIJea8Y817o8dPyzPyLKWcOfYiYRe6v9OlV/c91TjgHAn8MlAlDqYWJwS9FaiPJwnGwRQXaXs1xmMYZhawncS88HRQWN6TeE78qBlc6oZ+Z79+ww3gtPHO06GDisFt21Gj6C+zhlaTbLymAKysxKWiu04IpCOs/GOTZkSXHQ0m5gv+zUPJKyygOoqPz6rk4HC9tQu4FGxwfwQc/54y/fihGYfYieeIB2IriWJHDg0Ce0uhrOedLAUi4rVsiApPW7uOBAwKIkSjrFsEb3n5GLPzsbsXhVET1suZhuuV1M+Vmuurhd8/rP3/GZ3xhB6mlc552cbVCN4qMfkrsPVik0UIXcCKyTBXqBDS1Mcfe6BPI7Wmy7kyXzFu7s5PvC4QDLkMM4qdGLZvR5DaumUJqiB1OJDz4end+yiiLaKUFuBd1BnGlsrVOlIRcfo5weuvz1HdJ7+BMZJTewN+5cj+jOPjSC1lmYbMZxbAm1Qt8FxVbpw5FvLZ5f3fL05IbCWOg9o1glROICD5LXAf9lSblKSuCeadvTvMsR5R9sGBOnAWbLn/t0MXUEtQuKwR4SeMlEELRA6ZlFFKzRdpGhuU4IVfPr8nkMfQDFwf5ggCoNfBwSnLd0uYiwbtruC5x9ec/XDKV0mGR9atmQMUhEdLNGoIa0FIrQc8pCuDFHPa9x1zNzUjLOOdZnTzRzDNiR+VhFfw0OU4hUkO9Dasm8S5GxAPUjUTpJOl/yLf/i/+Y9j/H+fy5451PLIPYcjCjOreg6LCPM6QQPD2CMihxkkHz9Z8urtnD7zoGEoQ4Zeo+wxU7k2MaYMjiP7NmQ7BIjAIsoQPbNoZdEtqNqDdhgrWWR7lt0YayViOIrv3CGgPJWYOkI6DxNDc5WiBo6WvsDhlEDnHU0Zo9sBUwU0PmC02DGKS0gH6mXCZjni02LD+13O+jrjb/7se74tTnh1PcF8r2FqcWrAjh3WC9xaInND6A1nz9fcNwVdKImwFG3PYVWwtxHFuMGMHOO04mEzIu4sZhVSkRyhGOJIVpM4onjATgXWHsOJgoPjqd+xJWabBNhtQtsFWBFA7PCx53S+pf8QTrcV136M0pZq0EzzCh87ylAfD0naoSeWfmLhVYB/3iLfSf6Lz7/nRs2oNjml6Ci7hOVNTv/MsdEhplU82+2YfbLhLx+eMQ07SiJ048GDeB0dU64SR9OIY15+6DixPeFZR+VC0tMGs3PsNoqFPbDzEUXYYu48vUtRoeXsyYr7zZhnyY53Ty20gv/x7DX/YndOV8W472OSteCDn11R65Csh8B23OiMwzpg+nFFWUh2byb80fM7vmszunVCbC3NmedlU5CMO8KD5PxJyavtDGUEp483XN/Mjv7ipz1JP7DQJU0SUZuQ/Pme618VzC43/M3n7/g/RF/yLN9wfzNjM1ZkbcOjkwPKgMh7prpHnHr6ZcDuWYTvFdEamkhhLw2jH4/FmwvDoY+J13D1X0aoxCLWntHjA+ukIHgDQ+GPKOhOYALFMPUQeLzQxOOKoh/IsoprM6HbhFRZyOGHEcXpgf6xOVow6xSRSfxKoisBM2hdeBTjtYLPox2/eLzmn+8v2V6lbNIIvdVcPFrzsC6wGp5P7rkKZzRKY+8LUI4BwYwWd1CkSYfVDu8F/7q/5PR8g6n0UVwVetw6YlfFRFvP7dmMUEAnQNQCrzxioygODet6zHYcUwiD1A5WCrGW3Dczwr2nCyEYWp48X7P8YcLQaQ5BzHxWcvVmgZk4otjxR8U1659STtOaTRLy5MUN31wt2PRHFwqh4/C2wIWC0oUMG83FYkuzCZHWMtQhsve4QaENEDn8zNP+EBOcdNy8PSGswMQSN4boAToRoZoe5NHv7bcaEgczg2wE18sJo9OaJOrwH1r8KuDTJzccDjlVGWFbgfi3CS4FP/LojaKtYkok9plnmlaUDzm1DlHSs3AN+3fZ0Ul0ZvBW8cXzK97+8pL8w4b7+ynqAR7PNjSbhOHC0uUpMoUiqOi2EfUPBaoYMG9iEmMZLjXX2zk2gXTW0ASONGr4YrziL+7OCRc9ynnq95quyhiUJBYWE2te3c+InlUMX4/JL2s2tyPCYICdwLWSrcuYfrimfZsyD0uuGbE/CVG9pW8VRJbWhVQqJE46hHCIqUEsQ3ziWN/n7KMIJcFXAtWCEJ7tSYi91zBYWiEICkfgB+xW4ZwgmAwMRv1W6+XvtM/e1ZpxWCM7wbQoMQ8hm+0I9z7Eph6VD0SVQ/ij1ean5RgbeorKMrItvZI0+gjwQIDpNcq447h27BgmnnDtkI2AbcBhm9JNBd1EMViNiT13d1P6sad57LFjqFOJGA2krYW5g9MjIMZkMJxakJ7uI097KnDLALzAVQGVj4iMo/4xo7w6CurWuxzTakolsI1E5gO//O4R2+/GlJnCPh6otGYfJ4QYhjLECEnwKqCuY96+PEdUirD1yNyg7PGQIhNHu42RGIpyIN543F4TZD36vEFUArNwxE9qdGGYhR2ig0dhyePZmjxtOewSpj9fEx484rzj8IEgO204n24obM2hjThRFfaR569/8ZLeax4He/pIYGOYX/X8tRc/cTrZEO0drtPHh7LWnD3e8dVXT/mNOKW/GOiVwJ46TAzqZYD65tg1VJ/B29tTTncNl8kGsdFEZx0u8YyeVgTaEox6xkNPci/RhSN7ViKmPepeclH2dFc5vQpYRA028DRtiFmE+AtH/rMd9VvN2ccreumZli1dJPlX95eU3494/Ed3lFqj/saB3SHh1Tcn/LI+4evqlMZp+lPLtYx518/QM8M+UTTLlOeXd5y/WMHJwLBL6LXkZy/ucE7T1DGpH6jvEyazki4SdPcx453h/c2CdZnzeN/xZjdjJ1PKecr/6V9/gXwZ8no34cNfXLFIKvoo5PZhxMMhQV1BNDHUJiCLOoqLkiDv8Rc90oEoFW4CWdihHzecPV9x+Bm0u4T1oUAJz/auoCoTqnOIDo7kPRRtTzgbiA9AblH6CGS6tQXtWNH3AV2g2TyMuHy0ZnpW8onf4FpJvjdcvtgwebol+XiPSC1R3KMTQ7L2vL0d8S/LS7LA4p2AuwAzaH749WNWyxGTsudumtCOBGFnSVvL448eKDpDemO4vp+y+WGK3gr07qgs378ZU7YRJhFo5/AORGCxI0dvA+x1yPb1BJxgRE8463AvLPPTPcFS0sXHIBsnBPuFJlg6gsYdx8r7gJt3E9o+YPCKYG4oTUy498i1YthEvH44pXkk2BYh19cLfjgsMBPBFx+/4flkRSQtzT6i8gHZOwjWks2vp3S9ZnAahCN9VEJu0blBn/SoUhzfWYPCZp7yucCFRzdEPwF/HzB8mxJHA1UiUalBeOirAHEbQOeZlwZtHM06YXxScruecv9qRjpu+M++fMnJH+5Iz2pO+z2jswojFEOoCCpBeZ8ReEtwLdFrQfUmJ9CWsPFEaU98L9iaiNsnmoOJSbYehMCPHDsZE2488WlD2Hm2h5S0aElnFX5hIPT0U/CNRCaO07ahX0f8j0avOVxl/POXz6i3Mf3uuAKdfFDTC0lqBubdgbi0mDagdBF1HhK/dMSB4bPpmjA3iOmAODHc3s9YVQUPNsOGR5qhawRh1hFGHj2AEBbVCJouQlxphPUEK0HoHeG4p98pdGgxuae+yQiuFLqFtLC4QaMCS+CPDZQVkq4P2Jnkt1ovf6eLfasVhzAkOmmwUiDmA14J6scQGosVEpOCKBw6M4Sx4XS+pYkkrY0ozkqivSfNW/zUIgOHDcXRaqUs5szQTwLcxYDGEqQD8dIzO9+i5i1J6Rjnx2z1aAMUllnTcX6y50m2Z7HpEHhMc4Rh6L3EPjLHzgAYnlsQELQ9YW0YOkmrNHoysLwdczrfcXa6Yd0m7E4C+iFi0+TI3DGaVzgpSecNgbX0DxGygVFQoS96Mt2TLRr8xOGwLNodgzoKnLyEfNzg9gEPqzFtIUFBLyXtEOD/WsPI9YhaEDqD7wVFN7B/nfL63TlblyJGhu9+eIa0gqQeGIU1pQ550y5o2oyZ71Cdpl0mfP3tM9JpC48H2j6g3ke4QPDN7pz9r6cgPGojCGuYNj3BwVN1GvtDxDszZt+knMz2rBYhV6OMYS7IbgXDXcz2eoxZhrwfRpw+W7O7GWEeAvpdiEEiGsnlF3fkpyVP0g1lE6NuAqpnnlB0kFlcAd+sLrHvI+S7gGEVE9xI2tuU8izh9uUJN+TMv1gSHzztQ8zs6Z6Hmymji4atT7iTOfNHB8J4QHbQ1yF2UASxY34/8KHa0r+JmDzdcvN+Tl2F9ENAmHWkWcfrV3PyoGWW1ETBQJj27GzMeG2ZqoaHwwgKx5P5lndZTFAJRCuY1w2MLI9/cUMdKb7an3FfjjAzwXoWkT1viB4c+/9mQneTYG9C3FVI2BnG0wr9rEZIxxAL6heeSTkgS8FM1Dw9f+DE1gSdo3uTEt4ogluNOzdE2YCVgmjcIZVHHBTytMd+H/HF4QH/PkRIh2wk3Riu9jPeLU+oF8dDOueGtUlpmxj/Mkalhmdn98xdTagsVRfyxel78rYnCiwEktYG2Ejw9OmSVRLx7LbjI7VB9JLhzFIfYmyleLcoMCNozmD14wzfS0QrUFca/aBIOktq7HGiUCviW4h/AuEsQyDYbzIOVznix4T6qzH/gw9fcvH7t9AIzJOB4KLFBx47F7RTgdOeodGkj2t84hlNKmaiwQ6KdiEInzb4D1vWMkCsNHFnSeOOKB+wtWZ1O+HibMfspIQTx1xU2JmjPYXuHHx8FAabCZRxBHODHVmmqwFxo+kXAk6OZMSg8rgA5CAIVgIXe/wjw/DYICaG3/vr3xOfNORJQ3DZ8vSjPV91J3gPwRu4e32C8p6+DHj7/Tl/9s3HvH55ipCgx4406JBrxZk4HPMtEJA75ABR40hGLbEZ6C8scq8I9cDDZkToHGYVYRLwAg6vCrR31LHGdIrDMoVGcnjI0IEnXvrjIeoQ4GtFqnseVEo8afnuL85Re4EKLcFKMDkv8d/GPLyfIrUlVj3hyCAe9UT0/Ben7ziLD1zPRzS7gK/rGaF0iL3ghdshBxBPOoZYkekOiSO+FyQ/aty9xnWKxQdrrBGI057HwZLLky3ysieYdhyusiPs651G9Q5/0FglGAqPwlLsWsS7gDxuyXaGoD7WGSt/u+X3d3qMT+AYeslgFaFqELuQMO/xtyH2ZGCc1JQmRq0EZuyRtwF1JrFWoxYdn5/c8925pnWKYRuxGO3ZbkcMZx5rFcHao9MBfzLQKkkQeUzuOOwzwqhHfdmy6RNO35b0H7eorWQ4hbe6IO4kNoZwA34yENJx8uGKzbsx2yRAAG4fEoSOeGwxnT6qxbMOWcJitqc0Cf0+IrYD8w93rMQIbwNqAkZVT1R0iEZitCeYGmwpyaThIQqhFwTZgAkk0khu+xMGI8k/3eJXgu46osvkEeX7VuILB4NnNKtJ7MBOpUTW0p056uuYSFuaS4VsPWWVkV9bVCowZUQ7VqhxzVNRcRultOuEnUxg8Dy4HFNpgvege08+69kSY7CYwHGh9jCyhErQX/SsNwXfV3PkLiA7bcle9fRxwm13glwr1NjS1RF+1sKPGUJAqUKGVxHbUYF0RxFdfrGl20XsVMxvfnhK3BvaHxX8zLP/sUCee75Wl3gFYe+ZjCs2Tz1B4KEKmdiGh22CLjzptKa6yfm1fIaNNbZwhENANyjG85r9usA2Ib0J8YVnlFasTUoYDRweUkwWMC8CnBlIaovtBtbfj1FTRegHZAvbhaLxE7yC/PL4IhX3I4yTrFyGjwXp4BGtpXYhgXb4y4428tiNoDmEjEVP1Fls2BPNKoqbgbuHEdUfaiZ9iesSTiYHrmSBTiz32zF6cKDBHwRNFePHFc4LtmXO+pspToLyHhF4yHpUB2al0aOGQxDjXufIRwP6KsTdB/i38M3ZGdoODNsQnRiKnyzizGLHjt0mpS0j0lKwP5VkveEP/+QN1zc5r/s5baB5drZlOnT88vUznJWcvVjyJLjn5rsFVRgwTUv2NmenQ57JHbfPathJlHOMP9vS2ID6TY4sJS5z2IeASFpcDNJLgsozRAqfWU7qhoaU4dwSakvSCFTnGJxGXUP/WPJf/7++5Pw/vyWcdIRFh64logowVmOlI60aqrOEc9dQjVJ6oxiIEVODKTVnacX1zQKdGLZdhrzzDFNFUCp6BGab82cPc17EK0TvmP3eGrkcWF1NGOYOdRWgz1pkrWljgTeSeOfZ+xhyz0i27F2Im8Ei27O7yxlCAaVCb0B8YHk03jFsYn78l0/44ssHbpcpVigehpgzX7EsR1gUZ4cDqzQjGve4QVANEWEH4p1m/GTLj90J45OSfh0R4pFnA0N7nB4mLyrKscJ+m+CUpyJmfFLSNgH9EOAuDWqnkEuPe2KRjSc+a6n2CVHWkOyP3XETCfxwXA85o4iW0AURxfOK9i7kjQzQseFZsuHmecHt1RRx4rFtQHAr2c0SvHHM4x13bsqf/8UzHn90T2st4b+LmLmG71/PST888MP1gmTa0V3HqMST9hbXWIJFz7BMsLGEwlKVKWovYK1ZX6SYX0fgLf4XHSZyPE1KVjaFncTOLF0X4HsJoWL0WYWsNFd9hpx5fDzw+FDzkP125XK/0529tYrQOaJbSddHNENI3x459up9wEM55hBr5MhgEtifSMqJpvh8izKeXy4vaHKBupeIO839dkz+uCSxhunkwPjZFn3aoX9ShMry5PGa33t0S35Wk08axEGgxj36jwzzRUXWGbq1htcxYWU4+3yJf9HRx1DNJMu6oLQxKjakeUtKT3xWoT5o0KkhEA6RONo+4O7NnCQ0/A//6DcUH9UEwqFxBHFPrAeiaU/UOtzrGCU8wXmLGXtWIqL3iuy8RO4MVR/R+eNDFJUQ/jrkTB74+eiOqa158tkt8vMWUViKRc1fz98wXRyIao/bScL2SHdzUhJJg3WKUml0YpHe0Z4f8wbUveY6iPFrTf7e4V5HHL4b495HpC8l/rzHXg7sRYQyjsNGcWFKPviTW2Tq+K6as1rn7O5zsqgn1IbhNmLtpiRRT3EYSB+VvPj5exI6ujLEh6DPe7yR2FOLlI5nl/fkrcFtQtZtjgwc/RTaRyA+98gO6qnCrEPKPqKMFSdPNphGEEwMF+OS+eWO9MOS5FFDoTuKooGnHYE98hOSnxTN64y871A7xagbUN4SYoiswW01g5DkduBQJrDz/CgL9k8UqyanfuYpPiiZ+xoCz93VDNUcpydR1PHm1Sn9Q8wiOWByh7hRvFB7LuIDD+Q8XqzpVjEHEXMf5jS5ogkl6laz2x4T3Q6vxmx+nOHeRDxrttS7mHjSsUpjmk3EvklIGQhLQbCUuAg+nz9QdTGbm5SuV2StwRUwCI1JQTeCXivQsO8TMt0fY6c7jZs7Rs/3uP+sxZ467H1EMBp48XQJn3YMiUB6T6o6omCg7wNEpXj66S1XdynttwWtC+i85lU741ecMpyAGlvU4Pnu/SPMqScNe76+fUwfeEoC3h4KdDoQx8MR0lQHfDlaolJD9EmJPbH4L1vSn+9xHwzoBlQLQ3iM8N2F0TFIaaVwNyHyTiBWCmmhm0qcC6h9iH8XMDEdL5IlzgryTw48/fiBaDIw/KFAFgNXKiEeDMWkxryJcTtNsIWr1yeYTrO1MU0f0l4eo7ijSc+YlmjUMhpXHGzCk7xk+9WMfFExmpUE1tAvDNsgoj93+EYhcos/M+hkwNaKnY6RO0Gfwv1hRD2VRB9U2E86xOctTz+5xWSOFRF9GPLLm3NkD/fXE+Re0At95HMsDLsyJfaG/Lykdwq9lQxdSHfuebeZE+8NfSBomwi9lJibCEIPM0MTavROMjzxhGmPPng2TUpvFWneMxYdWdxirKZZJYSznmIYkGuJUI7PH1/zNN5Q7RMC7xkKTxAYig92NCpgfT2mzyQyhHYI2b4vGK4SbK9o64io9swv9/C4Z5gLhqminoRUXrMSKcIrDp8I/GQgOGmpq5iLfIt/GaHGFp87Qgw+hF7B6OMtZAYxSEI14OaOPGwYOgGPB8w44LTtmbw1rK5zEj+Qz2tGbwbiuCcyhk+SFXnR8EblaGU5+XDDFy+u6ceaQJvfar38ne7sdQmyl3Rzj5XHeFk7dzgBkTAIoUiWnlImREmDyAxqo6hlijgxtFcZqR8I9YAKLCbwhI8rknnD9mZEpHuqZULyomIWDmTdwD4IMZXiZxcrvq+nPDYr3i1P2Juc/VwRWcdItJw/3fNtPeXpusPsBE8udgwGVrVgNC1Z3k6QL3pmoxL3Y0JjHeGipdnGR5HNSHC/Lvi/9V9gN5qCjmhsEIlkpDpmYcPrLkF9UZH1jsmqo36T4dKAzy42GD2wIeNs2aLmAw/7KakbGEzA7lVKc34gbi1bE6NKh4wMD1XBr6JLprLi88/e8tVXz6gOCZntMCNPuIXGSNJ7zzrI6C9AlQpbODSe9m1K9krQnXqin+/pf8ygN7SnHhU5khtJE0pGj/fsdM677Zx3hznyViMy2N+O/kq80mGXAUFoMUbTeUXuLLv3GT8SE0mBCB1Z0uIyR/+ZQQtJuBOU0xA161n5YzH7OL/h66tLiBVbn3F+sWGcerbXI4LymLhXXU0YjGL60Zbd25ibaUrYgMJRhyC+ljAKmJ6U1GlE20uCAfoqpHwI8Tk8e7Qhsx3f35zTL0F5Qd0mRHODvrS4mwBvBUOgmR4q9heKVkdQK/ypwSt/hB+FDiE9m/sRdmYZyoDTi4oJLe9Mwulq4G/M7/mLM8krl2P3CiEc7k3A88cPfJYe+Mv9hP0QMzyBQnZMhMNFDXfbhLpLwCrCa0vSOgbpsYWh94Jv3p8y8h1GhYhe4F4YZC8ZUolQnm4Eo9mBVinC0CAjS/XdBLnoWbgd6x/nuMLjJpZEdJw9W3OhS75dLpCxJcsGmquED758z/CQsBMRb96d8+zpHbo7EIwKRBngOoltAnQJ5knP64dT5FqTj2tWpmCoQ3zgmImW3ULRLGPILMl8oFaKtzdj0ryjaUIuJwc6odgsc9S8R/5+SSUVbDSqFyRnB/KtZaUzxK8TTCiRxqMOjmEhcbFHesGb9YJw3hG1KRLwB0tnPb2VnF5s2N5kNCZgMhg2ZYq9MBSNQQgwfchYNewOMf1tCELhlgG324Ak6ekfYkzqmWdb0k/WvHrzlOaXJ0jvmM4b+r3BZYJ6HyOyAekcdqcRSY+9tIRnDYugxQu4uZ4QfNiQvYSLyZ6NiOj2MVHYoyY9pdNIAXc+Y3jm2JQZdIohB+El+eOKfR2iIsXIdbSRRi4d7lTA2JEqg147bpIE+0lPODhGec3mUUpzCMjGPb730BzH2GFkeJKWXG9yVGRpD5rMNdjMY1+F1C8sKh7oh4DX9RRnFBJHXzg+Xu2pTwSuB2cE475DHuwRH01A2USYveZnn77mp3BB8xBzX43RdoCJ47ApoHCY2DBsQqwA5Txv9hNk5pBWcNeOePHhBhE73q7H7M419k5gu4TGe6qJJv1R0d0X9IkgkoZqpJFvQ7yG1/spl092rNOIdqVxb1PsmUXvPC4Q/PLdIyiHI4UVz8PDmFns8R2MN91vt17+Vn/t/8+X6CR1pgjTHnqFmXri0iOKHldp5o+3mEFxZkuuGHMyObDLUvK1xg4OLmr0rUZWAn1WI4SgeZ2hmiN4xG0CVGSprlOeX9whTyw/vpljAsm/Wl9SPDQEsaZtQ3qviRrDs6DmdTdGvhxRfNZRPfZkvuH+l1PMhz2Egoe7CTiJvYrg9cCZ2nMfF9Raw8jCeU/8LkaWHhqFLUAsevqXOT5UR9+zsXgNsbAc6hhx7zm73DDcp7iwJwkGCtUz+nTP1eqE7KSm9DGn8x27N3PKmxH5aUWziknGA5O9oe1C9ipl+2bMN6lE7xQicnDiSOKOD9nwL8Qz0tmA2QdEgQMjcDOB7ByRNsjHx6hRtw34YLzhtczpmgBl4fDc41ZgjSLOOuo6OebKP+5ZNC1rmdDZgMObEWHtcbnH9568N7SZIlg7iAZmn1Skcct4VGOsJNU933z1lL5M6FeG+LKm+jpjeNqzrArESBK1A8NI0whNF2j+xl97yauXCzYmoUwCbADBfc5gAhSePpGMl46gdGwuNJ2Q3G9z8q2BT3uc8CRBS1VJukFT30QsZwnthWfIJVk10D12jLOGAMuqH7HvQlgMRNah1yGB8wxPOlwb0EnJoDRSeHoh0B832DIgOO1YasXdZgEDDLLj//jmA2zuEQ7MPkTFA6F1NEryf26eIK0gG9UciAirnp/ilN2QYVuFH1tOii3NXcpeKIIbicoGlBJ0kUKnHYf7nGjnaZcJ2c8OHG5z5Fow/St4i37SImPLfpcS9Q7xk2I9KhiPDoiRo/y6oH2sua1GrPsCiSdtDRe2o7uAwyYjVob1IYO95n08h0HhXgf4U0sYGPSPwZG290PE/OM9bRjR3SX0dYSMLDZQ7F8WmPjIWVhMSvZ3Oe4g2fqYadbiMti1AX9jcctDHPCbn54QLRqiAfrYoQqLQ2JTR2AN4QcH5vM9N6sp3U1KEPSIRBAWPUMXMPQa7eHsxZqyCaiGjIU90H6X09mEM7Ml/3yP+WmO1/D0k3s2TY4fYsLIULzzmGlIkTQ0TwbkT4pkYgjqjiGBLpK8eXtOlrd0BPTbjFwIwrOB7GCoRHR0CcQWcerZ30VkQUezDRFxTaQcL+YPrAgIzz2HH1MaHeKVRS0jzhcVb3/IkP9tWMxdxr6IySYN2cZxEles+5yhTnAzQRAIWqEQL2CeNXQbWLmM+WKPUANB1GNuMzirqI1mHtdo5fCloB9CdAsmhHe3Y4LN0X+ulSP/sOGwzEjnLWnZs/Y5wZ1HrGL2swhKhbGa1aSjbDUXlyvkr6Y0E4X+KcY+GnDrkEA5VGF428wwreb02ZpNlzHcxlzaPW+2J4TDX2UaWNjKhOzBMY07Ls7uub2bs7nLefQHS9Z9xtg0mLWkBaKTjlgY4pVHC3ecMg2O/iohCHvkrGMoQEeG/euMk493XO9OQEGSd7RtiOodbpDIjeTJYs3d6zmulVyNR9idZn/62xXo/U4Xe6Kjul0MksB6FIYktlS7gHKq6G4nJOc178sZZ2cbzrqG2MG6GbO/Shk+G/B1yOxn16xenqIue+rrlNAZplXNw2bCeFbR1CE/XU9ZNxFOCv7gk5/48ddnZBfw6mbOR59es9+lXPmCqyYl3TjGn5Y8LHNqBfmiZRkmjPoBf9YRDQIRO5qvR6yjmG7uKcuY/seI2aRkc5URxgZ50cNeIWtNeZ2RP6/YyhhhDe/XU3wncVNL/rhkJnv2J5qucLRdQYkmNg5TTniU7Bn2GsYDXnvUWYe8VWyXGf7c4q3i4DNwgmJtWCYB0gLzgSxoWcy3vH5/xvfbiNArWqGIvIVZj9EBLvXojUVVjvRZyXJbIEPB91EOew0nhmJjKW8zfOzpXYQpPFnc4lPo7yP8zw6E1yHRrMO0muBFS5gM2KuINO2gCxiSmJ+fveVNO2VbpwxvEtZvx/jQY01AP3HIZUy1TAm2gn6uuKXAW0FXBIyDlkgNjGWL05LbfsS0bHEG2rGAXjJ0GmkMyXC0Lr65XrA42XF9M8VaeJzvUfM91asUYTRaQqMk77KckWzJ8pYwtqiRoDivcW9igk4iWkka9Jzka3YyIRy1tN+H2PchydgQzns6J5mokjuZUb3PkRuJXxj8VhM+auiXEZdZxTfdlISO5ArakcVGgn0RkYYarhR7kzBLtoRLSXrieN2mBL3jo2f3tHXIYSTADbiHhOHM02eSMBwoNp5g0eMaiYg7+lwh/you2I09N1lEciUobI/4KsCPNU8vdlzkJX3WUr+Z8EbE1D9zBN+F6F6R5i3jx3seZTtevz/F3il8KMh9j14KmhyCXpGkA32nMEYR7zzumaUbQuwMylxjnYC9RloIRj3qu4iTj5dcBwVmE7O/GjP5+ZYPoiXfvH/Mqs4YNiFh3PMv5CVpa9HC8ahtEcnAps7wRUe5zbH7iEW440aOac0YW8D4ZM30rufikx1p7/i/vPyUE19z/TA78gZuIha/fw+tQIwMcdCwuhtxuNcE846w99zczbGFJ3SORPdUi5CT2YY877gTKZ1MWJoIBo1sPf2rEKEFTh2Dcxh7fCNp+xDRaKafbWmvM5Q17Fc5wVqgTiTZtDuuMl9AfQg4y/fcdQXbaYivwSxTvvij99zWBel5zcWu5tXrU3wlcUoyrBPsqGeQCrcPcFZyLg5ciRH5pOZ8WrJeFVR1RlT03F/PyFyPm4EbWYwXJMqSDpYkbJm+HXj/8xT1EJAlNe0ooZ8K+jag0B3d+4RnuuFtF3P+QcnlX9Zs5zHuyLkCA2ovkc8NY2e4/2mOrj3DWDJceHygiAZw0vMk3bC2Cb3R3C8nyMiR3Hl8rhiNWh4lB350BZ0NCEOLazTnL/Zc1TNEKrAf9PzF7pLeaXTpiU86xGtF/Lghe3BMti0HlaLuBOHeo//THU0dgteIlWR2WpJ/1HG9nxzzDmKwnTwyF2JLN/VIDTd3M4YpZHlDZ8EIyfDutwvC+Z0u9sFOYOaS1kqSpENHloPUiNQTOYPKLXk3sKwS9kQsq4JDlzDXPdKCexOTy5aH+wm217DxpFmLWYY0IiSuLBtbEMUDwjo+jzc8RJpyiDGHiPdpgDrx3PkRwy5COolKDOZDz0/dnGJaE+4ci6ikzwIGIUneQH2bMPtwR/9hjfqLiEOV8/yzGx7EhOVhRDZp8Yll0vRII1ifBvRSMh2X1KuI5pAQNo5n2ZZ7rdjdj6hbj7kXJM9LmtsYh6cyEfFGcJVO0YsB1St2fYzooRsEejKgE0O49Yyiim2YMKw046TGhIIgNXRNwP7diNPzHZ2OGesDWdTR2YBSST4SB364HxG4Afdlx91yjHQKd3DYSpIYTz2RbG9GFM/38FPOMPVMqx511rG8naCmPYVvuDwpqVXEQx6x3ye0dYhU0A8K1QhMAr/5v39MdSoJSzAPDvnM4LaKeOOIAgN3Cq095syTvJXUFwKVHu0xeMdhk7EcAsqHjEwMuDPHoYrQO9iNFHZmkWuNbT3vbi6ZfbZGrUE0HhcaHj4QaFIeijH9XXy0GM16Pnyy4rvVlPhe8aW4pTzXvPy3Cya2J31aUaOQO8/196dMHh+oIs3oiw2bh4LxWUmzi+naALsAWQlEAwOC85Md1gQ8PBSoreRVdOyI3I8xd0XAp49v2VQp9Sbk/fUJ4aTn0+2a/U8h4chQSsmZ3mFeZfRBTBA7tvcFo96ge5g92SKl57BOYXpUhwfOMv/jFe4h4Ke7M5JRh1qCbwRiGBhJy5vDBBd63pRjthOPvktZpgUn0Q57o7h4vuUuiwnCjvb7M37Ij0mR875lFwTQSfJmYCgEoXMEW8H/5On3/D+ax6y2I4LnDfpa8Pz5nuvXBcMMzATUa5D3AUjLIQ7BwZfRih90xizo2b+NCeYd9auIJ09WrFZjKicwQBz2XPmUk+DAZX2g3sKQ9KhXmlu7IHyw9H9NohNDScy2nPD+Xy8Ynx0QrWS4hEzWNFdTbAjLKse/D1mc7MhLzzY+MssPy4jxuAYH7duU4tMd/UNEPO2pflNwtxhTqA7zNsFcWNTYYQXIGwgii7wNERwzOfo6QG8l/RrKPmN2eqCsI/SsR48toxuLLI/7+91dRhsoqoeM6j7DD5LEWHzu+cuXTxirisu8pglDoouGapsgRhY1GoizjuUhZXTR0xnP7q5ATiShc7x+d8IQCYJO4O5jgo9akssDciupdyNKWfD4Ysnbm1N8VyDmksE6dCBxu5Q6lSgH0Y2nHodYE7HxKVlRsVwmbOMMpQeqfYI+KLqpJ3jcEYQD/X2M0RJxYVAPR46Itx7z2JDNK173I1ypSGtHkynoJemXNXUK46rhp90YrETPGqIbS6I92/uIOo4I4p5uHdN1iijp6J5aunVB7xX36zGb3hNNDL4GUXn6mUS2AdnQI5ewnwRs+oyViJF7hTEan9jjtDPz+F2An1iinyQmh3DlMV4ho+Pa1P2W8+x+p4u9yUFKjwws89mWfRwSYxl+SggU1E1E31jS0HC57VkvJeXJURyWfnLAvczYnYXoFJ7k90TSs/z15JgolUP/SBFuoPUBJoM3r2eIs579MseOBLOhY1kVbLbHm8ynlsME1HUAg2A9Sohiy3qfYfYhetGRPD1gQsuqTtHXAfVUMJuXrEiJRh2jtsZVglHeUqmAqkvgqwD1UUMetUTSYMoYn1le2xEfNCvaxNJEmsqGxxPxVqBfaXhkmYoGeSOpZUBVKNKoxt/EGK3J05J+kOjUU3eSbuy5zEs2TULVRIxme/QelnnM9EEcVeDest5FnFxuiRvP8rogKga2WYD+JqZIe7y02E4x1Io284h7CUXLUiSoS0emB06+vOf8fcf9i4r7ICVJe5abnLvrKTpw4BTiIHC7gCF15Isa+zrBx0etRrg6jstNEzEqavo6YjAadVyJ4jpB5wXhXU87Dwi9ZShgJ2IC6xETR5zXPM9XfL86pzrkiLOG8JsI1RzjlF0iOPxmghhAFwpSyUOomf+5QY80ctITPmnpHmJabcmuW+KZ5FVScPgpYaT2ZM8M7UNMOgz87Pd/4lerx9zczAkvaqpXGaeziv4QIFqBXEqW3ZTJ5Z79WBD2js1vpphLyx88esePb854Gh9Y7hJ2E88orHh/M6W3Cp14zoKaeh/w0GSUE8HMdSzXBRbBh//pCtU5fhhOEL2ijT2jSUl3lZCOGrJxQx8qWqtZjLd0RrNZTUmGgUEfD0x6L5me13y7njPKHU0qaUPPpJdE4w6/kdzsJsRbxc3EcXhX0LgMm3n6UHASVhzWGS6HbRUTCY+MB/S0wyeKgwrIrCG72LCbe4QZuOozPii2vNmN6GIYEgfGE096nFPMJjUvf5jyB3/4lje3Y3bfnjKbH4gftWgMqrGo1wHDwpMaw2GXUo47rvcLxsWewzbhs//8ivvDGHsboXYBOIkpQ4wSyLXiNhojK8HhXYGVPbKUxMXAk33NzZlDRYayjGidJkh67H3E9n5CUPRMFiWrb2aENwL1ecPoUUm3KxjWMTZ3XAY7NqsR3cIznCj8WmMfe9J5hb/LEHeaIYLmMWjj6HYBtY8JWkNqPD6ruR3GqObIjrBOcPNuwrCwBDdQhwFsJc47IECKnvy8Y1srHost3Qzu342Y+ZY+h2lcs/3Vgv2JwB88B+WRS0EQSeb5gTKNKE1Atx4z3g5M53uef3HDTT0iyTqezHd8W54jQ8dHT5e87wpGa49OLCYLcLchw8iRJA1P8jXf5XP0WmCcJkp62iYmWoN4iHgoY8LeEi4GnBAI58E6RrMd26sJz0ct171AJJZoNuDfjylmFd1PGc3I0SeOyBjG04aHQ8HCV6TPG2odU32fEMdHW6MAui4kezVgPulRhWN617DPcpwyUGq6Dxyq9KybI1115B350HE/xARW4caQj2vKIaQNBLQglCNXPTaMUKEjHhyD9phaklxUqNT+Vuvl73Sx7xae0crghOS+nuAaGEYeMYOLfcPFhw3Dg+LB5XxTzbmYbnGDRCtLFFp2scYZibhXHO4K5KOK4cIT9y11HSB6ibcS4TxDqIkMWCtJGnd8GbqCkR8w6wA1MqjGsasVU1FRngUoB0npqJRCDSCVw3SKxeMtq6spej7QLFPiRc/2PuZgAzhIujMYDhn+3DAcFHJsSScdXz9ckjiL8A7VObJHJdc3Y6a6ZrAhhIJwB23qULVnNKpJ9MBi0vPyuxN+Pr7h5u2IQygJsISzgeFtjvugxr+MyQbD62FMUINE/NUEwaMOim2QY0eWwnTsE8XqaozZySM5biEJ8KQ7Q3TWsulS/G0AzqNaiOYDOuhJveDys3tW6zFvbxcsox65V6y7jJWZEF1J5HBMLyOVyD04CbIH+12KFCC8x0uBTQWmAFXBTiXIHKKdRw7glEB6cInHpgpZC/ouQqQd6dURu7prcx7NV9x0I8wgIXaE7wPsI4NuHa4QhO80PhDoZy1yCNE4zCagOvFYAYvzDatfnhLsBNe3l6g9rBT0nSRNO3o7hnDD2QcHFust327O6QOJTRwCT/Ks5vZmhKoswnlm4TGMaPPjhHxWsx/H+NwyKhpef3WG1rARKdVDCB8PxA+O/W1M/HFJmyi22yNd0HoJ6cB6l0Mn0J80ZI9bfvWbJ8idRI8cT3TJ6jqjPBFYEzAuKlwToQrD1oTsblLSwiISzycf3lB1Ebd3M65djn5wTC9KPkxrfoozNruYuo6IjWEkWlZJTnWfEmQ9XsLUdwxItjON6BwST5521HFAMMDhxxE+ha/LU9JkwJwYgjcpOxXhC8vVbUbvQrJxyfh8h/aCK5mhjGS2NFTBwKvtFLsN4GeG+2pEPK7QYYc0A00XgQnxb2OCU8P+rsCcOao+RieW19UcsdOoRwP9NiZ1A+55gyg1YuLRbyOc9Milxj4eIHTYdcBLMyectqxWIz4c3bMQJcZLglOLmQkqIiYXe1YiZTJq2D8ci9BkcWDVT3GF444c9cQQhZawtOzGMUVR4/fHkBw6iZr2yJCjaBJB3BrCnad/AmWZ4tYB8UflEdXdaiLT01ZHGNTi7MDVzQlJ2vPB0we0Eby/nlB3CVXiyeuGwnWM33fcnmX8tMjxsw7dR8i9RPiAYSoQjaQLJGeTDe3dKVwa+pml6Ab0mxCfKuKi48dqgogsvlJ8a0+YFSVMIXoH2yJhPOtgIpiPD+z7mOhfpXgvMMrT70KQHmnBZ554J8mmPemq44YxH4w3NAFgIAhq9LzDtBHR4LlbTfD7kN4rpklHF0jMtaI81aSJZfFozRNRsX2ZMhSQjBt4KeFjT9IZTOGIxj3+hwgxcYSZQGwULhRMz3e0u4hGBATCQmbofubgoJCxRXRQnJec6IrDuwvcJkIEx/ucVmIuLaKV9CrAKYMVkmnasSp/u9n4v9PWu0ezNf6p4enFPWJsIHd8lq2IK4Pbgr7TuHNLkBrCHuwYFlXNZ4/u8JuA5LThDz9+S4dmk2S8255gViGxsxB5wr3HPenw8vigmQz8QdMeIoJbQbx0lEFA+5EhGjXoRzXjW0My72j7gElcEYxaJk3PNCvRHPPzN2+PGc2hNFz8/A6dW2b5geysgV806M6hXksubEWoDeOLirB3qHvNdpMzfrbnxWe3OCEgtfyUjPjy0/fYSrN9PyKetSSnHeZNwPu3J+xVRPZHO0gUF3+txE0dWE/0LkSVEP27gM+TNaUI8auANlR4J5hsBtJpw+nllvCs5tF8jWgF8Z1jXpRcPt7h/pOeRAywVrhHBiUcSdRjRh6hPfFly4vPbwkvDB/l96xfz1j9NGX4Kqf6VzMOPxbIpSJ8HSAEIAU+8oQ7jxgswntULXFW4CKPao68eNlDuALZOdJrT7J0uBBcarGZo59IgoPAVRoH+F7A+4jgpGOYeJyFQSkmSUVdxjgJbSCxlUZmjnjvEZOBfizonWbQgqFV+NDjS0X2ElZXE3wn6C8dKnTYRCJ6wcj06GSg9Rph4IfXJ+xnIYn3nFAziRrGryzmVYIy7niYGTlWSUQYDcTjjvouRXaCU9Hw7L6m1Yo//ug1Lz5+IOiAlWI5ihnNDvTbCHcT0uxjzMwhxhYfQDGpkac9YW/57tUZDIDySBzv78Y8/+ABuVP4uUDsFWsdITrBqa+Zuo70rKLwhrhRHK4zJr4luFZIJ7i+mvAXw4LJ9EBYDHSBossEy21GP/EEgWUx22MjsKEgCnpG7zwz3yBaQTd1yAaGQeMkjGkpc8l8cmBlE6o3Oe6NJkgMzy73nF7uqB8y9pucW5XQG0WbCPZTgTqzHGTIWiW4/ZHloEvB8uujQCsxhuxKoBsI3mvye0dybTEPEdJBvw/JSkPmB+LnNRLHp+k9H10u6bWED3tk4NAnHeIbfQTRGJi0LVHjMQ8p1+UJuybDacGH53dYBZ8trglbOJvuaBPJ0Eh2bcJhk6FCgyiPivfivSe/c0yTGmU8VR/RigCfOuzEMftsg3Ce6fMN1ZnAjkFNB6LQsFsVjKYVTRuyuy0wTjFkkA89/cxxbUaEk54/vLiiXCb8up/Rjzxm0AzbkP0uZa1Tvn8yI84MzfuMmoRw0uO0wDmNAkQrOMiI9dsZKMdoM+AbSRAYXvUzhi4gqKH4pURxxE9HrxV1GbFucpafadJdz5Oze9T3iv4QsLsb0SfHEhRuBLJwOCXx0gOW/txRnXuu8xz/tOOnPOGtKygjQTjveThkJKqneZ3hS0UUDgSx4fDcowNDNOtho+i/jbm+n/HNuzNuljMO346xE3j+yR401KGm7yJ2bYo/KLjXx5z+zjBXB3Z1hgxACIHAQSMJjCdSA1HaQynw/zJj+2dTtHckYU9wEGRhj5lZUgxZ0hLFPbYXnJzusRPHus1+q/XydxuE87/8n+N3xRHpKQfsmSBae1zRY5eafN5zSCKckXCl+L0vbqhDzd988QP/u3/1x3RGo7fiaBPRCrRFFB7ZCgIsQyhoQ0mwk6gWwgeP1wKbeFwmMBOLftD4xy3jpGJbjnisNlQXgqpOUDcKPe2ZPNmzuy1oUkUmerptRCs0QWSQd5pSBCAEo6Qhy1p2ZYTtFXlrEZmhygNMGaCmPd0yIYwHiqJm/z7n7KM11T5m9ujA27cLbAhnky2Tg+XHn87oQ4kcWT66uOXNco6e9OzaFOk8821JKTPsKkQYiZgMR6uQk4SDRzRgng88FTt87jiIAH5MiM4bbAKbmxF6I1h8vGb7dsShTmFiiE5bxnmNuJXohaGRCr8MKU1IuoHSR8g7+VddusQrEMoTP65odhHeSILAEi8NBx0TlBwPARLk4HChwIw8XgnCe5DmyLEfCgGPetwywHtBeufopoI+F0SNpfvEoLTDNpppWmK0ZJRVXN+eMHQaG4HeSWIz4D/t8G9j2lSgW0Gw9/RaobsjeU+2kn7uSTrLaNSw3yeoFvrkyBBQ0h5jQXOPlQK9hY8+eMedmGCEglIwOAUPAe3suFPO76HJFTob6GJBXhvSlUSMB2ZnFQ8PGRsdk5cDzz644+v3TyheN5hJhDu1eDMwnrVsXYayMPYNm31O14UI4fGhQxUOXcN4cqAxmrKPGQ093UOIzSBWlpNHOx4OBa06Jj2KyHFma6bTipe3pzgncEbiQs9nF7fcm4wo7rjZTglLh20C8suS+lVKcGJxB42dG8I7ccT/1jFrF5NZCzvN0Ap8r8kvSsbRgBtZ7n6Y0pch0eOG/joiH/ecffrAoQsZL0p+fHnJ5cWK9jo9CkIFR0dM6+iRPHqyRnSS61czbAzJNSDAxZAGHQ9nIVJ4VGT58PKWIBvYbHL6fztmfyKwY880ajB7jRs7uvcpoXUcHguoBdnWYUKBulX0C0hHLWI8IDea2oao1DBZ7Ln/5RknyYHprOJBxWjrWZIiW45rBRsTvld0E1ClwGkBM4MPPGrcMxrVjPYt13dnyPFAf+IJ3ygmouV8vuGrYIGqJLZV9E1ANm/x4siH+Hiy5tXVCU0kGZke5yUidLROUzYR8Z0kmTU0bQjOE6zAZJpAGoxThAeLP3XIzLLXIem0IWstOx8RtJao8TRnEjcISB1/krzmX/7qY2zvmT0pOQQRYmrgRgGCeNrRHkIOIsYbQfRK4dIjiyA+WLpPLfY2Ph7wW08wHZh8uWH7pqAVAWIQJGcVvnC4VciuT5jsevyZ49BEyK3EXRjG05Lhx5T8RUl9iOlXEeEXJem1YPcux84ddmHJfw0iPGKZ63VOjMHdB6gawvOG+e+tePnNJfF7iR95ik+33FcFUnpEZvmALXd9QVVFPI52lDJgf30Mc8Ja5Lmj6SKC0qMSC+ExofHL8ZKXjJiKLf/1//R/+x9BOP8+l+qO3bqMDYvFDjLPbVjQ3uWE9wPpBfQreDzacT3J+M2bM6LTmv/9/c8QmcV16ogqrATidCBeeyLTc9jlOCeIMovrJdIKgqDHjAIA+jOPGBnmL1vEhefp8zVf3Z6B8tyXE9pbgSs1oTK0g8SVnt26IOs7TpKGu1wh9gF9ExCHBnKB34Z0y4juEDAkktFlibwTlHGI6wSyEoRW4IpjJy1SQ/hRfYRkaKjuEhYXW3arlHYTs5802DOL2gqCacvukB5hPT/FxLmHjWIXjjk537KXKc+ouatSnj1dc1PnjM5LVjcZu23BdThG7j0uc5hDDmlIt1O4ALLYsfx+yhBJ1KJDnveYVYi3CpMLtj/MsKEgLwdcF5NHe8oywUtwWqJrj6wdAkEnE4KtwEbglKIziqBx2FiBOCp1TSBQjUc2EtU6vJI4CcJ57PMBfaMIdqC6I+MAJwhLcAU0PkSXnpPJgS6WmOuEYrRFDJDYgcBaKDXqo5am0QxIVOcZIkF80hPmHdUQobeSIDNo7fnks7cMc8/mp0vmu4EsaXh7NaOYt1g4iqkuBugUyyzDbhWd0ujrgOSyJvhZybAZE/bwx5+/48/vLumVJH2vUHPP8y9v+eFhTnhSEiqBW8a0M8m36wumqqb5XJHNDpwMHTuv8YEnfC0JvqyQ/XEloWpDNG84D0uWLkctDOUuxiYefdoTvnW8eHTDr4ZH6Kbn7X6OTAzuOjpOU0ILJ4KXr89xtwpOHCeyZllm7Cc5bR8ec8330CUaNPROc/b7S5Z1QbvwJK1DTSCoBOduT/U64uKjigeRU8YJIvCMZhXrIeOP+gPvDqf4zOL+MmZYKPoHx7fFOVZDXuUk9/Du9gKRO05UzU2YEV55xKctSWpZfzOm+HhHpHvaIUR2jvZCYLWEQ4huYPbxlt37MX6n6ISge59w+BCSSUNfBmhl8W1AKWKKsEd6x8Ib5KOB7XrC5PTAtp0gRgPOC7pDQt8cd/7PZxuul2NOny8pr3J2YkTyADdBTJwMTEYVI9XS/RgTjTvEmaWRKXhQG4XuwXUeuQu49gWisNQ6YPq249CGlMrzfjdjWntaq+g3IeHIkfU9wyGgXqb8+jRGTXseR1t2fUIwbejfhXghiI1lNCtp9inh4OhChY052hpbhfugQ74RxLrnkGvUg6SJQvykJ18OtG1EELWI0rFxESIUfLs65+/+wbf8X7/5iMObnOzDkv4h5KBDPt/t2c4duyBFbiWm1QyZJ+sGxCBpPrUYoUlVT5kE6EAwelSxuh9hQ4nXHr8M2GU5QdqgnUB5R51LVC3R2hEITxc4JlXH+3FGsy+IjUVPBpz2bMuM9lwQJI5HwY6rL8ewCuEQ8vzZivuvJ6QvDnTXKc1jePvrC4IObCKwQrB7M+LTx/ccMs1eh7zczpiZlm40cHUYoRtBcNGSyZ7luykW0N4RfVxT1jHaSia+5/tvz4iLnpsi/63Wy//gMf6f/dmf8Xf+zt/h8vISIQT/7J/9s//e5957/sE/+AdcXl6SJAl/8id/wldfffXf+07Xdfy9v/f3ODk5Icsy/u7f/bu8f//+P/iPb3KFjx1qC7vXBbtNDAjCfGDxYUuZOnZnktsgpM8FzSFiuylo//UY8SZAvglRe4HILdGdoPEhZhcSbh3OKRo0PgXZgWkDeNrRP7ZILHIt2M0iDoHi7j6lawPCZMCnHvmgMecGO/IEiWEx3h27gy7gp35GfxMxdIp+H1Jtj15j6R39SGDnDqEc3XXCtR9Tx4rp0z3tRLB7LHCpwxjJ9u0Yt9EEk56dDhm8IrsSTA4DQTLAVjMNG3xmEW9CDvcp8WnLYENkIwgOnqQdyIwh9YbbIcZHju/fnJI0Hn8bsb8pyMOWxWhPmBtsE1J8vkVLhzxo4qWk+GRPszgWVWUdk2tL+Fax/X7K8Osce9DYZUC9TzCZ56YdI/sja91LcCE055I+l9BDdwogEEbQFRKvJWo4diqyB2EE0oLsPE5LBB4vjqP98DuF7yU2OibC4cEHjiEVDKOjeyN9IxhvB/ofMyZxyddvHyEHQAmaXJJNa+QA9j5Cx5aTrCQat+h8IL6omaYV+bjCTexRhNlk3H1zwodyTTdyvDcj/NQze7qnGinM+Dj+58JS9im1DvCDoEkk9dcjHv7ylOE2IpCWf7u6QEjHIqgYpOIwkXx7NyduLUMfsLkdkYQDedgSdo5aaORSU77JePVqwf6bMdt/M2eQkvJXY/p1hLjsOP/9Wx7FexyK7VVGuQwxmxB1rXF/mbMPQr5LFoyLBvHRcNQUBB6zsIjZEfXs4oFSaYr/ZEvsDF2gmD3ds8sFn0/ek40b9OOWZO9ILyrKuxwrJMN9zGl1HP+LHvS95PV/8wg3U7x6O2dZ5ceQGCl4/8sLyoeUXgjG4xIxNTQfwouPb8j+8zUm9sjC8Mnk9ph8F0uU9ay6nJ9fvGO8KCl0g30fU3y+5XY14SKr+fSDa4JHA1HrwFraQqE3CnsXkycNNz+cUm8S5OOexBvYaEau5+H1nA0JJpD4yFE5zTQv6bqA7JOSLpUUz/dMLw6M0goXOrK8I/aGt/UMjGTZFMi5wb+NKR9y1EEhOsHlaMdtOSb8/ECbCnytiNSAmVtMwjFD/tSyNhln4x1p3DKNa/TIoJ42zHQHkaAOA7JRhX3WwwDVNyNMr/EIwrVEvAtZvx6TGEO1SehCuJhuyVVLPHioIVs0BLHFnTqGS4vNLf0uojpR1PPj/07oyfTA5N4ic4udWcpE09hjsdTfRNS7iId1gXeS/lMLJwbZwyKp+FU85f5+gkwNeiuQrTgyDdqAwSh6pYneCvpI4TV0J54bN8JJcRzFVwF2YVnsGk6bGt164mDAJoJ0P+DWmnYMsRgog4giblDCU5xVfPHolg/TFY8+uCeIBsKlYLfK+f3sFlqF3FjuHgqGjwbqZUpvNeZVzBAJ9J6jhoijFqi+S6gIad5kfKBWNH2IbTW6BndiELljX6VE6UCme2RmsYVDGY+PHP/l+RvmT0r4vCbNm//gmvf/y/UfXOyrquIXv/gFf/qnf/r/9fN/9I/+Ef/4H/9j/vRP/5Q///M/5/z8nL/1t/4Wh8Phv/vO3//7f59/+k//Kf/kn/wT/vk//+eUZcnf/tt/G2v/w9SJGocTgvPLA2cfbsjXMB0M7USwWoTcrqeY5f+HvT/5tW5b0zqx36hmvcq9dvnVp7znFkFkBJGRBJAkAoHlhk0LJDo0aCAh2Q4BAiF6lhWIFin/BSHRtUUP2SAboRSkI5O4cW/c8txzzlfveq96zXoUbszjKwWpdBIifOWQGNJq7PXNvffSt9ea7xjv+zy/J2K5GdO+ysBK1KVB1gJpPMKBkAE3cbRa4ZRgnxnUUY/UFt2A3gv8hcWct4RGcVTsWew6kjuPiSy1i6i7GD3rEQdBmFnEt0vi2jNOKxbZjnevT0isY0qF2xsckoBAZxaROca+RVgBFiLpCGuDsxLjHFEXOLwsED5gloq5bcj2lg/erfBfJKzLgjTu8B5Wc425qKm9Yf+6oMshRAFRSpwQ9G9SUt0y7WuauSDM4P1Pj9gUEe5ZjznpOMwE65Fk480grsoPbD6fUt5lhHtDsXeDV19YnIHtuzFP8gcePb+jr4ZkwGokSWc1VYiQjUAfQNeBuHMU4wqhHKoaOjN9LjB7UJ1DdJLkCsTMEkcWYaGdSIIQuFjQjyCo4Xt8JAgaEKDagBCDVSmjJ89rnBnGA/GDJ15b5EFg2kA3EVzezXGV5u5mjq4EJENw0lFWMvrkgHEBnViePH3ABXg8XkHmaaxBTzqS3qMfFNk1LK+mbK/GXN/P2O9TuocYV2p2DwnuwdBpgX5n6FcJ5VXOc7XHNYrx0x12HkjuHdmtx/40pX+f4vcR0bhFPWpJryWq8MQXPeO84dF8g18auligfUAmnmTlOR3tsUeB+lTQnwfsIjAaNTSvcvSt4v73jrnrc366P8YeeeoyxveCEHu6o4B4ZYh3gdGoplj2PI/3yKUitJLOK3wS2F6PmZQdsxtQpx0bE9NcG+zPElZNTrNP6O5i6kPE49EKM+rwpUJ7i3aBfhooNykPNsOeAwdFCAoz6aFSUEvSpyWzquH9JufpxQ6nBQu/p40EI9vzPFmTecvLHz4mei85me9Qsx71qOH9D8+Je0uue5p5YF+mSB+4PDVcRxl+6rFaEqaCdNogRKArAnMO2MJy927O/vV4+OzIiFJrTGQHm1UpaVEUvueymdJ8WbAtM/KuJxKWi2RP3Ao+OH/g8WjH7GiLaQPJG8HoOtC8yVCpI3+853S2Iak978opSdyxX+fEZy1+FSGUJ4p7oh1Epid0kjg4MtOSpS3z6Y5i3/M43vP8T7ynR2EaRz0VWKcgH+J8yz4hmdeY3hMpC41i9cUcXsV06xHvbhfkeUu/CEw+3hJGjvRxiROC3PXEi475yZ6PPruhNppk6wmPW+LG86vPrsjqwLNszXlXYY5azmYbxs93yImj14L5Bzv0QdD8dyOqLqLXkovphiJt4EHje0m8DiRrRxDgIgiVwhtBk0lGaUtcdMBQaJ2Ej+WG/OCY6ZaLScXx8Rb9I4FtNHWbYAtIH1V8cLJmHVLKhwxxEIymB+4yw8vlMbt9ht5IWqWRI8f9uwmfqjXZccOvf/AG1QeOX6wZ/9oDcuagl5x8ukQft4TznpAGHiYxai2xk8DNzRzlPXalwUoeHXYkO0+67IgWLRcf32KPHeFBU0QNJ7rk3/3gMS6zpN6h8/4PW37/k9Z/0sxeCME//+f/nL/yV/4KMJzqLy4u+M3f/E3+wT/4B8Bwij89PeWf/JN/wt/6W3+L7XbL8fEx/+yf/TP+2l/7awBcXV3x5MkT/sW/+Bf85b/8l/8Xf+//Z2b/v/pH/3vSkaI5l9ztxzzLV1zfLHBbQR8rxEbSJRLZDMlUeWMpJ5pUt9he47Qkdj1FWvPQj5BftxKDC6RnNbev5ozGDdVdRhsLTsZbfALrbYFdR2RZSdPHJBcNLkiKtsNXmh0JQQaeT5Zc38/wvWT6fEtzmaNwrNscX3heHN/zarUgaRxswEw8etGzX2XML7b4lUE+btjcjxgvHbMXa7ZXE46ebkmCpaojvgpzZOwQO0Wc9tiDxkpJXvXUoyF1zkgLXhCXHj5umJiawy6l0Rq9FzS9wXcK03vqIzjlgIo9nZbU1xmiFnRS4pQkkT3OC/pzR9wHQiM5P1ty9cUxodHEB4cVCmkDLgmYRlCclmw3OemNo3ysIYTBKhTAZhCvA91YEG0DNh+ejyuPE4Natc8FphxOO3nc0DURlBCUGCKCI9DjQTAkK0EkLV1t6CYCXTq6iUC1Evm0ha+ioS2nBe2JJ9oLopMGEXvm2YHbuwm+HaiFcXCoXeB4vOXmeg5j6K3CS0HUesROfu0Tt7h6KA5ho3AF+BctohFwExHHHeakZlOPyJ/t6d+nOCfR7wzRLlAdC0QQ5KKh9AlSeIIGrRzVCaSThnBv8J3EJBYXA/ea6Scb6n2CbmE/V3RvUkzvaaeCdOUIEYgo0HhDeNxivjBkj2qizrO/zpF4mlSjR5ZJUVGYnrskwv8gJxp39IVgvtiwXo2xleC5KHm/z3nyJ+5YyZT+/1pw+GWFGHmiLZSp4RvZkus+Iwqe45M1d/dT+vuIbpVgHjX0RuDuY1LX0U3BjwKjtqPaJLRaQeaIXhr8kYdvNESiR+SO1XrMWLSYDbS7GLeS2McwihqePl6xKTN225jdIUPGnieLB6oq4V5kjOcV+88n6Bqy0tKfedo2IjMtSWc5JBoy8L3AeoV2gSjuCVNHu4pxjSJaBz59cs3LYkrrFaFSfPb0luVtgUl7rpsxpvWkaUedKpLbgJh4ym1KKQ0JnjoWnI82NHcpfa+oNwnuxBOXgenRgfXrMfOPN6xljN5K7MFgi0BxXA62V+VobyJiN2hLmieOfFzT7BImXUcpDM19jGcI+dHaMx6V1D8b4TqNyiw2gVj3dMsYl0D+5IAPEn3a0PWa6EFS3He471gCEhsGr3+8gU2dMk9LFvM9+qThy//hKaOi5qEviM4a0oNDXkkS7Tg4TXfQ6AV4J5h9c8nd+xniJkK5gK0M7RS8hmQl8AY46glS8BsfvOEn0ZjDKmXhGm7ezoi0Y7cQHKUHjnc9rRaMWscyjnF3MbdPFckaUAE1sbDRnHzwgModsexZXY9xQVBejyjHkjTp0DVwHzFa7FC1ZL+P+XO/8pLfEScsb8bIB03yyZ7q8xEuhWlUs91nzCYlh+sMiyYIiLZgM4GwAfGtkmla0dxmmJXgZFzy1UmG2xg+sVuMbHjVzKhEQnJ34Hf/2//TL2xm/0eqxn/16hU3Nzf8pb/0l37+XBzH/Lk/9+f4d//u3wHwu7/7u/R9/weuubi44Nvf/vbPr/kPV9u27Ha7P/AA2MQp3iV0hSSkjpvrGaERiKc9vpMcfbwklT39UUBMHCf/1R28aChlRPS4YZzW9E8scurQ2hFdVKiiZaw6rq6OcJNAqyTuSYfqBJv1iPX9CO08Bo87KPpY4q5i5vmeOO8IhUeoQCg8Oxmhz1r0rEO8NlSl4dFnNzw/v0VvJLeHCWLqGIuO6bjFGIfdGXyl6K1mPjpQHBziJmLfpFy+OmV9GNFIxU/3p/x0c0bfaD7Il5w/NORvIUsbaCRtpPnl8EDuejLdIhcd5rQl7BV5ZzncF+jOY6VgfL7nfLEiG9eY4DlUKbKCeB+QNmCmlmzecXS65H/zKz/ETDsy15NZi1pK7r46wuagmoALmqAhaEG08YgO9g85WjiYgazBfP1zfTSc7oMB3YCpA2b/NQZWDfGcug7Dh1IAQtBWml5J6hOJiweHBAjke41aCoIFl4L0AR0cNpPoWuA1+MsIOwqE1KHbQHoP8aRhJwydkrzdHqF7BmTxRtHfxBxUzN3PFoMobanxGnACKwRBCSZpRRc0NhWIpR5Eg80gAqyMYfTkgNcS2xnEoqe9TghLTXoZ6CbQzIeNjCkdZYhxSSBaB7ACHQLF7yv6HxfIlwa5ltitgS8iyoXgZjVjHcUobfE7RZcLeik4LmsK3ZOklvm+47TYoreKdNHSrBNqG6EYEsdOj7b82icvWR4yVtsM8aOUPhY0Y4EMju2XU7qtoXMRb+yYD0ZrrtoplIr1ZynZvKPdx6gowE7xyo2oPx+xuRzx8rtPqG8zKpsQPmvwWSCOOvoENuMYm4As4SHKsBee2eyAaAT1ObgYmusMthq3jhE7xTiqOfgYJp6zxxUXsy3lXcYXPzzmtiwweKa6wQvwKYjCchHtmG17fnlxjT/t2RcRhyZFaI+/M2xJODE1URcg8Zz0B8bne8LYUW8TtPeYixb/2LHLUtL7ADuNaQSPmhqkpHIGlQwz9e27Mf1PCw6rFNtA8mD56OyWR+mO8/ua56zYtikuD8QbmN936OApXxc8K5aEe022HTbB/qAJVtKsU0aXcKb2jE4bXBJotKA4dJQvCyLvmE9L4thSvDiQHNdM5hX6pGES1/ggmB3v+It/+vcYjypcrRBTh4sEu9uCAxG7yzEvRkt01GHPAtdvjzigOPcNT101ZFBMBdduzBdfXXBTjwmnlrqMyY4rum3EmoxuIbHf7HiYZ7QfSmwCmzTm/ftTXGMIB4VWDhXZAS27dGjTw+OWUVrzXy9ecr/KyF4a2ElODyXF4wNq2hFj2biEr/oprdN8nkzZ7hPMqOS8OyDxmM7R3iU0neH6es7lekb9kOKRtG0MIjCzJf46Hpw6c8vhkHKnU7qZ5P+++oCH92M0Hs47muuMYlKTBM9GJ3gr2YoYc9YiREBaj/CBcNQz/ZUlQQmebluOl5awcNyuUkzc45OAwbHKMvxeY0Y99tEv9mT/R1rsb25uADg9Pf0Dz5+env78325uboiiiNls9j97zX+4/vE//sdMJpOfP548eQLAqs95m+XcvJvhv0g5jDXnH6yYTXeMiorVeoJZ9Pz6L7/CmcDlvz1HXmnGRcmL6AF93kCpsa0mOqnY3hTc3cxo5yAPgqPoQHOIMQdBkIHR8x06s+gnDemTkvGTholqSdOOTVlw8AlSBn5VXHG+bGh/MsbYIc9+m8V8MF9xU0659hPSSUO3jChay9Hxjl1IOH604eLFPWNTUb3KuVuN6TbxAAYZd+iiwz3qMKklMj2fJHccUbHfp7wTEw4yZt0VNInkKDmQHW84e/IwtCzfZbRa0leG5cOIkHtqH9Gngf27gtvthOLDHSaxSBEoRUQZabjoaY880XnF00/u+L+8/hY+EyyqlupVQXAS9aDJHwJm3KMvavrx4K+3I4lXAve1kt5LgbQe2Qq6kUSXAdV6ukIQxNCyd4lENWFo0yPxZji5BwnRzg/K49yhOoZZewDVeFQ7YGOFh8pH1CcCpEd7D37I+VZNwOwBK3Fzj24DtJA6R95YRORoM4koJVnUDoro4HBG4mYCddLjgyRvelwsUC2IAxAEmW6xjy3dSNBGGrE0jEJHMxa4kaevIvJVQL6LiK/Al4b8XUC1AZsFinHFpCiJduC1QDUBrgQhdUgbCBokHtkLfCpJ0h7ZB9R7Q7XNkDvJ2HfkJw3uhUU9b2imgeqFRxuPMo7FomKWVvS1ojqWdLkimXW8uz+GSrGrMuwIUtkyGtecnm7oW8NUtRSuZ/JszzoqCJ0k3GlM7Si3KXlrkect8+drXBwgcdhE0M0908UecdFilEMry9nsQLbvSUWHtRqxUUgCGs+hzNCZR/mA6xVi1HPwMXE7JD62zjCal6SjBt0FyiYirjzfPLtGV56+14zSjrTyXG8mXLsRXSzYZ4p1qrgYbxDnLcXFnvSshgtLMmmZxiUitox0S+UTGql4+ugOsdTERy154whBcOMy1GnHLKp4frTmd8OC3nguRIWsApnrETLgGwVzyKc9q/OE67cLXt4fETnHF//9BaKV2KuMfga2N8ilwjWSN+2c6suC5l1G8lohpz3RBsRLQ/8+5vp3ztldjuiCZvJ0x6FPiVuPfYjYrmKM7jhpK/zSoLwn7R2vH46pPnYcYs1//5NPkdojPmjIX+yRLmAaOGpKbK+4ujyiqC27qwnnxYHP8jU/vjzhJ7fHNA8Z2cFh5h2P/8sr+GFK91VB1cRQKiZdR0aP83D7csFJsmfUtOTXHef5hrNsg3MKkXqijyvsqcc0Afm8Axnwc0dbCF5GBeuZ5G6usLHkKzFjtu2wtUY8GKI3BqlAVZKPJysS61i5Avsu44neE8wAW+sjgas0H+oHrNUcvj9F2sDiZI8zmuoEGq9prSaTFr8bxiin6Zbpe8v41sPnKedRhS3ARx7de+TY4a1E+Y7Tx1vI4fEHK6QZAorCvWGZKnYnchgHPdGEq4R5XPLwWNDXmidHG2bjA35l/ijL7//i+v+Jz16IPwgLCCH8T577D9f/t2v+4T/8h2y3258/3r17B8A4qWikAhXoxpLU99y8nDBVjj4W/Dff+CnyNvDmvzsitg45qTHlsKP/4faC7VWBGbesRApBcpSVzGYHCt0w/86KtorpvaKoO5SA1WpEd5/Q/aRArBXNMmG0sjTLhO5NxubNlNX7Kb9bPmJ9O2Yz0SRZzdFiQz+G23pE+yanqSJGtETS4lvJy4cTQupZ7kbcNyP6jxyGQJlp5EnLn/nzP2RmKh6PNsRrwVc/eERzkxKPG5hZkm3A1IEy0pg9iMRxdZjyk58+4yFJaX2E8p7x0hE3jjpVnKodyjh8pfG9Yrw4cP/7x7SNoYoU8bxBPyh8J4lVT1UbfvyDD5jVLWln2V6P0aaDWuCswLaGcGto9hHxajhJ61IMrOsgcK2mzBU2VfQTQbJ0w2nYCdI7j9CBkHi8hn4siNIOcdbiYkiWHhdDV0jaucArifAe2XuivScogU2Gt3I7GU787Qw6bbBaoitHN5Y0M0WfKZwW6HuJ9xLeG0KjqJxh4WvYaZLjhpMnK0wF3mv6TBJqSbjXSB+wnR6iPzPYdxmqBb0DXUK2sphDQFcB+y7B/yCD1zGhlnTXMV5KfCpACrqxxGtBfxxYFSm7Okcc9TQngyhUWkGfanQlkH4QJPlxj5sMp0vfKmQjaYTC5/Dk5IHjYkfT6+HmOLV0I7j3OX6tudzmiIuG6KOaQrZUvaGRkkZDOmuQ3pM8PnAoDCe2Y/nDOc++cc8yi2HmMJGljwP2ISIYQTLp+NafeMXsWyueyjXeSbr7FHHSE/UWZWF9N2bse/pOo0TgJkmozyWsNWddiT7uMDbgKoUvAiq3iLHDjQNF7VCXmg9YMbu2lD8bs76ZUt7lrKpkaKePPJe/c4p6Be5S8jjZwVlH4XueZWvGocNextzKjDaT6J3gw3bJn5q+4uNiSXMTEWIwAfrLhNHjLd8QG25/fEKIAneHEZvXE9RS0r/OWH4x57Yb8cVhzqFKKI2h3SXsNyPKbUZ/FOjGDILDn54SfzEw10PqeGgKttMcfzpErCYPgbBSWCGJI4vaD5tjWwT6XQR7hSkD8SogdxJnoPg8kH4l2dyNiYuO7IOSkWpYLwfM673LiWcdJ/5A9ybHHCCPO8KJo65j+rnnxFWsHsaE3KNqx2Y5Ij54+h8V3PzsFL0UtFJy/W5GiBhGXguPF5KsdFy9WtA+CUQHj26gk4r86QF3UIwmNUeyZHLZ8ElzIBxr1jdjNpsCs+v49M+8JNp5ikPH0ekWXcN0UfNM7ckPluufHNNsYtpNxKxvSCcNX2VT+iMPscc8qzk9XWNHnjJENHNBh2F7pDlca7oqITU9WdYgtGd9NWZ9N4IsEO4VF6qk2Dky1zM6PSA8HF7nREWLrCQPV3MOo5i9jnBScLvMGcc1MnMsTraMFyXZuCG0EU1l8JlnnRjMwtK8HDFpOtYPY2zskT5QLEpkYgka7G7QQX0xmrFrU55fbP/Ti+0fYv2RFvuzszOA/8kJ/e7u7uen/bOzM7quY71e/89e8x+uOI4Zj8d/4AFQ1hGykagHiSp6jsc7XCx49XrBt9o1P/nuBU0mWBcpPpKEjzxy5og/rmDimJ4fiIoOV3jsZCgoLgdT6gFQ0SmOFxuk8cib4VTZjwJewziqqYJhtS7oOw0tKGlJdUcSLPUY1ElLazQ4xXfSa6qbbJgjLxVX3ZTaDe1UEQInR1sqYXhUrImDRT+rSWvHajPip5eP2ZDyerfAO0FmOmwOb+9PkJ3gzo0YfbylmFX4O028D6jCcv7RHfU25vTxEp8H1n2G8xq/N1TvCya3nnjco047EILjxxsiMYBxtncjusYgCZgDpGVABKjfZuwvCw7GQAxm1OGOA3QClwWUHfLtbQ59DqrzqDYglUe3HhEC/SQAAl0HXCLQhSM1LaoRJCuHtNAGjV9GyH6Y2YsQQAT0HpJbEE4CEpvKQeATB4IaNga6DqQ3npANQkBvBLoKRNseqTzxZrhGuICqJXIjoZfslznmqGG/yrn64RnMHdEuIJQnXglEI0gfAnk2CLxk5JBueF2libC1QHSCvgBTDlIYtRXIPuC8RO8YXk8WkLmlSBpkD9lLIIDoAtEqkN7A5OJAO1GoQ8Cm0EeKsszwq5hwkPRWggT1rKabQFo0fNUcsTZD5apPAn5jkHtJlHXEH5Ww0Zgbw6Nuz4uLB8xGsPvdEbs6hbkjf1JxQk0xr+gbx+jDHa8u5yACVaK5upqzqlOifhDS9rHkR7/3nO0PZ1zfzDl8NSatPGGtSDcB0Uik8Dya7klPKnbrgvLfHqF3A4ly6TPK2wLfSvo6ottr+k1MnrbIraK7SQhzT9FZ8o/WTD9bMz46sEh3nH57yePzFf0ULv7LHf1zSfZpxyqRtLuY1XbEbp+xLCJOPl3ydLRC5pb8tKQ6Unz/i6dsdxH/64++5GeXJ6zvCqoRvF8uuIpSyn2C2YHpA+NpyfTFFl/0OA2mhdz1PNvvSE2HrSEze6KDZ6RaTO0Je4WdekBg4g5jHPEvHRABojW4kYfOEaKAii3dweCjgMs8BR3CDKRMkkB5rnCZRwoPqcBnw/ul2if0X2X0jcHl4G4j6lxxnuxZbjLMeT3okx4i9I0km9d0UnL/fs5ENmjhcFNBLix6LQgXg05Bxg65FSzXBaOkIRvVSAfxrCY2PcVdoLGGxZM18S/vmU5K7jdjsg8PVFrz8Ytr3ETTHXl+9eISP/Woo46Tb2242s7xRwI1D9T7BNtKxMhx36ds7gpE5HBvU0aLimWXsLyfDN05AW0iyegpHzL6I0+PZzypyEYNbSK4f5KRND3xNTw/vWcsW7ocqkISzjs++OiO31ufsd5mPDm5Y9unZDtLdSoJ1SBwrKuI2Ft8HNCxw3iPCOC+yug2MfVtSv8qo31IWeea3PWsthnpMlDHiv1M0Sygvs7oVjHtPuZb0xX9TULtDE2v0MEjSsXVKP6jKLv/0euPtNi/ePGCs7Mz/tW/+lc/f67rOv7Nv/k3/MZv/AYAv/qrv4ox5g9cc319zQ9/+MOfX/Mfu76V3BOve1QUcMuI999b0FYaHfV8Nc/JvlUyuqhYRDt04phnB8SRxY0C0kOdKA7XBYvbDnUQNO8yxLXm5csF7VWC6QLuX+c8vJ3TvoBWSvIvACFY/2RK8qXATgXiWYv8TkWwkm4G/thRTBuO+obVj464vZ7zcrfAFh53EyGPOnLRITOPkh4167nuJjgref8/XnC06qjqmGAEB5dQ7QztOiYqWmwcGF3siLBUZcxqM6K9j9kvM+o3KfJiUDc3m4Sfvn2MwfP+aoGTgnjS0VqNU4L6Y4c99QgPf+LxW+J+ENaNopYPFneY1tN4Rb2K2VyOKS/zwXI1EeSiI1kFmglYpWAv8Zn/eoYlaJ4PO35x5BB2aEuLfpjp6wrS90NbPugBjdv2mrJPoAMCRBuP3gynXptI+kIMp/0DiNQivMDMO6QL6N4htKd5LAhy2BSoLmBKAUUYsLr58DZvFppo42iOJe1EIvww8xciIHroJuBHnmTWQOwJsx43c7giYBduUHTPA7WU9NMAtcSZYZNjk4BqJe1UIkOgz4ZZvNdi8Ps7gTeSEAe8F9RaUR9idBUIBtLXAuHAthoXwXI7QvYe6UB3gWDCcLMPgtmiJhGes48eOE+3qK1kdznCbyXlbU6zSmhuExajHeYAslRk94GPPrrj4pt3mI3k3XKGP+85+mBHugmEjWbWDO3xtGjZnyvG0wovBGPfk8ie4CTOSaptSrNOce8T5F5QuYjbZoyoJcltQEpoP3EoHOLI8VoWrH88hzuD19AlkuhpTTOSCB0g9tivXRZtDOHLmFFegfIE4/m34gm3Pzlm8z/MqcsIubA0B00ZKR6fPvC+z5ikFem04bYZk6iO2DnayxRzr7jaT7l/NWf/ZoSSnsNdgXzUc60z/p/JBfaxI/1wDwfDUddQ3adwZtFPWkbzmt1cUlYJejuMlmRiaYRiGSVES8kdBfJy6CLtdyk4EHuJ2Q00yGab8Ol2j3uVkl8cUMoj9gr1Z2s46el7gzcCJyXdAtTe00UKkQSi2pLee9q5RK8G/rquQN8o0qiHErrWEB9A7DXd+5SXN0dUmSRoqPqIX3n6FSJ21JGiRxEix7ncMJlUqJGl3cS4AmylEJGn8gnddYLygWnX4dYx0kL3PmNzP2KlMuxlwm01IbGD7uUvn/2MZ6uS8EXM/7h8xs1uys+Y8v949wJRSY7LlmzWsegdY9WQRC1q2uEnAaU6XK3I5i0eRacl/jYmXXSUynB8uiUNlidmRXcXM+8OrNYjyjomkQHdwCfze/RhuE/wuGfbZDS7hP3LCWwUpY356d05mfI8uthz+cU5+l4yOS6Jd9CfeNx26KTYywTXK6LSI457tt+do5SjaiKcEPhG4qREvIuxlaZ4B4frDPOVQr+KMJcam0KiOsqrjOV3C9KopbrKSVJLFrWwEfh/+4v12f+hoTqHw4Evv/zy51+/evWK733ve8znc54+fcpv/uZv8lu/9Vt8/PHHfPzxx/zWb/0WWZbx1//6XwdgMpnwN//m3+Tv/t2/y9HREfP5nL/39/4e3/nOd/iLf/Ev/qFeyxf2iEfhwIPPUGVAPW5J5y0ouH2Y0ivo65hEOITydG8z+j7CtZqmi/A7TXoNm6MUvpDIeCh+rtLIJFCWCfaxAiBtPIeZoPmlMBCvkuFDHz2rQYIRPenFHhsJkqLlSVGyzTTkHvMgedhn5PMKudPUC0t6sMxdT4ih/b0J9qlgdnJAncNmlyPTQH5UEe97VAPPFg9sZUQ+bdk+jEiznonruNtO0HGP94MYJtkHmqcW1wnqdUQWVfgoEL+V7OcpIg7I95p0VGFrwyIu+b0fvYCxGxTvvaLaJthOD2jVaqCo+bHncJtTND19PhCxsh9rqseCEAukh+PTPds+wS9jghyU+sIFkAEbBfRuOI3ytcjNxQw35VwjbMAn0OuhlRmigJ0EdBkwFahuKAbRVtNnIC4jSBzFUc1+neJ3CpsKZCeHv+GZg7VGdmGAL8UCYYcTcvwAfQbNggGpS8B7ydGTFf2rjPIQ4eae09uOnVI0vcQIT4eCUuH90I0IQaNii9lCdaGwi4BsPH2scBNHtAoIJ4mUx1uwhUaOLInp2B0KmthjIgERdEogpUeMekIbEW0dLlF4LYhUh5TglUR6jz6pyJcRLBXbQqFih5CBwrS4XvPtZzfcyYhnsy0vryKckkzHB958scDGxzgjiKOO+ErSfyTRWUv1kHG5PqIfA8uANJ4rTpidbemXCcE58ril8YooGTaCduGJ3NCyJnf4NjA9X7POUoQMhA9bIuWobzLUXmL2nuYU0k2gtynTomGXJsx8h1CCRHRsq4z9OCCtwgiH2Cv6YDBeYB4N4ruks9zsp6i0Z99I7CHmlJLtzRh3bhlnDbGr0UnAeUG9ztm1EcI60lVPOY4pL0eEzFEeItKdw2aG0XlJ2vSsHwpGRzXprGO5Lfh2vuQdBdUZRPMS+xARLxrWh5zTdIs77jmEAhF72CnQglha4lFPmxp85/jZYY4tPOP34GWEnQRqGzj7ZMNItHz55oz4paZ+6qmCRnhHpxQqhviiYiQduyJl18Q0C4HcSORKU3cKPEQbgUWQVg7nDFyPqB85eqmxSlFHAx5XHjz1kUNZz2Y5dC9ZwKTvse8j2pkaivA9dDNJ9miP8DnJvKZ6NyV/VjKxPe/vZpD3KNnz/osTNkcj+gzO8y33OqcWhoOKEFpyFkom8wM/uzpBxh4TKQ6uwCwDThhuRyMepRters4QXqBEoHuXkJoDuhIsX07xVlFFKWJh6asxU1lTf5mxmxnGJwd8LRl/c8X2bkxpI8JWEFqBOu7px/BBuqE5RBx0xJ3WSOvRpeTd/ojog5amjInzFhc7qpHCS0V83BLJQPdpR3gdoyoFVhGEIJp0hHcRNtf4JCBGDt+CRwzW4KVG1y1J4Xhl5sS7lihqsXnATHp8o3Dt/58H4fz7f//v+fN//s///Ou/83f+DgB/42/8DX77t3+bv//3/z51XfO3//bfZr1e8+u//uv8y3/5LxmNRj//nn/6T/8pWmv+6l/9q9R1zV/4C3+B3/7t30Yp9Yd6Lad+g/1WgrYt8jNLt4s5PBQchCHrHaXKsOsImTbDzvb7OVoE0qzFa0ETDK2Q+IPAjQMyhV0SISYOH8PkbEf9OudgIpwSGOcp4hbxUcBdR4iTjsR5ukbz7fE1P9bneKuZqYq7H064O084zXb0C8Uk3bG7LRhNetJbgTrvcS6wNgn8csfIBbra8KxYstERu8OMbZeibAAbMXp24FnYc0/BcjthXtU0j+HcdhzaGJ8G/NuMpoooNlueyB3kim0TIS8FVRZIJw3OC+QyxnWavteUKhlEWcrT3aZQOHLRsz2C4CT5S4eVCjcNeBOon3qmdUtZxLg3EfEdeB0ggl2Z4e8idO7oM4HaDCAK1UKwamhVhwCZx1mFEB4OIAoQYWDfR/uAmwqidSAgcPHQ6ndmAPAEMQTh2HEgwbEkxzgIXqCboZVvUzD3GpVBNxIoA0KC9sMIRmaWVAX6jcZHEgz4GHY/nkLuoVMkX0keZqOh0+CGMYSqDUE7rBHIW404sujXivaZ49ufXfL9txfMNpaTpORtnBBUoAgVybinvDOoucIfJHLRI6OOIAaISKwtfakJERSvNeEEWjUE/Zh9oLcaUXRYFTiLdlwdcro+pvAaGfW8mN9zJUcDM/heUveS6/sFD9sJp/M9KnJchjGRCtjEIySMDj17Idh8OcFFAXmA9AZUHxBBEEaAd7TbgkZq9AMQIKtb6g8C+rXEW4Fc9Cziip0yxJPAtz+75//28iNOj9eEleahKbC9RuaONhHI4LFeDjbMDyMSOnZVSgiS1gryvaWTEnniaJRBlQLVC+qzQHQjef7ZPYcu5VlfcdVEHA4D3e5NNSXtLL4DFbf0QrEOGhsEi9ByFWviJtDeJcyKGn8Z037imFaBapWwiJa0uWT/5YhQexppSMYNtjZ8f3nBkdry+HzNzXJK6i3lyxF9AQ+vF/C8YzTa0+xiLrIH3mcLXPga5rRWTPsdq4Xi6WjD4Sbj4+9s+OJnJ8TGsdEJc2X5337jR/zr5lMmm45KGrJnJdLDtKiQLvCwGZGJkuoqZ3S+pyRH47AIAlDNIdoG1FbjUuhiyeihw8vAT/71R4yyhptTiVlr9NbyhThFLHqKhx4dwD1y8KxGryLStqf/Btgu4ssfP0bcCw6XMc5IWpvgasXpeIOeeVyjWIiS68sps8dbHvSEs/kDV1ZympSYM8fh3Yjvvn7+dYiMZ79OGR1VZIcO92bEYSK5Wh59HQ9riLeWutDUVzli7LC1QXcen3tE5ujXEaOjim4UI2cd+01KGSLS2YE47+l3mr5XZMclK5cTNpL7MqdbRoQTQTZumKqGfZODsvgHQ1oDi4B2nlANHcUq0fgHSVJ59pFAXRv8aUBIQdsZdBLwT3vknaJWmigJ2AjGznL2ZMvlviCNasIKfB7QU4vdRpSvC4QT7EftH7b8/ietP9Zs/L/4f/7f4bKcvYxwvUAmUF+nRFnPY7Xj8qsTfBwQjxqyWUPz4wIXCaLbYSZ6V49xRSBvOvx8OMkncY8XnumjHVPbsvzdY5oPHfG4xZea5D6gjgKR6VmJmE2VMe16EIHx+Z6buxmTpGEZxZj3EnsMwkI8a5E7STqr2O9yQub5oN1QrnPEacPD3Qw/c3S7CHPUMpM1u31Gv5eoHOoyIhs3TMqe7txjd4ZZXZE+r/ni6hzhARVIoh5ST/QTQ/7NPZc3c+JLSZkZkpMKEo+/jJmerilvRow+2LNfZ1S7DCE8LkhGtsM0gTIyjOqWZpfgYokdBezEk64gffC0UiGcpJkK4u1AsoOBZtfnw0zelMMcv1uALAeRkbSefjbM0RMcXRmBCAQZiNceFwuC9nitf27Rc5Eg2oRBU6EtvZeog6BdCIx3sFXD6Z0wsPL7wa3XFZJ4b+keAw00hRoUzm6Yq7tI0D4fOAWkli5TJGuH1YoQD+Me2YHcC5yRQ2taBIzwNC7Cjj1+PFh+Hp2sEXKwLUWzlrqKMUuBSRzyRU1eOg7XOaWIkQeBHDlwkJ7WeCvY9Okg9jOOBk2c9Oi4p68j/F7THzvGviM7PrBsR8yKA+urKWejLdlpyf6rMbd9gTdQFA3hJkKmFtcILkTJbV+QvSixlWE+LVmtCqq3OU4J9P0wF9aHYbQijUcUlixtKd+McAUEDUo4mFoS73FGUZyVbGxEs0yZ53vWXY5xnvhRRXOfEt7EfOvTa35o50RvBN3jgLw0eEDmHiaW0kYkwhFbj8m7obNVSZLe0U88wQ/+9/gQ+Oanl1S7dAAC+YFPsDIp7dsYYaDNBGfFnu59zDqLGduGJtF88PiBpGjpb1LeLkfIuUPew/HzPUsyGiUp7zL8XlKcV8zzkqiEZgq3Pz7iaFaROM/dsuDobEsjYzZ3GelRg0l6djriV5MHfvxwRGxadp9Pic4r3PHAOfi028HYcv27R2zniqL3HLU70qxHjhSvugmp7OhPPW6jmB+V3K8nJKtAOm9YlzF/5vlb/l8//QA780yTBixsM027jDm675k83nP/fkTVZcRpjzQO/yamXkiiokNsJO7cE95GeC2IFw19rbGjwMl4x14b6jZiXjdsVQrTnqiB+m1O0lg4SNzYcfLr99Q/HfH86ZKrZszGxUx1zeX9EWraETWBxdmWqonpVxFloaCSJPOGse+4K0cUeUP9Nic0Cjnr0cJRH2JUDzq2ODeM8ZK3gm4qWDxa48eeq80M1cLieIvtJf44cK633NRjmjKhFZLMdOiiJz0ECmO5vy3Y9QnHx3uavaHPYd71bENEsHI4JAQGLLax7FKN+UEEXhAmw8jFzh2ikkMSYScwvcfcK/oXFqfB1wrVC4zoGacN57LmZ3fH9GlAVtCegjnq0F2griI+e3zL243h3/8f/sl/ZuP/x6z17ZiQZTSxRBSOfOVQPUR14GqW03zUEBs3YDK3imJWcxUXnH54T3kVU7iG9iFm+nS4AYossOszwsgyv4+4EjmHSUrYBJ7ebrinoNQx+Xct4U+3hDIQ3TrKC4m+k4SZpD9E3K8SkkVNXFg6CaWP8WVE1MLkShMd9dAJQqJR457NbUF02jDyDfezAvEg2dUF0aShXxjqVQq1Ipu1LJsxpmyYJA37JqH/nZjkeT+ANR7v8F8lXJyVfPE4Yv92TtH0zD88cNlNEAcBrUI4cD6iHBtsSNG9QHQgg0BrT1gbyhMHazkECQnBRbbhupwSS0d5pLFeE1UBd2QRpSaMLE1skHaImU12jvYI5MTT7TXJvSeaNuwWBlsrRA9xa4kWHf1O4yOB7AXdWH+ttJf4RPzccidSMfDxLYQwzE77ScCLQekv0oGISB7wHqyWGOHotCTECqtBSohXQ6cg2g4iQpcMnYmoCfSJJFkGslFPtZT42KGDICRfq9816L0cyH6ZQijLSPY0JuBaQ7gzXLsR4iBxDxlCCVoj4c6ga8GDUOiVJOlBNxb/IGmOJP67Kc1EkZwO4xjZgNRDN+I8qnlVJohHLce01GXEqhwh30RsmCGCZC0yahSy1pzEJVUX4ZaGmarY3MeoDxxWQP1gEA8prdS4naQqE2Q1xHi6qSfeedqRwGzAJRKHQb9RpK6liwXqRUv1OkfcRaS/9sD+Z2MuH8ZEox6OO4R0mNuBhHj42Zjj4w3Lqeb75TG2NITg4UrQHjvSKnB8vmLjMupdCr3DpI7oXvLhdy558+YU/8YQR45iXnIT5VSZ5vPlCX7kCO8S+nPLJ09uUD/L+Paza2QreR8luKD44M9ccXczRirLqi8o24i3nx/RGEV+19Mcg1kEDmVEEXWIVUrbBFTb8VhU3JUZdw8TJpuKJ4/XfOQrfqc+4c9+5zXfa454li9phCDaCz6L1/zOwyPqLsHFmn43bPB4MHz46Jq3uyO+vJ4T24r50wOFdpQ25/Z+hjCWWVrSNpq603AN0sPtKqHJJE0WeLAxs2nJvczwhSdsFboW7CeS+hAjSkU/6VmphLM/dcebd2fM9J7yqmD5TDBSNXHk2RYJsgaMAyTtOiazPfXBsL8ak/zSnnXIWR4iTA3caWwPatZhjWLybI87KDak2AvBj7pjbKVJop5rO0YQiOrAmd/y+vXxQCDNLM4Jgh80KhPXI3Sgfpujpj3tRhAOGh45koMjSXo2ucEoi7SS9lSjJh0HH7NfJZjc4qzmkBlGsqHyhtebBdNZSdtGsDH0haZdGUbZhoevZnTPe5KHHnPjaD/zNG/HdBc187eBzbsM+WlHsJJw7pCvFAUdu+OY6f4AsaCd6YH+oy165NDCM5UVt8kRYq9x48B4VHOcHbCVoogtX9xNeHJxyzJL2F8XqIXFLg1MerLKc+8S7PtfrPXuj3Wx1zVw1tHUMVHU4V4niFNHGjWoNhAuI3yhaZqY/FnJ48WOfQq7JkYsPNGuYydjbu7miKml6Htmm5b5kw3RUnAyrnj5/YwQeZpf1XRXMX4L5rOW2/0EL8GdwfPDlnfbIyobo7uAzR2d11xM1zyEhGjeQisoJg2d9TwqK1b3Kbd5gcwcm6ogem+Zn1dE3tMcYjwCpwOR9zjtUWlHWBtM0nKht1z7MUXqOPlsg63HJMcd5U/GtGNoPp/TTwxSB3ZjQSXGA3VtH6AxxLOe6jKFWOI3Kb0VmBoK02Ie1xxmMaGM4GmHbTWp7XBRwAqIDxLtvmbSHzu4NbhpIPR6+HswnAC9EkQPAdNK+rFDGs9+ZBBLhTsOmC6gt47yKkNkHnUQ9IXCaxBqKKpBelQL9YnEHAb1vNOSNjWo3pHlLW0T0RaStPOERhF2ciDq1UPMaTweEqpEJfFWEkQgvfMDP18PymihFELD6LWn+I0N1w8zKARqYwjNMF6IW48dD12HfgHxGlwq6W4Teu1JpjXJUYn491NCEnA2MD8+8GALxAbc1kAyjCVMJ3CRAiGQrcAJSbwMhJ3GGw9BkyqBuIy4YUH2tKF+MMhgYW1gJhBJYDY6sLyZcKhSRlELY8vINPQy0P1kzG4RqIuI6M7y/kxjXKC/GfIj2kghxw5tPSEMp5K6UAgPvvAgAqoP2CCwQhM2ivB7BveBh2kgrRzzb9xwvxtxt5sQ1QGbRyxEw04ndFaz3Ix5cbTm8mpGJSAkjuyixS4jOiO5ulngvCRdgj7zqOCYnlfsriecTPdskpTuTUKzDsgjSMueSkWIjWf+6MA+Mny1XnA0rwiV4qUb0VUS2UhevT5F4Yg+CMxNyS4y9BeOaAXyE4usNdm45ObVEXHW8Tg/ICvFkmSwarYR8SHAWWDbxfzgqiB73vG9zTGTo4r7h5x5XnH7dsEP92fo1PNT5ngL+c5TJYJ81rF6fUT/RKBtwN3FhOOOSzFFR57qSSBda8ysQZqEaCvJfc/4oz1vl3NiAkd9w/XdjGij+dHyAn9ikSFwsxpjKkuERG6hvlC0VlP9Xk6ie+pTQ+UiIukYnx1IK8nSGIKUJEmP8IPItHsfkJVGHgRta0g2gemhHT5nnUFqzyhtcAuYy5qljrHvE+ouZrQ4IFaS5LOK7l5hso6sdEQvOvhSkD3bc1hnSO2QehjD3TYj5K1EddBGEqMt0Z0CB22ncJlF7ST1scDcK4gCwQgYBeS4ItoMqZJx3BEqGF8G/Kxn+dM5buaYzA4o6cEJ1vdjmqceE2D24Zb9VYbbCM7bim9EG6pHivsmJxiBTB3zB4s8szy5q/nJE494GxHNW3YY/F4QCpjJnv46Zj3LiXYWIT1dY6hjg+0MjDreyYw871mFnPtdwXhtib7T0KmBlvj4G3dcXs7oq19swvwf6zb+r/wf/xGTi6E1Kj9omC87lnFK20QApL1jLyLkHp6ePdB1esic7iW+D3A6WEKiN4p7N2IqazLRkZZwvRzhJiA6iJ6XpJOOvjVo7WhbMwjJrMBeRTz55i3SC97s5jzPVjghiFtBjWS5LcjjFvcq4fg7DzSriNYYTj5Y8vkXT7Be0gdFNO4GZn85zKTjrMeOA3nouN9OiB8aOpOSLx183HE+2lDvcrr3hmTcso9imi9y+qNAFPU0PiJMHMkGvvVrL3nz9oTMOrY6Yns1JvM9/enQ0nMnDmMD+kHihaDPIP3oQLeO4V4T1Q7zuGPjUuSdwtgAkUPWgtBJqrNA8iCHCFETEH7oFOgm4LKA1o649xxETBABOxEoGxCHr0NtzjvCO4PLhvmjrgOSQHvm0UsJfojDtQUDIGfPkHmtBH0OohdEO08/h/ge8ANDv5lJop0naTuahR5gJwAE4q2nOlWDnuDroAvdW2QQNAs1CAw7iXCQpy1dkISlIihFOwsQBCF2iF5i9uBOe7Ljmkh6ukbitwbnBEdZxf7NmC4SeKt+br2CgRTYziG9dtQnA+6zKyBZB+pjRVCB7NYTfVix2RVD238rEXbQmGR9R+UisuMGuzO4bCD3qYuWqAr4uedksse/SqjOBdxKDi6iPcTEs4Z+F5GOWppGE7021AtBsgz4kcMZQEmk8pijFrHSlFOJqodwkUOi8VLQbWNMG9CRQyaOqPHQKHbbFNUK5r92T3dZML7Y8+7tmHQl8TsD36qpW00Qiov7PXIeWL0foTNL/S1H1PsBXvK5wU4ErVTEeYe3EltqksZTZpo0bzk9X7He54xcSzuTpNcwvrR8ro7JP9nzAQd+7ObUQpFhmY4O3G/G8KBh4XmsNtyKghfTNV99eYK6ksy/tSZznmf2gR98/hS70OSqp5p76vc5n/zKG27NiPVtweJiw2qf841uz9sv56i55xBJjtKKZ6Hk9+Mpueyo15rq/YTJry7Jk4arn54wvdhTVgk9krxyRNpSjSRtExFagYg8IzpenNzz/Z+8wCwawkEjXEC8i+itRgqPOOppnaFPQe8UdupI3weOH214m0wwFczMYNNkr8g/2HH3bs5sssfXmt2rMXlaU52CKyOoJSQBOe8Qazh+sWVzPYZW0teaFs1o02EXjpaIounIxy3LkOF7gb7RNI9BnTU4BTPfsF4VmCagE0+zSrBFILOWWFmqRJLdDgeOzYkeKH+Rx9/FeD3cE6X3FPMKM+9oVyn91BNvA/VUYCpBdlxxeJmTbVs2FxnqRuGSwNGTLQ/VCFWCMINYeeY77n8J2Cm6dUx2WtHUw/856SC0DFvD49MHbusRTdC4AnRiUV/GhBBI7x3lM4GQAlkLpo+3bKoMXwTMViDPOkqheXpbcR/nUDjSvKF6mdMZzbG45t/8o//2P7fx/2OWF4L9TU49UrhVgawHq4OIPLKR7CXITmDPLK9uzjDC4rUgS1u8G0JKkqWhP3HMX9W0VlMGaIqASi1q4qiDQWtJVIPceZpc099EtDPJ+KjEjDvKVUq86LhQe+5uJph5T9Y6trHBG9iQ0pxGhJdzXBbYq4jNlylhYvFXyRDT2ki8FxSPK2ZVi20U2y9yHuIcmTuORE9rPEef7minkiMqvmcXyEeB08WeZq3guOHs9MDVFwv0kw4uOsbSsXo3RbVw6caEnSL2DpE4WA9qbzrJyDUcdDTMa48s7X1MozSZ9OyeSmKt0VcCXXtcKhidViw3I1QHqlbog2P8tOTe54RKQjoIvXQdYDTgT0Vu6XtFfDso76VyhDNHExRyMuzy/U5jDgF3FNBLRQiDPS7beLosoEuJOXjELOBGUJiO/SGjnQXsFEQtkd1Q7KV2uFmg9Jqo8YRHbrAJVorqqUB0DtlLnBZ004C60lQLQfAC3MAIEApKbyAKpMbTxgPsRlhBdA/VuSA+LnGvYrZuTBQset7TjhWyD/Co4yS/49JN8W9S9EHikoBwAyVPdSCjQHQYCr2uAmbX0+WAEjTnge6mILKDQJKso88U+l7R2wg/ETRXKTYTyK0nWXrqOCYuO/Yupb7OmT5eM9323CY5zSoiTixuFwEC93mGHDtcDKqBKOqGZD4c4AmVon8u8CKQWsuj4x3NtGf5/gwfQdo5zrI9D4eCZpMSipZgJIEBDHT9ekHmOsq3U87tnjItMC/2LLc5wgRU76iiiEMTkX+7gtTjugStHJtDAl+H5uhS0IkIfdRhW8XF4oGraoZ1ktUXM+w0MMr37Pcj1FHP+ywjV3vsOub6zDIRFdEuRrSwvhoxftRwXDRc9WPOxluurqb8xJ0gIkn8tKNXkvuHjC/bI2Qk6IWmvuk4/fCelUh4r8c0lxnSwWZT8EFX8lU5pX0k0JcaZo7NZU59HLNVGdrXHJ1tSGc9y9sRfhuTPG4YjUr29xnWa6bPVvhWoaWkii3lZY7oFaNZw92XU2qt6LQhtpJQCvzjgFx5VNaRHVf4783Qc4e3ArEV1E9gkyaMaOkKRX+vEQ1kv7zhUdcwye6pqoh7Z7BzR1VI5HKIb5ZVT7/KyTYeN/eU1xnxlSA9q3i4maJyTxcrPp0t+aKZUWeSZOrR0TBqMqYjFIrPpg988fqYYA25tHSNoYwU+ajFlhrbGPqZRChLcVRz2CX019Fg88w9Rg9Z8B+rLe9WBZOPdmht2SwN7VcJ7rwiygN/Sl/zvd0F5xc7tklO3AaaCzfYY98nmARoBboStEKzMmBsw2Gfkn0p6EcauZNoH3jxwRWfr54Sn1Rcr2fIo470YBG3kmjk2KcBUcHhlwNiL3EwxDpPBNG0oX+bEeYdYWkQY8F2EpHUFrsJbJIEk0Kathxc8gutl3+si31eNPhOsbExopbsK43Lh+jTeA/hkcUhwAo8IM56uibi6emaap3SFfBC7VmtY+4f5TybrPjZ9QnTUYmbCkTm4EFTvBSoGcz0gXqV0D5vWNc53Q8KLj5aclNoHt5MOd606ImnaweuM6uY2ZMDK5lwrEt20xjfKiLZk6uGZmuQzjGzDb8sHrjqC66+GLNfGFTukF6SNJbQKeq5ZpcalMuZyS0/+OlTnANtYJ3mmOA4+41r7h7G5L+yYdx3VLcJvhpmbIdljk8DsbGMTc9axXTxYOUSwnPYJEOs7Fmg2AuSo5rsYGjnCl0ZglOEflDFBxMo24R4+7U/tZMEORAGRRTwUSC9Cugm0JxBvBH4ucC8U8MbLgS8EciphUuNUQqbgL6UuMRjM6BSSAZ7nCsCrnCIRuOtZPMtweJ9RzOGelkQSY+bgDxI0APkxqMGMWAvERL8ox4pBawUQQGtQlUBO/MDcKeGbiIRwg8kx3Y4QUPAqgGIE7YBY0AagUsD9blAuYC/SRg8hQF9r2BkAYHqYXdf0FcOlQWqTGBkj+0UXkKQkvzacvgIoreBeDd0I4I2CAQhCMxKQPCIqUcnltYq0kuo5kOssJeACcSlAxfwv9wS15L2kUP3Laf5ntBLaiNR7xXKeNLOsnEZZucHtkAk8Kkgbj0i9XTHARl5VClwFnqniHuIpi1fuinqtUAbj1tputpwnwna48A4PeAOEfXYE80a5AmMwsAO0MFzNR1RVJa2TKCEeN6Tn9Ws78Z0nUa9LgiJQ5ea7VGE3EuCl6TO0saScd9R3ht0G3jNEfJRi3uIKauIVDR8IY9ZhIr7aswsrnArxafFPftdSj31dMHz2fE9371/RnkTIUeSzx5d8XvLx3RKYVtJNmlQnaV1BnfqcDuBjRUyeKrngtsfLEg6wVV/RF5aJmcl0ZWhPDKs9wWLox3VOIFiYCNUfUJyUrF8n7H7MiUZtUSppT8NPJts6FeaHkGadLx/OGI8Ldk/ZOjSMp8fCI97Ln9yROE7UAHbKnTmybylvU4QY0e4NCy7KXIcoNLUZ4HkVqA3UDYZbtIT28BWxuR9i/WSA/BlPCUzFrEVRLMOM+4RjSZ5Kegzg/+s4l5HqPcRVJA2gf6NHqBQEmQvuHUp3gSezpe8+/yMySdr7H1K5ySR9Vz5KepGUz4DuR9Ed6KTuEhw/u073i/nLNqavYu5qcY8fvZA9dUxYWrRs55zWxJyuAoG09shWTGO0a0l/aT82tkB/+b2Y04mO17vT1BPWtz7COE8UeXoncFNPdFO00YCcWbpUo9tY1waKB+D38ZICd3I8dW7c7qRIC2huNhSL6D5qkBUhkYLIixtrLEZJNfgzgOx66k2CafpluWuwLYJ4WlDsIZGKSSCaVbTfz4CJTDjDrMof6H18o91sd8eUo7mPfGVgMIiTyxeKOgGlvl4VLE7ZLx4ekvjNXYM0/XQMl/vC2a24ZAr+qkgvDE0u4K09Wz7gnja0m0i7Agend/x+qsF7VVMNxa03zVEHzYcVMrbXU5xa6mEZLMukFGNGXUwkVgBKvEkzrK/GSGnjjhvSVWH3iuaoOmchqD42XzM/nWGMxJxJ9B/sifuPWVfEG1BBEmaSPLEcwg5ceEpNxlP9RrXKN4up4wuPU2RMckbVuWYk8+WXL2ZMrr3iKxHBkWjJWImYCfAKrJJTbOO6RcBUQ5e785L4jSw6SNCr1hEFa0Q2KVCtJIuEsjLGJuCNj3RWtDNvlbf7wVu4mhOJUGLIQvcS+yxxSeCaANu6tGloN8Zoj4gY4cp1cCVnwoy26GXUOfRgJ5tQW4N01/Zsvt8iiqBj1qaw4jIe2gFZgm6GtKnQi8IaSC+EwQhCGOHf50MCXtdwB1BvHQEJclDi3WaJhJklaM9aLwJqMbjoqGtLWv5tVtAIJzHp4LkISAbgYr9IKqzYA4gUof3gkRa/GVC1AYOUYaPQF70nLHibjGmu0rJxweENPTBkLYBm0qS5cD416Wln2i66ZACmH9t0+niCL7RIvG4XiI0yKJFpT3luxHn8wMTVXPZT2lvMu5XmnCQCBWgFqg+sD2L8MHTCYkMHnnUE/thzvnJ5I73KuNqN2P6Yk1Vx7hlRtZ2OCeI9p4+SKyRjHVD5TL62KOkw1jPaFxhmoh9k9AEwZGosCqwv89YZFsO6xwxdaidYvJhye6Qo94bcuWInEMtA6WRqNyhm0Cc14gkICOoX2fkqqU8EsPfY6f54MU1N+qIXZcwLltuqxnheBCBLU3O9HhH9/0EJjX+VvO99WOm84r4ecXmPqf94RgfRUS9QGQB6aCrDE8uHrhbjZmMSg5FzCKuuX+T0swV0dOK8XtNPdaUVUL8bMf6qkCNe7KNZRY/sHQFfqX4ZLric58NzPwtVCLmpC55EBnvlxqnJEdlh5gLNrHk4ScLirwmdIZFvuSL3RHhyLJcpehSkBw8KvL0lxHeCEQrsLnAxxCSwHjX4LYGeeSJbgR+J6hnAw75+LIlnAdW3z2iLTKi0tBMNe06wc8c9TIlsp74l3aYXlK/GpGNLVFRsYljul7geo24aIirQHrcoect3U+nrMspvtHs3kyG9/9xTXjQJEVNdu7RXcvkuOLVfkEROrZ9yupyAkFRbhMYw/jkwENXEJ3X6DLQt5K9TVjZhBfnt9wfTSjfTcielow/K/F1zObdCCFazHHH3VdHUAxAI6sl1JLQK/q9wC1api82jNrA/bpg02uir1X/5mnFfFWzHGf0lUEUjtAEfCvY/WxE3wSoPG4aKExPumjov5qRRx3tJIVO0CWKvlJcdzMK2ZFMLJtlQiY9fRVzVmzwTpLJDnFieZKsqIv/XOz/o1c46ekKwcgdSJ8c6HYxvBvhCodCQhSY5BVXbsyxPlAuM7pO89zd0Y8O1F2EsQ5lNQUN65UmqzzRuWVEyW0y4oWq2dxmZKanfqrJm57584puY1iNwDvDNO7Qe48569AvGs7Njld+zmi84/pmwrjv6MeBonHEWc/2fcG3v/mGxh7has8sLXlbTzC5JMExbXa8f70gLgP5tKEpIlLnsDFsm4S4rSkzweOTW+pI8KQ6kNmW86Tk5cOUzPUY5aguM9rrFLFosUuDzALxQ8BODW7miPaBcpWhncWbr290WmJVQL0pSFUgX7YcooxeS8TUY5aBMPHIFw3uJsN1hqL0qEYOpDkT0FsFySCoi1zAHL620QmwuUA1EiYOUSmakcKUnn4E0dYRdwHfquH3hUHoJ8Lws6ofjBFaEK0F9aEgEgN1rs8H5bpPHXLuke8NXfp1Wh4B1YPwAbUGREA2CjfyQwRvpakTjXUBtRNIDbIdbp4ifO3ZT7vhROIFVitM6PGZQgZBjyTMHWo3gHbETiAaiTpI6KGrIkzlB5hPLbn8YMooammmEWTQ/wmHrDTlZ4FoXlK9TSHxuI1ATFoaFVPELdvYEKUdyXzHyLbs7sYU84rtJmeiG7q7CFlJVjcTVm8WdCeBaCkIWpFUlnaiqE8lyX6gNmIDLveoacskK6nuUoppyatJxs1XCzjvsV5RLzOSKrCaRuhOcz7ect+NoRdUbTzQ3PaCOlLUJqJSgSztCUtF9OmezZt0iPK9aNnZBI/C3xr6RWD9Zjrww4uaShoaNQCG/N5juoCvNPsiGoKADpr5uGR0scf+4Jg+0rTHglf+hMcfPOBeHXGsSrYhJt4KVnWBLBzrmzFHv7bm+idzgjM8utgSMsshklQu5pU2pKKnsRFRsOSiAw2zy0C5FdTGk0865M4x/aLj/L9ecisL9jpiEjeYDUQbQ5Lt+dQ7amV4OMR89OkD+2nG+zYh23lG2Z7L5xPSe1AvOpI3huf/9Tuu3ywQG4GSFrXLST/ak9AhHgyvuyOMh/I2RR0kQQVsaxDvxZDs2IKLJP3JMIJR7YBq9jHYVmGnEKyEFvxTx+Gbiq4ztDuNWWmyWcs20SgbSFxP3xnSrONwNcLOPf7YoxNP9xCRBEce98wf7Zl+VPHdHz1hLGrCxhBV0McKed7iFHzy6C2r5YTNRHLXjTgWHeNRy50bIXvJ5OjA8v2Ibh4gBE5ExUEo/EGgzztS0dNGMXUZM/EtSnje3R+TPzhORxtuVwVrJaiWKeZB0NsYsXBUTzzRHroM9H3gaHwY3geTFlODkoEy0ZjYMlt6lucDU2WytBTBkjy+52dfPSYfd6gqJhQCe+QJuYdcEHrLfhtzYkuWUQuXBtlD6BQ+DpBbrIGAQ097TnXNqA28z0bc24zaGrLEQaO4TyYcfpL/QuvlH+tif7HbctM/opg2VF+OcQiiswYpPV4pGqEZNY542mNzMN6ye8j5kgsOGFQlmcd7DkgOMkE/6ahLiT7pUQfolwnQUe1ifCF4IvYspcS1cBgrzI3He8VlmPKpXPKlmyMbwbEuuTAHvrxfwNRxuI4o4oZ03rLrU+RJzxefP+YwUkTXkt1/1XHeH5g9PfD6Rxc0nySkbU/+rCQqAya0jB7v2b86ZlemzPcWUwrGUc9GR3zfnvGoKPl+Mie66emsQAXH8m48zPAqQ3FSUZYJyniS2uN6gdcOM63ZhwhlBSp2jEJPPwWkQKqA/WmM+zhgtgFfSfoUsnFDvR1uAEpZXBRhdh67cMhbjT12+HoAqUjrsL1AV57uuUffKWLnQFi6TmHar33xfgiHCZ0ipA6bgotgEte4ryJsIvAp2NxD6tBrOSSdZQLZfd3O7iViJwbxYCMJwmMnEi3DcGNMwBUDg17WAnUINGhkJVE+gHCoVuJNwBcCUYJLBb4z6CrQ5gKvBd5q1GmPulJ0mRpuxF4Q7iROCMLaEUpQNag2wDd6wo1C1eAuY/YmIp83JOc1I+tpxweWu4K48/QE9KOW5MLydPTAq3enPP/WAy8f5lTvCrKqYbvM6UcCPWuYjw88XE4RiSejI3yucUYQ3Q352vGnew6rDIdgElfEvaPBEJ55zqqK7dQgE8umMxy6MeG1gSRgjOX+dspn2RJjHLfjiH0VD1kJOLrGILSHpy2hheOTHbOoopWKh2bEt371Le/fzxidLVmpgkVZMhv13D5KeNNPmR/v6d4ntHNBcxczme+pfURyKTkUAZs40rxnJht6qdifRGxLxagF86sbqs0Y3xpsKlj/eApbze14RFJ05EkLvSAOHaHU3P3kmKY3xM9r1r1Bl4rlKkeuJa2XxM8PhHuJfgDba47+iyVVLZj0a9ofzsmKjuV2hP+zjm1/zqSp0Ecd8TX4rwzXT2OO0x1vF5pWGrptQvvekB41xLXj+HRwTYxedpinPWpncXPPu8tjrFVET1qqu4Q6UTyePrC9H7HrcqbrCvdOI4uvtS9xIHhwMw9jR7+PkF6QhI79VFOIlr5UmIsa+2UGRw6xGZgT/SbiOC0hbbnqItr7iKfzLeZIIN2BWz+G8454abGxJGodeAaXjPZku+G+UM4EHyc71MRSHSI2DwXps5o4a4ivDXYe+OLlI7Tz0AfCwtOYwDJk9MFgnWTzfoxf9MRRoN9GLKOEJGtYradEy5ZsNATZ5OOK5f2EyeJAuUuxZ54jUdG91hymg3h2clKxI8IpyGSHLRRxG7gYbUm/uSb8AF5wjz9S7ArN9qCIspa9yplND2y7lD0xXR0zut/wp8/e8NPVKUnW0d6miMKS7AfIjlcCfdRSpaCuJFZKxEnPottz5wuSNiB3gUYb/FLwzPT8qF1QLEqC1QMN9HFFfUjY3mRU5hcL1fljXezLJmV2cmDnEyZYbA47bTiNa3ZlhvYeNekpv3tE80lFKi3HFxtMG1jLiKo1XG5mpMctsxcb9rcJTSzQ9xlvdYY6brH3UGYRtTN8fH5PqwcAjGuhOTLojUTqlgefkt12FHnDOzejqB1j3dJKwe5UoWPHYR1TpA3KQTmNmZaW7mTA0871AWf1EKIzkWjR8bCZcHTTkX7rwN1mwki17PKU52bLMk3Z9Slx1HCWbYfQmktN38TEseXOjxATz9PRhrKOqbXGlIFvfnzDq9WU2iVDcdkbMuNAQW8N1njSa0+0d+wWMfYMegTFrKLcZuhSoCqB7kHUDAp6O4TRqB10mSAKgU4EKCWiHbK5pQd9J9AbQR9r1GuJmHhUJ+jGgsV4x0GkhEqiYtBHDVUbIZdiiKuMByAPfaAdaezcEylBmDrEe41uBcLYYd7cC8Q20BeaIMOAbg0CXQ0BOkENgSPRA9hcIutANxGoVmIa6E8DSeuwncGlYVAQ70F3nmYkh+S6uxhCwLSO8kQRrUD1g0tANcMowRZiOJW+ipHG4yOJH3toJOUq46AN7bSkfxcTP2/IVg5x6tGzlv1NwRevH/HiW3d8/rDAXCmUdsQHz/JIMJM1j+53XPsR55MVdw9TbKvwqaQ6lWTXge7bLWwjfDWgj/tc0WlBetxiR7C8LrBbTRnlyCBhLweIyNhi+sC3Hl3x5Q8XuKDxvScIz+5DgX87cBRGv7Ym0z23X825WU9xuaLapLSx5GcPp2SmZ7sqiDLLOzVhEt+y7DK+5TZM1I4vuiOMAJX2TJ5tWX5+hNWSs+mG2hh2TUy2suiFY3zwbLsCt1C0XYq+VkgVBv92HtBTh/I9E9uwdDlt0KiHhOyjiubtwFzQu8C+zDl6tiG9DjQ7zdHFnuLQ8FU/Ihpb5GvDy/CY6FHDN08uWYkcvTfQChSKo+keL0BkDpfEJJ/2LKuC2/sj3NYTEsh3cHiQiF6ybWJuqxFx7TmND3z7/B0/3Zxjxi0Pdzl5ZDm8S0lVRzTpePf6mMiDSS35omQnc/wBzNqSffvA/cMEogC9Qp61COUonUY0gcdP72mCYb/OOeQep8BUEtEJ7A9HLKcxfuoo4gaXaV43Y87XO5Y2paCnDYIw9czP1myux7iNQUlH/GFF8xAjpGf5MOHf7VI6r5GVR6se5ySbt2OKxyX1FyP8Ry19JyhGLbnzjLqOXZcymnW8nccsJhXxXYQeN4iVREaechujso64DdyEGRNT43eagIYQsCtNsuz4XvaYpGgpHyLGWcM4q7jHkL8TRF5yQGK85e4Q86SKcE8sN/sJO2tQt7CvUvCBJO7ofjhi/rgkeMHW5TTvF2yUx+cBfzQAjk5UxXZV0BwMYeSIVpLDfkKWOhoV6GuJ/6BH7zrCraJvNSNZkXz7wJffXZBYz2GTc/7ogWqZk1wpdNxTnilm9eEXWi//WBf7JWPipSE9rujGAirFOOowIdAaSWod9S7DzTy1jWm6mGztyE9KFlXD/S5mu8iI9o5Sa5w1uD7CNJZZUbMJKV0i8G81SWf5bv2IZqaYTioapxGd4JMn9yyriMZryvOYBo14l+CinmYZkf3JLSw1qhB0RhAaxcRWVCJi8uEW/5Wmepey+dgQx4E6UoyuBeakwU9gNCnphKK+TzFHJXnfcpUnBCW4CxmP6o73VwtOxnvKI0HYBbpTQfQSfBJY346JrUNUMXLu+P0359Ao8llF10dERzX9TUp2XvJwHJPcgys14cjx4skNXx0u0LVATQJh5kiylo1NkF4g95I2VSTKU59K0tqTbAQu0sRyEHq5vcRFkrbwmP2gPMcLXO7xVoMeBHLLWYrwGm3BbANlFBFvJV0TgwRpga9dorIb8qxl3pO+g91EYGqPrBVhLQjWE+Tgze9GApaD5S6IgK6GoivqIZhGtR7pArJTQ/fABNRSYaXGNA5pBm66Vw4RwYnYcesnIEHbITwjaz02E7iJR+0FhGEEoauAwNNMJboO9ONBdU/vGH+ygyct96+OiE565I2i6zSh1Pg0EKaOrU/4/PJkSKE708Q/1uz7AtUHojgwfREIO4tvBWErUTuwH/U8Ot3jn0BvJQ86I6kc7lmHvzf4WHLYa8JSknhLtHQUk4bwgeV2PCZWlm8dX/L28pjf/+KCcGJRkSMZNZgS+F6BrzSh02y+mFHW4PIh+OhOj8jqgB73xNKzvB9B5NFJi2ot358dkxUdb1cp7u0YGzSjh5pqIbDvxwhaPvtgxc1YY28S1K3i/njC8WJD9VXMbLGnjAzNXYwZe5xXg0g0VnigP/IUG0W/TdAO/MJCL7C9JpKeXkX4yjCmpVrEPJFbDIpNm5O7ftCW/MmWwneMHxwkMf25ZUKNjAPz8z3bz2esxxFHZsezb73h93/4IWrW0TtDGlv6oOjPOso84dDmJDieTDasyhH7NOd3bj5kv00JrwVZ3jF/cWBZjhACTA+z2Ya1yhS/uAcAAQAASURBVPBCYFxP0nWcJlvqpzHrH8+YPKrQ2nK4LahGimgtmM5abGPoXuWU64ztKMLsA6FS6CagqmGM1YgIHbUc6vxrFbnjspaEWtEbiRh1iOuU9XJEsAKhApNJyeomY1TU1HWE6BTrUUz6pSRMAywcqvd0iebwaoRWDnOlsBmUDznZo4ptHEPieXm9IGo9t18uoNGE2KPino1OiHtPlncUz0vquqD/SQxl4DxpuHw/Qq00XTHAspI9uA8brB/Eo98x97QfSHipEc8i4s5zktSUP03oH2visy3qfcSmGaBEyTrgI8FCr7l7N2FcdhgT+KVP3/FejrnbjOCrlKiEh/OMaAsqCbAUWGEQU0uIAn3iMdeK/fdnXJxsyR533Lwe424My5MJ83FJfZ9zPt7ge8lhKjkTG27zBF0otpe/2Db+L9bV/0e8+lYT7gz9Q8yLNy1dpWjWMQ/3Oaa1lFcp2ajhv/joDSfLhllbIeWAIN2GmKKoyVX3/2bvP35k27I8Tezb4mjTZi6vfipURgpWJaurB0SD4N9MAkSDSaK7WcWqzqqozFBP3XeFazdtR2/FwXmc94AIIIC2mY/cYW521t5r/db3sbw64gpPrBzziyO91oQLh2oDJ5ngf9lRfF3jLzyv9A531RNVDj9y/HO/4qGe0NQJ9iEhcQa9aHEV+JHDbBOMU4RThC/BPSjQjkT1PPcj5GuPvHBM7y3l3QjfSrbdiHTSM4pavnVnPG5n2GNEWad0uWB9N6NWmii1rB9nRBPDfTuhaRKiL1qKYNCRw58k3S7GI+jRqDuNjB1WSMoyw545TBmT/sOR492EqBGYKqU/C0SFob4rhrkvgm2SEi4Mp3ho84a5pZ+COgnE2w4tPGY+UOmik8MUArlV6DIg+0CyGXj3CEDAeNIRFAQlCUjiTwp9HJC7rdSo7c/IW+vpZoJ+6YmcQwCj0qKbQPK9wtYK1QVMITDFsPIlOkX2MGQFlPOECOKD/5mbr34u+BBVHjz0y6HFn278MK+PhwBYNx3Y9aILZI8BlTuakBDU8Lc5OfCBk51ANYL4QZEeAjq3+DgghMesGBC4NcT7gI0EJlWcjjnbxwlip+CHlPangmqssL/uONqESWM4i0tU6mi/HZPdSuwxAjcY+iyB//Lpij9tlpB7zFhhZgFtA/ubMe6o0L1DrTWq8SR7GBcNugHTakIj6cYCmyu2emjzT0LL+Gj54emclSrJRY/yAlkKwrcJfTsEpuxUYq8srpR0vYb7eGgzNxJziNDKE6U9VgkKeq7SA7Nxy+TBw0Zi7lKyccvsyz2HLCa1luo549BM+a/3V9z9eEazSegzgasV289jksJSlQmnP44II0ssLFEJbuIJcUAgsHXEHRMoHOfTA/Imxn5MkAr8MSIYSTGr+fDpktBKbuMZJD3/plyz8DVJ0tHVMbITtLWCbcTVoSWftnx9sUH/S8Ik71DOssgrns3gkI+cZ/luz+jNCV8LYu2JHx0qcRSThsOnQTizDRnLuGPy9sjLtzvGyqK3Me8WO4qo5aXcc3d/hv3jiORWsf24gEgNUq5Eoy87+ruUU5kzOz8x3rpB0d0pXr174vRU0MwF4/dD+DRogY+gvob2QuB1oOh6qMEWAS09JpJ4KUixfL3ZkWTdENJrA6NZw2kyPGcApBs+y/I2Zjqu6IPC3yaYQ0QUwhAC1ZAXLXN5gkuDSD1uH1M/ZcStR151LK4PJF9VKBPobQR7jT3ERDvPom4o/kkgTsDCUZ8FmlQRfdnw9//wgSzvef3rZxySroxxJ3gg57ab8ViMCUaixpab84LT/9HRoLn5aUVzMyDKnQJvAvYpoSlizAwe3ub0v7Z8KxZsqxz5rAm1wipFsAL/2hCmDrGw6Fct4hMciZF/TmnUYLgLDn58WtBKhf/7njQxOCvQk5aHmzH1n3K+fPGEmUAWDOUpx/1lpXd/3Tf7RFryyFO1MT+lE8TYc3655dRnHJuUfFnhq8D3hyWvdUn8oubhwxL75xGUMaE1qAvJsR8RFwF51jEpLRs9IXOOnVVENUTWDHvbheP5YYJet5hjQqMkSePpRgHzlCIvO7Z+hEsgjT1+JoibQBR1/Luvf+I/PL9mGnV82q0QQtDqgB81NLuEbFRR3MGx8ORnDdvHGXLRE9UwiRrMSlOHCHaa+KymehwxX524p+Bs3VJclHSFoGoTXKuYnpeUzQQvJKKLCanDjhx+Hw9FLxKEfYKaWsw+Y/T2xOF+PJjvhKa3Ma2QhG96qDQuDUzMIHCpnnJGaTfMcGOLe4gHXv0uGoq7Z0jMh0BQckiph0A/lkOtLxx1k0IY+PgugX4kkAFUM7TMfSwGlG08FOFuLpE1yGmgED1VnWFTgZkHQmKJD4pwbpEnhVaDCtbFEmEDLpb4hSPfO3wk8eFnx3wMAonoodg6fDykzZuVIq7BjwKqHpL4ZiQxlaDXEar3BIY1Il0F6gtBUOCWdpiZ7gR2GhBGDg8tGegXg3VPVWAT8JEnMYFGezhzjK5adp+npMeAWwQaG2M2CfJJYbWgDArVe+xY4qcOKT3uXpKNLeLgByeAhJfpjoezjM1mROgEsvCDC6ARHEOCzBxFYlHPinxR83yeI571EMY7pjQTi2ki7mKBTSG/rOg+5cQLQ9mnyKueXkrUTlKsOrq7lOR1jV71hHVGJAKqhI0YkZ9XFKOadTvivDhxOI4ZFRXqohpENLkgwdL4mCR3hA+SdqVI5g3x2FE95dAqfB5hD5LORUMRqwXdXuDjQDfSREdPOumo+gh6QZobto9jsqhDGIGcdthY4SM4dinFtCHSjn6d8tnPeHaBSVoi0fzi+p4/PF/y1bs1nRBEy5by45h/DROSEFi7hDQxtOuCm8OUSV7jnmKUkGzdCHFhETeC+LpnrB1X247NY8RmGfNqsUdqT7PPefn6mU2Vc7yNoMlxXrLuF6xGNdUrhekFVaZ5nR55/N0KtYB2qnj3yyf2ZOz2GU5JstAxT2qy0hG7jsrmuKmA3JNnPUUJp4WEICj7hLXKyGRP1A/5GHHQ6C7gLwN1JiHyjFxDHaf8m28+oWTgw+cFf7965P8u3lGffg5AFmNEN+Rs+hiWqmGvclQjaKaSZBbIfoTDYYw8SHQtUJnHbGPMmWRyeYC15iGeMJ6VLEzF0/cLPnRXkGlYeGymqLwgqQc65j/3rxm5jk9uzFJXrPdzZu8Mt2mCqSQi87THFNNqTlFM9h2EKkI9BOxlwFmJfpbIdz2NkaRVBDYQjuASSZMroh7c3BF9Bj329JsIk0oQnlHeMh43fHxzRkEHWeDN1Ya9z/i8n6OCw7cK/yEijXpOkSKbNfQBjifJ4XBO9KOmzwVmJon+wji7v+piL896xhcdkpiR7thvRjQfCvKrlrDw9EbTeQ0Cbo4jskjQrxXzq5pTn3LMcqJlS9tNCH/S2DM4xA6hYGNyLpd76jjjarbjh+YMuVEkqw7VB5YvNvQTwWY9JVWe8NIyz2ue9lO08+RvKoxKONWa7Oi4kTlRLYjedSwfDjzfL1Fb2NucKtEUhWJ1VWHLHPEkqCcRUSn4Jn+m1pr9qeBlsaOZRKwfpxTTmt0PM/LLDvNlj6gk9VOKjj3BwbEsGFtHPK2pbUzyJCl1hIwFcWTR4w6tPadjzuLJMn1ZUSU5WoNca6yMkcqz8i3qZc2himnqnPCqRxaOLOno31bY+xSrIO0FZA5jIlwGhIBLB7yslh79KIkP4NPBZW9bhUwsYRSQzcDGV5XH5ENArp8Nhrridlifs/nQkhfeczpl9CtBVELcO7AgpaM56GFN0QXctcMfNYRh3Uy5gH7RY1JJc8yRfUCGgdYnYonwnjjt6ZKYxFlcJLGRID5BOuvpRpo2HyiBSoDvIJ022M8p0kC0g3CUNAuBvYD4CLoF1XnyVUurFPvfQNZauEmJlgZaySRp6N7nlIuMs/mWYzvC3Wrs1MJGY+ee9LPA5pL2kkGvu5Yc3AzVB0wv2Rxzvn79wEFmfPi0Qo17QqkHSNEk0LwKmCZGFI7z0YkjCXau2fc5Ugv6OWyqgjQElA7MZzvaKsW+srQfJlzOd6y30+Fh6QQL0dH1MW2T4kaB2duafjuMW8zXHhBYASGBrcvoDiltHlFkhmOdYb1CvvA4p1FeEU1amqcc+7cef1TE+4iyzhCdQBSe1Do6G5E7S6s1ahcQQF704CRZcLQ2RmSOZB9Yvj6w9VO6WmKdJn7Q+DAQL1Uc6FINjWL88oR9r8nngc7lNAuYOsvf3m+IzgXv4xE4T9NHcBC4WBAdBS5XVDc5//D1B/7X8g0Khd0qVl/tuF0veXlVcZwMK2XhoqOfWnznWE80b+OGTRJz86dzDllE+MbzS7dm3+Zs0xHLWc3az4gTz1erB243c+q/87wOB9amoNqkXF3t+XMo0CGQB8PdxxV7PaV+AbIPUDhCHdHuc3rlCc+g5o5sA9HcYpSCF5Z+GyHSQNKBTSWbaU7yUbKROXJs+J9++IKZblBPnn+2L9Cpp4gaSBV9GRPFlm4JUS/oDzGRD9hM4MuEBk3qBUKCu3J8nW649SOciRAzx+F+QucVsXL4ItDVmnxlqE8Z0aqnUxoTQW8UeuLIz0tOtyM2RcbkLqK/lagp/JTn1F1Ciqe2CRGB6cHTrBy2UERpi7sMLFXFKUkGlbJuOe0z1mi+Fs98Pp6hThHj35S0fYbfK1QNzEAcYWIsfS25vKw5JRIVwkBTlYG7H84YTWu+md3z6fYCfdHQbTIUHruRbKMR2XmLWgT4MaV3imjtyRzYOvnL1su/6G/7//PrKjuxvZkxP/TYP+W8zPdcTk483c1ZPnZ0DzlFbpkmhuyVZffdnM1lxmM5wY09ctmT3krE1DK5rlF7iY4t07MTOMH6MKEvBPVIM74oWZ7v+cXymWzecrNd4XrJ8tWOYtRBHCi7hHFoSe4E9T6j3yW0dUxTpdx+f072ZPn4h2v265xeKbyRmEQRSU99l1JrELmntQmhkpB4fqyXrH+3IE4NT5spdp+Q/SQITzHLswNZZPAa9k2OqWLcLkY6weisxk88XYgwfYTxmmgrSRuLPOtIT4EmaC7iA1uTc/fdOdEPks5HeBUwWhOs4lkXlGi6MsYoSVdHOCl43k4oiQgtQ5t82WA6heoD/SQgI0dUiiFp78JwABDgFdBJZB/wQhJtB6FNuvWoFqLG4ZMhuCdcIEw8Yeoobj3LxYFEW2QvSLawWJywscSOBGYsSKzDZ9BNFcZppPg5rBegThT7uqDaZgg7PBR1F1CdH9zxEfRNjDoFOEmkHdS83RLSX55wX/bkFw2ZMGgTGBf1YCyzUNw5QuFRFrJHT3EXiCpBPxU050Pr+/yLDTPf8oXecbXY8HZyIJaWtk7oXoI9xdxeFISZIz1vkM8amwmiR4lqIWhPVHmizoMcZEDRzhE9C6rPY276EfttysX1jlcv1ywnJ9y5JcwsamoIWUDXjo/bJcYrmlQRMo+2w634ujyRa4NNAAmTtCZrPHln6LRCtJJU9VgCuyymWQj+/h8+Mn5zontMhwCglCSx5XKyY540XLQV8kmzKkqmJ0vXx/i9Juwi4sQMe8ePAfEhJsEiRSBzjrZMEAL0i474vCW0imYusEsYRy1q7OknkvYUE33UtE1EP/YEJ+lmip/WFxx0AijinmEerRxFVyFnBhck0oD/oHkRt9RVxHGf4DYRn8opn97m/L5f8oXbIGtJrDzjryvClWE53pGsBXWT8Idv35A1gbPzE6u0wT8kaAN1p5EHiVSBX13ecXF5ZCSHdPefH5ZMhEFqyyyv+KKtyHZwXrT0S8fHckxmHfJW0XUa1QoKYbg9zXiR17Q65vl5ztT0xKnhUeeEc0OFhvto2BPfa0wmCePBSeGR+IMiaizhNsI0CnMc1sYkAZ8F/u2rTyDg13/7hGgk/iTJN55NWbCXBWufcCwzxuct0krmoxqfAA8/dwovHO3bwc9+7ipe9iXZu5Kr1Y7EWu4+zrjK97wMBw4PY6oyZTRtIPN8nezYb8fstyN8EnBXlixuIPJksie2lqT0jLKaC11y8atn5L+v4MoSGQhHTcgd/+ZX3zHLSkweQEHIPeEEjBxWKGZJM+QrjCabdEgj+DydMn5XwcxjmoTuPied9fRnAms1unA054J81PP4uwWb/7giXXtmlwde/v0D0byjk4qHs4xsMSCogxGUXUR3pkjWQ6B5cllhhSA6BWwO+WdPfv2X3bP/qy72+IGYVvcpdRLx/vsLdlHGm+WROz0ldp4LWvhO8ewyFlVHeiOxXhGMBCc5XxzRN5r+dkDFmk2KeYqZh4bZqsT0mt2hoC0TDuWYP+kZ3S7BzDzWRqzLMZXQLKYnQu6pVER5JSkzTRCCSdPjE0GdRGzCiCZW1LYgX9bMshpTRrheUbUZT5/nNFWMzNxAS2sjXscbLq43mE8ppovockF3EXBB0PQJsQ9kTxLZDZ72aNGiK3i+mSFLgf2cEt1oyD3aePpEs9wYts2IOHI0ZTbIhGKFUoG4DMTXLRev13RXnm4k2NYFvZIkeY/YRZh1ihACVUuCEqRukD7YSIIciqhDYlOB1A59HFzzIoQhXNcOgTuvBM2rQEgG7r2LBUFKZCtwWoAQ2BzG1yX2KlCtcxoR014GgoTyKSd0ErFRsFW0maI984TEE9UBFTtUH0jEAPQRPzPzEYH45PGJx4wkXFiCBpd5EALVB1wU0KUgmvas05T5rGI1qagniky12MhiczBjSXcmCVZglcRMJdVLQT8fqH/9+SA8+fz5DGEFj2nB+mlO9RBTPhT4JDDKOhZvD4htBDrQPuREG0FxN5D4fBSwxc85AS2gHQJ/1bWGlUX8TYOrI/pJIDhI7jWtlKi9xP+U0fQRYxrGZw3f1Fvc+5SCnlApnJN8Odtg3gQOJMxtxzKpOZJyZhraiUTKMJAWFSQ7iWoEMvLctgX9DyOyzmLXCQUGf5fw46drDsec3cMMU0fsdMJJxqzOD5xdHlh8seN0X8DnBLm0BCXw55awjgkeIgYvffuU0pQprZKMPgr8p5i2SSD3xNLgogGEow2kzwKHQJwk8ZMkfpZEOKbLipHrkFeG7pXgHSXTWYW4bjgsEh7PY0ypGL0twQvKOqb8YUqiDd/+eM3i2DDLK7Y2w+4jtuWUVkH0m5Jf/d09v62fCannMU45mhQrJNUIRqplsev4p9u3VK1GniQuC+zLnJt6zGOWYzLFKWh2ecR/Myv0gyZ/XfM3V3dMi47P314iLDgNI2f4gTEhdSSLlirWSO3hJhn48QnwZYeKPPnbI3LWEz1LwlHhM0CBUwo7h/G9Z1z3wxruqiGMDO//lwXmU0IIDjW1uLHkN29vkY0nWhh8ItA7yf77KVWmaZ9TnFGoxDCigzSQbYfMiJ71jM47qtuCo0rwiaCdSQ5pRPPWYWOw0ZAdMTcpn/58hrGDXGu8rCg+QdyB30ewskSdoI0UIYVMd9z9foV2gX+Y3tE+xSRFT4vmXzcvmYwM07gnuVFMVUf2VUex9SxEw/PjjG05QgpYFCVj2SI3iuNDQRMpDusCM4K6GUyC4qAIU0fnNPGvK8w7AwF8p9nfjvn8vKRKNf0pxvzrmLpOSRqPV4LoqEm8w8wCoVW4XYyfeaovoZlJqgtN+Zz9RcvlX3Ub/15kLHLDdFJS3SzwDh4PBWNvSIWnsoJv/Rj/SqEeFPsopV1BQA8F5wgP6YTJouYQJcioJ7no+LKseZ0f+V0/x3WGsk+IE0tVx0SfxNC2LgzNv44JqYS/KRlLy65X2KCYzEvGccft4xlVnAxra30CE8FCV5hzSbiLefHbG+r9BdzGpFnDIaScT0+oueEQUkIZ81DPMF00MP0fA3aTYZfgLizmGFFtYvSTYvHliWzWcvu8YFxUBOVwVYSbBNJpS1slyAuHk3DfT5AOms8FGND3A+rVX3hcIwg3KWsSzFIQ7wXkDvczDe0hxPSrgN0rsnlLq2PqKiJ7Doidp58JdDX44uPS0a08/mfKlzQS8OhnCDpADvE60GmNCB4fgZlBnFjOphU7kxEqzaYcExYO4zyx7cFqXDrY2KKSIQznB2kRyRDScypgRsPtXBw1cTtYKqUIRDODMQo8+EQQThEulYggwTu8GjgF1VewmDY0rWI3ibCPOamw1EWEXgSq+wwdHP2XFreJ0R0I4dG1wqcBf2XRmeGiq4neWJ5tQfNhzOy3B55OI9zEo0zAF47yU0ouLCGH+BnaM0jWQ15gllR05RjZecTMEYLCrQxRJVHnPfm0w/6pYJI0XC72/PP9W9JVR5rV2O8mGCnpnmOMETSRJAQ4RRFJ6wgjz6efVoQzQ5RYXkVbtn2GepIk4meGfx9oV/BqfmB9WNFn8NafOF/s2b4Y0c4Fp9sYvZbYDIRy/PryDrvQHGxK2aSMXpzY6IRF12JucoISRE3gIDIE4L+NWVY9vVX4KmBeSnRkCI2EaLgNqRZcrehaQRw7othgNglm7pj0Pd4oImXoU4E4KZxQODcclrKs582u5rMc0/0uJ72syCqDPnekq4bjoaDTkt5nvPnbB9ouYrOAbZHz71/dsPQlP+7PCYklqQSEwE+f5qhlICkHxoF8YVnmOx43cw65pTcxySfYn0eIg2BqLb/46p5/+f1r9LuW7AGqKqN6npCLQPimo/lxhL0uaaRi+rLk4r7jcCZJXva0ZkLZZFBr8quK/rsRRkqi1BCuesRe4z24/mci4C9aLBJRK4yV+PEg2olShxWCYlky1Q0/7ZZsRznEkv/lj1+hvCHuA5vxiHxqWdqa7k8561jh8wBW0BaS7GDYf6PQnUJtBN0yMLMV/S7mc5wSN/Du/Ik/fniJ8oHuOWOdJkz+KBn9/YnUW3InkCtD8zGml9DYCNHGmJOEuUScFG0u6bucy+WOaCuJl47JvGJ7iDgPJx5/PyO+DvTrlG9nY85OLdl5g6sUTnmqNudYDXY7/UGws4uB0icdxT8r6ksJuUOqQB8U2glUbmhSjaxiZAjcf1jglEaNIN0EmkxxHU5IWspsSr3QhM8J/iTIrcWNBekpEE9ajn2K+W6KH3vyoyPKe47nCfl/E3/RevlXXezlQSK8IBo5vpg98f33L+m1Zl8o1E5RjwXnTcvRRswvjlS5JjolOCvJDoEQQ+ckp3VOuLa8KzYcHnJ+71ZUk4D8KaJXmuhdj7nTTMY1q9WRrLV8YE639Kh1wH1WrM9GZIuW0EgMEj9xyLFlGRr2+5Si6NAHxTjpuD/NkAvLH358TZF1dI2iOXPk4xLXC5o6oa8SJrWhLwRRNriQTSqGn48g9zFq1REVhjbOqFA0dcGlKOmDJK3gpIZbgf8YEy4GZWk87elvMlQLvhjocxEOlyh6o+iWEEfggyR7drgJ9EFRBMPj4wy+6dD7iBDB3qYkW0m+c6hSwMyRxR53iJHGAwHxPOCLY+eH/9l1j681TirECXwImAtHQU+edYxf1BzQ+KPErxUuBdmCEgHbxmAEIvGoNmAKsGeO5EbiIwFjT7oeJDWEYdedsUeUQ4o4zg0h9sQ3cLhS4CSigRCDahxBSoKWeBVw31jG5x2uFZhjTONBWYWqBf0YikfB/HrL4TqhajMShjSyTQWelvStIZ10dI8pUSPRU4+/j0lHhtJmtGXK69GO4rqm7BJushxVStxeE73xxGuBLRQuCRzShL4I9DNJ7B1Xsz3MesoyQnjF8W5M0jrkfcxPqwVx0dOdYs7SEnUlKGYNYQX2KAlxxEqeuN1NSYsO8sDplFEEOFMlmzzlRbHjKZvxw2jMb89+4vl2zjRv6HpNKALxpOe5zvjp4xck457JIyzHJd0LMM8ptpM8383x1z3yNkFJyXo7R1SKezlFaI9IA6NRS73N8XEgOgXEwpNkjvYrweopYO4T2stAbzWJ8agXPc0+RfbDQci+NYjI8zI54YWitpI2F4i9RL/oGF+VmE3K69WGh/WE9ZXmeB8xe3viWlbcXaZsXE6W9XR7TagF6kFztAVllaGXlsVky+/bBfqTYhQ5UtVzmCXsdhPyxjFPTzzonNx4mjLlvz+75z/ZhGOZIq0ilpbjY8bUN3SJZPdxwlzX9FvP203Fny8ULjIor4kewKtA2Scw8VgnuRMTzu6PxCvBGyo+3GX4S4gdqDctE1mzPxSMsSSNR44bXn2x4w//79fYQqHOe95ePrP905LdeURjNDILuE3MNDK895dID7NZzWRZcfhhQvYPJ7yQ3Hw/J6QeU8Am5FghwYhB5JQ4fC95oSuu+oo/Hi8GNPW0Yeo6dnaCngY+/3hOOEVI2+EuHa9ONWluqAVMXcfjPqNZaeS1wZ8GrfOYlvYbj29SouPQAVSbiFNasL/NCBr2csKzK5hnLf0sYvLigPrGUu0ymokg2kdYJTmtY/LLFuFgPil5fL9AekvTxCTjnmJVc0zHOCTR0aOFwMYQFY5op2leBPRODZ6IeaB/ZYm+6MkeYuRRc6+viBYdRdlTJxFdDLky6DRgUzD7hGAVPvIEAS4PFNIRPUuKs+YvWi//qov9wjWswwXNjcLUGi4C2d5hJh5LNMyNRz3iMaWuMlLTs8x3PB2nuEiRKEMXEnodYB9xf1hilOA8PnF/P0deeNo4oG8SXCvxIXDTroa2aq+xl4HJZctieqQSMeUmx9YxaSsIhxTtPIciRk0MYaephebYz7jQJ+RVy+f9gpmtsW86NvuCUASu3uwwjxGfmoj6XJB5g50Hui4hbgLxZtCymrNAKi3VU4HqILpV2KmiXnj8Q0QTRYiDIqvAFx6dO8w+gqefb+oFCCeQvURYNQT7Mkd81Gg84ijpcwWpw8dQTkE+SEIckCeJKixirwfdrFe4M4f34BuNOQfpBLqS2EyQVgGbywGL+5zizx0uDVgl0JlBnTTq65b2v8T0+yk2ExgUYh8Q5wI1M7jbBN2BmwXGohvQyEEiDwqBxy49NlJDKG0kMGiKWwBB0IN4x7gI3wm6HJxlCAxqQdYb+khhco+3A4JUhWFtbHZRstlPEDIZZszzmuzMcvw85hgk496QSotdCmJn4JXl/GPNjZ0jq4BikNYEFRHlFnfQ1GXMN7+65bgriJqUTV9QTFrcRJBvFQ0KfWFwjynKO1wRyDpD/EXNthqxjjWijjAjMWQDCuDfOngOtM8Zi3FN3Dc8flrgMk+SGR6f56SiRX5OePoyEDmP6DXlfYq/sISNwrxxnP6w5Gm+oIsV4ruYj/05bauZntV0rSYpOvouYOqEKPK4o4ZXJXrjON4v6GJJshfcXkxQd4GoFcTWEemAjT1+7tD3Eh/DdqqQyjHKOsy1pP02oXkJ3nvKkUQi0coRb8BeeJxRxBfN8Le96JgmHf7WkF+2xBeep8MCF4FNBC5oCidpJvDjx3PaMubsl2vO3u2p6oyuhVLEtG2EReO9wBUDgvbY5sRnPWPTc7tdMD87UVzXfH6ccH1+QDwr3k2fOOzHlCTUUQRnPcVH+JfkkieZ82+XN/z56RXpZcMEQ/tjSjRr+fQ8Qbaw+nLLvU4IwNXqyEGl7H+akmUtj2c5/aeEdNLjzgzlMeJgC0SA7gIWbcfUGr6tRqQnj5x5jpsRaePIpOen/+cLwspDAm2d8LRb0EcaKT1pZji7OPChv6LPM+I78CtHIxLs9wli7Nj8yxm9GsZwo6LieTehWwZsGgYmRexIs5bpzBJJw3OaEL+rabYjNkVKX0hmyxNKKuZxzfXFlt1mRP+jwKgY+euGXTNlvx+hM4H9KSHpHWdpi4gEo3HN/scVPii6PkJGfniWrCe4SBBWhvLDhHxmaR4L8lLSH6bDCGwqyI2jbCPshUVMe87Sili0zKqO59UY5xXqFDjLjtTXOXHS0u4z+gwSaQga2EtUcKhSorKe9G2DXieEGjimkFv2d2MW8wZZWtpdirASUo9dBcJaIw8QZp5X0Zp9lcNOYsaCfRzjI3g92f1F6+VfdbHfmBGagP8ho70WZLGBr3qi9zEsLF2nua+nXK2OPP9piVr0rGVOhuXURYxtT5MmNLnm3Nb4vca/8+wPI5avt1S7grk0FKue4zHndbGhv1BUDznJecOmHLM3KfvvRxSZw6iISDmckYQ3luxbT3vSXP96y309J5tUuMeI5yRHPycsfcduO2H05kRSOkaVZdeNSeIOXQNzi+8F7j5Gzw3aS4LXRBcNkRMcQ4ZUYEYSe6ZIyiFhPrs68uRHeB8jW4URirAdVo98pRCZI5QKETuSeUNWKxopEQ8RIgEbKZQY/NHjpGHTFah7TTJvYBOweNw6hjC0Vs0I4tjRxzGyDcTHARMrLPQXgSJq2amUZgbZWc2rrx55+N35YFSTAqc16llwyFIYeTJvEHNDruFEQuQ93gf8LGASSfuYDuuNa48ZCcqXcpCm4LBKobcB+3o4YNhUEFUWYQNKBuITA9VOymGd0njcYpBeRM3wnnOUCBFYZiXbkHI+39N9OoNZia8V0Y1k3HXsthkX77bkkWK/LWgecmJnuZ3P0GtJWyTwGONsQj8B0ShWsz0m87RNRP2YcXB6CDgtPTxH+GNAGUGtU8RVTzpukXNP+5Shfogp3nRQDx2A0esTp+2EXBi+EFv+xbwA49kkBXJsiYyhDxHHLqObedRTQvcbT9E7LmZ7bp9WTH+5p21iTlHMuJOcVopx3OIeI0IDceeII8PDacJVW9LtBL0V6A7ivCMOjt3nCbKSRA7kXsKVRVpJmvVIJEnhYOxInxQvsg3tNOa4VDzfj4iuOsqbDNWAOZMYDUp4dC/IhSG7qDi0U8TUEW0F/fRn8c9nR37oCe882yKi3ScIFVgtSg63Y7yGZp+ijhLXSVa/fqZ7TtnvM9CBdS2ZzhqaMkMpEJklaaCbatTKkUw6ql5zZWvy0Ynj+xlZ0hN5R3HZ8DI9UruEYqOo94JO5RgVMLFB3UTcPV/w8sWOL0Z7FrT8j5/e8e8mt/yTfM3RjHiuJhgbM9cnPn9a4jKJnQTYJ1x+fST6dUv5hwl6bNivIk59jOgFOvFs24KVKxmfemwu0UGSSoNaBsamZ3OWMnl3ZPfjDB9JjjpBx57V2YH9/RikRCQODhqdWbosICs4zCOQmtG8Rd9r3E8xEZKmiYmvOsRzgphZss6hDSwmJfuZoNzl6KgnEh3xsub8uePTP1/Qrzz7hzFffXlPFCzxbw13HwvC+xW+0Uzamvyqo8VRVzm9iIljw6eHM0QvUSoQvEdK8K96gpGk7xXGyCHLoDR2FRDGII+a/gKSPahLQz+Covb0DwkfVxFx7bipJMWqpS4j5lnFYzVFVgofBeLaYq8sHAdgl+gEViii1KGkYxR3mCA5bnPkqMdVESA4uhRxgKgJRCOD38X48RC+DqWCDp5Z0LzwZIlFfdK4uUefBD/q6V+0Xv5VB/RS1WPSAL9tyEpPGzRmndImiiTqGectciOpHwvsLLD/POVSlTQhYv7yyNOoIIt68m5gVUcve645MT0/oe81cmRhalGRo4k037cXfHhacvdhRtJ7+k5xMTqQ5x5KjVxrkhP84jef6MJw+7CFYLcZ4QvHzBj6JmUaepa+wSiBet2w/WmGLzzu53DYi2PNlT+w2HYkiWGctlylJzLdEn5bs19pqqkkuWwpRoMhStzE9IeEbpdw08wHklXv6MfDTFtZj9ZmSE0nQyq+mUG7yTmpGJtIzMUAq1F9wMwCZhXY1GNGHwN6bLBlDPvhdixFGFLiMiB9wFcadRSo9ucgnJD0U0nQgeocklcVIkC7TqmfxuymMXI27KVOXh9pu4S4C6STjvqUYj/HRIlFbNTADx8ZlHAke3BySMt3C4Efe2LnyecNg4Jd4DLJeGsZryriyqN6SZAC74eTtUsGgY6YOlwuadsYWYdhJ38f4VKoQ8yHakX9acKffv+W7rsRTZuxkzlbU2A7TXQM3P7zGdvnMbG0/CrZknwUjFtLfHSMazPM5JeODMPk6wPHa00cWda/XyAKR5G2GKsQT4NtL/27E2e/3nC2ODC/LkmXPWFsCTNHeyawtxkug3jR49cJr8Zbsqxnf8hQTcBNA6ZSEAWicU9mDC8WByY/CuQxcF63vOtOLH40BBeYhI60DsyeOtYf54ytITcWm4Jc9shW0JUZF/mRcpuCDryKD1x880z3KWe3mUA/UBfFrSZxnsX4hDYeV2us1ZQzQceQ/t/7lCqPsG1M4hQegTwF+jEo60mMoyg9fuzIDpZVeoKLHn0r6XPB2boh+immI+VJLPhRLGkyTbi0zMYV+cFhp4GF7zg7O9BOJfba0HwuiPHMJhUkgee4oL4dIfTgTQi9YlmUZKsGWcG/k8/I7yNOTlM+jjh8LpCx42a/Yt1MeLIZFA71ouPtxQOi97iJp1znJHHP/gtHFTSf9xP+2Ew4XCT8X//rl/RGDTPvTYZvFbVP+arYczE/cfXYomeDN339ecYhjjiWCcXOo9YR4qTwYghp/unDC2qf4H/IKFrHJO/ocqgjwXR54rgvEAquFjtyX5P1hlAr5F7yfMp5/WrDq/GReixZFBXT1QkVhu950nlmv9oT/aKlfU5QVz291USpY6Y7prInTA3fMSNVZuBW1ILprKTep9wd56jUcjk9cvFlyR/FOfrKYT8ViCggniNib+heC6wQVHWMLRyV0DzkOfKix84HSJdQAXdh4TjIlvzIk846wghkK0g/KmSnyec1+uBJrhs6oSD32HU0dOPSnnEwrKYnhBRcnJ04tEM41Szh333zES48+mOEulNw3tEnihDEgL/OAmab48TAykj/GCGrQTgUPwSsllQXEltq8AG7iYmCh9gjDPi5I34SqO8jaCF6VIOhs/vL3rX/qot9dCd5bU/4UpN+VaIKi2w9/VRw2o/g2xT1pGlWQwpcXHeckohR2nIqM5yXpJOWsInoNhldJtiWYw6HgrtmRvWc05YxwQpeTtZM8oqsccTa8d3jJV0XU303YhbVfPXmmbPRgXerBzabKX9z9sT/8O7PiMxz2I5Zrg2Vi4m/qFCpxRpJfnLUuwxZSkIjqbYZ+yrjP9+/5mlSoJPAy5cbTC2IVUPIAxezAy9mBy7yktfnT7QjgbnwzM6PSOWxzwlCBPRGwqUlAEp51NwQeoVbOUSpMOPAeG9JTp4gBKIXTNKOIAV4EMrjnUR3gfZLz3jvBkGMBt0I4jcV0gV8CnYMYe4RcnjgufxnfGwsUKuetOjo1imJtajMcV+N8Z2kLlPi3uNbjcoN6nVL+5Shncct4HjIiSpwpaL1EeKoyLMO4YeW4uzsxMWXG4KXmF2C9AHhwCXgG0V9m2Pzn3n1nSfIYeWuWQ2rVy78LKqR0BeSbixR2qEaWK5bhPc0P2WIE5hMoD5GpI8SeYgIdwnRRtIFRWoN9oeUT4yoZTyQvd55ThNN+qZCzByXf7NmVllO2xzfKKSG1ia0bczqek/2AOm94FRnlPsCJwLHkHC4H7O7ndJoxSItefHrB77un6kPCf+ni1tEE0Eb8WE8JkoM6jYm2ijUo2TaOd7OTjw9TNCrlvP0RO0i7uOUfz07I+099VNK9WFEaNLBLHhIeN5NkdoTlEC+7LgUDc/7CS5WqK8aDnXB4Q9zvBBIaXFOoZVDnhsuLh4ZXQ6bJlfZgV98eUNyK/A3ERf9CTU2jBtDtR2IjF8s1rz+5QOTvEGmngAsXM2lrRFvHZ/yMb+InnFJIA2GdTXBHiPCmaFfepQLpBtwjwnHuwn1MSPzloNJedhPmI8rEGBHgnjR0sWS5ezA2XJPtGp5Md+RzHviRcfxKcPfxLi7hH/69Jovf/NM9jGw24+o5oqySagLSV9p/Daifl9QPPX81M6YRQ1ZMEjlGPc91cOIR5fzWRbcRRN+8/KO2YuGk0mxS0fUBsZ5Q6M1f4rmlJcemzDIaeYGJwOm18ijYmdyRrOKKLPM6dC5IdKORFuKb0582e/Z64gUy0GlbA9TJtZQvD6xOY5pjwVhBIftiGTVoe81/lbzGCW8E1vaU0IfJLPFnnzZcHW+5XA35boo8RcG8axInWWel+SzGrnsKWpDnnQ8fFpyfBrxdJrgPmaMHgImKOJZR99qdnc5WRl4qEYcQ0r054jxuxPqZY/4GPF8mmC0Ipw7xrOGeAuhl0gT8AtLvxC4naafSia+p18FmolEjhw+D/h4CNmWeghR+51GVJJYW8wrS3zskX8Yiv7pdgTK8bwe4U8J/qCY6ob/+vGavlcEF+iVovYxyaqGwhMQZElLg6LbpcjC0S4UTklC7JAeiqMh2wSiUuBGgUnUYKXARoJkF0h+UOgwdCFBDNrqeLA9/iVff9XFfvVliYoF1gl8OqBIO6VRTRhSo190mGXAP0aoGkayZ1RbXD+Q3PQxYE8Kdd1B6mhtxLFJqZqEOLKkWYe9S9mUI3YfFxyfJvQFpMuWMHPkRYdF0V4I7psp13mFcZp38x0fNnP+5+MXXHzxTDGvqEWMyDz9dyN2N1OeqinjRU3WOLo3Bn3VEZBcLA7IkceWEfsk5tPjEjtRfDqeISLP16LiHy9ueWl3jJ8F/aVHTC07MtpFQH3RECrFXmT0n3PcJJAtWr74+o78ixMXcck8qpknNb6K8EKgg8WNPdXTsArSrUDhiYMjKgxqYtnZHOkD44uK5FWN/5AOq0//P6DMTuMjkI1ERiAue+w4MK0N8iAJqUf/Q0W/HMApFB6PwF45dk3BPKloqgSuer761RPJJ4V8VIhOoLeK7ORophLfS8wsYEeSXVuw+TgldAGKYTffzIYxQj8SuASCHDoA/UTh4+EgkBwCQYONJGHisJNBhys8hE4hMk93KUhKQTdRAxEw8qTXFXHpBq1rajALgZ8FosIiv+x5PdtDK+mlJK8t053BC8l8WnJsUp6yZAiOPhXIJ4UpNU2mkWtFclYSlpYRPaNFRfjK8urimUVaITeaC3Wi7WLyyPDj8ZxfqSf+y4dLHrKEvGhZ9i3i3BIXLT4zpEfH7WnKt92cvtG4HxKe5Rjbaqoyw/Qxcq/ZPs7oJwHHoKrN8g5xVGSLhijvqWVMHPUUpmPx1Y7CGepcE5aG5KwletPihEImgySoXiTc9xOKWUtFjBtDoizz84rdVcL8BziYnE7pgTXhNac84Ztf3qO+bkhe10xelrilZWtTqBXf9efk8x4mw6aEGYH4cbjVRs+S2kW4o6aPBHWbYg4J+rwjpx80qU1D7yTPHxf0XvN8P2fdjXErS0nCP65uBgreFz0UHvPS0ruI990U8ytH1yXoPUzLwHVfkX8KxB8FuWkx1wYpB/3zJOooKs86zYisw1QRTRWz/mHGh/9wycXZnrR3Q/crCVRlhsj8QG37nODeWf4sZ9zcrbCRQDcQ5o7EOPRekMc95TrDNBH2GCE8zOqG1UXLZFwTnGB8VpFc10gTMA8py/MD0bnBRYF+5vEvDcVlxTZLoZJ8kjOiWcd1ccCImO6Q8MOnK042YV+l/Ls3t0yWDTry9L1m/TTFS89hlGCVoI8F3oNaK2w+jNrOXu7oHnPks6KYNnT7BHuISJcN098cePHlE2NV468tQQ7vxfV8TzLtSPOGPhG4cUDsNaJWgKBo3aDxPUqKo8MDMnFo74bV3THYRmNyQSg1/tufA69LiVOS3SyiOfNUx4w6UXQLyXRf82JScmwz0lsIUuLmHttoqiZB/6Yk5J7pqCL3PR5YURE1wzhQHxXSSIxTyKNEtYFk5/B3EfIo0SeA4fIZHyEqA8nek24CkXWs17O/aL38q57ZP4WE65MhfwTmigyLeeEYpR2J6lFJwNxJ7FEh54FGRNzfL0mXBxZJzTqdoBYd7X1OLxTFj5aoAZnC6qrk026BWCua84i4DejCUVUZQXe0QdMeU/LMYj+niLxHjgKnOsNWnmja0duE+3LGRDhEATud0L30xDi4T3l6WJAvamydE99JVq/X3PzLOf0E4pkh9gZ3G+iSiK6LieOO/6BWuH95RZdIfiMfuboz9C8aSl0QNwLzMUOqgM8BCwqHMIJPhwXdLiM7O1JcH3Fecj/SvG5KPu0XyHmHSTTsJArP9NWJq9Ge2zBh/y9L0rwjVJLyd1PshYcCgh1aWckGfAQuEYTrnvmLksNTCk5z2Ca4c4GrNXwYE+YemwqKtkc+K7q5QnZw41eISvHmqzU3j2PcKCCaAYijhcPmAik8facRGqQfxDftEnQF/ucZX7SXw4ZBGegnEt048jXYNNCcDat4LgHhPaqS2DHEeyDy6E6iIocTMD827EyGnBniJ0+3iPB7DbFATnpU3pAkPSjYfZ7z5eKBz/EIEeCb5IGndMy57vhMTrXJqNc505dHZFEynvY4G5OXgS6D7mVAioDeGryX7PqCycWR9Yc52UWDKB2LruNstee/vr/Gb2PWFzl9Iqi6mJtjhFASUQt08ChtaCYJ4r3Ej4eMR/rasA4FlBKdWAphsBOJQzFOO/wrQ3YMHEVCfN1wPOUoPOnOUlnNlT7xeD+hmUn+7uuP/PBwzTu9oe/gxz5Hv2yZLFrKn0bMypqx7Hl7sec///4dv3j1zAMpue7ocknYS4qzFnrPbVXAQ8z3+4RF3rAzOR/vz5G9Z/WbHSaFUkmOn3MS0Q2Cl/nQiZEjg5hZrBdklyXSwfG7CcJCmySIVGCkJI+HsOVRFZydH5CzwOZhilaeVHb80M+Y6prGaqJFj3qOCQEOj2OOM8MvXm24baYoFxitHe/+5pYfxYznsmBd5eTLDnPSuADjWY19nuA6STKzfBmt+RRmiJFi8zznSp3oXlnubs+I95A6S5IPeujmMUVaTXtmmFqDzSVBO5KJ4/QwIhMd6mqAwgQV6PuIj/aMu3ZOm6ghPDuSZNrwJHPinWSjNKOzCrePkJuIrs/pa0FfxujCkjrHZOM4xQVnlwc+//kCFTv+/vqO371/gdkFEmWJgqUJMdERjocp7tJzXjWcjCJbVZwWMVWdgPPIu4LsvCJtLVUbExwU45b1wxS96CjfXzCfH5G7YWyZFj2b3ZhSxfiFYJlUqFuNiQW1Soagq3c0cYxXEuWHbmV3StCXLXEniRc1ZTVG7SVd4dGbgFAD1joNDvnj0O6vX0jGo5a2jmhexTz2GbmxuJEiOTlMPtA43WhATKsGHn48J8s6fAJh4rHjIQysS2jPIVEGpyU+GWRsNgoQB/Re4uNho6hdQPQZqheSkAai9wLiv2y9/Ku+2b+83FInEdWVoi5TmlwQzTpoJenG8830icmsYvyyhLEl6w1X0xNXomP3fg4/Rdj/14T8R8l82/NF9ERxfuLiN0/cPq0Y2x4uDBGObi7o5YB45UPE+PcCHQ3Ur8R2mKXnP/UXKMXQSryX/HenLYsbz6EtqHcZabAoF0jqQNI7/EFjNikuhXIz4rackf/2QHTZQm4pSenmCQ5B4i2nPkE8RMirHllYfhAzTiamaA16L7DdwLBPX9ZIF4jeNMSF4dXXa/om5nK25VDmHONo2PsfOfY+Q8YOcRsPONTOI0tB20f8cDzj4ThFvWmwTqCEJ8w9+jaCJBAvOoQI9BeB9guLnTtUJej/WCCAxesDWeFQHyOKTUAdBd5IsNBuc6JrA2q4UUo8etmxexhRr1O8lby8OmLmoKRBlgJUQH3ZoOqBZW0vLJOiQUyGFj29IDGWZO9xOUjtMIWk/AL8tUW6gBIOXQ/hPdV79PPAxhf94AwXjYCJp34hCV85dOSQXxvi2tOUGUZI1Ljn5dmeSsbsTyNCFfhUL2g/F0gTGI0DXRNxp1JSaag6iVlYvJAw9nTrhNW45pgl2C6itZrqkOKEolhV1EJz9zTn8uWW/fcziquGu9sF6zAizztcKqjGmmaXMv1gCBeeNDLE0hKcwpcJbqPpVpJ02nC0CQ9xjlMDYlq0gnAv6JqI6AhdDsefvd5aeVQZCEai15L4UXDqU/pcUZoU10rEfcZMdvgG1tUEMRoCbb5S6L0lWMB7Gj98btf/acbK1XASvI+m9JFEn3XIyBN1joDgVGYcfIYNitM4orxS7FSCvclYUDO5PnIiZvS2RFg46xqu3zzDRjMe1fRW0z1m5JcNPgko71HS4e2AiTaZYHRRsflpjvndmEg7Hj4v2bQjbu4XFNMWYT3+MUE1gUXTMqNhNT5yexghXhhuJhnbV5o/qxl9qYlvFIumI2vhXX4ABO1CEN4ORblIGh5/WNEuIvpXhv4+4Ykcc5OjasFoVQ1s+sxyeh7RVTGF6mETIZ4j5MTCc0z9sRi0yH1KlFi0C/Cypw8SqwXNSA1rwUnJ+CGQPECmLP3CEzqB/zQ8N3RmiaqAO0ZEwsFThOkVSas4tRlFK/nFxROXRc2/7s65/uaRaixZu5zzsz1aGEbXJcwdDkEpYtzYM55VTB8tTgrEsyaPe9TMcnta4KceAVTfTcgeevpdTNgq6mOGc5J+FlAzQyUj5EEyFR3joqYbDYUzX7S4ZaCtMtxkEFjVJqITEnPhqF8HykJzeD9F1IPh0DuFe+0wXiJ0wEXD1k1gkHE1fYR0ENeB7brAVxqRe5ozies1rDVhr3mZnJhZgznGnIiRHVSHnMm4GXbyz37ONl0HVBxIz1r6v23pLzzyJJEu0E8GZDBBYPKBhikbgZ3Byv9lCXp/1Tf7wylnPU5Q4xr5fgDexC/BnjT3yZSPf1wS7QHtmTwatm9S+iQinwi+utjyFCc8/zhj1jX8D//uj/zffvwN78oTnTBM/m6LVJ6J0YSDotzng1xFgXntmWQVcp1SJTEfijlJaNF7yWYZY+9i+omm7hP+MXngfZrztJ+wVTHRyFJVCXIcGC1auq0iUZY2i+gSSfg4JR73pO8t078r+VAtsV6hA1SdQj8oCnHCNYrxecP6LMPpAp05CuMwS0MXNHYUsHuN2kse0jH+FHHoxrRVRrTsSQvD6EFhtUTMLb6PWcYVh3iMagZZi9hrQiqIM0OdJgTlMb0iyjzuIAeCnlP4M0N01hH2EcFatmmKWmtCNFjmZOSQVz1uHREmjuyTwneSUqW4JGDPHFlhmI9KeqFRnzJcBI83Y+LIY0KMd4ok7jCfM4KQZKUjGMEu5Kjco9eS6pWnMIZwkyJbiNYC8JipxKQCrRy86zE3Q9DMC0G0+1likwxMAuEF8ckSX3qarUJuFcmkYyclvggo7WHh+dPuEv05QTtwmabbaeamYfViw/e/W7D4ouFFV/H5MOKr6InjZYqc9GyOOdV+xKezhF/MPyE6xf4po/qQM37RUzuYjBuicc9tNccW8CquuBNzPj1MCJUmjB2qDtidxF0L1PuYisH+B4Pa2I4CF2+2jHrHXS0IlUKd9XCv6ZG4mcI7mGQNq/MDn8sxi7zB7BXHeYbeAUfNw1WBj0CX6dCC3gX+a/kau3LEnefics/hc0H0XvFUFBTBsf8xp45T6iLGasWbr7Z8Wi/Qa0G4lDRO8rezR95/vuIUNPHMECmPO0XwOUGce6ajhvoQcTIR3e8L6qkgTwcBUugUN8uC2QeNfNdSmYRZaKm2CaO3LU2VERJBh2acNrRVzPyrPSNv+BgkxwuY0dE+xaQXNeZ2zqfFhLdXO6L8xH03AW/ofIojotGac3XCxxIrJfO6Y+MzinHD3qXIY4DYM15VnFxCHBtevttze5yxiI+USUGE480Xax6KmPmkovunlGzUsxMxuzpDHSROD1jnIDxdpelbjYodYQZ0nsnbI+0uRX0OyBXklyX2JiNzLV+LJ35I57xTa77vVpixZ/m5QztLNY3RnaM5Johlhx0bom8TmFlCBI9JDA7+9PECO3NMa4v2g4463gd6E/hxd04279m0I/JZw/Ew5rSH8axjX4759S8f2e5fkTaeZ5mT9j2rN3tqq1ErQ5Qb2l1CFDvExLKtRkOBTC2HU0HSGloiyn3O6ZgxlS2mACkF3VgyVh39IaZwluiix3QJcfCoGvpaIjKH2EEYC9KdI5GOSmoS42jzn7NHK0W2MYgPisnXJ6qfCtJS0M0lrhMoD6wsuhwMf48fZrSpIgmGk9WosQcbaINGnkBmAn2SuGOK3lvKLEY3DheD/GVD/ZDjpg69l9hLT0TAbTRh6iivQJm/bL38q77ZpwhmcUP0QXH590+c6aFdReFI1pZv3j7xxcUONfE0/xB4e7kmko7z2YnHi4Hm9ubv72gv4J8ev0aOHKeRZHucsr6dcTIJoVRE0rEsjlxM94ibiEla0T2ntGWCzzy2V7iPKcIEji7BRAJ9I9najO/1jFrFHC40897wVbYjWba4QnCME+xYIG4jghNkDwKxVnT3GUdR8NnMmImW6CCG3dYysPzbNVIFsklLqCVLGsJzRJUrjiNNkhpEC+mkR04d9sKxfpjiZo5ynzOZ1qSnQO8Vp21BsJCuBUoETnFMyAaVq73LMCmcJRXVLmd8WeIUJKuG+KolV5biiwEhOf5yTzcFJaHzCdlZg70aPtzeSdpvhjyEU5Lok6adSppLSI0hvayZXpzoDwlV83POwCtCFPCNGEJ3E4tPA2YT4+Qws+3OhnZavg0sTcsqrnG9olERthhwvEhBP1UExHDg2Arc+xRdQ7QVqJPAjEHoAfgT1R6fedxG87if0skIO/ecHkek2rBKT8jYkY8HSEfQnqj2KNygR40U62pK/Npii8Dvz6acVMq/TM+5awvWLuWpmuBWjlNZsK7H/HR3RtkXiBXsRzFjPcyak8gR9hHMLR/uFxgl6ZuEro1gq9kdxzgl6R9T5FEyFh0y9qSLlhe/eOIXv/1Ef4q4OUzI0gZdGNp1QlOnJL0bjvkLB9+07EVEtvUsJican6IPgrlokWrQ1k6mFfIgMYnEtBH5ZU0c/IDo7TV9DLeTMabXjIuG9OJEOYmwqcAVgZ2IuEoO/P38E8vxEZG0fPe/vibFkS4DvY8wQhJPO969fEAvDGwVqXHEwREiT3HwZKOOXkvCFw1q1dFdeMYHQ7QRPPdjlHbsdmOKq4bRWcPLFxu2XQ6VYvc/XWB/NxvsawFOHyeojeL4eUryRUUiHGnrWcQVYi0YT2qk9MzihuwpcL+Z0x1jGiXZbMdkpaWtYho/3Jjn+YnHZoz544jwrHn4POfUZNw2c+Jxz6nK+dyOKH3MTTtBnhueqhmm1shaDu18O4BqolJgRgE1NnAckK1uEpC1wK5j8rmhOHiCkTgp2PmM/1i/ZtfnfNeueC335LJnlpeIzPNC7ikLjVgYio3l/2A3JC9q3rx9ZipbbKkoxjWyhCi1TC5OXM53qIMgrT1vL5/5NxefWSYV37x8QD7HqFLiC8EpiWh8xP/nT18gnv0g1xEB1ppylzGOOoxXjDvD15eP2F7TBU36DLbSiO8Vox8s7hQN2z2xY5I2yJmlGQvioufVZMPq7IAbO/avYF9nxMaig8PdpMgQCI2ifKOwRUCMHd1zBr1gN42wXmPMsBlkY42wks3TBCM0yawnHXX4SCALS0gD4dqQva6RwRJqRRMpsBKXC/o+ojYJwohhDNMOHbV2pYgLQ+QdZupQE4t7YYlHhmzt+bv4mWJaI7Ohg/lGbtl2/7vP/n/zqzvGWC3oI8WuLTi+kry53rE61fxicWT7fsr6ZsTF+MhXxY76OWexOuIbiWgFB5Gy9QV9qjlUKbSCLhW8fPVMMu1wdyl9JFiT89DOeI5zxm9OpIUllYYvf/nAPG6J2sB8VKETh14P6y2bRcZ/d36DoueJHNEK/EbztBmTHjyxMeRFA1JgzzzZqsZrwfSrA3ZlYeEZGTeIbn6Wk7gx7E8F5e0IMbZDwQ+CxdkJaWGelxweM5pUYY3EdAqMYHTsSRLDi189IlxANYOSNwuOlexobISSnrDVRI0nJAEagfuc0T+nqEpiSsmvXn8kEY6mTmi9JjuvcRF070eEh4hvzh9Jjx7hf2b3/1ggKxgfhy9wH0tGWYdWHnnZ0XwFfhNjnoYgUX2K6RoNwhN6gSsEzVjSC41P/LCHfGmQkUM9aXwzEL0O6xG7bUGy81inwIHPPM35kMIX2qEeFLbwxI3HJRLmDgFEp4DJB40uAtKdISiBKizfnK2RhcXOPTYLrFUGVz372yk+DhQvKvo56F6QxJaL8ZGXyxPOKSa9Y7SoGZ9XLJIW5xTr75b4p4jp5Yn5iz21SWhDxMGl1CRgAh2ap2bM9v2U307uWYmat6MjEtBHSHqP0n4AjZz1RKse+aKFzKPPO9RXDcr13H4+41RojJQ0+4yqT5jnNfG0J/8/73E55LXl1KZUf5yw8wXf//CSWkRQKnZNTrsIOCUw74thZLUHOwqEKBDVA+Z3/+N0eDg3gvwYyM4N4rWHBr50R5LM8ugKvttf8SlacEpjZmc1ZuXZJymmSphVHenREzaK+3HBOG45iZSjTOhiidIWo6F8KLAJTGRP/kkwdQ3HPsdGQ0hqf6YHZ0KXYDvF034y3KqEgKVBNJ5mneGCxNeSEHnE2DHKBxXuOqQcfUI27bjpZvRoHn9aYpzm6/MnZl2P+aGgk4pNyCEDPe/JKhidIuR9zOrVjv6UkDvDRHRELZgfR6x8x/mkJn+C7ikbmBcjh7l0sIkxCWSjFlTAe4l1CpUM8hf5OSJ+FtQ/jbCR4FgW7GcRbh2jCwMrQxw5XtgjXZewyUeEdYI3kpV0bE9Tsi30XtHMNf/66Yps3lKnkvhdxehVyWTSIHJH0Tie1lNu3i/ZfD/n4s2W2z+c89/+yzvuuwnfP684VBFJ3qGPEjqJKBX5UyAozdusIjMeJyRhq9h8OyfpPM+bKd99+wIOahi5TT2qFqR/U+N+6Qcd9aJn+skO65u2Z/QEzZ8zPvsp935MlHr0URLGHtcP5j2bB4IVYAXFZ0H2MHQkzQgS68hrRybMELJtBG4RsBMPViKlw3cKs44RiSe2nqy1uLuI/fOIps9xnUI4YGbQwhNKCV7QXAbMPNB+GejOPRpH28WocWBs7PAsOQWi2FJ+Ffjd8ZxtN6KZBaLbQDOJ0K/rv2i9/Ksu9s+HAvtjhig8p21B3HmOh4Q+aH7YzjESvvn3H0jGHY1QZOctVio23Zh01XEZHXGdohA9476jXafsHqfcPy+I9gOWtf1coPcCeRz0ppPLijqOOLQ577+/4MHnNKlCeME0NsR7j1CeL0Zr/vPzS6KlIxaOeW2pTUxtYtTIkUtLOGpGoueFO1DUFllYhAzMlyVvp8/87eSOqktQftD1ipkh3zni50BzV1Cniv3jhKebOeo9xNbz7m/uCWFoS4ddRCodrhAYJOvvZkgDfZ/QlAlybHn6+XQpaoEvAm4M6qolqgMyNZh9TD5q6Im5/f05KYb0umacN+yeJ+RY+lgRtvEAq5kNfOvRI+RfnMizBkpF9WGE8h7baXwZob5PcTcpzTYlGvUk+8BcV7Ra4/XwXnshEP2Ay/RSkmwD2YNHWEG7GlZuMBD1bpi3O4k6Da14LyW6HBLNXg3u8yA17muDEA5rJYQwMNp3kiBA1xBCjBkJ5L3i5nFK0ThU7MBKkieB9h7fKIyN0NohEg9XHfms4fEi4l7FLP5mh0oc+8cR61Dgrgxj3YAEHTzlPuN0yLjsKiazGibDwWDVNdx+f85I9PQoPosxu/2IO59TbD3xyHJ2eSB7U6KFI3rdMP1iT/Rlw5t/c8PZ36x5udzzHE2p+mE1MG4CNQmhl5Qhxh8U628XqNxy/ndror1EX3aEXmEOEeemRtkhMJgcYDwvGa9KRArhAEYIrJOcvdki/AB5cV5i1DBD/fj7c8RaIUv4eDfjStZMz0oWkxPiokfeJDwdlggJURswJ81progJCCEY0XO8H6A4ySmAEcgvOuLCQAhMdY1uLV/86o5xaShKR+VjpPSMVc+7qw3zWUmkHLOkQ6wM08pw/mJH/dZz/fKZse/RbzrQwxbJ4fsZfp1Q/rc5P/3pmkrGlLcjmkSStB4vBauoQ70xiIUbEN3nLTrphxBjYWBnOLvYcb9eDCjg84ZZWpObnrBRqLjn43qG0J7LcOK/n75nFAyZs4jrjmzcU69zim1gfHUinhhcBPVCohtPKBztSCJ7QXMVoJeky5ZpcmJVtYyymhaNjALNc0J6VVOdUpwxhMSyuKqQItAFTXMGkXbc/3RGWWZ0SnEsM0QWSLKev/n6I9+8eyRKAn/6+JImD7xY7Cj6nuwZXqc1530LhactI7pOw0tL4gMfi4wyRFir6K48STDEq47z7EQeDMWyRnybYrSAqaVyMavZaaBhWkH/JYiF5SaMcGlgtOpJvxP0u4R475mIllFpSXKDHFn0XuJG/GyGDIQI+kSwkhVpZfFHTSM15srj5p5i1BCuHZHxKBEolxqRefLW0iaKOmjEK4MoFRwl/cLD1PFuPwSW9XlPPwske4hTi84trlX4r3uily0bnaJeNrj7mNFFSVUmuDNPetEipWeSt4Sl5Kkv8D/kf9F6+Vc9s2fWE00s5WZC2lue+gUqdVBLkosGoxV//PyKtLE0fQJjx4voxLjvONiMh26CUIF83NPXKcJ4wjqCqcCkAtUIxhcnQuohadmfco73Y9wmos0HwMXI9QQtKJ/Swdo2Cjin+Wl9xrU8EGWCdp0yXxy5WG64befYGEIyIGxfPR1Zhymr8yM8Tai3I/7xbz/zaEd8t77ki+WeP7kV6dRQEuGihPTaYWpN/eOIaNRzOT1wtzuDk2T9YUmkJc5CNDb85stPBKe5203Y7mZw3TLxhtNB0xoFUpAuWmwZkUmLTgLl+4K09IhI4xlyCiEKiHeGouuJXMAnEbEJPHUJbmUJPXyfzkjflaQ7SZNlOC3Qmaf/LiIsHeNVw+mpwMcw+uyJrlvqOqGbJZSbmAYNRSA+CZJlS5cooh8U3SsIRtLnEhti9AmiDYjCw6XD1QpZD1/4fioQVcBLgXIBkw/BPpdIlIGqzUixiCIQ6oGeJZwYmPjnlrDXCAfRFlqTIt6V9KdB9uHOLTqWZKOW0ic4DdNVSa0jurlE3kbMvthR9gk3YorbatIuYExOPOkJsWN5XpJ/NCRvDvzJXfBSH8hzaNYxpBAVPS5I4tjyZHPeqC2LtOL7qyuu4hOygTu3QqsAtYI5TEzL9398RTTreRLQ7RKSI0S1Qn7ZwYOi/cLS3qWMxy1X85LdIWX3h4y5aGhyxcj3CO1J7z32uqU55gQd6A4xNgiieUf7twp9kIxdz9N+SoyjHwmK3JC0mtQYXl6fWB8SmlcafZBsY02x8xyIEMcRbR+RTTr6U4LIDFEZ4C4aFK0CDmWOlIF43qOlw7ho2Lt+M/DHo4XhUOZsTMbX77Yku4asUbxqGk4reN6NqGyEqCS+VMy6njZJqO7nWCm5fvVM/WlKFSLiXhJEwPpAkhraS4FvFO4wGgyN9zFlKRGp5Y//ekX7MtBLiZKGWdbRmpR509GMA/dvY8rbAmcll2nJ049LGDlGX1ZsdzHji5L9fsTD05zL8Z7//PkdfimIQqBuYvxWIiYGM4LycUTSe5ozSI8Cd+1xGeh7gRt54sPgBcnOO45Vxkmk5M+GaA/2ChYXB8qPGb/9x0/8x09f8vJqx+3tDCUdaTTonG/6Cee+onmICElgv1RYL6GU3O0uiZY9L+Y7bm7nfPFvn9gfM8aN49gr2qlhF1Jm4xJuJoSFpTWKoAPZLZiZJJm1pCLg3kjO53vKImH6i5rdaUQ+2XPSMePKsH2YcpPE+AtD6nuM1Tzfz3BLy+xFQ2J6xHMOvcTXMXpv2Y1jXB6I7xhWbdcSt7CEGIIXjA6Ok4wJRiOiAQu+mJzYpxFVq7G9IFxa0klHqDVeKsKdIHrToitHWWZEsaFNJMFJxlnN5g9j+rVGX3XkTwPwxxpNUnuu0j3PIgU78PeXVc8mNazrMaPzGh0847zh5CQHkxLllhAELgl/0XL5V13sM++YzhpOt2MaYqQIuE6hW6i6hHTvabcptneYVOGC4/1zgZoZbCmQVqF8QLzpEKWg62PCK8teZkR/iKmvBpNTeIoY6Y7oTtJtC/CBad6BCpy2KYJAP3Kksh/WQUJAx44bM+O2LhBnFrvV+LFgHDfcfjjDv+5RDwnv24TiVc21LPkpW9ItBP+PD+8YXZVQWJ5+KEizDvOQEBuNAF5+/cTdeo581rS9oIxiLv7+kcd/vsDHw7xv5VuOIuWPP70cWuMIstzxbvrMfzu+whSKLO4Zu47Nekq8A+simlkgOQjka0uedLhKY04xLpXUKmV3P0dGDmaOc3lg5AVF2vCsFrRtjNsLfDYY9MQ+oj3muEzgbET2XUBmAtEFujceGQSRdHSZpxprxFqRjBx+CsZpeqvpv/ZIC302JObjPdgMpBCIVuK9RBmBi4fVv2Qb8HFANwK57Om7mPQBbC5AeXQXSHcBN3YDRlgND3wzD2CH9U07hXCn0StD6CT59xC9rmmeEr5KTny6yFA/pJxEAUYirnpm30vaoFF1xMaPiGY9RgvO3YHn3ZTxVcnKllw2J9Rbz7/evWaZNTRZgjCgLhruyzF57xn1gd0oELYxOztme5oyGhvuRhOUFZhKExnHMSSc/pCRjXpYGuLOkwmHVJJQx/SRRB40ZuaJU8vr6xPCG+xtiooEqQrMi5anjxeIqUMdFPe/irFWkyY9bRlhMkV6CEQHybS3hCfP7F1DJjtu/Io8arjoW2qXExGo3/ZkomdcQlwElLbYTNLvFatVjc1hSsNpEdHWCb1WKBeQuaNvIkQUUMEzrzq6RPAi23Pvp4ijJIksu/dTxKIn6wNbCpb7nrPVE5/SCXorKD+Myd+eSH8+FOg28Opyww/tiqv5lq/iPeaVIlqPqGyO7BxeC2oToW4VybynN8PKp+8HKVL+KPBfeorrGvvtjN9er/nu/Tlu5Ri9bCm/m+JFTG8i1FGwOc3J3pScJpogEmwGf95fIO8z0tcNO52QxJ6xc2Qva9i0HE8Rh2NBqAUiHgyOi76h32dYPdw29XLAtOqDwKWC3dMEPTXE2pJ3lvYbyJ8DpYzYLmP+x8df8NXymexJcZWW7B8KXnzzzHd3FwNMS0PqLbWJmOw7TlOJD/C4H/ML8cDtcc7rr5/58Q8zwkLR1THvJgeOvUJe9hiGjqF71IRxIN6CbwVUknY/wsyHJH4YaU4yRe7g//L2R/7nj2/x24iNSykqh+3AZoGdyyEaQnvJLmCOCY8+Jbvq6JoUoyV1GQ1irFrgUpjkNXaX0jpFPqpp1xmdiXCRw88H0qeP4PTHCf7KE84M8gjpywbTCNzCEqWWOk6RncYKjT4NNEdzFoiOiubDmGxtsFLifsyQOqAChLc9OMezzhFdYFo7km3gJp/ickkie8Re0dynjH9t+OZXj3zbzDmahFV04vn5f5/Z/29+2QQOTc6lPpEVPcEDwuMiAZXCFYMH3IwkZgz6uqF/6ajGCtNFg8N85pEO2kKilx0XUYkikH5VcfHlM7obqGzVNsUkQ3K7nQ/+7VprlA1Mrk5wZmDmyZxDKkvIh/kyI0fSBQ5Lzff7c26+v6ArJOrbBH0boQhsupx/2n4xtOp3kmUoyXWLcrAeZRSZRWmP6xT5g+XpZoaaWvy7nlQ69MjCSdOPJS4WRDgOMibuLRud0RYaXvW414aH45zUGF5fPzONavpWE3JPNDMDrvURQuYwK0//2jL9xzXxtCeKLaNZQ9Zb5qLhMj0RZ2D7iNIlUDj0gyJtPd5LwibiLD/ircAm4MaO1kfM5ydE7GlzQXTR43/V4rZD2y960SKUH75UTg2kvh0U0pAeYVLU+DNLEQxJ0eOWHqKACIF+KkAOEB2XCsTYIjYal4I7czCzA0CoCZgZdGWKn3iCGAA70V6QPAs4KdSNRHrH6OTIHsEmknab0KcRH5sZI9UhjSD9rHBzD5Xi2CR87bcE4SlsT/OQ8SbZ4hMYvSjpnKZ+HHFTLvj4hzPSnSVtDdFWcNyMsA8peRmYiIbnKOEsPWHKiMPziHDQlE2EvNXUTznFvOaFP/C2rzj7xZ4os/hjhC8H+pf/NqIea6ZvjmQYoqnFHiP6PnB4P6YJ/1/2/iTWti49ywWfUc1y1Wvt8px9qr+IcDjCBQbZFHlxCjBSikS0QKKFRAMltiULSEgaeYWUki1ICTpINEFCQrRA0KABErrBdVIZVxGO8i/Of6pdrnqtWY8iG/MnUgZl5g37Km5ayiHtxl577r2ndjG/Mb7vfZ9XUmJYTjJqbTCpZXa1Q8w8bW1onMbvDEoE9FbQJJKyjbBB0T4yHAYKZSXmsibtGtZ5Pzt+9OE96+UIvZOMhwXegzAeQSBSju2bKXWrKV4PUO8M8l6ROstYtQwf7YkelYxGBaenO2Is+lrz6fIM7wVbEjZdSvr8yA8NH7ja1Rz2CfuR4o0dUa9TdBF6bUeQPIicY2zYPpMkZxWjQUmxzHi9nLJ7NyIZtkAgijpiWaMLQX3qqTqDXHSYYUty78nuQ+/gyGHhj4x1TbvSjF3JIj2y+7UFkbbsbEI5UPipJ6jAsYnp1hEu0Hu0lzHtE4uvFTz0G7FjF/HwZkwHzGcFctZxaXcM4ho39n1L+XGDeFHjC0OnBZH1yA5k1iErcE4ySw9Ei5ZR1yJHnraKCAfN0HbIIHhoEuLEs6tTXt6e8vTZA/YuYWEbft/VLbHzpHvHoq7oSsXs4og+wIeDJR9/fEH7RFJPA/GLA8VZR2U0+qCYty2UAv245ny4o40FJ/Mt0gbmtiR3HYPQcvfZnPo6g1Ly/3jzhMIZ8AKx0XRCIRSoW83wY4iXAt0GusrAdYQoJQcZw7lFJA4GgcurJXJkkYOObmNQTyq0aek+yVAHQTsUuCAJgwAzixcBe2kReKJBS6It9W0CBz4XEOreabWNGYwqwthhJSR7j5IeGTziytIOA7ry4AUh8ohKULqIpo5otCZJa+5MRnPoa4U6CrqdwlnD5pMJrz6a0yIYh47oIeIi3f5A6+Xv6WLvgmJ1O+F1OkQpRzsPJBcVfm6ZPd7yvLlHDVvU4wbdBtxNSiIt0b4PQ1F7gbg1VN8Z9iz8z2LW12NC4hAO5EbTvM0RBMyo5oMvv0GeNgxlw2hjUfeC06t1r8pfGtzWEJ60RL//QDOGFkV1SCj3MdV9hlvG4AWqhmhg+x0nMeKtwRzB1YogoPIxc1dTvcuwQXC/GjEUDUGDetIxjBrUdyOSYDkPR/a/MuWtGJKdVPzY5WuSB884LhmfFUTBobtAvAlU1rC+HSNKzf4+Z3kYMVcdz/WG83xD9sEB/vCR9P0jw6wiWcH1yxOqLqKpDVPZIC4t60XE4VXO6+sT8vOKs6rlhy/f8vQLd5B4os5jI7jbjREBjHPIUtKMYdNluEyQzBp2RYavNf5gSF4p3OsEdYDEdYgQ6OL+hFV5gxOS+hgRC0t2XmCtQO4USgbidU/A0yc1Pg3EKwdF70E2B1BLiVyqXnHbQag12a0lfifQdW+zcYO+6EPfOXAjgR8Flgyo5gq76EN83KXFfjMhmIC9sgj6uE97Jlg/g62P2R5zrlxBWWbEeUPlNa7U/NTv+w5yHHBSMHxeoG2gHgfSSUVbGU4fbdh1GWezDWXcCyDjacl7792R6ZrgA+1jy+AhYEaC7tbyZFWyPeQ8Gq5ZfGGF7yReGqKloHiXkKaOVPb55DfLGaWPmT7dYYViOix52Ock86rfhBEY33oS09IloD+OUBUo6YiMZZoXmGlD5SOqCQx3Lc1TgbaeYpfwrY8fM5cFq4cxy3JIc51w+HTEfTnCdn1xk9LjnrR0BsyDhIuG6Y8vycpAfCtJrKd9m3BXjDkkEcNFgX+IMFVA3yncxzlouJUJbae5fjvnuMyplwn7NsdmgsLGPDt54IMX11BqfuvhESa2ZI9LjmXyua8/YZ4XlCZC1BE0gmzliDuPDL2INXzY0g3AVZKiSXhdzQkzx0OW8TBLKMoYMeqwlSZyjtOuJE5a9EVDmreYGup1Qr1OsTFMJgVoOLlaYzXEeYtwgeePH3CjDhk84RS6oeeDiwcmVwfaWKK/oYgfPGNVQexxz1qUCIjzjgjL+jBiVyZsNgOqbYrZC7RxjETDZjlg52Me2phRXBKdV9x+NqE9c9wPYr6lR1zkR6ozwfZugBw7vnh1zdv5kM+KCeZFwaC1jFzDl6o98VZinOe8KthEmvHpkaflmrtqhFeCZZOBh20cEZ3WhBaaLCBijz0Y1m/H2PuIs8EWETlE8CAEduypFr1w2u01goAet5jUMTw7orWlmwTqkWAtUppcILaaFsWxTKAD9axCDhyoAB4uFyuia0n2RmC6QGgl3komZcHVaMvzH74n23rCvUHJgE8cxybGbQxSeOwjj0MSRoH2mACS+kTRXDkmg4q21eQHh04tcit599GCZmUwW0gPgagGl0tE6qEW7LoYf9QcjjFrG7N8O/2B1svf08X++eQBOepYDA8k+8BkXBB7x7N0ie48t+mESV7R1hq7cHDZUGiNih1BC7CQ3IEuPfE6IEuQpcAfNJs25cFnGGERBwk7w6fbU6ptAsBssSeOO3Zdit5JvBNwFChjUUuJSwOyEER7z3RygEL00bCdR+SOLK3wFx3dOMBlh68UpvHIuj91vdwvCAeNmFkenaxZ+Qxjel55eFGzGJXsuoTyiaA+UcyiiueLO+ooZvJje66ePVDe5CTSYmKLb1SPmlXg9hqTWrLac7HcYmPBtR7yaLTnRFXsiSl9RFdHJFlLvKgYDivevZpRXWcMV552KJlEJYd9zNuPp3zyn67YfWOEnQQGlWPQWmqlefzhAz9x8Y7Z+Ig0Ht8J4rzmpK0IQdDeJ7QXjuOLgJt5xKBPfwsHTbwNhBOHnTtU0kGhqNcp9w9TQqOQNmC2AXKHmzjcXURQ0M4lIQ00JwE7DNSzvuOhy4AfACbQTPoUQGcEOEH0YGlO+w5BUAoqSVHHpFnNJCoIAfzrGPudjLVIkQ66EVBLEIFJKGnGgfGwYGEbrm+nzOqSN9s5hYtJVMPDasx8VBC/39IEzXU8ZHczwlmJGXV8tpz3mfF7iXxrGMQVOm75lcMjbCwhhvGuFwW9WWZsvxjx6WRAPGu5vZty+2pOucoongvs+y3DL1UoD6dNS9toRqMj/llDUhqSraPYJ3zwxVuqRPNmPUfu+3AlUUsckvoEujPfkwnXlnuT0hxioiNsvzElRILdzYhql5DREArF22KGN/BUbpk8OTBeHBmahqc/dM/l+yvkg0G8SnAPEe1zS7lO2X005c31AuJAGDv8hzVPf/wd5qrC5vDoh1boo2QwLzlmgu8+nHIoEvSnBhX6vHsz6BjnRxhZ1E5y8uC4X07It55MtBw+GrP91oTQCXwraXYJ5P3oLTQCs4P4CKlucTcxdhP3TP1ThxWKs/mOxytL2lj2TUS9TVithwze3zOb7hGdpKxirBRMn297EefEE1lHojsGk5I2BxcH3lVTus/BURdXS/7zr79geT8hnhaEy5r5tublR3OO9xlNG1F+USCGgbJIaY1CykDypGB4eUTtBam3jEcFM1GhXSB+XpBby6HphXBVKomjjq7seRWMPLISVHXE/WbEwUgapznOFIdjxid3J/haUw8VF1nB8TrnuMn59stzbtcTDj7i/UdrZoOSLC65j8YYOjJZ4WLPYFiTxS3mtYFGEt0p3EOfGRFqhXCC3TojvioIVx3JaQWZJ0wtYmZRru8I+Qh8APWJ4VwfSLzDbCHuHEPX9vyHvezBXKeWso1pg0KfNIyjivtXM+pEcfwAwl6RjBvkXrNcpCybjKXPaJXg9IM7YlnTU6dgPj6iZ5b30wdE5LFWU5+AT/uwLJO1FK1hMdyTL0oQASEC7qzPCkEF2vuIahszfOhTNetUwF1E5zTn6Y6wMbSP7A+0Xn5fxf6XfumX+AN/4A8wHA45PT3lz/yZP8N3vvOd33ZNCIG/9bf+FpeXl6Rpyk//9E/zjW9847dd0zQNP//zP89isSDPc/70n/7TvH379vu++ZtqxFd2a058QzeC0hmO0nDTjtFHSSkMu01G1jj0RmLfJpy+66ARDMcFIli63CGc6AlLWtDlYKWEQhN9LUY3AYvCHwzuJiZVHfpW8e43z6jLBLHV1JM+x72YKooqRV1rJm89J+HA4tGeUVYSzjtE7picHRnGNdmg4oefv2bQtrhO0uUCJxX54yOZ6+Da0C08ci+4jQaMHu3JqNk95FT3A/Kk5BEVd2/GnDZH6tuUly/P+M67BQ8vJ7z8ziV61PLi5B5jLKP3D+ShI1t3yEFH1UZ4B79+dsGNzqnamI9WJ1zfjaHoYTzJaUl5jOnuY2TbK9+l8HTT3lonCSwGRxg6Gq3YJgn1JmanI/LLgum2ZlnF/Jac0rQSpT3RuOMxFQ2aMPTMZkeSB4muQCaOsIwIJtCNAiEK6L3AfKJRHXjTE/Bk1z8MghSEYcC9sMhj/zWSjUV3YMYtg6xBd6HHUxYe1YZ+01WBKXpvvTQe1XkYiF5tXwJRv6mKth6/NFSJJB43DAY1NBKmAjf3VCMQeY3MLPPZgXAw3B2ntCZw+XhJULLH+74zyIPiu3envPruCbe3U45dTGcV5hBI3yjsgyEODmEhvZZkB0FdxzwZlpiThvpNyklSonzAtYrZrKB7SKi6CPcqoU4lVRPxZLDkan3ky9E9dmkYjxqiaYNxjv3Q0CWKXyvPEDpQK8U3P75kMijgzhB96cDRRcSVR236vImxb5lVDflPHMhvBY/mGyo04Qs1bhQQPrCLe4bCl770hly0NJ1m82qC2CnGJ0eGp0cO+4TSSDqrOMQGBgFba6IDOCnIQ0cAlssR7jbm7mbKSVNyMtzx0WaGjWFzzMk6D7cRai3J04Z4AxwktjJs84ihrxCzjl/ZPCbpLHLcEVU9pe2QaXYiJk0aaATLNxPqVUqVKUQIPflvrZBFn4ooC0EWHCNRo9dwF8Xc1GPySUk+qhmeFohWcIwN/iBpFp6Bs+xeThArjXKCysXkg4phVON3EfGsIYnbz79GxbvP5pB4goLxVUXZJFQXqi8QuWDQdbyIdvzwBzc4BCF1yK1ksHfInaLeJviJYy8TGmWgg93LEaSeL+gV06hERo69jnDPLeUm4ccWd1yNNpyzI70PPJlvGJQwSypcFshly+lgxwfpjuprA5588EB3jDicCPRZTXLS8B+aS9abIcvzFKzg9OmG08Ue9ywQPSspRcTzZw+080B40cJ7NXrUotOOJG+Rw4DsJKmzzOY7rhYr5qFilBS4mWP8bI+OXL9pcQnFuxy1liRRh601RxXRzD63ge7AWc0iOTKSFUEFqibCqr68pdvA4MUBEQW6h5hmn+DfJrjPIh6bEttpmAseLXYs8j3VXjFua15+9xzvJcnOI3PX/69eFrx39sAPfekdbaw4RhFhpZEBxFGhdgr2iuhBIUpJcx8hbzSm7PNCxr7hIGLyqwNp3H7fNe93s76vYv/Vr36Vn/3Zn+U//sf/yL/5N/8Gay0/8zM/Q1H8v7B/f+fv/B3+7t/9u/z9v//3+ZVf+RXOz8/5E3/iT3A4HL53zS/8wi/wz//5P+ef/tN/yi//8i9zPB75U3/qT+Gc+75ufrUe8YmdsAkGr8AeDOqTCFdrDjLC3Gl8p9HeE98IBp9K7EETdgb3aUS00ej6c4qcDPjMY6TFrCXZO09zEahjhawFQQj0vaRepTQPCf42Ir3x2LcxvIoxW4G8NshbwfaxYvDBHvOswaaw+7U58RKiWzi3W/K85vbNgm/92nOCF0QbweOn9ySnFc5JjrGBNHBp9vhdhBGe+48W7F3K6LRAHeDBpOyFYvjoyH6fU9QJVZHSppJG9d5QVykOLuL9xw/Uq5jotIEvd9iTgDCBrlW4g4ZCkckOX0i6ESyGB1opOewyRAjY2qB1y0jUcN7hph4/hGKXsHs7wllF+cRTDyTCBFytWB9zuoHgbHpA5IHaxgQLF/mBV0nG5t0YsVZ4J3AiICJPqyXZRYHPAgLwKhA7h7ASeaMIoVfHyg7kTmEqjyrAf5z2ASlj+lwAAZSKsNZI23dtALJRha76pLt6JqkvA+3CoazvA3fKQJCgN4JmCt1UYDbgj5qgA6U0qGHHSXmkMz2S9A8/fwuN4vrXZtx/OmNeNZjYcz9MuLnL+TDdMtM1Dy8niIPCtIH4QSA/i1BdIAmW6ahieF5Q0wsw37gpu4Hi2Ca8dkNOmwYztTzcpcxO9uziCDuFporwbxNCK+E+ovIR7+IRx0yhjKVeeI6Z4FolqPOWsO/n5NOyoJn1M0m5EtQ3KdJ49psc2UHuOqK0Q2WWJO34kR97zWVeYWee5j6BRqJEIPctOvL4uk8l/OjVJdEGxqJhNj9wNIZNkaOtp/x0QPEfZ2jpaFKBm3iSSU2cWJpCU06gGQjSacU4q5jcOO7rEe/enDCoLJ0SkDtkCEyfbTj/wgPdiUOfNeSnBW7imJmSUsf4QiNXkt2nI5KooSoSqkghJo52mXK0CeOyZTQs0KUgEZZ27qmmimNu6Eb0QJSThhdhzZnutT5uL5ENVLcZ2bElFR3b6xFHYtpU4ipNsU3ZbzL8xBPVnixqcE6yXQ9xlaJVEjt3cNHxdjvD3mcQ9Xny9l1Mtg3IjWJ8duD98QM2CeyPCd+5OSFqWx6dbTCxpQmG9HHJ8Kc2yK1gvtjTIElpGZ4faEqDO+lQ1vNU7wiFRqwUM1Hy3d+ccRPlrPyQPLZMdcVGa4ZpS1trdJC83C3w2rE3mjfXM0zWIacdZZngCs3+OqNJBK5QZOMKtZIchSG5FpR1hKg9X//kErlUtPcx5jPFiT4Q64bL/Z6uVYxDhx3CajnBeoWygZKIq/duiQ4OMezo7k0fuf02wXaSYqQojOkZ9gTcqB8bipXisMnwC4eUnthazOFzuFbqWTcD3CcpegdSgH5aszyM+PZ6zvtmx2l2BN/S7FLaOOE6zPCFRuPonnWIzOGMoH1I+OjlOd96OCc7LXBB8oXpA6O0Jqkc2U0gahzKBgZRzdjVDO9rEtFilzHbkLC9H1K+HKBWP9jGuggh/I71/w8PD5yenvLVr36V/+F/+B8IIXB5eckv/MIv8Df+xt8A+lP82dkZf/tv/23+0l/6S+x2O05OTvjH//gf8+f+3J8D4Pr6mqurK/7Vv/pX/Mk/+Sf/u+/TNA1N03zv/f1+z9XVFT/8f/q/oXSK1h0m8rSl7jOI6Wff3gTMEVQXqKeC+ODxSqKdRz5pKNcpNhEI7YjbQGMk0V708Z5Vz1KWNiCswKYCjENYQTsSZPcB4T0+E8hd30ryOiAiS3vat/UvzldUmWa3zukOEWbcMqZm3WUM9x1GNuxnCZlpSeO2n9HPAuLz9KT105h82GCPhmYbIUzAVIG2lkgFp7Mdh7FGBNgvc0wDfm7J0obqbc5w2zE/PdI+dRw+HcBpx3GX88XHd7z9ZEpzrxn+6JH8tuNhOSPMA8J4yrZnXsfXCj9xmFnLKKs4fDRmcnokrgTXPiMYj32XMjvfcx+nSALRnewL5uMa8SZCZV2PAY7gzBe89yPX/OebJ9ivjYgvSrIlLEWCfNxi38ZIIJ41yJcRjVDYHKICzCpQzxXSe0LuUbUgNIAGJxR26hh+F9qhRNcB4QWi89hcQNzzwm0uaCagS0E3EujGgYS48fiin+m3Q0G67mN/ZRuoTvp/yPbEE00b9HcM+rImkQ5x3uLvNem041yUvF3PWJR73jzJ0dMWcWvY7Iac5nvuVwN0EgiNhI2iy0EEsFow0g0mbzm4BK0djdXooyQAYezQK0ljFHbqGfqWNu0Lvb5XeA2TpqFIDG0OQ9uS07B4tOGbr55izirsJkbNW/Q7TYNCOo/PA63SnJyuib1n3Q7oSs3VcMvd3RjmDisE87JFZZ4u92xuhrwYPXDcpZSZ4tGzB46HjPXtmIOLeRpvKG2EO7OUb3OGk5JmBt27lDyrETqQNR3ruzH+acvswdLGkrDtw0LKJmbyeEfxOmOiG16JKcNJgWogSRu2b8bI3DEelJQ2ohhIvjy44ZOXFxyJmSYldRkTAv1puRYw9CTDBiqJPErG04I7kzGwHavNEH3SYm9jzFrgp33XyHmFflTxo92Kt1lGaPpkvWOVoActgxvQ5w1VEzN/vuH2sxm1MwQp+ML5Ne++dYo79fgaGiLee3LDZ7sT9Bqu5lvedGOyxlIZRXeIgH7jFJ+0KAH13mDSFp16VlHMZXJgVyaM7hoexIBWGkYri089z3/smptPzqiHgvZgOB/sehhRF+FHFltqTu9bViZmYi3mpKaMFL7QTC533PzaGSEozHlNGQyx80zODhy6iEHboY6SdhzYf2tC9sGR43eGzM72lFWCvmgo2ojRxZHsECi8obqPkZ2kngjCd9N+rKICovIk75UMV5ajSSiFxinIRg25bNneDKiapBfu5ZbolYZ518dO3yiyg8N6jYsFUdJQDvvPV1U/hlNdYJYfKU8EbWFIS8/ZbMtrM8IeDGnj8VawzyKkD5x/5Z7tb0xRJnBxtaHbaG42I7KhpRYK/zKmPfdMu4bOKcRSMPvCjuNv5hwvI8KThum+pdQRY13w8HpBt3CErSZWHTL2uEJhukCNAStwpw6Oily3qEnL/k7yzb/9P7Lb7RiNRr/TMvy/eP2utha73Q6A2WwGwMuXL7m9veVnfuZnvndNHMf80T/6R/n3//7fA/Crv/qrdF332665vLzky1/+8veu+W/XL/3SLzEej7/3dnV1BUAY+15I4RXdJkZnjvQuQPC4NBAmjnYKNoNo38MxhHOUjwKZaBGZI8kafAp1DjiBjQNtDkQeOe6w8966IbwjPTjIPNo4wo9WFF8AjO3nORKKC0HzYSDQf627lzOa0hBKhakDHlg/jIjeSGo0dRYRecfxkBJbT/rlA2FriB61HC4NagOHm5zyPiVdVIRWEE1aUBIRB67rCdv1gIOLUXEvrEKCe5USz2qM7FjLlPfiHV9Z3HIejqgjvNlOqM8DfKVjW2S8qU9JZo4vzJfMTEPqHH+wvcaYmrDUXIwOnOkj8qTlqBVVpXAGVClxI8/KJMQHiF7915O6JL4VeClobMSj0RZba14fZ/zq11/gXmXoRcOTxY7uQZONKmZ1ifk8pzsJFlEIQhaQlWAaF32U7KE/8aul6oU8Y3AnHl0HzFL2QRaxwBtBMw09/z7z1CfQnkE36At9SAIqteAFpgqEvUJ4UHX/PVzuaOeebhSQNhDtPUY6UtuhnrQc2ozdwLCqBiQvCtrS89HbGYcJjGYdZ6ai+ziDMjBQNTcPU9opTJICuReknUXX/ego6gLd3lC+HSDvNO0yZlrUmG1AX0vMdwx21Ct/008VVRkhloa5r9DeoRpBNRYkT4/M5kdi07GcJHxz/bj3ee89wUrO5IHQSPK8Zjw+Mssq4qyl8hHbMqObOQaLkrf1mHYoyKMG8wDRoGW9zclLh6lg0Hqu5nuK6wEf/cen3L5dYCLH6a1l4i3NKuHs3jNUDcIKZqrkcrRmMj6yOeR8Vs1R75eIQlKOJF/5obd0jx0bE/P08R0HETN8WlBMJCrvaJ3GW0lbxOTnBUY61j6lqQ3ioPjG6yvqMuZxusV2isYq2gSM6rh6ekvkHWIl8RL+0v/+l7maHPjR5JbstCQo6O5jhAqYOiDWms4ZxqpC7BQ3aUye1tQ5bEJKOw3kdDQjSBpHfZuyrVK6gSAyHelZQdgpTOjJbfOo4vJizV09QtTQWsP2IUcW/VioaQzSg9hreBVRWY0dWWTqGDw/smoyUtnxcDei7CKOpHTHCLnS2FxyNDEv2wV24SneZTjhyNLPSYevY+x/mKBuNetxhEo8K5uyeRgSNx590bBejhkOCqZfXlFuE+Tac54dOfiI9l2GXypSX+ErQZK2jOOKD09uIAkMnh5Jm47FTc3u9Yh37+asjznqaYucWp5e3WMvHG0mUZFl/iNb5NSR/eiBQ2ww447MetrPMuStZjEtCacdP5SuUDUM39/zpfkNPzZ/jbvqkF+seuJhbmGpkRWMVMMPn93hB55JeqQqDeFljG01BxHzUTylJCLsNc1DQruNUV3A3EueLC0/kq5JZcfbXznjZpjSRZpdouii3tVzyZ52HuguHW2sKMaS0//jLcmsRr6OWd1OqZYJ6ahG5i2+lL0jqFDob2j0rcIWBtEE0rs+pvjy+QPJSQlWMJj+YINwfsfFPoTAX/krf4U/8kf+CF/+8pcBuL29BeDs7Oy3XXt2dva9j93e3hJFEdPp9P/tNf/t+pt/82+y2+2+9/bmzRsAlPZYLbG5pJs6Qhoon4EdCIQVqI0kRAEed4gMCALhJNIEuicO86KkFpqhrpjomhD1wIk2EnSPHeOLA4nrs95dKule9Cei+ElBPqyJtMWdBz54/ABf7ouVLANRETCqw8aS7ps52ZuA9IHZR5781oMViJ3qcby7mMX8wHaX0R0N6kmJaCGsDW2myH1H4i3jqiEylrboT5ZiZMkHNUPRkKiOLGrIbh2ToqWNJdLDJs9YipT/6eYZ31VTghScXu0ZVRbtPKYOyK3s+dPv33NdD7iJYyY0HLYZufG892TJq9WU1XdnPPU7ZC2ILirG8tjTpaKAfGVQrzTtpGeph0qyGsSUJ4HhoOKRLvnxs2uizqN8YB5Ksqjh1WdT0p88MGigOuS4oGiC5nibw9wRK0u0g1U1oMoV7UigBpbu1Pfjj9Kjt73Oop3QW7z2AWcE6dbjjEJVgvheInMHoofu6LVDLCVOSyaLEpH1qNz8pEQA1vSjHa/k52QugagFw6yhjQQh9ZxEJef5jtv1jKIZUPgYpwMHK3jz6QwhoUk1RZWQi5b80Kv8q4GiIkJvIRYdjC161OIddFoiZaCYaspU05yCHcPU1WjVz/PNSiKXkt1qQHfpkRNLozT1ISakjrVJmYSaaVMzLC06tYyzkuYhQW3AWkn7Jsc2GrnSiJsIIQVpEah3Me9P1/zk8IbNw4jGGO5DjheB248XFLHm15sLftOeop40+JMe7uSGjt2HntVMwmnDy3rE3misU+y/M+G6HkOhOM336AaaMkbOOo5Nwv/87ffQWPRa8rqY499qDseUSDgmaYUbBsLUUeaCskgofMR02yJCwAwbvOu5DtU2IekCugZ9p6nrhAMpjy5XXAxW+OD4p//5J/hWMefNb8zZHHJCHNCZJXklsZEABPP0QKU1InMw8NzeTyje5cyPfcRwcUw55pobOYQvVBzXGWHgENoziwuqiaCYKNrKEIahR7p+kuNXEelVwXGXUDtJs43Q3uPOO+y5xT+12FZjtxHtOJCHhjhvqR9SYmNJvENdWsQg8N78mqfnD/ig4NqwWQ2wncJJw7d2p6wHKXUiUY8ailTjMvCtZna2x04898sJ69cTskHNXmcUr4aM6xZRK7Zfy2iXMdKDvwzc6zHtKmZvYpbtkNMPd+Tjhu5gsLPA7qnhbLjl6sUdibZMbzpU65lcO3CC83iPs4rDLiWpPG9Wc04utrw3XvIs2TL4wp7DNsZ2gotoz24WqFNJLQwPk4SPyhNoJKNjzYvLFTJx1HPBcFiRT0q+Y8YIHVjOEvw8EGYeWQqMtthVjD7AIK3pBj1XQJVg546vf/2cT6OcaV4TvVdibCATHeOqdzuFmcMPAnIjiHcw+7EN3TZmux9iU3rn16IiGTV8fHNBFSKytzAPJa3RtCOFjSW6tigr8LEgH1WsRhFu4mkzgU5/sAK93zFU5+d+7uf42te+xi//8i//dx8TQvy290MI/91r/+36/3RNHMfEcfzfvf7B4xs+ORpcY1BvJO1A4WKBGvQCt2BAtoJaGYwWJIlFjC1tiDm+GYL2RAdQI9gfc5KdBxmQjUcFASeKRmpiHNZ46jpCSChf5WyHgmgFcup4bQeoyGErg0sCmQpEn4JMwc487VEjt4JtnqACxBtHN+hDWlLfUTrDwSQkn0SMnxxwog8QUStD3Sny0lOaFNcZJrpkWSWknaPeZuhZi9gLmr1i/1Rw5gt8JTnuhr3HuQ3IINjqBKJAqDSxsFwkR96VMyoiokPgo++e8yNfes3zneHTkzGv7geMW8t3lycIB8eTlm45JAwlb80YIcHbvvDxyKLfwajwNK0kaEFaex5f3LNZjXhoYg65QScWpwPuUUenNdE7i1QRD1OFDAGxB3mU0AaKSOE9mFRghx5vBNHR06UKESCMHKIRIEOPrS0kMgA6oJ0nGKgXoEqFqoBbgzICpEdqiZhY4geJcAob912IwyFDEEhXHhspggIfeaTvvb9p1Qdj6MSRyI52o/ugll2EOe3oXmW8WQSmScU2NeyrjDx0JFnHRqVUKuvJji4lCItQICw064RuBNpYQu4Rba/wV6EHCh3eDGhOJOPTGlEFyirFzWxvszMBbRwuSA5Vghs6ZOdJzmuW353j8phuaQjPO8DjtSIZOA7bhOGuwz93dEvF/L09s8Tz7eWM7dbxweiB15/NSXaCeFGwPks5HVYY46g2KYcmIcgedNS+zhnLhrbO2UxT4sSSVOBea+Ifr9DHiKXJiQ6CL0xXvFqPqfY5kkBoFTIOtLkgXTTUD0PStsKNYR4XfGFyw3/55nvYCLQMSBzLaUxceUKpIIL0XcPjH3tgOm755f/8Pt08oKTgbN/w5t2c+ZOSmaz4xCwwlWf2o0eaNkEVAusNfgFiZAlHje0UWdIyutyy+vaCSuueyjlrietAlThCGaPHFfmsQN4ptiEiXXTcvlkwSUpkK8mSivImo/2wRawtJ75leZORv1fywhR859Upie9QG8F2qDCqJdKW7TInvBM8IJktOqKnd7zezftkzH2EagXv9JyBKrj88i3NZykqsWTPa4r7HFFKTNLRTAIyWOamoss9I2ep1hnx0NK2KfG8RIaAiwQuCdSbpN+0nFtCk1DVEflScbbYch1PkG8Eu03Gb2weE1t4kq345rtLTNbx0i2YlDUmtpTOQBZ4FU0YZSXzsyPNSlIpSZYEhnWf03H3jSlVoykwhLlEbzytBVcZTC2pTyS7JkUvFfl7Je/2I3LfUUSBqGrYbVLU0eBWEabxuAso2xihA93MI3cR6RaC0dRZf8hTZd/tU3eC+hRal7DLNCp1lKuUD+YP3DUDquuEuLB055ImCSgHw9AxPGm5WY6oDwnDWUWI+vAc+TqmjQXViUR7RVRaknUfb0srUS6gWqi2KdGbingZKGOFm/xgZ/a/o2L/8z//8/zLf/kv+Xf/7t/x+PHj771+fn4O9Kf3i4uL771+f3//vdP++fk5bduy2Wx+2+n+/v6eP/SH/tD3dR+ffHzG6L2Sgx0QfrjlJD1QvB5QRBr3rMXMG3LT0XyU00UGN+k7AcO8ItpBITSzRzu2r8ZERxAIrAYOkshalr4vanboIHUI1ae4zX1BezA0c4EPkrbUeGsQyqMqQYtC5oIgBeZB9vNZAroO6MIRdB8GoeqAO0ToN4r0ix3mvQaVdkzuHMdhTFMpTAl1MHSlZjg9skkM0QbEoRccdRuDOkrqs96GtvFDtPS4oYejYjQt+jZ15zHGIZOOxcmRb316hZeQ6xa5FpQYfvUbzzg92VEUMW4MD4cYEVtGccP7l0teFqeM4gqzjDmc9C0qdd70P7tLwcHGyKFDjhpU11KXEVXQfPftJdMXewbB4YvA9n6K8DA2FSdpwc4OyVzLcp8QSYv1BjNrsDuDS/t77PYGZyT5rGB/zECDTD3OStRKkpYBrwXtuLdT+kgiXCAqe756Ow6oLqB3PRQpeQX+wnFf5ogQcCnQgM0B71HeUxmNWQl8IricbrhtR7gI/FGSpI5o5JlsLB9PJ9hDRJcIlu2Q2WxPdB0wAw+RYFtnyNzhXyYI64i30OW9l9d7iR95jHKYxNIYSdx1tGeCvOrYhxwSUANLe5TYWWCWb6jvYtrzQDWUzCjZfjYhzh3jnWNdj8jmNfGjGiUdjHucc1wE3nu24u5+SDJqSS+ObCcRYQ/Vbc7HueAs3bNuhpigkQtLdeLZtgN8rTl6j36nqMfgjqpnDAToUsG2TYhjixaei3SPHlrWYsh2O0QMOsRaEcaWV90AD+jPNGpokcKDT4hxXDUlH8UDwmlHZXstiq4S8h/a0n2W9YjjmwgRHEcfw0EQHQOnjw581MyxrzVp2nKR7TjkEVYCaU9fvOvGzHUBR8XxkxEnsz332qCCxzqNN33imy47Fosdbz9dUDYRIbewNRyCxI4VbmVIZw2+k7it5nA3IE1LDq+G0AlWImPYOMof9sTHBuMDpYOm0bx3tmQrIj75bI6SgbDwFE7x5ek7ViGlEhqx8iStYDOPGQXI7yRdGSGUIDKeALSbmAcMemsRDkaDCkIgGR14kEPaOJC/FoiZRD+ziIdAe4yRp22fUTHvsEKyLHNUYpnJI/dpjJ0EylWKDIJ0UtNpxdIO8EfJHz9/ySf5kFfbMd2k5bPipNfRmAi1UdSDiJPogLiwVHXKtKyoZcxCVpRPA6f3itWvTjicSs4Lz65LScYNj+uS89mB//LmknCtUecNciWIHjQ2VqiLjpFuSI6abZcgk4BOOqJRRblNaBaOsJHInULbgPeKRFtU1lKPYnwW0E0vrEQIRAX1VGKGDaoMzEZHro9jHp2tuAwF125EflkiRSCrW8o3GbUxvIwN0XnDgYQ4cugHwWFkCOuY3Lcop7CZQuzpn0OLgJ95KqGJBy2sBcnjGkaO8iHDG8HJtvqdlN/f8fq+thYhBH7u536Of/bP/hn/9t/+W54/f/7bPv78+XPOz8/5N//m33zvtbZt+epXv/q9Qv4TP/ETGGN+2zU3Nzf81m/91vdd7BulObzKmSwOfDBaMrnYM/mJNfG05uJyzeBtoLhPWQz2xF84ED6sUc7jnEROWhYXG4qJgEctQYJNBJqAGVraP1wTPmiwp5bBsEJLR1x3xGlD/SajdhGjruXDZonSfarbf21MuEcdxRNoT/t5sXIB43v/aDeQuCQgBLhEEAQUp4bwKqH6xoj9Z2OGaYV5JRjEDW3Sn96E90RHQbKGLqEPYlkLstpBGoiXAnUEsVHIgyAf1MyfbGhuEzLd8ni65roec3cY8/p2QTGSzFRJNQk0zz3RHkbywFsGlIcIW2lUZpkOjxjT8dHDCctJxG2cYxeBoW55lq8wVa94LTYpmeh60Vqr8ESs6wHVPsbgKG5SVi6mcKoHE+Ul+aTmYRljS812OcQFyaPZCntq+5N+5PG5o2oNLu452PW7HHMnEWvdg4yQvXVSKkIQRGtBN1QEIUjvBCpxSAvpQ8AZerCOCvhYY4sIAugikDwEguidEepWIRqIpEO8aKhngTs3oJsGrBCklzXrJmdkLPWpQEUOX6levb4OVEVMOPG4WtOuEkKA0AqC9lQTqJ74vnMzdD2OWQAK2l1ERke9j0mSllIa5FXNiTkymlU0WeAkPaAua9TTBruK8dcxh/uctLHYhz5oSR4V9TGiTBSbkFI7Q1Y7RoOST+2I6lRwPBds2wz3JqJBs6oHpNbxsByTihYfCUzkUSEgDhJVOiZFR9dpuI0we4EqYXJoSV5K8tJStBGKnvD2ejunrmO6iUfdaNKThnTQMJIV9RT0tIMHTXOMWO8GxDt4+K0J3gS6r+WMXwXavWF/zGiqiKGuCTtNeNYynjQoAqoGt3CoRx1p5WiFRL7SbN7MKLXh07enJEnLpk1Q14pqk1JmGjd2OA2hlbgY8kXJoLI4JShPDDebOdZqlAicRQVD0fQ2qmOESwLxuKVbG+pdSrfwVKoXd+rzjsWjHcEK2rcJx2NC+xsGvOM4V7zuxhwPCdmzgg9+5A2LqCS/r9mtEpwW5MGhxo50UtCieHAZ2zxmsA7Y25jqsyFWSFLRom2gqyTdAB7uJ+yqnPMnK2b5AXWtcTkcJpLlfoC+kbhUIAvB4PTAe4sH/nj6mtlry49dfsYJBWeDHX/s9Ltk85LuwuILRWgFzTGCreFbZkRY1Dx//MBXrvcchKGaKDonOZ3sOHmyoQqGm8OEokgRY89gWPLSDbHvEgbGs3sqiYcd54M9aqmZNRYyyyftCL2S2FyQDyo0jibVqImjWqWs/8Oc4k2MjQRqraGT5IOKuAjEe0c2rokSS/qsIM5b5o92qGNHyBxoj+8U6nOXW7WQKBt6981Gcn2/QCw1ZRvzWTHGIxBaoLyg26fMnuwhCGxr8A8RU9tglGfwwZ5IdZhp28O4IhgnJZXSBC8JtieaRjjcJmJ0vmH0aU00bqgfO+LLmm+n8++r3v1u1/d1sv/Zn/1Z/sk/+Sf8i3/xLxgOh9+bsY/HY9I0RQjBL/zCL/CLv/iLfPDBB3zwwQf84i/+IlmW8ef//J//3rV/8S/+Rf7qX/2rzOdzZrMZf+2v/TW+8pWv8Mf/+B//vm5+MKso2wnHMmF82rBrB9RvckZPCoqbjKNOmEyPhM8Uw9hyexhyEpfEtua+mdCuIvK4Yb3w+C/VXEUbrm8X6EFN2Rm8gMnJkbIwjLKCo0sIQeJ+qkSvNMdMcm1PSRtHPGrobM7ClchxwwenW37t7oKqivEZiJMOao9aK+pUIlpBs3DEO6Dp4S+ik7S3MZ8VF+iFZzwpqCrN8KLmYrrm1fIEhyM9BpqnjmeXd3R1xOtqCjcGe2EZrTzlQ0zkBasmJ1m0NI2hepNxPtvxYMecTUpECNS1YDE6gIHTrODueoTwAqkEoYZ6n2FDwvtffMunm1PO1YFDZKiXCXrW4h5GoBXPoiWfThWHOkF8KvAa9MZjE5BTTzURJKXg+Xu3vPn6BfHSse8ymoXB1xAmAeMDofHc380QuSBNLLaOEJcd6p3GDQKjT6GeSHwqiPa9TY5OIHzvhHBZwHQePw+4g0Z0oEuNsX27MkgBQqDWEnNweCV6Nr4S/WZq34v4fOgpe3rasnj/gTfvTqCBxDk648i/rZk8KVkPBNdFgtSOYID7iGbuSJaa/dNAJhqqKMO0AtdGCB3wJiCPED8q8U6gbjVJa1GR46BTzEFDEvBWItaKOO1QM8tp2xCqQPVpjpAtopGgIWstvtNUSmArhYx6/v8gqpD3gnWqIQr4yHFyukZfjzk0Ca1WiNrjckEUHLZVOC+JRy3FQBDKBPuQ4QrQRxDesy2GNHNBcgfxeUkhNO0HDfWtokUxLhrcb8bcDxYwgC4LyJca8RA4LFKOVuDHHmGBG0lzFkjTmqAENg/sPk3o2p6dcJa1HIsBD8cJYWIpSk2VSayP+nvPHF0aGFcdN69mVMqgCknzwhEFh2qgNIbST5i6IybqaO9T8i/uuXq04fXtDG8Cj05W7L475PzZmrtu2BPqOkW4NiRnFV1QVFIzykp8EjBnNeGoiVrPbL6nzBWTsuPtVFFtEsJRIq1Gdx2MOw5nMY/O1qiRY7fOWYSC7cOIzXbMrs2Izlr8hWO/yznxNVeTLasy5VzsWOmU9WZAO5HodcBedHgnqZSh22viq4JoIyBrCQo+vj2nawxmYrEpqLVifHGkfGZoDobUSYbXliJK+HaR8eRqx8t3FyzvRjz9wi0PNqMtIhoV41OPmoIvJeKgeVeM0cveunadStJVYH614u1hSr7tqJqUNu9T9zLfkraeK7via80jDj7CxCsyU3FVHnioh2RZw5d+/yt+5fqKthXIJw2Pm5Jh3XD4yYrtbkgiO8rvJAQn6C5bglR99HghqU4lix9e8vCfzlCxo3aa9pgyySp21yNs2lPzok8lxbNAoyHa0sdvpxC5gHrRwl1EUJ48b1luBgig0hFREWhQfHG4wZ5pCheRJQ3LzZDJsGC5HmE7hU4dQYNfGra5RrWSoKB8GhCVxLYB/bjG3uaoFy2rVzmykQypUYOWj7+vive7W99Xsf8H/+AfAPDTP/3Tv+31f/gP/yF/4S/8BQD++l//61RVxV/+y3+ZzWbDT/7kT/Kv//W/Zjgcfu/6v/f3/h5aa/7sn/2zVFXFH/tjf4x/9I/+EUqp7+vmGyOJBy0+9bx9M6UOmstsS/MuRgRoBpLj9YAywLGTZLVjo1PS2xj93DF6cmD1csZ52LIcJby9n2NrQyclIuloq4iiEmjraLUhzxraY0R1FzEcV+xuBjASmDtBcyJxQ089D+QrzX+qnhAVlnxe8/5wzcfdnE4KTp7uuPMZYdd7/EVmuYh2HN5mHL8cyN9IjkOFPkowJYnoiETvdy6FIXUt7bnnUbnl5tN5vwPWgcGTPUltWbsx0bzm8sUdr+9PeHxd8taM2dRDpo82pBvHKzFhZBs2w4jUCexB88CIvJBoJyinIArZax5KyfIwwmtYHUZkZUm9M4TrCJs7rJK8HM2p9im6BRk8edxRnUOoFVHaElmBSB3vjmOYWbpGkMqSZgF2p1EN7HNNdi9pjEQCO5PAWYeuBOIg8RraDOJV345vhr2VLg4W1UrKM4E5CqSF6F4QWpCdx46BQ0B4Sbzx/WamgyAg2VpsounSvjsgCMTawf/uyOGTMe+/uOdVOcXeJbhFx6Bx+DRQd9Amjo+uzxjPC+p3I+TcEpaG2DuaE4leGdSmPxlHD71v36WCaCUwu0AXEk70gTubcPHowM1xgCLgjhFeQp0qtNHM1h2dU7wejwiRoj3TyFcpNoUo7XCJwB4UIeptSPGdgBNL1Rh8Hsh1iR0pss5xXU852pzz0R0sJwyfleg2ZnK1Y9VNSaKOP/r0I/7Vuy/S3kS93fBBEO08xY8E9GtDfOdwi4BzEl9FdEsNKQQHci/x77Vwm/SZElGLOBg4d+Te4t4mdCtNPqpoPwzITYTxHis8bRchnjfEO4M3gZdiSqIsunOErQOvGZeO49bw7Mt3FIcBmzplNjvQeo1cgQ26t0u2htx1WOmxAo73EflWEmWO8Z3lZnXCQUdMVElxTBkMW94eppjUUt7k/QFBOsLWUNUCWYLJHSHvmLWWd3FCfQpvVgvyskaMG9LSok2F1YrqkUd3iqjwSCfYljliJalTyaHKeovqbZ+SWbuI+igZ3HrcBMpKk2rH4TZH/VCLrBWZ6ijyGF17stOCs5OCT16d0W1jrGvJ5r0weP9uiD5t8BLUUfJituH1ZxPaSGKkZf5ixfYkxVcRM9Pwq5tLVNXrlI53GeU8Ynp2oLyOePZozZubCTLyxFclbZfQtIaqiDBJx+j9I9tNzpk+oq46RsITFZKnj1f8l19/j3szoB1JxAPMDpadyjkxBfGo5bCOOUwFv3Z/yTbETOhQgxb7yPPpZkr8LUE1lxQiIptVVJcKsdUM5iW6DvDJAD0VXLsxIgsUbcowNERHT+1SnP+cZPoypj0NZDcd9kVASEWWdJRFQjSwtLUm2TrkF0vkvUK90ugPK4TxVDZB65ZvfvOckx9fsfxkQG11jztfSTrb0wnFa4FbOEbvb9l/Nka+qLDLmNR0WANeK1QhKFxMN20Z3nUcyoyHZsTk4ub7qne/2/W78tn/b7X2+z3j8Zjf93//v5BMFa1WVK8GCDw2E5g2EFWBYiCJO0+UdmxMjNoJ0sYTC4ttFOUViMgjDhK9liRZS5UooqzFfpZxPJGcRgfKMkKLQKk1XxFLVhPFeVLya4dzoiJgD4bF1Y5DqckaaIyi2mgiHFfv71jXCVsdoY6Sn5zd8KATdg+aJQP8RmMs+I3kxYcPrExM5SJ+PH/gbp+z3qSUCxjKmpXKaIPCBA9vI/JRTRcLWiMZFh31GMJ9BEOPvY6JVE104VHTmvpVjl1H+BjaM88ordl2KbGwqAJCo4gyi17U7A8ZUbAcq4yFKiisYbJs2ZxHRFFHtc1gr5iNDpQPCcw9FQa5lfhpR3KnkCeWsJM0i9Dnye818WmN3RsoJeZpyUWy5/aTeS+QKzWqCAQkpB6kIL4ssV7SrmPUVuCVIDoEuOiIviOxqSKZl3RV1KvLA4SRxx/6uFQIpOuO4kxhCkE3EJhDIDr0rfN2LJE20IwF/tLyyOyoU8nWJYyGFd3eMIxr3t3PMV1AnjZwVJgbOD6VJMMWaRzhIWJ0uaf8ZMRRRFglyFxHOEjUBpzRBBmoT0BPOmZxSdNpVCU43meYYYsedRx2OYOkoZQa8aCRNiDbgD7rOGqDXktMGShe9JbESHR9Qt9RIU0gWYIfe6qB4ORQsyZl2pYss5zs6PFPWsp9wtXFkje3C57NlrhacQwx0gZsJZnYhpUb9GjlWKAuWjhI3H3vRbcZBAVCQjjpcJVCfY6Blo3AjvvIYLMW+CxQXwai4BjNj4xVxei0ZPOtKS+nA0KhiYIl0h36VcTVh3e8a4fIrUIcAoeLCHOnOEwUOngm1OxlhCbgO8lYVbTriHouSD7rKBcJOnUMOWB1Qr2LiRqPk9AlknTQ8OJyzW++vcR5yftnD5ROU96nbPKI03xPcUyo6hg7dSQ3ki4RJK3j2Yc33D1McUbCrNcgHKuEfF4SBU/xboC/7IgeBDuboPeyF6/WkmjQICNP+qjCV5JqpZknJbftlDhtaaqIxX3FPk8xUS/Ec4lAZw1K9F2etolIGs9iseV1NGJwVpA+OEJpMKcdy/WQIMEc+pCu/LVAPy9pbjP2kSF/54k+aNhscjIamkwylRXb9RCV9PnuctyhHxzZwDM4KXnTTGilAhHQItBIydA0pNayLIdYJE8HK/arPnv+yeMHjtuMYx2jJx0Px2FfUIPnSbTmphpzYo7sU8NRRMy7GnvQ7O5HyGnH/HJH+3HKPjOkcUfVGJJRQz2QDGzHQ5ejW4i97TuBlaK9i/FKIHKHjQTRraQ978g+EdhM4SaBfNlRDiPiztGMBQH4oas7vn48IU46ogOcqCMbm/L8fMmv7x5zbo50D4LDIMEPPf7TlOi0plqnDJOaNK/ZdwmFjYlHLSq22Dcp88WO1ZsxxILxoGKTaeKNIwK254ppVBIdBeVvDck/uOWr/+e/+3vDZ/+/9ZIK9j7huM2I8xbtArluaL2ibiKkAxsJ7MuU6EYRrQTOKTZzzeGxIBSK+BDQg44QB0aiJik9QgXE45p0UhK9AR07tjrGnNb85mBGZSPe7IcsJntCq/hDyTseHyrMneC4zBg+2mMedyx+dMvrMGAZxfjvZlwkO766ecxntxNQogeKfP7wlB80LKOYSFm6GP794Zz72yG7sSSyrvfrbzVT2/BsU6GM5xg03b0hOsJAdMjfSFj4GltE2FxQ+4wGyXY5wk084apj8OiIrTWHZU6ctMzrmmTYEictwXiKj0foG029jZjEBVEViEqBOXecTXbM4wJZ9raUtjNYNN1DjCoEyUlFdKN7DoGDcGKJcb2CvfYMSstgUfJ0tqOyhrtPFoStJuoC6V2gOfck5xXae1TjaR9iwtuoV9q3fQteOEFaBdqxwisojjmNMLQjgaxB3kuCCojgkR7asUa2knreE/J0HXC5xyWBZg5uYrGjQDov6c480aMK/zbG1RIfJF2QdFPPyZeXiMxjlWD/QuJjyEJL/ekATS+0+9LpDYO2RXVQuhiLoh0YbCrorhzpsCU+BFZtjmkD2TuBnQSCFNhv59gUSqFRbwy6gZGpUQ10jSLeBcaHFpv1f/vz/IAoJY+nn+smjoIy0pRdRCgVW5uiHVRDjYk6lHJw1KSzitUnEyLVt0ivd2Mqr0kuSqouoVEaGyTW9ujPbmvoGo1wEN5vkJlDnHbYU4veCKI6YJQlCh49acELBk+OMHO4qSc+eOpIsrofc1ARD++mFImGnSY+eCwS+yahm8Kn5YLqJuWgY9anKeHOwNMWXQj0sXfMKEufaDZrGTw+4k56Ep89M6hK4FPPdpHRnXjac0/71H7O2A+YccOvr86JzkvGecm8a9g1KYfIsMiP+LnDDcAMWqau7umGSUkWN7z5+IwjhgiL3oP/HPFbHRNWDyMMjkFhccPAqLQY4dGTDmQfimJRbJYD2vsUlUnKE02+86S3AiLP4cKgjgHfSBbTI7OsxESO5FGFmlniO4FQHbtVTrwLmNcK3UrafczqkwneSUIt0acVv29yC8mRsDIciwQfgZh4furxJzy5eIAzR146Dp+OkDpQe0N1SIjvQT3xHLKIJGtoI0nXKqSA4bSAewMPms3NCL8ysFd9+7wx7DcZ3/zNp7y8PSV8YrBvElQbyJMGmwcK2adjPuiMzacz1MuYcV6SZzVWCmyteViPEOOGeN4wXBSMnx4oqpSuiPACno3XxGXonR9BUWrF+MdXSOEJAZwQhKsWOkX7nsdHPRr78MgAgg5FelZiE/jWq3OmpsHuYtpZ4LqeIgXch5xYWL6c3WM6GMQlE9v0z811hKkD9T5mtxzg14bkThC9VKilQi8lu+9McVnvKBoPaoQMJIOOyDuGG0fx2ZDVbkB74dlF0Q+2Xv5Av9v/yuu9ixuMcqSmxW0MTgsSawkDh3YerwQ6eKIXBXbmUTogxx0iQGQ9yT4QtYGL8Y4wcexPFJXS/elXKMZlSf2jjmYgyM5K1EbiS8W6S5kMC9ZNTjxraD7suFExg/cq8h/aYfaCsDFU38mQW8XUVJhFQ4ciUR2mDNy1I8T6cwXpwpIWnpvjmNV/WFBfp7BThNwxuCz7B2ejuTzd4jrFsdFMTNnPgCeBaFhzFhd88cMH6imoQcekO/LofMuPJUsexzu+HD0wHpY8HqzI85pk1zFcOspM4paawmvStKGd+94vj6IaKD78/Z8x/+KS2ivqzzLK65woCyS6oWkU7twSXjTEbUc8ajAflJRn4HaSMlM4ITm9L3khdlgh8RPHQ4iRtxHWKpqhojnENGNBdi9ogyL4z2NqHzQ+g3xQ92ETrg+uqZcRNpb4SNINQu+dzfvWvI8ELgXZQBABP7eoQUf64DFFoMsk1miOV30EcvKlApk7sutAc5vy7nqOOW/YNykLWWCM48wcWb8b4e4NxnoGwTKf7zgMNMOzgr2PeeIqPtqeIIuAPAjStMGcNojEMn+xJh02jLOSy64k95ZDm6Ce1z0cSEiaxw5T9IUsn1SYScO+TSkeScRe0U0D1SWopzUD2dAayeL5Dq0hlIqkduRpTbSGEAXkk4Y8r/FB0kQKNwqERvCB3fPDzT3RrSIbWZKoAwHdpxmhE+SXFUntmc32qBLUTqAaGKiGyDvMpEU6yN/1m2W1Bd/09kWtehdD+ybBJoHx9IDILWlnyZOKh/sJm9cjmk9TOhRm3qKawOhFQSsldhnjpeHi0JBsBXJuqe8S4l0vaDWlx3zcd4ayuGMnYkbzAkrNITM0Y8ngvKDZx4gawrVG/maCulE4p9gdc6aigc8SzGvBt98syCYFTgmOGHbrnCRpKH1Ee58wiQtKZ7ADwdPzDWnlORqDrDRfOr9jcnLESoGetdRjQRtJKm8oZ70GodsaUAF/bhkOK0ZZRUgstdUcbgY4AathTDhqWHQ0ZxaLYNVlHE/6YKj1zRDx1mCfWqrMcBz14J8k6SiJaS4D+qzFDTzz9zYY6fnm//SEbT5mcxgggiO6lRSR5hv/8/OeK1FKmkxSzwRyKQgyEA0bRvOWMxqU9nxXLBjZlrmpYKfYf23KJCv751cTEK0gNy37b097eFPaZzAMhiV2KDkcU0wbGA0LZseGL01vSGcVxgbk0wo9ann18Rn376aYiwqVOsZRxWxyYGYLbt9MefhoSm5afnhyi99pdnVKkwpu7IgYx+PZkuM6J/lgz3yxQ9eBqo36cCsjsLGgPofg+tx6nwa2bUY+qXn0eIPZwZfcGv3K8MHlAzbQB/l8q+Kbcs7NSc7xboy4N7RFRFL1XWIZAt08kA9rQhyoF2CNQA0t+XnBV56/o5sIbsuME1vSoal8RJx16NwynB0RecfjXfP/rcT9r7p+xz77/19Y71ZzvJJ0e81g1NJ2mlKY/uGjBMIGfCE5nkSY1jM6K+nyAA854f2GKo8xwvLwbsrZ1YbiIUUIkEvJh2rJSz1B3kuMELiVwUgHWmBWkpvdgswJKhHzzcNlfwpYbNlVI0ahxd9GzE62PE/3NBK+UY0pZMyP/shLvv7qCXyWwMjj9wrxYChlQIwF9fs9I94ZidlHRJ9C0UUEH/DXw14RfNLgDIyjiuPXMp7/6JGPlxPKE4XWDr0L2MhwVyTsqxiTNoTliPIk4pPiHDFyJJOOx3LL4TZnOC35erWg3KeIjWbydEfoJEUR8427x5xRsBGSyUnB7s2Ek5OSw13C6dWSazvE2A4ZYvbLDFUL3FkgnnouogrRCJ7/vhV+AvflFHebEF7FMAlwFMSTDqykHQrEXiHeSuI1tFNwBuLS08UKYQVCfZ4N3knEHoz01EESHTz+QWHTHkHrM08jBXhBeiPwkULVATsA6T3+KzXWJ5jEslUx8SGwMSn6AJHxiEKRJI5yprhrhsS6o96kmIMgOys4O93x0TdP4LFls4vIGstvrk77ZLZHBbaJ0Hea4bhk9pU7Xq1PeN8f2O9Tbn1KXUmCh5vjCDGz2E5gdh47EOSlp2hjOinRQLQJNJe+D2MyirgOhMz1TIBW0jhFdPC0qaFVAhl9bvcsUraNQA87ss6SDBo6p7klJ6SaJgiW5YggPVbDQzdExIHqQVM98jRVCk86prOC9c2IMlboG0MrFXYasFoSTiz5fcfOJMip53BIMDWInUBPA8fTntzmagWVJoo8DB1WaaR25JOK6DPFPmT4GJwMqI1kqVJsq9AfabQBL3u0bz5pCLlAXyv0FbjrmGrRF8icFowgFx3HWUX1LkNlHvNew84ljEyNlp7HVxt+q7kgzHp6YXOIeTRd08SS3UcTiolCikAlI15HE85GOyaLiuZIT+hMG8ou4ZvffkQ8blCFoC0idOZo1gmPJzvuzBC/sESVxAnB0cVEd+AOpve/14Jh27L4Q/csX88o6ojyLifbdOhJR3OIGD94HkYxw0FNeTckXXrUBzWeQBkSdiIh3oM6SMoFnKc7yk3C4ZASpZLkW4rk+bHnW0jPfFCwS1Lu353h3qXoSUuIoFn0FiJF4NPdFC06RCORWAYcqBPFVbfjXTJBrSW3w5wo9ujaITKLqxPsqwyVWryTMLA0QqGOgmze4mpNktcUacpcHqmzvjs5nZe8+fiMaNaL+wYbzzGO2W0vGWc7zEEQIjjVe8oc4kHL8RsjLn9qyWo1pF5GvK1OiYSjdBmucUyjiqXKyEJDWGnyQclxleCjAAPL1ftLVuUQ10qeqi3bRynfXs3J6Te8RIG6jLFPIlrfcyychMNQ064NTghOLlfcFGPCWrMRBnXeMpMVZ9MtRsesTiTfulugE0uZKGKvKZRGNhFNcJh3DpfHDFXHtcp/oPXy9/TJ3mWBi5MNg7RFzjvi9w9M5wdS3REPWoTykEM2aEikZVVmvTdzFJiYGmkFJB6bBzb7AewgoWVvIl66BW6VIFaKdmsQB4HwvTgsyjuKWFO0Ef6gKVcJZRnx+jcecVjlLN9Oacbwdprx668ecXzdAzzycc3DuxmJ9fihZTgu+pPV447oeUEsHf7Ecna1RscWFRzdzDGc7dF7jW/BXViIAqYUPM1WuIHh2x+dIeOA7xQj09CeOtIv7OkajfWS+fjI5PmOLz96S/gs5jzeoxrPy2LO3SLluzenBCdp1wlG9LG9fuw5H2+ZHhvkTlPWMW83C4QIyHnL/A8+9JjgiedYxRSPIJxZGmeIhcMJwUrFrPKIX313wde/e0Hxakh9iOgmkHYdDDzT7EhIAm7qEU8bvOqBF0SgC+gKQ7iPkB29kEgZmlQTNJjYEm9AuIA5drQzEM4z2FiUDBjrYBQQok/IEx20F4H8pCSWltHkSHLoA1jeu1piXpRcnq5opaTSis4pLosCh2AUV8wutkR5w+pVzvnVlngPo/mRy8crctXSBkUdKRCC2Ys9u1OJwjNIG15/OqWWkh/68C2hEvhKwVJDK0npGEUd8l4T7jVeQFx4RAh4IxC1JLkTxNuAj6A+SEzW0AxAbXoSoDMCs+9bwiH2eAOY3r6YbAM+COSwY//tEcd9CnGgmoMo+zFRUnhkDWs7JD966iKm20ccDimzQ0NoJNWwxwonR0+yDui3irKIibHIjSTLa9pLh80FvtZwlByJcaUhbBW2VfiF472zW/ROUm4SxNAhR20PCMo8J6c71FFgikB43iA7j2gFURmonWE4qPjCT3+GiB3xuEHcakQp6fLASFfY1ymjjyB9J0hbT3E7gKWhWqaMTwrevZ7CTtPepXDSkWYtydax2g8ZPd0zTUoSaYlCx4iaQdKw6mLuijEtin2dEmqFqxS7MsPgelriPsIfJKtjzul8S2otoZTwLkLeGFRicWnAHPpNaDeD5hDTHg3zyx0yt4yShq6KqEeS5LzCPEjKTYY9CZSPBeU2IT6tWWR7woMhWjScqCNiKzm8HZOuIa4CfuhopoJ9k5CkLe5NzPrVlN0hR3RACNRVD9NSk46QeYqhIt4E2l2MBdK4QgYPaeCYGVqr2Q4i5F5RakNrBNmrQPLhkcg7oqzr3QsDsJ3CPrIM0oZmmXEIKd/87ik8KOp9gisM4iHi/HzD48GWEHmaAZSHhPoEtmLEs2f3fOWDV1y/PmH96wty16ITR1sZhgeHKQWXsx1i2JGkLW0w7G3CxcmaybQmvqrpEhj4FpcIsrSjuUuQd4pp0/Cr60sOO8P/4ewTJnlDuDdoArtdzsl7e/JBRZY3fdSz05itYDA7clOO+YJfI4xDOc+kbXnyeMX9cczX6wXXr2fwEGEqyG+gvYtR15pk2hLbQHgumFwdOCwMdfjBnrV/Txd7tZdEO0F2sMTjluo6Z/vxBDqBX1j8wtFpSXmdMxhXva0mD4xNg71LSEOHs4o4dDStoTwxVFKjdgobA1ZgO4U49rGF7cuU6G2fC28Kj6rBdI7s1iM/ichUzbDtKA8Jw7bFfpQSELRdRHJRsiwHbFYD9LUilR3+OobnNa0UbIqcnJaRaNCbgK00+rzFBY3NNeGio4sVPoZuGVPeZfzmt95j8YUV6klDOuh6T7SDx+rAeAmjuGQwKpEpfP3dFd/5+JJ2HHjzMOcrFys4A1OAPyiSOwGZ5eJ8w3x4RBwl+dITipi71YgXgwfSt5YGw75KaW9z7q8ncKdYpB1Xwy36M4V2HqcEiam5OBzp7iJSB24XIUtIRIcPEr+PaEaCUkTIoSPUEvndCF0FkCCOIByk9x6RWFzS86qTfUA2n7fzjzEugvJC4SKNEY52JrCtJrQCUUm6oGmHguMTgfpCgX5UI1eayDnsywzxiSaOOl4XE5T23G7HvDdcQyc4NAn3DxPk65h0WjE737NejTlcSI5Vgpx1WKeYTGq8gbiyxGvP+E1HuvOYfeDlzRn7JsE/6YjahrXLeytmEHRPHKGQdMuYw/2AwfRIMxWYjWA0KpAhEGaW7nGHih3JaYncwlA60sTyBbY0haGZCPwgIHKL7ALDtCarLOmTI11p2JJSK4VpQScWETyu1HReER40+iCQkSNOWhSB9iFGJRYza2h3EXfzBGpJHLcMFgWt7oWXQQlc0kcLi1JRbWLMtcRHgpAG1Ecx6l5hnaLJJGnSMgoN3/KnZCtLeJPgRoEFFbIQmNeagWzwpkcem8LSPe8I5x3Jewd+5EtvaR9ZrBFURcydH3AsYo4ngvHesVQxWx9xGGmaJw5bK7JhjR95orzDhoAjsBjuyZ4cEJXgKCLedDPONgWmc9SfpHSbCHPSYtKO2/2I5i6luM6wVuBuY5CQjWtM2edmdJ/DPV0CjTPc7sborUAbRyJ6audhIehOXf85E0szCDwbbkielNzczDB7QT2TqB1IG7h7Ne0jXONANG5Iq45B19L5XhEu1oLV2wk35ymPx1tezDfsBxqf9YLYxdUaPfbY0hAPOvLWctYWtKuULuvDY2bzPXVtEEeJjDzuWUd8dIxES1Uk3O3HHD4e4hoIicdrkNb3fxt1YCn7zbt9ZJme7TGHwJgKE3c8irZU3pAtChJhSS4arj68ozMCN3XsBhHZtKRLLZOo4PmzG4aLkrNQMBxVdFLSDgSXTx8oc8H4RqBDRxK1bGVKd+4QKxibGgpFHjpcCsUqQ34mqV+lVPuEg08wK8G4q7nVA/becKgNRaS5S1K+WlywepfxrWrG/tWYdFrz5uWCUEtGooHQuxWqk94ma3zgpz58Q9T2OR2Hu4xv/sYVRR0hFi3BCVTpiVuH7jzZtCQ+r3jv4hqdOzhIVtsBohUk/geLy/09rcb//b/wPzJ73nKsUnZtykBVpMOWk3nJbZ1S2AiTWQ7bHD1piHbgvaSNJXiQLqA96B2ELBACWCTqnSZEAvYCkXp4URNvobjJCEqgq55zLjtQLb04zHpk5iCBzkviLejHDXsXc15W1I892ybhND8gpUftJFubYgaW4zolTi32qFHKET860gpN/Eaz2w8Q0iFyh0kt8awho2MvYoSDU47c1mPab+Wc+oKrP7jmv4Q50+uO2mhGk5JymXM9TEkPjm4EZiO5infszgXlJkF3/Wy20RKfeyIRSFrPV85vObqYb706YRpqtm1G9t6RLijO64bXxQBWivJ5YKpq7MuE5hTaMsKPHaO6wd7FLOZH3oUheiWJOk9wCp97mlkg9pbWGsxa9JurtM/sTu56aEs3BNkJbCZQdaAb977vIHtUrvIeTKCVuge6VAGnJSB61bgDCPCoJUsb9scM7T06c9ijJpId4tLS3sdEa8HxKhDWBp1ZojcSc9oiRpau6QN7xrphZXPWhxxTwKPhmm4mWZYZXx684yHJuflswcnJlvVxgI8hWsMkLtntchhYDmmMlAElPckxEJaaZihIHwLHiQbjUa1AHQLuok/mc0dNZlr0saEdpVgnOTEFGxXhbhJy1fZz/XXSY3YL0BctlTJ9W38UCKkn2cDoizvu78cYH5BriZ941KxlPCwY7R13+zFFp7GZoIn7rHdVQ7oBOemQryK8Crh578OONtCMBcoG4gdPN5YgQFX0yZFTCzNLVcSkO4d9ZlHSE5XweLrmOy8fYYee9OjwWaBSEcZ5nBEszjbYu5gulTTTwKP5imqTsV0PIPVcyh2fhRmD0JGlNeu3Y6pIkSQdfBpj0g7XapoB6MxijuBST3oIXF6teHc/o5xLdNZbbcefeCbv74iflLz8rUco4amUYTwtSMZVr7HZa7aHDF8YOO2o6ghiT7TuXSWi6+f42nucVT1EyPbdJe087TEC0XMizKwj3Gp8LJCZQ/7XmNfjnk0aEUnH0DS0J4H99RA96BB7jdCe8vO/+cxb6kxSrDPkomG49qjTjjRqKI4J5bsBSdT0tmKr8anHDQJqqwg6ELWecgHZNfghaOlw/a+Qky8uKcqE/SEn9o4Yy15G+E2EqAX53lHlkuzoSPMS9yxw2Ke0XjGIW4KFoD10ktn0gBpZjiLGVQr3EDM7W0OlediOUecN9rsR+ZMalTrCUbN7NaI+84xLi5h2TOZ71tcTjokh3giSvKa9yfCZox5L1FEgh5ZRXrKTMd06Jqfr2+qvBvjzDrnRpK8CdiBwi45JXFI1KSpzlMHgRh5lPJNjS3s05B8cuf1sTlJ6uGg5UwU7azhsBsgbRXZZsB3E5HlD9SpnIFqs622V8XmFOG1xX89R0DNWpCc9NIRhw6/8X3/p/6/G/1+y1hPDu9s58iiQDaTfEdi7mOt3I0QhydKGrtGocYNZSeJxSzmQdOsI+doQbgxPTh8QsccfFYc2wReqt4u1Ej8OdLFkuHL4SR+GE+3AK4k5CtS8oT6HRDbEu4BaScRGorQnflYiHiT5O1i7lOpdilxpOikIQXIXhtTbhP3bHJ95WiO4Gq2wkWR9GKMOgiYTqEWLOW1BSLrSYJoAqWd459F3Cvc2ZlB5nj1ascoTvraec3bYUw4lRRmTbAXjacEiKpDLiGFpGZ0fMANP9xBjtxFqqShVhMjg0ZM1BxXx4mrJ2zdDfG5BCrpUUMeKcpUypuKTmznZg+fDF3eMd5bt/ZDjyJCqDiH7JLqDjLFI7u8nxJWnnQiaROJP+4e/lP3P3TxIXALSB5JVIL6X1FNJvQAQ30ukc3OHOfTqdQCzB1qJjmzf9u3AJhJd96cj4xyx7HBJYKIq7ADUvOVivEMkFvukZTgpKV8PqJWiiXqvvmgF/tjHrh6N4fj/ZO8/eiXr0jRLbG1x9DFtV1+Xn38idFRVZLM6i2T3oKsbJAE2+G84YZNoTjgkyN/BKQcEB+yqRnVmVWZGZkaG+KTLq00fO3oLDk6Q4xoFEEAZ4GP3a9fc3rPf/TxrpZr82ZFXoy2r9YiQnnTccHayYXXMMaXHdpobNaZ/l6KtZ3MYY3JPvwsJcaweJqRVx5fNmlflntfRCrGR2KmlmQjGtuM41qRXR06ne5bxET+z+E2Av42Q1RCkTHNDl4BuLSfnW7wX9JcGnCfSHZHuCM3AE+g2EfakJ3595NnrR5ZhST0WdKVGbhXjUYmSHukcSdCxXY34pEfUbcDPFw9czLaMg4boVqELQRS3NG1INZXoo8D2gmzlkB0I5VGto7kGkTmk85jRHx+G7ZD/UAUgBHSeZhtS7FK+/XiBCQRTamTicYcAHIijhEZw+DSmkBHhZYU/aD59OOXuaUY2qpnblvm8Je4sQdzxdBjhD8P/0eX0gHrRUFwI6iWo0HHxakV/6vhsviJ+XfHUjBh/fsDuA2aiJuwc+T8/kp1V3PzTKewVsoPYdbwK91QfYzZPYzYklD7CJ354mGsd6ajFG4FuPN3CDRvByKG9RQqPqQKafsgTnS53iFaSlYbnwYHR8yNh1iF2CnrBtKhZRzGLsOZ0XvF0zHn4MKdaDPTM6hjSPkboJ4laSbY+wQpBuqgQvUCNLDPbcqgSyipiER5ZZEfGz/eoZYsMHGHr0Maieo8a94RrSbMURGcVlAKr4fR6yzSqsUjyfU+6cmx8gtsFCAkEHr/sWeYF/ZmnPR9EVv8/EuUoLwcuySGhRbN9P+XT78/ZvJ1S7hJcAJO4R7cCKR1+q5FfdfS9Zho3JKcV/4u//AOp73HnHWHUc/v9GaWJUI3g2WxHu4+wp4bAOmazI3rck/keLwRnQcGvXnxPWDkqF4CHWdlhU4e5cISqJ9KOXZjgOok461C54fLFiijpeEwTmoXg8LdzxkHNeFTyLFtRzATqxgxZmamkrhL0XtDcJvirjvl1QZy3nF1vcA8hvIswQvFqvkM9KdgFHJcR+yb5k87LP+thf1lW6A4KFRDv/KDrbFLUQbApcxqhOdYR6jcxSlv8VqHfhSyWB4Le8PPrT2Si46gCzDEg6S1SQNw4Rralk4o+huOnfABWlLCc7FFBjzCWbOvJdhb5EOCFwMwge3akCyTmIaRxAXJmaAOF3CvmvsY9RGw/5kSPnuVXa2wmkBJmiyNfne14nq948bRn7huiWYtuIV9UpHGDXTrqWGHuFe1MQi8RLzyjvMVd98zOD9AKnA6oP2a0I0mlA459TO/ALwxy3jHeGbrI4wqBswO8IlY9r5sDxcOY0Fj+w/tn3C5yPr1d0Neaug1JZQ9Lw81xhks9XDj6XUg8apm82LO82DI/O7B4syHwDuk98UmL7KDvAuKVw6aeLoVGaII7jdooVO/BCbrp8HvtpiCUJShANcOfoPSEB4EZeWzi6fNhbWoDQVPHSGlhNqzOTDLcbfehpHxjCU9bgvueUViRpi1PpJSbDLVTPGyn+Gbol8dYhPXozg1Gw0TAJuDUNTw+Tvm79y9gaimOKbnteR4e+OKLOzbVhLPTDauPU1ZpRKx6vIPRJ1hWFW4yII2zheH96YTOKKIdTE+PZLrjX7/5Fhk4xLjHKTjWKfsyRq41+iAgcATG0T13HCcRQjtwmr/fXlFNBGHeY14Z9vucoBIQeMLzBtmCKwLSxlAUCdVcgJGUb8cEe8/hhwkcJG2sKB40P/7xDSPb82J0QIae8/xA4BzhtCV6FJjbBL1RSAMmkIzyBiMlTC25b1GhRxpwlULvQRXglRsIfH7ARlsjkEYSjnvGusZPLG5mMVrS1iEmFERHh5k4krAjSA06sRxuxngN+lGzkBW7D2OqnebbfzihNwF2H+CcwF5YgmWLTgw67wmEIxcdci85fD/BFoqf60fC3uK84MNqgV/0PD3OEBtNswr47c01hU8G0mLeE8x6vj3MOcYxSd4iD5K5qpmoGv1uyCZMVY161hK/LglHHW5uh97664pRXZPEDUEF8jZgvZ4gE4uRivsgpnzMCIQlbC2BNugXHeGzBjXuebwdIVeC0AzbwOBO43NPtZTE1zX9EvxoyC/o1JD1hvik5mAj6l08cP1NxP3tjOpDju8E47DCptC8cKRBR2c0kbWkjaU3GvHjGtl6ym3MUzmiKBJMIqjmgtz1hBWMVMP8cs/xXMB5j+zAG+g2ESPfDFct7zPQnqABKyVGSqJ7R/Zx8BCorOdoAnZJwPPXD3RCEZRQbROaPmQxO/A/3r5EFo6w8xzLBCHh+fUjwnjePi7olh4MyIkhkS160bK83hIaiysk3/zdS3ol6Z0E6an6kPR7iesko7OK6HmFruDin99TrDJCbfBGMskqfn79nrYN6CYekVjc2NPcpLTfZpTTFDmy5PMKeoG3glD1jG4c2y4mq6HXwzzoY4GT8LtqjroamkVsAvTxT7tU/7Me9v3UcXqxJdKG8LThxYsnVA3FQ8KVrbBFSOQc/dRT3SYU+xQ6gX8K+fx6S2A1N08LXsZr5i92Ayp1r+iMxjiPu+pI5zXVyfA2dRPP/STDG0F1LdnHES6D8rWnmwlEB+UPI8KtpzkDc+KpA0208ajA4WKwO03fBrSE7B5y4k8GXcGhSPh3j5es305J/9mROG3xjwpXSfZtjCkD5FHQPcUkUcf2fU5rFOuvx6y/G7OTAfUSAuF4/H4JF4Yk69iNFI86pq1CdDY8eHzKctaEyFYSX1eEzyrOLveskggOAgzkZUlyA6/1ivnZAeUNovVcVzXeCKJJS9MHtDNHmFpGa8uz0Zbthwn2NuJ5UxBbj3MSYQe6m7uwiHxIZ6veQyeIFh0mEeBAOT8E7wpBuBqCbsIDcqi6iFIQuMFyNxmVxNcVInYEpaPLFX0kEZ3E6QHu0r40SOfJaXh6k6ETR28VzT4mfgR1I8kai2o8RZliIgG1JH1Z0UcSddWglh3tOkIUEnkH/iHEWck/W9xS24CHfsSPnn0i2klOJwVRZMivj7DX9Dm0S0mUt2R7i8w89j7g8DFldTPly7YkXCk+7SdUTch46wm/D2iUxE4GCE/zyiKlQ8SO87qkfwiZJTX/7a9+y2eLDeHHAP+HhOa7nD4QdFVAGWl2WYSdOMI17PY5RJ7qEJNMa8JpC2MH2tOOBL6TTKc9u5uc9bc579dTfv/DOb9rzug/RKS1wcXgn7V0M4u8bDExw+nsdYvpNR0D0jgYGZoldAto55LuRwY7t8NGDMlkXKJyQzWVBNcN8ihIfE+3jVAHCCq4znYkW0NjNcp6jJfYSuPfRbQm4LDK8FJQHRKKeUiqOgj+eCWHQ60lD39Y0n+TER88KrDMnu85mhAZOf7m+IwNKZNXezLR81psyU5KpvMCZUD1IHpBf2Wp32WMdIN1krw3Azv9SeOcoF4lRMuG5hy2302QtUAse+K4R3/UVG1EcUwxM+gOITrvqF8bzHx4qOwDSf0hQ2+Gqm+U9oAg2AB7wfbrEXatePPlHXiIDgNM6PTzFSMaij4kyHrioMe1Q9DSeknzFLNfpcymBSow2FbjYkE7h+W0QIUDATF/8rRtgNGSfsRQuxUKuQqwqaIQMe5tzPOrJ8K8h1oTTFq+/PEdfeQ5lClBB0lhCa4ajkTESUuXCRgPyvFIG5JRQ9b0iK2inym8kYjfJMyaljpUvDx54ruvr5ifHJieHFGXLR/qMTfFjCjsUeeOsWuJ0pZ/sfyEcQopHeOuRW8E4l7QtyGbtzOebUu2T2PmYcfuaYR93VNFA2QrvT5ilw69rDEXlv1YE7cOEfTsbYKTArUS7DYZD9sJ//T+GXqlwApE6ri0JXUYkCQGZzVnzzb0WhKcNwSuhTGcPt9xme9wnzfsmwz7KSJ4EOTbnsnzA69/cYMYW8LW8fynf1qC3p/1sBeTnttugp1Z6onnRo1harCZZFUkBB8lbRmgVppJ1yBjw+WPHzjNK56ikN/4ObUN6NuYR50OJ+8YrJOUYUzsLLqG56crQm2QVkDsESM7eMgrRxtKnILYtqhFD1YQnnaot5qpr9Gtx2bgtgrZQusDZODQsqeVimVWMR0feXUoOXYh5tLx4W+uePiwQP5djFSWi2xPXnecHDqshjtmBMKiE4M3YF50VDYkvFNIC2HagpH0veJ4jBE3AbFyfN5tiGtHIC1Xiy2vXz2A9IiNouhCukCSRR3JuCW8tixURX+ihp72c4d41rGc7DjLK5oipDlE3LdjLuKCf/X6E5/qGT6EadJT7FLOVYndKca2RDnDUpek8xrPkNa2oaCpA5waTFFiZFGTDjNyyHmHzT1eOPoUgrXAzhxB5wYWfqGpE0W0bEAM9/zhoyQ8a0h+dGD25ZZ04wm2Aqng3B+5fZrR3cWIQtJeeDyKLhOIs57kvKJvFc5L9rsMcdky3XaI3OCuOr54fsvzZxvQgvPllr/fn7FKIqou5MGOUJlFnxmC3vHwwwLBkF1Izyt6qekvLasPY+Z5ifUhwUnLeF7S5457FXE6L/jZz26wjeBsumV+vWd+dSASlq/ma1zsaT8kyNizfz/m//XNaz4UE6Qd+tjm2hCkBpuBTC1qKxBHSbCVGCcpvhsTGI9oJP1dPAS9Mjtcn7RgreQrdnypd8RJh3vRkaqO8MclDQFvru/otEL2Ap4ChPbIUjDuekwMxglMpTn4CNkBC0MyaumPAaJViH5QfbZVQFnG6I+abZlzTEPaKsAtLWreM8/3vHUzgs9aFqKmDDRhO8BZ3MLixz1y0jHKGkbTGtXBkYDZtGD+YotggB2ZVtPPLO25o5Garg4IvSPMOx50QhY1lB9zop3nppoi8TzJDJs7gqNHHQatcbOE980cETk6KQcS5aXFjjzyq4rJsmAkWnjeE0U9ugIegiHgexfQP8T4v8vIS8vy2KBXChVZnJOYpUVHlv7zHqkcexENtbVqUAeXk5DuQrDOE0Tg0fOW5txRfZ1y/npFcvA0ShIqC39I6NoA4Tx9LAguW451go4coXOkeUvUO3brEe4xIFEdUTxc8cTCICqB24WER2hR+NSymB4QI8vhfc5MlzTR0CJ5nyTk8wojBS7yfCrn9E3AWNd0x5AyCGBisfuApgsoy4RSB9RfGoJ5gzkx9OeGhzihOKR8uz3HOsnur2c83czgKHGrkNWvT2jfZ5Sp4r4ZoSPLD6MUfTs0UKpMkWQ1k0nDlxeP2E6z/5TRobixKeWppDsEzGYH8q0hO3RcX26Y/3yL0B6zC+kmHhlKuo8pZ8sdwZsW0SjaTcxo1RGNGkzqGeU1N5OE8KRhtKhxClZk2BCsBldHFF3IWsRsthk37RhXK37x6gZ51qNfdxwect7+2+fYmaOZSuxN8Kedl3/OAb2f/e//O9QopjWaJG05vd6yvRvjP0SYRjHLSnZ9gv2qI3yEoBNUczg3JT+ebfnDYUpjQAeak5OCP9RzymK4/0p8TzRpSWvL5HnBqso4PuYEW+i0hE7AcTA1jXyHCaESA1FJLDu8kQSNpxMKtZWo5y39LkB5T7aoILZskxh5UPga8vOaNGl5vJmROcMX2Yb7Y4qcOexdiGos7ivL4/ucNlOMmpZmm8J5h4sEWju0G2pX/S4kTjrqqWRaNtT3Ge0cJlnJps6Q4x4+RYyqFpuBUZrlyx3v9gtOkoJobxG54OSdJX1d8pvuFJ107OuMk6rh6TginLeIQtKYgKDxvP7RI6bV3PuY0gScTAoO346ZXh7on0Kqh4TwZxW+lRz3GT52yE8B4bgjS2rW1QiMJCgtPoMkbnGbgCrTmEAMVr/WwczRBwJde6rPHMGjRO0U2XlJ7YPB7a7BZp5AWpo+QGw1Vgj03oOX6MQyfbbnfjthvLNUkSbpDL6TdCisUUPgKutwc0fbB+R0jGSNqzSVCUmflewex8iNJ/ys4fPFht/en2EfIiqlyeKaX14/8OtqSf7O4nLJ0caIR00zZYCLRAbZDyHQqLPYa0OiO4ouZkZDeZtjS0k1Vfxk/MAfujnxFtrDgOg8e7Hj4WlMJof7ajvyqKPEWUl48Aj9x9DpH/MQ6bKhPCQYOVwNhKvBOdBPPbntefXqHtVJlLZMZg3/cHdBnlfMOsf7f3NKF3m6E4VoJISDSEgasIjBi9AL0IbYW5KzmuM2xexDpHc0M4mZO/RWMj45UvUB+rIl3Rn2QcK47el/H9K9Fiyvd9T7GI6SzW7E6NUBfx9ij4o2kXSBYhGVjM4L7h9nyJGhOUSEONTW0MUxQWCoAoWOLPJOQuyRZ8Na3/SKZXBk925CPq4xV4aiTBlVBjnv2VXJQEcTBn/Ro3tPc5tiFo5ZXrLZ5siRJTM9exMT5B1RYlioEvcYcFjn7FVEtPc4o2guPNHTcOpPRy3tx4Hf3i89uhu+fvtYIITHh560NSTGcewjxnHDqkuJlPnjNY+Ee4UZg9pI1MQg1oMKtkUxihrWx4zAOSgkYmFJVoK21MTCUM0l521JEQcQOuqbFMSg4DZa0b/oGdkOPDRNyDwuKW8jks9q2j6ETpJPj4jHIeVfbSP2QUJnNDK0xI2jEQo96+k/xMTzhs5rLqMDh7cj9HkDRrCrc2Rn6DJNkBiMlYxnJTkd+31KLQLkTqGUYzwueXAjImUI0g5banTgiJ8faTcp509H7qqcMJDk8wNBIimNxp0Zmn1I1himiz0//PoZ6tTiI88iK9gfUsK3guILyahtMZuAM1HxIZhBbAEQW8n4+sir8zU3fszdwwS7DkjDjtqEhI1HTTqaQ0SybJhf7tg9jOkOITxo3NQxPSuo3sd0iSLoPE0+bI9HjwV/93/97/9kAb0/a6jOSViRfnlg93ZCkQ0dzrYKefHzB77/cMHhlcOtzHBSyUO2hxjbe36wMdsyxrSKLnNMryuqQnEhVzzszlmGLY+7hKZLqDrFusoxVYANAWtZhEeOmabqM7KnHjeSlDOJthblJG4VoITH9IMuEe1x72NcBi52bGchZ7MdPKaIyuPPLTsfg3K8Pnnk6805f98teP2TB542U9ovBPnBsd3ldGMYz0qcAJMbwmXPJG6Gk8xNhNLQXvfsn2LUHZQ7TX0NvtRsuzEqMfiHGCU9JtX0e8mrH68JWsPs0ZBcG54eZyS6pk1gUjrqMiK+D1Ba0nkIekclAl6okofQodaCdw9Tlmd7Dg/pILbxGZ2WFPsMGkV5rcgOkrFpMIkiHPfsi+EDXu1T9HGQdXS5xAuotwlKDLzuIPT4bUQ/B11JvBOoyhA+SVRoicKO7Kykvp3SSMU0KUlUx0qkqEqgeod6CnGxx8QCTjoaofGNwvQQ7sEbjXjWIZVjJCu2NkHmFofgy/yerUkptikRHY1UHD7OOe0qfnK65pMM+fb9KXYb0ntJMG/JHi3fbk9wYcQxdvjjYJWTaghxRYXDP7P4XqHfalQCVS5p1ylCeA5lTBcOzZFFVPD2ac7nswOrLsdtDaaLKOSILlJ4Fw2hKGuReKoU+plF30lkK4m2g3PgMI4ZbwzFTCM7QfXSke48qlbI1PGDmfOLi0/stwlFFEMjqJOAslZYLeDa4aVDWIMsNSJ1BBtBNxYQDVwDk0A4b7jvx6gY9Kgjyyu63Zj40SErOIYJbmGR/xBznFv0h4jiKmDxiz2mTtkfU7qPCVZJ4quK6jYhrj19p+nnDt04ji5EEBLOWugFWjiW2YGnesLF8xXb72bMr0v2tyPSy5K6CZhENVHh2SchsoMyCBlFFf4QIJWnXTrKfcas6einFr9SiDtIYkM1cYij5Nik5LJDG0PRJCxmR5LGse4TnlYLjmNFOjLEW095NtSCz+IKmwaUm5i6kvz04oFfN2fIUtDPPEEFcW9xqwB32aOeNOtUE+8d9UmAzgx53bEvAvJRS+tB5ZZIeU5sxXqcUfeaBQ1d5JGdQARDyDFILUcTEniHDT1See6aCVKZwZSZeYJZR+000cOgq27rkPNwz1MiqVYpnFqOJma8NxRBgPsupXAxXWo5nR8JbUsa1zRNgNUQeI+vJD4GO3KEHz3HRQAzSxdoKCSj7x3FS8U4qqlsSLTxHFyOs8MQDaYd5tpingJ2t2PO3+zwgce3koPWBHnL9u0MIeF9pPnsqzuKmxH7PmeZHRldHHn4+oSEnqdkxG4Tk1/XfHFyx3fHU/Z/mJGfHllfpvSbgF2k0I3kMO7xFbx5ec/bxzPG84q9iPh2tyB6kASZIzs9IpXHPnqSL2tcowmQzOKSsPRU3444W+5YfR4OgcVW4oG+DZG9ITh6zMQRfXH8k87LP+s1/i4O+PR+SXxaEXwI0FnH4vmOso05eb6lXKdEk4avzu7oCk10OVjkImNxR8XLyYqkdNTriLv1mHftGa0WfJvldFNYRgVXL58wRhEeHcIOp+PDPqFtQ4LeEV531KEiWkFQeUzIAIY5M6ja0y2GypOdOszcoLHUVUj5LmG5qUl9TfAoie8E/jbkzo2RwpNqw+13JxSbgdNuFg6RG0bTlnYf8dPlir949XZQcD4lUAvqTNHcpIR7QdR55rc9f/GLb1GlJNY98d4P3HctcELg5o78uuVIRHZWIieGp6cx85MDjdK0ShMpiW49Xa9Z+JL+ISU6rYmdob3RpMKRLAy+kVTfjsmSgbX/JtpxSo1tFaOLI0lnmeuGtU2ZzQuetQXpaYUE9FmLHXnyZwUq60mCbgjYaEGw88hbDWc9iepQlUf3HnvqmY0LWhewn0Wsvp8PUqGkpwkUqzjBFAFmG8KdHnrCZwYV9ZhNwOF3UzLfkZ+W+F7hEmitZnJ9ZH5RoBuPDgyBt3y3OmO7z/hnP/6BQiZM2o5YGubjAw9twKfHJYfHDJt5Lr54wPaS3TjguJQYJVDSEu0c2GHjkK8NJgb7MaHKFP7zjlpo0rMKrR1dqOisoj9xyJHhsMsodMjHpzGrcYQ9kYy/2HIMJfGiJpo3Q7q6EJheEj+CvB8QtmbskNctnPecL7ecv1oT7cBqwWlbsXy2xWeOZ/GO88mOf3y65O4w4cN3p0y+2DG2HQcdMf0vntBpixOCrLUEnSdKDWYJKrL4ucFmHnWjOdgYVQi6RNIITeEiupghnT7qGZ8UhGmHv+7oIj1AlLTDpZ7TxQ7zISbOO+YvdkjriYznkISYuSNMDH7ZY4Vg836K11CvEsRNwOdyTxhbPu1niEmPqARR3pH1HWInqXYxxyaBSnG/mqEzw6rP6BFcUHD5ZGAtKM1go2xPYPmsYBVkRFnHLKnokBxkxKbMCC9qim2Cdj3x2tOMBJQSUwTI2KACizztOBQphyDELwxtIPl9v2AW1QSZIZJD3c6vNMFeYIoQkwAK/NJRW42oBHrZEV81lKHGTQYFczatuXnIkLOe6aKgjBQtisW8gLVGPu8oq4DAD9uW5ecbfA9OCEwZ4BOPmBlsCOFKIWOLPEgmJ8VQAZVQZZL+JiIogI0biIC3Ofag0bcKiyRQnknS0D/FhIFDrhUmEGSXR1LVo887ijjg7FcPzM72mEASv6mItwybwdZjluCVp3MKPerJL0pS3RPPWsyZwzaaYxPTVCFfTu9pbzKu8gMno5Jg2fF+u8SdO8LGIXDcvlvQrCJ2NyO8APFNyPGQ8On9kghHnjXYx5jgW825PJAqi0NznAVM7muqbgAe6aBndnHANopirLEO0u88MT3RyKJbQfOU8Bev39M2IW8fz1B5T6wa5EpzlW5pugC/iVCTHlUJxNIwtQ2b+8mfdF7+WQ97ETh86FnfzLj80QO9VDx1OeG4ozikZEdLmnbD6fJ1x7NtQ/bouTxbk5zXfGzmFH2OtIKmi5DvBbNJxbzrIPM8MKL0MTq0MO0ZqYLjXYq3iuBBYZ1ilcVMX+3xJ5ZaBKhlx+s3tyzWFpNLZO/p5WAwU1c9YmHJrEFXktFfbtF/0dJHClV5XDd03dO4ZXxScpQBdhXS3CSU64HcFRYDl/5+PeKfvnuOKCVR2KM7j+sFUhtMq/lSPPL6Xzyxbuaksxo57xgt94zymvR3kLiOfhOSzFrW70bcfzcnn1XIWrLtM9SD4kW0p9WDkS8Y99SBojoZtLJp4SivFHnakr5smJ0e+V//1//EhWmYLUq+WS/JTmtqAtrbhPmxoYwlL8I9h03MN6MpQdSTTUqqhwSr4OlpjtmF9KuQVga42ONbhapBbxROSkwq6GNJLxS1DxGhIw//KM857UminvYQ025j3F2Emxr68WA9096h1JD8N7mnW0c87KcYJWmVYvTqQGckmzaGiaUtIqZ5RbJ36KNi9Zs5/T5EnPaEtSVeNnzTnoFw+JFldFlQbxPivSfcSOJ6WFubx5jypUf+cSviJx6vh/tOtVVET8PPOaoNKjOoCrIXJQtZ4gWE05ZEGaJFx+j0iBdQrhJe5Fvio6D6xwn0ILaWsPOMsoZwB8FxYEHUdUyL5uFhxjszo1t6tHfUJqB3AYukpNUCaxR2G5JM2uEzVcG9HZFlDcVIUy8ki7Rg83NJfQ1VFw0+gsiRryx+6oi/KOg6jdQO4Typ6FCfNPnW4WPJMQlZHcaIf4pRUpJ0htNXW+TI8LCZcred464N6rqlkAGzZcHlL+5havBCcHm15os3t5gJBElPWcS0M4gWLXWsiWc14SdN6zRpYxhHDd0CROyQyqMTg7jT+GyoxU2DkpOLA8UqY50q4hNDqyJkI7Ba0B4iFqOCvtLs+5iT/MjJyR7dCqb7Hpk4nnZT2kAxmVSk04G6120j0ntJsgG/1oi94mW0QXlPe9AUNxmmVdhVwDiuCRpQ855p0nB+vWc2PZKpjiTt0Dh03JOvHa/ElvmywJaKXZViZYIe9zSxJEh63DrAWImODaXWjMKa0PckZxXHMELUCmU8MjdEdNhOYZCDHApJvId2ldB+l9PuYuJRgwsE9VNMf6YwvcQte/oJ9GeO4tczXCM5fMiZqgrVO/SzmpPpgfH1gVYrdO95Lg7cvD2h+5jSBYq1SAhflahiAJe1SqIzQxNogsuGxmtGrqW/jRCVoOsVKjGQObYqxmsodIB2jqi3iELQ7iPMXPD24ZSySDAp6M9qwhKOpxr3EPC4nXLX5PQC9NxiftGyX6e4SrAY74lKT/uZxmrB1YsnZODpVjHJES6ris/iA9I60mJ4ACkeMlzg2TyO0FGPfhRkvmNCRxRYzl3Fm2RPf+lRtcAqge0U1UiS68Ofdl7+Od/Z//i//++Qo5guHHqdQWRxTnDxcs3qb5acfLlhfzdCW0s2rnkopvjQEyY9ldSM3guMFvixQyz6wfz0BP6qx3yK6DMQjeTClRxnmt7By8WGd7s5vhF4p+jqAOkNfaaQ1hMYR5D28DFg/LM9T+sxaqfoxpC/OlBuExZUOCVg7Kh+yDFFQKg6qmegN4LxvaW8VixkSzP21JGEHyKILUHW0z8FuGtLlHf4VmEahV8F6EXHz67v+fr2jIMPWeqSL0/W/PvfvaDtPbxyLFxN9XWMuRD4UiOFxWYC9UEh3zSMs5q+Dbl86viUxvSrEBn1BLGjjSTNIUQ87+jrkKD2xN5QJJpLV3J5uecPvz/jlz/+yKdPC5IHxx/CKf/F5295/9sZd6MJplX0U8//6pe/4a/uXpCtPA83c2Coy5E4bALP5Z77+xlOCPRWUF2A7kD0wybA9X/sereS+asdptFUoUZKT1lFJOMG+zFB1tAmAntqWY4K6u9z/CqgmQmQIDvIT0uurlfcHGao2LLbZ1gvuJBHwmVDUSY0X+eIn5d0dchpW7C+G7PIG+7UaADaRIKxrtG9Y2Uzcnrq/QDqsQtH76HPJNIJZDn00v1eoSsBqcNf9bSJYHQwuFWIOuvoLizth4wk6PCRp1yl+MzyXG853KUEY8P9PCOVHfJeY+cO+UOEiz1Z3FDaCFUMK/Zu4ZBPmm7GgLqNe9pEcZIcOU4lYWzYbnPm8wJpPOVTRtcrwrhHJY524pGVpNlGsDBkjaUuItgpVGXwmSR0DmbDQ5IWFkqJHxn0k8YJAWcGe9CQWs5ebDj93rCba+xnPdUqoatD2nA4GQd5T/q1pL1y1G2Ecp7Z1R5qSblPqUyArsFGnsQZ8ALZe4KflrTHECpBPO0Jq57dQ07yvCFQlqJJyGVH7lsaE9BKwYQGNxc0KFqtOK4SrJO8nj/x/fsLFp9vWBUjRC0RhcRJwc+fv+OhmLMJQ5a2prlPOaSacd2hlz1HFaC+C0mXNWjHrs8wrcZaiT44bCoRYkBcS2FIV+A+64knDbISrDdjdANuagmPYHPPKK1I6KnqmDpUSDGk53UnOP1sxWfLR/7Nb348NI6aYYDLlUCm4MYOvw+Ikw4aCQ8KEUIXDv8Oqz3KeMbLivVmzFKVrJOIeDVsh/zU4Z8C1KRj1HWEiWFbZLhdgEws7hjw5Ve3bB4TwmctRRdTxRJdgXsKqH1IHwnylcMj6AOJeNYM9+/C0bQhUdwjHhVeCvK0odimCOkRU0uS11Qu4n/5k9+yepjydXXK1bjgeIyotYStom8CFumRIgyp7xOWWYH3sJnEeCMw24BgJ2nPLeGkQz9ANdNka8fJ6y2yhvJTRvqsYrMfUbkQXYDNIBj1qI8addLBrGeUNGTe8vhpglUwOa1ZVSn1TUqQGHzsiJWlK0PkeUN5iPl5+8TvixPIPEKC2tT8/f/l//yfoDr/MS+RW6bP90RhT3ZZ0gcg6ZChhaXhxXxHnLZw1hMnjvnkQF2FVMeYqWjJXxxoAkWdS8y7hDA0lNd/9K8biMcdQe85Noou9/RFyHdmPhCXTEg+OyJOO4KflshxT5T08LKlkxpxZjiqAD+3jD47EO8d/dcRsvFU9zFrkeJKOYA7eoeMHHnacvHZmubnlsh0sGhg0hMEBg+YOqB9TKjzAPcYUpQJqhJgHW+ePXIy2vNNOaNKwXWCUVLy/fsFdILXl3vOm4b6IeF4GuL2AdGLI2JpmT1ZsnywVvW9JrCObxjTfJ+gTht07Hme7jgLC6bnR8a+5Wq5RlmHDQU/54HFtOHuaYRbOd7up+wJeW/nvBrvKcuEYhEjnyRx1PEm3PHXf/cF/q8i2tbhP2+IFy3zn2yIkg4v4baakMwb+kuLe90wzUqyuIWTnn4T4bcBrQ1o8gE+dFjlhIFBdeCsoOsDzt6sCC8avAbxGLC/HUE76EllD8m9xz3ryF4VMO4Z3feU7zNkNVT/+ojhfX+K6EYCewygF7Rlwuev1zQmYJIf6a1AlAJ31rNrMux9zN5F+E5hnCSwFlsHxCtB1Dqkhd5qulwiXzRkbwqEdgRPAj0yyNzg+qEeZSaOqono9hFR0JPWjo/1jJaQw8UgbRmFDel5jWsU/Zmjf9Uzf7Yn2Azreq0McqXQNaQ3HnoPZqgdHj5FlJHiuE0Ik36oZLWG/PKIqzT6rUffKl6O11xme2TomDUd1SodbGJnFhdrogeJLwK6TUgoekyrIfF4pxDOQ+YwCGwgUGtF+xSTnvXo0559kRHg6A8B3X2MSC3HQ8I+jKhsCLHDCUEpA2ql6cYel3n6sUPmFnPmqBKFfW542o5JdIfIHGUbsukzaiK69xnKgw88+zbhIUjZ9gnzquXTxxOO3+W0P2TonRjCq/OadTniy1efmKQVV/LA83yNSCznp1u+a0+5rUdwVJgQCh0yyhpcJDjuY7pVTNcEHL4doZQjcpaw8OhJh7g2hLOWIDSYiYNIUD2DQzhY8gDGacXoWUGWtLRdgAgNm3HIUzEm8T3zpkKXICc9b6INj98v+Q8fXxFEPX0Z4LQgy1qUk2hlibThZLRHbASsNMG8Jz87MpMVLvLIUmGk5nA7IvKGYpUNVzFSgYfg6wALuG1Al0i2PkaFbqAjFoNo6rt/PEEIz+TYIAJHeC94/eKOYNkxPS+YjktO/tkjwaijT6A7RITSYGo9mOT0sIGJwp7CRsjYEsw7ktagAsezxYZvHy/49u6cl11JuYmYTQ4QevS8Y3JVDDmctwnPkx33+xndIaLbB6i3IZnvCBpLtBU0fUDlE+SniL4LmbuewiRcfrbnUCWIRhCPGvSLhtFJgVOeairpjKa+Sbn5boH74AkKRzsTfDpMqJ4S0pOaYDEAluq3CV2tkW8Dkk/w4bs5s7CFncLXksW0+JPOyz/rgJ59DInGhvRWgg4QSHoCnv5hiTzp+esfXvAqP/BNPWVfSOaLA4F0uF6ykxF1GWLGDqUck1lJdQwQZ1BrAZ85IgOcdgRtz67LUM87or1g4zLCylKcRTROkbewPF3T7QP6ImCrQuTEEW0UIhYsvtwg3iuepjFzV7JfxKSqY7sZoZWExBNGhkpBHhjyvMBq+OGbE/JnFbNZw8dlAvWA+F1eHanfZ/hWEHjL2XlJ10g2t2Nenu743eOI/EXJzbsJ89YwjkuclVSx5ur1jl0VsDqO4ZMkPen55c8e2e4y/ubmhKiz/Gdnt/x1nOKmnuMhpZ1I3NggvR9Sun3IhdzzYKeoAG6KOdmDI540hBeQbiXV0dNoeJApm2OMsQr7eYeoBcGoR6iQ6nlMmkfI1uNLQf99QJ735E1PcaF4abf87ptL1ASaVNPkmovZltU4o1slzBd7KhtgP8UEo46uDQjmLdEPIe1UsPk4YT/WhNLjvMfvA6rM42NBUHnaLyxB61n8QfB2fE72ZYX4AOcXO3orqFLJ8R8X+HOL6T2mjPhVdM/9NCUPOvSPSkZPhscQzEJQPuT4ucUKg95qurnDTwT1NkMlPaVWhN4zFh2NSjkND7x6dc/v15dUreaiKFlmPbuRGfj565g4bGhlhNYDxbE/BMTjhigQhKIjjhxBLXkII6bPC3ohONEl5aeMfjE0NDqlwAhs5JG9pL+02FrROok78USbDltL3H2If9NzUBHNOsTHnuS6pehjtk9jknHDyWyPt5KksRA6nPfYkR/W4niWUcljk/P51SM3VUYtNKL3pDtJGQ9KYlE5dk9j/rGJEV81NFVEWWjEXmOeG2wVML3YU47/+ADSqiFHoTxPzXiAwNxImhnYVqNEgxPQdAGzs4Jlc+TmwxnCSOyJIQu7gVnvcoKDoO013V5z3e+Rv+hI6dCF5+TzNVuVoCaGbh/inOT7D5fo8waBJ2ks46Dl8GFCMK9Y9hXFLqeMYmRm2D6McMlwQlatGK72rMabDLnR+Eqg3ilE5qlngqCQJPmQDg+9RTcCaSWd1WSThrYO8AKiq5qLizXZtKYox1RhQN1EhJVnsev5IDLUvIX6jyHQzJBgcE8hYecxnyKao6BWGeFOYGaONlLsbY4KBN4IpGCAP5mBLyAaTXQn6WYDMdQbSdgY/HzY8EU7T/ispr9P6Jwe3BVacyhzqiIm/csdJoTbw5yTUUmvBEcb8O6w5MWzNe3dnOlFwdNqQjxtsbUYHrI7z14muNgTBX+8amoEM1HxYT3jJD/y8mrN8cOI/UNOmDf4SiFDz2Y9wh80JhN8Nx1hlKdUgmlSUnYZS1eyimaQWeRDOEhyOo05Nfzd4yXCD62ml28e+P7tBXltWBcZX/7khu++uyKPS7KkpaoiTsY1v2nOCXpJdGsJLzqU8ggF9SZBOsf4rKR7lzJ6U7H1Gd2lo9ylpCc1i1FBYuo/6bz8sz7Zy9xy342pXluahRjkNLmjqTXjdcuPxg98mITofkAjuj8E6LQj8pbJ3lAdY0wkiApD5SNcIJk3HeJDSLCWVF1APRHwi5pRVhFHPWbscM960pcV/ruAuHSET4L2dzHFt2MOF5LZ+YFxUtPIgGAP5d9NqUzERLUs0prUWo6rlMRY4kmDV5bjieC533O3HfPh7ZSPNwvMS4uZOj5+mjCWDaO8Rh0FcWmR8w6/DdjuclbrjO/8lNnPtxRrycm8RM575MEz1yXNmUeNLGFpkHHP/ikjmTY0NuVpM+bXzZzaKf7rZ9/jnvf8IZ4yen7gND+Sy56MjiDvGXU1c1my3Fu+/qfnqLViLmvkqWH6oz3JxPCzn99wfGYJXxhmriZ2Hf7OYKTlWpS8jrZcRwfqfUicN0TewjrgOAkoRhEqtqjLnk5KOGmJnjecfLVmPirQnadFwkM0vK8/jPCVopgrXn6+Rvae7THDaEn63nP2co2sJcZLhITw4MlNR3gYKHxOgdpLjicD//wy2ZGOazqn2D6OKMqUk1dbTnc1s6gizRvehgnqvOIfzZKnd3Nu7ASx8Mx2hjxtkAYuP1uTX5SMRhWjpCGIe3wgCGrwkUdOLJ/nax6mEb9/d0VvFEtaOIFvgpzN/ZjDPoUG6CSTrsZJgV8PbHPTK07yPVfRlijoCCuPXGmau4SZb4l6gZ06JvOSKG+xncSMPWbm6eYO1XmEdAgBIvCozjG5Lsi/3JONGnYiQi46Mtmiccxsje0U8knxsJqxeT8jndZDIJGSM1Ezmx8wKZRxSH/l+LafMA8rZC0I5i3MG7Kw5nW6Ip50g+zm1ONrzfn1msmsRIwMsodx2RFuJUnYD4CbGvqj4qHNGY9KdCGgFwS9RzpP1wXIqSELOmQp+eF4QpsL+kAgnKA/hKi9INmC3sE4rpDPWu6ilN+tLjkQ0bxy2L2m3CfEr47owCIii1GS6jYHL2hMyON+QhVJdOLYlzltJKi2CfGTQJYS0QlkJZk8Owyabelp9xFNJkleVDircY0iFobZz9boUUc6q7mYrtGzlvBFRXAwdFXIsUkoCYlUj/Awlx2696z7DJUbvBWkUUNfBRy3KfsuHsKBT5ruJsU+RjSpxCRyEB09gU0dqgeMQO8l09MDsbG4xKPvNE4CDrwWmGf2/2/6NCkYrfH7ALOKiErPfpUTesPlLx8I3pToyuOUp4ginv7+BH0A/e9CNjdjfprcctykJB8ko0nD5HJwbAg83S7E7CNEJYcNSNih/QBDE5EjPKvZ3E8JO1hkBTc+45aMly9WhJ0nCg27MkUUCiFgnNbEB0iyBqcFrQt4Nj1wd5JTzhV24dDOgBGcvNgSHxzToOQsLjgfH9g+Tri4XuO85JnY85t/eMW+zihtyGGkSJY13/kRX4VPTP94VZD4nvFpQTRu8CNL7A1bFVMtBFUX8ePsAaEFMjf0hwCRSFb/SXH7H/9Kxi0zWeMfA9ptRBtp0rQh6jtGZzU7m3H8bkK3jei2Me00II0M+llNPRJczPYsZU0tIqo2opQBq+2YyezI8tUa3FB587sAkTp0D/4pJCygWsfE05b46Dh0KTs7wqWC8cqiPinySY3QjteXG9xEEL8uGac1ysrhTQ88rZbUk0GVKB8l37y/YtVktKceLjrenN4z+p1HFsNpWq9h+uWGson4xdkT2euCbNoShR3TsKG/D7FngnDUsNy1VC9D7vSEfh3x6WmCiQUfvp2ilj32DPJXJSfLPaUI+egz/tafkVWeya7meIjpQrik4if7DfXXKZs046mc8vrzBz47XeOVoDsKZkVFfNLwsRnx3bsTwp3iWMVMXx0IY5j8qCM567j2BdOzI3/99jnqAJ9TsD2k+G2A7CEYGx7qEZs24cV4zTf35wTCcCwTdjdjkOCKkPCigsxy8nxLGnbMxxUBHdLCXFdkssXO4f1hwcV4SxQalBsY7u4hxI8tnPQDNz/xrH6Y8ku9Zvx1jK8laVpz/tmar+SG5ydbjnmA0x4ROJKjYP1+yijo+Z9dvkccBGxCXskt1ccUbgNmR8vs2JKv4UqWBJcN8aIimtSMLwvik5JPo4iT+oh/1pOMW7pIkswbzi+2nP+LFemk49gM/OxNFGMue/SoIwxbbK/43i74/f6cTime0ggXeeJpy+NxxLZK6I6S3mm8lQTOEVeOVLfEy8ESpnODnToWquZXyzt8JyjuRrRWMUsr2j4gmPbcBRkPDzOkcJRmqEjJxCBCx9W+Ig0dGxWx6UZE44541CA3mqD3uIklNx35RYUpNcYqVmHMaVIT9o42Gbr3myonmdeI2CK7wVpYdwFj1ZAde+JVTyUj/FHzsJ+SnFeYicdf9Jz8+Gl4sK2g/aQ42xpenzziGglO4FuNyT1+5NDTjuB1RWgcPIZEXzUo4zkJjzTvc75+ukTVnhHDHWvXB6QnJcFJA73g2u1YnB0gdTRNSKZa0p0nH9fsw4DkskIGwzDdfzPFKEF2WhLLHu8luzqlP/EEX5YsqTA3ET63JJOa6lzSJZK+DNi/CpjOC/KgRYeGDsWnD0tWv59yo0YET5K20xwuBVwaxs+P+EYRfNLoRzX8vMrjhSDagRj36INDSMfoqiC/PBAchq/+8i6HqSHI+iFoqsGtA3Rn8aVATQwmYiBbZgMGmsyxDhOCo6e9ibn9n+Y0NyndS0vpIkyn0e8Vl+0R+XlLdlHwt+vnREe43u35+OsLNseMSiuejdfoCmLVY7yiixWJNqhCICpBePSwgZPne5bzgm0S4o3Ejy0fJilMLdHBM6tbbAKiEfCoacoI9dsEvw7I2577MMLuA565LSPfIGI4f7nmwWdcJwc+f36PPmvY9ynFMeGHZs5aJZT7CI8jbAzqG03x2wnrb2e47xM+2BnrOGW8PFI/pjy+XyD3EvVDQNcE5LT4iSOeNXzTnyI+hrReUS8F2zrhavT0J52Xf9bD3vWCsg/JVEesDVnfM5Ido7zjKczoR9BnnrQzMDG4scNqT7OO6caQjSvm11t87jAzS5q3COk4rEbcNeNBDxtakrDBloqFKiFx+K9DhBAkaUfzwjG53kOr8AqKLmE7C7j/sOSir3j/OGX3boTQcIwDgkVHEBouihIdGtLcIi4s+2VAeFlzMd1x6Q+82pb8NNkQxJ7si5rnrx8o2ozHNmfvY/7qh2eIxjEZ1zyVE/YuZttk+JuQapfy9d0FJJZ9GCHODWYjKNcxUQZx7wkrwbzoqcqI/rcJue9ofpdgasXLScnFuKA7dWybmD9cjsisJ/6Nwq0EeiX5er2gfeG5fLFh3af84394xq5K2H3KaVuFsYJ6KrnK9hxMyH91+T13TvPt44IsaPETz/unGWon6C4s5A4lPGfnW8ad5ebXZ4iNQNQwSSrOXm8QytM1AXYXoq8bDqnm1HZs9wnfPp5S1hF+q6lHis5qRlnN8YcxogZ1tLSvLd0Lg514vrq4wwaC0bziy8stX5w+UC+hW3oeP8xgq/h4nPBP/+Y1Px7foI1ntrec2oaz8Mi+jLl7yBjVDtUbvqtnnKV7lHH87vGE25OU0XXF78MJXQw2hT4XHJuUu/sFwUaycyOq3084fBpRB4p1l7P7zRh7lERBi5w6osmAlJ3uevxBk+wt+aQm7CzmKWJ/P8L2kuBB8OzkCXmj8Ubw375+y9R2dGjkT0sWv1zRL8C3kpiWoISxa7mc7flGzYh3jn89/5azpqCqQ7oyIHirOL2tCc9r1oecqosBQRT3HL8fcduOOLvYMM9KXKE49BFPb+f4YPg93d0sOYiI25slZR7TTCX9fci9DDn7yRPZzhEcBd2HjLvvTulR2EDQG00UWGI8e5dwPunodzFx1OGN4MXyiTfRE9NjS/gefpw+8Mav6BcBH2zOu8cTRCmwscB0Eu8li2y4/x5XPdNpzflJgXmMUCtFIULET45kJyXBdcN9Nya6qhglFeGNIBcdvdHc2wmUkqUqKR5yDiahR9KsYrCCWmrC1g3123RQo5rfZjSBJn5+ZFT0yKhHCk9hIy5Ge7I7i7iLONyPuJRHNm2GWituiinHIiYf1ciN5HK6472bIZ8kk9GRyaIkpue71Sn3352QNgbRC0zmiFsHiUOnPf7CID30qUb+rEQoz26XoyeDQc5Lj12FCOkJlONcF4SBYZY2w9o5VshGDuv9UhMEhmAtkK0giXuyUYP9HE6/XJPNK/qlJaXFhpJP9ycUT2P2d2NWd1MOPuT7iynFXDEZV4g/aG7fLwjHLcl5xcvP7jh0Cbs2pXrmUSuJzx19pFh/O+FYRDTHmNHRMH0cthz7r2ccTUQ/gmzUoJY98suGFydrzNQS3Et2RUr4CDE9hyql3w8Ohvs/LDkJj3Q/a/l9f8b2+yn725Sv8hXqY0gcdYgXPUtd0MgA+WXN6LQaaJuzwVfhN5qn7YgmleRBg488fS5oXlq6XGBjz6FI6XuFq0DtFdEa2kbx23cv/qTz8s962O9FRNsHNPuYLgZ12XJfTFhPY87KHlMKUt0hUke6d3SBpLgbIUuJ2ig+vV/y6WHJqDLoG81hlcPYklwc0ZVEpwNRi2OArTS7bYpsoLqSEFjWuzHyLqD4x+kAuclAj3v4GNF0ATf7nLoKaHNYVRlVEbIWiuX1ltGPhzvIM38kMJ4sb/nZyUcmfUt80rKLQ/7fv/kKdzmoHW8el9gvWmZBwyxskI2naFIe84Bg0SC/Dwj+RvMhHRNmPS/mO/438QfG85LzvCJ+1pI6i38fokPHryY3fDyMOcYavzS4XpH+qGISdTy0GbuHMe6HGKskfBuDhm0SYd847icxp9cHkoNnvVrQHEL+5z/9jmV2JNCGfRcNms8bzafNmM8fSr7ZnlD1KfebKaNJQ19LrmZbXvxkzfnogNOePvDkqiEKe/zE89XVmui0pt9G3BynhLVHH0BNerS2pKrjY5sjV5qg/ONnIg7pYsF8WtB/SOgzge8l9TPFC1+gtOc8Lfnh9pRnquS6LpH7jv/nu8+pUcS/g//yy2+JThpehVvmrwv+6u4N5cecxyDit82ST9s5tdcU5yHp5xXhtOfkR1u2NsW8MkgLeVxTaMGoMgQ7SSJ6Et3T7wOW0y2LkyO+UvzqxSfezDeoryX1LqYqR7x/XPK0GZObHrOL4E1H9NExDirUZx3eQ6MCgrMGEot9CnEXlu//7gV9G2Byx//j9gvEyBI/O7I4VPQbzVVdMX2z4+qrNWefrZg82xONKoqnFO09N2HC98UZ3S4k/KTYPtO4PuS8rQfyV69x1z1oTzfxdG8Mv6mXPOkEE8HMNOjTliC0nC73EHjwgugHhd5L7GOEywUj1dFtQ7rxIE8RrUBox7htWRYVedOz62M2JmKZFPhJx1g1TLcG5Txvf3tGhSbtDMXDlNvdHCdDsouSJOuIe4cdgX6UpCc1qe1xSqBbz2Y1oks9tZHMTMP06kCNxnSKZ9EWVUOetPitRuwU2zZh/zjC9ZKz6w39zNGVIc8maxbrBrnsMYFE1R5deUyjEeVg3qOVOATSDPbD4LTlp88+8Wz5RBj13HRT2lXMUQSU64TVbsR1vkUvW6LUEH9+pA0ket5zHGvmp3tsLtjeT+mKkHg9kCWzeYXxkuCkxRlNhwIHfaNptxGdkEP3u3AcTMJoVCOVo+4j5NTgQrC3Eb6VdKFEGz9sMveK5F7gI4+fWEQnBniXU0TeUnzKWYUpo1HDfzZ/pD2EpHdDrihZVBxtSIukaQP8OiD7vSJYQ/K1pP91zvJVMXT9R46aEC0tctrjtSf+IFDK8ubzG76Yr/nyzR31VGAOARsZE2aW5kNKcZPTe0mctgjrUVmPig2NUcTzBvu6Q3pPvU3wVpK9KLFW4XpBd+G4Pin44ftz4m804qLDh47jyOO84BhpkiNsgxFJafBe0tylNFVEVrWYSpDTMTcVSWVRD4q6jvAa/CZArhX6KCjCYCDmfXFA1h598LR1AIH7k87LP+vq3ef/t/8DOoz/qNtUCAdJOITA7NixXBzY/TBBLnt6o+l3Ib4cTuCBtfT54KLXFWT0HNoE5S0+tahOQuaQxhMlPf03GX7ZY4xGFx7vFc2ZIyw9UjlkYrFTx1LUPBzGg/lqFTK+6fE/aVEHQWsU8cuasosxMcSFpxAhz7It2/WI3mr83DBtOlRg2d2NGScV6rwljw1bItYfx0QP8Jc/+sD/dLgcTFLSsFtniE4grjrytefV1Yq7Q87OJgRFS9jCz189sQkSvqsnmMdokDjgoIIs72h2MWcXB5oTy/hbyYdZgkoN86JldRwjcoMKPdU65i/f/MD97ya4c8mqSJlmLbsypDWacNqTm459kfKj8Zrvvxnjnyn+5Zcf+JvV+YBVlbBtY2ZRie5ht05xFQRWUeWKKGk4Od9yI6ac5Qd2nyY4I1AXHc1jTGLM0FtFIrXlv7z+wP9QXBNZw7rPmI1L7E3EsY0ZXx+oq4iLFxte51u+3S8oDzGHNuZ1sqKSIdZ5dh8SdAPipSeqHMVSIwtFcww5oaAMQ/xe464NPrO8aA/Y2HPzsGA8P1I8jQbhyxHqlyAeNKlpuRodeHczxz8fZEhv1I6HZkSU1jyWE9zc4raak7M99X3GYarQa4F2jr4KMSeOKOg5jQru9xM4SnzCgAB+klgh8SPL+KSkKBPExMBak+QdVkAyrdlvcqZRzWGb0Dchp+dbnqoRs6gmcoau1lQypKxiYmMAgQvAqMFQ5kPHRX9kpXMWwZGH/QQ/s4TO0W0jnPKoucGtNUnS43pJYSLkURKtwUZgl5Zw0ZIGHftPY3QBwoFcDIG4cN4Mqt6rBhl5mp0e1KMTh38KkZnB/RDB2DO+PvLZaMPj/zjnUzrhdb7mjoRDmSOmBlYaPe7pcxBH0L3gZLohmhk++QlGCF6+b5Anlm/FhDNR4mrF5NmedZ0jt4pmH9FpRThtyUzP+jgindQcn1Kyg6ObQKcUatHheonfBjgj0bUHCfqy4UIfuX2YIs475nHFfFzRHTW375acvl7z+GnOqWn4IHO8FcjnDZezLfdvl3SJ4FevfiAPGzb3UzJrOZiAd2ZG6AzHNsFuAsIG/MThHkK6uR0etEtBUHjc3GHP7cANOCi0dsOdvBCDkOa8o3uKEd4PobiwJ6jhkGqCjSTZe/pI0C39AKdpxR/Dio4OSXfqOBkfqPcxndAEk46sMXSdojhkhAfP5OWB/dOI+N7jQkcrI07TPVx19Eqz7RMiaQhbR3mMEdpynux4sDOiSUmhEr4Knng6jOnfJ7SvDb0fwohCeWamRns3ZHWmNRubctYUfDrMUZUkzDoOmzEud4jAIQpJ0HouvnwkLiVbGXNYpZhWkawt7quegwqZnRakt4JDE2M/arpTyfzNlsNdThS0jKYND8cJk3FFWweoT4rJosZ8H7F+pRiFDcebgeB4VAHtU8Lo3mJftphVTDOq+fb/9H/8T9W7/5hX/Chx6xCnIDstmeQVc9GwDSNSbzhWMS5h0N4uOuzC0k09TkMzkthE4EuJWinqXqNig88cInUs5gf6Q8ByemAqaqbjmtA5ZDn46H3oEJ3AjqA99bTPLYFzyNhiFbjbkPCk4foXGz5Lnhjbmuy8pthkTBYFsbeM0opx2HC3nRLc94M1q5LUWrF7O2F6sWeTxDzcLKgrRfF+xMlyT3Ul+KEak3SGptccXEjaG9R5R+QNkTKUbUi4bLGtYiot+cjyT5szTsOGs0VBcBTETxB4S3ZRIe8c+biEs5b9bcpHn5PkDfr3IXNn+PHlHT/LHrh0JVpb/vG7M/TM8VWyYTyqeCoTbAL5pOMsKLg3OWra8+vVJfXzgNop3q1T6jJCj1tOdQkHMLVi85gTOgvjIUyjTlpUK1kfpsx0w7bMCKcty5d7YmM5vd5ihcKdWiIs2Rr+tl/SlQGrLid5hLYMGJ/vycYVSlkib5k/Wt7enXB8TBjLjpPgyNvjkqfbMeUhxYw0ycueE18jJpC9Ffznkzv+d199Tdr3lNuIX35+i2490nu+uTmDFVzLA9mjYKR6lhdbzucF8m1Id+r4xV9+4m0+xb/y1DNJrxU/lHPWaYhRCnMIMB9iui7g8W6KFTCNKsKkR+eGXkvijwIOkvvbGdO0xJ0avPIEwsKLFnnd8ubVHW0kUNOe6GvBLK6pViGJ6zFPA7azEpok7om04fFpjIgcVRmx/jCF0GNLxevZA0HlEJElmTRYL0lVT3CvWZdjbO4o1HDXLLVDpBYfeEbLivHekKqeLhZ0Y49QfqCjTd3ANGgFQecpfYB41mBOHEwMvZEwM5SEmEQxET1j1XCV77HWY3chfSAIa4/IDVFhKMqYxybidpZjIvi6X3KII4KrhnxSY6fDQ3r2e0FWOpyRHCYRrQvg+xi/DbHPHR/7EbKQdEpwprasHybk04qT0x0mh+T5ER87nlYT2ChSM4QI61Pw5z2hNphtiGhBaosOzfCtOrZoa9G55fJyxV+8+p5gL9i1ESaAk4st79+dkY5r7vIYM3WMsgp31Kw/zQaS5KihsgHfP5zxtJ7wdw/PuPcjDm1MXSbIDgJtMIHA7SU+skQrCK0ntBZzZREnHWotkdHgXmgWYMdDe6JdDlmOaNoSRj1eQ2/UcAioII5butc9yGEzigNdCBLRE9seJhY8tEdNbQOsEdT7iHosWJ4dmGVHxNxy+DDCpw7/ec9hGWJTz6PPefx+weNuRFg1COUg9NRac/lyTX2qILU8v9xwle4ogmAgUb4usU8hNvCocY+yIKWndhHbxxGr3y9odzG+08TaYqeelhCJI3sHadDiU0f7zKLynocq41DE2PMeIof75wMgLA9azp46zvIj1jk+e7lhfHVg18bEZyV1kXD79gR9r3C/TuF9hI8FD13GIRHI1FL6iHDWUB4jZkWLiA38ssYHEhsK0qD/k87LP+th3xuFFg5nJbbUdF4h/BDc86XCbUKOkUZZMHfxoDPUDjfyLKYFJhyAKlI5uhPHyeSAVA61gd2HCcuzPbuHEU9FTvy6pKkDkrDGv2nheYcOHDI1PF8+Mep66nVK+xAx+toTKMMbu+F7xtyICftnMdV9StR4Ut9zOtqzbXO082S+ZzvNeXa2Hb7MtSBIe5JHxVfqaUiOP6ScsidtPUjPJg6ptSYQDusVYuQwUtB0IXUkebgds12NUDjWfc7jJGIbh/yHv7+iuU8xalDZ9s3gEa/OArZtys1vT1GVokuHdHE7E7xfj/n+2zPaseaDGYAbx7OA+4ec9uj5ly+/5dlyTRY3xHGHqSSLdcNlWZPFFSYWmEBgW0V2tNx/d8IP/3DGaOX5b15+hwo820mEfAqYnR45SUvUy5o2F+yeMhobsLzpWVR2QI9WCfOiRv1ekqQ18Y+OHPuQMB1WeDzrqKWmmAa4U0u9ifGVxc9b4rzGbhTvzIgeycXJlmTW8bo58s93O34633IMAn76/I4yUvx/Pjznh2KCODec9SUfdhOmL3b0reaz83vuDgv2MuEuzDm52lO0IW+TEX5pSULDv/3+Ne1dgk1B7DXdOhrCU05wfz/H5Q6ZOILWoyNHk0qabpCM9DeaQBhc7ulNgHrWDK2IwnDWHXgdrDg/LUiM491xQWlD+F2MWwiILSKG9TFH/EOA3gnKMKB2AVjBL3kgKj0ud/QT2N8M9473xxl9LKldwPEhI9wPKttaa5R0mFXE8T5jWnd81jwxVxWjccXhMefy80euztfY+4jR3jBj6Fv7AGwsyGTLxbM1ynv8U4grFYSOrDb0kUAZkBvJ/TcL3n97yjfVKX4hiQLDaGV4/dkdzVhz+qs1JoXcOkzmSU5qgsYQlIPwp97GzJKaz+ItjD321JJ/uUf0godvp2TTmmRWoc9L6CG7d3yWr9hHKfYJNr+Zsw0jMEPjw/xjjmwEshPsupSL8y36rCFJWkayJUg6yB2kHj3ryaKa1HQsg4r3vz/h9tOC368ueDjmFPc5d+WE7/sFOhwATt0q4upYopVDPyl04RmnDXKn2N9O2H+YUFmNmViKNkY3gi4UBLUniCziRYMRmmDrYdbTo6jGChEPmyGmlqYKcQdNVhvipMXtNMk7SSFDctGQXx3JohZZS5R0RLVBnvTDRmVsmZwcCRowI0jyjmIUgPCMvneIRiMrhdhpdCU4TQ6Ut9FgQnSScNyhPymaTwnZnWcpjrjAk53WyB7MM4nXsO8T4qDn428vOLydUq9Svv6n54xFw93TjEMaYr2kzhSjjePN2QNnl2v22xwbQDTuaGyA/l3Ip90cOkk465hsW86jA3bqaNHE2rAoG3Yfx5A6gqAnvfMsLzZEruOhylD3irk5UO4047hhtizpGk3fBVSPOaIVhBuQK8UxDjClpmgSXOqZn1Z0fYDNPLYJUG7IScRYxAGMUUwuC/RJ8yedl3/Wwz6b1KjtsFrqy4CodDSp4PxYckwUm1HAiJ5w1NF6jWk1equ4mG1JVEdWWOoupD83BA+K7TEj0hYS6C4tWzmgOMUq4MPbJe2Fp/vckduWl3ZNEg1Y0XKfERpHpDu2ZYYdCYKt5Ie7Mw5Fyk0xY73OOYlbRqdHdsWI4IeI+jGhkprubcwyL7i+WhMZhzGS+go4b4e60YOlNAFHERHKnpO+JHgUNEahGhArhdhKlgfDPKoJMSjj6Y0i7A0n1zvcx2jYYgjP6LHHjRx27hFOUrcRxkhMKJheHugYurr1XQqR51//q29pEo3fefzI8urH94RHAT9p+bf753zz/jlvv77AbANM41nvRpQi4eOHJWlQcxYXaO/JzzvaQDKa1cx/dODlmzX/w4fXeA8ydkx+tSLIW0QjqN9n/Oh4IDkO1cY6kryNY9JRTbVP+HiRsXse8KBTChMSOUseNvAU0NYh6bzGVAH6CMGsxz/3bIsRYS9wLywz03JoE6K9ZzSp+Y1csn2j+KvyBOMlv/3hnLqLGemWeOU5fJqyvwr5tB/T1CE/Oh5Yd1N+dLlGloL/KvnE09ucWkYs+oa5aOkfI+gl8qclnZTorMeF0DyluKdwOO2Uii6S1Gfwar7hPD5i2oD6GHFcRHRS00qFyxzBTrBvU16/2dD4EMYWZRwnzzac7qqBKviswWbDSVpoTzTqSX9ak0wa4saiUkebCXjtcaFASwdHhb82BNOemoAoMQSWQRrkBTbzdFOodIDvBHotsHvNW3HCOzNjpyNOz7dUNzkftwuiLwoOVwK57EhelCSvjySXJdUrx7vVEpwgyVuYWewY2iuHrCSL11uClxV+0XOV7kl7Q+8UwQ56pXn7N8+Io551nzKXFatPY96oPS+Pd0w/OxA6h3eCk/EeKripc/ylockkrQ0Qe8Xkl3um0yNiFbL7OOH84sDk8wP3n5asdhlhIFAXlt2HKa5VTC4PXC3XhJEhOmuQPejec7JpKR8zjncRbCTZv4eLxwPTruUYhizzA33oSUYVUWrof5czpqPsIto6YBQ3CAckHhVb9jrkzdUKf2pIxi0/n33k8nLFbhezFgm1CPjZ7Ba7C1AtpJWj84r2EA7ZmrHFnVn8McBNLD72aAPzZ3vysOX01YZx1KBiS1uHkDmSq5q0b8nOS/Y3GXav0dYwHpeYUFN/zMGCV/CoE3zqUOOeKOgIbgX6Y0BzquhvYhyQHCzxxlL87ZhCxfRFiKw9bRUOjIgx1EvJ5rnCacEx1gTe09xlSO1YLPfYRqPXcujXpx7nJcWnjNqFVPuYtgiZRRVR1vF0M6U1If3c05UBrpO8+Pkt7kWPKDRNFeG2GvVFy80k4/yiYPKuZywazIuewif0TzHVfY7JJNFZj4slp6rAjj27Ucxap0wTx6PLuZrtiBuH3Qf0QiMlqOctToNPHc+fPyBuA24Oc5Cevta4wjH2NSq1yCPUPh4qv6OeVLV/0nn5Zz3s/V4hT3uy05LsYBEXLUSWJ5fjhED0kjDp6EOGdaGWuLll52KaTOJ6xVJXyHC4OzS9xKwCktOGcNEiHvXwkPDzBpkYfOTw5YCALIMQ30rEaYdzkvIQ43tJOmvpc8nFVzvqSKGtYyIbgqjnnYlZVylJZ/g+HdNeWlLnSM9a6pnERT33cULbBsMTYzL8/d29pl8LFtdH3sd0W7dFAAEAAElEQVQjHv2Yo4wAiXWCyayiXzqM8zA11A8JRRowmx5YhgWjSYcfDy7zbRzxfTIhel7Bq4Y8rYg7Rz+Ds8WO4DDQ3ew+oE0EqWj5299fcBXvWDcjTuuWX0U7/nlwR/xRo3eS1UMKM0eTaOoiYeJqgmmLnxo2Sc6myLlSFfubMX95ckfYGx5cQlXEbHxCoxVX8Y7H9ZiiDziYCDk33LoIdxSMfcdmNyKoQGnHwtac+RJpwWYCayQaS5p3zKKS8XwICl6Iki4WBIEZMLS55R92ZxwOKcGyR3YC20ja7wN6Kbldp/i9pPyQU1pNljb43PP+04xOS5TryT470lvFIfVk04pyHXPcxPy7pws2WczaZByyiMlVSWwd3kDTB5hdiHsXM5kc6aaesal5ffFAdNIyEQ2TouN9NeF2N0EGlr4MUXLY4oQbh+wHa5a9C/nN7oSqTNh/PcKuIk4ag0sdE9tgxNA/3387gTuN3YesZxH9Jib1PZeiIu4tj23CfFIgV4rxvEJqhy01CR29k6S2Q9UOdVURO4usBfagUSuJHXkKHWNLTVSC3QVsSPju4ZzF4kDkLVo66scIFXd0ZUjXBMzCGrsNEVtF4C2h7hFPmk5L0ucFYd7hixCVGwIHl1nB6X2PlAOpsUEhU0O3jYg7h77s2L90fEjOKWyMn1lcA+vtmONCDKHTvOEiPpA2hp/96B2ikLxbnRAuG746feLwmBLWnsO7jOgoMSeOZ/Ea0QpGy5LtdsztJKOTkm7uCVPDJkxY+RxZSro4xESK8qcCew2J6PjC3LHpNcVjTjTrSEYNdR+x8Qmv8hVp0HPdFmRBw8vwFqaG0fLI2DRMm4Z1mfBX//Qlu7dT9NJwcbnh84s71g9jdGjRkaHZRzQ6wI88qodAWaRR2FAiaoHowSWOoxrCxeWHnChvOe5S1GXL2WTP6KpAJlA0CUZqTCIQkedpP0FmhmDW0Z14+nLYCrhaEa7g6TDGxIMeuU0l/bnFCUn5CtoXnmIRE570CGnxEweRJ5oMLaRsXGPuE0Ljye48LgAhBhT09ncLggeJC0G2MNk6XkYH6jAm6Sy6hF5KEtERj1r6FA7/OEV+Usj3AXHasf9hSnK06Gow5I3KnkefYseOj11Gdl3zqDJy0dFZTSsl4rIjnLc83M6IckO+qJm/2vIgc8yFoRh7vt1NufnrC3QhhoPF0VAvoDpG4GB5dkCEjvbZwGF5HhWEN5JexIhKc35b85OXd5yIknFeEwvH42r2J52Xf9bDXi07XKcR9ZBE3X+csFIJXSiRbyPUk2T/wxT/NiGswUmPbwV2FdBUIcLBsUzQ1uFDoJO0I0n/Q4q7jYguK8Z5xRfnd/zsV98ztR2zsOVfX3zN4+OUOpJU70dMjzXxtMV5QSsl/qJj3Yb87F9+x9WLFXJh+JdfveXsvCIQnkcz4mpRoHHsQ81jllCvE/7p9hr965ipb/GhRTiJDD3HV5of/fSJ+pDQf5vBRmMPwVDl2Shsq8hFx7PZE/JuSAC/TNYc2pT81HEQCt8P7gDlYZHvkaXAHTR1qnHS8FO5YbfPSa5K1KRDeI9cdBijeLid0Z4Kxuc1D33C//3v/4J/v7/GJg71qwp56sgmNcHechkfSWLB82cbnk/WRLeOtDE44bnf5PzdwxmTqGNJyw+jjC6Gf3X6kVFiEUpgzwYssXsXsSnHqM87vPJc/WjLfp/xS/1E12vKNuL5Z4+MkoY+EhxNTGYNJ4sD/WNEfZ8yzWpQUDxmrA85dR/zz6YPpMISfacZRw2rKGU0bflKP/Lj5ZZ//dkHZpd7zF1Icwj5yeyJ7PWB/HWBmwLCE7+VfB7uByd56jl5WeAmPcnKE98MddDvV3PqSrHoal7pPdmyojmFKO24SA+UecDDcUzXKuzGc5LtSXuL7oDA0fdq+LJqenwiUImllAHjtuUX5YbosqH5TFKMBL8TEz64OYVPOEsLDIp6LLGpR/SC/psMTjqElZSPMZk2PD5O+M/Pb/jffv412bLhp8/u/7/s/Vmsdeua14f93vcd/Rizn3O1X7u/b+999mmrM7iwFBBQWEgIlFyAZMWyZC4QKI5KsgWBK1uKCgOSLSQklJsIyTd1h3IRg+3EBhtDpeqcqlN19j67/7rVrzX70Y/xNrmY5ZMQbMWHUghIfqV1s9bQnHN145nv8z7/34/zas/T0ZKn0yV17+NvLU5I2tZnfJLDs5bkZ/Yoz6E7Recf/NyTQcGob5mEJW+/OoF7n3gLva+QhQd7RSw6dn1EoHpkCY3zmKoalx4m9nUgWF2OeXS+QjUCP9a8W42xJz39exqTWbyzFk9aytAjbwKcclzeTimrmLKK0BufE9sQxQbvQVHfxchCMPE7nJDs72NsDuHecLrsuFqNWfYJd82IfBJgjwwqMHz+6hFZ3OC3joVfMLYNgyc7+l7hRYZa+xgfxKzD+Q5ahTCSZhfjRfB6POPJSU5zBkUYEJ9XeC8rZAsn5zsGbcc+8ElPS+ogJQx6xNLnB6+e8sH0geS0pe4C9MQRjVq6xuPL6zMu8gmeMIcu2HmJJzQ0hyE5dafoxSEiZ6TELw5GueI+xSkHE00w6UiHNVPbUCYe+VdDgvbgTAh8g0ktJ8cbYq+j9yXaSoQ7aG+jQXf42WcetvCwvmC4KIg3FgqJHRiwMFxaut6j3ka4mcMIgZz1PPnGNfGsBgeh0ES5pe4DdK+wgWWqa5q5oz5y6MziVw4aeGhjPpovmZ7uOHpvTVf53OUjOu3TX8d0zsOGAn2iKS8ztlVCa33kTCNqWOZDotJx4uecDXOKmUffeWxWQx4Pt8jIcny04WRYYu4Cgq3k69tjVr89J+16utJn9TDEu1U0Tyz9ucY7bQ/8hLgjwKAiy4t2S9HEjAcFYqO4aVP6Iwt+T3ZWUX3LcpdFmLllLhtOq5bH/f+iuP2fvfbt7/K5m4NVSkaGNHd89OQW980Gb97hFh2qB+1B6ncM6LEJ4IGueoKgI8wdE1kRTRuQDpc4XC2RvSC+sbz+x49x9z7TTU0tBT8qFxyf7piNcoaLHDnXmLceg/f3MDD4G0nVhHx5f0JVRXwQLrl6M6ONBFYrTqId64eY54MloeoJk5bWKUynEOOOkW2pmhj5dcAszMn8hh/fHHH96YzOenjWIFaKbZeinYcMLOUm4Yv6hOVqhIlh+SaBB5/dLqbcRoRLgd36eL7GnIC784nvxAESZCyvihF2YrgLE9RWcKp2/MzDLWbvM/75NeXE8nU/wN8q5J2H9eHBZAT3ihEddNAOBI2VvNsOuNUpt19MkQX0oWAybHg+2vCvfXTBB8M1C7Hn5cmK70S3fLEfEw/2iB54HR0c2K0lqB3uM5/9fcKbV1POkh3XRDSnluPnK64fZoeZDXeYyv384YjCxjx+usLbCW7WQx7nBWJoeXG0wl9JRCeZxRXbI0mFR1WHbKOQVZTy/fqILy/mHKmObNLyaLbmuk+4LKbc7TMCY+neJuyTkOswZfv1iIexx1d6xL7OaPBJP9gfGPWrAHlkSB81rO5TzjcN3kYwKXree7zk6XDL4+ma2TDHLASro4iNDemcQt0r5FATJN2BFY7h+WxDjEYYyKcSm3sMJjX7KkYLSVgeBvvcVNNFgixsCY9bwrjlG8/f4XpB+TZExB1i0eCA3/qtc/5RdcYtMbUR7DKPzW3GdT9k/mxD/wha6+OtBUZC6B+GXhGwGO8ZfakRSlO3Ift1RpaWqEgTpj37XUL8FvL1YT7Ebn3ObxpOwz3Ri5J+F1A9xPh7gbIO82lCVwS8+3xB9RDzaj9l+LjgX/vwS07FHn8Lo1mJt5XIrWKVxNxsJoyimvSowB90+JOad6OE6bRg2Gm6kSB6WnIf+QS2504N0KmiDxQ/3h/x1XKOzBVFF6BiTdUGBDvBk+M1uvQRK++Q2sg8RpMK71axX8f024A0aPEuPGK/I/FaAqVZnO/ZtQEydnxWnfBi9ECTRySiJ/E7BkHN9z97ju0sXmLYPAzpVyFmJ5GnHems4pUcsykSxMBQXqYUPxzQvklJjwpEp7CNxGx8zMpH94e0gmsUMjOY2CGFI5zXkBmSsGNSdkzSmmofoevfnRDPY8rb9NBWr0P2MkQ+gNv4vLZTGl+iG+/gUygdsoNC+ohhjxx29Ceafm5pPQ/VOIQUhGsYrg1NLA9vhHxDNGvxrOXZ81uub2YMTwsCZTj7xh3lWB6iymjCNTShImoNcWcI1xxAO8OGYFHx8dWM7W/M6JYBwgNnJJsypSlCpHSYzKFKBUYg+wNboT/RSOs4H20INhD0DjHvQQui44pmG7F7OySbNNS+RLeS+KM9FyrFu/d5fH5g6/fvUuSVT3TUILVg2rbojc8mDvG3jnhSo1rLPyqe8bAcsns1wV9JwGG1wgsgHdQUm5SbhzFbE7KSIZ9tZ3SN+BdaL/+Vjt79/F/7P5APRoi1x/uPbqi1R1HFZHHPfZfiBZpvJ1dc9GOW7YAn4QY/EGwvEppG0reS2fOG1XqAXSlsBDNVsDjf8/HDKTI1HCc5+zzi8b7C+96euzczonmNHTmKhxRPGfLXA1QHTaIQzQGc4HyDO9d8+OQKtorJ04L76wk3JCSqI/cDJn3DTTkmqNyB0Vx6yGnHdNXSnArOdc31akjRRATK0DmJajiYmKYWakU8q+lvItq5w51owpXBzzXq/HBWxsASugpVhzS3MU10iB6KkSa8PvxzGCUwqaVNJNJB1HY0mWQ+rNjdZZjQoZyl9nzOsx07EyK3gjwKiU4q6MEsI4J5g7z1qGeO6V1PEyuetDvWMiNNNNeTgJdFSWkFl7sxs2c57Trk6emaZJzzG7/2HCMC5EDjxYaR7JFxx359sNDpSGAGlqQ/ZIP1Zwnj+Y7tLqV2Pl4nD0mKD9Y0Rcg83SMbwems5OvPjvmo2uNODB/fLtgHPt/+ubd8+eaExipS3bMpssP3848TgqMelWnem234wcM5hIbn8y0XuyFR21D0KWfkPMQx0R4SaVnXAQPZ4Z025JcptvDhcY+0sKgaXk1TvuHtELHFb2AXKZ4OH3iznnFXjVAd6MQhA0td+8xWHcm0Yj6ouXyY08UwHm652i54MbmnCyTNZcTRByve3BwzoKWoI5rSJ0UznefUkcJUkvXtkLQ2NOcQJB2bPCWowA0ModVESU//OmL4zS2ToOfruxk6OPjZo72jf6/HE45B2VELj7Nww8XqiPd/7oKs6/ntf/A+NnVMv7Uhv0+RseY02XGxnpHnycHjrUEvDGrR4WmHXfu4rU8UtPSR4NHTFe3rlFWZ0E0dyeOC+MGg9wHdI4d/4VN1IY0nEZ5D5gIxMDxP9rz9akT6osbUHhPRcKMzRGiJlSZQPV0o6VYhNYfzcq+1hM5ytx4hW4E36Th6b8nDfsRitKesQuRKUe4Tjs9X3JIh3yq+/fSOr9YLTOBIsoajo4pPbxaH45YHheuhn0IgNXWgcJUiMR3VEcxFjd86lLD0KKZVTfCo4dVywTYNDgNclYRSUrUhCkt6VFKtI7zuEBNOtKZrPYyW9JEgQdNrhfItxh5if+q8Iegtu5sR4aBFWoc86siClt0+IbhVFImHaTwCe6AhSt/CwKJvA7zI0AmJURJ/JVC5pDs2iKMesVVQScS8R7zxMSNIRI9G4nmWnQjxAs0RBWHcIWvLcpAROk0tPRrjczLY4VWO1WZAu44RwqI6gZ0YrCfwbg7PPTnZ4+aawdaw90PWMsIoUDc+g7OCIOzZvB5jQw4shTsf54MoJToBl1qy9/d4jaPm4BOI9hYvq4gRLPcD5HFPHHR8KLe8fj2lmCtM6TE/2rNZZuhNgBOgQ4hrc8AvGwWBYyJKyqHHz07f8tmXj9m+J/HvoTUhQdVSPfJ4b/7A8mFEfx9inncQwLzQ4Bz7UvObf+VX/oVF7/6VFuG4SuG3EoY9l1/O8QLDOCqRp4asL7EPAXf9hEwYitZxGw1IHywmhTDuyYaaYpky6RrWScTiaE8zFgzGBeduw3aTkvcxupK0H3V0ZYwOJBuXMPmyIU4MdSwRuUJriVQW0QraucN6knnYcf3rZwy+lXP94zFVFR1yv6lAAnflmN4XyNQSBD1ZUlOvEsSxIU0M68rnmVlTDWPuRETve5j60Np3ShLuLMUoQmaWeOkoFpIsqik6QaUjsqSlqwOK/RQrQElxUMbu3EG3+l5N4jSul5S5D9cB9qyDqSWZtuyuQt7/7js2D0PelVOshE2doANIs57BoMQ5aJuAfugQTjEfFWQSpsOKu0HG1c34cPN6CKm3AV/7AWGgmR1XXKzGzE2NddDvYx5PSm6cQxqBn3ZsLxK0TIjTDvXao5oGTL0duzpmGjTUL2rknUOsFXErkKHlm89viPyKu1GG1xoe7ib4gPYFq8Qn1xFl7jPxa751VyPVmt/8+pR1EhMUhnRmGH1rA+c9D5uUmy7jDxxf8OPtAn0V8O33b7kph8zkHdtdRrbqOAp6vvJGnHoFVhnu9mMG85oBNb0VbNoUNTSkheFtOUGE4J3WHLsNXxRTVOaw+cHE58ue0aCgbSL6oc977z9w+5vHNAbms4pdnhFsHUUYsi5SXOET3XZ40jAf7Nj3MUYqzKijuU/IhcSkEgW0oUfsl+Tb5JANXqW4XqFGmrqI8IaadZmxrgRd7aN2gj4D+aRE7yPO/JxSO9Taw34btDJcPkyZn+1YfOuB2zcL7i9HhNqyERlh1tFJhUkcw1HFJC64EENi0TPJKvLVkKr1CT2NjCDvAoLjlkxbWu3RbUMaozh6tOPUNBTvK8o3CzzhCOOOqklQ857N5yFZ1vKe2/Bb6oyB6FFK861Ht9TC493vHDF5b0c91DQXMzzp2E88xqsOX2i6qWT+ckOnDsS2d5cLgnuJyRz2yNBmAtsK5pOKV6s5s6OCByLqbYTMSmzhIbcS772caVwiFLxZzxCRxRpBoz3UG8XeeMz8kupU4Y97Xusxei1p6xDPaaK+Jz4vWNkR1kJUOkobYISijwWqt4ShptsHCB+iRUOQO3QtiNH0nqANfdKwR9/EJFFDU/vYxDIzhqKMiDuN+6CFPGFQ9Tx6vORtMeO783t++/IMpTlwAsYt9iLBy3qCRz3jeYM90azFAC/3MO98kvdKGulRNh6Bp/GdwW89gsax7IYEumHa1nhWYKuQ4/Mdby9PsCZh+5HBHWlm3pbt1QjrHOLOQzmBEwc2/26TcbxYshsH+FIzokLlAms01SYkjyICYYlUi34XoTOBm2t4OETbfOloex9ZdaT0RJ6mOVb0LkArRxzV2PKA9r458ylPBOZVgJ1Y2kr9bpfXYhFw3IPQ+NbQ36boEMxeMk1zfhidEX7QEuwjzp+tud+PaFchCyr2JqaXknomiPcKqeF8vOGz9ZTTs7t/ofXyX+liz3lPGueUVUQzVzw52nL1dkHwI8fk2ZZkvOfqsyPmz9b0nUd9GyG3mnwh8ZVFXQnUQHPyeM1qr2iVJLYdn1ydAgJjJYUO8Xu4fTtHSEu6sfSJYl+ENEVIMxSk0xZ14cODgOctovLoBnByvOFtu+B7iyV3g4jNJyPyiccszqlsQLAWoB3DccndDxeUx4rgRUnVKop9StA52jPFcbjnw7rga5FRTEM+yNbcfj5ieT5AuAOoxeUS0Tr2yxQ3s9BL+ljSlIfzNR0LlA/xziLe61AWjJU0X4Z849Ga31THZE87/tff/TH/56+/Q7BUaF/x1W8/Rp10DI8K9NcRj47XuCrirUw5ni2x6wAxrLi6WBykEzqiqmL+0It7vqg98qOOr7YL/IFBdo6hrZBAllb8b/w78kjy1X7KY7Nj6cUM/IaH6zHB0BJ5mir83Zb4k56kgO0yY3qUk94bhoHhVbHA1w6G8MFozacPc7ytY9433P3mmD4LKFuNVJLPzRRheiaTkmWR8d+/fYw5NsSFQJ+0LL69RtWClU7xv0rY9CkurPhSC3bS49GzNfLGI/tSUD0d8jws2MUBf/jJp2Tdc6yGG53yrekNP7w+4eefvCFwlh9sH1H7irlpuX0MtjrATVzn0V0M0XPD/GTL7f2EwBdc5xNUKznNdpTLjJUOiQLDxgaowBIvalwvMEZx8mzN6Kjg+WbLD5aP6PzDkUahQ/Swp9/6hDdQnkimpka3HlNR4S4VOu2REkxs+Va+4ofpMaYVZFrjGU33WDOOO8rXQ/xScBGMeXS2YnC6YvVmwLffv+LTu0d0gca99elPDMd3JemgxDGj24fEQjPOKtpdhP+4Q10KOhvSYelqHxs4ptOcB5dhJRSJh7tShyO6xDFKK64eZlzf+Xz40TtUrDEXCXZh+NmjG16vBvQjhTOSZZSQDCquLwcszjYsXUiaa1xm8RAM7x1bX7MxMaI1LM4r/EWPyAzVZ0P0UONuA84fb8kXHuVFQvhOsLya8+TDWzZJxplc8y4bsN9GxDXcqfAgVzrtqRqPdZCg1h5Z3NJWPjLtEFrgAoOP5s5GiAePcZDDRFPdDxif5ejSp+19iq9nqAsf70VPZ33sRhIvaup9hG0UZuuhlQQNL/WGi5sTdKhIFjl311Nir4Z7nyLyGB4XmFcKXUnWfoq3kWSPCrQAk/u4RyWXjEj7nn+yPifcSGRkiOOO/OsMrxfYsUB2YKRg/4MZdgDdwmE68DyFdRCtHLYNyFNBkjX0dYAJBa2SvBlPiHaOBM3tw5TAtSzDCP9HAtsqyrMa+6wl6TV5kRBdSXQoUYlm8uGa/TKiCxVlrWDjo57WdCMPV3jo2JJ2FlMHdDPQRiBXPqoXpMPqQP6TmsKGOOER7zTWHrTRUSHolaK1iomtub2f4iQkRw2Psz06EXy5H4KWTPySSjii1x516tOPLcED7CeKF3HF7XZI9TBgkFY0jaIKFMYp2mJA2jWI2CEzDcbyzfk1n62OicYNK/0vVoTzr3Qb/2f+2l9GeCl9IxAKImOZhiU3xZj4qKYzPrKw1DZkpFvC1lIJn/IUgi1w5eHSw27XBg7RwWhUUhOgVz7WF9jEIXBEl4JuAvFZhX4bUyeSyO+p+xAz1qhaEm0P0B3zsw3TqGD1MMZ6IOcti1HOxd2MqHb4SU/X+sSu59nJHavtiBKf9d2QtDLMnm5Y9xmJ6djWKbHqEJ7Dl4cYUXEzIGoMVeARVA4dQ/Lg6AegEk07cwyXFh1Imm2IiR3htD2c7WrDNC7ZbDJcpvnO+JpumfFuHLGrE56MNrz7+ojA08ijDtWAn3XkX47AwPPTFVdvpsiFRgrHvGm5aIeYXsG4xzMO77Rl0nQsSVDjjvOu5t2XE8TQ8jQuyMqad/2E8aBl6YWkUmM/E0Tf0Dw7vWKtMj777JyX5zd80pyyGOyRK8XaSzingswwvIFdCjerKXHU8v58jbg3/JY7Jd1bhj+35uHVhJfvL/ny3RxrFR89ueHT/+aIl88qyrlm+JXlzVlKvYkIPqw4bjvW2xRRShoU5k3A+9MlNyOffh/xYrbhSqVUm4B03JK/TRBnhpdyTz6QBNbwwhb8t9eP6ZwkPK2QuYe88PA/qhA7Rez1LGVM9F6Bv1R4QrMUCX5rqbuQLjqY0rK25Y89e80/+eoJL58/8Ml6QXMfowcwtTW18dl3EfGwhZ2HO2/p3yUoC13rYVOLpy2Z7Ahrg3zZHhSgErJOczQpePcwpk0E6lGLuQ9Qgx79NmZhK4og4Pj5BreXvM0nmMiR6I4Ew9msZO0kexXSf5kyH5fIRHN5NWewKGgrSScC0lHLyXzN/VczxHlHqX30LkBLwXvJmisvw1xETE73dJ1PSE8Y9dTLBDnrqJ3P89N7Lj4/IZIduQ7pIsnw0mGPNPUmxk96sklJPvGwVxFd7RMPW1TYY3zJyNastkOaJmBgW/pQETc9k6Mc6ytuvpjwzRfXnFLx28mYeVTw7sdn5M8c/VWI11rS0PDo/IFdH+NNO+rXA74Vr/jv7h7hIomWMKSlER5R2lFtY74Rrmmt5Grmo1uf7FFBVwSkd6CcZScjdCtQrWTwdM99PUBvA4LW4kmHP+rI8xivhPBRhfYlWgF3wWFavYPUauSoo9rFIB3jpKRpApDgjnqaZUwaHdr4wgr2dYTvOXSlMALC2qHmHX4p8J7UuGufeiRxjaRfhoyPcrrGo2giROjonSJZlLRVgFcIbO6RDmta4yH3kmog8QpJmLY0OsB3BrfQRE7TX8XQioMJsBfIwGIiUI04AH8WB699VFn6xieZNrz/7SvevlnQlx5e0FMQ0XqSNOwolgm0hw4p3e+mD4aWpDUI3+LNW4JZw/71mGhnGUxymqmi/TqhUR7D85ydiAl6S6h6ZmHD1f2Qs5MVu0HIZpdyMtsS7xS3ecbz8xX3dcy+j7B3PqFvqKyPahwMHPoyQkw0/uygJn6y37MZ+rAL6WMoF4LYabzW8ujRkndXRwzNhv/6f/83/+Uk6P3tv/23+e53v8twOGQ4HPKLv/iL/L2/9/d+8nXnHP/hf/gfcnZ2RhzH/KE/9If45JNP/qnHaNuWf+/f+/eYz+ekacqf/JN/ksvLy3+uF3+S7InDjuBlzSBqEZHl0o7oPMV2lTFvW8TOww80RRtRRQoERHcCuVIIIZGlQOYSfykRCKrLDPc2wE80Ulm8jSC5AY56/JWg/ToljA+0qcr5RJMaL9P4aKyRqG9URM7QS8X86ZpAaR6nG5abIanROP/giG/2IcFRw1U7YfPlGDwOk7GjnpvVhPoqZutFSAtFeyjEHg7744i06AnQRFYf1JOJg0c9JhCEveVYFmjfYXYeybzGx6ByQbLRHM1KuFYH2t3K4ClD8RDC1xH93ufm1ZTFYovWisH6kMnNLwfYly3ypKecCvrHltGo4Q989Ip3UcRglhO92KN2jkdPtlRVRBj2hE2HXQXUymP0C1vEUc+79Yh7Ep59d8Uga2mFQvg94UeaTgl+7c0HPKsqxr3m5pM5g7hmvc/Yj3y6i4iLuxHXn06oJTRDyb/xM58harhapnzGgufPV/y+f/1LNp/O+Pmze/KvBgTXguNkz9v1lMFjizWW+ibkY3VEs4wJ944PVxXXH0+Z0vEHn79mqit+9vEV9ycByeOO5tjx6uMZ+ipgvCjIzrZ0mcMsPaZRzdlgR5gL3rkM+bhhpHK+NVwz9Hrs3GJDyJ5VtEeO82BHdxtw5B3ywgvXsLcR40mBqgVZq4l7y39bnKPHll/fnHFbDamdj8gFDzKhu4lQ94rqLqYqQ9rrhL5TaK3wjlqOH6/49vde0Y7BHWv8XjAIWsa7isFpztvrCcmixbOObhcgjjradYRzDjnqSAcNN7cjNjrChI4XZomMHNI4ZNAxHVX4kUE+rtkUEfU6QUSaJ9mK42c75vMdse25u5kgRpr5aI//2sPHcnK05SYfIXJFaHtsKRlFJf68ZdWm7JqITZkyUg3lq4Cz9+9oUkEiewa6Q9ceWdjTDgEFTRGRvxvS1R6J7dF3ASZzVPuQRgQoaYn8DneqCaKeaNBxdT/n1cMM8aTj63LGf108ZbsZ8OnNOa6WRL1BTTRxpElMz+tXJ9y/nrL+nSlN5/PJfoYDRvv20LqfaJJZzVA2qKTndT/gdT2ifTUg6/Rhg3CdsI19culDYOkHAl0rtpdDwtcKvwBjFSb3MHcBie3x+kMhkdJyzh4bW/x5A+mBfthpnyg7xND6uxh/oLFa0rQhR+mexWzPaFTRdx7OSfTeYqVBxBbxqGXXJOS7mPq/n7CzMe1DjB8eJuv325TuISL2ekbDikAYqjYkDjuEcHjGoZxDtocuqADMsUamhm89uiM6rTA7j6IJoFCoRCMnGokjntV4W4EIDbJytNrHOIUMLO6kR7eKr79/jvIsC79CJwKrJf69ou08HAIXO1QhOD5bgQStJG0i6JEEnkFKRxV5bB4rqseKXRJgn2tGUcMsLRCdIMoNCMdVlxE8bihHHl1zON/XnyXc5BlVHvHF9YLqdUb/KsXmCjMyxGlLIloq7YN0hIOG4XVPOGvIF5L9MGSX+aSuQzYHLkr9KuXt2xN453Pf/kusuH306BH/8X/8H/P973+f73//+/zhP/yH+VN/6k/9pKD/9b/+1/lP/pP/hL/1t/4Wv/Ebv8HJyQm/9Eu/RJ7nP3mMX/7lX+bv/t2/y6/+6q/yj/7RP6IoCv7En/gTGGN+6he/axJ2ZUK4EZy+tyIct9jI8fz9ayZpwSrwcUOLTAz+uMWbdmSDGt0pmhn0Qw6CB2kRgNc6+qGjyyQ2dthWIbWjDRRi4wEC+7sUq6zVjOcFtvIQtz7OCpgYjkd7ZC3ov04QnWD8aEfidXSFT9JqZmGFP2sYZSVu6aOuFPHLnPvViOG0wvrA2LBQBeejFb1WUCpELXjYjJi93OIPekQHXgeToCSINM3cMp7lhEnPSmdUR4L+XGNiB+Kgp4y/UTCdrBg8LjC5h8bji/qI5KOC7LRk4pXYymO5GTL3c3Ymwkp44e9It44eQXeV8DTasf8s4jdv51jjsdkO6S5Dnj3acn8d49aSR67ig8maZ8drXpBjcx9xFyDWknYARR6RtBV6r7h7NeH1Zsr9p1NcKbnRA777C6/JT314G2C2PpuHAWfPHjBKMMx7oCfRNVdv5lgFL+Zb0qDj9r+eUX45QryRNCtJLiSd8Og+FsQPPcFpyTLw6AJFH4OqJc0ZlNbycz/7mtPphnIXsioGDI47xk3P/tWAM7MjnwWoJx11F+JdB3zz2RLhem5+kPH6N+dUTrHdREQ3gq2XcW4b7ooMN7AEveZiO2TdxryZJwzHHRs/JK8zvhc88DzbEYQ9YqipdhGrIuX5eoNcCbrrhPGsoE+gkR6qFJjM0i8c3rQnpuejxRXJqGH4aEf0KdzlI3746j3aMqBVHjefj6itRz0Mad4mdJ7iX59coh4kwSuP6OOAYOuQuUd2XrJtYoIbQZYeOAwXuzkytegTi5povlgfUz0ktJ5EmZpH6Z7x4z06FhSrmKY8wCu+9+ErsmnF2+sj7Bikb4m3GowgeSvIJjXvf+fdYRDOlch7DzHT4DmO5nsuRlM+++oJ9VcJZRfi1gq9sATGEgtNoQP6scOfdGihMEOLayX56yERhvw+RfsCOdTYjY/2ocsgsgdBkasOMwW/dPyKvvRIhg1dKrCrgEXVcj7KaVoPhgbjC7p9iHkXUJeSly+WeE86vAa48xEbxb6LkPUhYZAOGuxC0yN5PF4Rhx1FGSMbhVwr2PlYIfFXEucJ0q1hsCjAcwdSnPLQE0d81MC9z/ZixMtgRbpzZE2PeF7R1T7dQ0iwcZQ6IL/LsFLw/uiW+3rExRdH3K7HjNOK4/MV0cuWwXmN986nv41JC4PzoTwVVENBPzC4G5+ha0lzzfgbG3QC+21CF0K2tISdOez+fUmzD+mEd0CPK4HvDEYrPq5m9A8xSgPeYbC27zzCjUEGBjs3uMTiG3toz+8leJa28nGpYT+SFEXEw9WQV/6Ayob0UqIXhuNsh6oszgiaI8dDNyB5nuMlGi/VqMjyNNrivg4ZmoYg0DTLmGHdsyfEe1GxvRsyais0Djt2iB7klc96O0AtJRNZIy209xEoR3B7EASFG4cVEnXpE+0s8mmPKiUseqZezXBY8tjfMx93mL2P3Enuo5Ro2uBdBkgM0llmLwpE8i+2qf57buNPp1P+xt/4G/y7/+6/y9nZGb/8y7/MX/pLfwk47OKPj4/5a3/tr/Hn/tyfY7fbsVgs+M/+s/+MP/Nn/gwA19fXPH78mP/8P//P+Tf/zX/zf9Zz/g9t/N/31/4i6+MRj8INxSYjFD3OKPAcMtVMvIarVwvEvMMb9BijCGpLX/p0uxDnOR6dLblZTWidR5g7dOcxTGpqz8NehqjaYd7vsKXC02AA6yTBWY3sQAeSsvF5Ol1xu5oQtJZfeHLB9x8eUWSS2Bna6jAB3FYBNAr9ssEsQwJnSbKKlI7rywWDoCWZVhSbhMAY1Mua7j6iwsfd+rjHPY+jNdIJAm3Z1jFaSkTviJ6X5F+NIBeYlaIdKfQchrI5DMyQoMaaYH+YKt0OfGZpwbLMyIYNjfNYDLcsL6cMs4rmXYT3XktxOWDaNizPFbHr2e8GfBA8cH8f0c18Gt8jURpXCdKnNbsqgM9jzOMepWFeFZg+4vjJjr1SvNAFX92N2acBCEnuebi14vFiz3qbcDLZcHRc8NXFjPX1mMG+pzkWtEIgd4Jh0FGZgA9f3PHF7ZS0cpQvLe9NlzTrgLt2xLFs2O9C3quvWaZDLtIRA3pOZENkS97sJnTSh5nltG9Y38csPlwzetXzkCVs6hF76eOPOvTX0cFcFho4MoivQ0RkCB9XnO46zKKnvo3w2gYVeCzjDLPQHOU9o1HNm6/muFJSLywno4qr1ZA+hUlY8+jZih8/HOGufbJpzaLLuW4mSAvdKjxEBIc+zgm6UCA2CuYar3Kw0IjXEW6ukbnk6ctbvvr6FLUTCAv9M032WwqtBMFpw9YleIFBeRYjYFY1FEcK/WVMm0nCrMUXhxtyp32EtcyahnKo0DGcPVlyUU0Z25ajtuMtKQPRsb3PsD78/uFrfpA/5Xi+Jv9ihA0UjDUnx3u+3k9IxOH39jRYUoUhbunRhpIudYSlI76FzeSwa+SkZ/lmwik50lqG0iLGFZd2QvbKshwk9Mf2cIT1EGCODE+ynLurIbX1GC4KTOVRCx+jJdG44czbs7o6oHLTtEFsJBfllEezNfvMx1yHVAtL/GMP/X7PxK9IRi3Hds8PH55Q7DL8QKM7hT/QLOI9xSaiKBOSZyWDsGJVDEltx6kpuRRD+gD6vQDlo7UkihtyF6O04+njey7eHuEpg76KcO6QCAi1RV9E6JmhDyW+MYiRpd/4pLKjOXHYRiF9y3hUcLccIxNDUhuYaegFzkIYavJNQrwCfXzg+XuROTDb7xPcvc9wVlJ2ESZyaK1Q4x52ivCdgiPN5HRHEfocuZIv350xmuVoJOYyxMpDdM5pSSQ7mlRyNNyx6xIejXO22mO/T9D3EZ42KOlohSLzWlrn0Y5AtRBcKIR2uBTMYT+FFA7vZUV1kyAnmqMgJ91b3l0t6M4Niepx1z6Dxzmdpyi/GiJmPdFphScdxccjxMsafyOIbmA/9JFbhRsdfCbpomJ0vMe99VgPY6rblMmTHfvPx7iRQTY9fRQgeg4Y66MOXfjUnsIODfOgJDWa2zBhGLRsfzzBLXq+Pb/nZpkRPS+5+/GCJpIEG4G3qPBCwXFScl8ktKXP6XjPPY4f/O/++r+cbfz/92WM4Vd/9Vcpy5Jf/MVf5PXr19ze3vLH/tgf+8k1YRjyB//gH+Qf/+N/DMAPfvAD+r7/p645Ozvj29/+9k+u+R9bbduy3+//qQ8AcxdwbnPG/L/OpaRxlPuY/CHjshhRCY+yCYmcZjQuqOuQyaJABYbJUc5DO2D0bEfYWVrt439QkgufvvMYP9qRfW/H9GxH/NAzPN4inzX4k4ZZUZMsao6jkvPJmvUuIaktVsLvbE548fKWbOugkgTzFirFXFXEk5r4c4/4rUSvffxSUJjwoHqMDfn1ANsoqi5keTmmXkX0uU9z5PCU4d3dMdd2wCsxZutichuyVTF6F+A/bZCZY/ehh//tiuRJiZxo+uSAFDb3Ad6oR75fE0tNuY0RhaR6nWEryeqHR1Q2oL/xEYnDu4PBOKeOBGIv6UaSb/sPDGYd/WmAXivEUKOEOZyr7wXncYF41PE4zAl3cG3GBKWh3kg2KqQdw74YUNsA0ToCoZk/21BNDUOvwHiKH315TvluQDapCE96Ji92xOOO8GdKFnnFYpZzE8e8HO0odyHd24RPX53zqpvx5HRD0mj+yHe+JnhuEZnE8w0fjh/Y6YDGhQf2/4ljUGt0DEUZcfl/P+FH1TntLuXJkzXJeUFb+Ajt8BqLygXvxVsWP3dP0Dlmuw6V9dxfjtjdpVQy5fEHG+rSo7lMWNUBP1we80gW/OHvvML4kuG4IGo1YW5ZiZDb7w8Re0GfCWrP4yYZ8eLZPYoeNTEUXoB1B8KYQTI0mkfZHu0LXOFBZHGFwH8D/UNC70tMAjYEsfSwUwceNJsEfyeR9x79NsT/OiC/HlLfJNjEEG8NnrVUTYi88xh+aQ9MfOej1wH+BpY3Y2ZJTlb3vN6MOR1tEaFhWpRM8obPd6c89nfs/IjJtzcc/eySNhC00iJzqKWPt5SMZyW18BFDR9sG+HuJiQTxz+1ITU8Q9NiPE/y0p3aSsvJJBzv2acBClvTfsKRhR2w0Xi5ZjHPCdx7bUCF3AjEy5LfZIW/u9yhxOBve+hE6FGxXA26aAdf9GH1quOuGVLcp+Srl6McG/7SkCnwKEbAj4tfefUBxNzw4H0IQlaQpA+4fRphSIs4b9G+HXNxP6DYB1d7n/t2AFsVpU0DoMX68I/A0SkLUGBaLLTfrCX7QIx2kpxUus5zEO7770SvaE4csBFJD+rSkl4LZiy3Tpzvmm47YHYYOG+FD4DgJ9qSFRVnHUVdgUKilJA47Gjy6u5i41+R9RJ2HqPSgCja5QjYOmyuia4nJfayTVOeObuK4W4/ILwZc308IvJ6yiajKCJOCOTK4wDEallTCR+SS7eUIdorl/pBhr4sQFxwoecw15sjQvN/jHnUMswoTgZ45queOhAadCtqnBiEc6uag6W3LgFYHNMbn5Qc3DJKGySQneZGztTH7TUr20Y7zj24plgnzrKI9N8jXHnrpU/gBybBBhT2ulrDQDCcVq3zAfTUBKxCppSxiuiOLjsGdCbKuxxpFG0r0q5i+CJDd4Q3KSdeyOvFIhOGmHuKetyRRy+ebGfJlzcPVhDry8KzDjCye54j7HiMNjZRM1pa6SMi3yT9v+f3nWj/1NP6PfvQjfvEXf5GmaciyjL/7d/8u3/zmN39SrI+Pj/+p64+Pj3n79i0At7e3BEHAZDL5Z665vb39n3zOv/pX/yr/0X/0H/0zny9Hkpdtzet+wjho0Aq6ziOcVNRliCl8fGPxW8P1ekLW19TbiIttyKit2ZLRdgF2qWhrH+c57E1AKDXtsUbMaoJ7xaZMsC+hHXg8XTzwhiOamcXPBTfNgDhomb3Y0fz2CDEWFM7nh7fnTNOWaNZS3AxoDbhtjPUgHPfExyV3pJQmQBcKYxTGF/SVh5lbtDwYmjoXI1PNeNBQPkTYeQ/3PqEymEIid476maS5iBBPNXpqEUtJ8ybjvcUdWxcjpx3p2FHtYoyR7ERIHyoGSYt8HWJ78I2jzQTJpaSLYkwtcEcCXUnsOoC5xXtwfN2PGYqCvvTQTy1z0RENexZnay5+54h1FDN6lPOt9YbbZzs+eXhENmoxOsC7tlw8Thl8sCOQClcKlAnYmATbwaNBwdOzJW9eHyFHmvGsY1uFqN9JqbwYgo43WcI0zJGl4It5in5m8CqBLQ6RoQsmfHj8wKfNCIqIZR9gGo83uynLbUZRJBw9ynkic34nP2OcVcw/2hIbzd54REvBwy4knRr2gaBfOHzf8t3pNfuvffJnGf2oJRWQ7xPOPlpz9WZKZRSf3s0Iw556H9HuA+RI8GqS8Hr9lKlsuPtkind8uIkEVmKPBf0ywg0t3S5AN4K7eoqdwUf+LeNhzW/88An9RBCFHd5xT3Ed46xHYFrUWYu+DLCJ4M5FDGWDaCVp2FK/ydC+ov6GQTYgcxj3NZtzSSIdZR8ia0m4V3h7R9dEZPUBG81htIVeeERxR6VDzsY7dnmEP+7oAkMw7Lh4NWMad7QTS50nlIODVvXGTBiogkFWQq4YDxryPkCc9FznUwZZiQwEkZMUkcLeJBBbTk4KXOTIbcSz8YZCBazbIZ+7I/zXgruFwtaSNvbI0or2MqDBI3xcU12n9ImHkD2+r+nzQ4FwU41rPPIHny5QBJEhnfYIHIFzPHq25epmjKwc9w8TRNcy7GuklIhGsDje0qUB28sh/gc1senYypCwNCSnLVFiWZ6NGXU1nhB0dUc7D/BCzTIN8VYGWR3uS10VIEaak0pz1/kksidOakwimIxbrrdDHl4NyV5u2d9k2Jll3SUsZM3yYkwY9rhOwRbi04JeK9JJzZqI0UmLuI657hcYBN0HLV5rEb5D1ZDfDg6R3VLRzwTuyNLaECksXu7oTgzCQdRbGGuaSDLyWqbjnMubOc5CpHr6bYhYaCKlqVYJdeYTraHPBGOvpvUU70dbLs56djcZw7Zn1af0nSJsHT6SxWTLfjnge8+v+DqZ0ZUR3ZEgiyvqdwmuc2yTBF9pAmHYX2YU057SKBK/oDIRZR6D7xCtoMhj2mWIq33evpoxqHpc61EnCtmCuPGwH/W4NeiB5dakFH1EMnCMrx1mpmnWIWPV0EaSRkicUwRo5J2iHwj8yqJPNd6D5avHI0a7lrgybDA87UrWXsjpaM+6DPGUI4h6JqqiHzvqOiS/zTg3iiB2PHq25k03Ik7/JWfjf/jhh/zwhz/k137t1/jzf/7P8+/8O/8OP/7xj3/ydSH+aSqQc+6f+dz/5/r/ds1f/st/md1u95OPi4sLAOZexa3LmIuO57MlfiuJhy1pqxmVPX7nGGQ1fg6+sZitj1861FDjTQyes4S9QScS4TtEYhk8yRkkDXNZ8uiqYRWGFM6n8hXGE7zez+l8yf7VkJs3Ewrh07+NKX5ripIWF1iycU2kO4LcYd/GmAC0UeRxQOt88sanqxXjrMLzNXoXkJY99U2CGVmwjsBZ7N4nmDc8nS6xtSKetARxjwWwgiL12T31MRE0C4XOA7jzmU/3uE5y+WpBfZnS30Sw8hH3HqHfs7AVAZb+IQIrcEcacxnhSUsyrTj95po2lVRViNgonA/twjKc53ijjvv9AJFYEr8lx2cQFFS3Ce//7BWR0+SfDVl6CW+6BW5ieGVG3AUBRZpw+27K9X7Ebpuw7FO6nY/cepzKik0+xN0mPE53hElHfeeTFpr9sU+SdszyHnnSkV9mFMYjTDqUgKDtSVqNijT1XYSWHu+WEz65OuLYdIcssAiwGXzzj75jIwIuyPj24gb/wjDe9+RFwkU9Y9MHbPKE5scxcqkYlR0js+Wr5ZBVmTH8QU3UdlwNAorLPdfvJrhWok8d2y8UFIdTsRf/+h1JpQlqSJ2mSiXRBwViJTllw0m8pSsD7NjgIsNU14zbjv1DTKg0t+2AX1+dEcw7xBpGcY0sLeJryc/4K04+3NM0IbJWPH9vQ9g63JuAOOgRZz3+aYua9fiX3u+e7xvMmSb+UlEmHvFRjfQszVBQHauDWdEHrMMODEHWoDKDcg4/6nn4nTndPqS3EnUVsLwc8/RkSfvUIoXhNNkSBj3NFIpMIUqF7EHalslwz4D2cAQWOvJlRnfj09cS/+7w9xetobqJ0TvFIGy43w2JMs1itkNElkb5yFbQW4nnGdxVhEksWVBjOkWvFdG4Ia01/kai6sPAa2AtwoDzJEpZ8KG/j2kywTCo2a8SaunRRwemgKkl+03K9GjHMOy4v5uwuRtgYvAKQdGE+PUB4uJ6Qfc6QRSKsknZ9CltPECMLLsuZv9mRJiDeBOyEBXRoEVcBvzO9oiwt/g5bO5Gh8iZ07Tapw4Um4sxwUqSdRq5VuxHiixqiOgxY0tw3CA/C1GBYRoU+PeKsokgMwdATqQpvxpiryK8wOAWhv7c4MUWk0r0xOBaSbCE0bAieV6STGqykwLxIFGBYeZX7FTA67sFTSYwvsTehITWYGsP1ypcKNBC0mdAbDHfqdEDyyfdjLvbCbZQnPoHqUw8q7Ejixk5Np+OaTcRF/djhlnF3NS0gaTZR2hP4GIIMIymJYNtS/aNHbrzaDwPSkF6XGKHDnfvoz1J1BmCJzUy0dhKoYVChhbPOFwMTaRQX/iYuSFSPVaAH2p04bFMYvpQ4M57ulAQ1AbbSNSoRU80YtbSnhrSxyVh2KPHiky2lPuQG5sw6nvyhURaw10Xw9KHRmAaxW03gDufn53cMMsqzgdbnqmc2gZ887sXPIt3P235/T2tn7rYB0HAy5cv+YVf+AX+6l/9q3zve9/jb/7Nv8nJyQnAP7NDv7+//8lu/+TkhK7r2Gw2/5PX/I+tMAx/kgD4Hz4Ahi/3lLFEPiq5Vim3NsWtfPZfjyAzaKHYVQl2ZpnOcvwGzk72HLUN/cwijnqyk5JOKI6/d4eLHdVtSucLNsuMj5tT+q8TVC5RtTiwtn8cYXMP6UA4iSoFxUzRzB156mEuQ/TeJ4wshQjRtyEiNsjUIHyHjgRt6LHNE7bLjFZ6mBBM5vAmHdSSx+EatRX0SmITx3U1pt4m1ATIiwA/05SBhzdvGVUd53KPE47GE7T41F8OyEY1faBIBhWb7YCiiLC9JP94zM2nx8hSMFUl4ZOSSBmib+ZkUYsdaB7uUjyn8baS428/ENWG4Mrj4WZMLT2end1hRpa+FKS24av8mDefn/Llx495OlsijKCRkkVV4+0l6XmB1opHbJEDTZD1mEYxosZNLWfphvohxK0kv315TLv2+LbYoq4l82iHbxx+2GEAtYfAWY62Dc++2PDB8ZJSxriN4MPpPaKH231CeZGSDSs2y5S410xUhV1o8jakHDsS7XCZYZIY+sZHVI7HYsfjb+xYeAXHYY7flJRVjGk8Zv0eNStZD2OehBvOL1a0wwWZ6/iZl5fEVwJiQb8GPXK4u4B6LKgjyXxS0lU+5T7BPO/phoqtF7Md+FAqsjtomoDho4qsawmXhvdOHvClQTQSOh/3OqQeKE7/wAYlLebOQ64VQdbxRTSkaX36I8P8GzkfBjmj90qqFwbmPf5OEjlNfZkcnPc7QecUegDWl7TPNP1TQzOVdE8tLhJUEtyTBvfdijBsCaOWaGlwPaiop/rhiPCrkHoX8fh4C4B3L/H3gqM4R/sS8aVE1xFffPGIVRyicDRfhRQ3Ce1dQhx2TE9K3usL6iKAJzV+ZhksHVNbsboforXHCzY8en+FkYIIw1jVmI0PC83Ki+nbQ4PSrx39OsAMHSIX2NAis55MH9IrSXhQX0eDBnHvc12PuLqe0F8ekMPpUYlYaJRxXN3OqG8lHhrxqEPNenStsLEFz3F5lHAnUtYLHy0l/toRrB32IWCzzjidbVDjjtyFlFMJM00YdfBBhWccYW/I9wlSggPeFVO0JxgFFUEObqZpWp+gBrlT5F1EZQNcZMlPBPL8ELm8vJvTeIejqnoVH3j2E0PgWrYqpDYefadQlSBUPW6mCe8latrTv98TTRt0Luj3AW7nY7/RkW9TdK1YhAVWCgZ0HNcF42+tEcctWgr6ziMZNEjh8LeHzLxY+oRhTzqtkJ2gGUs+6xY4B9G9I5i1tA8R7XswOM5ZFhmb+yFNEeCVMJAlJoJw3BBYQ/H5kP4uRt74jEWNfR0RjqF4M0SWMBrt8JVGp1B9OkTYHt0KagI669E/1YjIHMBOsWAoW7qBIwo04spDWoPMNEGt8dYwmeUsHu15NNlwfLZlepojcvDWimBeUzcBXqJp8xAXHGa/XOmxrmPyKDiQI43BPW4Jxy0D29Nehfzo43NGiz1lCM1cs4x9Pnl7zs1Xo5+2/P6e1u+Zje+co21bnj9/zsnJCf/Vf/Vf/eRrXdfxD//hP+QP/IE/AMDP//zP4/v+P3XNzc0NH3/88U+u+WnW5fUc6Tl06XH3MEY7yfJuRDGWPKxH6L2PaKERCrv3aGPBvR+yOpfkOkIJS+K1qFuP3e2AdF5SZZKlTQkCTW0V0gAaKBW6DNAnhlAZ3NBgY3eIgASOoHN0D9HBGX6XkF8ODruUZzvMfYjehhgkTkH24Pjw8S1hbfGzlky0uJlBVZYQzXU+IX8syM4Kor2lv42hBTpB4nc0dzGqhNCYg+ZyBI/DDcfDPXiWegHSOMaiZr9JwbNE+YE5rZShTyEwhn0T05YBHww32FYRhD25ScibmNA3dE97LvYz6sEhDeBVMO1b7r5eIC99pklHtYlZBA0fPb1lX4VU+cEQdm983g0yPpws2dYxw/Mt9+sxlfSpIkk/deyTEBX35GVE3oeIFuJZg/9ew7qNOf/9ax7yGQtb4Cl4Pt4TPeloZ5a2NKyjIa/uh5x69/SN46uvJvjG8Py+5nTbUIc+YWpYxDmVC/hIbuhyn2ejPesq4O1uwmf9lK+GKRsVESw9fvTxOQMcl/cjPjgvyL6RIx5LruwZk6GHPwy5qSZ8fTMjNQWFCPhkO+NEbHh0ssd7WvN4tGTrK/709z7mfLzh4d2AUdfykb8hyi3X+YQqjwjuFFJaTp6vwBfcXab4pw3te44fXZ9wHDSsiwEGwbaPiXqD8eDH3pAbmVJ7EuJDXKsXkhDL/k3Kp//glKg3jGWLF2g860hLw7MnK0a/sIGTjqbxCJ/lDI5zZCNQwuGetHQLS/ftFnkmELnHrGh4MVrx/HSFl1mmy57TosYcGz5tZngbwfWbBfVM4KRAXvoc7RqaMiR9qWmmCi/QZA+GaVUzGrZMnu8wjw3zR3ts7nPdpPRzxweTe17fTbn0M663E4a2wnWCT/sj7kVI1YS0nY9fOyKt8a58wqXAIPAfJG3j4cUGX2ryDx1m4Jh2DeOsYDbe0xc+wWlN3oYI32IfAqQWWB/02FGLA+udDtrLlNfFKXUf8cjtkKtDW190Ej/piXaO8NIRPcCEAvWixY0sLrRIA3ev5iRxi3/e4A00d/sRu/UAtCQ8rukbHyUt8ahBrwKS14rjfcO0schFTx9KvEYcRCufBNjMojsPgcNTltwPDvelRmEKH2sE2be2DLMK/0bRuZD4CvyNwNsLrBLsZYhYe3DU4wQkSUe9jdn3GdMXG6KzCpVqotFBlKNGPcq3dFZx6w24uJ+zlgl+3JGMalSoCeKefmZR2pGMa9pYsHkzxmmBbAWuk0QbSIaa8iEh6zTxrOK6G0JoD4mY9xr6I0uTeUgsnnboBMypwYaS6jphn8doJ7msxgySmuGgJj3pSKMG0YE4b/EjAf4BKZ7NK/rcxySWk8Uad95T9CH1dcY2TzlPtpy8XEIhqXsFTrDcDHnzdsHyfkizDtncDumeCURkaccCr7c0RUCfCOxWMe4azr+xwvMMfSAxI8eNHVCuE55/cM3RsxXdiaN+4rjWAy4/X3Dxds71/YRuoyjOf2+196ddP9WZ/V/5K3+FP/7H/ziPHz8mz3N+9Vd/lX/wD/4Bf//v/32EEPzyL/8yv/Irv8L777/P+++/z6/8yq+QJAn/1r/1bwEwGo34s3/2z/Lv//v/PrPZjOl0yn/wH/wHfOc73+GP/tE/+lO/eLv2MaFg93aCuQvxkcjaMnorUEeaZgL+UYP1BNs+YhC05EXMcK+Jriz6THHTDnEDg7wI8I46ZKNwvaTGEWwF5pEmGTT0r1KMOkB2zF2Ile5g27PgrCBPA0DhhIXwoK6NS+g2ESaSKGsRqSGwhmFaketDi625TgiHHY90TTH1qZRPJjV1HKEDaEcepnaYiWOYtOSrhCDrwEiCnSCMK7Yfj2jHMc4D44O/ElRpSOD1ZJMKXab0MQRhh9t6eMbRtT526aMn8Fl+xCCtKdIA4VlmYUVpwsPPS4fEw464tFSpJC9j1O7QasuLCOcJHlYJPO5Ivm65P41QqcHtfb7hbfjEzJlMSk4p+PosZbqX5HmESAzuKmT2M1vyPkVsQI8tKocHmYIUDC4C6liRvIlx3234vpzxvtxxa0eYqSS6sTwertkxgactVko+cLdcHsdMP3yg2Q+JhwY/MFzeRHz89oTzl/e8lDVv8hFd6nMyWPGs3nE5mtLJgA/KJV/tZkgFr2+mRC9KpqXh6XjDqyxmrtcMdc3rZMjkZYX8cYBvJR/8rx74L5bfYJwWHLUlx7rh619fcO+NkU8akrhHqYYokkR3Hu0+JJg0VJuY9e2Q7/3MV2yLEXfbhMfelh+ZMf3XPmmU04URctpTXyd8VWQMhiWN8zmVFarqCCc1mVHcqoiVCoh+X8v1KGB3kwGCo3nOxsSU+YRsW6G0ZtxY1vuI7jZADix+oEnWAqxjHfuYvUdcWd5wyiDv8L5dU1iPcCgYByV9kRJsNI9+7o6miOgQJIuS+yG88UbIyrELPeRGMug69FPN/d2QJ0/vOZM1v3b9HuXVCSoxJE3H3gv5jftnuNOeaVVzb4fc7iaYiWE8qNi/GoFwyFyyGSeoc43JFSIURIXFxoZuBngWexUQOIf2Ye0SvAcoUoXnQ9f4SOWIN0Da4fYewXFL7h1iup0ALzbwoAjsYeD21mYYIclcDac9D5sBgXKgfAzgv2zg65hOgT62B3bATYQZeZwNS9arDBEYso1mUyS0gSD2W1x6KOaBZ5ifFCxVTL4d4A014VZizzv2LiZMBMNth8pa2gCCFfS+o91FBI9q5suGiatYRQHrZkgTegjfIbHI3eH40X8AOe7pzg2yV7BW7LTHfJCTZDUrl6BKwWyxp6l86nXM3dWMVHT4vmbXhUxO9/SrANdINlGC7xyyEfhZj8sV126A/jolvD9M1qsduKHGdwLd+ITCIhvH/jojXAvi7+3prxKeT1f8YPeUaJzznii4TlPO9iVXQUT/gSXdW1zp45zFF5p9eSj8rfHZ7zM8LIHX0jQ+AhDTnjz08TpDbAzqvMffGPw7jyDqcCcdwaRm16TMnm24+3pGsBJ0KsD5YBOHlyuoAyYfHCihw6yiNwl4gt5zyLGgsY5XPz4mWAr6FxZv3GJLj9oovvjsESw93NAiLAybHmEFTRWQGI0JFKYIf+qa93tZP1Wxv7u749/+t/9tbm5uGI1GfPe73+Xv//2/zy/90i8B8Bf/4l+krmv+wl/4C2w2G37/7//9/Jf/5X/JYDD4yWP8p//pf4rnefzpP/2nqeuaP/JH/gh/5+/8HZRSP/WL7wYOs03A75GRw6Yas/FoHjvoA4QwpIuG4Y3laghto3gxXXJ/O2bwpOCuGMLKRw0Mw7jmrk+QlSB5tqe/ifCfV3RSUZcRyEOkxb2NcPHB0RzcO9xcY3uJePDIjgp2TUSXgloqyqmDIEA1YD3w95DOG5ZVilc7krhDGIUILRflmOyhJ511lImPuvXo/YOzXnqOsDRkk5aHeUC4E8isZ9dEyIGHXHrUGw+RaLwKaKGPBN0uwrYebnLwovdKMAhrtIlwtaQbCkzoMKlm95Di1RoRKErPZzisyAOPod9Q5zE9B7rg5vMp9cihKonpQrJxybpPyP+bOcMi5+E0RmhwvaKoYqbPa3b7iE/zJ4SRpt0E9IFHvJEIDP3nCXQecugYTGtS2zN4es9tnXJa1OhNwEMyoHqVkYwcnlNE64bp84Lps4Z2qXgoYXzqmD/U5KFB3HismgHxtOXtfsxpUIJUeKFh/2rM6pkhGgiMM3iB4rfzx6hJh19YLpZDjvwV7UnMJgrpPxXs1IQubXi/q7nfT1n9dw+c/9wD290xxYliSM+nn5/ys9+75M3FlK/uz3jtOeZhwZ/47if8X9cvsB8nmGnD8MTx6MUFP/r154h5h60j2kDxG5+9xISg9paLL095+dED18MJzyl5qwQi6xlEmqpL6KWii6HwIN3A/mrAjQ2ZLnaka4uUhtU2xTtueRLsuH6YcDotCJ6WzOKWV6/ntI/hKCpJe8nFasZZU/KwHpF+M+c7cc0n7x7x6PGapY1YTBtqA0fHK+7alFWUHciQxy2f1ce8f/rAqOq5XE9xIRwPtyw3U4omZTbZ4nuOSLYU1uFZw8fNEe9/95KgtwyDmkr7fHz9hNwmxGlF7XxOzjbs5xFm57MsM4ggvJLISY+1ggiH8Sym9KCRqMwQlo4+VHipQUQWrxP0Wx/bHBSt3UCw8CvKXYwbO2ZUmMeCfeoj7iO6KsB44BsDvmDYGuanW15v56SLmv1DyHvjLbZU5A8Z3ehQzJZNyvS0Ib4D712AWIBpDbs8pVcSUXmMFhVmrnCtYzgtKNcJohWw9Bm9yLm+Hh02BLnEeIedprmPCLqDZ0Mj8Y9brPPRbzwmYUujHMWdT+FD2/hsrkNk6ZHmDnqBPrUEHxVMghZde2gpQAeknqboU5Qx9I3Hy9ktm/2AeiLYblKC3mIKDyeB2LG+H3J0ssOUEvvWpw8E/rlGCYcvHc3IYa1EVBK/dPSRItwZ+rHEmYBqYlCJYFRX7EVGv1ccDXK2Vynjuuer9RGT4xx7GdBHPUeTLSUJIT12C8mkRgx9Js+2FDJkfT9E3PrEs5pxfBAF6VbhJRrVKtxe0noewRpGT3LyZUrf+sTTlnw9QNx4NI9LNvuYdjtGeYJk2OJtFfZ5gwscw7LjetjzcDdhGDa8Xc1RoeLJdEdZCW76IYN9j40s9bMAAsHU1uxUhLmVKCdxkcZufRxQXA0Yz3KSeX+IU+4V2vvp2TK/l/WvNC73/b/0fyQbSOzWw571qC9CuvEhHqFPNTKwqAbEwJDannyTEvg9ctrTriK83tHvA4ZeTTHwMJ5ArSSMDF3n4ffukHv1DkURLQmMgchiF5r6LkUNO+zaR4574tohAsPeRQQ3EtmBjSA5qXC5ZBsHCAm+s0Tzms54nB6t8I3lYrlADHvqIkTcBkQnNb9w8ppP3j6m6kK6QOB3h6GU5KSm3fn0k4M+NryTWOGw6vDPdvT+ku3vTOnf1/SXMeOkovB8+iLA0xYbO7zaIDceLrKgBM6DOOio6gPoQ877w0R2KBh6DeGoxa096rsE90FDUweM6xZlHXXiE9SOD77zmk/uHzGnYtQabjYZyz6DkUPkElGDKxSDSY1/1NBqn5f+juUXA86frPnt4pQ+Eritx+Nwx/MXS37r7Rn1Vz7NyKedSb7b3LP0U+btmnLuU7Q+xmR0tUD4DdndjmdnPV/lJ3TGJ3tekeuDHGR1N0AYmM4Kvhet+P7rM9rKwyiYP9mxajLcncf8gy02NfRvDjfp5hNJ/u0I9fd6+GZKMqnI9xF16Agan0YHCN+yeLZj/0mM6Bz1UUJaavx5i0ssQeDwe4dNHftVRG8VlT1EncItWCsIg5a+DPGOOh69vOPyZs7JZIsLHYX04fOQfRdz9L0lqeupfpRiH1m2r0ekJxXbXUY8q8l9n9mwYPuQIUsJlURuBeJxT+l5BJ0l6Qzb1OMXHr/j4tMjTkYVu/uQK4Z4xy3iUx8/NPi9ZnHWcJFFB9yp8fBflPhvPLoptK2Ph8VFDkpJJxRp1WGcx5Sa6rml+ScjnkweuB2miLMe0ym8HsptzNHJhpVOGciWdZ4iMoOoFBPdUK/jw8yBMPhZz/4fzmkeOWbPN9zcTVGVILm2HD3fYmNH3oRIc4gx6thhO4Uc9thVgPQMainxfHvgtoeSo1HBSkdYJ5iqivohwQwtulGkViMKcSjsU4XTEq+3iJnm6ckDb6+O6MqAYNGQbA1dHxCdVOTLlDaUBFuDm4Dd+SyONvTrkHYI5eDwvUdhj0aQtpp9HZOFLcGVpNxF2ImlCxVyJwlnDSKw+IuGaVhy9W6BGBjioMMaQb2OUSONfB1Q+D5R0qIa0IkgOi9xKx9d+FT4yEJiA3cg7nkSaSBJW/Y6ZJbt6bWPrCS1DajaABUa7NYnCjrwHM+O73noU/KHAQktzSbCPe+YzvbkPx6iCx/rSegk4qSja3383uI9b0iDlnofwNZDSkdwWlNsE9JhjSok/chh73xqz8OrBUI7RCdwCgZeTRt4qEVPa8E8BHSxIptWVLsY14CuA3xlaKU6bFB8DycPmtmk7VClxKaWLj+AwvwKjH+A/XixxiaO4c4iXEOZxeBbwitFWUTgOdpjxyQsqU1A0mk0Ap0K4sLhjVqEglp6+NrS9h718OCYaO5i2kDihQY2HmrWYe4D7MTwZP6G/+J/+3/6X6x3/3OWVwHJQehiNiHZosazCvdUoyNJsAJPC5JJzWad4Y47OiPQm5Q4bEkfVehAIDpHvAwpdYgeWnrlETpD+KihWR8EFGiHwmGMJH4NxSrDjRx676MmGh588gRkBbHVVAsBErKux10GtE4ReRp/2FM3IVUX4HWWq9XsYDsroSs8VOsR6J6FLfns/pyjo4KHTpPe+VRdRPqtHf0yQFrAgffWpx9ZvBLsyGA3PmuX0i2griLiWU/XS6Jpg/EFQ9OwW6cY7dMeA4HA30rScUlep+gJ+BuH2/qE85qscUS1o92HrB4rzhZbtiYithpGlj/0zc/5J1fP2Lwb0a5jmtuE+yog8LYwcoSjhqqJ8K1D+tALQR0LahcRVZYyjHDf7bnMx7wcr1l6MV4r2QQR6//bU5L3K9xjwVHUsNwk7J5L3OeC29MpT4/vePIAZr3kk/6UPgH5OGQXRKSDgp9VJauHlMlZSXGTEdw5kicNaddzs06JVz3f+4ULPs1PuFuP8fyebiCwy9+96SjB7iIjOu859VuK7/RMjmoergb8G0eX/KA6Yi1TZC3IMo1agQ16vitX3F94LNcpW2bI0PH+kwd6T5I2BjXrUPceN15GYUPUaYtdhujWA+VQe7j8YkHThVy6Kecf3JNfjejjAAaOfZ6wuwyZeC3B0mKmlu0mw2aO7f0AIR113OIUqElHHsXEmSYKNJPjisV9Tv44wJUpX39xQhcqvpIDokXNc3+JW2h84fF6M0bHgssyofOBRnAkSm7fDpgNNuzbkPfPLrlaT1m9GYEHXudoVIIF5tN7Ns7HP85JjirS9rDLpjJ4nqHzAq7vptAohkctidacqi2Xuznv/8wFv/mbH6ACR28E+dUIHhmkg70NiToLrUQ/tdxnEZnq2FcRxiqOJ2v2NqLWAcFGMFY1+z7Gpo6m8unTg2DH1RLbS2K/I28j4kGDFAqrBE0GXRER+Qa/ANdZbCrIsoa7ckAct2TP9lT7mE4o/Lhnv004igtK5VP0KeHnluaRwQHaSvrcJxy0dEpR3cfoCHTW0xY+Hhr7yOCOOpo8xDkwM4OUgraOifdwlQYcPVqzvR1iI/iw3OK/v+THqzmrLMHbgZg48sDH1Yr2nSS8FIiRI0gMw5Mcg6QKJKG1RLInGrRslMemSTjua/rp4U3YYrQj2jju6gnDqGX9MODz4hz3qCeMDGlWspMRmTQ0m5hm6OG0h1UgPIfqFEHYoRPJ+/6eN7dTnGfJZI+JD9CoxvMomwFpblFKI5520EB3HyD3HtFRTZsJ7jcJk6hm8InFeyrgScnyYYy7CjBVwHBUUSpJuIHZox3lJ0O88x5RHNj9rlLMVcFX+QxhwSWOOoFsUiK2Cj0ysA1wnaW2EdxKvKGmTX0WZ1tS0XO1zA6Uw5XjvVkOo4a3d3NWTYaqIoTvDn/zdYucGoo+oGwU4biDh8OMSKdANAoVdCQ03JvB/2Rt+//F+j0P6P3/c5kY+t7Dm3WETpMnPsNBSbcJsKXPy8e3TJ5vcGODmRsmWUk6rsme7jk+X6PuFeV1yrZJaH2JloIhLUPb0kuPqgoxHnhbCEYtwaKiRyHe6yAw4Dv8gUY4ECPDeFxAL+hiQRa0IKHvFToDEks2quilxHqQqRbPdzyRO5p9iFl7iJuIcOPwtaN8SOhWHid3Lb6DrO/54NkdT7yCxbcfUO/V+EmPHVqMk/RTiKSmP7JUywSrFfGNwFUS3XvYWx9/A5siBS1QvcMqgSwEaOh2IfQQ3zuGWY21An0X0CnB+OkORpbwxmO7T2g3EUXs0dzEvLofk69jBs93PAyiA5Xw0Y7L/RHbTydUy+SA631e0ypFlmu6MuR4viUcdZSJ5HaXcdFmfN5O6bTAnrc8eCGb84BdnRAeNzSx40W84kVb0juPvA748h8t+MHtE5qjlMGLDh9L9Q5ev4tZ2B1f9iMaqSmWEW+OQma/f8XseH/ozswc+hh+pzpBaMEiKTHbACHgw2d3nOxLmrEkea/C9yXlpx75Q8xDPUImgpfPN6jOxyLpzhyJ0vwb37ihi4asJmPKwRh9PgQpMOc9N1cjijpASMPcdfjzlkfPH1DK4DYe+sRgssMAZeM8XAhKHiAfxbuMZ/mWxPUke4N/q9BOsvF9ru7HqAuPeFEzkA3ZuGIyygluJF3rUfcBSdIgth6TScGT+QNu4lPLEGGAgWUx26FijUwFy8RnNMjZZYI2EQRxjzpvibXBB/ZegJOOS5ERzWr2LqIrfbxOMB8UzE+2pLMS5VletzPu30yoxz5FEHL/1ZzNxQhPO+63I4wH46gimdb0VzG99nj79RHerOPXL17gZR3KWY7jnNk4x8SOIDCIWx8Cw/zRhpSOah2z26WI2OIErL+Y0NzFRI0hGLS0qaS3B7yryyxurkldx32V4mUa3Su0lBTbjKIPaSKJ/yDwlEFmBj22yABsZtneDSmuhrRVQLWNmYgaYQQqNBgnudmN2BcxnHfoGfgPivX9GBc5hq6h3wYExUEGEwmNWfsEStMpSa0DqjpAtg451ehYoJUiGLawV0x3LXIZ8iLOwcHFMObjm2OqiwRhLfgW3xmwAj/QBBbk+z1OWeyx5s5L2NqQ08mOYOfI1zHLV2P8tST4OEQ3kr70CLMOmyt2eUL2ZI+NDTaxjFzDaVUyTCpKETCclASXcOoVDMIGL9CkowaUo3OSzilcAA/bhK6VqNBQdCFlE1AtE+JrCBvLLCkwiaV6PcB9HREFPebIwMrH5R5RBW0VkD/x+MajO/RNRKo78jAkSlryhTigck97GqWonjvOzrbYE0P9LUM3FdyGGTK0mBCEb4hVj7cH6wSqgvMkJ89Dfv7snmzak4175qMNL9d73uzGNGXEtkjZtxGfuhGfbE+wtxIRG0RqaOODGdA86WikJL4GJPidRZ52mNZDZBqZaHwr6HcJY1f/C62X/0oX+1FScj7e0E5hcrrjxfyeby/uiMcNZ2cr3FzzcDkmfzOEXrLLU7YPGVFnadYJeRCglEEYkLXAd/ZA2rqK8LAkDxa1VJhQYC8CynWCFxv61mMwqRjXDd6dIHolcYWieZcwqRrSjaUJJLI4xOfKVPLkyQPRuCMZNpyerhk1LS5wPKiEOOpQU0180tDNoDiRbFSEGQpWSYjpBQ/lgNevZrx6NyetLW7lM7h0PH95g/UgWVv8tz7DuEIoC4see6KRG0XnCWwjcZXEKAFOUB9D2GukFojE0joP6x9sWjsXoazDYaiNz/1yRF7ExJ3G66FXAgaGXRzw6/fPcRqa24jJvUaWis+rxaFdOen43gcXjGjJywgWPe5n6gMJ8M2cJpIsCciOSsw+gMjSrxWjoz3JvCawBtuD7gR6bvjxZAqnDccv92Six1YQvl/wjgFCSPo0wE0GvJc98OXyMcdmw2obky72nN+05MsUi0Jax7ZWzKclXmmpjzXbRPHyvXsGk5rtFwnpccvLeM3dVxMu/Az1zNCex4hNw+jJjv/HxVN83zFNtgQ7w02e8X/55Bs8f7bioQ3ZrRRpsiN7uWGgWoZPCtZJwCSoKY3jshtif5yidgoWhmAtCI2hFwrRK5o6REw1thd0r2L2Jz7dPjy0H2eWdgz2XBNMWtTTjjNRofcB9ipk1yaok5axqxChpbUe1QRubqdcvD5mOiiJtONMlQRZz5v7BfPFDqMUdRmy2ybEsmd6sqfvPKSW0BzEI3Lr4e0d4aWi1j67H05oAsXxR/dkfs/6ekRRR6hUU9/6jIKaxpf0nmA0LhHOsiyHiJFhElYE2hKhyYOAznh0p5aqDpglOUYJ8usBr96c8rAaIQx0ocA4CTuPh+sxeR4T5Y5Q9sy8iqzSuJmBuSaQHe1DDKXAj3vGacns8ZagdhShT9hZ7EWIygVzr6QfOIK4Q+w9bCiwnaKzikHf0Q8ccqdIbI/UDrQgXDsc0E8su2WGCx2EFjnUJH5HPRF0RxbhW2QFbSARFsZhxczWDLwWWUrCe8ERFd5GcqQrslHNULRk7yzp0pAdVdiFoegjjlxNO+/JKotZBWTDCk9aVK3whj3HjzZ4O8FoVjL4YIspPIT2YOUzoOfsbEWRHpC/SdLjjgxBZ4kmLVoolmGM7yzVKqHKFJ3zKMoYMev/n+z9165t27pmCZVqmm/d9z78dGuuudy2Z+cJk5ECZUYCEiRSSlxzxXuAeAoegBfgApSAQsqUgPDnxI5tl51+Dtv76Lb5Vg0XPW4RcQFb2lK0JxgaGqP9tf31+0rhcKJ4DBK6ImRctlwMN5Rac2gUJhBEWQcrxfRyT1CD3CviuEVEhnxe0T1E+MzROY2fGVh0WAWbKCRtesJhixMgC0EWtyRf71EHgSn0cWulIv5wd07ZRdT7mCDumP/qEdVJfAzFQ0b1Lsd3Cq812bLH3kZ8k68pu5AkbQhbj200pjo6ILJFxem+oRYWkXreXE9ZTiO205DuY8yHryRGCaJJS5x3BGmHHXiiqEe+7AmfFcjEoIwnKyz9fYgcGORZjxz2PNcbfhGviOKOcCPIV458WpKeVuy+m/x/G3H/P33+qof9Lgp4UCl9pbmRAw4PCdcmRz9pmUYlbRFiB552CP42oL8Lj3CdQlJKDVtFeC/hNoT3GmrBQpZkWYPGQu4wC4eILP25JVKGaVnTSUXS95ha0qExoTrKFsbiiCc98XAXIhzIFryE8gSW+yFtG7CuM+7tkNHWY5zEhTCJStQS8q7DdMcEbblOeadyhrLBXVgYG6aTA/e7MXZqaedQLFOGpqUZSOpA0S0jAmcxlSZoIR9VEHhaG2ITmJ5vGcQlo0MPjcRrcLFFjXrSYUP50mNOLOZFR3GlCW4VBx/RW4UpA7oyIE1adOcZqIY06BCPiuY+4916zoXd8/xsSTtzuEtLZzX9bUT8XuGNoP0hJ7soibf2yPEuJc2/yzEjS601v/xqxbv7BZ9n9zwf7Xj1akW9DzCrgHxx4F89XkHkqIqYfpCTPTq6IsB8VJyLA+1Q8T3PcVbwYz9jHab4bczVyw291Xz6YUY/lQS5IMRwmZeI38fkGJh6fj56IP+q54frU1bbFBlzfCHJlLR1iIHl0+2U395fUC2gGMaEgSN+XtKEkoug4JtfX/PkZcHi5y3mY0r94Ln584z2MeTbesbHv7vE7kIeRMbF8EDeWsbzChUarr544BfPP0HoSD9aXm52nIwLDq9HdBk0XUhiDXHQ4T5GlFeePoS3P84JQkOTS8K0o7+PCRQkB8d8eODF6RLRQ6FCfre6wHaCzUN6NLfJlvUfFmSdod+GNPuIx3djJknJ4FmByxyugrzpOJ0ciO4FrpVcuT1iJ4/a3psxn+7HyMAzaisarzELxTaLMPcxb3+8YBuE+Az0rEVbR1QatmnAzkYkw5p8WnIiKgZpQ+wsX4oVJuZoNEOCAt8LxLzHP+mQgI9AWGjaiM0+w71oCVKDLqFOA8zMshsF9JHEdJrHjxN8q2h3ETo1ZHlDM1TIaY8e9MhKEu6OyOEk6Jie79lPgyNtcOcBj8gdXSDpI0VTRExHB4anBeKgyKqeJ4tHvrxYcj7c4CcWMoucdxxMDK2kbzTFKqG6SXHznl5oNtuczirkScfZcEuLJD2t0T+rjprb0uOs5K3MWC1zBmmFODGIxB3ZDXOHMZq75RixaClucqoPObZTkBlO5BZzUCz3I3Y3AwaDkkZL4tLhvaC5cBymiqyyiL0A47FDe7y2HLX8evoRi6CMNMUZ7BcBh3WGaULuiwk+tSjl6IymKmKyi/IY1K3BrAR2H0AEZhuiD+AOmnabIGuJu45QGAZRwzQp6PaavtH0dUAYGdzTnuoQo38MsZ8iBi92JLYn9Ybr1wuC7xRuqRGjnn7u8SNDHzmahWDwaLn7MWP8aoORkvjLA6PnO7jsuP1xQegsZR8y1z3puEGPLH8zvSW5lvQX8P5hwajr+NkXt/zy+TV+7qjbkG4T0TrNpT8wTBpS0XP+m1uii5pp0zIaF6j2+N6ojGLQNej3jiJUPPiMzf2Qqgv/ovPyr3rYnx4axF5xMt8RB4bHpwEfHmb425AfPlzw7sMZNBJ5EyBCDzF0ZYi8Poay+lhSF0cJhbnw2MhjWkk3d7QuoBQR82xPelWSThqiuEfPWqwGqaAbaKLSYhOHnHSksxr5tMENj6f5WVygc0M8aNn+cXZkUh/gH00+EVUOqxxtFSLvAtabIWWVUNcxw6BBSUc2qAkaT7GLCWuL6TV36xHGClo0duwoTwTm1CITSzDp6UIJqwAxMJiDotinqN6TXRX4gaH6kHG4lOiXNcGwRw17Oq0Rpx2uVoSNRZeC6HVAvrPon5e0RcRgVqKmPaITKAvaes6GW9SHkLNZQSo6DkXK3TAmbhxx5eml4M835xyGAcHzmtBbmi8MuyAku+xItKWvQqqBRhkYlj2/vT1B3wn270eUgeSHhwmLcYnyIB4CJIJ3/YAvfnELT4795cWwQCSW3bshPhDo3PDV2Ucy1RC9bbm9yVk9jJF7wenljsc2ow0Eb4Ix74ohUdjxDz5/z9xX/NlO+Be/f0okLPlZxeSsZND3pJEhyjy7u3OePNsRZS1ZZPilWnFCw+WbChla/rCa8dtPT9nNE7L3ii+uHkkbj1y0ZLWl6gPMS4dwgnDQcT0P0M7SbkPswsMeXHiE04SxpboUrM80J2HBE71nkdTcfZoiS4XpNclbjT0cATP1IcJZSd2GqNhCK7iqCpbrIbd3Y7pA0lVH70M2q0ieVFzOHjFGUtYhd9dj0r3j7m5GrQOepBuqdcJY1IxPK7pIcj2IOP/NPZPTPdPBgcE/XKMP8ORqy+WrJS71+DPPVbdFe8tktic6r/HjI0u904I8rInPK4oLjTeKvDEIwL2J2MkQ0UrqRPEmHqNDQ39isCNLg4Za0R1Ckqxh+rMlMu9pzjzySY2RguAAQjuoFEnScVq0LKjIVIvsIJi0nIQHPkuX1LuYgwrRwvL4xxn5PbRFCJ2glJoy1hzqBNso+qHHXFjYaXQF2UVJIUKM1Sy/X2CMAgvlMODhfsS3705p7lO8E/jYUe4zAuvRcc86iOleWkanBYumxRuBe4jwRrGshuTPC+RtQFNFtD/k2H1APqwpI8Vul1GWCR+Wc26KEatvp5h1QOw6orinLkMIwKTHYPHzv/3E4Pkee+HJ84araEsw7fnQTCH01LvoeDD7FKN+CrkIdzyNS/oLxzwpiKOe9i7h5u2Mk3xPPGjBCcyfM27uZ/RXlvikIXoU7LYpsve0KA6rDDP29OuYHTleOfRZgxAOqyUeQXAQuBDkk46vP3ugKmK2Oib5qiAdNZj7gJ2K8GNDnHa0TyztJiIzBtE5+iKATxEewdPBATex6MuGLGnoWo0NFJVWlHFIfncMybX3Md1PGU/NHn3VsO4yNiea778/J8w6DhPPqk6olUJKz1g2/Gr6QFDCj3cLzF1IUHEkBoZwU43Z3+Qwsiyvp9gu5H0zo9qlnFY1dmQpbUCEoHypCFv4L/Wb4xZ1/pdN4/9VD3s76xChP4bbrlP0R00TKuxWMZvvCIYdIBjRICtJ7AzaOepMEzhP3FpsCnhBFnVEsWHXp8QXFeK0IzypyacVbShJnUUPOx4/jBkMaqZ6i5p3TF7tUKcNunNHXnwvGcQN85Md6zQ6fpHchjReo7cCM/O8kSNGlweCe4G6URgkTQ7NGZBb+ruY5JPAWkV7iNmUA0ofYiuNOmvZfxyiVprM9vTrCPc2xmwDxLwjdMfu63lXYS8NzZnHzByFidCZo76AZNRQmBB/G9AIxdC2RGtBowW9VKh7jQVapyk/DsAJqncDmmXM+Yslw/kBcwh5/+6cfh1xexhymu65SHZcigPX72f4UiFjxy+vPjANSuZhydDVyMDiOonKLHNZQ2rJRUcw6YimNQSG01mJ0JbFaMdL9cCuS9nvY4wSpHHHc1/y8XrCsK+YzA6U25DUOi5/seJKbAlaz/fLp5hNCnnA2astzW89k/MDD7sc+YNikLf8zeUN55Mt5gL+xe9e8u1hRmIdz+ZrvpyvCXvJ4+OAygds3g0Ydi1ff3mL3GnmFzv6TxJz3lKE8OfpjMVox1O3Jbv3nFBz9c0KnTRs4yH9zuNHPcNtRxa1hBc1zX2M/BRS1RF66bg4tBQi5U+HKWZhqJ95busB+4eU92KECCzbSDNOC4INOCNIZxWx7BleFoxO9yQrT3CraZxmJgsOT/2R96A1g7gmvoe0NbSPMRaBOYTE5zVq3BMMeqoT6FOwm4A///QCKsFDn0PkKWWIN5JiqtkfUv7up8/xjxrROe7+bkH/EDGNSoLMsMtizABuPyxwiWOU1KStJZCW+3rEbpnjjGCiK/KTkjDuaOYS1YFbGG42E+LQwMiipWMU14zXPemsRjXQfDdkuR3RKEWUtoxdQygNZZkQ1oA6bnwGWUshAuRSs+9iXKVYbkf8YX8Fpx34oyTKJZ59GCBSR/5yR5R3Rxvc7tg2cBqaTNCqI8DI7xW6ddTbCAfkoiGfl1BI2n1Mu4nxjeB8uMHuQ/wmwCswQzC5o088exuzW2aEi5qzxSP/8Pk71Lxl/X6BnziaWNHvA/arnGobowYGOTU0yxjZCoYfPKbRfPP8I4OkIawdTgmck2S6Qw97Xn84Y/04ZCJrptOaQ53gS8nnL65Jgo7x1zsmQY1rFWWieb+Z8dHnRHsoupjuIaZ7ahjEDcUqw91FhHcQZJZsVCNrSftTRv0+xy0s3gnCnzTBRqBKeHq2JpIO2waIe40YONSgP2pupwZRKuo65PvlGXUT4SLIfMe6yAilY+prgrUg2kCetPjPejqj2UcRO2Jc4ohflcjKED56utuExobcrSZEK2hlgAwd19WQuj9mclqrud8NmAUlInSQOdxnHUufkUY9ifGYsackZFdmrJqY323OCLQhbo+AqoSe82h3DElHlitZk9YO3TmitKXoQz7ez2n/OMCsAjZVwsXVhtFlSStDRmXDf/v027/ovPyrHvZ1GhKHPcU2BXPkbkfOUbcxpj4avpyEUgXElyV9CnpoaBLFusrwL1qSV3vGn20Z7o8BL5EYstbwXK0JfifJm57Je0uNovqYIy4bWMP7N1f0neLQJGAl/fiYQs1kj1mGbHxC9Fri+yNzO7iqyYYNJ4M9AzpuVlO2g4DIWp48u2c2PCD+Q1UwPyvo5uBWGuEcZuIIJh0sA7gJiSc1Ag/LAHUdIDNLJA39nzOkcqisZ9rVxBik8YT3inFQo4VD7RVRaHg2XaJOegLl6NoQuw6IO4Py0I8dwcAQTjowAh9ZRmd72pFnv8zwP6To0KAKAYGnOMS8DYbow9GU1Vwcw4jiVvHT6hTfCIaFQcvjyvjryR2HQ8SPbxeM2g7VCGQpeChH7B+GvIkGqFctb6MRfxif081AnvXHld73Id93Y/ZJQJ9oqrcD2kRRf+V4+/EEvw8Y6Z56ATmG5rOAH7bnTH9TE90JvpqsuPzHDxxkSFmHzNuOUV4z0TXqXcDl/sDL5w/83k/59u6E0XnJrDSMw553bszbKKfKoDkkuInHfRqQjy0/TzfkeE6e1Fyc7PhWTvhTO+O8a3iR7Ag5bpVejA5E8fG0b2rNP3xyjRCwnsd8aAao0BBYkEuN+UNO9ENI+Foh7xU3H+e4EDweFRtU4NjIhL5XlJ9yShkiLjsGn+/olhHxaUOuO0Rk8ZGnWqdMsxKfeBqjsUj2KmS0KIhOa5RypNYi90eFc+yP/yudVNwkKb/+6iMXizU3PyyozwUuhe045LAISH/2yM5H1LHA7jVm7AmlRe0E0QrGdQseOqloe80sOd6frjYDih8GrPucaNRS2YB0bcmThs0uxS8DFrYiXzmCtGMkK9RJi58aPpdbFhdborOGx90QaxRm5HG5Q3SStgr56TCBQvHk83uSkxr/oLELQ/IgkNrhE0/pQzobkO4d0nlGpaFrNSeuQGSGoOP4JYpDnnbHa7nbHAqNSUBNezZZhIkFMnH4VtJMj9hktoJnn98hX1X0c4NZRshHTSAcg2FF8KoiSAyDJ3v+/cMZzWPMu+WEbhWhRx1m6GBoKNcJrlb4vUZVHJPftkQ0np/eXFLtUrpOHxXGYwsjw8lwxxO54cWTO968O+ftzYzt3YDuXcbHP15QVTHNY4zUDvVFhQCsPQ5fN3E0NxmuE4TC8FGNiXpH+uj426tbhq92nH22It54mkzSXxnUTmKGHtR/aAYFgjduQu81KT2tlthG4R9CgsCSpw1DGuLe8fBuTBR15FHLvcwZDUr6mUP2jlNXUjYh5hCghGP93RhODFPTks4aDlWKujJHxsKDwpQaNezoTz1q1vE4iLE5mH2AVYJAGyZ5zW6VUbUhwY8h2aFnOjvwaT3l3XZMcG/QK5g8tqz6kGYV8bAaM/jlmvO/uaXUAYWJiCYtz7Ilm21EelKzdjFD0zFSHb85+0Qy7OknoF9V6M6RbTzfdXNsoPj+/v8zIv7/H89fdfUuOAjKSCKsR8SW+F5QxgEmEpRFwuCkoBMKkyj6XjFXJUlo2UWawyFhHhUkpeILvWbzG8ntbz8jrVqSHnafSZ4+LxiHjh9nEa/We/yk4HYR4x4C2lYQBS2n2Z6H5YjBFwce6yH7XPIifaAvAt4+PSWsJUl4DM1tH1KGhaC6sKjLBtM7Bu89N49TxkGJkwL9oqbWAnYeedphW0m6d+i7gOjFjnWZkSpDi+bhMMJnlqhyEDlUavGTnmIQcb8bkS1aOqHQvqe7jWlOBNm84Dzd8n4/Y7A4UBUjbO5Q3mF7hVCO/BrkhUE8Kox3hGvBocxR55axOPBDfoq8V2RPK3Z5wEKU1E6T5CWHKsd1iiu95/0iw/WK0Bn+tD3j1bN7YiF5++EUWUqunqyZT7es3w9ZRRnNJuLqZMvH6xFrOaT+lDEZVBQuIOiBzNMtJNOmpow1eV6zOQmPXOunDf/gbMcmCPh4P2ZSO+51xjRpSCtLPQg4KM14bDh8TLhpxtzmQxabit98dsM/e3iFzwSjNEZUnnITERcCO1SkUcso7XgmDO1O0saKfik5m1TsxwG3ZUa0cZwEJR8+TLHPDKddTeLh//rwikHacnVWEtuOZu5odpqgFaS/3vAnM6FxipPRju3DgO0qJ5M9dRQcE+Rjx2DrCBtPm0mUdBRhAJ0iFT0DWbFVCXYf0ImI6KSh7EJsDq8fzuh7RW1CToY79tWQ/eDYCAhrsJFkayP0tWAsGm76DKk84/MDgbekLzf4j+cELbSd5rtugYgcWdJykhx4V0wIrSRoPfPpnsmh5+HTmPxvt/SHHNN5XO8ZLEpM6pi7BnnIcasQHfWU2xife9ZtijxY5KIlOXjW1Zh4UJLOSh68YrMZMAwbqj5k50IC1WOE5OZhSN0oQuNIspZDkxFHNd1dDCOLGFjC1hPUjof3M2LnKOYCdwhQDsxDfPwbitURlysdw6ZneT3Ct5qqT+lMiA8dInVwUOhHgfiqhesQF3p8rQiFQ3wKsAuDFQKpPPkbgRkE7MyA7qPE7TXZuCH6Yo3basLYogPDmagwPuTd6oTwWmGfe1orIQL5HwihcWowUiFaQd2GhAtLPGhxjeD8s0eKjzm1tPRPIRKGma7YfphwP9UMTMftPiN+WbJ/PyQThu4Q0IoQjaHUIQLAQXDaICpJMqppvhtwerEhUh291DwmisYrbKX4t99dYROJkp4oN1ylW970I6gkg6ileKZQB4mZOLLDsS5Z2IhgJ3DaE8oO6y3VPiC66sgKj9gK/CqgHEXo8wZxYrF7yd4nlDaEuMc5Tx8JtIAoPnbYTdKjrOP7d6fI3qM7h/AOHhWnv1iy/92EaqUQDbQnAiMFv/75DTsR0L0fHDcupxV2HyBrA8rjhaBvQlhJDp9ZYgRBBbKG9VXC3c2IYVqz/jAk3nSsEol3gvvbEUqAe2op7lN2nx8o3h/rdoUJCYcHbjdDeK/pQ8Uft4u/6Lz8qx72/acY+eujxzmIDF0uEaIjqhQ2BvWdxA1Dhqblbpjxarzhs5MlB+/5cXRCsw2Ry45/HZ/TIXjycknzfcpqnxIHBT8cTkkGCb7zrGRA3WfUlSB1liiyvGDLaLjl9XKB/jc5p98c6HL4eBiTN57BpOZRZZydV+wySbITxCctooaqtzRak3+14/BDylpmJLWl2mWEwuKeGXLTUjcxLhX4saVyAbIHcx2hzgyyBLQ9AicaSRa3TA8tay84iJBoJ2EI7m1IfSEI4w75IPnQnbK2KTap8NISCEvvJP5eIbXFZoJk3LK0A8SDQOSWeu6Jc8On+1MGwrC4WnH3bsZY9qQjS2R7vnu8QGpHkjSstxH6DwFMW3opufrynve3E8o2RpXwYrImuem576c8VjnyQdKfwLbT/Oz0hpswQz/bU1Qx2QPo2LDvM5pYYmIIg5bdKuVvv3pHFHh+ezvn437GmawZL0pOnePu3YjlbUwdOtJVwK+e37FqY4ZPS17uWnwquP/+hB8/Ljif71l/zKhMgEo7otLzfLxlF0msgNtNxlol/G9+9Xf8i+sLajvk++qELz674ZvK8sfijNtA8uUXS376NOW82jO/qrm82rG7jfmQDoik4R9M3zEP9ry9nbN9M4bu+KLYb8eIsx530uMeIYsrWqdJfoxQFehvSsIM2jbk8mzNZpXT3GY09xHTqy1tYijuc5r1AJ9bAu8YX+1Z/2mKvmqp+hAbwsHERKaj6WOCuGTgOg6rIeuhxocOGRhGWcPt+wlbG5OHHX0X0A0d/W1E8nlJ1Qbc3E9QVx12rQmt4/uHK/TQ8PRyzZkseSjHtG2IfdXxoZ7wpDxwX6X4kWNxUlKtI05lyeliz4/1GaoFX0v8xNJGinggWP0wp0+AxNHYABVb+kNI3UqGw5p+F+KcpHABcSNJfA+NoCsD1LRGlAFSG2RqqQtNInvSsMWvA7InJfVIoLxFPiQEe8/4rGGdxqQC5vuWvYzQFw2ilpAYfAxmJlDeYytBIC39DPYmZLD2iIMm+awhjwyHYQTrgDrXR7PfaX/EWa8CvBJsigiRWvKmQg8ErRCIn7eoQvNUbVm1Gf6qJ6sE+bBklLc0c3hYjvGlokOyHyRkSuKeGgLnqUVMpg27JiV4XtL9MMCcGnQtEYFg0PX0XuFHFl15ukDhvWCQ1KRjy93NhKh3+FYhTnpWzYAhLeXHFJkbLp48UPiUOgywUlE/xGTjPdXcM1rX7NyQJpSw17jAkToDCNq5J/cNg6TmYTvCrCTdOiZShroNMEuFGllk3KOVwBWO0d5wbYcMhzVFotBakHaWWkv8FLpDxCivab/V8MQx9C17F2PHDjXv0GvJh7+7ILctQeUYPj9w2w8JP2j+EJ+S5g3CgjZHQQ+nsNlnRJ8U0RcFfgTVMGFwVtOjmFw8sttljA4Nu1Zz8oNjOe+ITU+TCRoZEO4EgXLs7lKSWcWn9ZDTWUlwC32jWW6m6LVEeY8MDVXzl12s/1UP+5MvH/i0fso0LiiCkNHsgDIQJx1f1Dt+ahe8nK35cTdl2Hf8aTfnh3bMbLynKSIi2VOkGS7z7A4J5J5//E9/Yr2ecphbzOchM1pKKShcROwammVKWweM8553Sc48lkyvSoo7DV1M8uDYb1PGl1vObcmmD8lqy/X7KXrYMy4h2nq6aYC+k9zrGfGwIx40rB5nmBcd0+kOeZ3ToyGzNEVK87wlzVqyjaG5i0hKD7WDgaEPJaGx+Mzx2KWYCKR1mMhzcbHCTyW7b8cI7xkYw32RMQtq2EtUJakTTXQjEUYidoL2m5bu/Rifgkgt0joQkkj0yNQR5D13vz1j/8oTtIp+pTiP9kRpz1h0ZLQMvePHXDM6r2iVovOKSVqga095KWljw+p3p+hNQ/zMkA56mo8TZNOzfRVzpvfQR3y3zOgGnkOdEjjHTNY0oULFntOTHX/86ZxDkxJc1IzHDZVw/Gq24fc/nPOxHtI8tXzZb9jrmH99fc4/ePmBj98Nka3GPrU0V/BhPWGgO14M9kjXc/fHAYOw5dqkPNsfaGaCTgWcbSv+zd0lqzdjdGZ59eUtkWv4FGZks5ow7zDvY8oi5nBas/UJ9aeYSDtOF4+M2p7b7xe8VmNAEMQG7zRx3GDOHKNRyfqPU8pG8/XXH7mLUlaNJngnCFaSfHLgU6CP/m4TEM0aDl1MUUXUMkBetLQ2RHUQCkuxTnELx8/SNftG454WHERInwi6ZUp/nVIlLfq8JX086kA5aMLa49cBz842rG3McFAw2Gj2s4DtpxFeSBbZlqpUtA8ZyUWBOIQ09zk/ngc8fF+RjAxq7CkPIYu8YLvJEIGnv434lId8vbhhZTKmW8uLqzve3ZyySPfc2SHWKYp1SqB7nrk9G5Ege4F6VtP9GHJhSg6ZRnhJrA12LxhNavZdgmk06kmDXkvSzjD+YsPy7RRRCPYXIdO8AKHYuISo7pBOEEiDCCTzrqLqI6KfF0Q/aXQVIEvBQQYMPgl84GkTSecFQQhq1pFvA9qDxqeO0fxAUwfsZ5KiCMjGBj/wkDu8O1Iuk6hhOi+p7hLW70eskynpe08+q6kJ0Y+C4lwTTRvkTjKYFhxcyPYxYLeLeWq3rLIEmzhcKTG7AKMEz6Yr2Gh+vljxx+qEQdRxM3OIQlIFAfJDxHhS0B0Sos7QnQik89gi5LEc039QjAYVfRtQuwQ57Yibjup9gtCeU7nnthlz9mqDuR2ga09jBTuTsv9+wEwWzJ5s2AURndSEmcGllsg6rtyBugqxrSb8pIiDniRssSMwm4jTF4+sPk6odhku9AgHDxNNInqCaUtUh+zbHNN7ukIQSIetNPtWoVKQe8PupaG/P9Zq9YeQbgAX3yxZv5lgckEx0Py8feQfffGR/+Pua7YPAwZtz+DplrLXNO9SgsIzGu0o3yScPt9gQs++PCrE+9RzOVzTNQr5yfPpMsVe9JQ3KfYgyZOG1FqaMchtjC8kstEoGbBqYsTYooyBUqPnDTYEbv6yw/6v+s5+dT1iNjrw6sktp9ke+1PG5nrI4+sxqz6mEYq1CdEGxpcHzkZb+scQswk41DEOSXjZwszw+eCRi/jAjz895d+/vSQzPbsi4TVDHkcxDkVlA77IHpB3GvegqH8Y8u2HU0ShcGNNu4wwtYKdwreWT9sRaW15qAf8Ml5ybhtu7mZ8yEakj9CbEC0cRRNxLQa0C4coBNtdRrAUNC5klNVMXj0SBwaxDOiEIDjtkPMO96oFC6oV+LHDPUb0wZEYqCNDrxTlNqV8yNk9ExwSzWYSYueGdiAoP2Wkj/4ImPAeIaGfgVwrnDry/K0WlDo4Jqa1Z5OGbO6G9ANPci+gUjRo2lMwQtAXmk9dxm/tHIE+Jv4dmHXMSV5hG03faRJt6H/usM8kd+WA782Mcipo/ouOKtBYn1D1IWHc0y8jTKfQO8E/Ta75n0Tv0DgeiiFCeuIbQ9mEuFZw9zBh/ccBrAUn+ZbRJ89HM+UsL7l8sub9asIbf8KP4ZS+C5mELf+rb77jFxf3fPjzmPfNmHU3Qp63/C8v39I0AQCbVUIyaLkYVZx9ccBf9MTW428Uu5uUk5OSpg/YuhDVglSa68chn0dbxmclN7djng0eYVaRhi3hShLeSJyELlTUjwn7byc0OoBO8uHtCdLC+fNH2jlkk4rlNmc232N7SRY31DfHrIp1mufZHu1hWPVI7YiU4aBD9GnD62JEmSpWRY6IHSfxgSe/vCO3HU+fPSB2ipfPPjFsKlxmeR8NGbzcsVpn7A8x9iHk8ZBTHBIAztIdYt7TNiHRooFekKcN6UUJpcI/g64N6B8C9E6w2ecYp7Du+HOfmIJtk9LvQr7dnPPhxzPkUiOHFi9BC4d0kL3as3+q8eEx9yI6yeLFI+v1CFcqzCGgFJrgtME4RXsKrVb4QvH8bIm5MFzvxpiZxX3WM00LdqucdZceFambnn0f4yWEj571fgAOyjKmvPSIlw3xk4phUjMZHUBC3FumWcnpFyvqMuaQarzwJGlD0YeIxIKVDJoOmVrUWjHIalRoMYFg+TDh3WrOusvpZqC9o5t7RO7xrcCnlv/65C1dFSCjntRYDiZiPjuQxw0PSUZgjk4AHznqXYT0jmYo6WN4/bCgFBp/p4iCHmcVwnv6gWcTR6RRSy8UqeyIQkPwrKKPwSWOIg5oEolSlmztMa3CBWBj+JgN0BvIlp6mDdkkEYsXj2jjUaXgUWaUDyF2GZJ4w/nZhoWq6Jzi7cMJ6yDh3g1oh4Iq1HRFSF1HWAXX+ymm0CAcetwTTDo6o2hdQLOOEQdF1DpOnz8SN47AOLJtz9/87VuS3+xpL8BfR0yebTk73eJ3iqgxlPsYuRWEn1e0VvNDOeZfNgsGbUeqesZ5QbmN6D/FzEY7bKSoTIyLPW0bcpCa5kMARtDep7x/nFDKgHjYYCLw7xMMCqc9UeFIpxWBAX3RcWgztIdm4pBjQ7+OCGMD06NttLrPWaTFX3Re/lUP+2YusYXm9394zs2PC7JRR90GhEnHu58WjCc1t9dTTiePvMg3FMuccVCT9oYrjlWyQ6zITMf9Ied2PWa/S3kyX6GrEB97VOOpi5g+kCyCA3+wF/hQ0I4d6aTkRBT88uUHvs5XGKvY1AlqYrj985yuCjiMNGsZ0s8dT4c7vDtCQQ46JNA9JSEcJJNdT9CDAvyHGCMk7iHkvN/TPxy7z902ot7ENKngs2hJF0EbSQbnBWrUHznd8wrnJCaDPK8ofsqp24CfmwcG5dEXIAtFv4kg8bRaY5zCO4/IDco7pqMDMrYkW4vIDRNZo4TDPkYMtx63MDgtiXvHaFIymB643+QE2nDwmnYXMU0Kng23fLQjuhjOxmv8Q0Qzg3FS8Wk7oewiWqURBiZhRZR3tLcp1UNA8ybkVsSIVqCGPaIVdKng3/5wzv/l/dds3o3Y6oBJ0MOXPdk9/OOrDwxOC/40HqEvOmyueRpv0dJRLVO+fXvBm27C7HTH+LHj1fkdamaonGSnJL/8+QfmH2ukcMzSjsA7+q1h0PbMJhXBVvDHj6fc7zVmJ/nvb57x++KCn31zQ7dX+KHnwcb86uyeTRtx4ip84rnoG4oy5J9/94KgCpmp9vjCGjbEQUdnFXHUs5huOS9K9FnL/ilsPgyQ34eEtWfZD3BlgFsGVH10HIxPOuzIY1O4jlKM9pwNdyyutlSJJFwL9DuN7yUKz3RQ0hwi2iJkXHVUueR2P8bsAn5XP2H+zYaT0w1h7dnJiFZIxEFR2gj9smEkKyLf0UeCLGwZDyqSqGOrIiof0tzHqArO5IH/9m9+R5L0yMCjKsHg6YGr4YbJ8x3xpGe7zzmsM8a2JZo06K8K9tsUVpreSJwQNI8pu9WAtgwIvWV/PWD5cUJ/YWi6AJQnUz3dIUJPWwaNIYtauonjXuREg44vZ2vG44q2ClguR8yaGjVv8SeG1SyGkTl6IJ501LuIrHZ0h4BJ2fLZ7IF2HVPbkLvllLB1CG2phOZ+P0QlholqiKTBDR1KO/yDRglHYi19ILALQ7zzJM4QvQ2I32jctymHLkLcB4j62P/PjWVc1bhC8i/NGUZLzMwjRo7PpkveH2bsPozwQBK3oAVaO+K0o9mkbG6HYCW3zYCB7/jkBnT3If3rmOh9z6xpUMJRW0UUd6zDCErBdHV0x4tGks8rfG5xGjLdEGhLellhE8+EhvC85cfDlG4VEGrD436Ad5KYFhMImjRAppbsrELhEaWkNRoroX8XY1qNjC0ah3WSIDTEtUcHFs4N+mmL3QT42wDZSvpWUfkAl3nSztAXAZPhDrvXuDPLfZ1R3GVHba8THLYZzTZk8MWe0sZsVznJtOXsxjLxDWJs+OHhDJsJYtWxtzHJuOCz0QPrdxOitCectzw/WWOlYONj7FARWEe4FPjXCc23AxZVQ15YspOS89M1vxpeY9YBlpATWvK94/Now1f6EbsR1LuQzivKbcwgrQhGHUoct7B/yeevWoTz1f/2f89YK871lteHBU4Knk+WrIYJ6qNkPOtY24Cz8wO7MIReUL5OCHLD5EPPapFQnTn8Q4iRcDavuMq2/Ph2QTM43qX6jaTPJbnsmF0bPl7FzNMDexvT7zRsNaNf7jCNpi4DxKMiO62p62OadrsasDjZkbue5X5AltaULqTtA1RiYRXw9Ytb/vzHK3LTEV3UCAsP1QDfSULfo0aO0gWkxpCrhod2QBjaI+jktKYqE+bRgdVhRJB12AhC6/B3ATWasHAMf71j/WZMaB2Tz7Y8vBtjbUC6t7gQzLOewcGyrVLSoKPfBDCxtIEkWClU0pPNa76KHviX4or+MWIU13TrkO6ZJZ3UJD/CoxiQiB5Ta4IHw/BvarpO0Zw6zDKi85p4Be3EE4aGvgoIsp5o3NK8SRg9KyjeJXQiIrSGg49g4lAlCOkYy5paB0QHiTmzaGnRGPzvQ8TnBp/CF5uCBxtRW8XqNoavBF+nazahpt7HnO1bNirkrs65HO5YdAXfZyMu+4aR7vnAgF8vHvgq3vJ//+cvedNlTE8bJpOab7sZ37glTRux3MckE/ji4o6372ecXRaMPhr0meP/ub5i0jd8jBO+nK3otGa6Mzwbbfk/ffiKZ88fqO4SGqcwGdSHmJFsSL1jEdR8t5wippasNQjr6S8tVgr8TYTSBqs91mnaQPKi31FMFOZ9RG8C1LMjsTCPGmayps8lIu9pPyYE45ZmHTPIGhqvaaqIvgmQr0q6NmAkG+ay4vXjCSPZHPXN9zlGCpwGPeiJvCW4qJneWXYXkmofUy1T1LRjGNXsXo8YPDlA6DHbgNz16KCluBtQPheYZcRkfKD5lDK4KI4HBakxrSJxPfanmOpMcjl9xJQhk6Djo0oxgSDxPe1QYG8jCBxp1hEUkM1LrssxkbIkQXe8H28U867nts3oh462iAiNpZt4FnlBt44IQkPdB9hlhG0VoXfHL9rIMR7XEDnaRtN6jUHiRpa0dtifEkZfb8m9Zb1PiEXPwygmqR35tIK1pksE231GLHusFCSl51DHhI1DxJ5qJMno2Y0kQ9+RxS3r9YAw6Wk30RHD3QmkdASxITmtsZLjRif1yBrmomTdZ8iRIfeW+9WQcHcUXTWvIOocxoMxAbIBJ0D1HjGyxBh2QXQU7XRHK2Yc9oQNVGPwkefi0HGXHrG1UdQxC0pW2yH2OsEqQR40WCNpT6ErNUJ5np1syNMaE3hKH1D+NMTcKJpRQBwYJqOCfuzplxHb1YBg3iABHRqMkVRjif8UEY5b5FqQPymJpt3xd/NacAgUk1FL1UaUuUICqgLZCsRekl2UtEaTSkfvDJsgI3oU+NwhhoaTyzX3H2ZYLwkDi6slZhNy8fKR9Z8GDJ821IeQ589v+fg4p/kpJXI9tg7gSQdeoXxP6WNMLhg+9pitRj3pmZ0f8F5SheANtGjc9ynNRKDTnnALg88OVO9ydg/wp//D/+4vJsL5q/6yj5KW2iveHebkwz1nP1sS6A5RHK1TXexxQrJuI9qfNLQw/mIPGuQvWuIHwz8IrkmmLXMaYmt5+2ZKsPKIyCFr+JvZPeHK061D0rzmbLxjGlf846v3/JMv3hI20HxI8CUMZhVB7BCxwzjJ4WZAIAz6o2Tzbsg4qcgfHeGwI9075rsKsZH8+5+uaKaeIg7Y9TGNDxAezuYbwhcNatQTtJDGDeq8x00c6lrjywhXapK84bEckI4rukOAvwso1wn5aUHUOaR0FIeY4bAiPS+5SDeEO1BZj7/osbHH7gPaVBI6R1uE9KGgVAGRMNjcYwLLodO8CXPiW0P0aOlDSJ6VDA+GWdHS/5givUOedZx8+cjsWcWrcIPdCuRSHys59gjSiB4k6iCxIYyShqu+wU7grhxyOA8IQoNMHOmoQe4lf9PfMjUNbqeJuyOgQ99Kuk1IGWvsf9bDyLC5zRFf7BhMG56dbUk7iKzl/d2Y4rsBm33CGznAGkUfCzZ9wqPIeXZScPbVmrsi54toy78uTvkfunP63HMV7PHKMyo75n3NaGC4vh1y9Ysd7djwx+8veW/G/L6fsBQx/3Z3yq/Ht+DgWb6nKSPOJ3seDyH/3XdfMh6VJGFHe+6oR8du+bxtGAcVn5oBf3xY0AYahccZQRUobAhBapiND8eaZRUSTzsmlwf8856kswyflESLhm4fMu5aFostRaGxVrKschqpWRZDmjJmtRkgvlPEusNNDPpNxLNgy1eLG6zyOC/Y1ik2BD/vubpcERwEQ9FiVpqw9GwzhW8l06xkREO6d7RSEeI4vB+wvx0gE08uLHcPM4q5Ir0XZKpjYDqSRYUfWlTvCUct3gu6LgAPqhK0+wgXwHuRE4x6FosdQ9fRrSOSvCFPGr6K7vjN9CNh4/gyWFHepygc4l1AeZOwjiXhvCHqHSK2pFF7NOzdDrGrALVWTAYlnPWYQNB6hZx32LHHSo9LHH0gOPUFsjiaJ0VqaU8dG5Pw4X4MtwF9HRA8SuSdYv/diHKb0GmBtwJfH82DjZdET0vc3PL1V+85WTwin9ScjvakScOsrQi0oV9GxAd/3Mx4BxNDawLSqMEKgRhaorTDtBqnBeog0O8D1g8pQdwjxpanXywJbwXOeaxXqHGLyxw+EESxQQ4M06jis+d3hDtxrC3vPW0d0kw9qgf9SVPEgpHvCOKeehdTVCldFyKmHWrcsdcRzQC4DtDOE2SGW5MxOqmQTvL+5gQTCOrnCmEE0lseH3KK343oQoENoa8D+hS6T+nRYlcopqJCaEc1E1Aopq5CHWCXJPSjgM06pz91xLVD4nC1wnWSOOuQs56ulJAZ3G1ATk92VaAXLQkGtw7xoSdpjnmk4K1ntC2Jtj2Tb/aIaYerJNtPI3orMZlgO4+YfLMljXuaNiBJWkg86azCf90QzVoWL1eMaIlqx65IqW8yNrucNpWI3KJGhqtf3rN/GNA/Nbx89vgXnZd/1cPe2YCos9jMsxtk3B5GfLgYUlxC95lisrfsbUwbaLo8pHiI8d9nkHoC5Rh+U/Gv333OQLacTzYUdcA0KenPIc8b0PDdpznJ8xL1Rc3qhcJ8iqhEwGqb8eN3F3z562smi5J852CniaY1+9sBk6BGH8B6yZ0boQaG67spH8yIoovZhhHtTMOzllx2BMISxj3BW4lcS6T3LOsh9T6mRtMpSSc19II0atFXDfakpyJAKUc79uzLFE4M6RcH0mHDrRvRzTy9Vyg8adrTbTSvfzgleNESXdZMr7aUaXC8b6os5y8fyYYNcmQISjCPEaoAZTXnzw+U45CRNISj/vhFKgOaSBHNW8xccD7YkaoOu9IUI8n/a33JcphS3OZUXUBkHTWaGk0hA5LKMt1blLY0ZYTpNOwUTR/Q7CPUScdwWPKpzdmoBJ1bRqrEeHDSMl7syT/AXh5ZAaOg5Vw3LJc53+9POPvPt9hWkaie/+n5O+bFkSuePSuZjva4TyEHHdDvBR+uF8ih4d+9veCb8T3btxn5SY98qgiueu7zhM19ym9vzokSx3hneTbcYQaeoPBkj1AsNEne0mjJZ794oN9J5qLlp9+ec5JUfPPilovRjnApaNqQWVVjlwFfZiu+cRvGYU2S9CSThkYorFC425Bw66nfp5ylB3wXglM8kwXlKuHm3ZzrzRSTeZomwq4D9l3Cu3fnyGeWTRdTfsoIxi2Z6OlCQR9Jil8JahvR3KTUTvF2P8Nbgcvgm9N7xrID4Zme7bC1ZPbVI0Ud0Z1wXO3HDrMJWe1zugjEyNDcpVxdPXBxsiHIe6o2pJ0ZTqOSILb40fFqqCpi9vsMJT1iYpAfomP+oJV0uSaOO5Jpzfz5huG4wG00NzdTNjomcoZuE/H5xS0f/3zK28MYVQve/3RKFjQ0jzHJqwPR0hJ9hHAL/jYi0oY9MfY6QfagnrSYyx6RGwZxxenlBp94QmFZ+JLmx4TD7QDrJKsuJxx3iEdNt4o5fbJBFJKR76hVQPMQ0ElFflbSJYIw7WlvU/zUYFCQenR+PEjL2PF6fcKTx45+FaDuNfVDxi6KQHtUZqATqHl3PLh0kmALy+WYi3RHMOqwlUaftKxdSjf1dHVAm8ujGKtQXH+cEV40+H1AEBj8XqOFRV42dGh06FjtB3zcTGmeGsaTArXSRNoQvJc4ji6E3T5jv87oPmYkq55eHDn4XglGYc1ocSBb1JhIIIeWziuya8e7Py34eDMhaixtLI8HqtxSNCk9iv55T+p6sAK5UfhPIVHdk2QtgbRYAbnpOJkdWHU53757glmGkB0r1lKCWCvUnSJWHXrUHTkNKuCwyoheNriDoosD+lZx2ZZ8M1xic8fubsBVviW8rLCppX/lEOeOlczYuZjlzZi6j3jXTilszPCk4Lwv2BQZxZsBYtzTDxSn4w3tY4ioIXza8vbDOX/6dManOMEdNH14DNYG9wJrJYSOh92QcNKgrOP2u/9kvfuPfk5WLTrycG5Iz0tU4RmolquqwZ313HQJed/xdF3zZP7IYahZRSGbZU4TBLzuJ7jTnmaZcquHdP884vb9jKdBwTkNppfsVcL2bkBWtmzKjP0gZG9i7m1GlSrS0LD+3ZTdLqevAqaLA1dXS+Lc4p53TJ/vCFrHfqDIJxXSC5LOMFn3TDcdplWkvmNc9ce7/F941nFMryS+kegDjG4dcdbS3McYIxnrhlZJQmsRynOoExSeRPTMTYV7H1NtE6wT5I8WER+70c+frWhOJP1MYgaO58mam/dThD+uF8v7jJ+2M/YjRZq2EHhCbxGB4/TFCgpF/S5jU+akpy3PxI6mP+o0316fMJjUVLkmspYyDtiJgKDxXOo9PrPoTwG1Vjgp8PIIKum85k/hmL/7dEVwgPhaEr3T6BbCa0n/hwFWSrbnMUFm+B89e8tlVhL2njQ0LG/GPNqY8LcRuoL54sC//+kJ2aijKkKWdYY4MdhLy/+5f8bmC0dOw/3DgOzQ88XZHZ+lOyaN4XozRI4tAxqW6yHrKmXqWoaPLf4D3LY5amg52+/5z3/2hrII+PP7M4aiI552/JPPP/KwydncZVTrkM3vxkwwtLHn1xefWOch7/cD7v9+wAcz4bRuKTcZetjyh37Bv+WUNpaEi5puGcMqoFWCxasNrQoInOP9ZoqZ9uTnBR+KnNQZ3HXEoDA01ykHH9JMoVUK20vEp4CuU0zPdgStRaaG/OkBdacom5haaPK0wcWC/OB4vTpDHiSPrzPa1LFNQnRiKVyMrxXRqOPZ6YqyDXExREHP15fX6NOWrY+5eLJi3yTY25CB7xD3mvXrMelpw/ShQ0nLvkpYqox8fqTDeAtNAjI2BIOOdH5gNtwTNQ7vBCeyIwk7fnX6gflih0tAxY4IKKqIPTkPbsDoyy0ngwMHG7NbDbHPoX0iKMIA+axhPtsTpy1B4UmDHu4C+jKgqmOCyEIpCfKWy/NHiiah/8wxyQ5MkoooMrS7mPikIZUd5jbEC+gCyej5lvDnNbP0wP7jkOGkovmQIA6S6I3Gjy3RqMXeB7wYPpC5DrsP+KGd05cRTRXivWe5H5KmLa6TmFBQpZK+DQg+KHzmkAI2N0Ncoyi1xs4s+VmJOYR0Y4800AtJM4c6UOxchAwcs9GBJ1/fks0a4tqD9HR3EWanEa1H1QJjNcHXFYv5lrY7/j6GFwd071F7gZp0XDyv6QOBrj2u0mwfhuxdTNnFuNzhlwGiEVRxQDeB56dLfARMDUUdIYSnXzhEp5C3IdsuRTqPn1u6hYMnLXUdIbYa90VHG2rMh5hAGYKwx3UCtZX42B6/qEuFmBt04ImwdIeQOGrJpjX9IaQPgVOLSxzbzQB7nxAahzpA/X3Ged0zqyxBYmhCzT4OqaqI+KJi8usV0UWNbRVTV1MPJMZJ2nOPzgy7m5y7dkg8b/lsvCSY9+QHQ3jeUkmN+qiJ7wRBIXAo4sLBXYC8l4BD/6BRk/+Ey/2Pfj7/2S3T2Z7JuoNWgRNk1wK314zLjsdFyPjiQDZtufvDnLhzhEnPqGupQhjEDf5aUaSKUFku/9fXfPVffGASlxjRM7ENLvaEwhNVgs+DLS/GD7xMt7gERN7z2+KUbz57jw4tVRdy+tBz89sZpRRI7ek3Rxxp2wUM3vQMXu5BCOzA82E/YbR0lEFA0wSUgSJ4r5mMDiTeoHDHVOguwaWe8POKw0BT9BHSQJgYpAHP8culv42pYkWYtQTnNbIU9C8M7WcOmzj++N05eqXY1hnFfcZvPz3js8mWQV7xdLFCRQ59EMgHzd7GzNID4psSl/WUZUwgDF0fUAcBj28mvP/zOfpDQCJ7xq6iQ7AuU242Y+y/CYn2HME+gSSY9MirlnReEbWWeOVZmAo5sEzLmoye6LKCYYePjlY/MxQI7XE7TfboCLaev39/ye+TGb3VbGTCeFbgEZjw2HHd9BH7qWRXRPw3L7/DF4ovgi3zruMkqhE/xmz7hKvBluXDhHYqyYKOeqTJPzo+vRuxDFLi0HA+2fFDOeHwXLEqBuS+4/xDxadJzsebCT/czZChI6oMJ8mB/8e//YzZWUEYHpsCb8IBLvXcVhnqsmIwKyjigOpzTf0+4fA2Q/VHDWa1DViplCRpeTY44ELPuGrxtaRWiuCT5IqGapXQ3AZ0q4Dt/ZDtY84oKZG/KBlmFWfxlsV4z5Of3ZH7lqi1uHXIia3QCPbrnLqL4MyiC4HyjlFWkaqeMlZc/3TC9uOQ6kSxGO5JCs/yw4Tp+Z5w4+m1xBtBagztfUg+KnmohwSF4DQ7YKqApR1wP08IAsPw6y1iaPjUDliWCTK16MRwGu6ZNjWbx4R53RBlHXNd4SKJH8B9mnGX5bw3Y75rJ5zPNyy3Y4ZBzZNsTaZbPq7mNE8ldy5lNjjg15qbfsjw4sDpaM9QVISjlmFYU2wSPn5YHMOrL3rqKuI8PxyT0WXE7n5A7xSzsObT61O6SCIaxUbHdPuQmBaXWdRW0FtBHPVEncPOLUuTY4qQfhvSj4+kwvoC+hyMPKpeiyZGf13RZAHZuMYNPM9HD8jIMjg74LVHNJLniwdU63DjI+2yz6CdQLxoaNchvdOIVhDJnnTtqO5TbO7QVw2zqy2RMCjhkb0iulZUU7hfTrh7tyBoPa6WjC92CCswTy1RaNEdTGhxTuJ6Sf43G+Tg2IKJRw161DN7uuWAYFAawr3AZQ7Rgrw+bkN1B3raEmpDOD16Nv743XNMrQlfa9ReIq4D1BbaAfSRwG/1EX2sJaKR2EuPzA1u4Ah7x7BpSbOGtg+otinKO/JJxfTna6pYwVmPzA3eCp7Pt0STloOP2O4z7KcYQk/YOxLZ83EW8+16SlsHMHQcEs33HxbYTmFaje8UT6MtT2eP7Fc560NOu4tAws1qgj1oxvMC/azCvdXYgafPIZSWP+yucD2MsoK4svhliD/paQaCfnRsQrhGodOebiI4jw+cTA7Y5D9V7/6jnz+VCw4/DjBW84odTmgG3vLgI4og5MnlI+3blE/vh1y9vGeRHEg7x+XVmup9xuCkIDgz6JWjOkS8r2Y0+4Bv3ZSwd5RPITsvEZ2gHUvK+xSvJUuTsH3ICbxDBY7BRU07hKTreL2aEP+8ZlrYow9+H2Cz48nz/lXCqk8piphOKzqOWtm/PbmlnghmgwNPXt1xqGNMKzGxI1MdT54tmbuKLG5xpWa/zXBeEo6ONSssKG8IRy1P0i2bKkfea6K0Z9j2PO0OSAH1iaBMFDiwWnISHnjTjtBJx/bvh6jTiumrDWrYQyt5cAOKLsEnkknUUpcRrpXEwxavIb6oufrNDXre0j8k6HFPPqoJncNfGuyFpw4UugHtHF8/+YS4Cci/2sOXLXJssJVisx6C9lijcGWIi8Am0C8s8c5hdwHaO/ompJ+DfRtT/zlHWo84KAZhS35xYN9HuFKx+/OU6hL+u99/Qb6Et+8WvH6YcaYqhvMSLnt+eJxz6ms2bcLv707Q1rFbaIzUXGZ7RAN50DG+KmhayYuXW6oqZvyrhs/jA1WmufpiiwkFg7jn025MXUc0B4W3EncPhQ+5I2WsW77/01Oig2KalXRGEz8raU4dzDsOKkUMwXtBplv+sFkgWkHZxbRTgf+YMHta8Z0bI4XB9CEHGRMEPcKCfWLYrzIekgQ578F6nsiK4ElDrxVp3LKOYjZdgtkEdLcJo2GBVyByQ+FDiiZCtx6rPffFmD4S7FY5IrTEheftT2cs7YCxben+nBEOemZpxeZmwuOHCQcXcb8acahiLuIdi4sN5acMaSCat+RhQ/qiZvtpiOskzU3Gyg8YRj23aoC90wRGMMuqoy5UtEx8x0Q1PB1sWAcxyaymKiIe/tmI7TJlu0xQpy1BB/Nkj4898awlrS2nq5LzaIvpNEYLnj59IBy3aAOiksit4Ho9Rl2HdLcJV8mGwYs9V6crTp+vSEc1+mlFbCzhWUOfC34Tf8Qrj9+FrEWKmvTopSLpLdHOUe1SFpMD1gmCvGchSqSFSFqurh6oumMjIF7U+NxSjhUditVugMoN8bDlZj0jzCyn8y0Bhsl8j4oNXRGghgYSB4FHHyT9XUj+xnGVbHFC8HA3oatCkrRDvKgxF4axb1CPR3Rw+xijlaWxIfHXB1ohqdqIsyePVCn0CLZlhjaOQxFTJAFFrKlTycN6xM4mVG1Ef25QG4nPjx8Hy3dT5KgnLQ1x2MHYYKVApBaRWNxOEc6P/gWUwGYOKwTGKYRzRI8CaT3moHGNQrWe2X1PvUx4mMSkJzVq2CPnnklrMN9lqEbirMROLb2Q3JuYrg1Q0iOFw2aW3S5j6Fs+H68R055pXjB/6Hg6PKAnhovnj2x2KXyIqELN9rsh97894bwsGL32yEIwdTU+hC4T1FJzkh7oT8CkoFaSQgbIkeH0sWdX57RKcTopIIc+kCjribMOF0IYOILW8/H1KedfFMye7v6i8/KvetjnQYP5ZUffKl4/TEDA9+WUV25H+SnDF4roVYn5vCdKQeQeUwh+/OmcWHZsfj/gVbxlMmsYDQqedns+flpgNyH3Wcr5fMOZPBCd16SDmt1ccLcdQu/4kjVJ64h1y29/ek6Y94yfHuifOIpDgr1sUd4fB0bfkleWpHWkHxSyFsTGkOiO+srx+JiS0yPvA87mBXrRky5qVOSpnjiWcYJPLOvHAZkwDMYVs8stg7RBjzq8E8hSUo0kb//FE66eLolelcRpy6EIkHnP/YcJqhC4XqJXAtF6tjLGVQG+jHjyzYHmLsNtApwS2KGFoUGtFedtwY3JuZY5etGRNJbx1R5z1vPYZ6jeUwcaZyWmCuCzhvPPd4zDGmmgGQnyuObjck4daNrbhDNKLtjhtMDGgsF1j1op+kQQ3UpU71ArxXYU4nvBqh4wWBSsfpwShQZx2tHXAesm5WRYMzOWy9GapUwRTxsmriYZ9jQTT9lpBvT8XkyJvWeStIzfd1TesttEPDEV7TZEPAYMxg1LFfO6n7BaDTgUmsoGvHk7h3t4c5gwGu8IjCdvLfNByQ83Yy5He+ZfbVCdYpi01F9DFHe4UuFCzyFSfPfnc3Y/jLEHTaw7ul1EU0Qkw4Z02BDcKm4/zggqz7naI6Qj2nnErOd6OT5KVpoYnTlm0wOqAS0trZecrCuEgNXdhPV2xLeHKVf9juI2Jcla9oeUfheh5x0zX7J5OyGKOs7GW9qBgKGhtZpJXhKGHbnpkdaTRi1NLghOWrJphQs8/qpn0rWcnG84LSrmouKXg2t8odmvcu5sTsNx5e9byUnTUH+XIwpF8B/Uys2lZVOndHVIZUPchWU70CAtfqsp9ymrVYbtJOu3I+plysf1jE05QD4LaBONaTUj23K62PDd318dr4gOiot1we9O53zfnRLcC+I7yfV6iuwFMnCcne2xc8fZyyXZaUnmDHc3M4qbnDerBSUBZ/GOp2aHrAXaeAjgNhhAoUjPqiOxzUeMwxK/Dig3KVp1uFagq6NaOgx7/NQQfFKUPw2Pa+dCsvzjAr8L2NyOSPYeEXuuDhXdPmBfJ/Rjh140CAEOQRMrmjaizgQm9XgJlQioziTpP9wjxoY46hDnDemwxpQauw4JJh3tKsGkx8NzYzQXP7snNgZzF6EDS3BwPGyGlNc54iCpqoimDcgPDtkcU+4T06MKgXKOLGiZ3BnSrcNoiU2OKfjOq6M7o0pp/jTEFYrhrMB2GmaWZNrQTkFKR3onCB4lNvMIKQif1ogA+iYkHdZoDO/bKWUbM+wMT6Mdg6Rm1u1ZPhcsvnkk6XuS1qBbgasUh12KfJAYI+mcIrSWVBieXa3487+b0RvFfZ5yG+Q83A6YDGpOJgdkK2Ds0HjamWAwrFg/DtnPFZnqqGJFLxU+hHWfcrecMAw60tpincL8lKLuNH9enrKPA0ZBRV8aktKgNuqYQ7GC7OWBVDiaTczJq0f+2eMzbj+M/6Lz8q962Pd3mrCGNGkIbyT6QZK/2vMYRYzyHZt3A/p9QHGbcdtl1HXINouRDdBq2ibj3bsZy07z8P2Cd+9OSQYGe2Jof8op/35IVnusgnCv+W/+5nfMnm+5EUMO54qH7YDgU4ToBHaneGgH/GL+wCipufk4YxYXBJ0jaj1y3BOuPdPJjvFNS3514IQDk23H7SCicRo16/n9//ACtZQEkeEs3MMyoLhJaeqYRXjgWfVIU4esywGLoOCcmv/F19/zm/yetDecPt/Tf5/zn1VLhnWP3Gl2+5yss/Q/poxEi5xZgtQyWDk8EHQdf6imqHnPRLe4xwAReOZpwfx0QzEOqA4xTnrGXY2cNYjIwqeI9nVGJxQicXSHENnBYZvxdjdn/WZEMOyQwpG7lupDxiip6J3iQz/mQzDi/MmKF7+4JnteIe4VyQPokw6XePRBEAtDeNoyHhf4nSYNO0obIYYO0Qrkveba5KxI+H55gbJgtwE3DzNMLmiUYjLYk8YV8iBYDAty1bE4aejPj5Sz39sF3/sRXyxWPEkOJI3lC71jcbXhrkrxteBqvCHvLGPb8eebM5Yi4i4J+LQd0b4UhEHPpSpYfzdkv9G4dwHhwfPsb26Z1paJNcQvC2qniXeO0oWEVU/wTtN9SCiDgP7KYM4t4WcV+d/ucS87BrLF1ZI+t8TCkM0rLuaP9Ei0BFVJpPG4Z4avzm4ZzwtkD8Facnc753AlqV6nBHeK5GlB5HsOTUpvJW4ZEO+gbkKCvEdpR5NJwmHPp7sZOjQ0bYQzgoQWFj1KeAZZTTmUrKoBy68k46dbrjcz8rjhMt7xPzt5R7pznIweUb1lHUSo35Q0SlHlAZ1SKOGZjgvExBDnLaNBzaGKSLuOk9kx5KnPW6LQ8PMXd0TewFIfFa8TQb4oyZ8XjGiRjwHlPMT+m4TMdPy4n+GWEXUS8CAGXLdjZM8xwX4Xcf/dGG17bn+Yc3b+SJ63mBOLCz3bKKT4lCNbwep+jF60OCvoiwAVw8kvHknjljYUnNb1UdHaWoKXFWncsA9Dxs92bD+M2B8yMt8TP62Igv7IfJ/1DF7tUBsBHXRWU1URqyzhi/k9nVWczjZMdIUVksYGJHEHyiOkp7jJ6D8mpHEH64CbNws+bmY0XcDpbo+pNfTQzy3chvRDUElP2FvqBTxsRqwOA/pdSGQ83iqCCiLdo542jE/39LcpSdCRiY5hWpMKg7YOsQ7oas02iOnG7oj1jh1h2JPOa1wmABhOS/zQsTMJctTjhUAWkmgFOjCgPdlJSaY71GVLOqlwRhJZS32XUgYhJvbYmSPZWz5uxjS7mIdgRLVM+endOVHckGYNu32KOSiyrke/aBCJRSUGMXaIYc+/fv8CP484qSuCtEcMHXZhsbXm7Z/OUPOOPK4IrMHsIh5NhougDyXmqgftGYwqgtaTqY6+02xMQt1GqEIcrxFex3ReIhPLw3pMKKGNFbHqEVYwuTgQ3Gl+6kfswpA/788Qe0USm7/ovPyrxuVuByln44r7n0559vWSw11AbQJE6LicVVRlwtbGxOOOk7bhYRTwwld8KhbEdAQXiupDzku9Rl8dMBPB4ZBSVjHz+Z5Nk0EtCFvJMsn5+9fPWW+HJJ3n2gw4cQXBFx37T2NqFP/gyWt66RinJSe6Yf/Es94O6DvYb1Kyz0u2m5R47CkeM9ygpd9J+jvHk+GOznh20YCLcE8iWtY+Be1IfU/5/QBTOJazMfLS4N5rDtOUO1IOy3PakwBZwCQpSU4N+31M1wmKE02c9piNog8FfRWjpCM/KXnsRgQVtEITuOPL8PGQ0c48kXAsl2OSypK8KFD3Amkkg3nDtk4Igw6VGYIC2jYgnjT0VrEnInGGvgk5uVpjHDQDwXqZkX+542q65sf1KWIVsFkPmBuPtaDTnt0LQXDwhLGH/Jg1cA8h9jGi1SFGKaR0yAOYpcamHj+HzHZUDwlhAWHpUdJjIsHws/ZINzuvuK7GNJXm92ZG+J0mPO95sthxfzcg2jjqoeJP3ZRf3Tzwm+Ejf7JDWpvwt6NbUmX4Vw/PYawovWZqC9ZNRLJ1/ONnN9jHkE4KXv8053/8qzcs64SbYc7GxNj7mKIPqZuAoPIkO4/NBE/Zsp2M+OrXd/z9xzN4TMjGDf67EHOVslyGmFhhlMMjYKdop45QCm7aMV2jGUiDDmvMXUqfG36/uuSzySMP5YBinRFnLbnokL/oKX8c4pYJSdVRKImYWLqJ5V04Ytg09IXAWUnQOmwnefnylu5NQvmQ8/T8ga2OOZMFN4cxfZAh5z31OqbapPygcyLV0yjJ7GnJv6vn3B1GXDPGeEV4IwmyHlrQSjDMSsz7hPDZjvuHMacvVxwOCUo5Nm3Os9GOjw8B6tbBQvC9m9B/nyG1oL/whKHDWcnq/ZiydeSjhpN0z+6LGCsVvYzho0BmEiz4seMi32A6xeKzDfSOt5sFoYFPP55iU0+YtDSfUmjgZLpGDRzbQ0SKR3SO+WLL5mHIepuRm44gchir6PqIeqSJ7iTdwKFrQzxvUaWgTST6tKM5hJQ+RP5J4y567u4mZGcNTRsge8eTs0f6Vcjr5RmXX6y4dAd+98NzBmmFP+2xkSeblcTO8liMaPoYcwjJ6KjjALFVuGVM9tkeNdnRJyGdVDgp6bqYVkiGo4o0cGz2OeHIkOw8poFUNXTbkDrSiLDHFoon2SObbU4RB3z2+TXFdow9hPSt5OTlFn+w7Pcp8b1ERIJkWlEvA4aqJz4r2Cwz5sGO7n1CHwjaXrCrEpyV9I0gmFlUayl1jPKQLjVCOMo6JLkoMUISbwQm8IyDjl0VYyJH6CCZdyTGUzcx9aeEFI87KDZPYsL+6Pmw9yEqa4lKj+4N/9U//Z7//sdnTAcFtYhoHgN2Oma46JFdgNWSPhCIR8gmDd0uItkaXCaolcLXEVp70gLKRBBfS9yixyw8NAIZGvK4x9fgxo6H+zE6tcSJJfysYFWnhF5w2pZwYRGVZL0fUkbBX3Re/lV/2T+RBTerCcmXBd2sZzCr0G8VA9Hwh4dz6kgQy57kg+fmzZBB2/NTP2c0KeieQG0DwpcVeuIRoSdQHcXrlPjBU5YRVgpWKsGG4GrBZ+qAuNeoteCr4R3z5wdu/niKrRWih3/z4+d8V56yFgld6Ll+tyD4pBiNDwjpEB7koqdaiGMOIFV8/fUnuIBDrDmJep5+focDftydsI0iBouCv335mq+/eQtfNwSZRQBu4Xn7+1OKTcJjl/LQ5/RG0wvF41jy+s2crc2x2ZF+Nbzco8c9SdCxGO9wHuJxQzho6YOAUdtSxZo2P1ZeVAnRwdO3mt1ygB9b2rGgSyTNULI95ByqmDqTxLXFvw1p7hJmtoGDIqVn82YMvw+YNy1nT9aoA3z73VN0YCmbCBVamFjExHJ3mJA+elRoIPCYRiGuQ4R2OCFxEfjAQ30M47VjCGYdZ4MtdRBgTUj8eFwTysuG9sSx/27AvR9wU47RHZxFOyJraD83ZNOC98shaVrzX/9XP/JlsGGwNHx4HHNdZ0x3LX6pYKPgGv7JZz8ce+zaEFrBSdKwXqdUjzFLEsyp4ze/+Ynfyiky66Dx/M9f/Uj3LuTODDiIiPPhgfQXO+JFy4dmTjOFEzqe6op+5CjvM9wXPVHYIbQgTnuC2JCOGtJxS7CTBMoid4pR29F76M4Es5drtHOYbciHf38OsaPNYZ9orIKzvsSMHDJyHGzGfLYnGHY4IdB4mlXKdFDCQdHeJxxEyKf3Cx7EADexfGinlDbk6XBHdNmQup7+Y8z1bkrbh3gE7TIhjTqWNxO274ZEP2qSPcRYgnF7DHpFR4yv6GCwKOliQeo7Vg9D2GrkKqAMNOvHFLmRzF7umZcd/k8pDQHZZyWfjTekYcM/Gnzgm5cfGb/YMo9qEA51YjCBQIT+mNjvwQ48PnLYTlOXIW0ZcdIb7MSzI0YfBEFk0L1DBQYhFWGnWd4NuBru8Z1kNCy4XU95draEocGf9uTDGpNI9nWK7Dz66xL9oBC14ObPZyRRRx1rhrQI7cjGNcG4ZTHfkIgeuVQ0JgAvePw0ohtCd2ZZPY64Xs7QD5byOuWia+lvUigU+z4m6ixfnF+Txi2L8x3RuCX75lj/vLubYAtFUcYI7ykI0YMOoT2d0YhGIR4CXC/QgcEqgTrpCWcN/QBkL9BXDc2lp80E6axGvgtZroa4VhKe1eybhH2b4JWgO7fE84q2CUFYTuI9rVEkeQtOoDODW1jkxDDcG1TmcBNPr468/mjU4p2nuM0Z5wVy1hNox+jZATtyLGY7PrY55sQhDHSxIDGG+9WAqg2glfS1Jjhrj4hdLfBOcPXsAbMJqfoAlfT83z69wD9E1G8HFB8GoAWzfcvWxZj3IbtVRtR6kkVBu49o5xBIA71HrDTxZUUbSYptTIiFoUVPeoLG4zLP9L98wD3pKMMAJwWcGMwhoB3Aro2JWgdriXmdoG4VzU8Zi74krNxfdF7+VQ/78llHOO3YdgmfbhaMzg7YZ47yNiMtDPJe0yhNeS7xP+/Y2wgGlr99+h5z0AgJ4/c9/Rg+biY8NDnlXFDFin2ZE7WO9AFM6iGxfLydEk477Nyz2WUUWcSprsCC2GnyzlBsU6omYvt2gO48Bx/x5vUFotK0WuE6RR426NQwblsOLsHFkuU+50rsWT6OeUwTlPFHqU/t+f23L9knCXorITySpqJhi9eeKOxReJTx6NrzuIlo3+SEE0t00hA2npt2iLqV+MeAbh+wLIc0ZcRFtEPWEAw61iQEjw7/IaJtQsJFS5R2yGmPOGkZX+1JjcFuAqplhtaOMO9JjKUZSLZRQiwNVaCI1iAc/Gy4ZPbVgdB6qh+GrIIMVyjMfYTaQbiS7F6P2bsQHRicVSjraZ0gWIE6HAlgwbTB5xaFwyvwEtTAIAaGKlSMfg/hyiO7Y/UoPulYPDTssgBfKqpliq0UaW/J3jn+6ck7zN8n6I2j/D7E/RByNt8yflHwq69vCJyjiBRnFwUfFjlvX474V9++4m0/4m4YcqMj8rhl8qTkZFGwX0bkrqGYJkS94+7NjLsw5/9N3Z/EWrvkab3YL5q3X32z+689XZ7Mk6eyOsqAr8G36GQQgzsACSRPGCCBECmBwAwsg42rBLKAAUIyAwsJZJUs2cgTBoB8RXOLJquysj/9+brd79Wvt483IjxYddMqyviqjF26FdIa7Njv0tp7La034h//5/k93749w/U97711i5YOH0I1Pgi2gtBiRo5/U53zqRuTVBZ/0ZAXMb265eSdBV999zWdFKzuBwfOeS1Z3Q3wwE+cvSYbVZhlyrUZ0rQhvj3cRMVeIZcKvZUUVcxnYkI0bJgnOWSWh7JHWEGyBFEKsnd2bNc92oknGdeoVoARJLOS3rwgbixJ4fl3nz6HVUDca6kDjSsVjDrGRzvmj1fowKEaQWIc4VdL2lgwDXN0frBwjs921KsEUR1AN2nliE4qZOhxEQRHNXHrqKae4IMc+xDzYAdUk0M++qpJOBveYQPB97bnVJHivzn6DlXPsYh67NcJ+TKly/xBh3DWct5UjLuWSzdg12RcrkYs0BwFOV87viO6KOmJhnFXc3a2QISW2yxEDix7deDs78qEYa9gMzg4TwaqIWksldeYE0v4vMIWmvjre8Kv7RGPa06PV6S6ZnS048On1ziryFVIvYlxdwGbNkVvJM4dRGYitFAqAudYFn26c0H2wY5XXYYzAvMfe7RXKWhQ3qMKT+wthI5uERG1Di8EXRMQhh3nR1tOox0fHL8hmlVYoK9agn5NUluCcUM2rtiZhG2bEPmO4ekeU4VcvZjTOI30cOlGhM4SPS4QmaN5kdFmoLaCxycPaG1hamgTzce3J1wUOSKXtEbSbCI0Dj1qWVcpnZHo9kDxs4XE5xrhBEIfwF9q2lLdJ6g3AV97fH3gOBw5ernl4u07gtCy/2LI8MagCgGxw0+6H1MdRQe21GzvMt6dX9PZAJcI3Pd6VHnErokRlSTG8tD20RtF01NEW0dUOPakJMcVQS7oT0vShUcbd3hvvUCMDb24Qj6tUT8EHjT9nWWfp3h7cA61reYtlgjtiKQh+bUOb6E1mtZqFvogbrVzT7NRv63r5e9oXO5P/+L/ivixYNlkhNaR7DzV+IAvPO/lvN4n5FHKqa3IjnKenN7xz3/4IdNthZ94KhNBz+JXh4Slo+MN237A6nrAxNas1n3Sk4K8ilGPakZdS50HyNsAf2QxVYDqHY6+EtXhpx1KOFbXA8wmIFIWF3n8I0P3KoG+5UgU9OuWm8+nyPcrRCsRWUN50yMyHjP16Ds4emdDG0jyIsEYTRUpsrBBlIJilfH8/TuuvhzjAo9LPNYowsIelMCloBlIohtBNVXIDvRW0MwcopG42NPPamJp2L9KCZ5W+CbAnHbITyKcEPzEO6/4zuVjYtfRGo1Dos4afCepjeZivGSrY8yn2WF3/2VGOG45Hy55E46Z+YqHL4cUU0Xf1uzlARjS7wzrOCS5PvDPXeZhYIkDg7CezihcpbGaA7HMetxJh3gRoktH/liSHFfUVhPnHvEiQIQe6yRdD5gbZCmxEpLYMm4a7osUkTimb6/QzuI/TalDTdAzTMOWlQkxVwn7PIS+J/Ud8dOc68spR5Oc/2r+kn/5798jftrwYXbDXZbx8KtDAlWzP4+ZTUo2y5goMSxcSto1+FCQpBbziaIbaMp9xOPzBTa11NuUdFZyXQ1xraTdh3yQ3vKjzQmt0KRhSVw4hj+3xixDlrdjlHMUIiC6kqRRQ/C05r1kya+YE/x9QP9xzva+h5y36AKKZYJuPaKTuCctce5o9xHucYvqwN2GjJ5tCCPDejkgM4biISEaNuyDEDHu0GsFoWPoax5cxrQ9gKbIO3gCi9shUekJAovWhnRSUxIQZg239ZBYdrROozeQdhYTC6wQ0AmUcsgOmoEn6DxRaqjLkKixJMayjiLePl7RpI4Xb2Y8izZUVjM/3vLx6gTXSn72yRe8XB1hGn2I8N0G7HVAPK2JIkN+k3GsSwoC6kozj3JKETCe5jxsBkR4glmJvFeIB4WbO3bHkuQKet5y00/I2g59VGM+7THsV2xkhBGCQFvWJkXeKfpBixwZ3HVE7ysb3GXETkVUkSSODSL0iJuAUgQQOboQEtORasPOxoStYzzao5WjepHyzs9e8qYbsXzog5Fo59BlRxFG2DbgpLdhLWKezh94tZ9RXaeMZntU6CgTyUm0IwstH10dczraYfGIWtCECm0cy/WQQFpm/R1XiylB0mGM4izecnk9J7SWeFrTG5SUZYzILPUmogsk3UbT/2BDuY0JF+AeFKMPt1zWE47jHcIJ1oseqWzJ9yn9aUHnFG6tafpAJyF2hMowMQ1lpEmPC4LEcvNmTOo6SqV5Plnwcj3F3Ue41NHtApT1BCUIPBx3xGnDs+MVYqX5bDEnOC7ZfjTi7HxNHNe82MyRhYStpHvcEeoOGkWlJIGBTgtkIxjNdtj7mG4XUPsAHRucBFEJwtKR/uSeTkNexXRvYkRokCXE7zQMkgoWmsWud1jAG81INGTK8JXjBb/87x6TPGkO0cL3h/AmRUcsHU/PvuT//Nf+T79tuNzf0T3790Y3pCeSxcspftJw4yb0EoMcQ3tWM7vxaO3Ja40Wkm+tn9KblPRaw2WQcTzZMhk0tGewvu6Rk1AuD2Klmd7y/vk9VzImuBxy1r9DGMHHm3OyUcsu1YSBQQYWb2HrQ/ROU68Sgl5Lf1hTFBGub5FfxPhA0O9XFLsAFTnin8jZ5BnRrMY3EWHiEX2LSMA/92xfDXCzQ/Uwn+6wQYp9FeG3nv55zouXM4g8vbhmm6eMr2rizHA/yHjyzi13rsd+EBNcQbQB+6TDtTFRCb4W1CrgIt1SS433kPckqpNEgScd1ayI0VmH1i3xXcfyLGQy30OhWF8NeHgzAe/QA4O81/ieoybg8+qI3s5w9u6S+20ff2SpP4/pZYZ02ZBPQ0LjMccOdXfAW5a7iDqQuMQR3WjCYYuKO5rlAS7EKiTsW+pEEfVbTKEP/cJCoXNPNzggZ8W845Qtq2IAI8tumSBODZOjNTsVU2wjxh8VtGNDO1P4zwR3o5TZuOSzcED9SBBuJDbwrNoB/bAkbTs+KqYkScugX/H5Zk5mavT7Oe/1Nlzux3z8ekZiLW2n+cnpHT9oZzzL1tzriGAm0LnhuK64ftGjnAeoUUdTcOAgJIJeUPJxMMVpCFRHb1SzzWLoYmQb4KyiSQVh4bATwc4fGAY/zI/xaNJpTfMqITsu8ZFj22b0JyW7NiFtDFlUsm56+JMOvwyxhSLsH3Ib/LwjHbdUv5Jy/PYSE2goHNv7lNlky65JWO779J7m3F/3OfUFMtLUe0+0FNiZw2iJI6LZH5gF7U2PoAkwWYBFkhQdbacJZg11FaO2gvSopAokfdFw73o86e0OWNU8ICjg9za3fOvLE07fvcd7SX9QUz3ETNMccQ+jXkF+2yNfZYTzmoktuYxHRP2GIOl4XOyYyB1lKBj3Gz5fTVluU8K0Iakt4T3wpMNsQqogQE4EooVTu+IhHLHfBowHFWYoMAvNPo/Rs5aerJCx5b7toVcQqAZlQK4lXnomG8uDVjSZQLbgtOAsKFm/ZQhfZJhcE/Y7TBMwqGvspKXaR6zu+7CVqL3gs8tjhvOGxgYEg5ay1PxE74aP1+d0neBqNUV08HkRMj7e4ZKAMo8Ju462jbiah4dAm9cR+SBA7CQP+YBAOOJxjQo6Gq1Y3o/QDdQmhs5zG/YhtbSdoCc7ZuOK7+3GvC3WLBtNsqm5PEvJfMe6CDFSELxl8CjGoz2bhwy31wS1p3MBWnqKbQJW0MYwm21xlUJ6T9JaxKTDG8Xq1+bY5w3VOsUUgvF0RzARlA8pceM5HW5Y+wG7JIDMEV15/Cbgpx6/4OMfHLGoB1R9xdtyhXy65lU5QhcOX2t6vqIaBUSqw7+IObtY8jrtIfaKLhREK0m+GKC3EpcAs462VQTDGnMiODV7FlXKMKpIJju297BTAW6icVtJ1YV8LVpSf81ToDlbNixNxp1MmH3Rx09hGFSQHsBmrz49Q/Us3cTy/ZtHv63r5e/oY/z+UUm5yHgtelzvRySN4VG2xknBm++dITJJtUvobiKckwRBR7yF2eMNA1ND6KmFpKsFk9Mt3bQltoaTZIeMLP/m9illFbHfptzfjblZj/GXMffVECpFPK7RziEUDOsW02jC3sEKFPQM/ec7wsDy+NEDYWLQ1oPwtJnHxQ4VW3q7juhW0L0J0YUgjDoUnmKkaPII30iW350jbwPG8w3qUYeLIGocj6ItmalxrWD3KOR21sOHcNmM2NwOCYVDNoLyVNC2GuGgSwSu7/CZ5QszIUoc42FF0B4ia/unBcZK7poBo7TkaFChnhjCjWf5MKDNA/onOe8MVxyd7NGFoIo1etIR9VrmZxuCwPLdHz09QC0+ihHSH3y+J5LaBTh3ALOo2NIUAcJBNK+IWk8UGQJpMV7SG9YknQUjMFJiQ4F4o3HyAIOxUlCfggj84f96CLj7bIY5tqhxy9dnd1TbmL2PyAYV1mv8hxG9dwztJqA5D9hXCfcTTVJ1BLWjO+24+OCeebxj8LUdb3TGcjFAPG65/TJDTVquuozOa3ZFxmfViJP+htOvPXCRbTGdRneON7dD2GnCqGN6UbG7CEnw9G1L8kLgO8mAlqztKHxCu4k4TnfoScNmnxFcBhQv+qSN43iypacbpPAktiPwBmsV+zpCVp7NNsMOYB3Ghx5/11G/6pGtHbrXUYkAQo+sBE5C8ig/pJJFjoksCK4U7p2WZF5TZYpmKBEtbMuE3jwnTluS0jGWNfq04s6nrJuMIDZkvqXrOYxXlO6gwyh6ivhJQZy06LU4kPCMpwg1s6dLzMijdIdbhOwfMqLEsL3vY24ieqVhucvYlApnJQ+XE+ZpzqebOepRx68uHxNae3ACmD5+2mFDeFONaQJF1ynkSvLF5+fsjuBmn/Hd7QzVr3g0f+DtR0uuqxHypCNy9oCYLhRWSvJGs/5+H1YKcdaSi5Ag7GgzjRoeKG6nds3daoQWjrEuEZuQEs0miNjrgFILSqNQuSBwnrTuuClTVlWCRSCTDr9Vh6S5xKMqcJknrDsmHyyJn+W4zPPydoSIHMOo5kgUXKVDtHbIixqVdHTjAx9/eT8gGVaIO8k+DnE9xzApcQjMGNz1oZWpdx4sFHVEplvixh1Y8pVFxh0mlnTLEOEheZITzBt+9PkJvdOCTSgZzEo2TzVR1LF56CErCaUiWgrMVcS8bTmta6T3yAeFbTVNJOiswnaCoID9LiEdlfi1xjrB7tMBZakJBw3mMiTYONKgRLeSL793RBQaItlyezfBJHAkC4Kw4/yDDaJsuXEpNycZquc47u149fEp201G3zVERw1PHt+SnJfIzJK0HebYcv1iitoccjmCa0366tdTQAfg+o7AOAa6Ipy3TPcVVawIT2pWVUbjNM2xJ+gE4VYQWMfwziDHDeVHfYIfRdx/PKONJT81umQnA+armqhR1PcZZhQQvltg8gD/ZXw46fhtHL+jK/v/9lc+JDhSpP2Gnmp56Pq4azCvE6qZ5H7To/IKkwa4mzF/5OQHXM4HXJdDzCLGrmKu3vIMio5qmaCUpW0DShHy3tkt/X5F12myfs0i7xFHHf6sJbqMIY8w9QG1mI0rkl7H/HVO98hx0m0ojiJ+9ZO3OJuvWVZ9ZOsRuaJtQ7pGY+88j3/XkuW+x4YQmwDrALtTRMYePNFBh1PA0OGVJF+mtEWM7Tni2BAUjuVxhMxB3ysskv644OnJPZ+0p9RSMdSGvQkJjCPsNeRxgKqh33V0saD1UH86Qs8ds7pBRS25DXgm1ohQsA0U99sej3sb9vuY/LaPe9Jidob2LsZPLKZUqI3i9K17fOLJZx5bOMQnMX5m8Z1AG0e31oSVpz12iFzSaYELPbE1mHWIsoJuq+jqANMIVFhjTh1i6xBGEEUWGYBMLGU/oHdjYXKggOX7GDG2TN5ZcflmhvOS4r0dySrHLGOE7dCd57IbMO9tSUcN582ON+GM/Drj7WcrigfJm+2Eazlm6vdUu5DRsGAyrNnvI3jHcrUekvYr1ncDNvMEOTXcfz5gLhz5OiZPFfGigxMolhkffv0V4jYgHXXozPNrd1OaUcCJqIiGLbd2jAsdYi0RoYcOxkFFcRRSqZBqGdFkkl2eoLyjCx3BtcBHkiQytE8s07uG/nmJHBoeij61CdDaUwwloVL4ZcAwrMlVSDA3hHELOGwbsP10TBtI0rblujdi62O8E7gJKClY5z3UTlMS084cRTVAz1rsZcwezTgwJGtgXEEr8ZljeF9RblL8rSYMHPbMUbch/aMNVJI4NJR9yTDesa56qL3kWW/B/eUYkwrSYcV2qOi5HO0dq10PVyqSU8vlmz7x1hO823BrMgY7g3QSLR1xaWlCRTgwWGU57e+4u4i5vprDqGOgWrYmIEwMUdwerHV7Qfy4plsclNHDpxVXq4SuCdEPgs1iwOAiZz/ULIqDwOutJyv8qMX1BJ0+2OOmvZKVT7m9GhHIDllq6qmkXvbpn+/pOYfVkrnbcNtM6T/PeShDmjJkuOngwvJOuOeTNmXxkDFISkJn2e9j2lgSvZKoO48eeoJHJYFXVLuQRHbQCYrHAmVADVsWZe8QaywtVappEyASuEpA5BiOS1Rg2OiM+Ct7zOs+tudRQ8vPDC75tYcLlqsE6xTVOiLfZgSzNaOoZLXqHyJ5fU2sHat+RB1r9tshaWuRgL3ocMuA+MoTnFfsZIKzgiSH3esJ0aDDLkO8l2TGElQeVSgEnt/7wQt+eHNK1OvQq/BQaS9jwp1mNZb0u5bXLqV8EvCynnCc7rjt5tSFIgwNqpU8fveWlw8zLusJ+k5RnEL3MkSnDlkK3MsQ5R2DUYuJQpAQFIfY7ibQtGOYbmtmH6xZ+h6qUnjRsf1yiJt2RMoSiI76xLB5mXC0STFOE7YNdaLoljHfXz5mMM9Z+ISu7h9oi1tL76XAlFD1NeHdb2/P/nf0Yi9HLb5OMSbkngQ1MMy6ChN3mNdTBm8dUK1hbIgby7eWZ2S24/Jhimg1vWGJXGvGDw1lAK1SeMRBdLJUPMsLqp7nRT/hLNuyuetxPN9yuQqRW0G5T6B1zCYNvhZsHikiZ7nsjXBeMJI1+yam6EJmQcXzdcGnuwlFG+K/1mK/m1I/U4edeR9sE+NTR30XEqcVpQqgO1TBYiHJH1tc5JChZ6cDhMmoi0M4jig9YWawNyGvH85hLAivQ3bHFrmWVJli0N8z3VmKRcpKZ6Sypq5SwtbTqyryOODR7IH21mO0YLUZsI1Cgtzx7PGSX7t8xOnX73j/aIFZaf795TO6LyLUuy0X9zmLuofIBXHuKPIIMfLYTqIrQTew+ESi+x2+DnH8ek698tiVIrySdGNP2xfIiUGvNaUO0bk7uA802E7gp5Zs49lHUB4rfCxp9wFR4WliyYNKyMIWGzgePp6iU0tjFOVYE4aG+EZz3Oas0pTKBHzw+JL/+PI5X+Qz4tigHySn+5rh04aNdIhtwpOw5D9cjwifGqbZjuuXE7LHOeNpRfNFysY5bvIpwWnD25clr+ijo4b/+r3PeFmMKIKQ2yJDntSchhvadcw+VlRdwtA3bPMUsVU8lCPIHG7Ukl05zMSyFAlp1xJuIaoc3QC6iaAbW+oiYLg1yB40saB8NeZkUnA9UNQuhk4hLYhxy+4yI5jVhJeC9aBPNqxJgpbGBmSDmmqbwLdC5NmhNVM7QZ0oMtORxi2m0rS5xgMhDkpJNDLs9ilIT9A41E7SPPR5erbg012MdB1dAsnbe9pLQddq9p1EGHC3EbtTyfP4jjAUXF5OUBjyR+qg9leC7jLkJ373l1ytJlxv+lBL2liQxQ1TVXJ/PWIbJLy9WHJ2vmEatPw/Fs9pyhBx0fLLnz4nFwHvPLqm2KW8Xh6hJi1931KVEcOna6Z1A4lDRHB5M6NdxiShQd/CvqcYjnJ2t316T3bs7vv4zLM/hZv7E+RKMxgW9D4PuDguaHxM76IgvBTsLJxmDbuxRChLednDCsF6EhMOalzicQ+asa5gBlUZ8JD1WccJYe3YiAwdtZhNiGgFp6c3JHPD98sL2gdFeNKCdlSNpl1rVP+QHz9RDb1kx3I/YCQLXq8mdL2O8KilH9aUr3u8qE/w045AWHqqpd5Kzidb9jd9ijZC30iO3l1wvx0RLTw+qylUSPzgGT/ak+wco7Dj7iZj2EH1/Qw1a+kChV4L/GmHCgR61HGmtngtaJcR/sxhpMTFgpO2pQ2hG1rqVxnyaUvUwCfVmMW2R+cFpJ5+6+iPSrK0picatnlCuc6IBwa3U6i3DEfVmrbv2bwYEawEH12fE30KxAorIboEvRO0icANDrkc1mpEYDEXlmBnCC8VVSAQPcdXj67wNuD1w4ztqkeSO4wR+GNLgKOTkp9674r/Lj9HdII3txN+7t03/ODyiEjXREbQ1ZrdbYYwkuGVZTQtePj+EENHOxeIqMNFv70++9/RAr3f9Q/+MseDjptvHyMuzGHRO2lIeg0X4YYX7YTKhnAdIuctx8mefm24awfsvuzjn7S4bYC9j0jGJWLU4QuN3ygmcU638cRPHXuhMXnE1sT0spowNRQvMzqrGT/esIhihrZlHUZo5xnbmqOq5NX3z7DPW5o64o988DG7l30ulymXUYo6cow+c6zeUSSyI5INu/Uhw970ob/sqLUmnDe4VYC/DdFvVZw+XrFDc//pjLCAZFBT3qQEO0E7gLgztFLRzARBYuAmBA8uAm08ToPJPE4LhtOcJ37Li3yGcwKzCxiais1zSfQGKh3DuCPbWvppQzmU/MR7X/Ktb7/LKCkxb2JMAHuXovce/VbFuG4JBpY31xNOJmsWn0ywoaA7sRw/XrHZZNhNgC00KukIGkcTabLzgvYmhjcHXK4Pfx1EFHbUVxmB7FDG0V0Y7HVy+Kw2AUnT0QQSd2w5rfcs5xFtLKBSyFKgGk/nJdP3VkxLw7rK8FeKuzRj+qZgnra4seSyTimm0AWaZNVxfFTSNzVuG7PoKXpbTxEoomnFwNXc0iN8EIyOW3b3Afl1jHkieS62/EBM6a9bnpyv+bKa4hF8xa14laWEccvuevTreGBJclSxLHuE1hIUUAeKk8GWaFrxcjtDXQf4yJF5QzsAtwhxCoJhQ7OOieIO0ylkC8c/saDJQ8ptiD5pMLuA1msib2lXIW1y+MwNis5JOiFR94oehm4bYAcev5NEZxV5F9EFgswfksg2t30mJzvWRQ/tHb4DudD0HuXoG8XJOw/82uunqKxjNtlRLROGZzt2N31E6piFBXkbs96luH2A84Jw1PB8uKBaxTz4FJ0Z2iPPmIq9i3giVmx8wsBZ2o1ilHte9VOKIuZ3n73iv/3ybdzQMxA1bhugB4ZiIvBXEXZscZuQeGfJvrHl6X7P59GIvmy4/2xGb5aTE/G7n3/GJ4tT7h+GRC81amggADFqaV8H+Hcd1ijOxI7F5ZjjZs/6a4J8lXEc7KmtJtgqZOjwnUcPO7Jdy+5MI9cBx++s+fTjY0aTnFWR4rcBKrVQC2wZEKcNJhYYIZG1wM46xl3HtooQL0PSqCKfKyLvqFYhRKAGlmxvqGJJ7mKwEoRHNpAtPSYT8KjFac/Tes3nyeQg4A0sfdcijGC7S9HjChtJMtcQKE+1j8lomc7XfH51wXm2Jw8PAuighH5Y4jLBY7PFackn+zli0BHdCyyKNg+QkTvEU09b3CagrQMSV1IOI+ayJg8UbRNA6BDDDrvQiLsQG4MIHFHpqcYgK4mdWo6DPV0GRRNzMtpx89kEfxUgUkcS1TQh+DbClxJ51uIW+oAGtyHZvSWkY98PEc6hS4+NFHJiMAii+wPFL0wNsvWYQNEOBGraMKlb7m7GBGlHPKlxncDvAt4+e+CzcoRZxUT9BhMIckLixcES/I3za672fdygY3/bJ8Biep7pZE/9UUr8rCZ2BvdlwkLAr/zv/ve/bQK939GL/X/1f/wmYRfTFgHBcUNRxZydLtnahDOxo1sGXKZ9hoOC17dzencQ0RE8qyjyCIxCLSCa1hyfbBFr+HI7Z7huMe86gqEhuAx4sR+hQ0fcWGwrCANH0YR4LYlOK4qXfYR12OiQCPX243s+ZwyfxoSnNe0qQsYWuZek85zkqKK9TQl7Dfsy5oPHD7z8csy1GxBoixk7ghJcpdFxRzKpcN8NKc8Cnh898NHyhMA4ItfRSYm6ljQTiSwkXoFuHWZu0YHDbCK0cijtaDqFVo4kbaiEZtLfk2eK8KVmT0ywE8hRR6UVRgl0JQhwTI+2B/Z3HGG1Jxi0RA+KN3Wf2BrCUccpG+ooRKeGfZOQLzLqRUw79yQY3FXE9HhD+Rie9lc87HosbkcEqUFuFZ0WdOWhlxd0luBO0R7x66r8A5ZT91u6JiDeWtRK4bUjTFqqXoh+XCEdOMA0il0ckrxWMOlQ7pA7XV1lPD9as13E3OoEFwp+OrzhPgvpT3NufzDnJKrY9WFLSugsR2HB66sZI9nQjByJNWxNQjirkJeK5mHA2XTH5S4jOqoI5o76PqRJBZk9sB8eXo7Qd552InDHEAhHFR+sOuw0UdLSbCPcpGNoGwpC2l2E6yQy6+h1HaXXpDt/IKH1JFHr2FQJg1l56CXuA9TIkN1bzDZEJB1lT+MnoHGEqqO6T7g4W7DUCXojKDOFKg5H0bpvqBYxsW5JlKHQISq0NPtDWlmcNjyf33OTj9h/PKQ5coSjhunKsI9CBmnJQ9nHG0kmDIOLHe2XKcsw5izZsHoYEJ3WuO9lNJ3CnRsmsz2y1Vz6HsGVhHdrgqTD1goQpKrmdLhhtR2jdYv6TPIQ9wmURUwteRcileNpvaHZxPTeypGx54ffPYeex886zocbdsuMs/6G23xIZxQWgfsiITQd+uslqWzRjUdODFefnDCMi0OwkmhY7SYwM5gyIEgMRxTosSEtLJf7EV574sywqlMSZejrhvNoj/GOu6tjhm9tuH8zpJWKsHYUZUJylqNySVOHZM4g2o5uLqBRtCGMVcu6SJgHNU0jyeeSsgzJdE25TZidbqm3EZOyQjrJF90Mrzwy7YgiQ7wSbETEabphsR/QzSyjrGR9OUDE8I6+xy0CVk804fqQh1CbiN5Jhd5CYOH+dkDe0/gQdAPHpys2VUpTh4zSgvL64Gh5XG+5bzLMTDAoW3IdMOwauiFU24R4Wh04Ja1gFLbcND2sl2RRg10FYAXqWpM/Bhk6pvMtqoC+b1mmEc1lSu+tHVURs99mh/x643EThzKOwgVMwppyGyE60Dm0kYQOVCXRxpC6jk0vJuh1tFYjQ4fae9qhIBofBJ2680TTlptXI9T+oEcQ1qPHHXZySETEC7z0hLVjOGxZ+IjJdMfyswGuinFnLc+bLcW5Q65gsxgyO93waj+j13X4ytM+dZxGO64vpzRFx6d/63/927bY/44W6LEXdFVAvkt5aHtEo5a71xP2y5SbN1PeLGfs9hmb7w2YZjntkcWcWXZFTLjtaKWgc4Ltiz4/+pWnfH/1iLXMuL9IMDcxt9854rNiSlcf0qaaGai+xZ0Y0ndzeoOS/X2GU+BOLbO3Vkze2fCmGuJWAWLQYZYhvhOHnvzcUKwybndjbmSPy+UcE0k2RGwqTfB2QZAadCHwGqRwtK2mUorkp/f0hyWvXs95J9jgB5bx+Q4k+AzwAiUtqvME0xamlm7kUcJjUjBSED8c3rYqg7Nsw/4hZiwa1jol1S1ybBiogq/2b0gnFS71VJHkoerxRowQw4ZOC/a3Gdt1ymScE6SOsox5eXtCE0tE4FkWPdpNBFaidgKzhfHba9oxtD/q8cnVKbbR9GbFASk5NYyfbDj58B5hJFVP085AP6roBh5dO4T3BAbSQYVyHpE4ZOcpzyRnP3mLaAXVMgEn6JQg6Dyj2Z6ggiRqeHe8IMphFhUsuxikQDSC75hjnJBkpSUZNnRWMUhb5EvFqLKkhWA0LfHzirAyLOlRx4pkIynrmNm7W/JI8TPv3mAHAoVlIGp6gcFvNPY25o/99I/gfUc7UdhGsrch0grC15pJmiO3ktA6WAVEpqNFkvUrBrqmN6zYb1L6SU0hNfmpYKoKykgRSYutFVYIQmlJXcUuSWiSAONixscFgbGIuMMC4eOSu8UIi0QsFGHtEcqj7CGYpG9a0qzFKQmFQt1oRCVQJzUysdw+jHEawnnFo9GazBlKH6BWknqVoG4D5FaS7xIWuz51X2K14F5nUCmKzwaY4cGKGgSW7abH5W5APKkJZhWn/S3BXmBLTVdrOqexq5i39ZJNlRIHHU+mC9oRLHY9zCoiVB0voxEvGfN6O2b7SXpQeYeeWVCwvhzCVnH5Ys6+jeiWIT1asve39N7fUbiIh+WIO9tjHhW898FrmlBTdjH51Yio6Qheawa+IQw65Niw3SU83A+ompBQOvIqRqwV9UPCvkn49MszchmhkpabxZBuHcLMgBDEl56uDdAnDeHm8L6v1IDmZUqxTDE3CftdhEkEmy46eOwfQqw9MBRkBeVnCTSCazPmoe1xVOdkcYNcBIhLhe93SOlYLwfEqeEk25FcGvSkQUmHbzQcd6jAsV/FLH1K5xSze8f13YDVJiLqG0ZHe6LzktPpGnaH/vqjZIcIHJ1QdDGk71RYrfCFYtUPGQ5LqqFCDQ3RuMZXguyuY+Arbv2BI9+XDb22Qy0lVaSQU8NklNM735MkDSuXct0MsXmAKh35D4fs9glBaJCNIzirD/fW0CMi2JUxqAOZTgoINoJwy+FUL1Rs5yEI0LVFGQ9HLd3XG2zfYzeacplwOsmZuwLVSpT02Bh42tK2Cr9QeO1x5w1dIHC3mmqvSFXL3d0I3wZ0TxtGkz2fM2H9ekjzGPpPclYPQ4IHxaOzDfbcYQvNw3bEqFch1G8vVOd3dM++nXrm4y3braR93cc4g84c5W3KdpXgS4kKG4QW7NoIZxRFpRnbAucCik0CU8t0UNPUit5RSdwpfC3ZLPrI4xY39LgUotZSbhKcNMyahk2RYnseITvSo5b2Vcx2P2DctYQXDU0GaqUwtSb8ao5dhtihpROK6KVGIGnGoDxcfTRExQKzO+xuh1HJqswwU9DKUOcRYlKirSMKOq73PUQIrQjpYoGI5UH5LjpcALYMyKho7yKYN/ibCOEkfmKYnW7prhTNRDN5d8/qyyFUEtsEFJnmUbrlphjx5NGChRqyWfSI8TSTjv2qT50HBCc1qW0pdIAYwFv9G77IZ/Sz6nCK4Txq0FLvItKkoRmAeQhxJx4/ctiriPuZRrdw9PaCNHRcqDUff/IUJyHeelTsCPcwVgXFU0klErobjbgPiLcG+8jSlgGB7Vh8OkYY6B0VmIcQfW7xX0bMLxoYexLR8vpbJwx6Nf/h4Zxo2GEmMB0UbK76PBQ9ZNpRxgHmXpFuJBfHO54/f+DXPj2jloqTvcQbOH3niocfzHh6tONe9slWhifPl/zy588IjhrsVrFdJpRVyPs/8ZIX//4R/+x773FkOganNSsiyn3GLNizUkPSLyI2vRRtHaqD1SQmvlbEgw7fOFgKuhDWD/0DgrVOuE5CfCVQDrQ92L50ZOle9ODC0u/VnDxd8DKf8dZwzcf3cwa0NPuAduboG0v7niX4OOTRz95w+8mcYqkxUjIVDTf1CKMEhgDft8SdRxnYiZB4b7GRpJOCSFh2LsEOwaUS1xxgR9FKEu6g0AGqFPhtBOcNLEL00wrZCfResG8CRkENuaU2CaqFpgeytSSNRWYdZan54n5GtBfcTnoUThMnhmRjiaY1PtdkscGNFGufkI0N4+Md1JJAWtTQ0PyohwwsbaSx2hNLha48uzLBdgFoTzw2XLVDShuSNwF6J4lPDol6ywgmScF2kbFte3SXGnvkyEYlT9MtmyJBN7DSIb6T7OOAqx+ckryB4N2K3RiSDoKeOeQx+JbFlxN04AiaDgYdDDvMUiOmHYOupo0jVGwwS43xErFXBL7Djhz6voPUc57t2C5T9EDy4eCehzTgk+KY9F4TzAwZHfFZwbpKcUVANmlAdazbhMdqze2LEU2giTrD096G2aiC3oD1OmXgG1qp0SuYypLLYMQqDagcpDfgtecRW364O2L2aE9+1cOftMiuQxnJfpEhckV2UnAZDUneSIgOeRZmHTJ+a4c0numkIU8Sssrw0A8pbvvItaJ53PFY5gx+74Zvf/Kc+EYgvaaZeXwe0JuW7LKIUVvRhJLKBKRLxyzbsckzWh0gvCfcQSMVLoF8qkmNoWkkrVAktaNKNE9P77i76VENBTo1qAIG8xyMoDk2bPqa5/WGh4/HkFjqRwoXSxoZIFoFY4eQUH7cR04d+wvovhwRCovIPB2ezz+ak5kOaRQ2CKj3IeKs/G1dL/+LKvtf/MVfRAjBN7/5zR/Pee/563/9r3N2dkaSJPz+3//7+eEPf/gbntc0DX/hL/wFZrMZWZbxx//4H+fy8vK3/Poj1xBUnt7WEy2h3xpWn4zx1xH7kcJedISDFgKPeBWhIkumWoLTQ09pPNvRVw3mMmB0tqNYpGRtR1sFmOmhemSr0a0nHdZobRFesPMJLYr6JkV1nroJ0MJitWD4ZE9YeeLaYQcOqwTtJxl2o5HSIzuBSzzdyCNCi2sU+yQmnjaITzWm0KSV5Xy2QyYWlQsmtiZ+odh91Kf2inIM+nXAbpdxNlsxON8zebKhkAG5jbDz7hB0stHYqxjZgYsc1ipGvqZbZGzvBmzXGaWPCEODHBmktnz+MGfbJFx+fMR2mRFrw/yuZvIDQzJokNqTOoMaG2LT0VMNXyyOGPiWjzYnrJqMaCUOlb0TdNuQkazoLjwXyZ6qCQ4c6s0h4vf6eydcfnrM5dUMH1i+cn6HExIlLPkm5j6I2JCQXUF/WKL3nu3jEHXWMHhry+95/hLlPXOxwdaKp1+54YOTK3RieZnFLE3Cxoe0jyx4x8++/ZIykIhccmz3/K7zL3l7es/zdY5bKdSTms1AcH8asLAZ/VHLV82ad57d83k45D++eUo2rbgKY9ozxxcM+eX7x7jMcR7uccLjUk+yadl/nFD2JT9zdk+7l8TFodc9swdWvx06HpKMaCno9WqOny/ovRY8PVkRGE/wtMR0iuzhsKqrvkEmFoCuD84cCHm21tRVSPp2gehAdB0ftUdMVcHnmynRAmziEcKiLRQ+JNjB2e+6YZdniDNDqDuqued6PaKVhxuzbAU0EnaK/LqHHHT04xpjJY+GS4ouoj1yaG0JrhSif8hKqM48uQh53nsgVBZjNc1Dgo88J/0t3WWM2CguTpeIUcezR3dkz/dcf/cEbSCNGwbTgsfVnihpcX3H//znfoSzindP7rC3EaO0oL1K8bEjG9aEOOLIsFr1Gd15dg897pYjqhc9SCz66yVSHFpz+13CosmQu4PldpiUhzZHGdHsQ3q3BxFWHio2e01WGxbVAAHo1tM/qphfrPna5IE36wG3IsG+V3Ey2ZL3JHoNk7MNwVslbXUQ0GYPnm2d0Jw6ah8QdA4XCtouonMaJwTZoxyhPMsuQ99quteHkwLRSeIl+FcxqoNaZeyqjM0wphUxg3f2rN4kvFgcHUBeVmCvYvLblGYd4V4kuKGkfR1gGk3cduyFwkWeZHtIptxnIW+qDPkqpHclGKiGoaipiphPVkc45em/cagHhVUHlvwiijkebGnuQ8TYkBaO/ed9jJO0Y8jOcxqnCa3F9MArqI88bm75bDvBDjy3YYbPPHcPY4IvAkoZYFLJoHJoOr73w6eoW4VJJXSC08mO6aCgvY8II8OekGFaIreK6AbWo4j8VOBSj02gOod27OjeaQiSllYJtHcEDxqXOZS3PJgeqyphv01R94rgvYLVvkeJZnDXMVhZPuy29I4adiLGbwLSO8E7+zWxb9E7T3AlGc1KLo5WyKVGbBVx4/FaMDjfI0JHJTVWS6wS+L5HL357a+3/rxf7b33rW/zDf/gP+fDDD3/D/N/+23+bv/N3/g5//+//fb71rW9xcnLCH/yDf5D9fv/ja775zW/yT//pP+WXfumX+Lf/9t+S5zl/7I/9May1v6W/4c1yyiszYh2EyNOWyhx6xs3EH6rC4z0D33I02tGbFESBITot6WLYb3uojWIwyTn5vQ+M1eGmst8kiEvNJC5wVhJ1jsG4JN8nTAY5vUlJVYTIyJElNYGu8NeSNpWHHfCbCTsZIpRD3yoGaYkatyS6JYsalHeEkwadtEwXOe1Jh6sl1kvM25aRqogCg609/W3LeFhw9GjHq2iEf7tFdo5MmkMikzUsl0Pe92uqLxPO379HnTZEo4a012DfaXE9Sz0SJIGh7Qm+kBN2U0WRatp1RKcFdSQxy5APs3uCrEM9KFp36L0djXc8e7JmcxqxfDVEBB4i2G1T5jcWZyWUUN6FxC8Uk7AE7RlOc4LK40vFdtsnv09ZrjJ0zyAFqFIgW8eIguOgIMihNCGdF6hBS/Rujjt2qFvF42qHKqHdR5TPAe35ydk1gTnwssVRx1oN2NYJP1he8NH3HpMlNVHlUThsGdBtPbvH8Nn9EceTglHdcnc1YW0z6kWPV9WA9y4eWN8MqD6Oefh0yH948YhPFzO+DHtcjjSDsuUk3HP9Yki1T5DXmrffuqe/rRkOC5bbjFXVI2w9P/nODU2UQt/xvcWc5LRgsU7wTtAFkuO7gvALhRq1yOOaeh/yUPfxjwwfbWZc5QPu6z5dI+id50gBfmzxocc5gRx38LhFVyCER5YCfxfwXrIkDC2hc+hO0U8bgsxRNRG/++kLZmGO7yTxUcWXr04oU4HxgnZuyQYV6qImSlrCxKD6Bj+y1N2BheBrxV3RR9WCh7pHXBueTheEjScOu4O1VCgmV4aRrbCXMaZTRKHBR46zsyUPH4+xrWQxDbnfDsmOSn74/Wd0eYA8aumuQ9r7hKTrkHiklMTS8O8+eZfMGuo3MaUMuBV93NSiG7jfDFBRR7LxhNOG+dPFYSOxVYegknlH4Byj3BycLaUk7iztGJwSHPV2GBNgG8Vw1dE98oSBpUHiAk3oPMO7PW81W4o3KXYXUq9iHh765A8Z9V3K9SdH3LyZwE6hRh2118hnDaO39sxMdVChtxIpPd1W088KlPQMg5zI2AOtcK0QhUTElkor9ipEdAK9tlgBbSJorUZYT3AjaX7YJxi0mIVk/QFE44a5KRif7Hn67i1dT7DM+0gHMnCodyyVD7nWCa+KGWJsOXm/xhVg7kIWbUJ4UdN+rWVpe2xvBsh5S3haUwcHdHAQdLi+hdBjvMYbAe+3nD1a0a1CukQwDGvGlJycrJh3FaoUJMYwOtkRGscfOf6YM1PQrUOS4tB378aO/rBEjFsGZYVpFDIWBEmDl4L0piN4VrBepyzLmN93fMnwwfONi1d0ixhxbCh/wtNtA8gE3RCagUA66IUtFzcl6kYRzRqm0z1P378k2AqS1vFf15c8fVgxzOuD9upNghl5hrOC/ZHm/HjHfS/gpMw5DnY8EWuSccVn1RG1DWDisLPDqeUkrJhcGjySXRfhOGiOup6AnqMZCuLjmnYC9H4HqPHzPOenfuqn+Af/4B/wN//m3+Qb3/gGf+/v/T2895ydnfHNb36Tv/pX/ypwqOKPj4/5W3/rb/Fn/+yfZbvdMp/P+cf/+B/zJ//knwTg+vqaR48e8c/+2T/jD//hP/w/+Pr/vUDv3V/83xDPJScqZ9clFJ/+uvoxFQel1ryjH5V0RqG1w510tJ+n9N7bsbga0zct8/mWXRvTfn9A/50ttdYsboZE3jJNczarDBNKjD9UZs1UMBwUBJXAaUcbSsxdTDX1qI1CbSTitGYW7XmjxwS3BxjM29NbXvgJ3TrgPN1QVCnHsx3sFEdZwb8L53yl3PFGJBy5nF0YU8UBvhVwHREmDaZTmFWE1550ViFKiZaWqgsYz/Y8RBnB3uOkwHAgz3WVQow7orRlllRs7zQ71yfLatobjXMB46ykNyh57/k9//aT55Q2Qj2uMJtDf1MLT1lE1CkICaIGKWFAiw2hu41IxwWBlVRCUpoQtRWEl5L6DJ58eM+XywkX4YbdQLPa9REbhUw7AmdJpaG0IaIW2LOOftNhI0+Zx2TXnmogwQp8qYiOao7qgue9Dd/xx+yrhEor0rJDOKi1Pnz2gGo9Xgj05BCDOnh7w+1yRNh6ZtkOsdLsdESy8jx6sua7r05xkSBxHd/46hWLfczeJRQZ/C+f/4gvPxnRDgTbZUouQj5ZznkeLtnqkEoEzCYrFt894mffueK+zVh3Ea+CPj1dIzyMeyWrJkWlFvsQ0FUBru+QNwG9pCK4qHmoeuhXAeFRDaXE+18XLiYQmcPNQhUecXaAOOmNhEbiMo/fadKzgnFQIqXnZjEkfZZjrhKcd8xP9thOEYQdN12f6I2inArsNsAODnY60bcE1gMH+2XmDW0VEnaOpN/gtCdUHW4T8BAmZEuYPl+z2PQxhab3eI/5PGN+vOLeDRj2cqKVZhmHhCc19vMY24N0WOES6NYB7TpiPNshdppbmXLeNMikZXHTpzmHE7emHIX88eNP+aXvfYOtigjSQz5Er+gwpSb3EdmgJolazCIi05ZlrNEbRVJ7srRhdLLnLk/pbkKy9wqazzLkI8N9k6JXAmc0NnNQSJJeSxVJTKOZxTnNFymzTcv1KOX4fMNrOyS989TnHqE9vbIjsy3L2xH9n1xg8oh5mLOtUnY3fXwp8UNLpyUklllcUC1j8l2CTB3SHAhzt5dTotzjz1saFHFnKMMA0Sj0RmBH7qDLeZBUxzB4vDuk9UlQWYf1AmEdJ++uwEm41KTTku+vL/BeEDhHX9cHh8zrEGaOrtSERzVB6VEvBWHmyR+Dv9PYRuMliJkhXDtMJBFDh38ZU44Fg7DGzBxZ7iibkP6k4KS/4QeLC2QD/X2H3AjqRCMsaOEOIKRCE2YdatZQvehR1yFB1pLMKvxHMV0qYanYvwNyJ9CloDes0MIyfLZjoA2L25Tn0wUvNqdsCQBHXBmKLMRbib3RSGXpPa6o72PK+wx10RCFhr2N0C80z76yYH0XUH+ZEP9EzfphQOgtHBn6ec3tLCO4VKiLmmwhcF7g8XSRpHaSXtTQViF1oxneGXYfSvxaE288qvUHjcVQEfQ7ImXY1SlSOuLA0BQN3/s//G//xy3Q+/N//s/zR//oH+UP/IE/8BvmX7x4we3tLX/oD/2hH89FUcTv+32/j1/+5V8G4Fd/9VcxxvyGa87Ozvjggw9+fM1/OpqmYbfb/YYHQJy0RK1nWfYoblPcxGLHnuE8x8qDlaXcJWSqZTTNkUuFFYraBByfrJmdbQiyllYq7POW0+MlD+s+dmQ5nqyJhjXVQGKlACtoeoJBUDEb7Xj0bMH8Yn2ws2WOo/4OUQiG/T2lCLkqDqQmfdQSZi1XboSvJSJ0XBUTiiri83LM6O01u9QRvNZcfzrmrMspipAw6XiWrlD+8MXXYYeKLElUk8Yt63WfZRIxebam6iseXI/6LkY+KEamoW4i2kAiRgdS2GlW4I2nEQlpUlMQQKRo+4LdPmHdC/nX336b7k2IUx7/vRQhDl/4wgdEgWGclLhSQq5Jk4ZQGvImxEpPPGx49lNXlCY4LFZrgT31BFheff+I9Nry0I9hr8iWjkh0jGWNTByNUIR7iE5qwh+FrE3K//TRFbq00G9RoSOZVght6dua19WU/+7Lp6zeDNFLgawOoq5mCrbWyEHHoF8hnzY4f3ApBI9KChFA6phebOhiWI9DTr5+TzOHmxcHx8WjD+959vwO8cazuB7ihUXbjv/bt98l1yEvryfclgMubYZLPW/UgFFc4C41N82E0df2XKuQtx4vOAu3pF8q4gK6WHDT9dGRZewqggb8UUcvq4jf37GZaIpdjGgE9RS6UsPIIp7XtMcO1QoaFPJeorWhv7aoBo4e7RilFRdiTX++p1WS/T5l38UYoWCj6CzYUFF9kbF83WctDmE05THouCPcH0KJrBKIqxBuFakypFuIVoIoMjA3lJGimziaq5Tlvs80ywmPKuROcDLZ0JMtWQmJ73h9c4QdOZbbATd3fXpry/K7M/YqYtfGrJcZ3UchZhFRi4Dtw4D7vI+eGNrzhnBSc/6NOyobcHM5p7qL+L+/eA+30mhl6bcttlHsq5TRSU4St/R8S7mNWdUZL9cj2oeYZ70VvVFFEzk+++wIepblWUBqO04fbbhd9hENyEFHaQOMkySxYbnqo24D3EZTvOzz3gfXnP/kEjHvkJ9FpFtHO/UM0wL3JoRBRx0JyiNPu0jQynHlBpQmwo4d3VNDVDpCbUhmFXV92BBHa6CRiM6zqxJU4zGRgLUi2An8PkRVgqC2EFrCvSdIDJMPF3DaUBWadngI6nLO0yVQDAPyPKbdRqyihBd3R0SbgyPjIlkhJ4ag8XTisDEejQowAvdZxDhqWIwjRCkwU2hPLW3muejtSe8trQ+QOwUTg5QeX0nEK4VWFje1tHnIl6/OOFlURMbx37z9Q+wzRzFUTGdbjFVUdXg4yq4kNg/o90viQYW4h/1dn/08IA8DykcgPXQ9EE8ank62nL69QVtBLiR5FvCvPn+fL7djqjZgIivqgSZfZrRGox4ZTr6yptsFiEagkg5/FSIuNcHu4Jx62KaYRNNMQpbLIaKBrtAE9xrbxahc4keO+CNNLkO6XFMvYsxVjPCSRh10FpO0IPldG8Lak0hDh0RGFmcU6dqRvLaUq4hAWi6eP+COO+Twt3aS/V86fstNg1/6pV/i29/+Nt/61rd+0+9ub28BOD4+/g3zx8fHvHr16sfXhGHIeDz+Tdf898//T8cv/uIv8jf+xt/4TfPRBmxfI281Z8835FXM0sXUPkUdt6igozcs2Hw6ZF8n6GlLOK1QL0Ky5wXjfc2yHHB0tuNB9fm1j5+T1hZbCdaiR5SDijmIfO4Tgi3YY8eyzni9jVHC0TYBLnMsd31GwxJCz0RU2EpiZ+LAWd4KqkwTlJCFBoOhjiPG3wJrepQjRZ0q/EnDF/fHmAyelQ9opwnw7F5llAjUuOUoq5jYmnIR4pzg8+6c4BbME0XX99R1gGotYWYO8XANHKkcs1Pcmh5nx2tuNkOiLYzfX7JdZTRtwrZNSZ/WxOcNehliXMDFasduHFInmjoQyEXEydGGRTemuEkRs5oLU7E8lWxu++QPPZJraAJoMk+jA+y8IxIdnbCMq5b7/fDgNhg6dm/6DJOC9DTnKppwEZQUTxz71Zh/88uP2Y9jlKixuULWYL3mqh4fgl7GAZGz1IXCA9G8pvKKYVBSfx6RH0nElQYt8E7irmIMkjjr2L4aYlxwIAouEsJew12SIb2jrCO2VUac7Jm8vWTnI0Ztwzjr+NfrCy4edrz15J4mjHlTee6LjHyX8fzDe9Yy5lGVU8893//yiIdtyulbd4RTx3Y7oecN6d5zKSeMjkp8keJjgdMCjCQeNvitYKhqVoOEuJXYmwTVCERkkX0H1wGzZ1uKmx6x7FhdZkSZYX8d0P+gxLwaUJYxUVoS7R3mscD1HHFtyVcxI10j955NJIlbj3OHYCQ1bokW8cGf3nlMFZDKFuskNnWYbcSAhsqGRJOaeVCzvstIZMt9PSQeFMhhxyoOMHVyOH5eKbqtopoqFiaF1BJ2He9+9ZY3+xHlLsU1irTtiDAULmSwh105QMuC+82QyBriWUt9othshhw9KdBSYDchg3GB0vJAwjuqWb3uEyeGIOqYiJbWaT4tJ5gyYDguaKVmf9tn1i95sRsS7yG+FOAlu5OQVDc0+xDbN6jMoPs1SaEBx0efnCF6HZ1R9D9ccleMiW416Z1HbEv0k461TgizljhqKF6HxJ8qqp8TxIMaex1C6nl0vOR1O6aNJFnqOVF7HkyMC0Get8i7CFFK2rc9FJLgTiIAGRmOeytyHbMzKZvPR0RDRzx0mLgm6hm6+xg5rRm0jk2ecdyUFCIkHdcI5+mGDpRH3stD++y9FbbTjI+23D2MWYsJ7RACZTG7CFE4nl3kfCGGPEQh6TdaouuY5/0d22PHQ5cRPAhym+IbQbqwFDNNt4lxhSYfwr/87nt8cL7kO82ch1GC7llm0z27KqFT0B/VlE2EedDUjw8UvXTnsSMDucI1EisEtQv5td6M02DF/qGHqaGuQpwWDHRNswu4rUZMoppm0MKlohlFPGwnFGWEbyU28STeYyYePWggVZjbCJ9YpHd0kUJVAuGhdQIfQ/AgEAOPfGaIHgRto7CRPKSM7qDWEeGNY6sV9SAjasA4hX7cIaKGNlYMBgUbl3Ha2/Pa9Li6GzKYVvTnO77zW12A/wvGb6myf/PmDX/xL/5F/sk/+SfEcfyfvU4I8Rt+9t7/prn/dPx/uuav/bW/xna7/fHjzZs3h+ecdqRHBWZmWW4zmtRzNlrT3xyOyZyH0Hf4SYcadfRHFU/1hsx3sIv4zu0z3tgRohE8Uyt8z6EGHekChBWo0wOBz64CFBZ52tLaAAEMbcPUFcgOxFojCsVbb19RfBkjlKeREZ0TDMIKogPdyonDrpFAHqrRC8sb0+O2zBid7egNS56c3/ETZ68oNhGfv5lhNOiLCun8QbSjHa9PYtTU4lNPfFoQvF+hCkm6cfTDkrWIsVqgXwkC4VgRcVkOCSct902f1gV0Ayhf9xCTFvFuyTAs2TUJexdRhQr3bsuV7tF4hVpJhkGFHnVsF33oWeJpA3eaujvsKWJteev5PcQgrMUJiRaW3mfwlcED0bgjDDsu3r/j/Ov3XAR79LBl2Yu5K8YMt5Yvt3MWcUoYG6wJGdUtQQ5927BOI0wgUHuJNJJo63Hak9xAdAPNZUq3Cym2Ce1IIjhUpP6sxRjFvqcoIk1hYqpI4y4M46ikWKcE1vO159eMZwW2hXRveCCk/SLG7RQi8Hz35TlT1zL+oOLbxTmX6z7sO07dmupc8fnrOavXAz775IzLz+ako464Vgx7DSI2TMuGNhLcPww56u/QJaRdQ92GBKWH2KLGDf4ywMSCRBx86JNBQTQwnB4vcUIwuthxfz2mGkD4AJOnG8qBYPM0YSES4mclst9BLdHDjurzPvazBCMV4axlXaQEV54jvSd3IcnKkw0biCA8qpHHNfFRQ7fXVKNf1wm8ibEB7JsEs0zYXQ8p8whRSGyjiYUlqB2N0dhlCGeG8EmF22nGj7acTdcEvY50XFPaiNvPZohVgBuAmXq+EtzxR97/nP6iI98l+K1kv86wheJrpw9kJxUqF2T3UF+lqEFHk0GxSDk+XqGuFF2tiHTH7tf5/PG85fTxBtlKpBGUSjGbbpCFoLzMCG81spK0VUR7LvBCkMQtUa9B9zs6rxi3HSc+hxHMshyfaxLd8vJySnBW8ZMfXjIcGzoVUroQoxTZlaNdh+iJR40MSnjsfYjeG1oL96sR07IiaTwytdxMInirwY8tc99g+qClRfVbMlXTjj1d4nG1Zql7lKnGhnD8tXtcDGnVMTk7WAjn4y31yx5lEXOWrbnfD4mDjnobkXziON2V5CYkzjoGb20xXyb0iw79EJHfZIj3a9aDkPeCB956+5afefs1zx4viCND/cMezSoimDX8yI1ZfjbCLxQ7FxPqDnMXszUpeqUISk+VaOROsVAR33l1Rq4j0lsQsuPReEs2L9gSc/tqSPtRzLNnN/RVQ3IlqB5b5LAjEwbbSsLAkGw9wZ3k/kczAuewRUAQWMKtIP5CMgpK1JcBm48yEtGQjFucFPSHOaEwKG3RwuMCT1WFTOqK0a5jOM3xtSXpNST3hwTO9LSA05afff8l8uQAler2mhaJGx0AZfVE4EPP6GhHfSoQM0OARWQd/ajm3XTFE1Egz1rWRYa40yx2GfErgQwE+8uI+ir6Lay+/+Xjt1TZ/+qv/ir39/f89E//9I/nrLX863/9r/n7f//v88knnwCH6v309PTH19zf3/+42j85OaFtW9br9W+o7u/v7/k9v+f3/L993SiKiKLf/MbUmwhWEd4LShOhrYPqkBCmAokqBGIZcD5fc72YsLwe0m0sVnhuX83ovER5z4s3FzDtsPsAu3f4kcVEiu57Q9Sph1JhY0/bhUSXjvisoBvD4m6AlA43sfQqy7fvnxC/1zFVFQPleb3ss7Exbmgh86iNBOuwqxCpoDAB9UOMyz3ZTqBPC24WU5Kq5SuDW753/5SdGSKdg57FVIpuIjDfDfCZwgnBIDfs6wQXAnvBtukhpCBJKrqz6BB7ey5QX8jDBsQrZC5ILwqk9phf7TP/6oZ2kZFaaPzBu/z29I5OBrx8eYJSDj8VNFLREqC2HnNkqY4FqtT83OwV/yZ6SvPmiFZJ+m/tOZYVN0nKJu7z2ZsZdgKj7Y5kEVK4gCLViIFhcm+wY8f/5Oeu+exuSLuNMVPPNUPCtacpNRwYGejWI7XDBRJdK/S1REUN4SagnoBaKoTwCAvOC/rjnG7dw4wtYhEQ5xJSi8XhFpq7fAKdpOhZNmFEV2lGsiJXARejNb244ztfXLD/tR7JT5b8XLugHBr6bxxnT/d8NBxjioD3zy75kT6iX9Z4H/JgU3ZNyD5T/PzZmv/L977GXO3pXvXIsobmLqbaxbSRJModcnZACdcv++hRg5t16AUEnedu1UdvBffPevgO1jLm5GRL0USUM0lzNcTsY2zi0Z3nPosJWo0pA8TY4EcdUeHo7kPYOMKJYV0lmMoR92oG14bLeUavMHTuwGDvnCCdVnRGYhTYC0O8lnSVoj8qUSNLXsYHZXGgKTpJ9L2IZiTRo5ZHjxYEseHTfI7LUzJZM3hrg98F+DtNdFwT3gWEymG85vPqmPx1Qpso0qjGjjxFHhCkDW/iAbtVjLaOi8f3bG2C+ShmfpwTTAyLus/o6Y7tNqb8oscga+nmAptHXGcx7C0yE3gp2LYJo/e2DAYln312iiw08Ve2hNrRvJ7g55K8rxgrw//i6cf84P6UJX3irmNPRJNIZl1HfmQJf6j4lbvHNM89vYlBWosMPG89eaDaZdB43FuC+bu3fPEfT9meB/wkt/zQH2N6kr6tuX81IpYNLBT+WceLz+bEWcvg59eIVxp34Ql3LbvLhGYW0NUR47Zk/uyBN7cT2Avyp5LgPkPXAj23jPolvoXFJCWe1ogdKDr2wwTeeHwGMvJ8+fIRaVjxcD/F7xRq3iK2hwjW16MR3SeSq7uA9jG0JuDovTVhYHi5mKFLSRMJhtc7mEvEyGLKAB111E4hrUTJFhU74rRmX8ZMz3fELyTPzhf825unZL2arz26Jr/pca8zqs0hInrvUtyNxBQR1sLoeE+SdeyfCNp1BKuA9S6jp1rahwBdGYqeZhhU+NFBQ+VDTTTqSG8lQiraRBApw7gxzE9W3G2GbF71yBJD04Uk79fs7/qkgxyfebovE2zs+EL26I8LkpUnSi2Vk5QXIeo24MEkRM5Qv85I+h3ZrKT7JKEmoKsEH/spYeexhSLQFm89lZDoSCLfxHjpCcaL38ry+188fkuV/c///M/z/e9/n+985zs/fvzMz/wMf/pP/2m+853v8Pz5c05OTvgX/+Jf/Pg5bdvyr/7Vv/rxQv7TP/3TBEHwG665ubnhBz/4wX92sf/PjdG6PYTEZB4rJdYobs0AaxXtNqR9HXN7N2b5wwldc7Aw9YIamwd0VtHTDcFGIU5bgsSQKEMzh6ovMHVA+ZbHHVu84uDtjVp8p1hVPbY2xjpJfFITT2vsyGJ/fT9yX2Usw4CsVyLHhnDUIveK0DhcJfGBR3tLN1CAZ3a0I5i2DK49R+M9u2HID7pTnBLQgfcCdS0J7gTrywHVWUQ5kbiZQ4aOZhuha8/JfMcoLEmjhjCC/9kHX2C6gDbXNBcWuVCENxJZSepFwqbMUIOW1WVGKRXFQKK8g0HHXdvjyg3wlaM9c+yLBJ0LorhFTgxFF/Ckt0MPDjYeuZK0dYAaWE76O168nFJ+1ONZsWPkG945umb9uMcXoz5NoGjuYi66GhPDNoj4Fz94h7uqz2U14HrfZ+QbgmFHPG5QI0O8AJUL5O4A7iGzIBzmHJqRQjUCJNjUI4RHLST1XYy/Dwk6T3pSQmIRe4UwknCtoBPo0jFraqrblLbTNEKTTkr2+4irKuP9D14hHjnkVvNFkvHFL59QqpBFFFC0EbSKH76Ys2oitjbkrk0Ihoa5aIleO/6vn7yLWlvaVjF7vOL56Q1loOlOLKJn6Y48JQFvPbohOK6IT1q624TKhKBhXDWIQlJdp7hSk7iOtjgczc7mOceDDcnuoAOQa8m8qRhOCy7ONmS+tCJM+AABAABJREFUpR819GLDaFIQ9e0hTvwbO8omJPhMw4nj7dN7JqdbWqGomwBjNfUYnp7cYS5jZAvT4y0mEuRtxOJuSDRsSb0hMwdLZvxWiUs8yaSm3Mbcr4b4XFFqTXAP+y/7lHmE73n2Nz22lwkrFR+sepliE0T4Dyv00HBEyUldcjFbs1caEwqKMuHlcs7F8QIlHZt9j+v1mPvtAOkdhQs5+b03zJ8ucaViXUWEWYtpI1TWIgrJI7Wh/WGfzY9GDHYWQgG5ZnE/woceo2E8ztksMq5fz2gjwewrK9q7hKoNUall6SPmoqLsUrqh4NRVqAvD1vZQ2vFmOefy5YT+ac0LNeCjb5+y3KWky45X8ZhNEqCxyMozkBWE0J5Dog3HT5fYzHO/77O6GmFepNRtAHNP5hq6ucUNPKuiT0MAITRvUlzseZKs6esO/xBQfdHDLiP2kSbuN8ze3qHeqzE/bWjP4bZL6aqA22xIQUJVRchLhVhJvJUQewpi8qeCcF7z5PE9wTXcfzZFF4fEzXRYsR0N8DNHuU5wEtxWEy3ABZ7gRoMXFA8p75yuGPiOqqf4Mj8maMG9irn54Yzr9RBZQXOZ0HyRMh3sGV11pBgGYc3bXY5oBKO0gkoQLDxxr6HbB5B6XHpoCZqhI3hcMxjlzMMSuRektNyKPvFawGcRtZR89nBCZSPKOGS7S1klEYvFCPUqYLsdkN/1Dq3XzwJef+ec1ZdjciIW+wG7aUy1iWmHnlG/REw7OGoI4wZRCOJ3c6Jxg+s5gtxDc2BheAUn5yvGSYEXIIxDNrC3/yOu7Pv9Ph988MFvmMuyjOl0+uP5b37zm/zCL/wC77zzDu+88w6/8Au/QJqm/Kk/9acAGA6H/Jk/82f4S3/pLzGdTplMJvzlv/yX+frXv/6bBH//Q2N3KvGrCO0dbtgylg1t66gGgsoEMAJRaXIp8SvY9SDpV5jMEuxa3K3Gx54WRTquSEc5231GmceIQhCWArvVoB3+OjwcpccdXaMwK8nF2ZJtkVELRdJaAt8hRx0mD0lsSxFoZtc1eR0QyJZOKtoJpCuH04Ko9vieY7dOiReW20cB7ecx3imKIIRAEOzBP2vRUuARDNKG9nWAPRPIoGPZ9kj7Be++s+DmhxOq8SHdqq4DfnB3Qndhqd3hSI2exejDsft8nLNeZ0xGObYJWA8iwg4G7xaUu5DtxxOYGHQokRtH2Fqkk4TzguV2SBDBK9FHdrB+/ZRe0uA7xa6N+X51TnzicKXgRTTAe7i+zoi94b30jpfdnN2xZL8dk1UOXytaDQGKt9++4pPPHlGOLN5IosbT6YO4xkqBxNO1Gi8FMvK4TUQ3sQRrgdPQFzWlidE5bOcRduDRyxDhDiAVF1iCnmVYVyxFhmshJzgohVPD2WDF5WqC7ndEtePlek487hhYw6ubMb2LmsYcGAYnosbGinlac/dijHynhrlB1I527zh9b8fLtkc11dSBo8oD1mZAqlv2RXKwlkWH4JnLF0fEuSc8atCtpycNC1LsXGEbYGaJX0oGfUM5c+TLHhNXEYUN+qslWa/k/N7xxvfhMsImHbuTgA+SBV/uprydbSkqy+VmQP4vZ2TPWookZvOxQlUR+1QTGk8xcuA98UvN/fGIdwZrrrcxG9cjCgxDVXFvBpQve8zONjxsBwwHe7TxhHvLVBZUhISDlrcWFWfZmu9PLxgMS4LAsolS1ss+j7+2oFGaJtYkt55SC4a2on2VsYgiuk3I08c5Z24Pe81qHFHkCR+9vkDpQw5EKCz1NuY6nyE6T5pIPq0mHE1z7E3I7iYl/uoev9PMkz2fuylehrgu4qy3YbdO6IYtfutJz3OqmwR+kDDc1NRfh/06ob2VtEIyGtf4lSawNW/qPoN39uRVn+LzgCeTS7rmjNNwx0s3ZXa65zvXJ2RhA5mn6ySqlMjOMv4RuG9oPnh0w/WkzyebKT3XkNkOKsWzYEOZCm5PA1zakd8PAQgf5WQcwmtOsj27pqFextRnHlsplm3Ci2+d0EaS957c82kzQSuLVJa7V1PMWYesNWYV0psW5PRgoxHC4YcdvJG4kSTKO45Eie5p9j3Yb2O80zSbGHvScVy3DOYVX24m2MTDZYxUAlELZA39WUlXZ3zt7TvksCI493y0nRFsPfZ1grnoOJusaDY9ip3GpQKRgKkU4bBle5vhYoGLWyZhw2IecLvqkd2qw3flbcO4NFwN9aGNOWkZiYrNLqNpFLEynOUti7Hl7nKILBVxr2Y5ULTrjOC0ZKr2bMMZLu047RXUdwl1EuP7DqIOvdDIQhA8HISRjQsx6xCXe4STWBOD8NQnHuEs/l5QC0E0rxlc7LHrHgkdqmdYXY54Oljwcj2nNykO/JZO4SXw/38B/m8Y/z939f+Vv/JXqKqKP/fn/hzr9Zqf+7mf45//839Ov///Cu/9u3/376K15k/8iT9BVVX8/M//PP/oH/0jlPotRv7tNFJ4RGIxXUitA8ogIKo6ZGzQ2mKlph/W6GFLvknZtAmqFIhcIVKLGwlspXGtZKlSuj68nd1xbSco6SkeEoSEdu6xkSdcKkwjiBaOetFDvGcIGsn8aEP9JsUJSB/tDrQ95ykeS5Irhw4NS2K81NQXIDYKr+D8bM3qizG7tz36LkTVIPodXRng5h1agLqVdG0AHoqJR57AICupPu9hv1IT91p+7fIClXiiowr1SoKz7PvgrgOyHdQTkNeatLXUc0kyNbSq4a4cc/J4RVxJelXHukx4LDeIi4JXyyn+tGOYFNi7BHvWse1FqMqinKCXljx1Fd97mBFnhm170CsEsaHsIgaP9sQ3nlWUkmhLf1pQFhFFHR1sip2lf7GnzAdIJ0kWnleLcxha8kQR9zuYtnSLDFGB2oM59cjCQd+i945GabJbaPseAknuI+LC4wN56P/ND4l5jdFEymEGINqATREgzw3kIU16SBYMNoLX6ZhRWGMlLMmYrQw/fXTJy+2Q6yzlJN2zvBrwRTXh6MkGead48AP6tqJ4nfAoLKneqnCF5J4YFThCZXgc77n9Ykj47p6NiJGqRn8UHsAjsSCKJDkpqeiodyFmLpjd1Sxueqhph9sogn7DdgpJauBe83A54UYL3IVhf59QbjtCbeAs505E9KOa2zrG5oLvlVPCNxrVioPE+fOA4d5RPZb4ZUpXeh6pHfs8ZtTf86Wa8PV6y1WUEgrDfj3g+bNritsMkwrkrOVa9IiPGmTgKEyITzx3rk+9iXHeMT274vOHIwZZRfUqYesj2qnncbxGhZbzoIDY8vmLxwQKds2A6Kilu0sIHjVEzrI1KavNCBJDOGnpRxViAH0JmzbheLSmVBGFjfnk7oiz6Yr7xYjeoKB37bFlzMjWtPOAuo3ojWryPOFlOyfaCoo8QI8N73crPtsfY0qNHFsq7bhY7Bh/mLOv94yOS35kT6g/jxDLAP+7Qd1qxFnNdXFEJTU7Kzl+tKC+zQgXh1Oo+vdZeq5iawKaKmZ01uDfBPzzu/dQ2hI8qljvE5JdSV4kyLc66oeQwaRgd5MhvKeXd+w3McII1N6RjxLCpKU9Fxwd7UmXHVubYK3AW8GVyNA7RxvCQvWZX2yoNzEqt4x6G+6rEaaTcNaSRjVuqyg/9PjPI6JHFXrU0C4sxiX0PrMcf3VDOwu56f8/2fuPXuu2PU8Teoabdvm1/WvPe9x1ERUuk8gioSolhKqDxGegAz0a0CwJCUGLVokPAR0kEBIq0cCVEsjMiMjIuHHuvce8fvu9/PRzDkNjXYkWUna4qivl7G5pa5u11hjjP36/50npbyTyLyu++BS4LadUUqFzS7Ca/iJQaM1iuePBRMig0N7SXaccMgkX8OZqRbEacXmx5+anOaO0ouwjmkRhG83yWc2QOw59zOeQk64HlqcFtY1QiSPuHW0sMdah9fH6tJbHq8EBycm65derM/zUk1907DPNvYoZ2wadBfLnNU+rjPQx0EtDbyRh7HCJJb7V+Fgf2frjgDoEzO6Yv3LnHlOBy47TWWXD7/HjEs8RUKTeG7bFki4WECz5tCeNevrGsPA1URtosw790lPXMZvNH1Zo/0ctwvnL/8l/TvdFgvGBtjfE94KXf36HTODTbopbx+jxQL+KiIKj6yNEJ5hOSvYuJcwc3EYwcujY0SaKy3jFEEf88uKa+3LK9T9c4TNPn4PtNbKSRBtxvNdXgviiwSN4dfbE9nFKFPWko47P16cMKGZ1S5HHuFIR30qi34NK+sqQn1cgoPxxTBQs6lmPWBuGWhO/qqmuc9RiQMwt9T45ynFOLSeiJjxqTlXP6rWjbRIaZXBvY6JlTx51qNixIUN/1Njz4521WstjZzZxOC0ZLhy6DySvK8KjoR4Modb8x/ojPyyX3LkMs5NcXGy5HBV89+GCrorJFg1DEaEWA8sPDauznCgbKIsU7wWZ6vlicc/dfkRhxpyYAmUsKIldR9R1gnOSYW8Q55ZR1LLvUygV6Rcl++0I0/tjWEk40mA5H+2oP4/YNDnDhUcV4ljjmzR0mwSnBKoFr0BXR4KWOB0I9xFygOGVRVUQiqNkpV8E3CQwvfMs5gceD2PwR+Ogng/ksqNFkzjLQnSsH0bsX0tOQsNl2fHvDqd82RbcphkyCrw5f+Ifby/5eraiKBMuT3Z8eFyQnjUMkSDfCtLzgk8/XmIjyYs3d+w+T7FCMY97rpMEfoiRJwPsJV2iyeQAlaRtYsyyw1UK8UVLqBWU6ljLrAX5Vc2qzZk8DGTjAU6O3fhGG57lBx5+O6U61+gngVMSIy2m9qiFY3Je0pURg5KcfbHmUOekd4GQCf57X//A/+a7X9ELQe8Mo6zFfY6JLgYap8AH6j5m/vzAoUkQvUA0kErLJK44dGNevryn6iNuvrvAzz3zeYE/aLb7Md/+8oa35ZE9IWQgeVajPhlYWoQ8SqtW2ykyHxgNFmcV5UOOvbTMTgqKTxOMcDxLdmwPYy6nJbe7HDF2uFEgfS9oE00VNOGLgf59Dh7G5yX7MufE1TyZlGei4M9PHvk/Pn5J9HTEKJ9tWgarWb+Ijmnu8+GIer2Pj/Ka5x31p5yfPX9kEyVsfj0lu2iwCSx3jluVIh4k4auBsepRY0saLNcPS+RoQO8lPvPk0+OpNOqO7vdZabnrxgyVIVYD/S5hltWUbQS9xM88YjEQ9YHhU8rZvMSPe9RO0fWGei7QeLwSvIjX3OQTXs7WfPrdBYJA/rKE72J2MqUdS968eOTP3YH/6+oZm/WIOO6Zv6x4fBohvOB8VzKMJcMJ9JOAv44Y0GRiYFOOiA5H/waXA0MMYQ1xCqOtoxwSki9LzO0REBTFPWk3IIxk9TRh+WpPhKMYK4YfM3wWeHm+4XY3YZg52k2GWQVe/cUjj7dTTB+OBwEbI4tAGcckm8Cb5YaVzUhSS6MURRvBo2EhSx7UhPii4c9fXvO7xzO6TynzV3vKbUqjDKKH7M5TqRizF9hE0F05ogeFrjgKhnoPU4EoBaPXBY3TuE5xqls+xRnxtSA+7Y647c6gpz3RP0rUX7RsRYJ8NCxNRSUN/8mrt/yrH1/TnQZGas1/9T/6L/7r3bP/r8vTGo04aF7oLeiAPum5KafsV9ERwqA6huuUeNIz5NAtA8MYmheB2Fi8k3Snx1F6pQziIEmXFpcH/uX335KJnsWfPlIpwxAUajQwTWv8yx617Mlkj32M6Jzm4f0pZiVYHybcf1zitxEjO9Bj+Nloza++/kz0dUU7E4AnWjv0XpDvPHLi6BeCrc3ZLzXu0lIVKTZStG1M9yEnfhLIyDMLLV1rOPm65Ok1KAFFkyEHSF80dBeeqk4obse4VtK9dIS9wkw64rzHXjr6XOIl5FHL2Hb44ojrNKklSPi3716x/TQiewREYLfN2WxS9EEQ10elrJwM5J+gvdDEt4L2NiVrHboQcG5ZzC0X3zScn22x15qtTqnejVk9zRC9YKgMLgu0XtM8pXgrSFc97h+TYzrdCZLPArWT1M1RXFPONc4rwlYz5AJhob9O8U6S3oPPHdJyRBEPAlcZQhLwI09UOOaHDjkI7AikDcQHT1CefZvivEJsFX5QXIy2NMLw/HyP1o7P1ZQmGF6sGiLp+N2nE5TwxPOen2VrrvKC9Q8T0trzfFRRSsNP7ZIXckvZpxz2Oc2j5M6NSafH0Xv5/YQvljvadcLDP8wwzmOeteiJ5XReMr4oGb/aY2Pop4GmjpkNLf/Z+C1n8z0nkwOVMNg3A0OrOcsKfFAUI4OeWqw4bqx+2iwZgubnlzf86a8+k5zVdF8NfPUvPpD9/MCjzxm9PpC/Knh8mHG4zfFWsbqe8b99+8tjt/8gMGcthy4hajz7KoHcMihB4i3Seeb3jsRZnl1usErSnwtkZtneTak2Oe1EMlhNe51wCAlnb9Z8qqdUVUxSB9xDckTCXliGKsK1msPTiCxtOVkcqG5yDjamX3q+mK84rEf86ttrrp7vuKlncN7TjQZmr/ZEk543dsM+jiF2dDOJ/ZiSmw6bQ+kjonigu/AsL/fs1xn/6vaSfGsxL1uaVJG/6Nk/18hFT/SihkbR7BOwEqsF6aQlXFniZc0mRCRfVjRSkeqe7Zng4tmG/KRBPypE7Bm5liTqUGuHlIE4GhAmIEuBijzJlyV/ph85TCWz0JLoAb0RSBGomhjZSBZ5idmCKSRipYlOW5JvttAE9pmhm4NPIH5W82W6Yq8z7MeUpx8WxGYgZJ7oJ8nlFwX6ZKAdCR5+WPJ/e3yBbwSxsiQhkFtHFveIe82gDN+eP5F8Uti7hHzc8iwvsYNhHDeYRYdLAqnqifYeZwzDQ8TGZ4RGUt/l9EtQnYdbzWjcwdySvqywg2RdjjhcT2hzhTvzfH93QegU6c4jZGD+F3vkrWJoFPsuI0960DA7qTHKET3v+DGa0owE7iFiu0mxO0Hy4FipMVkYMI+KH1enFJ/H1CPF+mFK3xre7GoGZ9BXAy/GBaNf7hi5Bl0K2ksPv6gIE8fLv37iX/z1PzJ9XtKNIRUN/TLw+Fzx8mRFfwa9MExeFJA7/L1B/byjqFKU9pjesxsZ/EHzr9+94ptna04PDat3J3/Q9fKPWoQTzzriQfI0SVhQ4B8jxn3LPo0Ydoa61WTPK5LhqJelh2Hq6W4zDJ5xN3AICtVJzs4PrBcJd92U6jFDZpZ+Ith9NyVuLMtxyWo94ZBKVCeYxQ2PwwzvJOrOI142xMsa8ZjQ3yeImaORGtsZPrkc+cMUU3p87Hn1l4/8Wr1k5yNUD8tRSR9J/L0+kq2sISo8/nVL/E7RRwoyDyPLoUsJaeDdfoYyA5Qpg5IMRcr5+ZZlM7CKxti1Jr1q6YUEoeiLiEFK9ONx5yo6SZT1xzF2Z0h6z3Re8tTFhG8HkiHQGIXxEG4NN89nJJkncscWwUjW5POOrUpQv2wJb8c0VpK8qXAEfnqaU5gYv9b4qUDeQxEMNgP7OYIxqAaCELRoROdpv4bko0A0knHaUKgUtRGMXjesd2N06REmIJKB6LyDLqIvDWYrEC6g9hLmFpEdJUG6gNT0tAuJF4LioPFKMFc1ZZkg80CVaaQzZOctto5xKdwMM2gUpZTIsWN0UrB7N6NeZZjQIkMgxqNf1dz/6xn2V55nXxaozyAmPf9RfIORgvtlwulNz7v1gv7UckrBzcMp/aCYfLWmbA36yxpcjLzz7C9jcm/x37b0d1O6Hxe4sSe6BSEc9annbw6XDKMj7lnGDn8XUzWSFo38wuKt4u7TklBrSDzGOPo44raYYrRHWxAbw2dOGIIisZ7r316QOIu9TcAJrk9TjPUM36V4qWitwZmeS31g9BcV+7cXnIqOn/ZjktLzuJgQzx3ZpGUVJfRWs9/ltHXMulVI7fjvvPme3z6c0ZmI6ibmYXfC4BXivOdiXtBeFexDzOAEUQvDNqasElTWUt1GhAn03bH7/9CO4E7z/fo54rTnZHHAJVCXEWVI6IuI8npOfSmpygSBO1rMxHEkHqwgGjx2l+BCQA+w9Skih8lhYN9m/IOO6DvN5XiFaCWuCqTeYrUiFz1dlTDsI363f463mnIQzCclbuZxeKyB7ZXmq8MjUafJU8fjx4yzswNYcazabQN3YsRyUpJWlr+/e0GTKEazmmxcoGWEkI6ukiihaYVBlApZBhiBFIJPj6ekqYWxZegM3Sqh8BG7yjE9raiVwj6NEZcDedbQKcmPzZxDnUIiqEeSok0xbcBXmm7sKd7HhFZgnMN/0/L/un7N6csdvh6zbUYUPrDMGx6bjOzakV3WVEXM8mxPe5Og2oCfA+cDJrd8na+5/WJMWBnme/jUTHC15CAU6ahDaYsuA1JY3GSgWkWESDOtB3aHjLBs4E4i9oLDb8ZMxpbd4wJxEghXHTwaJB43d2AF0czivSKLGvI3JWWIOPw0Qo4s6XQgbj1J6Wm0ZTapSUrPvUxJqFl/YzCdJd+Ckg79pyXv7Jif3i4RXmB6S1jAidzTVTH1K4VxPVfywM1v5iTa4l553rDhIUnZdjnZeEBuoPKCxPTsPmeIReDPLz/x2z/gevlHvdh7d9S3ikazuNxTGo8twB8iukhhU4HoIppCof2RYiZ9gNSjOk92VqO6gd5rChmjf4roTn6fmt9qfnq4QJwG0AHhBMuTA2WTEM4tpdPMfrah+M2MZ9GOm/0YsYuh0nQXELeCwYBuoboeM36zJ7vzaDwP75aMuoHDlSAtHE0kCWuFiizeakLscZWC/REEoaYDttSMdU+hUhZFRzVXJJOBPoW4GBA7TX2bUcUBfoppXoDcJZj1cdwqB+imEj/1qE4CgeofJvivB9SToso1fzI94GeavoixvSF707HpUrKdJ/KBqHfsX0FaOOLZQJVrTjYDm3VEkvUklyX155xID/Qu4/W3G27FiIOPkG+jYzLVgnCB5CnQTyRS/f6UVCiap5j6iwFXBcTAUZ6RBiIRqJ8y1BBQ/igpmTQWP/e0EloVoU4HfK+w3qAfjtpfATRSoz4L7EQREJjK0zYRdiFQYwjNETxUHlL0q575xlOjUVcdZZtgrxPirOX8ZMNKzDgZeub/7Zrdvz7h08czvvjFlttPU+Q3e/LLmh9vzngVH3jfj6luNcOrQGRq3Lnjr8U1//tXY2RnqPTRKKd+EriVoP5VRKQczSZGiAlll/D6dMV9tSA5b2id4tn5CpUF7q9P6Z05fkgeNMECH2PcLxrEAebjmkOfEvaKKHZErsNvDVUVYa0g94794wxjLNFeEO8UkzcVW2NwUqN8QMlAHxRmx/E08wAPas5+m7OclezWMX/x4oa7fQ5esm0zpqcNfhOxnQi0NfhW8eJsRSs0//r+JdVYcrXcUY4UWe3ZxDFyYrnXBjN4duscEwYW45J9JEjpkGZglHSsyhnztw75pUW2nkQ26Muepor5Z88+8vfFBZ/KU/Ks5YvLW747vMK3kpAFVCeY9AP6VU+dHsmb5089ZtlzW45JlcUcUkQIiAuLaTzVWJJIS1fG7FSCFoGT+YFOSnb3I8J7g3jV0+w1mbWIsWfVj8jvO+pEYZsEE8Oz1wW/frjAu4T+mcDfR3x1/oh/l/KeCZOs5c+iFWne8Q/ecfiUkH9bcf2bUxIfULklUgoRIEwc09crdl1GFvUMQeLLmC6R+L0CCXI0kNwfr0X6EPPyfMVHeUo6tcx1jQuK0hj0DlwjMFvJ1eWGos1pTxx2HSM66F86bCcJf7skWgmafxpjfhvh04CSjvvJCKsl4lWAPma6GcgvO6bPCqyM6ZzGC0mfSH7jlnRVdPTJj3LctUYpj5tY6hmYQ6AbS+KDRomAUA5KwclXGz49nLLzE7wBnwgObYragsgdyaTj5HcdVStpnwcaq/CtxLcRwXqkEehWUX6eksqe8Kxltrfs2hHRaYWNA10Dvo+QO8VBjYlGFiMDzaVGLQKzuGF30CSLEjcoXox3fNjNsZEm0Y79h5xYWd5XS+xIEaLAuTxw73MMDrsxlDuF6AUMgkOasR0EbaO4vfn/zar5/8fzRz3GP1cVQ21wNynVv1kSHcAVhrFqmdCh20DeWnyjsAhk6rAG3MGwzDbQCeygMNmAUAERIGmPPftk3CHXitALYjcwP9uTYHGNRIcjJ/zQZEyeFzwkI57P1wxCYhPJNGoIAmSpkL0gdIJVnXPIInqhWe3GtMEQH2B+dsDuIkaj7sjvjwNSB6KfVYT2COLpYonoBFYqfOYpfUJ+F9juR5QfxsgbfeTCB0m3ibG5QA0Q3WqwAh+DGwcWcUUy6RA2MNIdQ6pgpQkBokPgb/72Jff9lPUsIqSecBMReQcvHWlvecmWCypGfsAVmrYxfOhHFPuIbFHz0I+JTzuKOKbKJT/en9C+ywk/ZPggoZGM6x6voV1qdAdR6+ilpIsl8mRA3WnGrqd1hmnXIZRnbFrcOBCuBuxJQCDY/XA8nYxeFqTzlmAVdIpkExhmRz2uTz1eCuxIoJpjNCVICPIIzxCfDVFr6U88Yj6wmJT0QiNOBkKAzilef317VLkWOefPN5y8KvBbQ//MUm4S7FbT7CJW38+YzDrunia8vV8ixh3+G8vy7EA8HRA28H94+hlVmWKRNFoTPQj+9Nk1h28UvtSc7Fus0/SxIF5Lnqo5PGkupjuSycB7d8bjYU77OUMGDwfJ8GI41vgiT1dG+F5TFQky8uAEM9MgT3tcZRhfFgQhqL91RDtQn4/4TxUCt3pM8ufFUXwybfDnPTjJ/rkixMdrFTUI3H3M+ocF1is++DHhoPiq3hNFPe/eXrIvEy45kJSe/EVBGSL625SdS7gwB9pdgkkHDoeMeC0wd5rDJme/HaG0Je4EfaWRkSOcDoRScR8dKYf+q55WaJp3GZlumLiGL0/u+De753waZgRz3ND/+OmMhamYZyWqCSyWe+Lzhs5LJvQ8tw3vkjFPfUqVKh7thPOzEn3Zs9tNoJdMZMfoRcnVpuZUlrw+2dI1muh3mnjfcnq24fSxJWsH0nGHOWtZuJaZt7xWBSeiQJSef/f5OXYGTRVRrjK6JkJsBV0iGZSkijRJ6umUZHjMiK+OSuts1NMJjV1HfJM8YC8szW3K8JhwNt5T7lPqNubbxT2RdgxlRF46GDnOX21Yzg+MFyW3j3P0aMBfx9y/PeVpPaUOmmff3jN/cWD40tJNFfmvDuAkOu/J8obFqIRO0s3AzqD+YXxUTuwlcTwcr8iCoOs1QUAxjbj/cHqsAZ/1RG3gZLxn9mj5ZrZB1J5LU9HeG6K8wyYBmXmiFuygEKVE7DWLpiEkAbVX/PT9c8JthK8UPBpUDbr12BSaZx63kmyfJ8SverqHEc1jRrSW6DtFqAxlm1J9ygge1NlxI19qg9tpuk1K+WGM+ckgNpqhV4iNQv8uxn5KCYWi+/WEp3Z05LGcHuvZn+6WHLY5d+WCh9vFkc2yiZmnFWbaYU469r+d0X7Oefi8IKXHpmCNICktz6dr3NTig+DFq8c/6Hr5R32y/0kueX51YJo0rFYTdrcTxE7hxoH2heMy2bDejwldQO4kYeTIekcjJNH5gK01F7MNtdG4f4wwy4KTZcFDmlF/yglLi3UaWxgeH2dsQ8r5smDnU4r7GLtOUeOeOB049BnybCDvBw4iQlvQyiPGHtlrRj8pumlMflYjEs3gFctZTdFlzN/ssJ8ylouK1o5wXmCzQCp7bG1IVw71ZcXQafJ5SzHOceuY865kI37fG48G3F7SA6IPiFbgDYSpg0aiZj392HMiGjZ1TKIcbSegMrhzhxSeVA+0RmF2Cptb8h24BuLzlv39GHU+UA4xzmuaLiMXDcIKZC9Z3c4wg6BUCukVZuew4oiyZQrDJKALaEMM5qhcpQpE+YCILFsyJg/Qt+CKGE4HukSS1J7DZsT5fM/tZkFqOkShscuBTiiEg4nr2PcGkoB+cuha0y8CKvYEL3CJR9wYfBbwOfTnMKGlEzmhNUSPYKeC1eMCnVn8KoLE0eWCd7sL5qcl06Jmt854WJ8QnvUsZyWLkyc+rE6wXziKIHn8cM7Zl2tumhlXTlMOCeqjJPUBdxPR/MqxiA7sSZAddJnk7x5eI0wgcxYRAip4ho8ZeWPpncJrwedhdtw06kDTJFghyS8LSqmJPhjEwtJeKk5PCoY4orlPGTc9eyXYVymJHugjwbbLubrY8PnzHDuBpPC4s4D9tiXt4HA/Ipm02EIzHTXILzrkLqMYRfQ7he4dKABB/9MIW1jCIJjjmAyeYdJhzMDu+ynJn1eU73KSUY06b/lKbbn7hyWLn+8gKOxph680TaRgJZGLjlQNvPrzRz5+PCOxnrpIWIwKsveG/nNKOLG8vnzizs65tQviR8tX2TW7PsUGxeuPDT+MFkxe7DncJbjy6DFfkWM28ihQEQ2rIeE8L3Gd5BfdhvVFzu1jTr9PcFqBcvgPMf4q8LvdjCA9h9cNy1HN47MJsfNsnzKIPXod2CdHMFYYItaD5LSoabPANB7og+bwXQapIJ0N1HHEj39/wS9fP7KJDOPe8/+4fYFtFGbZUasRZ21JFHfUswidtDzdT9CTQJtIlBqQyjNbFhS7nEOfUBcJNpaUjyPidmB/mRCPe3Y/neJH8Eyuue1O8MGhDoKk8YQhRT0TGDxlnZIJOD0r2K8Tpr6mezhqjtO8o4kj4s7Tzz0/Xz7y42+uGJ9UhEaTvqjoVoYuGIZa0jhFxIB90+NGHu0G3n5/iipg1UwJVwMpAyeuZ9uPONRH9TERnJ/sud3OcEiEEiRiILqHZiqIhwEKQf1CoITltGk5uyz5vp9zSGL0i57kg0KMHb00BASRHhhPS+QqsP844vmrWw6bBS73dJ9ThgzS3VE1jA44qYhaR4gE2XtJc+kxnyOIPfvv5ohW4ntBUkp8HAgjRxc0tpLHTFcrkN821AtB2KSY9UD1rcTrwORtwI0E757OiL+qsY+Bj/f/4c7+3/uJs57JywOP5Zj5iz1lFyOxJC7AZ802niJOHEp4Lt5s2csE/xiBGXj3eMEXr+7Z3o84u9yzP/Wcjw70HzL8NGKUtqyLnDNTsm0ibKoIQiKNJ7EBFznakwETBaR3ZKph5z0YyfO6oRoSouXAYTshXlTo0YDbGCprcKPAoqoo+4RpZTGxZ//SYq1AvJf4VpNGDf2lZSIrot5xd7OEq568C4xDT5NH7JqcLpL4Fx5/ExFpS/QI7UIinEMIiZP8/sUsCYnEq4DJOoZnDj4PJLOOaN5Tf85pfES8UghtCRqef/HA92+fETtB2Sju+jHpW8UwAx3AbhP0AEMmibbQL44IW2pBM1HEpufNq3v2v52ySQzdK80waIw9UgmHc4/5tmbUWbpBUHcJIghc5sneKszPOopzhVgbrEsZLyrCTUQ7DYSDIYo8+/UMkXrcAoS2DDOH2Al8pEgeJcM0YLYKpRw6tfRthKoF3TbF5D2DUdAqdA3heY+soDWKdCeYpi2jdCAzPTfrMy5Ha+ozy4PLyeMO22qmo4o+EohHRZMHCiKypOWmm/JsuSEylrJM+er5llWdUIeEaTfQ9DGPcsqb2QP9BRS/mzI894Rdj95qQgjI0w7xGNNIxUz2uFbTeIWMAi4Iwl3CkATCVmMngWKbMzgFc8feS8S8oxsJ7F2GSD3mg+Y2WbJUNWWd4pIAJfheYLdHbXOTaVoZYaY9zSplZnpc4rE2pT0RR4vc3YBLJOlPgn4p+X465WxSovaCP335yM1VS/E5Y/Z6x1pEvBxvefj1KUEo2iJlfTfB5AOjvCVkCp8KsjAwzmp++O4lz5ZrSpUwOjng+8C+SRl/2zJZlry5eOCHmyUn0QZlBZ/cgsPNCKskby8ikmZAtBIxskyrnuVpS9XFKKco7kbMTlr0neJBjEjkwG8YYYRnWMU4LZCpRU4tgYDpHNtnEGcW+1NKlwaSyUC1TsmnHYWPMHOPLSJmowNlF5GanicykA73FLBWIIXAnNcMXUTkHKI3/OZwBsZR3RnkXzdc+AMqhrOm5eZphrYOv00Y/0nBIU8xwcPFQNtEuPeabiL51fKat+8uyMc9upJQafRaoXeadhwzGrcEK3h6nCEJqOcd4sHg9oY7u4DckqieYWXYvU+5PwMpA7WJMYsOKk//YEifdZydVHz68ZTXlzve56e8TEp2NuH5oeaHmzNCLDlfbrh4deBhO+fTasrq/rgxJcAkb7j6+oEfr68QHyL0ZWB6WRJdQ/yzikZEbEmOV6w7hV70qA8K6QTRJ4lPJJM3BV2XQ+wRy469kISNRhUa5T1R6+hahT8DGkHnNU9RzvTSIj8FPv76EoVjfG6ptUF1IIVH14Emi0gOAa8k/QTya0e0FsR9YChjRAVuDNE3DcNvj1dXogkkJUSnDWbSU96OmTQDyajkUtXc+RzvFV2A/pnHG1A7yfQ6UMw8FH/Ywfof9WJ/Oj2QFY5879jYCWpm6dsEpS1iL6EXJGcNBxFTEiEszJ/tWbuU5bKlcglPw5jR7zxfvbnn7d0FMoG2jEnUgNppNmEOIVDUGU5K7soT0lFHVSQQAo8vY/675+/47ecTDpsR07OSw+cRm9eK0buUQShM7slHLcwa3D/M+ZO/vOPjbkKUWSba8vTrKWny+yS5VjA+VvPaJsI1MZlo0aGnU4JqleFnjsm0otwkKOmQj4ZAoBcSNQ+YKuCy3+tdfaCLA4wcsoOhi4hlT+gCZt6zbzOkVCRrA9/2vJzuqa3m0Mest2MGFJsoRagj/zx8M2CswA8S8aCxpx43t4RBw8TTG8FJdqBwKU0K18Oc4VIzHDRhUKhzix9AWaAX9N/npL98JDyNGU9rijiF7liBq12GeAogA/qzxi4Dqhd4J4imPaEDWWjUeMC2CrlW2DEgBHHrj9cXsaCLFEnjiA4SGQf0gyI46HON9g7zrKNJJFFicfuIZNTTjQxDFcNM8Hg7ZnTR8Hm/JK5q5Aj2IiUvoYoU7l1E8rLGvo0YXxZQGa5Ge0IrWcqGze2CX1c5i6SkzaDVBpUOmN9lbL/KcLeKcpNhkwSTWzotiRYD/gDiZU/yfUKVakLqUTuJjDz9+xQRBFpZfvb6mgc/omsi+tbQngZcmaC3gdh19AKCBpcHVBHY5xn2zDO5diy/WXHXTcmXNd322Ljo8kD96VhhiyeWn58+8q+G52gviHaK4UIglMPvBD4WJO8Um2RKJDzf7c+o6hH7C0O87tB3kvtLyb4e4aYeb2OidCB4QdDwzeSBzmnu93MePy/54tUDdap4kT1yX0woPswYqpgCmKUV//Lf/oyTcct/c/KZv1tf0QlNFALhrCcPA21isOOA7eAy2RNPe/52n9NHCbKBx90Me+H5T5fvuFnn3N4scKeOheioM4V7UvhKYt4rmBp+frLhvkh4Nj2wEhGjRUvbRchFj/xtRhEbVA/7/QgvJOpJI0890gZsBvJBHgVLXYrGMZct+28F4qIj9ZZGGuK9YhWNiB8hLB2dNfRVRMLA/vsJpz9b44SENazFCOkEahP4+6cvsL1G7AVyAJNaWiWRUpObln2VcpoVXF5teLc+Q2z0UckqYgZzBMJ0NxnEDjcOiNzzJnmi/M2c83HBD2qOPZUsfIPdRYzPan79+YKyS/gByehVyd/ulrxaVjSrKavHOS6XPH0aYU8FcelJbgLDRUC1kn6f8O2zB3btiFIaDvc5z/I9B6M4FCni3jByA7PnK3Z3E/qvLNwrBgGTuKUMMaI85qd2v1nQzgVT11EnAi8DXSOxWiGlxY8804sDdYjZ3GfIQjCMBfUzz5VvaW8zVO8xWwhSEM965JMmqICbiCOlUyiGhxhvBBigD7TvMkQCenrkE3gl4R9jmiQlnHj2DyNCHHi3yfjly2tWZc4exXARmMeejZiQzXtCOaa7/MNa7/6oe/a/+s//Z5ye9yRPgjs3pR0JMtPjrg1OKdLXBX0To/E4IYjX8PqfXfPphzO6s0D2KJkmJWuVUn4eH53dp80RbnGvEF91tE0ElSQqji8yl4LCI2qBHznEoBCvO4bKoGSLvtPM/nLPTibkreOwzllM9/ynv/gd/+V3f4r0gX2foS4b+BjTGY0bJCEJjESPmfQ0RYy1mpOsoEoMGM9ZU5LsDHf1mE0WkU1axpOGw82YQQDbCN2ALsGPPea8ZfiYE2YDvda4LGB6T5L32CwQpOAyrrj5ccE0qRFSUAwxdhJ41hz4mE0Zux6pPZO05uGnE37+VzfsGoMaOR7enrCYFnRGcegShAjHwFArGcueQsYMnWbedAylwY4C0UnPcNCYt78n1r2p6VcJfSaRy4FX54/UbUy/T3gqxmjp0ZklACPZUd2MGMYB04COLH5rUOc9xnpqFyG2CjntcbVhfFKx3+YoGxiWAXUAryVmJ1AXPcKCsxJRC0IWmFwdmD/b8/2/fUY2tdgiQmcDg1ZEwqInA5PTEhU8t9+fUZ0IkpuASAQ+DUymNfsh4Rd+zX6X8JkZz882vDnfsP08ZlYONKnhd8MU/3ygvsl4k674sD3nYrqlbSNsBps6J6ohuRMME8fzr9asyhHVpzGkjnHeslweeH97Rlo78j/Z0e0T/F6SRpadjmmDIW0sfpDIsT/mLgQsy5bNkB+RnfOeeAhsRhFyEHCncfOjl90NinKmMF3gzeKR7Sym3KeMd5bibY7wEmQgzGCIIbqTR6T02mOXHllLbCwZJgEvBUEF4qSHweFfBOTEMfxmhFoMWAWztmOtMiau5/x0Q5coTGq5/eEUEXnsvSH+VcXwFKFPep6f70iso7jJuDMj4t9z6e0+YjQM9NOAFB6rBd4dmwpu4kk2gvZzSrZoYO7ovSLaB4Zx4C9e3lL+OCI+HXiQCd1GU5Rjzk4P7FvDs03HD9UC5R0nvzjgBrivpthBo+70cTOVOkIEKMGlOFAWOX2QuLEnHvfY65jpVUmTKs6alutiijrrEY3AWoX8rOlOJK6X+NyTj1vGqx67FFTrnPPFlrt6ii8003eecmpwCajMoh8l3sCQCuJ5hzpI2MvjCC6CNpOY0YB4a+gvAnoX0M8HSmEYmQG3U0cVbwK/mj7xaT1nZBpu7k6Y9j2t00y+3dEdYurbjHzW0iaSOBmoqxh3H7PUJdULGKyiXyeQHsE3+p1BjB2T5yX9LqIxEu80s/M9V33Lo0u5rmaku0DoFUIF4lWgzwQqBLrTQDSyXF3sGD5JtnaMDwK7NQSriOIe800NdxH1Y4pUAz6XqBZcJLFSECTEg6eNJFEdiB7E8X2AQ1qJ1+DM0aEQjQbMB/DPPc4qOjTKBYIKcHGsbEdZTxMrwk5jHo8KdINnOPPYQaFjxxcXa7apofaK/KxGbRQP352RLmqGjyn8kyf+7n/wv/qD9ez/qE/2i3lBUSx4wpAMnumy4fD5SOGSe8Fo1NE7R68FdpWwfL3i7e0pkYPhXY48qVAjz+Vsy045qirGPUZMs4ZNPsG8TxBTsOOAejgCFvqpACUwSGwqUALEfUIYDzhtsBeSYDxRsIwqS9t5tibl/vMFlTHolUJOHKqVdGOIbkG9aIgmPXovKJ8SMix6NFBVKVfLFZsmo3MxuzZj+SdrzN+P2SYx682Y+IMh+6KhzSXiqicMErmTCAXtEkwvMTUIK5hmLUnacCtGaAsfGaOXlrwc0BNHVQMTydqmLOqW+EkS/1lBsYpIfl4R9z1FM2GyH7joS0ztuSnGRF8cswWzaU393YRiEpMEi94oGh0fT6Q1vPYFhY54imc4Len2GdIJZC0QVcTb8orJtIbYE88rLnxFmFo+P56wtilGCnzgOHq2BptIGBTZRU32g4KJpekMcnEMM7Hw+EJhgaUsaZqYNonoq+go1REBl0HoJXbmuSvnpFPPLKtYa/BWII2jiwRL1bK6n9IUCYvXO6LCMPu25WE7xjbR0XRWa94+n+BjQ7QD0Qp++HxKdZuRTi3P52t8GeivM/StpGjH2K8ch88p0mrMLyvifSCrHVWcMpzA9W6Bu4/wY4/OejYmomrmuIXHZ55mn1EaRby0VE1M6AyiVSgxADCWLaGXzKclO5/jCBA8IoVqrI73v3FLMR7Tt4bCaSQwySrsleN3d+e8PHvEjhVdAnZ3bHJ4LUjdgG0M/VSgB4/NwCkFCkwZEFHARYF40qLHlvIUUm3ZrMaopSUPHVuZsTUJ6bil2MTsqzOi0sPWYHyPUZZn/2zNqTzw/vGM8secD/Upqoe4DgznAn8wnJ7sQQbGoufmfkKdK6yTiEqRh4Hxac0wAv9Vx3LdkMWW99WYw5liYlr+/rtXhK2gaCOywuHHgeW45lM9wd7HDKOG4YU/TtLeRnQ3kuQL0Iua9jDBXjiQoGqQ44H1pzn+0nI637Pf5vQoom8atl1Megi4TYrvDc9eril3Oeaq4PA4p0+O//90XvG0mZCuJfQeLyUPb5foWuK+7pDfWKIHCQ8SeemhEHglCQH61DCWPdVMoTtwCcR6IMk61qcxunOoE0s8DIzTlnjkeGjHnISadtB8/+kcuVLs5hk+DgyNQGlHlnS8Otnz6/4ZfhAMjzFJ7jk5LfBdQ9mmqKxF/jYm7juG6IjQbS4Dmfe4O0P3mKJ+0eI2kvJmwo9ujDaOdCOPIdpB4BIIHWAUKIt0kJ5XbJqEfZoh7iJcFlCjAN4jpwOXec3tM/BDjHAStgYnAmIIGBWgk7gTiDYC1QlcDDaHeKPpZp5oH5ASRA920IQMQuOxuSSdN7ibCLEYqIqEEAWWSUO/mWAV2OnRvNmfQyiObRYXC9S4ZeItJhjaIiKVHfbC0ipFOHPUm/QPul7+US/2q3bEdNbTJQKyARfD+Ysdh3XKOKtZ300ZjUrCxqAuLB+vL5HPegavMIuO3kqufzjFjAe8k5B6wsJSWwMmYJ1AdhAbhzuDXikUHnUIhLEnRIKgwOzAvXS0iUTu4W4/BycYtSWTn++4rab8+vMF8eue9mnEs6sV1/0EtZZ0kSK7jehWEQev8JcDYR2TJTWj0z0f/vYZ9sxxujzQXATKLkEZOBsVrDYT1IuOdir4Wt3x4+0VvhJo7yn8EbRjKvAvOpCKZhJoPo1ILgIhDkxEj48C/tQyGEimPfbDiG7pGC9LmknOY7EkmfakDNw8nOCUpnQSnwmKEMFUEDoFH2PaE4+JBjoMnVLENQyXDpNZ+lXEd9Up47hFSU937lFrhZt5MBBdC/LfeORfO2yh0bcR+7Eg7juiznOS7bnNToibgB45bKuI0oHYdGyLETJotBzI6EhOazb9lKAF8TZgM0E9JMg3HXwyBAPeBkwTQB/bDpsfF2gXiPBsPi5pluL49bnDHxRnf7WiqwL/8ewTv/UL8IrJvORwl6IlZMGyHStUqYjuoQ2Cj3YGOvDXr+/47X7J/cMYkQm6KmJxUfPtlzdU62ck2lKvNLpVoDy9UuQvCtzaoE4Gsl8UVM4cpTRCkcQDcmnBS04utwzvT7CbFDqFmQ6wCqixIA6O3XZGWDh6J1gIRxgs6WXNQzVhKDXCwuEpQWcDej4w1JqJa/H3hnHlCNFAXlk+bkd8LVfIV4JdkSMTx+hzx9AZhPfEB481CvCkvyrpncIaxazs6PYxZZFjB49PWhJvCYNCXjjGqx7XSSIBbQQZA63RpNqSHQaqqWD/MKbrIk5Dw8NsxvipJywcuy5DvQe1tVyXp4SlYz6tqBPB6WLL7e6Yhj59tuJhPeciFNhNzPp2xOBb5AyypGf0XtDvPes4xoWAMQNdq4lGlqz0bJae4hCDGrAHxV1iePknW0iPGxf3eiAtA+FG010GouCx0x6ZDZQ3OXnU0xWwsykE+Kuzt/y/zRfkt5bi3Zgy16jvE2wRoeY99Ar/NmEy62kSQ9dphBVc6h2bdsJwH9NEEhsUkXJ0jcG9AHJHFiwh8sjTFrEDdRswHqyWhLXm6sWKFs2Qg4gsbaEpr0ck5y2PhzFDpVDKk7xqiTcxgxbor5oj26FLuPndCf2gGHpg7Jg9L9gXKeswRiIYdx28bJh2lv1dTptJshtPFEMtU4ZlICEQFYHhVOB7jasiTs53NLcZLhWEQVF+IdHpwIvhkY+LGf5dzNWsxD2miNBhM08VKV5e93Ta892HS06TA1ncsesywnPPJHToj8cKczcLuEYjcoeuJbIPdAtB9TKQfxSoLqB66Cfgpx5zI37fEoDappjB4+9jotQhd4KnZgkeDB7vJd0ZmJOB4SFhGEOWDfz4/hnppKPapLSJ5GkAPe3prWJsBl5d3vHTH3C9/KMe4//1//p/ytXVwPu3J3RKQxxIt5AnNVWZ0RqF9J6v/ulnDruMp8cZr+cbru8XhNuI0Cj8+YBI/PFUnWqkDujpgBk83S4ielBgPFZL7IkjaqFPBEnS4ysJO0XwimEc8EqgXIDcExLPMjSI055UDKwfZ0QbR09M/MsDTRsRtgZXaRI94IygyiVSHVP4c1Fzv5sjOyDymMbjtGbedfziZw/8/d35cSR2BuEhYmI72n1MWDp8AL2SOCWJrIUzS30O8qAQtSC5aliOG3YPOd4HTGopbkeEpTuGVlJHogbqyPBitKXtNaunGYwc1h4DhKoFueh5uXjk4eMJRRIhCIitIZl09KuI7KzBecloWnOzWZC2jtHWYn8xMB1X3Lw9Y/SsJA2Op+spkbLoPpC1jspohkN85Axcdfg00FURai9RqcUHSbSGYAXqIBguHCIOyFIRxhY/DagC/M7QnRyrPFJ7lAgMI09oFOPvoT6XIMLvk8NH8ZALEmYO/XgcwZ4821OjaYqE6EWD2xqqVJGFgYnt6SKJ2CqK3PDGbXGZ5FZmtGVMqgZ0KfjFxS0Xi5r/081XdO0xTDn6ANmbA+VCETUeKyTrYkL8KMkuas7nJVsMbRshRKBoImj1UaAEmPOW5PaoTN4sYtLaMaQScYBwHpCNwHbHuuYX+ol//uUd/7unb8hlz2Y9OZ58bSCftahlD4Ogej9GzBxMLeNmoAwxs3lJV8T8fPLEj7slKPhGbLnb5ayeJkh1pFnKg8R8U/FyseNjPyK5FnDl6bYJxlnUyNGdePzBEOc9vZaUP4wxscUvPc/rkgdG/DfmP/L94Rl9JGlSid3EmGvJOO5IvqjYbsaERjB63zH9px3/WJ2hs4GXyZ56iOiFIqw06cuSvLKUw5GSNr2BD+UCUQbGyUB7FliqnofHEWYLzQL6DNK1wJ4G1EZy8mrL+nGKKzVKOZxVVAvBeOPovEELTz8JPPcFXsFdOSMXHWncM/12R7uKqTcZ9TpjlpfsdjniwtLYmKS1mD7QSo3MPOpO45NA0IHJeQm3mqJLEQ7qC8F811Jz1Bkn0hJMwDYKEXmcU6SXNUp65F7QY+gSwfN8ixskj92Yie4ZImiqGBM75HRAriXxooNSsfI5QgSyymGtRkcD8mygXmWIxUD4GB/FOU8R3gjevLnnbj/DnLWITuDCkS6oXKA/xNiVIV4HvJbYcSBeB5oLQYgDqjiKgbwB3QvsiUP/HuyjXraEQaIvO4aniHqimIWOYMURBjR2sNfEy5b+Pjla5raCbNyhG9i1OWFsybYBmTmawSAGgR4cXaaQA8hBoLWDfcCMHIM3hE4ge0Acx/4Cj3ABvDx+rlaawHFSkqyPV6Eh8yADauKILxtWTc4LtWNvc1wMtTWoxBJvLfUrybgbGOSxzup+dPy7/+X/4j/gcv99nq2K+f7jBdlJy3Re4hF0GTx1ExpvUCtF8JJoUNy+O0GqwP39jDe/vCH+yx3iZEBMjsrQvokw6fE+RlWCbtAM6qig1QPEL2vURhMOGrFXtGWMLSP6TIIXCHtcKIQ8+tTNpMdNPC9CyeFhjKshTD1uL6k/jFiailj2fDW9p0g0/Ykj3sF4b2k3MQ9PC1QdWM4LEuPQo4HeClb1mNt1Tlgfe+fCw2hcUxtD+8ZhR4HmRNCfBZDQjiRDY3im90yqARUcWTww3B7vYQej8F5hGoiHgH+K6Z5Sig9jxrpj83HCw3qO6T3uU0rwkiRv+ebkibEdeHy75HK5w+QD5qJDJJ5qFxOft7BwNEGzGXLEyMFaMP6ywqeBpx8WCBWY9JYmaJ6fronyAV9pztOWoYwIWeDi1YZ8C2KtmOkaLRwuA7UWEEB68EagBolPIPlZgXcKf1BE0mFfWLQNDGcOoQI2CcTXkknfU3xx3LTJEBBOMtItZJbmKkDiiIRFSc/woGhRSOs52JjJqz1iEFyMD4izAXPakbyumLoeMRgOTURTxUy7jtd9QUnEezHl/95fERtLVDsyOhbfbHnspzTXOYfdiFXISQZHf+6p6ohPDzO6MsLUUN7lJKvAOG0YpS0tmsXG0d5FFJkmXvTYUxBxoD8X2NKgtgJlHDL1bO7m/J//5VdcRTtIPXLWH0NHQdI3hqZIcEEyellC5PGVIpu3R1xrMFSd4e/vL6lChPsY8XmUsBvFiGcD9VQxpIJ+DM1TyufNjGGTor5tcVOH3wriqOXidEO/jwkCmk1CeGuYj2vO0wrdOS66gqztubYL2m16dBp0ERejLT723E9TSpfQ9hHBS3bnGT8cTli+3JKdNMTL/uhUaBX2i56wNtyt5zz2M4rv5oySgb7XWKlZi/SI/rUGGxTqZcfipGF+UeGswj1FNBjKXQohcFGWNJEm+wDLnzx6NmASh4w9olKsqzFFFJHLluR5TXWIqMqEss9ornN6LdntJsgkYA8RsbSEg8ZtIrwXUAvC2UBIPP7S8nSYIFNLmB575aoPlFGMywKykQylof/9e2SUdMfPnC7QPSXk2nHyZodWARUFwjTQ1jF7m7DbjZguC9LD0RnS3yX4m4zVkLN86pnMKrJuYJY1RFHPbF5ieo/8YPB7g/qg8eaInO51RDsYttsRxdspei8YtjHxDuRbg4sFzZk8vj+7QBAQbSG+EejCg/ZkaUc2ahF7RT3RuLlH20DiHeFTQhdpXK8Yix57FzNNK8znI3/ErSOiJhB91oxVj9xJtjpBDoFsFfCVoh5LQhDHU/jIMqFleOUYXg7HrJSStC7CIY55iwDSOoR06ArQEvlFh02Op36pPLqGYQx24RGnlnYsaWLF0CrkRc9hqVh8s+Wvnt0iRpYXz5/oo4jwEFMeMro2wlYG5/+wg/U/6sVerRRnXUPxlLO7nqI49uiTYIkKCDqQZw3XjyPGcY+yMAya69+cM9xk+LFDfx+hukC3ON7rJHGPtRJ5pwnyeK8TVEDcm+Nptoeod8xDja7AFAExsijlGcYCWQnEVhD9aAifEprPOanpUYlFTx3pi4baa0aFJdpKfqxO8bln8JKUgUQ6ni32UEpEI6lcxGtf4G5zZq8KxGXH+26OeunQk4FM9FTG4NXvwSdbT9o75NIiXzZEkeXssoATxyGLGBKFvYmP1x5O4mqDHFnCm55UDaRxh48C7tTzdD9F9RB1ntms5MtfXpNlHUtXsx7F9Lng8FpyW81pveHE1bDoIXFMxxXNd2OSNSzikqWrcReeh4cJfIjJhMVVim6dEK0CpY7IbI9delZK0T130AlunuYcgkE8aYQICCuIftJoF7CxJAwSPwpMpwX50PPFySPJaUNvFIVNmFSOpA6oSlCPBZFykAYsijiyDIuAedMgjafdpBRTgzoIxCdDdSLg+e/ZAo2ibWK4juhaQ6gUt787Zfc0prgdY4RnKmve9VOSSc9y6LgILe2F4JtsxaVqGMmey8X2GCjsYj75GboU/Gdf/8RfzW4ZVxa8QPXHayIzHujLiOLdCKsFdcjY3Y9IphVeQf5NgfnWEUYS+xhj1xGRsZhHmDUt1h83ps/rktlfHOhfBt59uqRfJ4RVhFCeYRrwWWDoNX7saWuDtUcS2FM5ZhI3DNcZF76iSxQnz3Zc/ZMnpJVIHRjPGhaXe/TgMKVHtII2KPzYU3UJ1YcxkYfeRLRFzFzVCBFQjWeWFkxPK7o2Yjx4Lv7pnn8+uWP105JT17JbjfEWPh2W9Bjyu8BORwytomkN3YWkHwUetlOaVcpqM0Joj3GBZCuolUFfdkjp+er1ivfXY7K4p5vCdFax6zNqrehPPOaixc0d21WOyCyx84zNUYbUTCXJXxVcLrdM/ltrwhcdcmzR3hNOBoIOLC52hKVFClhfzxhOJDcPJ/SNRv9ZwfyrHdGiw5fyeLJsOU4KRg6cxFnFs7MtsXckw7FhtG9zXK9g7DE1qFIgO4F5fsy12CtLKuwxd3JmKc8hOut4bHPe3Z2iE4t/iHj6YU5ycNSJRA+B7ds58vnAEDT2yqJzi2oELhOMtwKuHPupwkrF4b88Q91JuipB1R6EQDVgc8/nmzneC5IfJbp37FcjzL2g+23G1WRDvArk1xavAgFJ/VxQXQqECATtkaNAJWOcF8jcMtYd04sDziq2JuH8bMebbIUuBGkNUXbcwOEk0V7ARpMua7Kow2UBOzsSNlUHRAF/ZQkHRTTpCS8s8WlH30Rke4cfFFIPR4hZ7/AIrJEMMwFBYI06fr9uQKwU8kmRrI+TiPRVRfiyRT3vYGmRV8f3dSIseePI045mG/Evn57BTnP93SV9IjHCEYTAGTDpgB53f9D18o96sZ+OGtpMECc9uhC4XtNsErKsxc8t+rIle1WxfxhjC4PfakwbGH7I6YTCrwzicoDUk4kB6QJt0ISFxb8eOKk64jLgvKTtDT4JCB9weaDxEf2FR77oGWaBYRaQmWU2K4lOB7oTqNFcXGz46sUGlyiK6wzx5PhituIujLn82SMXX6658jvSTaA/RDQzuHPH8Xq7FMTJwG+HJd3oiFtUY8sobTmsc+LecaYqxqHjzRe3jG8dw1yxoCY+BFQlmX+zh8nA3eclpg68vnhk/M2OQR/bBIOT9EWEqzWHLqHTitmyxE4CKnH4lwPOHBO2Dz8ueS5KnvKMw8cM/2CQn2OG25iTruX+85L0+4hYBJ5+OMHm0EeS7dsFw1TQS0OXS/KspdqmCCvYP6TUq5S1TbkXE+TUIl92zOoe7RyjpMUbQXg50CSK8c92pBcNKZbEDrivOxZ/9YR75pAnlrvDDD92jELLvKzhcIQLjT8HovXxg5bEoyJHdNIdqYdCoicD9TcWGTtk4n5fVYP5uKA+08gsoM4GghAcDjkv4g1eQHoPIgDa8/OLJ5TzvFwPLLIWs7CsfjPl5mHO35XnrG4nFHcj4pklEp7BKtzI812x4Df3l5jxwNnZDpk7fmYfGO40Th9PEWbSk74qieOBdTVhRMfj45j+EOEVpJOWeG+JKwdGYKXEj3qi1zXhZc/aJhQ2JT1vsHHAx8ByIDmvMZlF1+D3hii1xNlA1MPV5Y4ksrhTS/2F5/xqw89HKx6eJjz9dslQRZwnJavtGBpFmAaSrCfeCsy9xN4k2FpTuZhaaN5Wp8fWxN9lDPcZ6/UJqglU65hWS/4v/9U3/BAm2MIwGM/Vsz2+1Zg7xfys4OUvN3wz2jD2Jdn1Mfj33B5YRDVha9hVKdfVgomuqZ4S5Hig6w2h0vx2t6T4ShOdtbxJVlBKLuYFz4eaP3NPNN9NcH8fo4Rn7hvESUNvAqKHL9MnDkPCg87YdiO08BS3E2wayPIelTrK24TqekRZp0SRZfnYEQfL4AzDOiEZPBUGN4Wzb1eolSRj4OT8gJhaIumoBoMsHG1tQHj0Lyv8ieVZvkPHjuSXBToajnS+bxqep3u+zdcUVYrwoA6Kr5M1X/knjPX4VrFVEXLe8+zswMmqZZbUqNjTf0x5NdlxoVq+uVzxTbsnmgw0RnHYp7SHmGKfU8w0bgGTpseM3XEz2jrytSeRPanqEQhC5AmAP0CfCT43C4SD/kyChCA98d4fN5YjSXOuSDae4KGdKewzBwtLPms5xBGykIix5SnKyHvLPtJIJygzg58d8zZSO+pNziE3lLuM/iZlqlrsPBALh1l2xK1DNTAWDYe7nG4iGJwi/wC61Zj5ACiMPVaW5XLAZpJ455Gxo3stGP60PdoA/+mekW8RcUB3EiECVRMxXxY8dGOqhaTzhv3DiF2ZMTc1o3aAxON7iZPiqCFeaap9SsQf9gb9jzqg1zwmJL9yiEeDmDpmSUX/QtHepAwziWsN5TqHGIRx9KVBqEA/ByuPTuQqGNJpR9canFDoNiClJB48Z8/3vL2Y43cab473pGHpiacdshNMuoHzqzVvry94rbaUhxR/4RB3gqyAUMH38QX9u4T4dCC+atnYjHzc4hvFXZggek/hMk5Fze1oRo9Bvo0x5TEBO2Hg4AOtllTvpkjjKfoIc3r8GdbliA5DoRLE3DKPLbO05Fm24u33Lzjte+7vxyQukPziwGY/QhYDHBSZ6sifN2yrnDjvcYXGjQTZtMGvJKOkI3wwrEXMo5cYD0+fRohDir7saT8nyLlFWkH7KSMqFfVZILpR+HDs/uIV1ZUn/ilFGrCZ4HE7QSERyiE7yaANPIIaH0fq1d2INO/oDzHSCkQtkIPCVYoiMkTK0cqEtBuI8hZ7nSBTRx611L1B9gJqR/ynNb7RTDrLNhpzmvdUn3OGK0t01hyVxdGAeZCMZzVBpjC1sEuYf7tDxQGjPZdXGx5VhvpeIzpJ1nlu9Ri3BNVYxOeUe3fCXX3O5Ks96aREf1aExuFHFuM8f7ot+KE9pXzpcTrQ3RjizvPLLx4YTODDRLG40Rjdg1dsno8wsaMrNJxbhk1Mkwfmqmf+akdUQLtOUV8ccB9GtDLCXwRMY1C5JcQWO1MMpeT6MGM8qlGpxbUK12jyaQMbQRFFDJln9EWF6zSDkySiJ5xbPq7mRJElWsNy3BE7z6/rC3qlYGZpOs0/vH/BLK9pyhy9hv5cszg7cBAxWe+PFbipYmp7toVh2CW4BKI1lAvF/eMC2xrMowUNHx5Oad7AycixuZmSX3Q4GRPywGOT0LRj7KmiWUi+9AUj5WjkGDG1aAtp0rB7P+eEmt3bmPHE8jD1qK1BPEXYtMXPW85eb+gyiWwEVWHoSkOtY5KiI7p0TIKnawOTkxJ7H9ONDPqgmMwr7vsc2QXCIcJ6x2JcMSSSqHBMXx6obcTwuse8jbDa0znD6v0Izi0+Fex2I2Z/ukG0oL5ume09oZBo0XNYzFCXDWeXBY9dzuzqwPXHGerEMsp6xFeO9fUYIqhTxbaJiNWALzT6XvPT1YLuTPIn6SPnzY5fJ0vSnSa/Ktl0M6r7FLew7Lcx+0PMclzwN0/nMJOIPVAoxqKBk6NcqjuDcdSwsiNiH4iet1Rjje4CBxEz3g10Z6CkgAqQin4GuvHQS8yTgFmP3ErcryxmMLhEIBE0XqOEY8gCL+uK3ih2jzO09MgYPv94xvnXK5pTz6FPkUvPIMB0HvfMo0uBlp7OROSVo5eaXUiQE0vtJGHQ2KmEg0H/O4VegmuPY32vgMwz7FJ0OCLSWSu4M9hJoL2EIDXJlyWZ7Yj/vCE9q1k2Jd1qxOZ84GmUYgrL7noKwMLUfP40Rn3TYVvFxmWcf7mn+rREZY7ZoqRxmv5TgmmOUq8/5PNHvdhL4fliKPhHe4GfOqqHmPO45urnj/zj52d0RuErhSwloVX4RFDGBqEhu6hIrGLrUwanWLYNVRRhEbSNQbWen8ZzJnnNRua8UFvuGdN7g+gNY98zxII8cnyx2DBLK/zcUdUJ/akjXvRkZqDqE+Sypdpm1NZgBoEaNAZY7Wec5AWTwvE0yli+2BIeNNs3R2a83Hk+7k+J8x4tHWlw1IeI5llgoiV3mzkvz54IXUI/aH7xz9+y+jRjGnp+c7igP8T8Zn0GM3dk4CtFLzWjHnRuiX+EtkmIIshGjmfzPT++O6PYTGhSTd8ZZrolXoXjhkoEDucSmyn8x5RggMLgrUdVAptLCA6UxybyOBqceqIyEAZDhOW0L7hljk8c+efAcOLxQpPeBWyn6GeSX83v+fXNGcOZPUpZhKeXEhoBg0KeWOTrmvDeEPee89MD77dLDjYhFQM0BptJRtYSF5YfHs/JzxvqziC0h8JgFjXcKp5fbng/XLAuR7DTQEwYe8o4ZhI1aGVRysJmfMTxxj3tQYE5bgiLLkNoj+rBTi3lNuNOj9Cp5X0/owsRda8ph5R03LPb5ETxgD7tYGX4m7cvIfXkfsAvPOWnDPVy4PF+Qj879vizqDvKS0qBmlsO7zPcJYRdRllqktgR7wPiZxWu1ZxuHbs6ZpF0tK1BVYZiN8JHMFY9XaSpY0X0O83FL0oebUZRZIziFr8W7KKUs+keF/XE+0C/CPzw7gpz0oGAZ/mWeptT7lP6k0B5nxMuA35kmacVc92hhGP3m9nRVuc1m09TQgRxbdEVyMYzeic5fB1hkoC+SUCCUAFTSG7dGcME9O8S/Nyx0AOHzQSUpEslI9vTdYapGuh+m5G0CpsIxCaiP3NwNsBvRzRes5x0HEJKfN7R5rDtc4pPGYu0obSG6Vl3VMOWjum0oBxi2kEyHAyunvDixYqOjGqTcr8bMQ49duHoH1LaxxRnBGYjiBKPih0yeIpNRrIcUAKe6YKmjEgvDtx1U+xdxLYZ0StJ+9McAchOsA8BNwpMd4HdLKH3CrvOyN558rOGbZ4SSUd/IlBrhRnVvBVLoifop0eAlDaapaxoK82/Vc8ovWIcHP+4v2SR1Dyfbei1IDeCe53SS0X1mBFHllgN9LFExdA0EXnbsyDwT/70M3+XnbJbjzmsMsQehtSxtA3dWOMGgd5q4ibgLy3J9tiVF5mjnWmcMYS5IFpJhDlmbVTvUc+Ho6FuENzcL3jx5ZrbRYr4dJx4hbHj4XFG5B0+SHRwmIMkmzcUDyNk4aimYBq4ePPI6v2C9imCODAISdjBuByoEkk3F+itR04DqoF83tBVEb2Gs5Mt+zyhDjFSepa/XNNfp7iHmHavWfUx3hrUzpMOjkFoxAqmZ3ts0DRDShgkn96fM0wDYpXSTwPmfcz6HMTLjvg3Eft+hHOKSHrUWrB49Ycd4/9Rp/H/+//F/5BGzHi4WeJOLLbUzPYt5suexV1LPIeP7+YcXiuslGSNpckledKwWFSs/+aMaimI1oKkcXTPAqEPUEYo4bBf9yT7gPqyRr0zzL4+cPvxhPyiovhuiow9Hkket6TekZiOdT0ifVGzfhqT5y2+MuhswGdw+DDhX/zye/6f779AbyRi0pC96rC1Zl9mxFmPd5JOHGUw0WfBep4QbzhiX5VDWYGLApfnG35XnHNBSTxvydtAtdMo7dGZZ+szunVMX0ZM5hUhCA4hYRbVHFY53njGWYs4t9Tvc5Q/dkzbU8G46mm0QewlPoLRT9BcCpwWGBcIjSBoQT8SKDxSekKp0E1gSCVR6bGzAL3g5KqkOyiabYIQnv4UwkEfa4F1wD630EnkRmET0N4jLgamoeUgIq6ebfhYLOFDTJq0hEpjZ4Fk2dD6iGwYKJzh66tHbh+WuDNHeTdikRd09hiwGiUtbmWoU40uJLrz+EkgjDzKBapYI3qBsgGpPSa1WCnJow4aSXTW0HpD7zX+U8zJix3jtufTdklkHGJ8/B26SCGHwOJyz6+qHT+uTvmoxyAhrQJf/OqeDz+ecKgyTi92hNyDlZy1PW+LOWnWE9WBbZPSnUCqesxeEi8bvBKsQ0y0FsiDpL+CpAzYp5jorGVa9WwXGnMH0+cHhNNs1mN8fmQJ+Cgw3/fcMUYbzzSraf3x99apQzRHcl7fRjirOAk1fXOE0Tin6BvD8uyASwLhU0LfRAxjh6gVygaWP9sQWU8hI7I2cJhKDj/MMMrBFqJ7hb0KhF7ggeTRE2LBcCHQt9DOFVf5lt31mHqpSFYBF0n82DNNKx6TlHnfozKLWA5M+p4+E2yLHEnA3iV0QjF5dISrnnqfcvLqgH7RILSDVtE8JLQzge7AV5pR0nJ1ueb97SXLUc3nm+MVUDSxuLWmu8/wUSBVA0MsCE8GcdWT9Q6Reqoipj/1ZBcV7A1WCkaiY9jHR0td5vjm/J5/fP+SNGt5ebHmYmO5MTEf2iV9ESHeGZQJhATsy4Fcd7R3GVYKTOSIRh3uThO/6tjXGaFXcFDIyKGFo/URS9fQ7w3DmSea9XRvc5yH/ivLz6d37J/G3G4WBAP/0Zsbrj/OcL3BzS3yo6A7VaSlQyeOpo8JrUJsBP5PWs5NSW1j2lqTXDT065hoJTnsckzjCaeOoYowVSD8fiwtPcdAnhQUrwXj1fFqrl9HtK89STQgv9P0UUT8Zc3rkyee1jPkTmImA0wHDjal9wrxSZM9a6gPCfqio/ztFKxAhnDMRwVQ/UBzIdFB4KRgQJI+evCgO8HRfwk2O+ZTROKIIotOHBuXM4+OQrU2VsznBeVTijOS0IvjPbcQ2ChgbtURCZ7AJG359tkjP9yf0OxSxqGjthF9q/H5sVGhVGC0qIkbx67IsWWEPXGYaGB0I1ifdbz9H//P/0Ma/9/n2TU5q3qMNYLwYPBCUUxj1qsJ790p/04vKaYRp1nB2NTEeY/6fYq6eDvm5GxH8ggEaL90hOqI/1Ta4cYeUUnkYiCRlkrFPKxmjEtL/CTRIweFYBCCtUjZC8P1ek4YBaa9xxuBUGDyAXudMLoPnJzt+YfHC6QIMLHoBMpdxnxWMB3VnKkDsbOIRmKVIP2mYrSoqS9AlR7XaL5ZPGGawO5hxHxd0r7NWH9c8Onjgv0o5WGYcPP5lPhOkHQOnVjazznlOiPRPc3nHH9i8RPoFwI5tmTLFu0CbaKRhaDdpMinI3pWFJr2UhB6iVfggYDA5RbdeJT16GmPasGmAmQ4nrC8YPL1Hh9b7MKzeLHHpYLUObLOYkoYMoGwEqUDcmKxI/AZDE6x7VJCDe+eThCZJ39Z0DyHZimwZ5a6TujuU6rYkJz1PLQT+taQHY5AI3HRk00bXAz7kYGvWmIzIHKLuxrQFy2MHOOLAnQg2gVCEFgUnTV0vaa9SWlrzdNuwrbIYaXQjSdYuD7MURML3TFsk540TPyBci7ZtRk28ezvU66GgtgPuAC/fX9B6BQycTQTgZt4OhQPh4yvvnokvnek85ootUTTju6QEJ+0bN1RzJRlA1oGTOwRt8ckNMbTKE0zFoSPhk5F+HM4qBgqQbzoMLOe+JMmmvVEk56XV4901ymikUwuD8R5h0oGTlyNmQyo6NgIkKcD57bC7o/p9837Od3nnJ/9xXumv9hgdoFQGEKpkQ+KWd5RrTOe3s9ISk86DMhPGhcL2ssAB0k7koQM6l8EwqXnmTkQnlv0rOeTnGIXAUWgPw8k0xZRS4bPCePvJaWL6asI+zcjHr4/Ib3RyDW0Q8z8xZ7J84KTf/5Al2jMryrywRHjWR1GFO8ziqnm8DhmKA3jswr1vOX6txe4G83NfoyLjmbJs/iAReFiOJnvCCFgpGNYBMStpnsyDFby5ptb9Br8h+T42roxdNcx0bxlGxL6IubXNy+QASpiBgzXbsyn+zPqjyOSysO5ZfrtjnO1wt9IkrVHa8fp7MAobola6ERCuIuw+wj1ILEGOiPp2mMv364iTPCk85ZXoeDqqwfyuCHbecrrMbsuZ/lqz+vlln94OudJ5JSRQlowP++QjeSQRHRPEeevVyRfFFRfBtx1wi6OiLD0m4Run/Lt+ZpBwvLVhux1iTnrGJ+XCGMhhhAJmhNJ85KjQyO1ZBctdhXRzgTxg0RvJd/+ckVaOc7Sikh6Tl/s0ZcNGx3RPkSYrKPoY6yRNB8zGjTFhwl0ErORmNUx0W+vLDYzBC1xlwN0gvzBIy34WCLnDvd8YHjmaJ8Fomcd8szS+JjCx7iJO8K2mpjJ0OEeI2IbcLXBt5pBSMwBMtnjlcB0Ryrjvkj5+795TXkzQrZwaBKena+QAmQjSW4EVgrKdYbvJPFFg5CB+aQkPBj6E8fFdP8HXS//qBf7Wzdm2ER4DX7k8csB16sjZnVkuXIlkXWs7mdMVIdaQ6oGPn4+pxgbysSgnrW4pYedxlTws28/M25a1MQxbzvObcms7RnJnuFTynCI2LydIT6B9foobVhrpq3luS1IO8uP9Zyp7Rl+zJj5huWXGxRHwE3xewKTjiy20IQKrv/tFfvrKduHGSe6It0P9A8R8lHyH4/uSJxndNVwNt7zw+OS6irQiAh9Cj4VzKOKJB8ouhhfa3QBq2GM8AJKRXZeEy4GzHnHcBKYDj1/ln1GPhiKv13Q/DRCfJAEB6kdmIcWcwjorUD+Hg2shuOLOPQKmx9DaXrZ44RgeEjoJwJpQQjPMIJ2DqWLObQZG51w10z/v4jLPDCMOW4MGo3ba/S9JHuA4ATqXhP2BjtVjPaWr59Kvv3ynuW0PPK/9yneS0g80U+KL6ePpHFPexqoY4W+anm4XbBVCRc/f2KSNIRGk102qK/rYwhQemLraXJFJnry8xqdWJ6PNsxsSxDQLQPtSDAoiawkLgMbFNU+w5x3zG3LyZ+vMBt42o1Z7xa8kAVvhoJ3wwwuHD/71QP2yVCK6Kg3Vccd/7COqT5MGKQkvaj58R/P+frlhpswOaaXHyPOTYHRluAFfuYJG8OFLmlCxPiLAyBg4XBpoMkVJvU4ISnvcoa/z2lUTPPdmOJ+RP6q4HE9Q0WO+6cZ6YsKO/NUu5x9mdFXEeWQEjlHpgai4EmajjZS4ATSBSJxDHC9/+45+l4REXBxwKvAw/2Mt//mnGjU0194ijKjD+o4si3UsSK5OOqK82lDkg9UM8F2pJguSsZJRyo7hAnHnvYIZrMCnzvcucdP/fFv38c0I4UuPeu7EaM3NfFlzeMkoZWGt3KBmlhGWcsnOWKyakn2gumf7Iliizjpmb8+8HgYE1nHus6pDzFp2mFnDoPj9t+dMDSK6cuCZOZQU8f8csd8XPDirx54PtlA7qkPCYn1COmQhaSfQiRg2MfoHgYtSGcty2mB20V8+O0Fn8op1X2GcILlqw1R0VP0CeOfNZx2DathTO80u8cJZZPQBY3PPWuTHjvgMqAbyEvHuPr/sPcnv9ZtaVov9htjzLpY9S6//VXnO0WcqDIjgazgQjYACRkhhGSJjv8EJEQP0ckeEhKIBj1EIxHi0rBEy5aw8MXXpKkyIxPiRJz6fNX+dr3qWc85CjdWKKz0vchOsEMOyUOa0t57zb3X0lp7zneMdzzP89NEg2Hx/Qc2xx5s4fPLE7a3I4owxAss9/3ooGGpJOWbiKSwCBwuN+yLmD0Bo3GF2HmUTyTmNqBoY947ucOb96RLyzqIiL5bkHeawFjscc983NIYn9R22Kc93bcM9ldqvvXRW148fcfZYs/0u1sCNTA7LRmN9+THFdNZgfEFdeiRfVgQHdX0G5/124zrdoL34FFFEbsfzRH3Pm0d0VUhrH387uCG4qMGE4D+0ECl6E8t4Z0g/k8hqhTYWGDOzMHP/+2a4YMB71slWdISTDoG7eFyi9UK/15icagSdiZiI2L2YUA6qpG5RsaGvvdo9odtJhKDFA5VSvwHiVc6RCUxU8ub2yOsBL9ydHMQw+ESbQOPJOmRRz1VF/K9J9f4E02ytz/XevkLvWevjENHEN5r+lOJf38grQkF7DzutzNcaEELbtdTgraHFYw/KiibmNT0ZLOaG5UTVIbvz1bUWpJfFJzkLW+DObtuhlgebvJOK+rew4YO8xzUyqIfDYSNo9r7bHcZqjCkCRB5uA96bm2OvJdQSoJNh7wwEEC/D5gle7bTCLU3RKOWyNO8uzvCtYLktMFPNP/l7RnyqGcwkuVujDce6NY+vh6oy4yLbMOyS+kDiQoN1vm4kcGMHFWrEMcd7SCJCmhcjFsM2EpQ2hh/B50n6B5ZvEIRrgTN2KfJBZGy+Bcdw6sYkzisFeRpjUo13rRnV6T06wAnDznpw9QgShDCMQpqxFSjK4VehXgjwAh0JPBaR+dJVA9DJhDaET0Y/MoxnB5AQNHgGCKJqCV6IgjzPavPJsgZBMFANHKHQIzCpwgU/+XrZ4jQIaRl62LSvSa7srRZyiaKmMz21NpHOksUGoJ5x+Z1zihvKHcBtfAZpCLJO9Zzhb2XjESLfgjI5g3d4FH1EaKSuNxRex7P0yWViHlSt+w/7Lj5fEyStTzNNnz2zRP6xvHoxYbdLsUdQbQ2WCPonoASAyYA98rn7GLFe3XJfTzlR/sT9FygQkdQW277ESrpD35k1ZEHhpuHMUHaU1kfj5ZwrEk+8WkeeyRPC8piTL8LsaeWIREwHvD3ki0RNnLkvWaXBChrib0e0/o8my2RvcdWhpR9AJ7lkaq5mcTcb3NkL8jTFuM8+kbSKp/0vQolJWmmcZcB3QiMbxB7n2BwuMrDeIIo1hRTj6OgoiCitYdJghvgmVpyYRr+wJxyNtkxahX3JxFch6SxobI+dqIZGkN+2tINCWHU0Uc+ya9sadYR7VVMOu15UTecTAq+MDOuVUr/bkyaNdS5wt3C1f2c/DU8ebJHf5FgI4+oULxgw9snGeZdSPx8OMQ+nzSE+KSu5aHMaa1PJiUmUFwOY9yxwg80OnD0nkff+8xOl0RvY1zsUPOebGQIGhCl4GY1ZXpcwOuAxgrCao/+tmJZjAligblUfLN+RDM9ZHx4fk8b+uApXC1QW0U07dHrkOZM4lmHuQ/ock2uOvTeQ2tFqyM+/t41m28iyjImmGnE0YC5DRlqDyc8KCVhrzAdBIXAv/IoNjEisMhCcNtNOJ5tqDcJdmS5rzNcCzqEYNFxbRJOL3a8+eIYNTLUhHRriR0k3Srmcr2gmElk5DDXASLXfB3n5BND1/n0r0OEgK/djJNna+73Y5K4xzmHt4dOK/wbS9gPmFjgP4jDtsBU47byAPgqPTzfESzBbA7QLGHALAz+hzXD24h00lCpiH0VMQ/3lDcJ3l5SDglDHZAUmvGLHfs2pOojwsCCA5EaorwlvBNMZE2pPezCkIctxSNJuU45Tvf0vs8gQogtXRcg1oIka6hSnz6RpFkHaw87NjgBfedjAxgdl3x1uThos07rn2u9/IUu9s4K4kDTzb1D0lwNQ+LhBpAzDT3oE4O4C/AqQftEERWGKlZ0e4+tjMn8inlWIBP4fDPFaUc79gnikOohQoUG1x4U2NNv3VG+ztHHlouTBz6rH6M7j2BpWU18okwQaIdaQp34tCNDLDRhbxmFJeo7UKwTplcN2yOHq32eioKvsgWL0wqzC5gdFdxfTagvc14lCV6kkY2mus9Qxz1aCZSn6TceJoMHUjjtEQ8hQSHAGeyJZrHV7PYpwnOIo4E06xhuM4L9IVXqppgRTgf8G4+0MDQjhY0g8TVlqhDjgcr6iA97jt4NPGQJF798Rdg4Pv3sMeGLGjkZaLYHLK3fC168uOF1M8ZoyUnY0u59btOIcHD0xscbDGZhkGsPHYETQOjoTgRFLPFqgd/bA3N+DGrwkO8kxUnOazMj31UIafnlZ2/46tUZInb00QFZ6xw4rZgnNfKkpy9TkuMOG8JaRAT9IYxn924EVhBIQ1nGEIFVAicdj7wdt9sRVQjqtc/gScohREcgQst4NxCNO2rjs7sfkacNP/z8jCFSTL7qqacRr4Mp3lnHfh/Te4Kv9hMukg2vxZRTUTA0IfF5gZVw8xzqdzH2ccnsaM/1wwx/bdCew9dwfLqlvg7wM8evvHjNH+6eMqQOb6XQTpIFPU0dYl4IGCS725xR2hHGA0uVoYxjWgwMs58GlZyU3L+eYcaONldkemA3FxSbhIqQIDjkRXit49PrE3QOodCQw3hekKaGYQf1Q0y4dxyf1bxaTUmeVZS3E6JpR3Mb4t15CBzy1DL4krB0qI8HTB0gN4po0pJ6PattRjA1lMuAah0S5QMYj2qiOO8qjJXYxsc1iubIEu4MVRagdopMGnYrH/aGQUcsvz3w9eUFbedjE4fvW/xXhu1pzjze0xRHBNMaZz0Sv+PouGO5znBnUO98okWJfw9x3HP+4YrLyznFPiU6bWivfAyHztdxuOP29Qn0kvVojOgEUglu3iwIpaa5S9AmwiSW47hkeBXz6Mkap6E7dkTXIbOLmkIp1jLDeoIu85GzAXfjo0OHtwkOufC5gdbh3EG70Ft76CS0ApM6gvMWceWxig5JjZ0OuG0z8u8WJKuSqvNQneHbZ9e820xZioxuUKS3mvG2py4O+fwmd4i5Jrj0MGPHyqQIKSiFhycFqeip9z5V4RhSn/27I0QriWVPLxVBMhBcSUQoKLqEZoBg7Q6t771A30XUxSGJVPSHgJtk3GC04nYzglqS9QO+b+jWCdYZ3FaQ+z1DL3C5o9cKpSUmFoROE7SGYhTgx2CnliE3TL2GOGgI/YbLfoqIBkQR0oYhYnGw4nU3GSNRU6WH8B3XeNiJRc1bFkOLPxnYuog2DWgI0Bqy44ZANujNlFFeEwYDbSTRVUc0tljrUMZQtQFurBE1eNIw+AqDREnLdGhxWtEWPro46DTm4+rnWi9/oYu9jBzR45JmlWNaiX/S4RWKQYJwAtlbxJXClYLusSVsBJnVJJctlyKmiTyGh5yk6Gm2ITSKofd/+oFJ0tOa2Gm0Pgg3ctfRT3wmnkaWIclNj5l5eJUjmGn0TB2CNoQiiDv0Q8iQCLrcsg9TTv2C8/ce2G4yvmXXWOmhrWJmGspNglPiAFrIIIk7uiJA3ge05cH/u0j2aKN4kCm95xN0BtMckphkbJhP96ymCUELUdjRnjtGJw0T1fLF148YTRrqXUQiO8yg6IzP6LhkMwuoe8OfEe+wpx2fffmEbz1a8p+Wjwh6y9ZEiMKx+3zC5n5MuxAkkWFXpqTNgE0E/RDwVG1ZXiXszJTXfsyQO4JKQiEIlMXNLXIrafMDXSqpNDo9xAyjwMYgYovacfg9C0ZI3pUzXAxtFeEELH90gnrSM1yO+PB8w9fbBdK3DFJQXGfY1nDx8fJQJJyPt3FkJxXLaoS3Emih6JTCCzVyI/ADhw49vuCILGk5GW1YDxP8a4FFYLYeYWMpYo8KQTTrKULJ9ps5cadZHDWoPz8QvEtQwC/Nbvjd6JSbmxnmVDOOK7QUNEJxXpUkquD3X75gnNb0oeT3vnyCAXIacm1YpT7quGff+9R5xHvRFucJ6s7HlYpuDMnGYlYR0fsNnfPokHhuoHuZUC8caqzJjgqUMey/nCBmBwLZ8XjH1X7K6bMdupPsrMd6nzG52GOE5CSo2WwSVGrwY02vffyp5qHJuNl4BPGAnkr2fcDooaWrAsY0+J2hvEuJWovxgUFidxI7GYgmLadxSf0qQ5w1lEVMK0O0knx+c46/kZQupxprbK8IBwdOsSty3CmYVqHfpYyeFAS+pbrL+eblMaO4ptrFOGupbqY4p5CJPSCinUBcC/q7kJs4pT+HMlP8Ke+Oz5izKWLExGBeJTDVFG1MYgak63j5MGfbZSgnmHo1GT3VpxnqXLO8nBGIAXWkkS8DujOLMT6nrqA4EgSvFf7JgHwQRO9rfuPPfsb/9ZMPaV/F6AuDNzd8lc4Y73tc6TMUChE45NIj0wO2kfjznq5OCK8l2XlFMXEEkabtfVTWoweFenCob3z00w4uPbKLhpO8YOlFLC9nhJWh0BGdgi8rD/3EIGoYjyuaPma7jwl8h0UiCsiOKvapj3frYQOHGCDfOXTl4WeCMDeETwcyN9DsQqSwByKoFfStxIYCfyvozyypP4D2MdvD4ySOYexQtWR4PpB2A10VkDQWVUmUsvAQ0AcW2YMN5AHZfR9iE0f1FIQGbwvZtaX6lkSfW0Tt6J9YjrIt8bRntRqTDXBlZshRTz/4+POOwROk/UCZS8qJJLSKrK+5a0doXxAPGr+Ch3aKf2/48E++4dP6nLzvKauA5uuMPozJnlZ8NLvhP759n855PHvxwPXbBdG8xbzymb+3ZTckRH6P+TTh4teWvN6NGUcNV5cL1J1keNwzCEkkLe6z9OdbL3+uz/b/4eE8x/3DGHnjEcuBJBjohIcxHkYJtFU4BcwG0kajJejHBnsGR3GJ2XnoVUjRRojMIo86kkflIS5XevhCsy0TVKbZ/XjK629OWLqM+zLlJ9+c0Tz1SVRP/QGk8wbPNzAInj7eMMzB+aAWPbHWUHhcXc95/eaY6Ape76bUY8ft9Zidi2icj6jEwdN8scFIgY7AZg7fWMR0QH+ToDRE84Zx2JBGHclxzfx8DbGhrCL8wTHLC7zHHXqTUP9kzHqVI0eaIB6Yne+4DTNmT3YoX7ONQkZ1T+I0P+mPeL1bYNYBn39+QnQpCH/sY4REnzlu95NDbOydovg3C9y7EFOF9NsQPXX88PIZTRrhRxpyQ7Rx+L3DTg0mdfSnljZTCAPR2uFqSV4dAEb5uAHP4d8LRK1IVUdQguAQaqStQkSW9GmFng3s1jlu6XNXxXih4clsiRdpkucl8d7y7s0Rbe/jPItpJc1/mSDXHrIXnCUFJgTb/JSM1knCBhCQqp7ahLTOgwC8FtJpjf+titFFiZxa6mWMfRvRB4r1UQgIPkrXdFJw2U74uh1xNPRMXcezquKb29MDoU1J3IVm1WX4oUaXPlE6IJ92TE4q2tSn+6njIbxRqFIySStmWcV/fPUBbuURdwPRPVycLvkouafRPq5QhGqgU4eV2XhcsEgK6uuUpoyR73eQWJJJzT0p+bimLkJCY7nwdvjWMt0PHD80vPrJEesiQ+HIZM/MrwiWAmUN+eM9J0cbctvhvpbswoD5rKBdxsxFyfje8PHHb5jnW5yC0agmDzs+iLZs5cG/zMpDFJL2JqHdhchGENSG0XnB7GgP9xJ5OrDd5DgjCR8EWEGoNLs+ol9HRGlP4A3UqYc+d0SlI9iCWkt0r/hT/i3zqkGcG86mFdHzFmck2y7idy+f0mkF9x4z05A8KxCFx9D5yJFmU6WU/zlBNIJHH97h3wnS4+oQ4nLrHboNWlLKAHPiUInh9L0l6yGhsx7dM4Nyls75jJzm1eUcs/NQ5x1Tv2Oytoxp0Q8eajwgAoNnDkecdAxKMRQ+fmOJv1MgUrCtgkIysj1T1RJvHV5gCP2ex6pmdrFl6yJuVU65TCjWCZs2JUk6RGDpUkXzcIi6Lncxk4vdQTMzCPTcgO+ohwBpHToB9dajCyVCHLRQXSDBCbpOUbzKOE0qRvOGo195IP3ODjc2uJHF/amK6P5ABjRTi5AWE4ObWOTxAM865KKnnQn625gykxwfbxDOYRPLOK+RGoK9I7poOBlv6VJF/pkkfScAh44FYqMQAo4fbzmNS6ptxPJ6QnZUcnM1p0+hG3zi8wrpW0Z+x4NOOQ8KctmRmoFO+6Rpe9B3WUszVrjUogy8+tEF5jommdWkwjJ4kuO4QGj4w//wIeY2gDuf3TLj6NEGpyX+Y037LmGoPVrn4znHL2d36EEhjeO7ckWXS0wZkSYdadayluHPtV7+Qq/sTacIjcMXlrYMIXT4noZKoozFhDB9vqWtApqvcsJO0L/NuTnK+f4Hryi8EP9pT7HKmAUV5S6hMAFhAXYiediN8Z2FQlEdCVzlEQY9IjbY7gBfqBeKQSuydsB1IY3x+aYaY31B+KgiNJa2iZGxQxz1pLeCXRBxEZTcfDWiH3mkqiNaCqqJInreoC9jhtrHx9AvILiVhK2lyDxEI6guQ4Tv8K3FGIFdQS99wIBwXN3OyaYNRoG7N2zSnNOLLeG95dnFirWKaHufo1HBfp9SlzGTvGO3ShnuYmxo0VIihEDHAn8DCAHCofYSJ37q6R0sfShQ1uLdC+oswPtaYS4sulM4z9HPBIGzyPgAphgqhxgEw8wia0E9hKgHxW7m4zcSpw8XdbcOcDODKARhb6GWuCFkHSi644qzoyXL/Zz+swAVCG7ynNgaZtOSoewZNhOStKPUIaOzhq3KD3aY5y1dDcnQ45/1NJkkvFY8/XjJ5zdH6KnFbEPOFytKldB0AaO9wds7ypnPR2bNzTDhdsiIV2DODTe7MXvf5+m45O21h37wuAoydOgRZi2/vFrShI79VcirYYa5CfGCgTDpDxarrWDQHl7r2AQxWdhSdD6P5I4sbLi5mWAqH++0o9lFxDcO9aHj9XmCrTyEhcQOhEnHEEo2dYIJHV7uENrheZYo7bi9mxGWDj2D1SbH9YouChhlDetPJ+yeKKJCUKQetBYdKPK8IcigfhNz8fiO5d0EIkl21iIGS12FtCOFjyQ52/Pjr54f/j9SR7MOePRkzydvzvF8jS8tlQmxTuD1h+0uXw7oTLHSKdG1xT4x6FWA93TArAOGBFTj2M8kXgnWOMojyfy2p1pHqMgRn3WYUJHaFqaOl4yo2xg/6vhGKTxh+OjRO969mfMeFT/ZHjOaNrSvQ05PSvo0oN7HB9FdE+CdDHw3v+f6LqMsUqxncMce4Y3E24AJD5HZ6aJmXaWU29EBQfypT38KYi8Iop7PvzolSBu6M/BriR+2bI9CtK+YLpqDOyE3tNcJ3VjSpYLhuMeuFW5zCJEyoSOZNFApsnFDtDV4VcPwyz3LJmfZTghCza+evOTrH19gs8P+NWPN+OkWsUuxNyEMjtxv2IuQ1O+QnmXy/Q2t8BEoSuNhrMKmFpwgeKcwkcAtDv4b79pDrzwGY4nSBnXccFeMGF7GOCngRUd8B8VCkmvoCo8+PpjeRCWIz1uOkz2vvznFTzQ6ssgHn7qNiANN34RUdYpeWNRWsG1TqolPcOvQU4e0gBGYhSaIDbOgphk8htuY8fGeehux/f3FYbKiNNNZwd3dmMdqe0jUyw46l/lQ8RAluEoQdZZq5SNnhpNlw3WYMfQerANEAuuvJiAlqhDc9xP07QFPPhsa1jqhfZXihMCVPuU2wncafMfj+RJzFvNlM+HpfEm3jPimHJEFJfaRo7tKaKcDu/jnu9b+hS72waylb2NGxw21ienuQsKjjqDVWKcw3iGn2qvBBSAyTZj0tH1EMwTou4BnZyVfKMuqyBgFLeJWYULBSVNTeD7Ehsb4HJ9vufnsiKEKyVXDOKsRG8MujRCdY7PPGKSHV4FKDC6yBNqgQouLDYFnqTcBYjWgJ5LNkSMbVezeTKhciF9a5ETTbCLCScvTfk15JBGB5WGeMh8q9p/McAvD+EWFtxM0bUA1UbS9JHkHNTHR1uE+0PjtIZpS/UbH08UdVy9PUJWkfOMjpI+XN+jHGnulaV2MuEyRVtBPHYEEV0h0egBw9IlgGEG4FLhYIzpFWFrEuKcvQ+AQQzv0AS62JGmHJjigSq8BJbGloskgmfQMhY9nHNYq2vTgt4++PnxeQhraFwax9LGpw2sVZqcghXDaEu4dvQhZ73zSaYM6sqgGqrcJ+bwlFoY7k/H0xS2vLs9Qs55u5zM9LkncQNWEmKklFZqh9Um7Q5b1Jw+HfdjVcsQiKmirEJlqgquA7XFApDRuEHw+HKEWmpEteTxu2Ace3drjl7wlVyrEazVX3phOK9KvBpZmzJPjB7zAo/Y8bCO5SLasZz6xbBnWPvVdhP/tAvETSWAlJx/t6WYe61XGMHWUmQe1o7uKkaHDTSxvhpw6DGEHoj8IpHZ9QuANWKnw3kiEg/oxtBuPIjDEUjOcaZK8p7xJuV/N+ODFLcVcEpSOMkgwZ47R0tIvLOV9hrKOgpBJ3NAuI5bLMabxUPMBuxfIrUA8GRi2Ptp42MTgKok37pAVvH05P+SPC0vfCmSrwLMY70AbjDtHGzvUbUDfWaJ8oGo8+kHhK4NfKob0sAd6FNXsVimiA5HC6NmW3Talsop0UjMyA2+uj5Fee7hpjzWsfE6er9gvM3wFcufx6NGe6XHB2k+4sSM64ZHGLU+iDW2R4h91vL6ZIo4tvT58Ztm85fnRircPRwzCY6Q7+jrgSbem+0BgbiJ0FjG+0ejnjjLwGGcFzS7k4/iOd/fHrMqU+lgwHjrWXsgHz5a8fnnCD375Na+GEV2o2LcRg++RZxX9GOxdQDZpWYxKzqMtv3//FO9py+6bI85fPFDsMtplzGYSU08k46ShGfXM8hI3SIbaRxaO6Xc2qNGAfvC4fH2MjRw2s9SrEDNxuI0kmdbo0qOLfURqYX+YdFhfwFFPEg7krme/C4mSARlYrBFwMiBufGrjiOKBoo+IzhqGm5Deh3w5kMQ9V+2U+FlN8XJEPGnY6wjRSaS2+KHFKtCJxMgDMMdsfYJnDRiB6wXJYFmnAaddQ12HZN/fwbzm3e2CrLMMyQFUk88qZKKxO7gPYzJ90LGU+4SPsjXtZzHp+ZLsuMWbdqw/n1Kc+sjB0SaCbNIgC4XNwbwOEWNNmwiCrwUuE1gnsDNDKyB4E+CakMBYnPBINpr7YUa3CtkOAVUdY54O+E2AUBL11eE6PKpbav3/B+H8vz3EzieVLWsbYaxkyARmFSAAd+khpEV3HtVUYs57gljTtz6xN/DyR0/QTvFuNSLbW/x0IP1wj00t0ajl+GKNpyzP+hprYPnljPc/viLfdITW4O8E8cIhVocLf7Io8T2D96glCDUpPWEyYIdDdGwU9Ixch/+rNcfPN9R1hM0cjA1B3lM/A20VUjpcL6lGHvVIkvsdR3HJ7mrMEAm0C+DLEHMdYnoP9h7qdcBQBXgNGCVwjaI0EUI5hsGjus/Q5QFwoh9bTtsdqzbh8rNT9iahjyV9KuiPHMFeICYarxLIDrojh5mbn6V8dUeC/Fs7mhMo4wAbOHzfEGN4JlbI84HIDkSdZXa0x/00BS84bglKR18FGCmxjcI77nj2y+8O5K5FRcBAMOoZie6QlrWGbFLjznuCxzU7EVPsUspVSrWP2V5OuN+MKYaQxfdWtDqg+cOE82F/IOjlFmsFwXEHtaItItZ9it74nJ2vmT7aU3Yh9Qmke0OWNZw+XrFpMh7HNf1VQhGH2J1PfxNS9wF9KAmSgdb4fJXklCqg8zx++OoROxcxrAO85w1PKJDfcQxPBW9Vyn0eYQfLWVKyP/JQo4H7dzOqNqKYSTb3I9TzDsaGt2ZC6zyqTUzfK+xG4rWC+YsN3zu9Rp5qzKsE78YjOG5QkaFv/YMN79yQhi2Z3+I/bQh8zbxumacVSW9I+sNq3FnBxfdveHARXRnQ4zHhAATqFo5+FeKsQe99os6y2Wa83syxU0c/ElSeR+crdCgROAar+PD9S9RRjxs8bKvQIw87MihrD9enVSjPILRkODIobakiHxVajk43yMWAyyxMNC609LND7oK/h9RqgkRjk0OTqZs5NlVCvwqhk2RBx7tuggmhuknREey2GXKpuHmYs93lBJ3jchoitOXtD2e0RUAcdkxGFelpxdVqThINqE5SuoT6TUrkepJWc7FYsvcCLt5bMhpXlIQkJw3laci7mwWNy7C+x/AMgkmPXHuEScdEtLy5PaLSPl5gOVYVF5M1z+IVbx9mOOX4g/0Z6zpDa3XA3naa/hQsAtFK9kVM1Qe8vF/w4vE1MrQcL3a0b2NkDUSWV7cnmJ/EVFXI437Pw+WU2/2YIO7J39+jGkd9mSALhwgsI9mhlyF9IPBSjTjXEDi6I/B8Q/qoRBwNh06XEyyigotJwcQb8APDc1XD2uM7j264MBXxqGW6KCjxsVogOlDPahLXIy40Q6coywijBdPjPXKA8E6gGoG3kzgJ8kYRXElQjv7YYs8GZvMd3nlDOioJ0xbVOq5mCbOP9kxtw/W7OWpwNKfukJMwhZUXcbcZkycts6ihR1HvI0KpudIpj//EPclU83Z3xGpISZ9UlENEfAd+CTIxDIGk24eYicU6iSrBzQ7JfDsTY1PHZF4yf76lObUQH9ID/R9UaCkZRtDvfYwncFcBVgvs3qOKQyoZ8LqY8Euj659rvfyFXtnbRmB9D6UcSgwoK4njnmIboc4sXS4Q457otcdwLtmGEpk6no3WXEYLhAfx1uDNe+pVyGY1pw58Bq14+9kZ/ouGL1czPGkxteLNas7oWUvdh7CwSO2w0hAuwSaS8aKgvvRpUh+rYzLVME06/KwnG9Xcmjn91zlNojjNdvzZ7pr/o01pPcX3k3v2fkDSQ3jacPXFnG7jc1VEDIC/k0hnsT50yidwmlT1VMsYMe1xWw//aUuzjGGqiY4a5KuIukhQO0UcG0xiOQkL7h+N8QZLW0jcDqJ0wHghqrFEfsdw7aOnjiDvcQ/hAYZTHMAPqRgQVpCcVdRXGcZJbGCZn61ofY/j2Yr6NsG+8sgmMJ7vuB8lByGSFXQvNOf5jklUcfnqmN3lCB061kOOPrcka4f6SUb4qDv4Uz8qKLcjTC2JJw2d8VEbn+BRh9d6NInFjC2rYkRvFO0sYriMkHqCn9qDwK720NYwpkcCzRDz8otzTOmRPy8p+xCVWsKsR99GnC52LC9TjmRFOBko72OMUbitQPaOepaQjVrckabCZzh1fNRtuXyYwMziLRVN6xg0B/hFCuezFW/DOaozZIOEWQsfwP4mI/F71Fgzjmv8Ny0PQ0qxH6Fiy/btBIXh6KLgkSh540ccj7bc7+ckSQ+FZNsphG85yfZEYqCahrgpDHcpVkqOv3tD9y6lzh195yMzS+p3aCsZTSr2y5zkg5qHL8a8+N41bx4WeIXAnw8YY6nahPC9Dv/Wx+YdwfGA9iC9MTRNjNkEDFpyu5oSDxbjWUQE3zq95e1uRFpodvc5zWNL8rJjG44IB4sNFCrV2F6yymOc9dAbcIHEr8Epg5xpgr2kiwXuaOBJsub1qxnVLiOsLOHThoXXIv5vY3QYYnJFlnWw9mHW8/jpPQ9dzsRv6Tt4aCYUg6E/Fqh7H5v2+A/q0AEaPOZpTRIPB33AVDDSHQLL27fH5HWDP3XY+vB+Wx8Sd/BzWwGBMJSphxCa+feXcKvYeCGD9QjVIfb57GjFm69OyYqWb52vuTw9JMW1y4iqiHHSYU3AaNmQRzX5kz0PVUIjJOHRwEOZE+UD05OCz5sLpHR49wdHgH7SMxItb9UE0ysWSYle+Sz9CcZKfDVAAEnU0heHPXrZCo68mkKF1OsEEUmaY0s+GwjDGvU2IZvXNJHH7XKC2EuMD0UlQVjaRNI3kt0uQw01R892VJ8nvL94YPnVjMX4gZ/kR0QCYq0ZeT3XakTkGeyjHjdI/K8UBI7hyBBkA14CadTRW8HN10cEL2qmJ3u2fcKz6paNyKm14vrqnPi0QXSCsogJB40xFltIjmY71lcTyto/RNjuPbIXO/pE8pPqBL+H0aRmpDsIOCwGohCZaMo2Jsg0wjPEnaYOPLI7TXsmsfcR06hCo6k2CRd64HRUsnh/T+0kS5MS5DXVKkfRIwrJ4EuiwmADQd4N9LsIk8Fnq9Ofa738hV7Zu4XGho7FaM9o3DB6fwsSvIWmCxX+gyS4VjhfEGwkybSFWvLwhyfo1sOtfNreZ79P6GYwviiQqcYb95gLw65KsYNEbA5Zzf0yInoleXa+4dS17K5GPMr3fO/XXzLVLUPnk79oMK2PU9CUAZuHlLnfod55iMBw+nTJbFyx/2rE726fwsZjvHHYseHJ6QNXJuKbckw3sRBaxAChP3B6ekeaDZiZI3jU0B4Lti4CaTk936DGGu8bwShsiFvDSdehto4o75m/v4dCMm00E9PRdAHVPsIpBxbktYcNLWo60JoAuZaorKe1PvUjRycUXmAw32lJk54saQhyTXzWcPrhA2cf3LEREZsmgo2CzBB/VLHtIwwgS0d20nIcVERhz6pPqDyPybyksCGxMXh5j2oE7A7v3c6L6E8NWVIzLnq8tSI+aZie7wkfV1z4Bd7TmmzaMLc1+7uUdFKRPy44/9Y9olT8ufdfEw4O8S4gSjtsqkmnNflpydHpFmGhWqccdw1PpgeUZt2EbN4mOK9nHwUE1pDEHcPFT0WGHwx0KIplzG6foD6RBFeC7l3M5A3MTwsCBrojGEzI7KgkHWveVHPWNuLdbkblPK4fJtTriHHaUA0BtXcgZnVZSNtEqJMamzjiWcO33rvk7HhL1QQsvzzi6/UR+9SjTD3aSJLLFpsbOiXZbVJ051FsU06erVCpRnSSt+WUqgoZpEStFBdHD5h3MddfLjATw+shZ3RacnM/o93FPDrf8sjb0YUBj09W2Lkj+t4Ot/GJB43d+shOIDKLlwyEYmB9N2a4DhE1yNeKH9+cUpYRLnXUF46p0dQ2RXUQXCq8RDM0PnaQcHngUjgF1nd4oUUGjiEQeCc9odUUNzkv91OCpy2+6g9RrbVifN2zOfLIxgOnZ1v6c42yhoSW2yxlo2M+0zPWy4xg7Rg6n2B5SJAbZzWB39PXAenGsrnPWa9z4kXHo2jHh5MHgsByvljz0Mx4++qM7TZHpgbZCyqnCDfgd+AKH7FVtPuQtNN0ZYjXQVodwqO6XHD7MKctI+7UjOsupa09NjrBw5GnFY9cQXpcsfMD7h9m3NyO+NNHl6ippthGDGO4q0dcXh1xnm2JapB7gehh6tVsXo8Rn0Wcn6wodyHLJGTsOrzBUsuAofVpdxH2wWfvB3ixZnk/Ie4stgwI455xN9BcJbRvU8R5R5Y1VDLAGw2kZiAVPa2vqKuI8XGN7hVj3dIMIXlp0YPP16+Puc9DvhZzgjeK0gV4Zz3vn90znpZk+SEdVDzuqT629BPgoifLSxZxTR8BUtDlgqaIaOsAceezX4/ZJT5t6zN9tkO/jYis5vT4kOiXrA+hYftVzkcfXJONW9JRiwwt1VVGs4+IRx3BouXxeAuRZTVk2CNNpxRNKgGHXXmYQTE0Hnbj0TwFIyH5eE+7EOweRsRJR5lZdtLDX2mGdxnRpYfb+oz9hkYHfO/4ntBqOBpQmcYNisAYPKX5eQfV/0IX+zO/QjbyQHxaW9rXIXYQyGzAazQ6OgiFbGzxQ82zZk8iB/pIEpcGmzhkYLCxhbVHex3jrMAsA6omxNWSxAw4C6IWPM42PPn+LfcPKXdvc3QsmIQdb1+f8tDn4EG9jQhuLHHSEWAQi45+6/FwP0PcBUQjRx5VdO8Z7qqc/Vxx8mLN+0XF7/3++1gt0W8T3H1AuIfw/Qp5GXDbHyGfdcTnNd+5uGGxKAl8TWQNu9djnHCUSchitiOcdlyalGIc0piQ+j7h6PGW5EXNp9UJ1TIh6CyPTx4IAo0OwYUWdTTgXvTY36gwE7CJ5Vm05lm+xJOaR/mG9z68Jo814srjLNzhXjruliN8bQnDgYdyxK5LkEc9dmy4uZohdz7NXcRD7nOWbLlQa7oHH3MdMHpU4M874q1DRPbw/keCpNWEznC7mlIQISYDq1cTNncjIm1oywAxCIRvWXYZY70nweD2HlftFO+9iv/wxWOOZ1ui5yX7h5S7ZsxYtui9z3I5xu8teV5T4rPSCfvg4HfWsaJc52zikGUUsg4iRGAJFh2TvuPx8Yr4OyXvx/dMTloW1z33oc/VU49qFeOPB7LC8CK75/h0w//w3muSfUeqB7Qv+BOja05FweikpLzKSOIOr3dEhePj53d0Y0dZjxAPimoT85PlIz69POFH3RHRWY3YeojycNOgkQwLi8st7XVKeZezv87QCMo+xAXwrhvz0ckDL5Id0/ODEvvN6xPWDxn2LqD4wynTlWF2ZZEjw0fJPZnUfJrO6ITiVTnH7j1s4yEzzbaNmDcdbXHQEJzqltA3dBPoqpDomkMwkDfgkFSJhwzg0WRLsBgQucZFDhPA5LRAOAcLgxk7ZOzwY41/2tJ5kplocScG6zl05fNoKHi8bhkmBw2HGhlefyti10bUhcc0b/CkZfJsj+ki5CcJ3pXHt6M7IuVwqUAWkvG4xATQdgH5d7eI6UBy3PLe0ztErvne6Q1dKPl3u/e4bid8/fYcGdjDDTox9Nrjvs9YvxtBo1AbyWKxJb4SBOnA3XYMtSAet7jAknstbRNQSR9/0iOlZdOkzPuW9Bb8zhE6w12X863xCqkcUjjc1uffri6gdcTTnkAafGPpfMXtcoqRgn4iaHzJrZfRhR4ytNx9c4ReB6gB5qOCD6Itwe1hm5CdBCOYu4Z4NxDsLPVtTPLenugagntBdxPTa59ik3DnUp4G68PW2NjSKYUcBJ5v2P0wh4lGzQbiJyXvsojuqaYJFU0qSE5rGNVEQc+fHF3z5etTEq0JUsPJoAnWcBGvUOcN2ZOSLvBZywBKiUMgI4sYDWy/nFKuE/pTS7CS1H3A7m2G6B2rfU71agRLH/+oQ2UDEQNfPRzRN4r6ZYroBbUOaFYx4s47dCTLkJvtlL7xsbXi+HsP5KZnNtkT+5px1qBGA1Ey0G8CbC+p1jF7QpJ7g2kE/fwQcPQ1MzocVSWoQkmrD4TVT9UY6QRxqBEedJnEjBw2gKcXm59rvfyFbuO32sO/g27m4+4k6VTTGUG4drgHRxgM2FNzyPa+9LhmisKicXRzgXCOvvERdwKdSVoPvEodRO2e48Qr2OoUx2HPXTjDf755TOcpjl/s6XYjrlYTpLWErUYryIOe+KOW3TqiWBzgKM3IolvDcd2wv00pliHzZwXbasz76ZZX+zEvr44QraVrE+bHe3QgqN6mDIVEfbeldwH3yxxpB3786VOGGZgAvMiwGyvSuKcKfL6qjpkNFUElGe4lOj/AKOZRw4/vLwg8jRxpGqO4uZ4jth74lmAtUSkMg6BXHtYe6FKrKsWXGn8y8KBzdl/l5LZDO8ltN+LpBw/oq4DmTDIblaw3CmrFpkoZbiKUhW4kUL6l28dcf3IGU403GfDmjuIhZREX9M8NQWtxH2r6JkDlA96lx+hxBWr4qYWnR7+LaIqE5akg8Vt4e2irjX6wRy0V9WZEv4pw+wgnBd80MYv5jlw2KGNpPsvowsOKJnmvIBz1zNOKT99eEJQQRlAMBy2C0hblWT7KV4ymDT/64hF9IHBRS9RYHsoJNT7R4hBT+4HZsDwLOVk2fBFnuFgz3Xj8H5bv8Sf/7Btevz5i92bE//TJhzz+wQ2b7Qiz0KS9RqWae5tw/+mY6UVBs0nwW8evnl/xH7dnlNIHLVjJBD8yyFbQ3idMnuwpEsWs6WhUjD0ecA8+8tqjzXye+FtcPHB7v6C9CenfKbJnJdXtCGUN5sKgPUHjPN70I4pbhV6HHL+/J90friehBUoLmFradzGzRY2KHO5bLQvdIp2l2mWIwmI9aI8kiew5nuwpdyEPlznZouY1GW0sD/v3U4FpfTb7AJkYyDWiDpHzDldJOqvIJzXTVctqiA7OkKc9u9pn/TJGzBwmhzRu2W0T7MyQv3IUvcJWHsvrDGslZWiI0p53qznOKo7GO9b7CQDyouNsUnD/2YxZ3rBtUrzO0Xohxc4jdAO536CWkraLkMLRZQ5lHf6dxFUKnTjMxBLblvemaz4hwFMD7kHh+YL6KkWedTTqwBDoTcjjb9/w9rNTRl5DMHaQDWjP4PTB/vXJwwlHVPzSxYbPbmcIv2e1ySG1rBqfLKvBCayVnMx23G9niJWjCUAZA086XOETxQPVekS3ENy8nqB2km4iEVYirWO9HCHHGs8q5NYRnfS0iQLrcBOD3IF3L6l0yis/YIgFQS3wnzX0vc/gC165KcIOpBvH0AbIE0saDDx+tOHuYUJxl6ECi9sZ/r28wEssE6OZhgM2tRiX8uBGZJuBZpNTuoDYaIoqYPwwkM4c1gjGac22Sxi0h1z0iEIxVIrHx2seVEIeDuz2MYuTPXerKYEx6MGnu1b4nqU3EikNUTKg1wHWG7gbR7SDx/ykYLKC9aAIFi3PF/dcLs9xgaZtY7KjGrOO0alAXAc8PVtz9WiCdxPRDI7f+PYrfsgRZxdrilcpd1+GNOcJfe+j6oCIgc6TyPMGoX3MlYccGV7ezn6u9fIXutjvLjOkr3ArH5NArwNsaMiHAeF7xM+2lLuYJpbYc8f3R3fsvZB7CU4pYjEwzD1cJuljxxBBsNOYQRJYy+pmTHdqyZYaL3awAHWvEJngYTMiyVvKIWLWDTzEMYnTtFWAMA41NyzKjvpdROQF7KVgWWe4BrwPGuQnER88WzEgScMe96infJUhpGUTBYeZ/WKAncLbCiaTlk0XYmoJC03SOfp3Ec0HBmkd7VVMXAlcaFmdxASDJNpA0FiCC8dlOSeZNcyqDlkL7rKYLOxpjnwq6xNsLdpJTCDohMIPLc1dQmgNXRticwtXAa2D0osJJj2t8XCJo/B8gtZQZiFB51gVEfG0Rc077M7Hm/SE7tBO7mOBlYLuJj1Q784tqyojGAw5Ayxa3p9fcTWM2Y1SUgOVtoxcj9xFtE5jnxpOZYXeAa2Pzh1vXp5ycbRkWWeoUuKkQ/qW8VuDzhX9I0vRx/hji9hpEtfxUI4497bsVYw0jsbz6Xc+xpecZlv6SJKOG266hF0leXp8T98EZF5Dux/D3uP5yYZbPWJYBbyOZ5i14utRhKjhQrR8e77k/2IiPnlzQVn5tDbkz/7Sa77pEk6DmpftlOxJRfD7lv5RQC98zKsQcWQJ8o4fujlt7SGUIE1aGgLGO83mWDEbl1yMNry8PaaTHumspsFjerKhlAHldcbbc0f1LuW8rukaQfykIdsaRqcblt5hb5I6wBrFn/jBJf/63TPEs4G7PqU3HtOwZl9mxLJjLBq6932e7/bc65DTsCMufW6rhMgfaB9iprZmPQ8QXscwMTQKlOwpZQCtQ840mX/A04rAYiU4T5BGLbIX+HnPIqu5X4+pqpDX+4inRyVvl2NOvR1t6dMKTWIs+s4ne3/Ptxe3/MGnzxGLQ6Tqc9a8ns9xOw+FoQo8ksLQHmn6KUyPlrTrkFO5h3igkwoRKexrj3bqoQPBTTNmqH36TURw1uEtB4yW4MEi2bP0ctJ5gz/V2J1PsBL84ZsLQmtRI4tWHjI0+LJjlFa0yudsssb2iqsfzaDyOf2lDZfDhA9Ht7x+c0IUaDzTUaQeuybgX+8ukEqSFD2x06Rew2U3o/BjXOnhC8PVpydEfk/8rYLyYUIwHWhfZihjCX+1IK5Dbj85QZz3aM8g9x4i08TJQL2LEDuJyA2e1HS7AFcIjPWRNQSVRT7viUpLsw5xUuH7A/o+RChHsFSkxzWbPkSdDEir0U4xWjl245S2DdGDxNcWsxCIeIBbj5ssJywKXrcTjJaIQWJ7Dz115KZDWGhGhsqX5GnDdojpZ5LgNfieoTeS86Dm3o/5xs1QK0EwHjj/lQde3hzxNKm426WonTzEbieOVkm8iSb0eobXGY+fr7jczsikprpLUKOGvvbJ5hUPJqV7auh3Id06xL0J6XNB+MoSTTrKfUx8WtNUEcNNwA+7x/jv1RRvMtoiQMx9wmTgItwyjgbe2hE2thxnJW/v5+R5Q7FOSVT7c62Xv9Bt/HwYSJ+ViNjiUkcS1ywWJaNMo75bUdqAj9ye9JUjf2f45vaIofcIOkt0WtNWIbrxsHvvkL/84KEGDvjTXoKArNA4IXHPBt7qKW0boM0BDtI9hIjM8JCHHO0bzNQwRIJqLok7y+zFBjXr8B519NonP2p4/HxJvQzoO4/lRPGqGLFrIjZ1Rn0qsY8HwncCJS3PLu5QG8n+LqPYJYT+wEm+w5UeximcEQzXIWbr45QjaByy9ghbSG5AJ9A+M8i14uhtTXCr2G0y7q5mqB+HSCMY1S3ptcVsffRNgPPg/fE9Xu+Qe0UdeHSxIGgcQdoR1RavFVR9SNIblq/nqIUhzlq6XcjQHiZRz4M9j4MCddQxEh1x1OOUY0AgPUPaDgTpAFYwn5WcNBXVVNCsYl7bMcurESo5tCx3Q8J+k3AU7/E7hx8PfPj+NYGUTIMCtxSkWUMy6vAbh55YdAZipOmfWspVxnATIbE0q5jguKUbg2cdKx1TfpJzYhuerAqi45pH/o7VPiPpB+amxesdq9dT7uKYTRjyppyz1SFV4RE8+IhbRUTPk3aPBbpVwGTbcNPH/LA9ZRsmvComnJ3uOZut+OHymH4fc/lfjkguLZufzLiNjqA+iHkCK7m4agmUJew0xpPIWX8QXt5a8uc7PGHpkFztJyRhR9sEtDqgCySbNqErQpR1cOOTPFhSOtyZoV3GPJQ51+2Y5KhGXvrEfo98pXjzZsapaxgaj7oN6bXHap9DYJlf7OliSV0HfBnk7GVEdTPmczlmrwLYKtxYs01CpIbhGG6vp3S1T8aA2MsDsWx/0EXITKNqgd34DEJS/XiMsZL+KuLmzRHtQ4zyLH3k87bL0VODM469DfGedOhI8CcvXnF1N+cnf/AU785D+pKmyFiFGW6m6c8OgrlsXOMrQ2gNbuOxqzKKsc/DdszV/3yOrjwaHRCeNEhnkAVsljnVTYIoJNU2xgVgZhZ/I9nvUlSqSV2Ht4VJ1PDsvRWL05KjFyu6KqQ8kvQ5mMpjmld84C+5vZpw3U3oZz5yOrBqU/Z1zI9+9B67NmYwkqoKkEsFWw/nCWwIOxniP64R1jGaVSyme4bUIeODyK2dCIoiIbiXpNXAcGwZRoLi1ZjA0wwjS4zGN5bAGtgpRCFR2mIHxVD7uFsfvwUbSVSu8Y57umcO3R5cIewkIjR4O+BlgLr0CDdQXWZkhca0CrUXqC89Nl1EeZdwFO2IhKYdCRCCI1OjlSTqDLfLMWIrsFoSBT2uVfjWMn+0RS0Gjo52+POOqg3xrME9BJiRoFglhGu4jwJEOvBb4TtoFJ0O8deSD7IV5knLLvXxkwF3NCBCQ1wZ0BLXS/oPBpb7nKO+4nh+0KHolc9clFzdzGl3IfU2IssO7pTelzgjMIGiOoVkVvNbp9/w9GzDsJAMR5b6P49ZVSndESgfxq6hrGK+bGc4B+LGY2h8fF+jbwPs6NBV/nmOP1ax/+3f/m2EEH/kOD39fygKnXP89m//Nufn58RxzG/91m/xk5/85I/8ja7r+Bt/42+wWCxI05S/8lf+Cu/evftvevFtIhmMImgPZLb+BGSq2W5izqcVgTJ8cxIhP+hJ/8yW5AcFjVJkqqfcxbhBoJxFhAZ166F2hzZYUvX4xx3TX14x+niH96t7oqQjXTmCuCM9qXAzjZg4XK2IWpidNTgEC9VyNtmxJOZHy0d0vsf80Yp0UXGrEr6+WZBtHM33B1ZvJtB6iPsAX1joJX0boEeSP/X0krduwvTjPe7Y4GrJkIHx1GFf37kDjpdD4IQZO4pnYEOHuleYTjIkEld5bIi4necUgYc90kw+2mBPLeKtR+xrTBvwvcd3nDzZkm06bndz0sow+2jNr//Jz4jnDYvpjllWE//KFr0wYAV25bErEsx1SPMmR176+BV4DXy6W7Bfx8Syp4o8pIP0vT1TW5GNWvTzgfGs5GK2Iola9ueKcNSTnZW4DtJpxc4FvE0ini52mEiybEcMrUf/Lub3fvIBe53wNpmzCzNc5VG8y9FC4u0FQ+4g1cilRO4FobF4/zkhfSkYfjSi/ckIu/MYDQP+aU9d+9QLQagNVROROMtp2tBUIcNVzGRSoWrJRduib0Omdx1p03KpUrwTzXcv7jl7WvBLJ9ec/8ot/akiCgZuVzH2nU/4RvHpwzFfyWP+t7/0KR+Hd3hpi0ss8ahDvtdiIgjPK6JZRfOeYRmmFDIC3+EvFfU65tEP7libFJcbdm3C5sdzbm8mJL0m0RpxFzAzB13C5KHAu5K89+Edy3HEMCiC/SF6NAp7dlXM9PmWfG94/KeW3KYRDypCx5JxNZCGHSLXmMhxfz9h99WIrBt4slhS/RSYs1j2yDufJpeMj2uOZMXorDpst9Q+7nVALyUn3p58Z4idPtzY1aEQ60Ci9pIgG1DxgdHQTx1MDKp1jPqGbF4S5z2N9Hj/e+/YBQFiofnRZ0/J5ICadvzpD1/zbFsShD3tyBK88qCWeJHBFh57HaAXllFSM84q5E4SYHALQ7Q+YIubNmAvE5xTuE4RpAOiF5ykW+wg8JQl+LAkPm2J/YHCRARez8YP+KKaUcWCWzdCjg30grYNGPDYvBvxn18/R4SSofU5tjWnx1vuTIZV0KUCX1r6wcddHVaFWdAhCwl7hXDQFBE39Zhnakf/TUJ8K2lbH3/lIDBU0ofM0s04RFJHkr0MDnwMJ9gvU4bkwBPxa8d+ofCedkgsQWtp5oJ+LLBKUEcKEwjyvodBIHODOulxWlKcC0wgcBbquUR4llIdFhytkCSPKsK1xQSOze2YPhRMdEe4dty9nDPsArRTdJ2PiixHruZktmVYGPAs6ZWjbzwa6/FetiMRDYOvcMohrEUpix4Up+GeJ17NF1GGfTJwcr7lphjx5vKI9WXOOK5grhEnPeqDmpMf3PPobEkVK2Jj6IyP1ZJ6E9ALRT0X3A4TTKfgdUz0RrH+eobaC/JqYH6+Q2BR1rH5fMz/6d/8EvUQ8GSxZLwyJFlLtqgpi4TdELPWGeVDir6MGK5CvNBw85Dx62df0p4KbGYxkfhvqnv/reOPvbL/zne+w83Nzc+OTz755GeP/b2/9/f4B//gH/CP/tE/4vd+7/c4PT3lL/yFv0BRFD8752/+zb/Jv/yX/5J/8S/+Bb/7u79LWZb85b/8lzHG/LFffH9ksdc+2fd2jB4XzPqek7rn/L0lVhhS0TN8nXLiFRgkM79GB4JFXhGV4EmNQCPbAZOAGhkckumi5nTfwrWiKQKCpWS81gwh1HPFVoUo5+gzh8g1kex5tx4zFi1DIw+zXw153FH3IT9++4S2C7Frn0kz8Pg3bsn7gTRvmC/2qOBARAvXkIxbRsclb5qcpLL4D478K4uJHb7SVLcpRktkbhlGDjdITAS2lzgPkpMKFRtMCl7jwB4U9x9Ea1Lb060jlusxbidoqojbT48xVvHyenrIK6gDFq5lCCSbNxOWb2aEBWQnFZs2Jiwd31EPZPcaEzpG45LRcUFwUdNOBe0M5k9XqFFP4fv0wiNSAyZ2GE9AeEBCjjc9GxMzpsVdBpQqpNgnyL1ilwRsicjmJS4wFCMw84FEDhgf5umGNG7x7iCLe57uGsoy4l0xhU7hRYeOgPzGp/U8fGNp1yGyFrjQIVqHXzioJMWPxzz8lwXbdxl9KngyW+ElmtO4phIe1y8XnOUl50mNLy1f7ud8dLHif/Pd1wx1hLsSzOY19yJBbzoef7zlbjdGPmm4jlKaK4dc9DgPwp0jr3v+x0+/zb93j3CJoCTGKslEdWgpOd23eL2hxSPbGbzOETTQJ4ftj9Xb6UEkVnjMs/IAUBk56olkZyKG1HGlcuYXG9QCmjPBfZFjAoetFN0E/OOWUA0cewX7NiFrGx5cCL7FDRL7ZczQeoeo6cCRntQY5cgrjZhprvUYkRi8W0FvPVRoILRUlzHrOqXdhaTHNdEapIH2Nqa8y2mUx2abIgrJ/t0IsVakWUOatFT49MuIJO1QK0l03OCfd4w+KNh0CXrvQ+r4+s05weA40S3KGR67il8/v+FdEvDqPGVfxDyqO779vRs+OF0ihUOHYCOB+TLh9pNjdg8pvVS0SpGEHdl7JWEBVB6DU6gGtA9yoqmfGdZFjlpJvAZwgnOv5HxU8PSDJbs0Qv84xbSK3T6lvktoO5/TZI9nLaaT3KmMLgfl3CGkK5LEUccju8dehXiFBW0JowHnKcpVQukFzBd7PAzZ3tC8ypnGJV/WC8yxxZv3nJ2tUe8N6CJEVJJuDFUR4y8huBFkpWZqa47SPbntmPg1A4ohFUSVpS8DLAI57wmX4FYecqUIVgr3zqccIlxmqKVPv42wAbi9hzSH8BvRCWx3cD2ZUCAKj00zYlh4dEOIyezhf5aUIYdQGWxo6XofdzTw0cUNnfMQyjGmJ6wtt3lMlPR0L3Ne/eSYvU1RG4W3gfBLhbsJmE5ayj6mXsBuk0OpuO1ytruMbiPxsTS3yWFF7w3YdxHv3h5xtZuSYAhuJaNFSXjUQm9BWYpXI7qHGH8vWe4z6lgRP/TYI4M4H6iKEO9xi4eh/84hUKgk4FW1YPpsi0gst3cz7L2Pd9oSOEM/M4jRQZ8UGk3mNL+/f4ZTgtm2h+CPX/P+e8Yfu9h7nsfp6enPjqOjI+Cwqv+H//Af8nf+zt/hr/21v8Z3v/tdfud3foe6rvnn//yfA7Db7fgn/+Sf8Pf//t/nz//5P88PfvAD/tk/+2d88skn/Ot//a//2C9+cbRnclLQfZGQnFd4QnO3yjAIlrcjgqWhC+BNNSe1Pc1djCgV1yolrQeCxy3GSkzsYVPD5KJkWhfE+cD/8Btf0wUe6o2ieDPiQWbkYY/cOPwGXKMQpSTdOuyx5lcu3mCQFLni+m7OudwTigG/NvTrEG3AywYAisGnqkKqOuKBlFoHyE5iTg3NQ0Satrz54pSijbhPE+rHgu8+eYvKD0pTcdwj3EFgKE57rIDIGo7jgrOvG0bbjiHip0xlRfhG8Wo9x76LoFTk2wGtPERu6B8b1ODYpOHB4qIjrq9HjKnxRM/lfkZ1m/L5j59RypD92xxRG6JnDU+eLfGOeiobIEoJtz7fP35HLUOy2iJHA9Og5X/39DO+d78j+1ygjKRSHrsjn6H2aa4SypEkr1qOvQJVSYpNQvxOIq98vm23NG8SpIY+kqjFwPhJT3zS4J51FEXEFkXQOeRaIZwlRBOWMCgPhGDwBZ2nQEKwcfiDY0glfukwoUAnEuaO8i7j1eYIrQTlKuS1zGlPLcNpx+vbERPTI3eS4T7gP62O0R9r+lXJ3UPIVZFSy4T/+PUF40/XdF96zH/vnnjmWDwuEdrRDT61DuhvY7xkYDwpiegR64FtEeHfCe6/XLC0I5pIUhHQ3sTY4adizFyjLjps5TGNa9qvM3oleBqvOM83KGeZuZaJ7altwE00Ikk7qi6gLQNkD1I5qsTDyzVHpqW/jXhbHdNZD9cqkq1D5AZOBgatGG0M1Tqh8gLqqWLWd0QVyMkA5z3+0hC+V/HseImKDOfPHjhedtALhkeWLlekkwaTWJLTmujusIrP+oFo5+huEtZditeDEeDtJLHWDEaBlry7OcI6yYsnN2TjlmRvOLo1fJRs+P7H99zvM+6KjFWV4oBmJHhXZ3xTj/mmmRAUDncVMhl6jJH0ZwbtC0RiiHcdwaynHw5tfNmA2kqSs4p4MHSvE9Re4q0l3bcH/MogXgbUO8n1csT+5Zgs6rDHA8MjTSQMYzrE64Bm8Amc4dHze8K8ww2C7EnBB9ktw9cJ7354ysMfzFH5wLGoEI3HdpciH7fYUiG+CWlWMakZSN7fo0eOSdPiTzpUJQikZvAPKYnUillSEQUDweDQjywg0DufYojZ3Oc0c0G1TVCRQXYOcXOAcAXq4OhoTgShGvBPWnDgeRYvsPidwPoC6ztcLxDS4Z112NyizltU7xCtOFx7Dx5COIwPUTdgJnD8fI3fWZR2tGOYj0rmeUHmdexUwHvPbliuMspdzM4mrO8mPCzHiFrQOh+5VYyP9uSuQ8cwzC0PXshqm3L7co7pFcFG0PceLjrAyNJWE+8t6g9j2s9zhpsQGom7DinvUkQ+sH/IePPmhE0zJlgJVCXQjcL2EpM5rBO4Xz0kdtbuYDNt8JnHFdOoxo8GjnYNXuF4/c0p5VWO2guy9wr8HXg15EOHGQTTcc3pxZ7xk4Jx2JCkLc63HD9b/7Fr3n/P+GMX+6+++orz83OeP3/OX//rf52XL18C8OrVK25vb/mLf/Ev/uzcMAz5c3/uz/Hv/t2/A+CHP/whwzD8kXPOz8/57ne/+7Nz/tdG13Xs9/s/cgBsvpgRaMe3fu31gWwUdYTzig0xxx8/0B4L0vOaSVhTZj7jRYvzoW99+khw7BpmRqNSw0w2XK0nXD/PKI3HV8UUv1J4Jxr1g5LFRjORHf7Ykg2axvqIQSKlhZXPv314wfYhZ/g8w3aKh+0YVypEP3C2WPNotOMsr/DVQNBbwo8rxEwzzwoIDKOkIlzBaGW52UwwrYcrPaLjhjjseVvP8UqPeOPgJqDUIb61B8vOShA8qslUx9Uipdl5SByTrETNe+QA/TagOjugYBvl43wwVuLcAe+arBwj05PVPX4puF9OEJFBO4n1ID0qkalhNwt4WRwfolnjARlavFKwVz7+WUs3ARu7g+Wxk0SN4X98+zH/bvOUqk4OfPfKO+QLBDUPQwpGUZUpMjT0zzt8ZcheFESPO65thl17ZJ9LpvVhv/HlqwXFVUae9PyFX/0UJj3v/co1XnTwxLfbkDqS2GODF2iQYBNHe+Iovi1o3/tpV0QddA39kUO9X8FCE+4NcW1ZfLCDq4CgNLTfhMiJZT1T1M8N32wnXC0nDKuQ/gc51TsPt5Pc/qFH++9roseG+E3L6DsJhR5jckMe19hO/bSVakgKSx8rqrHHcp7h7x0iH6ieGxp8aCQiOsQt+6nm0ekKU3k8fDWnqqODXXRkCIzjTTOnuM8ZEAedxipEbDySxlJ9OaK9jbG3ATI1nKYbvEvF+vWYb5bH9E804oOG8iqjbzxM4+FlA7sy5fx0zQc/uGYhKuJhIJj0vL6f89AmqHuPu3pC90RwpBoe9mPILWuTUueK6iGF2wDVQrHJqERIex9jnveH7kpiEBcdJrOoSmBCCPcO4zuqpw6bOZo6JOosY9nw6cM5b1YTujufh7nH/7l6zL+5e85wPPBpM0NGmsb5TPOKYptS2IBH+QYdSQah2Mc+MjBkXk+6gQ9HK2wbs74as61Tpn2H7xlUBcnXHougxCEQs4H+qSb6SmIjQR0rvgiOeZpvUKuWbRPTe4ruLqapfEATfFiyKkaMpx04waO64nG4Z1bA5maKyg3yRcN+ESIuA+7zlOkHa1xwuGbM3PLku3dkRzUictAKPGtYhwmiFxgNwzLkzBWHyYxw1FcJo6ilH3wsAi8x+MqQyA7VCYbOO0xceguRQ3BIixuEpM8FgbGIQuKWHsmNxVsK0I6hV/iDQZ8a3NSgKsloXBEqgyct7VNLPwY9M3A8IAcO2OpvayKnqXcxNnNUgc+JX+A1FhdbTC1Zfzrm0/tz3CBxviNcHhYt0b0AfRAbmxA29yO6yMNJgWwkfeeR+j39kaVIFPWZIxr12M7nI2/H692CYiGxZwNeD6N5yfS4YPFsjRlbgqOekWmIbx3tJkBowezZhjDpUJ1FJBYx02gUCou46Bg8RbAU3P9oQvU6Iniv5iococ8swVowHRXIxx1GC7xSsskCXCJJbw37OuSamP4nCbvrnEKH1Crgdj3+45bf/67xxyr2v/Zrv8Y//af/lH/1r/4V//gf/2Nub2/5zd/8TVarFbe3twCcnJz8kd85OTn52WO3t7cEQcB0Ov2vnvO/Nv7u3/27jMfjnx2PHz8GINU9nA58sTxDfuVx06bcvjqmeJMzvvToPh1R3qXsZEi2GxhqjyOvphs7dn3K689O2JQJ8q1Pex0jLLznbTlSDX/w+VPqHKx/yKeurWD5TUqnPbCORV7gGoluD2lapvOQdx4utjA/2EqWmzH1UYifa46OK571K8yJQ+4lue6Yew1/5uQtM79ja2L0ICnOxAF3awWsFLvPJgzbgP3lmN541GWEd9IRbgAEYTSgnEHd+dzdTbFS8fw37g9itZ1At5LhscYMCu9BIADvuCPXHd2JJc46pAGDoN2GdMbDGw9IDfHaksybg5L+KsAvIC0MydOKNpZ8cnvB3XLC1sU8m6wIlOGLbx4Rpx1nz5aEec9VPcbsBWI0sHh/hQoN9sFH1JKNPKjBbeBwleLms2MkgpOoJJWWCZrVj6YEjzpGj2vGYc+s60mSjipTPL1Y8eluwcbkvLw94tGTNX2osHNzIOYVkqPzDce/fM/i8YaTiw1x0rGI94yf7ZDfa/hQ7ZjS4L0KEDc+uzcT7NSQey2Lx1uSsGfZTNjlHo31idSAHjta6ePmFt35xKcWmYFMPZ79QOCLhOZbOa/smA+HG+xVQJMEJJ1hvBmQY0efSTSS46IlWAkGT2FygbIClQ8c3RmmeYM+suTHFeU+ZhQ05M/2yFwfREVCMvVqfvXxW5RnCaxj6Dwa4VH0If0mwCmByyyRGUjmNY2viHzN8/dXyFxzNDSEeUdgDMZKeNLSBzA73TOpLObGp14mPJMbwv7wHK7wGLYBeVohCsnXuzk1HnHaIQfoOg9v1INvOTldczTbEj2AnRgS1aPvAqruYI2UqSEaNLIU5CclP5i+JWl7Qq3xQo2VhxuyFRCWkH9QEW4daWWIrwTlNqYIfK6rCUoY+kAgpcUsfVbXYwgs77+4OsQ1j3uYDbRzx81+hJ4b5L1HuBaUuxjOBsgN23Ooioh8UfPB6IHfGH2DkJrjpysyM5CWhtRYpBM83Tac9xXPvS3evGefBzRGQud4/c2Ct1+dcXk9p20EL22G9DX92YGkFhpDd2HIbE/1B1N06dHXAd6op3KS4iFlyBxdIvEedTw0OYvjHel3CpLvb7m8X9Dcx4zDhiGQ9BEMAYTfKDonD3vjxqcLBfHewiBgq7AcJsLRoAl6i1pKVH6IgHaBw3kGEwqG6mD3DIMB79Yj+UxhIhgeAlqhaO4T/OWBxhfuQaWGPG3xOojeeHS9j60lNnSkUU8W9exFDL8Xc/R8SzjVpGJA1go5gBlb9MY/OHZCS77TB9Gx7zBaoMcOP9CkXse6TrEPAd5O4O8Eu4cMVTvWjcLfOWglZZNQLQQj2SFSg0kcj/MVq6sJ7esMtXMHZ9AxrNZj1F4S5gOzqmU6K5nddnRzcbjXTjRGOJrnEht5dD/KMZWHd6toY8FDEuOFmv0+pZUeqoFRWnP2yysKGVKtEuJvVaRJh7gOCNaCcPX/wyCcv/SX/tLPvv7e977Hb/zGb/DixQt+53d+h1//9V8HQIg/Kjpwzv0vfvb/PP5fnfO3//bf5m/9rb/1s+/3+z2PHz+mNCFBD2HvuA1GyEIQjg3incenxTmDUfidwHuvY7/KWGWC9nUGe0EQCeRwQCbKXmGlJXha8e7VFNsZontow5D7JEaet9ze5UyzGlFbtvZw0THWKKHxOoucDIiTHvEQYQWkFyWyDkkHTftFjj6v+CROydKG2LPEl5avA5//6d9/TC8VUWjYjQWu8xA4hLJUF44kbWn2IdIOeOnA9FnJvo1xx4Y0qw8iofcausrHtorySPG1nSNahRkb/D34scWcdLDyITQ81luyM83Gxqyvchh1dIGk34f4yqLbw4q8O4e6iHj63hK5ETQiYJfGlCOPC7VG7z22NyPGneWVOyHE0K8jlloSqoFqlWITx9FKso58Chvj+Qb/xqef+syqhu5jyxNvz6uniu46o3+Z8mSxA9/x9c2cFxdb3m4yXiZT/Mbg9wKWghBLMY8IlcGXmuYuYpg2VIlExQ65dghfcy8z7GcBYaT56Nk7vtiGaO0TFdAbxe0qwvcP/PB40tFPFU0dUJcR/Vixv8uRnuB9vaN7o3j+4pZ/e/OCk/GGeqzovzRM0oZ1ecT2/Rx90zP1eoZI0JWCt1VI34U8UjXjRx3T04L/8G+ekykoI0n3xHC62nG9n5AFNW0vUHvF5SwmvoV40eOlhoUt+ObuhOABwolmvcsIFi07GfDZfkF/k/BLv/Y1P/nDZwjpiPYOnZgD0MiTmAtLlBqq65igdrxtczqlDl2AbxTNRKBCjcgMZqsIs4G7OCByisW3VnSXMdsyIRg0tTvYS92dQn7YotuMoIO+DXFGsI9CfAYwgrduQrwUiMjR38WgLYtnWzZ3GeY+hPkhsbIOA5Ym43+u30dZQdz2h+jrLCDYKiabjpWJEUcV4VlD3/pIafCUIagd/VWMd9yysBXF1GISh/XA23g8+Al972HqgK4ISdxw6G4NAhM7vOqgJ9HvIrJ5xfR8x1t9RH5aU7fwalhgjxS7PqGPBWfna17rEY8uGn50f0w6KglKmK4NKrFE4461kuw8hWgt5bkgnw981y5ZFyM238QQe4ijntNRxX6ZYXOFbATxoqVpPZbNmHHe4EJHeZnQSYmN4e2rY46CBn0ZYR5r2kDQNxGjrKb84RT/RNOcS4gcbSzwt/7BNjvq0cojVj36nU93rNAhaCURMSij0COJQGAzwIH1BaIQ1JlP1EE/Eah4oL2NSTtBO7EIH1rjQeBoypB8WhEMGjXXRM6wrjKUclzsai43M+xTS3nis1nNCEtJnfgkG3FAjxc+YupQzmBSSRUofGmIt4fnpoNeOE76isYFaF+RNAPvfXzL5bsjKh1TXWeICPxcE0oNTrCUCUlSsr0dU9egUWxHCvFTXVMXSpAOLRTjcUMwq5m2PRUJx0NLcFyTpT2bPOX1/YRG+YwmPR4wdB7y/QaxDGmuUrxWkEwH/Bcl+zJAakewd5iR4OvLUyySyZOKRnuMbfFfK3n/Xxn/XT77NE353ve+x1dffcVf/at/FTis3s/Ozn52zv39/c9W+6enp/R9z2az+SOr+/v7e37zN3/zv/o8YRgShv9L9u/Fe7eYLKGRAbNkR1pKrhljnx2Ec8fphoeXM8x1zCoPSb/S0AlU61CtQxqH7EE4Qfq05Cgu+SI4JloKdOPhabC5IRgM4qLH7h0qNCjTo3cSi6LUAXqkyJKG8OuQi8WKP9ic4GWOUdjwXDf8oTvmskz5jrrlnR0RNz6XzQgR97iJY9hJvEuPOIQhcwyJOGBLgx79eYrKLL4y5K5jkwYEk479Q0bvh9h3Ef64R77xse8PJGtF8kHD9iompEOedIz9ivI2R3SGKC3YBTF1oCl2CZtxwKTpcccDcm4x9x5qIykeC/wziROG1++OmdQtbmEZtIKdwOHBvY/cQv/YEe8Ni1mFOGvYEPCXj7/kf199l9GyZPan9yy/DJiMSm6LEcFTgzzp0Y1B3Hl8PZwyO9lzmg2YyLD+OmYYa8zY8KVJeZKX7L2MbFwyG/fcbCbo0uPlas6z0ZaTWYk5d2SqZaR9ussY2Ujmix2+b9lNU+pVwsPX0wOn2/cZbsT/nb0/+bVs2/LzsG9Wq971qaO+EffeV+Z7yaSYVGFVlCzDgNxwQ4DUUUM9tWSLEGAbsGyAZkOAIcB/gVpuWrAbbpCyDEoUnSQz8zHfy1fcKspTn7PrVa9ZuLGfCbhhSwnaCSXA1Y2Ig4PYxZhzjN/4PtrXErNpOT1u2WUdo7jj2+0x/iHiq/6IL+M7pspx+rTh8pdT4oXl779/zrPv3TFtBlhohs9G3L3N+VdHv+TOTLHG0dcL0seBeD8gnqU0iaYNNbuQM6Emem7ZKoM+aokvNY/lGNLArksRiUdmnuA16UXJkEOzjbjqZmRYhlHAaI8wniEo0shyUpV8eqP49N0JiTmga3th8EIyfrWlfl8wWTq0CnSjjrbMaSt5CNadD2AF+V4QmZ4osojIct+MCWvJdFqhbSDcxegsMCiBMxAM7HWE/BATXVgEh/FNv4xJsLRW80ruufOGgQSzE8Q1DIWk7GN40yEHwTRqSUWPblJiG9hXCbYVJMpxWleIWYBfpWQ/qljfpwwGVC8PgpGXA00Vk/gBEXvsdcxVmBGCJOkHmruUZN7RrnL8yKH2EhkClY5RzuIjiZhbGAfEQ4R62pGanv6y4OjlhjLWGCLaXNFFnkXWUz3puOnGnEz2fOoyOmk4s4qL8Yo//vgMpT1jb0ntgAua0hzCc9tvFshnGnIwo5ZZ1OOuFI93EyaLit1ZTBYs24cRxnvmFyuq6wKdOvzCYjYaHTkmQ08ybXm8HWMvU1QbEOPDyq/znmI5MNQGzhxtHTOMA1IG2qDQykMHQyGQtYQQkEmAXmKNJt1ZQg4iPqT2o8IxOE2yCgTt8UaSWE/oNLY4mERd4RG7w2oaKlA9ZPSzgI4CQknCo0R7x5+mC+J4IL6C6KSi6mNkEojrADNP5iy+DlApgpH0U9AbyTAW9Eee8LxHXWnkJmJ5VjAbWpaPE84ulry/OTrQRHWPmx0OOv4xYhAKmwOxYz54sltBbWNM6vBNwCaCfiTIbgLhYiDkErn3VK4g1h3XfUadRsTrlCyvyCJLKjx63KAacI1mOLPEpYbUI4Qnmlum0y1J03Mar/nj/QUXoeLhw4heKsIEyu/GmBLqWfNPUn7/zM8/UR+h6zp+/etfc35+zqtXrzg7O+Nv/+2//Y//vO97/s7f+Tv/uJD/3u/9HsaY/7e/c3Nzw5/+6Z/+fy32/5+evFXsNjmRdtRVwk03xscHgYu/jrn97hi9F+AD5lZh64j4PiCQ6MHiY5DukFbvpObydoLYGtwuYsgF6EAet4z2DnEV0TQR9kPCE7Pn1csHRs5y1Ha4QbLZ5ewTzZ+sT7E5rJuMhw8z/mF9hiw8vVD85uGCqk64XOc4BVnlcZcpXgu6I4GTgmgviLpAYTsSHMPLgWJRYlRgfTkl2QY2ZYaSHvMoKSYVZtKTPysP/35wNJsEOwnsZERYGjY/G5Oe1hx9uSJMPWuX0GwjnBeMfM/nT66Zjip+Rz3wl48/Mnuyx6QOV2mKD4K5bjE/bmiHmJwBpwTbNqOLJG4Mg1fUZ5L1SHN9P6KL4DerBYn15DlcfzdHKLj7hydEQ+B7z26ZX1qOkw7bGJLSs+4y/I1gEZdso5jtfsp8GPjp9Ia3foTZC8K3CZcPx4wnNXpteVWXVH93zPGuRTeW+8sJ/bc53EeIAMvNjNU3ExoXobTlUcf4lab7kFA/i4iXgi5k3FY5eel4+GZOtIbzfI8pHO8fZux0yvu2IP7Le7aPCfnXJYt7T/lQUP8iRknLJJT83eENn/YZl/kJo6zmebLh+IeW6LzjX5pf8WRoGFrN371+QjNoPttXvHjseHOygXHPv/h7v+T3nrxFzzt6rw5tSB+hYkvlIhbTPWbcE3YGTjsmz3fEwdFsY1aTBPaaVT3iUeSsQkYbDjS2eNFx/uMHtk8U+7uMxB/yFNEeTBuYtD1RPGBngeKo5WS6Y/AKXQriZmDTpDwNLSoNoCEaWeLKEUeWxfGOp1/ekfsB1UFXR/StIQSB9p6dUvi9Icp7hpln9tmakydLXj+55mW6JEjob2N2v5xgOkG5iQlLjV5Lyl3Kp35B++uC3WnEx4cFTgp8adgOB6hMdNwRJp4+hfGkxjxvSYRDa4e/Vjx7dU+41/ggcIMCAoTDRcBHEnXRYZ3ihz94x+jFjlx3NPsI90VD1UfMk5JiUuNuIpKio5P64G94m1DaiOHoQH3cTwTfXC+IX+5J5zva0rCPDE0sSRct2cazu5Csk4S9jpiOO9Y+4/So5CevbwmdxqwkGQPJqCM9q5iMasyTBlUMqJVC9oLiJjB8SLi7mqIjS5p1nP3gnnYE7sJiI0HyqqE/FfiVQSgPuUNJT5cLQiVp6hh1bLFzjz32RFsIs4FpaJAjj7UKhySRA6IRKBEQJhC3AeHB38XI2GOzQDztOOkrQhoICIKV+OiA/uVBEb1TmOpwEPEpuL2hjyTJeY1KHV4JJqolbDV+4nFCoQZwWSDp3IEroRtOT1Ys6hajHDYPHMflYavnZOC7sCAbddT7lP2xoppLbCJJvEM4OD1bE9WC7bspbaxwTwaCDgeeSetI+p7u3DPddvwbv/+nNMkh+PxhP0bFnvGoQdUCtZNsrseovcCXGjdIdGaJqkBxHTDZQDcWVNJQVgmfHo75+e6USkTcz2J42pEvGmLnyEcNNhI0Q/RPUn7/zM+f6Wb/1//6X+ff/Df/TZ4/f879/T1/42/8DXa7Hf/uv/vvIoTgP/gP/gP+5t/8m3z++ed8/vnn/M2/+TfJsox/59/5dwCYTCb8e//ev8d/+B/+hywWC+bzOX/9r/91fvzjH/Ov/Wv/2p/5l58mNa3MOdoNpHLPk3/uE+++XbBejQ8Iyr3Gf97CZYogQGLpFuYA/XgqyH/l6b8IYBxJ2lP+ZkySDlijMDtPtxC4OuLV+QNrqxBRIL7o+XR1jOw94qxjuc6Yh551l2BUfyAoZR7/EHMyNJjnJav3M3588sjH1QS3jbGLgaGTlNuU5KKiryLaROAQkFisETSlYfigkUcQjgJ2gHCr2MkCnfaMs5bNpwnd1OLuDidrdaOZjbZsbxNUL/Cfe56crXj0c5IysM8j7voMowOlixkqQ4jgq/fP6J8PbPoxJ6drHu9y3DSghENEgdmrDaubMccna0Kpae8maGmRxwPtdQqVYJJX1PcZrjbY32h+YVLyH+54vImoVETaO+JKUMURH48K3MSwe5sxGIn3kmEpWCKxlWf+gzXLzYhJKKlHgeTWU+9imr2iXkjSRDN8Dum3A8NFw7aPuflqQTsVxEtBTsc+N5hK4ozG6oC78PjLiNG8pWwTxM4wife0F4ZNlyGbQNdrTOZwJVR1TPaypK4lfR5TPxqWn0ekpWDdeqqtYfq9Fdv3M0ZHG3bDBWETEa9XzH+yof1JzNs/nTK8s7z7PrjckSc11eMIMR34djlGVh63h6fFll98eEHTRSTZQBMLEAe7WvHo2T7EbI8kUWIZv97iK4lOBkbUbLUjuZe0wtMLje4ON6z4Hvq1YX81ZZrucTbCXOxpNwlq7LCtQhUWdEDPOkaiY7NK2VyOSHLLKGtZJD3fNGP+MJwQxzA29aHgRQGMZ/fthK2YEGKIsoHJrGTtJS5xJEWHkT3DuwyROEziWI0jTqI99yJnUx087H1uOZk1NB9TnBR0kQIp0Ftwe00wHmJBspEI42nyGOcFMZbyN2PUTiKmAyZtefrqjl98en6Ap/iIfZPizh3yThMbhz61tLsY32h8GvBWE6uBD78+ZdMUyMwxyWtiaznOH3nY5Typ9tTPKtrWkErL3it84RlahakF46TBfmc4/+GSb359in3W06SGbOuZfG/D8t0csXA8y1dUQ4xuPZsmh0gwmQx8tZvR9AYjPPVljp4cbre1O4zqVrcj2EXYEwe/SemOBLFr6YImwnH7OCWOHFmwWOHYDyldLPGxwFgHO/BWEhPgWY9fGlyQqApi6xjGgmwZsHVCUyj0vCdsJV2v6IQi2QiiLfRjgZQBH0Mat3RlhrhwyAqiSUe7S4gHhx95OqkwN5Li+yseH6foGsRRR1ilJN5yezMn7gLD0lC2Bl1DuUrJt2Ajgd6Ay0CkjvZU0nVjiqI+GPKEQywldh+hEPSZIBp1LG4FD3VOenwgMY60B9PROMPJ8yXv1seItcK8F+gK0t/bkbz0tG2EcJ5qiPnZw1P6M8HTYk22Sqh8hCKwHBm6XUGsB85ePLJ8HLPKYog9P5V3rH7PsL4/Ir4+oNb3byL8VUpTJCgXcE8DUXmAiYUQqPcxx0d7omTFL/9bq9z/754/U7G/vLzk3/63/20eHx85Pj7mr/7Vv8of/MEf8OLFCwD+o//oP6JpGv79f//fZ71e8/u///v8rb/1txiNRv/4Z/yn/+l/itaaf+vf+rdomoa/9tf+Gv/Zf/afoZT6M//yexFxdt2weRG4/nrBcpXxvRf33H1aMMkbdhuDGgydF8gGhtzgc0ishbWmm3paKSie7lF4fBbQW0E2DLggSe8E7dzw80/npMcNYaMgCbx8dsf91wuGCTxLdthmwKie/FlNV0Y4DRdyhxSCT5+OyU9qNl6RDANjU6HeNLy9OcVcNDSDQYiAWXlsLEj3HnsiMTcCIQXaCbbNGB9DZjy+laDA5h7OelTwxE1g2MUMJlASMX+6IRKW7dzg14p03lJ1Ec19jEQTfMDFjsV0x2Y1wknBsIwpJg37NgEpYBAE4+luNFflGUOnKZ9HyHBIDJs3NWNXc5WmTKIa7wUqtaS5pV4Z2jSieRhjfCDpLH5vaCPI7y0lOWnnaL3BVI5oNCCCohzH9KViuC9IlINnPb9495xZXmHigd6mKBEobE/zXcGlXOB6S3mdw8RhKoWfeajFIfT29JA8zlpLbQ1qYunQOK/4Z55dcrvJmE7WxB8CrhB8z2wo1xFX5ZixbIlvBorYcXq25e/evWT0tGbfFdxsNbZTPFxFoDXVGCbxhrtuweKh5b++e8XUNbxIPvJhMeW6S3kzXbKyBdGj5Pn3H9k+Ezx+c8SbfMtjlR/arAOYpaZIBupg8E5xPR5z/GSPG8DLwP1+xNGoZL/NkLKnXyUMpSJ7XuFji0WgHyRHP73n/pczwrHjKopg1FJ/yGhjSfSVQp5ZmqB5Nb5n5TLudmOKxBLlDX/l2Qc6G/Hrao6+CYT7iHrhabsx+kGQpD1tGRGHQBUrZObo9wZ/b/BHIBDYdUylFF8urnlrZsjJgEVTuZhYDqTfSqwPDK1m/zgiRAE5c+ghIEsQgLQQgkB1DrSgzyShEbjU0wWFDAHZQtw4miPJ9bfHnOR79lFCf+LZ7jLMpKedQ/7B4I1melERm4FtnzKvOjY+QqJQs4FXbskjGeOt5YM/Bg13lUC2kqNpxSRuGSaB0eB5/+kItCP/csft9ZyPw5inrzesqgS1c0RvalQpeaq2XJUTHvsxcTbgCUQrxfTNiq+rGfs4ojGatpacfLbkYT0hNoH2JCC3imja0VnJ5IPHjQ9f2ck+MHQKeTIgpo7pqOJxPSZOevgqxhwfwm6qGJCdRNTqQK3cOLZdRBgUeh9oZ5pIOvpYoODQ+HjUqFpQPw2YLWA87UXAPAr8HnQj2Poc4QWmFGx0fAhk5vZgD7xMmTyr2U5yqvuCrDzkJ/qtQY48+zpFbQSUAT+VhOBw2mEqiQi/HRElh85RrwX/grjjZ+0xa50jnEFpz205ha0kzQbOz9bcLGfMjxuSzWF8FHlPv4XVlymikegNpN8qhhwy0ZOknmzfs+wSol3g1U/u2GjDssrwreK+nTCsIhJh2YaYMA6Hy9S9oXocsXi1pXYz3IPhN9UTZnrN5GxP8zBCXQzIT5oqhpHqGPaa4TqhaRWLruN/8jvf0T8XmA7+6Od/vml8EcKft2jvn/zZ7XZMJhO+97/+j5nmcDauyOOevRJcNzP6QSNKQecizA6GeSC0kmgNcuAAhxBwPl1jLxxlmZCMO9ZXU9KP0JwKVOQOM+7UEX3WsH0s4FOEOh7Awuef3fHu52ckz2uiqOfmbs7L0Y67bUYZG57kG7wOyEbyz39+yf/lT78gvoLw0wbfKubpwM3dhOlix+ZqjFxLuqeei8mGh+2Y+M5h08Muf3zUUVYpwkH2vR3m24jOSAYvSQbH7uUBODH68Y7E9CTJgJCBD1+fEdJw2IHPPe4rCE809IJ8GChHBrFThMIhTgeyVWDXJ1zIHUuf0UhDvvbIRtBFEu0C05dr7poJonDkuqEsc0KtiNRAGg/Mk5L9JqWxyeHUTCDEINuADxJlPKGWxGLA1ho7EkjtUcbSO4M9djyrt+Qjz8NckkU9D82IUCnSXeAs3vOpmVK5GDPpcJ3AdhFp3sFa045BLxXCgTrrsJUhsz1DMIjI8eKHN1z+yQm2EPzLr97zh+tz6qsU/7wn+U7ROkn6KCi/hBfFknKZQVOzDjkvsoGw6IimDcn/PfDNekb/wwzOYdqX6Fbyg/QDH4YnfCxHPJ8/0HyKWT0vuGgrfBsxHjeUhaC2mv4+YTUkIAQmc3gFk3pg16VYDd2ZY/Rg0ceeU9twZ2LqNuY42TPsI0Z5x8P6AKsx0w53H2Maj68UcTZg8h5GHr0V1KmhiyV9pYjXgs9/dMmn7ZxYWdplik8CVax5ojaU9wUbkXD2xQP3D1PsOiZIUD4cePYIghWk3W931lUgyTtU7dnJGN9ERCct6lHhTweEDpzNt6yWBdoH7E0EEvo2olUK3XDYF/eQ7B2iEZSfC6K737IkQsClAtkKuuPDWlS0CSTfCDafRQQTUCIQqwF75jiPKlbbDL801DNgZdBb8LPA4uWa5puY519uuV6P2Q0xQcAiNPRVzD5XLFzH73/vE3/76gVRcFyEHepi4L6a4FcKlTn2H0fE5w2LtOThVzN+8uyW23LMeqbQW2ikIh8cdm+ogyG87DnNtySlYqgl3/UzxMISDQHEITuEgb42KBeQk4Gh06SjjnlRUl4XlOsM6zRpM+AGzbDwJM9qXk0fuK3H7O+Lw+uxMlgpSIoOuzaYeY+3EpF4MtWzuh1jIkc/aFQPXgXSOw6vyZnHtxppOYjC5gEjHGYlUJvDXn8zFUigz0FFniNZsisM7X1GSD2RsKhFjx4N+MsY9gofQXMWkG9jEDCZV+wzhf46wmwENhGE1GPnkM9Ldjc5U9FhbKCeS1RiGawm7d1hXNQZzIua7FOgW0hmpuaqnSCCJ4kt0gf+1dOP/FdXL3j0KXEXiHrHyYtHdh/G2InAB0EjDDJz+MyTq44ejbzXB0yukNhtTDJpoFLMQ4NdeELu8TvNQ1fw7NkD9bsY9xzadcLReH9YL34bE180NGVCNmvoqojsqCbfSpY+5WW0pF97/k//8f+B7XbLeDz+/3vd/AvNxpcS4vmADY53381ZNgVqJxlKQ3iIyNcOETtEOJiqRABpAwRQPXBh2a1yhvcJ5dsxuvOH0997cE7h65h6m7H/NIKtAhnoVzHuKuHbP3rCfqwo9wlVnaBD4HGdI2VgnLTctyM2bQ7a8g8fT4jSgWAEYiPorxM+NgXZ8z27fY7YHtzeaqnxleZksSE9GVAExuMG5yWp7RC5JTwa9hxmo67V1EToUjL6co/aOZJtz25XcL2cE6ykVwcL2FAb7DNNctyitEdnnpHsiJ406DYwCS110CgLn8wYLQP5NSSnDWiHrgPEns03M1wayJKe8VFDlB22BPCwLXMu/ZS56PjxF+9RymKKHlsE8sbiswOYI6QOMT0EfuJVOJz0VxqUJ7KexycRn3SG+5OUZpWQXSlMC9VM8PBhxFAbjB7oqoh4CDw9ekRVEJ01pNpSmI6ggZXB3EmGTqHHPTIZ+OarJ0xPGtxa8rc+vubF7J4noy3nX3lmnxzpzFL8bsXz8Zru0VBuNeV2xKsf7CmyltvNjJPQ0/4ocPzPW46LBv22Ixc9s7glJlDnmlFiCRU0uWLUWJYkrN8nfBgKlHU8H0pmSUvsPCryRNIiMoc7skTjFrmXRHikk3SNxs8GJnnN6b7itduzVTHbhxTvBdOu5SgpGT3dI2cOcTagX7TUuaHNJU2V0DmFrTVx8Ixf7gneczFeUz/k6EVP7zQ/nd5xOUwZjERrz+p2ilvFqFqg24AQAd8pUAGigB0FbCRYjEoqadgfGSav9iTHDYvjLfOfPiBbyflszf5dwYvrkum8REUe91v3u+pBcpgHSwtq4vBGEFUOH4EtYDjiwG/QYPAkeU8fIjY/Ur9tK/coYXEDKBvwDaSi53m+JfWWqA3Yc8dUl4Q/SXmRlyw3KfsyJks6pkND3SeM0pb0AbZNwn/98RlNG9P0EZPTlqwUpDeB82jDvG3xlWL4dcr9hzmL5yXnRU27i5iolsVkT/rgMZFjMtkzf75jtraMloFrmfJOjdGZY7ztWGzLAzjH9Lw4faQoHT4OWC0Rj4IJLfcuZ/5sQzTuMSctVWawCRjrab4t+Oq7p+w/jGHk+OLNNadRyejW4tYH2l1/k+IKj9kLNldjjos9SdaDOHwfJumAuWgYxhBfqoMr4fwAsQlC4PcGi8KNA3rRkcc9Jh1QiSPagW0U8uFwwB6LjlgPjMcNF13JcbpHTiwmskyymuioIRp12OuY2YOlO4J2ARxZxm+2RElL9+uU+CHgPsZsfUJ63CBLOPYlp36PDIEoOcCFUAKN56qZMp5ViCDotxGzUcd/8c0bjo/2TJ/sODm/p6Bn+TCnKQxx1GPHgfS8RlaHLkVRHop42SU0+5i2jjl6usai6PqI7iSw+W7C3FpU5JgmNf0mpjkynIqKs2xLeiV5lm+IvywP2xujQwd6OYq47CfcRRFfvLzlq3zK23H251ov/0Jb7wanWL/NkMeC5y8e+fn7lwgLIRJ0k0B0J4iOe6yPSJaBMAiGQiKUJ1q0LH1KlnRUnWH65Y67hxnuDLTyiN/uz8tFR1hrhkgjZw5Kj08O4b0gBPUQYfcR3sIP5nd8XM0IkaWOD4Kdm27GC79iWjSIH1XstinaeyablrPTHZ+8Zj89rAWp8cC9yznZBLQNYCXLKGNUD+yFQXRQOs1Zsmd5PSE7bmm0IpSKdmI4Omq5bWZkDxJxEmimAr2VeHMIvaSXcHG65WZ9yn4cEznHMPLYKQQnD7z7xDMaLKoV9KeWTZsixhIlJOm0JUkcdZnT+oj+6pg47Th79sj67YRyffg/unwTseyOcCi09cQrT2gF06yi0RFsNFV0UD+KHwz0q4RYO5QSmF3AVwm7seS53nDybMmDPqLc58TfBbZPNFFpca1EImhDzEMHQh+UqRZJnWlECIzihqZOmJmW4TtDdyQxewldhPKKWSjZ/p058geO/EXL1/GC2brmOs2Iooh/5vkdfhuI65LFtuJb/QSL4KaZ015Y1v9nQzfN+dxcst9nPCxi3v3mDT/58ZpPX8PVtIAfRphvDaNnJfb3B7pdwqbK+bDPyboBk1rELLALBvFg8DIiOAjHjlHesF9kFHmDiizdr2c0J4G33RSSQH2fMmQCVVjK+ymyg+O2Jfliy/q+IO17+lix0zFqFSB3LIqKfpC8sydUmwTtBcIIwk7xzi6YnDRMdM9yPabrJflxjS4FTCzxnafODc0qpTvzBCGIvOUo35HQcn29IEwExWlJ6WK6X6R4KTHOY3vN9oVi+/Mp4UnPIDwuDoSJwyw97A0BaF18EFElgWgT6E8P70/RAGeWsNfMWot5tuT6ako4apE4wrXGfW4RDh7vJoyymnfNlJc/uqXrEtRLS79X2CPFt/spSW15UzxyuZ4hEstnT5Z89d0JaWzJRU91n5B2gsVRxTfLI2ypaYeIPjqImfyJZVgMSOV5uCv4v4mIeGFZLGraTlOPNVHquHYjXuZrNlXKdZqRzFv8UhLvHa9+fM3945TquwnFyx27u4zj5yt+VDRcNyk3bkFtI2am4fKXZ3ghsRLidUA40JGnzzzFSYXoBc/1mm++OsUI8KlAlApmFvMgGW5j2umAzR2bm4LUDqRRT1cYksjRZRI56alHEdIq5K3EuIAcBGHkeD594PZuhpWCKhhwgkh1MBJ0RhFsIMsa9iHiuKsZbR0f3ZyjFxvkaqDcpeQrDwtP1vc0KsFfdByZBhmgu0nZ1hksFSFRCAPD3PHlm3s+XU2xc8smjbh7O8M1kvHTkmlUcdvNEWtB3luO44Zym6OmnrvfTDm52BMqQbvKCA8Rs7TB5pLxqGWVaISH3S4ldRApS1q0qLuMIm1QOhDSQLVLUHvBq88uaWYCak19nxE9r6iCYnCAh4/XR0xtS208208njHRHlPa/zdHARHTYTQRzwWVV4AaJif58m+p/oW/2RAE38TxeTvn5u5d4Ba4I6FHPVDU0maJvNFkz0B5DexJwMXA00B8HnhVrqrGk+7Gj6mKEBbOS2E4he9CVYHJUcvFsyexoz/FsR7SHkAX61z2pGZjaCs56xJuWX8o5y6nBnLaopeJpviQZW+6GMdVXIx5tThNi5j/e4E892zuN7TVJNmCOWnjSIwQ0RrPdpqgXDWHs8JUgyhxSB8Tcsn4i6X7YwZcNXgt0LRg1lseqoO0iHlODSC2pd4xUB04wm5SIzHN3PyeYwGhW0WUCvZboHrY3IwaviC4V8RJG0wo1gFkJRl0HsWPrElb7EfG9JFKOoQhUhWL13RQ/8sw/WzGsYuQvEtxDRCg8mEAythSLmu+9vOTkyZqniy1m5ahPFW0dEWKHqySRHmgTSbg74CkfsjHv/+iUWVTxA7/hfLzGjHvSk4awO6A5fRrogyTUimETHZCdDkRhaW9SQBK/blGveqpjhblo0WVPdGX5l599ZL1IuN0U3IWIJ7NHjOs46TvOflPx3Z8cMa89rZ9zPSxIPjn+2vl78k97sj8euJjvSfKSh9MR0WLAvk340fGWn787Y/sqIb8K+J9Bo6B/q6nXKWbRUT6k5Lrl9AePNNrwUu6ZvbdEJ79Fpc4Hxqc7qo8F8VHDsI1Y3k6YPdkSOoE7sRQbR/KkZhaVOAMydQjZE78p2VQ5K1ewkylZ22NwMLcU4w5yT+0juqsU4zzTosZdJXyhNvRC0VURn+6O6ILCS0FdxsjE4iTUE03dJrg8IAcY5Q3nJyvuygmVjJgf7Zkse87Lntz0PH31SHZjWa9GMLU8XM7QT1rqmwxXGbK153nYIhXoWY8dgR716LMOieDsn3uANBCAUdFwdLxHFQNj07NtYqJ8IK49Z8meGM947Zg2PXoXSC4aPv/BJ6o/GTNb7OhEoN7E7NsUWxvik5bVtxlq3lF3EZfvJ0R3gvloxenJii9e3qEGz/JhynpVMCiFE5LN5ZSo84gHQ/gmRf4yRi2hRbO0GT+7OeeyGrOPIuqPms/aDbvrlItiy5ksiW8N7sqQdi27mzF3f3xMMSt5LlrGWce6zXkbCm7Wc84/W+EHiB8get6QxD1iL/Fjz3DmaLqYOBpohoiqj/nq+gnNPiYyPV0h0DV0TmOPHSLyhEaibzRSO4ZBM4wFie9hZLFJwJtAMm6ZpDXRuENry5OzLT4O3N3MsduIkhisQPlDl66WhjLR+F6R7jzxA+z6mJ0wWC8P9Mb3Cbkb6IRi2Br2bUI/gVZq6m1K/4cx/YcUv4xI5h3+6YA9cqQXNR+6EfqzGhaO1+kD4+c7Tl+tOJ1seL87YrCKk5/co79sGYj4V/7St7SpQKQOte+48wWzpzue/+tLPszmrGXEg0yI64C+U8hWMmwNu8ccUTi+//qKBIeMPUeTPUwtoob9kDDeOFzqeTiR1IVkPKmJk4FZaLCDojQRqpckj4IHmdFeZaRhwCERj/A7p5eYk45yiIkGT7b88y32f6Fn9r/zn/yv0CahXaYkRctwl+DOLHqlCKmnPQrkNwI/SNRsYHiIsSPP2emKXRrTbGOiWUsqHEOnYaUOYgYCpgqYbuD7P77nZ798ju80MvIMUqHygVApzLSDvfotFETzPN1w6wtUJQ77ukh8bfAmMLI9zks6Z5i/2PCwG9OkkvNmy+Zyhjnr6VPJ/HpgZyLaXYwaAt4dxA72mSVtHf7C4qTACEfzkNJ1EUZYJuVAGHmaxwQ9sZiTju3HMe7YkcU9nVfIDoZNTCQGXGlwRhxY2Y4DqrNSyNiRHrV4L5A+sNkU5I8ONw2QByZRze5qjFdgTxxT3VAbjY8D/ruURFlCgLBXMB5QmaeYVigEu2VO3FuGQpDVHftRxLBLEG8OkBBTe6qx5uRdz/rNocWr7z291iQE9LinjQM6EbjLiGqsSactJngoHLtNTnojcGeWER2JGbiWBc/jLcvtCNkFugz8o+bNZMfHfkw87jhVFdfLMTjJUdjSzzXTx56TaYtKW/7gq+d8drpE5oFdnvJXhnd8m57yy48XHI33tEpiQsnu45ifnC/5VoxIqoY7c0wQnk7E/BuvvqbbCf5QnTO8zSiins4o3pw98NXtCZNtw3qSEUaeflAUNz2TH5c0TUz1boR61hJ2inYOs7xi/1jgB4nfGVQJxUmJPx7YqYS5a1luR0zebLCVon2fE6nAv+Cu+aPJ/EDM6zyNi9hsCnp1SPq7uxg1GfBWICYWsdX0GUwnFXwytInGnw206wTRCUbne4z2NFcpMnOkvedlXfI+KdjahFfDhjKXqKlFfIopI019W9CfOUIjiXYCd+wOafFuQHeS5rmHSjHa9oinln2ZHmbaInB0VJHZlm/qY7LW8uLZmk/liAvT0KnAVsXY6wirJUoG0rQlFz27TDM8JoRK0TvDk8/uuFwtDrPiHH73+TvefX1BtcnIXu5QtSRTPWkT+MYtiGTPE1PxaT/h6esH3tsp/n2KsZ5w5IiTnjJEJA/QCEM2PcxqJ6pmfFxR1wm6EsgIuhRCLxiLhp2JuLi0VK8s46jDOrAXnk/VlOoxZzp0lI8pPoM4Hshaz2afgYeQBvCH7E0dDMNRYERH2x+seaZ3dPcpvlWEOOC1OLgMPhqMHBh/vuPxwwR13tOjkPXhdjwYSdsZkklHuImQCsgcdqNotcEee4qtw+8UYRQOafwflsiPEd04kNwHms8D8l2ESgfyvGd1NyH0ApeCi8EMgTCxPFms6dcJ5T6ibRIQcHq0pj/27PY5Z3pHs05ZqYSzxRrvJcwceem4ebvAjqBD8c/KS8KTjm/3JzwbrfmmOWIcWsJ7zWqWs3A1u9RQ2hg6ybw4fFZnZzvClaEvFH0eGCU9YXkwJCofaJSmeLUn6hzjO8vjIqO2B/pnbD2MHEOtMd6jrSA8BloZowm0icTsA7NJSbXPqJ97TAMuDUTKEu1BFlv+3v/sf/9PZ/b/XR4HpLMaIk+3iw+iBKvwxSHVG+2gDxJZgXKBWPSA4H41ZbwbeH72yOfFPfJB4SqNeNFB7pC9xPUaPQ78/N0TwsTjYkG7ADEbKIYDxCNeCbQPTJ7tmR+X3NzM+Pz4gYunD3Te0K0Tpg8N3GnudEb/xcDZqwfCVvFq9IDcSu7FBBkHqjKhHRTSAiuN7zXaBdK8J/+dDWfpluPXK1QxcMaO/SrFP0jEVjL0Gj3pWYmU7UuBdI5k58miHrFSiObQzs840MpYOEaf7Yg/qwhe4KYOp0FNLOpBUTcxVR+zCilSe5g6IuvxG82jy1HSM8SC1A8HWmDakXQe86JhM9PUKoLCo171iGNLI2LaPqaONA86Z1hHPB7nJPP+EMC6iUAdAkHJtaAOMWFl0O8M6sGgS01dSMJeQ6kRueP7X94gB8jWnvnRlh0xkXX4mceXmrVNDwVHrXjYF+j7QGsOnnDtAu9FQXfq+B9+/1u+7ueUhaJ3ipXNOBnfUSnJP/Cn/INPL3h6vOGyLPi5OOOhS/kmOsP3ilYI7lcFr/IHhv+8JPmiZfSkpf/bA7sq4aQrObupSe4s/49ffcZ/8/ga0SlM5hkWnjYRfHqcYISjzJPDOtW9QF8Z4lc9OxFj72JMYmlvU/raED8K/F10CK01irAYCCNHPVF0y4xxMxA7x9w3jC8D/ipGGYc/svxX4zOi4EhCINlBf51STQVYiKMBkw6cfvbI688eeLJYk57UJJ8k9uuUUHjEHrKsJWsd2XKgKAPDVwmzkz25s+xuC75ZHRMZy1i1NFrT3SU03435nc+uiXpJ4IAoHSUNZ2dL4vYwox5OYfZmjRWS9tSxnKSU347I445IH3zk613CjR1T0BM9bXl3syD9KKgkTGRH/yFjiCQ6BH46uWWycgzrmNmm58yXhCtDrjs2t2PMo0A5mIuG39xf0NykTLMSrTx9UNzfTNkQUwwNoyZwuRlzdrbjpW8pNoF8WvPsdAWdINp60jtBj0YknmF3cBvUDxn31Zh02nJ2tmd5V1BepQyXhuY6Zf+Q8dXRmLdiytufnXN9dczD3YSZa4kfoHA9PBsQE4uXEI165HGPGFuivcO/ahnmnjxruQhb6l2CC5LdkFBtMuqpRCwsfHHQb7u1oTeCfgSP11O6qTh8nraKsFPs9jndTQqdRD9IdOSQlafdR7hzjz9yqAFoBFo5XKugFjSfcjotce8zZALSB0YXFU5IdquCbOuQkUNZTzI4VD6QriziD2LQYI4G/OnAkx/do497ut5w5kuaPqa7cCTzhrPxDrO0tOuIq6sj4qJHGMuL7JGv9YwmVeTjmqvbObF3yJXmTk4YBsWdLRje5ehrQ2gk2w8TRH1goSQvKiJrCaViZjpkF1C3kmHhmeiG+FFgteRmmFBe59hthOs05RDTOM282KF7z7yuGaRBqIAcDYfNBiWoY0PQgaLvKb63RY4sdhQ4mm0J4b/nitv/Pj3zrKTepwgfkHiU8xTX4BG4OCB2Cl0JxJFjiAX6d0uEccxebpnMKi7GGz5cnuLzQNI73C9yvBBMLnbo1zX1fUIpI1JlideBseyI2oBbGqJbSeM1+5lk982Ix3cTTo72xA30VzliLUknLeLzwPj7O9TM0t2lbG3GZsh47EZkZsB2GusFsXPoleJ2lLGbacykAwt2ftA1PkYZMrPYu5ir2zniTpM+CNKkJ7kRPKwmmAfJpLJUXcr9cLixhcLTVDFdY6hjRSEGVCsoLwuaJqI/t3gviEY9s7Ti9Y9v+d7xDa5VpLcCIg+5P3CyvUBuFE2sSKSl8RHlLqMvDZNnO56croil5XS2IrzoKDcpWji6raHyClUKxg8W87Qlqy3byxFBCiJ7CLsMWuMigWogveHwBRULZB8oop461fgmorosuCynmMiyqzOuvjoj/qQ4ifec92uKqOanzy7pjOLy4QifAg8GEXvy7UDxrEKddMxXHX////qK34+vSb9WPH2y4Sc//shDNqWJMoSF+V9asTqPOK82HD8MjJOOwUl+fnXG0W5F8nbLPtP0Z+dM/2jJPsQ8+x807M8KNnHKk+89kpx0/O7LGyLnSEvLXx5dU+9jzhcrbGWwBewzjTptuThfEc8a4sZxoRseFxHdNDCkAtFIhtGBES46QWI9qbD8zvQWIeDZxT3VJmWXa8zZgbKYCYvrI/plTN8YOqkZPibcM6LPJMdVx7jtCEuNO3KER4NeSdYuZb/KCWMHMexFjPbgf5OxGO0JrzxF0jBMoXADagv/zMUlyVnD/jdj6kiRntd0L4CF5b+8eU37xMKbA6UuzDwfowm+1JyNNhTjmtOb6hD82igSf+Czd7lgOqvophIahbrRCBOo3xZIGdicam7vZ7x/nKOF5WS0Q2wl/2h3yuKkIW4Un9bHfGRC/rKivUvpHyN+kK/opGL7bozdGmLTs59o3E4zbCTtWHJXjRgd1VgjmJ7XzETLsjTkuqGRho1KSRcNtjOoeY+YWaQX5E8rSDzVU0HmLcu7CVsl0MnAYBTt83DAw6bgeoVAsD0yNL0h2wV8q3n1+pa79ZSRHUi+1aTWsysUpxcrZOJILzr0ZYQXkt2xYttlhLkjUpaFaw4FuxakRYdoJbIY0A2ExCNuI8SDOoQtBdBK0qSH2JOqntl8TyU0w1VMkyjyYUAKz7TsMbeSUGva+DDztscg1pqwNsjEkRw3iBvD/iGjiiJcDMNrSzg6GDZdFkgGh1hG7I8j7u7HVKuU5Fqw9xFfnC3pW0OpDcEE6iqmvc342fIpnAeqm4LivGQWVyxGNdO4Y/zQ8fH+mOZdyq7Q2MeIXZ/ircIJgbmXuCMLscecdCRf7jm9eMRXisfNmEeT4IvA+18c08aS9iXgBPbEH4J6NxlR6ojTAW08SeiZRDUXYkdTJYQXPe/TKX44HNb7MkJLT/FmT0Bw/MNHonnH8PfS35rwFOm8w2X/tI3/3/r8v9r4v/e//V9SyRmn6YZ9l9EvY+R5z+RiR/WY0e8PDvMsatEnPfs6ZeosYjqwv0tppGFCw5DCsExwscCNHdOoJYsbrusppgHhBDwonDlIRUSt0OmAWEpC6vBnlsw72l1MSAPPX96zXeXUqSK0mnYTETrN6OmOxhmmUUNfxrRGMew1Zg+hFej5QKUN87imnUOx8izLApVa2iEiTjp8q8nGLWbU037KSTvL/jHDjwLFxtI9BR05TBPYNBl+7lFLiTrv0HcKd9h8YSpqNiHDOE8iB5YyQ20FcmTpMhBSgAO5lrg8MN1bdD7gdobVkSHaCEQHxA61GBCFxz1E+NOBUHjaVUK8BxE7kq+gfG7QwlHYATlybJYFPg9klwF7HkjVQN9q2mDIbxxeS2ws8AuH+aTop5BVlu4UQiMR369xjzFTUdF/ncBLz7Noxc0kRzxqlHBUX+e4saY/9RS3/pBvGPfsVcxitCO8Tzh+WvFgFNPQEZykHku6XxVUC1B9i7mQ1NuEYr3lSW+5VCntzvDl6w1Vpmn//kBfRNSPKSdne6LIs/I5w7rDHkf8s7/7nofHUx5iw/bnExZ/eU1WD3xbnyBbGHYR8y9X7OsM/6BI0p4o8nSNpsHgkQgHRdayshlp7aDwnBxvOekG6iLweJ/R2whbabojMMLi3yUME5gelTx98cDyl1PuooJFVGOtousi0IFk0bItU9IViMJR7jIi64hmHVJ58ALVCpZlwWhUIyoJL1vqOiErWpIq0DtNVxu0sxRl4DFPEfOBOO0pv5sQtR6bgU4cvnDkpkcqz2pbHA7Ea41ZtHSbhCYYzLXC7AIq8bRfDijj6e5SonUg/bxiNK153BTk1vJQjXglNrRKs+wzzLTn6WjH9ccZr+ZbonigXGmiFzXvr06xraTpEl68vqUwPZd3C5ptgpIO12hsJDBiQIeASyEed4T7iCJvuGsmBOPJGsteRjxr92wmMb1UzOM91WPGixdrfC9J+8C7xylVLtGpZSobxEbRC41dK8rP4CI0FIPnMo1p7g2xCrh7RXcakFYSCc8iKrm/nKOPeqLzlv02w7WSaV5TbhKGSFI8LfHlIRDcKI3oBOZRMZxbghOMsxZrJbbV5LeWQRv6oHGZJ64C9UKiS0FIPLIWuBxMFQgEhpEg6gPZFzu6q5Tw6YAtHwqBNI4+lRjryemJnjY8hIzUWcJWI2NH2aTE2pKPGoQMfOZW3G/H3GU5QgX8TvH0eElrNG0bUWmNv4+Jjlq08AQv2D8WJFlP2xlGusUdOyIsJB6tPPc3M4KBp2LDJosIG0O3TAgjT4RFZo7P2fLz6oRRazFPa4besFkXJFlH9CjpBoXWntBKhjEIAXbsSeVA1Hnc1JMuA0/mJXUmEQS2HwrWSQxnHeEmwdzDMBjS5xVq1CNj6NcR/dHhsqQ/avACmwX+8u+95etfTfmD//l/8ufWxv8LncbfJBERnuohQzSC4YUjXSpGS4mZd/RHPU0VUz/GmDagZj3VrSHyjn4Xs3i9oaojni1WvBXHJNJhgqP8UNCda2JpieKAeBdTaQFWMgTBLG6pXUTxqjpoO5uIajHgCo9eCj784RN8Fli82lAhMS9a2k1MnPacr0seP8woznt85IiOeoRW9B8jah8xPd4xXCd06wiw+FHgtam4SToWfc1umpDFPaIRzMdrVlcT0qcVauYYlKC7KRg2ijb36OBwe4eIDKN+YF8m2MgTmYHtdYF8OoDwNDkU+5buhYB7jQ4QBrBa4E896v4gh+nmmsIG4mCJJp6hOoS4qDX+5AAcYR+RND1hD5FyNIPC/l5H2BjaSGHXBu8dufX0a8UQgash7MEXHi089hiaqSdaC+Kiw8sUIRzVZwH924MP6xTde4ZNiv5Jw7Qa+Gp7jso7opUGA+nL9lDYixr7SpA/bdg8jElvLH4Voc8t9anl8X6C6yV/6dUV/831KyYE4iKQxZ4fccef3J9gziM+3XlG1Zb61SnX7xSb1xOi45bn7Rp7b7lvx+jFgJrA57/TURQb/t7XL/kfnX3ioXrG904eWD7kfIwLTO8xO8/JlxtW9wUno5LLYoocWR7bjNNnG/YfFizSinoicW8Ncezxe03IB14crfj7v3yFGhyiCETO0hcKLT3nizWMFNshQToYh467FiamZ/oQeG9zXv/kjq9+fUaa9ejE0eiYvPZ0iWAkO9JxTywP75lNXeBHnr7VHM33bKuYYR2T+5JNVeAN5NOWTZvR554+9sitIXUDJy9W7G5yqGLScQPOUz5kDLlERQ4Ggc4H2mWCazSqVkSlRw4C/5lDbBXRyYA4qRmKiBPZYCuYHZXc78aIBh6bgjYXdLFkoXqu+4J6JPn2eoY/cRzZms+lZdl6PmUxZI77uynL4DAC2CrqhSCuJcIHsJrGSIKD3hriJaweYmQqCNLTSYMx8CgK3EqiYs+6GkOn+NXjCZnsOdIV+bxjO2S8mW3YXWfkr0t2DzmyhqnqiU9KPixndHWESAVHR0v2zyP6KiN1Pfmk4frDguHCk+Se8GAYpS0rk1HfZwhxyNGkjaNaJwctdqyQk55OCWQS0GVAO0/3IUOEgG8E7qlHtIdDI0eWmbSUVYr3CmFBS0dSBepMI13AnLYkN4JSKpQ52AtlK5GFYywP+ld51BA6waKoSfKOFSP8zvDiyT1rncA7zbBUXL+ZEo09shhwqwhdWN51C9yjQThBMQz4B83gEyoj+eJ7n/B3Ec1jDHGgXad474h9x3CZ4nAkUuFTQfUmIms81jsuRg/clxNsBCGD1V2CCJJuJGi/nhJ0IC96nIA065CTwOfPbvnF2+eMkh4yS7g3mHtFejGwrTX9fcx1JXn54o5f353S6RgpHKeTHWezS97nZ6x8RtMYxvRUm4TOapL3jrgIqB+XuMsYgeDbuyNWnflzrZd/odv4uR/IswZbxXSRIV6BVJ4PesqyGrP9NKZbKZwWTNKBbBsIT3rCSjF9vkUqT9Z5fJBEk56+MzyEDHXR4TaayWNP12q6E0+0Ad174s4hhEfNO6rlATzjGo1aK97kjwxzGI0bkuOG5iblqKtwtxERjr6PuNtOKX66ocwFzdcFu/djdssCqw151DGKW05++Eh03HL6asUP+iX3txlHVYdNNG2kuapmXH065n4YowpLM9a0saIPBjqBm0BAoo8G5mnHUbRn36aIiSU6brFK4s4ddhOx6XL6VXKYy93GDF4zaIGcDZg2kAXLJGtQn7WH4+7pAA8H8pZLgb2iGUP1YQR7RRz1hJnjr/7kG9zOIFJPlrdkaUveD9g8EDuPLCx+6uhnAT85kMi6iaJ94nEJFI+B3HSEY4uLHWYreXpXctxXjFWDUo4hkYS5owuGB18wKWrSXxr8Fx3lRLGexGRpyxAk5d2Y2dea+J0iLAXzz9a0Cdx0BbO8IdoK/uAfvOao7pGvW57GJdJK/rg+Y/bZhnsZUyYLumFC9gmev+h41d3AxrONCz7/6QOMAsntgF0KPrQpdug4mff80ftT2jpmO0qxVpFNW05PNnDmmNqWqFIYe2hTn28tk7zmTJaMdp52iBAbxaD0obV47FDG8asPZ8i9RG3A7hX7uxxZCgrXc7eZ8uByZCt4ebrk9bJhIGUoNdftCK09v/x0innwuDtDfCVRK0mdaPLrwEh2NKXm/v2ErYiJnWd+tuPl8zUfzZidjDl5umJ7Pz5w0CtF9bGAVuDXmrO2ZHG8w/48o71J8COPGLe0s0ASWV6Nl6R5d2gj/0mKXUbE0mE2kF9BtxBUTwRdp5jSMtzFhF6SfgO8h+gKtg8FJ6JlNtujxj0X/Y7TaI+vJLOmJ+ssNhGIraLJDb9+e0rVGSb1wLEuGZyia2P6xxjhAmYrGEZA5PFAXAZC4dGtJ751DAXo1hO0BB/QpccKyZAJggB2gvRFiTzqCA4e3k65vRpj3mu+vZ+z6w2vJlu6KsZk4K5jvvnqCb1TyG3gi2TD4+ME4WDUOMazmmWdk9z0uBTCh4TgFf2xR3MI6TqrEFvF6mpCuIpQswFTHsaX0njizhKSwG6f0Z5AflHj33RgAtYIhkhS1QmPNmN8Vh7GoROLRVJdCBbpnigZ6D5mbD5N6P2BQud7RVx7jPXYq5jBaoSVdMFQtQnrJqe7z7CDZNPm6EoyXjT4zxwPH6bcNqPDzbtS9FWEa8yBgSI8Ju9RbxqoFdlHwe4fLOh6c8hSRY70eYnaSXYuwR8PqEVPdwqzsy1nZyukDJRDxHU7oRpDd+4Z+sMqpNQe9VYzmjR4JNGtZLSzNFoSecuHXx7jgjysWa5jQqtgYvnJ4obBGZozWIaEX/zRa9ouJWwE2SawvRnxzR++YDbZg/a81iu69zmukuiNII4s+VVgv8no2oi+i9i9mxClw59rvfwL3cb/wf/if4MWKXKjiLaeYSwZxoEw9ujOH4QOWY9SHkOgf4yoT0BtJcWTkn4V0boI2UI3E6jEMjnZc0yL6xXvPx4j9gKrJFiJqQJ+FAi5w3cS2UuC9OADCQcxw9ArvJMMTnH8esWw05QyZj7fs/nVjJFpsCNB2xg6q/FGYB4kovBMnu2QqaXfR8i95Mt0yVftnC/jNR8mKXZt2FcZrlOM8ppx33G/naJfNdTbBC8FzgvmosXPHH2rWDwOPCbZQYjRQRdL4i2AwJcKGXvCvYHzHjGxhJWhmwRm44pqk8Je4K1GTQY6a1D3CvOiRraCJpdIPKeqYuEGXC25GhLCK4e/ihi5QB8Lnn7a8M3JjNKnpLInNo4hEvTqwDxwVynYgK4EPg3MP9szxFAOBlFJfpg8smsTrq+nYDzNXGJ6j54NjLKGxij224zzdE/9KSMZd+xuc5qJJDUd531H7wRH5xVf//IMPbHYR0n9mSCuA8fPtmTfBZZpTG80Oh1o+pifHN1zczXBjgd2f5LizxXTcY2soNp7wtzR3E6JTcVPf3LPzz+cIDeSybwle9aR/KojWwjKo5SvfnVKrxTRXeBf+Zfe8vTsgf/j3/k90qRnNBm4rAumSU+fBWwjkSMLTlDoju3t6AAJKhwydxy1Dff1CPzh9R6dlKx+M2fx5YojVfPpwwlVb3iZb2kyQVNH7GTEyA3UNyk+EXRjyPoWeoPyHpsLKBXnTx953I4ohwSVOuZFSVcIMmepyoQehTPwk6eXbO5yuntF2eQMM4FsBFHjaZ2mPxYUG8uwj+heOUzlUTtB/LqheltgXrbUDxnyXqOLgTYSB+FIB3YRsEkguXX4VDMxFWuVo/cg8oEfLh55W004H2/Y6YjVIuKkqnl8nJK1ln1IGCc17WNC5yN86pGPmqGA2YsNm13OzDYsRUYsHe0+QrYHOpzynvYETmzNbnm4DQsvsFLic49PPARIloJ+JEn1Ie0f3yi8Cijl8a97Uj3Q3yeYlaCdBvzecLzYshcxRdRTxDV35YRo2lFhcDcJc7MjSEU4slRX+WFvO9fsugyMZ6R6ROTRlSdKe7ZRhvsQI/YSOwaVHfzv27FhHlf0qxhfBFQ46GezpMJlEipFU0hmpmHbJpg7hZg4+saggsfEltBLohLUZGDfJTgtGNWWOlZERx3D2hz0wnhaNHbiESuFPu35K88/8Y/ePuFJX/P1bkGcDuAkvZAHXO2zivJXE9pUoZ624EHvAkoJ2puM5EmNu41RW/BPLP02xqee4CUhCugWctWhXU8nImwWmE9bXp6s+fbmiLLVOKWY53s2PiERjsErRvWAjWBTF8ghsHi9RgbYlBn93kApkecDT9KK2zqj7A/J+hOzZ1hHuFKycxk4icYRAsQbGCYBm0qyoma2KIm3Ejex3G0muJ1i8mKPvUpYnymyOlDf5vgkkPgNP/vf/Y1/msb/7/I0+SFk0o8PtwGfW/Ro4KSvmJoaEQTJ1vPkyYonnz8wOdmhlgqxV/gbc9B5pp7JaclYtMTO0T8k2KUm7CWzSY1xgaiESA5E2UBwgmzSYtYHWYeSnhBLmuMAhEOIKiiOypb1+wmbqiCLO9a3Y7yB3cSwX2c4r0iznmgtMMETKUv1zYjdMqe1hnqX8Kd/fI6vJb+8PsP1inVIUdIjJpZdofk0LZDTgaT2zJ9uyc8r9Gxg16YMvT6kg+cDZg3+25RmnzAte5ouZrCKbgx60WFOWkKp0QT6XmNuNMKJg+9cG4R2GGMJxwP+Rw2n+ZZXlJzrkvn9QOMVN9cjPjYJ07zldzdLRrMKUkfWdfz66RFNk2KUg7PhH3vEXaVI1x5z1GI2EpfBJG/Y7mJ2Vzn6TrEY7dliuLY5PrdMj/YH+clak63h9mZGgyHrPT89uebixZLlUOCU4DSp+B+/fofPPV2nePt2wnDsKY8F3Uwy2gxI41mXCZd2xBBpKqHpfpHzolhjJVAFtm1OeZxDpVm3OeWd4fTM0jcjkpOBv/J0yT/62Ql9LtnFY6bXHrdKuH4146aKeJUtGb/ZYiYD+VmL2wf+iz98w5vZhjfPHulyj7jo2CaSXRnTVjHZMJC0jvXdGCU85/5AWjsXFZ+ujxGZo55BdNxi/lQx7npCrfj64wVyZJEe9EnNss1o3hbIpUT6QP3Ko9cw61qcMAgLjANRMTB6uqPUBhEHiDzJYHFK0txlbH8z5WKyRsSe3zn7xInoUE5xY2bUM03iHdmkZe9imlzRSUUTKSLn0FcK5QXxcQ+fIvJ5jbw2iKVCWYffGMKgGDIwRU/+ODBKWvQbi5SB+IuGo2dr5OcNo2cV73ZTgvc8jFNeLbaE9xlRLfj91x94mT/yxbNrdoXk5CcPyJOesDLIkSVeBoKVxMNhTTOxjlprslGHDIfP1bAIiE5SG03kHCGFo9drRG6xUwtOIoQgZBYrDypW1QmYW5KoJ6SBl2JNc51jxx4Xg+0Mwgoe7ieopWK3StmpBLs3bK8n+E3EdFxRkqKnPX1/GE8Onx1gN6PvrdGnLcEFho8p4t5QXxdkWYsYH4KMIQikhT4+GOt6LeFpTzUWVInEFoEyShBfpwylQS413XcZYquxGbhWkgwWkXlELWiNZDdTtKkEJxC9IH9ekn65BwvaOIY6Yj/EsFToTwp6CWvFL69OSTroLwaiWU/TxAwJLD5b0j31xPHA/HiPOBk47htUJWkTc8hOVAL36wyRO9TJwVHiC08wMJrVBxuorBhiyY9/eEu7UOjkMPv/5qsT7nRC42JCd9gEWHQ1P55eEg+O6rJA7RRx0SNTTy4cT/MatRYobcnnHcOgWT9myL0kfmcYtjEP3x6x8Sn12GCnB1NqkB6TWerXnupEIjrBtoh4zFI2p5K6T4hnHcWkpxsitseGoTdUUhONW3wUiJ60f6718i/0zN7UAW8OfvqpatjvE3yj2T31yBa09UzflLy/PyKrB6KzFp/3TGlZ54awTvDriB0COw3EV4I2E3waG1IsF/M18bOBK13gVhHFfctI9wjZsXuVYK408ZXEn8HvTG74cH98mJWXkjBxTBpPGFuqx4Tvn9wgC8NdGXOfToj18FufeItUnrTtsCbG/3GC+1EHix4xAb0PFM+2XL9dkEUdadoj7hUbn/Lss3t2F/qAQ70fEbWBtBa4fCB8jBFIHs2UdN4zrBR51rPdZzgD0RZErJhGLTd2DoVnlRsKZREIGmvwXuJiAUeB0R1cqJJ2Lrj5+oghaOZiz9blxHVLnSd4BO02Z/siYnASiaQ2Kdm8RXqIJy376xGtgOgXkmzUs49SZAfFtKOxhjYTjMqe7RGMZYOsA5shwc0949YymdTkWWB9l3CW7imdZuYakBE3H8dc5QVvXt9SuJabT3P+81/8gGjp8IUnuegInzRp3JFEPVWdwLcGV2gu9JrWGPylQv5oz9fLI6hhGizmQfAvPP/AL789Irz19BPNd5djvIxYjLb83T9cIL8nedVXfCdz+Nzy6d0Rzz/fMD33/OLXz1k3Y2Yv9xydN/yj785YmgxRghJjktHARFrWDwlqZnFjx/YXE/oXASWhqC3raYQbJLd1TnJeQ6vIvKfpEvK/uiFpeqKtZG2ByUAae351/4RpVtEXEWZuaQdDfKMg9ijvMWvQc0tvFdGi5nyx5JvfPKFWEUlwtJmEu4Qi7mmGlI/LOfOzPb/6+gWXoqXIG6Kl5LMf3vOLhzNGo4o8a6miiEnSYDeG7Kgn7GN2U0X/foQcPG6nkLlFN4HhxKNPW46TlrSz3A8jBmuYJA3aOPo3Peu3Y0IMbRVjoxjbaeZpyct8zW+qBUeTLdvEcHf7lLDVvHl6xdxW3G2mpLOWrtUQPN0iIN+ndHPQncOhyXbQxTF6YSmyhvabguqJowqGZOToLyyXNwvEYiBpAsSWs9MlR33Hz8qnzI73PPRTdNFxbCtuhgkDki+eXvPr5oQmkwf632wgutWUeQyDR34zwreK+HhguIu4X2jGQ8+k6lnrnH/xi/f86nHO5KTh7tMck3mSIbCLA8sswiPJ/tRglWR2skdY2KQxNgjyrGfiHJcPY1TsUBuFjANZ2uOzGLORBw7+kUDWAhSoUhIW9rdrcyV2yDDWoScWnx/WY283E1gK1GmHbCU2DyTxQKg1Xkp04lA2sDgpmR43fLs5wuCpp47stKLfJbQ+4rabMvms5HS74/p+jh8UWdFi0o5+YmiNIvPQSkWWt4ye7rn6+RlCwet0w2oi+VQn/GxzwTQr2d+MuJMpZueI9hL/fEClPSfpjghk6S8AAQAASURBVP02w3eaQQlcDHcUTNkznVesbwquignx2OE/5gxPOjIs07MdH9cL+qcHimUVp6SbgJo53MaAEAgn0dYh5pYXbKn6jNw13H8zohtput7w/fEj7+SEKPRQSvTIoW80+dM9L6s7lvt/OrP/7/yM4haZWuTYsp1qOLUI42g6TX0iMIlj4xMqDJss5nY/JbSK5f0EeW9gdxBtDLNArAc6rTDnHU0s6Qp4+w+P2N7lRA8HCUuZRuyGBKRHNIdEZreAkAQu11OOnm6YTRri5zUqcbRo+M7wvWd3/Ko64+u7OfLokMwl9fReIkaWnIHOa8YnJfsferKihcJymuzYkpJtPRJHsenY7TPE2JGfVHyzO2b5aY41gkQO+D00mMOqnVSozOJnDqLDHFyfdKSzjrR3mIlldFFyH+fYI39owzmIz1voBSp1PF88Mpvs+eeefaAaaR4fppTfjpGJIxr13JuMLpOH9cc9BCvoUsmoDeSdZ7saIQbJ7tMIv9FUbUyfAmNH/KpmEJLYHgQw9VQQP6+gh34KM1NTa4U7PyhBC9HTpBG6V7y9mlLvY+6jiJP5hqj37NcJv1pdkO5gt074+PWUh4cxrhMEKQidYf9pzBAL3HeGTZUxDBr/qqf7zPG9NytYDMhBIG8yzODJ3cAqjxmdlXy3m+P1QH2UcSFXREIhnjpWJoPPCjITqD91CGl56yc0ueLORlx+MKyVJmSBURj4sJlSn0lc4fnss0d+9L33xF9p6rsM2VpsL3jdrchPOnRsyRc1olY0uwRXK5rBEFlPVcbYvcZIT/mY4zeG1S9mRFtP/asp9ddjonyg2mSIucXdRhzLGmUck+9vac4EXSHpnSbMHP7RcPWnZwzOkH6ShEFRlAPNAlwKZz95oJA9q6/nPA8H+uIuivBBsLweM8tr0o8BeTYc3tu1Zp9G7NYZ3WcePXjkcPhZ/XNLNZNUTwTBS1TwvD66YdtmVHWCXQj2TcrwmLCtM/wzS8jCAS2cOSCwtDmXmyntLuaxLGic5oenV5y9XmISS6cjgpPowXO22MB5j1GO3igKO8BI8ERsiLKefKhR0tK1hpN/6Y7xuEWtJHJuyfKe/IstR01DvOgRgLea+yxnfLEjlS1u7PDfxTxezeid4vr6iJtPC3Jn0QMcZ3sm04qT2QbVBTLZUZ0GfAJhpVDCEZcBGXseb6fIRnK9nnPdTLl6d4TcC0Z7exi1zR0j3WF6j/UKOw20kWTIBaeTLUfFDpE7Wi3QNmB2MB2VpHeQxh3pl3vC3GGnA8IKYmVReOypI5QC2ytWN5MDv2Mb0V9l9LXBzxyp6YiynguzJeo92c4TSoUzEi8FkR7IzyoWccXlx2P8IBknNSH3tErRd4HT4xWF7PFbTXVTMM0rYm8ZlKT2hu6lwx872lzgO4m1hnaVktsWMXO8Z8rV7THyQbOxCZtdwfn37pk83ROdDlz88I6gA3YwvLs+Yy9ifrk5I3m07OeS4AXLekTdJYi5Jc46mscUaySyhGFv2OxyvvfskqT0DA+Gi/kKfdojHWjvka8a5NziHLwOG0IR6EaHlWGT93S9QuHgtKPrNNO6gxDgOiIylnYfYd9Itjr5c62Xf6Fv9p1RFHR0m4TQKfocpBckfQVjR9vHDJ8yYgdDqpF7hT05SBmCh0Er4uc1dhnBTpEfVaTTCroM3YJ/A8nS061j1ElPJQ0yAXs3QXSSAJidIESOjUpRtWMjIvra4JQlHbXkkwH5CM8vHrjfHnN3OwcJYQiYCkopSYqao7RD9Z5846iilNDCZT8jzzq6E8+gBJtFTLrtKH2E2EgiCbPFnmjc8PhxgRAC0sCi2FI1CWnSE+0C8aJlaGKabwoYJK4ITBcN1SqlSTQJ9rCLu1RURpKcd7S/KugvDAL47uaYUdmyz1PSoxq9gjtZIEtQTSAEQSItw04SttDEkkpEjHRLu9IkkaVIOja7hPm4Zph57H0EHuRJTwHs1xlF27ItND5WLEnJ046oNJjUMrr0lFvFezMjGw8kk55dSOm/E7SJwpcaN3dsfMzQZOixZ1Y2rHWC/dwxv/SUfcJQC/TIwtYgOoEr4E2y4qv9gv3bjFwMxEc9ahlj40Ca9XzsZny+eCTLNfGw5v23UybPa1Zlzuz1nmqi2X07YnrkyLWlkhEqctRNysYn/Hh2Sdk8ZeVjzikZLIzHLdfvJ8Te8HgW8fLokX2W8LvFHX/w8TnSCxai5EW+ZfcsYlueYGYDUa3o5hAPPTSKeNaxqxOi2MJFz/S85PHXRwQlmZiG7Sil7xVmZLnxGUdfrmjvE2oiRs9rVPBEnwL7LscZSX8E8WhAFg6/0cSfJP0TSWQc2/uYE13xrsiQyqPWCn3esrOG+GOgaTTjSUkkHYPTxI+C8qVgnvS4x5hivmWdpAQdmFeONSl57WiuUv7hp+8ThCQtA7qW+DRhP/MkomcYIpRw2LFD7hTT7UD4rMf2kt1djq6hbRQf/Zy/9Pod1794QhZgdNehi5YrDtTAzPXUHyC5CDgvuGOMvOhRscdUsLEpdTshBAPHDpEGwnVEOwI7dbTbGBl71jZlpGpOyoaPyRi2Cl0Lqhfit2tvIM4C0dbR7iXDscTfGC5VjHaBPlOMo5auGuGUwuWHwGo5xBTeYfKGX348wWhHWGmqE08xNDQPCcIftlyenT9yeZwdjHoE4mmDcpadzXHvFe2ZJ857GjS7OiOJB8RHwybLMCWIkWBQgv4M4k3Aopg+37PajCjymmaIiU9b6o8Fxg6ERlGNFFNZs10VyJFlyAVSOYL1+GVE/xjhbiN+/iHne/mSb8sZn//4krvb/EAAnHkelxPCVlMc1dSRJLpOYRwIpSaatZi94km8YzlRyOeW49CzechZnHVE+44m1tSbGLPyuBcD7ibhrpiwHxJOopq7Twt8LElO68NF7ipjW1he5A8o5wmRIPso2B4XjM72bKsURmBuoS80HPfIVeDjz54gnMSkA+1Dwlm05yZMKd7sWPcpr7MV1ZHh8tsZi7Mdcgg87AuGeSB50qJXij95+wyxg3eTOS6F2b5iV0SIXvNwM6F9/LNr3f9Jnr/QN/tx30Ph8ObAvFeJwz63fO+nj4yHnrFvSeRANB3wUh5maFbhgsIPChNb1F6iB7AJ7JsMMwQyOSAzz4kuSectv/e9d0zO9xg61HFPctpxNKsOwYyF5/TFEhcJbvZjzL0i6gPNPqG+Knj8OOfnt88Qt4YhAhk7Zi82xB8V093AF8s9I91xfzdieznGPG3R0hEqRRQNDIPg/XdneHkItG2jhHKiaJ84/OnA6nZEc5ljC1gc7ZiEllZp8vMKOkH8vMbVhtPzPeHI0c5Azgfu9uMD09pLOiWR0pOZHqyg3KbYU09+UdNngrqKqC8kbR0R7yzbTca8bVG1olt4VH3AVpoXNeq04y6MDglYrQkjAVOPaQJn8w1Vk9BtYoxyRGbAryNsIlgc7bF7w2yxZ9a3OCdRHbyM12STloexYfJ6TfKyoR5L7n1BGnUUn20RhUd+0RByzzhrmM5KsqhnFaeYFmQjKbsEkVmiiwZ3GtCFx7/s4VFTBcXNkCMS2D8T3OwKbAx51POaFYuiZFgbPAOPt5bxjzoW0hJdWoo+UFwL9MxyszXYm46+0/TakNwGjFR86y/oy4jNEPP2doFyiokdGDLB5OOh0Fz94pTqoeDvrZ8TdobNSLPUGb+5OuHjboG1iuFjSqp60sqjCITjgTfRmtna0/YGFQLtdcbrZw/k84b22/zQgTKe0XlFflGzv80JCp7XDRPVor1nFY9wzxzJ85rz0RYQiBjU5w3iTYsvNbf/5dEBc1w4/qdffEUSLMZ4hAwMrWIzihguAvfkdF3E8cWa+KQlDQN5PPDZ8wc2TxSvjh/I4469iZCZw20NSdqTlg4dH5SvPgddeuRWID8YXrUbJqclPiiiLZhXDSqx7B5GTG8tampRmwjbaP747Sve9yPszDJ6ueNOTDmfbhgPA9VlyvxNjXw+IOYdx6MtwcCxqHCDwvSB2cYxT2pEahl9GOgiySu7Rj4oJk2PflBUywxlLJs6IzxGTLY97Y8HUt0zWlRE3y+xO8OuyXEplD6mbFLkXiGHg2abtSZkDiE9qpKHXEHnkMc9zbcFybw9bNb8dh6/6seQetLS0feat98d4YNgoUu8kNTfZVxt5gwfMloTw405fGdZR6gFfaqoMMjuIPCiPWi8M2fR2iEbWO6Kg2fBg1YO8WBIgyUIcVgrrsBuYspVitgozHtNh8bMe45frLj44oFBK4Yu4t2rhE4pfvG3X7IYlXAdsbofo7aSeNbTflMQ3Un8sUOmDv20JtkG0rzl9v0Iu1H0peGyKrgfUm7rw8G53UvMk4Yf/8uXyMxhnEN1gqS1bESMrxRp2nE6VIjC4hOPbuEumpCMB7AC+9SRTlq6fYJaKnyvCB7s2jA0hq3L6YoDmbT1mr6NeDuaYgfF+mGEeNDcRTn3uxH1qebm9ohhF+GlRF1G1B8KdOsxgyeeDujyoGc+/uEGg+d7z26py4gX6fLPtV7+hS729z5ns/p/svfnMLat63ku9vzN6GffVLtqdXuvvU/Pw0YUdWn5UoYFOhAEOFfg1IEAxYYDwYYDC5CcKLu5ITgR4MCCDV/7EqIkihQPeZrdrrVXV33V7Ofox984mMQ1nEnJgQ+gmVbVqAnMMcf3f9/3vs/bxxp9iMJ0hyzp18WI/eWA7apHXsV04UHxnn2QJDeCNG4QnUTOWiZPNpwM1hy3OwZ5zWM54GE/QBSStU6IRzUPb8fsrnsMkprYOE7jNc+OVnxy9sBpuKWyAfPjNePzHdmmJWkNurX4M4d5YQiUYXUzJGjAScG8Nggc9knL6pngpsvoOs0qicmLlChqGU/3CA+uVsjEkDQGnRief3ZD774ifA9J2CGmhrKL0KFhnYWs+iHSwK5K2A80y7sRt7sRH0WPrFfzdLrELEPiS4mynn6/xBuJefgbrOrAEfjD7i3/5YjQOfxJx16EpEcV/bjjaL47pJMJQVgJxHGHOO7o+Q79dUj0ThF9lLieZzTfEzUW1/McLwrS3BB9VDSdxo4On6PSluyoJH7WwD7gSLZkxWFf9ov1OXanqeuUlRkR9ToCaTkeb2lNwLo4ENDC9nBIq/KY+j6lvE5hreiUIn4vEKllMClo7xLk25DaBqgW4h/k7O/6zIYFF+GO/8X4LdJ6ujrkTJRo5TFO8pjEjDd7dpsUIyWLXUj2SU0/3FNHiiAx+Fea+mnK2XaLdJ6zXkFv44j/QhLUnuG4QPcNr9cTLn2Pdq3ZzgL6Z3vkRY2JPNUypjszjMsW/zZiu8nYmcOUxpaaqkhwv4wJGs/Y1ezvYzwe/SEklxGiZ6nqgO8/vyd5WvI8W5F4g1sGdA8xRkC7C7ACMuVY7PrM4g0zX/AsXtE6ST8r0IFhPM458g3WCNTU8DvPrrhXMf/26gln7Zr1PsLsQ3ypCdKO6KgicQbtHOubHm6nOdnmVFcZN29mdJcxVaHx3ybo1xp1fYhqNVcJxirEo0I2hwjqri+Jlp5GSa43Y7LKcBZvKQcHu1bzbR+fK8y5IX25QwSO8jHBv41wTrD4dk450OgXJY9Jig8FLpP0v78h0pZ43rKeRoTKsN5nhKOGwWlOkBqeZUuyzpD9dI+YHIBTtlIczzakL/aEwrD60zn3mxHNNsKfW9KthfgAi6q/6dMPcsLOMU1z3FqD9sjOgZE0Rh9WePqgOXKawx64CokLT9BBW8bIk47YdqS6Q+cQtR3dqaNqQ5qZx9eK1dcT8l1CWcUM85Z0VBLmh+veXE5IhcFFgi6SFCcSMe+wRxYTc8i/aAQyl+hCEKwUuvU0XmNWId3AE36e43uOYNpiA6iFRhvP3keEJzX9tkX+hWZTJSzyAfMfLmhfdazyjKPTHYPne9Y+hsRB4qiURrcOnRn6T/cMz3c8sVvEQlGGim4RYsZg24D9+yHlmwG20DT7mNXNiI0fIkrJl+sZ1kj8wJIkLa/UGlkcmpu4tny0I7La0n++I3QdjVekpcMrj9CWuohwfYsdOVRgGb3YoMct7BXBjULsJUFu0V4cvt9XAf6sQzlPFHfUm4RX6YZgf/h7xpZWKZqpR4UO5QSnRzueJEvOnqx5fvzIN5enNKHkej3GBWB6v15c7m/0GH9MdbDWqABlD12zF4AQuJkh+kKRf89CHRB2nqCzNEPNfpOihGPatIS1Z60j8qaHumiotUS3nm2ZMI4KPt7NCQS0/YOITC873tYz0qQhcB7bxjQbgZIt6cLx8nce+NndGQKHchaxlKQvcsa+YrkaEHnD7jEhe1pSNxGNEMjAIzND2nU0Q4FPHcUypnIBcuJRk4Zx00EruPpmzmBlyY8kpjtw4r2ThMZT7SJSLPYqRpxZQnFIxoqfFSjnqd9kdBNNdOuoTiC5EnQ/lKTeYD/v6EpJFwkm4wI3tgTbkKb25FXA2DfUOmDVi2gvE3zfoQqPWntcpDCxYGVSgqQjOC1hH/Nb8xteVxOSVzX5JuCLn50jlSR5XrFdpdiTDlJBUggW92OqWDHcdnyMDpYXIwWx7hBOMBUFegUcO5omYG0V6caR9SseTwX7PCLLaqwD/SARsaPuC2ws0HcKvGchhhB40rihqxLqNOCH8ytsGtNcJeRW8W+/fsnp0y3LJuXLxTG1OYzaXOp5uzlh/mLP7m6Mdy2pdrz55ZRw3pHgqd8PMIGiDVuGDw0fRn1+8N9c05OWL/7iDP9dwOjVFvPVjHodEfUMeRUT7Vv0+5DqiUZ6EPcKqyVBbGDoyH4RMO833EQR5n2CKMB9BWXao+7XuJ3GNAc88zC33F5PuH0YEd973l0MSHXDhgQlHPKi4Vm25XGfMaxbxk1NOQto9yFrl5AtHc3Is18n9OcFNhSE3tK4gD//6ilmKDiOlojiQHQcRRWtS+g/XXH7OKHexqSnOXSCJte0p1Bch3SdwlWSN5yQ5A5hBE4IXMbBCuoUAo8TAvCEnUM4SbwU+E1IOU7Zi4iRqyi3CbKRWCVw+wizdiRPCtrXGYsgwtcWM7Lsr/pMnu14eNtjuwhpX3re1lMqInp7x6uLR/76F+fEJxXTtMOuFY9lj+tpRhpa3n44IzvPyasUc2HZiphRVlCoDBsqgi1IPP1Zx6roExQWf94hLdzpPvTBbxIGVHQDj8sjTCIQDhoVoLRH1p7YGsSpxSwCtvc9qguPXis6EzKIS9pVSBEL/DcjyvMD0TP92lNNA+zUkbqGchyyI2BQOoSomLzasL4ZUCuFcgYtHC61dJuIi3jDfTnCbjTWSeSzCtXzNLuIcCVoThR6aJErQbXKyPodqnKYv3EbmaHDe0c2LalCyaYaIU9K5maPDyBxBxG030luopQRBeu2TxB3mBg+H97w2Ix5uB1jasN2I/GBQ6UCuw0wscQeGcbjHVUZoYKOVofgNX4rCQKH7CyN08jTFmk960mAv1TsFhlB1GG8Z3PfJ6sMoTVEP8ppHmOCS8fn339g2Dq+vR3TVfbwbA9a3BCeTHO+2D5BTzti6amk5vlsRblMeCgjTs7WqM6z0TE3NkLNOrLW0/YdYe2oleaJ3NEb1Qwax1+rEYO4xj4mHBd7ViIkyjpe2IrlJvq11svf6GJvQ4EuHWl/w/YohE7gKkX1bY/RoKC5aMl2kmYf4BOPO28QwiGFYPRsR5sr+jXkZYKbW0aixawzWAXIfodqoX+asxRjwqTiJGq59wP6/ZKyjLlgz3IGzU2f3qhjuesjxpvD/jQ+4FlneUsRCi6vZ8i9p5+15HWIjCxq3DE0Dqk8JQl5GiK0YVukuGXE+XTNMo5I6VhuMrJpxUlvy200xq0DePB0PU8gHOImACvpUo85cQw3jt6gos409TY6xL56id1lDJ8VxEPDvh4wiisWQcqTIic8dhTv5hTTkNZAdORQncRtNGWoCEtHNxTYRqIchGcl7Tcp7YVl1DTsuwQ1FdhS06SSn1+f8+wHN0x2HV9dPiUYNExf7Zmd5/zFzz8lbj3Hzf6wB5WS1kjKYxBbT4CjySPqm5hEWKqLhl7naHONCB3CeaJBQ9FE+DI4OAgeeuiLAo8k+AixE1RjBdbRSYkxHAh9HxWZaqkGAllBbT3Pnq/4ohpyYTdEVcsf/+63XN0O2O9S/sN3zzgabshGnlWSwY1lYvdswx4q0WTLmtVDhhlpPs9W7ELFzkuc8Hzz8Zhw3FJMFc4FRA8Knzp+/9kVopF8eBihXhUs+5ph2ZHvE1of4prD9EddK6zV3HTDg1+4cMixQZSSTmvi1lF7j0fS3MVcmhQlHaqATmuStqPbRYShxZkA4wSvXQrK8fV3Kd2pJTENovVw3HJ0tOP6qyP0wHH/5Zy/dfGBP88GyE2AG3b4WnLzMMVUIWYh6X1vhdSGzSpBfBuhMofwAlto9INgeT8HAVp5aDzhxhHuBc0IeleWLjlYAr0C0/M4cfBRq63EJILId1QyoL6PEPuI/VigI4NREll7hJHwOqIKBe7EcUzOpkkJhUEIz7vtjLDSYBRsDOXDkN6rHam3XN73MZlCfSF5fJXhRwfio19L5MkeHhL27wfoUUuvtuzDiPyrHmFiSOcFCzJ84PBTg7sU+CH0bsCUAaoTtOeWTNRwYmiLhGDe0BmH1wE6F+hZh0HT9R2NDUgEdH1BcufxyiLuFHvfp557dGow554Mh3naIVYhRBqVQ/+ipq0VYSsYzgsetimruwHnUc79rs8m88zXFZiOpQxonhjKxKG2lqP5EqkdtQxp84D63BMpi7uKsEZhpp5hWfNY9SF1nHz/kfu2h6sVx8Ocny1PGZztaZ2gNzRUNyFh7diMY37nB++oF6d0eYi1ki6PCVrD128uDnjf5wZSj9QW30q6RqEnllAY3FbCHM6+d8e6yFCFQUYK0yiiVznNJqJbByTzms7AUFr+Zz95w59cPSWuDPrPoX4u+b2XH/jrck5QC/I8gonlzdcnuFTQmxfsZYBfB7xtU47SnGxUMPx0w/Cj4bYZYTvNVTCkqwKmzYHH/4vqjB8lD1yGfSLRHfj7OE5ON4TTGhrNPK3YrmNMrlmZlL/ze9/y5u0Zk8HBdSQqwdqEv9Z6+Rtd7ItUcXq2o0oUbSPQHuKtp7GSlUiIa4ssJMGgxbeStg5xE0f64BnVLaswYpyUXH8n6U4sTSbITIMbd8wv1nRCcTpa4a3ENorHuz7KeNK1pXlUvBmNKFxIdG4JtMWeOH7WzhB7yXxTcjsfoH68J6sknbaIsWPjUnwTEBQd02zHouphtiFH/R3jfstNPSSwnjKB1aqHmXn62Z6iF7LfpgzDljDtaFOHuwmJbh1iBP5pi7+PkdJDq+g2cHeaoV4H+MyBgXjcYvYKhKB+G6OSmqhwTKoOfd5ya3uMVEnQGJZFhohabOwZqIp+v0KfGAayZnXRsXo9pmg1amLJ7h3N34ji6kDhQ+jNSuK05eFXc24MBA2UaUxUdKzuUnQJOrPcTUNmUcnRiwUPdyOoJe1jgP9+Rxa0/K2XV/zl8gKxU0Tfy3n4iyNCJxj3K56oki97AybBlvXlEAd0i5hJ19CoGBU6+tKwn8aE24MavOspmkSAB2ccy2+HrG7GHP/0iuYypXjp2I8U/+c/+T2mWcnfO37HL44b1o9DXv3ea7a7Y9pUIVpP/TrBJi1pLyC/CAhuJPebBPtDi+lB4jpGGLYPIb1ZiS4lP57d8dcfn/CrekSlQrKe5ZXrWOQKN3CY1JAYg1IOWsmrZ7dsHoc8pCFtJeBZR9WGJK5lRs5D1yOOPF3rQQvEtOEkXGDmiuX7MX4vsQLMVCDWHowk3AIxuE6iHwRFoYluoGoiFoHCzB2iOEBQvv1wwkm8x84leaRxjyFuBPFZgZaOJRndMqFax+iKv8l5SHCrALTFI1D24AZxSiCExFWH7taFAuUPIT9SeGQrEEZgh5YwaRHrkDaTSOeITlo284i46whywfR0zXrTw0jJcNtSrGNaNFUSUbYxwcZRZQlH/RXboE/XV0x9w2OgUc6xLFPUrUBkAh8FvJpf866c0Y8qymWKzQNIDjkYF9uadazZ1TGj8xJnBf1+yWKZYrYht3ZK2Eqsd9Q+RArwsUDtBftthny0mGNoTxx2pUiWFrnT7Ich6XmJvw8IBy1J6mEbI0Ydw5Mdt/cz4nsYrg0ms4STlmKToi4jTO/ArzdKsNgdoEt1I7lbDhme7un2AW0ncVOLvg9ohoJ1NQQEyw8j0s6RiIbtNiYveocGRTtk6A76kGcHnQ2RZ3l1yLEQ0uOMYJBV7PI+bz5MOJnsaSKJXIasfEL/eUH4TtMWCV0RsH/ImIsCeROwfimQ1lMeCV49WXIXxfi9ZqAa4iPLx8chL17c0uUBS5Hi9gGXvzjjeL7mdpkSxJbpPKd/a7irM6LOEVSOvzW55Rdvz/kfeIYNBV2qEL/XoXeGn317RvKkwzyG2EJhR45uLgiuNJt8iHvSIo46BrbD7CTffHXGo09Zp2DHMJQFo7ggih27u5if318QtY7LbsJU7yljRdeGsHbYc8eXX57T6pB0XPNZvcEoSRQbHpsBDZoyD4lyz/GLJVePJ7/WevkbXeztPmBHiKkgXmqyecFqmOErT7r2+CpARJZuE9J7dMQvCsx5zeev7smLlIdlj6/zU8LTik+jDVfXE4o84eJkwcPHKa5nWS4y/nj6nq+6I26LAbPejsdhTK0hWkp6aYstPZeDPvHOEM064qFBJZ7QWgqhSAND6QN0aHnpV3RzQRcLVt+MCEaOSHQ4BJtNn6Bv8Y0k1h34A7jHakVYeao84HE5xP20xd4FxJ0juSjZPgwQmSfAUsYKOW0puxC3VQSBwA89ie84SrZ81CN6acs6zQhjR95FBGOLKxVyEaCwBKOW+XtJUQdk/YbShKxLRdBYbh6P0K1hPtrysZ3SHBuelGvuyhHSgfICyyFq2OYxg6hh+zjg2U/vuZMhxceMi8k9Hx8jVOQ4DXYs2wz/KIjWgk0UoI4tPzq+4bHN+PPlBbYMOD3e8FLtueSMeFKzrSOSsCYuDEWToneCauoZvBeU0whhJVaBERJde4Q24BTRyh9iOj9t6RKF0pY/ev6Gf//dBRfzFUUecpFtuDRjVL/j/3L5OXYZEkWWu2+mpENPz1csXmV0l3A+bckXEcP+HnUSsO9ionVNct4waB2fZGs+qJSpLvjqz57x5W9PSUclk6rFP9nRK+DnZvI3cBTPJMk52jWs9jHNEVSJ5PF2QBZVVC8c7tuEti8JnrcEXY3fJYzTLTePI05UTrapuSqPkI3BjQ2iVejM8ZPJgisGbG8yLv7ghrKKuFuPUI0gCTtcT5LeK4owwe7BTx3aO9onBlcpnr1YkqNZMaSLBPLLiOBpyf5Dn/pEgJDoeU1RJEgPkTZgD3qHLpUI6Qhyj58buiNNGLXIsaEMFb2VI8laShMcup5WkiQV26FGryTB0FBPIdm0iLuA9nsNS39AO0cb2LsYVYNooahilPZ4K1CVo3mfMD/Z8LGaEN06ksZjZMyri1s+7o/RQcfgewXLzZjhe0u71ejPK+pNwOAkZ79PWGuJmlW8qPY8XE5oUsHH24TkQWIiGJ9v2e77BN8I+EmNEQqvJK6TuABsLMFZojVI15EZz/JUIkJPaULS0IKBJoN6YBn2GtpvM0hBTAyd9HROUbcJ4c7RRBo1tqTbjn0aEB3VmKsYmUuaU8tw73m3SdgeBcTfCpCK/KFHEAr0UYNZh4zignUvpOca+scrQgy7uxF2I9G/v0NYgYcDlyPVyNByfLGmv7No5cilQ5wZ1qlmluREpeO43WPXmsdQwa3mZ5tXjHROkwZsP/G4TYiwljgwPIYRQkM9hs57WqXphnC5mNHtQ4JRQ2UO4t316wmBBJcIShWR5yn1qaerPLJI+O/vvocwjrTzVCNQ447BKCefhHQiYfurEWbgEK0i2gtUbKl6DlUITKUY2PZwOMERekew1aRJwzYMcEeG7WNK9z4lepUz+GgJpg3RUcVqG9OsQtCCeGq52U958nTD9WLEybbh3e0MPZX8KF7y17cnpJWl0QFtJbm7HzE4yn+t9fI3utgPRwVhlTJuWu4Dze7tEKU9uvA4NCYWsActgc9q4qzCto4vbp9hUo8KLeY25vxowbFq+aWIYQAfqjFqL2Gtiauaf2+ecBP1CCP49OkOX1q2DrZZQhw6VOD5bXHPF2+eYbcamdbsbIC7i9juQlxnGHtDe2L5UE6ZxxsGg4LtDyPcVcjRyZbNJiEuWuh7jk72UArqVxwiOb9LCQKJlo4mCJHvNVFnCcYdVR1jRg5WIWoLYWShFFAAG0lUO4KTCvvLBPvfetKipX6fEr0wnFQFuYvodiGbTYj+pD2Ilb6JQArkRYXJJeJR41pBngQE45YghyZTjBc1s8GGy36P2X7P41djxNwT91t6XUufhmooGcmc/S7hx2cPrDPDWVVytdJkkxy109A/KN+7QLPKNWKpuHo54HYxZjQqSOdbqjcxf6KeEn+259htuRVDkvGO+nJE9TGgiyQ+dWSf7Nm9HRzG2ENPsJCYUKCQGCfBg7QWeVxxFLXsdwlR2vG7f/s93fuE2+2IL99EnGdbupXik2HOwyxgu094pwfM9iV1HfIieSA63fIYxESlZ7Rq+cnvvWble3xzd0y89vC0412Rcr8c4TNFeFISnTXcVn3GuaTZJHxwMe6dQseGsDFUkebjPsEZidt63q0S1EqxS3vILx1ZUtOtUsRA06xTXBdwm0/5cbRg+UpyHw7QWYm9jpGTDhlZ6izimoz2iUHahp1PWb8f4gbA0JL3IN1b5NjjhwLxLkSsBT4RqIWGzvPt+yOO0x2dlnSrEP2kZbkZgFRIZxCNx/UEunEHOtizCrPWmMQjB5bgVuD7gpdnN1ybKZXXjNKa5qrH9OWaro34PHrg6+6IuoxYbVM+0Y8Ev1/z8XFOdZsiY4sZe4K9QEUOF1qYOUQDTawx54b4ViDzw3TLKUHxEFM0Edm85aEd4DtolzFv1s948qMbVmWfaFiy+ssJSWPxA4g/Cjht6TWGXn9LEjZ0hcLuA0wnmUR77iYDYt3CLuTxbgAhiEThP2jcU0vqWvY6wgw8qhYEmcE4T1J5NoMIfVGjOKCqWx2gKkfV89Cz5FcxbiTJ7ju6qUTWgiw+5HTYVNCrO5rbkDp2SAPuuwQfgzIgbgK6n7T0BzniDpwKqC+gXYQkqqN2AUEhaG3ISVSwjQPMKsTUMdtXIN8H8K4PA8PQNPSPSz4PLvmqOmETBzyuBph7jZy3jKOWznvKdUKYOd4/zEkHOVFgCIcVt/TYiJjYWmQjydIGda+Qn7Qc24ZvVjP0sKHtNN1NBIGm8QpvBcJLQmEJlSWdWnrHOWIHwUnL5e0c3oW4SBGsGoJJRx6E7FJPn5buXYIYtjRVD7cOKF94vD+4e6wX6J2G9JB1IEtHJaLDBGQnCaRhGO8YzXL865Sz5zsg4OYTQVXE2FgikMxHJTf5CKsDnjQ7mkRQ2oiohvRJyd1DTPMcnlY7vns352zYMI0KfrE+5uR8y/2HIaX59bLxf6OL/di07EYReXlQ8qrKI/CEjcVXEGpFc+4Zv1qwKTNM08O91nRCMExyEFAdWdYi5rs3J0zmJbsyQbXgnOT5J7e8v55jlpLe0KJOGm6XE+pYkK9T5FrTyJDPvndHb2TxAVR9RdH0USuJrgXCO1zkCeTB5hI93TOiJq0sT9+XXN5nNCnozDB8UnB5ecxqcvAWuxy0FayCjPMnCxabGJd4RkFFcC9ZlilEniRsaQaeRil0rpgcbdkc9XBO0HhJJzV8Yrl7O8OWinYo0TeOdQ9aowlax9nJktU3E5KBpRhqon5HFWpcBF2sMFpy2tvRXh3AOPV9xOZIsd1O0PcSEzuiScvnow339xGljghPc7Z1j0QbxnHDWVSwosdfVccMnux4onc8nib8ne6KP339KXmqcJmD1PL48znRi5r1dyMKawmFQ98L2jbCDRt6zrMLUsxeU71w+FIgathdDuhGIBuQ7YF3rhuHfWrpCglOIIaGqbKYVUTTav7sV8/pX+SULiSPFX4X4TzUVrJfHMbfs6xj+9TjWsGrdI1+sNwXMe8fjwmN5Qev7rn5s1O+OR2ydxqsQnYNyghaK4k+KJ6MG6b7mo/FjLXWBI0BI7AqoP+kIFhK1oHHxp54aZGJgeuQ4ENHcR4inWCfZMQ7jy1i6twjngtk43kfDGjfwOQHe/JlTB4pftLkfBeMCWLDLpOYRUA/PwjIROwQAahCcGFq7vc98rljHJWH6OW+R20D1MDiJRz1asISzsdrLosZdaTwo8M14nHD08mS5SqF44pyqNCyQxiLLDImdcP6OCa0HW/aY9K4JTEd1TrBasVy16exmmroD7yL3JEPBLfDBG1jJqMcsw1YkhI7Sziu2H4cEtRgnYKpYTTNyR8yWqWwZ4740tPMPdI6Rv2K6jIjGTXkXYQdWfxScv/miNHzLY9XE8KTDukt0lvWYcirwZ7R2vJXdsrqFxNOvr9m0Y9gAcvrMeOTHJto1D2ogacVkmSWU0Qh0aSjzjXjm47RpxseuozuMULGjnzm6fbAMoaZwfU0uvMclJnQ21t60z3rj1NcCjbXiNDgtpq035EvM9q0I1o69p9IghIy27ENDzQ2H8LNNzNsetAOPDt6JPd9NA7lPPjDKsgmHet1SkZFnQYEg4448thEEo0azLuYzSuJLBy3aZ9dG8HOkQWGRksiabFeom4d4tRxui/Iz2vqOoDAQdrh9wKhBXmVENyDmXXIi4YkbHl3N6TXr+gHFdv7PoUICPsto6ZheT+gDSMGvmbW3+Cniokv2MQxD7cT2m2I63tooDqSRIFhLPaUjz38BLqJ5z5KKbqY+LxlrFvqRODvIqLXnvJCE44b3FAQnjScbms+qD5ZXNO2ipGsOVEF8rcty7dDulrw7PsLrn5xQisc9kXLqk4ZugZfOdSsQ61D4nHL09GGj/sL5MBhleOqTFAJtDJkEm3Qe0tRxJhU8EwveP1rrJe/0cW+uQ4oRtmh2xQWqzSyBReBXkuakcQbhw4Nn+oHLvcTfv/JG/7szWd4I+gJw1G85L7r0fTBphpXCILQ0Wq43o7pRw2NUMRRS69t2AYRXR7w0x9+5OdfXPBZu+f19YybKqOcCayTBJUgPKkwQYTvOfZOkb5XJL/VsVwNWUR9gjUEd4Ls05JSZZRG8XgzIDmtaK5TdGTY/moEY4tKLPl1RmhB9A2FCNChIF5Al0ryC010o+hPSpoZlEFI4gy7IiVKWoLc0/Qd/koTjltUD2zkKe4jFNB1km/zM5LUME5zapkhHyVB56mH4DPPOCoplgmnp0t2NqYftsgy449/8CV/ol5xvRmgrOAXxZwodNR96JcxttUY0/E2GfLld8fMj3e86q35rp7QLwWdhV+5E+af7FivpiTGIEKDftJy2uUsNYzmOdfdEHsT4iLPzYcZru/pDT2lS1CPCmKPObKERUekBGUvQ1hP8uDBGkyl6eYC1UFCSxpV3LdTnvxgRf5XB3tbbTQsFUFqeHH6wM1/nOF9wEIluF5Db1lzp/tUTYJZAXcaf+qZny+5tyGVS6hDSAtL9e0BgFIKzcv5A+8uj3kxWvPLcs6LYMnKZ4xFzmI7Rt5K+kNHswxpdxkaR+sFavk3SXdHAp03mKcSvRKYAVipiXcdPVraHxnMBw3HlpEoWVxNoO+5jHso70gnBV0RILWnOZdsb4fQd4zqDi8tq0pjBwY/8iwe+vydH1/hZzVfXp3hpcNehWyGEbv9GL09KOlpITIN2ySlrzp2QmK84OwHj3y4nfG4HnHU22FnHQ8f+/i5IVxoYgvrs5Q0bDFlSNWXmLsBUa/h4WZEcicOaYj3Mc0mJpjX+DagVBojBJPTHUNdEp07GqHprhLMh5AikzSRQglHrDpkohF3gvyJYr3pYU8848GeqVnzbjOjSyHrH0BZwjnam5j6yFH0FWPb8N3HKWpoYAFyL1lcDdGJQTWOTQ82XUKYg4wEatyhYjg5e+C7m1PKdYLcSOrYcH8/IklaTOZIJhWqCrA+QO0laqeoRgJtLSJXIBR1HWInCtspsJJ06SmfHlIPo2uFjB2d0phjie8ZAmHhRhAdtZgnAv0gSTKD1J6zdMevyilJVtGNFWO1BZ1StwFPjxd8Y87QnWY8zLm9mSITSzeGRkYc/daWYB0T1YbL7Yyj8Y7qPqH3gz3y6wFZWlP3HNnKIXcBl80YJxyVCTBCEleeYCegC8gmLcc/WvF2NadxIWWhodL0Zcn6NsafWkQh6BYamZYHumYeEE4bloshDZLLdowUnsl0R7dVjKKcRZbRXzlmT9ZsXqeEaU3/eE+7GhJnLWnaUF2m1KkiMBYVNxSnGdG2I/vJIfmw3MTc1DH6/hBKJhwk5zXf3J+Qm5C2DBHScXUtaYXibJjTVQ1XZkBaCkYLA2VCfqSI45qvqsnBhgtoJ2ltSJAZ3F7TnUiiwrKuYpT3fLTjX2u9/I322d+nvcPI514TWYMwMJIVzihseChoEsAJzJcx4cbwH68/JdCGoSgZUfCwSpEGjmXBrGjQHWjnefrJI6PSYMaetguYtDWbXYZbhsw3DesgYPRyhx23kFl2YUD4tEJKR/90j3WSk/kj35tdEi8kJpI0eYgRgrh0yBtP6WNuix6LNkM/aqJ7gXkMqb2meHuIDzW1gtuAItNYBaXXmE2EnziCiwo/tkhzSOs6Gm84cgXBDoxXPH32iLpXdF4x2BrkSUc6rjgLNsz7O+KkhbOaZ2d3ZIVFHrXUtSY7y7HuMPaapXvCxjE3BXZqeHN/yr3t0+83tJnk//WLH/LHn7wj2kM06fjtVx+RRzXhStBWCUemYSEyzK0ifF7SGs1/eneOs55bm/BVPeLuYci7YojoGeo6RL/RNHnE+9WMwDvurqd0iwhhJOEeVC6IrqH79z2y9xDuwXtJVjp2ImNrErrRoVNqh4L8WYANBb1rh2o8vg1ZRCmz6ZbwVhMdOfq2IVaGnmv5NFiz/I8jzFDx6r+9ZXxW4Y87RqLj9za3BPeSdh9jnULWnofFhOGl4iHNcN8F9LKW0ff3vHB7JmHFx8cpFy82rCPJ47ZPKULyLmJVZERpizhuaPeSfCRQG4Hqd6h+i0NgvaAdS+qTEJMI9FGL/LSkNy1Z/a4kOKuYbzqy721JTMPNNyPMEAgk26HGTSzlfcq+jnFesC9TxEKR7hxZr2G/6ZOPDisv9yGBWvEX63N634R8v90TO0NyVtNWAa7UGBQN+tDVeUuW1mwXfaptwlom3NxNqduIXr9i2aQE9woXgloojJfIiwa/CMnXCbbUZNfikP9+sSZ0Djc3mB547QmkwdaK58dLnp/d87S3xC5DCp8QdYJFm+KdIO3XtE5ih5ZZusM2EoElSUqmsiKJWoZXlv2bIR/qMaNpjhgbViQE1qAiRzMVeAlh6bkPE+qhwGqYf76iOvfUU8c+0WxPJEHu6RUdPnS4wNM9xtTrmI/fnh6U5AEEykAhkFvIbxPUg6T81QD3RYxaK1xmkdtDeqNaSugEugS8Jy4PATou8piJQ1agamieeahBCYvNHMl7CQbKnsI/SGZhTpq09DuDSi36ScXsfI37OqCTjv0moek0uhFUdwnh0nNORXsfEOqOqSoYtQ190VBHBw/7LorwpaLeh0RPS+q7BOsFJ4+G9jEhVI68TNg/Jmwf+3S3hxjnhY4Zv1hD6kh1w3aXcT5f4gFXCcZPNzROo2JPmBoC4XFDz2YWMk9z0mnNUbInDA94YG8EbRWyejdkNMupVyneCISCoBGQa8pMs7wc0e1D3GPIfpegFpKkshxFBXp8CFbqPrPsi4TGKvS8wToB5zV5onFPO27zMQUhohXIBrqRZ5qVjIc5izrglBqx0LhW8ngcMvzhmmru2XYxy6/HNN/GmGWIW4ZkO8tAtKQnBW/qCW3fIxzEQce8t/+11svf6M5+dL6j/TpCdJJaRGAPDzMXSUTg6Z4bjBL4u4D7+0Makgg63NATlZKHZsA+TohWlr1JCVfg+p5Sa6aXEcXxAWoT9joe0oQn4wUfPpxQDBQ9LMG15HLYwz0K0n7LzBbkJTwOY0IJ1URhlz080I4FRgT07wz2Jmb4P13SvtdEI4cvFXLS4Y0ktoackDbQSOEJloebTXQaMo8wAp11eA551mrUMaCjTBM+1BPqdcw4KGjODYvLAbsjhbYCQQidJ5h2PGx6dNcBmeqwH2KCM09WdfiuI08ixKXCzC1uq6CQjIKSh3bAvC4IhhWrKKYdap4Hj3xXHfOv/+P3ORnvuVsMaIoEsQ344WTBTRpwq2OClSf5tOLktuOTH9/ys/snBB8CLuMR3/vpJb+MLoiijmaTERaSJlUEtxwQnXWGTT0ylYSVJXCGciJwnaOaSXp7aIwiKBxpaGilQzgIVh7RgA0PhxZdeZxy6ImjRKOvI/JTi/tLzVU3Jp3UDCYV24uARZlyf5Lxg2jB+9WQpg6IjjpGbcF/6D2BWKLvLP6l5zwu2BQBb/Ix30/u2X43pm8Eu5nnpkv5dLrmy/WMy/WIZuyZBls26z79pObTz675+f/zc+S446bXZxKU2KAj7wKCtWD62QZhQJaCKggYiorB2Y6PxYQnsy0uGsJSIXuGY9fyUSdk45a6V6GVhcBT3vaYHW8xj310zxHmgBVE5wX7taQ7NUSrAFl71A66Z4Z4a7hr++w+B9/zOGXwtweORTqosJsQKR2TpObxLiBoLY2MCCLPyqSIZYA7aZCXId3EkYwa2jakcgq/ilGNRbYCG3lGcY48a1l3Ce0qRiUdsnXYow69gUgZvi6mPGNHYCXXWYD9mDFqa3pPG8osoZtawncGMTB0a0lPNvzw1ZKbm4zahLw6vcXIiGUVUSuYhjm9tWfZpoy143IbIrWnzUP0o6T3soOJwd0FbHQfPzP0pgXVrwaYRGIyMH2BrjxZVDN4nlNeRzx7eUedSr59OCHoFOGopfyQEYwNIGj3B3R0pCxuJaknkrBwdKcefX2wElZHgq2PydqWug1opgK99sjyEGdcvXIMyhaNhJeeZh0RDzvsnSIILdF5xXKRUT6G5LGkWsQkI8NxLye9E3SlxjtJ3mUkk5av7YTgwdOcC6KFIxh2FFVCXFrKZYJugdCT9SrUQqCPC25SxZtln3alkRnElaOcG4aDhioWjOhYEVM0IT47cBtetDtu3w64eLHGVAFto7GBpj+p8Klln8bErcVdxXT9DlMG3Pk5n54uGXclX67O+K4fMflkRbdRlE8c7jakHRq+3B1z8dsrologR46jwYqH2xFdDOIUgsRjgDqG8G9taDchXnvO0y2yCVjNYP3YQ4eWHi3r7YC0cEQvc8o6RTwGLPIecmh4crbhzW5Av+uobIgYG+6ajE/dho/LCWdHK/bmsLJRuWNb9nDvYggN5tTjY0f/uEBhab9Kf6318jc6z/63/w//G9rdhPjR4SOPl4egAyEcNhF0I8i6DqcPe3ybCaT25PuE0+MFTSZZfxiRqpYukHgvMKlHGphFJYXR1K8zktM99TRAKI/5LuHzo0f0Ucu7aoDeykP0bGhI95Zg55m+yLmveoTThv1tjye64G0xZOoqes8rihZEevD3D2VBqwMetwPkWqAjx+h0z8omxHuPCz2ZMeyWPYTzcNHRyyrqq4RIGzYmIW4sJg8xUpBmDVw0mDxAduA7SdYZgmHH/f0QIR0jSuom5pM/vObnN2cAzHcVYuhIRjVFFbFfpOCBVQihYdLLEYXCpIJmrxhmHYNPd4QWfvnzc8anOdv7lJ8OH1iIiKITtNsYJS1PTnK+7kaMxzXp0vChHfN8tOXrYEBWOqTw6F7HetkjXoHGopYKO3BEuaMKNaF0qCcVTkt6tmW56KOKw4hVPG9p+wIlDOo6AO+w+xDbF6jI4DYBqvV45VEV+CNLklR89uMF3yxmrO97qFIwMzU/ubjhY7/Hu3rAvCuJxg1FGzF754lnNQ9kPOmt+fPHCybzkv6+5f6mz8nLPUO941YOaZYhpTyEXjxVO8bne/7quwvUuMXcB9R9hdbugEx1YLXAWcmnyS3fMYeNRrQgp4Zw2tDcplALRCWJnpUHrGcToHC4oUPlcPyjFcm1ZZXGrIlRk44oMBQPKfPxHjaCu82IZFYjturw8KtCrAgIwoNXOLMdQb/DKwisIZ3VbOqUxBr26wwvBclJQfmQ0tQBaVrT7UKkEdSRIEgNQQmDeU47gP1Nj3DWUFxlKOnR4gBE8l4QpzU9OnIX4QVYCaoSdEIR7DwugtPhGl9pLsM+08mOZhETjlpYK8zxgb/vA498E6InLYUO0FsJAr43WfHxskc5V0yTgt8yDxhjWMgZN6s+zalFtBKzDjFWIqylHgT4VhA9CMyRR89qftC/48vimG4fMFoZqp7CTSx/ePyR795OmU93bEjYVQmulOQ+wlvwVXCgIhaa5AFKG6Bz6A8rDIrKBXRKEniDSQSykoga/MDhdwrZSJRxuASc8ggD5Tmk7QGWZceG5iEmvFN0M4/pQRo2DE3HfRQjHiHUHttogn5DeNZwMthxtxmxXvSgFkzKinjU0gYK1Tf4WceLbsvPvnuBygXhpMEkkDUdcmyopSKrLOtdxkmYs7uLaZ4Lpv094kPAjoR6IKCUsJOoJy2sQJWKItKEcXvQJwjY6pigFkSyJZCWfRAyrBvGgx07n5GdFbitxDUhT8j56v0Zz8cr7mSESx17m1BVERMKyoGmfYyI0pbx8Y5w0BF1hsd6QP2mh5OCV7/1kSqWfNhPiVtPZFvELzJk4LmbRejAEjwelPp+bgiMp4olf3f8gT9//QLtLZy0FPcR8aNETi1lHBC7Ft+H82zNfTnAvY3xR4a6jEjChnYZYQNF2Br8iWEwLIlGDdsvhqi25N/97/7Zf82z/895qdDiI0/9DPZPFeXsoHq1U4/KDC+P7/hs8oh70rF/DkYJgrjFpp7N+wF2exCFVAQ0QoGD+FoxSiuIDdVDRvSDgiBzVFrxfP5I+jJnGYW8/vKEcO+hUKTe8FKvaVJN09dUFxb9rKL6kGEWIfdtesCJTg06NWzeZjz4jGBW06aK2/UI1opAO/yRYf+hj3mIGE93xMJS1hE9WyO8YyArdo8Zv/3pJXsfI5aaVmjCT/a4s47gtGYoa+wypLIBNvHUKPImon++R2uHKQT5RPL2VyeMyxaxlZgqoCag+WWGWwbYbQhe0E0c0bAhrxMexgnlOmQiS5Y2pjSK198eESnLJDdYLbkXEf2zioUdoQqJOLOU3wUEHzT3H0bcrEd4J7nZpzxZl9irmKj20EjitSCcNkTe4BTohcC0AeFWoG6gvM2oblLq/zAglYY0MNixpc4jVAlx46mOBd2pgDNL2JiDo2JtselBlGZSicsPcaK325TjtKB3UuAzy0Nf8WfLc25J+Hsv3/F3Xr1neT9kU6fsewp1Ypk8z7k+jQmlwdWebRzgBvDYxKzsiLxIWWYJdhEQVp63xZB//+Y5KxK265RNEpOtWzoOUxO5E0x7ObOnG2af5Qyf7QnmLTaUmJmj2wcIcUhIa0aebZFiVyHho8A5Afcav9TcfTXlfT7D9qAnG/qXgmAPyRVE1qMQDMIK14F4lNiZR+80OjDIe82py+lQdE3AUVTRlhHNTYLaC6oPGU/O71E7MHchSdSA54CKrSXBcYsIoNuHVFLzbLbBfzhYwvxNSDapCGXHH5y9ZnC8R+BJXhu665DfeXFFepqjYossDwzxwHZQCy6rMUsZE2Eo1jHOKcZRgQoPwCXVCqouwI88Yq95xY4kagm8g7REPLG0WrEuU/7T3QV/tX3Bt3KIiRQv2TPo1cjjFi+hNRGyFASFwPYOQt90Cb9YPsHtNcoY1v0As9eM2g6/D9n0Ih6LPuvXI8pAQ6uwW01XRETSYO5itHdsRwqZWMwUutuYwahh/GxLelrQHnuSB/CZJRh26MwgAg+nLebkMIr3MTDwRMKQhC2jfg6PIcGkw44dqndAcmNgRYS8DyAVmGMHR4ZOWKSFN9fHlPuIoIS0cpRdQruMOMnW7DYp2zcj/urdcwJhGZ/siIMWP7D4zJN/1yd/zNh9O8TWkis3RB0ZVOdp0NRDSUmA2WqGVc2wrvmsXDKbFMQXBYE19MY1XkrWMkUXEtl5qjqm8QF/d3jF//zlR+4epuzWMcPCsbwbkKeKv9qckw8kLoNaBmzuR0jg89M79FmDDTzpsCZ+UlIWMQ95n3c3xzTLmGhSM3i+5a4dcPXtCclCMOg6zCpi1Q/xWce8qkhUS29Y0/clryYrJpM945Xh55fnmIVCCM+TIicSkMqG42frg0tjEVJdZty+nR+mIH1HZzT6UWJHoCqDPWlxJ4YwMfhtQHudcNrfsP/wX1Pv/rNfWdaShHv2OiB6VNiJo53BJKgpq4BNGNFOauzbkDCwVD6kHTpmvmTbxajMoy8ljRbEznD8+SNXl3OGs5rl7Yji1EOrETNLuIHoUeFKxfK2h73oqB6HoB39RcPOx/hKMjrdkK9ThuuSs7NH2ihAnte8fXfGq/mKnz/MaV5q0qZFaUEhNKNeic4s6nXIOgg5Ga5JWsubd8eoAjLX0p5CaTTVZkjvBn5RXtBqhdQgxwajFHIpabea1SghyBX9TzYksmN/OUYcV+hrhReC6igk6DxVHdGLDaGyFFGAChrWacJ8uqZaJbQpCGnhJsQ2mjDsCLXhRo7o9gGXXx2j1pIka7lpUz4dLbjejLj+6wmDtKJrFN37jPqp4TRb8/VmjggkrhP4DyH5C0k4aajKAKs8MrAEaYfuFAQH1nSwtCAkLlTED+A0NEOJqDRIA2WA1h5fhnRlgPpxR7sLSTqLPXbYR0F5rAhzj9DQxQLVeV7+zorXfz6m/QzUWnLSK5DHDY+vB/Rbz9U3IwZnml0UYneSHz5/xzJP+evvzgh6ht95/pEvbs5pbiN6z/bU36WMfrijKTUXZ2vqOKQbex7ej3nmctbSkbcRF+M120mIfKexqSMJO7z0uBa+WJzSeUWTh+AEbDVCNXipYNzh2gC5kjQjsBF4FC4D+p5gq3GJoygjpPHsmgh7nxEmHav3PZqRPBzK6prLQQ+5PjzEpfYoY1mUfcRpi68cH69GmAE0tWIQVPT/YMnyZ2OyWUNRBXS5xqWeRLb0RQsPAfmxxYSQ7R3tpSCPQtxGIKaONDJ0fc8X5Skyl3zveMGDjjiZLLlfD/GRpK5CUm+REbhIEtwAVhHcC+QzSzHQDNqOcW25G0gC0dHdJ4SzjjTtqLcxV9dj4uOStg14d3nEk9maLgFRSvYmIhgalLS0iWJbBfSilvUuwx5bhLGIR03QgQsknYRdEyP3HttzSCW4mC+5vZoyjQr+U3FEtwt52KXY0CP3kvJdhB97zNxRdYqJKtCNo2wSXOMIS0eVafKHIbI2YDmQ9jIPK40pQV60jMKSnYlQmeHpaMlqP6BuQ+SwwS0D1h/6qMwxbGu2RUQ8b2isxiUeswgg8gQrRZ2AaxXChIR6D0aQpjV2aJkmOc3UETjPw/sRXSwIRh3NVcLxfEsuAma2JNhELMseZu6ICo94buE+42iwITkteX91RP5dSnKR07cVRRUStx3uQnE3Sbm9GqNjgzeadZ3x+atH7GrMvC25bgecDNaUm5QyUfwPd085+t6S1T5hvY3oNjG65zDeI/G4YUX+OCY6qWGpqW8GLJ6GuFyhWo9FkpmWLoU4aklaz+a6T/g6ovueRb+XyBeOJgHean766gPfuhnCW850RTERtA8xV99MaUYQ9Q3D+Z4nr+5ZfTPBtymFjHj+/IFqH5NgGD5tCENDbQN29xku5RCw9uOaYCso4oBRXCKMoogkJ8MlRZWwKHtw+uutl7/RnX0uNXUi0C3I0xY/tEzSAjFvMVKwf8x4+HKOehBYAWHumD22jNoKd2zomY5g2iBLSeUDbv7dOcOyQyvL0FfM85rwQbAtE2yuILH88ewjwVlNX9WMRcWL0xUkjnoRMehVbBcJ+UPKB3tE24VMq5adTXhxuubn7ZhHk/AqWRBJQ1HFUEpkIQ6CrVGE6Axi3rLOImgF/rzFnBlWLsMJkM7DUUvpAqLaIV5WxB8UkegYzEvcCYyqDjVvEB8itu8y0uOSWircWYc8q4kwzKM9g/MdKrJko4pwVqM66CrN4tsx9A3dLkQsNVWkOHu2Ik06Tl9uCUJDZAqcEMheR+0D6lCy+9gDLEwsR+OSqh/y/d+65cWra5Y3PWY6Zz7eMTwqiJ+1h9XAo6LfL0h0S7iBeh3TyJC2dxipiTOH1wIXgFeesHUAdJ0mjw8RXTrvcKHHZBCUHPjqDwpxq4kfLTiPSUAUEO8t0XnNX+5n7IYJv+uXfGr35B9jtr/sc/Qi5yLd8PBuwJs358jG8/J8zZ8354gM9N6g04YP745wRhI9rTkzDYl1XN8N2ZUJfqmpM8Fjk/C7L24YzrZUKQRxxye9DXarCZ5UaG353rMrNjpmuRzRfptS3qV0PSBwCAv+IUQUErMN8RJE4IiXIK1HDDuSk4KTzx7gRyX2RYdbBehcEg1axlGBPfbkcUjoO/onOfeiTz+tmC5rLoINqoXgeU38tCAd1Dypa3784pZkbYlay/x4g8oV47OCF6NHxLSl7klwoEqBGzp6/T2nTU5f1fhW8ovFc2x9oO2x1CwfhuSLlKVMMGPHu9sRbV/R61tO+ntGlUU5iI5a1FrS9TztUGBDQfupZXa6RKSWHSHfXE9xyxAdOn7y9BpZS1ZVitsdbJvVMiORHVUQ8qB7NF1AfZ9iPjO0nWLfxpgALtsx31yfUG9iPj++OQBpZgfK33i4J0obur5Hpga5F7RFQHud8Txccf0wwVaKn3x+RaAMetjhjaA+dfQGJanoEMctuyDkphjSf2yZHW9BQzBtEIFH7TxSeLyGYCUIV9ANoQsE9ouQT4cL4nHN6+0xzXMPJwasoFgldIGmLiNuqjF2egh10bGlWseIxBGrFtdKwqUgXXhC49g99lDS4ztJMG5ZRAleSpYmQ7+oUZ1AloJZuuf8bMnFYMuy6bHTIcfff2QcNPgnFp/CrL9jsetz+8tjKBR2bCnvM7YfBgyf7Vi9CHhUMbttghp3BOOW4dM9wduA1z8/QXwTc3M9AwUmgi7XXG4H6H7D8sOYvOmxT0PqiUALR9xrsWPLRzNB1oJIGLyGW5Xxhycfmc92DM93fB5sqZcp309WsA34fnRPqGukEJitxny/4/h4S9p1mBPLt/kRVJL6JuX2fsDD7ZjdKqUwIQjP5HzLfpOyfhiwsQmXR5rPn97z/otzFu+HfE89gPKUc9iEESL1DFxDEFpspditEoJBjZOCeq/5/sktm1XKettnu8uQ2v1a6+Vv9M7+f/nf/a+5GY7xtYSdokwlbRMy7JXUHzL+4NNv+Prygv1Q8epkwXfv5py0Oe/MFHnSIu41vpO4ThHHDfUQhmlFHHXk9xnpiz1VFZG/7zHuKi5OV5RVzMNRgNpKjsKa1a/6TH+05M1359gUghswxxCkHQPfYNaS+cst6w9DVs8UvY1jOM8RwvFuOUf2LDyEhLXFDMBpT9JZ+itLfQLuvKO6TlBe0C4j/NiShjUWyUDV7OoUvYD+04KlSqhWMUFgkN4jYkfUWnzf02xDItUytTX3UUYsDemkpLuM6bzGZAJzF2BMgBrVDPolXR6h+gdW/9BVdJsY2yheniz5YHq86nYsRUIwqlgXPUoT4CLPRFRMw5bvdmPifoO+V7ROUJ0K/GVIOOoQPUukDU2naa3i1OQE1/BYDCmOFMJCFBpE5xE3CpMqhIP0sz3VVz1EYKET2FDilCBIO4I7Tyc1XebRrUB04NUBtqM7h1oK1ElHk0mycUPXg+dNia0UXd9iV5rFQLMhQRrBK7fBzTt2/ZB6FXAclOSlJjOexbJHuUz4o//mW5oh/LsvX3A03bBd9Jie5Yd863UPt1fEn+ZIJ2lvE5Ko5aYXM+vt0ZeK7RNFsBXsFxkATkikcchS0A4EugOh3QFO04LuGeLGUXYhveOcgWoOyuz6sO7JCkc7EMyDPW++Puc03fF2MUU/rwliQ7iC4olAWGg2EQPTMptu+MCYzisu9IbyMsVJQTdw/NHnX/Pd+hQqycOiR8+29M9q7ruMeO35ZLLlsU25fjthfST5UbDkl2aGyAWy0CTTil0XgQICi26hX7eUU0XPdMwoOH+25q9+9ZxCBMgGXN9BqbBOcjZb0h/u+a49wu8CgqQjrSzbIkPGhiA1lPsIJS3RSvK3fnjJF9WExWMfO/BEytBVAfGooq01vpaoThBsIRWGoo4I+jVSgelBVDt87ClcRBVJxrZmcrJHPETsYkWw8qRhzcL06Lwmm5Qo78nrhFZIiBzSQxcKxFaR7BzlUwc7TbgRsFTUU0E4ahhTs91kjM/23L8f4lNPGBo6JbERRDcKjMSljtBZ+tOC7WMPoTx+YjF3ITqXB3a+8XQoVAvP23uu/Jxu7knDBq8EOQGBsMhBR1ofRKxjUXHXTzCNIrxRnL1YUAYBD3cj5ElD1DoyWTP3JdfdGN+zVFWEbRVWCoIcktoST2tc4ihXCaUMoJHoFsKoo11H2NOOBEtnJWKrEIGnHXrCuGMy3rN5PaYID+r2flJhPqRUY4EcdKhCMOwVTB8r3ppjdGqY9HfsiBFeMOyVrHY9gnnNb2cLvnh9SrcUlD4iFi3JccdJUnCnIuRa0WnYyxi9PzAJqipCVJKk6Kh7hzApYwO8EbihpUkEM1sRjFt4UPjsgBt2s45ZUlAtIz777St0rVhWEUeiwD1pyfMU/cuEh1lEuwroYkGXQX9WYK6SQzRwsuHP/lf/p/+6s//PeV3ahOY+oVwm7GXIb+k1f3B8iXgXkBrDW3PEaVaSlo7ryxFyr9ilCboCuRfo0GKFJH5SIPoGYQT7zcGKpwYdQX2ITdWBwwWS7x8/8GGUYauApgy5NyH755b9r1L8kSHRHfVU0R9V9KOa3umeMgl5/XCCFo5wBSK07ETEbT5C5JKg8qRRAyHIXDIcldhIsDwK2OYp2YNH7yXsJb2gJvUtlJI4bQh6Bm0cT4+WFLcZvA+ZDvaEcYfvDnnMgztHoA06sdSZ4rKacNTf8jJZERZQxQFheBDI9U5KVGhQiWV722fQOMJCkOaOok0IhoaTl0uCuKUXWfKZYL+J8V4xLBtSYxkvHEfNhg+b4WH3GXgmr7boSYf4TmJihS0PIqpHUoLKc7xruSpH3O8nGKUIV4748eAG8IsQFyhk52kHkH/oIaWn/v2WtqewgSBQFt9B3VMgINx5bCxwPTBjh5UCRUczFeziiCA1FEKzvU9pU8dH0+O02fPys0cuznbI0PFqsOLRh1S7mLwMeHVxTzHw7MuIG5kgxg5nO/7t8gnldcpEVdi9h3vF5S+OeH81xpeCbN1w7hqGj4Y4KrhLIpK3ErOKsAOo3/Qp3veQHSjlCGqHji1oT7gRuN4h1+B7ckEYWXqq5eSzB7Q0yFbw+N2E9TcTHn45p8ojchfSOsm370+Jew33YYpPwSce1cCj6dMtI/ZVjJCw1AnffniCbRVjSnzs2Q5CZt9bUoUhf/36Bdf7AV+Vc0wiWM5iYpdjK000N7wvh3yxmLOOA6J7z9U0gqElfFKDcBRNSC9sUBXEhefZcMleReilpNjGTMYNi+WALjzwD8yJRS8Fca8hOKp42A75Lj8hKyxp2pA5w5OnS04vFsTjiiDtIASfgJp3/HIxpwolQWhRa8lo41DpIRRHXocQQtgdul16BiEc4lpSe430nt2njmUcEYwbToMdeRlz+XGOOKooQ8liFHKzH9AsY8wqRHgoZEAvqZgfbYjTDjxYKxmFJfZJh76XhNcC6R0/+OE1/XGJXAqqrzL0UhA1DjMBhwANqe2Qj5pqBmHhMBNLMimpbUAStsihIWwcqpIcH6/JhtVhXYEnKBzXYkYzAyUcNoS21YhGkM4qhllFWcbUTUC+ifGdIgtbylPP9iEjKzpILO06Yjba83ePPrL8bsrGxixvR+iN5Oi247guCBqLraDaRSCh3EckGMLAkmU13X2EicBZidHgJbihw/ctf/z5V2Rrz/6uh3qQhP2WQFvadxlyZJgO9gzKjig3NLuEhRsSJg0jl1NkAXUZM0lybjdD2tuY7dsRf3Z1zuTTJUYolPSI3iFQ64PpoUYt+UywihLSx5YqBCEdiW7xU4M59ujEwcTBwDB4vmN2tGPqK4LCo3bi8N1ZBEzjnAk164eD1fCbnz/ly+9O+FhM+E/ffsI375+w2Pf5ws+5fxyyz1OCo4auCFlej6isZpLu6W9/vZ39b/TOflBbRFey9SlNo3hDHycdZiholiHqfUwVtLRVgHpZMxYF0aCjeJ3hU4Hzkun3lnh58LEOlg3KaDaLHtlpTr1K2LQJNhQIY/m/3n4fBdi9JjkvGW4Nj2HIuuwj9/C9ZMWX4YxSHlKP7D7CDjwIh85aJrXlcdVHFod8aBF2+K2iEpIgsvRe7DkbrfjwcE4dKqKo48l0xabO0IGh2kWYXBFtPbYMyJ6uUccN397OCYeGznvirwX5OEAcGYJ7zUOUoLqWYWNwDbQ9R9lGbMeeYh9gHgO2iSfs1zQmwHtJrC2RqxlUHe8+jhCftqSq4VO95HqUssk1+07wkzBn+vsVextwfzMkDi1Hf7Di/ccJmo76uodfK+4+lZhA0j6TyLjGrjStC5jsOpwWhGmLLFK6niBaW+qZJHxSIwoJXuIKjes7os7T9SXhcUXbCpz2gKD1h04Cq/DysJvXO7CJQG/ABYJaxASJwU48tQbtLC8+vWW5HvC3f/iRyyZjJHKqtwEnvmD2PKcKPcnec/9xzIdqjv+gcBeeY1mzqDJ+9OSe+zTiPpc8+pjvnexZ7CVd5/ncr7m5m/H4VPKT5IFfrM7ZKsFkWvJ43KfZpkjpDqS/ToADV2u8FygBoXfYVmAagVGwDzTxpKS4SgnL9BDO5BTsBeo6RGOxdcwqVvj7g9CtM4d7N9p6yklEu9YE3hNEnhBHFUqi/cErfh5tOR5sePP6nFHcoFPL/HSDe4joqhCv4MX3b1h9OeVuPGH3mHGWVzSjhqdnjyzqHmKjCL3haLJhu+yhEovOBU0dkewd/sgjex2vslseux71LuLLbsyTMMdVmiIWBHt/gLtISVNr/MRBEdK5kNHpFipPgGD9RcbkWcXjccDkuz3jzwo+Ps5xtaRZKOougNSxqGImbkf+tkf/LKdYJYhJe8BMzz3aeeKRRRSaeqaQK0GvNph9SDv0JE2NjTWroof/KsUNodUgUoGuoWxDOhSfv7zlzevzg1tg6Mkqz/6hhwo8PoRhvEWfWpb7hK2KiHuGygm6mUe0CXoHbSoRtwGmd+jsZS1oJpZgJZGbkPwoIOoZBvvuYHF0jut2iNkdPPrhCkwfZOPRO8HxxZZNlaJHNfLbhFUwIEw64qRDBY79SMK1opxFPD1esYsTPj5OCVpBiGM9T/l/fP0DvAvomsP0LBIN+RyKZUYtIi6GG+4eJ7hdj7RpCHeCrg9mHaOOO9wixGiBbSWjk5w/nnzg//4nn/OnzSvc0FF0IXYUoO80qgUTCEwR4LUnVIbRJEeOD+LE++WQ2miKqwyjBI95H9EIgqBDVwp6ksXPZzRziW4N7RRuVhnfj1csXmfoRuB1wF4ERBvL5z+6540b0i4HaOEJko79fUacNFRNCCuJi6GKBb1pxzjZ071O8ZGlWQcEziNmHf4qpJ572EjqCdRXI3TnSGmo5gp7bEldR/fe0D09NCGBtlyo3a+1Xv5Gj/F//H/830KU8vzFLQ+PQ0aiYbcMELHC15K8i8isRSwE3TNHG0hE5JEHMTFEjl59EA+1TcCzYMXm6wnr4wDVOcJxw1bGJGuP0oeQBVlIBoMC50FagUsd6irE9y2f6RU/q09ouoDZsw2rdY/e3lJPwXnBUNZ025B+XPFY9VCtoHPqMNYvJflQ0qclf+xx/vyey8UE0Uqa7mAtcqsAtRFkC8v2pUTHh/CU42RPeZ+xrmP6cYP9NqY8kWgMz1WBPalx24AmFHw6eOBNN8c4RbmNGTUt4XlFcu242Y3J4xC58/x0vqBQAplZ3i2GmKHgrLflWbjnaOX4oh6xcjF/++KK97sRYeCxgeXGpZQmRhrPyqaw1WhlURV0Jxa5k1gvCbSl1zUcvdhQPmQs3g9oe5LQWJyTSOUO4JYazMihe4aL+ZLX2yOCBYQrTxMF6EKgJy3eSEJrKeuQcAs2FgCofof3gi4SiOSwuw+sY6wa2kvN4GXN7PmSbxZHTPOO8aZhn4SMphVXH0f4U8NUtEye7jAfI+TY8pffnWHWIWejPX5ieDZZcX834v0sZrapWH+VUc4TPjd3XJoZ9VxwEe9ZP6ZMny1YFgNkK6keElqtiDaAdHSJQPyNtkQaqPsHm5fsG54cLyhCzf66R/cQ45wkWkG4PTgMTCxQxiFTi44t7TKinkO0BK+g92KPTTy69biB44kq+BhmPBYZcQ7T2Y7pdMvjtzMmcUE46Lh+NyG6qAm1oSca1nHE6u0ImXo+SZa83s6Yvlxzyp71sk/feV5/nOKNwr9oGXU19T7EAEWb4gIIG8/85ZLbDzOcFyA9U1mxe+xhRg7VwUBVrPoRmWvZpQFp7klPCob7DqcEgw181UzRO09w1qIqT39WoUYtH94fEd4e9sgmEmR7y9GLJdfvj2DSEQlLGjXkXUxTB9jUE2487UASZC0XozV3vzjCDyzdOiLQHX54ENB5CUJ6omHL1ocMBiXdXUyrJYPnWxbLIXqp6B/lsFRUiSYVHdUuQkjHYLbFJJptlzB3JeXbHgURctxhWoUqQbUCaR3hqKYxEUo4Gq0QTpB1LWUUIO3f6FM+apSxtCOJlYdDow9ACI83Ah97ZOAIRYdTji7QqJ6lF9Ws73vIyDM72lIVEc9UznvRg8zg3wf0Fi2FzvAvW8SDIul17FRwOJBmDrsIoZHYBPT2cKDsepCpFquhMiHCeLoJRLpjtC9pyphQOvZJiO15/E5BdZjGSXPQR9ip43S6Icgs75aTA4+hVqRVRxmE6OagO1DhAZFb24ixKBh6eN9llNOA+LwgXAtWbUZ0BQQCM3PENHSE9FXNZtMnUi1sFNG0Ij/WdJuAyBtoJB2aeFzjMkcqOubxjtX9iKC0LFcDyqGi96GDgaYcH+7j2FvaRuGMRHp4NXnk23hIvzZ0kWCebLlezsFDLFvCNx3//r/73///7xj/+vqaf/SP/hHT6ZQ0TfnpT3/KX/7lX/6PP/fe80//6T/l7OyMJEn4oz/6I7744ov/n2s0TcM//sf/mNlsRpZl/MN/+A+5urr6L3/zN4pPXlxTfsyodzHvbo9o3g+oHhPO2NFqhQ1AvKoJTiuQ0LcNz57eMV03jL+y5Hcp1dseWddwWU1ZqITKaHgvUPcKdRUc6Fx9i5aWC7Vmu8qorjMWywHlr/rsg4Aij3mzm+JrSa+ybG77RBV4faD69a6g+npAnHQU72N6Dw7VwDgpGPiWrQgpr3r4WnF8uqQvWiabFrfX+I3GfJOQ9GqSi5LgeUM6rGAvOdM7lvdDqiJi3jVQwfO/dwcDS39ccdsP8RomP14R9C0/212wbWPGTUdgLa/OFsgvQx7SFDP0qA3EuuO9SHlbjHiX9Dg63xEElru3U950A74WQ25NymYZ8d+vnvFlMuaj7fFlMcfkAfkipnqfka4PnPekMwSPkuQR9BZ85AmWgn0YcvNxyvY6xQnQe4+REr2EFoXCE0SW2ZM1pQ15vTpG3SlseBBv2bFDdRadg98q2l2IbqA+84ikI1kZxAL0rUA2gunphifRiqrTXHV91scBl5sex7eel2LL78zuiI4Nb/WIv1qeUJ5YyhPHxyThZ9884S9WZ3xxO+eV2KG8ZxGEfMiH/PLdCVd1n24ZUhGSDB3zKOfHP34kHTSM9y3VwFMnAptI/MhRS42dWVRiqC4s9ecGn3mGT3bIpy3qRcPk2ZqXJ0vmwZ5ykbD5ODwcbpYKXYBwh0ONC8Emh+lU6/VhD2lBK4cPao5/fM8kKlChZf7JErsOuPw4YuQqdGARA0PgPR8e5+xTzS4O+PB+zmBUkVSWu+sJH16fMcoFI9ngrefqZ1NsDPlVj6/zY7pA8tiTyKctXR/am5hGSXwpGB6VhFlHUjoGe4MCEjpOXc5xsKV8HxPkEJaO5qlhRcokKMlUh8JjGsX6sc821ayuBtwte0zmWz773UtSafj0yZrf+vSax2+mSO+5ONswOC4PzxopWTwOUbmgnxtE6CiKiGRcMUoqosAgRof1CUZwezvGnhjSoEOMDcG8oy0DqkjCWmBCKO5S2Gnq+5Q2FMidYrLtSLaOsHI4DSCYT7bUWtJohS8Vu8UAt1cMthb5qGnbANs74H1Db8Ef9sQidjQmZLDp0J2nRwvaUYYBQWgIloL0jUc4h5t69FGDwqJaj5AOEVtM3yNPGuJhhV8G6L7n6HyNaKF+SJDaczLeICqJWwd8WI3otsGBhDnt+Ad/9y2zSYlVgqavWBc9nso1Gkd0L9EbiXCHQh63BkYWe96xVxF1F6KFpTvxpKolOq7Y9lLyuWY9DLFC4muJbMBOLG5o0U9LkrjhJNyx/tjj7ldTdHdwyvhCUaQBciORNchK0ApBPQgouxQ58/x1OsN9Ykl1yX6fsr4bEG08LhP40w5agYo8Z+MtWecJHh3RpOHF83tk6NELSzqrOfrBkvh5iZ93NGVAf1hSrBLuugFh2JEPAvy5ObiIvi9w54dJiagldq+Row6hIJ7WXIUZmWzp0dBvW7Z17xCPrGui0FCMf71b9P+i/7Zer/nDP/xDgiDg3/ybf8OXX37JP//n/5zRaPQ//s4/+2f/jH/xL/4F//Jf/kv+4i/+gpOTE/7+3//77Pf/XzTgP/kn/4R//a//Nf/qX/0r/vRP/5Q8z/kH/+AfYK39L3rzttbIxwhtPf+TJ+9JNwZ35Ak+L/lgpiTvBXmZsBA91vdDlHH4NyHvfvWE1T6j+J7AB57srIRU8GS+oPdJzsveEvcJyIXg4mKJHBmeJ0uG7z3LtsdkkGNjCExHeNKhPyiyjaUZCaLSEjwp0SuFWEq6D5rHsk8eSaqBwBWS7STheFoSDDtoFMuHPrE0hEnL9vWQ9a7H4nHAPogxVYAfGTLTUV5m1E1AGtUEtxoVGZQ++Gs/eXqL/rSi6SKubUpwVB92iWHDsunx/uenbG5TnrkCmSsefILxcPvY57EXU1YpbRfiEsGzVw/sMk02K4mXcOv7WCTHxxuqTcrbmxHFvoeINbsmxXWShYtQXU1RJ4QbQTOHcNQconmriOoI9mmAHplDnvePSvwmoHmIEEuJ1w4vBNGdJNp6eh8cXQz2ZctqOyDuDPpKY/tAoWlGikHRYWaCBkW4sngHOof0ziG3ki6Cri/Z/oGlOXPc5UMesxRRHZgKKE/8tOI/tkf89cM5/7f8Ge+uJoi14Ki/I80dvaKhH+zx2tKrO5Yi4VflFHtk0U9qhIL9UNNkED1Kjs9XzH5rw/Fsx3/44iVZ7RAzw0+iR3r9mhOb099YJsEebTzxSQmjA2JVjDrqI4+iIxuVRNZwd9nnWDfk65TolxrzPkF4R1AcvPfx0xKOOlRqiH66Q/da2nNL9qMto6dbnvzdR6Ko41EktHXAVz97wVbEuOOW+rUmsZaT4Yb1JiMtHeN7Q77LEHcBS5uxWvapXIAIat7sRizyPsc65/SHK4Id6FGLU4Jll7K6H9EtY/zU4I86dC2pg5Dt3ZBOSeRRy/ZEkt5L+mHHToVcDDd0M4nVArlVJFeS6LQkF8GBWx854qMSIsfz8wdOL5YUJ6CvFS+GG14NVry7H/P//ovPkbEj6hwP+wCR/c0IeGJAgzkyuGODdZI4bCkeU/SoOQhAK0Haa0iMxV9FdLcJba0R45Z+VhKEB8yqeeJIopb/D3t/EmPdlt13Yr/dnP7c/kYf8TXv+16XmS+TL5kkzSQFuUBC5ULZgCFAHEiAJpoIGgjqIJjWxBbslDWQxRkBCoIaCBoZECAYhtVUCSRcpIvJTDLz5eu/PvqI29/Tn7P39uASCVCFMipBgygCXsAdxLk7EDci7rlr77X+6//T1iCTDmMkvagi7hfcLYfotUUPGt7x1uhpgx+06JkiGNR0PQhGLcU2pnqVsCSCgSFaOCqrkVtJuHK4Zqc3oGfZngm2e5K8Dmi1RDqHvFPIyFK/5bCxQ2qDLKEdCqJejTSglEEOWppFQI8Gud9iXgcUnuboZIGXtnhpwzaPsKWkdYpsGUMlEdIR55Z/9Zs/zd1lSNNqrOfohoay75HjEwxr4sJw0F8Rqwav1yI6CLa7E7obGNxpQ/+qJbq3VOcJNnSY1FGnEj2pSUYF/rBBGNg7XvGN0xvEqKM9NnSNxBlBkDnaQ0PgWsJ7UA3ITOAbg+o5qkW0m4KYh0xGG8yLCBsrTs0GTxuCYUv/nRVi0pKokq3n8+pyyk0V00wUbBXnV3uMXYXzNN1Sk9/HrOYJnnUoZ6guI6LxzohqYWK2oUf/YIPyDQ8e3PHW3h3JXsbhV28JzgpE4FDDloeHS7ZVxGiU0V57WCOwvkPEHTQCr3Ls++VPlO/+uPETJft/+A//IWdnZ/yzf/bP+Nmf/VkePXrEL/3SL/HkyRNgd6r/tV/7Nf7e3/t7/Pk//+f52te+xr/4F/+Coij41//6XwOwXq/5p//0n/KP/tE/4pd/+Zf58MMP+Vf/6l/x0Ucf8R//43/8iV783sMt0VKyrSNmtz2EB1UscVbiCol+p8J/lNPzK/orQ7gQHH64ovQV/pMc6Vm++vQSfMe6iZjfDTj0C9pa0Wsr5J7lrkqIvJZAdMzTgPbeJ9/s+NkGRWBbhj83Qz5pGLoWOYEP0znBg90o1PaBx15/y95xRr+XEwcVaVBz16YMpgXffueCgdcwCXOQggd6CY3ktRtibn2Sd9b0Vw3NA7v78LjU3OS7OehQWOTLACksdelRlN6u1GgEvnGM/JKnj++oM5/C+rSh4mIxoFoHxLolDjveeD1+8RuvODxZsPdkhh0Z3uRjuvVutK0dOqabnHbgqEcOO9PIkeEgXPPWdEGQG6KLjrAxpNmOUdDby0nbhmixI/LJUQdWoDeCahmS6Bqz9ghqQ98v8U9a2p7eQTjGsH0iMV9rUdOGaGlRBQzLGl1A/6Md0W7wkdspgq8dtA4nHF0M5QHYIRAKmqEC6ZCdwAgJ1z7VqxSTOLwMTCc5ynM+DO4Z+hUPtyXZWBA8LCg+jaFnyX53hFWKuF8jjtqd2E91dBVsrkOebLYMPzW8f7tGn9Zsvj/m9c2E0X7JwTSj8QQn9yXP/QQzk/zo/AFy2pCFPm+fXRH7DZO2oj+3jEcbjgZLwuOahYhY73lsp4q78e49bSfQTRztBLozQ/xku3MmM5bD0QKrHMePZzzuzZCHDXMZcj0bc/N8SvZySHMbMYgLxFLvqITvGUQrKJ0PkaG1isPTDen3JK11qBcSELzFhs6EuFqQHmeoTDP0Ko7MiqTrODQZ1SwminYe5uN1g8ok802KpztSVeJj6ApNNCl4dTXmxguxfcsfbE7R2uAGHaZvcRba0sMUHjruCFaCoHJMowxPWcyexc87skTz3R895uh4wWiR0adESEuyqYjeqjhpMqZBhc4Fp6MNY9uQBi2DsqFD4w8alibCtIpDb8NxsEb4Du9hQb+/IQ803lZSriNU2mE8MMcNhdL0pzl+4QhFy7YOqROJFzd4Q4OpNJ9mY7ZlyMX1HnmqqJchUdQQ9BqSpEJj6NYeWIGTAuaaTkrMaHfabIxCzDXdfYjswAta0kFBGDc4CwUeqWpoxmA3Hk2rcG732WdSC/ce6kqjrKXKdvCo6K2cttLcPZtQtxpTK6aTNV2t6bmaYFwhZ47tKuadsyv6T9dkeyFx1vLk7IaBrDjfjPCXUMxDOG7IspDmVUTpPEytqI1GW8NQlQRRhzwwrE41rdGEqoO1Rm0Edu5jVx7Ct1gluG8SrghJgobbizH5vqaaOspQwEzRHFhk2IGymBCqtzusBaMh3BhWbcxqndI+6IiTistmgDypqSeW7DalvE7Iqwivc6hCoEctIupYjDXTD+d0vqA9D4lqi7pSyFrwFZbI2CIGHaaUiJ5BCseZv8QUPmFnkZ9ErIuY6vd6rC/7SM8im52LaYEkSkvimWD0wRoZG8pVyESX+L5hm3pU4Z9sB/0nSvb/9t/+W771rW/xF/7CX2B/f58PP/yQf/JP/smPn3/58iU3Nzf8uT/35358LQgC/uyf/bP89m//NgDf+973aNv2j6w5Pj7ma1/72o/X/OdR1zWbzeaPPACawDGXPsNJxj0hbmxIt4behdy5Xs3h8KrENJLoyYZV5HPRxXwtnrHtYharHl+eHyLnkv6dQZSCF8UAe9Zis474tKRxiiDreJFNEb5DRA0jr0RnoBwsbUwwl2ybncK5aTT//fkJW+tT3McMZUV3G9CVilO2nBxuUAvBpNqQvhY8fz2gWym6WUSc1LzRA8Kw46HYsLe/ou48mkShX0uazMMtFQhHWfg09Q4oUW5Dnm32d2re0BLMIb6WzPyQi7sxdq1pG8UgrJhEOR8+uOBgU7O9jjFO8puXD7laDumUpHfbIPyOoG1JtwYZWMTTlq8H12RZzCoJ6CaOaFJzI+PdbL8J6PKAbRbSDmA91WymirXvYz3otTVSWrxiZ/vZ8xsoBOqooZ5KqlDhOYMeNpiDFs9Y9sIN3X24E6xFBucJzMHuhgsXFhNa1Bu9I7AZRXWkCTaOeGao+oJ6AGLawjdLRCF2o2DWIjOBchbR69ChYy5jvEnNYZgzayMedWse9hckXy3Yi1oe/uKM+bbPsk5I/ZK90ZrxsCCODIe2RU4qvvnWNV/Ue0zXNeYY/rdHn3LzfMKylcRnOek3N6gS7FAwPNsiMoVRgo8uHjB/OaSZBfR0yeZyQJZHrJ4N8buO1kg8ZcjmCf23V/Te35Wrg2HNwfESNa45Gc1IHm1pB4Is9Lm42qMSHtmrPupeo+4UJI6wXyGnNT/78CXp6RZbaO7uh3iNo7kK6W06ilTwejNgfaAQlUaPLTLpuGxjZGR5p93i1pJeP8dPOrZhTNmFqJVkP1lxOFiRupaq9nFC4OKd0M4IEA14fsfeqmE83TKqapR0mK2HDsyuFB13xKri9OSOg+MFBJZGSig91rMev//FWyzWPSbTgiBpqI3m//bDb3A5HiDnHme9NVkvYNaknHdDVrWPjRy3vs8y8rh2MWFcMZI5vjB8Td1glx4v6wlfrA4YhjWytvgYpv0tftCyvUlIlwZZCdSVBw4WVYK41dS5h76QiBuNqiTbkSIZVBwcroijHWDFH9a4nqGchSyWPZpOYQQoDEaCUaAK8LbgakmZBTR9QaMU/qTGIGlDgbkJMKUiOK5QcUuxCVC+oTx0+L2WyfGa6DDHjTvqI4uuHGHpaIzGeFA0Hs1FhKkViWiIwpbz8ynlWlP6ioGpcGhEaPntz9+hUJogbPGN4+ZmzDpPELlEC4sd7zaJbaAo9iW2lphAIPsd6rTmMJ7TrDy2no/fOLQ0FJmP3oCfQdivCPoN+NCXNUlYc/XJAYurAa6RiFwiC8nANiTbFv+loj2zJDMDBtxcM0hzwmFNlaid42ctsVcBadERbi1+bZluy13VJOrwSoF1Ahda6sKnlQotLbf3feZOEe/nVIGiwCcyDc5a0sIwDXI6X9JFIIe7yaz1OiRfBqjOMA4Lxk/WWCNQrzQni4zWCuafDUiXhlkeU1iPWZUS5rvPUzW0dHOPZeH9JOn3jx0/UbJ/8eIFv/7rv87bb7/Nv/t3/46/+lf/Kn/9r/91/uW//JcA3NzcAHBwcPBHvu/g4ODHz93c3OD7PqPR6H90zX8e/+Af/AMGg8GPH2dnZwAoz/Do0Zz3g3Pabrez3HqaSliC97fkU8VlO8I+9ymf99A1tM8SnpUDwnGJCy3byMM1EGwsctihPg64uRtRPAy4CUK81nLbpUgfRGSwUpF5irO35/S6gtF4y2TRcVBlxHVFnBaEcYutJQNXUa5DNssEsYLLdszlzQhPGF7P9vliO2XZhORvOdjvGJUtuhUcHc25XfdwnuBxWdD6iuqR48npAhFYhqIimpZ0ieRH2QlSGgJZc3qzxQtrmkjgT2u6lc/55QgLfPDwElFIFm3EnR+RP2kIv5pzpNeYpaYdGbbPeqjI0FWaLtE0sSQMG7If9LldDEiTkvFwy76o+F89ek3nwcbzyffZWfL2PbZVxHTRENwqorTBGsnoeAPaYlKwgeE6jEmnBcUioskVlZZUJxZVgnctKQ7h6npKsDCUjY/4KGBRJsiNwEh2m50DTedDM5TYxCErgdGC6khwdLwhPSrJRwpxHoAQtH1He2ghNRBbItVydDLj/eSeH1weUa8dwqv4dH3IZ3dHMNO8ej2hKxVuIzi93nKxGe+w429X7MVb3no4w2skz8QYqx1u5WEuNf/tf/uI+ZuAzVUPs1VcXI+4fjNFDloMcDsMsJFlcrziRGUcvj8nOsmRpcB7I6iFon7Ro76NSUxLsQ7ZXvRYvxny2f0UvV8R9UqmsmTbD8ikx+1igP0ipik9bl+MKVcBVJKwX7MfFQQnOUI7Xl9PSZMSOVMMhxmHpwuO9JZ8HTPeNqiNZWi3+F/LaI4NpmfxkxoTwpf9mPYuYnPZ53c/foTzOn4mumXjAlbPB6xfj3j88I50P2fS35KsLWlmeT9e8jhdMBI50hi6PYvqGexGIyPD4HTDA70gFS2PP7ym2wZ8Q93SczXDYcbw7QVq43ClpNiGrNuQ9sCxinctLZVDtw+36xhXCcgV9W1IswpojcTVEufBKCq4GcfcqRSvdrza7OFFHdGVYLRuuJz1ccuQ7C7h/nrIap4Q9WoWdYJuHP6tRHcOF1mCw5o43vXSGRhiOlQlUb7h7tMJ+XVMMt9Nc+iFREqHvhMUeJSnoA9rgmuBlzk8v/tDyh3QSsLCwbAjiiu8NaiNRBhB22k6J/nKdMZpskbonfdCaxVF5bO4HyDPffyNoOsLemcbxFHL6HiDntREukE9KkifbKh9id/vaB476O2ErTLdgcMaLWnXPk+GS8y+I5/t+AheZXcjjLlCCkulJX5lGDzcMHx3QXxUYKaG2+OYMGzxopagMlgfPGUQDiaPFmQENIEkeJRzvHePulKEXYsat/iyYyxLHIJ7F9O1HtZqzMynVRrnC1ynaM8T2tcR/Sdr2vHOCdIrLPdVj67VtJXmejsiGZTESU3Xd5i1h40teJbQdFgrqVuPYjbkYbzlgVuTBBVH3po2V7w9WnLxySFyJhmuLXuLluX3J/idwx+0XPsDni33WK17kEvqJx2l8XC5pvMl1csQIuh8SZJW2FND7RTzNiB+kON14idJv3/s+IlG76y1fOtb3+I73/kOAB9++CEff/wxv/7rv85f/st/+cfrhPijv4Rz7n9w7T+P/29rfvVXf5W/9bf+1o+/3mw2nJ2doZ3ht+ZnvBftfK1dq3bs96nckbkWGjHsCM3ObvNArlkuegRpQ1toRsMM715xKnNu5zFq4RH1WrpS0K4ikrAmimsqEtK0JLyxeNKx0YovyzGedvSv4GWS0CXgfhDQ/3CNHFqeFgvKyiM8Kfj0/JSNjUnTiq/25nxpxvR6DZqKTZ3S8yraW5/Db9yQ+QrZWQb7GdnWZ+EUplActAUXScLx+yu2geQoq8lDwf2rESdvr8jPI5ZdnxNZ8GI+oRtVRF3HT73/imfPT7i7HVApSZxWnF+PCQYJWlhmocdkuGKSZrw43mOL4sMnr3jz0REzEdGc93AnjqzoI68F7R6slM9/vDqjfe0TvlfRK1s2R97uFNnBTMbYxrGtQvyN5XY5wUlFOK0QpcJtNFkYEh/u+tL3TQ+xcVRW4CmHzCReYVHaUa013WQ3o6sXAhwI6/CXFlUKRNJRJZLyzKAyiQRe1X38e9BK0jUCfykRU0urFSJsGSclqddxVfRZz4c88FfIqcFGHkm64t6G3CcBhfGwy4RBW2OmEHgNk0XDRBS8FCOWlykXn4e4nzJgO5rW8eRbd2Qzn8s38Q5VvO4xfLqlqSzHTcPFyyGcGFTRkRjD0WjFc5WQZRHyuOZ1NsbPJe5BhT73adIQvwY2Ad64pcwC2Hq8rvpYK9GBQT0u8HsNrQMSS7BxaN9ymK6oUknnoLpIaIfwcjklnOQkhwVe0DHJWmY24PFwyYvVACkt4qijuYrwhi1V5hM+yjFf+MiBYfj2ioVWPFrPcXea+L01zeaI+GnO7Hlv5wswMkw3NbMH8PXRDZ9lIxaLPmKu8aYN9b2Hn7YEqsOVkvzTPr3jOWq/5nUxJvAd/93NY8SkAw/mdUz3pCNuDflVhBsJ7FIjV5omdjipEZ6hd1jj9SVaCYSTDM5WtC8nVM9TbCJ4cLzizWKCFzbMNz1sLdGhBSdQfYMTPiI0mHWEFB1yqSkPLB4t7jag02Bzx0A3nD2ZsZ5F3HUDlLRUHphacl/28ALDflMwTyPiUUV5G9ElDlB4awuJIWw7tpHEBQ4TgVQNdq7wOwGNxn+novosQfkCBvYPbYA1k3jLF9dj2omlWflIbbCBo12HBG1L52kwEOQdB3bLeTnkrhzQf7Blj5zX2zFSKrq7EDmpAKiFQiUC5VvGpmbeeHROcFvFuIHBKx1OKGylsAXIhxVlHhA0Ahl1ZNsQKyVJU+GMRKeCbeeDdGydj38DwgV0fcH99RjXs3QBdFnIfax4cLBmaQOS/YJXtxM2dwFdKIiuoXlscDd/uOH5dkZyq8i3ERQSL+4otUZ3Bh3X6EtNdxUgrKTqaxLVYhuJ9DvkvULGBs8ZmusAqQ06sChjmA4qhBbc3sb0zmrKWczPfPCal2WP+LrFDgxFo0kPC9aNQvgWFv6uNRM2bI4k/msP+YOY7HGNfeVDYukflQhtuL8f4O9XiGtFXoR0IWSvQ5qg+EnS7x87fqKT/dHREV/5ylf+yLX333+fN2/eAHB4eAjwPzih393d/fi0f3h4SNM0LJfL/9E1/3kEQUC/3/8jD4CZjWkzny+vD1GlZDrICZOGntfQlRoTOdr+DiHpHBQ2IPrGmod6w8QvGc4c/WHJ8KiABxazkgwezRgmOdHTnMZ63JCQrFo8HNVGc5GN8G8lpyqjf9HxzeE9KoLNImX7ruSu63M1G1Emitt6yEam9Po1D4cLaB2/8/IBy7uEqOy47KcsDjT9UY58WPPZZ2ekccmzfA/tG/rphvp1gDcTvHW4ZDKrWGc+aWuZ+z6hMbw/vWcgG6KuZeEHZEWA2Dr0XKC15UevT8grn01PU2iNedju6G8VHB/OUcIipMK1aldKXHk8//yYgS6RrUDEDrvVxKZjGFRMKRgsDYuJwGs77EaTm2B3k+03dHuGeuLonRTsjddkh5JmJHY0r9BxsL+hHe9GHeVakOcR0jiCe0FYGbxCEr9yNKGgdop+1hDnhmAh8HKzGzsKJV2kKB9Cp6FLHEejJW/vzQjDlvQakm1L4tWoDHCOugdJUDJNc4rOwy01E1uxFAFfthNedEMWs5RJtOZgb8OAil5Y4axG9S3v9u/5s/tvOD3d8OnsgM3LPnPlsX47wq09pFBsdMAXsxEukjQPDI/fviH2OmY3Q74ar7k0KVY79Mrx5OE9k2hD3WjeEUs8aag8BY3eaSMuA1xqEZearvZoQ4e580nHBdGjjOOvX5O+v+IrX3/BA7fmQbxlkBb0g5J0XJAmJTddH9HCm/kYJnDo5VRSs7zs0y586sznxXLK8iblzf2QB2/foUeGqggYvLXCKYje3jLuZ/gHFd6ookkhMoYb2yM6LPj48ohqGdCtAjjpdj1yl/FJvkd7GXCTxWwvEoo8xIWOWmisVdRWYweG/l5GJSW3yyFtILAfJTwsM4Zdw7FcsznvUb1JGLY1lILwtOTx8R3Ogj1qGB4WDI9zmvmuuqBnCmUd3UYxyErwwFUSawWfPj/DF4Y2dnhhRzouaDxBPXUsZylitutlN/sW70ahKoctFF2tiQ8zJt+Y8V+++5zYq3l9M0XnhmTZMSlqynWIFBC9VHgvPVapjysFixcj1H6zEyB6O3MfWypWWYrTIEu5+//mHqqWJPs5Im2hUKjTFjVtee/onuoyofI0N8+nFPiIGw/VQDCDtO1IVY20AtWw82BoPC68AVHQsL+o6WYBMxWzJzO8zLF3sMS6naHP3qLFnzYkcUW+CUmTCtU3bOYJ61mK61nsyGAThxO7Pro4rQne29L6Eg8HDuL9ggbJ/WxIhyA0hmRaIh40uPcqnh7MOf36NYfjNW9tCtSXHum65UYHXGc9Pv/yGC9qMRGM5x2BZ1BLhYstToLJNYswpJk49N7OAdPUivJAUN1GtM4jrlumyQrXCDabhG0d0mQ+nScgNzS1RrcdWnRMZMn4yYa9x2uuTcxor8XvGebHmt9ZHnPbBphBhzWStfS5XgxJk2pnc2ygEIpQtXS1ptOCxpM0FzHmrMObNASmYzOPUcqyvU6xGs72dlbpbtwxFf8zTva/8Au/wOeff/5Hrn3xxRc8fPgQgMePH3N4eMh/+A//4cfPN03Db/7mb/Ltb38bgJ/+6Z/G87w/sub6+pof/ehHP17zPzW8DXiNxfdagkcZpZCwVmQvE8K5Y9/bIq99mnkInaC8iej+3ykv8xHXFyOuboaw1axXKR+ezbChYPVZnw5BngekSYm3EpiBxK9apirn24/fsOz5HPYyqg/gP2WnaNtyEM2JvA7dgcTRvRLciYhN4dPkim3nU7+OsA7yfcGn8YjchsS9ituqR3MT0VrFZT2gXgeUy4DLZ4ckreG/fv9zbu9SzNAj9QyvX00QRrBeJnxxN0H5hqtBipKWod8ht3IH+pm0bMMAnlY4AYNhznzRJ6WhKjwWm5Q6EMxswH0Xk+UBXmdZrPq87vdwtaRnG1qrKBuP3klGswwoq4B+1sEY7AufcFDiDRqawsNdeRwGG+zQMJcx0yQj7Azadni5YRkLAr9Bex3tgaVG7QRaI0NnNE47hAOVSUQjWI4CVg8VOjdUU03TF1SHYPY67FnD8JtrfNUR7ZXc30W0dwHFBMyeoJkFmLdb1LiCxPGzH3zJeycz3lcr5tonu0pw0lEaTWsVldO8nk9YftmnrgKKbUBV++xFW67SmOsm5ot2hAmgCxzb85T9YUGyMuBAvV3jDzo+2exx7IqdYYhyKGHJSh9z71H5HiawNJchN27ArQxYrHpw4ZOKhkdPrzh6ek8wqfB67a71Me6Izkrqxx32qCNIG4o8BOWYlymv7YDmXpHIhn2xpik9jvfm/NTZBaNBycnpgkA25J3P06cXHJo1h4dztpcJx27DZC+DvmXpYgYy5/SDax4MV9hJR2M19+s+/oOCkSzxtvBnBucEo5pP3QFZERLqlvR4i2kk1yT84JNTpDYUgebzZp+Nigg7S9ov2U9X+EFLnQfsb2tmdwOmScWjpwuCmSaetFyOArqzluoqYTpZs+9tWJiYSDdEXcdVOaCeBfTCksYDYRz+SYk57vCHDV7SwPs1F9eHtKmDo5LROKPLFLN5n9X5kMYq2ruQftsitcXuGZwP3qMSz+uo9x3Nww49aDBCsJ5q7l+N+N71mI0KyBqfu6gH7zSsH8K2C2krRf20o3rSQmxg0qE2OwvdwDPYXO3m0hcKKyROgdtvcUODPqxpHndk0sf0wHpQO8XpyT3PswEctEjjsMHOibM1mlFTIgNLeRNRbENaTyIbQbi0NEMoVxHVJmTvnQUPvQXlPGJW97gTCfV5SHgN+6uSyBnKKiCvQoaHW/SwoW00ftLgrwU6bnH3HsKBflpg3upwkaV4neJtoR5CfJqxyBPEVtJpQS9vsbmiKj0OVyXBG0E6LNle9gkbQ7C3xfUMvjP4ztKfG4a6pJjFPN6/R+/V+KMGKoHaSmSvw74K0BvHmbekGkhQULUefg7Cc3RTw2IQUh5JhC9AWbDg5w4/bRE9hQZMKKmmksWizyxP+Phmj9s64eVqzJs/2Me2kuss5dXdPvksZaBKxrYkvDC0SvCgv0RGll7b8pbNObIFw3GGe1JTPDJI3+L1Wl7HQ4q+RA673XSA17GRAfF+gU5bzr55+RPluz9u/ERl/L/5N/8m3/72t/nOd77Dr/zKr/C7v/u7/MZv/Aa/8Ru/AezK93/jb/wNvvOd7/D222/z9ttv853vfIc4jvmLf/EvAjAYDPgrf+Wv8Lf/9t9mMpkwHo/5O3/n7/DBBx/wy7/8yz/Ri7cVPNxfMns14ny5z8NH1yy2fdxGUR85NiZEPShplwEDv+TgYMuyTdgsQqJxhbffcX2fcukE1cZxONpyfhpgtSC4ELSewk879LhhsRggT1u2ShPKmsskYNl5PE3n3H8xJB95DFYd2YHAtJKXxRQ56tiuArLIJ74Oafctcdqg+xBj8Kwl3rRUNzHDR1u2+Qg/aTnubVjUKd1bLZuV4jfzY8JRR3+4RW8swgxYbmPio5LeXsYXxZCHgytCJXn2+gg7dHjTlnDtkGsFhU9jNENX0ruzDPczFosemzbAFwalHLO8Tyc0k5MFvOxjzgOqWDLPezhvR89aVxHRfkFbRXw2OyZSHUwt/sYhvQYTCFZRzPLLEVUiCYQh3wSIylFOJMM458/1X/Mf24ckL3zyQDJ8uKZ7EdFtfIw10GpUZTGpwe8MLgW18GhGO2ewZiSI9gveenTN7XIEjcBuNW/O91BGk/g1XmipOp9g3JE0NfqwxqsSbi/3uStSbvyIoJDQCbxhh9XgConodRR3PlUgeSfbsD8oeS77XN8OKa9DvLQm3tZoC20F3YmhnXt0Nx7B45aeqvj55Ibfyjwmq4rtA0t3LRETy/lnQ3pxgbqMiZTiVozo9ixWgjsuiMqK1X2MSjN8Z1g6BaWg/3jLpgvohMBLWg7LLderIds6Zt/bUox83h0vMLeaL6oxx2XH0cmc33/xmLeObnllR/Rlw/o+IY1rtnnEw3fuuOt6DE63fL6Y8Ljc4IQgLwMeeAXLi5BnSYjUjuo2xKcj6xLG/gq59fl/ZY/Y2JDBoGDZRJjCJ1t46KQjKC3dEUz6OdlVDx1YNhLoBOttQlN5mK1mvL+laQWB19A1gu/fH2FQHOgNh1nDjUzZO6jY0yWXQR+3kMwWQ3qjAlt4mFAS+B1FHSCtIZQtMvdJk4LSU9Q3EWFcgPCpNyGDJ0seju/5bHOErTR2o5kUG+zbNZsvQ+Ky4cmjcz5ZH6OwMGo4OFowfz7GYYlmHf5ZgVMOcyfQfoenDNt5SnzZEm6g7e/Kzc0elIsY40EImLWHs5LBbUdz0hI8LVguU5AQ245N7VFLD1UJOitw6e6eslby5cXxbhY+cOgt6I3DaUuVSrbrEOFZROYwA4WsHW0fzJFBto628NgfZJTzkBqN9nc+A5UncakjyVqWyqeZBzsGvXN4vuG67rN3tiQ7T+k8gcGjNy4pNwFOCLpKo+s/FOkJwUiU+JnBbELKFNRGID1HeJax3A5ZVz61p/nRYo+g33CzTLkqe+jOcp0m+IOGrK/oGh9/UnF1N+HwYMH1zZi9h0tm8xAOIN2vyWcx85sBwZOCfJngjVvYenhRh20lD0ZrXlwPEbEFBXKjMLcRzTsdxodwaaFT+F8qFJZ2kdANYX+yZTUOOTlbcHUzYXi4ZdUkPPrKDdcf75M/tvTeq9gPChZXPVwu6fqC794eE3stoWl4dDrHtJKLT/dp9lreS645F0O8uaPX3+Bt4c7FaGMZjHPO8/FPlO/+uPETJfuf+Zmf4d/8m3/Dr/7qr/L3//7f5/Hjx/zar/0af+kv/aUfr/m7f/fvUpYlf+2v/TWWyyU/93M/x7//9/+eXq/34zX/+B//Y7TW/Mqv/AplWfJLv/RL/PN//s9RSv1EL/50UpDeBRwMb/hBeUB+3qcJHeYBNBF4wjKdbGjnMblWeKOK5dUY60viqCE+zDGdYDLJES8U91rjpGCwl3E/C4lqQ6+29CYFb/SAch0iZopHX7litU7Z9wtuvxgThS210oTDCrHVFJ1P8lZOcL0by5HRjrj16OSON7M95L3jrccX9EvBj5oh3RiyuWB/usFJuLU9jJNEpSGQhmYEzSqgcTA93jCUG2TaITYKlKC3X3J3MybFcDZeshEeDZq61EgDzTzAE5Z1nTIab6mPDdYa6iLEpi12rRjoiiYxFErhRobepECtI4TvSDPDvEnYap89kSOvJCp0tAeGQHRUL2LCsIEEUq+mGUuetivkoGMlY0rnEVtNm3t894enNK3PsFcwdyPqSpAGljZwdE4RWIPZBzWpsV8GqAuB0pbw/Q1N7tNZDxc46nVIfhfDXFEfWqJ7RYMinxp+7uwlP/ziMU/8NW0rMdqSKXi+nDD2cyLTYduAUZSzbTR169O0inRQEz2qsFcJwaRmMQgZNRu6H0W8e3RHceTwOoWNW76cDhCdpO4bukVIz7WIreC3f/iYIg740vk8FjM2U4G69dh7uKCvWq7Glp7bMcovbqb4RwWn4xnPyiNaI/jybp/TZUZ/1VJPJKrXYG9ibGxxNz7n2wMGjzeEo4ZvpjP+4G6Puie5jFPMhWY86Xj9+Qh/1LJdRkTSYkLJMCkZeQUv7/fIbcCgrQlcx3GSoR50eB859lc1z19PWD5VRLVhawLCQUfPVrhcssgH9OOCpvVotOT6esxIlHSJY+hKilCR1SGe1yC2CjOERmtE0rHvl4yrgqs45J3JnBeuz818QBh35MfgX4LJBKtRgt9reDvP+Xi2T+sJ4ocZuhA7V7hKUQXgxR3rL0a898ErXrZ7jI9yWHfsLQ3PFymjsmZjY5JxjvA97HlIsSfxNgIq0CcljDryVUx0lqOl4bPFEWKu2ButaZWi/1oz72B/f0UnJOsypL2OSB9tqDcxiypBx5ZNEdI8suhuV6J3tUAYQXgv6C1qNgONWkhKTyI/0mz23Y5/IKDY+oSjlqr00GtBslfgEksb7ubr/Rz6D9dsbhPKfY315Y7E5yzmYUv0TNP0BFrvlN6EhrbRJL2aah8uXo13lYWwJeoa9sucdL/ms/mU7XWPHg1Rr8NLHHWtsQE7v/ilps007dCh7gIypQmdIVk22BPLNo+JMoHQFgfcXYzphCB6KyMJS9ZZTDSHuNzZ1nJpMImkuo6wrdoRLvuO5jJC3FuGDzfUrxPEm4DOF1y1ffyDClMJ/EbirQyN73HqZbx8OyF+IwhFTehXzPZTvKClXobMtyFBvmvvadvR+hp7YKAUqDtFpxydL6hPHHtdhSki4kmNcYqHwYLGh8Now2IdE2zh3g0wxw2y09hMcXU3IY0qdLfTwTgfcqcpqpj1sod50NKdOZIGVi7hLX/BzWJIOICh6bjPJZ50kAiSvPuJ8t0fN/5U2+X+V//7v0N2GNFce3iHHQebkkUccH83QNUdftXi/VxDtgkZNjXtaUdzF2EbQRcIRmdrqjzggV5yeT+hSiSH4Zq7qxF+A0G/4pvtPdcDxffuH5POO1RiefDBnOcvp0ziipuLAfKoRqwUZmQ5TlZ4uiPzPTY3PfYPl7udeaZoQ4lWBh23rC57uHPJw0dL3sR99LDGdIrwjeR2EuJfaPaPVmS+pms19SJAN46oX1MPHf2wZBCV1K3HWJZ88aMTRgcZbxZDxtOcxTJFLhTqrKbcBPSKblemdB3pNCdvQmZdiuy1nE3mu77TjY8xElI48NY024hqYAktzL4cMH13hXnl05YCdWC4TwK8tMWc7xy5JrLg7OGcZ8sJ7z685+r3h2TaJ6sjikDvsKyNwLsyqJ+pCWtD+aOUR1+74UeLE0ayotEWnbgfjxrJzHH4zXsKQgqnKYWiqnyi3KIWku7EYLZgfIVoJPKo4sAWSOuozkPcWx1F49NvG+xCIR+0tCtNsU5gJcHsMJtdIgjThqr26B9m1LmPqAV2C0jBvl5wErfYS2hGPp8vpvz8V76kWaf8zuaYn59cY1vBZhsyixXVSNIXLcPzhmdmwC998Irf3J6SeBXlykOWHu9N53x2N2bwcEM3dGR3CetFiiodQ1FT+Yqi8/DmEptYxFbhpobjh/c0dyFV61N4GqMhqg25CUhdg95rKM1OqNUieSdZ8arrEbyRGCTysEElHeILzVBVeA8s2yKgrhWmkjQD6Hk1NZr4NMcuPNYXCaNJxTt2xe+1+7s+7p1H0GtYEZL8QFJPJNGkwlYSnGCZ+uxHG4pViFWCsGhJohLhCQ4ebzn/4R7vTue8bPrcNSn+S0V14IjbjmHYYFTLchDigEmUsVr0mPa2LIfezhHtDTSPBJ40eDX0GrPjKZw0yLXmapOiW3j3wRWRavnu/RneZ4p8TxNFNf4ayljjWoE/LqmaAOVb4rjCXgRUgUY2jvHDFetFSnkX4+/VoB35IiKwHcHasZ56pE1L13fU+Y4jEIxqxI2388Do7ypj/ZMt7UWELATdwFKEGu9OkQ5z1mWMshB7Na0vsb6jnQUM+jmUkqIJqBPQNejYIF/62G8UhJeCZNRwnfcYxxmmU2yzCOlZXCVQ/Q55UNNdRpwMliyXKUWqODpYk72OqAofUoeYa0ZRSWIKrvs9VAZZqnEjQ3Mf4jcGOe5QcUc9D3c2wIkgSmrarU9lNJ7qGCcFIoBpkrO5iFlcJnSx5uDpDJ3B5XyCqwUu0xBbXCEJ44b2QQeF5LAsOA97eCvBwdkCrQyLywE2dIyCnItqxEl/xdAreNWN2G5i/FVJG8V4vZZTb8Wbuz08v6NNLO0iJBEtroNoUFOZnRW6vfVQQtCrG26nISfTBZtNRBcKurWHWO4om/UAeKumURK1UuyFW9xasWwSOiTxnaH3MKdaBdBITOyolSayu6pT8DBnuOpwl4rj3pLFI483qym9JyuyZ5qPfvX/9Cdml/unGoRzLhKGScuD44L7bUKtPCo8pKpRTy312iNfa7zW4RSMRMXlbIhqO5LaUNz3sU867uZ9bKWorcfFfIR/6/HovSt+tDji3w9i/JVDl5LqUBLFLZ9+75RwWHJ1O8A3FuEbXCRQjcNNHSayDM8r5MSy2UaImSIctNDvyOYh8Zc+3VSSPKrwj0uCZz2WfsTRYE33QBO+iii8Xf9e14YwbKl9iTQGuxG06wi719E6y7zoEzxqqDtN82lKODWISpDmHW7aEqmadFIhQoUYt1SbgK2JaIwmEA1pnDN/M2DoVxwULS/rAV8/ueCTj0/otTV3LsBmmqfHS756cM1H5mDHVu8BpaK+85nYmnrkEJHl9y9OUBv4/fqU/abi2Oa8iTWMG8zS42FvTfu24PJqwIEpKPqW67ZP/2xLfefTP8spheZwsqL6vMf9WUCWaOq5ZjNLEalBVJJOO9L9gu0ior+pqb9Z0y9bVvOUe9lH1444admc97ADyxaBcBpPG0rjcxBvuPAGTPycmJbZqxEhHeNJzgaPplN4cUcSNJQbn/2DFjlo+cHVI/xhge81/MHlGUYLTvsrPu766DvFSoXYoUEuIR7WHH5jw0fnE77PBGMkdRfwyMso9+G+Cnn30YxVFnH7MiZaOLapRG4d7anjwWjB5ccTmj2HiRVi0qHe+FwUh6RhRRkLOinx6CCyqNrQlZL6IkFrQzu2xFvLcz2gfR1TKke/V3DiNmxWITNiqoeCs02BrQTH0wyHJNlb8fEXZzgj2K6HNB4EsiUrNZden6lpuRQehwdrFi9GeFohn9S4ZcTKhETTivo+4jRc0jvKqFJJ4Ax26dMbGe42CeXnlhUBb9YJbujwcsf46ZaLTR/XExw+nfP6vocpFf604v7NmIfRgussJbx2mCcN9TuC08GS8FLzg+Uh8w7UnUaPt9h1QF1rpnsbXlzsY2qJ13dUH+wwr81GUW0DXL4bDZ2LEDLBL/zcM17eTeg92nDuhsgrjwGG/njOomkpfUV2nxCuHCZVVFIQlJYkrSgyn1iVuFJRThwutpg7vfO/SA1qI6kDRxMITOURLAUO8AoYyYJtErBpQpS1xPctVI7SD2grD5m2+Jmke9DSp2CbDjCFoogVeiVwSrAkopMKFTkwgnAL4qBjPyqIJgWXWY/kaU76I0e81+AnkjfrBFrL/vCOWvdpDxVlJhi6ltG4ojYKIoV0gmFT0IwceuAQS02Xe/iyxnSwv6rAOn767Jzfdo949ck+9UDx3rdf8+IHZ6wv+7RW0TmFJw0y3/19cOBuPWQjcZkiG1u8W423hFk7olIafwXipGGTJ/hzxeVwwrwY401rpl6B23cMDq55fbvH682E3rJFPm2JZxA+vMO+CbjyBpSVJkwbrJKIA4u1EnsFwQrykY9/UtFex7jAIQY7My/hCfbSDc4Izpt97mSPflxhFIRXFv/WstZ9XGpwgUCtdmjxbuHjS6iDiKJfcfhuwXf1PhhJ4FnkD2PkXfsnmi//VCf7VksS0UEnyQLJMgkxtSbtd4zZso5DDvtr7lYDgqhlc92DxLD3aM7dmzG589iPMrpnCUUo8RKDlxiakcenb06oEoVXt6yFJowMXtjSNhrtGep5iEsdxknCQrAJFKM0I+t83OuE95I7rl6PcX04/uCezQ9HzO8HeF7H29+6YrWe0lQeP/jsLbAOri2rXkgStgQHFcVFAEnHfm9FheZI1mxHASzBSyvanmNb+pQpLEVMNKy583wGM8ti3SfYdOijAmehyT3644Ki9Zn0NjQDCa996u97uPc0T9yWeRsRdQW+7vjkyz1E0DHb381Bh19KNoHH937/AYtZRPNU0Ovl9A/X+HeKzkme9Da8fDblW+klV/0+L+yIfNwQlCGyhImX0afmlZvSzTS9XkFuQQhDsrHEDzfM5zHjqubZ3Qj39S3rt6HeCDbfHZF+ZcPj/TuuL0dQGsQ3a1avE2Rg2UQRveWGbhWQPiwoG00UNmRViNk61KWmNQH+pKK9CQlDw7oMOAs3ZL5keRURJwVCO+L9gtsfHYMPuraUl33ctOUTM2b4quE4WVJcexgBm/cc/r1ivJfjLkPavkew9WjuJT/z8Bw7tHy+GoKEqtBY6fAaWPQ9wqahwvHGDynmPY76GxZthDqpcc98jq9Lll5KNZCIw5rwjWCYbuE9SVsqsruId0YLnokBSlnKCx91smPa+4VBx4ZupWgqD/1WRqBaZCmos4DLfEC7CmidpF4FvNr6fHt4ydQv6AJH7eCUkiYRBEHH3GqSpOGDwzvSRvBxPkZrxboIEE9LEmGoG5+wq9G1AiNIT7aYWiG+F+J7giYN+fnDS55djvm5B+f8zvoh746X9NOMrIlYJDHbwmMvKvCW8Ozzfd5Sc+ZqQGk9+scZrXXseWveZBMOFxl122dzmOLmLWNZUVqf7qRB3WvsSmLHjvsihUYjWpB3MJ5u+MrwjusDH3/qePN8j7qViFKxd1NSbwOMDzfZgGIdEfkdz+6GeLVAxLsSOU5QT0BXAgH0RzmLOoLUkW0ibOCQix3UhwctzAOCG0GtIroDh9go/A3IiSFKS5Z1jFc55Frgxx2mVhSJwoagth7R3EIl4LDB+9xjOerT9SSp12H7LXs2Z36zhykUvnGouCVsBcH7BTpzNP9dSjs1HPa3vLoaIk4N221E0qt2/vRDx108phYKr9nZ8ZbnIbUEtRYQCVymiOIO7w8U7/wXL3m+OaMTgsfxkk9XB2QHiuMw5xN7yEFX8cUkob80nN/s00wt5TLC24KNIRpWFG28qzJJdt4VtcBLaopZhO4ENnZw6xGkFplaOPeoRIBrBTq3+L2KzPjI2idMK6q7mOhGYPoG76imqxSVp2lnCb1hTVS06NbQ3vp0EWivox/llA80jw8v2Y9yfuviCZNBhjGKXIbsnSy4eTPhRGy4LEbEmYG0o5xFu3ZjX1I+UbjEgG93oLIGrA/KOoqTHbSneBPxYuoRKsejr17RvumxmHiI2U/Wtv7jxp/qMv63/q//O37xnUs++q2HXB9GiEbihoZ9UbAuQ0rrMxjkdAsP2ykapZCFwBvVVFIRuY6sr3gSzpm9GaHoeO9wzmf1CG49VgOfJClZznsMtjXSSMRei+cZ5mVCaDusEoyGGaHtuBUJsoMgbHmUbnjzw33UQckmj0i9kq2NAZDSghCEM0ueKMbjnLcOb3gjRqSu4vVyjwNVsmh8siokkS3NTYDqdZhKE4xqCq342tEVw0vHR3fHLAY+cis4yzbclX2KkWTYFaSHBXkTkbyWNF8vKbVHsQlxmWISbcAXvButeFUNOKjWfDkY4w+rndpXl1xfTpikGZPelnme0vNquvOIyU/d8qoek1/0UJ4lNTWzRZ9k1SHuPczQUQSKvmo5fHrHdTUkvG5YjlLCXk0hNJXVeAvJod2iA8PNJMS+Cdk7WhHUlvNuQJg7vKol3GtZtglirtC3Cu+oRnsdc5egBi3KdZwdrdlWETeXfXpezeTRkhfzPeLCwMCgtpKq1bQpnHlLZudjJnJL/LjlepNQeJqBqynWEe7WJ8w62im0ncKF0B9vyHuatG0Ju45Lv8ejbUXcqzi/GuIpA7Vl73iDXcW80ilvRzdcZ0MeT9d8vNonDBqaViIbR5I5ukOLOGiwl5qy8HGPO8SNT7f0EYMWFzqMlfTGBe+bBWdxwW99+Qi3gCpQDL81Z3TVsZ77LNsR9VSAcEzCjNWzPsm0Yd5FTOMt8qBlvukRfCKpJoLAwrBruAs9DvSax9OclfCpsoCbeYp4XBPJHTBpf5ihMsHFcsxpl3Fe9FivYninZjLe4OY+14sRstudbH764TkfPztGSIu6lJgjkIGlXzZI2bFRER+c3HBX9Vg0IYurHoFp+MWfesaZXPMf3rzNVkRUSlOuPHy1o/lF+yV5HdCljnIVchhuOfIzlF/zxZenNHuObu0T9yuqwiccVai13AlGnWE83rDd7MRzU68iu/DJipjSBDx5eElQtzy7O0XcK+onHWHcIgpBf27oBpaNF+JnjrwOSW1D02r0pKapPYL9imoe4jJNMANx2BBPSzav+7SpI7gTlPuCoOkwSpKmFW3oKJYx0ahCXmtMrmligQgNNnY7j/gLjZ12NIeGsLC7nrPVqKRjPy9pah877ViXEYFuOchKbCq5tTGn4RrxecBVP0Y24B7XNK8TpG/pH21Y3vZRyqDuNM4IzLs1+tyjjaALQfQ73MzH8zt8YyiVxqvAWkHnS6Jtg3WabuCI/YbCeUhlGRUVIoW60RSehlqQ1h2bNiDKLK3akUmdEDDs8BeC3mjLrBjghR3MNc469EGLWWhkaKGQWCN3+OwTYK0hdjvMxUoiHJQnDtU4vJVkoApqqemOOuptiFMWvwLTt4wfLgkWlrugR3Bj+d989Tn/aXPC+ssh9srHSXZwH3YkSvYb/LUlPS5ZPBsR3XTkJxp9WNNZBZc7PLguHfXEEeSgpKUWimBh0QV0Rxb/p7ZsL3t4mUX7G777f/i//ImV8f9UJ/uf/j/+PSrTp546nuoFq5d91geKKKpR/o6wdVP1CHVHU3r4tqOOFeSKrxzd0YaOH90f0Kub3cjI0BHs1bh7jweDLWpQ8uZyD5tJQtVRbgOafYs96khzQ/vCpztwqGGH7hxmbHC3PvQMzTIg9Fu+atZsI8Hl3RDzdk13H+AaSZkKBmGJ7hz1PMDPLF7P8FZ6j2sC7qKANzrl2G0ptyH13MdIx0jXrPwAedSS6IpwKbjsBrubXxj8l4LkQU5yWOJhqb4bIWJN6FvioKY4a7m6H3MYr3l9P6W/V1D7kiNVEFFz1yaUeGSbCGMlRgl6rqbZarzThqFfIjYKXTlGexs+vTwhSSuEgfw+hkYSTCuILNYJjs2G2TbgoF+w0SGedfT9grv5kMHZmld2xFvegueXR8hc8DhasjYx9+sY3YKIO8ZBwXzeZ+90l2QmSUZtFOKNTxcIgmFDO/NoHnY4KXdOe76ARuAayeT9OVHVMd4azm8HbE8lcqE5PJgTdR2j2nHhJ9wvUoKgJW99bKfw58DAYhqJ7znGD9YsfY/gWtBkHl2pEQcd3rIlOG3pLyTCrPEmAkKfDw4uWb4Z8lvzM37qved89+VTvM7iTRpOxwvkRYDZN1xmfTxtdjazG4Xer3fz3YXGaQhFxzr2GB+uUTea4vM+Xc8SJQ1nIqOdW67fimDh8eR0ySfn+4xEg0ksvahk9qKPApo9B9MdVGkx6zGiwEscy8uUaZQzHtZ8eTUmkA1V7uNOLNovyYOQbzS3XG6neHsNR5stb/w++TYk6LVMxmsWRUJ3H+LvVQyCYiemrDXrZz3sOxXuPKAZSGQt+PDsmutZxG2Q8rQ/I3MhvzR+w++dH3Lz/Yj9X1ozX/bxFAhjmN8P0EmDjAx27sNZzXGT0yWaVefjqYpRUnDxnw5oDwTxWUU/rbhWCe2LhGlvw6JJcKmBW81gVHG4v+bNcogrBfbewx+0nD684+r5AYFoybcB9YGAmebRT59zfj+l3+wcBos6YjP6Q91ALhCrHe3MWInMFP60pCs9vNriTluKLMAlFv+lpksF4DB9B4Wk50pyFyIagR82IAXa67CBw89grQL8Nfh+xyYI6MUlutfs6H0ve0R6l2CVcjSZjxi3DI83qJeKel+yzSPioCLAsI40Zh3gSoVUFhs4uloTLAXNxBLMwIaOXr9i78Gc+WyAOqg4mFuevZwgjjsS11EtA+p9S9R16BvBYhTh9RsiWuxNQCcFnu12WNy7HlZDfJCjvutTv2cxlaJtPPRGYj2HLhzSOoySOM/hUkMrNToX+NOKsvDRW1D7LW4lkSsBUmI9QTPcQbPqocTLoH5saSNwhSK9cRgN9WAHKKtjDcLieh3yqKO/7qhvdxVaoQzTRwuCC8WlGGJn3o5sF0r6247NPEX6DR/+1As++uQt6rHDu5aYRlNNIFAd8lYj4g5Z7FgcXu0o90HOFcI5wspSHEMwrjFO4JqKH/zd//P/v2f/PyWU6nD7HenacXUzRRgHNfQrS9bT3GYJQVdhDThtqTOPKCg5mpbclRF3VYTG4NcON2kgtcjOYSvJ/TLm6FGFq6HpQWlCor2SwLfseRtuL/dQBx2d0mxtwFF/TbEIMUNDex+iCrADx0UX0JvmcAnqRiGcpb4PCMKGUHUktuEyCPG1ZS9d86P2iF5SkgkfbynpVgHrfkg7lkS0dEZipdgBMHKxw5M+8xh+bUXbalo/IjchVbMbLYkfW2ZVyDvTO1Yu5HbTR/uOchPin1ZklY+cSS77CsKY4NpRjnxGSYaaaxY2xglNmyj8H8YUHxhErRjNYNMNGCQl+X1E6Wu8WkBiaDYeJpOMBhkXiyntAJRRqMOa7k2ALSSBMpSzBL9VrIcxp2d3zGZDbu9iysiHniUQDZXTLPNkJwT81CcYOirh0VmFGUvioKa9CBHOIXuGvs5Zln3UemekshdmeC8UM9WjdC17p2u6JMEFLUI6PM8yW8Y0zyOGwjDfD3g6mbMtQ+6XA9RM4I0MXavIXid4osNOHJWSTI/XdE4SWEesa16tp6h+jHwF3Rh6N47b2MdUkpefHRMdVPRuLI2xVE6zkBH9JEPew+Gg4LYT6LhhaX3qMqB3lMFGUqcCf+voLSTdvKJ6GKBDx6bw+Gy+R+jXZCuP08mSL1wfFbacRCte25QgrHnrwYJP5vvEYcXjYMb95RTtW959cssPXpxSC8XWk3QLn6++c8UPL48YmYaNFryzyvhYx8xPYsp7D9d1lG3Mf/G115xfDvnscsK9GdBexoQPchCCVZ1glh5nUUZzUmJfKjaD3fvXdYrPsjF7Byu6uwHb0qOIFf/27pTRwMGZR2l81irEIPgwvWFbh6SqRLea3vGKRa1YrFJWwsO/lhRjH5U49AcN8bSl+zKkPdLoyNEaWK56iKMadxcw7GfQN1S+w49a1lcD1KSlaTTPvzhF30nqbxj0uEXlHu6wYfbJHioWiMCylRFizzEoaqJJRaN8kn7DbDnAJga0ww86mkCSL3y6RqGFI6gsXSgIkprC+PSTguo6pe156NIhWxBbjRh1hK0l14q6lRA6zHsNZSFh4yjHkGSK09GKz3WPtQgJTYNsFbIFtoplHhNHDq/tMDU8HG24zlPkRtOLC9Y2xvM7cAK9VagKrGfxtWOgSpTsmK/6jOKaux/2OD/oqHSwO/0nLcFJRXhhIIiwE4PR4LY++trH9zsqraFQREGH8S3OSrKLHn4A4sbsPnNNR6U8XGqRxS4hj2SBKRWbvkIuoU0d6jrAlzvTHlaKOpFEa4fot3T4yFJSjQS6AAREX4KagCwsbSoQxiFbQecpcA7rC/RK40qNO8goYk2UVrQbj0XWQ5Y+mQ7oPShoNgI9d6wPNKXn8Izmo9sHuMYxVDltP0SLCnffg77cUSCjljLxMYAuHWFjsbXCakE5EaAc+1VFO4U3F8GfaL78U53sM98jein5yjdf8iI4ZGZjImOZqZCoX9DzG0ZBxdVmwJHbYJWimljONz2yRPMNfUcx95BTy+tqjN4avnZ6xaXXw1jH8/UetlQEokMNCuKwxV5J/MRn+DDjPk/oGsmR3cKrgLYJqR4ZXOpwPpTbEBFD9crj/W+9YnuVcNUNMZ6BwrHyIsSFz3QvYx4k3AYJjRCEfsviuo9vO+Zdiq4NLCW1F7JsfUwKx9MZmyplsUkRhy3r2x6mUXiZxBw4qsYn0Q1b4SEayfx6gOq3uI2Hyx3zNqLLHcKAF7ZEC0N/ktG9BZXvqK4CPOlIewWiEQzDhvu9PmLj0euVrBYJ6zomDUoarVC5JD7MUKEhX0Vo0TLvEmQMwlmyy5S6TEnDhtYKGFlcq0hosL5gcTGiiwRmApN0y/K8z+pQoWcSz3WMv77gyh1glMClMNA59dKjWUbggdc55L3Gak2QW3pFyyIOiB7klJUHtSX3BJ3pc8qW1y/GdCcVB09m/Ie7U84+mBOtBQdRxjKMuG4j1Hsl8rmP6Tma1tGTFV5kuTcptpUUzwLasUZVLXexQkiBepgTfOLR1Ap3UjN7PcIpKNcBk37Ohyf3/G51QH6XUDcBRd1yNFzRvgzpfX3L+raHilqCWtIsAiaigHOf5kHOcbTmk2CPQ5UTnBW8eLFH+9Agm5aHxZaFDGkuQ8JezQ/MHrYQNFbvbGlTCxW8+H8eo36hQi4My9dDmnnI9GsLbCHJVgGvrnbMivioYrYd0L5lObzM0TeaySgjKzXPvJTLV4/56cEtI9VxH0tGp2u2twnDUcG2CGhLDw4t7+s5H+f7+E0HK413XFJXPlfeiLC1vDNYc3U5ZPA44/rFAO3VdIEivDN8+NYl3/+Dhwy3LatJAijuqpjYqzGd5PSypPeg5CrrUWxjMu1jtwHDw5rtKqbNFGHSsHey5vZ6iNdY8jpmOl7CQiM9i35QomYSv9fijRpErHHzlmrP5/hohhzsqoLl6wQ97DDAO9M5F5cjvJmHLxvmQURwUpBt+wSTmtoKPNdxPNpwGaXYxqMbW9S4YLOO6E8LukyDdJipQa0tpvAQxhIel/T9HFukbNKI5EFGbj0GQYlLa+pIEJeGy4spetgQOUeuPEQN416GMY6BLtEp2LGlXAd8uthHaQvasdUeRkj8yGKEQBw39FxFnHds4xAvrHGpJO98UtVQegrxJsSrHdJaDlTOpM15HSa0saG3apGpo155NLGH3XiE9U6se5/1wYHxIFpDNQFVaVwo6aUFrVH4GMSpQeQ+tdI0hYe8lrQRCLOzUccIpIF2IAmCFhN6ePce8biDgaGeBZhIUA8sJhSISuK3gHR0Y4vYyD8ULFraQ0OYCRor2L7qEdQOM48xsaV6maJWkkiBuUwwGpQ1eGGHkx1e3FEJTfqgYuuHTI7WbLKEJuyIZuwcBgOByMUOblQ47JnBzUCVjmDtaI5gMfJocs3+3pwv/gTz5U/koPc/t6grn/rA8fpqH5daei93O+KHT285bjK8jSBrQupIEqQ1y36IFJIgt/Rf13Sf+IyjluSg5hvinuEs5/vbfa6yHnkWchZtSQ8LinVI8TIheKWwZx0XvYjlbQ+9dYReRfGqtytlH9T4oqPnVwRBw6nb8JX9C/yDjvvZkLtln+06xhlBf5LhS8Phz98wfLpB4NhuIuSdYnnZ4yTdos9KxHGLsNAeGXQNnZbosOPifkLTA1sr2kYRlpbYa9BJizWSaVFTP0/INxGN06wWCXfzAUJbhLboxnFYFwQraDOfrvJYuwhrFOnGMAgyjt+65+Foho47ZL/DCwwGyWzRJw814bhEaIs3qHGHNZOjNa1VPDq8Y/BwiycMj8b3TJIMIxSDqGRyuiQ5rAiWIDeCw705ftgho91pdxRnJFc7xXRcW6RvSU9zqi92ozX+UckxW6SynO7d0wsL/GGNfdAivd3N7saW/vsr0oMcqwV5GzLtb5nqnM0m4vx7+/R0RVpZPp4fcThYsTlPWEqPRRqz/OGQ4fdgb9FRj+Gbb79iL8lZb3pcFSMsgl5cw9c63j2+w9tv6KcV4bQhpKM4gzyRfPLDU8oT6CJJ9Z5FT2u+W0z4YHxHNzC4iWGdxVxsxtwcRNydj8m1h809xI0HK4/tJkY3jqPhknrQUno+uoHLF2NEtytX9vsV0bAm8WvsaYsrJamq8WeKZJrTeJLOBzqB+6mG+llIX9XcKZ93xlek3xX0W0u4cpyJFcpvcThcK/hssU8nFbcq5P2vXzAYVgQ5yHznnd4b5YgvNZsuxO51hF1H4ireH19yq30aK7FTx3/5wafEJzn7Jzl/9htfEOmG9w9mXJY97gPN43aDf9owC1N+0b/lf733gs/f7GH3DKOvZnRaMjrZoHKLmQWYhU851Dx7s4876HjKnMSvSWmQHYiNYiIKTtgy/D4MvZzUNST7JfY2QFaObBvRtJpu4th2IVkWsSGkdSFvhyu2y4T84wHZVY9337niG/1rOilYtj7vP7hkk0hMBHt+waOze0YnK8b+Fl1BVFiuZmN0DtMgo200Ru1KuHXt7bwe9g2RbhGBoz3oaB861vc9bvIh5TKgPygxzyN0Jqm3Pu2t4n21QglDs9K4iwBzvQMepV7NyemcRkpuXo641BFJ2fLg3TuGUUH42sK15qQoODuYk9y18NLSKypir+G+7lNon7lJmd9HqNaxWqWYMqDTApEY9r+ywmiPL7sxwnq0TlI9NRw9mlEcSKqepB0I7NstZV8g2KGkw3tBN7SYgUU9KbCpI1sk+KWDVlBHEm8JmfGhk7uWJpZg5eilOWK/pUsddAIz83fAICHYDHe+DiIxxEc5/hx0JndHWMEOLlRpHAJhwWiBute7HnuzE1fKDqyCPX9L3HQ7QJPnsH8IGTJKwasAfaN2VsexwTVy9z74vTHupcZYCaNdFaNYRoilpBsI+j+1proNEK2jnkKxr1DThkYooqBm1cZ/ovnyT/XJXjsL1z5l2lCWAc27oJVhWcT84oPn/Derr5N3itAYbuQQt9Ks6oS280l6Fa+3E3527xXbxHITRXTvCKRv0apFFo7LgU+38XAR2Nhwk3poKyjKgMlRziO94flyjBzXLJuYrIt4sn/Dmcu5vR1zdxXxRk9x9z7y0OCGgkd7V8y+d0Aa1xzbNa+/f0QdSdx1gHnc4l17tB82LMsAlbV4ncVpUG8irHb4pkVvIUwbyjcRxgNfW6Q2mL6jmgfoS9gcRHgnFdQaayWmUIhKYEIHuULXjtbskJay1Mhc0Ot1nBdDVC3w/A5vvIPUqJ5FO8Hhk3vOvzwkkB0WBa3kaLrkLgjh94fc2yFN5vNsfYiILP1JzmodIScdPKqggLvzEardmZqkJufj1Slx11E3HsKH7TplaQXOCI4GK84HPcpG0zuueTuc89n5KUFiETcB63RAFwjagSVuOjYmwmUS7Tou7Qh71PK4lxOJhpHtuJ+nhH4Le4Kob5h1KfVnPm7a4gJJWDr20oLp2zUX9yl1ExCNamwseBLP+bivsGXEnpeRbBwIy72IWV/EqHvDV3/+hu9vDqnXAeFtR/huhpCSZD9DVrBtIopYk0Y1w7zBFD7Tt1eMbcHzL45wCI7qAi9qeSP2aQcO6Tm6k4KLckRwA0Y7nnk9RCsI1jAIakoUs3pCd+cTTSqkJ8gChT+ucZmiXza4TGMPLLaRcGqwY8v+eYmdR3zjW7f8/t0hba35aHRApDtG4y3zVQr7BbJt8LYxzz8bUYQevaikqEM2dcgwqfBODbZv4UaxGWraxvFyMeHsaMXtos/D/pamb9mflLxcDlluIupFwIUVPNy/o1lLFtMQs/R5ul1wXqW8mo2oncfJ8YaXd32+/u41RSFIe461UIR9y0m64vf0Cds64IvlAa4V5COJF3Soo45eVHCxGVMMA4KopDnyUFufvbcWLGYRrZFMVzVZ4hG1HS4yqF5L3BpmP0x49GduWC56vH94z2fNPoubAWFhOZ9NmT+I8Q8bujWsCLmfB3T3HvWVRzNQ9HrZTqx47tPuG44Olizue1RDgd/rqAuJt18xUhlLmaLETth7r/vsHWYs2z6bTYQRjjSuaQsPtwn46NUhrlKEfkt/W1EdQms9+nXH/c2Id5MlnzEluga1D8NWMGLBp719hLRkRUA4KpgPQvzQsr3tYfo5tRB0jccwzmlXAVYbqlKiMNhWE48Ljns5hWy5X4+xOLTuyJ4NKAYxSWkhqGiFJopLMtkjWFn0WQOvAtxGIZ2j2YaIwxbXd3QrDY1EXguM2gns6jGwdGChObYsigRRObRnUYudwNF6Auc7kleCLgHjKdZ5jBg5kqjCOUGrAmQukcrueBqpo+5JwplD+g5PdTinYGSRQlIEmm2kYatQziEQSN+hq91mQDhQN5ow86jUDs9tpo5HB/dc3U/Y1iFBt2NwuMDhLyCLA9KopixDkKBqQZd7iMziHja8Nb3jR3+S+fJP8Gf9/zwm4y2l0DR9hV8YzNJHDDqqy5jfevEVGBp6G1gGPuMuRx5URM6S30vUtsUFmt/59AFDWVF4mjjKOXIZz1f7DI5nbFyCuYpJpjnWB1MpQs8yfHDP7cdTPibm8devWbwc0fiKt5o1J6bh+88fYj2Hq3zcHVglWdY9TKkRmaC1guu7EcuFw00MSVrTDS3DoGRzLKlrD3/TIQNBqTwGkwJpBXa/RXuG6k1M4NUoYamHAiMsZ705z788xEYCawXjcY4TjmUREFYWM27plj7+5Y6P7fYscxNyxIb7pkdhA1brAF+3yFuNFZp7O97tvGtNedDxqHdL6LWo2sKjir1txX0ZU5c+XtLgYkstYK8umG16FCIirDvKT3pw3JKlmnBjUaWlXoXkNmbsctZdwmS6Zn2b0jvNMFceXV+QGIvoBHIjyUzCXT1GjVtqo7na9xCbkCBqOXtwz/XlEE9VDNpdVWJe9kiSLUUlyaWmLhXtBMQC3KRjJnwK7RFEHe8f3DIXAyY65+bzAeuxZug3iEHJXtfx8nxEdRPSWB973FJd+5zsz7jopdzNBgQPGppU88INIFMEXsN7/4tzrvLpri8b+7wn5tyIlCdHc367OiLZyzmelqznCRefpqx1gggtZlCy/WiAfNDx9OgeUQqWbYC5DulMx/4wp/IVXeaRPK1Z3CWY5xH94wwvbvG3jmIZkGaWpr9TMK+6AH1SUXUe9tbHhIKRX9L2Al4tR6i7lrYVfPXhLXMZcJX3md30OCm2nH+aUIQBj85mPF9O6Hs1xx/MOa1r/u+fvUu4dAxOcg56OWkp+Hw2pk19Hu/PmDURN1XKKovx+y2vbUq31FQzDz1u0ccdEZrmgSAatwxnDftfX3O3TjnSBWm/4ab1eHu44PPLKT6Gh/01X4p9fnFwyfO7MQ8mS+aez302ILqRqEFLW3gEtuHG79McGOJtCZVgmmzYZinNLMQ0O5fA1Z7GbRRVBIOlZRsHbCS0b0Geg64kv/fyLbS2NFcRx6e3lMM+ohREQYcLBEd6SzvzEAcZ6zTGqySbLiYd1IRRTR14LOcj3FYjtxCENSrdIWCb3MeTlvY8ID4piFXD/d2AD772mo+eP2RQt3i6w/MFk+OS57KHHDQUQlBpn184fcnLYsgo6CiGjvvLkGhjwFd8fHdA7XyCpKIeeAzqhvRsy+JiSDD3CN7L6L4IWV71Sa8MzWmH+yigORN4mSCoLHmk8LeG/rrhB8UJQjpEJ6CGYhuQlB2ZClBzjVMOb1qx3KakrsXUPnURIQJHWHTEsmHthRwGGaaVeKct9Tpk3aaItCPKHbWSyEbSIlFli5s0qMCgCtgMI5Q0JKaj2QS0A5ChwZuBiwXUgtL6hGmDmXZQSeRaEuYdTSAYZwWzMMZq8BwI5fDHFe1lRGM8HI4gaJFG0HiKYFpRlgFu2jHSBetXfVwgiGaOGh/nw0U0wXUSda9oB5awscj5btqmvo3QtUNagc6gGxgiYfGilvVVH7f/JwvC+VOd7JeXA5K+Q2WS2hc8GM64no+p+6BiQ+skOrH4TYvuLJt1vOt5hY7pWznWdJxf9/HCGq+Fbe7TKY2tFDebAU8e3PBlErPRPr2kZBQUaG1JrmCYlHSZwvteRLsnCUvB4tM+11cpOrX0D3LqsMGljkFZM5/1MEct+SzAPmmZ6Jxl0yfooFh5fDC9oRGOWT3G3EtmfcnTqiCLfdYmwtvvmBY1jVE0ey3bWULfLzCex9tH17z67JCu1HgPKrqtR2MVKxmS7FWIWhAqg+jXlLcRYdchvJ0t5qKNwO3mQYO5o19bmsIiD1vEK0HehQjf4s8k1/f7JF5DPRA0jWZThjRaE6ct7z6947PbQ4K8pjY+2nXYe4/ByRaHIIsFXs3OvrcNsddgp4Z6IPHWHbc24cnTe87P9zk7nJNtI15dTUjezymVD3NFG0Dq11xcDjEBDEY5ES31lc/Z03vuvtij7hR3ssfZ0T2pZ7lzIf5C0AwhCioeDFb84MUpcg3Hj1aUA8ndVY/bpseobUimNaGo2GwHvDO+5/lsQtb3eNxbsJgE1JUifq/kxfMpmpbp8YpDueGNnhLqlmRQcfBgzuWzfby9mmhQUWrFvQ0R44rrKiEvQwgMptP0Xc1N0gNpUJngfjXEhJpps8a8Cdh/uODFm306IflwfMNCxmznEW3rUdzGvPvohgO15vvBESY0HNcb0Jr5Omby8JbXPzjCJoJopUA66q8UmEXA6jKFuOZ/+cEL4qLl5vqEn37/hv/HHzzl7HDFpYxRgwrWUE4d1U2Kuw2Ig4bz+31+IEJOpnN6aQnK8fx6wmG6JH8VsJWaZ92YMKz48IPXfHq/T2s1j82W2dKQHXnsH66Yn/f57eSItGv5fnHCsFfz8XyfOC6ZRjWt57hrEiJhILXohWOyX/JwseK3f/iIdC9n8fmY+gEkfoPnSWzniEYV8bxl9WrI6GtrjFDoyCJyiTGS5lWEUYK2J5A3IM4quA/w0g6W0W6US0FVxMjYkN2kqBL8NdxUe3SJpEgNQdXRv+uompTzJMaWlmRUoj5W9N/dUv5ogDYWs5AMDiransalFoPggVry8vdO2fvaDTYWvDwI0Pj8zOCc/37+kLNZgwivuf1iShNJ/szhOSaUXM5j3EYgpeVp74bfefkIu2dYRyXlMsAvBF64K6EPRwW2rljrgD2VMc965D86RlUOLS35F338TqArR3Eg8f0WOwZv4WOFxvUbotZgTztuupTRZEtR+3SVppwKqB3qpCRpNPlAoqcV4DioKjLtUSMI15bykaXad8TKErUNadhBDbJfc7sZoo5r/KBDW0t5nyC/UhIpkCtB8zLAeh7hexvq8xDRSppQ46UtVoMfNDQqwCSSSHQQOopNhNYdftKQE4LQCGVYipDRt+4pf7jbrLHQbLoeGkdUtIjgDytUmcb60OY+OjJwq1iJPiqymE5hfIH1IVg7/j/s/cmvZduWpwl9s1j12vXep7by1vcVXuAe4RFBplCSgbKHkGghWvwLCEEHJR0QUjaQUvwL9GiSEiBBREbKA48ID3/lLc3smtmpi12vuphz0tgObW896Um+msdO6xw7a+wxxm98X24OQWpVgZUS3/Q0/uFSIHgUtMcSJVuixNLlHmhJfxNxOi6I9/8I1fkHPyLucTcxwdcldhXywc2J/BbfdfSlQq58oqimTgTbOGS+L6kaj2FSo5Ti8nHEMCkoULROMUkzdndDxnFJe+3xmM3w16ArQ9/57Fuf3PMI/RZZSRLV8H6UUAaKF5OMvVKYxxBPN4RBTX4foX/vsX4VoE563C5Av8jZ3g6RCXCnKT0fh+U3nB387JnDWxiireO9N2VYNrS9gucd212C2Sia1oOJwZ+1HHcNd9cz6s7HnHcoKxkfZ4jQ4ltFqmpe+Dk/FFPcVjGb7jg53fD+4wmp6VjKhGBekm9i/NzRnHYUjcQ/bvF+G9INDvt9nOIoK1h36eHfZw16UCHvQ/rQsXc+axOTJjVe22GVYLyvqYaCrfF5HT4glOS2miBHDUHjSKcNnVE4IbBrzePHOYujjKrxQcL5LOP9ckBqG/resBhl3NcDzInB1x3poMT0kqNkx7vHI6rORzUCc6t522rsTYi96EgnBV5tyaqIXRTipy16o7jeT0n6Bm9S8nN7y/tuwvS6ZZ/5tLHiNow5mWWQevSPwI8exz9bcWdHNGNBGjXc3YyQA0FS9dx2I8Zty4ffnsLSg5uExc9WNO/mPKYpP8+uUa9bHm8n1J0ibjuCK0ckOoaTkv0gPJgDt4p96FH3gof75wyCkvIu4dfNSyau4uzlmv02RoaO2/dDnn+95mjdsI8UH4oxf3V+g1WSd7tjmMMgKfG+cfCVo8gS1IPHF5/e4rWWv9mdINYe54Oc/8tf/wlfjx74bjWjawK65w0XZzuMlXxox/gvKratx2fzNevfDam3A6ooYI/HqdhzeTlndJ6hi5jPn9/x3d+ccTPz+eRoxzfXJ4gGooee0bzBdAE/m6xpS5/ENnyMY36qBvRScEzLbhtiXYc3bRCtoyhjxMuC3z3ND2a4U8No3nDvefwyfuLNmwXVSOCFPU3uwUDgJTWTuidTgsYTmMzDtB591BEtSmwd0NUBbhuQ2p7HZkDgLNGsJvc9grbnxWDHj0ZSNz4Sh7fo8D4mhGFN+xSQzR3l9wfefxPDfns4R/xZ/8j7acfgPKdsAtYupqw0J9Gepgn4WM5xU0u785C545PFGlUI3t2eEUjDvy4+wXgSpoKjJuf/fvWakWhwnoWJw1wmvPEu6GIOgKrLlBeDJe8GMwInMJXCFT5sBUYr7LjjebehPpbc1yP0gyBpOqqpop0LvEJALXBDGOwL9n7As/mOOxNi7xVmcaD9EVi4VvhnPcIK7MSS3nZ88mzFo0lpbwNcYjBaY0JH84uOYVrxqi/54XrO3G8g6lmGAcE+4ovzG1b5gNl8x/ZuSN3ExK8L9tsEb+QOPP9XW262I9ysZ7DvETjc8IAsbvIQJw/ehD7zkJnDnvbYvSAQPeKjoR0q2Avsp4bihxFWOmrpE7yuUbWHFxqqUtMvesw+IPAModdBYhGDHvs+RCsHBpJbS3Xh8CsBvkNdenzx+TW/352i14raeLjYMmlqGi9ECUvYGfpMolYO+7zDSp8281jvZn/QevlHHdCTwOgXO/J9hJx0pLZF7iW21wTSoo4PjPCRafny6J6+FxTWZ1eGPOATvM4pEg+eNKnrGPs1ZmHwvJ7aavZ1QnxaMjgrmU32nHzxxOnplp+re2Rryf2Aog3QmeDmdohVMPAq9EoQXHWE9DQzS1tJik2EKgXZD0PO4y3iQSOFwz8tYWboK0U5kLhAIfcCYSWyVRRtiKoErpAQGTws/exwQrJ2McSOVmq6BKgUvRAUxme7SWCjyS9TNp5HhyKmZbNLuHx/SnIPpfH5+cU1qoX52QY/bGi1JFxUJHSUQ0k6KTE/qxl8uWPbx7jNQT0bbC1B5zh9sUU8KR52I14GK2qhcUOLVzuaLx3qxmPYttx+POLt7ojhvuer9B43sHwqt/wpj0xNyTiuoBPIDIrHmNpoRsuK0FqqWDF4lRE2Frf2USuJ3Xjcv53jdooP355SfUzwraFuPVAO9ajxSoeuYLcc4G48/iS+ZhSXZATIrytE7JC+ZfdhxHftgoiedRrRf3XY8d3uhvw0iKkCycdozOOJzzofsr8b0OwCsjKmrwOej1dESQ2PPv7oMHY8e7lh/EnG3vOQgSEpDSx9vru6oPkxov2Q8qfjW+7zAfs+YlkN6IUkPC0Zvdox9GqqXYhZ+8TWMJI1R4sN7RDyMqJ9DBh0FVXn8eaHI8p1QOJ6NuXBKHZ5FNB3mq6RzCd71EKw3qXEHxXjeY7Z+Pzt9TmUim2seex9tjLgaRYyTSv+yfNrVCH5uBlRVB5B1CC3iv8ieU/jOYbP96ikh/OGaNIQjjvClx2rNOJivue3d2esz3z+9MUNWxkQj2u++Mtbun9uGH6Wc7Ud8XfZETdFzA/BiEHQ8ufjO8ZxSak0zSc9s89LzJ1if5fyevLAlJrP0xXnukSWgifjkwYVbx4W9KEk2BmGqmFCQ5lFRHRcPY0JMIy3Hfs2xAJNE1CsYwJniF2H2Gjq3GNqS9y0R+dwKjL2VcTdU4paCsRe0C8s/Z2PjRyD0uI7yB5SwhcVH25mPHw/oMs0axHxr+9eU6Waq6sTfn6+ZK5LYt2x//WUmekoPI9uZnhaJexnHg/fT1lmQ6w1FH7AYrJj8nKDlPCxWSCWHr0W5Mqn7xXOOpwHTkL5GOM38NQNkBuFWXnYVh3WBh7EcYPswKYeuyo+rNuOLM1LA61EbwXBA7DU8FFi5g6nBNcmQhmB+eTwgb/SinRtMC87/HnF6Dg7gHrGPd2wZ/8QMfx0izkCf+WIs56TvCC/Tbl7P4QcbNIh9pL2MmX545zr6xmbNyO+f/OMQnvEFwUjv+KT53foUYOdNeRDgThvmJ/tGJ1n5IlGTVtEYBGJRZ00BPS4iw457Yk/CPxHx66MKV+D88BEAv3eo3QeDT7prMS0kvDRImKDzAV6J1DGYUKJN24gNVTGozlyTD9bk3yxp/66x6aOJhV0R4ffwds3p1BLxpMMYRx2YNm8VORfWUyn6F8ZtHXYSMJTgJs6dkFIN/jDlt8/6s5+5uVAiNl65EYhpx2p7GhrD2qojxWDRcMiLPE6gfMU53LPiz9d89vNEc0yINlYTCRRA0OqW8RGs/cVLAxyI6k9iQgE9cOQ42bPejNk1YxYjPc8FQO6E0u9ConynmhaMnpdcf9mzsfZgpNdgd45CB19rKhHEl07WAnaEJLTivUmJY5aQmdwe4GpQRgJvoXnHfbOg9ZhH31M1FEpHzs0hB8UfmCRIQzantPRlvcP5wSDhrZR+Ou/51De+2zUAL3RLGWM7iz1VpAnoO4tv6pfMjzfk90M6JUg7lqixrC5Gx+IUGGPfxXSXkjkLwvc30TYyKOZSlbbIaLtEccd2zbEbRScWoofUpSxKOPQ3zhOFgVvFnOe7wrEoMXkAcU64d2pIbgUFEcSb9RS7zyCkxpv4+ENDbdnmr8qL/n/PF0wGjYkvuAxbKgnDt9a2iefeix5/ukTw6rj3z28xKPDn7UEQc/2ekhSOvxZhdHw5vqUbK4xQlBlIZFqoZE0iaTLQogF1UihKp/hvCIvY+p9zKDr6HqNt5I86inCwOCyp/g6BM/y7ftnjJeWER1l75FEJcPJlruPz6j3Glkojo/3LC8U/gfBfTLADQ3/j7vPST5rsaLDPgUEpifrE8LOkriSs/M999mAm3aEnzj8rQAPvn5xya8nF/x4eULyssLXPbnVTFTLVGW8XU65sBVimnMdJjQ7j2hasqhgcNIzf57xbjniuNkQbAKM80jGLZ1XsKxj7FZSSUUnJZHtqYXiK2/LZhry/zYXjLKaT82O4pkkE5K9L2kfY37+83tud2OuywS7U0QnNf/+/oI//eyWf/f+JR+CMYnoWX4zxIsNbiPxjxsuJgW/fjxGhgbnGYa2YSkSPl7O0Pc+IjXcXB1hI4foA967If5OcRG1LJ8CFl/sUM7y4+aI7kExH+6ZRAV60oEzPK6GBH3PSbInD32KNkQ0jn4ZMPqLFdVv5viyoi48gqMW3zds+phoY7ELQeF7+FVPO7QM/Ib+OiXTmtJoDJJ2PcCXFv3c8E/TB/7DD+cQHYBe56ri//Wrz+imjgt23NYR+50mGbc0H0MY91S7APVlyy/DJ351f8zQlWSej3vwUcZw3He0qWTQOZZbjyEt8vM9q8shYtQT5Jau9jj5+RbuBAwFxTJCRoavTx7J72My6bGTCi410S9qTKcJVUf/iUXchxitUY1DOUEuPPxPCrwHSR55mNbhT1uOohwxkBSeYLscUl1LYt2yDSX2PiLVhqdqQK0lP//zW95+84yjqGQpY7ZtwHBecbee8qen7zmuHcXrAL1zRCcdYqPY2oBiGZC/HWKGlvRlxuCoYvU4xJ/XRF3PTT45jNonPfqpx6aW9jpEexarHFx7NMMDRbKLHNGtAutQnaRLILoB6Rua6aFhMMph32vc2AAgFi3qTYAKoJAang6QtKc6xe8dKu7xrKMeabq1R7KoMZEjWGos4J61dGjmr3Km+54bNSYTAW4qia4OVtBKKLrQceLKfwzo/UOfdZsSX4VEo5ovw3v6KYgiYPxyz++zI2SZ4J3XuL7n2+wIUXlkkcfqNkQb8DxLOVJEW8dmH5LfHGM8KLXmE7chnBoen1LiRUPhEnb7CG8PKu5oQ0FoOpJrKFzP/GhLcxtyb6Y8Xzxxdz1lORwQT3pOVxn5TKMuE9ypYRWHpK5lu4+xkSO6O/jgs3pA8Bc7wqgl9Dru3y0oTyyqsPQjh7xSBIuacNJy7HW0qeHj1YJpn5OrMZ+dPfBjNyWaVYShIxMeQdDjhYZu0WNSmCxb9MCyj3yqIkRmgvz3Y7wC1EWL+ylgmcZ445a+9Nldj/BVT7UJkUuFe2HxagOloL/xcRNNbGuU7/CPGj4TGftXAQ/bEX0XIl5YdOc4PVsRd4a71RDfHIiD5buUJupptGW41WSBj20nOC2ZFg3uKeRGOmane+7fLjj2cl6/eOLjcsrGBihPoO81OwbUa4gGDbPzjMf7MfFPmiENYtSyX8Ykfc+6PnABxKankiFmYYi8FiUNx+JAbRucV3R5gFgqjucZaxXSlQI2Ejnq8e4VXtoRvmwQpYc7qjBjizkxvJA5v7s7IbMJ9+2YIDE891cs5ZD31YzBtw3h/LDmoBIop3ilS+6c5NpELD5bYz/OaBuP7dRjcCnw5x3esCF/SvBqSd96fPerl1RTj+l0T70JuFFDwt7wzhyRVhU/e/XAr79/jo4aRic5F1nO3969RMY9QWT5m3/zCXrU8cmLO1bXEWefLBkmPXsZcuznvM2m2EZSxwpNT4/id9uQ/9m//A9c3yz4m8dz7GjPQHV89+aEaF4jnzW8vx/jFj2PDwmLSU7WBNTjjv/221eoyPGsaRhR8Ht3QltrxMKw/zhi4xxuK5n/fMXqcsJ9EBF2Fjs83IqzU3QDUMKQThu+zLa86+b4oudnZyv+9voVetbxn0Y3/E1/CntJkQaY3w/oRg6/N7RjqAjp1wEq7ulPD2rWSDeMjgpaI9HXimRtiU9Kon1HFUY0ex+vFfhDQ7hxOCn4s/CW/VRSpPCwnlBbj4nIWG5T3tuE8bM9f3l8ze82p4xkR3gTcN2MuVdDgmmHTg1uowmjli++uOGby2eM/YrfmxHhvMDEgkW0Z309w9aK58mWx7FiVQ34Or3nfTVH7yXPTpYsb4ak05onp5nSoSZbJnt4GLTsG59vHuccUTOSBW02pEsEr/uSn5oxdgRB26EeNGbYkD5rqXqfbiZ5GZR8sFOGlJRCowu4sjOUszTLEOU52qFAbHw6H3ZO4FWO5u+Nh3f5HJMa3tgJz88eqccBcidIE01vApRU+KJlN47x9oIKn1HZUAwh70PiylHlAf02JJspBku480K63MerDPnjAAJBZxXB6xK5kzx/vuQ+mmAJ6HuJevAwoWV4ltO+iTFOYEKJ6CT2yscIieotVglsp/E/QlwbDIbtNsXfWPSgwV4rylmIdwejL/csJjkfVEpVSVolcS3YMRTEh/fWwCIquH0/wdaa+LgmHTbsfxlSVY6y1WAdZfSHrZd/1GP8bmrYeR6NJ/l9uuDDzYTtqWAV+ajI4K8Fu99Pef/bc/zfesRbyzCqcFvNn6hH/vPkkpmf08aCeN4RphasY7A3/NXP3uNODf+Df/GWugv5/PkSD4s56mlOLW3492jHkWWyLVlVA06HGaPjijJVDEzNwKtY9THfBcfcZSOqUPOUDbAjqPHwfcOEGjtwmCePs+dLhkWP2ymq2wjROIINeOsD0GUYHhSNcW142CTkbchI1gypYaW5qYZ8ku8RmcKvLBd6x3/v5RWpsqj7A8Bi1/jsIh9v3KBLh9OOZFFiAohER3BW4fUWu/UwEqK+Iwlr/Pf6ALe41fRKEW8M7qJHxR19plEPmv/p82+ZNR1GSAarnsVTzdF0RyEDth9HXD9OKLKI64c5AEZwUGyuD6czp7M1bavoCk1xHdK2Ck4NzwcZ6rhBDRzlMsaOOvzOMH4oaHpF+SHmvk/obiNu3hzROE35DHolMbHAmzXsQo9k0fCfnr4lPa8Q4w4/bDlzW8JJxV0zInxVcKr32E4ybTLWD0PM5cEQaHuBzRQ2sdidot4FVJ6iGgoGdU+RB/zH5TmR3+IaiVpq4qWj6zWmVISPAj3o2ZUR0a3E83uev1rylA9oBgcDWJ9pWhT+cY0XdDxNPYomoCs143HOn3/2gWhU0d74vLIbJmWPReJGBjHuiI9KZq8qrn6a4b2oiIc1D7+b82O1oGsluhJs0aRxQavBfQjpesXN+yOu3syY+TmXOqZLNHLe4Q8bjucZfza+Jzxv+VV2xq++O+NovmfdpPztj+foysJess1inh8/kWwsw6QkcwHa6wlch7HqgJJV8NP9ApOAf1QysTWfPLunedEQpRWXzQTtLM9HK1LTYAuFuqgRZx0UEnfp893HOcOmA9/w/XTAu/6Y1+Mt/33vjo8yxkws3ZcHCJVIDb7fYYSieoqxToLncIVG9PDyYkPxY0qXB4SThvrzjn0bsXka0eQevZbUzmPQtv9/Q153bHlnxjzamGIXMx3vGZYdd26A9Cz3xZjHOOL37YxSSH6dL5ADyb+8eMuRaMgin3uT0m88FqOc7//uJUFn2d0NyTYxNT77MuIynx38EReOH+SIqw9HfB0s4dIn7Hq+GiwJ76GNBEsVYmNHfxshb3z6FrbGp/EEg0HFTTvgw35GESvsWcfHboDfOXQLxfWA2dmeyYuCcibJlY908P5vj+ikog4kdq8p85h+7YP+exZ9LvBUR3fkDljrkcEWCpcrxFay+zikuouR/yHg8dcLnp7G3IohwVGHPyzp1j6fna4YDCs+HT6STiv6AZSdj96DMQLxwafLPPRaUjcebXGY8oidxD0F2Nxj8FbQX0eoW4/144gX8Zov7SM66HADgwtgb316T+CvQXagMoH/JAm3DhDoGkxkEfTYa4XRDl0J1FJStx5uCMm9ox8Lil3Im7fHVO8GmNQSeh0nXsaxzhmohioLGJgWUUn6kSV+ndH9FLCNAvqtxq59puM96YsM91X1B62Xf9Rs/P/J//V/wf33zwlki3feUdzHFOsEERu82tF7glJ4KAzhDrpYQC+J0opS+BgEs5MdXacIo56prvjh6oRX/pp8rjkhZ7mL+bzP+BgP+NgOSB4NrdV0M5DaMktzzgY5v3l7yuT1niyL8XcwSAq29wMaXyPPWo4mO25+f4IqBMGswrUC0QgmOifrY+KuZXBeMosbflUdI37yOf75I5O4p/yY8raI0eFhHKWPa/Z5wpHOmH6A69MA2Tl00pPQMZ4UXN4uGB4X3Jcpi31L9SGmGgtcagmflURRw+puTNtqgrVAhj0u0/QTiyglZmrwPEu8tIe1SCXRW0M3VIigw1eO2ld04uBT90oYXmSkouUk3vHjhxOkciTHDX8xvOW2GfF3v78gPaqxs47tPkY4wWhQsH0cEqxATVo6o+nHDmUtftiziDIy5VFWEb401JnPF5/e8MNvnnEc7ihjzUnb8H01pQ8EY9NQjeG527L1Q4aLnNXjmOEwJwbu34/Racfe06gnhUwtsxcbwi0UXUBpfepvU7zjhtzzcLXEWzToa007FAS9ocs8hvMC31jmz/Z83I8p6gDZOPwWuPWwnzS8DLd8XM3gQWOed3ja0BWawed7miwgKB1OQi58bCfxcotxGjvtmZ9tsb+JGP1yS+F81m+mzM+2NJ1moGu2eUiVetS1z9n5iodvF/RaEjWGoK4xUpCrhGRecxZk3NuYYWMoH3wIeoplwsCrmI53rBgwlwZnLO/0kOR3lmf/2YqnLuUpTxDW8eVixccPE1Dw6vUDH344wpWakz9/pFzGfC6WFLHHrzenmF7hhy1q0KHf+0wWGUN6HmeK8SOsI491fpAqpX3LX43u+X41Z/VSkMqSvtDcPsxxe4k6bem3PsK3JNOS8Als77OPFIx71ErQKU3adggc0aAhqyImsx3X10f0MdBI7KzHax2dkugGkk2H53qqZ5JP1Y6rNiXPI0ZpSdYGnKZ77I8h1u9YyYRR2pJ9DFH/9EAA/HS0ZFkN6G40rlPkrxxd7jGtGtRxw2adkoQt4rhl/X7Cz+NH5Nhy3Sf0VuAePdpIYIXAE5Zpd0jTV+kBr/v6+R3Hccbbp1OWP0Y0QcRcr3mUE8yi5yv/iVj1/PhwRBF4DJuOwaSm7TRpVHB5v8DbO16FS76P57iF4VRlbJ4OIcR9ERBKczhJC0qC3PEoEvyN5M++/Mg3dwuyXUr6Mqe4jZFJR58I4qJnKRL8TKA6Sz07MDG8rSDperoQTKFRDfQxeJmj/MQiC0l0J3Aa7MASzqsD3CY1tLnPydmaZgxPP07xOgj7jm5/GPOroaWwPh6G6HnOvkpQK0noteR9SGQ77EofEvCjmuqFww87WhRtqwk3YCeW/mOI1zpc4qDnIJlykvAKhOewQuBvwJ326MhQbGOcZ4kWFW2v6RoPOoGuHDZ2GC04v3jiyQ2QHxR2CO6s46Qr0GHH9jYiUJblcozse8pXiiEtqW1Z3YyZ/vlH/tX//P/8B2Pj/1F39oPWMr7Yk809nm4mbNdDWqlQHzVd7tHWHtEj6I3CtRLbSfqJIff8Q2fXWTb7FK92VE8+1+WI6fGem82YVJZ893iE6x13IjyAPKqWYNJxdL7ltM9Qa0ncOi7rIWjBOktp9gHhrGLVDkBJ8ECtFft1QnKWM/90SRB0hFGHLiW+M6iNo48kTSB458aoQtLNLLf3MxSWJ+ujQ0EvNH2raHMPvREUu4jLeki9CzBWsd/HmNiRXQ4wkWO9j/giXiMDiwp6umeHBL9/Lal/GEArCDpL+LJAjAzgEI4DxKd3eLKnP7F0x5YuhT7V2NRhjUdpA/pYIDuH1zvspw01mkZJfrM9x3zWse8j7F7yr+rnONkSPCs5er1CWkcUtYSywysc/llJv7CH+1gpoHfoG017F/Hx7gixU/iuR2WOWZqh7jRtBPfegKyJ+D6f8mK0YywbdpFHZCzr/YA6D9hXMaNFxrm/I3qsSMclg6QkkgZVS+reY7dLadcBTSgRac/oIiOvI3TniBcVSlrqkUAnHe3IHXabl5qu8Lj8MMG7lEQfHabyaEoPb2NwqWUTBvCsQY067NKjWsV4fUfxkFA4j91EkqORgUH7PSJyyMgQ3FlsJbGJ4/5+xlHeM3qxY/s4YFcnbHQEoaApAhbzHW3po3eCtGvphWD6RUb0quX1n15jV4Knq5SB7dmWEb7fgZEMvIKfvVgx+2WOCCz7oWCVxZz1FfUvBJf3Q06DnGjVc1w0bPYRXeXRGsn9eoxODZOjPYm1PK1CbqMR7+7nmEbBsEdaaFqfv/qTS+pAcseA1+mef/HVB/rbkGRQoYOeNSH/xj9mm2q6x5Cr6wX3/ZChV+OODanXoIcdxBZpBGbi2Pg+FBKuPWTsIDVMi5aVTdj1Eduh5rYdI8Y94EiCmuN0jxd1pMGBbin/ScGLiy2qhW+DMQvZ4I1b2GkGs5Kr7YTNS8kyGiC+aHC9ofc96usEIRxPt0O6pSY+KsltgHjrES7h2Vf3vEj2yHtN8uSI9wcf+kOR8O3jHNkJbKbJPY8obhlNchbPNrRDQdkHxJWlSxxLL+LRDRDKkf4s45V5oDc+etgTfyP5fnmC8HtGJznhtmO4MsxP9iz3MdlTQnxaUC8Ey1ceycucQVgxkzU/f3nLn7+6wvdapNcRuI5iJHgQCaIRBC9yLqOE4y9WuLGhKj2UsvjCYkvFRoWE95JeCagU3pMkeDq8j11gcSODmVuaL3qQgv5Vz/S6J6l7hDloo0Xv2DYxwoC9CQiCjs3liPxXYy6SLTZxjE73TE72pJ9kdK86cALTKerHGK+zGCkoppJwVtH4ivqFhUFPXQbwfYh5F8APMfpO08eC45M18qRF/zLHvGyIwhY2GlVb3FkPPngZdBMwuUe5DYnCCqskWRVCJ4ifDNr0qEGPtxKoXPDx8YhxmtOfOJpY0Kx9bi9n/PTjGbskIbsZYKfQExJ+UJRlyOPtBDsx7H8c/UHr5R91Z/9P/uv/FbPjjqurI1hpkllFVkWHfWgDOAFDc2ASawkIhs8yiljyT6aXZPsRH2WIcpbyfYK08jCmuvKpXhrkE6TOEk1rKunjyYpurnh+smT3zYinakjnSexFx5EryKTPfpfg6x5ZQjsSnK5rwp3g/GcbrtuQHT5lHuM/OJJ5Q1NIWjS58vGVQdbw6dmKN9mUqZexCmJ6o+DBw3kOWUi07ek8jTdvmAQ1XavwXMd+k2KcpB0bdG2hUTyb7tBCEJeG9/mQ4Vcbbh+myDufYNJSKYVpFPOyot6HdAl0nqSbG9L3gmjaHPSaP8Z0A4k96jB7D38jEM9a9IPETzvqKdi9JmwsFQohDcpJ7MTwbLTntbfku92CazMi2ArKOSSixV4F+KaiCFLE/jCS7qVAB4ZA9vSVB7VgcrKneu/RPZe8aDY8DiOqOkIpSysUqWwpLxOm84zJuCRvNffdEFNoXOg48jPqB8vpq5Jxa7jzQqoyoBMKedQyvOt5TCOKJuKz8QNvNsf0wYFqdqwLtkWMKRUyPBTibhUQJi2h6OiXPvG45sobEu4dvVHYqSGeVLBT9HeHIF+wsnBsSBYVpadJZgVPv1kgXjTIXlAbzS9OHrj57YR2GWBfNJiRZF5WjFzFbTAkmDbUm4BP5hu+/+0ZR5+s2N4MOY3ueT9bsChr4mmJn7bk+4jb35/AwNFYTeI3qEtBvZDEuqe7CWhft/zi+T0/vj86AF4uasSVoxtqzNZjWPeIIehZg87BpRZ/LyhqH3nW8awp+FDHFHHAC5GRDmvug5DyKaQKJHItGR0VrLOUr5IVcd/xkxoc0M1W8PXskduPIxQCN+vQfk8fCoospI0FXtRBphjIhvIyofM6BrOOdigJ94az84w3qymyVkSjiuohwk0MKuipvxtiQoeUMLzYHyhrNbgAnskNV2IEHpwM9tSPIa2WlFXIcV7woFP80lFrzXG0YVdGeKXg2astj/hoc/hZyNZRFhGLqiD+es/lzTFd42EzRXRaUfY+et/TTQRFoBmWPV8nd1wWM9qROzQcuwFi3tFcxzyrM+6SGIXl5MvlgXyJY/d+SHpekrcBR6bhJhuQjlqOqpw36yPaiSNMWqgk0oCRgtYqLOKgoHYtWRExerZHZQJ31NE3mjY7mCPDO4HZeiyeb3gMIlQr6FtNnYDOBaKUIEFbi8gVbaXxM+iGDhs5nJX41iCcQa8U5VwSbCA6qyifQrpEIBqwFz36XuJpS19q9E7QfNYRXCrqOQS5ow4VnLX4lSNZ5OSBj/5dSHNqUStFjUZah7cXuGnPdJixWY4QpUAOO+zaR7aO7syidxKLoDvv8RuHSnpOgyVF5LEqh0zGBfvrIcFRTfUuxdVg0Sivx8PQ1D7u/zfWr6A8g+M6Z79LsAF0Y4fqIHheIirJVgQctRn2IaCMfdrwMM3wVwIrwGiJEIZmIpg8bfgP/9X/4R87+3/Ik70Z8fH7U+zew/kQyQ61kej8QIDygp7AGNovW/x5g/6kZOhX8E7xH68u+DEfs92mZD+NUXNL+KogVSXquEHmAu8XDesjH/lZTT6W7PyE0VFOkYWUQw/nC2wvEbVgn8fkdYhUlt4qAmOIs4bSl+yE4v2HMU+7hKb1+HT2RJg0vDp/pHmIiaYd/oNCflAsftxjM8vZyZbYCNS3AfJOk9jDJMDE0B05ZOFIuhYTWp5MxE6HhIsalOVisOfPh084o7n/ZsDHN2O+380oB4rldkQc9DTnltpoFrM9QW1pdj7dicUZhW4dg62D/iDEWNkY96rDSYGpNKoUdLGg7xXeeU03tBzlJTLp6c56GushW4U76egrzXWV8O8+vGDlh3y2WKGxXKRbdAEqsnQ2JnYl9Yk9jAPvwascunaETyBXmsd2QD31mV/3XLsxJpSYgaU3As/0FL5Gn9fcypQbm+B7EN05TGpRaUf35DP8omPtYt66MTdmRDGUxM8KdCHYnyu+Prs9yC1Cn9Ngz+lwx18O7xnrFtMoXOxodx6T51v804o4qanLgKIPMXND+CDw7hSqAf9RMgxL7KOHVgbrCfxPGp4/f4LIkkxKhuOS6EVOUB+wqlHUoOqOtpeYQJJGHcG7wyminVjStqOoA8K05Xfvz+g6Td4E1HO4ZYFrFbuBx+5hCFvN6qcxbm4OH1KCjkD0NM8hfGz54vSW6SIn+MHj7f/znEHmSM8LpLFEieCfj+/wZg116eNCy3y84y9fXDGWJe5lzfE040hm/NCP6d8niHc+t2WKyx3P84KZqfEb0L7lpZ8Rlo585fPd7TFnk5znr5fEdIyqkovX9xi/p9WCTZZS5QFHx1vOvT3/NL1CZZJqIpi83hFNHNPjPc1DyLpLeb8ZcxzsKTuNFY5g2JBXIboH64HfOlTn6K8izNpH3PnosCf2G7yVAu14fDdj0x/OPeeioF36TJKCaNQgc8l2P6CIAoazHbNRxnSSceRl3N9OuRcD1HlFOVL8+NMFiWlorMZ7XtGMLZ3v6IcSG4J+FyIlrNOAfRuxfRjSrSOicc1QtPhHNcszj7jtEM5x8/0Ru6eEp2xIf+xY34wIWsu6jLn49JGi0Vz1I3qrcFqQ2g5fG6bDnOGkQAmLdy9ZUDGQDWne42eOtDCcZhULUeKEQGwUdmapp4IuFFT3CeUmpKx8xMrDdxZpwC09usyn7TXCCIrn7mDO8y02stAJ+gkYIbBK0A2hvIvotUJXFpOCuPdwnaStNN0AuhSSG+g9Ce3f++pzgbrWdLVm93Z6sGSOauJlgz+sCUc10oGdGUSuWN1NCNueZgKulzh52M27TOMMiFagbj3ErUa/9bj69hmPlwvEh4NZcfpqzXCcMz3fkfxyjzhukccto+cZ4+OMwFnS8xw7N/gbwbpJsZ6gjw5NpTnr6KyiLTz8laCcaPKBpkMgigOsKIoqRCeIgxI1bwk2AhH/YfvsP+o0Psctw8gwnlVc3o/Z5xH+aU3gWjYu4SjZo5WjyAN0ZPhkes+qT+nPJH6tqEOFlwncs5by0WMc5pRVjFn0iCsPq8AXhqyK8O4k4mXL9X5C9FZiIxANxL2haBSjYUbSCx4ZYRrFTFRc6wFS9DTHknodU3gwTTO8ScPcWd6vj1BxR7yy9C8ytgTshc99M8LbtEw/3cEdWKBTEqcc02c7slVEWnf8j/7kB36fzcn/44hmEkLmkK1i2Y/ZBodRfvTPSqo3KUHpKH2B8yWuEniZRA87qlWEEI5yqsCCUhajBX5j6T9t0HtJ8KQJWok+LcnKCDnu8CwUQ6hXPr7X85jGqL2g2UQoaQ+3wA8hXtJhHkPsqKD9KeWyC5Fa8KnZ8ZsyxdwovvzlFTtC6tpDzx1V76OWPnkcoKU9SG42CpErkk/XLOUArxZcmJyr+znKWfzTlvomRltJ61Lej1KMr9APBvllg1tYNg9DknlJUfmMBiVWCxBQJYLqKeW374a48xZ55aFTQ+c7vs88xLc+6hxezh75XXtBtk4ZJhXbNsZPelQCm9UA76ShtRHOd1gPZCNpPYXvLN4etl7EdhfiSskk3GILzXyx5/FpQi8lo7Thp/UCBpJyJJB1wFcvHrncTXh3dco4ynm5WLF+SFF7iTHQWIXNNaEoMNuUdFDTmJidCkm+LDi/F3yfTLFS4n5S/OLn13yoF8wyy2vvkv/ms9eYO4+T8w17Am7ejXFDy04EvEg2BMMdu0ef1vP4V4NnFE2IvnZMTp/45umIF+mK1ZcDXt03vK1HdInPN+8WSOmoBpIvxkv6SvBqsCTPQ2afb7m/HXMe7XkV3fP0MOT2PqE7NXTLEPGk6b42rB4G6KHh2+qYPNCI+5Sxt4YeLrMZ46Rk5SLUpWZ/FKE7S9X7eM5xfrRmtUlpQ8Hp0YZNlhCmFZQdlQ4ZiYbbaULpYFRahvMtRsHGC/A2huQ/W9HXmvurGbHXsH9lGeqSJ5cgpi0PuxGmDwhqy9ArKJ8iygB6CY8fh1TPoSsjxk3Ns/MlGxMTbwSbC8ivBOEgZnq2Ze0i9o8hoTDE7pBNsM9bPCWQW49gXFDvAzCaeu1DpSjTHm/ccffTgoUoeLqZ4ClHUPfkLqR2CvVWU54JYr9jMd9y9XGB7MFMLXEjEKOONx9PCelhIPAfFIUfIGNDsQ/wGotMDdZIaMQhdX+jcRJUIYjDllz7RLeCduroOg0CCHv6zkMOHPgO0YG56AHBSBbsmpgm1jhhkI3EGYWd9tjqoCb3SoesOIRghw4hHeSOo23D/XZK/cwwqmpmeUHxaUMXQ/9NCr2kFhqfHhkYDII6UKAseiWojg7ddxcJTK8Putxc0ynY3A6ZjnP2A818VjGKOqrrCIaG7ccRyWnOUGZ0VuOtBXV0kPvEn2Q024Cu9lCDDv97ATsDY4l5F2DE4esybvE7R+ZCxNggnSC4UdRAViR/0HL5Rz3G/5P/+n8D45Cq8hnuexaqYZNoND1l75PWPWUTUPuKMG0QPQTzGv+d4vXPnlh9mHK5GdL2GjluOY725HNJsY/QNxKxsLhLj+YYoq7n1cmW280QMekw3cGhnO9D2l4TqY5BUiJGlqdmwLir2UY+w7Zjsxogcon9ssbbCia6Yl0kPMsr6rGj6KEyEfVA4g1bgp2gdB7JrCR7SrCRJV5DOq85emzwXzV8x4hR11FZjVGCsDeklcP0kirTzNKcW4ZI6RiJhskk5+1yzqduy7KPeIhjRCVJcovNJXVygOUQGeLO4CY9beOB7+hK7xCCGVlMIPBdT3Mb0SaS0aDg/OUT99/OGZ/u+bhe0IaCMTWsPLqnADsyvD7asEoFpzbnx80xk6ea1SDBWMXi0zX1tzGN81AGxOsa/aTZ5zGyPxROnfQHgEd02NcFSUdbKry1pIw0vjXowEAuMYUgOanZjTza3Cc5y2Gv8EtoPYESjrTuUKOWhyhhoXMmXsm796cESYdZeYhXNbHq2T2knPlbrrdzwrBBxobIdejQwMhQ7QPa9yG+6qiPFEUT4JegsETzinYV0rcKv3IYBFUkCZ2hVQdwxzCtmI/3/PTfviBdlJSpIj7O2byfMElyykSycBU3d3N8ZSCxuAeP6tiiMoEVktnRjmyV4C8a0lHJ8mZCOiqYlh33qyEuhzZSLPqClYkY6BoxgMm6R6SGp36AiSyTZUf3paUSgt06Jd0ZCuvxYrjmYT+gee1Idz1p0nOidrx9e4J/WlMsBOmvNPs64tlnax62EYu64j5KEL3h5CJnNK1pPgYUzmN0XHLVDJhkLW/NDEpBHLTIo5ZqFzKzFWeDDW+3R3QfY6oz8HWHQaLCnrILCPYOO7FMjvY4K2Cv2IU+5iFgHJTkwoeNYr7YY2rNabrlx+sT6gnEuscWit4q4kWFfwMFGnVkGBQ9e08jNAymJf7GIVNDuQ/47PUd363OOD5Z8/QwxhrJvoixDlQJ4XFNt/UxtyHeoEPEPSdnax76Iem3gsL5zF/nLANFUnWsdgNOw4zchizyks0goAg1plPI2KB7y7BpufMG2I0GBc+7HQ8mxTmJGnSIOx/zsqHd+fjjDiJL9xSQxhV25SFyeXg/bQzNkeD800eUdNznQ+QHj3osidOaQPVkdUSlDjCexXRHu/PJihjtLGovkBct7TJADHqkNPT7gHbmkLMWUSiGr3aHtVzakgce7snHGkknJbPzLdUuJJMerhO4RjH5Dcy+WHPZzVAbibQOqxzCCMTY4FpB6tVkkU9QWvYDDyEdqa6o2hAZG8KwOYTwkoPtsHyI6EuN9QXCOaI7QX9sMamDlUZVoNrDRMImFqHdQcyz0RhfMPxqQ/NNyjTNeOiGpF7LsK+5LyZUnkJlErSl9yU67vCNoYvB7HxOgh3Do5pGCd7tZgS9JfRbktxRWYUdW6rrBDkw9LEjytb8x//yv/qDjfH/qDt7fyVopeJLlphzARtN8XZE/1mLQNA+xfifFURPluIpYhDXtD8mbJ1P/0bRDhytlLhKInPF3WdDurc+QWtJvT0vdM5vR3NmwxqZObzGYhJLgY9Ye/SlRs1a1OaQ5NwTcb7f4c22rMqUqLXoAqTfE35ZYxJHErfcr0aM05qPVYLUhkYq9JMiNS22VUjfoB41KoQgamkyHxn0fHV6xzcfnyG3HrG1RM8KyqsJdgxe3LGvAkwE5rQn0zDrcgKvZvnDlJ2c0lrNk4roUoGQlvBakD+XnIQFbqjpP8QYK6mUYH685+lyDEsPFztM4vAyQe00IlMozzBPSzZdyDcPZ+ipI/9whNWKad0SlYL6i4qm8ZC+42M7wGTQnUPdKMQvOvyyofsxoWo9sshnfpaRf4hASAyKeFIi7zSl9OgbRRzU1LFEbS3iTYRIwJ/XJGlJV3gchwUf2zFOHvZ+3nsPfy8wuxihHWWtcB4cj5bUc0E0qAm3Ies3U7aTAU2smJ5vKE8Oyd/iISapezYmRQpD3gV4j5D8YomXWzZ3KVQd9UtLjeZlvyOPA5pQ8aKu+P7jDLHTyNhSf95yZEucDJn4JZs2we01eRbR/XqICg2fn99yZcYMq5JimCCHPdpXnE02VK3PVgW0hU8QOLykw2Ues+cbmjLAGkmZhSS2RRqH22iGYYmZl9wEKaKQrOsR0azmaF7yQz2jG/Yg4L+4eM9/83efshUeg3VGv014/nJL5QXI3LF/SBh9UVFvHYWKqQqfrjtcaIhFR5D7jIYVR88zJv6On9RrKuPx1fGK2zYh8BybNyPumpio68nzkP7IcZ9o5KNg6JWIHMLQEA5z1JXknVngIod8WTM+qQ+K0icfuZXQW9SsxaaWMLPctSOioGVaN2zykHoToZxAj1q+dhseVQADeP7FA2+eTrArjfItetigrxXnnz5wVw8Jk56TrmZlNWWqybOY2nlM9Z5qJNk9jYi8nsvVnMGwZCBbUtOSBT5NHVAXPq5V4DvK3kd0kieT8if9mu/jGTayaNnQPk0JEkcSNRTK4/PljrfZAhu2xK4l0oZNEeM5x4vBhmdhzt/ePqNH8tEfM/Eq8v4QyjVjwcha+lYcAmY+YAXVdYwbW47mO5Ztij02CKm4L8Z0Wx/ZgZ70FGeOkoDZRtIoBU7gYsv2w5DRsITQYQOLiSVj3VGmPjpXhy5+4NDHDRgYvdhhBUTjmvWPYxavd6hPMtp1yMBruX8Y0I9AXUsC11L3CjXuGZuG3ScbgluPlQypIom/FAyaHu/TiuGupXmc0jxz6MLgGkFdRYyjkvbOZ/zzgqwcEMqODTFdp/EGHXHhqIqALhEYwPbiwFZRBqaWrtQQO/SDpGtDTAhd7Gi/Txm+3POwHsFDzy5IWE5SAucQ+tB4xCtHPbcoZ2iMxluB2giexgM2fsSJ3uFjaVPQO5/j+Zq70MO+9zAYulwRVT06Fn/QevlHvbPflxG11by/P2b9ccCAjtP5BrvzYOUhPUvf/71BSRncTrMd+HizA5O9qkI674ATJYR+6RNtHKOzjMwkfFPOUQ8e5Q8p92rIbzYL+kvF63qNmLQkZzkD03I82VIqj7wIuTFDlndj+r0moMNNe/xpy6nKGOuKi7LgZb8l6hzOB7FVxPcOXTnaMUjnSFXN0fGGoLJE95JPxB4ax/vlhPalgeOeyUVGMOoQviPyWj5LHon6nhOVI52jrALWv434eLuAgSWrQ0JnWFYp+23COOvop5B0DduRRrcOcdQSzSvUsKO79aGVNCcGUoN/WjN6vkM2jurCMj7bEyUNCotxf5+TSHu8YYunLP3Gw8sFvRb0J4aLyY7j+ZbXyRIvbtm/GTEWNWZuaJ88/nl8xct9ia8dtlIQGroUFl8tCV1H1PckFyW6EPgO1LzF3zm8xtD9LoZa8iGb0DpFn0rMVUirFNI4moUleF7yyZ/cMXyxZ1um9IngrhxT5hFWSdp9QKprHt7NORF7gtrhY2GrEA2EYYfaS2wtqbOAogxwfxvTZintLqRZRtw9ztB7QXcV8dN6jNw52s870kVOcCfIHlMukg0P9yMWdcl4tufkaI39ssYF8Kv2DDsy/Ngc092FND+lUEu+eXtGu/axDwGTVYk6aki8lvmg4uv4Ea+zzD5fE8mapgiIxxXNLuA2jJmLGv+dwCA5frljMi25vx3z58N7Xhw/Efg1f70+5+R0h5n3nHgVJ59u2TcBeePzy/kjL7965PWXD8hA0DaaL9SW+AHsqSHYC45cxnU7Jq9CwqOOZN/DSJBbD3/W8N3HBTd1QqcE5qInWHTk+5BPmhwx6cl0iPqiYeVH5HlMMfIoq5DuMeR8tiYODvfL/mlNlQr8r3IGcc34rmO2azjWOUlh2N4OsaGjOAP1uiJ9mfG3/YIf74/45qdT3r85Q+8Exioqp8nrgN1Y8zEYkj0l7PuAb9yULlGUdYRfWY69PUeTHUhBOslpJNhSYfqDFvpBJxT7iKoLcVrwiy+uGJ7nvP7ZDWrRkT0m/NvHCzbBQc6yfBqymOypHiPyyCcNSn54lTJ5uaEzkuwm5TpJD0yHpUceRlz/OMUPWlRnSVRLhcfsZE/g9UjjiLKOaNjgY2jWISQ9cdejS1i/nRJnjsAzFLGirD36yQGXG6YNE1WjrKPOfCqhsZ1ilJaUJ4K1jjBOoKMOjaF4CtFpRzMVdAt5uNr51qO/jdi/GyFqwc16SnhWM0sL3KNC7AXboUCOOipfMT0riI8sggM581KPKX4zQimLF3fMXYmnK7xFg/AMK5fQGYVqQXdgPYFaSxpf0i8s+/sB+WNCpyQBPZ+d3aIuWqKLgsFfrnBTi0MS9BYkiPOGaiQpTwR94ugDAQikcfhrgV36XO9nqGFH8lVH8EWB7nt+dnrPaJKTTAvMlw1hXCClRU476kQgvyhRuaBd+9xezWGlWXQVk5cb5hc7toFPttA43+EtHY1R7P7RZ/8Pf2ziYGw4m22ZhgU/fXuMdIZpl2FPBL509H/rYT6z1CHkvobQklQdw3mHtJr+wedBDVGeIV5aymOJ9xghIocrFfq8I246xpWlj+AuTViRIneaqlcUjWRHivItyaJAXSvqI8t8sefhfow/q4ltzdqGzIKMy80MV4KcOlK/YrdK8ZMO1fWEXksiWry8pY88CiJc5VFNJcI5ghuJ96wnUD16B3Xo0/sQCUO2jQlmDVsVYK9D9HHN4GcZ5V3McNyQ15JB1KCfQ5P7jEc7mr1jMizI9yFlE3Escx6DEDlq6feg1wY/bik8ze4hxZQh7rTH2yuaVw7nHLZ1JK6DyqObW0wrMYuaNjr4t50H0bDGBD1yp/npmwtoAkTcU9xH6HGHjHv+Zn3On508MDnfss5jtnWEt1LoU9CznlpLVjdjPG1Qi472txHtkcCsElwkYKeRyUGTWY8l9AJlQY164qSluI1ZxpKjccX6wvC0GjJZ7DG/T+gnjjhteD5cU2UxDx8WHOmc8cslj3rE/nGICzv8rzNEK2lXPp6ucBPL0LTwGFLEguC4ZN0EiLOO/tpHRha9lezWQ8J5TWE81rdjUJKHfITZKOJPMnzdYwcdXS65uzsmeFngn3e8Wjzx/cMZ8p0mfFXhdlDbgKBraJSifIx4k50y/WLL+nGEuFPkMiD+0CNf99hKEQw6ZCLwVcefTm+4J6RSAvMYYs57ssuUC7llEyeMkoZbmxCuLLs2IhKG/3B3hv68QD5Y0k4SFZa3coQYCYwWbO+G3KYjzkY7siYg1wGnn63YLVNaI9mWBzeAyAWfPd/w9GZAOTEIbflhM8OLLIGXsStjyCVy0tL0CmkM+klSSQ9amIsSfdvhJYLVxyG9cHiDhts4YVfEqHc+MrGk04J1nSJiaNchn5w/cOOm9FYTL2q2d0P8iwb2CiMFewIWTUWd9JhSU2pFFLR4nuFJpRQi4fEu4Wy6YV3HjIuOz5498s3DOW+fxlgPgqVACsE4KnltM7p9TFsrwntFdQyUgtS0BKEhW3hcjCueupTQ6yn2CdnblDoMsZ1HeFLT3wZ0DcSm4ce/PsdFhx26aiUm97AaNn1EFwrUuEbeWeQGXn51j36YUu4i2oHk6Pmax7sxlVKUa5/4dUWHx/xiQ/5+CGODFAK9kahBT6AcYmGwHwOS0xpxrwmMRe4FrZH4iaHbesiBxT75uLHBepKw7BFPirIYMZ+V9Fch3909w4165q+2jK4ENwPFJOtoEzC3PkIdGPL7bYiwisdditsK5IsCWygyrVCFpoo9OLKMVg66nnbmIeOeLgX1TiMiRxC0BD8pmibkvY6xnqAdOWZuz7O/+MD7yzl9rfCfVci1Ik5q1E8JAkUzA5W0qMTQ34a0vcS/gSpKaDOJnHToTPDdcEwrD2tD6yTVxLFoKzZlQjzoGIQ1q0nIaFMTflrRVh5+YtAbxa8ez5BDmAUl6sTxEIxxSGzzh+21/6h39n/xf/pfk+kxr2dLjBBsvx/SPbMkpqVd+rya7PgumxPEPRdFyZ2M6CNBZTTpdz3Pf7HiTXVMU/qkJzldr+hqj7PxhmWdUjQBKS22VASDmqNpzodihLMK9+DhBhaNY9xX1LGi1xKTa0LRMTQNq6cR41nG/SRk0PY4aVFWEfU9tzYh6jvQDlUKvnj9SNg5fv/TKc/NlttByvGrJW8/HKME9LHgX8w+cvU04a4ZIgKDOm5YjHbs342RnUF7h5vjqgk4iiuWBDwfb3jz5pTuNuDln99z/TjmYrSjkYr7b2ac/cUjvrP8uJvxSbxmW6dsE4n5McIrIUxbirkkCDrMzsO2B53jcF/zNE/QYUeP5Cza8fA4oY0FNnCcXjc8jkP0SYNzgrFtYKWpEsFgWBF2/YEWFxjkoGe9Sfll/8i7aoYNHQO/4c3DMbqVBEFDt3a0SUg4aCnRyBaiW0F2Jgh3ENsOFzgq4aEeFeAQFqwSiNDQHjtaTzKxGYOl4WGekt47vFc1snYsTcogKen3Gr9znL/aEiUN2WVIO1ZUVtFuQnYmQGQCZR3VTBC+kwRxT1cqxq/2LFdDzMwyoEVU0MSSrI0YvHe0M/DCDtto/LOSLgX5pBHaUgqPia3YbQa4xB6mKRswQ8dXwyc8K/jRjGivIvy4I2o7Mt9jMdphE9ish3zx8oofb8/RxtHUiuOgpsPxtBugjONksqOoA0bnOZ/1e97mMyrnM1MlobX83c0pi2lOloc0CbwId6yaBD2vedwN+EptWFmfo7TkjojdxxFfvHqgyzzerqYMpgX9VlP5PoloWRxnNJnHvvY5OdmT5zH/49M3/OvqiHfvTvny4gE97bjPE1bvxsS6JtcRZmyYRgVPxRBvJQjT5vAyvRqiZj2jpuHz8yXf3hwTHlcsrybIcU8hPfy9A0/w1fyBq26A+yEgPispVzEct/iZo60DPNXytAgIt4KgsIxeFaxvBjQpuNjSaYlzsLAVftjiBhZvBbvLEWrcMvAa7nWK5yx9rvE2UB5LZGB5FuypPsTsypA2kkyPMzapRt9oRNQTC8tgnrO6mtCsfGxkGY4qmvcRTghUC1YLzMBh4kMgTA872k0ABpzv8DGYRmIS0NVhP61fV/R3AfoW9J8V7FzMWFVUheZ4nLEd+PhJi1eDqAyDsWGTRwxax/0qhdjSLBzd1odcMYgrRlHBUzk4aF+VwG40nQ+hMdhWogsOJ2XeoXCpaUefeWhnMP2hmAZpQ6E9ZlmD5xn2vo+ctvjGUr5PEUaQzEr2RXw4Py5AJQaOWup9SJC0+DfQTQ92O5zA+EBsiU3L7GTH03bE7nqIHnYEUUcnJGKpITBMxgUu95iPMoom5K5JMKsAVUAdH3S18rDRQm8FqoZuKHDK4QlL+F7QLA4QHasPjH1igwwMXu9wzSFvIErJ8+GSxeuMpzxi+XFMk6rDzycCSonUFrfX2KGleTK8+d//b/9xZ/8PefKnCHkED5shse3ITIy7ddRRiE576A8Mdyskd41PszAMBz1+6aH+ZUPZeGjbMqYhyi0fXERgDaqB076izTraxmf2asX+Q0JlYkSmiLcWPayoncJFluU6JXA9jHr8K+hfSuS0RU0L+rBlcgfFNqI7h7P5hu3NgCRuaDc+8daRLwQ/fDzmP3/1nvG84EM7Qt0K3vhndEKQjmrUyuP3wRGjUcupvyK/HiC9lqurI+I10DqWTYKnen7x4pKf7hbsx5qKBt1YzNcV6ahmFm/ZWc16k8LAsr5L8U4aVCloAsUmC2lyjeeBiAztXYidd1S7AGkcshTYvUR83pLee7grRT9X3C3n6Kg7CIe0I3peo3oPmUmUtJRlhFo0LPyCW5nyn5xc8Zt3r4i7DhtA8XHA3wwi5tMtRR6xKlOGQYVrPHZ1hJ1botYgthI3d6QqZ/AXPaaL8H1J6DVsH1KSo5Io7WkeQ+rWw5v1tCOg9JiICjeQbH0PeS/JAgmVQhSCs+cb8tZj/NmOu8cpu9WCZ/2acuTjex0DKlZByBfDJcGi59fXZ3yZrLk6SmkqHxXAKkuhdbBTFCrE9YKBqNFpQTcI8GOLXrRUP3kstj07J/EGJQZBm2mcJxhfHOh6s2DH0h8duorVGcm4oum9QyI66hmNM4puwHU1I3UVethwuZvxy9cf2Lyd8/toSlclpNcO//OGmS65up9zkmS0xue/q57RtZq/mN1wVw0ZTnYc9xvqjUZ0HRPbc1ulzFTO5KHjQY9420zwKktvJeNJzuvjLVUX8FM5ZnaS83P9yG9+fEY8z/jTz564J8SedJh/PyAfRpha8n+7+5TceAzGFVHb8mE1Rrw7oHS7zx3y8SA0KaTH1+qed+KEXkkeshGv/JyjxQqhJXdFSj6TPHVjhtOWz1/ecPswxdeGFs2VjCnzgNGzhqC0cFay60KGgwP50M0NiRYMBj3d0LK+SWgaTY9ArR1D0WB2PlkyZPZsg1srdk2IaxVTnfOQT9CxQ0wMamQYuJ7WJfRLzc1sgP+sw1QdY9sgh5bXwy3ZTFOuEv7p7Iol/4ZCAAEAAElEQVR/89NrWg9GJzvyVUr9PqGPQVhQjcN5DtrDBQ61o5IBwnPoWtB7ktoTmASCFcje0Z466l2E14HXO8rHGBEKRi8ajJPc7SfUiWX065B87oh1R/9B0p53XHYhpg5Iz/eEmeT58RNEmrflmIl1yHHPbLynXXvoTx2jtOS7Hy4IMXQnjlpq/J1FWpDDHm+l6YcCaodXClrlMSosvdWUOoRJj9/AKKkQLiA7lgdsdmKx1x59fLDxxT8oYmuYflqwHMXUwqOPBcG4ZkLNcj/gZLJFdA4RGNKo5pdfXfLudoZODaUK2OwGrK/GeI2jCiQvzh4pHwWOmt1JiNp4xGlHuQsJXE8z0JiZAywXwy27qyHVz//+jNta+qVP7yn8O0mfClopkJ3Af3CoecsHMeHy/Zivj67hYs9NPqEtPWzaM1o7/uTZI383muE6wedfX/HmD1gv/6iL/fx8x3p3QnJak64thWipQ3VQsFrL6jRERh1R1bOTEZ1nUb4j8Vu82jIhZzMO2awjNlYwSgrividvA9ZEnAYZ/lnDw21A+1wyCBqGpaIoE9rTlsaTmDogSDpe2DXZfcT6pUI5S58ppsOS/H1AUcTYBQjruL6bkR6XsPWIxjVfHu24+XbExov4jTei9ASffXLDt/MTOqHQQU/z6OEmIJYh11HIQB265eY+xaBokPiFQ/odTllu2gnTeUaZTXALS9V4LKItP+5nBK6nrgKGqqWdG+bzHR/Xc2QvWL2foi465NNhdNoZjRr3yEsN5z2x19BYj3TR8vQ4ReSSyHZEjYMnMJFPdF4TBRVPIkCPGlypsBba3mPoappAIp8Uv8+fU0iP7cMxMjeHsz0T8LBbEPg9HZr0pMIbtjSXHrWAi/MnLvMpXw6XXP5uQaY79FbBouPhakJYWjqrQAp2pxqTabQWiHGNJwRzVXO7GWCERRuHVBbSjq72DpKOacXJeMPdbozuBHfbMaHX0fcS50mqteZ6M+XFp4+cXSz5oRsxDDui05p8H3Mx2bO9T8hn4G8lfa3I84g+7dGnFrWRVEsP3VlcI6k6n2DYk3+MOdF7JrOCsgmojmtyqXGVwzYHB8NOBBynGQ9uSKZ9zoSETqMmLUdkNLXH5t2Yd1c+YdygwoMZMB1WmFLwJAa8evZEdRPw8Jsp9VCiheHfP7xACrgcjHnV3ZPHQ2QDtfUZDUuOpwV9aYmbmqYJmJ3t2N8kbG2IBwy8jsXJmvg3IdvThPGwIPyTLZddzE2Tov82oNeCp8sBXyRL1vrwIWj4es9DHhGvDXsvwO011ZsBNrbEfU/4CFUVYNrDOibpO/a+YFNPaX8f4Z0abKTxraOXko9Xx2wIoReESYN98OiOLE70hCc53Srhk7M7Hn8aEqUtm9sUhMfs9ZYjb8OP+TM2QhC91/SBpPMP4c5eC7Zvx0S3hua1pB/AQz9mFNecT7a828zI5oJ+BnIFwYOkFd4h7b1wfH30yPtsyMffneB9WTAva755d05jIhBQ+AdWuwkcqhF49eH0VWfQR4dde+8prA+R7cD4h+mFslhPgJH0WuBfg7MOaQXNkcAhEJnk8nqK6yRqB94ayl4Sbjoez0P8lWSyKRBFhL+HXThgICt+zE8IG0vvKS6vT1CTnnrWk5UJcVoycQo9bslrn3/28gPLwiMPBhyf79hvY5pBSVd6LE1MWFk652GBxTwjmys290N46xM4RXDUkmcxZmiw0pF+umXVpgS3ijZwqE6zfTem9jUS8CJH6FusltSFz9XVGXXlo04bPGn46XbG0DQsL0cUXUDkDBcnK2rhcZ8NeXt7QnWdYBc9MheIXjDoW45meyp8ylQSNgabK677EZPjmpNnGU/rMY2R+K9q4tThroeYGIKNQ8c9/amgkRodNcSt4c3uhKNBxmmXcRkOsZ1A/LzgUgRMtgWr+zFO/WHL7x91sV95ISKTLNWY1pS0YwGVQO8Pp1qryzFx2LINQ9RK8eLiiY9PC/reYIVgtxsSPy/wJw1+4djuUlxUU/Qeo7hmvRnyp+O3/Kp+SdVqbKVoQo1+3RHuoL5wjKcZ6lLztB8jFoZngzVXH44I5zkuk4w+LakzDx4UFokMLc1tRNr2DI4q5qdPyFFB+c1zsipkvxyw6hPEg0fk9Zy/WnP5eIw76un3mtlkwyYfHEZBe4lqBM46urTDjy3dwLHcJ6w3B9XkD98fM5E1SSa4ayI6LMrvCXVHMVas24QucvS5RqIId45oZSEw1AONevKQocV8DLAzg6oUttPolQDr6DzFYN2w/gxs5lHXIXUjqIyHKhzP4zV5HdIbR/n9gOYkhNSyT3yOZ2vumwXa7ykWmjioaDYBbaWRTpC9G9FFgjhtkL5kv00g99jqBBaCynqkUUfzY4AbQh0qRpmg8AM841A7cL6gGQaUUmD8GKfE4WsLeDbdsDQx8aTk5mZA8B7u12OCDFwEzkAaNTROsS4ThrOKXejzUzEmNz7qStEuHPGsRAaaIGgYH1uyH47oK4EZWcK4wZuUGKtotY8rPXqhCccdkRQ87RLOBhm33RBUz5WYsLhuCM8rCuVwkcMTPbSKLlWcTDPKrU8RaUQjUY+SH+MjgifBdJazLgd87ZW0Tz18VjF82ZDXPp+6ilfhjrLx+bdugJ41vJou+fDmlM+bB4pQ0ZiInYv5pN1w/jrn9j5F7B2Po5jA9RzLjLsfJrz42YpyFbLyE1K5oV5FzI5q7kTC0UXG9W7C2drQblJOZmumX+/45voUs9ZsjI+9DRmvRgSDnH4MF+OcD5HERoLU7+iQvD5/4uNyQvRgCL4XjL7IyPCZjAvef3Wg2Y0GBS7XdEBY9rCXhJ9VvGgKfiyPsXvDUPbcfXdEE0uqRCJPBKO0YKlj3HcK/0SzW4zYjD3m24KVP8Zqh2oFwlmcEhgjqSdAB3onKG1IEMPVcka59Q8I3KmBSqGf1USrw/eLR4+/ls9IkpqzixX2yqPKImayhNOG8EoyDCvUueM+jYiuJP2ZQ773kM9bRKFxM4P3k0LXDp17mOAAovF3AoXFGWgDQY9AWIG/shgPrJOoTtB6inDlQIAVBxiX1ZLo6vAhsugjdNDThR7BB4Ha+Qz/WUX/U4x34VDKYreabTHC9Ap2mu/dmCBzuEjwbXNKbgMm84zbxxFiK9kQY2OLrCE4L7HLiM6TDOYNuzolTSuqkUanOUUeIZQl6jsGzlHZgPFFRjfW8N7HJFDEDvXosE5gjaD4OGCfOrxcso19tAQ6hYjg6fs5dwNHGtVYK9G9YddE7NsI02pUYhDTnjBzBxqgdrwabfj2/ohg1vJcZCyXA/A0P3txw9s3x+TfzRGvWhqjGWaWRni44DDFYO/RBhJiiyslPZJOW6gUa5OipMUWithv6XrFZT4mkS0jCu7r9A9aL/+od/Zf/pf/O8ImID0p2doQqRzHds9jNSSRPdUMpHCYBw+pHaO6xowFpfIIM4t42bC/S5nMMsosotkHDMYF+/sB7bFBXfvolyV+bzmpa9ZVhDxqycsA9+TjBLSBZDws+DLasVom3JkI5znatY+Meua2ZkWM/6pArXzyUPDMlmx3MdmF5biqWK6HhOMau/SJdMNTPcSllkWS4SUdWRvgFY6tjQimDcHeEZaOp+UIJR3eq5JB2dAJTetLZmHNXTbkdbLhh/sZf3Zxx4/fHFMJj8nnW9JFQfdDzFU0YHCWU+1Dwt4gnSMIDyMtmfYUdwneo8KOemwMopD0viB+tDRTcbiHbQU27hCdQsWGrj2gLJn0xMMKi8AJyb4NGN84Zl+uuTVDvMoRpjUPm/HByb2SyBbEWcO0bri/noIVCOGYHGfsq+iwGmlApT31LsRIwZfHS968X+CLQ9q4lRJvK1Bpj8k8It3SbAPcxCCtozcanQPPWkI6KqFJdoaYnm0dY+PD+JRKQC0PFxLK4BLL2XTLw90EzxlEZOg8hbAwOMrpWoVdaxrnMY0K8u9H9KHAKyzmq5YgbcmXEfLapx+CnRgCv8X3e4QEu1b0O49OaoablsWfPdEPBMvdkPL7Id0IkucZfebh3UEyqolOKrLLIUHS8iTiw919BEePLXrr2P5CcDwseLOccT5bYX8X8Bfnl/x695z1yKMrfZQ0RLIjLyJez7eksuXddkQcNpxP96weUtIkZ+ulNFbS3oWkoidPBSoydL1G+I4wN5TG5+Und+xEyMP1lHYdkIxqZGxQO0HnCyLZsX4aEk1qWis5He95fBzT9RLxaU33LiFJKuKoZmkSZC3wbg2q8Hj+l/fohWXZe7QPmiyKycsQEVjScYH6PmQUFywfxnTp4f+AKCVup/HOKry0w248Tk629EqjteG2j5jJFts5Zn3Pj78eokJNkHTsRj59rwldT2xbytuE4aJiO9DM0oynD1MMkvH5jjb3MY8BQlrIBW2q6BYG3TtMp0iSigDD8cWGbhnSCY+7pyFCWYLWMk0zqixka2KoBf4W7MLQ1x6qsYe/NQOyc1SnoDqIqp5eqYNmVR3Y7geTJTQThyoVTjmCrUU4gdXQTx2uVTjp6Aaga/Ayi5WSdnK4Qe8G4K9Adw47dFgjiZ4VeG8VRRsCAmEd3dgBh2yDrgEcQdKSewc7nm4trlcES0d/5LD2gJf1SzAh8LyhN4pgB5+OHjg+Kvi3y+ckVc/wLGP5Ycqnest3TCisz9jVZC7Alhq1lfQDd+AbrBzd4O8/cO0UbmzQ0mISB1uFzA7vLU5bzsMdK2K6u5DFsx2qtFw/zPFyh5AcKKC9wwsF3knOYGPZ1wHNM4GzAn2jqUdgOKwAtdcTJh22kcg3CvVVTRy1yCefVeDjW4tuD6eR49cZd8WIZFgT7C1Zafn1//L/+I87+3/QE1lk2qP2lmQF7pOOx/0IE0uSMAfjU6AZz0q2m5ThWcbHfE5ie+pUoTuNCxwRHcvthKg17JcpjVTIW0W4NzhpmSQ5X3x5zX/35nOyLOHViydsEPHhbkxMx4Uu+E05J6odrVV0WiA1EEiu+hGhOSTR/4fHP1GEmr9++4KtDvD3HUsVI4aG7ClFNg5lHfPjPY9dStdptlcJJnH8/PSa6t0Fbe3jFZZtexA8TH/xRLGL2CQhJ3VOO3PcPIwJwobvdnP+k8+u+P5pxP5M4oUN203KskzxX1UEZUtEz3C2ofgwIOt8BkVNIw19q5nqkvpTRTN0BFuJPOmoljH1FwarJHonEHtJ72miyoJwCG3RNYyCAiYtWlsCazGPE4ozRbWcYa1gICuQksDvOKlyNl1KU3uo+4A7ETN8mdF8jKkmgqf9kNBrSW1Hbn1eiJJ3sYepfK6zITK0nDx74q4dopYhMgJ2kE5yiByTvj7Y02JLeZ8cxoBHLWXlE+aHgFMx8GicRi0Fwre4AFQBo32HrqEcSao+Qj5qmDtEIWDkaCoP+zhg4WXEScfHnxLW+CgrENKQPRf4rSLaSga6pUwVTihEd0hwT8KKdZXQlAF+2jNTW65mQ4qnY3Tb4TmLwmJrybDo2D94fD16JPki57oY4GJHvQvwfLCxIxA9qwvFF2GOnhgiV/Plw47ZpOT9UPHvf/yM9sjxqdnzbbtAxj1fvrpHGE3+JuXqccjir9boO8H3YkzWD/iTeUWZe/R3Hr6xRGnLfFDzvT2wG2ZVQ3rc0r2XvFmd0FYe87JitwwxNyFmaBmpjP6FJV8lvJht4aTm/f2cLgE3MCTTgvUuRZ43dLXgYTuCAJKjHFV5ZM8Nj10Kv1E0nxqms5Iv4yu++/0Fxz975PZ2Thc5ylOJpyvqLkJvNOL/y96fxNi2pmmZ4PM3q1+7tW3t6c/trzfhEQFBFZUkoaQrEsSkBKiYgIIBEgjJBQiKUkrJoBQIBlADhoUCCQaMgFIilAWeKUUKggw8nHD3cL9+/Tant37bble//qYG65ZnRZFQhGdmKEPKX9rSOduWmW0z23t///997/s+1iP0AMTZ9zFCSM5v53x4eMXGKOzLlOt7AcevW14fxagnkjbyJN5wv2i42E1JHnQcTfd8Tsjxezf4mxl3FzOUcIx0zXhU0d85do8tpgxwkSZOG5LcgPQo4bHLgPhhRRlpIicphSIsPR5JcFqzfD5DFiATibIOnCTYgs8sci+RnUfXlm6ikLVEGUtDgGwgtFAvBDb0SCOw0YAFUb3HeTCpHHIKQkG472FuaUJFuJYI4xH4Ie7WK+LG4hOBCxRN5gkbh5nAepsTJBDYQSNgY4jWIHpHnwp047CRwl0EyIVASo/aKoQHq0DUAq8l6ScOG0uCyNEWGW7kgZ4fXDzko7Ln0ektqpZcbqe0BNyMA5pOkPka1TvGNMSnNVdvDnh8ds2zyxPE1NGMIWzhrckNFx8v6KOAeuwIJo6w9kRbQUfIxWxK6g1mZqhNwFZG+EWP9YMtTpQBNvMIaRGZYteE5LLh7dGaZ3fHLI4q2tyz8gmR7lCFpEthgqcTEcErwb2Tkjzb8e14RtHF1H3MbLZn7FpeFSGN8/i8IzXNb2q5/C19sv+J/9v/ldyHyKOO3c0I10l86ImTDpd4mjcZ9qQnbMAaiQot8aTleLHh9Ysj9KjHVYpeSjqviIMe+zrGTDxBCYwsbmLJsgaxUQggjXu+ktzyjbsnsNbotENODeYmIi0dyg2nXrvRRFHPjpjpuByCclaQGI8et1wzRk4MWhvktabwIXHvkEcdYdzRFyHNPsKOHWnaMkoqijbBC6he5HRlhNMMCmijUGcN9ocJSWy4p9es+pB0Dl87vGKzTfhvdo9JbyzlWPPuvSV7DbtVjnqlsR+0mMsYXcLc1oinDRf1FFMHhJUjfFRT9CGJMwRvNNG9ivomRR51+EZw/2jNm4sFYtERBYbbzw8YJRVBbsi9ZStDfOsJRz0pnvUyRbSW/p7n6eSWZT2iXifs68FL/dbklk+vTtFLiZk6gjuFOOzpryNOH9xyV44oVYDeC5R0/OzZG77XjLm8mqMCi9grOOjJ5zW7VYaODSEOI8FcRvhekR1WrF1MdCFxhxaRWXyhSK2BNxp90lEVCSGGvglwU4vowRUBPvJEkwoRW/oDSeQM77sNRRXxbL2g6xRp3tDYgD71BNridgFu7Ii2kN4rKdYZUdghDzuaIiTqPUo7mjcpdSoJDlq4DjBWIsxAawzt4LMOJh2HYcFdMeayGaETg28lX5u95oWYUX1/jMwGKMnjbk870YiRw91K1psY73u6gxBx2NO9SXl6b81qkyCnLV/tbpDvOHRn+KVffYdtEBFKw+/+qWfc3uR81kw5uDGIhx27fcThdcftNGGzznj88I7n6yn3T285r6bcX1asfjileF+QBS3WSgLpeHTvju2tpvIp/sBQnOc4JbAxZLMK0Qim245VkxKdNkQ7zzJPCI0dwnw2EmaGxHjO7q+4/c6E5pFg0yboBvK8ob5IcRpyWspIw12ADi3d1DPpBwTw/bM7Xnx0RpMLRGiQsSdzBp9bfKuQNWTzmn0b8Vis+eTlPbIvb3Glpi0j6nWMSnvC0iEWlmYXEY075JWmTBU6tbhGkjjDqG1pT4cQr+moIYoMmyJm/XyKEB4ZOVgrtLYwM/jnMSYDM/JEd8Op3imBl55oC91YEBQerzy69PS5wEeQ6J5+HWISQHk8AqcHOqV0X4TCpB1FHyHboXCralCLu6nnw9k1b5ZTqk2MZ9gwAOCgXgxQnmAPfuwIpcGsBvZDLyWyE2AcLpQIAR4BmcX1ikBajFdI56AdRG02EjQLT3Lr4dDgC4WzEtVCe+ARTkBuwUHwoMYuQ07HGzYypjUB82DYIHLS0ZsA5TwfnF7y8ffuEzpHFQTYvULNepwA1ysyYxCrgTESHTcQO+oqROaWA1XxZj1DmoFl72PHwXtr+pcR9kqz+NKWu2KEDz3mKqLKFGna0CjF+03BrYkGONlLw/VpwuYuQ2hHPGlhZMiMRXeOA1VR1Bnrtedf/Rd/83872f/HrODaEXypxoYK7reI6xA1NmxtSB62BHLwrnd1iBRDbKzF8fLbp7gzQ9dECAGy82SFp4pD7k037EVEcq9FBo5Vm5C1hm0c0FWaug74pZePOH6wxd0T3LQjxqFB5oK6VzCDeDLEV07P9qiw4ZHY8tQUfGt8yNrlTI2hDwSTzlAIzWg/ZLvvTURqFboXA8Zx3mFXMdtIMNp0NG2MkpZ78xXn/YJw1FE2IUeLLX2jSKMSM4E3zZT7Rze83hzx/7x6hxRDKC3xUUuRCl69nKM6B086+nuWI1NzqWMenS7pmpAXtwfIjUfjGZ9UlJ0mLy1+H2CXAV0tcF/qoIHptuFLRyvkUcDz5RgRC+RZQ/9as880/WbA6sa15ELm9NMSTjqkcARC8MP6CFko3h1t+IHSyNCy3uWEe2iMZvSyQmUGvWrpPqwp6xCjJZnvURoS1fKLr+8TpR1Rb/DLAJd5ahnQXwYwMpjLFKU73Erjx56TD2+5H1d8tJ6zcRmzqKHLBJHpaMsIfc+w8zEuETQTCStJlPSwCUB64luPeGSpJ4LgoxDzxPORnFObGLkOkBIqF+PmjnTWInqPv5OUSOpc4C9yEmsp+ozeJsxPtiy2Hc8vDvEzR6h6eimQEtR+8AHLRhJ9HhB/WKN7z2qfkcYWgNmkxAWeJTFi6+gf9MitJAkrLkzM20/u2LQJcdJz/MGe/Fcltwcxr6MMGcCL7YTj6Z7DozXuIqJ8pfno9TFn6YbuKkQfWXQp+OTlEQ8/uGERG8qF48P0lv968y7xpOUkXbFZZkjpuW1GmFXEaqeozyRq0TA/u+Pl82Pmowr2nrKJuZ/tqBPPOh0RWgtOMBmXbDdj6ljTSkVTZoyPSsZNz2y6Y7dPmZ9WvL49xIaGF+cL/ENLWSRDF2SrqHcpLnf0XrLfJZjIE7qBZXE43tF/KyR+Yrjbjxg/2dFXOQJPWhn265zksIbYUXUh/U7yO776nMs3Y/IHBanrebOdEOzAx47AORwO0wQ4IemvQqZJTbfO6BnCqtzY0UeC7TpGNBIRt/jIE+Qeckfa9nRlAHlPJA3qoGW9TgagSzNsgPrnCfXBQFDrp55QDN21aDVEWNtoeM8ock0aGEShMImgPxiKdVAIVOcxU0/RxAjnkX7YDEjraXMIVoKP96fAkBZHL2nnIFtBn3t06ZCdxIYQXwo8CqkhSns6QkQlwEuCwmPjIRvEOfCJp1wIghXIkaOvAoLS008dwU5gxkClAUHQe0TkwCraCSRLiezB3eV4BcvnCxySfgobG2AiSaot7TpG3G85X09QwqEXLWqlWBwVxKrlJszoVhF9o3BSo2tHvISf/pnP+bXLe+x9SFpY4mlLagyztOZmNeXm5ZzEGZq3oE9S7EYxDWpuRzFCePxW0+qQj1cx9/I9B7OSj4oj+tuQ02WF/skSF3sur+f41uHHll2RkoxairX6Ta2XvyFX/+PHjxFC/Du3P/fn/hwA3nv+2l/7a5ydnZEkCT/7sz/L97///V/3Ndq25c//+T/PYrEgyzL+yB/5I7x58+bHevDmSFJXCW0vid54xmHD29Ml/8f3v485j+hSSUqPHFmSUcuuSWiakMU7K8RFgLgIuDdao/CoyCKkZ+Nj2k6ztjE7G3GwaWiFwhYKbT2Bt7SH0Lch+/MRJ6pAbxX3dYGeGUQn2C9zvBWsiwxzG+BfwL/YPuammOA6xVWSo6WjtBE6thy8t8VdRDyOV7w1uiXEow87ImeIsg79LOJiP8f1kuZ1zs4nJCcVfS1JGkeziunXActyxFqGKGVZr4bduXwZsL3JqTcxu1djsu9qxFbR7yN4HSCXmvqTEWPZ8NmbE56vD6CW9JHGO0XxcoT+VNO1IV4KujNHRYh5HVOKiOv5iF+8e0qc14zuF3xpdsvitYHIc//ojuCkxrxy7APF4dmaWVbSLyM2F2OEgQU1qev53vkJ03lBXjnaFxmBtBxFO+wJyNxQzwOCBva7FKM8ctxSzSR9G2Ajwf2zJU4o3D3D/PGW0aUjKDxJ1BPOWpyAdizIgoZynXH9+QT334+YvfbkqqfZRIhaEp9UBKMWhYW5ASMwY0fbBDRHnvbY0zxy1DIg9J4vf+3VEJ38JoZeYhOPPetoF0Nx4UaRvBH41BJvHaO+R3tHG0tCYxB7yWads8sUo6Akz2t01BNcfFE8Mok0nuDCo1rHbZFjdiGr1xMuihG57Vi/GXOgSt5cLbi7nRFeKEJj6JsMkUOxTnm1mbBPNd11jo4U7z255qwpeO/4nC6GSbiluIvZz+D8RvPlowvaU/AzR3kKz0xOcNDwZjfmX+1OKb6d8wM5gQODeqOJriAShqPRjmjtmTzeYiNPn8HMNpy+Nkw+gQ/Uik+3x8hYculGvC6mGCcYn+1ZnG5YfTynLQL238mo25DaBuyrBO08Myy7ZcbdQchehvQyQAYW1UiiaqACRqpD7CTx5xItHG7siLMWnrZUDzwFIepLPf19y9JkuImDscFmnsLFyMDQNkOu+yxomKqaf/2Nd3gT5NzlEdfPFoQHDYvDLWFrcHchZydbwtYR1g4p4S6JwQIe/FlHaSPaMfRTycHDHfswYRmNWTcpTkLrNbPHG8aPdxzlt2y6hKcPz8nGNYvjLd1I4N7qmL61RYaO7EFBPK6R7zS4dw2kHmGhnQqsElQngvaex2eOsHIEG3Da0xyCbAS69ISFw0bDjL6dC4LNoAdwARAMyFq8R++GSNdoM5y0pbUg/ofkuT6HyoVkqkdYsLHABQIbD63+bjZkYoz7Dqkd+vmwGWjmAllJhB+6FiaWeCFoDoaqFG09s7YlWoEXQxQwQoAZPO84AVuFLAW7VUb2GvzrkLtPDog6T28lpJbYeu6yGLMPmMua6WiHPelJdIlUjv/2+buo4w5ZCLZjjbqTVF3IJRn5uGaSVQRHg31xXBpOmor6TYa6EogG5Mgwe7zh3ZNrpqcFn/yb+9heETaW9ollLWM2XUqwUsg3AeG5pL+N2N5l2Mf2x6p7P+76DRX7b37zm1xeXv7o9i/+xb8A4I/+0T8KwN/8m3+Tv/W3/hZ/5+/8Hb75zW9ycnLC7/t9v4/9fv+jr/H1r3+df/yP/zH/8B/+Q/7lv/yXFEXBH/7Dfxhrf+M/eL2AnUlwRYB6aHCl5GI747978Q69UoiRIbu1uE7QIvEMLf7b9RQRenQFt99ZYDYBnVeoO4V9E8NW43Z6yOIOwF7FdEWI0A5baBo0exvRoCmuEw7Nnst2ROUiogKi55LorZLHZyseHe74weSE7iKhziX9KhgEYnvH/bNb8sbQhAo96zCbgE9/cI92G1JfpnQXMWILLnFESYsILVhYnY+pNwm6FtQ+oGhiijrBKIlaauoy4vZ8ilwq+qlH15BfOKKdRTXDHE92AmrJk2bHro4przO8hGD9hW0ndtj4CyHNex26YsD+1g7deKR1HPgaeau53o+4+eYhJz90LL+V06SS468u2ewz3K1m9l5LVwZcX82pyohZU/PB25fkQcvkdMfJgzuy4z2bNxN2L1J2XUy7D5k/qAjHhuZEcKgrNt89wOwCxqYnXEvktaIrAgJhuPvuhOlkS9cG+DtFljXorGfR1ahaEGgLR0NuwmFUcnuRMXtvjTprEB7eCdaYA8vToOBMF+h1R7T1hKFhGjToacfpdMNRusOgiFbw1mTJighTC0b39symO9T9FhMPwkXRSfrYY9/ryB9VmEBRbyKMEEjjkQcGmw8byM5pVqOEsg3obiNsqzBOQuiowpB2qnGPDG7kmEcVwXsNbQ7Ukjjq+Pz6mMPDDadPb8lnFUlsCbXneFagEwuFZiE77k83XIUpb8oJXzm5ot2nBJGl61K0EOSjmj5L+ZVPHzN54dA13N+X+IuQOO944tZ8JVqyWufsno048B05PbdBxuHJhnFUwmVI1hvqJ4503nA/KuglpF8tmNIhnad3km0SYKwiEI7dZgCYzCcbUt8TnPXEa0f6yhMVjuVtznevTwjznsvPDrFI/E2A/bWUB11NbzTFJqFRGpf4YT59odAtnBxs+G2PXjC+cbzrtsjCk45a8qyh2CaEvSUrPGnWkiYdaW9pZrDKAsyBI7ItD3d7stbQO4nvJUZKnNHEGM6LGVle4efD3zM5rAgeVwS9J/lEE95KzDJm6htWRU7ba96Kd1QuQq8E4a2g8iH2RiLRnG4rti4lntfMxgXH7y7hYUtfKfCeTZ0S3GuI8o74SUn4tYLwccVoURAf1vhagPbce/sG/6THTDx24uHQEr5d0R956vsCiceOwB2ZL0794EI/VAUhCEqPV2AjT3sosLlHKpCTDmk8IrF4KeiFYitibDh0GMzU05x4vB7CfnxgcXeSeg5mPOgJggKUd7iRw4w8cd5Caoe4aaORLfg3IdXZQN0jtcRBS6Yb5llBemcJYkO8GqLGTSIGb/6Vo98EtKsEfStpxxZ3q8HBToUsuwnjtGZyWvPObztn/KBk1tdYIbm5nRIZx+loy1x1jO4XNCbA3IRMmw6lLfVYMb+/YXpY8JNffjXQ9LYRN23Ocpfxsz/7MW+3BU/PNggFbqMpX+aYbcBurIg6i5ESs4zITf8brnn/U9b/pJn917/+df7pP/2nfPrpEA1wdnbG17/+df7KX/krwHCKPz4+5m/8jb/Bn/kzf4btdsvh4SF//+//ff74H//jAFxcXPDgwQP+2T/7Z/yBP/AH/qO+7/9nZv/Vv/N/IXcBjdQ0RchoWoOHVitkIyjrmHA77FynBwWBtewvc7wUNLFE7xhIal5iMz+c3K8k8qzDeIH/YivkjILAE9eO+p4jVBavPAsxKOknScGqGKEywzv6lkTBxeWY7tCz72IeNHtetHO8AiE9vhNIA+lpOZySVyEucxxQsytS9FriJPixwQWCZA2JbNjbkDqOCOMe6qFt1zcR0rgfWT+kchCJLyw5EvmwJn6lMI3iICspbyOqmUIrD1cKs4DoQuDfbWmVJm16yigE43FIdA/6XoPZBMS1Q76UGALMTwwq605IxF7xnz/6nB++nvEiGOF7zWK0w3Yh0aSi2GRU64jksqP5mmfcGLwVRMZxqUb8xPyWu6uIS5kTvxFs3hMcz7fcbsaEFwpx1HN6uOLNr5yQfFBin0UQe5oyIklaqibCHFuCangqxytBP/4iY39sORltuX01w0wGYZ3PHV7BeN1jO0v6kw2Hr+Gz9Zif+MoVuzLkymQ0ZUh4KWBh4e2GUdiwPp+wX6ckG4iDFvWoxQlJWDp2E01Th6T0UEq0MqhRx+nxhs9en5HsHZVWZDuPuw0oT0AGDlV4xu8V9L+a0h0Z7FzS3sbIyKBamM8qTKMpihh3aJi+tEOM7MSzSyXhTtKnkOwcowc7pIE9CR8c3HK877iZRPzg1RFGMcx3O02kLT9z75x/9ewe+nHPoulZlgEPn95yuPH86pt7TFTBge5YzQN+l7jmmY45vxtzIWYEK086qdmvM9K0QxvB7GzDuh6x/zzGR4J0XHM/2bBVKZfkiBWMjis6ofGbgP1CcHp4h+kD/BuB3SboJzX7VzntgSQqoAkUZ8mGdiTxhcR9ElMfSURqae9ifOSIWks9UiRrRxcqwsKjS0E7A0JHnjRUhMSLmtO8YLNKuAtjgqjnMCyoypjuWUZ77DGJ5+3ths82J3gFh1++pvxWzuR37Nn3EepWUbYJQlu6VUx0K5ChoV0wkMykJYh61EcR4bsV5kpjXYCdGuLHNZUJcJ+k2FLTz784fd8JdNpzdLzBTDzLZ3MmuiaY9DStQlrBnU+5f7rk9Q8OedgV8HZLsUvQuUEHjr6UBJWAI8OqShEK0r7Dd5plnSFSSxa3TI73yEJSblO2dxmL4y1BZNguR4jlwGSPjxrKXUIqO5yDNK4pXUKoDf4uoFQBPvYkVx6ZWVoboITHa48JBEJ5+syj94NiXlSKbu4QFlQH4VLgA4/KDV0gSJ9JbDxoNkwgCUpPfNhQm4BAG9pySLsT0g9iwArMyBFshxa/jTz1kSDYQbQZ8kx270Fc9Zw+XLL9NEcdOrZBgluFzMZbZmFFctrgneCTzx7gY0dWW/ZtRBQOr7s2EthAIENHelCClcxme4rPR7gTg3uZ0GiFv9eRN4Zp21OE8F5b8Jqcy+sJ7ZEkKMAUAcEaVOSgkCRnNdut43v/9//yN21m/2OH83Zdxz/4B/+An/u5n0MIwfPnz7m6uuL3//7f/6Nroijid//u380v/dIvAfCtb32Lvu9/3TVnZ2d8+ctf/tE1/2OrbVt2u92vuwGYTch8UiJXCu8lmy5BriVtFRLTk9Fhxh4XO3ZvcpqLlLHv8AIiZxHak5xU3I+3zGd75m+tET9dEM9r2kTi0gGnGsYdQWjIH+yJpEF0cKAruAuIQ8P6cooa94Te8lzOuQv1MHfyMAsq1qMALwTGKdgpgkYQNZbq+QjXS078jvygQs167r97TSTMsPuzGhVaImsYZS32gSde1CyerOkjiZt6fGLQ3iGVwx04dGvpDxxp0zERNfrXYtwqQNSKuzpHH/a4mSc0juZAYYSimwtS2RGsPdFRg9549LxHB4ao6ajRRJ2jHEv6k2GT8kiuGb92nLZ7tDJ8dHnA5/WC1BvefXDNapszHRes25RqBPqkxb0LupG4QLDtUvZBQLTr+dVPTzk6XUEv2TzQ0ErEXjAVDa5WNG3A5X5Cfr/EPI9JnwzRxtGoodOS/tQMf0/jEXcBmeiQymHGHmslV68P6CceUUpQICJHIg3Zh3t2b4fsVylvLge9wfe/e8r+IqCrAmTj4KCjtRJzE3Hz3UOqOiY9K8keFDz80h3/Jz5nIRpkbuk6TXQlcZ0kOylxUiKU4Go1I9p61HEHTiIPO7oPO95/cs3jd28w7w4hQu5tRyNictkiPIitBiRibLn/1jXvP3lNGrZ0x55o1pG1hvHOoHvH15pb7t9f8d5sxZc+3TNuOl796pzvv5mjGjgKKr5kr3lrviJQBnXdsXUh9aHipx+84bZLqFTE57/ygMNJyUy2XDDje/6QuIR/Wjzml589IbyIyF50hIsOH8LsuKLpNVG859Lm9FKgjIfYMU8aHk4K9LlFC48bSTZdRhMqXOg5yXacNIb6LiV8p8eOBJv1GDl33FuXPEw3HNCwkTF9qekPHO1Dh7MSKwRHsy1RYJjpkoO0wKQSGVi8HNrndmFhZOm0GLpqCdyYBCMgmTSkveH25Qx90FMREAnD+/EtL+8OmR/uWLy3RArPbN7yOL3D/CChkZpeCcK8h9zSH3isV0TOkZSW8dGeLof4J3f4W00RRYznBb5Q7MuYdOdIj2vIHOOgJjyq8RNLpxWV1Mg3AbOTHXavODFbrBfM0j3ptOby8oA0NPgPel4tF8yoKZYZZxTEgaGxAft/OyWJOlgGtE1IoYLhvQiBuxVcvVywXo64azLM1LGVEdG0pZs51Hslk/c3tHPP9PiOxU/f0LxjWR3FWClJfmpL8t4OEVmirMJ92NIdeR4+uSP5yTXiyyXt2z3m0CF6CQeGaAX5OxvyF564cMSmx4xABxa7C9BLSXUsfyTQlD34Q0N/GRI0jiZUiNDRHnusFHghsHOHiwQeTzfxg47mbogZ7iZisLyuBdYGvLo5wUUB4cgQdB4/sjRRwNX+gAPVU1xMoISTozVi0dIfuWF81ITIYLBNil5w72RFqjrsKkFK6MqQuo5Q845wJ2hQFCF0MuSNjtnVMdmsZeIaRnFNOq/pptDPPeZph6wcx13545bfH2v92MX+n/yTf8Jms+FP/ak/BcDV1RUAx8fHv+664+PjH33s6uqKMAyZzWb/3mv+x9Zf/+t/nclk8qPbgwcPAAgbz8vlApF4UmEQraSOFaZXbC7GNLsISsXMtYTWUbcRBQFi3jPJSlziMZuI3UzR9Yr37l8yNj31WBAFBrQgfVDhjwyLB2t6p0inNfH9kntqxwklv//kc8ZZw0+lt0zjlu51gjOS8GFNdZtydTlj9zKjl5IpNTI32AOLP3bce3KLKQPuyKnuUpKo5XU14+RoS3DUYA8srtBUmeZuEtG1AYF1XF/MWCy2NLng3uwOjgzJpMWEAr8QBM8UjQsxFxHhFrKDGntqILOUXYIOLe1jS/eopz8z8FMFW5fQP7asTUr3wBErQ6oM9pFjmgwtysO2ZP7eGvm4ISl7Dt7e03yaY7Rgn0pG45LSRNzJiHTREkkLAk5NQbATBMcN3ARUNmSeltR9xE9++SU6NXy2O+Gt2R1ZaZCVYP9iwraPsQuLXEvMOuQ9fUNwv6bZxkwfLZGF40gVnMR7pPCIO42PHDeTmG4X4RWDx1s4kqzDS4fOO8RSs84DNuc5mTD0vWL6U1s+/InXfPiVC3Zxxsi3KCnoDwVZ1HCvqTn68pJHwZKvxjfIDn7w0RnfkMe8WE1Y+xh1JUnihn4dsXs1hl5Qn6d8kK6wB5ZjW3Hv3Rs0HiugRjEuW0Ybw4NnHaVT+MRTNTFx3sHCsHi6YhFu+MGrU77tTxGhIDnuKGzAxmlGx3tG45JVHbLTmtt2RP+f9LjcYg7AzT1Xz8eURvHp8ojPni8Qcwtnlspoxp/AR998SDtzjKoOdWj5fnlEfWR5rO/IFyVuYfCHhmBqsL+rIH+n4Q8/+ZSnT1aQGeayogxTPljc8oFYokYVb/3UNa/UiP+2fMTrxyP8QY8L/eCUqCVu7JEWbn1Ecx2z+dcLmnWAvINTsWb2EyuuXULiGzLd4HM3cBmMw6Ye5wR9qenagLswZbMaEYSDMM0dOtouRN5pvIDGBTzJlmTnhomvqWxEsUyZVR2xLGl3IeKsY99GXNcjVGQomojmWxnm44y53PODi1P8Oz3BXqBKgbmJkB20U4+fOPoy4DBdE9QWuZLs2gT95QJ31nNZT6iIkLeaQ9vSCUl8VtEeQNh73pXXzJKSbZ1RLQR+rzCR5JWaMZ7XXJczzD4kX5SwsLx8cYhvJZdxTjRqqYzkzW5Gm0jMqWW7zVnkO3yraVcRgTCkYUc9jshGNfNJwSiv0bca8yzl/NunuJuQ7nVGkWgSaVi2M9ZXY8RGY28DvIe7H8zZvJwgEk8nNMm8oZvCKojQgcPXehiF5A1ZNowZgqzn4eEScdBxPFkxebwnwCInZuhsyOF0Lo963NQhDdhO0aeCRgSI24BIGeIl9CNBojvUShLcDXoWETrCrMfMhiwC7Swu9WSXnnANei0pu4TN9ZgWTRx3+FbizwN++K17FC8zCDzL8ynF+Yhw1iKOOuRJSx0rEtuRH5RUNyk2ha6VvP30ElFJnrDFrkIePLkhUI61z+lRvFoewcghE0s396yzEIMkmbaEYU+rhrHHdv5bJEHv7/7dv8sf/IN/kLOzs193vxC/ntHrvf937vv/Xf//rvmrf/Wv8hf+wl/40f93ux0PHjzAThzppKHbB6ikR+NRhWA0bhA91Lki/lzTuZho0WCsp19r4k8k+zCCWGDTnk0QgBD8ytVDTk523F0t0KFDSwcdpJWjL0Km44rSBBwnWz65PMULj2xq7kU7vvPmFO8FwsGrz08GUlUl0c6hDzwjVbBvY3Qt8G8UIpBc3J+CFbhjw6nc81AWiAiqU41fCsKVRAiHUYJEOtSspbmKOHl6x9g2VGWAGjv0UY1aCfKoor/JUO82OCvIxxXiRUDRRYTG0N0DMe6YVy271yOifLC2+K0eMu/HEM4baEOeqh3RaYUpBv/oZ+aQzShE/GCEzzy3eoq78UzGHdmoYX+bYA4H5jR7SR9AvQ559GiNs7CWhs3VGBlKmn2CiyVpWPPi2SH9yBP5nurIUSvPuKyo0xC1EWgcrdEo5fg0muMKza5IaU4cmRcsVxldKAi9g2OD1QqhPL4WiEKCk4hFzT5XxL1BTg3TtKY2Y9Kko9hEnPiCykpuv3kPMXOMJgXjlcNsEmRp4W3H9VWO+TeSWDTIJmB1oMkfbsjjlqg0rNuYk+M99SaBVlAfCR7YiuSgg9BjrebZ5TG//8Gv8u0np7i7OcVdxJUa8dbxLc9WJ4zDjkYpOjVEeJ5me0oXYrqAo7jibbvnqk55zhS2Ae2ZZVelQ8JZKkmLDrtu+Ja+j6sluoObLmR+WDCfF7SHmq4IcXeazgY0S0dwv+I2yvDWIE5aJvOC7a0ilYbVJGHXRLDTFAtF/LimXUm6ief/tX7E+yc3zPI9/ic8vk34wXqBvgro+wj9SY0Vmk55AuthB6oTvPP2G67vpqTWsexSyjIkGhnayPM4XpLklme7BY9v13grSZ9WtM+n1MnAWOiOFK52RElPKwQ6bOFWo6ctHQpCEPvBqy5qiQ01jA03LiMWcLlMmOQNbpXxzB4yebTjeL7FBYL+PKIpI07urdj3CY2NmR62mCDgnW5N/Ljjm+Ie7TogWXo6J0nWgnDS4VK4nEw4yPb0lxEfTpd8fj6h9yE6cWR7Q+o7LkRGPDbUfYCsJNm4wR54DoMt9sWC7d0EvRZMRhWx6rj85BBfaPQ7JYk1+G1EMq9pdiHdJyli7fg0H0Mi2B1o8r7Hr0OuikOiSYfuBWnssQGoc41pcy7zHDcbQFGTrKI0IW6nMaWATYCJHbO6p7/N0AKijcBqjV8r+kgQvfJ4Lak/nyLmgpWO0AT46xD1Q0GnPN2JY/RmCNk6/+EJykqubhfIS/CxJ+s7+rsIXTtsIDEuIDAClzpU73Be48PBaSCfS0IHzkM11YgccIAcAoJaL1Eji33aE75R+ErQHoCLQDmLkmCXEWFqGEnH/i5HNbB1OeAHF8cbiVAKV6TY1GOFQCFotKC/VIyfVFSfjBglJb/87C2ytmd9qMi6mk8uT2hsQLAVLE7vSN+vWF7OUDcKOTIEqaN1msXhjjxsSGxMfaKJrrvfWNH9n7h+rGL/8uVLvvGNb/CP/tE/+tF9JycnwHB6Pz09/dH9Nzc3Pzrtn5yc0HUd6/X6153ub25u+J2/83f+e79fFEVEUfTv3O96ReAtuzAmmhjUGuazPe064jpMmXUNzVRhhcD1klga/JFBvVGYnSadNaSy40bH6L2g6VNuvCZPOtabHB86VGzxdhDR1ecRXRnSPAqHlKo+5Jufv8Xboyu6CNSzkKi0cOhwQLyxuLc69NjgXiksillWsJtmWO0RfvCxikpQiZhv/9uHjE8bql3M0f2K23pE9RAy3UIpUKnHWEVuWy6u52gvuB7lxE3Pdj1C5z36QYOcd8SXg5r+zVE2eJfXCi8hG9WMD/bs3iToENKsYWcGOIULPNHGI2PDM5cTfyfDvt+z3uW4fUjySmDf6shrA4c92zbFbjVh3WIFiNjRd5oqUfSdYvSo4tnNDJk6fCMRlUQdd0Re4mtYFWOsaZgXFfHPVKyKlNGiRClF+BLMTgEC7YCrkDsTEJeefGXpRiHtoaO7jhHXjvrAIqc9TghOsy3Xq8WgC4haknkDfUS8gXYkaJ1Gh47q44zqnuTOCdJ1B0971EZQXWSsI838SwUzVfFqN6NeSEaZoTmAr0yXvH71iPrjnN3IkwYt5T6hrVImuuF8GqIrwSdyzqlccX0XE8cd+7nkX79+i9FBxTv3Lyk3IyobodoAoyX/6ZMXvHk54SLMODN7LoKc3ZsR22rCW09vkTX877/8mqvXMf3eg/CwhtgYVOzYrWKebxdEjw3juqdOFOIAViLBPgtYqhTZQZ42fNhuWMqEZZRxGlb0nedeVLFbS8pQcH07pQ0kJ0/v8JWEuzFmG3KQ1Lz5KKKQGd9qYxanO/ZFhC8U9XlGNxHcO9nwg/0CpR3h3uEmkMwrIi+4rCaUNoZ1TytSJqcFvtMUgaZ+KNhf5vSBZH89IsgMvtTIRU9ch6Sy5+4ugcBjrxJc7HCFxCQQ34bI0x6Z9ZhOU594AtvjdgGqkLQE1AtPcK1QU4v3Ap33LKZbjIBJUmB8z06mnPsJ/TbCO4G5GyMPOh6MDJ++OUBIeG9yQ9ml7PcRcmIRO4GUks5H3JaaJOl4XowhdQTSoHcCFTmWQcY027NeZ2AE2WewfntMoTLUcshWP5zvEacOedhjpKSZDBvZUdhQ3GbMH6xZfr5AbzTRgwq/ToYAqNDBStLbkHA/zLTLqSS9MUMx3wjkvKeuh9HQaNvSH/bsb2L0ShIIgUkH0Z7aQZGGqNYjgebMDbPwkUMuNdJ67IlBnAcEgaH1AeIqQLcebyW69EijkBODTz3txxleeZIKvPZ0J1Ce5wQduBFI6wjWQ8KfkgZ5B13qsFZAZHFSYVNBvLaoViI8tLPBlqirgXHvbzVh0iOlG1wHpcIbD43EC0k/d9BrNvuMdGsxsRp2D6nF2yEts18IdOEJtwLpoH1o6fpBof9SHTMqOtZXMxgL7n94zq4dsfs4J3yhSX+mgEhRL2PI4SjfUewyguOGiW3pp5K6Drj5dEH+dMPe5qii+HHK74+9fqw2/i/8wi9wdHTEH/pDf+hH9z158oSTk5MfKfRhmOv/4i/+4o8K+U//9E8TBMGvu+by8pLvfe97/8Fi/+9bsewoTAQbDYXi/uyOsDJsbEz8QlG+HtHXA5lOJo6mD/H7YXfqEo8JBU0miK4ETkEctdi7iPU2QziPc5J43NKPIV86KDXRuCPOO5o2ID6uUZOeN+McCg2hwz/qIPAcPNjx07/tFdn9kk5LNlmMutex7RNUaHG1QrwMCe4g6j152FI+UKx2OWZhGYUd1io+iFfoFrLHe/wqwCWOz65OiY8aEtVzEJXsthlq0VIqzf2TW5S2xGnL0sY8mm5w1zFJ0POl4yXJ1vP8xSn5BxXjtKX+ZIy4DLGRRBWacpfjrCITHbswZv1ygis0cesHZW8Gj+/d8qHcEAiDP+t4e3TDZFzhGkVme/peIbVlVwSc5Fv235px12XMxnsCaZHC4zYamRuODjdkj1tuLydsXk3ZX4xY9Snt0RDVabRAW0cw6ggqiMbtMLbYe5oixk4sk0mJqCXztOY/OX6FeOUZH5SopEemPdUuor1IqcOQfhXR9AGp67GJIAosbAR5UpHqjr0O2D2RBI9qfOkIlOGIii8fnzM/2ZHS8/ndlLfyO+YHBUHSs2sT5KijyDS7NGBU9fhGMIkqCh+yMQm21MQXgvEGjvYOvdT8id/+ayzmBRcuQU8aPv/2AS+qA6pNzFUz4eloyel4TxJ2LKuMX748479+9hbZXcc795f8n9//iKdRyYls8b3i6HQPEryVVH2EfRVgraTbBGzTgMN8R3LXUPUhF3nCy5sxQQNNGVBvY9oDx9Xnc0IHC9eQlo64cAQbQaR75rLm+WdzysOI0/dvUZGh7EPqVUTgLY+/csn4sMBHYBYOEXjG7+4xraC5iXkaL7FGII46skcFb9+/Ifct6qAnOWioLnN2ZYrtJSK3rNOIN9dTrpsc6wWlDwlWMDYdWdqQ3Suwpz2hd/z0V14wvnYktOgHNaNRhd9q4lmDmFrkVqNeh4RnNcvViHTU4IWgLSK2bUpVJtQHASozdE5zergmxdCvNC5xXLw+wG1BW0/hhiAvETqk9TSpxhWS+WiHGvfIxtJsI6oqpo+gzgXdOsCnDnMbEyhHVIKfwjwt8a2kmSqEF2zzkEqE7F6MqJuIqHO4ex2rl1P6SnH30SF1JvFTw0FT4QJQhQDhUdqgj1vSpEUYAd4jxwZxExBcK0QpCQqB7KHqQvSFAj/MuG0MSI9sBLIWmEBgQokTYhDahYLOarr3DNWhwi8j7MjjmsEaigQbfXFLBPiB4EcjMekwi+/GgvpEDKJXI0AORdV7icCB9oj90I2RbtgohteD0t4p6EaSoHAI54lXDl0JvIJ4NQTzqJeavg7RG4VILeEWbOoHC2QwBAiFpaE+lDTHftgAVAoaSX0oIXK4McN4QDrC0pEsPaITBI2nsSF9rKATbD6acbsc0aQh5bGi/35ObCxZ1JJsPNGF5PTRhvnhDtNozpYd2kJ35Oh2IYvrnqL+X16U9/+9fsMne+ccv/ALv8Cf/JN/Eq3/h08XQvD1r3+dn//5n+edd97hnXfe4ed//udJ05Q/8Sf+BACTyYQ//af/NH/xL/5FDg4OmM/n/KW/9Jf4yle+wu/9vb/3N/zgtyJGC8XsrTUj23GzmtCHEvlSYwNBdOfoc4GZQOsUKjH0q5Cz2YptnNBVCiqNTWBse6pK89bTC86Xc5h6tkWK2GlC5Ri/t0XeJqy6EfZSElhH/TrFRpKn4Y6bU09zonlfrPjo82OEtKxHmmQJ20YTNo60sJhg8H6b+45dkWADqAPJg7zjwW6J+QlJknQ0m4DJvR0vmjGikbjrkDDv0AhGtw5/FdC7EFUPOfTNMiTq4bbP0IXgsh4hDnt2yxiTDJCfToLfa0TuWb6eDJnWsUVtAqK0oYgDgh7e02s+8jPsAvq15nC+IXjYU5yPsKnkZT4i33jCG0W4qDmPxkxljZSG1WpEkHfkTc+OhKloeef9a55/95Dwt/dgPbsyIrvX0r9KKR5G1FWAXUeMFjXd64ROS4wThAcN2UHFmd6zzTR3v7Zg36d4I0F6grjHS1jvcrKgJSodt0GGXwjO+pJPwnBgnN8KhPYcTTcsmxzrJaFz1BPFbFxivOb6h4cEjxrSs4ruJkGNPPsmYXuVMTve82p7gBegC4FxjjZScGBYVxFdEZAGNU0C/rBDhwr2CYGwtCuNDSSnhxtaEVCWIfV6RNmH/D/OZwSFZz4pOA4lhdbI2HDmGtRzaG5GVIchZivoNjGPv3bFmev4jl8gdob/6pMvcXh/yXYqeSu75s7lRGeOWdKw3E6JKoMOO0TS02hNIUPG77fEa8H62Rgx8cTTlvZNDMrx8tMj9MLy3mTLdzfHzNMCtw9ZmJb322suj2YsZYBYerr3PfGFZb1NsZGgvx1z63IWusQHHtNItHdsbnN6pWGv+Y66zzwsuL0Yc7kYMxItTR3RozA7iS46HhxuqcqIPDJwHiL2FpNDXwQkcUvwfjGQKc8zws6TFZZ0a3hRj0imNa1X7IsQ1wcENQR3PcGJoAw1yUHFWbCFqeBKjUho6KSi+t4E93bDZO3Yvh7DgWebZpShRhx7+GGOnRiMkCyaGhl7CC3x2uEYCqiJh6CZvFEUSTgUv+MevwyJttCPByW6DDpalfLo6R3nrw5ISkVizOAx70LEErLTgtv1Av95jA88Y9FhJxaFpylD+iIgDxo2LmH3gSe5cIyyGqHAjjzFNMZoT7IH+3bP5GjN7rMJTOwA40oN97o9TaywfUxvIjAC2UG4GxDfNvUIK7DBAMzR1aCyF1caUXn6TBDuBWEDXjtcDCYZTuk2lLjAQx/g7RfWOekJtUGuFAiPCyS6cAR5T1tF4IYNhegkTgt8JNDGY7xCdR6XeLCSdqZILlvMJEAZRzeWeG/oY4W0oBoHnYBKIpxHbwVeQPJ6GBN3eTDE/CpDncthQxP6QTdQgB17hHfIUtBFAX7scAcW1ylk6kl9S9UH3NYTQttD4Gi9orea3eUIUY2J7tc0UhE0AfGlYUfE1seUzxPm9/f81FvX/PfffsjJgyUf/Yar3o+/fsPF/hvf+AavXr3i537u5/6dj/3lv/yXqeuaP/tn/yzr9Zrf8Tt+B//8n/9zRqPRj67523/7b6O15o/9sT9GXdf8nt/ze/h7f+/vodSPkSZkYCYa5HXALtAUdwnu0BE8srjeUSs98L9rid1pdCmwC8f11RybeGThqU8gKS1NFdMcCj6+PSPwhnDvCRYtvRekxnPx/BArJdNRxfXdiCNX0V4ozJHgLs/Qpw2LvaAH5sd70qzlu/sjpnFDfrqneZFiIofdKPYuxF0r7MiT7h2NC7hOE6pJhDOeaSvZVDn5pCZaeuJZxW6dIws4SLbs2wxfR3BscN5AHzPdVTRzRWljhBSIQnFvvuL6foI4D8mTCrVVTKc113cZHgh1x7vza34le8TR4x18NOM42hFOCx60cG4mjJKe9M5z/nrB4NJ0yMjDcQNdTD5quLycclEFxJUjGrUUmxS39dx7coM3GnlQE+ieN9sZwblmNK0p0oD0ScGRq6lGDW8CRUnA/N6ezfWY2cEO5yQnBxvOuynldYxsJXHa4cuAPgF1K2nOPEFvCXXPOK65KUYU+5itABFZlHRorzg+XXPzbIGadfg7Ra0DIm3Rl4qlyMjSnvFJwfV2gh9Z7j2+4fbymJs4o7qZcTQuaENJ24a4TiDWktnxDkLH4eM7pkHNy++fYMqEKG4ZBS3rMkPmhq7SHE0LIg3f3x3QfJyxeRd+Ml7x7PaIch8jPmiQe0F9G7I89kweWg6SFt30lIsAk0K3TfiunvDAVZy3KZtcs66PeTjZ8Kaecdi0zNKGB8kO9UCyCkO2q5SwsRzdL7nZjaj2Kff1nuBhg8tixnlD/N6G62bMbpvx5XTJRz84IQt62lITPyyZHK/5uDxm82rE4emOuoxY/eKCedZiRcRsUlAfGuxtyHKegvIo7zl4ukb+SkjxweCqqMqY5fM5VBI1blj1OfES2lgTZD3lsaaxMdup4qrIcAGIQpLteoLE0JchvjFcFwliDKMOfNYRvdNTbmLSSUu7SnC1QiYW8eWScdWyC0I0Dpd4lpsxx/M9kTewUpipQB+2VLuQZaeZHhTYPqHMJdFFDycOfxHBsSPsHS7wPEg2fPLmHnJmkY2ny8EpsIniMNvQjhL6jxJmTUd1G+HnhmjSoDwc3LRUk4DVqzHagV9q6jQgnjQ0UpAkkuVuPuTpB5Z2LNj2Cd55AudQqSVxHYk37G5y1EOLaCXrJiPLWpqNYnxW4ntBMOtBelwG02RJfxtgDzt8p1gFIUnaEdUNrdFIK3j48JrtxxN2xITjFlMFyFLhQof1EuUHCl6XaJJbh02hfeCRG4nqHG4kUaWgm3mkFbhSIDtPej349T1yiMB1Hi+hm0rcLgIJwjjCPdhQDrn+JYhe4mYCYS2ykjgN0cZico1qHH2ukL2nH2l07ZHWI6zHxGBShY8E4cbjpUMaTzeShDuPaj39dmCLBNqja4FqPPVDTzfzqMozf2vLrpiwmG5Zr0fkD/bUz3MKrXG5Q68krVeolYQDMFMHBqI7qKqQUd/RzAb4k3weUi88k4MKuw34pU8fcnC8Y735d0fT/0uu39LZ+B/+F/8lHEYg4P35Dd97fo8o6wfikjbURYxaS2TgEPXQlhLCI0uJ0I57H17y6vKI+KDGtAEuc8RRz26Z4R0wN8heIGrJZNujZpaDveFNkdIFirceXVJ/PyR87CjzgMm0YLXJubueokrB4nRNMqkpsoDRtmcher69PuXIltydasw2RFxp+iJCTAf7WJtCUjnKxbA7zcMGKSE/qjAaqu+NCc8aWqfo9wG57ti9GhOsh4Q4MXLEtqcUEeHOM7u3pfAhhycbXi0P0XpIhcNBYB2Lky3P9wfktaWdOrgMGRUt5XuC3BhkYOmWMb0e4jYff/mC3U3Oo7ev+Ne/+gEqscirgA5J0DvUvY5HfcXr2zHK+kENf9iRxA2X3z0kfNLQ7CKyvSV62JAJg590bK9GdCgmYc1lkBF+FnAartnnMV0q6GrF2PaUOqDrNWHnYKXojz0Ukv7UcTTdUu0ibBkQpR2rLiMILNJ6QmMHxnkmEY3AS0F0r8SfR5QjyfRsS7QRmBNLtrNso4h863n3a2/45X/zFmnWc/9gzevPF6Rpg5z1rL4/goVkNt/jck99FRMLR+8ks5M9zz89JZy1+ELy4GTLveMtV2+mdJeKN2rEz9y/5C5QNFKiPk4Gn/LUMJ52PL+Y82S65eY8x39YM3KOZqd5pUYkryx52NFMA9zMsrA10khE4dncjDlerCmDmFIrxBb0zjDJO25mKf13UpL3GvqxpV9rxtpSJpJOSfpdwMFox+pmSh8JTpItU2PYtCHrVU5/4Plpu2Rz6JFrx28/XfON549ZL9SAEy0EKE+StPSNRhtocjBaom4UXR0OtLtKI/aS6F5Ds40GwMnjHaNbx7N2MeShCwi34BqFsg511GGLEKvg9OiOm/V06NS1AbHq0MZhU4HILO23xwQPaw4frnl+dYTVEK6HebQYGaJFw4ksqJqATZ9hb0PuT/eUCh4/vuW7t6c4q/BGIH4Y08eS45M1soO7MuPxyS1Heckv21Pu3dTISPB5PefdxZKXL+f0Ew+N4t47N1z8qzP6qWN8VNCXIfql5vStLTY3XG9GtCjiH2j6+446leTTinqZ4o3ACQZHSS2ZjEsQHq8E8aQhtYYXL44J+kEIzMhh5kNRHI0KHmW3TO9VrLqcT16cEs1anJFEs4ayDzlSJcvNmH6tCQOHmXnqNiRRPf5Ow2VAd+Jw+wChHdEN2EDSTwaynqpBJwajxRA5vlT4wOMnFnmrh+L6RdSuLj3heAjW8k4BHpMJXATx7YDflZ3HJIMMxQaCsPDI3tPMFe1MEK38F5hfi00G/VGfDyOEPgc9/HoGNsnMg1HIDlCesLf4Ug0cgNrSHgQDATAVA/SmHd7bRGyxWmC9JDIWM/PISiDvFPIrJcGkY/dsCoYhB0FaNmWKTg1VHxLuBgKffrtGB5bmULC9GDH9gUUeWuzUYe5Zjqc7rJR8/iLj87/y1/63bPz/mPXu0RUvl4/ptORiecDTJ3e8qMeMXY/pFGIvCXbAiYWdYuoadgcanxlc4rkKc47evmO9HOG2GgpoMkV2AXY8eF/TjcPmFnGvJVMdy2nI0eiG9MZz51JOf/eG9cczwJNJWCpB+mhPvByeTL3XHKV77vYTnOsZbw0Xekp7LZimFcm841Zowshy0u95ebGgO2hJ9hap7PAGoQ2lDWgbTSsDtrcRqeowRUC1iZCBhxx8Muxkp2/taTqJLQPuzqfY3PF8f4oMHJEDVwnEsUGOHedmgrrV1EZjPBwdb5md1bj9hMlBya3JORgV3HUZvtNUfcTresbkc8ukMxwmWy6iKcGzCDf3PDhZc/VqSjeBtHBkhyV1LtmZhOgrBbyJmDzZs90nhLHl3KREKEyksJcR12mAT6F51/B6N0FdKc6yW6pJgM099bOcZNIgPwvpHhme5ivMI8/rj465dFOylSG8kohTjYoYmACuZ20yQiMYdnFDUbKNwp9Y5FaRLT0P37niV77/NuXYDkEiTcTnF4eYrWIdaqrzgLPHd9y9mhNYwW/7XS+5vJxx9XJCFQfc0zv+d1/6nE/XJ1yuM7J5zazruQgTVhc5ZhtSK4VoHMGt5NeKI0bvbdldjmm2CV/9ief0KiTBcjbZc3k7IsaRnCtu84gsaoispVqEvP3gmsvljKoISUcdz1+cENCjTg2bXLP5PMUee4QU9FnKamIQtSCdtojW4a41vtVUI4Fae9KjFllaDCELUbAqcrLW45/B4fsl6yAjq3teJzGr85zwsOXNeo5cWFRsSW48KjKEwtL2mmDnKM4U8UYQ9FA7hQgs/laTPKyRDdQ3Ay71wXzLq4spm1qivSdwFhcIwhZa7QlmBn3UUMWC6FJRiZC3siVfWdzy3908Iokabu8myHVPVHnqqaVoY/z5jDkNx+/ccjsfs1nmBNJCpegzzVU/YeZaHss7FkcbPr2d8/GrU+wm5MRsKU1OON3SeYU5sJRtRCZa7nY5V6sZJlBUW8vhqEO9UXyyOSbLa8K1gVPYbDJE3kAf0zzL0Mc9dah41We8f/SGZ2KMuwzwTy0uH+hsu1XO8cGG7jqk6FL0pMMFEusU/cxiEextxrTpkAaiO4/NLZ1VgxNm1DN/Z0NRB3z86i2EH7oOzV2M6TTmKsIpuMhjImmY3Vp270H7JkW2gs4GiLTH5Aq9FsiJQVxpTAQuGlJHw93Qqve1QKlBWGfHLQINpUI3jmYuia8HUA8eqj7GpaBbRzsdgmZ0OcT89idDqpzeDRIy4fji9C+I1g7dDN/DSzDZ0K6XHfjafxHxKxiG/5AeldhVMrzM/XDSFxuB7h3dROKVRnZDZyG98tgQ2sUw/zeRQBaKoPFIJJRfBPjE4F5ENCpGIIg6h0exnMck1uFeR+gR+MBT3JdEZczBfEchA3zmqL7mceUQEPTe4Rs+//SMsWk5Lms+/02sl7+li/2XD6/YzKe0m4jQOG5URNCCjB1B6ihsRDWGSTzYdHbbkC72PBA7ykRRdJqr8wOia4k/MSjjEcrRnghSZ/EVQ1ZzLfFtzKbP0GuJeuK4uhhjck/5zTHtA4kNHCK2EFpSZ5nNGy6XM27kmLgbMQ0rflgf4rVktOkQq5jyrWB4U8s6Jm3HqsoI8w67VXSBwihBLAzNNsPZiLN7dxSNpihT0tzRHVVUKqU7dEz3hvuTPf4qYF1mA2jlGNTdsMO1qSB5KWjGA3JUrTWyduRhy/peT/pywG7udUD9OuFgXnO9nxIsGvzU8EAuWX4yZ3U3JryTPGPCwhc8UxNIJclxT1w73A9DzJFDtJbbMGL7JiF8WjCTNV0qURmchmsaFdLWmizuiPeO6GxFdOCI9pbP+kOqIuYk2HEbTrneTmlSybsHl2xHI5pthJ565HHP62KM3UtMJnjUbtnuMtokACWGVtskxJUBue1pZYQoFEI48J7E9dTbmLDx7McR33vzgONwQ10liJElqhqOXnUsQ4/bQfTSchtOEAZEpXj+4pTzPke1ktnJljebCf/0l79GmPeUgUIZwZPijks3wjzseGd8xadXC96oGdN3StptyKRqiOael2gul2O2kwg97kg3kkprdNhzcaRpbmPKfUzaWJpY8t3zB2Sfgv9yx6v1AZP7BfU2GoRQXcDROxuIHOwFly8PiST4eY/zAX3mSHVLnzd0tzHjpOb65YTR9z32q4a6jpBPW67vMtLjhrbP+KnJFd/bHFPf61Gmoz1PYGaZ25Y0arnZzalkzFujKz41h+SjZkhK+wK0IgtQOOqZ5OmuY5NGRNZipOVqk1OPJUpA0DgCb6mkptaKoPb0VUB/G0HusHNDuJfsxjHfuzpGxo5tHDE9rGhryfHpmrtNhNppzj68Ibcdqgwp6bizimy+p/04Zz9J6HPFss7Y2ZTo4xmqgThv2RPwxi6InOPggzvsTnN1NSM9rCianPDWsxnHCOfYTofgJhdCvIRqHMBYwYtoCNoa9di1GwrVJmDx1optn/DRs/tIJwlFz8nBltfXC7oIRkVHkUWE93piXyIjy3RasPzhAnUV4HcKiaBTEnXU0rQRQjtkatEG6i6guM6wdUCzjIkzQ78POTm+46adMJoUyJ2mRhLWjl2S0FwqksriQo9tNOIiJDCO7szRC0moh9l4tB6EgLiBTOelIlg7urmgPwyQe4U66Ol6DRL6bJiTCwsmEYQ7T7jzyMbjQoGILSaVGBMMLXgHXnmkgWYh0Y0DK4YTe8sQxZtAuHfIHlQzCAJxA0XPZLC7Swl2YFLQ9fAYXORxkQAvqA+Gn0FaiO8sMHxucyiQ7RAHblI5jIH2CqcgKD1trNGlx009baDoQoEqPQaJP7CITiI9yP3Ai1mPQnZtTBBaXCcJNoL0pOfZD+/TG4k8qdg+D39T6+Vv6WL/X33vq4y+2tOsY6qlRlx5ODPsNhlx0jIdl3TbEGcledhhnjicDtjvI/AOuZOYVmJPzfAkLz1NIEhrixtBNOqYuoatzYhXljgruZlkrH84IzqtcKsY/17HuDK0E9hUKYduz+51ij1wfHh6zu3dgnvJhm6t+bXdmNZrxKEjuHV4C5NRhbGaPQndTCCjHvNZShh3ZHvQO4eNPEo4Lq7nqAxk0NNdRszCLcGsYJdrdOW5KSa0oUb2nsnTLfom4+RrS67KCWEn8BcRXoPIHOmopnmTIF6EzN9uae9L9F1M9yrFa8uLbsw8LPB3miaJ0NcBspTUIiCclSSHHTupOYs2vN4eEZ0ULEXCajXnvlsThBWT4z3f7h/ztl/xyk9Jtz39Wc/S5FgrIR1Utf1IsX1xQB43VNuY3gbEuWF1NcZagSsCxHFHGWgOH6wpVhlx7dh+Z4SVAuKBynZzmiJ7zYOHt1ypEc0+gFrhZj1NEUDQE18L7NwTN4a6jsmf7thfZZR1SBQYRg9q6s9StndjHrHjk3gOU8t7i1tckbMN4G4/pr0K4cs7fvL4GVeTOfFhRTsTFJ9mtH3E8WjN7bMDXo4ykkVFdx7xbX+MR/Hw8ZJ1FVN3ks/8Efm5Q90z/Gdf/QH/8vO3EXtLKxMejLe8siMaq/FW0cZQxJqDviae1RRPM9yVpo0lYqmJ05Zql9DX8PB+yadXU6QRcNqjPrPclxU6cnynWzAaObq7iIlt6WLJ5Kzk7OmGzXpE+LghEYJP5YLg2OK3ilejlHe++pLPv/2AbqoJMsO4qehmiv2nC1TvCe+11KlmEe1YkXBw0w1q6E6QTyvugpR8C/0uYvtAkfc94VqxFxGyFQOb/UYg+hD5mCHyORveeE0j0NozOSqQ3vPmes5NDZNNz/Txjnzb87Ke8fz2PmoM02WP/Cjl4+V93MxiF5aw9BAE+LlDp5bRzjA7qHkTjFGjnsNVyavqEF8o5FFHNCl5fX6ELD1Fk1KfDxYyg0MuesRaU+4SijgiBPbvWmSr8EYRGYdvJVLBPCrYRxFmFXC7nOJGjqQ2qHsNqoD1LiMMe6KjhtIniE7wiIZP7IQuEOw32TD6cj3OKwg9PoVwJ4jHJSq1iKlh1WXccxvu7nKKqSJ41CGkYdb2LKsx8YOKttKUqzHRomAfRHgEaikwRtFFAj/1aAOHox1v1AhVSOS4Jz/38KDHrMIv2ucgjMdkEu8FwcrirUOUGhk4+lANm2IHunYIwGSAkMAXNrtKg4J4bbGnFuuHvBPZDyd+GwqEEUgLeEd02lCuE0w8QHpMKsEN8/ihaHtsougzQXrlcHrIPRksKtCNwScO0QxwnW4s6aeD6K4gRFUSG3uCckBGCzsI97wEJgZrNN6AyTxJ7XEIrB/cELqBLpeI0KOsp3+eoo49h7Libj3FBJ7dKkamDhF6OiXpFv/h/Jn/udePnaD3v4ZlM091mVGOJPbUISuB7wRqIwd2etwgxj3ee7764ecwsxzmO8pAU8aK0DjyR3tmVcskqMhOa7JFick9jVL0y5BX2wP2KsRMPa+7GW0b4FJPrA3JYUUjNQ++dIXEo58rbl8c0PUphVB8ejGjbjR50HExikjf3hE8qkgmDXZmyMOW7LBGHBr+0w8+JQ07ZGhJ39rDxDENK6owJpx0yI0k092QrnVsMSeW82hEMm2450vERUC3CQkvBlDH9NoSRIb17ZiZrmn6kHYmGT3Z4fea7T7FtZo2FdSxon8RIxrBPCqJRy0T1dLcxcR9x5NwS7mQLOXAcA5SKHYpE2G5eH1IPG4Q1qO/r4jqlukLS0HAJ7enHE43dGgesCFbVJgywN1pDnc1477FTjwrG+MzR9OF2MlArLJGorKB/xw5y/TCotbQVCG2Faz2Ofpeg4sd+k7gwiEQRLaCV5dHyKVk3JcEOxC3Cokne1jing62rePfdoOfWspdQtcFJLOWFsVFMeUkXuOQ1E9BTgx2FfKimPPmKGVfxfBhwzjbMtUVz7pDbvuM5tMRj+IV0cOG2aMNUWR467efs98mjEYVZ+/ecLcasZtKVlHALowwSvN4vCU86ziqKv7ZL3+FN9spN2ZCmAjuuoizfE1Kjxp1TP0g2IzSlmAliLKhlatqaALJbpfRd0Mk87cvTjCvE07SiiO9J5sZZOS5ajK+Ji8J+4Grve1Tbrox1T6mvk3Y5ppXr495/sMDvnr/gtBZ2ipi+XLCs9dnKG05Ujt+3+Nn+FDyvtpgFxZz7Jh0Lasu46naIb1HHnS0RxamFhtoqCR9CcswxN9EVMsYsfMk1w7pB+hOdywwTwxOD3ausPKgHCIyg/jrV3OWRU6EwYSwbUNevZzzQk6wtx4ZWISH60nCs3JOP/XkUYN0Ht9DbRT1PuRmP8asQqp1SOZbxMcxV2rMOK7o7hniUUu/jqiLCCsVuhpOeFobwgc1/iocwFqdIX3lCZOe+GpwIAgBOrVIaSlWCUudIcLhdKnWAnGrsa9j2o9H6NiyJaZzCp13vP3wig/vXfDKZijjeGg3uGcxo7MCc2DR8xamhvSgpDPQ3ndUJmS1HnGSlBw/3tPHArlRdG9SxEfRYK0bObouoA40yTt7rBIkquFBeEfsDaN5SThqGYsGl3gu/BTRSXTlBzfEgcBlnuTDHd3E0xx8carGYzM36IC0wCaDdkiaAVBDYOkOQXqPkBanBTYEaQZ/vHYOk0haOQjmUB6CIUlPNQLhPF56+kTSLgfv/1DA/XATfoBKdR68QDUwqluc9rjA4tVwym9OPaoDVQ1cEmn9kCtQCyobkbzwQ+cglfixHxwBfGFpbB3hzWBZ1DWoHlwniJzlkd/iE2DsiHYeXTn0VmAnbjhYTQxi2uMnDmkEvlRErSFeOZ7my9/Uevlb+mQfBQYdKsQ+wJaaTFU0fQRHhkBZdAG91xwc7PmlX30fInAjTeYa6k7DqSFKW/IRZHFNgyaKej5rT2h6jaohnbXMwopNEKFiC1cBwf0C7iSPRhuWIuf7v/YIP/JkueFrJ5dU24Q7p3AngvWrmIsgYbdP8LVHGKAW9FMYHzb0L2I2ywnfmE7Qox5RC5RzHB1uKGYB6rylloJJ1Q7c7LnjUFdU4xjTJFx/5xjZw8hVqHnPyWTDOTOuNlMCa7AHnt2350xOW/z9lvZ7Y8SBQ5cOOk2XSmwXIL0cFKmbGDG3jJ/s2S9CFknB9/7tPRZvrdG54V64IUgt5+sZUjjOnizp2oB2FVE/VPg4pj7cU1iNsg5aibyEl8kRzaVmdLYnspabOCOMOqpNhCkiRtRUscaeB2TXID40WKEQT3oaITgsa6p9SpwZ9vuIrO/pygFxqRqHOmzoXABaoIUhXjSUocIvDf5Bi11F7N+MkKnh+IM15iqBVxLx0JJNG+oyQh82TN80ZA86jqqSVZmRF45puGe9DAnjmvY8JZSQ/GyFuxNEjUNMa1ZVyrjbITtJ1cTUz8f8zH/2Gf1PSF53U7iRjA5LwtaytilT2eB7yw/OjxkdFXRS8/Z8yRPT8cn1MTpq2d8esCsyHp9cY04d53dzZO/onaawAWItcJ1ilLZsRATlINSSOE6nW5o0YBVIMue4VCnWedwE1nnEwajghT1gutgRakftNC9fLjgJ76i2OSaXvOhyClK6kUfmBp9aXOowneBfLe9TxZrvvj5GnxhkI9iHmky1vBIZvpHcXs+Ijmtufci46mmbACLP+LjAfT9HZlDoBA4GOIryDukMba8xU0fsLYEBv1LIzEEMIjOYfUxUWmzo6WcQFWBuIlToqCYCX0lUanCtQF8o9qMMoyxohbiJ4MQyHddsdcq6j1FbsIce1YR0nUAYSVo6ikRhrMO+DpiVLfVI0p/29KVGx5Zg52nuO4IiQO08adzhvaOMNJWWRDdieA3cKopDAQnImQElCFKDEpLqOkOFDqHg8vKA2/1AnJQdjJOacpnz4J0VfWC561JMr9DXIeGkw5IwDyvqLIGx4HWbs1xpJlVH5z3tRtBGAc0mIN32+Bwap6gX/SCgrCLWu4ju2CECQd8pAjkQMaVzyHFPFYSonUK2nt4o/LOcYGxwrSRQnm46CKClUdRTgXKD2p3MUccSFXvCyuG8xLXD5sFLwHuKdyQj19PtIqItSGsxscBJhdce2QIM5DxVDKd2P+Rs4bRAdgOZU1WePpXIbhgXNATIhUHUii4WRHuHLsGG4KUYsLp+eBxBAYbh36obinwXKFQvcMEgMhROEa6/IADGg15AaE8rFZsuJjOG2oVo4xBCYkLw1qKk4eb5gtm4oFqmtGNBtPTYI8nmMKB7M/sPVLf/+ddv6WLffpbDQzi7rChGMbsoIlQGbwRtEbL7LIWZ505npIcNUeeGtK3A0CnFTNZc/3BOON8y2nle94fc/7ggl575/+GO5TbH3oVsVjkqNISHLe7YkVxD99CxLHJ2mxQVW7o6xOeWj9s523KEuvSo1NE1IR/96n06GxA8LRk/d5zeX/Hp6zOK3YTqZYAWjkYq3Faj9hKXWFY9TA8LqrOGeW+RUyjbEHXYMI4KPJ76OkXVA5Yy7D26lFyFM9IfKupDidiG+KvhxWaFwr9l8QuLEYoECwMkEFENTwMfgM0kbh9wvZOM84rvXT5gpjoOxjVXdsTz4pAsrIguBBfRnDBsEBvF2DZEtwqXOl6mYw5Vyd35DKstwUjTLyzzfc2qSPnyg1fcPssptwmikKSqpd0FZHtLIT3mABJlmd/bkeHYLTP0YcfDkyU//N5D1C2YuR/ClLykTyS69ijn6RaDH7cZCXwD0aJm0w5iwLmsWCx23F5O2OkAXUnEZUDwYM94VlGsU5r3YHuT0peSfNYwbuC9h7d8z0+5qkZExw7TalavR9jXMY+ervn8xYKD44IX3z8diG4Ty1e+csN3P3nI5i5hnm3JDxruRMqVHaGeSZZjTfpWRbpqiKIeETreXEzopCbMLZ+rMTpy6ELSecX1D+aoqcPGlvIiIz6uiXLDqguwAYy6lqZPcXiMU9w+O0DgefvhBcGh5WU/YRYVnC4K/s35Y8z3I0Rm2JwIctPRlQEnyZpVnOAXDqcdm+UY1Qj6e5Z06+lfx8QPS3Z9wtcenPPpco62munRhpfPj4iVY3WbozpL7g1mZAkrzzgoWdUZMrTorWB/nhMIcDtNKC3VfY/VAfFGIAPACOLO02uFOXWo12KIc1043C4hzAzh1mEmmsz21HWEdgafQ9Ya1Kyn0gHWKcQHDf4yJrhSAzSlcEjp6NchfhXA3ICC4LWmv29J2g7dadqFor5LiBrwkSd+f0f1vRnmdYIuQGYOkw/FohuDm3iEdCTOUNYRkkFVLmOHyFqCkUP0Cmsk40UBM6j2Cf5a4zpJErUkccPOJySvoR+BmXq+NrnhfJ9weXWAXQckjyvGb63Y3mU8CpbUmwwjBfZakq8Nk9wwfrvh1d0EtbWgBV0Kuz4g3HrUfsAuy3AIrCpkiO890UlNVHtED0dpwUU5Y3TQMWp7+n2IbRXhShJYy85p9MjSxQK/GYR2ZuxIb8Twe9hLTC/Q3iN2il4OsdzRHto5qEIS1I7xKwtKE1qPDz0m9WgzjELaqcc78aO5vLADGhcPylr6TA3PzfSLtv/QyUc1nn4OWgiiytErj43lQEJ0BpMonIY+lSQ3Di881akk3EK0Hoq+7IYTvXCeJh1QvzYSCCtopxDsoI8loXH0U/DPBoqo8IN7wkcgAsjfCJrOU7kYx9ClaI4FNCFh5+ij3wJxuf9rWcJ5xHVIkSowENR2oNyhSNeWp+9e8vHnp4g5mFiia4nPDOVFiplLri8OiE9L4sOaJpOoS8vtOCGJOx64LbvrCbUQhJGncwH1SnPSFIy+suX8YkHVepo6ZCYr9l9wjd0xpK6nq2L0jUNMDa7SBBLKuwRxJ9m0OaoSmFJDLogfNZzZjmIfIU4c2y7B72KKVyPIHW7U0o8dyaynfzXmZa55olfsSvBKkFxZ9kGCDSFYerwYWk429KTXhvYxtCJAXw4QDxkM0Z6RHeItuwOBkpZQWQgd8hzsbUBRZ8hCUSaaV+cLhJFkVy1ZbKifgKg9p9M1P6zOaPYR/dShnKfaxVzuY3qtCBPPtYwxPaxWCUYovqNPwAv6VYR0nvGkQlWeOghIH5SIG0VlNc3llJHomSQlrvQ8Lxac3NuzyyMe6C2f5gt07djZBPmZQuUGZh3diUenLV0T4xqJ2A1pXZ1SnK/m2BFk3uCfOEQGegym0TB2/Ofygv+mf0DW9Vw0GVsTcPPRiLe+fIXra+6NSj4yB2Sy53aSs20iVGjoFfSPLXnRESrD57dzqssU2TuWcY4qPOH9Hm7AHEoWsy3NZYzRgt2rMU4Jgi10T3qSjeP0bMcmmOACyfbZBDGW9FchXoKzkv7TFLcwHK4bdG7Z7xMS3bAzEaoV2BPDRDacXy5o3gTEuWUbphSzED+3pNP9IOS6TNg1Kf2J+X+z9yc7tnVrmib0jGrWq15W7Xr/5TnHy4ggQgEJmQ2ExF1wDTRAINFIJCREiyb3QSuVCCSUEURmuIdHHHc/xV/uyrbVq571HAWNeaDtAnSESz77ZntLZjbHGt/3vs9DvmihlYgB1GogsoHeR7xaPiHmgr2e0h9ipIcPDwtEHFj/xYbHkHM+PXL//Zq5qehyQeQCzW3O2a+2uCcND4pcWfzU4kw8shoIDJeebN7jYof0UIoIFwnkoImOgSEWhEgShCB+ZDTEnQzhYiD9bNHfdnipSFRLXcfYXpH7gd5LCAqxFzgtiCcNyU1Eu9SIUyCPG/Jpgz9AuAiEdeByeaTDUEYCIQLxyTJcgqhg88OS4Zkn21lwntO5QqqA20f4FPReYueSpolJGWicIVID6UXDngxZKpT2qFnH7LGhKTPEKiDPBzprEEKStJrQKLpZQLZQ3k75+/VAKTVhCn5qOVYZ8sFzGZV8PMxxueSSkva44PyLB+bTno+7JZnu2YWM4ps94jZFpj22jelfWNaHHvFVw+5myvKLAzf7BUkZmOUVURl4aKak28CrtqZ/PXBfFEymFovg3k1JPjiiZz1NiOgXcLbekUSW/f0U1UL6zHLnM/RHRX81dtKTeUOZJVgliY2nfx5Qu3GvHyJQS8uzsxMP/2lOPwuIXtIXArEIyFJC7JANODGG7oQNCOGIj2qk/3nQ3tPPJcFKrJcQj7dxWXqaM4EW4N34/USA9tm4JooO441/mI2VQq/AZx5tPfGjwtQeYcB5hRr+XxW/QGglfWdgOv77dhVQ+5EtoB8UXSQIE4f3guhNRfiUIbuAqT35VUe5/eOel/+oD/uAoNUaGVvixiP3CpyBtwMnFeF0hnzR0RhJqA3hIBDWgYI3tuJ9UXAMKcffTXi+3jCdlZgU7tsJ1YcX9ELhOk3TKdR8INIDe5dSHQT9TYbIe1QnOEwieqlYi5bjpwzTj770PO2QwlFWGTJA4WqGRUQjDaGW46c7L+l+zAk2xSuItp4sUsgrh3gA5zzdEJP83tHHORmeZpbwoTnHJ4HoafzkqnpIbyzNpUJGoEuHOTi8kvhaElsQB0X5TCClQ/YBF4+4TIFHVpKwtkQ3kk4JlPHk/UA/GTjqhH+2vuN3f/WSKtPkuqXtIpZ9x8P1giLuGEjGkNXUoiqNFB7pA5wkqgHRKIQVROuB5phgboEve8Kjpj4k+FWAUtHXGcMKkhJWx5bTNEZby8YkNDvNy+iGtWn4+989o8k18bRDC0t7oYg2BndS5KZluM2JX3TIUtO6cfIh5tBpATtNuXL88794z+67M+K4YUZA1IH/y49f8PbFDT81z7lMT3R9xnDh+WAy5q9qPuwzmlSSbBJQgcMpYYgDuoQXxZ4qjjm6lPOiRLyx1LFGnRSf1RS19ZjN+POo8gSNR94kuIVFF44218TFwEEYDk8rxEkRFo64DiSdw/aG6LKj/5BRfHFkv5mMK5hbga48pyuDxDOsA9p6qjbBGYm56DDZwNO7OUE74qLj2CZQOKIN2CmkacfxlDHJGorXJYPUhFaSFZ7NDwtWqxNJ0VNOAv025fTbGeJZT506qqcp7cxiJj3iAOm95y/f3PHfLV/gpWV+2XGSoy/BmgjXK9KzBnOEBs2FPrGZZGg5UPspwgtixlrY/hDhpgOhU/QJJDYQm46qjcc9eG/wg+YQMsLKoR4Vu7sJIvOYIdALhV16nNGoiUJ18GK15UEUND4Cq9BPA7JUNI8ZR5vg8bRDTPpiGJkcvaBLRrFSPQP9yvFisqPyEZVL6IImE5Zhq+hLgy49LCWihemrlkoa2ET0kcFUgq2bUlw2xDl0VUwS99idIZw0k7ynywX6D7fVP/vlNf/202uqTwlh4YjOLEY6fj6cE5mB6pRQYSm+PnD7sOTuN4ZuIsh1Tz5paE4a/lBBXX21xxrHuTrxoVrxZvnE8SknFwPlEFMpRetS8m9O1A+GH+/nxFtLJQ1qVpO6nuge0i8a7NGQiB511VKmGrtV1M5wOT9wT8LZ8yf2es5i6InOLHw5kD813O4XxImlQ/Hmyy3f2fk4yZkNHLUk/qJlWAnEQfJCl2xXBrFXVG2MelDoyKIzyyk16INAPwZCKnAIaBWygaEISAH0Ht1K+vMxqBd/U3Lc5ihvid9rghXYTGFTgak8shX0xZgh7OcCd9SoRtDPBfF2FPjEW4FNPWlnsV1EP1PoShDvPfp5D4OhzxVR2tEOhsg7nNf0vUac9yNN8gm6Q0wr/riIm3/Uh71deKKsw9cGvxpohRlHMzvFICR7oTDnHWoXM5tWDEMEFvyV5X2TUNga4z11yNj3Kd1x3Pv5qae8j5lNasJeQTyO50RhGXJJuI/wc0vvJOGZQz1o0hraMiXSgURY+knM8ZQi9yAvB4ZGod4nyFeWSdlTuwQ/GwUQ7XnA1mAngWTZ4YD5qWeXRqi9QgpBNxkRsTq1xCeN7CTxDtR0wO8Nsvf4SCJ7Qffakf0QcLmgvtSkDx5VO4T1JJHGJ4IQCXLXUhUaWWu6WSDuFSH39JFgOq84HTOksYTU87v75yRzR9NqHm+XAJRri3eC8DkmDBK78phWUGwcTaLRyqNmjnDmoQzIa0McD0SJpdUJchCYVU9Tx+jjiKrUNiCsRN8qumeQT2r2P86IXjdk04rrzYLhIaF/6xgQRNoh7yLkFy0qEXy72vBBTNjbjNlnT/mUk7+uOUtLbu5X6GhA5Jb+xxxbLdgPKcNjxvL5Zx42c+7aguXjhKelZtkFHvKMuLG464RG5+g+MAwxW22gFHRW4VaeF+d38PeGzy9jJm6gvs1RX/Ssdcvxeg5ecvWLHW9mJf/m80vax5RgAjIBc9AobXlWlfRKsmLP+2GBmwZoRnjSgCTCYX9IiCpodTJqP1cBWUF7KXCTQHSCaOKwZxbXKvQBcj+gdI9bdLy42GCSgZvTOe2jQb+qGU4JZjXwi/SR9j7nQ5NTlwnZomZ+UdF+njPD8a4qaA8JxVnNcBlhUksZaVyjcIeIfh7wsWJQisNUU9wM1B8zHqYRy/MjNiiiQ48tZ7gyRk56OCju9mv6s0AmNa+yHQ/frxFfOfwsoD8r0jcNTZMg3vS0UuD3BrOF+kqgjilqCLjJaDkbppIFYzC3UC0Pbkoce15MNrw7PcNvFR93K3waKExHs9YkzuLx+CD+UMsUKO0RNajY0UnNEEHsPWJhCT/FbN2ENhWITiEqTbfXuLMBd+7GUXPR42LB58c5aIVvDCGzTLLxpq/dwKJtqTtLM4nQKchWUmQNyaDwkcSKwL+/e0F9m41aXaUJ14b+bQ/3hmEuRmbASpId4KqqKF3K29mR35VnvHyxYWMSgnZ01UgnJMCMnraO+VBmDL1h9ZePuFozmVY89YLd3YzF+RHdaHb7KcvpiW4bUYuYQQgaoclSR1NGnFV7Ts5Q3hfIu4ib9ZLoq5r7uyXypBi+bjltUy6Olm2fMplWrLOGT+/P+TTNOXu5p24j1qHlv5h/5t/6l0xEwOw0L+Ue5IKhVCzXezY/z/nXf/GRjxcpf6YbHPDXf/MGORuwrcE6uHy94Wko0NLjvhsxwOaqQXjPk0yZxz1frXZ8H85oGkOEJTeOoYtwM4jrQDeV4+heCZQY4UHdQqMr8BGkDx6XRiOetwHh/bgauEvoz0aip5w7onvF2+meu5DSCk2RN7hW8bQsKH3EZP7Hvdr/oz7sWVjUxwRJwDYxsRxxlkMC0YPCeYF1EeJgaBeS2WXJYZuzGjrapGezL1hRkiY9WdQi45aH7Yz1+sAOxWFbEFae4CTpxDMIxdvFhut+wXlc81gXmEeJTcaDKt161GKgQaAeA8Pz8TCnVYjY42pBm0pkAvLwB069cFzamscsQz9JKp0islEiovpxehGpAdcrxADiZIj2gfpSoAxwMgTj8ReeYRj3WOpB000hv3dEx4CLJLpy9KsRK0n7h9RsPvb81XQgFIFJVnH8bgES7MkQXQOpRF8JLt7V7P9MIRJBojvkvYGniPWzIzfzCH3wRAc5KnsH0C97+lbBJsIPHhYD3RtPu81IfY98PjCRjnqbUniLTSV+6v/faWaXx0g3jubUVUt9TEmOCuegLiTxrw1F7Igmmv2ZJMk6ZucN/WeYr2rKmab8IWdIBVEjmU0Hrr2g7w2iNUgf+Ok0xRtFMql5+DThvZ8gZOCmyDHScutSzl+Ovehp1DIJHY+/XbE+r0YOtgdbBETvcYeY7/cr2pkhL05kRcfDY05YN5SFYpbUbE4Z76oVkydHmHREZgyStjIghaYpNOU+JZ55LhYHNi7DSzGqYg3Mzo64UrN/P8MVmnTakUwcw5kiVoFUeGxiSNY1zioIljAVFKrh6XFCcV6z6gduTynNLGAfNPmppXYSuVUcNjOut0vmf7bjFCXYHDZ3M+Syp0og8x220TgvsZuYSRl48/UT779x2MeYjUyhGetW//F4yddfbohLRTOHaq+ppopaJtg8oEvFUGv6iaD4ecDlilZoPoc5dg3p72OG6UB/6XE3GUwHZp8d22VMlA+IzzF66dBO0E0E6ijR0nE+PxKtG27+bkJtZmRf1pQ+4sOnC0InEK860ofR+cBUMDEDzdEge8HxUiNqxSSt6KQiaxx1E5HNO5KHwGFtUDLQpYI2GJKTxZUKUwX8xQBKEPWBWHgONsLHECJFtgkU6xN/8fYT7+/PqX6SlOsC12b86S/fs+ln/Hh7ju8ENgxwNt6m36oDnzcTosLyfLolEpZNN6WOBc44ok+C+GXPoctI447Vyx3VyXDSkuSiJZ63zBt4PEwZHhPii5qkdwwo4nRgCCMSvP2YEqRkNyi0cega5kWFeDUGC3NbMZwm1ENCKATRfcTQBazS3N2uMDbQXQZE0RPdKbRwFHFHlWSorSSKLbUwNNaQINjKHHHVY62i/n5KtGjZkfB/vvmGk0uY5yVtlfPpPIXYES0s//n0jr+6SPiPPz8jp+FWSJo1vP7n13y4P6dYVHS9pmsN2cNoRF1e7Xi4neAGSdQE8rmlLiQ/V1O6ZUBtHFnUsS0LzHQgOkG4DMhWkzBQ/4FhoI1jyMfgndkF0KNpERlw00AXQ/pJIuYWNxck+xY1G7BB8sGmmIMkxIIyMlRNykp0LF4cub37R+Kz//+Hx2wlIXXIRhIMzELNMYyHpugdYu2Y15Z6FriMj1TS8DLfcf9xxqxo2PnAUCXkFw2f2jmmBXnuOH4ucEqA8YgAuehI5MDxPudUGNwgyecN2yoj/7qk3OScvdnSt4bjw4TZpKLNLbl0NJeaehsjDITc4XrFbFpTvRlvKJEaOAwxi0nFRhYo5fEIHmzO8JUn+yARG4P7akA9SayWRMlA7AXtypAePPrksXuJSATR0SEHib10I341HVP2NpUEDcEJzNGPwTYU8RZM0tJuDO2nHCY9s0nPo05ZzQeyvzhhf1iwexsTgkdt1LiTXQ0kj4LqOke+HCiK0fZX/jijX0qkD/gMRB9Qxo75CiydUfg7gys16osaZh6/V2Smo69iMtlzMhE6s9hYUNqMIZd4CdZFXN5X+AWotaNXir2IUTcS1we29xHn//qW+/uMMEjCwhIfFXnSctemvE12bFTCPzv7xN++e0m5hsINhB8j7r4S/Kp54m/jM/a7HCYOVQqednOk9pys4ZjnhJnCPmXYIqCHQChGG+JOpqg55LMa6xTHQ4pYBg7bDGWhJiIcFZO4JVtbXq4OXJPTWsMiq7n5tMb2Gj14HrYL3NkY2uttRGwdcQ2nesLZlxs2Xya0g0HkAW4Nk8uS00OGm4JKByobI0WgzyS+UthBUmmDKAP/bT3nq2e39O8zkrllMxTIxHE8puR5B8uW2ht0HRAfYzLZMbw31L5gOxSIXtIaTaw8ZdD83cMF8kVHdh1YnRqGbwLRckAcFY+nKV4H+qeI0ynBSUdROya6x71mDC3dG4Y0Rt8FfBywHZgOdA9tGZFMB/oXnlIbQitIcGTlQKp7dvcZPYKkFphnDW0qKYTl592c6NyT7S3b6yneSvRBYoIlLXrqM01609KeEnoNXglkYUlP0AFDY5C14oghnbc0Q0TbGbgBO1VMyoFyMDQTg/ECbT3hqqeyEeZWkzz12DcKaSUhDuTnFd+u7/jx31yxfxXRvQ7IRmK+qvhZzHmpjjy/FDzsZnxhDmxOhqh3/Opf3LH9IaF8N+XpwkE/kvSW8sTuXCJW0NUp87Sm3mW8XwW2X0VYlzJJag5NRlvGNNsUIQPVNqNVntZEdFWESBxJLZjqivtuhmwionlHflFx+jTBD4p1O/A4m+AyMLVDnwR+6XCRJRcNvtL0TvEinOgvPFop9j8ViBc9X7264YcPz3DKk0Yt2aqhfUjpnqKxo58InINwa5CXPTK14AQtmmjZo7TH30v2s4zfNAv0VYvWgX2foqSivo7ZAzaosRI7t2zaGOkksXNsRUY1j9BpT77s0F6zTEpOv5sRrQOhd1RzRTRYEmEJLywGh9+nKGsZJpoQLM+yI7eHGfVM41NQhUW/U7TPAiGCyHv0W8u/+OqaX5dnyCjQPOS4R83kX56odxmhEVwsKh43CqzHnxRx6/+o5+U/6sN+eDZQ/F3E4EZj0WZRYL9wZF1HYwyxsVwsSw65GpPhK8tuMyf+uoIdTGY1+9spwjvynafKFOqoWV/uuL5eI1LPyjdYIZA7ARbuvj9HasvHhxXpfaC8m+PygH2IGZ5iohaq7+b4CPRVRZcJXq4f+RxNx9vyEZouZUgE+Slw8fzAZpqwPNtRfRfRGgUTS647qusJNmPkOHcGZQaSjWAoFDYS6FIQf11hP8TIHYhI0E8kCIHpBH2qyW4d3khUH4j3jnatCNLTTxRybtEve8oqZn1+4KGd0gdJX8Bc1vCNp9omxDiGS0+9S4i0Q2wNiz85YVNNYyNWaUf5w4RmohCve8ydonMjgz6XHb0SUMrRQGUc/WqcsnSfUpzUWAFZB6YTlEnE0Bu8heEkiPSAi6E4NZynNVf/+sCvn57RnRKsEahW0E8hnjpCK7m7n9G2Cp/BRVZykR8RSvOpnDB9/US+aTnVEZ2QzEXFL1c7bpYxHz5c4JMjupL4vcS3sE4rdk9ThJejcessYLBk04H2KUXllnDSKOOIzjtW94HSCbY3My7ne/ospXgPYe04Nwfa1ozq0FjwQ5ihnyTyoDm4JU4a4iMMuUL3gWjag2REcxZjt7jdRRxcwvxgSeOKqjEMF47HsqDoPO3PGfJ1gyks9mMKRSC1jhALssHRKMMyNDSx4koeMVPPzFdYEfPusCSaOZKjp/lhQhwc89QSekVTJzRzQZgFummgkAP91OArifmQoLXn+YsdH+8WDFJw3ndIJbiaNrz7m3OGJmL9r070O4P1gry0PBQZkRiYrStOIQMgWg+EzzH+dUdYQL+P8aeYOGvQg8LElnqTMLiIMvaEbkxWYwTcJfgpvBdzdAg0E4EoBgYhMKWAwhL1PQdS5IOEec18sWN3PyPqQDkQeHwrSS47MJK9S3B9jHcSM3NEelzl6WiExiTXI40xPLNwH1HsJS61dK8M2gbSciB0Ap0E/ur9l6yLhlnWcSTC7CXHp4Lnlwd+31wSBGTCcj833HYz0njg/356TrVQ6LbDfdHTfCxQDu6vp1wUB05nivBTRv8uIysaklMgkg65Mbj7jM2gCdFY93Urzzk1sy8P3DwsiHVH8ALtHdmq4co6ShvTt4b2c8aX33zgx/aM/gBdafB7A5HHZp7oNFrebOHwlSLTPQ9+wvCzJis6vvryho93ZxxNhp0EVkWFtRJ3MgQvRrBNLbALh9mOH+RRgmObYppA30Y4AVHv6CLF5LLidMzHiVYeeL7esGtyLqKag1O8OdvxY3dGOxhMcKSiZ5rW7GRCysBw0mxFwbP5ibTxbC8DVgqytx29lnyz2PLD05JzVWGi0fq3J2G2GfifvvodP3UrnmRG1MKZOnH2dUkyl/z+uOLbsOf9pOD55R2f9zPKY0aEo9/FaBuQe0XnNLG03P1mRfrqxOZxxuQ4MF2d/qjn5T9qEc7/5H/zv+C2W+MzQf5O0BcSmVhYOMhH1efQSN6aWz5M1oQoUDw6ttOIw65AeMhNRzkkJI3HRoL8qqRvIkRq6W4yzsy4178/FTinuJgf2D5MsF5RuIFTZBDZH37p7wJDIdE1yMHjLj1nr55wkWTfFxB5fKlI5y2xs+z/boH72oL2DL1GIDDnDf1vp+R5Q+c18lGPBL3j6HyW1tOeKfKsxW4iQq1G2lQSkL0gOnmcCfgXjuhO4F9bEALrFNFjIFu3bKMEvzP0E8GLb+857TPyqKH3gNT071P824FpVrP7uyVDrIgeIU1OhLXGO8n0iyP3Nwuii4Z1WtJeZ9weFzCzqBbknaafSLKjo1kLbAx68OQfBEMuqK9AGE/eW8JRIXYacdmPOsm9Zmg06qwf96m15vziyFq2nLTm7q/m1HHCkEkQAV0KRORRs4GLyZEbnfPq4pHhb6a0X1nKfYaWHhVbLkTD4uS4aTLqV461rvn9zy9ZzEtcEAy9oXYGbwXGjSsF30tkJbFFQNmAXTkWw0CLRP8mxj7zyEVPdHQ0ryUUjuF9RnCS7GXJl5NHPm2XNEKP05nGjJUs6QlS0HgDjSD7NGYYbCLQLxukgGlRsX2Y0cWCYu/ZxTHTvkcoR3LWcvp3OUIa/BzCRY+KoVhUHK4nuLuIt6/vOH6a418PnJqIxfuO1X9/z/V+xeQYOPQpbT0S0eL5QL8xdLEikpZ4MjBNax6fZuMkRnrcFwNXQ0P3PuZmlSIDJGIgTQeukgOf2gXuSVNPFOpoufhmy6bPuZxVtF5zfzPnWV1xYyactxVtbGgUzCYdt+2Us9mR7bYg3wZcBvN9x/5rhfiton0rRstcojF4oltBflHSa43/FPMnb295PyxoxVjvEqlnuiypdynHY4YfFGgLXpGZls4o2GvUxYCqgcyzND33MiatHcdTxrdXD7y/WxHmHhfBxPYcibC1Rt9o4s5j157qHOJoIPrBUK9GvapwkIUBHyS2U0TrHu8FXnmMChzKHJE65EmC9sR5T9WkJGrgPD5xuhCEo6YVCufGQ7XQHdU2xRmB20Q0mSA7eWwuCBLyoqFqEtKkp+sMC9nSFQE5t8hHTfy8ZtJaPu6W2MqQ5y1DBipylE1M3IIfJK9WT3zarqCVtL0hn7e4e4M4KdK/PHC6LUYtr5SoQRBlPSEIohpMNkDqiXVP1STEXeAkDKKS6IkdP8TNHboY6K0mvlbE5w3VKcV4TzcJJOuORVZy99OaTAyoix7rFdoGzruKbNLiCklUWT53M/Y+pXWG1PTYYwSNJH9W0kTjuySqGX8f3BicK1OJeIqY9zXHaYzMLN1DxvO3D6jI8XSYUrie4bsI87yjlil9LEgPnloYJsuaq3Dku4dn+LlH7iVBCqYXJ7ZNzrRvqe6nSOuwC5hMG2qpSI8w7CP8lSU/nfh3/9v//T+JcP4hT1VGTIqeY2rgwjFfd4hu5B5vF4rGCvwk8H6yJFnVlNcTvFWk/1Gz+pdbPtzN8Q+GaOYwVy2qF2y7jOwUiKVlnR9YrSo+1FNM6Rm04uE4I9mNOkWnNOdxjdtJGmDINOmbirqPYKtI/7RiMIJhFxFf1rRNBI2i3mYcK40/C6h3CSF1vL7ccPg4xf8+RV72NI8ZshjoJxBtxpu5Xlp4EOR0nERMNAiiylG/FSQ3kGYNbR8TpEQ8SbwN8CBw/7qm3aWEWnFIc16tnihdgnjrePo0Z1h77KnAzhy2irh6fuDuYUFyUvAUoWYemTjKWcbyYk/5ueB0n+Fahf3bCeUvHQyKF+sntPdUi4hhKXCPE3w77nBjHKHU9Gcef2aZZ92YSdCS6LxCec/QGPYPE+LPEF/0nD3bsr+OsZnjZjvnaQik0xa+sIi+Re1iTOpoZhHPXm64+7DiZloQecv+ZoacKhauRbmKvot4npR0TnOMNafIsHrouZMrCNA0MW6rCEKgvUTGdpR9mLFOEyaOoCQewUWoqF0MB4NLAx4JE1jKjuvHKc0+xjQCpQJJ5XlqZohaExkYBkW2aCkfsjHdi0VPHGaj8EYyWAX9SCiMzEArJ5w933H7tCJ6UzM9OdznmK/1kZu7hOJFzyaOYK/ohYCDotvErM6O3Awxn9Mp0hnUo4RLh/lnHQ/dBJdA2wZKq/CxxESeQYOYO9RuDNn1dUQ9pNhCICeedh2Qh4jdEHC9ZJWeCEaw8pabpwm/M1dcFifURcv6vaE+9+Re8FBH1PGAOgm+zPasVMtTEhHPamSr8e8zFm3PMbG4jwkiEbiFx+819QtJcgocl4boOqAuOyLpGB4Son7gsmj58GmBeNXzpFNE3hMeY9rzgEAgrqfIYkC3gUFBH2uKvUNYRTeVLFTLEAT9U0q267n/y5TGGt5885Hq/oLPdoL6uqa7T/E3hmOheDvb8dNqgr7x1F9JsmXDZRiou4jEBIbjCKYq4pZuMPhbjTizY13wHoZsBOeI1hOsxCw7zIMiW7dkDxBWlm5v6KeC4BXuc0JxUeOC5Nhk5OuGg0sQ0qI/JthFQHWQrhu8lXgvSNyAEh4973n8sIaDZT09salyqq3A52DuoZIxwns6H7G8OpFHDpd67j4vsClo68no8LVC546u1ZT3CYv8RBlHuN6QVsCtpvylJ4l6mscYnbZoKXj9+h5ZCZ42Ew6reMzk6MBZemRznPDqqzuuTxfoiw4TK9K4Izsq6nvNNpuS6oHJi5LmGBNpy67LqTcpryYPNK1m4ns2LodSoWaefO/YHzTfTDfc2IhM9/yzxQ2/O6ypZUR7m+HmnknWc0gMy1mP+yFG/EXPMDGUm4z19ISpYBOnRC8EVS6xXpE4x2ml+HpxS1MmPMmMXkmSHyX2PKC8o+kiwt7QrweIPOqsh23EqYiRYUCveiLlOD5k9Mn/B1r3/y+ef9Q3+3/xv/tfM39jOR1yprOay/mW66cV3T5mrxOmq4q2jFAnhZwOxJXHpdDUEV+utxRJx9/88IKv0geiR8V3syXZqmXe9gzbhN1jQpgGxMSBE8Shp/9pgi1GgpI4SKLeIXCEXtKuFH7mkW9b/nx5w/ebCzZtTvFekP9yT7iOsVbjmhEF2zYxTkG0A6ECDBKfe5Z9Q7XL6Kdi5DDfjbCHEIF7NRB7h7g1tLEk2sD+TyTJMTDNa45POTYRY3LfO8TSYZY96/zI8bGgN5pLVVEpGIJhd5fBRUBsNEncsI0z0scROSx8QE0HbA51rNHeIwMwtbyYHLg9TUkbx6HPyCpHkowd8ybS9DcRdg4Rjloa5rOK5iYbX0YTj5UgJ45vnz/y6X6O2EgGocfgy+doNKStHPpG0i8FiXeUIiI2A9GdJFtVVLEhWMkQSbK+p58KBiMROjBsI4rQkwpHmwr6xvDLyYaftjPWL0p2TUrRDjztJoTc82y251pM6a0m2Qim85JjSOiriCgMzNMjezFh1jrKQ8T6aodfBT4+rsjyjqKzXM33bEzMrOz4WK8IDzHeS2ZnR+77KWbwRC6wsDWHusDFsJ6e+NAtSKYd/TYapxTKEx3h6l/dcXu7xEaBPtYkZiDPOtpHQ5w4vADTBU5Xkrf9nt/cvCC9h24mkH+wNPb3MWHmsa3G1wqz6PEPEfjRiCemA6KRcBzpZrNfHXi4nnNhapqJpHlSRDPPUIix/uclr18+cvu3K/yvhrGl0Eti47DPHfL3ijcvH8mfeX58XFCYjtPRsF8k2EHzS7ehVpqn7QRlA/q8x9w5skuL0oHPdsKb+MjN368IlaTPBaoGoaDPBelVRb1NMYUlEz1RKaheeOw2wm5iQuGJzxvc+wQRBdzRIJSFJBBOEUEEsgdPtwCmHplZ5DuFLSQqGVde9uVAeIwwWc+k7VFTR5Z2fPjhCnPesL48UG/SMazYamwQzENDmRnkQWEvPPlhYJ1UPKopSdMzfOlAObY3cxgEfa8JChLTMxctW5uTZR22VBhtEWtLtcmJywAPmuy8Yp9HCAkvzzd8+njGUBpUOeYCbAzmvMUmkPQjOnx5ceTpt2tsp5AvOnQ2IGpJ3cZEb2p8L3l98Uh3k3LMDCLxHN/NQAdEK1HWIwpLXoNdO+Kopz9GdGZUb/eJ5PSUk4ue+pRirCdZtiSqJ8Hy1E5QseVcl3zuZ0yiDt0E7k5zVD4wtIbz/EAfKSbzlpdpyQ/1lOmHjnYW8XFzRtQEZr6l0RFDFBhyQdRYwjzwrX5k/+sJN88L7AJ0bHmxK+mahMgFeN5SHAc+ywlqEOjnHeJzxF5HmPnAgCLdBuxeU8405gCvv7rn/d0F//LFNToEfggFuz6Fv8uYnzX0hac/RMTRgEg9pyphCJKr51uqLqHqYrRwiO8T+lwQRRZxkJCPBML+3DF9D8MCLlZ3/Nf/8//jP93s/yFPvxwBLqvFnrm1bN6t2XUTvlzsGAaJPRrWecWRlP0hZ5LXnJ/tkR+m3N7OWOsKcfA8Pi1JUwcqYGvNVfGIi088qBfEgyP0Av8pplkH0pcNwyYjRB4RB+wfbkaiGIiNo58EXj974Kf7Nf11ipgJei3xv5nBwjMUkqT3DFWMqAXpqidMJIOXmMxhukD9h1qWagPSeoISIwoykbgzz+lkMEYTREBllrfTLVUUj9zpRU/vNHPRMIQx4RxQpB8Nvh0ov3bcfJwx9Jo2E6TlaLYSueOoUxZ9h/CK1sQjF/7RkGQNcePpX45Y1Bktt9sZTsEhJIRhFEvUmaI0inlWUSsNjUS6QNp6inlP32c4LWinI7REnSQ/bJboxDEVluZFINzGfPHnt5x+V9DkAZtFRIeI+S+OtO/WI3439tg2IsFSy1HKkjwFUt3z2M+IdwNSCXyiWH+7p9kmeNXS5R619wytYRY3+JUjcwaVOIQORJ1nPdvzbL3n4XFF4z2z+ZGySWjrnE5FTNIdzljsg2F/yJFaIjtJsJrrp0t8bpFEiEVAvWihUtypAnVQ2LOAcJ5oWeMeEsxs4PNuylw11DcpGiiKlnomsEZjS8Pz2Y50VXOTT3j8POPcO2ZvG7776YpMtqP3W8OP3QVJ59Eq4PeG6o1k9iTplg5xHHkRZi8QsUQvO0Qn0L0gtTWHIqGfa87SHa+Gltv5nM4HhkdDsSopyfkfvfiZ2+OUMjbsfyx4/hc7noiwM0FjDJ2GtIPORHx6Osc+StRy4O7ThF4rwjBCc36I1kxER0NEFNmx8nRpiU6C+axEdI73P63wkeLVV/f8eLwgzRvMdKB/SlkuSrLeE9eB8gyqKqF6HyGDZ9J2Y9JfODoJ8aOjNgqbSeI7iQiehanYLjOwkrh09FLRzQzDPICUJLVD/2hoz0A5RbuUdE8Z1VrgkoBqFNXfz7DnHp+BkwFRCSofU8aKIvFkUccvX+1pxUCXg+s9Uik+fX/G6+cPJHXgu/Uc9WNEMRkou4wktpzNTnzQC2QpEJsIsVX0JmBzTd1OUMHBzPHx3TmyFczXJSeVooaRb+E6iTkJ2jNIT3D/NGf1yy17nyIawSRuqeuM2Rd79HRg++OS7+9foSJHIWtEK2FqoVIMZsw0VZuMxa/u6a3Bv0s5RAa9keRXJy77ho9KU7cRDOOkIEkCJ5HhzhrMpGN3N8WnkizpmciGGzmneHvk8FSw7Bo6p6lchiDw198vibTFNzmnuxQTC7IHT1AGgiZKPXk+4PNAaDw///iS6kVACFhEJw7bnDsxY7ZskE3HRzfjP5vcMF/c8tcf36BvDRdJxUw3/HS3JoSALTyJBNlCdwYf2iWp6fnJFZg7TXLV8KfVgfbPt4jG8GO74GK6x04Fn7dL8sGhlOPQpLSHFPWkCA780iIrTRgEPkj0ySHSQHyr6HJPGARd/8dV0/yjPuwnDEwSx7FK4VNguug4xoYNmkVR8Xg34/5pScZAhsUaw+HjDJNY9j7mmM5I/qLhC/XIj0/nuPc55cLz/6jeom1g8AqXQvIgsVNgqpDXnnTj6HuJTQT9mSc2lj4RnOdHHm4Lfvzb50RTS6sU0xsPwtFOFEJIZrLFXQDOoRtw1wnOgLvw+FoihYSrQGQGsqinuo0QTmJz8FKQXgvalaM510QWqsSMPMkIdNEzbzyTdc9PwxR/0lx1Dbltub00bOoJ5poxKJeAOQqGWPFq8sSHsCC6l3TEMHOYSUfbGsTKs1xU6Lnj6GOQiv0pxy4DUnvkXYw6CYZIYPea/LyiO8bYkwEVqI1GGHj8Awo2aMiTjnjWM4kth9QQ/iaijT3JXtDMLXfVFGkNnR+wmcYawePHxeih9pKhVgignWu8VETOIi4sl8OJwy5nahperR/5O3vGj+0cZSXZpGEYYtwy8LjPyZcV2glUZKlyTT51pKLh8WmCu49pI0P2umJ3M+XN+hEVC05Pa37UE9KfFFU7drrjdcuz9kj8YuDmZkV1SPnqVxvk/YzKCDoH6qQwi47XZ1vce8VNNePq7RO+lIRY0lmFXThkKZl8tUff55wizcNPU5LnPW+zgTd9Tfs+4+P5HO7kGLyTBnvRY24EYTAk5w3HKIHUsp4diQNcXR744eZypDqSIrcCk0L7yqM+C7JlTxgEvRz4RpX8UC94++KOp7ogm1fjz3Uj+euHZyAk2dDTzBS39xcY4fCVBhnGIGnR8PLFIx8/nSFyjwuaN7984LqbUGnF1+kTk0XFV+me/+t/9WfIXzWkwbLuLI9RzA+HM6LEUmWCeXLkY70ku7OIV4KXHGjSltZJ1LxnpzMOTxmJtyR3AuEkzcpgngL9zhCKQOMSdBswz1p80FRa40RMH0BVf1CtIknyFmaCvPYs04pmobjzU2YXJWKjCOuWtomwzwZ41AwvPYt5h7+OaVJB0BCtGv40rvihO6f/Ycqv05TkrGSWV/Qu4rDLEZ3k6f2SEzHFuqJvFP65Z12W7M8UTW8QvWDYJmgRCINAvm4JewGdYAgC4wV+UEyfH3EGvA0MrSIuJSIooouG2apCtmYUhEnBpKhQFnoXc9KG6FEgWoeWjtmkpXcaSkU2aahOGTYDauhsxOWLLXUseHqakjej6a65CtzpgvebFbnvOc+OHBcZQyHYuBSrBXWp+LIs0YsT2WfL4nXL31drirSj30VE3pM9r7mt56jO0Q+GuIS2TykzMG501PcTRsBTExCDpGpjICDvBTJA9CRxEVifkE975mdHrjcrpijyk+PfzS4puhZRStRnyeFtQvqsYWoqqqcUUcJ0VZJ0hnBQ9JXmq/mOm22Cy8B/TvnOGaTwdE8JobDc382wCVyc7/lCHPkP22eoo6KY11RDRtp6qmiclHVLgWkt5VSidwKJp58K0nvYXv2T4vYf/FTOsPuwxiwb5LLl1OVEqWdVNDxVKdoFonVFNB9wTynWKV4sNtSF4vH7KUnREN8qfhLnDPsItexYFS277+fYecA0MBl6xHOPPWSoJmBzQRcLrBdgAjEOm8HV1RY5H5hfeh5/vSa5rAmNoYs18aIlnbe0HzMOpJytD6wfPM2ftFwfnqEczF8cYGNoymgcPdpA1aQgBPUliNThO4VAcbna8D5Z0RWOV6cTfaTQD4z+eTegnwJmIpFZz5+8+cz3vzljs50iBugigV95FrMSeRLsdMq1nxPdKHwc0DuFbQXta0uke8wBmkLjdwmKcZJwEimqcZjc4/ajR1q3gX4Kk2XF/m5CGCRy1kGtCHtJW4BMHfKoCB9i/rP//AP/5ukFbq/wC01QnjwpeXV14P7TCvNFx+F+gctGnKX2kMiWYS4IdYxcWOJvepptSuY6uueeT9UUrUZ07e+3Z5RrzVxU1DpiVpyovitQtwp96YmtZ9jF2J1h/lXD8ccZi/OanQns3kjUoyT+GNP3hk/DGu8lQ6JYRiWHVUGyaFGtRE0G3g9z0g8Bl45QpJ8+rpjGNV0w5NMWvVW0zyw+8VTzHC8kNz+c4UsJKORlh6kdHrg4eNrsxMTVVK9jqqeM32yeE097+HYgLgeKScvBpwxPMe4hYTo/0ShFs0koKod40+H+fcH+mcP9KEmFoxsUUToQekXpY7KqRkwExyihngpCqfium3HQhkzE9O8L7OAZJpLEWqr7Kd0isO8KkqIn+axwzuAvLLoZWeLNpuAmjscK4QnMr0pu76ZcTk9sQ8JdP2dLyofbC6pLQbKLGBLN7UNOJBw6swjjMKnFG8HsrOR4nCBLzfv6AvMoGJymS/+gVfWCOPK4rzo6N4YoXSOJ0p5hZxgKS3MhyKzEaEecgCsVibb4pUfGA9N84HA35fXLz6hNxDEkPGwmZGf1WI97FEQXHav8wN/tXuJXA7OsIbc9W5XwxeyBjw9nHHc5fydzolaQv6nYhoSknPC4zXDbmPPZkaYfaYVLWdNcJ0Rva4wLqGcd3YcVIlFMnyybN+CtJ9xK+k2Ck4JMWmwUIAvQe3qnefbFA/tjwfrVDqUcx+spInjmWUW1zDlXPaennDiynKKYXHVcTjt2v13iNwZedQx3hm6XIpc94TomvrQgQC7/gKm+1aymLdujoDkP2INmdWq4UgdurzKaPmLzOGOdV+Rpz0df4NqI9BT4lOcU7xuul1PqWiEqRXk34aUvOWpFvTIUh4HKRfijHquYBEQScINCdgKnFdo7wjPPcDKjx+AoGAqIjhDvxslnJ2MWkwPrpGS26Hh6WvB0ypjsGtpDxpBKLr84UE0UnDQy9cxly7FJ4TygK5gVNe+GOR+Sgu7niPyyp1jW+K2nesyglJjJ+O48PU4IacR3j1d8c/XE8YWmVBH+XtBJiX7eopXGWY2QgfgpMKRjiFpVgnYJs6d/Iuj9g580OLLhhH3UWGXI/uWRSTNwbg7s7jPSr04oH2h+X+ATTXre8Jv2kvO+Ilk0xGbAGY19TFBftIQyYXOaYDwo5bBBMXxOqJ1C+kA7lfhHgU8F07OK/SnHnTs8gtYojn+zoDmDeG6R30lMb6HVSC2ZXdXcdwUL27L7cc5WCeTfeOJVRzavOfw4R1+18KNGSEGbjkQnmQjUiwZTgX/REQLcbBZ8+aef2dQFdSLJTg53IZCmo2sU/THw4uUDtx/WfHyc0mYRJjg4aS7Kmuq5oL7NMcWA7mD4g0NaLi2NjjCRRX4wdFd+BEmIgZfzPU/tlI1JMHrkVJvME5YDXiiSZYOrEoa7FBdDflURUs9MWO6iAp8H5HHcL9LDf/Xjl6jUjmPDRBB5S+g11x/Wo1TIy3HCkHjEmWWhS6aHgUMesekVZRKTXo8HcONj3O80rlejkcpJahMT7x317dj1Dk1E/GcN3cJw9nrH7vs53Ukze1Uht5qr2Z6ftmcUZsDPHElsOaoE4x2TFyXhWmOHAukgfhBQG1Z9y6ZL0E+aIfOo1xWnPkbtY8ouRihLblvOvt2wfZhy+7Cmswb5qsU+Gvo1RFXAd4bzVzv2P0xopo7r2xVHHzPZdzSFIKQWP0Aox2qm26e4JUwXFYdtTttHxFWgO3O4hePrbM9tt2LTRJzR0SoNP6WYZKDLJSIIvBQU9YDeCR6bKUGBGjRhpyjrDKHAaQUbhT+CXQmyj1Bfje0EFwMTi7o39N/0mHlPuE6QW8nZ+kBpIhbzkn2RcvduzuA1Orc0TxG57hC7iKrPEHNLzoCtNQvVYacdTZ9TPkVQjnRKEPQLx2AEJnGjhKVXZPMOsZf86tkdPzNFOUn96wL1tuX0PsKuArNQUy8MS33iYtJx87QgMQPNNqGUEZMjFHbg5sdzlrKDWuJz4MlwXEgWz1q2NzPqECM/R/iVo/w4pXUBd+b4+MMl4kmx/rLkxYsHfnCXDJVGDgrdeozwdL2nrsfVXFR73AuHxWOPEcN9zsPFjG/PbtibhG0zRZwE/SJglwJVSZa6In7esrUpy7MTu2HG4BSPPy15PX+iWNasohO/ezC8Tp/4/vaMeNpB5FmYA4coYWI7NtspV1cbjl+Pa6Rzas6+PfLhtKI5JCRRz9N2ylyfcImj28VYqfj8WDCddrhe4XowK0uXKZr7FNcorp5vmMiBadXw7vOKyaLFRmKcUn7hmasSO/X47yQ+C9ycYoaVwOwTlssSV0Ea9zzFE/QmEALIACJzFMXosI8nPW2soJWYCszgsYlkmDGCvBJHdZezv4XKGPLzBjEIqmNOiANaeHZRyqk0/Fm95+lM8Zcvrvm/ffiCHQldEvG0Kfji9RN/fv6Z4UryNz+/oi8VzSTCXwwjhXTiyFvLQIN+0NQLwe6YUV1AeZ8TkIQO5G8TImkpF3JsTAmBrADtRhLjnWTzMvmjnpf/qAN6/4P/w/8SkSXEz2t2h5zQKlbHilKkcG84rBXpRcuX2SN31yuSfOB8feK3v37BrKhJ3ja0g6ToLN/XZxS3Pa+/2XL0CcEJbt+vWLUN+yQZd8wHkDpgaoebC4hgqUqOaYz4oqH+d0uiE6iva9q7FO093Vow7xr8IjB8l6ONpR2iUdzQQreC+dWRVmpW6wPX2yX5rKVWGn2EsDNE5y2R9bxQR47fBnZdirtNSA4DndHUxCjtMfGACoH51ZGHmwW214ijJJxZukOMCZ5E98ybll2UES86mlNMH0nSR8FiVfHwMAEB1itEI3FTx6QYa4Ch1vQrz1R0SBs4iAQVW4bblOw64DJBNm/Z64hs0bJYnNh9njM8RkSJpckFwUpWiyOnxECjqCZjXWeyrBAysFAtT4cJ3e8m9Gce5TyxtBRfnhCdQGwVj12B/KhxiULoMdvgDIhWYteO2Z2n0ZLIWuoiRsaOs+l+FHwkAz4JmD5QpYpBC+gkZ6GmvCvILytSb7nLE2Z2QHUDvovJ44bDkFNuUhJniZ51hEbSo1GxRftAGWmmquV0O0EcJfpVw8UXGz5/OmfYRMidYnZ2JPSCQ5Sg84FwUMgvOuxDzGxaMzjFoUmRjwp3YclKT7cMcJQICUk+EHeWx36CWfX4TzHKwhCN/O8QwRev71DB8fk/XWKVBARSOPyVJQiBrxUi8XyZPXBtZzQPBdIHonmH/IOFsY71GNAMATqB6AXNlSDeBdzKITSEABkDp8QQW0d/7nkbdqM/Ieq4281x7xLCzNMmEtkI+qCRU8u/evkTd49zbm/WJK8rhm3E5fzE498v8E7SXQSiSc9ZfuK0T6lTw5kqoQ8cQoY2HiYWbxVGexbblk03oTqm5JcV9i4mP6twXrAtC2Z7R/R1RVklVD5GCUfaezhIhtqABJcGZr/3nL6WqCpgCos4KqxXNBcBrT3xNQQrGPKRaNk/dyxnJ7KsozOK9hTTvUvRL3oGKQkexL2mnwG9IusH0nzgUCZ4Lf6QeA8s4woZjUa+41TTdBHaghUC/d4Q5p6z6QGXQXYX0K86EIGw06MK10Yc84h9l+JaTZZ2nC8OY2Ascqx1yYePF5i8J5Sax2qC2QrcFz25HMYUeSl5frUZA3Trjvox4/lkx9HHrIqS+BBoRcTQK2aXJU/HCWGvCVng4BIWoaGTmvqY8Gy9w8SO5EmwmWnmq5qHf79ArS1J3nMgwW4Mz35xz/FhwtBE7EMCjwaXB5Z9Q6cNtlW0E0G+CZzmEmUhOQSCFega+jmjyfTg8QWkq4a6j/ny6oGgBT98PEd0AkXATzzqo+Tbb+/46eMFxS8PnI4j9Go1q6n/Q074RcfhYUJ+XqPmHe4UUdDx8NOS9GXN1bSmdJqHuwlfz3a8lyMYKw2WwSmGxuDLEVHuLh2d0iACPg0kZcB7hTwJvIHQnPjN/+m//KeA3j/kiWYdrU05fppwebnj0BS0U0P9PoOFZ35f8/pqy83vzvjTVw/8d90ls586QjrwlCf8j+Vnfn37gm7d8mfFPe+WS35qV4QA+RNc+IqulFyFkse+QDgBAwxK4D10K883yz0lM9JgaZRFXHlkKxmMRKeWEDQnHfPt68/88D5nwKDs6GoeZqPwIp+01Collz2/+uYju1NG4ST/+fSO74clzaVDtIqqFezuC7ohYn0TeFjktAmk0YD2Ht9J2jLiFBukk5iTpGkiqAVJsJgDLF5X2FRjGNjXGcsny/ybI0+fVmzrBNdLbCbJ/IDIAlo5+kpjY0kkHOpO06wMCQ5ROBLvoPY0L8EMATdIVKMYnnJuvtbkzxsyPHUqoZdMdUv1YUK0bKgngklr6R9T6tOEMHXQRKgswLqnuGyRBJ7ND+Rxw/VPl+yTmDjvidcDzinaTJIUA7Y3dMYwuajQTwZTx8Rzj9hbsl+cYO6ZlA2dUsTCorcSX0Vky5ZyMMjI0llJUIaqSbC95JGE5YeGq3/9xIffXdIRMUtr4q8qtu/mcNDwvKepE/SNRl22ZFNHFXkS75jFFc3vCzqpEXGgu3QcngvsNkKeQKqASnvsThMeDI2PaIVB7RROi/Glvdrj25RhFrC9GmEkwZBd1tiTob9yxI8CMYxeA4Lg/ndL5tR89a8+8cPmgqQev7YMgoloWa9r9iIlrKG9iYj1wEy29HNHN7WIG4c/j0jdGJyTX7TUQ4TZRXTnkkL09L0hX9b4RvPMVxxVDJVG5Z76s6GbxVTCMPvzE/bvU1692tAOEQ8/rNAm8NufXuPT8f/rrURrx+3tDP/KIz9qlrbCqB7VQu1j8ronGIW4FVCmtGuHvZMYPMEFbosEaeFi12BrST31dN/ndEETd4Fmqen+05xp2uCSgDlIBII6NQzngSjrkXtNdaVI35T47xLaIUYmDhU7VqqmeT9h+KYj3BucEmSzjmgLVBGP32ooJVWdkMeOWVayfnXg7qc123xCrB22UuPfrm5olz3dIUaoQBx3nI4pWlriSctcN6j6D2jnJzNaLI+Su3qFEIHC96Q3Ef3CozaGX/2LW/6bX3+N7QTPL/e0TcJhk3HLnMh5spNn8xKmr4/sb6dEypHFPeq5w1aapkp58Sf3aOG4vV7BkyHUgvRGsX9RMH19Yio9f8c53042xPeW/YcpmXQ0UwsrS6g0ad2jreNYRNzs5nz5+obV10fun55jRI89h8nzhv6HlGoas74qua9mzGYdX8d7/tafc9oZ5CA4yBRKSZZ3qF7ggyK7laguEP+i4ng3gcwR3UtUAjZRyIOg36aEF4Gbzwu8CaSLhuH7HPW2Z1YPqMxTuxGr29zNmLuOi4sj87Ti8zfgm4BXIxMkXMecF0c+JlPEs56+U5Q/pNTfen7xxQNlG6Hv4dVsx+1+jq0joq4n2nj23whkqdDOo7owBsYzkM4zxKAaQZj8cQl6/6hv9v/if/ZfUv1pgtpJvv72mls/Zfi7AtfD1TcP1EmCXAxYL9n9foVLA5dvn6ifxl34PFgeleFZVzFMA2WTsj/moyq2ASMdcjqg/zpiIKJfe3w6Us0WZ0eGladqY65We6xVHH6/wJaKeiaRrWRV1GwvBHkzUOQ9+y5DVoL+XTpWZiT4X7bMFiXJpMeXit1uQmp6lPQM+4SJ6RCXLYNT3NdT4jYwTypOE4V+ktTS8M3rW37enXGoM2ZxA0+a7hihtKMXGjPvcY+Gr//0Gp1Yji6lvJlQtxH2KUIuLK6R6CEw+/rAkYiiaGm+n5A9LxGNZNPkWK9H7WPak006BqnGOl0jsWcB+aSQJ4lbOmQtsBnk9EgCXTeCVsysHw/JoyOuHWIV6GxMdNZxdbHl9GHKq+dbfr4/QzWO+lLwer6h8oYdGWGnifaexHoWrw/clTPOTMmn+zPivGW1KLm+X+OPmmVb0lxKmtiQmZ7TjzPcZISXyE7QSE3kHO0QsfAttVCoZz39+4yQj6P8pomIsx59EORZx14k+ABJOrCNY6K7gJ8KzBCQeGZtT3M26mqrxNAfY9Koo68icAKxHMFBkxvo7mLcKrAQNTuRMUkaomTg8eMSIQLZqiZ0kjZSWCRpNNAlAtlBumqpv5syFJA8OVwyZjFEH3g539B3CT6DTkoWuqfcS9p9jC0UIQ68vXiAQfKpm6N7KHTL7bs1y292LI8d37XnZNMaOfE8kxXOaR5uJ9TW8MwciHPLp3KGUwJxG40q0dnIGm+kxh8MKh/wrQQv6TKIqlFPqu8ERdLRnQfc1hBnA0QB9y5huPKoDuTTGMJUvcWfBVw+Skf8bUQ/kQjh8VogB4c5SXz8h3qqkAgL0asaWUl2IiE+gDk6BAqbB4YCZBsIenSUp7LHZgK9E+g3Le0hRt9Cc6aQE4cpAyHx2DJC2kDIPcqCeVsjDopIWJpY0wmFqyVRCUiBfFTY545QK4YC4h24XsLakUQDx2NKsaxRDoplzaAFJh7ohSIZHA2G/OS4G6bwKaI3irjoUU+SPO9Q64GlLmkbxcfDmukv9sTaMekHftyck7sBdzLMXx6ohKEaYlyvCFtNtmqpjeRFfOB4zHGlQjzvSayl2WTU+xSpPPq84/liy/W7M9w8II1lWQ6UuWYe1ex+nnP21YH7OGLwmtfFBnEy3HU5p13Gm+ePJElP12mEh8N+wlx2zOYNdafYdBmiD6xFi18G7o4Fpz4h20ucHrXl3SkmnA+IWuEDTBrLkRg1BAQgWuheetROjordPz3R3GbUE8k01DRxxHpRsr5vePdwTrOSOKvIdh7xq5po8PgPEXzVMyk9Yj6wOea0wozT1djzy3CLyiIWpmPwhv9QnxEOkk5opA2Y3QimOnuzYStS2qlE7RTpombIJP3vc9yVxTQB2yrWpwZ/duS/+V/9U/XuH/Q0bz0T3dG9ELwPS9JHweQv9pzlR364uyLZBL6JS+4+zKhWLQvbkZ88m7scM+m5Z/SDl3PN8ZhydnZgEdfUdcSmz/GVRlmonkvifITueKlJpxUqsVymB/pcofrAXTUhWbbszhOetyeaiaJpEmauxkeQyZ57XRBiRT5pkU1E+6ue12dPIzks2/PT/TPk2nLmD3xyC+LY8pf/ve/5bx/fUH43Qe8NLYrm2YBJHJH29MFz102wu4g4swxKQiLoo0A661FywFlJZwI/3F9i4oE3k0fKwwxjHEPiCQ8GU4E7H73ophaU2wncRlRZhu8k2oLxFnvpkTiqQ4rdG7J1Q5gIbC+xzwT1McHUgjNZUvcRwYSx9vWyQzUxQ22QTmC+6EmbHttrbONYNQ2bwwRVK959d0Yfa+ZJS+Yt9zcL2jomv6gpEXSJZNjE3N8U5GpgerYnO2uwO83PHy6J0wHbK2QaiKzj6DPsJ0XQnijvqV2EKcdMgLgzxCKwW8XkDwGxHLGplIL2FGE6gcwD+Z8fmA092/uYNhf8ItkijnOS5UCda+gF6RaaK2jKiER2FHFHG6CTGrfwFL5HiECelQzziO4xxiJ5WkQo67CfY04vDEZZ7DNHcDBdnrC/XaGMR712iE2CmQyIW4MUAXTAfNnCJkbvoZtKDg8zonlPiUYeJU/HFHXRUpMxGI/aSm70iqaKiAdPu48YZhGylJx+nNJVMJeO5l1Bf+75FHJwAlcqQg4fJ0v0j5LVl1v2NkVddJB6vhoqfvj9Gcm6ozaetjOYFrAgvUDtAyJx2GeCOG5p2pQ4tojPEQFPPO2p/AgkyqIGJRWV0ePHxXp8kRsNw8ohe4hrx5AoOiPQTcA9c+hPArMP2D6jP3Pkj2E0VSqJuOoZjMY0DqkDw0QinaDPA3IvaDKJ/pDjA/hnHWoQZL5juBAUYaCsFV2iuLrYsSlzeiXxkz9IpSoFR4UzEv224rgpEArMzGFjiTVApYhXPf7JUOqUdNkiGklfaZ5iyVAFpFSoy576IMmvKjZNivisMc8bitYzTARZ1jOPWkIeqIXmMGSItx2nmwnp+YGPpyWZtRjhaI45j48zYm2JNpryZWByVhNHA52IudvPCHFg8qKl38WclCF9XpNljipX9HcxNw8XLH6xwzYa/VtPExekuiMqAlZKHqsMWUMs4G63oph2nN92pLnndEqpb3LqTPFycqKPJNdVweNtihaOhphQSUyi2F3HGOGY7BxJ3lKRcsgj8rTH1xIGEI0kOuvQj4a+EMR7xmryQWGcQxtPeVMQC4s6KrJkoJlqypuExbJGC8tcOvoJZL0glR1Cw2dVEG/h5AOJDbAzRAacEaTFwN/vXxD9pDifHLn45YH45KlPEdG5xe0MLgNVem6OC0w+cNmW2ERRluPao18E5Nbg8cSTHtdJ1PKf2Pj/4KfXClVr8ucl5SljmdfcPeUoF3ibbthnCa6TJKsepRRpE2giRfKiJg6WoTXsy5wy0oghcP9pCdloOZotakws2D9kFN5xKgQT1SEOUFzV9EIRLSzvf31OFWIWq5J5XMNli3vUtMeEsLD0g8HeG6pJIJo4+kbRPgfzomWoIp5CRr3PyGaWcKdplUItHIuqxRrFv7t/iz9q0ucNu1ijHgRtH6F2jq/kjt8nUxLhKDPHrHVgLBM1sPc5thZ4JJ3U6L0cmQBK8+6nl1ivkVcdIQ+YSUPZJhTW4t4lZL/Y020T6lmgMqPK1B41F74i5D2P1ZS0HUUWMvYcq5TkvKG/LSj6nnYq2T2kvCq2fPp8Rr0SiAZk5vBufPGHjUEXHUo4CIqtzrAHRWcMSdaPAJ/LgXVXst3PaKWhv58gTpLZVUnpNMv5kX4X89vdBa9kyffVdFTtWhBrSzlVnLqY/GTp2pj+7eiEN7sx9Ogrg0wtIoHoNDoJJk+OwQnC1BPEqK5M9EC2DdzdrUhij7r1VG9ihkhw5jxPTcLM98jJgNgnyEYyzAT2aDDag7d0tzH1NCLtPeFcUO1SRBaI8IROMP9qj3zj6X5eEL2tkXbs6Nr5SEarnlKOfYzygqqLKPKGyA2IOPCvnr/nzq/56fMaUUmah4zylCJVAA/RPQynlEnc0z4q/EHSPhYQBOK8xfWScJMQX7aoLvBquuf9zTlyZeEhwqxazPOW+ncTfKMIrztoDQeRYO8Tos/gXlp+7EaaGzMIDTx7vqULip1OiA8C4TXTYWDpW74rl8jMYfcaLQJdagiRR9aQ0VNNYnLRY04B2QTcokecFL4AM+8xbYBdRO4H+lozTATpZ4ldWFwhCK2ATqNaDyLgkvHvhkEw5AFjA/pm3A9TuBFq5TzSgE0lw0yjDpJTEvFy9gQnRf68Y3edMxiJuRywdpxk6YPGFp6uTJGvWurbHOEE2SZQi5hkHxCxoJuB6hUEiZt6Wq8Ia48MgknpSaYW20BILC/NgY9uTuI9z6IdsrI8yZzINCQn2IuUWoP8pEgvW04+QxSeQ5MyWZTIVqGE4OQ7BiNRSmBjiTrAbN5y/zRDOoHPHPPQkBclIqqIm8DjdkpyVVOUkkOjCHmg+k8pz89qPuSzMX/0PsWnjotne15fbfhPx0v+pLnhB3OJO0qefpwT/0mFOEFlI4at4sfTKN/hLkJfVeg4YN/HPDMHPqUFxbIkuZfUX4KrAsODhGmgbTW6Ffgg8Knkvp+gLh3pLfTFaNoUsWdQAvNBY1oIWhG/7unmijNZwVxyNAndLCBPCvMuYNKeGRW31ytkbmkjyaR0DKUh1QOzSUlI4elxikkd08uandRc7SQ+s6yXO/Y2Qw4CPXM8/5Mt7zdLZB/Yi4T45GkKQ9w4sqKm2qeoSpKnnnw68Glf/FHPyz9uq///x0+o1ChSeYh5YQ/ctQmL53taO4JM9tsJv/cLPvRTeiHZXcDico9tNL3TuCRQvDkyKR1/Gj/w6tUDmezpThHlMaWZCGQUWK8PfLl+YHZ2YpI11EajWvjb719xrHLErcF/irhzE+xjwuPDnCETFE8B3sWkf6s4nXLWp5bsKXD1fMsXz+5RsWPfFMxmFdV/mBDWnulgaYymfEqRcaALo3Siuc1HSE00cpi7Y4yaN7wWJ25+WpP3FpV0NEazermn3iS4e4P6aJjfeJauQZ8EadLTG028avEaZmmFuzFk14E86xhmUB4y6l2KzSHKekzsUKuB7pmnVwpfavzncQSe+gExtTTXBUNQiKlDR542V7y7vyCad4jYk5iB7F5wVpxIig6dWY5Vxq7NqDJFvBtpfVPfwUnSlRE3v73gbzevqeeS1IxEMqcFO5/g1w53ndLYmG4T8/vdGt0HZqsSOwucr/d0hSD1DhYDMnKkbnSNBwF1neCCxJ87krTDpQKC5EFnuDhQnNXo3BJjsVry8bCiFRFllWBDyuf3K1qpOaUCfRTMjxVN0DRlRJtLjl1K3cY0Tyl9qxmeOWTmibKOh3crNI7E9Mgy4PPA5uOCapuTnde4p4jQQlE0sFccriT2yuGdxM4dooepHdBdIMSen/yC6zAhflGjJhZz2Y610GNAWMGwELRa05TJCHSaBNpzQEJ7jMnmLS6Bpo1wg+JuktN9bWnOJP0cnv3qniMJ9tIjFgNxE0iSntx0hMQz/HnH6s2GYlmxXh8QS4tcD7SfUvrrBO4N/VPM0Cse4pRPkwyZW5T16LQn9pY8blHKcr7e0wZFkgyUfUqdKawBdadg5fA5XNiKYu949mbD9OsDw1RgZyCkp5sIhlbj/ciKtzNPEDAUgfR+jHmbJqBuJAhPKDx6LwgHhdDw4uqJPhXIyBPWDqE8n/2Uo0vQXjAUgmgy4AZF5APWCPYyItU9L64e8WL0VKTBIi57nAE9sVysdnx9fkemerjoiYUl/tmQHQL6JCh9RLNLSfOOtAzo5cBEdVyGhipR3BQF7SvPoCI2w4T6h5y2izBRoBpissaTbzymhrnr6D7G3DxMca0mzxuSdY15UzOfn0jlwC/P7ugnATnA6XbC7e2SdpOgpEPOLZEfcb+rrKRYtFSLiHftFL+JMFiCcGy6nN1TQdoJ3tYlt8sFcpA4JWmew+Fmwm5bUHYxYTt6PuRJQhLoThGnp2z0gkiJ7j3V3YRTZuicZtfmeDsm75N4IG48URmI955n8ZFVWuIYPSRMHXInSfee4TLQrAXRecfk1tM8phxDTDHt2P56ifscI88G+hUcDhPqNqbTgiEZd+jTNyeioyA2Du8EW5fiK4maDexmEeUQcX0/J+0Dh2xUAyffVjghePf7FTprcDEc64ynIkP9wXvilUDFgTcXj5yC4mlicNt/6tn/g58oHpg7Sy1Gxa04aB7UlOmyxZaaQnXsP46jSPMpxkwGftO8ROUB9yFFvWoZDhFNkGy3BWdnO+w+Ymgj+ijQfE4o8oajikhrT6Qc+rzE3Wl6ZVjvez4PGTbyWC2onhLOfUuR9FRWcuxiVAD1Vc9+ElHu14SXPYWSfNisyOOOqRzY2Iz3WYqtDNG65e52TR9LbCdwexjuE9ReQAG9V/gri9zB73cXI+FqXaJtgHmge8j4Tl4SZp4LdWDwmpdmy1/vXjM8cyQyoL2nf0xwBWA9TmiU8uxtSlT0tGvPND7SVhEcBPP1iU01pTsmXLx94jhMSOcD+ariepgR1Z6o9aMW9FkyesWnBzbljCEVTOMW/0nSR4rmpxnxuoPUEcc963agOYM0Dsg6on7usT1MXUMcLPfDBDVASUTzFJPOG+pNgsZj/IDtIkQakBbw0F5nJC967n+3RicOuxl7ru0C5L0hv6gZfEwSHMo4hqNBTgOuF4SNQgTgzNJsEnwQiNgSHmNkGhiURGYD02VL28Ukv4s4FBofwV2zQKwG1md7TrucTDfwLLBLJ8Q7x0I01FmEvhXolae1EWWkyJKW+CBRwRLfKboLxas/veXd/TmXyxM/1gsmzcBGxkQVzN+WnNqCOlY0kUbeBu5OK7Kip/uQEouAKAKB0fNuNUxtB2mgfR0wdMiDJHtTET0TJLVll0XM1IaDTblYb6irGLxEHUG+Gtj8tEAFQRsUk43DtJ5NnpANLW++veEhTLh/nNK6lEnWMD12nB4Kmo1G9578ywGfWioZk9EzzDwTVVHd5jg9BtTKfUJBT33IQYhxX+tHSZJqBXHS0T5E+EWgjySnNuX4c46YWGTuUAj6dSARDqRgcXnkbr/AzgWy9vhU4FJJkAH8mCfpF2Pv2bYKvm5JpWV/SolsT2paDnVB9rMnXsMwUWQvGnQRczpl+EbTl5AnPcJH1GWGMA2Jc2jnkFaTTCxHG1M/87h9ATcQWkEIkmxZ01woZqLjUCcECd0iUN9m+CxwrUBtFF43yBcd9kNG3yj0XpGcPF/9Dz/xm9+8Qr1pCdcJ1cSgGoHMLL9vL/jLrx5o3p/hV564Ccw+thxSgdaK23ZBGxSzi4YqxEySmjxp2VUFHBVvzo58fDeltoKTTFCyJ131NI8pceKRHuoLxfl6zxMz/u3pinTZcXrICe8jhjWEeSA6gktA1WO1tz8LRPcRIgjsArwEWSp2siCUkrlscNrj71LC1I2GPBEIAtRZj/+UImPP7XFO1A+EhUe0oEtBpjr6QhCVMEhJHRuyqSOxlq7R1K3i+Rf33IQZ9n2CThzPLrc8bifE2jJxHTc+4wu556/EjMWkpuxi2jriTLb8hfnMf/30lhyHPm/JdoLNocAuA5NHxdAI+mDwt4qQeMTEop8UXWqQery0FZOW27MYcReYTUv6tfmjnpf/qG/2MzraIaZPBE2qEJcDr+OS5jbHeE8vJCLyTJsB+bxl+zDFb2LMXhC9bKiOKVPVcu4q+l5z46a4dKzEmQ5kCNQY+k8Z/W3Ku88XfHh/wcPHOZU0bM4054s94U3P4yJh8vrEPjUkq5a0dJw/2yFlYL82RJ8ladJRRD1P3y9obxMwgf4k+Ca5w3UG3QR2h4wiqxAry1YnxCeIFh3qoiMogaocgkB02VKqGDNvoRJMn53wMVyYA+Uuw3nJ+3ZFvwj89eYtNpIoF0am89mAUAHRC44hIX5Z4760yMjh5g5VCapNxnJR0h9jyiqlR3H+ekO/j3FCcngjsEiCEAgBYmZprzzowISOskuIlKWWhqwdmD6v+dWvPpJHHeKgsL3mcMq5vVtx+N2C22pOuneIdzFvqh3mPjBZVBTHjrmrWbuKyFlqItRs4Hyy50FkxLElnneoxJFlHWdfbEeLXupxXkLicV/1yFYgpGd2eSJ+1mCdJOxHi5c9mnEs/GXLvK/RHzXdKcZJgasMTVBYqwgTh7oxVGXK4tQhtQMXEI1A1IrhfUpFTHTWUpIg/yqlEB3zVyWlTmjLmN0LM3L/B0Xs3Ji+Pwr6fUTzPFD2Mbe/OxtpjJ+nfHH+wKHOUZGHQdBep4itwnUKCodfO0zw5HuLLCz5FyVn5wf8wvHi5S0JNeqfl7S9Rv3WUPqILO5wu4heSk4TQ3PIONzNKEzHVHb073L6bUy+87hGM2hNeoKXVxuiP6uIXrW8udxwkDHbekL/LsO1hmhwFHtLswY5H1CXPeG/KNGvmv8ndX8Wa9uanmWCz9+Mfsx+9Wv3Z58uGmNjXKYoKKOik5DhhhJgbhCgkiVLiJAMGFSAEAW2jJApiRtukGwZIbgmCykhrRRVJFiFHWGH48Tpz27W2qud/Rz9+Ju6mJFRMllkZahSofIvzYs115hrjb3nmuMf3/e97/OiSxiPC4bPNoziilHTkhaOB8MVD7IVxgn8VUA2rAgjg7zWmHgvoNMNmFQhj1uE8zQf7LtIetphrKYdgBl4Jm+vMUhs6qlcQPBugRwZ+iEEpzXB45JMtGSioTmUROc1D969Jnta4MeO/jIhbi3CKpplQriCwdsl9VTybjCnyECEjvIqQ8QWn3raoWASVTypS8LXikhYejR9K6k7QXbvUG8CukKTBh1h2nP0eEnaWEJv2HUxJrM81Pe8dXKzzzEoA2yr0GHPG51yvx4xEi36TmGODebYcfeNCY+f3iMiz6N370gaSz+WqMATd47PignZ45LQWyZpxW1wwE7klNuYugtxORTXOW4V0l6l7D4eIueKeZ/y9d0Ru03GoshR0tK/iBlse/KuZ/bWkq4IiZ1lVyVEzhC0nnRj0XGPkKAHHRNdQbjf9JSy6Cc1yajBZw7/rGU4KFFbSXDS0A4ENoSyiijvMqgFYqWQtcBKiY4thYmpT8FZSTRqePLeHeGkIzxo8YViN9FUuaKegh9YxFLiZz3BrOP0aMF2HDH32b4zkfZo5XBjz+m7S9TEsHIp6nXIf7p+hJs43ixHiNoTJT3FO45f2T1mcliiM8P2dsDdLEZmnqCF7VVO5cO9FdJLXCAIrjRe76+309EOWgEO3hkukREsX45J4vb7ul/+jt7sRa9oxx7fS4qrnKoNeTWfUg4lrdFEpUcODOqkxc0DnroVaWfxNwFHquJJvmY1HyJjQ4QlvPTISYefGc6f3RIFHaz22Ml7m+1tZQtJHYds7zLaNzGzZ0sOXI3eCMplSj0CLhS7IGI7DuArFSJ2RM9KxpMtplUkzjA53/L0YE0xCXl5dUwatzw6WWOUpIwD4jXEQc9MFwwWFvlWQ3BWox40fOXslsGwYjLb4lpNPKrZVQl1FXE/itBpjxwYsnFNcZPz7CuXxNOGwcMtq02O8ZJ8Wu2tgdFe5NNqiQklbh6hFpI2VqxlQjytKV1I4B0391O2PsZHnkQZbroRwoKrFNmsYjgtUYFlG4SkD0vsqSUxluX9kLYJ+NbHj2kJ6IuASVqhjCc/3SHwWC/YHWiydMvqiyFB5iivRviJYDwraRcR3WNLkHYEnwXMo4TovMY/6EgGDdlRwa6PWZNQJhprJX5oUblBfqRQoePoK/csipx6FyG1xwUgvcMEIAuBvYxYnUQwdhB4xHcscKoSZK0hfS0h8MjXAcs6ozwQpMOWZNASv11gRlBfZdRvUvJBgzroUA8bNncDeiexA8corNDCMzraEfUWsVNso3A/PtklyAraPuD2m4eUgebjDx4hN4LAWkS8p+Ppoxa7CIlVj1pLwsOO9SzEj/eCqEs/RPYCJzSj8wazUYidwD3pCRb797vRinKd4BrFOC+R5w1J3PH5Z6ek5wWDWYU+bJmGFUcnGxazgLpT7KTm/OE9Wxeg3mi6i5S+ChDXmjA1LOKU1d0IIR2z0xXnmwrdeuJ3CjLVIa8D6ouMuoqwM4vSlo/vjkHv0aKrKufkdIE7srheYSeWPhWUiUIlDu2gzSXa7Cly3RAC4ZA7yeJ2hBoY+khSpwHRrSAXLTq0DApLISOaUFHkASfHc7KgoblK6TrN+0dXfPlHLqnOwHqBrxVeweJugJuHvJJTdl1CVneE5xWuVPQDqDYJ2yqlOLT08d4aZhtFMdSUfczgaQFbhZsZ1jZmFrWEG+jKgPLUU7chj4+W4BS7ZYbOe0Jl8B8nrD6dEX8ieU8tkc8a4q0n/CjEDTzX6ZBCCorLhM/nh7QTiKzFekEyaqgWCcUmoVkkyELQp5BHLW21F2UKBOEdaOvoAkl1KihyhUMwTSvsiUMPe0oT0k5gXefkacObuxlGS+I7aG4SZC0ZiprXxQTVQ/JWSdR7moHAvt0ha4EbOmTvaVHkj3dobahWKX0i2I0VZuhBeWwiIXOY3GOlwow88cbhd4pI9oQ7T5sLxEbxejXirC1oAkV/4BGBQ552dM/2IULiQY8e9NRdQNnExKqntSH9sSNNO6wXiEJQFiH31QBpAQ/xvSUZ1sQPC4LTnsYEnEZrJq7FBwL1RUg+K2kLtU9KHfZ4LZDKE6SG1HVkrkc6TxR2aOmoQ42xAoTl1z96THMb021iUmu/r/vl7+g2fhc7RnmFKxSbLiPPa8KRpbpIwYLYKQ6+UjJf5ISjDvOoJV4G1LuUy/UYVysIPZUPCfMGkTvaywQTejbzBHncEc8M/as9KlEq8D0EjcC803Csdrx6cYw0goHvqLoAVUM5CPBin67n1wGBceyimMx3HIgCebO3yhTTAQdByXU9pp86ZCo5lhs27b7VJT8PucinxNOW9edj0qglsi268fhtwPViRrT03CRDgqXFPBIMV47qwNF1IeNBwbocctMMyYcViTGcHl7zSo1ZLyP6iSet9ra78VHJej5A54Z+rjnKtiSnBbtVxpN8x2erGa5SuMsEPXF0yxDv9hVtG0LnNO51wuC9grzvqS4zWh+SndQMsoa5TUljg7pTtCeG3kiOdxW7TGECSVB49NTRTkJUYMlHDfUqRVvPfTug9SHq1tIdOMIfaKHWDNOS8iJnniW8O73h7P0t1zdT/Mgi431aWD1y6Mk+7fDuYoLoBcm4pd5GBFlHPdIkryQi3KNa7S4kSlrUztOqPTlRnHXIO0F7apHC4JsQFwp051nHEaITpCuJCzzT0wJlHavrIWJkGX6s4LwjWIXEYUfvNL6TlLcp7mAvqPStgq3EHjnGpsMMPK3QsI1oM4HsJHYZYHPoFylT09HlBuf3s0a3SJBDQ0FEnnQMVMNyrLm4PiDPK8yRp3/bIgKYxjVBHxDXnutmgMg87osY+8BxcNhTBDD3GZtFymhQYWuNGDge52vasaG9HfLp8iGV0oSHPcFKMTpcczdOmR1vkd8KeZGOsCdwa4dI4RmPd8SFZ0vKVkSM0pr7mzFiaHm5OCKKHYNpgXmdIIzn4sXRPjRkDjaV1KdAILCrEOMlQnhcLEjSFlGFqIHBrjTqXqGOHMNHKxZfTBBTh7hS2KeWUBnSssPHnhEd8Y3kdhiTtw5faz774CGBtUy2HfeB5OBgTeX2GFcbwTIKOO4amj7iSLW8sTGycujjhkHV0kSS/KtLig+HyMQSLDV5bmiXEWdfveWHxzf82qcPuW0yfAcuhcwb2hncq4TWhujKIq1HhI7k3RoySXOr+EZzTPyBQHSKduJJfitBnViQEj/1exvmfcrs4YqyiVmvMkwGSWoIlGGlQsKtxY0sw35HcNiy2eaItz1nJ2vuugGBMpQmInc9UdiTXQhGBwVNoFnfDHhyOmeRB9CDVo460PsRSBdwlw6JlUW0Euq9qLR7HTF7vmZ2UPHp52fEhy3TpGF5McEsQ/zEQtgz2PZo0yOcpHgiQO/DyNRGICNDnWh0IhGXIaoHLTzucU846ZFpi11IxHFHZlq8smSNpV4lOAVB6bFG0dyl6MZw1FfcpDndKqF+KLhfZ3QLCfcBvrHEj2rSyrC5jTAupMolvZe8/sZDTtst8shSTht06ohGHcVththJzIlDtx7pPa3XPMnumHdDyk3Ew2GBCXo4rbh5PWZ4smPrcqLEULzMvq/75e/oyn6oeoZ3hv42YhRWZHHLqk7JxjXvPZ3zx37sE4o3OViBto5PV0d0H+XE45bssEQ+rnn81g0i8EwOC/J1i8khKAUntqd/nWJ3wZ7SFlsiaegzMJnH7QK2M4XrNAiBSBwDUTMcVkSRIdlY2jLkqN6Q9x3jrubqZkZThtw8SLETT9Nrbu6GZE/2BK5gA3dtjrlIKF2Acp4o7RjkLWfHGxSwdjm/fn/KtR9wfjDHRdAmAc0wItgIKrFXEWet4fbNFH/Uo0PLuknxY0vjA7rbmGAFdKAii9aO9tsDxqued49uGUUNXSBZfTil6QOK2xTmCu417qTH1RrbKPRCIVvQBWwvB7jY0H6eUW5i+rHndLTCForFMCA+qbETg5HAVcDu5YiVzbCbAKktfSDZznOi0NCOBS9uD7lfZUjn6RKBP+iZiIrZwYbWKiauor5JOXhnwVTWLL4945OLM+6jhPxCoHpPnHUcTLa8dXZPKAz6UqO8JxE9RgvMRUL6zQBjNH6n6QkIFoJuEcJWoTJDXLq911Z6go+Dfbb4SYEfW7wROCsZloZHD+7ReU/nFOlRxWS4w3aCogsoL3L6VpA4A5kB62HoSbAMpiXJqCYsLFnQ0p8bTLgPajFS4EuFasBFIIDoUlNvY/ydJn7tcUbgrkLsPCRae1yl2BUpueswB45aKyqt6IXCbQM2QYK6FWzLhHDXcxqvGZ9t4V7zwatj+puYsHWoRnCWrSB31JHAPmgIcYyOd5wcLwmGHemkJh9UTF3B0LXMNwNujhNSY6jvEuynMf025PVnx1xspizaFBrB3f2YQFqmcYksFRJH/cGQvtcIDSp2yDcaIrcX8bWeaAFx0tEMBL1U5M+3dMP953I875kcbmmGks5pmipkMtoRxi3yrYazsMTWAj8xvDtccvdmxItoyIPJlmUeYyKwxwY5MmyONOfxFvdpTLmKMYVidL5FrwWrm5xSBOxEyHt6zsHBmrfDLcV1ynaZ8/LmmH66d72oUU9TBDwabNlcjfhoPcOnHqUsOOiUJBq3HPia3/vkBf6JobEhRR3TNiFmF9BepPtRxlojvMfK/XgkinuyQU35JiOSPeVtRigMm2WOWezTBFPdMh4VBLGhjwQOwapJ2emIq3KMWwSIRpHZHiks+l6SuY7YGu4vx6zDiBdyghGCYNTwYX/Atkox8T62WsV2P+ayCnmt4Tqg3QVMs4bkVnA63UIr+Pz+CL0RbDYpm+WAuohIK8sxBXHfQ+bRieEHfuQznqf3+xhob9HTFjXpGSQ1SdRhU48dO8Y/ON/fgN1p7sOYw7rmQG54+/CeY19yezfl7O05B7rk9OGKwaiivYvpHnnMoSehZ/tAoecKv9rfeAvlsULhXwc0O40pQnon2RYJaW8QoaX7gRYOLV998IZCK8xlhLcCKR2R7kkmDemopheKF1fHxLFhcFaxKGPeOr8nrA3WC5plTDhpGcYl0+nu+7pf/o6u7EcHJTf+kPTplrDx3GxG6MKynoR8tDtkqUI6QHlHpTSR7qkPI4ZZC9oQNJ77z2aM45pXN8fMwpIH8QbestjOE6Y7qk9Ge9vHoxZVs5+lbQWqEJQXAygVD5Mtl0XGLCuZvxrjB5bQWXoruUlH+FKhhy3a9WxVguklZ4dLyjKh9RH2LsKFgk0RER4YxNihUsvDRyXXZNROs3092AtaIo+fOkQH224vWAm8w5YKPwMrwMwjomFHdlTiO0lNhPGS+W6A2Sa4SmPavS9UILCJwCtBpzQvvnUGud1jYJ2EnaOeNASvoT6x6I9DeLyHtyjrUY0lOOoobIwUAtF5mkAzdC1lEeNDj+s1ZqewCLRU+NShaoU7tpwdLXi1PsCMDCwVcWkJBgY5ayhWQ9xVRJs45NgyincEvaFWMfNpxInaIHcSddxSXic0QhLee/JpSVOGPDIF1UZwKYaolaAZOugUpQs5SAsKhnSHnqiyVA8UBJ7RqKS+julTSdB5aieRG4E66vBbT5h3lBc5ydOKTkR78JJzfH5zxDDseX50xXWck+uKtUhJXctAtswOK+7XQ/wKbO4wpSanZ1A3rCaO6suCSd/R3oaUMoIxTPOKQ13wyh5hY4itwx3vCY7ZrKLbKuSVJNxA+LSmFxKNZ2dDokKipgYlHOFOUEpHflZS9wHD844qCtFHjutmTGNCFA7rPOePFqxsggosqhUo4ylvU5pthqgU8UnFbTqEWnOwKbj1ObfuiKAB/YVCPWppZYhvFChHm0hiY3GVYqQa8nHNbTvi2O64ez0ke7ij6COSL1W0u5D+XmFPLOLQ0ncKP/QE9ETPasauYn0fEz9uaF6myJXAJJKbcb7PWUgMetCyXqREC0HSNjRnlvYywgQCMsu35BR51BE6xyzZ8MLPEEaQXAuadUIUGlaJ5uGTLXnjqW1I8UHGaNSSJhV3VxOaxPKt6IDpJy0+a+GBIVsa1CLDRQHJlUf8YEO3SagWIXSKjz874yAo0G81yEricsXaJPzgu5d8u56R6o7gQY3pNburnDyt6bWnWKVI5cjzik07wKxDKm2xi4B4B00eIBpBpRXSwtFgy/hkw8efPyRrHN1KY097GBjyS0l0VCOCEHsqSXaG5e0QdygJJh3rYkAlHaO+Yx5EeAWVUHR5QLgAKQXBDjajgCCEaOOwGcRJS7FNcL3mvkz50uMbolnHzRdjDD2nz++5up4x2xj6xyVPpgWfz8dkxlCGigUhL9oZulEcVg2HT7a8rEeUNykzX1CMBZvjCLmUdNt4j7ctWw4PKz5+GlAXA3Y2Rm410YOSz1YHzIYl80+O8FYhlaW/SSiGktFBSdUqehUizwzuLoSBxyjPKOhRWU21iBgeV/gImnmEtxJ7EbCcSJLJkGFSMlAt158fQCXQJx2N0gRbgfaW49mao6xlayNu4ohv1CeUyww5D0iUozERh/mGD9z0+7pf/o7e7D9eHHJwbHBFwPwqI0s62ncd4VbgVpqL9ZRg6EnPSo6HJZcfnPHl4J6P6wH6dYR+0NB7xVqHROOG2eGK+y9mGO1pVxF94AmcZ+hb2sIjM4texWhtEc8bzCZEVHD7VGGsZf3NESry4DwqMURXKUYrsrCh+iJHRJbpcMdaxxipaLXERp6065gXA+pIEe8ER2dLtssBqgLVC27TIWkFwx9Y01UBw7ZjdZuSPa33AJYDx9YMsEIgpCf1HfUmpNtEyA5cIZGxxOoAH4NKDF2s0act9W2ILMDE0EuFFB7RCHorCeIWX0nuNzOUB61a6qlCBj2+F+hxS+81UegIqw53p0hwdLeKpRwS7IDQI4agl6Bij00tfQppXtGtQ67bEW89vObF8gifOy78mKPFjlT3lKlCbyVvyVs+LQ8pkTw/X3OxmzBoeuYMCO8ko7fXrLwiWOznt+suxx/3vElChqrHNgp54HlPLxlPKn7t6pwyVPzuP/AFzSbGAJ98+xTZCOxAEj5syLaCwmncs31OeBhbtOuIAkNTCpoqJjGWB6c3rO5z6qsxVRigJ5LyPmN9lxAe9gyfFix/c8AXxQF/9Hd9wde3h+zuxugCehOxSzzm4wRpJeZ5xygv6BeSwge0Vcg8HOB6iQg8XnrcPERI6NpkH9MqPTaWuEWAywQmchgU5p2a2QuBSfdjAF03vPflK3YfzFh9pBk+79mpCBs5pHYYH+JLuLQzxE6Q9Z7bYoI89ORHJaoPkKknCWpsqQn7njrXFFcpHBj8dYh51DE9KtCd4+KzI8wYlHEkeY3RCjHqsNcaH3uW2wH9SLK7y0hn9R5gtLMIKdEHPTrqkV/EjH2FQXL3Zkg9i3ACDsMdl+MpCsFRtWVzKDFWMc4atjbm+eSGZCy4XI/IVh2rkaZtImztyZMamVqCHfzG9hw/sLgITCXxJ5bWCyjg9STh8bM5tzcxYdYgjGG3TVBnHc8fv+EbHz/DTGHhNC72FIch6kLQjSA66DCd5Ed+90uMV7z8xgiROOpTWN+PODu7x8mQ7jLh9fKEjQ5RoUUEHhaabFSx8jGDxjBSJV3qqK8TTh/fU8UBq/sB3ns0Pc+yHXd9RlEmjIuatgv48P4p+dMt1X1Cc+BxVUg6F8QPdsxlzCQvKU1A3cb0gcBehpRdiBwIVKXYKcWzsxvuvj3D/m6HWwgGuqOTgoyOchMhdqC9RQpH8SZHOIEUYJzgm8EJaVuTZD0jXdIKRXzUUK0koTJ80Yyo04BWSdxaEa4lugmZq5Qu9awahWolQWZ4UY9JPoTzRyW3YUZvJW2v2QYRq2WEnPSc6x2R7Xm5PcbL/Rz8sp0iHYSv92x6MewQN5LNccpxV5CdOT66OUQO9u13m4Oatmx/K0cNLWSWtg8htwSyQ/qAsHZ8Oj/ATzzuOsQohY/BC8VwUiISwVjvb4BHD3aItSMf7rtTnFTsVgGV0OgnDV/MZ6iq+77ul99TG98Yw9/6W3+Lp0+fkiQJz5494+/9vb+Hc/9vEpD3nr/7d/8uZ2dnJEnCH/yDf5APPvjgt/2ctm35y3/5L3NwcECWZfzJP/knuby8/J5P/uzpPT7xBK1j9GBLbzTZvcevAqbjHWYXUlvNfD2kuc6wpeIDNWFwUmFjwZfH8317yO3xiV/cHbNrE1RiSJ8V/G+ff0o6rFEDQ+0CVlWGjxwxhuO2RHiPj8C+TkhKhxoYVGhwoaf0MV57Dmcb8sqgnUVtJetthukVm/scLiNEqdi8HpA/3BGnLcYo1tsBY19xMczpBpI46viR//1niFIR945WKRodUVQxRZtg70LGSbUHhlQBtQqgUthOEk5agklPPqxgq8nGFSJwiMhibyMw4IRAB5bk1u/RoLCPmsQzHJacig1PTpaoHYiBRVpQicMdWsSsp5OSNOlxaAr3HSZ60hCd1fjQE80a9Kzj/HBBMO6Z5gXGCqT3CA/rj4Zo66CRZKVjTcpGxoTjnuTRjhfFbP/vm8KvXjzHhqByg+gETSJZ7AbgBNo7RCOhE8jrEPNxxvybU9xNRGUDXt+O+Hw+xveCJOv4dD6hnvV8vpoyPt6RvL8jnPWIhaZX4AaWKTXDqCENOk6ezOkuIoInNcNBzTQtefXhKfYo5Hha4QPPZ18c0RYBbSbxK83tr56wm6T4HP77bzylu4gYznaMHq9pzjxFm+A7RTcQLFY55SJl/HCHGhqSaUMxEzS5QIWW8KTZo4iBsLLIxx3qqEOnPds+gWYvxIuCHrmTdKEk6C2j8Y7HBys+//Y51/cZzVc9lQrotWSSlqhC0g9BhhbXQ+8U9TPHNorxjUbsFE0dsfUR15eH3FZDyiLm+nJMGwpGp1umX10QhpaLasJn5RH+wKELiCuH6gSN1+RZSZsJlPSoaUdw1CCPe+KswyDRR4bwpMHVkl2RoDcSo0AfNwTK4O4DlHQcNI4fHd4iSsnyYYhaKN6e3WJbRVpZtlnAt/spJvFsRzEknnRcM/UV9JI+gtoH1CZAvQxw9xFFlTA92MJdgNxJosrz8sNT3DyhuUmZr0Yo1ZOnDb/xwXP0K832TUZRR7QfDLBfJHQHAn2taeoIUwT82uuHfP3zB+ixQR30tFJzWLUU2wy6fRhRXYTEYY9MLUf5hvP3bxCdoP0iZ/lqxH2ToFrwzzqaiSSLOtS45+HhnMHzDVWrkdOOKG85en+DTh1pVtGUIQtSymVCUEF8WBMqw3Fasfx0QruM8UbSvY5R95IqV/jF3p0yEB13nxwgNprmMiP9xNHcJwQfBKRRTzhqCbd+P255oZEGXOxQzsFBj5r11C6kfjWg2KYsyhyfW3ZJSLOOSMYN6lpjpUAlFiU9y7sByUHF82RJMJe4O00kDeNhzfmzFX7SoXqPazX6pKU8kcTOICzoEO50Rjf22E6B9nur9KWiHYP3kjA3VE3MWFdszxQLp4mOatSox2nQG8HixYD87QohPd23B4z6lqOg4P3Jiuio5MbkuEc99IJk1KLylijoycOW8jZDCs/O7GFbrpes3oyYbwbM6wHbImWWFDx4/5pxUGNbTee+v7X29/Tbfv7nf55/+k//Kb/0S7/El7/8ZX7t136Nv/AX/gKj0Yi/8lf+CgD/8B/+Q37hF36BX/zFX+Sdd97h7//9v88f+SN/hI8//pjBYADA1772Nf71v/7X/Mt/+S+ZzWb89E//ND/+4z/Or//6r6OU+l98PtsipVnkmD5g2NXEjwu6i4SH6Y7b24zT9265fnGIvlXcywlD1bAVIa4JSOKO/+GjZyRZSz8PUZ3Fxx4mNVUT0W01/7l8l1FSopQkjzo2u4j0UcVw03N7M+XhozuKw5Dl51OmssEMLcV9Bh7k2KKVoPUB3ZHCr/d8bZl4bCCYnm/YzhKyyuEe+/2MV0iayO6Z9YHELzXMDDNT8/LNFHJDcZ3jtUOXHjNSpGnDZpmTZQ0PRivkuKNapnAXsLExCKiVJlgEuMRRbhLC1tMjCTbQD/cEKhNL3Ajs0GO1IC0tXRmzIiILG5q5YPTelu12Cp1g2Hc4L2idxCw1VQ+68LSPDcnW8f7hLR+rCcNZwbrO2Oaaup+QFZZ6EGE3IaIV4DVSGMxNRHLccjRvuQ1iJrrh9iplN9KEDZweF2wWIT4S2Ai6bYRpFb6X2A8z7AgiZ0knHS71bLYp7VLjIlCtJxu2NCLC9AkRBrURVK/GXDzKqLoIrcFtFW0uyBKDudd0Y4V8FTI4LRjpnl05ojoQMM+wuccdWPSgo9ASO+zIPu3pncYQ4lqFqwXtsUBEluHRFruKOThZs4sk8w9mqMMOcS0hdXircd5TLmLaXUjsQQwFiQBVW7o6JCtq4rxiWw/wRUhJjIkF0cTgsbiB2f/NYPCVYiMCTDHgOF9xs5pQ+IjIG/w8Zhg2BAI27QCFR3aeZOEwqWfoDWJhaE46orngmpzkQUOzi0gOewY0TE5a5lWM/kZEk6fIsGaYV0wDw5UcIq2kixR52jN2Dbq31LsEO7R0lSQL95VTjKG/jTnIS54dXfNbL5/RdArRS45/cMXCalTk0ZGhq2Osh4uLEX5iOf7KHafRht/84pRVnUGl2IYBUQfjk4IWCR+lzJ7sKGxE0HvqKmLgG/pW09+GdEcG3TtibwlLCM5rztINr+4PmD1ZoxYSF8D1zZR5MEZ9FOAHHtkIfAjdfUKUdXR1iDg2nH55zUU3Ij0rSbeO9khQ3mQEomdXRmy9QvY9ycKQNB4RePpe0X0SM+8DNo8g15awcLSPHV0oWTYZ6WCL3EjmTUZlIpZpTlB6hPKkN5Y8KXjx8gB93pBtDA9Uw2U5ITaWg0nFm2qIDz3d1V5QOnAN9TZBzCzdWuMKRVrsgUtbImSnUceGievYnMSEtxZmlvsmJ3CGbiJQrUYfOiQOLwTJoKMZQ3cbcxosiceSZaJJPoa5mHF4vubODNhuUqKo26fsjZaUo5hindC7iFWb4aREGkHRK2QpuDUBdS/wQ49PekSpsbchbezZ3Ya0hy1mESE7hesE0YXCxGBzgZYOryWBtPBOwfVyTHcfkOqOXmp0CaSeSDqEhe4+5vBsw939hGWfErQOY2Puiffi5U2ILwMOH91h72OiQ09rFSw0hYqJdE/sPLWUPHxyx+HdkBd9jrkP2cwcaSC4/3SE5Tvn9H1c31MQzo//+I9zfHzMP/tn/+y7z/2pP/WnSNOUX/7lX8Z7z9nZGV/72tf4mZ/5GWBfxR8fH/PzP//z/ORP/iSbzYbDw0N++Zd/mT/zZ/4MAFdXVzx8+JB/82/+DX/sj/2x/6/n8T8G4fyf/m//R+7uHvDCTpksW9qBpChi1CKgeWxQK4XUjmHVU4wlWjqaIkLNFe0Ekrwlcx2bNiAvPSfn+2CV6KyguBtQ1yHKeIwSMDE8lAtOn215eTuj2cTU/Z79zkLjpUCtBdlJRSsVQdjjJYzyirvPZtgc8qTGNwozdOhZS3+d4KeG3PVsfAxrzXi0Y9nkWAXTpmZ4tiNZC643E6pEke3cHl6BwB0a0qil6UOytCXtDWsR4l/EhI9qym3McFaw2A1QzqMasEhiafAS+jLAPWpJw47mIoN+PzZAe1RsqH2ANg4lPWFoqLTG14rsvGSzSwi3YGYWUSgGG0sVanzo8U7waLymdhKV9zB06J3AOsn6PqHpYlQt9n2l8w63DPCxw/eSB48XvHV4z7KO+NZnj9HyO9S2NSD3HmpfCvxCU+uA8LiGz2MElnjagYDuOsWf9tShRG0k7ViQ1HuNhpUS5wWc9qiPNTaWdCNQzhGtBPbQ4jYB9AI/tMhGoEpQmaEaSrRyxNt9q9wiAPaaCaMwXuInhlHQ4vEMVMflcoKuPSdfvScKe+63A/x9sI8WPoRpXNJ7iav0PqSoTYiCft8pGvXkhcGNYUHEQzZ83B4RYumakCDp6awiiA3iWuPHlmAjicOOldhX+i5xjNOKfhFSB5pg2uIjxx/gNS9Wh3wRDwluHT5TjO8a4sMebSU/+kMviE3Pv/jwB+mnnontqeuQTgjiswp7kRCqnmIXMzI1b/3ABS/NEf3XU+SxYfJkTXqluJEZm7sBeV4RPaxZ7TLCneerh7eUVymfLydkTUf8fslAtiw2M1aJ5t38hjfpAHGrKSrNM33Pa30Epx1h4RGdxL6J6LSgzSWzTcnv+dGX7HzIf37zhMPZlkpolq/HhGHPbFSwcyHZS8PJ21u2TcqVyVEbSZcIslFDvPCYCBarEflByanesrMh289H2FgQZT1NF1BFisRaPKA2e6FdflhwvZrwUO/ojvYV5/zlhDhskZVCHvfUJiA9KinrCLHdjzOsEBgFwUcxg7hkMwoxSBB7zodcaGaDHbaV6EHHIkrJhxVRC22v6RvNQHQUK03jItTA8f67r/it+QPOxmsWqxFPT+64qoekUUu/Dbm9HxH1DrsJcA96BveOtg5QDvoUBltDQYA7NYQLSTRu2K5zQmf2Hb2rACE9JgcmFr8ICbwlMD3Vyd5mmN1Zjt9fcnlxyFtPbilXEetFzs5HRJN2T6WLHFiBH1u2RYprJO8c3bAjYvXhvqiwsSfPGuSkpyxDUAK702jtSA5r0qBj+3pINRKEoSF5oXA7hdKWJpMcHm65vZzCWYeynq7VtNF+LzCZJ99YrFG4UhEnHbHvKVYZ/qQnocdqwemDNbe/eoDxEhNIqiOBtB7dCmJ6mPQEysGVxz0QVCogqx2TpmGbB2xuBgStozvwzGzFusvQpac72vHtn/n737cgnO+pjf/7f//v51d+5Vf45JNPAPjN3/xN/sN/+A/88T/+xwF48eIFNzc3/NE/+ke/+5ooivixH/sx/uN//I8A/Pqv/zp93/+2Y87OzvjKV77y3WP+y9W2Ldvt9rc9AP7vr97l5fyQvlZUmaK6S3GxwE3snj5lJeHOE1cWvRAUOiCwDhE6dO1pNhHtKsa7gNU05GWcE2Y1s7LhyXhNrHpc5lDK4RYh182MqzczqnVCWccMaOl6DU9a3Nhg32rp8726NEwNXRNwdTdFHfQ8mC0p65iNiHikt5yZHYeP17x1uGA02DOp2zZgtR6CESSvQPWw2A754tNT2nOLvNYsXEp43OAGDrHUmFWIbwW7OkS3lsg7+lhgeoX1ktViX7mFlUdYUB3YUuM6SX5UIp0nHTY8/L2X+zzz1uO1xxT7tDflPLbWNEVEryQudmhjmSbFXiEuIW0c/ZMe4T16rtArybJLufMZyy5jfjui3kRsbAQ3IUPX4REIZTFbjdoJ9FoQhh03Tc7XP3/ExacnxJ3l2Vfe0CvJjoitUrjM0kfQhxIdGdRlQJsLomlPZwPKTUp76nByP4iITxqGSUUXSmaHa8LjmmjrkJ9Ee3TqpCWKevROYvN9+093Hu0d8aVA9BBs9sx2nICFxgz29C9qSbCFttNgIShA3WlW65xgYCgJYWaZ/eAc5R2f3x7TVSHdIsKEEj1X1MsQv9IcUdBcpWSNhdcR3SqgKSIWKmL1ac5hX/KhOcKVGvlpiPX7VuGk7TgMSrojMH0AB98BtcRw2Bek9T7cKDmsSJOa3NfEdc9/un7G6FGJt5J+HCALyfIgY77OuSLlP3zwiE2ccjosSK/g+LblaFPztCvo3yQo4XlwuGV4WmFmsKxGdOuI5FFNPOzY7FI+uz2i+U6nrlql3F5OmaQlwlm+fvGQ4LDjref32MeOSd/BsaMZWhqnKW5TtPOEveUrs3tuRkPEo4Zp2eIrjdkG1EpTzRSBtmwfBvz3/+HLfPjyjK4N9hkUv5mjcKjes7oaYqWgfEtzP4iYRxGncsvwpMB1kvX1gGU5oFjme2vf65jFfEgkLMmXdsTnFW0gOPzSnPSogomhDSXJoKafh9zVOTo2XOUxdxdTik3MaFgShpA/LvGBpysDbndDQu/Idcfptmbia4IbzeRki40Es6Tg/HSByA0qMoRZsxegTiTlIMCG7JX6RUiZKEpC7kVKGAiePL0jCA1RaBi4noVN6IaWwgY8zuYsf3PKpkmQjQTEXgfSKMwmIJh1eOXRbU95JNDSE9t9Z6F5nZGdF2Q3Fpt47IGjjyWikYidwicWLXv6txzDkx2PwyVx0FG6AJkbPrs/pN3uEy/zWY3VgnqdUF+nOCNp1jHqRuO94Op+Rr2JSX/XGjnqccM9R+VstOL3hXd7B5D12IEjqSxM9hG7b2ULfC8pnzi6kce/1dMJzapPGT7a4u4CRsc74oMGJJiBY1z2HKQ16lrjBp5eSOpjgR1awqhDas+jQcGiTzCpIMj7/cizt4jwO+PPuEdXnjZWBLHHNJpJ0XPkG3zkqbcxJgM96onm+1TFaNRiU/Dh9zdw9nva7H/mZ36Gn/iJn+C9994jCAJ+6Id+iK997Wv8xE/8BAA3NzcAHB8f/7bXHR8ff/d7Nzc3hGHIZDL5rx7zX66f+7mfYzQafffx8OFDALLGMkxqBsOG3WrAaNAQ9B5nJDaE8/GSZPadyjUOGNFiEFgvQe/nibZSJFtP2hjiuWC1GrKMEnZS8WC8IZhL8IJoBfpesXwxohOSRPQUiSbcgL/dB3IEsYHQE0jLrovpvEaEnnaecFMPUYcdKrAMyprqgxT1mcbtJLefHnDyaM5RusQNLMEaJkcFXahxQpG9V/D2XUU2aRDO0xWa6LxCPK8wKSjlyMOW7SqjvE1BedTQEMU9OEkY9eQn+wsORz36sCN7ttvHVeYVq13OXTHkwTt3tOeeLGswY0dnAvpAgQCXOlQp8E5QqoD+2HH8eIncSoqhpCsCXObpM/AB7MqIpHKoNXsOwnqPqOSdjiJVCG8Q3pO0htT1ZBee9OuSsWroQ0EfQic0Fy+PSKMO10ms1biPQ2gF/cjjx3syV9BArwVVJOG4RRtLm3hUBdV1SnudIApJ9e0BzTLZY29bkJWmDyXZ2qF30EeC7UBhc49ODS4CKyW79wS+lqhiz8E38xC2GhNKjBLEK79P3pp1eA2BNOx+Y0zxMkNfKhbfOuTy4+N9JsMipH1mUYFDOse77ywwZcBFNcVLv4fvROA7hb8MqHcx6qDh5m7GoDOQWfpEoQJHO/HYI0tsDYnsUaWg2UYExy3BRlB2McGdIKo8ZR9TELOaj3GfpPS95tV6QmgdB9TIiSG6t9QziTeStcn4b66fYYeG5KDl8jjmzXHCfZ9gO4Wew2Kd0CjJg+mK1cWYSmkWyxF2FeDmMSYH2QtGD7aEzytEJ+gXIVlksFLw+XbGrUz5wdkFa2Iu6gm7bcYwqLmLch60NVJ5XkUDbC7oFxErnxB4S72LGBwXKO8gcbQ3CU+ma2wRMK5aQiz6pMd7ic88x8dLmGuKPuJ+NeQgqJipBt9pjsc7np7M6UNwqWc0qHCFprQhm1dDmiJCVJLWa+p5TPyZYqoKwh2M0pp+uCetxcIgqv3fRF3FTExLfFay8REysIjcMAsrcILx+Zr1qWCcVoyOC8bDGn/gWSch14sJjw8XiEbiWs3yekhJSHWfEF4rtjZhvcsQL0OSrSO1PWWrebOZog9aLj49ppKaw7rm/eiW+jbh068/QZz2RMZy8mDBJK44V1ui3tKeO5oqojoUyJO9Zmd4UiCkp48FxbuO/iqmOpDIxCEAm4CNBOoeYmsoJxpTKU4Pl9wsJ7hUslvlhNIQCMOmzfFKUHUB9jogEo70vGQwKREOOOh5+/gGZj2tUgQVPDu4IykspQjoN5qF3bfn+yNP7xW7qyHlt8ZE0nB0umImS/w8wCLZVgkEnjLQrLuYoLIsfIL2jlT0JDtPFtTslEact7jQczgreDxeMQ4azvINfmh5tRju9QVDKHNNcNDjhcQb0JHBrRR52TGoLcNhz0zWuKllOZPUmaQTkmgFRkrILOtvTWnLABd6bPf9ndl/T5v9v/pX/4p//s//Of/iX/wLvv71r/NLv/RL/KN/9I/4pV/6pd92nBDit33tvf+fPPdfrv+5Y/7m3/ybbDab7z4uLi4AqKSiXwdEeQvS85Wje4a6oZs6RG646kfYVnI3joknDS70hJ2jm3qcYs/GP3B0qdjPGG9jwjtYLYYUrzLuspA+FfjIksxqhjS0TtNtIqptjL+IcGPHoOwJ7T5UY2sjtjc5otjnGdtVSDKtCZIOsZL0SD5JD+neE8zvR9ysRjw+XTNbeiodMgga3nv+hu7UIUeG5lXGdp1yS8y6SnAS9HmL3WnSzhKW+xZ7tw4pjyRy1PPg+ZJhXGFzR6gMfR2w6RNsBplu4bjloVwyebxh0yX4pcZcx1xWY1RoKbuIsPZw0GNSj8n93uIWWNKghdhRfjqkWiS43KNXAnUdYoxCVxCuPaoSdGg69h8+1wf0v5pjK4VFwpOeFEu2cdSJpHwqke90FGWM3UqqJiReWyY0PGNH1HiCuaSaaAgFurY8SW9RSY897UhdT+gcwaxjkpYM30BuDOm9h8ATrwSbUYzqPJx39DOPetQwsB1NGWCfdgg8TggGs5Lq2OOfdAgP8bXA5J7k0mKGDh+CzTxh70CIfZXfgOkl4bQhuNmPDrwSCL3/vwvvBLPbDuEFJ1+0xL7HnDr61xHP8hXRWclA1zgp9hf5fJ+IJ63AXqX4k57RoCRswCb7kUzWWepNxKvNAWYVMPvSHHHaUtsA0UnqRNIegW0l9jZEbvb0Rnli6B/1VJEkinvKIqLtA0ymSK8l9IoODWvNzfWU9UBTFBHs1H5UEzjMmaMkpLtOufzGKc0UbL0fj9WpIkw7xNRgkPzQ4wswgjDr2M0HFK8HBMYhM8PizZDLj07Y6oj+KiY7KHnnfE6F4tN+jOhgYhryyhNfCrK8YdvvtQpmF3DoCuQbxdjWXM4inPZkoxqEIHCOPtqz8HVuiKYtxgn6CN5P13zx8YzRpMRnlmYZ8DRY8XCwINYt6lGNmGsqFeLmmn4TMN21tBcZ5i7ivhgiWriYzwhuBTUBVR/itMd3Emsk1zKnuYmxr0Kq3xiia0dzmxCsBZf3MyaDkrVMuZEpNyT4wDKrG0JnuL0fEYQW2yvkQhMsJWKrSFS/F/x2HinAHxh86uHQ7mfOlynrNmXY9GwvB3grqULFo6M1h2cb9HFLEPXoTU8z9ohC4p3AdZJwDfQQVJ57mVKWMbHuODxdo96uib9Sc5yUcGiID2qEsHTnHucFJ0crHiRLLlZTvAabe4JhR3OT0rzK2cUhGxGR3TrC1uAHhnYdgoag80yamlUUEneGpOtxnWIzCZjNdkxOt9zezviinKGFRVeCkdrjqlWz73T85q8+p91Ee5jayEIrCGLDeFogrMQ+tUxkQ6Asx8crMtOTdoYuEPtr2FwxiEu+fPiG04N7btYjhmWPW2vuRM70ZI0IPMmNZRLWjH2LMh532HI9HHF2uKFdau5VQnWZ0txF3Jkcm3n6kcMUwT6kSQv8yOESkG++v2z87+nW4q/9tb/G3/gbf4M/+2f/LABf/epXefXqFT/3cz/Hn//zf56TkxNgX72fnp5+93V3d3ffrfZPTk7ouo7VavXbqvu7uzt+3+/7ff8ff28URURR9D95Pt2AHlrUixCv4KPlGAaWwdrQlZo43fO5M9vT9prl7QiZO8KNQJ60+FcROvY0I7CdQGaC8EYy6BuqKsZtBCejNUcPFlwsD1jOBwSXnm4g0GuJLhz9QHEwXnIjBvhvRxw92LEMBiSdIznZUbgIRj04yB4W9HVKl3jKXU4zCUlCw281MyZdg0fS9YIv5IT6fk+BMgkEtwHVaUieNOxWKY4QfxtQPRakw47GBYhtQJ85ZGR4cTXbi9IelPRHDi4SArOf04sTQxJZbKS4vpxhA0HsLWhLV4RoYbFTh6xB3gREw5Y+loQHhnaxdw+Ym4B4DvU4Qh0ZvNN4IO4tJlOYXIDwBPWegKbXEt14TKSpkYhJj7CKIlaES4/swD7syMcV6ibh6HTNJ58/wE9qROCoy4A8bmilQu0CZAqd1NxeHTI+2CHbFmpH+KhhujW8Hqck3hFIS5a3NLsBPGjxM4cqHHYRgvKYI8OuiUlHjigzZHHDepui0x79WUY4ahFZC48NDRpHiIlAdgLVWhLf04+hkTEu8sS3nkYG6ESgWo9wYCMgtviFpI0irIK5zglfecJU8EJMac8th3pHeuJoKk07BjtyyGODuI5ow4AwaJnJjvLJjuqLHDWw9PMQGkmiDE6AXCuy73Q1hk8LbCIo7yNEJhnJDttIgrijJkRbwYmpeWOGHL215PajI2J6wlHPrkjxnSKMDMNBwWqRIUuBDz3htaR/p0NsBF86vuf6YMDDsy2frmcMHq0x24hoXLOYD9E3ijBp+R/+H+9hlNxXgpGnmAoOKMmyBsaez48GDF9axPs9fad52Qw4nOyQk55+F0Co6O5Cqjgg2Uhm857R0y3zOIDOkzcN6yzgaFJQ64R6HRBvDF/68gXl1SO4CnljpuixYdh0BI3hV5ZP8IFifXWE2kliXdFHilQ4NiJGGEH8u7ZsVYjegr3T7JoEP3TEJx3DWcVmOyZKe9IHNbPblvU4pjIxw6xi5jsqIdi1CWKrcQpsHeCsopsLkh8u6e8CjoY7tt8akr/XEiw1N+WANOwo1T65r5t6/KjDVZpYGExmCLxnFwY8lCvemCG92ud5ID2nj++4nY/ZvRigR4bbeoSrFC/sED6H4fmOzaucpoiYDUrEUhFMOmz2HUzwqwhx1BJfQ/5oRd3sXUXHszV31xM+rSfkcUN2WmKmDXYZ0C4TpPNsghRXQ5K2tF6haonrJaqB6FYgygA36DEDwDuG4xIcvP/0ko+KIya2pwdWLiYd1Gy+NQYj8Y9a5I2kPg7Qbp9iKHtwZt9560uJmVnURUj2TkFxPcDljoFrGNDw/HzJt148ZFmPCZOe9d2A+GHFzeUI8aHCjQQPwzWfrw64nw9pc0lrIg6jCvW4ReiAu80Iiad6LLBNBAOLbg3paYf5pOPiixFdHXLQldyJIaqAsNnf9OEgUj1qa+mN5rjbMl8NKd9qvpft9//n9T1V9lVVIeVvf4lS6rvWu6dPn3JycsK/+3f/7rvf77qOf//v//13N/If/uEfJgiC33bM9fU13/rWt/6rm/1/bRV9RK01a5EQW8N8PmZVZqR9z9E7C3orCHvL7ial7xTJoKEN98Qk8SbEWIXfyj2jPNyLc8zQU26SfYyrF7jTns8vTskaw+z5Av+0JxAWNzWYXCBKyUJm1GUEwjO4M0ynO7pDS3foEJmhMQF1qGjmCYO1o3qTYQtFunFwHTIOatYioW0jRjtL/SJHbSVmHiGcx6ceowXbXYYbeFqjcQH4Tu6tRL1CHPQo5TDzCC0tSnjcRYS/DUkOK8yR5cFohdaW0+Ga+V2OL/Z53EZIdm2MWQdwG2I3AV2viTuHPmwJhx1qrqBV2BiMURSnAr/VyKv9/aJMLW8d3aB6j5COYAfeg/MCG4DoBTaFwaAm193+617SjOW+8nod8vr2CPfU8GF9QhC3TKhQXnDxcsxIV7SBZHi0xd8GxJeSdhVTLjOqPqBpYvoqYEuAWGki0RMGBjXsmYQFk6bFv4kQgUeNLMmkYZaXJL2lzQV9rSlXMd4Lqm8OieKeg3wHM4se9BzpkvjdHeGgJdw5+glsBxHbYJ/RrQ878rMWF0hM7lGhRTlHWlnixtAdOkwomFITzqHPJE5JhBOkxrGcD3i9mWBqzaDvUDVEtcXPDO7QYD9LWX+SMXQtshaIF2pP7wo7kocFvpH4zT6IJX0NdJ5mEXH8cE06aBk+2nD8pRVtH1G1Mf42YnuXY9cBN58eIXDUNqBo9w4OaTx0+xjpJO2YjbY4IXCZJ/GWrop48cGEuQq5JWYzVPwuvWaws+yWGb6RhI/K/Qa50+iFQq0k3ghUbmiuU+4+naI7zakpOTndcVqWRHVH/0HMyaBCaGjnCc2bBDaW955dc/xgzfyR4uVqSLXY0y2bgaIN9t0HnfQMs4rdRcrqNuMs3NJpxXBlSH5Vkt9aksrS1QFMPCeHK9SgR2hBUSWsTEo6bvbEwWVAstk7AfzMYXI4mWxoqpDmk5w47jj0BaEwhEMIMsvAdNgQrqOEciXoe4k5dhw8XzI9LmBocW93WATtq5gPt0f0DyyT8Y7bPOb82ZyZrzha1vhSMoz3BLko6BhOa/JFR2wMg61BnTUM25phXKATSzqsiT5LmAQN9tTyzg+/YbfKaGzISbzhDwxfI64DmIJIJc08hczuw7awRNLivCTKHM+mG1wM2WFFuvLMb8fUWqEbsIHALENmvuH4bMXRdEkrNV0gaRPJdpuSZQ32VYhcS0wKvVYI4WluI3yhiKOOaViRDBte3B0SZj3dLmQwrpgmJfUyIcwMeV6jHYjf0xKmPfFJjVpLShPSD6EdCYQBcR2SHNe09zHRxhN2liLRXN5N2V5lpGclU70jqD3T0x3NXYJ8u2H03oKBqMiyjnAjaTLNsOgZqprr3QRCzzQvCHcOcRkglpLjsORQ1Txmg/8oQeR7HVQ661isRoSdJzqteWdyjUws4qCnTaFvFWYAL/opVayh/5/vdv+vvb6nyv5P/Ik/wT/4B/+AR48e8eUvf5lvfOMb/MIv/AJ/8S/+RWDfvv/a177Gz/7sz/L222/z9ttv87M/+7Okacqf+3N/DoDRaMRf+kt/iZ/+6Z9mNpsxnU75q3/1r/LVr36VP/yH//D3dPJuajHbCHnUc/xxw/VsSCclW5OQ34GpA6onkmoTMNh6fKDIoh6XKYzWmNQhnSeoFKlsKZxEtArRuP3cdqFYySFio+iUIz/e0RzW+G1Mn4COOob07GYSmbecmAK7U3RFyOS0YF7kxKVnl0iOxQ4TBmzKnNg4SB3qSccwaFgUGWKlCZShP/bIxtGvIpT2SCMI0w630vhe7MMYjEc96JCJoaw1PjLUpUYkBmW/A19xEhMAuWEyKphvByxchn8juKkUuxdD8rDB5oaNTRCdRHYel1lIwTqBntYUrwakcUuhFUnYMqLlJpb7vHAl8HWwv2VsBV9cn+xjKc3eHueGIKxH1vI7VhjHg0dz7r6YoO7ALv0+2nMq98z7rKUzGnkj6bzitj1AxS1tHFC/ZTHzfZvdzhyiB9cplnWO6iEIDMp1tFVE3HjKKuP4dMfmZY6JBcG05/ytG8S9Jqk9lzan+GywD9G4knRTj5KOx8mCNydT+iDArCeQOYo2plpo4gclE1UyPFvx4fwQvXYEjUCftWjnuBvFRFVPeyBhKWHo6BcBdmDBCXzg2bmAdNRTRyHJrac+EdCG+85LoRGBoepiQm/IpeFgvOE6HmHuU27iAdlCEp42bHcp8RI476k/zJASlvMhtvXIziFKTfhGcW8niHsNhwXXrzKaWHH0aI7faPzEIr51QH/sEI3gwBcUNkJXFrcMMMuIRaYRylGZEIUneLeiKwNc6tlOQzAON+tRO8t9kbCIA4a6pgks3Wcxo8OWNhE8yRbc3E8Y+orFhwPaB560NxzM1hxOGz7896dUmUJWkvydgpubnK5XlM8Ek6Bn3uQMesV1M6OPBPHzCnMfsftgiDjvGYsG/0bBU8vL1RHTByV2GVCn+1jkJ8+v+PYnj3lwvqRHMyg7+lggrgOskhg0Uls0Fvo9nKooY5xyhK7n//D+x3zj373Dw0nJRuVII6hqzbob0AcWlRXYD1KKiea03e0z3I898aam9hG7u5xHTxZMwwJ5YGkuE+TYsmkihgdrPl6eYLYhV7sp1BoT7pkWRahJhCM7L8ldy0bmHMQF3VRycX+A3ijGbkV96CjqmOrAE0vD6WjNdZ8yXZaUYcJNOuBJtqPdaMLeEB4XaOWp5wlNLrFWYIsA8cDyyLQEX12jqxFNHTIyHSw1WW7R4x3qUvDsB2/4+PIMtYE1MaICETjsRcDg3rEzQ4bDhju/D6GJTmuaXhNaB2tJW6Z89vkIUkekDDbtObUF1TamiyS+FripIZobdj7Gzfed3d0qI4g8g8rQS0EmW8okJihg3uTI0JG9VdI0+5FKono+Wx4R9h2Pny25v9zfwKW9w78MkPeSJoj5wkf4M4PsYOMSBk3PQb6me6MpA02nNSqF9oFj02ooBIf5isE8om801il2FxFi4ClPBPGHEddtRCBh8qUdZqLZdAlMLPo+JC4s6/h/uc38f431PW32/+Sf/BP+9t/+2/zUT/0Ud3d3nJ2d8ZM/+ZP8nb/zd757zF//63+duq75qZ/6KVarFT/6oz/Kv/23//a7HnuAf/yP/zFaa/70n/7T1HXNH/pDf4hf/MVf/J489gC697hQYJcRbwYRQnqiG4kbOew84PG7N1RoXGkwPiB6qySLDOWLjGpSk9YKXyq6IoJccCxKNlmGqxXSWdxCd3jokAAAVlxJREFU49YaYk+TBphNip8HjEc126uco8MVLneUNkNrw0U7Js06gtIR3wve/8obLr95wnhQopYw7zPytMEsQupOk91L7h5otGVfUQaKbpfTK0Xk9rMnmzrqm5gkb0gOa+pdhDvomNAQtD3L9Zh3pwteN0PKVBH+7i3NKmGSl7TLCJs47MDx7vklL18fQ+aoFhFm5mh6hWw8YWLoioDs+Ra/1hgRYjUYIRGFoN+FMPaoQc+qCVHK4rzCH1rcRhEtoTt3CAehtoitBu1BuD15zwhs4MlGDa9eHOFaBU2IOt63J4dnJW0oOE63XK+mDAY1rBX579py1Q4h7VlfjBgvPOtRjhhZ9GuJtJa8MWTv7LhaTUkuItzAg/Akd47VZkh/YrFKoMKexmiC0nMRZcjQsa0T2HmI9ulfk7blsjggeCORpz10krAH0UGHp3g9YJs73ljHIGzIGkPwfs+j4ZrBJ5Zv7h5SzxxukSBKcAcgHcSdpQbMDOxcwkGH7zXdgdjfnAG21diJRRXQHXv6TlN1I8L7AaO3VujTlvUi4zDacd8MsEOHrSXaC7qBIChabJIQlKA8REtLP4Bk6WHcUV/lyFIzKC1Wx6w2OV3t8LknUIZ3ohVBbHi1m0AKba0RE0P62qLzHjvxuK2mLkMC4cj6nkJrpm3J/PWUxDg+yA7QFwEu8niraIjIxI6uilhNYw5++J7w05ASzw+pW9pCsuscV12C+2pHdB2wmsbktUacNgy2lkYpHkYrqquUOgsQvWCmKsbJjvDQsTMpb+ZThI2IDzqqzyIi4SjTgM9eneO1h9zxm99+BzXruXp9xHFa0E88NvBgDDps6V+nBBWU9xEcGnRk6bzG9gruBRfBESozfNjNaHJNMG4RLwLsTmFCRRWEHAxqmjTm6NEG9fGQm3nGdNTgkpbIGFwr2VYx212ILyQmkgTf1Lh3QyYPKqqxxUuBmPTE2lK8yfjK6BqzCbhZ57y5nWJOLdWnCf1TRzDuSA97ZOg46Ao2xwHNVUr9acYqGaJFTXjosENLe5HxW/aAd2cL/vPylLDzHB7uOKsKyrFipVJOT++QI4dyHdfzKbfrEVhBrzrG0x2zacnrcsLwKwWfbI7phWLdxzyM1pw9XPNhN6VC4DKLnUdsninoLE7so5vTS4E4b4mMxY8d5WZAPGp5PF6wfZVyORqQqp5JUNOrkCjr2X0r472Hb/iwPodekl1BFys2WUIiO8oipQsko1nF5Ljmcj7DlBomBmsVNvXwVktXSygVpdLsNiFtGBBLaB5bTNITvJLUSpKvPW0kCY9bVq9TBoOSXZ+jtacPBKIVBCc18uOAN28OsW85orhBHXiuqiHBXKLDDvPljrKOULd+jycnwDeKuLCUylMfib0w8fu4vief/f+/rP/RZ//O//n/wmxqKGONXYQE2jI62iE6sE4hcsvmakDfhriJIT4vyVeOQgVErSUftkSd4dXiACX24RThpUSMLWq1V/T7ViJbiR40NDLc09eeF6grzUG6ZXEcQCXpBpL6LiFWhoFvODVLolPL3IxpDwzrZUazinn21jWvL44Y5hV50HDrc5pdRFeHhG6fTd43GhE5opOaYVyzvRmgGnh7dscnH53QJRDkDjpBs4tQ3uM1qGlHNmqZDnZIJ0ms5fOrA8bP1/t89kbRek3TBSjjsZcx2jvaA/aV3bMFhYs4tDXXLsddJNAL9LSFixCbCkzqwYFQoEJDKnp6JWnKiGDa4IoAsVY4JxBjQ/aowNYKlRq6mwQtLeYqpHcB4aQlP6hYyRhfSSZNy9nRhm/cn5OsHIOTgk2b0oqAsO+JjaPXkuoE0t8I6KaeZCEIBj3tJmT83oqu1xRvMmwGXSzRWYe4jhgNS3LRchtmxAc1plDUVUSwlegNmAy6UKFbh94KoklHU4bYocWnIOf7D2o1Veja02ce0Stc5giPGrLes+tDwr4nsJ7hox33izH+NkAGdq9Qj/dApWCwB7n06xATCzLT0xpN8KTCvYxwXqHTHu+hDRW57yl1wFGwY6kTWGqaMURRj7lIkKLf0w21ZHa24S7MSOOWZh6jncMGgr7TyFLQzTzJnaeaKHQBZJZmIjgKC2yjUIlBe4+PPbULSKOGxWZA2Ht6L+mcImn2VLx81JAcVcw/G5E8rSjLGHsZEdwLugcOpOedJ2/4VMwY6I7oSvH7Zpf85/6IHzl7yaefnnMVxZz0DVG2BxJdvp7y+3/gC/7b67foheD0eM1wDneLHPmgwX8RMnu+5jrJUcpRX2SYnacfBRzFO8ROUEeafh7iygCkJ3xS0dwlZH1Pk0ueFhXbseRNHnPal8xVTGCgu4oRqWUwrqGD8fmWqy8OifIO6T31/T4IRmm3F8W+SrARCCdITiqEh/IQgs5xGldMb3tu05D0oEbdhtxvE9y7PYu7IXFgKDcxSM9kumW07cloeekOEK2gyQSu1ZzO1pzMliyuR9xXA+LjmpGrWdmcLKlJxw2sFKKSlCMFO8miyHGNItU1g0nHvM9opSKsLW8f3vL5xSlnh0uWr0ecfGnOq2q6R0WvA0aipW73dMUexZMfeMMXl8ecLSsWIkWfN4zjiqJJqG1A0HuaLmCYVvBZyDpJyPKG5j7m4ZfuWemA9iZGTnv6DwboJzUPjua8fHOEkZIgNhz6moWLyRcON/QYK2m/iKmeCsKlR1mBLTTuxDCpWtY2JlCOeNxSXuRkk5rOK+RBT5S2DLeGxWKIji2rA4VuBP08YHpQsLvKsV7sYU5jT50qdGx4uCtQjeTuNkU/6amWKZODLa3R1EVCfeCRa4XNHekbhzv0vH96z+tySHWdcPD+ku2bAfYqQvUec25Jk54HruL19YjKhLQzT9h6KAX1keCt6A3/7V/5v37ffPa/o9n42aiiDjPSu574tOG8KPl8NSQ47MlNze3thPN4wWaS0uSCJ/k9n3/2EAW0KqC3AWrQMzwoWd8PkD3onceMBE2kEFbs2/nKkz9s6OeSHk0xT0mPGl6uZ/TXksBb0k1P1HmYOayXFFnG7YuQ+FnDzeWEsWw4ni3QtSYb1gzaluPZlotvHKKODcfDNUHgkC3supj3Hy34+qfnrLcx77xzzbeKUz6rpgTv1nTrBJtAVAhMLshosYuA7kRQ3KU0VwmTd9a8eHPA/+bJa7Z1yLe7Cc5IfKdQaY+bRzRnjsOoIECxihLu7yYExzWFDQm9xTwr6e9jtPeYAFzowQjwIIQnKj1VnxAoQzJuQFn6TGA0YARvn92w7hLqKoAioIsEttZ4t2+ZBucNIrKIhaCKFI4Y90oSH7WIZUjxyRhzZgmFwUWCbaIRG41+4VGZIb1SqGHHNguINuwDW9oQOTEIDcIKBr1hG4aso4Cq0lgpMXcRs2FBlTnkwGMfQHubENue/LBmuxlRESASvw8tySze74Vp3oJVEtkLgsJB5DCXMV1jsVOJSRVJ3TMVNaEJuW4zXO7ppx4xsNAJko1jJwPSwwa3ijFFgNBgViHNQCM7aAf7Gbeew/ZxgNSOm9UIYgdWozcWVySEww6/2Lsg+lxxdzfet0XPJVQgA09ThkRraI480ZWgTwW692Q3nvZAkJee7SwlLg1lHjIbFYRvDP4p+4x444lFi6tSkk4gtaBLIKwtd/dD6mNBu81Qg57RqGSXRQR3Gv+DNctqQOQ95DA+2vFrF+fchyHbYkThJJE2xPQ4ozl8vEZ4+O82T3BGgBIs6oxtKRme77i6PkBGFvt6xuT9De11ykm04kN5wGDX07QRdRGi3q6RcYdee0ZhSVWFyIVg9LTg6VFNjaZfJ5yEW2oXENSG8/GGN5MD8scF5WWKXmjSQEEvEW8Cwgc1hZHIhSAd95SRwk4cei0R1rMJA9Sdxth9J+XFckI52CEDx1U5orcRfSSJS4FPHOfBjs/qkGFeM1U97z5c8vGrKaf9jjqWRJliE6QsyozuPsSMHfY2IpIN936KxNO0sCkz1KUgGhoi3VOtYg43JW0WMielcQFp0pE6j6gUN3czTp/MEWtB5zSvXx8Qn7WQOZarlMZp9GHHcNMxnPbcX06w9xFLCYNnOxabAQfpfkSpNhLd9jgfMntnjnnU4nuLTT3Hqmb3WUb/wDNrGtYfZZhzg1SwMQmN0ogbjU0C7m1AjyKIGnwrOOw7Lp85JsMGAsmqSdA9uFIxVynRHFQl2dUh7QRGgaHbKGyt2DUZRexwRxAEfo/8Lfaq93mbIUcWKk3bK8Z1jZj1yNcR5aGiqFOef+We159OGI0LNnWMiDyicEROEEwawqpjmw0RtefVfELdhHgvWXx9Rv5gD0XKTnYsVzk4zedyQB0HpLMS14eMG4dNQY489vsbevc7O+LWV4oftHNU75gXKZvbnMgYeBFxtZtw4ErWowjTasKPFdd3M0zpKaWmtZptnbB7OaTaxqRZy8lbC0Y/tqTPPFHpiOgZDipcCBSKZ+N7otojtKW7iwi8QxcCPVeYdYhdB7iLGELHapGxylMWdcbTZMlxUnPeNdxfDom94fazAz5bHRIddLheUb7JqW4SItcjYogHNaeDDX3u+CIeI6XgvUdv0Jkhj1vOioYgNgShYXMqqIYKyv3DLzW3n85wlebz3ZStiNDeEZWONN5DZIKDhqfH94wOd9hKkTYWHzvsZwmL6yFh51GNIBi2FFrDcY8bOsLK75WwU0OLJpk1dKlkE4W06wgKiVgrZGh5+RvnBGoPeWFgCe4lrlSo9yq6XPBwsKHvNPGoYew7bKtoTEC4EqjzlvoERA8T15DZntPZmmTUkE0rnIH++d7f66P9e9KEiuC4ZVh1iHtNVHrMhUQXcBzV9E4j7zQD09GvAgZFy1vLJcPWUpqI2eMVTaJwqSPteqyVhN4SrgR63NHuQpwGJSzhuAPjMbsAm/o9AOZSYi4j8m3Py49OKZ1kfLDFSIU6apnqEt8Joi9tiR7VbFWEGXraAfQZ9I3m+eNrct0QVZ7c9qQHNWFria73hDBfKWRikDi653troH7WUD8QCO/wsceUmuYioyNkMYzpRyB6T7SQ+zZxJ4jCDrSnG0lkKdBLidxpbBlglWD01R3+w4iwEDyIl4Q3AicFnHX4aM+yuFMZWyLiwPB2vsAtQub1ALsKsRmUTcTteki/DOn/Y0r5Osef9Tw/WvOt4IAdCdv/nLCrIt4WW775zUfses00KUmyGqsEg7anLGPWqwFRb5EjhzxpOS0NsoDlThMMOjjuaYUmzTse3XX4TchsXHC/GlDogPGoxRwIXpHz5vUImzp6HxALsKuYjcn33IKLCJSgbkM+mh8hnKctIvxVQD9x9KmgdCFxZ1He74EplSd+KbGZ25Mp2308763IWDUpvpJ0UuK0QF4psmtHGDacyi2DUcnWhnzz/pC5G/Du4Zrjt3cUKiRowZeaxgeENcQYVtsBWdcxHexYXQ9p72KSoaERAaNpQfagYD1OCZ/WpMcVD8dLkB5Xa5K0Y3i4Y1HnvClmjB5u0ZGj/PaA9QdTEBBnHYm1iJFj82pA/Sojvt2/9+t5zlA1vHl9SNR6MtfRe4WvFS9+7YzrxYR2BPmowj9sEaGnaUIKFaK1Q11p7OuYzc2Ac7/lebTmf/f0BeNhRRT2lPF3Yo3PC4KoY9dH7F4OUDuJ7jzhQhIuBN4LXAhB2JG1PfUmphhq+l6Try36WhHfCrr7GBE64nLvzBGlRD3oCWctUWlZigStLYnuCL2jiRVfmBG7LGYepKjrAL9VcGI5eL7mwfGCfGTwCtQOtmWEd4JJWKKPe4pNij0z2NW+ZW/von1Xoxak7GmYT37/BfnDHeNVj/3+6vN+Z1f2P3J+RWFHqFMw24AvDhXSO/SJQS0EK5nDrcdqOJ1tuL4aIzPP7HQDrxRrke2hM4OecpFiKs/58YKoTenaCGb9XnjyuGG7DilXh/SJwFmFTgxtp0mPKkShcPOAvG1YP9R09zFVGeB6iQ4MN82Q6bhgpxOSo4LxtMQ+CMhsTzPWqI+i/UYsLEdRQfHJiP/n+m30QUN0YPFrjZp2fHp7CgJE4qiOLZU3JBdwerBhEeYc25I3yRQtzT6i9V6xqlOaVhEbi04s4l7hnSIZlYhSkOSGupdMVMP2PiM+qVADQ4QhaAWrNiXGIAz0cQAzh4kFqTLYNmC3SgmnLRPZET1q2a0zguOK7jZGC1j/p0PEM0s6rmGnEC8DuregP7N8dHlC22t079HeEwgPmaPZhPQKfORolGS+HOIiQ/daEese12n6fM+ejwrHUVixTmKi3uFrybYN6UcwbRtWxzG63Nv3VGowU9jmgnaZwDDimpRx0CF8x/1yTBtJ1NRTeYXYgBmB2nrUjUJFElF5vAM3D+iOINg5TKuonIBAQOa4VRl9EdB9FGKmoL1BBw6xUYxcT32bYWvNaFxyTMFtNaRIc6TzvH69t4IFkx513NLsou8IICVi7Rm9tcNXknWdIguHegV1HOyxo9oRrDx9Dj7x6Frit/u2fT9jb3m82c/UO6uxBwKMoB3tL6bFkSC+8yjveflbD6iigElUUmeK5ksO/S2wNkDMeoJrMGiS2GF2MYPDFaKWCAPdEALpeDBZUeqYapVQnQl8HOGdwswj3m4WFDam/zHLm8sxd2VLLTS/59Edu0rzjfUjzh6vqAjotxA5hZAgpKcONKJ3pFlPPKzYXA/pYk0dSaJCEWQNLFNu1yOS85p6E3OnIpyRHKQF7SPHj2a3rG9SPu9nZM8LljbGLiLqoaPYRuiRQ4UOuRH0M8uCBKshEHtfud9JTCIInAGhECNHONf0mUAqSxr2yKWiGgi6WJP6ls4qbOTp7wNerWc0oeKwsdjMYpRnt47477aPORY7Hr7acBOM+D2/93O+cfeAPN1hUoFvIq77AfEXAx49v6NeKZZ+gI09V5/PUIFjcrylWCeUVcbnfY6wQK3oVhn1IXug1qxj3uQEoeV8uOB+mGHfRDRtxEiVVE8dZ6cbutjzxuWoRsJOs0k943jPAqhONE0HcWeYJjteFVMeVVt0qchaQ3S+psgG+HIfPOQwHI62uJWmdAm71GHdAB8IfGLhOsTZgFfxFOckfh3gco8qJDQCPzHoBXTHoKKebh0iQ0cVaMJ7gT9x1IegC0N7keAeW6JPFE0iURtHOoeWELvTyGHL4ElFsvVslwlBbPbj2qHj/S9dc3k7pXsuGOQVu2XObpWwuRthjOY4X7GSA/RGw9OO5vNwPyYb2P9Xe28Wa1t23vX+xhhzzHb1e+32dHWqt122k9gJAXwJnUKEA0JICCLHMeIpCAebSJCIgIIQwX5CCAmCiFBeAjJCMSggLhcHjCGyE3NtV1x22dWe/ux+9bMfzX2YpnTrOtwb+7orZ/2k/XDmHGetuf577vXNMcb3fX+kEczaFJUrhDHo1GF2PaKVGK344itHnYvlvZi37Jx9S+PlGzrYf2E1xbkMOTCEw4ZqHaFXimroiWYBwnrK6574xYDL5Q7uMXCHluJhgmoEoob96RwztLSzCM41Dy8PIHTsujnVF/osHg/wBkTq2JssEdpyvhqiDDReU5YRKnaIyOOvecLIUGw0WEW0X2Dvh4jAQSy5uDOmf3XNg+eHmOuG7w3mPNnM+e3xIdObOXfKIXkWEN8smB3H6GcT6re3sFFEkwo9NNQXMZs2ws9h0l+xs7fk8njInqx4+Ooe9T5cHc849xly0tWRR9bz5NVjfvv+dZ4YnJOrmKJMOJUhroErBwtW84xhklO2AcX9hMJ7xmEO9xIcHvtEQ99UNBNBdBlgXYBuHaGwuFJgggBaR5pUuNMQERjCqw0zkeKd4rLq0dMO86THzCKGgxJbRKhaIgKHcxBGhnRU0g5BH4e0A8/E1rhWI1aOOlQURYBSjtQ0HO3kvKxGLKuY+opj8MBjhhYnJMrBPIyJK4cLoHUCCkUsGoo6pWda2tYzsC2pNrRasMkkO6rECUn9co+yB5m31FOBXypk7tFzsLHsVjckeCeITj3hBlwAci5o4wgVgkk96V3L5maAKQR2LvHXW1obkomGSVBw109Q72hQqwrua6KoYfTMmrPzMeU6IZkLnBKUOwIlPAPVMO8n+EqQ+RYbRYhLhapBxlAeCEQlui6Kznf7p7sSuXG0kUJLIHP4UiF2uzIuswhRa4E0Aukdi9t9wsOauPFUeUS5CmiRxM4SP5KTn2fYoUC2FkYWUyg+d/cI92SJO0kQLciZQqxhIkqaTcrNvTnzoeLhyYAbB2esfysm2LG8cLGDfkXx6b0D2p7g4//Hm+kNWkZPrlGzgN3DgjTxLJcZewczLk76xLsVLyQJUe5ZyYT+o0suH47RWcvKay6ziNpLoqJBrAWVCJBXGtS9gGWvjxOCh/0+JpaYQtLe7uEiifCdwVJ2aWnHnqj16MRiiog4NsSpwZmAcFijQkty7vFSUe0IxFKjGo/TwEbSWs3VR2e8emsP2VjayFMWCWpYc3M05/Z8B1VDOUrIqwifdbPPwXgDC3hY7yG05eIyI4wawrXjD04e4vst//niBipSjNKcKZpkp+LBbAJjQ76IsM6R2Ja88vQP18zujQi1oX7aEh0rGhdQr0OGeYvaMzzwE9KgIHx8Q3lnxGogeGy24bgaYYeGzNYEi4DhIOdkkRFPa/bIua/6VFWI32lxiYWV5Nz0MccxctwyPGkobILRhrrQXH3ylPkLI/bSDQ+jPr1bjlMvecqs6c9D7tQp8U6BvRWhr9QUVYQqBPbAEK4CxIXAPdqSOcNesOHh9R5Z6WjyHk1P4DLYzXJ6ueNWFtMK2ZWGBxLpoH17RTKpmZ/0GRzmtLOYRaNpDhy8PGCQGXLT4zSxPDE6Jxk0fOHBHiptIXbojcWPDfPzAe1YQGjhJKTodVVFSdgS39JUxlP1XGctfgxq1LI8yWgy1a0ytw7Xl9y/N/qWxss3dLCfXFswW4bEpWNRdZ3qjIkIHgSIXYvTHnESw8BgrcRvBCp3RMMWNbX485hLlcCLitYFTEZrzjd91EyyrgICZQkuFF57Btc2XLQp/lwxurqmmUdECEJtaeoAWQS4MgBp0bnH91psoVCFxFxzuEITBi0zmRDecMhVwLP2Cr1+Tmk0Xz7dx641r5zFYAUq8FQjSdMGKC+QtWRx2aMXVgRa4tcBLhA0Jwn5QFPHUA1Aa4tKDZvLCK0sctwgS8E5Kft6xb1ihLvUiB1Lr1+yvOhRqRA5sJ1L3LqrPgiyhmId0h46gjOB/rJGvLnEnSbsHC5xCdihxBeKOo9oa42bC/KgK18c2xJzYFBnIHBMXmrId0My2SJqSXQf8qFFVp1zlxWSdhUy04ImliSPVoR3ItzjLXUmGWUF9ZfHqBVEGIIbNcciIiodohWo+5plIPEr8AKyhwCOZqi6ZexAwJ7DWUl0WyCMQl5IlibhMvP0xg3xJKfKNWpkkT+4gpOM9SIkuAS1kngFNhSYBKSG6AKcBmnAS4HrOdoQgtVXatQDKHe72e5esia3GXUtYBnQFIrTywiZWMxpD6/hSCxxj5dcrvtdZUltMdcFTSTQp5IgdCxVhG4d8uqa9jN9/JFBrCU+EtSRRJ956h1QpcQqaDcJbc/QP1HYva7EMSrAWLpub2chfmihASk8YmAxKuCpK6fczScUr/RJ2hYdW/L9gMd7a56zCcMoJ3+QIXoGLS1Fqhl+uSXaNWyIWB+EyL5l00Q889Qxr1xOcC8pemvPps24rXooaylrSXFdkmQ1SQF2YPn+J+7yXLXD8aLHFdUy0929fH4+Ir2ZExnLah3TLAKK8wSbOfRlgBaGpvZc3t9BjSwuFGxyTdSCfBgiLYgLSfO44WIQMc4LXL+lX9aEfc+iSJkezLGPeZb3h5g67Jbwew7f98Sxgb5hsGkoK024U2NUCE3QzU6NQzaKsIC6p7mz3mM62vD4lTNOlgMeJg76nksdc13PODU9aqHQ0iGjFi08/ZnlIUOKI7iqNlTLGDEU9KXlN1aHfN/0FR4Pzrmj9nh4ssONwYLz4wm114SXgmsHl7xyvM/CCSgVq1Xa5cU411l5Rwr9XEq00/X/GG5adOuZlxm7esl1fYHIHOO9FZerELmSlE6jQotLIVOG5SwjMAqbalokbaVBCaKk4fBwxv1mFxUbjHTIY8Fwf0MbaM4uRsgrlspBoiv09xk2Lw15NkzQ+xW0DcIY5COdJ0LVb7F1SG/hMNcbzEmEekWymUY0SYifS1Z9R19W3aodgrN7Y+ZnChV61FLilEdZS5S2lKWmuBczGm/Ada6iZgJmHdCMPdMbC9xJj+JhyrPNkMEzc/7wk7f5+G8+Ttx3bHoeBoa60kSnAqsFzkhoIShh3WYkZwI1NPhAYGOoHzeowGP3HKkxVApU3GX228dK+OVvXbx8Qwf7B3d28CalqgSRtYgjQ3jgqY5j3J5FvRCj+w0NAmUlTSSJrWdxMkCMWrJRQ0pLoyOu13PuqwQkpHFF2qs5CfuQd2kNq/Me1kqE9Jxt+siFIGwEZdYZM7DbklrDugy5/tQFa6HZX7dcRH1OCQnTliYK6Uc1s2UPXQmKdcLlOiNYg3eKQVqgasFGaZgYkhU4KbCBZJxtur3SQlCsNUlaoxJHcNQQzFKsUoweWTOOCkJhOZgsELli04TsqYLj4z5i1S3ZHmZLprLmzt0JPdUihp78OMNllnCuSERD7TTtShEtJPW+o7WS3n2NiCA/zciGJYfRmnOdYXZhlJSElef+2ZR45VnrhOQ5jRzDD+zd52EvgVlCrQXhuafsRRROoXoOvRAcPnnB7HSInlbYO31snTLeW5LfSQk9yNMQKRzSQhVEtIUmMi0YQduDJKjxTtAYTXRQkAdZV0lhPMrBlf0F9zf97mEmlOQ9gbIObQ1iqVAXAdiIwaDiosjoZSUDUVPvdFa33kiQvusUpEXn/d5IdOnRzoEReAcyl1jpcbpzDwznoJxhVvZRjUQ8FEQY2He0qxBXdcl+KnA87PXJ7mh20pLUl1xmKZebDHlJ17ddWPQrMeXEw1oQxxa7kviJJY0aVosMBN3+pOp6tEdLiM4VTU/SKke68pR9SbQxtGVAZB02dHgl8Q5YSdICVlczjJfYQHCZRsS5xw8Nd/KMdGPxdyLaRNEUkv7GEnqonwxYHyc43ZXFnr6yi+obLhtPr5dzVk3Ixg2TUc4T4Yz79YBXmyFiVNMsA1ohiI4qPvbwMfpZySTLOT0f4vqe8JE1UQPnJ32SzFAtY9wmQAqHvhT4GqqTlDRowFnqeYBNPVZDsJY0e47klK/kJwiK2YB83EM3jv7BmjObkTQtQWQpNgmrPEWMDdm4wGjBONtQzBPqRcJ6FbF6CnTuqXRA/0pBawKCLwbY1ND0JNGF6Npdv9jj03HEUFeoeyE+rsnnKTkp417+lT1tSXQ/okglc5Wi05YUQzPoEvCurZZ84fyQw5uXfPZLj7EoE1hrqsgwni4RhWAkamoZcvuVQ8JR05Wbeku6u2FWZoQzB7HH5Bo7dGSPbRhcWGo0HLQ8M77EWsXo8Zrn51NsMaJXWFZCsSRm0jZsvjQgj0KCUcP5gSZeeZgFKO+5zEb00prFMkNeKjCSPIuIRxVTlTM/jFgfR8i1oB4G+JMQn4GquoZbMpbYeUg7aQk2nk2dMLi+Jpsazi8HZLQsjiRmoQlVizGK6Mmc6laK1RItPBO7odjRmKGjLEMS1RJODP6VBFMpkshSH1iGsqSYRZB6gkuJWIOJJcXtjOYAjO8szfuXgk+tr5EHEaaRVI1Gv5KQGY8REt04TNZ1Oe3cxD0u9ZgRqNITRJajvTW31kOEFuwPluSrFO0bbKnZ2ORbGi/f0Al6rQlQkcVcbXFW0b7Y2dKGuiV6NSBwnh4N5AFUIAOHawJUZAiso33hK21BJxWbKx4awZueuUd8o8Q+aiDwBGlL6hp2HpSEuaEfF0wuDVIK6jIkXnqsV1Q9iQgsOy7ndDNgtuyzkIo3P3aL3XTDA5kx2V8RpC3JuMLHMDhao6Wh0AHZsKROJKVSON/VtzcTeGT/jMH1NZXR1CjWRYaIQCjH5gsxp1XGI4+eUM9jzIOY1atDzhcDLk5GbExIbiIemj6h8ew8tSCVLVG/5n7boxJdV8HUt6RxjZ5JbAylVkzjDXJgqHcdUWVRxpGrEKUcdRWyusi4s5kwOxuQn2UsNp2ByaiX46cWO7XUQYBeCiorKaqE8jyhOU3IM02ZKcK5JFgJOOrsK30rac8jZAt6p8ZGApc5RN+wEhFyv8ZeN/jMd5a5Z1HXECQCMwsplYK1wM8lg0FB78x2tf6qJb8VkQjDoVgTLj1BDuG5BOuxqadNJfWDhOKTQ7jU5IEGJ3g6vGSiCvQjJWJiqCeCcG7RZwLfdxSHgiZSlDeBHUOQNAgL0kKYNUgcdqPwxwEO1bX3DL/SvyF02D64iG4fby5ov5xxcmuHB8UAbSvEqGWsC4I1NKHAP1KjpUOXhiYSyFwiLgNSY4gWvuvJX3cPJtJ50qeWtLsWG3nCuaDV3RaE1CDXElkKmHUOkc5Jit0A0Xccz8dkcYNTkBkDEnq5wdcBkTWYne6LMrrsWrrKxJKcQW+cE41qxrbEeYh0y3h/Q1VHeA2bKuTLxQ53Jxk7vYIgsfjI0w491/ZOycuI1irSB922julDKgzrOwMu2x4y87Q2YC9e4XdqaAVGBojdltGyRAQOn3qaVGAGDiEdCE+wErSxIMg9LLrGV829iOZ2yv3P78NakdchfduQ3hbsBDnkivokob2IuPjyFPv5jOBEUpUhwUuaQd0wHBbkPqQtA+ojgQsUaiERzsFKYbUn+kLI/P4QHwtuHm/YS5dEuxWRMDStQteO8oojShrCyhM33f08aktEITmVPXRi6YkaNEgvUTU0WvLqfB8fQXCieHx0zNAWHAQFInJMJ2sEkDpDVYcE2pKEBcl+3iVROkmQe54YX5JvUi6rhBerMcGFYrYY8kq5Q6FCesOSvIgIjmpEz3QWsw8DlseDzr0ST+oNVIrNC0P0uEXEnsA4CD0P6hHtwwix7pLWypMUN9c0z/fwRhBdy2nyECugyhTFWYIJRddXfxWxN17SLCNIPFK6zqkv9uTnKTYVlCrAjwzHxzu05yFlGSGFJxuVCKAdO/pxjdKeZFiRRxpShw8cyW6FeawhFTVFHPCua3d4y7Vjkqhh99JQ3U8Rc4V7JYFCYUJBG0lGjy0wTxoCaUAKTApBLfBCEktLe9QZfV0Qk+gWnbY0xxFP7p+xSQOCFYgvhd/SePmGrrN/+h/8bXpTEIWkuZ3iKoULoR2C056w9ETaYDYauZQI6XBHBldIBsOCUDqaGN6WnvAiY8rzlN4jOX4eUJWasLWcVn2i/Qr/YkKrQdWCZt+Rri02EUxGa+6vJsRxQ4ShlZK8ieiJmoN0RS5CrBQsNynUgh+Y3OJ2MMY4RbOMqG1APy5Y9wLiScX8i1MS11AEmihqiXcqjkzO8fmQ4dGao6sXPLgz5Ww+IjiscIVGOcfVRUtwY8XtzQ7gcbJr4Vj7gES2JGeC3lsXXGeJP2hZfHHEC3aX6d6S2Rd38GOHsRIbeygCRrZiQUx0KnCpJ1Cu616nBGFWIVcKl3fmDkJ4gtgQCcNGaLT1iBqSqGEwKPBrxantgZU0pSYsHT4G0bOIYYNcBVQ2IBlX2LnueupXqvujfrLGSoFuPHajiOaewNPt0W0Cmust+lzRW1j81LJwMaoCBVgNSVyR24hoVHellGdht8QuBXoDPnHI2hOsoX7MIs41JhGE1iCerGmPY6K0ptn3ZGeeoG/QLwWUi4h8T+JaBcKDlQgceuOpR5Jw4XChRFiHTbplvjYV2EgQlJ5wCU51WcVBYXEKfCJwCJJJzkYkuEAQrhyjKzlnswGy6rrw2UCgW4caGdSpIu9JvBBES4+qPF5IhPcURxDmBr0S8EiL1QJ7GWIyj7YeWYIdeLgI8InHhR6ZS7J7nvwtnuyJFcWtPiZz2JUmTFuk9wSlQCiHqRQMLSLy8IomMJb0esUiz0hu5IzymjC23DqbEguLDxwD17D2EZkztEayd33JC+spcil5xpzx4uUBzRBu3Lzkzt0d9p66oNWS4jRBBp6sbqHvWd2PEaUjfrQl8J54UBHMBffu7hN5S+0DImnQeyXrh31ULrscAwTVjiQouv7qXkE889gYvPI0Vy2uVvQXhnwYYGMIll1+j4sE6YmlnkpCYykmAlWAPGzo54YqUtRCES5BPFSsbyiiCwEOfOARE0M8KQkLh9xY1NOWs5MRQrmuFfQkR7ee/aLl1dMd4iQnvgfR9RYXwqxN8DNNOqhIhwWX9RB7JpFjj9KWlYmJZUtWG/xhixCewZllPom4pnJefHEfHZfIKy2qZ+FeTK271SmEYE2ISzzRQ0V4tSAZV52J1jzrjL6mLbLothB9LnGqc8EUWYs2HnethXWANNAqgc5alO9W43RqWGxSgkuHUJ7IKq6OFrxyZ4/wesHu7pL5wwGycmRPbjj5/D7CeuzIkcmuAqRsQkTmOOwteWj6mJWmH1ck3pDnEdUspplAlHuCM4UdOMRuSxNIbsRz8ns9VkGIbRTfc3CXYzPszKJMQOYr5Mgybh1Banj19pRkY9m5MePeYooJBf28Zbno4UNHnLbUrYYCVCOId2q4lMwPNNnKgIKnRpe8tBrz6M0LHrYZbRNwvX/JxeWA5fkA4hXP/u1f2NbZ/264Or1guTigjgVWKETPIR3oYYt3gr5uKFcxg35BqTvzk8Pr59y+td+1gz2qQAtOSAhzwbHQGKVpm4jIQthahg9zXKy4+Y4HnJ2mGCfJfEvWA7dbYU3I9x095HQTsWmSzsRCB5hLzUM7wi406mbJOMk5b4f81u0n0LGlCSTRqCQJKsJRxV4Lm0WMwMNKMZ3myEGLKiGfSNzac3YxIl+mXJIwfXROtUroTzfY05BXBglHpmYkCh7MJsi+pecMwjj64wK3G7J4aUgq4f66x4He8LbJQ5pLTTBd4hPPpLW8eDmGw5ymCEhEjUskOmsJ5hKbetwmRPUczTpEOks8amm1RORgC4eQCp97kisFqvW4iWV12sdYTWA618FgA8FOwWjUsDoN8fs1Za5QwhHFDWLXsrxIodftKwYLSR27brm8UQwna9pAMB+mJOMK8zCjHkFrQlQDPrPIwCIjz6aMiNIG7wRpZcgDjWjpHpoeTGl2oD+3VLGGpUDVEG86t7mm6tHuOSQBw6pAHLScrUccxA3iiZpKJ0SnnsgagrRlZRJIvpK8pyVkDrESqFJiQt9Vimw8XgjsyEIVIEKHawQuklgNbQ92Aku+krieoOp7Tuc9ktoyeWTBxWpAL2pZLVOU81Q3DepEE7iWpqcxfUG0aXGBQivH3t6Ss9GQaClI9mvMA00bSkRmMEYjapCN7WxLR5ZkWNKsU1gq9s9aFv2cy1WPHjVlGdAIQW+6YZqWtJuQVRtQLDT/295dvnQzITiW+CdaNpuE2zrmkXzDgS+4L/r0gxJ9o8D8dsplFTIcF7zw8iFP7Z6zLmKeV1PeevMhC5eRLzQRBvdCiDmQEHpYS/pXcy7ajHSnpLnIEFlFdOm599whLoPJ3ppg0qLXmsuix2EhCCKDEQGihHKHzt1NdrbEXnnqnc6W2rUB8S1FvQN+LdEWtBM4DS4UqMDSDiVeQjMUjHo56ypDRo5KCoqzGBsDxiN7AoVHDg1towlygV0F1GlIMdMc7s+pkARJZ5OaeRCxoq4kd4uEEEs9y9Dfs+TOcshEFqiVpDk0rE9SXGqppIIoIHCWKgXrPKVXDHwNgQMP6yRESccXNlPUtYYoNkx3NgCc9UP6cU0VhOhxjV8pdo6W9A4MLrXUlwmJMZShwScWuVC4dfdg6APR5Xx4EF7SWElsG8KyRYWOeZWClkRpQ7FOqNoQ3cD4+oblrR5lE/LgcsT+3pJgXDIvU/zEoCLD+Z0JAodaKdojR9UGiJVCHTS0RnG2GhCdKpJzwf7b17ieoD8oOH6wy+S8ZnGkMYmjH9b4lcSmEh9KGDrC1lCNLC+nQ3pry6aOSQ9z7O2I9WXE4nrTNffJHMsk4LLeZ0ALmWei17R5SDER4B3RuacOBapvKVNJEwXInkHVhrYn+KIeo64ZbL8hfjEhTSvu3N8l2q/QTU29/A72s/9O4/LOmEw2TEdrzGG3f+ulIFgo+m1LVUQYKykHgjYF1WsJNpJBVlFuYorPZjRVwPGLU8pGMriW4y41aWtxraCKJf6mJC9j7nxyCqXisupTtpKHw4hg2FIkilsq5aQcEo4b8kaTzhzR1YI2FoSJIVwK1ouMR68fI3cb/JWK7GhDOG0wGmwVMLYV+0XO93zfy+inCuyhZRrm9LOSUigev36MrKGJBdo7Vpc9jhYV61nW+Sz3WlqnKIsYkTpELhk8smTcy9lsUqKooYkU1W7AM/KEdNywWqesp4ogtKyPU740n+DXGnMvYRTn2E2XHW7PI/J1gkwcSjhqEyCPGuxE4KqAQFqi1DB5esPRI+fE+xXRcZe4Ut+O8FYwSnOytEJfqWhTycYkLKuQMgvYuIiUFnsWUj1MCJYCHVviC09cOVTbPSB4J/HKs3IRq7sDgsBivpTitaAVCowgKDzBRtD4AOcl4WVXwpNlNYXWBL2G3qjk7moHF3ZL0dODVZd3gaQZCupJZ86jN474DMwy4KLsYW5lqNZxejXgTGbQClwA1dRTRhpVCVTtsDG0fWDd1fibGHwg6A9LRNP9H7FRBIWHnc60yEYOoT0HwYpWS5TzyBoGK8ONw0tc5jl/MMa0quu7LsGdh4j7EcFGQqUIc08ctZT7CoMknpYURFgjyScBVkFx07N5BOpAI/D41FDtKdyexZ8FmDsxzUighOMuAxahJlAGG8JkvGbHrNgIzczEXBYJ81kfHXg+tzOmsJryNKFdhqiNIPliwPG6jykDdo/m2NCzPkvxRy3p0xtuujmBcxQxpFnJE9MZg92GtQh4KPuUE1gTs9h0DzahMjxcD7loUpwPGOzmiDsR7SpCNgJhoZinnN8b470A6bkIY3TPoEcNZiRIlh5dWVRscWNLWDpIPKr2KONh5Lrf0bTrJ2FGHiQE+xXpYQ7fu6E8cBgjqYRmdH2FyxVm4GkPLcFGdMv4rYDdhvGNBfivrBqMgGON2zWcjWOqUjPs5zw+XBBdKdjdX5IvU+qJp+1ZTORxpSRsPPOiR1XF1KsYGXnk2BH0W4LQMujlRJEh3FgiUbNoUozpbIbtPMAtJNPFhtBZqmXC5fGQi8sBV8MloTTE/ZpNkTCebKhuZ1y3C2yuKTcx63nKk0/eZ1fnjLOCcFp1D6qmm9UHLbgYVGYQraDtCaZ7S8bXluzuLzBS4AaOoHG4jWI2HzBIa6KkorwiuIxC6kVIsUiolxGrZUoiG8LU0Lu2RlpPGjWIaY2rZLe1E8Kot6FNJHc2UxZNgvKem7szJldKdkTB/nxJMGy67qDWcXp7wCwM8KFn1MtZzVMuL/rElWXoGuxS0hsWyKXErBXNUmMWIWltyesYWcNxOcIcGPxaUWxi6jGkF5ZryRpzGTKoGnq5QQw9e7srBkFFTzbcebjPcTmiWia445D8S0PKPKKefGt747+hg/2hKgi9o24DruzOEddq/NRQTj3rOmY9kCjlcU4QriC9mpPXEfpY4b3ElSHlMuZPvu0FYuGgkshacCkT3I6j3O/KOVzPox9pMGuFVAb3hEMUkgUJjRLUFyH70QpXKHQD/b0SvQEtDOFORZsBa8mDywnZlZysV6EjQ10HJEGDc4IX6l1E37KsUkyjMEqgjirOkpiea1jFIdEjOf5SE3mLP9a8VI3p+RaBpycahmXDYbwgXsL42oLzByOksxxkS06rPuGwJWkcn95c58WH+1yIhM0mpiIgDG3XGW9o+BNvepVNnhAZQ4FmeGXFaGfNtWLJI9MLEt0StF09dmpa/FmIaSWn9yaU8wQvIXtixdIn1ElAE0gC5SibLoeg3fFEgwZ/pWH6phk2D6hWIaXQtD1BoRSV1TROY2uF71miwtIXNRy11GtNNK3xVkDoUZOmm9FHnmYK7a7DG491gmbctX7tyRYRWoJcEGKxCFQl2JssOKn6qEmLTbque0EONhbYxOFSR29liM8ta0ImvibdONLcMRgXTL7nAjVw1I1GWGhHgsA4ItNt8xT7kiSqQXjKsxiT0SWOJV0nO7GUeCeJlqAvPW0b0BQBbuSJLz3tJuTsfESdCFTWdSqUpSBadclnOI99pMH2us9eb6Ju/184qkXMwnc9xJNhxfK8x2CWo6XDxF3pYBso1MbjjjXU4Deqy6MoJLys6Z0bXKuIDkqMUQwluNOI1Twh+XzBNC0IjaU6D+F+RPb4hp6uqIYQjWsmkw3B1Q07yZp0UHelqqEjClsejDPCac3D2Zh5EmFCw60XJrQ9x0RtGKclWVAx2tuQqganJLqEOG1YLnuc3tmhKQOCnYbHnj5Dh4Yi6VrHFkWEqAVOQl1pylQyvLkgeGYDBspMdlUvOy16DoHxUArcSiEMuIHDjzpfBZt6dL+h6Et84kmSmre89R57j86xE4vutYzbgqhykFrqXQcCgrkkPSxor7SY3a5dtzps0LmgrrsGT+UsQU2LrlLgfkwqG0YXLWJqiOquoc+gXzJoG5yEIHA0gWRzb0CbB/R7BVlWd+6Ew5orZwU7BytC3+I/lxFKSygsyVsLXBEwliWm0FR5yO3lDicXI043AxJnWL8yoMlDvlweIGNHoFpUbHn1lUMSWmwA2bomzEHu1RB6ROxIbQOxZbe3YnR9waVPkEvJ+brPapGxN1ry5sMHTB+dYxLPRRYzPVgSqxa3CZif9VG3NaoQ6MBQRAH2WsNlHCPnivxuDzsPMbOI/CyFWcDFvCtf9peCzReGuGcHXBQD3v7kMVUSkF6pUUuPO9U0KqC67ghbaJYhi1kfn+vuASxXZBeCaL/uHFMbg30YEd1TYEFeL5FXS+oHKVwENIsIKwWq3xKNGpo3WV5qhjyWLpABKOfZLBJOv7jLxkTUa03wqkRGlvU6oRUajyC47CYC30re0Mv407TkpRf32HnzJU7CTpiTByFRa1mrFG0dFo1bBfSu5bRCog9q2IQMhisqrRlPS/7b6Q1WaJpGQqnR0nOol0RphRwEvHJ3yrqOCA8Nb75ywm3ZoxwoHpcrHuRjcIJm35Pogl4lcF6wY2peFmNuppe8eHpAM7XspRsOxpdUt/qcRBmpMhyOZ9z6zRsMr+XcWu4Szyy9tGE5i8nJ2Ks32LECATtVzexQdl3mZp5xlhNNStSDCDU1nLQDmlZRJYKrdc2l6DPcqylfTZg+smC57nGxCRkOK+pZgnMBZZUyeHSJTBriywBGho+fXMMBu8McXYfMygFYTykkQ1d1NqX9FlkKFquEnb0VS5NgZ5p1JBAbxWmkGVF3LXb7NXUe0HqFmgmkgUpENMcKlCa5gPqKQZ8EFBOBmyXoXGBDQTSuGOUtM53hIgsRxDdK6rMYkRh8rVDSYV3nYxAY0LjOG+G+pPdYThvD8Zd3EBZEULMaBgQXIKXlvOxhtCQMLNJa7FjQb1vqvkIuJS6XnSlS03kgrM/6BNMWLQ1RZalnMYlrqfsauZC4oaUSkuS+QiqH9B67CBFDjygFIjVgA0Rk8ZHAGEnzmCdYSsK5p9jE+KEDL+gfFMxdjPCScCZwRw7/IEJtBO0OyAr6YYWvHOs6w0eedLfAbRTVUCKPQ/yhpXnE4u7FxAtPtR/TppBsLGYiSB8K7K5BOIka16gHmibR3funDQuZ0tMN6xeHuL7lNFOMd9akvqL5/oh+3tCMHeF9ic0kd0+meCEJ+zXjSc4m0NRVxNmzU9Skpa40iYDZKiMaVIiRxR1HnDZDRCGZ+4z0lTXVI5rYOep9OLRLHtzZI9mp0dOKzd0hwZlErz1MJa0SvHJvyuT6AuHgoh0ilx41tkz7azZxBA8SVj7FGUFwoyVcRLjMIbWjGghE5im8IwlaruysyVtN3kSIKxaxFsjY0sxigqXE7VhuX3Yd51q6EqzybESgLWm/YnXaw+w4XK24f3cXedqg9hWJtYR7JcUyIog8ddj14Hh5tk82KLh3PCAUlst+zGRZwY2COhOsL2KqUBNcb8CDXWqckKQ0XQJi4ng0PeWeGLBMA94yecD/ubqOmGqGoxw/tBT3+0TDGoVjfN4y6wWYBJK5Q9WS+HpFpRTaOXaHS2arHnWmGIwK1nnCvdNdbKmYHi2R3jDdWXF+MqZeRUTTlqYJOY9jWErq0wS31OhpTTBoKEzI3TxiM0vZ28s5ET3uXO6QBQ22VdgbDb1FS+wMq1WIbxRtzxO3lrrV1FNQdUAwbpAokocWLzyprlmMUvwa7smEYNryH196nNUywWeKdR0jNNDKLmdkGWKNQgcGVoK6HxFUjrOTPq3TXHvbJZcXGc57/JM1OM16kSLvRIS5h0QgtEM1kEU1zAOcC0iyhltlnzg3FCIlnlnKfYUqFHYd4nY9shBEaUMexARYxrsFTuff0nj5hp7Znw0U1955xmqome8GmAwGS0PtAsLWIjeSUFvYMcyiqEu+Oo05j2KO9uZcubkgcQ115BmmeeerPqzRM8H9asTJcszDh0PGecPhoxfYxPP8i1c6L/YAvnR6xKpJSXcqyBUycMyWPdoHMa/GQ2yrOK4HvPUtt8iGFfMi4V455qKNsbFlY0LOzia0U0ddKZyDjdSULmA4KIl3K0qZUKwjNqcZqzambDTTBy1vv3YbOW27xMONYpWnDMOC4JWQNG649+VDHutdcOdiRGtheXtEunBsMs1Q1sRJzTRZQ2aoFhHFIkHFhnodMj1YEMctaWjIct/ZrVYWc6bJTcgjwTnOCoKwJbu6Jl8khNow2NvgDgzN1FFJzSJJWDYZqzpjLmKcF512U0tvusFUX+kXcNgSPAgg74KazSA+rBCAfDnivOkhVNcIJlEtzknUuKU9i1ChoZmHZFFNen2N6UEVBpjEE6SGJgL3MEJFlp5vcIWiNQGRsSQ7FT3RoqtulmxjzzCpGfdq8IJxskHVsO6FyNYR0uK9oL0MEbdCVmUKkaM+DphcW1I9YklvebI7UO90Gf6i7hLwVCnIDxWmJwm8p80ku/0lqhL0XxTIGgi6JD6HgBrmqx5eWx7du981fjqPKIeS8go4o7BaUJmAqgxRjUcph3sQEGPIwgaXOeLCoD4f46Wn6SnauUbIrsuarjz1WFCFIWVPUc8yiolC4giNwXiFarrZyuBoxbBtiCtHsYlZXgyIhGGdCoJ+S/mYQB/W6FzQG5aMXMH+3oLFRZ+1jHEa2lbRVzWTKMclHtv3WCuJjwq08axdTBy1+CONzAXX4g1OCR4UE9LHNqy1Zv1ggG01gfOYHU8VBJyfjBiONgSXgnUVQehwWpCmNWamaR/E9KcbzGVEdFsRV91KUWRb8mUCmaXUCgJP72BDE8JFlZGPNcZK2hGsLvrIixDjAtxZRP4wpTqPMcsIeTckGHfNlzZFgoo8YtwQxy1DURCkIQNdMR2uKJuI9KgiVd0SvKo8bCSzL+1gep5GdjawbR6yMjG6BgpFltZYJXj88JgwM2SlIdmt0LFlMc+YbfrEa0FSOAoToTYK7R0KT17GbDLJ4WDOmUi4DEPGquyqDZDsHcw6TwkLtVK8fP+Aehag70KzCTEh6FFNdmNNGUoei2asvzTuVoCmLWWrme4siHLQc4nte1zYWVD7ShGtPReLIW2tmZ332ck27O0tGU5zgtTCTONrycZG2MuuSkCdBAxurLlx9ZL+qGCoC1wjUSvB+kAhM8v+skEODGboCAxkM4Oru4fgHbfhB59+iVGW4wPfVfYkwLWaSiiU8wRZSxA6TCYoRcCd+zs0lcY90YLwDExNuw7RwuL3DEWk6PcLvBDUm5BlT1NH4C9Ud88JRdKvCa62BHm3VSeNIG0Nh2++IA4qRsmGEEu1lnhrv6Xx8g09sz9te8w2Cf39nHyRUvkAFSuG+0u8ViRGsngwxMSCvb0ZTivK+ylh4JitM7SwzIsUVyiiiSXeePK+Ihg5+q4lDmuO6xFBWrE863HDLHl1V2CLgBvxnMtyQGQtszKh3y+wjUJOGzZasz7PGAc5uQj53Jcew/UtgRGIhWJjYqq15HC6IH+QUS1idtMCM1SYEuLaEmJJX424f3/A/3bzFe7ux7x6vM94VaOutizSkM3tHo0zlJcppvQ8VAk6A/lsArHg3pcOaSJJ2PdwrsjDmHatqB6pMUIyq3owC2gJkJlhh4LqQcrFcgc9aLg3SRBeoKTBBxKrA1CCF1ZHhK4lK2s2RUQVS5CKZKfEnKbEhcOfSdTU0HqFUd1sqFc0yFBgXxbIXkAvc1Q7EvEwxPQ8dghOgC4Fti+I+xXJ1QpXJYgHGlk4VkWfoHRII7HXLE8+csr95/cp1zH68w6961AXAj91GK8wZwGhNqjYUjQR0kB07qkCRVH0UMaTti3xMGd9t8/GRlQmpA0kizRErTy68gQr2fXjNr5zQ5QQHEukCPBRwPozQ7InC8qnIpg7stohp4Z2ntIMPU4KdA67Yc7w98+4db7LzIYQ1ahTgZsq3D2FbwXqEtzEIScN0aM569uDzppZdLOSug3RpSPYq1HaUq5igsRhC0UbeRiC0B7mgBPIgUEjcZMWLzz6QlEPA5LHN5QPh6A86X2PDUVnzRsIaD3+EoZxg9KWc5+Q3agYmZrNImNvb8XL8z20cxT3M2qj2AhH/aShB7jjHi83nTlJZDq7Y6cdSxPTriW+kgyKlvxhRhbVOBx+17LbVPjYspxK7t7p8+Qjc+6oAcUiQZ5rqiZA6q7aI57U5KkkSC2RaVldDmj7gnha8Ui55v5mADNFGrasqwjpHIlrEUcN/cc31EIx33j6tuutr2rIq5DVOkUqyOaOdhNS7XmcAgaOxBmqITAL0KElyhqqZYpYBvRWFqsdQjhc4uhd3zBb9RglG+pU8OBign0Qd4l+RnVlkxcScdUjd1rihUdGLfM8xRrHTrPuKlDiht5wQ1sMuDPbZXe6xlzGyNbhGkUUtZzZjFHZYjea43RM8oIgvNp1oauXMdFhQSlCxnHBWvSoIsmwrdj0NIsopnjYQ3jHMKiZu5Bw19CUmrIOUHFLL2o4L/rsHix46XjKRHWvPUkKChmQypb5gwFlLNFLhTWeaONwBhbFEJV6kqClWUSYVFNGArlQFJEkiRuKMsAjiA4q9rM156sB87tDTFKyK3JOwz5J0WDaGFtI4lHF3XmP6WjJ3A7Y13Pmmz7tWUwyN5zvZpz85oTdKwvmJsU9iHAKwmMQw87TwOQaJSzuQlNNJalokCcat2cwsaDxgvQUImMpfACZI24sWjhAwFohA0MUWYoqIlYtZSzYP9/grxmKyx76sOLm3iW7YcHJYxFvvr3kt/eOWElJ0DPf0nj5hp7Zvz095+rOjH5UdcszX4wIvGU/WiK178ok6BqWnF8MEKeaNoPp4YLF5YAz0SPIDLIQtLdSqkCShAYvPPk6YnnW44pcEQcts1WPLx0fIbTj0K5Bd3tnhQoJsQTS4QsFa4XJA9Kopn4QUd9LMUvNpK2ZBCVR1JLKFl5OCE9g0CuQwpM/5Zhem/PE9ROujtasRMyXqzE+djw7P+T+bIc33XxAvqeYz/qcnkyovKY6T7s92qXqllJ7nvaqQ0kLsSec1KyJefL6BWoj2VUbmi9kVK8mFC/1ECea9LCAoaXMNHJqCNIGGVscgrKMaFCs8gTZQtMEJMMSSoU8sijZlX1lUYXyjivRJaOoQF2riNKG629+CFNDJlvGz8wxI4fZE+RBAolDDxrK6xZRCnTuyRpDMK7RucMISXGcoZaS9poledsaudNiMkF96GEV8ODeDg6BdZ0lp9eOZFiivcUIBdcrhm+a06y7TnhtKjBrTXTmUcIQnTn24iXtSYydOurrlubIIyYtMvXUE3AK1LihHgsQHq8cXjpEKWhfTAlWnjA2hMqgA0MvLmieMszSiPKw865Gdq1UH9Ln7HRAWDlE7RmmRVc/XAKBRUWWtte195QXkryKmS37FELjgabRqFLgMk9da2wV4MaO4dEaBJigS+BrjiOCGmwP2jykrQNaq+jHFaMbK1ZnCe1vR+yMV0yLksD4bt9/+JVEtz1JqwIqpVlvUtxC4y81uYnoDUpOZkP20hVR6agI2D1YEqQNynme1nNqF7D40hiZWqyGqNfghOCpR+/hRobdGzMu6x7yRo3KBTJtERceCsfmQQwvRWyalLtf3GXxoE8zizA9zyAt0LnH7lvqRtOKbo9+8dKEMgwI7ga0t1LuxSOMCdBP5wxvLJFeIELP6k1dWVjlAm7qS0LnmIUx83VGuwop7/Q6A5qiqw6RjSe717UR1gUELcRnEpV3/ShKF1JPPY2SJJOqa67SQrvRLDcZda1Zh5rIG/JQYfZtVxfqBXJk4JmKg3LD/u6MMlQsiLsyTS2Y6Zi1ikhFRVJbRGKIxhUnOsTsWMSxJkrb7vc6KLERuD5ktmV9ELBoE272L5mkOYmxhN7AWiLHDWWqqIeCqagpihhrJNYEXJZZ571QZLShwFlJcdrj7PYOKrIMggqtLGJYMQoLZC5RwjMve5gDQyMVrCTeCxob0GQCUwbIU0VThMi+oVAh/aDGIkjWHjGT6CXE3uCE5PS5KWUZ0gjFRmlqERCuJK2MqPYdJhA0u563P/6Qxe0xdRlwv9yhzkMoFNVU442kDQUP7ux2BjwDDxGIWhIsJMluwTAsIHZsopA0rBGNwCvHtemC+ARCZ0iTEj+yxJEhCBwEkF5fU2tJWlqCS8UajTYOpTysNMu9hKfDOYG11MuQe1+eYC49xge83N/ltK8ZXFjS4luboPeGrLNfLpeMRiOe+ts/g1z3GcYb4mc22Acxh3uXLGXE/eMp++MlZhFy3mb0fEP5ICN+NKd/bcXJ3R0mdUk5UJTLmGRQI1fgbxjmDwaEaYO8GxLtVOxeWeAdPFiP6e/myM8HJJOKy/mEMG0ZhiWrUYC7E+MqST3xTB+ZM5/1idaeOGtJk4qVCtG5h3lAHmiaRcjh0ZxL0u4PNQCONXUg2Uk2NMOuJ799LuLKo3PuHu/ilwG27ixNEVCNBNpZwjMwWuF3WwLjqULZNV95oqC416MawdG8oJzFGBvgPOAEKve0R4ByRM6hjircwwhZQJi11LGkrjRq2bWLJfBEWMoyAmB8uCTKPXbouLw/ZHewYRRUPGx61AQ4JTBSIB1c8UsuZEqVR+AEooLEGIpeQNv3hKcSKTzNEPSsazepTJdgp2eCcNiw7gfQCFQhCOaCeirQupvRqNzjwobwUYOzkuY0IRxXpL5r8em1J5xJmmH3eYUXBMtOx2bY3VtB7jF7nrDyCO8xpSY+t12NeiwJTFfWZqXssrcbC1rhBQgJLoT+MKfZ9QSFYJGnBMIhhUe2HrMJCKxDP1myKWKytCYxhsvTIW0MobOIlcKGEBQCPayJs4b1LMNokDVYLTEjS7CS6DXYtHsg0UuBiwQ29Lisq5xgrQgKgVcgUotzgsGkYHMeIrVAKE9bhGCBicFUAUEJwjnqsSCoOpcxGRlkK+hd3VDmIe0iRIwNfqNQmSWqDWWm6Ac1y4cDvO7skAdxwUqH9KqWqoywmUfgERJU67FfKT989Molr97Z4XuOHvDl+7vUcYCtu9meqsAOHcFS4EVnFaadpZ6CU91+qPMKPKim+93GaYtZB6RXclqj2DQxOmlRlcC3ICOHScBcxBgt8LHr3kcLwrpz9QsKMIlHNgK7bwgXAp93X9DtyCMsOCGQOIQVSOehkvivuN/F/c7cR+aCaFiTrxOitKVftSyLlGS/oMwjpOu2kYyUBGcBXnncyCFqgZQOMZf4zBMflDSrsHu4Xyl02nKtPyMnZBmFDGXF2eWI1iiE9sS+ZdLfcD4f4q1Aho5+WjAre3gnIHWk0lAh0dbRriJ86CB16LWgH1RstKYuQnzo8F6SxRWtVSjtaGYxTnlEarvKklpB6mAdYAMIG8c43XCieshG4J1E7dRYCYO2ZRVqRCNxbff+LvGo1MCt7ndiYggrD9ZTDwXCiK4/xLDFR13VhF0HeNM5CurcI/EEuzUrF6M3AhNCPAeOGtqNRictfhaikrbrfxB3D2ZeCnThOLp2wapJaStFKLtS3VoGKOsJ8FxNV9w6H2NigQDioCUSLfVpQjMGGVuEALGG+IWG8koEgUKFFlKHqQPUuOGR8ZIyb/nf/+4/ZbFYMBwOv+lx8w0Z7O/fv8+1a9e+3ZexZcuWLVu2/P/i3r17XL169Zv+Pm/IYO+c44UXXuDNb34z9+7d+5Z0H/q9wmq14tq1a1tdvwlstf3msNX1m8dW228O/1PX559/nqeeegopv/k76m/IBD0pJVeuXAFgMBhsb8JvAltdv3lstf3msNX1m8dW228OV65c+ZYEeniDJ+ht2bJly5YtW/6/2Qb7LVu2bNmy5bucN2ywj6KIn//5nyeKom/3pXxXsdX1m8dW228OW12/eWy1/ebw7dD1DZmgt2XLli1btmz53fOGndlv2bJly5YtW353bIP9li1btmzZ8l3ONthv2bJly5Yt3+Vsg/2WLVu2bNnyXc422G/ZsmXLli3f5bwhg/0/+Sf/hJs3bxLHMe94xzv47//9v3+7L+k7mg996EN8//d/P/1+n729Pf7Mn/kzvPDCC68b473n7/ydv8PR0RFJkvCH//Af5otf/OLrxtR1zU/91E8xnU7Jsow//af/NPfv3/9WfpTvaD70oQ8hhOCDH/zga8e2un79PHjwgB//8R9nZ2eHNE35nu/5Hj7zmc+8dn6r7deOMYa/9bf+Fjdv3iRJEh599FH+7t/9uzjnXhuz1fV3x3/7b/+NP/Wn/hRHR0cIIfi3//bfvu78N0rH+XzOe9/7XobDIcPhkPe+970sFouv/YL9G4yPfOQjXmvtf+mXfsk///zz/gMf+IDPsszfuXPn231p37H8iT/xJ/wv//Iv+y984Qv+2Wef9e9+97v99evX/WazeW3Mhz/8Yd/v9/2v/uqv+ueee87/+T//5/3h4aFfrVavjfnJn/xJf+XKFf+xj33Mf/azn/V/5I/8Ef/2t7/dG2O+HR/rO4pPf/rT/pFHHvFve9vb/Ac+8IHXjm91/fqYzWb+xo0b/i/+xb/of+u3fsvfunXL//qv/7p/+eWXXxuz1fZr5+/9vb/nd3Z2/L//9//e37p1y//rf/2vfa/X8//wH/7D18Zsdf3d8R/+w3/wP/dzP+d/9Vd/1QP+3/ybf/O6898oHX/kR37EP/PMM/6Tn/yk/+QnP+mfeeYZ/6M/+qNf8/W+4YL9D/zAD/if/MmffN2xp59+2v/sz/7st+mK3nicnZ15wH/iE5/w3nvvnPMHBwf+wx/+8Gtjqqryw+HQ/9N/+k+9994vFguvtfYf+chHXhvz4MEDL6X0//E//sdv7Qf4DmO9XvsnnnjCf+xjH/M/9EM/9Fqw3+r69fMzP/Mz/l3vetf/8vxW26+Pd7/73f4v/aW/9Lpjf/bP/ln/4z/+4977ra5fL//PYP+N0vH555/3gP/N3/zN18Z86lOf8oD/8pe//DVd4xtqGb9pGj7zmc/wwz/8w687/sM//MN88pOf/DZd1RuP5XIJwGQyAeDWrVucnJy8TtcoivihH/qh13T9zGc+Q9u2rxtzdHTEM88883te+7/yV/4K7373u/njf/yPv+74Vtevn1/7tV/jne98J3/uz/059vb2+N7v/V5+6Zd+6bXzW22/Pt71rnfxn//zf+bFF18E4Ld/+7f5jd/4Df7kn/yTwFbXbxTfKB0/9alPMRwO+X2/7/e9NuYHf/AHGQ6HX7PWbyjXu4uLC6y17O/vv+74/v4+Jycn36aremPhveenf/qnede73sUzzzwD8Jp2v5Oud+7ceW1MGIaMx+OvGvN7WfuPfOQjfPazn+V//I//8VXntrp+/bz66qv84i/+Ij/90z/N3/ybf5NPf/rT/NW/+leJooif+Imf2Gr7dfIzP/MzLJdLnn76aZRSWGv5hV/4BX7sx34M2N6z3yi+UTqenJywt7f3Va+/t7f3NWv9hgr2/xMhxOv+7b3/qmNbfmfe//738/nPf57f+I3f+KpzX4+uv5e1v3fvHh/4wAf4T//pPxHH8f9y3FbXrx3nHO985zv5+3//7wPwvd/7vXzxi1/kF3/xF/mJn/iJ18Zttf3a+Ff/6l/xK7/yK/zLf/kvectb3sKzzz7LBz/4QY6Ojnjf+9732ritrt8YvhE6/k7jvx6t31DL+NPpFKXUVz3RnJ2dfdUT1Jav5qd+6qf4tV/7NT7+8Y9z9erV144fHBwA/L/qenBwQNM0zOfz/+WY32t85jOf4ezsjHe84x0EQUAQBHziE5/gH/2jf0QQBK/pstX1a+fw8JA3v/nNrzv2pje9ibt37wLbe/br5a//9b/Oz/7sz/IX/sJf4K1vfSvvfe97+Wt/7a/xoQ99CNjq+o3iG6XjwcEBp6enX/X65+fnX7PWb6hgH4Yh73jHO/jYxz72uuMf+9jH+AN/4A98m67qOx/vPe9///v56Ec/yn/5L/+Fmzdvvu78zZs3OTg4eJ2uTdPwiU984jVd3/GOd6C1ft2Y4+NjvvCFL/ye1f6P/bE/xnPPPcezzz772s873/lO3vOe9/Dss8/y6KOPbnX9OvmDf/APflV56IsvvsiNGzeA7T379VIUBVK+/mtfKfVa6d1W128M3ygdf//v//0sl0s+/elPvzbmt37rt1gul1+71l9TOt93AP+z9O6f//N/7p9//nn/wQ9+0GdZ5m/fvv3tvrTvWP7yX/7Lfjgc+v/6X/+rPz4+fu2nKIrXxnz4wx/2w+HQf/SjH/XPPfec/7Ef+7HfsUzk6tWr/td//df9Zz/7Wf9H/+gf/T1XbvP/xf89G9/7ra5fL5/+9Kd9EAT+F37hF/xLL73k/8W/+Bc+TVP/K7/yK6+N2Wr7tfO+973PX7ly5bXSu49+9KN+Op36v/E3/sZrY7a6/u5Yr9f+c5/7nP/c5z7nAf8P/sE/8J/73OdeKwP/Run4Iz/yI/5tb3ub/9SnPuU/9alP+be+9a2/N0rvvPf+H//jf+xv3LjhwzD03/d93/daCdmW3xngd/z55V/+5dfGOOf8z//8z/uDgwMfRZH/Q3/oD/nnnnvuda9TlqV///vf7yeTiU+SxP/oj/6ov3v37rf403xn8/8M9ltdv37+3b/7d/6ZZ57xURT5p59+2v+zf/bPXnd+q+3Xzmq18h/4wAf89evXfRzH/tFHH/U/93M/5+u6fm3MVtffHR//+Md/x+/V973vfd77b5yOl5eX/j3veY/v9/u+3+/797znPX4+n3/N17v1s9+yZcuWLVu+y3lD7dlv2bJly5YtW752tsF+y5YtW7Zs+S5nG+y3bNmyZcuW73K2wX7Lli1btmz5Lmcb7Lds2bJly5bvcrbBfsuWLVu2bPkuZxvst2zZsmXLlu9ytsF+y5YtW7Zs+S5nG+y3bNmyZcuW73K2wX7Lli1btmz5Lmcb7Lds2bJly5bvcv4vPbJyyAq2IjkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fit = scalarimage.scalarimage(data, indxer, datafield='fit'); plt.imshow(fit);" ] @@ -380,10 +515,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "ea57565f-5ddd-4be5-aa43-793edb30b6f7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7916.796 50798.13\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pq = (data[-1]['pq']).reshape(imshape[0],imshape[1]); plt.imshow(pq, cmap='gray')\n", "print(pq.min(), pq.max())" @@ -435,10 +588,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "64d0f40a-b55c-4103-8335-ea27267f6e1a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 60, 60)\n", + "float32\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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C0/tp7dO9Iw06r1FgLblqI47qn+O6deumoqIilZaW1mo1NTUqKyvT8OHDj+aujDHGNAFyfhLatWtXHV/92rVrtWTJEn3mM59Rly5dNGnSJE2ZMkUlJSUqKSnRlClT1LJlS1177bVHdeDGGGOOf3IuQgsWLNCll15a+/+33XabJOmGG27QI488ottvv1179+7VxIkTa7+sOnv2bHwsNMYYc+KScxG65JJLlCQJvp6Xl6fJkydr8uTJRzIuY4wxJwDOjjPGGJMZjba998CBAxu4eMjtQ44icgFJ7IKjlrubNm0KdfpeE7VpphwpSeratWuok0uHnDKUHUfOIXKVSewwHDhwYKiTW5CcV+Qqo5xAibO76IvO1HKc2lDT8aZ8s7R95JodN2DAgFB/4YUXQj3N5UTngpyqdE1Qnh29n86plJ4rF5FrPiLlLG7btg33QdcduXGXL18e6oMGDQp1SoGpqqrCMZGTlVyptDapnTq1905zjFL79yhbMK11eX38JGSMMSYzXISMMcZkhouQMcaYzHARMsYYkxkuQsYYYzKj0brj1qxZ08D5Q06tli1bhnqaw4ocUH379g31V155JdTJvZNrppzE2XGUx0ZfAKYxkbOGjoXEDihyd/3lL38JdXLWkOMxLceM9rFy5cpQJycVOdGok+hbb72FYyKHYefOnUOdXJXk7iK3ZZrb7OKLLw51cq+98cYbOe2bri86DxJn6VG2G7nEaI0vXLgw1NMSW5YsWRLqdK3S2qdzSg5J0iXu0kr3QMrPpLVMx5WyHCV2vEX5hWlZffXxk5AxxpjMcBEyxhiTGS5CxhhjMsNFyBhjTGa4CBljjMmMRuuOKywsbJD9Ro4pclilueMob462Ra6e0047LdSps2Cam+mss84KdcqYoo6r5LohxwrNTZLmz58f6uSa69OnT6hv3rw51Ok40XGVpJdffjnUzzvvPPydCDpO5AIqKCjAbVEOHa0zyiUjZ1lxcXGokzNU4rw5cirSOaU8MZoDOa8k6Ytf/GKoU74adY7dsGFDqJMjMM2xR87GOXPmhPrnPve5UCfX4YIFC0Kd3JkSd9IlyAVHbrq064ug+2nk6CSXZ4SfhIwxxmSGi5AxxpjMcBEyxhiTGS5CxhhjMsNFyBhjTGY0WndclB1HTipyt5AzSWInELnjqEsrZS2RG6dXr144Jsomo+6In//850OdsrAozy7NzZTmCMtlW5Sr1alTp1Cn8yBJl156aU7bIp263NJxGjFiBI5p3bp1oU5dYHN1BbZq1SrU+/fvj2Nq27ZtqFP2Xl5eXqhfeeWVoU6uubROvZS7RtcFuUmpkyi5wShTTmLn4fjx40P92WefDXXKsyPnWloH2sPJeYwg1yE5QJMkwW2R4y065mmO2/r4ScgYY0xmuAgZY4zJDBchY4wxmeEiZIwxJjNchIwxxmSGi5AxxpjMaLQW7bPOOquBLZoC9Cgcs3v37rh9sqnStsjSvXbt2lAnq2haMCHZTsk2+fOf/zzUS0pKQp3svGSbldhyTfZmstRS2CWFiKbZfMn2TKGWdE7TwmQjaA2kjWn37t2hTpZrej/Zf9evX49jSgvtjKAAXbJi0/VFFneJQ4hrampC/cILLwx1Ctalc33RRRfhmMgu//TTT4c6Xdt0T+nQoUOo03qVpB49eoQ6WZ/JPk33TPraB9n0JQ6+ja5hsoBH+EnIGGNMZrgIGWOMyQwXIWOMMZnhImSMMSYzXISMMcZkRqN1x9XU1DRwarzzzjvhe9u3bx/q9H6JXSDkgKKwQdIpsDOt7W1RURG+FkFOI3KukdMtzfVFDrLzzz8/1MnVRvorr7wS6uRykqR33303p23RcaLw2dLS0lBftmwZjomcZXT82rRpk9N26NxR62hJ6tevX6hT2CodJwrNLCsrC3VyCqZta9u2baG+atWqUCeXKV13acGfO3fuDPUrrrgi1MmhRk5PcinS8ZbYsUeOuv3794d6rq5NOg+StH379lCPAlrtjjPGGHNc4CJkjDEmM1yEjDHGZIaLkDHGmMxwETLGGJMZjdYdV1hYqBYtWtTRqPUxuX2ora4kbd26NdTJZUK5bps2bQp1ymxKgxwlNCZy6ZAjhtp+X3XVVTgmcsGRi4scZNRqncaa5kSjlsUjR44MdTpONCbaTlrGHrVmJ+cmtW9+7LHHQp3che3atcMx0fqn4zFs2LBQ/8lPfhLqlAeY1i6eXG00psLCwlAfOHBgqNN6onuHxE6xuXPnhnrfvn1DnZxldP126dIFx0ROzz59+oQ6OfNo3nS86T4nsaMzctemrYH6+EnIGGNMZrgIGWOMyQwXIWOMMZnhImSMMSYzXISMMcZkRqN1x61Zs6ZBthK5TAjqCClJvXr1CnVydZCzjJw1Xbt2TR9cQP1OsgdZvnx5qJMTiBxIgwYNCvW0Dpy0LZp3rp1BV6xYEeppbiZyIZGDjLp2UnYXucTIlSex04ly7uh4kMuJunmSw09ih2Hz5s1D/ZFHHgl1cvKtXr061NNcqbRmKb/wjTfewG1FUKfZoUOH4u9Q3hyd09mzZ4f6+PHjQ50yLOkeJHHX2vLy8lCnbsrkgqPrulOnTjimXDrKOjvOGGPMcYGLkDHGmMxwETLGGJMZLkLGGGMyw0XIGGNMZuTkjps6dapmzZqlFStWqEWLFho+fLjuvfde9ejRo/Y9SZLo7rvv1vTp07V9+3YNGTJEDz74YKoTJGL79u0NXDzkHiNX1ODBg3H7lDe3Z8+eUKcMMMqII8cN5ZJJf3UERlDHVerqSh01yQ1GmVASZ0lRxhl1K6VzRC6dNIfV0qVLQ53ck2ldNXPZNzmQJGnEiBGhTu4kckzRHA69xj7NdiReB7Q2BwwYEOqR+ymNNBchdTGl+8Pjjz8e6rT2aQ5pHWjPPffcUE/rEBvx61//OtTpGnr//fdxW2effXaod+7cOdTJFUjXV8+ePUOdcvEk7hIc3bfIkRqR05NQWVmZbrrpJs2fP1+lpaXav3+/xo4dW8eWe9999+n+++/XtGnTVF5erqKiIo0ZMwYXnzHGmBOXnJ6Enn322Tr//6tf/UodO3bUwoULdfHFFytJEj3wwAO68847NWHCBEnSjBkzVFhYqEcffVTf+MY3jt7IjTHGHPcc0WdCVVVVkv7/L5qtXbtWFRUVGjt2bO178vPzNXLkSM2bNy/cRnV1tXbs2FHnxxhjzInBYRehJEl022236aKLLlLv3r0lSRUVFZIa9gApLCysfa0+U6dOVUFBQe0PfZPcGGNM0+Owi9DNN9+spUuX6re//W2D1/Ly8ur8f5IkDbSD3HHHHaqqqqr9oQ9NjTHGND0OKzvulltu0VNPPaW5c+fWcWscdHFVVFTUySCqrKzEDon5+fmha6tLly4N3HCUq0VOtzPPPBPnUL9r60HIzURZZl/+8pdDnVx2lCUmcZdMcvmR8+WDDz4I9T/84Q+h/s///M84JnJGXX/99aFOeWX0hPu///u/oZ7mIiQnFbl3Dj6p16dVq1ahTq4lcuVJ3AmTnIfUkZfWLK2Nr3/96zimVatWhTp1xaWcNnLZ0VpOczZS/tgzzzwT6uQGo3sB/TmfOimnbeull14KdVrLl1xySajTukzrYrply5acfod0chFSl2DKyJTYCRzl/tF9NCKnJ6EkSXTzzTdr1qxZeuGFFxrYcbt166aioiKVlpbWajU1NSorK9Pw4cNz2ZUxxpgTgJyehG666SY9+uijevLJJ9WmTZvaz3kKCgrUokUL5eXladKkSZoyZYpKSkpUUlKiKVOmqGXLlrr22muPyQSMMcYcv+RUhB566CFJDR87f/WrX+mf/umfJEm333679u7dq4kTJ9Z+WXX27NmpX4g0xhhzYpJTEUqS5BPfk5eXp8mTJ2vy5MmHOyZjjDEnCM6OM8YYkxkuQsYYYzIjL/k0f2P7G7Jjxw4VFBToxhtvbGDdprbOBNmwJeGXZ8n+S9s66aS4jpN9Os26SFZYsniSzZLsmi1btgz1tWvX4pjIWk/z7tixY6hTOOuQIUNCncJWJW7BTm2o/+///i/Uc/2csqCgAF+j8dKavfzyy0OdWmxTy/G0dsxkr6d5H0xAqQ/ZhUeNGhXqaWucMiSp5fjTTz8d6hS0SWMlG7bEtnFqsU3XHVm3//jHP+K+ia5du4Y6BdzSdZd2HUWk2cZfffXVUI8s+dXV1frxj3+sqqoq/HrLQfwkZIwxJjNchIwxxmSGi5AxxpjMcBEyxhiTGS5CxhhjMuOwAkz/FvTp06eBI628vDx87+F0baV2zOQAIVcKhWBSO3By3Ejs+qJtVVZWhjqFFpaVlYU6hURK7Go7tGfUoURhhhIHS86ZMyfUKWRTYhfcvn37Qp3awhPkhExz+VBgLTkPn3vuuVAfN25cqJPLLq1tNbWFp8BfCqikBPzf//73oZ62nvr37x/qdDzoXFAALLm7KFxU4uvud7/7XahTIC6Fz5LTLS3olUKI6Zonxx45YskUndbKna7J6JqnYxrhJyFjjDGZ4SJkjDEmM1yEjDHGZIaLkDHGmMxwETLGGJMZjdYdV1VV1SCDirK7KPvp9NNPx+2/8MILod6jR49QJ6fR3LlzQ50capQDJ7ETiJwm55xzTqhTbh21gt64cSOOiTLiSCdXD7mWLrzwwlB/8skncUznn39+qJMD77LLLgt1conR8X7rrbdwTAcOHAh1mh9l8k2fPj2n7fTp0wfHRG4+csER5Jii67FDhw64rZUrV4Y6ORipLTdl6ZFrLs2JRuvg7LPPDnVyidF2KN+PHLeSNHr06FBftGhRqA8YMCDUf/GLX4Q6OfbSsubo3EX3THKqRvhJyBhjTGa4CBljjMkMFyFjjDGZ4SJkjDEmM1yEjDHGZEajdcetXr26QbdFct2QTm4pibsgkouGsq2oiyQ5ztJytbp16xbqlElFnTDJjbNs2bJQpzwvSXrttddC/d133w11cjORk2/58uWh/q1vfQvHRN1HqUvr7t27cVsRdO7ILSVxt1JyQNH7yfVFGX5pWYS7du0KdcpapHWzatWqUN++fXuopzmsaLx0juj9dG2Ty27JkiU4JnILUu4aOcvmz58f6uSao26yEnd47tmzZ6hTrubFF18c6nT9ki6xazS6Pzk7zhhjzHGBi5AxxpjMcBEyxhiTGS5CxhhjMsNFyBhjTGY0WnfcySef3CATjhw01NUyreMqZcRRNhPlRW3evDnUr7vuulCnTpsSd02kPDZyzVHGE7mfyAEnsXPu7bffDnVyHZLDijKy0vLs/vEf/zHUyUFG64aOxzXXXBPqv/zlL3FMdO7IATVq1KicxvT000+HOmXKSZy/d+6554Y65R1efvnloU7uO8oJlDhjL5eunRLn2VH+InUelXJ3W65YsSLUyRVI+X7PP/88jomuL3KZtmvXLtTpeFD2Y5rbkrIIo+6+5BoOx/Kp32mMMcYcZVyEjDHGZIaLkDHGmMxwETLGGJMZLkLGGGMyo9G647p06dIgBypXxwhlq0mcEUdukldffTXU+/XrF+rkUCNXj8R5UTRvyqQi9xM5/yivTOKMrhEjRoQ6udrWr18f6o899liop7m+yDFFTsVt27aF+ocffhjqlHuV5vih40HOvJkzZ4b6Rx99FOrkTHrllVdwTLRmycFInYhbtWoV6hs2bAj1Ll264JhoPVHGHl2PdP0+++yzoZ6Xl4djGjNmTKjTWJcuXRrqdI5o3QwePBjHRG6+l156Kaf3kwuYri+6p0h8PKIOu3bHGWOMOS5wETLGGJMZLkLGGGMyw0XIGGNMZrgIGWOMyQwXIWOMMZnRaC3abdu2bWDPpCDA3r17hzpZm9Neqx+aehCyPVNoJtl8KYxUkhYvXhzqZAMn2zO1Oy8sLAz1tJa+ZHsmOy+FWlJwJdnryaossbWa5jFy5MhQpyBKsrvW1NTgmMh6T+uA1g1ZZ3/729+G+oABA3BMgwYNCvU33ngj1Mm6/d5774U6hdsOHToUx/T//t//C/Xzzz8/1GmsZJMmSzeF/UpSRUVFqJ933nmhTvZjam1P1m2yPKftg647ej9dK3T86B4h8dcQIts4Xe8RfhIyxhiTGS5CxhhjMsNFyBhjTGa4CBljjMkMFyFjjDGZkZckSZL1IA5lx44dKigo0DXXXNOgNTI5QMjRQcGVErvgKByTgv3IfULtc9NaH7dv3z7UKdiUwkJzbbFNbjqJ3Tg0DwpupbmRQygtBJNcgVGQosTONTp3NNa01uwURlleXh7q5M4kJx+NddOmTTgmOoYUWEvrg1qOv/nmm6G+c+dOHFPHjh1Dna7tyy67LNQpGJbW66xZs3BMdH3R/MiBR+3AybVJwbBS3DJbYufhKaecEuoUYkvt1MndKvH9tGfPng206upq3XvvvaqqqkJ34EH8JGSMMSYzXISMMcZkhouQMcaYzHARMsYYkxkuQsYYYzIjp+y4hx56SA899FCtU6xXr176/ve/r3HjxkmSkiTR3XffrenTp2v79u0aMmSIHnzwQXSfpLFz584Gjo+BAweG7yUnWlo2Ezk2yLFCcyDXEjn20trnkotr9uzZoU5zoNw1crSltYimXL7KyspQp+NBUKYX5ZJJ0owZM0L94DqsD507ym+j80CuQ4ndXXv27An1du3ahTqNlVxwaVmElHdI64P2TRl7dD2Sq0xi9xqtA3JCUp4dZZZdccUVOKYXXngh1MnttmXLllBfsmRJqFMr8jTIRUiuQHLH0TmlTMg0J9vFF18c6tH5pvzFiJyehDp37qx77rlHCxYs0IIFCzRq1ChdeeWVtYO47777dP/992vatGkqLy9XUVGRxowZk2rZNMYYc+KSUxEaP368Lr/8cnXv3l3du3fXj370I7Vu3Vrz589XkiR64IEHdOedd2rChAnq3bu3ZsyYoT179ujRRx89VuM3xhhzHHPYnwkdOHBAM2fO1O7duzVs2DCtXbtWFRUVGjt2bO178vPzNXLkSM2bNw+3U11drR07dtT5McYYc2KQcxFatmyZWrdurfz8fN144416/PHHdd5559X25Kj/WUhhYSH265CkqVOnqqCgoPYn7e/uxhhjmhY5F6EePXpoyZIlmj9/vr75zW/qhhtu0FtvvVX7el5eXp33J0nSQDuUO+64Q1VVVbU/FEVjjDGm6ZFzZ9XmzZvXdsMcOHCgysvL9ZOf/ETf+c53JP21S+GhWVOVlZWp3fry8/OVn5/fQO/SpUsDnRxI5HIiB5LEOWPkfKH3U9YcuVjIUSRJ+/fvD3VyG1EuFOXi7du3L9TJaSSxa6lv376hTt0lycVFDqSpU6fimP7t3/4tp33QOSKoG2paxl6unWPr5yIehLrrkrORsuYkdn39y7/8S6j/4he/CPXPfe5zoU5rOc1hFXXhlHiNf+lLXwp1Ok50XMlhKim8/0hS165dQ52ca3/84x9DfcOGDaGelo9YUlIS6rSe6H5D90baNzlDJe5yG+XQ0TUUccTfE0qSRNXV1erWrZuKiopUWlpa+1pNTY3Kyso0fPjwI92NMcaYJkhOT0Lf+973NG7cOJ1xxhnauXOnZs6cqTlz5ujZZ59VXl6eJk2apClTpqikpEQlJSWaMmWKWrZsqWuvvfZYjd8YY8xxTE5F6IMPPtD111+vzZs3q6CgQH379tWzzz6rMWPGSJJuv/127d27VxMnTqz9surs2bPVpk2bYzJ4Y4wxxzc5FSH6m/FB8vLyNHnyZE2ePPlIxmSMMeYEwdlxxhhjMiNnd9zfinbt2unUU0+to1HeUVVVVaindTGl/Kzt27eHOjlDBg0aFOrUhZBcVBJ3O6Q/Z379618P9UWLFoV6/eN5kDTHHnXVpLFShhXx+OOPh3pRURH+zrJly3LaB3XCJCcQ5W2lOYeo6yo5FWlb9J26f/iHfwh1ciNKnF/4X//1X6FOx4kcbfR1CtqOxOufXH60D3JnUhdTctZKnDFJ9xXKiOvevXuoUx5l2vcn6TjRuqHrpU+fPqH+5JNPhvr555+PY6LjEeUjkhM3wk9CxhhjMsNFyBhjTGa4CBljjMkMFyFjjDGZ4SJkjDEmMxqtO+7dd99tkANFGWDkDKEujhI758jhkmtuHW0/zc1EeU6U37Z8+fJQp06OlJGV5hwitxs5FckVSC67Q8NvP+2YyG1EzijqzllcXJzTvmtqanBMSZKEOjV0JPcTOZDKyspCPS11njLOdu3ahb8TMWLEiFCn9fTcc8/htsiJSS44yjVs3759qNPaTwtGpmy8J554ItRHjRoV6nSd0vGg4yqxe+3CCy8Mdcq2pONHDtCVK1fimGgtR5mKaddvffwkZIwxJjNchIwxxmSGi5AxxpjMcBEyxhiTGS5CxhhjMsNFyBhjTGY0Wot2p06dGlhAyQZLds20ME1qk/unP/0p1Mk+TRbt999/P9T79++PY1q3bl2o/8d//Eeof/vb3w51autM1tK09uv0GrXSpnmvXr061CnokCzgEttIqVX4hx9+GOr//u//Huq33HJLqDdrxpcLWY9p32+//TZuK6Jly5ahTrbZtH2QtZrszbRvCm1Na4P+u9/9LtSpLTeNiUJSKTCW1r4k/fnPfw51sly//PLLoU7HlWzVaWvghhtuCPVDO1cfSrdu3UKdvqZCoa3UVlxi23VlZSX+zqfBT0LGGGMyw0XIGGNMZrgIGWOMyQwXIWOMMZnhImSMMSYzGq07rk2bNg3aUVMYH4VBfvzxx7h9cl+R0y5qYStJnTt3xn1EkCtFknbv3h3q9913X6i/+eaboU7zpgDONJcOtUGnsZLrkEJHp0+fHurjxo3DMS1dujTUyb0zb968UL/++utDnY5HmmMvbbwRPXr0CPUBAwaE+pQpU0L9q1/9Ku6DAn/z8vJCnc51eXl5qJ922mmhnhZgOn78+Jz2QeeI3JkUEDxr1iwcE13z1BqbdLo/vfLKK6H+d3/3dzimDRs2hDq5/+j95J4kpyc5AiVuLR6FE7u9tzHGmOMCFyFjjDGZ4SJkjDEmM1yEjDHGZIaLkDHGmMxotO64CGqZTa4ecmRJ7BQjNwk5X046Ka7jlDV38cUX45go8432sXfv3lCnDLU2bdrgvglqmU2ZaOQg++IXvxjqlJFFc5A4D4scU/VdlgehzCuaW7t27XBM//M//xPqlMXVtWvXUKd1Qy44csBJnLtGLkLKY6M5UM4izU2SFi1aFOrkMiVH1ty5c0Od2ssPHToUx0TzJqcdOcsoO44co2luy/3794c6XfPkVCQnH60Bcr1KsQvuk37n0+AnIWOMMZnhImSMMSYzXISMMcZkhouQMcaYzHARMsYYkxnHlTuuoKAg1CsqKkKd3ByS9OSTT4Y6OVy6d+8e6uTUon2XlZXhmMjhQs48cj/RmJYvXx7q5DqUpEsuuSTUf/nLX4b6xo0bQ5060L744ouhTllYEjupvvSlL4X666+/ntOYyEn12GOP4ZjoXJCLi9x/dPy+8pWvhPpTTz2FY7r66qtD/aWXXgp1yq2j9Uc5iNQlVWKXJHVBpn2TA4+yz+i4SuzAGzFiRKhTt2Ea64IFC0I9LW+QXIS7du0KdXLTkfOPri9axxK7dI8UPwkZY4zJDBchY4wxmeEiZIwxJjNchIwxxmSGi5AxxpjMyEuSJMl6EIeyY8cOFRQUaMqUKQ0yv6jjJXUS7dChA+6H3DXU8XL27NmhTu4TcpKkuU/IEUOuJepGuWnTplCnTo6UD5cGudouuuiiUKccrsPJsyPIXfj++++H+uWXXx7q5Pqi3C5Jev7550Oduv6effbZoU7ONepI2rt3bxzTGWecEerk4nrnnXdCvWPHjqH+xhtvhHpax1C6Luj6ou7BBF1fEyZMwN955plnQp1y+cjZuHDhwlCnewp1SZW42yt1rb3yyitDnfLp6J5JLkWJ8+ai9bRv3z59//vfV1VVFV6XB/GTkDHGmMxwETLGGJMZLkLGGGMyw0XIGGNMZrgIGWOMyYxG64771re+1SDHjfLbyFFEHRAlzh8jJxA5isgxRblkaflLlP90xRVXhDq5uKjTIY01zfVFLh06F+SuIZcOdUNNy/oixxS5COlckNtn7NixOb1fkvr37x/qy5YtC3Vyeo4aNSrUKeuL1ozELjVyK1HGGTn8yPWVdt2RA3Tx4sWhPnDgwFCnfERyutHakHh9XHbZZaFOLrhBgwaF+quvvhrq1EVX4oy4pUuXhjpl5pEDj+5DlIsn8fqIXITV1dWaNm2a3XHGGGMaNy5CxhhjMsNFyBhjTGa4CBljjMkMFyFjjDGZcURFaOrUqcrLy9OkSZNqtSRJNHnyZBUXF6tFixa65JJLcs5/MsYYc2Jw2Bbt8vJyfelLX9Jpp52mSy+9VA888IAk6d5779WPfvQjPfLII+revbt++MMfau7cuVq5cuWnCqs8aNG+4YYbGrQJ3rlzZ/g755xzTqh/+OGHOc1JkrZt2xbqaS2wIyiws6ioCH9n9OjROe1jyZIloU7zppbjabZxCqmkAMThw4fntG+y5h5OsOm6detCnc4d2fHJ9rxy5Urc98svvxzq1JabbPG5WuLJXiyxVZ/aVpNlmOz1ZMVev349jolCVcmS/9FHH4U6WfvJup1mr6//VZBP0l955ZVQ79WrV6jT1xZo+xIf21zXJoWw0vqjQGaJbePRdffRRx/pscceO3YW7V27dum6667Tww8/rLZt29bqSZLogQce0J133qkJEyaod+/emjFjhvbs2aNHH330cHZljDGmCXNYReimm27SFVdc0eDLXGvXrlVFRUWdL/vl5+dr5MiRmjdvXrit6upq7dixo86PMcaYEwP+ajMwc+ZMLVq0KOxlU1FRIanht24LCwvxEX3q1Km6++67cx2GMcaYJkBOT0IbN27Urbfeqt/85jcNGs4dSl5eXp3/T5KkgXaQO+64Q1VVVbU/aXEtxhhjmhY5PQktXLhQlZWVuuCCC2q1AwcOaO7cuZo2bVrth2MVFRV1PoiurKzETKL8/PzUD+iMMcY0XXIqQqNHj24QyPjVr35VPXv21He+8x2dddZZKioqUmlpqc4//3xJUk1NjcrKynTvvffmNLAWLVo0KE4U0keOkbRgTnLR0BNeSUlJqNNnWDU1NaH+uc99DsdErXvPOuusUKd5kzvuwgsvzOn9ErdLppbZ5AqkIE8KiaSgS4mDM1u1ahXqr732Wk7vp4BKasktcbv43/72t6F+7rnn5rQdcoaSI0uSqqqqQr2ysjLUKeST/pH48MMPhzq1fpekSy65JNTJldqyZctQv/rqq0Od1h+57CRpxYoVoU6hsXTu6P70+uuvhzq5MyV2JNI1T/cIWuPkgqOQUoldtIc+lBxk3759euyxx3Bbh5JTEWrTpk2DnvatWrVSu3btavVJkyZpypQpKikpUUlJiaZMmaKWLVvq2muvzWVXxhhjTgByNiZ8Erfffrv27t2riRMnavv27RoyZIhmz559WN/7MMYY07Q54iI0Z86cOv+fl5enyZMna/LkyUe6aWOMMU0cZ8cZY4zJDBchY4wxmXHUPxM6Wpx77rkNHByUM7Znz55Qr589dyiUfdavX79Q/8tf/hLq5BgZPHhwqJeVleGYKOuLWotTzhjlSFFL6Q4dOuCYKHOLMvBWr14d6pTfRuG25KJK+x06HuSkoiwscj+R00ji8VK2W65tzalF9F133YVjImcjreV9+/aF+qxZs0KdYiepJbfErkdyd1Fr8XfffTfUp0+fHuppLkLKnqTMN3KWkaONrpW070TSGiSXKa1luiboXkDuQolz6KJ7YFoeZYP3fup3GmOMMUcZFyFjjDGZ4SJkjDEmM1yEjDHGZIaLkDHGmMxotO641atXN8isojy24uLiUKeMLImdHuQ+ocwmct288847oZ7mGqE8O3JYUX5bz549Q51y67Zs2YJjIndNrh0e+/fvH+qbNm0KdcrtknLvFknnghxZ5L4bMmQIjoncawS5uwjKy6OxSnw8qEsrZeyRy45yE+n9knTyySeHOq1Zcmv9/ve/D3VylVH2nsTuU3KckbOMOtNu37491NNy2si9S8c818xBCpSmYyFxrmbUfZnu1RF+EjLGGJMZLkLGGGMyw0XIGGNMZrgIGWOMyQwXIWOMMZnRaN1xNTU1ysvLq6NR/ljU2U9Samc/coGcdtppoU5ZcORiIdI6PJIT6Lrrrgt16gBKjhjK7Uprr04ZZ5QpR84hej8dP3KuSTxv6n5LGYLUzfOKK64I9f/8z//EMZ133nmhTg5Gyisj1+Hzzz8f6uQqk3itPfLII6FOGWd0TZB7jLLpJM6V69SpU077JnchZT/S2pDYpUbuQhorZceRm/OMM87AMZErlXTaN7lPCwoKQp3y8iQ+F1F2J13vEX4SMsYYkxkuQsYYYzLDRcgYY0xmuAgZY4zJDBchY4wxmdFo3XFt27ZtkFVEbp8//elPoV7fXXcolG/VrFl8SMjtQS4dyrBKywyjMa1bty7Uya3SuXPnnLZz4YUX4pjIQUbbovwsygw788wzcd8EZeBFGVYSu5nmzp0b6uvXr895TD/96U9zej85o/r27Rvq11xzTainjbWqqirUqVsp5ZJRR1m6JtKcjeSoe+mll0Kdug2Ty47mNmfOHBwTjZdcbQQdJ1r7dJ2mQc5Dcs19+OGHoU7nLtc5S7HTzu44Y4wxxwUuQsYYYzLDRcgYY0xmuAgZY4zJDBchY4wxmeEiZIwxJjMarUX7gw8+aBA8SUGb1Po4LSyUQj7pd6gdLtmqX3jhhVBPs6++/vrrod6xY8dQp1bkY8eODXWyX1JwqsRteqPQQkkaNWpUqC9ZsiTUac5pVlGaN9GmTZtQpxBHCqstKyvDfXz9618PdWqJTEGlFLRJVtunn34ax0TrgI5Hhw4dQp1s9xR2mRZgSjZm+krDyy+/HOqzZs0Kdboev/jFL+KYKMy4RYsWoU5zmD17dqgPGzYs1Gn9pUHW9KVLl4Y6Wf63bNkS6hs3bsR9030i+gqE23sbY4w5LnARMsYYkxkuQsYYYzLDRcgYY0xmuAgZY4zJjEbrjuvUqVMDZxG54zZs2BDq5AyR2AVCrpE1a9aEOrXeplBQarEtsQuOnFHkIKO2xOQcSmvvTfOmltnUDpz2QYGxFDoq8fzItUSur/nz54c6ueBozpK0b9++UB8/fnyoU5AttUcnt+X111+PY7r66qtDnZxlpaWloU7rkpykaeuJ5te/f/9Q//znP5/TdigQl9ywkrR8+fJQp4BRcsqSq5JIW+MrVqwIdXKo0T2CwlPTjgdx6aWXhnplZWUDja6HCD8JGWOMyQwXIWOMMZnhImSMMSYzXISMMcZkhouQMcaYzGi07riCgoIG2U3k1KI8tldeeQW3f/rpp4c6uWvIlfL888+H+tlnnx3qlNuVtg+iqKgo1GkOK1euDHXKo5Kk4uLiUG/dunWokzOKcsZoziedxP8+ImdP27ZtQz3XttW0NtIcP9/73vdy+p0333wz1MmdeeWVV4Y65bpJ3Pqb2sKT25Lapl9xxRWhPn36dBwTuQXpuqCMPZo3rae1a9fimN5///1QHz16dKg/8cQToU65f+ecc06ok8tO4tw6aoNOWW20xukaonuHxPffCLf3NsYYc1zgImSMMSYzXISMMcZkhouQMcaYzHARMsYYkxmN1h23c+fOBtlU5PSg3KRevXrh9skxRc4hcmuRG4fcLZRjJnFWFXWqTHPXRHz88cehTl1PJXZ30TxorO3btw91yiWjTDmJM7fonFJ+FuWP0XG96667cEzkvqIMsOeeey7UqcMoOZPSnI10LsjZGGWASVLv3r1Dndb4F77wBRwTOe1ofZDbkjr40nGlOUjsACUnGs2b3IXUHTZJEhwTXRfkFiQHXlVVVajT9XXgwAEcEznw+vTp00CjzssRfhIyxhiTGS5CxhhjMsNFyBhjTGa4CBljjMkMFyFjjDGZkZM7bvLkybr77rvraIWFhaqoqJD0V7fH3XffrenTp2v79u0aMmSIHnzwwVSXGvHxxx83cHORQ41cQNT5UWJHHWWcUVbV5s2bQz1Xp1Ya5Grbtm1bqJOLheaW5rJ78skncxoTOawGDRoU6rnmmEl8vml9bNq0KdQpj42y46hrrCS9+uqrOW2LXG30/vPPPz/UqcusxE47cpCNGTMm1Kkjb/fu3UOdjrfE55XWJjkYyaE2duzYUH/66adxTOS0ow605Dgj926ubkSJ3ad0vdD95owzzgh1um/RcZX4unvvvfcaaMe0s2qvXr20efPm2p9ly5bVvnbffffp/vvv17Rp01ReXq6ioiKNGTPmsFrJGmOMafrkXISaNWumoqKi2p+DFTtJEj3wwAO68847NWHCBPXu3VszZszQnj179Oijjx71gRtjjDn+ybkIrVq1SsXFxerWrZu+8pWv1H6Jbu3ataqoqKjzOJyfn6+RI0dq3rx5uL3q6mrt2LGjzo8xxpgTg5yK0JAhQ/TrX/9af/7zn/Xwww+roqJCw4cP17Zt22o/F6r/+cKhnxlFTJ06VQUFBbU/9DdMY4wxTY+citC4ceN09dVXq0+fPrrsssv0zDPPSJJmzJhR+568vLw6v5MkSQPtUO644w5VVVXV/mzcuDGXIRljjDmOOaLsuFatWqlPnz5atWqVrrrqKklSRUVFnWyvysrKVPdVfn5+mA+1ZcsWNW/evI5GzrLdu3eHeprTg7KkyAFC3RepqyuNlTKhJHaczZ49O9QpQ23w4ME5vX/+/Pk4Jjp31Alz+fLloU7mlPrn+CBpziE6r5SHRZl8Q4YMCXXqyEuOIok7pVInUToetM4oWy3NsUfXBWWi9ejRI9RprAsWLMhpOxKf1/Ly8lDPNVOO5nbttdfimOh8k5OPXIcEuQV79uyJv0NOO+qOTPchcghTRtyLL76IYyKXc7QO0pzJ9Tmi7wlVV1fr7bffVqdOndStWzcVFRWptLS09vWamhqVlZVp+PDhR7IbY4wxTZScnoT+9V//VePHj1eXLl1UWVmpH/7wh9qxY4duuOEG5eXladKkSZoyZYpKSkpUUlKiKVOmqGXLlqn/CjHGGHPiklMR2rRpk6655hpt3bpVHTp00NChQzV//nx17dpVknT77bdr7969mjhxYu2XVWfPno1/ujHGGHNik1MRmjlzZurreXl5mjx5siZPnnwkYzLGGHOC4Ow4Y4wxmeEiZIwxJjPykrQesxmwY8cOFRQU6MEHH2xgxV20aFH4O2TXTAvBpEBSaqNMX7ht2bJlqJPVluzCaWN67bXXQp0CTGkfpKe19CX79gUXXJDTPsjeTNbStO+L0ReaKQST3k9/XiaLO60/iYMzKUy2uro61Cks9PXXXw/1a665Bsf0m9/8JtTpmL/77ruhTnZhsm7T+yUOXKXwWbK+0zX/ta99LdTTrOyU0kJWcwo/Jss6fTXi0NzN+hz8nL0+9BUSundQ23k6d2mf32/fvj3UozDevXv36pZbblFVVRWGrh7ET0LGGGMyw0XIGGNMZrgIGWOMyQwXIWOMMZnhImSMMSYzGq077gc/+IFOPfXUOq+Rw4VCNslJInHAHjmsKByTXD1nnnlmqJP7TkoPXM3l/W+88UZO79+1axfuY//+/aFOoYwUwkrtvTds2BDq/fv3xzFdfvnlob5ixYpQJ7fWunXrQp1CMKklt8RtucntRu442veFF16Y03bSfufZZ58NdVrLBK2Biy++GH+HxkvOQzquAwYMCPWtW7eGelqfMrpP0PkmpycFDdO9IM2VSs5ecgXSmMiZRuch7f5E13Z0fdXU1Gj69Ol2xxljjGncuAgZY4zJDBchY4wxmeEiZIwxJjNchIwxxmTGEbX3PpZs3bq1gROE3F3kMqmqqsLtd+vWLdQp22rfvn2hTm66tKwqglxqxcXFoZ5ry3FyAaW54+h3KI+N2nXT8Rg1alSopzkFKeOMHIzkOKOcLDp+ffr0wTFRRhy5C2kf5CSidZnmjqNzR1lmdMzffvvtUD/77LNz2r7EuWt9+/YNdTqnlKVHdOzYEV8j9yTtg84FOfbI+devXz8cE61xctpRK3daT2m5mgSt5egc0f0ywk9CxhhjMsNFyBhjTGa4CBljjMkMFyFjjDGZ4SJkjDEmMxqtO65Lly4N3Drk0iFXT+vWrXH7lLWU5qKJIJfTnDlzQj0tE42cQDQPyl0jt8/SpUtD/eSTT8Yx0bHN1ZnXtm3bUKcMP9qOxB0sV61aFeqUZbZ48eJQp26llE0nSeecc06ok8uJctdo3pSh9oUvfAHH9OKLL4b6yJEjQ50y5SivjFyVP//5z3FMEyZMCPWVK1eGOjkS6VqhjrxpOZLUxZTOBTn83nvvvVCntUFdiyU+r9RplhyJlPdGGXuUwynx/YM6q35a/CRkjDEmM1yEjDHGZIaLkDHGmMxwETLGGJMZLkLGGGMyo9G64zZs2NDAlUMOEHLvpDmsyC2T5qKJIJcTZTal5bQRO3fuDHVyXtG+hw0bFuppOW1lZWWhPn78+FAn9w5l9VGuFuVUSexgJNcSnaMuXbqE+jvvvBPqY8eOxTGRc7Nnz56hTu4hWn+UDfb000/jmOh6efXVV0Od8sTI3UXnmtarxMeJzgWtP1rLNAdyrqXxhz/8IdQpv43o0aNHqKc5yCi3jlypRUVFod6+fftQp/WU1u2VzlHktHN2nDHGmOMCFyFjjDGZ4SJkjDEmM1yEjDHGZIaLkDHGmMxotO64k08+uUGmGblucskpOgg5pqhbZK5ZVZ/97GdDnVwpEueokePn1FNPDXUaK3XhpPdLnKtF2VOUcUbupHbt2oU6ZYlJ7Na69NJLQ53cgkuWLMF9RFAeoMT5e+TYI0dn7969Q53GWlFRgWOi7DhyjVK3XLq+Fi5cGOq9evXCMRGU+0dZZnT9Ugdf6nwrSSedFP9bnFxwdK4p546cbmkdaMkdOmTIkFCnc0T3G3KYprmD6ThF+7Y7zhhjzHGBi5AxxpjMcBEyxhiTGS5CxhhjMsNFyBhjTGa4CBljjMmMRmvR3rt3b4PWtGQRpHbWh2PdXr16daiT9Zhsz2QVTQvmJFsjzYMspGSfpuOUZsuk+ZG9lGzmFLY6fPjwUE+zHpOte968eaGel5cX6n379g31devW5TwmmgdZ0ynIk87dnj17Qp3s55I0dOjQUKevOtBXAaidenFxcajTmpE49JT2vWXLllCnME2C7h0S27fpWqX1R3boXNuESxwkSvtYtmxZqFNwMN1T0u5P9DvR9UL3gQg/CRljjMkMFyFjjDGZ4SJkjDEmM1yEjDHGZIaLkDHGmMxotO64RYsWNQjWHDlyZPheCoPcvHkzbp/cbuSASnMhRXTo0CHn7ZAri9oDp80vgoJK09xxFMRa37l4kFNOOSXU+/XrF+rUWpzcUhI7xah1+qhRo0Kdjjc5r1asWIFjopBKckZddtlluK0Iat+cFsy5YcOGUG/dunWo0/xycUVJ6eeOxkuBmnRt5xKmKXHoqMTrhn6HHGoUMjtgwIBQJyekxE5WgpyytJZnz54d6uPGjcN9kOO3TZs2DbS0dVkfPwkZY4zJDBchY4wxmeEiZIwxJjNchIwxxmRGozMmJEkiKY6toDgQirtJiw+hKBf6QC3XfdD76YP7tG3Rh625dC+U2JiQ9iEi7YOMCTQHiiGh7afNjT6spnmQkYH2Qe9PO065njvaB3HwushlTDS/tA/pc9kO7TvtuqM4F9oHzZuMCbRvWq9pv0PH6VjfOyS+Vmnfua4/Og9pUWe5rIODGp2/Q8lLPs27/oZs2rQJ2wwbY4w5fti4caM6d+6c+p5GV4Q+/vhjvf/++2rTpo3y8vK0Y8cOnXHGGdq4cWPONunjlRNxztKJOe8Tcc6S593U550kiXbu3Kni4uLU8FipEf457qSTTgor52mnndakT1rEiThn6cSc94k4Z8nzbsoUFBR8qvfZmGCMMSYzXISMMcZkRqMvQvn5+brrrrvQEdUUORHnLJ2Y8z4R5yx53ifavNNodMYEY4wxJw6N/knIGGNM08VFyBhjTGa4CBljjMkMFyFjjDGZ4SJkjDEmMxp1EfrZz36mbt266dRTT9UFF1ygl156KeshHVXmzp2r8ePHq7i4WHl5eXriiSfqvJ4kiSZPnqzi4mK1aNFCl1xyid58881sBnuUmDp1qgYNGqQ2bdqoY8eOuuqqq7Ry5co672mK837ooYfUt2/f2m/KDxs2TH/6059qX2+Kc67P1KlTlZeXp0mTJtVqTXHekydPVl5eXp2fQzvjNsU5HwmNtgj97ne/06RJk3TnnXdq8eLFGjFihMaNG4cti49Hdu/erX79+mnatGnh6/fdd5/uv/9+TZs2TeXl5SoqKtKYMWO0c+fOv/FIjx5lZWW66aabNH/+fJWWlmr//v0aO3ZsnZbJTXHenTt31j333KMFCxZowYIFGjVqlK688sram09TnPOhlJeXa/r06erbt28dvanOu1evXtq8eXPtz7Jly2pfa6pzPmySRsrgwYOTG2+8sY7Ws2fP5Lvf/W5GIzq2SEoef/zx2v//+OOPk6KiouSee+6p1fbt25cUFBQk//3f/53BCI8NlZWViaSkrKwsSZITZ95JkiRt27ZNfv7znzf5Oe/cuTMpKSlJSktLk5EjRya33nprkiRN91zfddddSb9+/cLXmuqcj4RG+SRUU1OjhQsXauzYsXX0sWPHat68eRmN6m/L2rVrVVFRUecY5Ofna+TIkU3qGFRVVUmSPvOZz0g6MeZ94MABzZw5U7t379awYcOa/JxvuukmXXHFFbrsssvq6E153qtWrVJxcbG6deumr3zlK1qzZo2kpj3nw6XRpWhL0tatW3XgwAEVFhbW0QsLC1VRUZHRqP62HJxndAzWr1+fxZCOOkmS6LbbbtNFF12k3r17S2ra8162bJmGDRumffv2qXXr1nr88cd13nnn1d58muKcZ86cqUWLFqm8vLzBa031XA8ZMkS//vWv1b17d33wwQf64Q9/qOHDh+vNN99ssnM+EhplETpI/S6CSZJgZ8GmSlM+BjfffLOWLl2ql19+ucFrTXHePXr00JIlS/Thhx/qD3/4g2644QaVlZXVvt7U5rxx40bdeuutmj17tk499VR8X1Ob97hx42r/u0+fPho2bJjOPvtszZgxQ0OHDpXU9OZ8JDTKP8e1b99eJ598coOnnsrKygb/gmiqHHTTNNVjcMstt+ipp57Siy++WKd/VFOed/PmzXXOOedo4MCBmjp1qvr166ef/OQnTXbOCxcuVGVlpS644AI1a9ZMzZo1U1lZmX7605+qWbNmtXNravOuT6tWrdSnTx+tWrWqyZ7rI6FRFqHmzZvrggsuUGlpaR29tLRUw4cPz2hUf1u6deumoqKiOsegpqZGZWVlx/UxSJJEN998s2bNmqUXXnhB3bp1q/N6U513RJIkqq6ubrJzHj16tJYtW6YlS5bU/gwcOFDXXXedlixZorPOOqtJzrs+1dXVevvtt9WpU6cme66PiMwsEZ/AzJkzk1NOOSX5xS9+kbz11lvJpEmTklatWiXr1q3LemhHjZ07dyaLFy9OFi9enEhK7r///mTx4sXJ+vXrkyRJknvuuScpKChIZs2alSxbtiy55pprkk6dOiU7duzIeOSHzze/+c2koKAgmTNnTrJ58+banz179tS+pynO+4477kjmzp2brF27Nlm6dGnyve99LznppJOS2bNnJ0nSNOcccag7Lkma5ry//e1vJ3PmzEnWrFmTzJ8/P/n7v//7pE2bNrX3rqY45yOh0RahJEmSBx98MOnatWvSvHnzZMCAAbU23qbCiy++mEhq8HPDDTckSfJXO+ddd92VFBUVJfn5+cnFF1+cLFu2LNtBHyHRfCUlv/rVr2rf0xTn/bWvfa12LXfo0CEZPXp0bQFKkqY554j6RagpzvvLX/5y0qlTp+SUU05JiouLkwkTJiRvvvlm7etNcc5HgvsJGWOMyYxG+ZmQMcaYEwMXIWOMMZnhImSMMSYzXISMMcZkhouQMcaYzHARMsYYkxkuQsYYYzLDRcgYY0xmuAgZY4zJDBchY4wxmeEiZIwxJjP+P9WETXi0I6qMAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "startcolrow = [10,5]\n", "ncol = 200\n", @@ -462,10 +644,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "12433080-9bb9-408c-b252-024ebb80d58c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radon Time: 0.5023874205071479\n", + "Convolution Time: 4.2476018741726875\n", + "Peak ID Time: 1.176631631096825\n", + "Band Label Time: 1.7350942937191576\n", + "Total Band Find Time: 7.944864416960627\n", + "Band Vote Time: 3.974799749907106\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "datasm, bnddatsm=ebsd_index.index_pats(patsin = pats, ebsd_indexer_obj = indxer, verbose = 2)" ] @@ -485,17 +690,38 @@ "metadata": {}, "outputs": [], "source": [ - "datasm, bnddatsm = ebsd_index.index_pats_distributed(patsin = pats, ebsd_indexer_obj = indxer, ncpu = 12)" + "datasm, bnddatsm = ebsd_index.index_pats_distributed(patsin = pats, ebsd_indexer_obj = indxer)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "098a5371-ad47-43bf-ac41-a300db4cc30a", "metadata": {}, - "outputs": [], - "source": [ - "ipfim = IPFcolor.makeipf(datasm, indxer, xsize = 200, graychannel='nmatch'); plt.imshow(ipfim) # xsize needs to be defined for array inputs. " + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ipfim = IPFcolor.makeipf(datasm, indxer, ncols = 200, graychannel='nmatch'); plt.imshow(ipfim) # nCols needs to be defined for array inputs. " ] }, { @@ -508,10 +734,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "c373ce83-ca1a-49f6-afe5-695c75ab68eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 60)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pat1 = pats[0,:, :]\n", "print(pat1.shape)\n", @@ -520,10 +774,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "f7f8c560-b14b-4f22-95aa-9754fa8ebe6e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radon Time: 0.007874124916270375\n", + "Convolution Time: 0.027734542032703757\n", + "Peak ID Time: 0.008475000038743019\n", + "Band Label Time: 0.015364582883194089\n", + "Total Band Find Time: 0.05955320899374783\n", + "Band Vote Time: 0.0017500410322099924\n", + "('quat', 'iq', 'pq', 'cm', 'phase', 'fit', 'nmatch', 'matchattempts', 'totvotes')\n", + "[([ 0.27121148, -0.37868821, 0.18609422, -0.86510607], 1.7882881, 1.3826607e+09, 0.701611, 0, 0.5727154, 7, [0, 1, 0, 1], 7)]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "dat1, bnddat1 = ebsd_index.index_pats(patsin = pat1, ebsd_indexer_obj = indxer, verbose=2)\n", "dat1 = dat1[-1]\n", @@ -545,10 +824,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "0d60bb4f-917d-479a-8c04-f328154b9770", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radon Time: 0.008000832982361317\n", + "Convolution Time: 0.03154970798641443\n", + "Peak ID Time: 0.008697875076904893\n", + "Band Label Time: 0.015277625061571598\n", + "Total Band Find Time: 0.06363083282485604\n", + "Band Vote Time: 0.001741624902933836\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "indxerCPU = copy.deepcopy(indxer)\n", "indxerCPU.bandDetectPlan.useCPU = False\n", @@ -602,7 +904,7 @@ ], "metadata": { "kernelspec": { - "display_name": "PyEBSDIndexUpdate", + "display_name": "PyEBSDIndex", "language": "python", "name": "python3" }, @@ -616,7 +918,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index e1ade33..2049b09 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -37,8 +37,17 @@ from pyebsdindex.EBSDImage import micronbar, scalarimage -def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None, - addmicronbar=False, graychannel=None, gamma=1.0, **kwargs): +def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), ncols = None, nrows = None, + addmicronbar=False, graychannel=None, gamma=1.0, + xsize = None, ysize = None, # these are kept for backwards compatability. + **kwargs): + + # kept around for backwards compatability. + if xsize is not None: + ncols=xsize + if ysize is not None: + nrows = ysize + nphase = len(indexer.phaseLib) npoints = ebsddata.shape[-1] @@ -57,28 +66,25 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = ipfout[ebsddata[-1]['fit'] > 179,:] = 0 - if xsize is not None: - xsize = int(xsize) - #if ysize is None: - #print(ysize) + if ncols is not None: + ncols = int(ncols) + else: - xsize = indexer.fID.nCols - #xsize = int(npoints) - #ysize = 1 + ncols = indexer.fID.nCols - if ysize is not None: - ysize = int(ysize) + + if nrows is not None: + nrows = int(nrows) else: - ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) + nrows = int(npoints // ncols + np.int64((npoints % ncols) > 0)) - ipf_out = np.zeros((ysize, xsize,3), dtype=np.float32) + ipf_out = np.zeros((nrows, ncols, 3), dtype=np.float32) ipf_out = ipf_out.flatten() - npts = min(int(npoints), int(xsize*ysize)) - # if int(xsize*ysize) < npoints: - # npts = int(xsize*ysize) + npts = min(int(npoints), int(ncols * nrows)) + ipf_out[0:npts*3] = ipfout[0:npts,:].flatten() - ipf_out = ipf_out.reshape(ysize, xsize, 3) + ipf_out = ipf_out.reshape(nrows, ncols, 3) if graychannel is not None: if graychannel == 'fit': @@ -86,13 +92,13 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = else: gchan = graychannel gray = scalarimage.scalarimage(ebsddata, indexer, - xsize=xsize, - ysize=ysize, - addmicronbar=False, - datafield=gchan, - cmap='gray', - rescalenice=True, **kwargs - ) + ncols=ncols, + nrows=nrows, + addmicronbar=False, + datafield=gchan, + cmap='gray', + rescalenice=True, **kwargs + ) ipf_out *= gray**gamma diff --git a/pyebsdindex/EBSDImage/scalarimage.py b/pyebsdindex/EBSDImage/scalarimage.py index e2c4191..787da34 100644 --- a/pyebsdindex/EBSDImage/scalarimage.py +++ b/pyebsdindex/EBSDImage/scalarimage.py @@ -32,15 +32,24 @@ def scalarimage(ebsddata, indexer, datafield='pq', - xsize = None, - ysize = None, + ncols = None, + nrows = None, addmicronbar=False, cmap='viridis', norescalegray=False, rescalenice = False, datafieldindex=0, gamma=1.0, + xsize=None, # these are kept for backwards compatibility. + ysize=None, **kwargs): + + # kept around for backwards compatability. + if xsize is not None: + ncols=xsize + if ysize is not None: + nrows = ysize + npoints = ebsddata.shape[-1] if datafield != 'fitinv': imagedata = ebsddata[-1][datafield] @@ -52,18 +61,16 @@ def scalarimage(ebsddata, indexer, imagedata = imagedata.astype(np.float32) - if xsize is not None: - xsize = int(xsize) - # if ysize is None: - # print(ysize) + if ncols is not None: + ncols = int(ncols) + else: - xsize = indexer.fID.nCols - # xsize = int(npoints) - # ysize = 1 - if ysize is not None: - ysize = int(ysize) + ncols = indexer.fID.nCols + + if nrows is not None: + nrows = int(nrows) else: - ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) + nrows = int(npoints // ncols + np.int64((npoints % ncols) > 0)) if datafield == 'fit': @@ -95,22 +102,19 @@ def scalarimage(ebsddata, indexer, if len(imagedata.shape) > 1: - image_out = np.zeros((ysize, xsize, 3), dtype=np.float32) + image_out = np.zeros((nrows, ncols, 3), dtype=np.float32) image_out = image_out.flatten() - npts = min(int(npoints), int(xsize * ysize)) - # if int(xsize*ysize) < npoints: - # npts = int(xsize*ysize) + npts = min(int(npoints), int(ncols * nrows)) + image_out[0:npts * 3] = imagedata[0:npts, 0:3].flatten() - image_out = image_out.reshape(ysize, xsize, 3) + image_out = image_out.reshape(nrows, ncols, 3) # perform desired image resize else: - image_out = np.zeros((ysize, xsize), dtype=np.float32) + image_out = np.zeros((nrows, ncols), dtype=np.float32) image_out = image_out.flatten() - npts = min(int(npoints), int(xsize * ysize)) - # if int(xsize*ysize) < npoints: - # npts = int(xsize*ysize) + npts = min(int(npoints), int(ncols * nrows)) image_out[0:npts] = imagedata[0:npts].flatten() - image_out = image_out.reshape(ysize, xsize) + image_out = image_out.reshape(nrows, ncols) image_out = image_out**gamma diff --git a/pyebsdindex/__init__.py b/pyebsdindex/__init__.py index 56ac752..897a995 100644 --- a/pyebsdindex/__init__.py +++ b/pyebsdindex/__init__.py @@ -7,7 +7,7 @@ ] __description__ = "Python based tool for Radon based EBSD indexing" __name__ = "pyebsdindex" -__version__ = "0.3.8" +__version__ = "0.3.9" # Try to import only once - also will perform check that at least one GPU is found. diff --git a/pyebsdindex/nlpar_cpu.py b/pyebsdindex/nlpar_cpu.py index f0c2928..5b57d1d 100644 --- a/pyebsdindex/nlpar_cpu.py +++ b/pyebsdindex/nlpar_cpu.py @@ -690,6 +690,11 @@ def getinfileobj(self): else: fID.nCols = self.ncols + if self.ncols == 1: + print('The number of scan columns is set to one, which is unusual, and may indicate that') + print('the number of columns is not saved as metadata in the pattern file. Consider manually') + print('entering the number of columns/rows with ``nlobj.ncols={number of your scan columns}`` and ') + print('``nlobj.nrows={number of your scan rows}``.') return fID else: From 7403b4db724c115a22f3fc841de7b7d10e9bdc36 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 27 Jan 2026 09:36:56 -0500 Subject: [PATCH 92/92] Need to include psutil as dependency. Signed-off by: David Rowenhorst --- pyebsdindex/__init__.py | 2 +- setup.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/__init__.py b/pyebsdindex/__init__.py index 897a995..52a7f0f 100644 --- a/pyebsdindex/__init__.py +++ b/pyebsdindex/__init__.py @@ -7,7 +7,7 @@ ] __description__ = "Python based tool for Radon based EBSD indexing" __name__ = "pyebsdindex" -__version__ = "0.3.9" +__version__ = "0.3.9.1" # Try to import only once - also will perform check that at least one GPU is found. diff --git a/setup.py b/setup.py index a5e0e9c..3a551e0 100644 --- a/setup.py +++ b/setup.py @@ -96,6 +96,7 @@ "numpy", "numba>=0.55.1", "scipy", + "psutil" ], # Files to include when distributing package (see also MANIFEST.in) packages=find_packages(),